ASI · The Annual Survey of Industries (ASI) being conducted by the Industrial Statistics Wing of...

EDITORIALManufacturing Industry

The pattern of human consumption has been changing over the decades and changingquite fast in recent times. A shift from consumption of (hard) goods to services (soft goodsincluded) is clearly visible. We also notice a corresponding change in the pattern ofproduction. In fact, each influences the other. It is, however, true that ManufacturingIndustry has to continue supporting all production processes in a big way. Thus, the roleof ‘manufacturing’ remains quite significant. Of course, with ‘lean manufacturing’ conceptsand practices and with automation in its advanced form, size of manufacturing units interms of the number of workers or employees will tend to be smaller in general. This fact,along with the traditional practice of treating units with at least 10 workers (20 in caseelectricity is not used for production purposes) as constituting the “organizedmanufacturing” sector, may imply an increasing segment of manufacturing industry unitswill be marked as ‘unorganized’. Having said all this, ‘organized manufacturing’ sectorcontinues to be the anchor of economic activities in the country and this anchor must getstronger and more accessible to provide a boost to the National Economy.

The Annual Survey of Industries (ASI) being conducted by the Industrial Statistics Wingof the Central Statistical Office is possibly the only source of credible information about theorganised manufacturing industry in the country, with gradually increasing informationcontent. And, plans are afoot to cover the Service Industry also in course of time. Once thisexpansion in coverage takes place, we will get a much more comprehensive picture aboutthe production set-up in the country and its contribution to National Development.

One thing, however, has to be realized as circumscribing the contribution of ASI data to aportrayal of performance of Manufacturing Industry (for the present). This is the inseparableconnections between ‘organised’ and ‘unorganised’ manufacturing. The latter plays a bigrole in supplying inputs to the former, while the former in some cases provides technical,financial and other support to the latter. In fact, most unorganized manufacturing units areengaged in producing accessories or spares or semi-processed materials or finishedcomponents to large manufacturing units covered by the organised sector. The performanceof any registered factory generally depends on the performance of its vendors including‘unorganised’ production establishments. At the same time, most small units outside thepurview of ASI depend heavily on demand from and support extended by the organizedsector.

In a somewhat different sense, the performance of any industry sector is affected bysectors which provide inputs to or receive outputs from this sector. A study ofinterdependence among different sectors which are linked in terms of customer-supplierrelations should be undertaken in respect of important dimensions of performance. In fact,there have been situations where a customer industry had to wind up its operations in theabsence of required inputs coming from the domestic supplier industry. The converse alsohas happened with dwindling demands from domestic customer industry sending outsignals or threat of extinction to the domestic supplier industry. Exploring foreign marketsfor customers or suppliers may not be convenient or economical in all cases. It is alsointeresting to note that the nature and extent of such inter-dependencies have also beenchanging over time and deserve appropriate investigation.

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Statistics relating to manufacturing industry and meant to portray a reliable picture of thisvery significant economic activity should be understood and interpreted in a holistic mannerthat provides a balanced view about the intentions of and the contributions by differentplayers—both domestic and foreign—in the growth and development of manufacturingindustry. Thus, inflow of foreign investment which is often associated with some stringsthat pull the recipient industry in a certain direction—not necessarily desired in ourcountry—is welcome in some sense, inflow of foreign production technologies –some ofwhich draw upon more energy or generate more pollution or lead to reduction inemployment—in the name of Research & Development initiative may not be always awelcome move.

All this and many more related issues call for adequate attention of investigators who can–in their turn—demand more information about different aspects of functioning ofManufacturing Industry.

September 2016 S. P. MukherjeeKolkata Editor-in-Chief

SECTION I : ARTICLES

Page No.

• Ramaa Arun Kumar 99Subcontracting and Industrial Agglomeration :Related Phenomena in India’s Unorganised Manufacturing Sector

• Nilabja Ghosh, Roopal Jyoti Singh and M. Rajeshwor 122Understanding Demand, Supply and Price Behaviorin the Dairy Sector: Using Official Indian Statistics

• A. K. Panigrahi 138Contract Workers in India’s Organised Manufacturing Sector

• Sarmishtha Sen and Subrata Majumder 154Employment-Productivity Profile and Labour Demand Elasticity :A Preliminary Study of the Organized and Unorganized IndianTextile and Garment Firms

• Sajal Jana and Maniklal Adhikary 179On Spatial Concentration of Organized Manufacturing Industries :A Look at Regional Perspective

• Shiney Chakraborty 192The Price of Prejudice : Employment Trend and Wage Discrimination ofWomen Workers in India

• Panchanan Das, Abhishek Halder and Rahul Dutt 226Export Competitiveness and Intensity of Technology inIndian Manufacturing Industries –Analysis with ASI Unit Level Data

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The Journal of Industrial Statistics (2016), 5 (2), 99 - 121 9 9

Subcontracting and Industrial Agglomeration: Related Phenomena inIndia’s Unorganised Manufacturing Sector

Ramaa Arun Kumar1, Delhi School of Economics, New Delhi, India

Abstract

The unorganised manufacturing sector in India has been an integral part of the economyand its inter-linkage with the organised part of the sector in the form of subcontracting iswell established. The benefits arising out of this inter-linkage may be enhanced due tothe presence of agglomeration economies generated at the regional level. The study triesto estimate the effect of such economies on the prevailing subcontracting arrangementsin the unorganised manufacturing sector. Using the quinqennial enterprise surveysconducted by the National Sample Survey Organisation (NSSO) on UnorganisedManufacturing Sector Enterprises for the 56th (2000-01), 62nd (2005-06) and 67th (2010-11) Rounds, the paper establishes that the effect of industrial agglomeration on theprobability of a firm being in a contractual arrangement in the unorganised manufacturingsector varies across different industries at NIC 2-digit level. Overall, the localisationeconomies at district level seem to have a stronger effect on subcontracting than industrialdiversity.

1. Introduction

1.1 Production subcontracting in manufacturing sector is one of the key characteristicsof the interaction between organised and unorganised manufacturing sector. A firm maychoose to undertake all activities in its manufacturing process or outsource/subcontract apart of the manufacturing process to an outside firm, whether it is sourcing inputs (backwardlinkage) or after production processes (forward linkage). In the Indian context, subcontractinghas seen a significant and persistent growth, which is considered to be an offshoot of theeconomic reforms that were initiated in the late 1980s and early 1990s when industriallicenses were abolished for a number of industries along with trade and investmentliberalisation reforms. There was an advent of increased competition due to thesedevelopments for industries in India as it encouraged private sector to grow. The competitivepressures thus, created on manufacturing sector were aggravated by the strict labour lawspertaining to the formal sector enterprises which led to the proliferation of the unorganisedmanufacturing firms (Moreno-Monroy et. al, 2012).

1.2 A healthy inter-firm relation between large and small firms entails a lot of benefitsfor both the contracting parties. On the other hand, subcontracting that takes place betweenfirms of different sizes creates inequality of power which may also be detrimental to theoutcomes. It remains to be seen how subcontracting has impacted the smaller firms thatbelong to the unorganized manufacturing sector in India.

1.3 For any subcontracting relation to persist in the long run, the access to a largenumber of potential input suppliers is crucial as competition among them would reduce thecost of subcontracting to the parent firm. Likewise, a number of larger firms concentrated inthe same region would enhance the probability of the small firms to get contracts. This1 e-mail: [email protected]

The Journal of Industrial Statistics, Vol. 5, No. 2100

mutual benefit arising out of industrial agglomeration in a region is the key factor that willbe the focus of this study. Economies arising out of industrial agglomeration have beensubjected to a number of studies and discussions. The earliest explanations date back toMarshall (1890) when he explained three kinds of agglomeration economies that is, labourmarket interactions, buyer-supplier linkages and knowledge spillovers through inter-industryinteractions which increase the tendency of firms to cluster in a region.

1.4 The presence of own industry firms in unorganised sector in a region may providea bigger market for the larger firms to choose smaller firms for giving out contracts whichraises the probability of such firms to be working under a contract. On the other hand,concentration of different industries in one region leads to knowledge spillovers generatedby inter-industry linkages. Both these forces of agglomeration have an impact on theprobability of a firm in unorganised manufacturing sector to be operating under a contract.

1.5 This paper analyses this interrelationship between agglomeration andsubcontracting for the unorganised manufacturing sector in India. The National SampleSurvey Organisation (NSSO) conducts a quinqennial survey at the enterprise level for theunorganised manufacturing sector. The data from three quinqennial surveys, that is, 56th

(2000-01), 62nd (2005-06) and 67th (2010-11) are used to empirically establish the interrelationshipdescribed above.

1.6 The effect of industrial agglomeration is envisaged by the likely economies that itgenerates for the firms at the industry and regional level. However, the effects on differentindustries’ subcontracting status vary as there is an element of heterogeneity acrossindustries. The subsequent sections discuss and analyse this hypothesis empirically tothrow some light on this inter-relationship that is existing in the unorganised manufacturingsector. Section 2 begins with an overview of the subcontracting status in India and theliterature that explains the nature of interactions taking place between unorganised andorganised manufacturing sector. The review moves on to discuss the economic geographyliterature that throws light on the process of agglomeration and various economies itgenerates to enable firms to thrive therein. The literature on the inter-relationship betweenindustrial agglomeration and subcontracting is then revoked to enable us to understandhow these two phenomena are linked.

1.7 Section 3 explains the measures of agglomeration that have been used in theempirical estimations. The descriptive statistics based on the enterprise survey data arepresented in section 4 that tries to correlate industrial agglomeration and the existingsubcontracting status of the unorganised manufacturing sector firms at the state andindustry level. Section 5 gives a bird’s eye view of the data sources and the variables thathave been included in the study. This section also discusses the likely effect each variableis expected to have on the dependent variable and the methodology used in the empiricalestimation. Section 6 presents the empirical results followed by the conclusion in section 7.

2. Subcontracting and Agglomeration-Related Phenomena: Review of Literature

The literature providing evidence of this inter-relatedness of subcontracting andagglomeration dates back to 1890 when Marshall proposed that agglomeration allows forsharing of inputs and facilitates the emergence of specialised intermediate input producers.

Subcontracting and Industrial Agglomeration: Related Phenomena in India’s ..... 101

On a similar path, Stigler (1951) argued that increasing local market size leads to greatervertical disintegration.

2.1. Subcontracting

Subcontracting in terms of manufacturing process takes the form of either assemblysubcontracting or product subcontracting, among others. Where sub contractors producethe entire product and parent firm essentially performs the marketing activity for the sale ofthe product, it is called product subcontracting. Sometimes assembling components toform a finished good may be a labour intensive process, for example, in case of electronicindustries. Firms may farm out this activity to small firms, which is called assemblysubcontracting. There may be conditions where the large firms employ labour on a contractbasis, or they are temporary in nature. However, this would not amount to the process ofsubcontracting, but would be in nature of informal employment. The study restricts itself tocontractualisation of a firm’s production process, and not that of labour.

2.1.1. Subcontracting in Indian Context

2.1.1.1 The unorganised manufacturing sector in India is highly labour intensive as it haseasy access to cheap labour, while it lacks access to capital. Owing to the small scale ofthese firms, employing mostly cheap labour which works with limited capital, makes firms inthis sector low cost firms. Nagaraj (1984) explains that this comparative cost advantage isthe principal basis for the growth of sub-contracting. It is also believed that subcontractingwas fallout of the rigidity of regulations governing factory employment. Micro-economicstudies have documented the widespread use of market mediated work practices andoutsourcing of products [Bhushan (1984) and Ramaswamy (1988)].

2.1.1.2 In the Indian context, subcontracting dates back to the 1970s and 1980s muchbefore the liberalisation reforms were introduced in 1991. Ramaswamy (2013) notes that thesubcontracting intensity in Indian manufacturing sector which is characterised by thephenomenon of ‘missing middle’ in terms of size distribution, was significantly high for theemployment size group with turnover below rupees 50 million, that is exempt from exciseduty under the General Excise Exemption Scheme. Further, within this group of small scalefirms, the subcontract intensity is found to be higher in the labour intensive firms which arelocated in states that are inflexible in terms of labour regulations.

2.1.1.3 Evidence from Indian data on unorganised manufacturing sector indicates thatthere has been significant subcontracting taking place in the past two decades. Bairagya(2010) reports that the percentage of units operating on contracts has risen from 17 per centin 1999-2000 to 31.7 per cent in 2005-06. However, in the subsequent period of 5 years, theshare of subcontracting taking place in unorganised manufacturing sector has reduced to20 per cent in 2010-11.

2.1.1.4 According to the NSS 62nd round for 2005-06, it is found that 87 per cent of theOAMEs under a contract work solely for the contractors, while the similar figures for theNDMEs and DMEs are 63 percent and 70 percent, respectively. However, in 2010-11, 91percent of OAMEs worked solely for the contractors, while around 86 per cent of theNDMEs and DMEs together were working under contract.

2.1.1.5 Subcontracting usually takes place between firms of different sizes and thus,unequal power. The large or parent firms can exercise control over its subcontractors. Also,they may provide the subcontracting firms with raw materials, capital, product designs etc.which may help small firms to enhance production as well as their productivity with betterresources, compared to firms operating independently.

2.1.1.6 Based on a field survey of unorganised sector firms in India in 2005, Sahu (2007)found that technology transfer has been happening where small firms have got assistancefrom large ones in the form of technical drawings and specifications, tools and marketing,etc. In addition, technical advice is provided in respect of machinery, process, and materialsto be used, and so on. In rural areas, a large proportion (57 per cent) of the subcontractingunits worked with the technology completely prescribed or handed over by the parentcompany, and another 23 per cent operate with the technology that is partially or occasionallyprescribed by their parent companies; only 21 per cent of such units operated with theirown technology.

2.1.1.7 There is enough literature on the impact of subcontracting production overseason the parent firms. However, the studies on impact of domestic subcontracting on theparent as well as subcontracting firms are very few in case of India. This may be becausethe subcontracting relations between large and small firms in unorganized manufacturingsector have not been that strong. In addition, data poses the greatest barrier to studyingthese issues. This study fills this lacuna in the literature by using the enterprise surveysconducted by the National Sample Survey (NSS) that reports the contractual status of eachfirm in the unorganised manufacturing sector. The study very innovatively combines thefirm level factors with district and industry level factors that affect the contractual status ofthe firm.

2.2. Subcontracting and Agglomeration

2.2.1 The factors influencing the decision of firms to subcontract or agglomerate in aregion may be numerous, but there are fallouts emerging from these processes that areimportant to understand and analyse. The location of the firms, large as well as small, playan important role in determining the possibility and pattern of subcontracting taking placein unorganised sector. For example, locating firms nearer to the large industries may fetchgreater opportunities to get contracts as it enables larger firms to ensure the quality ofproducts supplied by the smaller firm. Location of firms, therefore, plays an important rolein affecting the performance of firms in unorganised manufacturing sector.

2.2.2 Industrial agglomeration is coming together of firms within the same industry ordifferent industries in the same region. There are a number of factors that may influence thedecision of a firm to locate itself in a particular area. The earliest literature on agglomerationphenomenon dates back to Marshall (1890). According to him, the tendency of industriesto cluster arises out of the agglomeration economies from three sources: labour marketinteractions, linkages between intermediate and final good suppliers and knowledgespillovers. Firms within the same industry may come together to take advantage of thelocalization economies such as specialized know-how, presence of buyer-supplier networksand opportunities of efficient subcontracting. In addition, the inter-industry benefits also


exist such as complementary services, availability of labour, varied skills, inter-industryinformation transfers and availability of low cost infrastructure, that also contribute tofirms locating in close proximity to other firms in the same industry or other industries. Adeeper and a more fundamental explanation on why industries choose a certain area andnot others is asked and attempted to answer in the New Economic Geography (NEG)literature (Krugman, 1991, Fujita and Krugman, 2004; Venables, 1996 etc.).

2.2.3 A competitive market of input suppliers lowers the cost of outsourcing, thus,reducing production costs of the firm which is outsourcing (Ono, 2001).

2.2.4 Ono (2007) estimated the effect of larger markets on the likelihood of outsourcingbusiness services. He develops a theoretical model to show that greater local demand foran input induces more suppliers to set up shop in that locality. This causes greatercompetition among the suppliers and thus, reduces the market price of the input. Thisraises the probability for the final good producer to outsource. Using cross section data forU.S manufacturing firms, the study found that plants in larger local markets have greateropportunities to outsource services.

2.2.5 The clustering of firms in the electronic industry in the districts of Madrid facilitatinggreater subcontracting has been shown in the studies by Suarez-Villa and Rama (1996) andRama et al. (2003). Clustering creates externalities for firms located therein that are internalto the cluster but external to the production establishment. Proximity makes it easier tobuild up inter-firm trust and reciprocity that leads to greater subcontracting in such clusters.

2.2.6 Taymaz and Kilicaslan (2005) have studied the subcontracting relations in Turkishtextiles and engineering industries. The study looks at the subcontract offering as wellreceiving firms. It finds that geographical concentration of firms in the industry, that is,clusters are important determinants for establishing subcontracting relationships.

2.2.7 Thus subcontracting decision of a firm is influenced by the regional factors.However, a firm’s decision to locate in a particular region may not be an independentdecision. Holl (2008) argues that in estimating the effect of agglomeration on subcontractingdecision of a firm, there may be a bias in the estimates as the location decision of a firm maybe correlated with the error term that reflects management ability. According to him, thefirm’s management strategy may affect the decision of the firm to locate. For example, if afirm is efficiently carrying out all production activities in-house, with no intention to engagein subcontracting, the firm may locate to the peripheral regions to take advantages of lowerlabour and land costs. However, firms that may not be able to carry all activities in-housemay need to subcontract and thus, decide to locate in agglomerations of economic activity.

2.2.8 In the Indian context, the effect of agglomeration on the subcontracting status ofa firm in the unorganised manufacturing sector has not been studied. This paper would tryto analyse what determines the firm’s subcontracting decision and the role of agglomerationin influencing that decision.

3. Measuring Agglomeration

There are a number of studies that have dealt with the measurement of agglomeration.Agglomeration effects are envisaged as the various economies arising out of the industrial


agglomeration process that are beneficial to the firms located therein. The different measuresused in this study depict two crucial linkages: intra-industry and inter-industry. The spatialunit considered in this study is the district level.

A region co-located by firms in same industry enables access to specialised know-how,adequate pool of labour and efficient subcontracting through intra-industry linkages. Onthe other hand, the presence of diverse industries in the same region leads to greater inter-industry linkages. These linkages are a breeding ground for innovation and knowledgetransfers (Chinitz, 1961 and Jacobs, 1969). In addition, regions with diverse economicactivities are endowed with financial and physical infrastructure such as access to betterbanking services, advertising, improved transportation etc.

For the purpose of this study we consider three measures of agglomeration: locationquotient depicting the intra-industry linkages and Herfindahl Index to measure the extentof diversity or concentration of economic activities in both organised as well as unorganisedmanufacturing sector.

3.1. Localisation Economies

3.1.1 First, we consider the degree to which industries are concentrated in each locationin each industry, which is measured by location quotients. They are simple measures ofregional concentration used in regional studies (North, 1955; Lall and Chakravorty, 2005;Pardo and Arauzo-Carod, 2011)

3.1.2 Measure of industrial concentration shows the degree of concentration ofeconomic activity in a particular location, region/spatial unit, while, industrial agglomerationshows how this concentration is distributed spatially.

3.1.3 Our analysis relates to the agglomeration effects on firm characteristics, therefore,the dimension of industries as well as location should be contained in one measure. Locationquotient (LQ) possesses both spatial and industrial dimension. It is the ratio of the share ofemployment of industry “i” in a particular region to that in a bigger geographical area, saystate or the economy as a whole. We take the share of the industry in the economy as awhole for comparison. Thus, the location quotient would represent the extent ofagglomeration of an industry in a particular district relative to its total presence in theeconomy.

The formula for LQ is given by the following:

LQir = (Eir/Er ) / (Ei /E)

Where,

Eir is the employment of industry “i” in region “r” (district in our study),

Er is the employment in region “r”,

Ei is the total employment in industry “i” and

E is the total employment in the economy


3.1.4 If the LQ is greater than one, the industry “i” would be considered to beconcentrated in the region “r”. LQ, being a proportion of two proportions, can range fromzero to very high values. For this study LQ would be calculated for each industry at NIC 3-digit level located in different districts of the 18 Indian states.

3.1.5 There is no consensus on what the magnitude of LQ should be in order to consideran industry being concentrated in a region or area. Various studies have used different cut-offs for indentifying clustered areas based on LQs. Some studies have used a cut-off of1.25 for identifying clusters, such as the study by Miller et al. (2001) that studies clusterswithin a range of UK industries. Whereas, Isaksen (1996) and Malmberg and Maskell (2002)have identified industrial agglomeration for industries within an area having an LQ largerthan 3. More recently, Chandrasekhar and Sharma (2014) have also undertaken a study onspatial concentration of employment in India using a cut off of one using location quotients.

3.1.6 O’Donoghue and Gleave (2004) have modified the LQ to a standardised LQ measureto incorporate the importance of statistical significance in using this measure. The motivationwas taken from Duranton and Overman (2002) which asserts that most cluster measures areindices that do not report levels of significance. Therefore, O’Donoghue and Gleave (2004)in their study identify agglomerations where the regions have statistically significant LQvalues, rather than an arbitrary cut-off.

3.1.7 The location quotient is a discrete measure of industrial concentration measuredat the spatial as well as industrial level. The objective of this study is to look at all areaswhether or not they are industrially agglomerated, in order to distinguish between theeffects of a firm located in either of the areas on its contractual status. Thus, the industrialas well as the regional dimension are important in choosing the agglomeration variable,which the location quotient possesses. In order to have a preliminary understanding of theextent of concentration levels in different districts in the 18 states considered in the study,we have used a simple cut-off of LQ greater than one to identify areas that have concentrationof a particular industry at NIC 3-digit level.

3.2. Industrial Diversity

3.2.1 Herfindahl Index has been used as a measure of the industrial diversity in a regionor the composition of local economic activity. This study includes two indices based onHerfindahl index for the organised as well unorganised manufacturing sector separately.The index is measured as given below:

2

irr i

r

EHIE

Here, irE is the employment of industry ‘i’ in region ‘r’ and rE is the total employment inregion ‘r’. The maximum value of the Herfindahl index can be one that indicates that theregion is dominated by only one industry, while lower values of the index indicate diversityof economic activity. The presence of greater number of industries within a region in bothorganised as well as unorganised manufacturing sector acts as a catalyst to the process of


subcontracting. For example, the firm in the wearing apparel firm belonging to NIC Division14 may outsource its textile or fabric requirements to the small weaving firms that belong toanother industry (NIC Div. 13), or the pharmaceutical industry firms subcontracting theirchemical inputs to the firms in the chemical industry.

4. Some Descriptive Statistics

4.1 Before any empirical analysis based on robust regression method is undertaken,it is useful to understand the trends in the different parameters or variables across time orcross section. This is important to understand the framework in which our hypothesis isbased. Therefore, this section provides an overview of the trend in subcontracting andagglomeration in different states and across industries at various points of time. The mainsource of data at enterprise level for unorganised manufacturing sector is the NSSUnorganised Manufacturing Sector Survey in 2000-1, 2005-6 and 2010-11. The surveycollects data on firm characteristics, different operating costs, wages and salaries of hiredemployees, employment, capital, revenue, gross value added and its assets and liabilities.

4.2 The overall growth of unorganised manufacturing sector had slowed down overthe 11 year period from 1994 to 2005. Although, initially there was a significant growth in thereal value added in the unorganised manufacturing sector in 2000-01, vis-à-vis, 1994-95,with 6.7 per cent compounded annual growth rate over the six year period as shown inTable 1. However, in the period of next five years, the growth rate fell drastically to 3.8 percent in 2005-06. The trend was reversed with real gross value added rising at an annualizedgrowth rate of 6.8 percent between the five year period 2005-06 to 2010-11.

4.3 The real gross value added is calculated by deflating the nominal GVA in variousNSS rounds using the wholesale price index (WPI) for manufactured products with baseyear 2004-05. The data on WPI which have been sourced from Handbook of Statistics,Reserve Bank of India have the current base year as 2004-05. However, the various timepoints considered in Table 1 had different base years, therefore, the base years of the WPIfor the two time points before 2004-05 were changed to 2004-05. The new indices were thenused for deflating the nominal GVA from the various NSS rounds.

4.4 Employment in the unorganised manufacturing sector, on the other hand, hasseen a falling trend since 2000-01. Total labour employed in the sector was 37 million in 2000which has come down to 34 million in 2010-11.

4.5 The corresponding GVA per labour employed, or in other words, the labourproductivity has been on the rise at the aggregate level. The period 2005-06 to 2010-11witnessed a dramatic increase in labour productivity, a growth of about 6.4 percent. However,the commensurate high growth of GVA at 6.8 percent and a fall in total labour employed inthe sector in 2010-11 can be construed as the reason behind the high growth in labourproductivity in the five year period. This means that with relatively less labour employed,the unorganised sector has been able to generate a high value added in 2010-11.

4.6 At the industry level, labour productivity has also been rising over the period of11 years, spanning 2000-01 to 2010-11, based on the real value added and total labouremployed from the three NSS rounds (Table 2). However, some industries experienced a fallin their labour productivity from 2005-6 to 2010-11.


4.7 These industries include basic metals industry (NIC Div. 24) where productivityfell from Rs. 100,629 in 2005-06 to Rs. 60,329 in 2010-11. Likewise, computer and electronicindustry (NIC Div. 26), cotton ginning and cleaning (NIC Subdiv. 01632) and othermanufacturing industry (NIC Div. 32) also experienced a fall in productivity during the sameperiod.

4.8 Around 90 per cent of employment that is provided by 16 industries in theunorganised sector also account for about 93 per cent of the total firms within each industryoperating under a contract from a larger firm. Within industries, the highest percentage ofsubcontracting is being carried out by tobacco industries, followed by the textile sector.Tobacco industry is also one of the largest job providers in the unorganised manufacturingsector in India. According to the Ministry of Labour, Annual Report 1999-2000, the totalnumber of beedi-workers in India was around 4.5 million. The entry of foreign players intobacco industry as a result of economic liberalisation has forced the main beedimanufacturers to shift to more backward and poverty-ridden areas in search of unorganisedand cheaper labour to subcontract their manufacturing activity (ILO, 1997). This may explainthe high and rising share of contractual work in this sector, which rose from 35 per cent in2000-01 to 45.8 per cent in 2010-11 which is mostly (99.5 per cent) carried out by ownaccount manufacturing enterprises (OAME).

4.9 Textiles sector is another sector where substantial subcontracting is present.Taking all industries under textiles and apparel sector (NIC 13 and 14 division), around 38per cent of total subcontracting in unorganised manufacturing sector is accounted for bythis sector.

4.10 In the context of subcontracting of production activity by a firm, the location of anindustry plays an important role. The next section throws some light on the trends acrossstates and districts of the unorganised manufacturing sector.

4.11 Regional Scenario: District Level

4.11.1 To measure the extent of industrial concentration in various states at district level,the location quotient has been calculated for all three NSS rounds. The location quotient,as explained in the previous section, is a measure of the extent of industrial concentrationin a geographical unit compared to that at a bigger geographical unit. In this study, thedistrict level employment at 3-digit NIC level as compared to the national level employmentshare has been considered.

4.11.2 A measure of location quotient greater than 1 is considered to be indicatinghigh concentration in a particular district or region. Based on the calculations of thismeasure at district level for the set of 18 states chosen for this study, comparisons havebeen made for the highly concentrated industries across time over the three time periods.There were 858 district-industry pairs identified as highly concentrated areas (data for alldistricts can be shared by author).

4.11.3 It is observed that the state of Andhra Pradesh has 16 out of 23 districts that areagglomerated by textile and wearing apparel industry. These districts include East Godavari,Vishakhapatnam and Hyderabad among others. The other industries that are concentrated


in different districts include paper industry in Vijayanagaram and Hyderabad, jewelleryindustry in Hyderabad etc.

4.11.4 Towards the north of India, the district of Firozabad in Uttar Pradesh specialisesin all types of glass products. According to the district industrial profile given by theDirectorate Commissioner of the Ministry of Micro, Small and Medium Enterprises(DCMSME), Government of India, there are 4222 units operating in the district alone withan employment of 33,776 and a total investment of Rs. 12,666 lakh. Various glass productslike glass bangles, ware, tumblers, tubes form the major clusters in this district.

4.11.5 Likewise, sports goods industry in Jalandhar forms a major cluster. It is one ofthe highest employment generating industries in the district. The footwear industry in thedistrict of Agra in Uttar Pradesh is another industry that has a dominant presence. Thereare about 5,000 functional units of leather footwear in this district, including 150 exportingunits. Employment in this cluster is reported to be around two lakh in the district profile ofthe DCMSME.

4.11.6 The district of Howrah in West Bengal also has a number of foundries producingrailway components and spares, apart from other casting metals for industrial purposes.The Foundation for MSME Clusters (FMC) reports that the Howrah foundry cluster employsaround 8000 workers.

4.11.7 Location quotient is a simple measure of concentration that spans the regional aswell as the industrial dimension together. This measure has also been incorporated as anexplanatory variable in the empirical section.

5. Inter-relation between Subcontracting and Agglomeration: Data Sources andEmpirical Model

5.1. Data Sources

5.1.1 This section explains the factors influencing subcontracting decision of firms inthe unorganized sector. There are very few studies and probably none for India in particularthat looks at the subcontracting firms in different manufacturing industries in the unorganisedmanufacturing sector. The factors considered, in accordance with the theory, that determinewhether a firm receives a contract or not are taken at firm level, industry and regional level.

5.1.2 The study uses the NSS 56th (2000-01), 62nd, (2005-06), and 67th (2010-11)Unorganised Manufacturing Sector Surveys to analyse the effect of agglomeration onsubcontracting firms. The survey collects firm level data on the characteristics of the firm,its operating costs, principle receipts, assets and liabilities etc. The firm level data has beenadjusted for annual levels, as the reference period in some of the characteristics of the firmsuch as operating expenses, receipts and value added was one month.

5.1.3 In case of India, the country is divided broadly into states and each state is furtherdivided into districts. The study includes 18 states which contribute about 96 per cent oftotal unorganised sector GVA. These states are: Maharashtra, Tamil Nadu, Gujarat, UttarPradesh, West Bengal, Andhra Pradesh, Karnataka, Delhi, Rajasthan, Kerala, Punjab, MadhyaPradesh, Haryana, Bihar, Orissa, Assam, Jammu & Kashmir and Himachal Pradesh. The


districts of Chhattisgarh, Jharkhand and Uttarakhand were not named in the NSS 56th

round, which would pose a problem of comparison across time, therefore, these threestates were not included, although they are important manufacturing states.

5.1.4 There are various factors at the firm, industry as well as district levels that determinewhether a firm in the unorganised manufacturing sector would undertake subcontractingactivity or not. Since the subcontract status of the firms is given by a yes or no answer inthe survey questionnaire, the dependent variable would be a binary variable.

5.1.5 Apart from the internal characteristics of the firms, there are several external factorsthat play an important role in the unorganised sector firms to get contracts from them. Theconcentration of firms within the same industry as well as industrial diversity in a regiongenerates numerous positive externalities that the firm can take advantage of. The studyhas considered two effects that may enhance the probability of a small firm in the unorganisedmanufacturing sector to obtain a contract from a larger firm. These are the location quotients(localisation economies) and Herfindahl index (industrial diversity).

5.1.6 For the Herifndahl index organised manufacturing sector employment data iscollected by the Central Statistical Organisation under the Government of India, called theAnnual Survey of Industries (ASI). The ASI collects data on factories that are registeredunder Sections 2m(i) and 2m(ii) of the Factories Act, 1948.

5.2. Empirical Model:

5.2.1 As mentioned before, the dependent variable is a binary variable, that takes avalue of 1 or 2 in the NSS round schedules to show whether a firm is having a contract witha larger firm or not, where code 1 signifies that the firm has a contract. The empiricalestimation therefore, will be the estimation of probabilities based on maximum likelihoodprocedure. The probit regression analysis is one of the techniques used in this case. Sincethe dependent variable is a binary variable, using an ordinary least squares (OLS) wouldnot give efficient estimates as the changes in independent variable affecting the probabilitythat the dependent variable takes a value 1 cannot be bounded within the [0,1] range thatprobability lies in. The probit regression coefficients give the change in the z-score orprobit index for a one unit change in the predictor.

5.2.2 The coefficients on independent variable are indicative of the direction of theimpact of a change in variable on the probability index. The magnitude of change inprobability owing to the change in an independent variable, however, is not a constant asin a linear regression. The marginal effect of a change in any of the independent variablesis based not only on the value of that independent variable, but all other independentvariables too.

5.2.3 The estimation results in Table 3 are based on the coefficients obtained from theestimation which indicate whether an explanatory variable increases or decreases theprobability of a firm to be working under a contract.

5.2.4 Variables and Empirical Equation:

5.2.4.1 The study attempts to test the inter-relation between the phenomenon ofsubcontracting at the firm level and agglomeration at the industrial level. The factors affecting


the subcontracting of a firm will be looked at, at the firm level, in addition to the industrialagglomeration, which is at the district-industry level.

5.2.5 Firm Level Independent Variables:

The firm level characteristics that are considered are listed below. Since they are firmspecific, they will be time varying.

a) Operational costs: Operational costs are one of the important factors that determinewhether the input suppliers would be subcontracted by a larger firm. A firm with loweroperation cost would be more likely to be working under a contract. Operational costsinclude electricity charge, fuel, rent payable on machinery, transport expenses,communication expenses etc. These expenses are an important part of the overall cost ofoperation of a firm. Hence, they may be an important determinant for a firm to receivecontracts. The log of real operational cost per unit of value of output has been consideredin the estimation using wholesale price index (WPI) for fuel, power, light & lubricantsprovided for industries together.

b) Size in terms of value added: Size of the firm is also a factor that may influence thelikelihood of a firm working under a contract. Bigger the size of the firm, larger is the scaleeconomies which enable them to undertake specialised operations. Log of real gross valueadded has been used.

c) Capital Intensity: Capital intensity at the firm level is measured as the ratio betweenreal value of assets owned by the firm and total workers employed in the firm.

d) Labour Productivity: Labour productivity of a firm is an indicator of the efficiencywith which firms are operating. It may be an important determinant for the large firmschoosing to contract out a part of their production process or inputs to the smaller firms inthe unorganised manufacturing sector. The variable is measured as the log of labourproductivity calculated as the real gross value added per worker employed in the respectivefirm.

e) Status of firms in last 3 years: Status of the firm in last 3 years in the NSS roundsis enquired, that is, whether the sample firm has been expanding, remained stagnant orcontracted in the last 3 years. The variable is reported in codes: expanding – 1, stagnant –2, contracting – 3, operated for less than 3 years – 9. Hence, three separate dummy variableshave been included in the estimation, viz., firms that are expanding, remained stagnant andthose which have contracted for the last three years. The firms which have operated lessthan three years act as a baseline category against which the coefficients on the other threedummies would be interpreted.

f) Whether accounts are maintained by firms: A firm that maintains records andaccounts of its operations would be more preferred to be given a contract than a firm thatdoes not maintain any accounts. The NSS schedule contains a binary code for firms whodo or do not maintain accounts. Therefore, a dummy variable has been used for thischaracteristic of the firm, where the dummy takes a value 1 if the firm maintains accounts,zero otherwise.


g) Location of firm’s premises: Two separate dummy variables for firms that havepremises within the household (Code 1) and those that have fixed and permanent structures(Code 2) have been included. The rest of the categories that do not have any permanentand/or fixed structures are taken as the baseline category.

5.2.6 District Level Agglomeration Variable:

5.2.6.1 This study considers the own industry concentration of the unorganisedmanufacturing sector firms as well as concentration of all industries in the unorganised aswell as organised sector at the district level. The presence of own industry firms inunorganised sector in a region may provide a bigger market for the larger firms to choosesmaller firms for giving out contracts. Therefore, the chances of the firm located in such aregion would have better probability to be working under a contract.

5.2.6.2 Secondly, it is the geographical proximity of the small and the large firms thatplays a bigger role in the small firms in the unorganised sector to get contracts. Although,in the new age of advanced telecommunication and better transportation services, it maybe easy to have a long distance inter-firm relationship of this kind, the chances of a contractualrelationship between a larger firm and a smaller one that are in proximity would be in anycase higher.

5.2.6.3 The measures of agglomeration included in the study have already beendiscussed in section 3 above. These are own-industry concentration measured by locationquotient, industrial diversity taken as the Herfindahl Index for unorganised and organisedmanufacturing sector.

5.2.7 Industry Dummies:

5.2.7.1 The concentration of industries differs across states as well as industries, thus,it is necessary to see to what extent the inter-relationship between agglomeration andsubcontracting varies across industries. For this, industry dummies at two digit NIC levelhave been interacted with the corresponding district level agglomeration variables. Thecoefficients on these dummies would indicate the extent of the effect that industryconcentration/diverstiy has on the subcontractual status of the unorganised manufacturingfirm across industries.

5.2.8 Time factor

5.2.8.1 In a pooled cross section, the effect of time on the dependent variable can begauged through time dummies. In order to look at the effect of any variable overtime, aninteraction variable between time dummy and the concerned variable would be included inthe estimation. Refer to equation 1 below that shows the empirical equation with all thevariables explained above estimated in the study.

( , , , , , , , , ,, , , , 05 , 10 , , ) (1)

itd it it it it it it it it

dt dt dt it i i t

SUBC f LgOC RGVA KI EXP STAG CON ACC HOUSE PERMLQ ORG UNORG LOGLP Year Year Industry DISTT

In equation 1, SUBC is the binary variable for contractual status of the firm, LgOC is log ofreal operating costs, RGVA is the log of real GVA, KI is capital intensity, EXP, STAG and


CON refer to dummies for expanding, stagnant and contracting firms in the last three years,ACC is the dummy for firms maintaining contracts, HOUSE and PERM are the respectivedummies for the location of the premises of the firm, LQ is location quotient for theunorganised manufacturing sector at district- NIC 3-digit level and ORG and UNORG is theindustrial diversity measured as the Herfindahl index for organised and unorganisedmanufacturing industries, respectively.

5.2.8.2 LOGLP is log of labour productivity at the firm level. The unorganizedmanufacturing firms are mostly labour intensive, therefore, the productivity of labour is animportant factor. Including an endogenous variable in the probit estimation of equation 1above may lead to biased coefficients. On the other hand, omitting a variable from theequation may create a specification bias. Although, in literature, the omitted variable bias inprobit or logit regression is not observed to be affecting the coefficients of other independentvariables in the regression equation, our study includes this variable.

5.2.8.3 Year05 and Year10 are the respective dummies for years 2005 and 2010, with year2000 as the baseline category. District dummies (DISTT) have been included to control forthe time invariant district fixed effects like infrastructure and government policies such asthe SEZs that may affect contracting. The industry fixed effects are controlled for by theNIC-2 digit industry dummies (Industry).

6 Estimation Results

6.1 The probit regression estimation results are given in Table 3. Probit estimationshave been undertaken for all three years on a pooled cross section data. Model 1 includesall firm specific variables, agglomeration variables and individual industry dummies. Model2 includes individual industry dummies interacted with location quotient to capture industryspecific effects of agglomeration. Model 3 includes industry dummies interacted with theorganised sector diversity variable, while Model 4 includes interacted industry dummieswith unorganised manufacturing sector diversity. The different models have been chosento see how individually the three measures of agglomeration, namely, own-industryagglomeration measured by location quotient and organised and unorganised manufacturingsector industrial diversity have an effect on subcontracting probability for differentindustries.

6.2 In pooled time series cross section where the sample in the cross section is not thesame across the years, the effect of time is estimated by including time dummies in theregression equation. The year dummies for 2005 and 2010 have been included in the regressionequations, with year 2000 as the base year.

6.3 From the empirical estimations, we can see that own industry concentrationmeasured by location quotient (LQ) of the unorganised manufacturing sector has an overallnegative coefficient, except models 3 and 4 where the coefficient is positive but statisticallyinsignificant. The weak overall effect of LQ can be explained by the fact that the effect of LQacross industries varies significantly, although the overall effect is negative. This showsthat the likelihood of a firm getting a contract in an industrially agglomerated region isdifferent across different industries.


6.4 However, an overall negative coefficient indicates that irrespective of the inter-industry differences there is less likelihood of manufacturing firms to be operating under acontract that are located in areas with greater agglomeration of own industry unorganisedsector industries.

6.5 Agglomeration Effect: Across Industries

6.5.1 Effect of Location Quotient across Industries

6.5.1.1 The effect of own industry agglomeration of unorganised sector firms has a positiveand significant coefficient for most of the industries. These include, in order of statisticalsignificance, tobacco industry (NIC Div. 12), textiles (NIC Div. 13), wearing apparel (NICDiv. 14), motor vehicles (NIC Div. 29), furniture industry (NIC Div. 31), among others. Thisimplies that the firms in these industries that are situated in an agglomerated area have asignificant likelihood that they may be working under a contract.

6.5.1.2 On the other hand, negative coefficients are found for relatively few industriessuch as coke and refined petroleum (NIC Div. 19), food products industry (NIC Div. 10),beverage industry (NIC Div. 11), paper and printing (NIC Div. 17 and 18) and repair andinstallation of machinery (NIC Div. 33), among others. The likelihood of the firms in theseindustries is not affected by the economies arising out of the industrially agglomeratedarea they are located in.

6.5.1.3 This is an important result as agglomeration cannot be construed to be creatinga similar advantage to unorganised manufacturing firms across various industries in termsof acquiring. There is significant heterogeneity across industries and therefore, the effectsof agglomeration may not be uniform for all.

6.5.2 Effect of Industrial Diversity in Organised Sector across Industries

6.5.2.1 Industries with significant negative coefficient include food products industry(NIC Div. 10), beverage industry (NIC 11), wearing apparel industry (NIC 14), rubber andplastic industry (NIC 16), coke and petroleum industry (NIC Div. 19), chemicals (NIC 20)and pharmaceuticals (NIC 21). A negative coefficient is indicative of a positive effect ofindustrial diversity in organised manufacturing sector on the probability of smaller firms inunorganised manufacturing in getting contracts. This is because a rise in the value ofHerfindahl index implies greater concentration than diversity of a particular industry in theregion. Thus, a negative coefficient implies that as the region becomes more industriallydiverse, the greater is the probability of unorganised sector firms to be under a subcontract.

6.5.2.2 However, most industries have a positive coefficient such as tobacco industry(NIC Div. 12), textiles industry (NIC 13) and leather industry (NIC 15), paper industry (NIC17), printing industry (NIC 18), transport industry (NIC 29), among others. For theseindustries subcontracting probability is higher when they are located in a region that is lessdiverse industrially.

6.5.3 Effect of Unorganised Sector Concentration across Industries

6.5.3.1 Apart from own industry concentration measured by location quotient, theindustrial diversity within the unorganised manufacturing sector in a region is also an


important factor that may be affecting the probability of smaller firms to be working undera contract.

6.5.3.2 The empirical results of model 4 in Table 3 pertain to the interaction of industrydummies with the unorganised manufacturing sector diversity measured by Herfindahlindex. The overall effect is seen to be negative and statistically significant. However, thecoefficient across many industries is positive implying that industrial concentration, ratherthan diversity is affecting the subcontracting probability more in some industries. Theseindustries are tobacco, textiles, leather, paper, printing, rubber and plastic, basic metals,fabricated metal products, electrical and electronic, transport, furniture and othermanufacturing. The knowledge spillovers enabled by inter-industry linkages that isassociated with industrial diversity does not have a discernible effect on the probability ofsmall firms to be operating under a contract.

6.5.3.3 The weak effects of industrial diversity on firms’ likelihood of working under acontract, compared with the results of a positive relation of location quotient on the sameare indicative of strong localisation economies at work in the unorganised manufacturingsector. A contradictory result on the localisation economy effects prevailing in India’sorganised manufacturing sector has been estimated by Lall et al. (2003). They found asignificant cost saving effects of industrial diversity using plant level data for 1998-99 fromthe Annual Survey of Industries (ASI).

6.6 Firm Specific Variables

6.6.1 A relatively more capital intensive firm has a higher likelihood of operating undera contract as the coefficient of capital intensity is positive and significant. It is known factthat the small firms in the unorganised manufacturing sector are plagued by technologicalbackwardness. Therefore, larger firms’ decision to contract out a part of their productionprocess or input requirement would prefer to approach firms within the region that aretechnologically more advanced. Also, a firm with its own premises, whether operating in ahouse or having a fixed and permanent location outside the house, has greater chances ofworking under a contract, compared to a firm that does not have any fixed or permanentpremise, the baseline category of firms.

6.6.2 The size of the firm in terms of the real gross value added (GVA) has a significantpositive coefficient in all models. A larger firm has a higher likelihood of getting a contract.Also, maintaining accounts increases the probability of fetching a contract for anunorganised manufacturing sector firm.

6.6.3 Productivity of labour employed in the firm has a negative but statisticallyinsignificant effect on the probability of getting a contract. The operating cost of the firm,on the other hand, has a positive and significant coefficient. This result does not tally withthe expected outcome. It was explained that a higher cost firm may not be very likely to beworking under a contract because it would be costly for the larger firm to subcontract to afirm that has a higher cost. However, the variable included in the analysis contains chargesfor electricity, fuel and lubricant, raw material for own construction etc. It does not containwages and salaries to employees or raw material expense. A small firm using electricity mayhave a high operating cost, however, it may be beneficial for the productivity of the firm,


that may partially explain the positive and significant coefficient for the operating costvariable.

6.6.4 A firm which in the owner’s opinion has expanded, remained stagnant or contractedin the last 3 years of its operation does not have a likelihood of being under a contract. Thecoefficients for all three dummy variables are negative and in some cases, statisticallysignificant.

6.7 Time Effect

6.7.1 The overall probability of a firm getting a contract has been going down over theyears, as shown in the negative and significant coefficients of time dummies in all models.One explanation can be that the number of firms that have been working under a subcontracthas declined over the years.

7. Conclusion

7.1 This study has empirically established that the inter-relation between the firmlevel phenomena of subcontracting is affected by the spatial phenomenon of industrialagglomeration. Using the NSS unorganised manufacturing enterprise surveys for the years2000-01, 2005-06 and 2010-11, the study has estimated the effect of various agglomerationeconomies on the probability of a firm working under a contract. Since the variable denotingthe status of the firm is a binary one, the estimation strategy used in the study is a probitregression.

7.2 As discussed in theory, the proximity of input suppliers to the larger firms enhancesthe likelihood of more subcontracting taking place. However, the empirical analysis givesmixed results. Districts with high own industry agglomeration (as captured by the locationquotients) has a positive effect on the likelihood of a firm receiving a contract for a largenumber of industries. The agglomeration economies arising out of industrial diversity oforganised and unorganised manufacturing sector, however, seem to be less dominant ingetting the smaller firms to work under a contract.

7.3 The overall likelihood of firms in getting contracts has fallen overtime, everythingelse being the same. This may be indicative of the fact that in the recent times the percentageof small firms working under a contract has come down since 2000. Consequently, for anumber of industries such as tobacco industry, textiles industry, leather industry, paperindustry, printing industry, transport industry, coke and refined petroleum industry there isa negative effect of organised manufacturing industry diversity on the likelihood of asubcontract. That is, the presence of larger firms in different industries does not seem to beenhancing the linkage between large and small industries. This result does not imply thatlack of presence of larger firms will benefit smaller firms in terms of getting contracts. It onlymeans that localisation economies are stronger than those generated from industrialdiversity.

7.4 One of the reasons behind the fading linkage between large and small firms is theinability of industries in unorganised manufacturing sector to upgrade their technology tomatch the quality requirements of the buyers in the organised part of the sector (Ramaswamy,2013). Our study finds that the firms that are capital intensive by nature are more likely to be


operating under a contract from larger firms. This indicates the potential of the unorganisedmanufacturing sector which is capable of having a strong linkage with larger firms throughsubcontracting. However, the inability of the sector to cope up with technology and otherrequirements limits this phenomenon.

7.5 Subcontracting in unorganised manufacturing sector in India is an importantphenomenon and has a lot of potential to enhance the performance of small firms in theunorganised manufacturing sector in India. Given that the sector employs about 85 percent of total employment in manufacturing sector, it is all the more important to ensure thatthis sector should be made as viable as possible.

7.6 Subcontracting has been taking place mainly in the highly unskilled part of thesector presumably due to the lack of an appropriate channel for larger firms to outsourcetheir activities to the better performing firms in the unorganised manufacturing.Subcontracting exchanges that are in operation have not performed efficiently to helplarger firms to get access to smaller firms through them (Sahu, 2007). This calls formaintenance of a database of a large number of small firms operating in the unorganisedmanufacturing sector, with information on their financial health and other relevant informationmade available. This information can help larger firms to forge access to the small firmsthrough these exchanges.

7.7 In order to encourage greater subcontracting and also allow smaller firms to gainaccess to institutional funds the financial institutions can provide incentives to small firmswhich are linked to larger firms through a contract to get cheaper credit from them. Thiswould enable small firms to gain access to formal credit and also bind them to perform well.Secondly, being linked to a larger firm, they also benefit from knowledge spillover effects interms of technology upgradation and efficient production techniques, which in turn wouldensure that quality standards are maintained.

7.8 The recent initiative of launching the Micro Units Development and RefinanceAgency (MUDRA) Bank by the Government of India is a step in the direction of includingthe micro, small and medium firms (MSMEs) in the gamut of institutional borrowing. Thebank would re-finance the loans up to Rs. 10 lakhs made available to MSMEs through thescheduled commercial banks, regional rural banks, non-bank financing institutions,cooperative banks and micro finance institutions. First time and young entrepreneurs andwomen entrepreneurs would be encouraged through special schemes designed to suittheir needs.

7.9 The government should be cognisant to inter-relatedness of the phenomenon ofsubcontracting and industrial agglomeration while formulating policies affecting industrialagglomeration in order to maintain and/or enhance the linkage between organised andunorganised manufacturing sector.


Reference:

Bairagya, Indrajit 2010, ‘Liberalization, Informal Sector and Formal-Informal Sectors’Relationship: A Study of India’, Paper Prepared for the 31st General Conference of TheInternational Association for Research in Income and Wealth St. Gallen, Switzerland, August22-28, 2010.

Bhushan, Bharat 1984, ‘Ancillarisation: A New Production Strategy’, Business India, NewDelhi, January 16-29, 1984.

Chinitz, B. 1961, ‘Contrasts in Agglomeration: New York and Pittsburgh’, American EconomicReview, 51, pp. 279-289.

Duranton, Gilles, and H. G. Overman 2005, ‘Testing for Localization Using Micro-geographicData’, The Review of Economic Studies 72(4), pp. 1077–1106.

Fujita, Masahisa and Paul Krugman 2004, ‘The New Economic Geography: Past, Presentand the Future’, Papers in Regional Science, Springer, 83(1), pp. 139-164.

Holl, Adelheid 2008, ‘Production Subcontracting and Location’, Regional Science andUrban Economics, 38, pp. 299–309.

International Labour Organisation. ILO’s Pilot Action Project for Beedi Women Workers inIndia. [INT/02/M57/NET]. Available: http://www.ilo.org/wcmsp5/groups/public/@asia/@ro-bangkok/@sro-new_delhi/documents/projectdocumentation/wcms_125467.pdf (accessedon 6th February, 2016).

Isaksen A 1996, ‘Towards Increased Regional Specialisation? The Quantitative Importanceof New Industrial Spaces in Norway, 1970-1990’, Norsk Geografisk Tidsskrift- NorwegianJournal of Geography, 50, pp. 113-123.

Jacobs, J. 1969, The Economy of Cities. New York: Vintage.

Krugman, Paul 1991, ‘Increasing Returns and Economic Geography’, Journal of PoliticalEconomy, 99, pp. 483–499.

Lall, Somik. V., Jun Koo and Sanjoy Chakravorty 2003, Diversity Matters: The EconomicGeography of Industry Location in India (Vol. 3072). World Bank Publications.

Lall, Somik V. and Sanjoy Chakravorty 2005, ‘Industrial Location and Spatial Inequality:Theory and Evidence from India’, Review of Development Economics, 9(1), pp. 47-68.

Malmberg A, Maskell P. 2002, ‘The Elusive Concept of Localization Economies: Towards aKnowledge-Based Theory of Spatial Clustering’, Environment and Planning A, 34(3), pp.429 – 449.


Marshall, A., 1890, Principles of Economics, 8th ed. Macmillan, London. Published in 1920.

Miller, P., R Botham, G Gibson and B Moore 2001, ‘Business Clusters in the UK- A FirstAssessment’, Report for the Department of Trade and Industry by a consortium led byTrends Business Research. London: TSO, 238pp. 2001.

Moreno-Monroy, Ana I., Janneke Pieters, and Abdul A. Erumban 2012, ‘Subcontractingand the Size and Composition of the Informal Sector: Evidence from Indian Manufacturing’,IZA Working Paper No. 6785.

Nagaraj, R 1984, ‘Subcontracting in Indian Manufacturing Industries: Analysis, Evidenceand Issues’, Economic and Political Weekly, August 1984.

North, Douglass C. 1955, ‘Location Theory and Regional Economic Growth’, Journal ofPolitical Economy, 63(3), pp. 243-258.

NSSO, Unorganised Manufacturing Sector Survey, 56th Round. 2000-01.

NSSO, Unorganised Manufacturing Sector Survey, 62nd Round. 2005-06.

NSSO, Unorganised Manufacturing Sector Survey, 67th Round. 2010-11.

O’Donoghue, D., and B. Gleave 2004, ‘A Note on Methods for Measuring IndustrialAgglomeration’, Regional Studies, 38(4), pp. 419–427.

Ono, Yukako 2001, ‘Outsourcing business service and the scope of local markets’, WorkingPaper Series, WP-01-09, Federal Reserve Bank of Chicago.

Ono, Yukako 2007, ‘Outsourcing Business Services and the Scope of Local Markets’,Regional Science and Urban Economics, 37(2), pp. 220-238.

Pardo, Ángel Alañón- & Josep-Maria Arauzo-Carod 2011, ‘Agglomeration, Accessibilityand Industrial Location: Evidence from Spanish Municipalities’, Documentos de trabajode la Facultad de Ciencias EconÃ³micas y Empresariales 11-02, Universidad Complutensede Madrid, Facultad de Ciencias Económicas y Empresariales.

Rama, R., Ferguson, D., Melero, A. 2003, ‘Subcontracting Networks in Industrial Districts:the Electronics Industries of Madrid’, Regional Studies, 37(1), pp. 71–88.

Ramaswamy, K.V. 2013, ‘Understanding the ‘Missing Middle’ in Indian Manufacturing:The Role of Size-dependent Labour Regulations and Fiscal Incentives’, Issue 480 of V.R.F.Series, Institute of Development Economics, Japan External Trade Organisation.

Ramaswamy. E A. 1988, Worker Consciousness and Trade Union Response, OxfordUniversity Press. New Delhi.


Sahu, Partha Pratim. 2007, ‘Subcontracting in India’s Small Manufacturing Enterprises:Problems and Prospects’, Institute for Studies in Industrial Development, Working Paper2007/01.

Stigler, George J. 1951, ‘The Division of Labour Is Limited by the Extent of the Market’,Journal of Political Economy, 59(2).

Suarez-Villa, L., Rama, R. 1996, ‘Outsourcing, R&D and the Pattern of Intra-Metropolitan-Location: the electronics industries of Madrid’, Urban Studies 33(7), pp. 1155–1197.

Taymaz, E., Kilicaslan, Y. 2005, ‘Determinants of Subcontracting and Regional Development:An Empirical Study on Turkish Textile and Engineering Industries’, Regional Studies,39(5), pp. 633–645.

Venables, A. J. 1996, ‘Equilibrium Locations of Vertically Linked Industries’, InternationalEconomic Review, 37(2), pp. 341-359.


Table 1: Trend in Real Gross Value Added and Labour Productivity in UnorganisedManufacturing Sector

NSS Rounds Year Real GVA Compounded

Annual Growth Rate (in per cent)

Employment (in Million)

Labour Productivity (Compounded Annual

GR in per cent) (Rs. in Crores)

51st 1994-95 47,795 -- 33.2 14396

56th 2000-01 70,642 6.7 37.08 19051 (4.8 per cent)

62nd 2005-06 85,533 3.8 36.4 23470 (3.5 per cent)

67th 2010-11 118,924 6.8 34.8 34087 (6.4 per cent)

Source: NSS Reports on Enterprise Surveys in Unorganised Manufacturing Sector

Table 2: Labour Productivity in Unorganised Sector at NIC 2-Digit Level

NIC 2 Digit Industry name 2000-01 2005-06 2010-11 10 Food products 18,388 23,235 36,510 11 Beverages 15,508 16,164 26,017 12 Tobacco Products 8,222 6,650 10,091 13 Textiles 16,469 18,911 29,182 14 Wearing Apparel 19,494 18,575 29,681 15 Leather and Leather Products 21,848 26,946 37,362 16 Wood and Products 12,443 13,148 26,930 17 Paper and Products 24,396 20,095 45,202

18 Printing and reproduction of Recorded Media 37,950 42,408 67,442

19 Coke and Petroleum Products 28,469 35,628 48,739 20 Chemicals 20,264 17,294 32,803 21 Pharmaceuticals 14,156 53,542 71,949 22 Rubber and Plastics 44,466 49,498 49,456 23 Other Non-metallic Products 18,881 28,150 34,040 24 Basic Metals 54,231 100,629 60,329 25 Fabricated Metal Products 28,409 40,154 61,064 26 Computer, electronic and optical prd 50,071 82,251 73,843 27 Electrical equipment 47,588 62,098 63,205 28 Machinery & Equipment nec 39,827 70,513 84,476 29 Motor vehicles, trailers and semi-trailers 51,015 57,648 92,459 30 Other transport equipment 55,303 47,314 75,913 31 Furniture 28,229 32,928 49,001 32 Other Manufacturing 27,680 42,198 39,769

33 Repair and installation of machinery and equipment 27,555 31,660 50,416

01632 Cotton ginning, cleaning and bailing 16,009 45,015 35,301


Table 3: Estimation Results for Probit Regression

Independent Variable Dep. Variable: Contract Dummy

Model 1 Model 2 Model 3 Model 4

Location Quotient -0.0001 (-0.09)

-0.008 (-1.26)

0.0009* (3.04)

0.0007*(2.4)

Unorganised Sector Diversity -0.49 (-1.41)

-0.36* (-3.34)

-0.08 (0.77)

-2.11#

(-1.67)

Organised Sector Diversity

-0.47* (-2.99)

0.04 (0.66)

-1.91 (-1.92)

0.04(0.56)

Labour Productivity

-0.03 (-0.69)

0.04* (3.68)

-0.004 (-0.32)

-0.01(-0.96)

Operating Cost

0.15* (7.11)

0.10* (15.4)

0.12* (18.3)

0.12*(18.2)

Capital Intensity

0.06* (4.25)

0.03* (6.8)

0.03* (6.3)

0.03*(6.3)

Expanding

0.04

(0.62)

-0.04#

(-1.86)

-0.04#

(-1.87)

-0.03(-1.23)

Stagnant

-0.13*

(-2.15)

-0.16*

(-7.7)

-0.16*

(-7.8)

-0.15*(-7.06)

Contracting

-0.036

(-0.44)

-0.14*

(-5.5)

-0.15*

(-6.2)

-0.14*(-5.4)

Location: House

0.225*

(3.05)

0.26*

(7.7)

0.21*

(6.17)

0.16*(4.8)

Location: Fixed & Permanent

0.16*

(2.05)

0.34*

(10.3)

0.23*

(6.9)

0.19*(5.6)

Log Real GVA

0.16*

(5.51)

0.05*

(5.4)

0.09*

(9.7)

0.09*(9.9)

Account Dummy

-0.081

(-1.38)

0.03

(1.38)

0.025

(1.16)

0.008(0.38)

Constant

-2.9

(-4.5)

-2.5

(-20.1)

-2.3

(-18.3)

-2.2(-17.1)

Year2005

-0.05

(-0.95)

-0.04*

(-2.3)

-0.05*

(-2.9)

-0.04*(-2.2)

Year2010

-0.91*

(-15.28)

-0.85*

(-35.8)

-0.85*

(-35.8)

-0.86*(-34.8)

District Dummies

YES

YES

NO

YESIndividual Industry Dummies

YES

--

--

--LQ Interacted Dummies

--

YES

--

--Organised Sector Interacted Dummies

--

--

YES

Unorganised Sector Interacted Dummies

--

--

--

YES

No. of Observations 120765 117355 125492 117823Pseudo R-Squared 0.28 0.19 0.12 0.198Note: Symbols *, **, # denote levels of significance at 1%, 5% and 10%, respectively . Parentheses include the corresponding z-statistic.


The Journal of Industrial Statistics (2016), 5 (2), 122 - 137122

Understanding Demand, Supply and Price Behavior in the DairySector: Using Official Indian Statistics

Nilabja Ghosh1, Institute of Economic Growth, New Delhi, IndiaRoopal Jyoti Singh, Institute of Economic Growth, New Delhi, India

M. Rajeshwor, Institute of Economic Growth, New Delhi, India

Abstract

Balancing the pressures of WTO compliance with direct and derived consumer demandsand the interests of the producers is a looming challenge in India’s Milk economy. Afterbeing driven by a successful cooperative movement for decades, the sector needs a review.In an attempt towards understanding the milk market this paper identifies different datasources along with data gaps to trace the market movements methodically in the recenttwo decades.

1. Introduction

1.1 Milk today is not just a consumer item. It is an exportable item in a competitiveworld and, with the rise of the organized food processing industry it is also becoming anindustrial input. A very large number of agents such as farmers, vendors, traders, processors,cooperative workers, manufacturing companies, exporters and consumers depend on thedynamics of the sector. The challenges on the demand and supply sides are compoundedby the pressure mounted on India to open up the dairy market as part of India’s obligationtowards WTO.

1.2 To monitor the dairy sector high quality and reliable data will be needed for broaderpublic view. Attributable perhaps to this inadequacy a lack of statistics-based holisticfocus on the structure of the dairy economy is observed. We filter out relevant data fromdifferent secondary official data sources to examine the directions of milk supply, of thedifferent components of its disposition and to assess the movement of price and thesupply-demand gap. Conducted at the all India level for the post liberalization period 1990-91 to 2010-11, the study keeps validation and consistency of estimates in context throughoutthe analysis.

2. Background to Indian dairy sector

2.1 Milk accounts for 9% of India’s GDP but an estimated 70 million households manyof whom are small farmers (Birthal et al., 2008) earn from livestock ownership, 69% ofworkforce being women (GOI, 2013). Despite being nutritive and a part of the regular diet ofIndian people, milk did not get the recognition in policies for India’s food security in theway cereals did. Little effort was made to ensure that the production met the demand of theconsumers, let alone understand the possibilities of expanding the market2. Seen commonly

1 e-mail: [email protected] Public initiatives such as the Milk Control Board and the Operation Flood (OF) can however becompared with the public distribution system and the green revolution respectively,

as a useful tool for poverty alleviation, dairy remained largely an individualistic, unorganizedand subsistence activity.

2.2 After independence India had a large number of dairy animals but produced ameagre 17 million tonnes of milk owing to poor animal health, low quality forage and humidand hot conditions in many parts of the country (Raj, 1969). The National Dairy DevelopmentBoard (NDDB) was founded at Anand in 1965 by a parliamentary Act, focusing on thegrass-root level producers with the principles of cooperation. By 2001 India became theleading milk producer in the world but even this was the beginning of yet another journey.

2.3 Today, visible shifts in consumer’s expenditure pattern towards milk products3,the appalling status of malnutrition among Indian children (Arnold et. al, 2009) and a closenexus between milk price and food price inflation (milk has a weight of 3.24% in wholesaleprice index in the 2004-05 series) are empirical findings that link milk closely with consumerwelfare. On the other hand, farmers with little or no land look for an expanding market forlivelihood and incentive. In view of the significance of welfare of the diverse stake-holdersof the dairy sector, the continual rise in the price of milk in recent times interspersed withintimidating slumps in the international market makes understanding the market necessary.

Cooperative functioning

2.4 The World Bank-financed Operation Flood (OF) launched by NDDB in the 1960swas a landmark achievement in cooperation that transpired on Indian territory. Althoughmany milk cooperative4 predated OF the government’s active participation in the processwas a turning point. Every cooperative in Asia has tried to emulate this success story ofAmul. Though other cooperatives too made a mark5, by and large, the cooperative movementin totality actually failed in most regions of India and large sections of households andfood providers even in the urban sector remain to be supplied by informal traders and milkvendors.

2.5 NDDB addressed the milk producers’ access to technology, feed and facilities atdoor step or proximity. Technological progress was mainly manifested in planned breedingthrough artificial insemination (AI) with selected genetic features acquired from othercountries by cross-breeding so that the share of indigenous cattle diminished in India overtime. NDDB also made access to veterinary facilities easier.

2.6 Grazing animals on pastures was a traditional practice but grazing on crop landsafter harvest serves the dual purpose of clearing and manuring land for the next sowingwhile also nourishing them. Dry crop residues from cereal and sugarcane fields are lessnutritious but they are common as fodder while cultivation of green fodder or forage crops6

is a superior means, rare but promoted by NDDB.A switch from extensive to an ‘intensivefeeding regime’ in which animals are fed in their stalls with commercially prepared nutritiousconcentrates made from grains and oilseed meals is however emerging (World Bank, 2008).

3 According to NDDB chairman Amrita Patel, rising incomes have led to a shift away from cereals tovegetables, milk and meat (Srivastava, 2011).4 The Kaira District Cooperative in Kheda district of Gujarat in 1946 is an example of early cooperativeventure.5 Brands like Vijaya (Andhra Pradesh), Verka (Punjab) and Saras (Rajasthan) are examples.6 Guinea grass, paragrass, lucerne, berseem, cowpea, velvet bean are nutritious forage crops.

123Understanding Demand, Supply and Price behavior in the Dairy sector:...

Liberalization and the emerging Market channels

2.7 While the OF was certainly a mile stone, the liberalization of the Indian economyin the 1990s introduced India’s dairy sector to another new era. De-licensing in 1991 and1992 and signing of the WTO agreement in 1995 gradually opened up the dairy sector toprivate capital and foreign markets7. Exports of dairy products began to grow from a minisculelevel in the 2000s, creating pressure on the domestic capacity even while imports of cheapermilk persisted as a threat to the viability of small milk producers. The government, yetunprepared for the conflicting pressures responded with trade controls (occasional levy,invocation of SPS measures8, sometimes outright bans) that created discontent amongexporters and processors and even perhaps deprivation to farmers. While trade partnersespecially EU clamor for access at the multi-lateral level, Free trade Agreements (FTA) withindividual partner countries will not make managing the economy easier (Sengupta andGautam, 2011).

2.8 The National Dairy Plan (NDP) made in 2012 and the National Livestock Policy in2013 in consistency with the National Policy of Farmers of 2007 were responses of theunion government to create a framework for a sustainable livestock economy underlining adeparture in approach from intense cross-breeding. The new government, in particular isreported to be placing considerable emphasis on the upgradation of indigenous ‘desi’breeds through a national Gokul Mission.

2.9 With the launch of economic liberalization, besides the state-promotedcooperatives, profit oriented, private, organized and more resourceful firms of both domesticand multinational ownership have also shown interest in milk. Even contracting, despite allthe ideological and historical contention it encounters is already seen as a promisingchannel (Kolekar et al., 2012). Not surprisingly, even though cooperation, seen as an ‘ethicalsocial behaviour’ (Abbott, 2014) proved itself to be a successful model worldwide (Chahaland Gupta, 1986, Ibitoye 2012), reappraisal of the NDDB’s role has been proposed (Kapoor,2014) at different levels.

Sources of Demand for Liquid Milk

2.10 Tastes vary within India but with globalization, dairy products have gained greatersignificance in food habits9 across the spectrum. The response to the changing domesticand international demand was also manifested in the rise of food processing in the organizedsector which grew by a massive 378% between the years 2001-02 to 2010-11 against a riseof wholesale price index of all commodities by 66.4% over the same span.

2.11 Milk is highly perishable and so the net available amount falls short of theproduction. A large part of the fresh milk produced is used for self-consumption of producers,for informal sale in local markets and delivery at door-steps in rural and urban neighborhoods7 The government was limited by a commitment of zero tariff binding made in the WTO agreement. Thetariff commitments on milk powder were re-negotiated in 2001-02 when Quantitative Restrictions onimports were removed. The Milk and Milk Product order (MMPO) imposed in 1992 was amended in1999 to allow flexibility to the processors to adjust their scales of production.8 Agreement on the Application of Sanitary and Phytosanitary Measures (SPS Agreement) subjects WTOmembers to base their methodologies on three recognized standards (Wikipedia)9 Milk is seen as a ‘high value’ (HV) product whose expenditure elasticity food is shown to be relativelyhigh (Ravi and Roy 2006)

124 The Journal of Industrial Statistics, Vol. 5, No. 2

Understanding Demand, Supply and Price behavior in the Dairy sector:...

in unprocessed form besides being marketed through the cooperatives and dairy firms.The price received by the farmers (farmer price or producer price) differentiates for thequality and fat content and the data even at the cooperative level are not systematicallyreported on public domain. It is indicated by The National Commission of farmers (NCF)that the milk producers in the Anand Model of milk production get, net of intermediation,about 60% of the final price but given the spatial and qualitative variations and with theorganized market and the unorganized sector both involved in milk marketing, this figure isonly indicative. A second estimate comes from Kulkarni (Kulnarni, 2014) which puts thesame figure at somewhere between 75% and 85% or at a median figure of 80%. Given theretail price as officially reported the Kulkarni estimate indicates a higher producer pricethan NCF estimate does. The matter remains unresolved.

2.12 Milk can be purchased in raw and liquid form (to be boiled at the domestic level)or in pasteurized liquid form or it can also be purchased skimmed of all or part of the fatcontent. A first stage processing for preservation of milk is the evaporation, spray dryingand similar techniques to produce milk powder or condensed milk more stable than theliquid form. Further downstream, both milk powder and liquid milk can be processed intodairy items like curd, yoghurt, cream, butter, cheese which in turn can be intermediateinputs for other food processing activities such as bakery, confectionery. The differentconstituents of milk such as the fat, the butter milk, the milk protein and casein also haveimportant uses in pharmaceutical and other sectors.

3. Data on the Milk market

3.1 Production of fresh milk is historically reported by Ministry of Agriculture,Department of Animal Husbandry, Dairying & Fisheries (DAHDF)10.In this reportingprotocol, milk is treated as of uniform quality. Data on wastage in the supply chain are notreported on a regular basis but quantitative post-harvest losses (PHL) were assessed byCIPHET (2010) by a onetime survey. Also the PHL for milk was estimated only throughverbal enquiry and field observations and only at select stages11.While this source is by farthe most reliable one owing to the methodological rigour, implications of underestimationcannot be ruled out.

3.2 Unlike crops that flow from land as the ‘capital’ stock’, output of milk flows fromlive animals constituting livestock. Animals are not only valuable sources of farm householdnutrition but they also serve in land preparation and organic manuring and provide fuel,haulage, transport and farm energy while also serving to stabilize incomes. The milchanimals are comprised of cattle and buffalo as also other smaller animals. Buffalo, consideredmore productive than cattle in feed utilization (Khajarern and Khajarern, 1998), makes upover 40% of the milch animals in India but around the world they are a minor source of milk.The data on livestock by type of animal, sex, milch status and also by indigenous or cross-bred classification are reported through the Livestock Census only at five years interval.

10 Collected from annual sample surveys conducted by the state Animal Husbandry Departments under the‘Integrated Sample scheme’ in three seasons namely summer, monsoon and winter.11 Not covered in the survey are operations of sorting and grading either because such operations are notrelevant for milk or required facilities and suitable methodologies were not at hand. Storage at godown,warehouse and cold stores, storage at wholesale level are also not covered because the units were notavailable in the specific districts surveyed.

125


3.3 Feed is the major constraint for milk production. Although animal products andcrop products are alternative human food, because the availability of dry fodder is associatedpositively with the access to land and its cultivation with certain crops and that of highergrade feed competes with crop land, indirectly milk production too depends on theperformance of the crop sector and the weather. Area under different cereal crops, theirproduction and wholesale prices are readily available from Ministry of Agriculture sources(website) and so are corresponding statistics on sugarcane and oilseeds. The same Ministryalso provides Land use statistics with data on pastures. Aggregate estimate of the areaunder fodder crops is also reported by MOA but detailed information is scarce.

3.4 Household consumption data are reported as value of milk and milk products byNational Sample Survey Office (NSSO, various) on per capita per month basis in its annual(thin) and the quinquennial (large) surveys of consumer expenditure. Data reported byNSSO are known to be reliable. Using population data from Census (1991,2001, 2011)undertaken at decennial intervals and interpolated for the interim years using compoundannual average growth rate, the total consumption value of milk can be calculated for allIndian households and for households of different types. Producers’ demand can be seenas the consumption in agricultural households presumably comprising of NSSO reportedcategories ‘self-employed in agriculture’ (SEA) and the ‘agricultural labour’ (AL) householdsas they are both likely also to be milk producers. The NSSO however reports annually forthe composite milk consumed both in fresh and primary processed forms (dairy)12.

3.5 To bring the value of milk consumption to physical units it is necessary also tohave milk prices at which consumers buy. The wholesale price (WP) of milk is reported formajor markets or mandis by Agricultural Prices in India (MOAa, various)and these pricesare averaged to get all India prices. Price index of milk and dairy products at the all Indialevel is reported in the website maintained by Office of the Economic Adviser, Governmentof India. However, the consumers buy milk at retail prices (RP) the data for which are notcontinuous. Rural consumption raises further complications because milk is either farmproduced or purchased locally from producers13 and the rural market functions differentlyfrom the urban market. The published retail prices can be collected from the same sourcesas WP and averaged across months and centres to arrive at all India annual retail prices(RP) but these prices possibly do not represent rural consumer prices.

3.6 Under such constraints consumer prices for different sections can be conjecturedonly using their empirical relation to the secondary data on WP. In the years 2001-02 to2011-12 for which both sets of data (RP and WP) are accessible monthly, we expressed theWP as a proportion of the corresponding RP. While the proportion varies between 0.90 and1.08, the average ratio is 0.967, which is used for projecting the retail prices for all the sampleyears using the yearly wholesale prices as benchmarks. The retail price so derived isassumed to be the price paid by urban consumers.

3.7 If representative estimates of prices paid by producers and other rural peoplewere available, the rural price can be obtained as an weighted average, the weights beingtheir shares in total rural households as available from NSSO data. The producer price can

12 NSSO includes consumption of milk and milk products: baby food, condensed/ powder milk, curd, ghee,butter, ice-cream, other milk products.13 The NSSO values the consumption at the actual paid cost or at producer prices.

126

Understanding Demand, Supply and Price behavior in the Dairy sector:...

be used as a proxy for the consumer price of farm households with the intuitive rationalethat these households are likely to forego this price when they do not sell the milk that theyretain for home consumption. However, a consistent data-base of the producer prices evenfor the cooperative channel, is not in public domain. We have assumed the ratio to be 60%of the corresponding RP going by the estimate given by NCF as stated earlier. We also takeinto account the alternate estimate of the ratio at 80% based on Kulkarni’s estimated rangeand produce a second set of estimates of the producer price.

3.8 What the non-farm rural consumers pay is a matter of conjecture. To the extentthat sales to these households are residual to cooperative sales and the cost of marketingis lower, this consumer price will be lower than the producer price. On the other hand, faithplaced on the quality, the visibility or production and door-step services may translate tohigher value but, the fact that such sales are made locally on foot or by cycle and mostlynot in graded forms14 makes this less likely. In any case obviously there is a competitionbetween the cooperative channel that provides assured sales and prices and the lessdemanding informal sales process in local markets so that the two prices are likely toconverge. We therefore assume that all rural households pay the same price equivalent tothe producer price.

3.9 The use of secondary available price data to derive the consumer prices is justifiedand validated by matching the derived producer price and the retail price with imputedconsumer price actually obtained using NSSO’s quinqennial survey data published inAgricultural Statistics at a Glance (MOAb, various). Consumer prices of milk imputed usingvalue and quantity of milk and dairy products consumed (NSSO data) in 2004-05 are Rs12.22 and 16.30 for the rural and urban sectors respectively compared to the derived producerprice and retail price of fresh milk at Rs 10.42 and Rs 17.37 that we estimate using wholesaleprice as the anchor (Table A1). Interestingly, the rural consumer price derived using Kulkarniratio is found to be closer to the rural price imputed using NSS data in both years 2004 and2009 compared to NCF. The total value of milk consumption is then divided by thecorresponding consumer price to obtain the quantity consumed. To obtain per capitaconsumption of different rural sections, population ratios calculated from NSS reports areused as weights.

BOX 1: Specifications for Milk and Milk Product with Harmonized System Code (HSC) Forms Specification as reported in DGCIS Liquid Milk (HSC-0401)

Milk & Cream Not Concentrated Nor Containing Added Sugar or Other Sweetening Matter

Milk Powder (HSC-0402)

Milk & Cream Concentrated/Containing Sugar/Sweetening Matter

Dairy Products Butter Milk (HSC-0403)

Butter Milk, Curdled Milk & Cream, Yogurt, Khir& Other Fermented Acidified Milk & Cream

Whey and Products (HSC-0404)

Whey & Products Consisting of Natural Milk Constituent Not Containing Added Sugar or Sweetening Matter.

Butter and Fat (HSC-0405)

Butter and Other Fats & Oils Derived from Milk; Dairy Spreads

Cheese And Curd Cheese and Curd

14 In organized milk marketing technical methods are employed (such as the 2-axis formula and theuse of the lactometer) to maintain compliance.

127

3.10 Liquid milk occupies a marginal place in international trade. Powdered milk isexported both as whole milk powder (WMP) and as low fat skimmed milk powder (SMP).Trade data is reported by Directorate General of Commercial Intelligence (DGCIS) in termsof components by HS codes15.FAO also in its website FAOSTAT reports India’s exportsand imports of liquid and powdered milk further disaggregated as skimmed and whole milk.We use DGCIS data on both milk and dairy food products as follows.

3.11 We make a crude estimate of the trade in pure milk interpreted to be either liquid orpowdered milk specified in Box 1 in liquid milk equivalence, though these component formsvary in fat content. Using their proportions in trade as reported by FAO, the respectivecomponents of skimmed milk powder and whole milk powder are expressed as liquid milkequivalent with no distinction employing conversion rates16 obtained from industry sourcesand added up. The dairy products also use milk as an input but need to be treated differently.To obtain movements of total milk import or export in real values, their total value is expressedas index and deflated by a composite milk price index which again is the weighted averageof wholesale price indices milk and dairy products with the same base. Admittedly,generalizations are compulsions given the data reporting system and the heterogeneity ofmilk and its products.

3.12 The Annual Survey of Industries (ASI) conducted by the CSO generates data onthe amount of fresh milk processed annually covering only the factory sector. The informalsector (using less than 10 employees or no power) is covered by the NSSO in its Survey butonly at fairly long intervals17. The value of fresh milk consumed as industrial inputs fordairy products (NIC 152) as well as all products together obtained from ASI is deflated bythe wholesale price of milk to obtain estimates of the quantities of milk processed in theorganized sector for different purposes. We use unit level data on ‘input consumed’ providedby ASI for the organized sector in the decade starting 2001 and identify the item fresh milkusing the commodity code ASICC.

4. Results

4.1 In table 1 the production of milk is seen to have risen impressively in the recentyears from 54 to 128 million tonnes from 1990 to 2011. It is further expected to cross 140million tonnes in 2014 (World Bank, 2008). The compound growth rate of total productionand net production accounting for wastage works out to 4.2%. The performance is reflectedin the productivity measured by production per hectare of crop area, per hectare of fodderarea and per hectare of area under all potential feed materials much of which however areonly by-products. Dedicated fodder area constitutes only 4% of India’s cropped area.Animals are increasingly fed in the stalls and are also known to stray about for feeding.Stable cereals and sugarcane acreages, declining pastures and highly volatile fodder areataking a special downturn in drought years like 2002 are other major features of the changein the feed scenario.4.2 Production per capita and productivity per animal grew at lower rates. Thecomposition rather than the number of animals or the amount of feed produced seems tostand out as a factor behind the production breakthrough as seen in Table 2 also. The15 Harmonized System16 100 liters of whole milk is equivalent to 9kg of SMP or 13 kg of WMP.17 The data has also serious problems with the codes for identifying the items.


animal stock remained nearly stable in the study period especially after 1997 census. Thedecline in the number of indigenous cattle after 1992 which is offset by the sharp growth ofthe numerically minor group of cross bred animals and a slow growth of the number ofbuffalos shifted the composition of animals steeply in favour of cross-bred cattle suggestingthat NDDB’s emphasis on improved breeding was successful (see also IUF, 2011).

4.3 The average Wholesale price, Rs7.72 per litre in 1990-91 and Rs 10.30 in 1993-94crossed Rs 30 in 2011-12 recording a growth rate of 6.04% in the period 1993-94 to 2011-12which is comparable, but a little less than the overall price rise (inflation in WPI) of 6.14%(Table 3).Thus milk prices did not increase significantly relative to general price levelespecially in the mid-1990s and in most part of 2000s until it caught up in 2009-10 (Figure 1).

4.4 At 3.9%, total consumption grew nearly at par with production (Table 4). The ruralconsumer’s spending on milk and dairy products grew at a slightly higher rate than theurban consumer, but the latter’s total physical consumption increased considerably fasterreflecting the urbanization of population. Between 1993-94 and 2011-12 the urban share inIndia’s domestic milk and dairy products consumption went up from 26% to 30% but therural sector remained an important contributor (70%) to milk consumption.

4.5 Farm households make up 45% of total population in the country and about 60%of the rural population. Using the assumptions given in earlier section we calculate thatabout 42% of milk consumption in India is accounted for by farm households down from45% in 2004-05 (appendix Table A2) and the SEA type households are larger milk consumersthan AL18.We have assumed uniform consumer price equal to the retail price in the urbansector. Since not all the urban households are served by the organized sector (Birthal et al2008)19 both the rural and the urban price may arguably be an over estimate. Based on NCFestimate, probably considerably less than 30% of milk consumption (Figure 2) attributed tourban sector passes the formal route. If the estimate from Kulkarni is accepted then ruralprice is higher (Table 3) and rural physical consumption is considerably less in 2011-12 at 47million tonnes compared to 63 million by NCF estimate and the rural share comes down to63% from 69%. NDDB estimated 20% of production (128 Million Tonnes) i.e., 26 milliontonnes in 2011-12 passed the organized channel (NCF, website). This is comparable to theless than 27 million tones of urban consumption for that year calculated using NCF-ratio.NCF states that only 20% of the produced milk reaches the organized sector and of thisonly a half passes the cooperatives. We estimate urban consumption to be 19% ofproduction. The IUF (2011) reports that 65% of milk is consumed in the unorganized sector,including producers and vendors, compared to over 70% estimated using the NCF ratioand 63% using Kulkarni ratio.

4.6 Milk is traded mostly as milk powder and baby food. Items like butter, cheese andwhey are other traded items. Since the import tariffs were renegotiated in 2001-02, India’strade in dairy goods went through a transition. Table 5 summarises trade movementsbetween the years 2001-02 and 2011-12. After rising to a peak in 2005-06, exports of milk18 The AL and SEA households spend 5.3% and 9.9% of their expenditures respectively on milk andmilk products. They spend 58.4% on food against 51.8% spent by all households.19 Direct transaction is common in rural and peri-urban areas. Restaurants, hotels, sweet shops and manyhouseholds are known to buy from informal vendors. The informal sector is the one that is not recognizedby the tax administration. Mutual trust and not rules and regulations guide their transactions.

Understanding Demand, Supply and Price behavior in the Dairy sector:... 129

powder and dairy product declined significantly. Betterment of technology and managerialpractices in competing countries and increased subsidies in EU are some of the reasonscited in literature. Exports of most dairy products declined after a lag only while powder milktook the major impact. Meanwhile imports of milk powder as also of all the dairy goods likecheese and butter rose sharply. Some dairy products are both exported from and importedin India at all times. Liquid milk export sustained increases but its net export actuallybecame negative.

4.7 Domestic industries process liquid fresh milk mostly to produce powder milk anddairy products. Milk however enters as input in industries in various forms. To avoid over-counting only ‘fresh’ milk, identified by commodity code ASICC, is considered to obtainthe extent of processing. All primary processed versions such as milk powder, pasteurizedmilk and condense milk are excluded as inputs. Besides, bakery, confectionery and otherindustries also use milk as an input but the dairy industry has a predominant role (Figure 3).Both components of food processing rose in the middle of the decade and came downthereafter. The extent of processing of fresh milk for producing milk powder and dairyproducts has been about 11% and less than 1% for other products, but came down in 2010-11. NDDB (2009) provides another set of estimates of milk processing as ‘butter, milkpowder and western type manufacture products’ and in the form of ‘traditional products’without further elaboration, and their measures indicate that between 8% and 25% of freshmilk is processed. The processed milk is both consumed in the domestic economy andexported.

The Demand Supply Gap and Price behaviour

4.8 Data drawn from the different sources are explored to track demand-supply gap inthe country and the prices in the milk market. Given the issues we face in dealing withcomposite data on milk and milk products and with complexities of conversion to milkequivalence, the results are indicative at best. A reasonable assumption about the producerprice or consumption price of farmers is also made difficult by the absence of and variationbetween alternate estimates of the retail price’s equation with the producer price. Moreover,there is a large informal market both in rural and urban area for milk on which data is sparseand can be collected only from primary survey. The prices in this market are likely to belower than the formal prices in the respective areas but the assertion cannot really be madewith conviction in the absence of evidences. Comparing our estimated total consumptionand net production a large gap is found. The gap would be larger if Kulkarni estimate isused though it would narrow if possibly lower prices in informal markets are taken accountof.

4.9 Figures 4 and 5 show that the real price of milk (WP of milk deflated by WP allcommodities) increased only from 2008-09 bearing little if any relation to the estimatedsurplus while the price rise is accompanied by falling exports, rising imports and decline ofmilk processing. Indices plotted with a common base however suggest a fatigue in domesticproduction that may not keep pace with domestic demand in future (Figure 6). In practice,the surplus measured as derived consumption quantity less net production is attributableto processing in formal manufacturing industries, informal enterprises and in the servicesector, or used for other purposes such as religion and storage as basic products or arisingfrom methodological flaws or even remain unexplained.


5. Conclusion

5.1 In context of the current changes in food demand within the country as well as theopportunities and threats in the global markets and to harness the opportunity of valueaddition and employment generation the milk economy needs to be monitored, encouragedand guided with understanding.

5.2 To understand the milk market, data available in the public domain is explored. Theanalysis of the price rise does not indicate that producers have gained substantiallysubsequent to liberalization. The interests of milk producers and employment generationmay be compromised by lower demand from processors. Since processors may be holdingstocks in the form of milk powder, data on which are not available, their long run incentiveis also undermined creating a vicious cycle. On the contrary price rise creates discontentamong urban consumers and exporters too. The challenge of matching sporadic declines ofinternational milk price as happening currently creates policy challenges with littlepreparation.

5.3 With globalization the milk market will have to be monitored continuously requiringimproved intelligence and analysis. Although much of the information is available publicly,inadequacy and fusion of multiple information necessitate assumptions. Besides aftercounting the varied sources of milk disposition reported there remains a gap betweensupply and demand that cannot be explained without data on its use in the service sectorand the informal sector. More consistent estimates of producer prices of milk are requiredfor which primary surveys in different parts of the country have to be encouraged. It ispossible that the NCF estimated relation between retail and wholesale prices is anunderestimate while Kulkarni’s ratio seem more consistent. This shows the importance ofdata reconciliation, coordination and strengthening of data collection.

References

Abbott, S. and Panwar, R. (2014). A Study into Marketing-cum-Processing Co-operativesocieties in Punjab with Special Reference to Mullanpur Society. Indian Journal ofEconomics and Development, Vol. 10 No. 3, 291-297.

Arnold, F., Parasuraman, S., Arokiasamy, P., & Kothari, M. (2009). Nutrition in India. NationalFamily Health Survey (NFHS-3) India 2005-06.

Birthal, P. S. (2008). Making Contract Farming Work in Smallholder Agriculture. New Delhi:National Centre for Agricultural Economics and Policy Research.

Birthal, P. S., Jha, A. K., Tiongco, M. M., &Narrod, C. (2008). Improving farm-to-marketlinkages through contract farming: A case Study of Smallholder Dairying in India. DiscussionPaper 00814. IFPRI.

Census of India (1991, 2001, 2011). Office of the Registrar General & Census Commissioner,Ministry of Home Affairs, Government of India.


Central Institute of Post-harvest Engineering and Technology (CIPHET), (2010). Project onPost Harvest Technology, Ludhiana Punjab, Report submitted to Ministry of Agriculture,Government of India.

Central Statistical Organisation (CSO), (1998, 2004, 2008). National Industrial Classification(All economic activities). Ministry of Statistics and Programme Implementation, Governmentof India.

Chahal, S.S. and Gupta, J.R. (1986). A study into growth of milk cooperatives in Punjab.Dairy Guide. 8 (9): 11-16.

Department of Animal Husbandry, Dairying and Fisheries, Ministry of Agriculture.

Directorate General of Commercial Intelligence, DGCIS (various). Ministry of Commerceand Industry, Government of India.

FAOSTAT (website). Food and Agriculture Organization of United States. http://faostat.fao.org/

Government of India (GOI) (2013). National Livestock Policy. Department of AnimalHusbandry, Dairying and Fisheries, Ministry of Agriculture.

Government of India (GOI) (website). Office of the Economic Advisor, Ministry of Commerce& Industry, Department of Industrial Policy & Promotion. http://www.eaindustry.nic.in/

Ibitoye, S.J. (2012). Survey of the performance of Agricultural cooperative societies inKogi state, Nigeria. European Scientific Journal. 8 (24): 98-114.

International Union of Food (IUF) (2011). Indian Dairy Industry. IUF’s Dairy DivisionCountry and Company Reports. http://cms.iuf.org/

Kapoor, Rana (2014). In search of a second white revolution. Business Line, February 10.

Khajarern, S. and J.M. Khajarern (1998). Feeding Swamp Buffalo for Milk Production. Book(Eds.) by Andrew Speedy, Feeding Dairy Cows in the tropics. Food and AgricultureOrganisation of the United Nations. Daya Publishing House, New Delhi.

Kolekar, D. V., Kokate, L. S., Bangar, Y. C., & Khillare, G. S.(2012). Review on contract dairyfarming: to boost Indian dairying.

Kulkarni Vishwanath, (2014) Amul, mother dairy have not form the cartel. July 1, BusinessLine.

Ministry of Agriculture (MAO) (Various), Agricultural Statistics at a Glance, Directorate ofEconomic and Statistics, Department of Agriculture and Cooperation, Government of India.

Ministry of Agriculture (MOA) (Various), Agricultural Prices in India, Directorate ofEconomic and Statistics, Department of Agriculture and Cooperation, Government of India.


Ministry of Agriculture (MOA), Directorate of Economics and Statistics, Department ofAgriculture and Cooperation (DES), Government of India. http://eands.dacnet.nic.in/.

National Commission on Farmers, NCF (website). http://krishakayog.gov.in/NDDB.pdf

National Dairy Development Board NDDB, (2009). Base working paper on Strategy andAction Plan for Ensuring Safety of Milk and Milk Products. www.fsssai.gov.in/portals/0/baseworkingpaper_june2009.pdf. Expert group on milk and milk poducts.

National Sample Survey Office NSSO (various). Household Consumer Expenditure. Ministryof Statistics & Programme Implementation, Government of India.

Raj, K.N. (1969). Investment in Livestock in agrarian Economies. Centre for AdvancedStudies, Department of Economies, Delhi School of Economics, University of Delhi.

Ravi, C. and D. Roy, (2006) Consumption patterns and food demand projection: A regionalanalysis. Paper presented at the Workshop ‘From Plate to Plough- Agricultural Diversificationand Its Implications for Smallholders, organized by the International Food Policy ResearchInstitute, New Delhi Office, and Institute of Economic Growth, New Delhi, India, 20-21September.

Sengupta, R. and Gautam, K. (2011). A Case Study of the Food Processing Industry. India’sFree Trade Agreements and Micro, Small and Medium Enterprises. InditeGlobal, New Delhi.

Srivastava, Samar (2011). When the Milk Runs Dry: India’s New Dairy Policy. India Forbes,April 4.

Wikipedia.http://en.wikipedia.org/wiki/Agreement_on_the_Application_of_Sanitary_and_Phytosanitary_Measures

Wikipedia (website). www.wikipedia.com

World Bank (2008). Agriculture for Development. World Development Report.


Figure 1: Price (wholesale) rise in Milk and all Commodities

Figure 2: Share (%) of physical Milk consumption in Rural households based onalternative estimated ratios

Figure 3: Quantity of liquid milk processed in industry (Vertical axis for Milk powderand Dairy is in the left and for the other industries in the right)


Figure 4: Movements of Real (wholesale) price of Milk and domestic surplus (netproduction less derived consumption demand) in the 2000s

Figure 5: Imports, Exports, Processing and Real (wholesale) price of Milk

Figure 6: Net Production and derived Domestic Consumption of liquid milk


Table 2: Milch animal stocks and their composition Number of animals ('000) Percentage share in total

Calendar year Cross-bred Indigenous

Buffalos Total Cross-bred Buffalos

1992 5792 52001 40275 98068 5.91 41.07 1997 8355 49875 42730 100960 8.28 42.32 2007 14407 48042 48641 111090 12.97 43.79 2012 19420 48120 51050 118590 16.38 43.05

Growth rate % 6.24 -0.39 1.19 0.95 Note: CAGR is compound annual growth rate between 1992 and 2012. Source: MOA a,(various),


Table 1: Availability and productivity indicators of Milk economy Year Availability of milk Productivity of milk in relation to

Production Net production

Per capita net production

animals crop area fodder area feed area

Million tonnes

Million tonnes

Kg Kg/head Kg/hectare

Kg/hectare Kg/hectare

1990 53.9 53.5 64.4 555.9 411.4 6505.7 357.7 2005 97.1 96.3 87.3 896.9 739.7 12038.2 648.3 2012 132.4 131.3 106.6 1100.2 1031.9 17236.6 905.3

CAGR% 4.17 4.17 2.35 3.22 4.18 4.46 4.23 Note: Con. Post-harvest loss is 0.8%. Animal: Cattle and buffalo, Crop area: Area under all cereals, oilseeds, sugarcane; Feed:

from data from Ministry of Agriculture.

Table 3: Price behaviour of milk over two decades by alternative estimates (Rs. /100 litre) Year Wholesale

price Retail price

Producer price WPI All commodities

NCF Kulkarni 2004-05=100 1993-94 1029.1 1063.8 638.3 851.1 53.4 2000-01 1537.8 1589.7 953.8 1271.8 83.1 2005-06 1711.3 1769.0 1061.4 1415.2 104.4 2011-12 2959.4 3059.3 1835.6 2447.4 156.1

CAGR% 6.04 6.04 6.04 6.04 6.14 Source: MOAa,(various), GOI (website). 2011-12. Wholesale price index (WPI) of all commodities at base 2004-05

Table 4: Derivation of Milk consumption in India

Consumption Value (Rs)

Estimated Quantity Annually Consumed (Million Tonnes)

Per capita monthly NCF ratio Kulkarni ratio Year Rural Urban Total Rural Urban Total Rural Urban Total 1993-94 27 45 31.74 33.38 11.94 45.31 25.03 11.94 36.97 2000-01 43 76 52.21 40.07 16.46 56.53 30.05 16.46 46.51 2005-06 51 85 61.06 45.22 19.00 64.22 33.92 19.00 52.91 2011-12 115 184 136.64 63.04 27.20 90.24 47.28 27.20 74.48 CAGR 8.38 8.14 8.45 3.60 4.68 3.90 3.60 4.68 3.97 Note: CAGR is compound annual growth rate between 1993-94 and 2011-12. Source: NSSO and calculated by our method.

Table 5 : Years of maximum and minimum of Exports and Imports Commodity

Exports Imports Highest Lowest Highest Lowest

Liquid Milk 2011-12 2001-02 2008-09 2002-03 Milk Powder 2005-06 2011-12 2011-12 2001-02 Total Liquid Milk Equivalent 2005-06 2011-12 2011-12 2001-02 Butter Milk, Etc. 2007-08 2002-03 2009-10 2001-02 Whey etc. 2007-09 2011-12 2011-12 2002-03 Butter, Etc. 2008-09 2001-02 2009-10 2005-06 Cheese And Curd 2008-09 2001-02 2010-11 2004-05

Appendix

TableA2 : Consumption Expenditure and Share of Agricultural Households in Consumption of Milk (2000s)

Monthly per capita Consumption Rs

Share in total Physical consumption (%) NCF estimate Kulkarni Estimate

Type of Household 2004 2009 2004 2009 2004 2009 Agricultural Labour 20.9 43.7 7.7 9.3 7.0 8.5 Self-employed in Agriculture

64.2 109.5 37.3 32.6 33.9 29.5

Agricultural Household 47.5 82.1 44.9 42.0 40.8 38.0 Rural Household 47.3 80.6 69.7 68.4 63.3 61.8 Source: Monthly per capita expenditure and population taken from NSSO (various).


Table A1: Validation of Prices and per capita consumption derived from NSS quinquennial consumer survey and Wholesale price data

Rural Consumption

Urban Consumption Imputed Price Price used Rural Urban

Year

Expense

(Rs.)

Quantity (kg)

Expense

(Rs.)

Quantity (kg)

Rural (Rs/ kg)

Urban (Rs/ kg)

Wholesale (Rs/ kg)

Estimated Producer's

(Rs/ kg)

Retail (Rs/kg

)

Consumption Estimated Kg/Capita

NSS NSS NSS NSS Estimated

Estimated MOA Kulk

arni NCF Estimated

Kulkarni NCF Kulk

arni NCF

2004 47.31 3.87 83.30 5.11 12.22 16.30 16.8 13.89 10.42 17.37 3.41 4.54 4.80 4.80 2009 80.55 4.12 137.01 5.36 19.55 25.56 24.17 19.99 14.99 24.99 4.03 5.37 5.48 5.48 Source: Monthly per capita expenditure and population taken from NSSO (various), MoA , Census Data.


Contract Workers in India’s Organised Manufacturing SectorA.K. Panigrahi1, Central Statistics Office, Kolkata, India

Abstract

The trend of contract workers in organized manufacturing sector in recent years hasbeen increased significantly. In this scenario an attempt has been made to examine thecontract workers participation rate and wage difference between direct workers andcontract workers in the organized manufacturing sector in India. The unit level data ofAnnual Survey of Industries (ASI) of 2000-01, 2005-06, 2010-11 and 2012-13 have beenused for the analysis. From the analysis it has been observed that the proportions ofcontract workers have increased significantly and reached at 34 percent in 2012-13. Ithas been observed that industrial activities, namely, tobacco products, where highest(73.29 percent) proportions of contract workers are engaged in the production processfollowed by other non-metallic mineral products (57.98 percent) and manufacture ofcoke, refined petroleum products (49.56 percent). Contract workers participation ratewith respect to different States/UTs in India has also varies significantly. States, namely,Bihar (70.05 percent), Odisha (58.47 percent), Uttarakhand (51.98 percent), AndhraPradesh (47.87 percent) and Haryana (47.06 percent) where, significantly higherproportions of contract workers have been engaged in the organized manufacturingsector. There is significant wage difference between contract workers and direct workershas also been observed and this is also true with respect to industrial activities and majorStates/UTs in India. From the analysis it has been observed that contract workers averagewage per day is Rs. 156 during 2012-13, whereas, direct workers average wage per dayis Rs. 404. From this analysis it is clearly observed that contract workers are getting 60percent less wage than that of the direct workers.

1. Introduction

1.1 In the present era, outsourcing, contractualisation, contract workers etc. are thepredominant issues. Most of the organizations are in favour of contractual employmentrather direct employment for smooth functioning of the day today activities. Thesecontractual workers/labours are available from the market on the prevailing market pricethrough certain agencies/contractors. There is no direct relationship between the contractworkers and organization where they are contributing their labour. The agencies have tosupply the contract workers and received the commission charges under certain terms andconditions. The suppliers have to manage all the issue relating to these contract workers.In this scenario the entrepreneurs are little bit free from issues relating to the contractworkers and prefer to maximize their profit. If the entrepreneurs are not satisfied with thework done by these contract workers can discontinue their service as and when they like.These factors are tending industries to hire more and more numbers of contract workers tohave greater flexibility to adjust the number of workforce based on economic efficiency,better utilization of resources, optimization of profit and bringing cost effectiveness, despitethe risk of lower worker loyalties and lousy pay.

1.2 What are the differences between direct workers and contract workers? Therefore,it is better to understand the difference between direct workers and contract workers. The1 e-mail: [email protected]

138

major difference between direct and contract workers are discussed here. Direct workersare directly recruited by the employer whereas contract workers are taken from thecontractors. Recruitment rules are applied for the direct workers whereas no such recruitmentrules are applied among the contract workers. On the aspect of job security, direct workersare highly secured whereas contract workers are not secured at all. On the basis of workinghours direct workers are benefitted through certain rules and regulation whereas contractworkers do not have any regulation of working hours. Direct workers wages & salaries arebased on certain rules and regulation whereas contract workers wages & salaries are notregulated properly. On the social security aspects, direct workers are in advantages whichincludes medical allowance and sick leave whereas contract workers are not get suchprotection. Among direct workers leave rules are applicable whereas among contract workers,leave rules are not applicable. Finally, direct workers are protected by labour laws includingright to freedom of association and collective bargaining whereas no such rules for theprotection of contract workers.

1.3 Some of the studies have observed that employers in a globalised economicenvironment favour flexible labour strategies where they ask for the freedom to hire workersfor a fixed term even for perennial activities and discontinue their services when not needed(Sood, Nath and Ghosh, 2014). A study by Neethi (2008) has observed that contractualisationprevails in almost all industry groups and it is highly region-specific and industry-specificfactors have their influence in determining contract work intensity.

1.4 All India Organisation of Employers’ has studied on the issues relating to theindustrial relations & contract labour and observed that during the recent years, employmentof contract labour has become a contentious issue and a key reason for the increasinglabour unrest in the form of strikes and protests. They have cited that the major reasons forthe rise in industrial unrest could be increasing dependence of industries on contractlabour for requirement of flexibility. This segment of worker due to anxiety of job security,lack of social security, exploitation in the hands of contractors, low wages, unequal treatmentby Trade Unions and even abusive behavior of the permanent workers and supervisorsdevelop rebellion feelings. This study has also cited some of the instances of industrialunrest during recent past and conclude that the surge in violence disturbing industrialrelations has become a concerning situation for all. On September 22, 2008 the CEO ofGraziano Transmissioni India, the Indian unit of an Italian auto component maker, wasclubbed to death by a group of 200 workers. In another incidents, in March 2011, a DeputyGeneral Manager (Operations) of Powmex Steel, a unit of Graphite India Ltd. was killed afterhis vehicle was set afire by irate workers, in November 2010 an Assistant General Managerof Allied Nippon, an auto parts maker, was stoned to death by angry workers, in September2009 the Vice-President (HR) of Pricol was beaten to death by agitating workers, and manymore. The most recent worst form of industrial unrest was witnessed in the Maruti SuzukiIndia Ltd., Manesar plant, where workers went into riotous, leaving its General Manager(HR) dead and 100 other officials laid up in hospital with serious injuries (http://www.aioe.in/htm/IndustrialRelations.pdf).

1.5 In this context an attempt has been made to examine the contract workersparticipation and wage differentials in organized manufacturing sector in India with thefollowing objectives.

Contract Workers in India’s Organised Manufacturing Sector 139

2. Objectives of the Study

(i) To examine the participation rate by contract workers in organized manufacturingsector in India.

(ii) To study the differentials in participation of contractual workers with respect to major industrial activities and major states in India.

(iii) To examine the wage differentials among contract workers with respect todirect workers.

3. Data and Methodology

3.1 Annual Survey of Industries (ASI) data are available for the organizedmanufacturing sector and those industries are registered under the Factories Act. 1948 arecovered in the survey. ASI schedule is the basic tool to collect required data for the factoriesregistered under Sections 2(m)(i) and 2(m)(ii) of the Factories Act, 1948. Block E of the ASIschedule collects the information with respect to employment and labour cost includingcontract workers in the organized manufacturing sector. In this block, information withrespect to direct workers, contract workers, supervisor & managerial staff, unpaid familymembers, other employees, man-days worked, average number of persons worked, wages/salaries etc. are available. Therefore, in this paper an attempt has been made to examine thecontract workers participation rate and wage differentials in the organized manufacturingsector. Before analyzing the contract workers participation, we should have better ideaabout who are the contract workers? Who are the direct workers? How are they differentfrom each others?

3.2 Contract Worker: All persons who are not employed directly by the factoryowner/employer but engaged through a third party i.e. agency/ contractor, are termed ascontract workers. Such agency charges from the factory for this job. In ASI schedule,Block E: item 4 collects the information on workers employed through contractors. In morespecific terms those workers employed purely on contract basis are reported in item 4 ofBlock E (Govt. of India, 2014).

3.3 Direct Worker: It includes those workers employed directly by the factory. InASI schedule, Block E: items 1 & 2 collects the information with respect to male and femaleworkers directly employed which include all persons employed directly on payment ofwages or salaries and engaged in any manufacturing process or its ancillary activities likecleaning any part of the machinery or any premises used for manufacturing or storingmaterials or any kind of work incidental to or connected with the manufacturing process(Govt. of India, 2014).

3.4 In this paper, the contract workers participation rate is defined as the proportionof contract workers employed in the total workers and contributing in the organizedmanufacturing process.

Thus,

Contract Workers Part icipation Rate Total Contract Workers Total Workers

×100=


3.5 ASI unit level data from 2000-01 to 2012-13 have been used for the analysis ofemployment composition in the organized manufacturing sector. However for depth analysisof contract workers participation and their wage rate calculation, the unit level data of ASIof 2000-01, 2005-06, 2010-11 and 2012-13 have been used.

4. Results and Finding

4.1 Employment Scenario in Organised Manufacturing Sector

4.1.1 It is interesting to analyses the composition of employment scenario in theorganized manufacturing sector. Figure 1 presents the employment scenario in the organizedmanufacturing sector during 2000-01 to 2012-13. From the figure it is clearly shown that in2000-01 the total employment in the organized manufacturing sector was around 8 million.However, over the period it has increased drastically and reached 13 million in 2012-13. It isinteresting to see the proportions of workers in the total employment. From the figure it isevident that workers constitute around 80 percent of the total employment in the organizedmanufacturing sector and the remaining 20 percent are other than workers, which includes,supervisor & managerial staff, unpaid family members, other employees those are notdirectly involved in the manufacturing process. Over the period both workers as well astotal employees have increased drastically. From this analysis it is clearly understood thatworkers in the manufacturing sector are the back bone for the organization for manufacturingprocess.

4.2 Contract Workers in Organised Manufacturing Sector

4.2.1 Now it will be interesting to analyse the composition of the workers with respectto direct workers and contract workers. Figure 2 presents the proportions of direct andcontract workers in the organized manufacturing sector during 2000-01 to 2012-13. From thefigure it is evident that in 2000-01 direct workers constitute around 80 percent of the totalworkers and the remaining 20 percent are contract workers. However, during last decade ithas been observed that proportions of direct workers declined significantly and reached at66 percent in 2012-13. Moreover, the proportions of contract workers increased significantlyand reached at 34 percent in 2012-13. In beginning of the decade there was a big gapbetween direct workers and contract workers participation in the organized manufacturingsector. However, over the period the gap has become narrowed.

4.3 Workers Size vs Contract Workers

4.3.1 An attempt has been made to examine the percentage of contract workers withrespect to the workers size. Figure 3 presents the proportions of contract workers in totalworkers by workers size of factories during 2000-01, 2005-06, 2010-11 and 2012-13. From thefigure, it is clearly evident that higher proportions of contract workers are engaged withrespect to higher workers size. It is observed that those factories having 5000 and aboveworkers, more than 50 percent workers are contract workers. Those factories, where workerssize is less than 50 significantly lower percentage of contract workers are engaged in theorganized manufacturing sector. There is an upward trend of contract workers participationwith respect to workers size in organized manufacturing sector.


4.4 Contract Workers vs Direct Workers

4.4.1 An attempt has also been made to examine the number of factories in operationwith respect to the contract workers size. Table 1 presents the contract workers size classwith respect to factories in operation during 2000-01, 2005-06, 2010-11 and 2012-13. Fromthe analysis of ASI unit level data, it has been observed that the contract workers arepredominant among the 25 percent of the total factories in the manufacturing sector. It hasalso been revealed that around 75 percent of the factories are functioning without anycontract workers. From the table it has been revealed that around 7 percent (11891 factories)of factories are in operation with contract workers 1-9 followed by 20-49 contractor workers(6 percent) and 10-19 contract workers (4 percent).

4.4.2 It is surprising to see the number of factories where numbers of contract workersare more than that of the direct workers. Table 2 presents the numbers of factories inoperation with respect to the contract workers size among those factories where numbersof contract workers are more than that of the direct workers. It has been observed that in2000-01 around 9 percent of factories are in operational where numbers of contract workersare more than that of the numbers of direct workers. Over the period in 2012-13, it is alsointeresting to see the huge number of factories (31908 factories during ASI 2012-13), wherenumbers of contract workers are more than that of the direct workers which constitutearound 18 percent of the total factories in the organsied manufacturing sector in India. Byexamining contract workers size class among these factories, it has been observed thathigher proportions of factories (around 25 percent) are in the size class of 20-49 contractworkers. It has also been observed that there are significant proportions (around 38 percent)of factories where contract workers are above fifty. It has also been observed that 12percent (3744 factories) of factories are functioning where more than 200 contract workersare in the production process.

4.4.3 Table 3 presents the number of contract workers and number of direct workers perfactory among those factories where numbers of contract workers are more than that of thenumber of direct workers with respect to contract workers size class. From the table it hasbeen revealed that in contract workers size class 1-9, where around 6000 factories are inoperational, on an average six contract workers are engaged in the production process andin the same industry on an average around one direct worker is engaged. Similarly, in sizeclass 10-19, the average numbers of contract workers per factory are around 14 whereas theaverage numbers of direct workers are only 3. Similarly, in size class above 200, the averagenumbers of contract workers per factory are around 473 whereas the average numbers ofdirect workers per factory are around 117 in 2012-13. From this analysis it has been revealedthat the numbers of contract workers are proportionately increasing with respect to thecontract workers size class. Similarly, irrespective of contract workers size class the averagenumber of contract workers per factory has significantly more than three times that of thenumber of direct workers.

4.5 Industry-wise Variations in Contract Workers Participation

4.5.1 It is interesting to see the industry wise variation in contract workers participation.Figure 4 presents the industry wise variation in contract workers participation. From Figure4, it is evident that there is significant variation with respect to contract workers participation


in industrial activities. Industrial activities, namely, tobacco products, where the highest(73.29 percent of all workers) proportion of contract workers are engaged in the productionprocess followed by other non-metallic mineral products (57.98 percent) and manufactureof coke, refined petroleum products (49.56 percent). Industrial activities, namely,manufacturing of wearing apparel (12.35 percent), textiles (14.07 percent), and printing &reproduction of recorded media (16.85 percent), where, significantly low percentage ofcontract workers are engaged.

4.6 Interstate Variation in Contract Workers Participation

4.6.1 Figure 5 presents the percentage of contract workers with respect to major States2/UTs in India. From the figure, it is evident that there is significant variations have beenobserved with respect to the participation of contract workers in major States/UTs in India.Top five States, namely, Bihar (70.05 percent), Odisha (58.47 percent), Uttarakhand (51.98percent), Andhra Pradesh (47.87 percent) and Haryana (47.06 percent) where, significantlyhigher proportions of contract workers have been engaged in the organized manufacturingsector. However, bottom five States/UTs, namely, Delhi (10.83 percent), Kerala (14.17percent), Tamil Nadu (19.54 percent), Assam (19.86 percent) and Karnataka (20.09 percent),where significantly low proportions of contract workers have been engaged in the organizedmanufacturing process. It has also been observed that the most industrialized States/UTs,namely, Maharashtra (40.31 percent), Gujarat (36.55 percent), Uttar Pradesh (35.93 percent)and Andhra Pradesh (47.87 percent) significantly higher proportions of contract workershave been engaged in the organized manufacturing sector which is higher than the nationalaverage. However, one industrialized States, namely, Tamil Nadu (19.54 percent) hassignificantly low proportions of contract workers have been engaged in the organizedmanufacturing sector.

5. Why Contract Workers?

5.1 From the above analysis it has been observed that percentage of contract workersare increasing over the study periods. But exact reason is not revealed from the aboveanalysis. There are many reasons behind increasing contract employment in organizedmanufacturing sector. However, some of the possible reasons are given below:

i. Contract workers are the substitution against the direct workers:It is arguedthat employers facing stringent labour laws do not want to employ more people inthe production process. Employers in a globalised economic environment favourflexible labour strategies where they ask for the freedom to hire workers for a fixedterm even for perennial activities and discontinue their services when not needed.

ii. For short run/seasonal production: The enterprise may interest for short runproduction for some specific kind of job. Therefore, the enterprise may hire contractworkers to meet the short term demand. It may happen that for seasonal items/production, enterprise may hire contract workers when required and fire them incompletion of the project.

2 Major States/UTs are Andhra Pradesh, Assam, Bihar, Chattisgarh, Delhi, Gujarat, Haryana, HimachalPradesh, Jammu & Kashmir, Jharkhand, Karnataka, Kerala, Madhya Pradesh, Maharashtra, Odisha,Punjab, Rajasthan, Tamil Nadu, Uttar Pradesh, Uttarakhand and West Bengal. Other States/UTs includesA & N. Island, Chandigarh, Dadra & N Haveli, Daman & Diu, Goa, Manipur, Meghalaya, Nagaland,Puducherry, Sikkim and Tripura


iii. Special kind of skilled workers for technical work: It may happen that forfulfillment of special kind of technical work which may not require regularly. In thiscontext, enterprises are very much interested to outsource the work through contractworkers.

iv. Minimizing monitoring cost/regulatory cost: Employing contract workersthrough contractors minimizes the monitoring cost. It is the responsibility of thecontractor to follow the terms and conditions for specific job. Therefore, contractorwill be very much responsible for the completion of specific job as per the termsand condition otherwise forfeit the payment.

v. Avoid fringe benefits like annual leave with wages, gratuity, bonus, etc.:Contract workers are not in the pay-role of the factories. Therefore, leave rules andother welfare measures such as gratuity, bonus etc. are not applicable for thecontract workers.

6. Wage Differentials

6.1 In the following section an attempt has been made to look into the wage differentialin the registered organized manufacturing sector with respect to contract workers anddirect workers in India.

We define,

Wage Differential Ratio = Average contract workers wage

Average direct workers wage

6.2 The average wage has been calculated on the basis of total annual wages3 to totalman-days4 worked. So, the average contract workers wage has been calculated on the basisof total contract workers annual wage to total man-days worked for contract workers.Similarly, the average direct workers wage has been calculated on the basis of total annualwage to total man-days worked for direct workers.

6.3 Table – 4 presents the average wage per day, wage difference and wage differentialratio with respect to contract workers and direct workers. From the table it has been revealedthat the average wage rate per day for direct workers is Rs. 164 in 2000-01. However, thewage rate has increased significantly during last decade and reached at Rs. 404 in 2012-13.Whereas the average wage rate per day for contract workers is Rs. 90 in 2000-01 andincreased to Rs. 221 in 2010-11 and Rs. 156 in 2012-13. It has also been revealed that there

3 Wages: Wages are defined to include all remuneration capable of being expressed in monetary terms andalso paid more or less regularly in each pay period to workers (defined above) as compensation for workdone during the accounting year. It includes:(i) Direct wages and salary (i.e. basic wages/salaries, payment of overtime, dearness, compensatory, houserent and other allowances;(ii) Remuneration for period not worked (i.e. basic wages), salaries and allowances payable for leaveperiod, paid holidays, lay-off payments and compensation for unemployment (if not paid from sourceother than employers);(iii) Bonus and ex-gratia payment paid more or less regularly (i.e., incentive bonuses and good attendancebonuses, production bonuses etc.).


is significant wage difference with respect to direct workers and contract workers. Over theperiod, the wage rate difference between contract workers and direct workers has increasedsignificantly. From wage differential ratio, it has been revealed that the contract workerswage rate is forty five (45%) percent less than that of direct workers wage in 2000-01. Thereis an indication of declining wage differential ratio between direct workers and contractworkers over the last decades.

6.4 From the Figure 6 it is clearly understood the wage differentials between contractworkers and direct workers during 2000-01 to 2012-13.

6.5 Wage differential Ratio in Industrial Activities

6.5.1 It will be interesting to examine the wage differential ratio with respect to industrialactivities. Table A3 presents the average wage rate of direct workers, contract workers andwage differential ratio. From the table it has been revealed that there is significant wagedifference has been observed in coke & refined petroleum products (0.29) followed bymotor vehicles & trailers (0.42) and basic metals (0.46). But there are industrial activities,namely, recycling (0.93), cotton ginning (0.88), leather & related products (0.86) and productsof wood (0.85) wage differential ratio is comparatively low. It has also been observed thatthere are industrial activities, namely; wearing apparel, leather & related products, cottonginning contract workers are getting higher wages than direct workers. Activities of wearingapparel in 2000-01, leather and related products in 2000-01 and 2010-11, cotton ginning in2005-06 and 2010-11 contract workers are getting higher wages than director workers. It hasalso been observed that in 2012-13 the wage rate of contract workers was almost four times(3.85) of direct workers in the industry of tobacco products. This is in sharp contrast withthe earlier years of 2000-01, 2005-06 and 2010-11 in that activity. It may happen that forspecial kind of technical work enterprises are very much interested to outsource the workthrough contract workers by paying higher wages.

6.6 Wage differential Ratio in Major States/UTs

6.6.1 Table A4 presents the wage difference in organized manufacturing sector in Indiawith respect to major States/UTs between contract and direct workers. From the table it hasbeen revealed that contract workers are always getting lower wage than that of directworkers. There is significant variation in wage difference has been observed among majorStates/UTs between contract and direct workers. States, namely; Odisha, Bihar, AndhraPradesh, Uttarakhand, and Maharashtra wage difference is higher than that of other States.

It excludes layoff payments and compensation for employment except where such payments are for thispurpose, i.e., payments not made by the employer. It excludes employer’s contribution to old agebenefits and other social security charges, direct expenditure on maternity benefits and crèches and othergroup benefit in kind and travelling and other expenditure incurred for business purposes and reimbursedby the employer. The wages are expressed in terms of gross value, i.e., before deductions for fines,damages, taxes, provident fund, employee’s state insurance contribution etc. Benefits in kind (perquisites)of individual nature are only included(Govt. of India, 2014).4 Man-days Worked: These are obtained by summing up the number of man-days worked by personsworking in each shift over all the shifts on all days, i.e. both manufacturing and non-manufacturing days.This figure excludes persons who are paid but remain on leave, strike, etc. Manufacturing days will meanand include number of days on which actual manufacturing process was carried out by the unit where asNon-manufacturing days will mean and include number of days on which only repair/maintenance andconstruction work were undertaken (Govt. of India, 2014).


It has also been revealed that the eastern region significantly wage difference is higherfollowed by central and western region.

7. Conclusion

7.1 From the above analysis it has been revealed that contract workers participationrate has significantly increased during the last decade. This is also true with respect toindustrial activities and major States/UTs. Because of flexibility in labour laws, contractworkers are engaged as the substitution against the direct workers. There is significantwage difference has been observed between contract and direct workers.

References

Govt. of India (2014), Instruction Manual: Annual Survey of Industries (Concepts,Definitions and Procedures). Central Statistics Office (IS Wing) and Field OperationDivision (NSSO), Govt. of India, Ministry of Statistics and PI.

Industrial Relations & Contract Labour in India by All India Organisation of Employers’,Federation House, Tansen Marg, New Delhi. http://www.aioe.in/htm/IndustrialRelations.pdf

Neethi P. (2008) Contract Work in the Organised Manufacturing Sector: A DisaggregatedAnalysis of Trends and their Implications. The Indian Journal of Labour Economics, Vol.51, No. 4, Pp. 559-573.

Sood, Atul, Paaritosh Nath, and Sangeeta Ghosh (2014) Deregulating Capital, RegulatingLabour: The Dynamics in the Manufacturing Sector in India, Economic and PoliticalWeekly, Vol. XLIX, No. 26 & 27, Pp. 58-68.


Figure 1: Employment Composition in Organised Manufacturing Sector during 2000-01to 2012-13 in India

Figure 2: Percentage of Direct and Contract Workers in Organised ManufacturingSector during 2000-01 to 2012-13 in India


Figure 4: Contract Workers in Organised Manufacturing Sector with respect toIndustrial Activities during 2000-01 and 2012-13.


Figure 5: Contract Workers in Organised Manufacturing Sector with respect to MajorStates/UTs during 2000-01 and 2012-13.

Figure-6: Wage Rate Differentials in Organised Manufacturing Sector


Table 1: Number of Factories in Operation with respect to Contract Workers Sizeduring 2000-01, 2005-06, 2010-11 and 2012-13.

Contract Workers Size Class

Number of factories in operation Percentage of factories in operation

(%)

2000-01 2005-06 2010-11 2012-13 2000-01 2005-06 2010-11 2012-13 No contract workers 147531 138312 124449 133486 85.63 80.48 72.28 74.53 1-9 8387 9551 13022 11891 4.87 5.56 7.56 6.64 10-19 4765 6066 8506 7668 2.77 3.53 4.94 4.28 20-49 6138 8213 10928 10831 3.56 4.78 6.35 6.05 50-99 3064 5040 7157 6813 1.78 2.93 4.16 3.80 100-199 1303 2505 4123 3974 0.76 1.46 2.39 2.22 Above 200 1102 2178 3991 4439 0.64 1.27 2.32 2.48 All India 172290 171865 172176 179102 100.00 100.00 100.00 100.00

Table 2: Number of Factories in Operation with respect to Contract Workers Size during2000-01, 2005-06, 2010-11 and 2012-13 among those Factories where Contract Workers

are more than the Direct Workers .Contract Workers Size Class

Number of factories in operation Percentage of factories in operation

2000-01 2005-06 2010-11 2012-13 2000-01 2005-06 2010-11 2012-13 1-9 3278 4079 6865 5894 21.70 17.82 20.15 18.47 10-19 3155 4393 6026 5344 20.88 19.20 17.69 16.75 20-49 4644 6533 8661 8255 30.74 28.55 25.42 25.87 50-99 2322 4135 5932 5477 15.37 18.07 17.41 17.16 100-199 916 2002 3383 3194 6.06 8.75 9.93 10.01 Above 200 793 1743 3202 3744 5.25 7.62 9.40 11.73

Total 15108 22885 34069 31908 100.00 100.00 100.00 100.00 Percentage 8.77 13.32 19.79 17.82

All India 172290 171865 172176 179102

Table 3: Number of Contract Workers and Direct Workers per Factory in Operation withrespect to Contract Workers Size during 2000-01, 2005-06, 2010-11 and 2012-13.

Contract Workers Size Class

Contract workers per factory Direct workers per factory

2000-01 2005-06 2010-11 2012-13 2000-01 2005-06 2010-11 2012-13 1-9 6 6 6 5 1 2 1 1 10-19 14 14 14 14 4 4 4 3 20-49 31 32 32 32 8 8 7 7 50-99 67 68 70 70 14 14 16 15 100-199 131 130 131 131 33 30 33 33 Above 200 635 504 502 473 103 98 116 117

Table 4: Wage Rate and Wage Differential Ratio in Organised Manufacturing Sectorin India

Years Wage Rate (INR) per day Wage

differential ratio Direct workers Contract

workers 2000-01 164 90 0.55 2005-06 198 116 0.59 2010-11 313 221 0.70 2012-13 404 156 0.39


Appendix TablesTable A1: Percentage of Contract Workers in Organised Manufacturing Sector in India

during 2000-01, 2005-06, 2010-11 and 2012-13 with respect to Industrial Activities.Activity Description Percentage of Contract Workers (%)

2000-01 2005-06 2010-11 2012-13 MANUFACTURE OF TOBACCO PRODUCTS 63.39 68.33 67.59 73.29

MANUFACTURE OF OTHER NON -METALLIC MINERAL PRODUCTS 33.07 49.26 56.05 57.98

MANUFACTURE OF COKE, REFINED PET ROLEUM PRODUCTS 19.25 43.82 49.83 49.56 MANUFACTURE OF OTHER TRANSPORT EQUIPMENT 12.56 31.93 45.23 48.20

MANUFACTURE OF FABRICATED METAL PRODUCTS, EXCEPT MACHINERY AND EQUIPMENTS 27.70 39.73 46.54 43.89 MANUFACTURE OF BASIC METALS 23.56 33.73 41.40 43.44

MANUFACTURE OF CHEMICALS AND CHEMICAL PRODUCTS 20.12 31.22 38.24 41.79 MANUFACTURE OF MOTOR VEHICLES, TRAILERS AND SEMI -TRAILERS 11.55 30.79 41.65 39.79 RECYCLING 36.15 46.24 20.84 39.04

MANUFACTURE OF ELECTRICAL EQUIPMENT 12.64 28.04 36.02 38.24

COTTON GINNING, CLEANING AND BAILING 27.57 33.46 49.75 35.48

MANUFACTURE OF COMPUTER, ELECTRONIC & OPTICAL PRODUCTS 11.46 20.81 33.84 33.86 MANUFACTURE OF MACHINERY AND EQUIPMENT N.E.C. 10.77 22.70 33.66 32.73 MANUFACTURE OF RUBBER AND PLASTIC PRODUCTS 13.28 24.14 30.64 32.61 MANUFACTURE OF FOOD PRODUCTS AND BEVERAGES 20.53 26.15 30.58 30.63 MANUFACTURE OF PAPER AND PAPER PRODUCTS 21.94 27.30 28.59 26.38

MANUFACTURE OF WOOD AND OF PRODUCTS OF WOOD 9.39 24.37 26.48 26.33 MANUFACTURE OF FURNITURE 14.90 17.63 22.77 23.10

MANUFACTURE OF LEATHER AND RELATED PRODUCTS 18.85 19.91 16.01 19.66 PRINTING AND REPRODUCTION OF RECORDED MEDIA 5.71 10.50 19.02 16.85 MANUFACTURE OF TEXTILES 9.17 12.52 14.94 14.07 MANUFACTURE OF WEARING APPAREL 5.79 13.26 14.46 12.35 OTHER ACTIVITES 21.74 24.03 28.09 26.67 All India 20.42 28.54 33.94 34.26

Table A2: Percentage of Contract Workers in Organised Manufacturing Sector inIndia during 2000-01, 2005-06, 2010-11 and 2012-13 with respect to Major States/UTs.


State Name Percentage of Contract Workers (%)

2000-01 2005-06 2010-11 2012-13 Andhra Pradesh 44.88 53.43 48.07 47.87 Assam 7.22 16.33 19.75 19.86 Bihar 38.24 55.24 64.28 70.05 Chattisgarh 24.77 36.08 42.91 40.81 Delhi 6.31 9.21 12.57 10.83 Gujarat 26.91 34.12 36.06 36.55 Haryana 30.26 44.66 46.65 47.06 Himachal Pradesh 15.74 20.19 26.35 29.99 Jammu & Kashmir 25.01 31.09 48.34 44.98 Jharkhand 12.40 12.33 23.24 35.45 Karnataka 11.30 13.48 21.13 20.09 Kerala 4.16 9.22 16.32 14.17

Table A2: Percentage of Contract Workers in Organised Manufacturing Sector in India during2000-01, 2005-06, 2010-11 and 2012-13 with respect to Major States/UTs. (Contd.)

Table A3: Wage Differenc in Organised Manufacturing Sector in India during 2000-01, 2005-06, 2010-11 and 2012-13 with respect to Industrial Activities between

Contract and Direct Workers.


Activity Descriptions

Direct workers average wage per day (Rs.)

Contract workers average wage per day (Rs.) Wage Differential Ratio Ave

rageRatio

2000-01

2005-06

2010-11

2012-13

2000-01

2005-06

2010-11

2012-13

2000-01

2005-06

2010-11

2012-13

COKE, REFINED PETROLEUM 521 586 1181 1407 132 197 406 331 0.25 0.34 0.34 0.23 0.29 MOTOR VEHICLES, TRAILERS 277 349 503 634 116 149 261 200 0.42 0.43 0.52 0.32 0.42 BASIC METALS 274 334 477 637 143 137 236 254 0.52 0.41 0.49 0.40 0.46

246 274 401 555 103 134 240 188 0.42 0.49 0.60 0.34 0.46 MACHINERY AND EQUIPMENT 228 281 422 572 111 143 267 148 0.49 0.51 0.63 0.26 0.47 CHEMICAL PRODUCTS 200 251 390 504 95 129 235 229 0.47 0.51 0.60 0.45 0.51 COMPUTER & ELECTRONIC PRODUCTS

194

250

430

555

119

145

254

154

0.61

0.58

0.59

0.28

0.51

PRINTING & REPRODUCTION 180 214 326 462 98 144 236 65 0.55 0.68 0.72 0.14 0.52 PAPER PRODUCTS 160 200 290 358 96 124 211 116 0.60 0.62 0.73 0.32 0.57 OTHER TRANSPORT EQUIPMENT 202 284 458 525 129 173 276 281 0.64 0.61 0.60 0.54 0.60 RUBBER & PLASTIC PRODUCTS 138 180 278 358 95 115 209 151 0.69 0.64 0.75 0.42 0.63 FABRICATED METAL PRODUCTS 173 202 341 434 94 135 282 236 0.54 0.67 0.83 0.54 0.65 FURNITURE; MANUFACTURING 161 219 312 392 131 161 298 90 0.82 0.73 0.96 0.23 0.68 FOOD PRODUCTS AND BEVERAGES 118 139 228 298 85 109 200 127 0.73 0.78 0.88 0.43 0.70 TEXTILES 129 145 222 278 109 127 203 52 0.84 0.87 0.92 0.19 0.70 OTHER NON-METALLIC MINERAL PRODUCTS 141 172 270 355 99 110 186 347 0.70 0.64 0.69 0.98 0.75

WEARING APPAREL 90 129 215 275 106 130 210 42 1.17 1.01 0.98 0.15 0.83

State Name Percentage of Contract Workers (%)

2000-01 2005-06 2010-11 2012-13 Madhya Pradesh 23.62 27.49 33.15 33.50 Maharashtra 18.84 31.07 40.51 40.31 Odisha 28.74 42.01 47.97 58.47 Punjab 16.46 27.90 28.52 27.89 Rajasthan 22.73 33.47 36.29 37.22 Tamil Nadu 8.03 14.57 19.95 19.54 Uttar Pradesh 25.21 30.38 36.44 35.93 Uttarkhand 21.22 43.02 50.18 51.98 West Bengal 10.50 18.86 30.41 33.27 Others 21.20 31.16 41.45 39.16 All India 20.42 28.54 33.94 34.26

Table A3: Wage Differenc in Organised Manufacturing Sector in India during 2000-01, 2005-06, 2010-11 and 2012-13 with respect to Industrial Activities between

Contract and Direct Workers. Contd.

Table A4: Wage Difference in Organised Manufacturing Sector in India during 2000-01,2005-06, 2010-11 and 2012-13 with respect to Major States/UTs between Contract and

Direct Workers.

State/UTs Name


Contract workers average wage per day (Rs.) Wage Differential Ratio

Average Ratio 2000-

01 2005-06

2010-11

2012-13

2000-01

2005-06

2010-11

2012-13

2000-01

2005-06

2010-11

2012-13

Jammu & Kashmir 135 142 224 297 89 105 183 240 0.66 0.74 0.82 0.81 0.76 Himachal Pradesh 120 154 260 330 77 110 234 280 0.64 0.71 0.90 0.85 0.78 Punjab 125 166 245 314 94 112 174 258 0.75 0.67 0.71 0.82 0.74 Uttarkhand 317 303 329 411 92 115 214 271 0.29 0.38 0.65 0.66 0.50 Haryana 186 223 353 435 97 135 252 294 0.52 0.60 0.71 0.67 0.63 Delhi 138 171 295 434 124 140 271 342 0.90 0.82 0.92 0.79 0.86 Rajasthan 137 169 273 388 111 120 211 299 0.81 0.71 0.77 0.77 0.77 Uttar Pradesh 151 183 294 355 94 118 191 242 0.62 0.64 0.65 0.68 0.65 North 164 189 284 371 97 119 216 278 0.59 0.63 0.76 0.75 0.68 Bihar 174 202 477 306 58 83 138 206 0.33 0.41 0.29 0.67 0.43 Assam 93 124 250 264 79 89 144 244 0.85 0.72 0.57 0.92 0.77 West Bengal 189 221 310 381 121 123 210 289 0.64 0.56 0.68 0.76 0.66 Jharkhand 296 360 517 801 293 118 229 245 0.99 0.33 0.44 0.31 0.52 Odisha 214 305 435 579 74 89 231 293 0.34 0.29 0.53 0.51 0.42 East 193 242 398 466 125 100 190 255 0.65 0.41 0.48 0.55 0.52 Chattisgarh 228 229 370 678 138 138 196 280 0.61 0.60 0.53 0.41 0.54 Madhya Pradesh 167 209 322 413 122 103 208 268 0.73 0.49 0.65 0.65 0.63 Central 197 219 346 545 130 120 202 274 0.66 0.55 0.58 0.50 0.57 Gujarat 158 205 304 376 102 136 232 299 0.64 0.66 0.76 0.79 0.72 Maharashtra 229 288 446 547 108 144 264 350 0.47 0.50 0.59 0.64 0.55 West 193 246 375 462 105 140 248 325 0.54 0.57 0.66 0.70 0.62 Andhra Pradesh 137 182 310 423 49 77 165 193 0.35 0.42 0.53 0.46 0.44 Karnataka 153 185 333 454 103 140 258 390 0.67 0.75 0.78 0.86 0.77 Kerala 143 164 250 330 128 127 190 265 0.90 0.77 0.76 0.80 0.81 Tamil Nadu 119 143 248 327 88 131 268 316 0.74 0.91 1.08 0.97 0.92 South 138 169 285 383 92 119 220 291 0.67 0.70 0.77 0.76 0.73 Other States 127 169 305 361 89 122 210 292 0.70 0.72 0.69 0.81 0.73 All India 164 198 313 404 90 116 221 286 0.55 0.59 0.70 0.71 0.64


PRODUCTS OF WOOD 87 117 194 276 82 123 200 107 0.94 1.05 1.03 0.39 0.85 LEATHER &

PRODUCTS RELATED

101 129 201 258 109 121 231 73 1.08 0.94 1.15 0.28 0.86 COTTON GINNING 73 90 163 248 70 94 169 120 0.96 1.05 1.03 0.49 0.88 RECYCLING 112 166 195 321 95 98 322 198 0.85 0.59 1.65 0.62 0.93 TOBACCO PRODUCTS 72 93 197 211 46 47 68 811 0.64 0.51 0.35 3.85 1.34 OTHERS 129 172 320 431 85 123 268 112 0.66 0.71 0.84 0.26 0.62

All India 164 198 313 404 90 116 221 156 0.55 0.59 0.70 0.39 0.56

Activity Descriptions


Contract workers average wage per day (Rs.) Wage Differential Ratio Ave

rageRatio

2000-01

2005-06

2010-11

2012-13

2000-01

2005-06

2010-11

2012-13

2000-01

2005-06

2010-11

2012-13


Employment-Productivity Profile and Labour Demand Elasticity: APreliminary Study of the Organized and Unorganized Indian Textile and

Garment Firms

Sarmishtha Sen1, Syamsundar College, Burdwan, IndiaSubrata Majumder, Sundarban Mahavidyalaya, South 24 Parganas, India

Abstract

The present work seeks to trace the pattern of change in labour demand by both organizedand unorganized T&G enterprises in India. Changes in average labour demanded acrossdifferent size-groups of firms classified by firm-specific average labour productivity aswell as intermediate input costs have been analysed. The changing share of these groupsin estimated aggregate labour demand is also noted and the presence of some kind ofstickiness in adjustment of labour demand with respect to productivity performancescomes out. Estimation of labour demand elasticity all-India level and for selected textile-major states suggested the limits of price signals in guiding such optimal adjustments. Itis necessary to trace the possible sources of this stickiness in the structural rigidityinherent especially in the vast unorganized segment. Such an analysis has to treat alsothe endogeneity arising from two-way causality between efficiency-enhancing policyefforts and the two segments’ existing employment and growth behaviour.

1. Introduction

1.1 Recent interventions in the Indian textile and garment (T&G) industry andespecially the National Textile Policy 2000 changed the pre-1985 policy approach radicallyby gradually removing the major protective measures for the small-scale decentralizedsection while withdrawing restrictions on their large-scale counterpart (Srinivasulu 1996,Roy 1996, 1998, Niranjana & Vinayan 2001, Galab et al 2009, Kathuria and Mamta 2012). Thisprocess signified a shift of two types: first, from a policy era with greater emphasis onemployment objective to one – explicitly focused on efficiency enhancement based onequalization principle of the market, and secondly from policy-guided inter-firm linkage tomarket-driven connectivity between organized and unorganized segments. Deregulationof the industry including de-reservation of the garment sector, the latter initiated by theTextile Policy 2000 and mostly completed by 2005, is expected to have removed majorblocks to profitable restructuring by the large units - including those with 100% FDI - whileallowing them entry practically in every area of T&G production (Singh and Sapra 2007,Chaudhary 2011). With efficiency-enhancing optimal re-allocation of resources by organizedunits becoming easier and possible now, we can expect a significant change in theemployment generating potential of these two constituent segments of the Indian T&Gindustry especially in the post-MFA period. Increased scope for firm-level optimaladjustment of labour demand suggests that greater part of this employment be concentratedin the relatively high-productivity segments of the industry.2

1 e-mail: [email protected] Assuming a perfectly elastic labour supply, it is plausible to argue that observed changes in employmentare guided mainly by the hiring behaviour of the firms.

154

1.2 Increased trade-liberalization is expected to have offered significant opportunitiesfor expanding production activities especially in the organized segment. There are evidences,on the other side, of unorganized units becoming a part of this technical and structuralreorganization through the large-small linkage like subcontracting (Maiti 2008, Sahu 2011).Labour demand in the unorganized segment may fall due to substitution effect arising fromuse of more sophisticated production techniques associated with such reorganizations. Ifthe organized segment however, contracts out the relatively labour-intensive operations tothese enterprises, there is a high possibility of increase in labour demand in the latter dueto scale effect. This potential however may not be realized if there is little increase inoutsourcing by large units interested in more intense supervision of production and qualitycontrol through in-house production.3 The unorganized units in such a situation mayexperience very slow expansion of production and labour demand eve in absence of anysignificant substitution effect.

1.3 Thus, we can trace this process of adjustment at two levels: through followingchanges in aggregate labour demand at the sectoral level, and by studying the micro-levelprocess of making optimal hiring decisions. Through an exploratory analysis, this paperseeks to examine the pattern of change in aggregate as well as sector-specific demand forlabour across the two constituent sections of the Indian T&G firms in the post-2000 years.Further, it attempts to evaluate how individual firms in different locational set-ups adjustedtheir demand for labour during this period, in response to changes in wage. The estimationof elasticity of labour demand (LD) for selected Indian states with considerable presence oforganized and unorganized T&G units may help us locate the significance among others ofvarious policy parameters in inducing this demand. The broad research questions are thefollowing:

How did the distribution of LD in the T&G sector and in its organized andunorganized parts change in the period following Textile Policy 2000? Is there any evidenceof direct association between productivity & change in LD in the period following theannouncement of National Textile Policy 2000?

How does firm-level labour demand adjust with wage & how do relevant locationalparameters & policy factors influence labor demand elasticity? Is there any variation acrossthe states?

1.4 The first part of the analysis assumes importance in view of a stagnant productivityperformance in the organized T&G segment and an actually falling relative performance ofeven their bigger unorganized counterparts during the first decade of the present centuryas noted by Sen and Majumder (2015). In this context, can we expect a direct associationbetween productivity growth and higher employment due to movement of employmentfrom low to relatively high-productivity sub-groups expected in optimal re-allocation ofresources made possible by reforms? Here, we treat the observed employment as theamount of LD assuming a perfectly elastic labour supply especially in the vast unorganizedsegment with approximately fixed wage. More rapid rate of increase in LD among the low-productivity groups however, would indicate the presence of some kind of stickiness in

3 Reduced traditional marketing support from government is expected to have increased dependence ofthe unorganized units for access to market on the large and organized ones.

Employment-Productivity Profile and Labour Demand Elasticity .... 155

adjustment of labour demand despite poor productivity performances. Next, we examinethe firm-specific adjustment of labour demand by Indian T&G units - both organized andunorganized - in response to wage while controlling other factors potentially influencinglabour demand in thirteen textile-major states.

1.5 Rest of the paper is organized as follows: Section 2 delineates, based on theavailable empirical findings, the possible link between employment, labour productivityand labour demand elasticity in the organized and unorganized T&G segments. The nextsection entitled ‘Methodology and the Database’ first discusses the methodologicalrequirements of this kind of an issue. Variables and the source of data used for this purposeare described in two following subsections in it. Section 4 presents the findings of both theexploratory analysis and the regression exercise respectively in two subsections whilesection 5 contains the concluding observations.

2. Links between Employment and Productivity: An Outline

2.1 Using the Flow Chart 1, we attempt to explain the possible channels throughwhich segment-specific labour demand and its average labour productivity may be relatedin the context of heightened market-based competition in the domestic economy along withincreased integration with the global economy. Trade liberalization and complete abolitionof MFA require that the T&G units, both organized and unorganized, enhance productivityand competitiveness. Indian textile production base is known to have suffered fromaccumulated structural weaknesses resulting from fragmentation of productive capacity,lack of technological upgrading especially modernization, and absence of advantages interms of scale economies. Restrictive textile policies of the pre-1985 era did not allowcapacity expansion or changing input-mix in the organized segment according to therequirements of production efficiency. Thus such a policy strategy was viewed as anobstacle to the achievement of export-competitiveness. The restrictions on the largeorganized units for optimal restructuring - through capacity expansion or capital importsand subsequent capital deepening process - were lowered in the post-1985 years especiallyafter the National Textile Policy 2000 had allowed entry of large units in garment productionincluding 100% automatic approval of FDI. Organized sector in most of the Indianmanufacturing industries have resorted to increased use of labour-displacing technologiesdespite the remaining labour laws as has been noted by a number of empirical studies in therecent years (Nagaraj 2004, Rani and Unni 2004, Banga 2005, Sen and Dasgupta 2006, Dasand Kalita 2009, among others). This may have exerted a negative pressure on employmentthrough substitution effect.4

2.2 Trade liberalization on the other hand, especially in the post-MFA years, offergreater scope for venturing in markets hitherto restricted by MFA quota. Traditional tradetheories would suggest an expansion of the labour-intensive exporting segment. In fact,Indian T&G exports are known to have created a niche market of their own while producinginnovation- or design-intensive high value-adding customized items. It has achieved exportsuccess particularly in the high-end fashion segment on the basis of economies of scope.The T&G enterprises are also catering to the expanding domestic, especially urban market

4 Increased use of capital may have been prompted by absence of skilled labour while increased capitalintensity may also be a reflection of increased labour productivity.


of consumers with rising purchasing power and demand for tailor-made and highlydifferentiated T&G products (Roy 1998, Teewari 2005, Singh and Sapra op cit. for example).The expanding market base presents opportunities for greater specialization, efficiency-enhancing restructuring and improvement in labour productivity at least in certain segmentsof the organized T&G units.5

2.3 Organized segment however subcontract a part of its operations, especially thelabour-intensive ones, to the unorganized segment as a means to achieve cost-competitiveness. There are empirical evidences indicating use of increased share of low-cost labour in the Indian T&G sector as a cost-cutting and efficiency-enhancing strategyespecially in the years after the implementation of Agreement on Textiles and Clothing(Abraham & Sasikumar 2010, for instance). Maiti and Marjit (2008) on the other handargued that with increased international price of T&G product, it becomes profitable for theorganized section of the T&G industry to outsource increasing proportion of productionactivities to the unorganized units while retaining and specializing in the marketing-relatedactivities. Ability to serve the increasingly globalized domestic and international markets -of such a linkage between organized and unorganized firms however hinges on developingtechnical and organizational capability of the latter under the supervision of the parentfirms. In such a scenario it is plausible to argue that export success of the organized parentunit may be accompanied by an improvement of productivity performance of the unorganizedsubcontracting firm. Downward pressure on employment arising from any possible increasein capital intensity may be outweighed by expansionary tendency of employment in thesegment via scale-effect.

Flow Chart 1Productivity-Employment Link in Organized T&G and Induced Effect in the

Unorganized T&G Segments

Source: Authors’ Understanding

5 Organized firms are expected to respond more effectively than their unorganized counterpart to theseopportunities.The phenomenon of relatively high increase of employment in labour-intensive exportingindustries has found support in empirical studies (Kakarlapudi 2010, for instance).

Employment-Productivity Profile and Labour Demand Elasticity .... 157

ORGANIZED T&G EMPLOYMENT Search for Increased Competitiveness

Greater Trade openness

Expanding Domestic Market

Relaxed Optimal Restructuring by ORGANIZED

Increased Scale of Operation

Use of Labor -saving Technique

UNORGANIZED T&G EMPLOYMENT

Labor-intensive Operations Outsourced

Survivalist UNORGANIZED: Productivity-Employment Disconnect

Modern UNORGANIZED: Relatively Rapid Employment Increase in High-Productivity Deciles

Organized Specializing in Marketing &

Monitoring

ORGANIZED T&G EMPLOYMENT

(-) (+)

&

2.4 To accommodate the phenomenon of intrinsically heterogeneity and diversity ofthe unorganized T&G segment in our analysis, we have divided it in two distinct groups:one mostly survivalist6 and the other dynamic7 section (following Sen 2016). Enhancedmarket-based competition of the recent years and changing policy regime are expected toinfluence these two parts differentially. For instance, market incentives are expected towork better with the latter group, which is better-placed to reap benefits from policy supporttoo. We can expect a direct association here between productivity improvement andemployment generation through competition as well as collaboration with the organizedsegment. The traditional group, on the contrary, is not supposed to display any suchdefinite pattern as the urge to survive, and not production efficiency, is the driving forcebehind its operations.

2.5 Trade liberalization has been found to be associated with increasing labour demandelasticity in the organized manufacturing industry. Unorganized segment however fallsoutside the scope of labour legislation, wage variation may be low and competitive laboursupply does not allow effective assessment of the effect of wage-variation on demand forlabour.8

3. Methodology and the Database

3.1 Methodology:

3.1.1 In the first exercise, we examine the change in segment-specific labour demanded(LD) across size-groups based on level of unit-specific average labour productivity as wellas on the firm-level expenses on intermediate inputs. We undertake further an exploratoryanalysis of the inter-relationship of change in average labour demand with change inproductivity and changing share of different size-groups in estimated aggregate labourdemand. This part of the analysis makes use mainly of summary statistics on averagelabour productivity (APL) and LD to demonstrate whether or not any increase in LD islocated in the relatively growing section of T&G firms during the post-2000 years.

3.1.2 The issues involved in the estimation of labour demand elasticity are mainlytwofold: specification of demand function and choosing the proper estimator, and selecting

6 This group refers to typically low-productivity units either operating independently in the local marketor is connected with distant markets through traditional and often exploitative chain of intermediaries.These firms generally lack the preparedness to adapt with efficiency-improving market signals or torespond to policy support.7 This group consists of units, generally mechanized and modern, linked with globalized market throughcollaborations with large-scale sector resulting in technological spillover, increased capital intensity andenhanced production performance.8 If increased wage is a reflection of use of labour with higher productivity (evident from fall in the shareof low-productivity self-employment, (Goldar and Sadhukhan 2015)) in the unorganized section andscale effect dominates substitution effect we may not observe the effect of wage rise as expected onlabour demand. Increased exposure to trade may give rise to asset-specificity and the export obligationsmay call for long term relationship with the skilled-worker. In this case considerations other than wagemay dominate employment decisions of the unit concerned). In fact, elasticity of wages with respect toproductivity was higher than unity in most of the organized manufacturing industries including textilesbetween 1990 and 2006 (Mitra 2013). Nagaraj (2004) observed that relative cost of labour did not seemto have any influence on employment decisions even in organized manufacturing at the turn of thepresent century.


the variables for estimation and consideration of their characteristics. Assuming an unlimitedlabour supply and identical technology with varying factor price ratio (assuming comparableunit cost of capital and different labour remuneration rates) across the organized andunorganized segments we attempt to estimate segment-specific labour demand elasticityfrom the following labour demand function (following Hasan et al 2007 and Goldar 2009):

... (1)

where, (with competitive labour market), (total emoluments per labor-day), is the logarithmic value of the wage series; (-), =organized segment dummy(+), = proportion of subcontracting big establishments in unorganized T&Gsegment in a state (+), G=garment-dummy (‘0’ if textile) (-), =effort toward productivityimprovement through capital structure (simple mean of unit’s ranks in terms of log Capital-labor ratio, capital in land & non-land capital-intensity) (+), =effort on productivityimprovement through input structure (simple mean of the units’ ranks by & logmaterial costs) (-), = simple mean of % spending on electricity in total output (exceeding1%) and state-level proportion of such power-driven establishments in the unorganized&G segment, Y=output value-added of a firm (+); =random error..

3.1.3 As individual firm’s labour demand and its output (a specific measure for T&Gvalue-added of a firm) are simultaneously determined within the model, both are likely to becorrelated with the random error term 9. This may result in biased and inconsistent estimates.Use of instrumental variable (IV) regression method can give us consistent estimators byreducing unobserved or hidden bias especially in the observational studies like this. Aninstrumental variable has to satisfy two criteria: it has to be highly correlated with theproblematic endogenous explanatory variable but should not affect the outcomeindependently.

3.1.4 We use instrumental variable regression method with the help of two-stage leastsquares (2SLS) estimator where the estimation is done at two stages. Suppose, the populationregression relating the dependent variable and the output-regressor is as follows:

... ... (2)

Where represents other covariates, and is the error term representing omitted factorsthat determine . A non-zero correlation between and , due to endogeneity for example,will yield inconsistent estimates. At the first stage, the variable under consideration, firm’soutput is decomposed into two parts: a part that is not correlated with the random error andtherefore is non-problematic, and the other that is correlated with the error term. Hereindividual firm’s output is regressed on the chosen instrument, which is correlated with

9 This happens due to possible reverse causation of labour demand to output giving rise to the problem ofendogeneity. Endogeneity of labour price or wage is taken care of by the assumption of perfectly elasticlabour supply typical of a developing economy.

159Employment-Productivity Profile and Labour Demand Elasticity ....

but not with . The second stage estimates the concerned regression coefficient with thehelp of this exogenous variation component i.e. the problem-free part of variation in firm’soutput. Thus, the first stage of the regression links firm’s output with the chosen instrument,

.

... ... (3)

Here, is the intercept, the slope and the error term. From the regression, we have thepredicted variation in firm’s output, as - the part uncorrelated with the error term

in the original regression. In the absence of actual regression coefficients and thesecond stage regresses on this predicted variation in i.e. . As As is notcorrelated with a simple OLS will give us consistent estimate of i’s.

3.1.5 In this research exercise, we attempt to estimate the labour demand function bythe 2SLS IV estimator. At the all-India level we tried two alternative instruments: Y1_ins=percapita Net State Domestic Product at constant prices10 as has been used in similar studiesof labour demand estimation & Y2_ins=each firm’s share of NSDP apportioned accordingto its share in total GVA of these establishments for each state).11 As we are working on tworepeated cross-section database we estimate the elasticity with the instrumental variableestimator separately for the years 2001 and 2011. We carried out this regression analysis forsample units in India as a whole and separately for thirteen selected states with majorpresence of T&G establishments in both organized and the unorganized segments (detailsgiven in Table 11) to examine state-specific variations. Variables e.g. LINKAGE (proportionof unorganized units linked with bigger firms through subcontracting), TECH (the averagedegree of mechanization of unorganized establishments in a state), and per capita NSDPhave single values for each state. So these cannot be used in the state-level regressionexercise.

3.2 Choice of Variables:

3.2.1 Studies explaining the pattern of Indian manufacturing employment growth andthose estimating labour demand have concentrated on the following factors: the indicatorsmarking labour market rigidity, increased trade-openness encouraging adoption of capital-intensive and often imported production techniques as well as cheaper inputs, presence ofmajor supply-side bottlenecks in different states (Nagaraj 2004, Das and Kalita 2009, ICRIER2008, Kannan & Raveendran 2009, Nataraj 2010, Goldar 2011, Mitra op. cit.). Unlike theseworks (generally dealing with the organized manufacturing sector, Nataraj 2010 being anexception), any analysis of changing labour demand condition of one particular industryhas to take in explicit consideration the specific interaction of that industry’s structure with

10 This measure, representing the state’s level of prosperity, influences the overall T&G production of thestate but is expected to be minimally affected by the T&G industry-specific developments, which arelikely to influence the sector’s labour demand also.11 Pairwise correlation between firms’ output measure and the proposed second instrument was quite highat least at 5% level of significance.


changes in market conditions on the one hand, and the same with relevant policy parametersthere12 on the other.

3.2.2 The analysis has to be sensitive to potential variation in effect of same structuralor policy-level change in an estimation that covers both the organized and unorganizedsegments. Change in policies instilling greater flexibility in the labour market may havedifferential impact on the two segments. Similarly, increased trade-openness is also expectedto influence the two segments’ response in terms of production and employment differentially,especially depending on the concerned unit’s structural location with respect to new marketopportunities. The impacts may vary less if production in the organized and unorganizedsections is linked through subcontracting.

3.2.3 These interventions - through influencing productivity - may affect the labourdemand of the organized and unorganized sections of the industry. As noted in theintroductory section, recent policy efforts targeting the Indian T&G sector have attemptedto establish a strong connectivity of large and small firms through subcontracting. Thus,the extent of variation in subcontracting percentage would indicate the extent of organized-unorganized linkage as well as the pro-competitive stance of the policy regime. The processof mechanization (alongwith other infrastructural factors) adds to the speed of productionand increases the scope of these linkages by ensuring adhering to the delivery schedule aswell as the quality-specifications of crucial and big national/global buyers. Indicator ofmechanization - a proxy of productivity enhancing technology-centered effort of the firm,based on the proportion of electricity consumption in T&G units especially in theunorganized segment will be an important factor to be controlled. Individual firm’sorganizational location as well as the product-group catered by it (textile or garment, forinstance) serves to incorporate segment-specific variation in the Indian T&G industry atthe unit level and across states. Following Biswas et al (2014), we have controlled variousaspects of firm’s productivity-enhancing effort through the channel of capital (byconstructing a composite index of different measures of capital i.e., different sub-categoriesof capital) and channel of other non-capital inputs in the LD elasticity estimation.

3.2.4 Due to non-accessibility of systematic and direct information on many such factorsover time, we have tried to control or reduce the extent of heterogeneity by controllingvarious structural and location parameters of the sample units.13 Thus, we incorporatestructural location of the firm: the segment it operates in (ORG), the product-group it

12 We have not included policy variables, such as amendments to the Industrial Disputes Act, the reformsintroduced to scale up the social security contribution by the firms or to dilute them effectively as a directmeasure of rigidity in the structure of the labour market due to our limited access to such data. Thesechanges, labour being a state subject also, can vary across the states considered here. Neither had we hadaccess to data required for constructing measures e.g. index of state-business relation or investmentclimate (as in Caliet al 2009, for example). We could only devise indirect measures against those potentialinfluences. Similarly, information on industry-specific and productivity-enhancing interventions (such asTextile Upgradation Funds Scheme) targeting conduct of the firm operating in the market and marketinghelps to aid optimal response to market-driven forces in presence of pre-market constraints, might berelevant. Firm-level data used here however do not provide such information.13 Use of direct information is also constrained by availability of comparable statistics on the samevariable at the two ends of the study period. For example, we could not construct any such comprehensiveindex of state-specific policy efforts and structural features using indiastats.com.


produces and markets (G), degree of labour-market rigidity (RGDT)14, extent of connectivitybetween unorganized units with bigger concerns (LINKAGE).15 The last two factors alsoreflect the current policy inclination in respective state’s textile and garment sector. On theother side, we have controlled firm’s efficiency-enhancing effort in the production spherelike KP16, INP and extent of mechanization. By concentrating only on the relatively big T&Gestablishments and excluding unorganized T&G firms with no hired worker from thisestimation exercise, we can somewhat reasonably treat the effect of trade-liberalizationpolicies symmetrical across the two segments.17 This exclusion allows us to focus onchanges in domestic structure of the industry especially in terms of variation in the effectof policy on the labour demand at the state level where the relevant policies include existinglabour laws and pro-competitive interventions meant for the T&G sector.

3.3 Database:

3.3.1 Unit level data on the organized factories from the Annual Survey of Industries(ASI) database were pulled together with the information on unorganized manufacturingunits provided by the successive rounds of NSSO (using the framework of Sen and Majumder2015, summed up in Table A1). The exact choice of time points was guided by our accessand availability of unit level data and the comparability of the two sets of industrial statistics.The data are in the form of repeated cross-sections and our study period refers to the yearsbetween 2001 and 2011.

3.3.2 Each observation in our data set collates information on a number of variablesincluding those on input and output bundles for different individual industrial enterprises.Necessary adjustments were made to ensure comparability of categories representing thesame variables. We have considered only the perennial enterprises in the unorganizedsector, which operate regularly throughout the year to make the comparison more compatible.For the same reason, we have only included unorganized firms hiring worker on a regularbasis. Two full-time hired workers were taken as the relevant cut-off to get the final set ofobservations as in Sen and Majumder 2015. Similarly, both the data sets were converted at2004-05 prices - for the exploratory part - by deflating with the help of wholesale price index(WPI) for ‘Manufacturing Products’18. The pre-reform analysis has been carried out on the

14 Scope of profitable adjustment in labour demand in response to changing wages is expected to be limitedby the extent of rigidity prevailing in the state’s labour market thereby putting a downward pressure onlabour elasticity of demand15 LINKAGE acts here roughly as a proxy of state-specific textile policy effort and labour marketflexibility (i.e., indicator of the general state of development in the T&G industry in each state - as thatwould indicate the scope for restructuring the entire value chain spanning both the segments);16 KP - (capital pillar) indicates the nature of technology development through capital structure. Theinteractive terms of wage with capital-based effort and other inputs-based effort are likely to capture theeffect of these channels generally on employment generation and specifically on labour demand elasticity17 The one- or two-worker units (known as own account manufacturing enterprises, the OAMEs) werefound to face significant participation constraints leading to lower incidence of large-small linkage there(Sen and Majumder 2014).Estimation of labour demand elasticity also loses relevance somewhat in unitswith preponderance of unpaid worker and with difficulty in distinguishing between the worker’s andentrepreneurial roles.18 To simplify we have not resorted to use of multiple indices although use of WPI for textile products andWPI for textiles machinery or that for general machinery to deflate output and fixed capital stockrespectively would be more appropriate. However, this is a limitation of the measures used in this work.


basis of 9056 unit level observations and the analysis of the later period uses 6522observations19. Composition of the firms however changed in the estimation of LD elasticityas the latter required fulfilling different criteria of analysis. Per capita NSDP value wasdownloaded from the RBI Handbook of Statistics.

4. Findings:

4.1. Change in LD across Productivity-groups and Firm-size: An Exploration:

4.1.1 In this section, we attempt to examine whether or not any increase registered inlabour demand is concentrated in the relatively high productivity firms or in the higher size-groups (based on firm-level expenses on intermediate inputs) in the industry. We start withchecking if average labour demand - measured in labour-days generated - has increased inthe relatively high productivity sectors or in the low-productivity ones. For this, we haveclassified the sample units in different size groups based on the values of average labourproductivity (APL). In Table 2, we have divided all the T&G firms into four quartile groupsof APL distribution. Summary statistics of LD - obtained after applying population weights- shows that greater mean LD values were recorded in the upper two productivity quartilesby T&G units of these two sections taken together in both the years. Just the opposite wastrue for the unorganized segment at both time-points while organized section displayed thesame pattern in the last year. Median LD values followed the same pattern of recordinggreater values in the lowemost quartiles both for all the T&G units of the industry and forits unorganized part (the same table). For the organized segment, median value was relativelyhigh in the top quartiles, particularly in the second and third quartiles.

4.1.2 Table 3 shows that increase in mean LD over time seems to have been concentratedin the rather higher productivity groups. Disaggregation into the organized and unorganizedsegments however indicates that the increase was greater in the lower productivity segmentsof the organized part of the industry20. On the other hand, firms at both the lower and higherproductivity ends of the unorganized segment seem to have experienced this rise21. Reliabilityof these findings however seems to be compromised as the coefficient of variation of theextreme two productivity quartiles is quite high (see Table 4, for the selected summarystatistics) i.e., the firms in those quartiles are not homogenous in terms of the criterion ofgrouping viz. average labour productivity.

4.1.3 Thus, we disaggregated the units further into productivity-deciles and tried toexamine the pattern of change in LD across these deciles. It is clear from the Table 5 that thefirms constituting different deciles (except the topmost one22) of the T&G enterprises andtheir organized and unorganized sectors are relatively homogenous groups in terms of theirrespective productivity performances.

19 The final data set was obtained after excluding firms with non-positive values of output (the relevantmeasures-gross output or GVA) and/or inputs (capital, labour and material-fuel) to make it more reliable.20 This was also true when we considered the rise in median LD values across the productivity quartiles ofthe organized segment (as Table A1 in the appendix shows).21 Median LD also followed a similar pattern for the unorganized units and for all the sample units takentogether.22 The concerned CV has however decreased significantly at the last time-point.


4.1.4 Table 6 presents the change in mean values of estimated LD (obtained again byapplying population weights) across productivity deciles at the two time-points concerned.It does not throw any clearly discernible pattern of relationship between the productivityperformances of the firm and change in estimated labour demanded by it. There was rise inmean estimated LD in almost all the productivity deciles (except the third decile in the entireset of T&G firms as well as fourth and eighth productivity deciles of sample organizedunits) during the study period. The increase of the mean value for all the T&G units wasconcentrated towards the medium deciles especially sixth through ninth deciles. Estimatedmean LD for the unorganized establishments increased more at both lower and upper endsthan around the middle of the productivity distribution, as is expected from ourconceptualization of the segment in Section 2. The pattern remains the same even if weconsider the median LD (see Table 7 presenting selected descriptive statistics for thedistribution of estimated LD).23 The pattern is not so clear for organized segment: the risewas greater in lowest three deciles, generally low in the topmost three deciles and fluctuatedaround the medium deciles. Thus, changes in both mean and median LD values do notindicate any definite relation between size-groups of productivity and change in averagelevel of LD for the organized units during the study period.

4.1.5 The other possible way to get an idea of the distribution of LD across productivitydeciles is to check if greater part of labour demand generated now is concentrated in thehigher or lower productivity deciles than before. Thus, we proceed to examine whetherfirm-level change in LD has translated in any improvement in the share of higher productivitydeciles in total labour demand. Table 8 presents the changing share of productivity decilesin total LD over time, taking all T&G units together and the organized and unorganizedsegments separately. The sample units in the sector as a whole definitely show an increasedconcentration of share in total LD around the middle section of the productivity distribution.Once we locate the changes in the two constituent segments, we find a clear deteriorationin the distribution of estimated LD as the share has increased mainly in the lower productivitydeciles of organized (except for the 7th decile) and especially unorganized T&G firms. Thestriking observation here is that of a significantly high increase in share of the topmost twoproductivity deciles in the unorganized part indicating the possibility of a sustainablegrowth of firm in these productivity size-groups.

4.1.6 Next, we carry out a similar exercise by examining distribution of estimated LD inboth the years across size-groups of firms classified in terms of firm-level expenses onintermediate inputs.25 Firms were grouped initially in quartiles as well as deciles based onthis criterion. However, the coefficient of variation in intermediate input costs is very highacross both the quartiles and the decile-groups. To make the comparative analysis moremeaningful we have resorted to a classification by trial whereby all the sample units werecategorized in 17 clusters. Our aim in this categorization was to retain as many observationsas possible in the analysis for both the time-points without allowing a very high coefficientof variation of intermediate input costs in individual clusters. Table 9 presents the details ofthis classification and the selected summary statistics for expenses on intermediate inputs

23This was true also for all the sample units.25 This seems better than the possible alternative schemes of size-classifications - based on size ofemployment or by stock of fixed capital. This is so because size-classification based on intermediate inputcosts is expected to minimize the problem of slack or underutilization.


in these groups. Findings from the exploratory analysis of change in estimated mean LD andshare of the groups so obtained in total LD are presented in Table 10.26

4.1.7 Almost all the size groups experienced fall in mean APL but quite a few of themregistered positive change in mean LD. Considering all the T&G firms in the sample togetherwe find clearly that the increase in mean LD is higher around the middle-sized firms. In factthe medium-sized firms had recorded higher share of LD in the aggregate demand for labourin both the years. Notwithstanding the declining productivity performance in most of thesize-groups, a few also exhibited increase in their respective shares in total labour demand.It is clear, however from the above discussion that there is no definite and positivecorrespondence between the change in LD and level as well as over time growth inproductivity in organized sphere of the industry. Employment growth in the unorganizedpart however seemed to be concentrated towards the two ends of productivity distribution.This probably indicates at some kind of stickiness in optimal adjustment of LD according toproductivity performances in different segments of T&G firms.

4.2 Estimation of Labour Demand Elasticity:

4.2.1 With the help of two-stage least square estimator we have carried out aninstrumental variable regression analysis for estimating the labour demand elasticity. Wecould incorporate all the variables enlisted in the section 3.1 only in the LD estimation at theall-India level. Here we have reported only the results of the second stage of instrumentalvariable regression (presented in Table 12)27. As mentioned earlier, we tried two possibleinstruments against firm output for the all-India regression: one used in similar studies onestimating labour demand elasticity (Y1_ins), the per capita net state domestic product(PCNSDP); and the alternative instrument devised in our study (Y2_ins).28 What we findhere is that the labour demand elasticity had the expected negative sign at the first timepoint while it was statistically insignificant at the end of the study period. The responsivenessof LD with respect to wage increases significantly if the concerned firm was in the organizedsector while it decreased when the firm belonged to a state with greater degree of connectivitybetween organized and unorganized segments. The direction of influence on the sameresponsiveness of LD however changed during the study period for factors such as labourmarket rigidity, individual firm’s effort in terms of improving inputs, degree of mechanizationof the state in which the firm was located. Firms had responded more through adjusting LDto wage changes at the first time-point while it responded less at the end of the studyperiod in states with higher labour market rigidity or in states with greater extent ofmechanization in the unorganized segment.29

4.2.2 Labour demand elasticity became significantly lower with firm’s effort towardimprovement in the basic non-capital inputs it used but did not differ significantly with itseffort in terms of improvement in the capital stock and capital intensity in the first period. It26 Similar results for the mentioned quartiles and deciles are not reported due to the reason specified here.27 Results significant at least at 5% level are discussed only.28 It was explained earlier in section 4.1. The first instrument however, never proved to be statisticallysignificant with alternative measures of labour market rigidity or even after excluding the regressors witht-values less than one. Thus, we had reported only the results of the regression using the second instrument.29 Labour market rigidity does not allow optimal adjustment in labour demand despite wage rise. Higherextent of mechanization in the unorganized segment probably facilitates greater subcontracting byorganized units.


is noteworthy however that the elasticity increased significantly at the last time-point withenhanced effort toward improvement on both input and capital fronts. This seemsencouraging as very little lowering of wage can bring about a substantially high andstatistically significant increase in labour demand through complementary efforts in termsof physical capital and other inputs e.g. raw materials. Individual firm’s location in thegarment sector lowered the effect of one percent change in wage on the LD adjustment inthe first year possibly indicating the relatively greater dominance of non-wage factors(especially stronger scale effect) in such adjustments compared to that among the textile-producing firms. The product-segment wise location of the firm however had no statisticallysignificant effect on the labour demand elasticity at the latest year under consideration.

4.2.3 Results of estimation of labour demand through the 2SLS-IV regression for theselected thirteen states with major presence of both organized and unorganized T&G unitsare reported in Table 13 (second state dealing with the instrumented variable). Only thesecond instrument Y2_ins is used in this regression exercise. Like in the all India-levelregression the instrument concerned was found to be positively significant in all states andat both the time-points.30

4.2.4 Adjustment of labour demand with changing wage was of varied degree and indifferent directions in the selected states. Estimated labour demand elasticity had theexpected negative sign in seven out of the 13 states in 2001(Andhra Pradesh, Gujarat,Karnataka, Kerala, Maharashtra, Uttar Pradesh and West Bengal). But in no state it wasnegative significant in 2011. The value was positive and statistically significant only inTamil Nadu in the first year, while it became positive significant also in Delhi, Karnataka,Maharashtra, Rajasthan and West Bengal at the end of the study period. Among the latterset of states, Delhi and Rajasthan displayed statistically insignificant elasticity value in2001. The estimated elasticity was insignificant at both time-points in Haryana, MadhyaPradesh and Punjab. In the states viz., Andhra Pradesh, Gujarat, Kerala and Uttar Pradesh,labour demand elasticity was negative significant in 2001 and it became statisticallyinsignificant in the latter year. This indicates again the possibility of non-wage factorsdominating individual firm’s optimal adjustment of LD.

4.2.5 There was a few uniform patterns also, similar to the one exhibited in the estimationfor all-India T&G labour demand function. For example, labour demand elasticity alwaysincreased when the firm in question operated in the organized segment rather than in theunorganized part of the industry.31 This increase was statistically significant in all thestates at both ends of the study period. Labour market rigidity generally exerted an expectednegative significant effect on the elasticity values (except registering a positive significanteffect in Punjab, Rajasthan and West Bengal and without any significant effect Gujarat andKerala in 2001). It was negative and statistically significant at least at 5% level in all thestates in the last year.

4.2.6 Similarly the responsiveness of LD to changing wages was lowered by the effortstoward improvement of non-capital inputs (except an insignificant effect in Andhra Pradesh30 The coefficient value increased for Karnataka, Rajasthan and Tamil Nadu, remained almost the samein Andhra Pradesh, Delhi,Gujarat, and Kerala, and declined somewhat in the others.31 As is expected from our discussion in Section 2, labour demand elasticity has actually increased atthe end of the study period for a typical organized unit in all the states and at the all-India level.


in 2011).32 This might indicate that further possibility of enhancing labour demand byreducing wages may be limited if the effort in terms of improving non-capital inputs isalready at a higher level. Operating in the garment sector also seems to have exerted astatistically significant negative influence on estimated labour demand elasticity. The effectwas negative significant at both the time points in the states of Maharashtra, Rajasthan,Uttar Pradesh and West Bengal. It was negative significant in Andhra Pradesh, Gujarat andKerala in 2001 but later became statistically insignificant. On the contrary, it was insignificantin the first year and became statistically significant (-) at the end of the study period inMadhya Pradesh and Tamil Nadu. The elasticity value did not differ significantly dependingon the product-markets catered by the firms in both the years in the remaining states. Whylabour demand adjusted in particular ways with changing wages and why the estimatedlabour demand elasticity was influenced by relevant locational and other factors in theabove-discussed manner in the selected states however, needs a thorough mapping ofmore concrete state-specific characteristics and their effects on the labour demand conditionin a comparative framework. This however lies beyond the scope of this preliminary analysis.

5. Conclusion

5.1 Indian textile policies in the recent years have gradually removed most importantprotective measures for decentralized and significant restrictions on the organized sectorsgiving way to the free interplay of market forces. In the near absence of growth-restrictingregulations, optimal re-allocation of productive resources including labour through greaterconnectivity between the organized and unorganized segments seems to be an ongoingprocess. It is then logical to expect that the segments with lower-productivity and efficiencywill be outcompeted and labour demand will mainly concentrate in the relatively growingsection of T&G firms. On this backdrop, the present paper seeks to trace the pattern ofchange in employment in both the organized and unorganized firms in the Indian T&Gindustry. For this, we attempted to examine the inter-relationship between change in labourdemand and the same in average labour productivity. Steeper rise in mean LD as well asshare in aggregate LD was concentrated in groups of all T&G units, classified by intermediateinput costs, which also registered substantial fall in APL. Disaggregated exploration for theorganized and unorganized segments indicated no definite pattern in the organized partwhile both mean LD and share in aggregate rose in the extreme size-groups of the unorganizedT&G establishments. Thus the exploratory analysis pointed at the presence of some sort ofstickiness in the behaviour of certain segments e.g. the survivalist unorganized set, withrespect to productivity performances necessitating the study of firm-specific employmentbehaviour.

5.2 This prompted us to inquire the nature of LD adjustment with respect to changesin market structure and in policy shifts influencing the structure or having a direct bearingon the firm’s conduct. The particular approach of estimating labour demand function and ofderiving estimate of LD elasticity with respect to wage was adopted while controlling certainstructural parameters. We sought to carry out the estimation using the two-stage leastsquares IV estimator at the all India level as well as for selected states with major presence

32 Like in the regression for all India units, we used the effort in terms of only non-labour inputs amongthe non-capital components such as raw-materials and fuels as a measure of input-based improvementeffort and found similar results.


of the T&G industry. Certain difficulties were encountered in the estimation across thetextile-major states especially regarding the choice of instrumental variable against firm-level output and instrument devised for conducting this analysis turned out to be statisticallysignificant with an expected sign in all cases. What we learnt from this exercise, however, isthat the responsiveness of labour demand to wage in particular and market incentives ingeneral may be weak (the estimated coefficient of wage i.e., the labour demand elasticityterm turned out to be insignificant at the all-India level and for most of the states). Thisstickiness possibly owes its origin to (i) inherent structural rigidities such as labour marketregulations facing the organized group and pre-market constraints faced by the unorganizedenterprises, (ii) lag in adjustment arising from inadequate preparedness of firms to respondgainfully to market signals. An efficient re-allocation of employment through flexible andoptimal adjustment of labour demand in response to signals from price mechanism andother market incentives will require, as a precondition, an in-depth inquiry into the sourceof the observed stickiness.

5.3 The above findings are subject to limitations imposed by the quality of availableinformation and variables used in the analysis. Estimation of labour demand elasticity in aspecific sector with the presence of a huge unorganized section also has to introduce finerdistinction between different market forces/policies affecting this phenomenon of stickiness.Methods of analysis have to be modified further as the estimation covers both the organizedand unorganized segments. The former – being registered under the Factories Act - is arelatively homogenous group in terms of structural location while the unorganized segmentis characterized by inherent structural heterogeneity. Nature of industry-specific policyinterventions is also likely to vary across the two segments resulting in the error term beingcorrelated with the policy variable (since segment-specific policy formulation may itself bemotivated by changing employment composition of the two segments). Thus, any futureprobe based on this preliminary analysis, needs addressing the possible endogeneity biasarising from the two-way relationship between any state’s labour-market regulations andindustry-specific efficiency enhancing policies with the performance of the two segmentsconcerned.

Reference

Abraham, V. and Sasikumar, S.K. (2010): Labour Cost and Export Behaviourof Firms inIndian Textile and Clothing Industry, MPRA Paper No. 22784, [Online] http://mpra.ub.uni-muenchen.de/22784 (Accessed 6th January, 2015);

Banerjee S., Chattopadhyay, S. and Lahiri, K. (2016). Export Performance in Textile andGarments with China as a Competitor: An Analysis of India’s Situation from the Perspectiveof Structure-Conduct-Performance Paradigm in Roy M. & S. Sinha Roy (eds) InternationalTrade and International Finance Explorations of Contemporary Issues, Springer;

Banga, R. (2005), Impact of Liberalization on Wages and Employment in Indian ManufacturingIndustries, Working Paper No. 153, Indian Council for Research in International EconomicRelations;

Biswas, P.K., Banerjee, P., Choudhury, A. and Kukreja, P. (2014): Industry Structure and thePattern of Innovation: Pharmaceutical Industry of India 2000-2010, CSIR-NISTADS, NewDelhi;


Cali, M., Mitra, S. and Purohit, P. (2009): State-Business Relations in Indian States: Papertowards constructing an effective SBR index for Indian states, IPPG Discussion PaperSeries, No.25, Manchester;

Das, D.K. and Kalita, Gunajit (2009): Are Labour-intensive Industries GeneratingEmployment in India? Evidence from Firm Level Survey, The Indian Journal of LabourEconomics, Vol. 52 No.3, pp. 411-432;

Galab, S. and Revathy, E. (2009): Understanding Powerloom Weavers’ Suicides in Sircilla,Economic and Political Weekly, Vol. 44 No. 8, pp. 12-15;

Goldar, B. (2011): Growth in Organised Manufacturing Employment in Recent Years,Economic and Political Weekly, Vol. 46, No. 7, pp. 20–23.

Goldar, B. and Sadhukhan, A. (2015): Employment and Wages in Indian Manufacturing:Post-Reform Performance, Working Paper No. 185, Employment Policy Department,International Labour Office, Geneva;

Hasan, R., Mitra, D. and Ramaswamy, K.V. (2007): Trade Reforms, Labor Regulations, andLabor-Demand Elasticities: Empirical Evidence from India, The Review of Economic andStatistics, Vol. 89 No.3, pp. 466-481;

ICRIER (2008): A Study on Labour Intensity and Employment Potential of IndianManufacturing, a report sponsored by National Manufactuting Competitiveness Council;

Kathuria, L.M. and Mamta (2012): Understanding Financing Practices and Constraints ofSmall and Medium Enterprises in Textile and Clothing Sector: An Empirical Study, Asia-Pacific Journal of Management Research and Innovation [Online] http://abr.sagepub.com/content/8/3/303 (Accessed 6th November, 2014);

Kakarlapudi, K. K. (2010): The Impact of Trade Liberalization on Employment: Evidencefrom India’s Manufacturing Sector, MPRA Paper No. 35872, [Online] http://mpra.ub.uni-muenchen.de/35872 (Accessed 6th January, 2015);

Kathuria, V., Raj, R.S.N. and Sen, K. (2010): Human Capital and Manufacturing ProductivityGrowth in India, Paper submitted for International Conference on: Science, Technologyand Economy: Human Capital and Development (Annual Conference of IASSI & KnowledgeForum Hosted by IIT Bombay during Nov. 11-12, 2010);

Kathuria, V.R., Natarajan, R.R.S. and Sen, K. (2013): Efficiency Comparison between Formaland Informal Firms: Evidence from Indian Manufacturing, Journal of Industrial Statistics,Vol. 2 No.1, pp.1-23;

Maiti, D. (2008): Organizational Morphology of Rural Industries and its Dynamics inLiberalized India, Cambridge Journal of Economics, Vol. 32 No. 4, pp. 577-591;

Maiti, D. and Marjit, S. (2008): Trade Liberalization, Production and Informal Sector of theDeveloping Countries, The Journal of International Trade & Economic Development: AnInternational and Comparative Review, Vol. 17, No. 3, pp. 453-461;

Mitra, A. (2013): Can Industry be the Key to Pro-Poor Growth? An Exploratory Analysis forIndia, ILO Asia-Pacific Working Paper Series, DWT for Sout Asia and Country Office forIndia, International Labour Organization

Nagaraj, R (2004): Fall in Organised Manufacturing Employment-A Brief Note, Economicand Political Weekly,Vol. 39 No. 30, pp. 3387-90;


Nagaraj, R (2007): Labour market in India, paper presented at a seminar on “LabourMarkets in Brazil, China and India”, Princeton University, New Jersey, [Online] http://www.oecd.org/employment/emp/38355655.pdf (Accessed 29th July, 2015)

Nagaraj, R (2011):.Growth in organised manufacturing employment in recent years,Economic and Political Weekly, Vol.46, No.39, pp.20–23.

Nataraj, Shanthi (2010): Productivity and Firm Size Distribution: Evidence from India’sOrganized and Unorganized Manufacturing Sectors, Unpublished Doctoral Thesis,University of California, Berkeley;

Niranjana, S. and Vinayan, S. (2001): Report on Growth and Prospects of the HandloomIndustry, Study Commissioned by the Planning Commission, New Delhi: Government ofIndia;

Raveendran, G. and Kannan, K.P. (2009): Growth sans Employment: AQuarter Century ofJobless Growth in India’s Organised Manufacturing, Economic and Political Weekly, Vol.44 No. 10, pp. 80-91;

Reserve Bank of India (2014): RBI Handbook of Statistics, downloaded on 01-08-2015 fromhttp:///www.rbi.org.in/scripts/publicationsview.aspx?id=15795

Roy, T. (1996): Market Resurgence, Deregulation, and Industrial Response: Indian CottonTextiles in 1990s, Economic and Political Weekly, Vol. 31 No. 21, pp. M31-M41;

Roy, T. (1998): Economic Reforms and Textile Industry in India, Economic and PoliticalWeekly, Vol. 33 No. 32, pp. 2173-2182;

Sahu, P.P. (2011): Subcontracting in India’s Micro and Small Enterprises: An ExploratoryAnalysis, Das, K. (ed), Micro and Small Enterprises in India:The Era of Reforms, pp. 185-207, Routledge, New Delhi;

Sen, S. and Dasgupta, B. (2006): Labour in India’s Organized Manufacturing Sector, IndianJournal of Labour Economics, Vol. 49, No. 1, pp. 79-102;

Sen, Sarmishtha. and Majumder, Subrata. (2014): Relative Efficiency in Adaptation to Pro-Competitive Environment: An Exploration of Unorganized Indian Textile and GarmentsEnterprises, Journal of Rural and Industrial Development, Volume 2(2), pp. 9-21

Sen, S. and Majumder, S. (2015): Relative Efficiency of Organized and Unorganized Segmentsof Indian Textile and Garments Industry: A Preliminary Exploration, The Journal ofIndustrial Statistics, Vol. 4 No. 1, pp. 37-57;

Sen, S. (2016): Adaptability to Pro-competitive Environment: A Study of Unorganized IndianTextile and Garment Enterprises, Unpublished Doctoral Thesis, University of Calcutta;

Sharma, A.N. (2006): Flexibility, Employment and Labour Market Reforms in India,Economic and Political Weekly, Vol. 41 No. 32, pp. 2078-2085;

Srinivasulu, K. (1996): 1985 Textile Policy and Handloom Industry Policy, Promises andPerformance, Economic and Political Weekly, Vol. 31 No. 49, pp. 3198-3206;

Teewari, M. (2005): Post-MFA Adjustments in India’s Textile and Apparel Industry: EmergingIssues and Trends, Working Paper No. 167, Indian Council for Research on InternationalEconomic Relations, pp. 1-55;


Table 1Variables used in the combined dataset and their definitional compatibility

Variable ASI data NSS data

Intermediate input (Rs.)

INTER: Expenses on materials and fuels consumed

INTER: Expenses on materials and fuels consumed = Annual value of (total expenses – other operating expenses + costs of electricity & fuel consumed)33

Capital (Rs.) K: Book value of fixed asset on the opening date of the reference year

K: Market value of fixed assets (owned & hired) as on the closing date of the reference year – net addition to fixed assets during the reference year

Labour (number)

L = Total man-days worked (both manufacturing and non-manufacturing)

L = (Total number of FTE workers34 * 30 * number of months operated * average hours worked35)/8

Firm-specific APL

(Rs./manday)

GVAalt/L (GVA alt=Ex-factory value of output – Costs of materials and fuels consumed36)

GVA alt/L (GVA alt=Annual37 value of total receipt38 – Expenses on materials and fuels)

Source: Sen and Majumder, 2015

33 There are a very large number of missing entries in the series on ‘expenses on raw materials’ thusmaking its direct use unreliable.34 To make the labour-data comparable to the ASI data, we convert all workers (both full-time and part-time) into full-time equivalents (FTEs), by treating 1 full-time worker = 2 part-time workers.35 In the 56th round data, average number of hours worked daily is not given. We assume here that each dayconsists of an 8-hour block.36 Information on Gross Value Added (GVA) is not directly available in the ASI database but it can becalculated by deducting expenditure on materials and fuels and other operating expenses from totaloutput (ex-factory value of output and other receipts e.g. value of electricity produced and sold).However, to be consistent with the production framework used in this study an alternative series of GVAvalues (GVAalt) was calculated by subtracting only costs of materials and energy from ex-factory value ofoutput. Similarly, a new series of value-added was computed from the given GVA data provided by theNSSO to arrive at a similar measure. Observations with negative GVAalt values were dropped to make theexercise meaningful.37. Annual values are obtained directly from NSSO 2000-01 data. But annual values of receipts (and othercategories e.g. expenses) for the year 2010-11are computed by multiplying the given series with numberof months operated.38 It is necessary to note: (a) this category includes receipts from other activities e.g. ‘receipt from trade’as well as ‘other receipts’ – in addition to ‘receipts from manufacturing activities’. More accurate valuesof production could be obtained by deducting the non-manufacturing component from total receipts. Dueto the presence of considerable number of missing values (especially for NSS 67th round) this operationwould have made the data series unreliable or a substantial number of data points would have been lost.


Table 2Summary Statistics for Estimated LD across Productivity Quartiles, 2001 & 2011

APL Quartiles/ Summary Statistics

2001 2010

Obs. Mean SD Median Obs. Mean SD Median

All T&G 1 89656 2810.83 5499.34 2160 143748 3506.18 4979.37 2700 2 92919 3027.54 11613.65 2250 153098 3322.07 10659.55 2520 3 71979 3683.42 30176.82 2160 47792 8087.05 72505.80 2520 4 18300 11634.75 56786.22 1800 12254 18705.07 74868.17 2520 Organized T&G 1 2342 31345.48 92550.11 6820 2531 55608.33 244148.60 9150 2 2124 52930.66 184055.21 7518 2448 57295.57 200046.90 10496 3 1940 42890.29 105120.09 12671 2434 53657.81 142558.00 16174 4 1917 31805.08 88569.22 10300 2618 36977.30 89907.97 13505 Unorganized T&G 1 67074 2609.44 2078.12 1980 75093 3548.47 3103.39 2835 2 63970 2810.38 1792.95 2310 75310 3268.24 2648.13 2520 3 76644 2719.50 1573.16 2160 87223 2988.70 1981.51 2520 4 56842 2077.73 1682.03 1800 109235 3173.59 2653.55 2430

Note: Used population weightsSource: Authors’ Calculation

Table 3Change in Mean LD across Quartiles of APL

Note: (i) ALL-All T&G Units; ORG-Organized Segment; UN-Unorganized Segment;(ii) Used population weights

Source: Authors’ Calculation

APL Quartiles/ Mean LD

All T&G Units (%)

(ALL)

Organized T&G Δ (%) (ORG)

Unorganized T&G Δ (%)

(UN) 2001 2011 2001 2011 2001 2011

I 2810.83 3506.18 24.74 31345.48 55608.33 77.40 2609.44 3548.47 35.99

II 3027.54 3322.07 9.73 52930.66 57295.57 8.25 2810.38 3268.24 16.29

III 3683.42 8087.05 119.55 42890.29 53657.81 25.10 2719.50 2988.70 9.90

IV 11634.75 18705.07 60.77 31805.08 36977.30 16.26 2077.73 3173.59 52.74

Δ


Table 4Summary Statistics of APL within Productivity Quartiles of Sample T&G Units in India,

2001 and 2011

Source: Authors’ CalculationTable 5

Distribution of APL - Productivity Deciles in India, 2001 and 2011

173

APL

Quartiles

ALL T&G Organized T&G Unorganized T&G

Obs. Mean Median SD CV Obs. Mean Median SD CV Obs. Mean Median SD CV

2001

I 2264 54.35 57.35 17.74 0.33 706 133.12 137.64 67.02 0.50 1558 48.79 50.67 14.36 0.29

II 2265 108.11 107.25 17.31 0.16 704 353.50 350.76 67.09 0.19 1559 86.67 86.06 10.65 0.12

III 2263 214.16 202.35 54.39 0.25 706 698.90 671.49 155.13 0.22 1559 133 130.31 17.9 0.13

IV 2264 1397.42 725.35 8790.51 6.29 705 3016.47 1789.02 15606.61 5.17 1559 407.29 248.63 706.86 1.74

2011

I 1631 69.57 72.34 22.86 0.33 844 202.70 220.20 86.22 0.43 788 54.11 56.14 14.64 0.27

II 1630 154.13 148.24 33.83 0.22 842 454.94 450.45 75.38 0.17 786 91.80 91.02 10.02 0.11

III 1631 391.64 378.30 116.34 0.30 843 781.50 766.39 127.13 0.16 788 132.27 131.40 14.07 0.11

IV 1630 1608.01 1065.53 2328.48 1.45 844 2327.64 1578.04 3051.91 1.31 787 289.73 212.82 367.56 1.27

Decile Groups/Variables

Summary Statistics

APL_ALL T&G APL_Organized APL_Unorganized 2001 2011 2001 2011 2001 2011

1

Obs. 905 652 282 337 623 315 Mean 36.11 46.11 63.64 113.18 34.12 39.16

Median 38.74 48.49 66.32 120.16 36.51 41.85 SD 11.51 13.99 33.71 56.85 9.68 9.65 CV 0.32 0.30 0.53 0.50 0.28 0.25

2

Obs. 905 652 282 337 623 315 Mean 61.84 78.58 157.69 240.56 54.92 60.24

Median 61.99 78.84 157.90 242.45 55.26 60.68 SD 5.64 7.39 23.89 23.64 4.62 4.87 CV 0.09 0.09 0.15 0.10 0.08 0.08

3

Obs. 905 652 282 337 623 315 Mean 80.51 105.82 243.24 328.62 69.15 75.08

Median 80.25 105.71 243.94 327.08 69.24 75.16 SD 5.44 8.61 24.90 27.69 3.85 3.98 CV 0.07 0.08 0.10 0.08 0.06 0.05

4

Obs. 905 652 282 337 623 315 Mean 101.37 138.76 327.56 426.56 82.56 87.93

Median 100.98 138.23 326.26 424.89 82.46 87.86 SD 6.57 11.25 25.78 28.91 4.01 3.77 CV 0.06 0.08 0.08 0.07 0.05 0.04

5

Obs. 905 652 282 337 623 315 Mean 126.10 189.72 423.63 533.65 97.81 102.38

Median 125.64 187.22 423.67 536.29 97.64 102.27 SD 8.23 19.58 30.97 31.28 4.81 4.39 CV 0.07 0.10 0.07 0.06 0.05 0.04

Employment-Productivity Profile and Labour Demand Elasticity ....

Table 5Distribution of APL - Productivity Deciles in India, 2001 and 2011 (Contd.)


Table 6Change in Mean LD across Decile-Groups of APL

APL Deciles/

Mean LD

All T&G Units Change (%)

Organized T&G Change (%)

Unorganized T&G Change (%) 2001 2011 2001 2011 2001 2011

1 2891.78 3881.49 34.22 23829.71 40429.08 69.66 2512.16 3591.28 42.96

2 2664.59 3524.81 32.28 40307.73 68659.32 70.34 2708.02 3718.59 37.32

3 2967.57 2964.90 -0.09 45339.76 53861.32 18.79 2658.42 3353.88 26.16

4 2925.65 3169.22 8.33 59124.31 58123.16 -1.69 2920.34 3508.97 20.16

5 3234.78 3621.61 11.96 39680.28 61848.23 55.87 2762.64 2906.28 5.20

6 2802.50 5628.13 100.83 45701.31 52334.29 14.51 2972.01 2982.55 0.35

7 3657.62 14567.99 298.29 35762.74 56583.89 58.22 2511.18 3187.26 26.92

8 8160.41 23640.63 189.70 44879.70 44842.64 -0.08 2441.69 2754.12 12.80

9 12051.51 22101.82 83.39 34866.95 47202.84 35.38 2283.56 3164.99 38.60

10 13972.81 15418.24 10.34 25194.24 26867.42 6.64 1753.63 3266.34 86.26

Note: Used population weightsSource: Authors’ Calculation


Decile Groups/Variables

Summary Statistics

APL_ALL T&G APL_Organized APL_Unorganized 2001 2011 2001 2011 2001 2011

6

No. of Observation 905 652 282 337 623 315 Mean 162.09 274.84 546.38 654.07 115.20 117.95

Median 161.34 272.48 542.20 652.90 115.22 117.95 SD 13.20 31.78 43.25 40.04 5.31 4.58 CV 0.08 0.12 0.08 0.06 0.05 0.04

7

Obs. 905 652 282 337 623 315 Mean 222.46 420.34 732.45 812.14 137.00 136.49

Median 220.34 416.83 728.24 810.09 136.86 136.09 SD 23.18 51.40 67.68 52.13 7.31 5.95 CV 0.10 0.12 0.09 0.06 0.05 0.04

8

Obs. 905 652 282 337 623 315 Mean 341.70 625.96 1028.76 1036.13 170.25 159.60

Median 335.97 621.33 1020.01 1034.44 169.82 158.95 SD 48.85 69.70 110.47 75.94 12.00 8.12 CV 0.14 0.11 0.11 0.07 0.07 0.05

9

Obs. 905 652 282 337 623 315 Mean 621.44 960.59 1599.84 1432.42 229.31 198.66

Median 596.64 945.70 1572.59 1406.63 225.16 195.90 SD 131.08 132.50 241.86 167.91 24.84 16.32 CV 0.21 0.14 0.15 0.12 0.11 0.08

10

Obs. 911 654 283 340 628 314 Mean 2666.63 2710.03 5364.45 3817.38 695.02 443.10

Median 1582.82 1825.78 3304.60 2541.53 428.26 296.43 SD 13763.33 3383.92 24467.80 4401.06 1049.43 547.15 CV 5.16 1.25 4.56 1.15 1.51 1.23

Table 7Distribution of Estimated LD across Productivity Deciles in India, 2001 and 2011

Source: Authors’ CalculationNote: Used population weights

Table 8Change in Share of Productivity-Deciles in Total LD

Productivity Groups

All T&G Change (%)

ORG_T&G Change (%)

UNORG_T&G Change (%)

Share_01 Share_10 Share_01 Share_10 Share_01 Share_10 Decile 1 9.37 12.64 34.93 6.52 8.17 25.39 9.24 11.16 20.83 Decile 2 10.40 12.58 20.98 10.82 13.62 25.92 10.85 8.47 -21.92 Decile 3 9.26 11.60 25.25 13.58 10.47 -22.86 11.03 7.75 -29.72 Decile 4 11.29 12.33 9.25 15.45 12.05 -22.05 9.56 10.00 4.61 Decile 5 12.28 12.94 5.44 9.94 10.96 10.31 11.41 8.47 -25.74 Decile 6 11.09 13.13 18.39 10.70 10.18 -4.86 11.78 8.87 -24.72 Decile 7 9.46 7.36 -22.14 8.94 10.73 20.01 13.10 10.54 -19.56 Decile 8 10.50 6.75 -35.70 10.09 8.82 -12.61 11.15 8.33 -25.26 Decile 9 8.85 5.95 -32.77 8.64 9.15 5.99 7.15 12.05 68.55 Decile 10 7.67 4.64 -39.56 5.39 5.93 9.85 4.80 14.38 199.37

Note: Share_01 and Share_10 are respective shares of productivity deciles in total LD in 2001 & 2010;LD estimates obtained using population weightsSource: Authors’ Calculation

175

Productivity Deciles/Variable

s

Summary Statistics APL_ALL T&G APL_Organized

APL_Unorganized

2001 2011 2001 2011 2001 2011

1

Obs. 905 652 282 337 623 315 Mean 36.11 46.11 63.64 113.18 34.12 39.16

Median 38.74 48.49 66.32 120.16 36.51 41.85 SD 11.51 13.99 33.71 56.85 9.68 9.65

2

Obs. 905 652 282 337 623 315 Mean 61.84 78.58 157.69 240.56 54.92 60.24

Median 61.99 78.84 157.90 242.45 55.26 60.68 SD 5.64 7.39 23.89 23.64 4.62 4.87

3

Obs. 905 652 282 337 623 315 Mean 80.51 105.82 243.24 328.62 69.15 75.08

Median 80.25 105.71 243.94 327.08 69.24 75.16 SD 5.44 8.61 24.90 27.69 3.85 3.98

4

Obs. 905 652 282 337 623 315 Mean 101.37 138.76 327.56 426.56 82.56 87.93

Median 100.98 138.23 326.26 424.89 82.46 87.86 SD 6.57 11.25 25.78 28.91 4.01 3.77

5

Obs. 905 652 282 337 623 315 Mean 126.10 189.72 423.63 533.65 97.81 102.38

Median 125.64 187.22 423.67 536.29 97.64 102.27 SD 8.23 19.58 30.97 31.28 4.81 4.39

6

No. of Observation 905 652 282 337 623 315 Mean 162.09 274.84 546.38 654.07 115.20 117.95

Median 161.34 272.48 542.20 652.90 115.22 117.95 SD 13.20 31.78 43.25 40.04 5.31 4.58

7

Obs. 905 652 282 337 623 315 Mean 222.46 420.34 732.45 812.14 137.00 136.49

Median 220.34 416.83 728.24 810.09 136.86 136.09 SD 23.18 51.40 67.68 7.31 5.95

8

Obs. 905 652 282 623 315 Mean 341.70 625.96 1028.76 170.25 159.60 Median 335.97 621.33 1020.01 169.82 158.95 SD 48.85 69.70 110.47 12.00 8.12

9

Obs. 905 652 282 623 315 Mean 621.44 960.59 1599.84 229.31 198.66

Median 596.64 945.70 1572.59 225.16 195.90SD 131.08 132.50 241.86 24.84 16.32

10

Obs. 911 654 283 628 314 Mean 2666.63 2710.03 5364.45 695.02 443.10

Median 1582.82 1825.78 3304.60 428.26 296.43SD 13763.33 3383.92 24467.80

52.13337

1036.131034.44

75.94337

1432.421406.63167.91

3403817.382541.534401.06 1049.43 547.15


Table 9Descriptive Statistics for Size-Groups based on the Values of Intermediate Inputs, 2001 & 2011


Table 10Change in Mean APL, Estimated LD & Share in Estimated LD by Groups based on

Intermediate Input-Costs

Note: LD estimates obtained using population weights; Coefficient of Variation is very high for the twoextreme size-groups here and the estimates are not reliable;Source: Authors’ Calculation


Intermediate Input Cost (Rs.) Groups

Mean APL Mean LD Share in Total LD 2001 2011 Δ (%) 2001 2011 Δ (%) 2011 2011 Δ (%)

1 75.55 64.85 -14.16 2229.33 1493.35 -33.01 1.56 0.16 -89.87 2 86.66 74.87 -13.60 2275.97 1895.08 -16.74 3.96 1.93 -51.20 3 100.17 89.31 -10.84 2502.07 2157.64 -13.77 4.02 3.78 -5.99 4 118.17 107.25 -9.24 2190.41 2527.53 15.39 5.63 7.48 32.86 5 120.78 125.43 3.85 2123.22 2605.91 22.73 11.58 9.94 -14.20 6 138.51 124.85 -9.86 2541.16 3308.67 30.20 15.45 13.19 -14.60 7 154.62 129.76 -16.08 2937.92 3874.89 31.89 8.92 10.49 17.60 8 150.75 186.44 23.67 3333.20 4476.69 34.31 6.09 8.16 34.00 9 213.15 183.56 -13.88 2920.01 3709.68 27.04 4.06 4.21 3.83 10 301.32 321.15 6.58 3100.73 3828.56 23.47 4.43 4.78 7.99 11 484.78 402.75 -16.92 5421.48 6957.86 28.34 2.97 3.88 30.54 12 784.78 649.25 -17.27 10733.97 6562.04 -38.87 2.70 2.53 -6.26 13 1796.18 976.06 -45.66 25386.82 20248.27 -20.24 4.34 3.34 -23.04 14 1094.29 1061.60 -2.99 61714.41 42772.72 -30.69 6.17 5.48 -11.08 15 1175.77 965.72 -17.86 141039.13 101069.46 -28.34 6.92 6.03 -12.86 16 1038.30 1206.43 16.19 334531.97 232869.06 -30.39 7.74 6.81 -12.05 17 1634.34 1528.50 -6.48 708048.89 605618.24 -14.47 3.52 7.73 119.31

Size-Group Value of Intermediate Inputs 2001 2011

Obs. Mean Median SD CV Obs. Mean Median SD CV

1 <=500 162 272 300 133.41 0.49 22 328.18 360 143.15 0.44

2 >500 & <=1500 330 1003.13 1020 284.52 0.28 113 1031.87 980 296.76 0.29

3 >1500 & <=4000 410 2597.12 2520 712.96 0.27 284 2722.80 2712 675.01 0.25

4 >4000 & <= 11000 569 7372.01 7320 2121.63 0.29 462 6992.98 6960 2022.41 0.29

5 >11000 & <=30000 1174 19814.78 19452 5476.57 0.28 575 19571.06 19200 5493.85 0.28

6 >30000 & <=80000 1129 50242.27 48681 13848.71 0.28 586 50923.04 48920 13603.96 0.27

7 >80000 & <=200000 828 128295.20 122400 34279.43 0.27 345 125068.80 118200 33677.55 0.27

8 >200000 & <=500000 559 319902.80 307200 84617.38 0.26 250 325144.00 315600 82326.21 0.25

9 >500000 & <=1250000 519 836336 808200 224169.10 0.27 278 826172.60 812370 212584.10 0.26

10 >1250000 & <=3000000 615 1946529 1825200 504439.60 0.26 271 2062392.00 2026764 491482 0.24

11 >3000000 & <=7500000 443 4790441 4606810 1263527 0.26 331 5110867 4992290 1367846 0.27

12 >7500000 & <=20000000 503 1.28E+07 1.24E+07 3610075 0.28 456 1.29E+07 1.26E+07 3596787 0.28

13 >20000000 & <=55000000 599 3.47E+07 3.41E+07 9696519 0.28 631 3.58E+07 3.51E+07 1.01E+07 0.28

14 >55000000 & <=150000000 621 9.32E+07 8.98E+07 2.67E+07 0.29 754 9.48E+07 9.25E+07 2.71E+07 0.29

15 >150000000 & <=375000000 355 2.34E+08 2.16E+08 6.36E+07 0.27 602 2.42E+08 2.34E+08 6.41E+07 0.27

16 >375000000 & <=1000000000 191 5.56E+08 5.17E+08 1.52E+08 0.27 376 5.90E+08 5.53E+08 1.64E+08 0.28

17 >1000000000 49 1.83E+09 1.35E+09 1.57E+09 0.86 186 2.30E+09 1.56E+09 2.35E+09 1.02

Table 12Results of Labour Demand Estimation at All-India Level, 2001 and 2011

177

Table 11Distribution of Sample Units across Selected Textile-Major States, India

States 2001 2010 Organized Unorganized Total Organized Unorganized Total

Andhra Pradesh 124 341 465 156 270 426 Delhi 173 235 408 120 290 410 Gujarat 225 452 677 262 237 499 Haryana 169 98 267 255 48 303 Karnataka 230 284 514 168 163 331 Kerala 130 354 484 84 173 257 Madhya Pradesh 65 144 209 42 80 122 Maharashtra 245 1040 1285 332 222 554 Punjab 172 299 471 304 64 368 Rajasthan 161 181 342 202 110 312 Tamil Nadu 878 1181 2059 1114 560 1674 Uttar Pradesh 162 990 1152 255 368 623 West Bengal 113 645 758 139 564 703



Variables/Year 2001 2011

L_Y2_ins 0.73 *** 0.81 *** (116.04) (126.78)

L_W -0.16 *** 0.02

(-9.53) (0.98)

L_W_ORG 0.18 *** 0.56 *** (34.84) (43.79)

L_W_RGDT 0.00 ** -0.00 *** (4.06) (-10.82)

L_W_LINKAGE -0.03 ** -0.03 *** (-2.58) (-8.87)

L_W_G -0.05 *** 0.02 (-14.05) (1.39)

L_W_KP 0.00 * 0.03 *** (1.96) (26.41)

L_W_INP -0.07 *** 0.00 *** (-38.85) (6.56)

L_W_TECH 0.00 *** -0.12 ***

(2.86) (-44.68)

Observation 9056 6521

F-value

F(9, 9046) =6339.00

F(9, 6511) =6414.32

Prob.>F 0.0000 0.0000

Adj. R2 0.8674 0.9001

d.f.

9055

6520


Table 13Results of Labour Demand Estimation for Textile-major States, 2001 and 2011



State/Year/Variables L_Y2_ins L_W L_W_ORG L_W_RGDT L_W_G L_W_KP L_W_INP Obs. F-Value Prob.>F Adj. R2 d. f.

AP

2001 0.78 -0.17 0.14 -0.05 -0.06

464 F(5, 458) =858.48 0.0000 0.9025 463 (34.92)

*** (-2.63)

*** (6.44) *** (-3.08)

*** (-7.57) ***

2011 0.77 -0.14 0.59 -0.00

424 F(6, 417) =272.28 0.0000 0.7937 423 (38.66)

*** (-1.87)

* (13.63)

*** (-2.38)

**

DEL

2001 0.67 -0.16 0.14 -0.00 -0.01 0.00 -0.07

408 F(7, 400) =338.03 0.0000 0.8529 407 (30.02)

*** (-1.80)

* (5.44) ***

(-1.31) **

(-1.09)

(3.00) ***

(-8.93) ***

2011 0.71 0.64 0.31 -0.00 -0.00 -0.10

410 F(6, 403) =592.35 0.0000 0.8966 409 (33.83)

*** (8.08) ***

(8.98) ***

(-6.80) *** (-3.08)

*** (-11.67)

***

GUJ

2001 0.70 -0.26 0.13 0.00 -0.05 0.00 -0.05

673 F(7, 665) =976.40 0.0000 0.9104 672 (37.58)

*** (-4.07)

*** (9.12) ***

(1.13)

(-3.50) ***

(1.75) *

(-8.18) ***

2011 0.74 0.60 -0.00 0.03 0.00 -0.11

498 F(6, 491) =620.60 0.0000 0.8821 497 (31.86)

*** (13.35) ***

(-6.65) ***

(-1.83) *

(3.92) ***

(-11.89) ***

HAR

2001 0.76 0.09 -0.07

267 F(3, 263) =524.58 0.0000 0.8552 266 (28.04)

*** (2.96) ** (-8.49)

***

2011 0.60 0.22 0.49 -0.00 -0.02 -0.09

302 F(7, 294) =481.14 0.0000 0.9178 301 (21.87)

*** (1.42)

(6.06) ***

(-9.73) ***

(-1.84) *

0.00

(2.61)**

(-8.54) ***

KAR 2001

0.56 -0.17 509 F(4, 504)

=834.53 0.0000 0.8858 508 (10.04) ***

(-2.32) **

0.33

(9.52)***

-0.05 (-5.74)

***

MAH

2001

1281 0.0000 0.8641 1280

2011

550 0.0000 0.9077 549

-0.10(-

2.63)***0.36

(4.00)***

PNJ

2001

468 0.0000 0.8709 467

2011

366

F(5, 1275)

=1628.37

F(7, 542) =772.48

F(4, 463) =796.61

F(6, 359) =879.66 0.0000 0.9352 365

0.69

(47.53)***

0.59(25.83)

***0.77

40.14)***0.56

(20.20)***

0.27

(19.66)***

0.65(13.88)

***0.13

(6.75)***0.68

(10.81)***

-

- (-

0.008.24)***0.00

(2.13)**0.00

(-12.76)***

(-

-0.07

(-9.77) ***

-0.04 (-3.75)

***

-0.011.44)

0.00(6.17)***

0.00(2.74)***

-0.07

(-18.43) ***

-0.14 (-16.99)

*** -0.08

(-14.99) ***

-0.07 (-6.51)

***

RAJ

2001 0.64 0.00

342 0.0000 0.8409 341 (19.22) *** (3.77)

***

2011 0.73 -0.00

312 0.0000 0.9084 311 (23.68) ***

0.35 (2.83)***

(-6.61) ***

0.00 (3.63) ***0.00

(4.08) ***

-0.09 (-12.26)

***-0.13

(-10.19) ***

TN

2001 0.62

2057

F(6, 335) =301.34

F(7, 304) =441.45

F(5, =2579.41

2051) 0.0000 0.8625 2056(46.95)

***

0.15 (3.68) ***

-0.11 (-5.18)

***-0.06

(-3.38) ***

2011 0.72

1637F(6,

=2405.481630)

0.0000 0.8982 1636(63.86) ***

0.27 (5.40) ***

UP 2001

0.77

-0.00 (-16.05)

***

1142F(6,

=1000.431135)

0.0000 0.8410 1141(46.79) ***

-0.35 (

***-11.82)

-0.02 (-3.54)

***-0.10

(-4.24) ***

0.00 (6.43) ***

0.00 (2.32)

**

-0.10 (-21.04)

***

-0.11

(-18.60)***

-0.05 (-14.92)

***

0.11 (3.62) ***0.51

(7.74)***0.27

(21.77)***0.53

(19.09) ***0.10

(7.86) ***

On Spatial Concentration of Organized Manufacturing Industries: ALook at Regional Perspective

Sajal Jana1, Garhbeta College, Midnapore, IndiaManiklal Adhikary, Burdwan University, Burdwan, India

Abstract

The Present paper has been designed to explain and analyze the regional pattern oforganized manufacturing sector in India at three time points: 1987-88, 1997-98, and2010-11. We intend to examine the extent of spatial concentration of the same across thestates at selected disaggregated two digit industry levels. The specific objectives havebeen examined by looking at employment and output figures of organized sectors inIndia. The results suggest that concentration has increased during the years 1997/98 &2010/11 respectively and the concentration across the states is not uniform at the timepoints being considered to the present study.

1. Introduction

1.1 The question of regional spread of industries is very important to understand thedevelopment potential of the sub national regions in India, since traditionally industrializationis considered as sine qua non for economic growth (Kaldor, 1967; Hirschman, 1958). Empiricalstudies point out that owing to increasing dominance of the Private sector in industrializationlocation concentration of industries has increased during the reform period & this may beconsidered as one of the major causes of regional inequality in India. The literature ofregional economies suggest that industries tend to concentrate in order to realize tangiblebenefits from being close to other firms & to consumers, market access, thick labour markets,better infrastructure, transportation, raw materials & resources, agglomeration benefits,knowledge & technology spillover, externalities etc ( Saikia, 2011).

1.2 The location decisions of state owned industries are influenced by considerationof balanced regional development. The role of state as industrial owner and industriallocation regulator has been substantially curtailed under the regime of liberalization &structural reforms. Therefore, with the increasing dominance of private sectorindustrialization, it is expected that industries will be more spatially concentrated in leadingindustrial regions having developed socio-economic infrastructure, which will further leadto higher levels of regional inequality. For balanced growth, the concentration of industrialactivities must decline over time and industrially backward states must also attract goodshare in total output of the state thus in turn creating good employment opportunities.

1.3 A brief review of literature highlights that numerous studies on regionalspecialization and concentration have been undertaken both in national and internationalcontext. To cite a few, Glenn Elisson (1997) developed a model to show that localizedindustry specific spillovers, natural advantages, and pure random chance all contribute togeographical concentration of manufacturing industries in United States. The study by M.Brulhant and J. Torestensson (1998) showed that industrial specialization among European

1 e-mail: [email protected]

The Journal of Industrial Statistics (2016), 5 (2), 179 - 191 179

Union (EU) countries has increased in the 1980s and increasing returns industries tend tobe highly localized, concentrated in central EU countries and subject to relative low intraindustry trade. A. Hildebrandt and J. Worz (2004) applied regression analysis on individualindustries to investigate the determinants of the patterns of regional concentration andspecialization in Central and Eastern European countries (CEECs) over the years 1993 to2000. The study concluded that a massive reallocation of production and labour forcestrongly affects the pattern of concentration both in terms of production and employmentgenerally increased in the CEECs. In 2006 the research studies on Economic Transition andIndustrial Concentration in China conducted by Canfei He, et.al. focus on the result thatcountry’s employment in the manufacturing sector has been increasingly concentratedsince the early 1980’s while industrial output experienced decentralization in the 1980’sfollowed by a centralization process in 1990’s. The study undertaken by Z.Goschin, et al.(2009) have used the measures Herfindhal Index, Krugment dissimilarity Index and LilienIndex to explore the main characteristics and the interaction of industries in Romania on thebasis of GVA & employment figure. In contrast the present studies considers two keyfactors viz. number of total persons engaged & gross output as a proxy of employment &output figures respectively and applies location Herfindahl Index and location Gini Index toanalyze spatial concentration of industries across the states.

1.4 In the Indian context, empirical research carried out by Alagh et. al.(1971) concludedthat the least and moderately diversified states like Maharastra, Tamil Nadu, West Bengalspecialized in resource based industries, while the less diversified states namely, Bihar,Rajasthan, Orissa and Kerala specialized in capital and demand oriented consumer goodsindustries. Ghosh (1975) computed Gini’s coefficient and Herfindhal Index to show that adeclining trend exists in concentration of twenty-two industries over the period 1948 to1968. Awasthi (2000) in a district level study in Gujrat has also observed that the flow ofinvestment has occurred to the districts that have proximity to some major industrialconcentration with the advantage of forward and backward linkages. Another study by Lal& Chakraborty (2005) concluded that new private sector industrial investments in India arebiased toward existing industrial and coastal districts and that the structural reforms increasespatial inequality in industrialization.

1.5 In 2006 S. Athraye et.al studied the impact of economic liberalization on industrialconcentration by using a dynamic model based on time series data on twelve industriesover the period 1970-99. Singh (2012) has computed industrial concentration levels for thestates based on Gini’s coefficient and Herfindahl index for each year between1979-80 and2006-07 using ASI data. In 2011 D. Saikia examined with the help of Gini’s coefficient thespatial concentration of the unorganized manufacturing sector at the state level. The findingsrevealed that there is a decline in industrial share of the leading states in post reform period.Empirical studies in the recent past have shown evidences of a wide range of factors thatinfluence industrial location. The present study adds value to the existing body of literatureby considering the following specific objectives.

a) To analyze the spatial distribution of organized manufacturing sector by lookingat the share of states in terms of total employment, value of output in all India.

b) To examine the spatial concentration of organized manufacturing sector and itsdimension of change across the states at disaggregate two digit industry levels.


1.6 These two objectives have been addressed by selecting eight major industriesgroups across the 15 states of India. Another notable feature is the cross-section timepoints chosen to the present analysis are very recent compared to that of earlier studies.

1.7 The scheme of the paper is organized in the following sections. The data base,Coverage and methodology are explained in section 2. Section 3 highlights regionaldistribution of industries in India. Section 4 concentrates on the results & discussions.Section 5 outlines scope of further work. Section 6 draws conclusions & policy implications.

2. Database, Coverage & Methodology

A) Database & Coverage of the Study

2.1 Database & Aggregation

2.1.1 The Present study uses the data from Annual Survey of Industries (ASI) compiledby Central Statistical Organization (CSO), Government of India. The ASI organizes the dataon the basis of National Industrial Classification (NIC). Until 1997-98 the ASI data wasorganized according to the NIC-1987 classification and then the NIC-1998 classificationhas followed until 2003-04 and since then the NIC 2004 Classification has been followeduntil 2007-08. Annexure 1 provides a brief explanation of selected industries correspondingto their National Industrial Codes. A concordance between NIC 1970, NIC 1987 & NIC 2008two digit levels has been made to build a comparable dataset at disaggregated two digitlevels. The variables to be used are gross output & total number of Persons engaged as ameasure of output & input respectively. The industry groups chosen for investigation canbe mentioned as: a) Food & Food Products b) Beverage & Tobacco Products c) Leather &Leather products d) Metal & Metal products e) Machine & Machine tools f) Textile &Textile Products v) Transport equipment. These industries have been selected on the basisof their contribution to Gross output in organized manufacturing sector of India. We haveincluded leather industry despite its low share in gross output because of its highercontribution to export earnings.

2.1.2 For the purpose of a comparative analysis we have considered three time points:1987-88, 1997-98 & 2010-11. The Present analysis has been carried out across 15 states. It isto be noted that the contribution of manufacturing sector in total employment for eachstate exceeds one percent.

2.2 Coverage of the Study

2.2.1 The present analysis confines to only the organized / registered manufacturingsector & excludes the unorganized manufacturing sector along with electricity, Water &Gas supply undertakings & repair services units, all of which count as industry.

2.2.2 We have used employment and gross output data of the organized manufacturingsector to represent industrial activities. By employment, we use the concept of “totalpersons engaged” as used in the ASI frame. On the other hand, Gross output comprisestotal ex-factory value of products & by-products manufactured as well as other receipts.Suitable price deflators have been constructed with the help of the official series onwholesale price indices (Index numbers of Wholesale Prices in India, prepared by the office

On Spatial Concentration of Organized Manufacturing Industries .... 181

of the Economic advisor, Ministry of Industry) to deflate gross output at constant 1993-94prices.

2.2.3 The study encompasses 18 major states of India (three of which were bifurcated inNovember 2000) which are listed as 15 states. The bifurcated states are: Bihar, MadhyaPradesh and Uttar Pradesh. Three new states were carved out of Bihar, Madhya Pradeshand Uttar Pradesh respectively. To make comparability the data for the newly created stateshave been added to the respective states from which it was created. We have marked thebifurcated states with “*”. Thus, the states included in this study, arranged in alphabeticalorder are: Andhra Pradesh (AP), Bihar (BIH*), Delhi (DEL), Gujarat (GUJ), Haryana(HAR),Karnataka(KAR), Kerala( KER), Maharashtra (MAH), Madhya Pradesh (MP*), Orissa (ORI), Punjab(PUN), Rajasthan ( RAJ), Tamil Nadu(TN), Uttar Pradesh(UP*), and WestBengal(WB).

B) Methodological Issues

2.3 Measures of Spatial Concentration

2.3.1 The term spatial concentration refers to the extent to which a given industry isconcentrated in a few geographical units. Geographic concentration of a specific industryreflects the distribution of its regional shares. A specific industry i is considered spatiallyconcentrated if a great part of the production is carried out in a few regions only. Severalstatistical indices of spatial concentration have been proposed in the literature over theyears, which vary from traditional measures like coefficient of variation, locationconcentration ratio, location Herfindahl Index, location Gini index, location entropy indexand location quotient etc. to the more recent measures like Ellison-Glaeser index and Moran’sI etc. However, each of the measures has its own limitations.

2.3.2 The Present study uses a set of traditional measures namely, Herfindahl index,Location Gini & Concentration ratio to measure spatial concentration of organizedmanufacturing sector across the states in India since any single index is inadequate toarrive at fairly reliable conclusion.

2.4 Indicators of Spatial Concentration of Industries: Location Herfindhal Index

2.4.1 The location Herfindahl Index of an industry i is defined as the sum squares ofemployment (or output) shares of all states in the industry. If Eik is the employment (oroutput) of Kth state in ith industry and Ei is employment / output of all the states in the ith

industry.

Symbolically:

The Herfindahl index is increasing with the degree of concentration reaching its upper limitof 1 when the industry i is concentrated in one region/ state. The lowest value ofconcentration is 1/n i.e. all regions have equal shares in industry i (i=1/n).


2.4.2 Concentration ratio is defined as the percentage share of employment / output ofan industry located in the largest four states, ranked in descending order of shares of thestates. Higher the ratio implies higher concentration.

2.4.3 Location Gini Index

Following CeaPraz (2008) we have applied the measure location Gini as the sum of thedifferences of the concentration rates by the addition of the differences of the weights ofeach industry and the weights of the arithmetic mean are obtained after the decreasingclassification of each state’s concentration rates. The index values between zero and one.An index which approaches zero value indicates that the distribution of the concentrationin kth state corresponds to the national distribution. A value of the index equal to one meansthat a specific state presents a strong concentration in a specific industry.

Symbolically:

Where n is the number of states. Ck = Sik /Sk for every state in the ith industry. Sik is the shareof employment in ith industry from kth state in total employment/output of ith industry. Sk isthe share of the employment of the kth state in the total employment/output of all the states.

Àk is rank of kth state in the ranking of Ck in descending order and is the mean value ofCk after arranging in descending order.

Finally the empirical results based on these measures are reported & analyzed to focus onspatial concentration in section 4.

3. Regional Distribution of Industries in India

We begin with analyzing the regional distribution of manufacturing industries across 15states and four major regions.

3.1 Table 1 reports the employment share of regions and states in organizedmanufacturing sector in 1987-88, 1997-98 and 2010-11 respectively.

3.1.1 It is obvious that the southern region has gained employment share from about 29percent in 1987/88 to 34.8 percent in 1997/98, whereas the eastern region has considerablylost their share from 17.7 to 14.4 during the same period. The employment share of theCentral region remains more or less same during the period of study and that of the north-west region fluctuates over the years.

3.1.2 A state level analysis of the employment share corresponding to Eastern regionshows that the sharp decline in the share of employment share is largely due to sharpdecline in the share of West Bengal & to some extent Bihar. While the share of Assam &Orissa remains more or less the same.

183On Spatial Concentration of Organized Manufacturing Industries ....

3.1.3 On the other hand, the insignificant fall in the employment share of the Centralregion between 87/88 & 97/98 is mainly due to decline in the share of Maharashtra while theshare of Gujarat & Madhya Pradesh decline marginally and that of Rajasthan remains moreor less same.

3.2 Empirical result showing the share of states in terms of Gross output is reported inTable 2.

3.2.1 The share of eastern region has significantly declined from about 16 percent in1987/88 to 8 percent in 2010/11 whereas that of southern region has increased marginallybetween the same time points. The share of north-West region has consistently declined inthe respective years 1997/98 and 2010/11.

3.2.2 Viewed in terms of Gross output, the eastern regions’ declining share is mainlydue to the decline in West Bengal & Bihar. The increasing share of Southern region isattributed to the share of Andhra Pradesh & Karnataka in contrast to Tamil Nadu that hasconsistently showed lower share during the time points as considered in the present analysis.

3.2.3 Individually, West Bengal, Maharashtra, Bihar and Tamil Nadu have significantlylost their share in gross output, while Gujarat, Andhra Pradesh, Haryana and Karnatakahave improved their position in the years 1997/98 & 2010/11 respectively. It is remarkable tonote that though Tamil Nadu has improved its share in terms of employment over the years,it has a stable position in terms of gross output throughout the years.

4. Results & Discussions

The tabular data presented in the preceding section (Table 1 & Table 2) cannot provideinformation on the extent of spatial concentration of these industries. In this section weexamine the extent of spatial concentration of organized manufacturing industries acrossthe states at two digit disaggregated industry levels. The results derived by employing thestatistical measures as mentioned in Section 2.5 & 2.6 are summarized in the followingsection.

4.1 Analysis of Results based on Herfindahl Index of Concentration

The Herfindhal Index computed in terms of Gross output & employment of organizedmanufacturing sector disaggregated by two digit level industries is reported in Table 3.

4.1.1 A close look at Table 3 provides a better understanding of the degree ofconcentration and the variation in the direction of change in concentration across theindustries. Concerning the evolution of disaggregated two digit level industries in terms ofemployment, Herfindahl Index shows a dominant regional concentration of Beverage &Tobacco Products, Leather & Leather Products, and Chemical & Chemical Products in theyear 2010-11. It is noteworthy to mention that same ranks have been preserved by thesethree industries compared to the previous years, 1987/88 &1997/98 respectively.

4.1.2 In the year 2010-11, Textile, Chemical, Metal, Machine & Transport industrieshave shown an increasing pattern of concentration in terms of employment compared toboth the previous years, 1987/88 &1997/98. However, Concentration has drastically declined


for Food & Food Products industry in the year 2010-11. Out of the eight industriesconcentration has increased in as many as five industries in terms of employment and infour industries in terms of gross output in the year 2010.

4.2 Analysis of Results based on Gini index of concentration

Table 3 also reports Location Gini measure of concentration for the selected disaggregatedtwo digit level industries.

4.2.1 The most concentrated sectors in the year 2010/11 are Leather & Leather Productsfollowed by Transport and Beverage & Tobacco products industries in terms of employment.It is noteworthy to mention that Beverage & Tobacco product industry is the mostconcentrated industry in the year 1997 with a spectacular drop in 2010. In the consequentyear, Leather & Leather product occupies second rank in concentration with an insignificantdrop in 2010.

4.2.2 A significant increase in concentration in terms of gross output has been found infive industries, namely, Textile, Chemical, Leather & Leather Products, Beverage & Tobaccoproducts, machine& machine tools in the year 2010 compared to the year 1997-98.

4. 3 Results based on Concentration Ratio

Table 4 presents the results based on the computation of concentration ratio.

4.3.1 States like Maharashtra, Tamilnadu, Uttar Pradesh, Gujarat, and West Bengalappeared more frequently in the list of four leading states in many of the industry groups.Other states like Andhra Pradesh, Karnataka, Punjab, Rajasthan, Haryana, and Bihar appearedsingle or couple of times in the list of four leading states in few industries.

4.3.2 Concentration of chemical industry has shifted from Uttar Pradesh & Tamilnadu toWest Bengal & Andhra Pradesh. Machine & Machine tools Industry has shifted from UttarPradesh to Gujarat. Leather has shifted to Karnataka to Haryana in the year 2010 comparedto the year 1997-98.

5. Scope for further Work

The present study uses a rather broad classification of industries (2 digit NIC level). Thishowever leaves a scope to the researchers to study regional industrial scenario at moredisaggregated level in Indian context. Though the present study tries to examine the spatialconcentration of organized sector based on two key variables, viz. number of Personsengaged & gross output but the trend of concentration over the years needs to be examinedbased on the available time series data. Other important variables may be included to getbetter insights.

6. Conclusions & Policy Implications

6.1 Differences in the results derived by applying several measures of concentrationare clearly noticed. This may be due to the size & importance attributed to the industry/state. Despite the fact that the analysis presented in this paper are data explanatory, the


findings are important in understanding the pattern of spatial concentration in organizedmanufacturing sector. It also helps to understand regional development in India.

6.2 The findings of the paper suggest that concentration is not uniform across thedifferent industry groups. Spatial concentration has increased during the years 1997/98 &2010/11. The less diversification of Industries widens the regional inequality which is amajor bottleneck to achieve the balanced regional development.

6.3 In the year 2010 according to Gini index we observe an increase in spatialconcentration for the six industries, Food Products, Textile, Leather, Chemical, Machine &Transport in terms of employment & a decline in concentration is noticed for the industries,Beverage& Tobacco and Metal industries in the same year compared to the year 1997-98.

6.4 In the same direction, according to Herfindahl index we notice an increase inconcentration in terms of employment for five industries, namely, Textile, Chemical, Metal& Transport industries and a constant trend for the food industry during the time point2010 compared to the year 1997-98.

6.5 The probable reasons of industrial concentration throughout the years could besome region specific factors such as cost structure, characteristics of labor force,geographical characteristics, investment climate, political condition etc. The factors thatare likely to be more important to welcome new investment in industrialization are availabilityof transport and communications, water & power, services & social amenities.

6.6 Therefore, the backward states should emphasize more on providing appropriatephysical infrastructure, legal & financial infrastructure (corporate law, accountancy norms,banks, capital market), and social infrastructure to attract new industrial investment so thatinter-state variation in terms of industrial development can be reduced. Hence the statesare required to reconsider their development strategies, alter necessary policy decisions,and change institutional structure to attract more industrial investment.

References

Alagh,Y. K., K. K. Subrahmanian and S. P. Kashyap(1971), “Regional Industrial Diversificationin India” Economic & Political Weekly, 6(15),795-802

Awasthi D. N (2000): Recent changes in Gujarat Industry: Issues and Evidence” Economicand Political Weekly, 35(35/36), 3183-92

Athraye, S. and Kapur. S (2006)”Industrial concentration in a Liberalizing Economy: AStudy of Indian Manufacturing” Journal of Development Studies, 42(6), 981-999.

Brulhart, M., and Torstensson, J ( 1998), “ Regional Integration, Scale Economies andIndustry location in the European Union” School of Economic Studies, University ofManchaster, research paper under the stimulation Plan for Economic Sciences in theEuropean Union (SPES-CT91-0058) and by the Swedish council for Research in theHumanities and Social Sciences.


Bhattacharya BB, Sakthivel S. (2004): Regional Growth and Disparity in India: comparisonof pre- and post-reform Decades. Economic and Political Weekly, 39(10), 1071-77

Canfei He, Yehua Dennis Wei, Xiuzhen Xie (2008), “Globalization, Institutional Change andIndustrial location: Economic Transition and Industrial Concentration in China”, RegionalStudies, Vol.427, August 2008, pp. 923-945

CeaPraz IL (2008): The concepts of specialization and spatial concentration and the processof economic integration: Theoretical relevance and Statistical measures. Journal of RomanianRegional Science Association, 2(1), 68-93

Ellison, G. and Glaeser, E. L. (1997) “Geographic concentration in U.S. Manufacturingindustries: A Dartboard Approach” Journal of Political Economy, 105(5), 889-997

Ghosh, A. (1975): “Concentration and Growth of Indian Industries 1948-68,”, Journal ofIndustrial Economics, 23(3), 203-222.

Goschin, Z. Constatin D. L., Roman,M. and Illeanu, B. (2009). “Regional Specialisation andGeographic Concentration of Industries in Romania”, South-Eastern Europe Journal ofEconomics, Vol.1, pp.99-113.

Hirschman (1958): A Strategy for Economic Development, Yale University Press, New Havan.

Hildebrandt, A. and Worz, J., (2004), “Determinants of Industrial Location Patterns in CEECs,Working Paper N0. 32,The Vienna Institute for International Economic Studies.

Kar, S. Sakthivel, S. (2007): Reforms and Regional inequality in India, Economic and PoliticalWeekly, 42(47), 69-77

Lall, S.V., Chakraborty, S. (2005): Industrial Location and Spatial inequality: Theory andEvidence from India. Review of Development Economics, 9(1), 47-68

Chakraborty S. (2003) Industrial location in Post-reform India: patterns of inter-regionaldivergence & Intra-regional Convergence, Journal of Development Studies, 40(2), 120-52

Saikia, D. (2011): “Unorganized Manufacturing Industries in India: A Regional Perspective”African Journal of Marketing Management, 3(8), 195-206.

Singh, F. P. (2012). “Economic Reforms and Industrial Concentration in Indian ManufacturingSector: An Inter-temporal Analysis” International Journal of Marketing, Financial Services& Management Research, 1(7), 36-53.


Annexure 1: Selected industries with respective NIC CodeIndustry description NIC 1970 NIC 1987 NIC 2004 NIC

2008 Food & Food Products 20-21 20-21 15 10 Beverage & Tobacco Products 22 22 16 11 Textile & Textile Products 23+24+25+26 23+24+25+26 17+18 13+14

Leather & Leather Products 29 29 19 15

Chemical & Chemical Products 31 30 24 20

Metal & Metal Products 33+34 33+34 27+28 24+25

Machinery & Equipments other than Transport

35+36 35+36 29+30+31+32+33+34

28

Transport Equipments 37 37 35 30

Annexure 2: List of Industries with Abbreviations.Abbreviation Industry Name

FOP Food & Food Products

BTP Beverage & Tobacco Products

TEXT Textile & Textile Products

LEATH Leather & Leather Products

CHEM Chemical & Chemical Products

METAL Metal & Metal Products

MACH Machinery & Equipments other than Transport

TRANS Transport Equipments

Annexure 3: List of States with Abbreviation.AP ANDHRA PRADESH

ASSAM ASSAM

BIH BIHAR

GUJ GUJARAT

HAR HARYANA

KAR KARNATAKA

MAH MAHARASHTRA

MP MADHYA PRADESH

ORI ORISSA

PUN PUNJAB

RAJ RAJASTHAN

TAMIL TAMIL NADU

UP UTTAR PRADESH

WB WEST BENGAL


Table 1: Share of States in Total Employment of Organized Manufacturing Sector.

States/ Regions Share of States/ Regions Absolute change

1987-88 1997-98 2010-11 1987-88 to 1997-98 1997-98 to 2010-11

Assam 1.31 1.55 1.31 0.24 -0.24

Bihar 4.93 2.75 0.84 -2.18 -1.91

Orissa 2.06 1.80 2.23 -0.26 0.43

West Bengal 9.36 8.34 5.01 -1.02 -3.33

Eastern Region 17.66 14.44 9.39 -3.22 -5.05

Delhi 1.75 1.37 0.96 -0.38 -0.41

Haryana 3.04 3.16 4.30 0.12 1.14

Punjab 4.96 4.59 4.84 -0.37 0.25

Uttar Pradesh 9.66 9.73 8.64 0.07 -1.09

North- West Region 19.41 16.85 18.74 -2.56 1.89

Gujarat 8.67 8.80 10.20 0.13 1.4

Madhya Pradesh 4.82 4.67 4.86 -0.15 0.19

Maharashtra 15.66 14.76 13.38 -0.9 -1.38

Rajasthan 3.01 2.94 3.40 -0.07 0.46

Central Region 32.16 31.17 31.84 -0.99 0.67

Andhra Pradesh 9.17 12.09 10.25 2.92 -1.84

Karnataka 4.98 6.28 6.16 1.3 -0.12

Kerala 3.14 3.60 3.00 0.46 -0.6

Tamilnadu 11.45 12.85 15.31 1.4 2.46

Southern Region 28.74 34.82 34.72 6.08 -0.1

Total of 16 States 97.97 97.28 94.69 -0.69 -2.59

Other States 2.03 2.72 5.31 0.69 2.59

All India 100 100 100

Source: Based on ASI data, provided by CSO


Table 2: Share of States in Gross output of Organized Manufacturing Sector.

States/ Regions Share of States/ Regions Absolute change

1987-88 1997-98 2010-11 1987-88 to 1997-98 1997-98 to 2010-11

Assam 1.25 0.91 0.91 -0.34 0

Bihar 5.26 3.51 0.77 -1.75 -2.74

Orissa 1.90 1.81 1.97 -0.09 0.16

West Bengal 7.20 5.08 4.41 -2.12 -0.67

Eastern Region 15.61 11.31 8.06 -4.30 -3.25

Delhi 2.10 1.86 1.04 -0.24 -0.82

Haryana 3.50 3.94 4.60 0.44 0.66

Punjab 5.00 3.88 3.18 -1.12 -0.7

Uttar Pradesh 8.88 8.70 8.51 -0.18 -0.19

North- West Region 19.48 18.38 17.33 -1.10 -1.05

Gujarat 10.65 12.87 17.25 2.22 4.38

Madhya Pradesh 5.20 5.36 4.56 0.16 -0.8

Maharashtra 21.32 21.00 16.79 -0.32 -4.21

Rajasthan 2.83 3.45 3.21 0.62 -0.24

Central Region 40.00 42.68 41.81 2.68 -0.87

Andhra Pradesh 5.93 6.88 7.35 0.95 0.47

Karnataka 4.26 5.17 6.11 0.91 0.94

Kerala 2.71 2.42 1.75 -0.29 -0.67

Tamilnadu 10.62 10.01 10.10 -0.61 0.09

Southern Region 23.52 24.48 25.31 0.96 0.83

Total of 16 States 98.61 96.85 92.51 -1.76 -4.34

Other States 1.39 3.15 7.49 1.76 4.34

All India 100 100 100

Source: Based on ASI data, provided by CSO

190 The Journal of Industrial Statistics, Vol. 5, No. 2

Table 3: Spatial Concentration of Disaggregate Two-digit industries: 1987-88, 1997-98, 2010-11

Source: Authors’ Own estimation based on ASI data.Note: See Annexure 2 for abbreviations used to denote the Industries.

Table 4: Location Concentration Ratio of Organized Manufacturing Sector by two digitIndustry.

Source: Authors’ Own estimation based on ASI data.Note: See Annexure 3 for abbreviations used to denote the states.

191

NIC 2004 Code

Industry Description

Location Herfindahl Index Location Gini

Employment Gross Output Employment Gross Output

1987-88

1997-98

2010-11

1987-88

1997-98

2010-11

1987-88

1997-98

2010-11

1987-88

1997-98

2010-11

15 FOP 0.101 0.093 0.093 0.098 0.965 0.095 0.319 0.458 0.476 0.504 0.306 0.447

16 BTP 0.371 0.414 0.313 0.108 0.111 0.102 0.753 0.760 0.603 0.564 0.540 0.657

17&18

TEXT 0.115 0.113 0.136 0.124 0.120 0.132 0.448 0.436 0.504 0.512 0.530 0.579

19 LEATH 0.277 0.272 0.259 0.284 0.242 0.190 0.693 0.690 0.786 0.764 0.768 0.849

24 CHEM 0.135 0.149 0.168 0.162 0.207 0.185 0.405 0.384 0.538 0.439 0.484 0.456

27&28

METAL 0.101 0.089 0.108 0.099 0.098 0.110 0.528 0.554 0.428 0.528 0.542 0.368

29-34 MACH 0.116 0.109 0.140 0.113 0.136 0.161 0.423 0.444 0.585 0.407 0.454 0.580

35 TRANS 0.117 0.105 0.137 0.163 0.153 0.184 0.583 0.525 0.647 0.667 0.642 0.757

NIC 2004 Code

Industry Descripti

on

Concentration Ratio Four Leading States(in terms of Gross output)

Employment Gross Output

1987-88

1997-98

2010-11

1987-88

1997-98

2010-11 1987-88 1997-98 2010-11

15 FOP 52.64 49.00 49.32 52.43 51.92 49.32 UP,TAMIL,AP, PUN

MAH,AP,GUJ, UP MAH,AP,UP, GUJ

16 BTP 82.03 84.89 51.29 57.16 58.11 51.29 AP,MAH,MP, UP AP,UP,MAH, KAR

AP,UP,KAR, MAH

17 & 18 TEXT 62.36 59.18 57.87 63.09 5844 57.87 MAH,GUJ,PUN, TAMIL

TAMIL,MAH, GUJ,RAJ

TAMIL,GUJ, MAH, PUN

19 LEATH 82.48 80.58 71.94 81.42 77.37 71.94 TAMIL,UP,WB,MP

TAMIL,UP,WB,KAR

24 CHEM 66.21 69.95 59.11 67.68 75.29 59.11 MAH,TAMIL,GUJ,UP

GUJ,MH,UP, TAMIL

GUJ,MAH,WB, AP

27&28 METAL 54.66 48.89 43.43 54.41 53.11 43.43 MAH,BIH,WB,MP

MAH,BIH,MP, AP

MAH,GUJ, TAMIL,

29-34 MACH 55.62 56.00 62.56 54.26 60.80 62.57 MAH,TAMIL, GUJ,UP

MAH,KAR,UP, TAMIL

MAH,GUJ, TAMIL,KAR

35 TRANS 60.50 55.26 66.54 67.21 66.87 66.54

MAH,HAR,TAM UPIL,

HAR,MAH,PUN, TAMIL

WB

On Spatial Concentration of Organized Manufacturing Industries ....


The Price of Prejudice: Employment Trend and Wage Discrimination ofWomen Workers in India

Shiney Chakraborty1, Jawaharlal Nehru University, New Delhi, India

Abstract

The present study reveals a persistent gap in labour force participation rate betweenrural and urban areas and between males and females in manufacturing industry with asubstantial gender wage differential. The unequal distribution of women workersbelonging to different religious groups is also prominent in manufacturing industries.Over the time period, a further disaggregated division of manufacturing industries at thetwo digit level portrays that women workers are mainly concentrated in food, textile,wearing apparel and tobacco industries. The Duncan dissimilarity index (ID) formanufacturing industry reveals significant variations in the distribution of women andmen across major Indian states. The most important finding is the deceleration of women’sparticipation rate in the job market. Wage estimation result also suggests a substantialwage differential between men and women, but the difference has been reducing duringthe post-reform period. This study also indicates that gender based wage difference cannotbe explained only by differences in education, experience and skills etc.

1. Introduction

1.1 Labour market discrimination has been a serious issue in India as for otherdeveloping countries, particularly during the post reform period. Discrimination is normallyfaced while entering into the labour market and in securing fair wages on participation inthe market, or in both. The International Labour Organization (hereafter ILO) definesdiscrimination as “any distinction, exclusion or preference made on the basis of race,colour, sex, religion, political opinion, national extraction or social origin, which has theeffect of nullifying or impairing equality of opportunity treatment in employment oroccupation”.2 Wage discrimination is generally expressed as an unequal treatment of workerswith roughly equal productivity in terms of their pay either in the form of allocativediscrimination, or other way (Peterson et al, 1997). Gender disparity in wages has been acommon feature in the labour market, particularly in the developing world, but the degree ofdisparity varies widely across different ethnic and religious groups. The study providesspecial emphasis on gender inequalities simply because it has serious implication on pro-poor or inclusive growth in an economy (Birdsall and Londono, 1997; Deininger and Olinto,2000). All over the world women face discrimination at work, with the global average genderwage gap in paid employment at around 16.5 percent and over 21 percent in Asian countriesin 2008 (The report of the International Trade Union Confederation, 2008). In the Indiansubcontinent, also women have been suppressed and subdued by the hegemony of socio-cultural patriarchy for several centuries (Ghosh, 2008).

1.2 Against this background, the present study focuses on some aspects of labourmarket discrimination in Indian manufacturing industry by looking at employment patternand wage differentials across ethnic and religious groups during the post-reform period1 e-mail: [email protected] defined discrimination in the Article 1(1a) during 1958 of the Discrimination (Employment andOccupation) Convention. For details see ILO report 2003 (pp 16, Box 2.1).

192

(1993-2011).The data used in this analysis is from the National Sample Survey Organisation(NSSO), which covers both organized and unorganized industries3. Disaggregated industrylevel data at the two digit level for the time period 1993-1994 and 2011-12 is used for thisstudy. It is important to mention that four different industrial classifications are used overthis time period. For the surveys between 1993-94 and 1998-99, NIC-87 industrialclassification was used, between 1998-99 to 2004-05, the industrial classification used wasNIC-1998. Between 2004-05 and 2009-10, NIC -2004 and 2011-12 onwards NIC-2008 wasused. Therefore, a concordance exercise across these different classifications has beenundertaken to make the dataset comparable as per the NIC-2008 classification and finally 24industries within the manufacturing industry is considered for the present purpose.

1.3 During the post reform period, the industry wise distribution of workers at thedisaggregated level indicates that women workers (about 63 per cent) remain concentratedin the low paying agricultural sector, whereas the proportion of male workers migratingfrom agriculture to other secondary and tertiary sector is increasing. The unequal distributionof women workers of different religious groups is also prominent across industries. Thereis also a considerable variation in terms of wage payments for male and female workersirrespective of socio-religious groups. Although the observed differences in wages betweenmen and women provide a broad idea about the gender pay gap, this needs to be furtherdisaggregated across the industries. In this connection an attempt has been made to lookinto the female work force participation and wage difference in manufacturing industry inIndia as this industry is largely dominated by male. However literature on female participationin manufacturing industry is very limited except few pioneering ones (Goldar, 2000;Chaudhuri and Panigrahi, 2013; Ramaswamy, 2015). This study is one of the first fewcomprehensive studies on gender wage gap in Indian manufacturing industry which hasfocused on gender and socio religious groups. The labour economics literature is repletewith numerous studies on wages and earnings in India by legion of eminent scholars byspecially focusing on gender wage gap but there are very few studies on wages andearnings in manufacturing industry across socio-religious groups. The present study hastried to bridge the evidence gap in this regard.

1.4 As labour market participation is not likely to be random the study uses Heckman’sselection model with two-step estimation techniques to find out the extent of wage gapexplained by education, work experience and other social factors. It analyses how genderpay gap changed during the two decades of economic reforms in India with pooled data oftwo independent samples taken from the 50th and 68throunds of the NSSO on employmentand unemployment in India.The rest of the paper has been structured as follows. Section IIpresents an abridged overview of aggregate trends in employment and wages inmanufacturing industry. This section also outlines an assessment of the religion and socialgroups in determining the employment pattern and wage levels in manufacturing industryand indicates the presence of gender segregation in certain industries across the majorstates. Section III contains a review of some relevant literature on gender wage gap. SectionIV describes the methodology adopted for addressing the questions and briefly describesthe data sources used for this study. Section V reports regression results that explainvariations in earnings for men and women, over the years and discusses them in the light ofprevious findings. Section VI concludes the paper.

3 Annual Survey of Industries (ASI), also collects data annually on organised manufacturing in India.

The Price of Prejudice: Employment Trend and Wage Discrimination ... 193

2. Employment and Wage Trends

2.1 There has been much focus and discussion on the evidence of significant declinein women’s labour force participation rate4, particularly, as the country is achieving asufficiently high rate of economic growth. The latest NSSO round data on employment andunemployment (NSSO, 2011-12) shows a decline in labour force participation rate both formales and females in rural area. But in the urban area for female workers there is an increaseof LFPR by about 1 percentage point and a constant LFPR for urban males. It can be seenfrom Figure 1 that LFPR is the lowest for rural women in 2011-12 and the lowest for urbanwomen in 2009-10. It is surprising that rural employment for women fell by almost 8percentage point inspite of the launch Mahatma Gandhi National Rural EmploymentGuarantee Act (MGNREGA), in 2006-07.

2.2 Labour force is basically the ‘economically active’ population and thereforeincludes both ‘employed’ and ‘unemployed’ persons and so a lower female labour forceparticipation rate is a result both of a smaller percentage of women than of men actuallyworking and a higher unemployment rate5 for females than those for males. Figure 2, indicatesthat among those looking for work, a smaller percentage of women than men are gettingjobs which are also supported by a higher rate of unemployment among women thanamong men, especially in urban areas.

2.3 However with an overall low and declining female workforce participation rate6,WPR of women workers in manufacturing industry belonging to different socio-religiousgroups indicates that after more than sixty years of independence, Muslim and Christianwomen’s participation in the manufacturing industry is significantly lower in both thesectors (See Table 1). This denotes that social norms confine women’s mobility and entryinto the labour force and keep more women tied to hearth and home. Overtime the WPR ofHindu male and female has decreased in rural manufacturing industry. Among the Muslim’sstill, urban Muslim women are participating more in the manufacturing industry incomparison to rural. It has also been observed that overtime the share of Muslim womenparticipation has increased in manufacturing industry. Among the social groups, WPR ofSC, ST is significantly lower in comparison to the others. One of the essential cruxes thathave emerged is the low probability of socially disadvantageous workers to get jobs in themanufacturing industry but for all the industries from agriculture to services WPR of sociallybackward group is higher. This implies the further disadvantageous position of thesebackward groups in manufacturing industry which is considered to be the main engine ofeconomic growth.

4 The labour force participation rate (LFPR) is defined as the number of persons/ person-days in thelabour force per 1000 persons/ person –days.5 Unemployment rate (UR) is defined as the number of persons/ person-days unemployed per 1000persons/ person –days in the labour force.6The decline in women’s recorded/recognised workforce participation rate (WPR) is quite different fromactual work done by them as a lot of work done by women is simply not captured by the data. By usingmore inclusive definition of work, and by including women who are involved in both paid and unpaid workthe overall workforce participation rate is consistently higher for women than for men (For details seeGhosh, 2014).


2.4 With the onset of globalization it is believed that with trade openness, theemployment opportunities will increase. But in reality liberalisation does not encouragefemale employment at least in organised manufacturing industry rather the use of newcapital intensive technology goes against the women workers (Banerjee and Veeramani,2015). During the post reform period the Indian labour market witnessed an increasingfeminisation of agriculture and tertiary sector employment with a decrease in secondarysector employment (Neetha, 2014). In this connection it will be interesting to compare thechange in the share of male and female workers in manufacturing industry and the trendand pattern of ‘female workforce participation across different socio-economic backgroundand characteristics. The manufacturing sector currently employs 12.6% of India’s labourforce and is of dualistic structure i.e. the prevalence of a formal/organized sector whichcoexists with a large “unorganized sector”. Over a long time period this dualism has persistedin the manufacturing sector and this situation is not going to change over the next decade.Goldar (2013) has perceived that over four-fifths of new jobs will be created in the unorganisedmanufacturing industry. Even the organised manufacturing industry also witnessed a risein contractual employment at the expense of regular employment, which reflects deteriorationin the quality of jobs. This uninspiring performance of the manufacturing industry furtherreveals a skewed distribution and concentration of men and women workers in a fewindustries. This heterogeneity in absorbing the growing population in manufacturingindustry is mainly because economic growth has benefited capital intensive manufacturingindustries which depends more on skilled workers as opposed to unskilled/low skilledworkers.

2.5 Table 2, indicates that in 1993-94, more than 50 per cent of rural male workers weremainly concentrated in manufacturing of food product, wood and products of wood andcork, other non-metallic mineral products and textile industry. Rural female workers weremainly working in manufacturing of textiles, tobacco, food product and wood and productionof wood and cork products. Major employment providing industries for urban male in termsof percent share in overall employment is textile, food product and other manufacturingindustry. During the study period textile, tobacco and wearing apparel industries is themajor employment provider for urban female. Over the time period, there is a massiveincrease in terms of percentage share of employment in wearing apparel industry both formale and female in rural and urban areas though the share of female workers in this industryis higher. Table 2, also reveals that tobacco, textiles and wearing apparel are the femaledominated manufacturing industries.

2.6 There exists an extensive literature on the organized manufacturing sector and itslacklustre performance in employment generation despite extensive reforms (Goldar(2000),Aghion et al. (2006), Besley& Burgess (2004), Gupta, et al. (2008), Mehrotra et al. (2014).With this heterogeneity and limited contribution of the manufacturing sector to employmentgeneration an attempt has been made to look into the gender wise wage pattern in themanufacturing industry. With the prevalence of wage differences in the formal manufacturingsector women are forced to take part in the informal sector because of their economicrequirements. These requirements have pushed them accepting low-paying jobs such asmanufacturing of bidi, cigarette, masala and other related products for their livelihood. Nextto find out gender wage gap in manufacturing industry wage ratio (female to male wagerate) is reported in Table 3. The value close to 1 indicates less gender wage gap whereas the


value close to 0 implies higher gender wage inequality. Table 3 indicates that gender wagegap has reduced within the manufacturing industries which have a higher share of femaleemployment and also for manufacturing industry as a whole.

2.7 After discussing the employment distribution and wage gap in manufacturingindustry for both the gender further disaggregation across the socio-religious groups ispresented in Table 4 and 5. Out of 24 manufacturing industries in terms of National IndustrialClassification (NIC) 2-digit codes, tobacco, textiles and wearing apparel industries togetherconstitute more than 80 per cent of employment for Muslim female and over 60 per cent ofemployment for Christian female in rural area during the entire time period. In urban areaalso Muslim female workers share is higher in these industries. Table 4, discloses that overthe time period among the female workers Muslim female’s percentage share is double intobacco industries in comparison to Hindu females which echoes their pathos. During1993-94 to 2011-12, major employment providing industries for Scheduled Caste (hereafterSC) women workers in terms of percent share in overall employment are manufacturing offood product, tobacco, textiles and wearing apparel (see Table 5). Over the same time,among the socially disadvantageous groups more proportion of SC female workers areinvolved in tobacco industries whereas the share of Scheduled Tribe (hereafter ST) femaleworkers are higher in textile industry in rural areas.In 1993-94, the percentage share of urbanST female worker is higher in tobacco and textile industry in comparison to SC female.However in 2011-12, employment distribution among the socially disadvantageous groupsin urban area has reversed in tobacco industry implying more percentage of SC women intobacco industry. The observations based on statistical findings that are noteworthy here,is the nature or quality of work available for women workers belonging to disadvantagedsocio religious groups. Women workers are mainly involved in the low paying unskilledmanual jobs.

2.8 Next, to identify the wage gap across the socio-religious groups a disaggregatedanalysis is carried out for usual status workers across genders. In Table 6 and 7, a simpleindicator of gender pay gap- ratio of female to male wage rates are reported which suggeststhe presence of caste and other forms of social discrimination in India. Table 6, unveils thatin manufacturing industries like Tobacco, Textile and Wearing Apparel gender wage gapexists in spite of higher representation of women workers. Women workers irrespective ofreligion are getting less payment than their men counterparts in the manufacturing industrywhich reveals the miserable situation of women workers in the industry. However duringthe entire time period in the manufacturing industry as a whole, there was also a reductionin gender wage inequality among all the religious groups. Within the sub divisions ofmanufacturing industry where female’s share is higher gender wage gap reduced for Hinduand Muslim women workers in rural area (see Table 6 (a)).Table 6 (b) indicates that over thetime period in manufacturing industry gender wage gap persists across all the religiousgroups still for Hindu and Muslim female workers there is also a decrease in wage gap inurban area. Within the sub divisions of manufacturing industry in food, tobacco, textilesand wearing apparel industries wage gap reduced for Hindu female workers and for Muslimfemale workers also wage gap reduced in these industries except food product and tobacco.The gloomy situation of the Muslim female workers in comparison to the general Hindumay be because of their low level of human capital endowment and less access to goodquality jobs.


2.9 Table 7 depicts the wage gap among the social groups in manufacturing industryin rural and urban area. Gender wage gap among the social groups has reduced in 2011-12in both rural and urban area. Within the sub divisions of manufacturing industry, industrieswith higher share of SC women workers have experienced a decline in wage gap for them inrural area. Similarly for ST women workers also wage gap declined in textile industry thoughin food and tobacco industries gender wage gap increased over time in rural area. Wearingapparel industry also has witnessed a massive decline in gender wage gap in the recenttime period. Urban women workers’ belonging to socially disadvantage groups also haswitnessed a decline in gender wage gap in textile and wearing apparel industry. So from theabove analysis it is clear that gender is predominant over other forms of social discriminationin terms of employment and wages in the manufacturing industry.

2.10 Finally the Duncan dissimilarity index (ID) has been constructed to measure theindustrial sex segregation in manufacturing industry. The Duncan index of dissimilarity isdefined as

(1)

Where denotes the number of female workers in the ith industry and denotes thenumber of male workers in the ith industry. The value of the index lies between zero (nosegregation) to 1(full segregation). However the index is interpreted as the share of theworkers that would have to change industry in order to get the same relative distribution ofmale and female across industries. Gender segregation across manufacturing industriesupto 2 digits, is computed for two time periods 1993-94 to 2011-12. The notable finding isthat gender segregation has increased sharply for causal wage labourers in both rural andurban area. State-wise analysis of industrial segregation is done for the seventeen majorstates and all around the Indian states there is a wide variation in labour regulationsbetween both the genders. The Duncan dissimilarity index (ID) for industries indicates thatthere is a considerable variation in the distribution of women and men workers across majorIndian states, which is very alarming (see Table 9). Over the time period among the seventeenmajor states Assam, Haryana, Jammu and Kashmir and Madhya Pradesh have witnessed adeceleration in segregation in both rural and urban area. However in comparison to nationalaverage low gender segregation in manufacturing industries is observed only in MadhyaPradesh and Tamilnadu whereas in urban area all the states have higher gender segregationin comparison to national average. The low index value of industrial segregation in fewstates reflects the better performance of these states in comparison to the other states. Atthe sub-national level the wide dissimilarity implies that a larger number of employed peoplewould need to change their existence industries to restore the distributional equality betweenmale and female across industries.

3. Review of Literature

3.1 The investigation of disparities in returns to work for same occupation or thegender gap in wage and earnings has been a significant area of concern for theoretical andempirical research in economics. In their pioneering papers, Blinder (1973) and Oaxaca(1973) first formulated a quantitative measure of wage gap. The Blinder-Oaxacadecomposition technique (hereafter, B-O) that distinguishes between explained variations


(such as level of education) and unexplained variations (which are said to includediscrimination) has been a key tool in the study of wage discrimination. The B-Odecomposition technique is frequently used even today as a summary measure of averagewage discrimination. Oaxaca (1973) analysed the average level of discrimination against thefemale workers in the United States and found that women were mainly concentrated in thelow-paid jobs and the gender wage difference was mainly owing to the discriminationcomponent. Duraisamy and Duraisamy (1996) found a larger discrimination component (67-77 per cent) for the year 1961-81 for persons with post-secondary schooling. They havealso observed that labour market experience tend to favour males whereas education favouredfemales more. Kingdon (1997) analyzed the gender wage gap for urban Lucknow by using1995 data and found that 45 per cent of the wage gap was due to discrimination and 55 percent was due to endowment. In an another study Kingdon and Unni (1998) investigateddeterminants of wages on 1987-88 data for urban districts of Madhya Pradesh and TamilNadu and found an average of 75-78 per cent discrimination. They concluded that womensuffer more wage discrimination in the urban labour market owing to their low educationalattainment. Das (2006) estimated the determinants of wages in 1999-2000 in the casuallabour market and found that 27.5 per cent of the difference in male-female wages was dueto the endowment effect whereas the rest (72.5 per cent) was due to discrimination. Bhaumikand Chakraborty (2008) indicated a decline in gender earnings gap in India from 0.46 to 0.12from 1987 to 1999.

3.2 In the Indian context, there are a few studies of wage discrimination based oncaste. Bhaumik and Chakraborty (2006) analyzed the wage gap between upper castes andSC/ST during the period 1987-1999 and found a decline in wage differences across castesbut an increase in wage differences between Muslims and Non-Muslims. They found thatthe inter-caste and inter-religion wage gaps were mainly due to endowment differences ineducation and experience and not due to discrimination. Madheswaran and Attewell (2007)examined the wage gap between higher castes and the SCs/STs in the urban regular salariedjob market and their study indicated a lower return of education for workers belonging tolower castes than that to the upper castes and that 85 per cent of the wage gap acrosscastes was due to endowment effect whereas 15 per cent was due to discrimination. Theyfound occupational discrimination to be more pronounced than wage discrimination.

3.3 A review of these studies indicates that gender differentials in wages still exist,but with varying trends. This is an issue which needs further examination and elaborationat a disaggregated level with recent data to analyze the trend in wage rates over the periodand to capture the change in the post reform period. Wage equations are estimated toanalyse the role of various explanatory variables in wage determination and a decompositionanalysis of the wage gap is performed which is discussed in the following section.

4. Methodology and data sources

4.1 Most studies on gender wage gap use simple OLS regression techniques to estimatethe wage equations incorporating one cross section. Das (2013) and Sengupta and Das(2014), on the other hand, estimated Mincerian wage regression in looking into differentaspects of wage discrimination in Indian labour market by taking 50th and 66th round NSSdata. This study has followed the methodology as used in Das (2013) and an independentlypooled cross section from 50th and 68th round unit level data, based on schedule 10 of the


survey is prepared. As the data are collected independently, it causes no problem in poolingthese data over time. In the estimation model the intercept is allowed to change over timeand a year dummy variable is generated to interact with other key explanatory variables tosee if the effect of that variable has changed over the time period. Information on wage andsalary earnings is collected separately for each of the wage/salaried work which is dailyrecorded for a person. Here, earnings refer to the wage or salary income which is receivedduring the reference week by a worker on the basis of usual principal activity status7.

4.2 Following Mincer (1974)8, the wage equation in the frame of pooled data fromtwo independent random samples is specified as follows:

(2)

4.3 There is no universally accepted set of explanatory variables to estimate thegender wage gap. The general consensus is to include factors, like education, maritalstatus, and number of children present in the household to explain the wage gap (Nordmanand Rouband, 2005).The natural logarithm of wages is used as the dependent variable. Inthe regression equation two quantitative variables are age and age-squared. Here age is agood proxy for experience. But the use of age instead of experience may lead to erroneousresult for the individuals with interrupted labour force participation. The quadratic term inthe variable age is to reflect the decreasing wage rate beyond the peak of the career, whichalso reflects the possible diminishing return to human capital accumulation through

schooling. The qualitative variables used in the estimation method are as follows: isa year dummy variable equal to 1 if the person belongs to the 68th round (2011-12) and 0 ifbelongs to the 50th round (1993-94). The variable is another dummy variable, the value

of which is equal to 1 if the person is a female and 0 for male. is a general educationdummy variable, with four levels of education, among them illiterate is the reference categoryand is the technical education dummy variable the value of which is equal to 1 forhaving technical skill and 0 otherwise, is a random error term distributed N (0, e).Variousinteraction dummies are also included in the equation (for details see appendix). The intercept

7 In NSS the persons surveyed are classified into various activity categories on the basis of the activitiespursued by them during certain specified reference periods. The usual activity status relates to the activitystatus of a person during the reference period of 365 days preceding the date of survey. The activity statuson which a person spent relatively longer time (i.e. major time criterion) during the 365 days precedingthe date of survey is considered as the usual principal activity status, activity status determined with aone-week reference period is the current weekly status (CWS) and current daily status (CDS) is based onthe daily activity pursued by individuals on each day of the reference week.8 The standard Mincerian semi-logarithmic earning function is generally used to investigate the determinantsof earnings. In Mincerian earning equation the wage of an individual is assumed to depend upon level ofschooling and job experience.


for 1993-94 is for male and for female it is (+ ) and in 2011-12 the intercept for male is(+ )and for female is (+ + ). The coefficients of i and i represents the effectreligion and caste on the wage level for men and (i +i) and (i +i) respectively reflectsthe effects of these on women in 1993-94. The similar effects of religion and caste onwomen’s wage rate in 2011-12 is measured by (i +i + i) and (i +i + i). The coefficientsi act as the effects of general level of education at different levels for men and (i +i) forwomen in 1993-94 and the effects for women in 2011-12 are measured by (i +i +i). Similarto the general level of education, the effect of technical education on male wage rate can bemeasured by in 1993-94 and for women of the same period it can be measured by ( +).In 2011-12 the effect of technical education on female wage rate can be measured by ( ++). Here one more crucial assumption is the same effect of experience on the wages formale and female workers in both the time periods.

4.4 Most of the studies on gender wage gap estimate the wage equations incorporatingwage earners only. But the presence of a large number of unemployed people can lead toselectivity bias. Though the problem of selectivity bias arises at two stages of employmentprocess- first at the stage of joining of labour force and second when a specific occupationis chosen, this is called occupational selectivity bias. Owing to occupational selectivitybias wage differential takes place and due to entry barrier of the subordinate group anothertypes of discrimination takes place. In general, the first problem is taken into considerationand correction is done. But correction for the second type of selection bias is not usuallydone (Neuman and Oaxaca, 2003). In the sample, wages are observed for the workingindividuals and simply by ignoring the individuals with no wage earnings will make thesample non-random or incidentally truncated and here the problem of sample selection biaswill arise (Das, 2013). To improve the efficiency of the estimated coefficients of the wageequation this study uses the two-step estimation procedure. By following Heckman (1979),the equation for entering the labour market as follows:

(3)

where is the difference between the market wage and the reservation wage. Thereservation wage is the minimum wage at which the ith individual will be willing to work. Ifthe wage is below the reservation wage nobody will choose to work. is actually notobserved instead a dichotomous variable with value 1 when the person is participatingin the labour market and 0 otherwise is observed:

=1 if ;

= 0 if

The wage equation specified in (2) is appropriate only if is positive.

4.5 A decomposition technique is also used to divide the wage gap into explainedcomponent and unexplained, or discrimination component. The decomposition of the wagegap following Blinder – Oaxaca (1973) is widely used to measure labour market discriminationagainst women. Decomposition method separates the wage gap into two factors. One can


be explained by different return to individual characteristics (endowment component),another portion of the differential cannot be explained and is attributable to discrimination(discrimination component). The gross wage differential can be written as

(4)

where and are the male and female wages, respectively. Becker (1994) extended themodel to include the influence of gender and other personal characteristics and propoundedthat in the absence of discrimination wage difference between male and female is the pureproductivity difference (Q) which is defined as

(5)

where Wom/ Wo

f is the competitive wage ratio in the absence of discrimination. Blinder –Oaxaca stated it in a different way

(6)

where Wm/ Wf is the observed wage ratio. Expressing it in logarithmic form this can bewritten as:

(7)

4.6 In equation 7 the first term on the right hand side is for discrimination while thesecond component is for the difference in male–female productivity-related characteristics.The Binder–Oaxaca (B-O) wage decomposition technique requires an estimation of twoseparate wage regressions for male and female workers and can be estimated by usingHeckman selectivity model9. Thus, in order to investigate the sources of gender differentialsin detail, the wage functions of men and women are estimated separately in the frame ofpooled data from two independent random samples in the rural and urban sectors.

(8)

(9)

where W denotes the geometric mean earnings, is the error term with zero mean andconstant variance, m represents men, f stands for women. Separate estimation is needed for

9 The equations estimated separately for male and female workers are not shown in this paper, as theinclusion of female dummy regressor in equation 2, is self explanatory. Further the decomposition inTable 12 has been done by estimating equations 8 and 9.


equation 8 and 9. Now a slight modification of equation 8 and 9 by denoting X as the vectorof mean values of all the regressors, and as the vector of estimated coefficients and thenby taking a simple log mean wage difference between men and women, the equations canbe written as:

(10)

where

By adding and subtracting Xm f in Equation 10, the equation can be written as

(11)

Again the addition and subtraction of Xf m with Equation 10 yields Equation 12.

(12)4.7 Equation 11 implies that in the absence of discrimination, female wage structurewould prevail in the market where as equation 12 indicates the prevalence of male wagestructure in a non-discriminatory market. Blinder (1973) and Oaxaca (1973) developeddecomposition approaches to partition the gender wage differential into components causedby two factors. The first term of the right hand side of the equation (11 and 12) captureshow the male-female wage differential changes in response to changes in the men-womengap in characteristics. The first term is sometimes called ‘observed X’s’ or ‘observed gendergap in characteristics’. The second term measures the unexplained wage gap for differencesin coefficients or returns. This term is considered to measure the level of ‘genderdiscrimination’.

4.8 The data from the two quinquennial surveys (the 50th and 68th Rounds) onemployment and unemployment (Schedule 10) of the NSSO have been used in this paper.This allows for an analysis of determinants as well as trends over time. The major aims ofthese surveys have been to measure the magnitude of ‘employment and unemployment’ inquantitative terms disaggregated by various household and population characteristics atthe national level. In order to capture the multi-dimensional aspects of employment andunemployment, data on several correlates were also gathered. Each quinquennial round isfurther segregated into four sub-rounds10 and covers the whole of the Indian Union exceptfew regions11. A stratified multi-stage sampling design was adopted for the survey both inrural and urban areas12. In the analysis of the NSS unit record data, usual status workers ofthe age group 15-59 years are considered13.

10 The sub-rounds are from July-September, October to December, January to March, and April to June.The number of sample villages and blocks are allotted for these surveys in each of these four sub-roundsare equal.11 i) Leh (Ladakh) and Kargil districts of Jammu & Kashmir ii)interior villages of Nagaland situatedbeyond five kilometres of the bus route and iii) villages in Andaman and Nicobar Islands which remaininaccessible throughout the year12 The first stage units (FSUs) are villages for rural areas and NSS urban frame survey (UFS) blocks forurban areas. The ultimate stage units (USU) are households.13 Only the age group 15-59 years is considered for the analysis as it is the most productive age group forwage based employment.


5. Results and Discussion

5.1 The sample selection corrected wage equation is estimated with 892,170 datapoints from personal level information after excluding children up to age 14 and old-ageabove 59 years in the pooled sample in which 652311 data points are censored and the restare uncensored. As the wage for nonworking people is unobserved, a probit model forlabour force participation needs to be estimated to test and correct for sample selectionbias. The estimated results are shown in Table 10. The inverse Mill’s ratio, the estimatedvalue of , as shown in the lower panel of Table 10, is statistically significant. Thus therewas sample selection problem in the data set and wage equation need to be estimated aftercorrecting for sample selection bias. In addition to education household size, and religionand caste dummy variables are also included to capture the effects of these groups on theselection equation. It is also assumed that, the household size have no effect on wage.

5.2 The intercept for 1993-94 is positive and statistically significant. The coefficientvalue of the household dummy implies that as the number of members of a householdincreases their probability of participation in the labour market decreases as they may beobliged to spend most of their time on non-economic activities. Again the labour marketparticipation rate is lower for the workers in rural areas, one of the possible reasons forwhich is job opportunities may be lower in the rural area. The estimation result also establishesthat one gender is lagging behind another gender in participating in the labour market overthe years in Indian economy and this is further intensified during 2011-12. Here only wageearners (that is the paid work category) are considered. Hindu women had a higher chanceof entering into the labour market in1993-94, but the rate declined in 2011-12. On the otherhand Muslim women’s probability of participation in the labour market was lower than thatfor women in other religious groups, and further deteriorated in the recent time period. Thepositive coefficients of female dummies for backward social groups in 1993-94 imply thatthe labour market participation for them was higher as compared with women in highercastes, and women workers in scheduled caste had larger marginal effect than the tribalwomen or women in other backward castes in the country. But the participation rate ofthese lower caste women declined in the recent time period. In a nutshell during the postliberalisation period gender dominates over all other forms of social discrimination as it isevident from the estimation result.

5.3 The coefficients of dummy variables for females with various level of educationare negative, implying that education did not affect the decision of women’s labour marketparticipation in 1993-94. However in the recent time period the rate of entering into the jobmarket improved for women upto primary and middle level of education but deteriorated forsecondary and higher secondary level of education but comparing it with 1993-94, it hasincreased marginally. Women without technical knowledge had less access to enter intothe job market in 1993-94, but their chance of getting employment although increasing overtime, at least in probabilistic sense as compared with their men counterparts. As thehypothesis of sample selection bias is accepted, the use of the censored sample model(OLS) would lead to incorrect estimates for the valuation of wage equation. This is alsoindicated by the Wald chi2 test14, which indicates a significant correlation between errorterms in the selection equation and the wage equation. Hence, Heckman’s technique will

14Wald chi2 (26) = 442579, Prob> chi2 = 0


provide better result. The estimated results of wage equation, specified in (2), by OLS usingparticipants in the labour market only and the normal hazard (the inverse Mill’s ratio)estimated from the first step as an additional regressor are used in the earning functionestimation equation.

5.4 In the wage equation the constant term for 1993-94 is 9.56 and the constant for2011-12 is 6.89. The weekly wage in logarithm form is used here in nominal rupees. Thenegative coefficient value of the year dummy indicates deflationary factor for nominalwage in 2011-12 as nominal wages grow simply due to inflation and the paper will concentrateto see the effect of each explanatory variable on real wages. If wages are measured in 1993-94 rupees experimentally, then deflating 2011-12 wages to 1993-94 rupees is required whichcan be done by using the Consumer Price Index. But it turns out that this is not necessary,provided a 1993-94 year dummy is included in the regression and log (wage) (as opposed towage) is used as the dependent variable15. The bottom line is that, for studying how thereturn to education or the gender gap has changed, one need to turn nominal wages intoreal wages in equation.On average, women workers got significantly lower wage than theirmale counterparts in 1993-94,but the gender wage gap decreased notably in 2011-12. Thisimplies that the situation of women in terms of overall gender wage-gap improved over thetime period.Rural urban wage gap was significant irrespective of the gender dimension ofworkers. In 1993-94, the average wage in rural areas was 52 per cent lower than urban wage.This rural urban wage gap may in turn induce worker to migrate to the urban sector insearch of job.

5.5 It can be inferred that just being literate, or literate with primary and middleschooling are not enough for men to earn better labour market rewards yet male workers willget better labour market rewards with an increase in their level of education. However forwomen workers different results are coming during the post reform period. With the onsetof liberalisation human capital accumulation plays a great role in deciding the wages of thewomen worker though this trend does not hold in the recent time period. Alternatively thereis clear indication of a fall in rate of return for women workers for all levels of educationduring the post-reform period in India. This means that education does not help womenworkers to get higher level of wages in 2011-12 than in 1993-94. Skill premium, on the otherhand, was 68 per cent for men and 10 per cent for women in 1993-94. Here the negativecoefficient value implies that men workers without having technical education earn 68 percent lower wages than the male workers having the technical education similarly the technicaleducation of women workers help them to earn 10 per cent more wage than without these.But the skill premium for women declined by 20 per cent in 2011-12 which implies thatduring the liberalisation period women workers without having technical education earn 20per cent lower wages than the pre reform period.

5.6 It can be suggested that Hindu women workers earn 26 per cent lower wages thantheir male counterpart in 1993-94 whereas Muslim women earn 41 per cent less wages than

15Using real or nominal wage in a logarithmic functional form only affects the coefficient on the yeardummy, Y11. To see this, let P11 denote the deflation factor for 2011-12 wages.Then, the log of the realwage for each person i in the 2011-12 sample is

Now, while wagei differs across people, P11 does not. Therefore, log (P11) will be absorbed into theintercept for 2011-12.


the Muslim male workers during the same time period. In the post reform period there is afall in the rate of return for the Hindu women workers in comparison to 1993-94. On the otherhand for Muslim workers there is no change during the post reform period. Across thecastes, during 1993-94 ST and SC women workers earn 1 per cent and 19 per cent less wagesthan their male counterparts in the same period. In 2011-12 there is no change for ST womenworkers but for SC there is a decline by 6 per cent in comparison to the previous time period.Comparing this it can be concluded that though gender gap has reduced from 1993-94 to2011-12 but across the socio-religious group women from the marginalised section areunable to reap the benefits of this and women from this group are becoming poorer.

5.7 Given the wide spread persistence of the gender wage gap, the final step is todecompose the wage gap between men and women separately for the years 1993-94 and2011-12 to bring out: (a) whether there has been a significant change in the gender wagegap between these two years, and (b) whether the relative contributions of endowmentdifferential and gender discrimination have changed over time. The decomposition methoddeveloped by Blinder (1973) Oaxaca (1973) is applied here. Here two separate Heckmanwage regression one for male another for female are performed as specified in equation 8and 9 respectively and the coefficient estimates from the wage regressions are used todecompose the wage gap between male and female. Decomposition closely follows Blinder’sexposition and uses both his method and his terminology. Decomposition takes the averageendowment differences between male and female and weights them (multiplies them) by themale wage workers estimated coefficients. The differences in the estimated coefficients areweighted multiplied by) the average characteristics of the female-wage workers.Conventionally, the high-wage groups (in this study high-wage group refers to male) areregarded as the “non-discriminatory norm”, that is, the reference group.

5.8 Table 12 describes the results of the B-O decomposition technique. A positivenumber indicates the percentage by which the gender gap would be reduced if male andfemale are equal in respect to the characteristic assuming that the characteristic is rewardedaccording to the estimated wage function for female/male. Negative number implies that ifwomen are more like men in this respect but the wage functions remains the same, then itwill lead to an increase in gender wage gap. The average gender wage gap was 89.4 per centin 1993 -94 which further reduced to 37.4 per cent in 2011-12. Table 12 indicates that thediscrimination component is larger than the endowment component in both the time periods.

6. Conclusion

6.1 The present study, based on data from the NSSO, seeks to analyze the structureand trends of gender-specific wages and earnings in the manufacturing industry by focusingon ethnic and religious groups in gender dimension during the post-reform period (1993-2011). The study has considered only male and female workers who are in wage employmentand aged between 15 and 59 years. In the recent years with a minuscule growth inemployment generation Indian economy is experiencing an informalisation of the workforcewith a persistent gap in labour force participation rate between males and females. In theurban areas, owing to better and diversified employment opportunities LFPR is higher. Inthe rural areas, due to the agro-based subsistence economy and poor infrastructure suchemployment opportunities are not only rare but also less remunerative.


6.2 Female labour force participation rate can be lower on account of the poor workenvironment for them, which discourages their supply response. It can also be due tolimited employment opportunities. There could be other reasons too for low FLFPR: First,women belonging to higher economic status may not enter into the labour market due tofamily values, cultural or societal norms (demand side argument); second, shortage of well-paying secure jobs for the educated women may reduce their work participation (supplyside argument). Over the years it has been observed that female workers are mainlyconcentrated in tobacco, textiles and wearing apparel industry and gender wage gap (interms of female to male wage ratio) is decreasing in manufacturing industry.

6.3 Regression result from the estimation of participation equation portrays thenegative effect of the household size on the participation rate. It also outlines that labourmarket participation rate are lower for the workers in rural areas than in comparison to urbanareas. One of the essential cruxes that have emerged during the post reform period is thedeceleration of women’s participation rate in the job market for all types of categoriesalternatively gender trumps all other forms of social discrimination. With the primary levelof education women workers probability in finding a job increases in the after reform periodbut higher level education is not going to help them to get a sophisticated job. Educationdoes not help women workers to get higher level of wages in 2011-12 than in 1993-94 thismay be an effect of a decline in Public sector jobs with social securities along with largescale informalisation of various jobs. Wage estimation and Decomposition result suggestthat a substantial wage difference between men and women exists in the Indian labourmarket, but the difference has been reducing during the post-reform period. Decompositionresult also highlights the higher incidence of discriminatory practices in India. The presenceof high discrimination component indicates that gender based wage difference is pervasiveand unless the stereotype behaviour of society changes or women’s position in the labourmarket undergoes radical changes, the wage structure will continue to be imbalanced andunequal in spite of the presence of the “equal remuneration act”. So any effort to reduce,must address gender inequalities from a multi-dimensional perspective which accounts forchanging perceptions and notions regarding women’s role and contribution among differentagents of the labour markets, in addition to the enhancement of women’s employment. Adeliberate government policy and efforts are needed to reduce the wage difference and itshould be aimed at empowering the women who suffer from discrimination. Only an inclusivegrowth strategy shall lead to lowering of wage differentials and removal of disparities inliving standards of people.


References

Aghion, P., et al. (2006),”The unequal effects of liberalization: Evidence from dismantlingthe License Raj in India.” CEP Discussion Paper no. 728.

Banerjee, P. and Veeramani, C. (2015), “Trade Liberalisation and Women’s EmploymentIntensity: Analysis of India’s Manufacturing Industries”, Working Paper IGIDR, 2015,Available at: http://www.igidr.ac.in/pdf/publication/WP-2015-018.pdf, Accessed on 8.7.2016.

Becker. G.S. (1985), “Human Capital, Effort, and the Sexual Division of labour”, Journal ofLabour Economics, Vol. 3, Issue 1, part 2: Trend’s in Women’s Work, education and FamilyBuilding, S33-S58.

________ (1994), “Human Capital: A Theoretical and Empirical Analysis with SpecialReference to Education” (3rd Edition), The University of Chicago Press. Available at NationalBureau of Economic Research.

Besley, T. and R. Burgess.(2004)”Can regulation hinder economic performance? Evidencefrom India.” Quarterly Journal of Economics, Vol.119, No. 1,pp. 91-134.

Bhaumik, S. K. and Chakraborty, M. (2006), “Earnings Inequality in India: Has the Rise ofCaste and Religion Based Politics in India had an Impact?”, IZA Discussion Papers, No.2008, IZA, Bonn, Germany.

______ (2008), “Does Move to Market have an Impact on Earnings Gap across Gender?Some Evidence from India”, Applied Economics Letters, No-15:8, pp. 601-605.

Birdsall, N. and Londono, J.L. (1997), “Asset Inequality Does Matter: Lessons from LatinAmerica”, OCE Working Paper, Inter American Development Bank, Washington, March,pp. 1-27.

Blinder, A.S (1973), “Wage Discrimination: Reduced Form and Structural Estimates”, Journalof Human Resources, VIII, 4,pp. 436-55.

Cotton, C. J. (1988), “On the Decomposition of Wage Differentials”, Review of Economicsand Statistics, 70, pp. 236-43.

Chandrasekhar. C.P and Ghosh. G. (2014), “Are Women Really Working Less in India?”Available at http://www.macroscan.org/fet/aug14/pdf/Working_Less.pdfAccessed on 30October 2014.

Chaudhuri, B and Panigrahi, A.K. (2013), “Gender Bias in Indian Industry”, Journal ofIndustrial Statistics, Vol. 2, No. 1, pp.108-127.

Das, M.B. (2006), “Do Traditional Axes of Exclusion Affect Labour Market Outcomes inIndia”, Social Development Paper, South Asia Series, June, No. 97.

Das, P. (2013), “Measuring Sample Selection Corrected Gender Wage Gaps in India: 1993-94to 2009-10”, Bangladesh Development Studies, Vol. XXXVI, No. 14, pp. 81-97.

Deininger, K. and Olinto, P. (2000), “Asset distribution, Inequality, and Growth”, WorldBank Policy Research Working Paper, June, pp. 1-28.Available athttp://elibrary.worldbank.org/doi/pdf/10.1596/1813-9450-2375,Accessed on17 April 2014.


Duraisamy, M. and Duraisamy, P. (1996), “Sex Discrimination in Indian Labour Markets”,Feminist Economics, Vol. 2, No. 2, pp. 41-61.

Ghosh, J. (2014), “What Exactly is Work?”, Available at http://www.macroscan.org/cur/oct14/cur31102014Exactly_work.htm Accessed on 9 January 2015.

_______(2008), Never Done and Poorly Paid: Women’s Work in Globalising India, WomenUnlimited Press, New Delhi.

Goldar, B. 2000.”Employment Growth in Organised Manufacturing in India”. Economic andPolitical Weekly, Vol 35, No 14, April 1-7.

_______(2013).”Sustaining a High Rate of Industrial Growth in India in the Next 10 Years”,Paper presented at a Workshop on ‘Sustaining High Growth in India’, 25-26 July 2013,Institute of Economic Growth, Delhi.

Gupta, P., et al. (2008),”Big Reforms but Small Payoffs: Explaining the Weak Record ofGrowth in Indian Manufacturing”, In S. Bery, B. Bosworth, and A. Panagariya (eds.), IndiaPolicy Forum, vol. 5, Sage, Delhi, pp.59-108.

Gupta, P., et al. (2008),”What constrains Indian manufacturing?” Working Paper no. 211.IndianCouncil for Research on International Economic Relations.

Heckman, J. 1976, “The Common Structure of Statistical Models of Truncation, SampleSelection, and Limited Dependent Variables and a Simple Estimator of Such Models.” Annalsof Economic and Social Measurement, 5, pp. 475-492.

________ (1979) “Sample Selection Bias as a Specification Error”, Econometrica, 47, pp.153-63.

International Labour Organisation Report (2003), “Time for Equality at Work”, InternationalLabour Conference, Report I(B), International Labour Office, Geneva.

International Trade Union Confederation Report (2008), The Global Gender Pay Gap,Belgium.

Kingdon, G. G. (1997), “Does the Labour Market Explain Lower Female Schooling in India”,Available at http://www.econ.ox.ac.uk/Members/geeta.kingdon/Published papers/JDevstudLabourlowerfemaleschooling.pdf.

Kingdon, G.G. &Unni, J. (1998), “Education and Women’s Labour Market Outcomes inIndia: An Analysis Using NSS Household Data”, Economics Series Working Papers, 99201,University of Oxford, Department of Economics.

Madheswaran, S. and Attewell, P. (2007), “Caste Discrimination in the Indian Urban LabourMarket: Evidence from the National Sample Survey”, Economic and Political Weekly,October 13, pp. 4146-4154.

Mehrotra, S., Parida J., Sinha.S, and Gandhi, A.2014. “Explaining Employment Trends in theIndian Economy: 1993-94 to 2011-12” Economic and Political Weekly, Vol XLIX No. 32.

Mincer, J. A. (1974), “Schooling, Experience, and Earnings”, NBER Books, National Bureauof Economic Research.Available at http://www.nber.org/books/minc74-1, Accessed on 12July, 2011.


N, Neetha. (2014), “Crisis in Female Employment: Analysis across Social Groups”, Economicand Political Weekly, November 22, pp. 50-59.

Neumark, D. (1988), “Employers Discriminatory Behaviour and the Estimation of WageDiscrimination”, Journal of Human Resources, 23, pp. 279-95

Nordman, C. and Rouband, F. (2005), “Reassessing the Gender wage Gap: Does labourForce Attachment Really Matter? Evidence for matched labour force and BiographicalSurvey in Madagascar”, ECINEQ Working Paper-16 in Richard Sebaggala (2007), “WageDetermination and Gender Discrimination in Uganda”, No. 50, Economic Policy and ResearchCentre.

Neuman, S. and Oaxaca, R. (2003), “Estimating Labour Market Discrimination with Selectivity-Corrected Wage Equations: Methodological Considerations and Illustration for Israel”,Discussion Paper No. 2-2003.

Oaxaca, R. (1973), “Male-Female Wage Differentials in Urban Labour Markets”, InternationalEconomic Review, Vol. 14, No. 3, October, pp. 693-709.

Oaxaca, L. R. and Ransom, R. M. (1994), “On Discrimination and Decomposition of WageDifferentials, Journal of Econometrics, 61, pp. 5-21.

O’ Donnell, Owen et al, (2008), “Analyzing Health Equity Using Household Survey Data-A Guide to Techniques and Their Implementation”, World Bank, Washington, DC.

Peterson, T., Snartland, V., Becken, L. E. Olsen, K. M. (1997), “Within Job WageDiscrimination and the Gender Wage Gap: The case of Norway”, European SociologicalReview, Vol- 13, No-2, pp. 199-213.

Ramaswamy, K.V. (2015), “Labour, Employment and Economic Growth in India”, CambridgeUniversity Press, New Delhi.

Reimer, W.C. (1983), “labour Market Discrimination against Hispanic and Black men”, Reviewof Economics and Statistics, 65, pp. 570-79.

Sengupta, A., Das, P. (2014), “Gender Wage Discrimination across Social and ReligiousGroups in India Estimates with Unit Level Data”, Economic and Political Weekly, Vol.XLIX, No. 21, pp. 71-76.

Unni, J. (2005), “Wages and Incomes in Formal and Informal Sectors in India”, IndianJournal of Labour Economics, Vol.48, No.2, 311-317.

Wooldridge, J. M. (2009), Introductory Econometrics a Modern Approach (Indian Edition).Cengage Learning India Private Limited, New Delhi.


Figure 1: LFPRs in usual status (ps+ss) during 1993-94 to 2011-12 of all ages.

Source: NSS Report No. 554: Employment and Unemployment Situation in India, 2011-12

Figure 2: Unemployment rates (in percentage) according to usual status from 1993-94to 2011-12

Source: NSS Report No. 554: Employment and Unemployment Situation in India, 2011-12

Table 1: WPRs (in percent) of various socio-religious groups in manufacturingindustry.

1993-94 2011-12 Rural Urban Rural Urban

Male Female Male Female Male Female Male Female

Rel

igio

us G

roup

s

Hindu 85 83 78 73 79 71 73 70

Muslim 10 12 16 22 15 21 21 23

Christian 2 4 3 4 4 5 2 4

Others 3 2 4 1 3 4 3 3

Total 100 100 100 100 100 100 100 100

Soci

al G

roup

s

SC 18 18 3 4 18 18 13 14

ST 7 10 9 10 10 11 3 4

Others 75 73 88 86 72 71 84 82

Total 100 100 100 100 100 100 100 100

Source:Estmated from Unit-level data, NSS Employment and Unemployment Survey, 1993-94 and2011-12Note: Total may not add up to 100 due to rounding off. Considered only the usual status workers of 15to 59 years age group.


Table 2: Distribution of usual status workers in manufacturing industry

Source: Same as Table 1


Divisions of Manufacturing Industry

1993-94 2011-12

Rural Urban Rural Urban

Male Female Male Female Male Female Male Female

Food Product 18.06 13.91 9.43 9.62 14.68 9.06 10.37 7.56

Beverages 1.29 0.9 0.83 0.91 1.05 0.76 0.89 2.09

Tobacco 5.16 25.41 2.34 22.63 1.9 21.7 1.07 13.24

Textiles 11.46 23.36 17.1 24.08 7.69 19.55 15.08 26.84

Wearing Apparel 6.85 9.21 7.45 13.3 13.14 22.36 12.53 29.25

Leather and related products 1.38 0.53 2.57 1.92 1.09 0.66 2.88 1.17

Wood and products of wood and cork, except furniture 12.9 10.17 5.27 3.64 13.02 8.96 5.12 1.95

Paper and paper products 0.64 0.29 1.29 1.82 0.78 0.35 1.31 1.74

Printing and reproduction of recorded media 0.87 0.21 3.1 1.4 0.55 0.24 2.04 0.64

Coke and refined petroleum products 0.53 0.05 0.57 0.18 0.31 0.03 0.62 0

Chemicals and chemical products 2.11 1.94 4.19 5.43 1.44 1.88 2.34 1.46

Pharmaceuticals, medicinal chemical and botanical products 0.38 0.09 1.43 0.91 0.9 0.28 1.73 1.03

Rubber and plastics products 1.35 0.15 2.53 1.18 1.69 0.66 2.75 1.38

Other non-metallic mineral products 11.94 8.35 4.46 3.59 13.36 7.6 5.29 3.51

basic metals 1.95 0.26 5.36 0.93 2.59 0.42 4.17 0.21

Fabricated metal products, except machinery and equipment 4.46 0.47 6.81 1.08 6.21 1.67 7.43 0.85

Computer, electronic and optical products 0.57 0.12 1.65 1.31 0.33 0.17 1.42 0.5

Electrical equipment 0.75 0.12 2.27 0.8 1.35 0.21 2.28 0.67

Machinery and equipment n.e.c. 2.95 0.62 4.95 0.73 1.14 0.21 2.12 0.11

Motor vehicles, trailers and semi-trailers 0.23 0 0.88 0.03 1.32 0.07 2.07 0.25

Other transport equipment 0.49 0.06 2.4 0.32 0.34 0.03 1.59 0.11

Furniture 4.69 1.38 2.85 0.48 9.19 0.17 5.07 0.43

Other manufacturing 6.72 2.34 7.52 3.65 3.64 2.85 7.47 4.86

Repair and installation of machinery and equipment 2.26 0.08 2.76 0.04 2.25 0.1 2.37 0.14

Total 100 100 100 100 100 100 100 100

Table 3: Gender wage gap by usual status in manufacturing industry.

Source: Same as Table 1Note: (n.a) implies no observations or unavailabilty of wage data.


Divisions of Manufacturing Industry 1993-94 2011-12

Rural wage Ratio

Urban wage Ratio

Rural wage Ratio

Urban wage Ratio

Food Product 0.52 0.28 0.65 0.40

Beverages 0.13 0.03 0.95 0.39

Tobacco 0.53 0.40 0.70 0.59

Textiles 0.31 0.29 0.65 0.58

Wearing Apparel 0.43 0.32 0.92 0.58

Leather and related products 1.98 0.43 0.78 1.19

Wood and products of wood and cork, except furniture 0.22 0.16 0.39 0.78

Paper and paper products 0.23 0.19 0.44 0.65

Printing and reproduction of recorded media 0.32 1.19 1.82 0.38

Coke and refined petroleum products 0.39 0.75 0.94 n.a

Chemicals and chemical products 0.23 0.31 0.46 0.37

Pharmaceuticals, medicinal chemical and botanical products 0.74 0.77 0.63 0.81

Rubber and plastics products 0.39 0.52 0.80 0.30

Other non-metallic mineral products 0.42 0.31 0.73 0.50

Basic metals 0.35 0.90 0.38 0.27

Fabricated metal products, except machinery and equipment 1.13 0.51 0.43 0.71

Computer, electronic and optical products 0.44 0.67 0.34 0.34

Electrical equipment 0.31 0.64 0.33 0.77

Machinery and equipment n.e.c. 0.79 0.65 1.39 0.82

Motor vehicles, trailers and semi-trailers n.a 0.37 0.25 0.89

Other transport equipment 0.76 0.64 0.41 0.61

Furniture 0.45 0.03 0.68 0.75

Other manufacturing 0.25 0.28 0.85 0.89

Repair and installation of machinery and equipment 0.00 1.17 n.a 1.37

Total Manufacturing 0.34 0.31 0.55 0.49

Table 4: Distribution of female usual workers by religious group in manufacturingindustry.

(a) Rural



Leather and related products 0.64 0 0 0 0.59 1.18 0 0

Wood and products of wood and cork, except furniture; 11.27 4.07 5.71 9.9 10.77 1.85 14.79 6.31

Paper and paper products 0.35 0 0 0 0.49 0 0 0

Printing and reproduction of recorded media 0.18 0.25 0 1.98 0.29 0.17 0 0

Coke and refined petroleum products 0.05 0 0 0 0 0 0 0.9

Chemicals and chemical products 1.75 2.1 6.53 0 2.21 1.18 1.41 0

Pharmaceuticals, medicinal chemical and botanical products 0.11 0 0 0 0.2 0 2.11 0.9

Rubber and plastics products 0.15 0 0.82 0 0.59 0.84 1.41 0

Other non-metallic mineral products 9.69 1.73 0 8.91 9.34 1.35 4.93 12.61

Basic metals 0.31 0 0 0 0.59 0 0 0

Fabricated metal products, except machinery and equipment 0.56 0 0 0 2.16 0.34 1.41 0

Computer, electronic and optical products

0.15

0

0

0

0.25

0

0

0

Electrical equipment

0.15

0

0

0

0.25

0.17

0

0

Machinery and equipment n.e.c.

0.65

0

0

4.95

0.29

0

0

0

Motor vehicles, trailers and semi-trailers 0.1 0 0 0

Other transport equipment 0.07 0 0 0 0.05 0 0 0

Furniture 1.58 0 0.41 3.96 0.2 0 0.7 0

Other manufacturing

2.42

1.23

5.31

0

2.95

2.87

2.11

1.8

Repair and installation of machinery and equipment

0.09

0

0

0

0.1

0.17

0

0

Total

100

100

100

100

100

100

100

100

Wearing Apparel 30.28 45.05 8.73 13.32 7.76 5.94 22.47 15.85


1993-94 2011-12

Hindu Muslim Christian Others Hindu Muslim Christian Others

Food Product 4.93 2.7

Beverages 2.11 1.8

Tobacco 5.63 2.7

Textiles 28.17 25.23

15.06

0.91

22.82

22.31

7.89

0.12

43.16

26.14

12.24

2.04

20.82

38.37

3.96

3.96

34.65

21.78

10.42

0.84

19.96

14.9

6.58

0

35.08

32.38

(a) Urban



1993-94 2011-12

Hindu Muslim Christian Others Hindu Muslim Christian Others

Food Product 10.66 5.85 12.55 8.43 8.3 4.13 17.09 3.7

Beverages 0.89 0.43 4.43 0 2.29 0.15 5.13 8.64

Tobacco 17.38 42.95 7.75 6.02 10.08 24.62 8.55 4.94

Textiles 26.32 20.06 11.07 3.61 22.85 39.76 17.95 32.1

Wearing Apparel 12.41 14.71 21.03 16.87 30.89 22.78 30.77 39.51

Leather and related products 1.96 1.85 2.21 0 1.42 0.15 3.42 0

Wood and products of wood and cork, except furniture; 4.23 1.23 5.9 6.02 2.44 0.46 2.56 1.23

Paper and paper products 1.67 1.97 1.48 9.64 2.04 1.38 0 0

Printing and reproduction of recorded media 1.48 0.31 5.54 3.61 0.87 0 0.85 0

Coke and refined petroleum products 0.21 0.12 0 0

Chemicals and chemical products 6.05 3.51 5.54 3.61 1.93 0.15 0.85 1.23

Pharmaceuticals, medicinal chemical and botanical products 0.86 0.18 4.43 7.23 1.42 0.15 0 0

Rubber and plastics products 1.29 0.92 0.74 0 1.63 0.92 0.85 0

Other non -metallic mineral products 4.3 1.11 2.21 12.05 4.48 1.53 0.85 0

Basic metals 1.2 0.12 0.74 0 0.25 0 0.85 0

Fabricated metal products, except machinery and equipment 1.22 0.25 2.21 4.82 0.66 1.07 1.71 2.47

Computer, electronic and optical products 1.56 0.37 2.58 0 0.71 0 0 0

Electrical equipment 0.86 0.12 2.58 4.82 0.97 0 0 0

Machinery and equipment n.e.c. 0.67 0.18 3.69 6.02 0.15 0 0 0

Motor vehicles, trailers and semi-trailers 0.04 0 0 0 0.36 0 0 0

Other transport equipment 0.36 0.12 0.74 0 0.15 0 0 0


Table 5: Distribution of female usual workers by social group in manufacturingindustry.

(a) Rural




1993-94 2011-12

SC ST Others SC ST Others

Food Product 15 29.23 11.77 7.58 5.56 9.97

Beverages 0.52 1.58 0.91 0.19 5.23 0.24

Tobacco 17.42 11.53 29.13 23.3 7.19 23.46

Textiles 10 18.01 27 12.5 16.01 21.9

Wearing Apparel 12.36 6.32 8.83 19.89 14.05 24.24

Leather and related products 1.63 0 0.33 1.33 0 0.59

Wood and products of wood and cork, except furniture; 23 25.75 5.17 17.61 34.97 2.83

Paper and paper products 0.43 0 0.29 0.38 0.33 0.34

Printing and reproduction of recorded media 0 0 0.29 0 0 0.34

Coke and refined petroleum products 0.26 0 0 0 0 0.05

Chemicals and chemical products 2.06 0.32 2.12 1.52 0.98 2.1

Pharmaceuticals, medicinal chemical and botanical products 0 0 0.12 0.19 0.65 0.24

Rubber and plastics products 0 0.32 0.16 0.19 0.33 0.83

Other non-metallic mineral products 12 5 7.93 10.42 8 6.79

Basic metals 0 0 0 0.95 1 0.24

Fabricated metal products, except machinery and equipment 0.69 0.63 0.39 0.95 3.59 1.56

Computer, electronic and optical products 0 0 0.16 0.38 0 0.15

Electrical equipment 0 0 0.16 0 0.33 0.24

Machinery and equipment n.e.c. 0.26 0 0.78 0 0.33 0.24

Motor vehicles, trailers and semi -trailers 0.19 0 0.05

Other transport equipment 0 0 0.08 0 0 0.05

Furniture 4.12 1.11 0.76 0 0.33 0.2

Other manufacturing 1.2 0 2.92 2.46 0.98 3.23

Repair and installation of machinery and equipment 0.26 0 0.04 0 0 0.1

Total 100 100 100 100 100 100

(a) Urban



Divisions of Manufacturing Industry 1993-94 2011-12


Food Product 10.45 5.8 10.05 6.72 3.54 7.9

Beverages 5.6 1.19 0.68 10.08 4.42 0.65

Tobacco 10.45 19.13 23.59 9.3 6.19 14.24

Textiles 16.79 22.69 24.57 17.31 27.43 28.4

Wearing Apparel 21 9.1 13.48 24.29 30.97 30

Leather and related products 0 10 1 3.1 0 0.91

Wood and products of wood and cork, except furniture; 8.58 9.63 2.69 6.46 6.19 0.99

Paper and paper products 0.37 0.66 2.03 2.84 0 1.64

Printing and reproduction of recorded media 1.49 1.85 1.34 2.07 0.88 0.39

Coke and refined petroleum products 0.75 0 0.18


Pharmaceuticals, medicinal chemical and botanical products 0 0.53 1 0.78 0 1.12

Rubber and plastics products 3.36 1.32 1.06 0.78 0 1.55

Other non-metallic mineral products 7.09 7.12 3.01 5.94 10.62 2.76

Basic metals 4.85 1.32 0.71 0.26 0 0.22

Fabricated metal products, except machinery and equipment

0.75

0

1.22

0.26

0.88

0.95


1.49

0.4

1.42

1.03

0

0.43


0

0.26

0.9

0.78

0

0.69


2.61

0

0.74

0.26

0

0.09

Motor vehicles, trailers and semi-trailers 0 0 0.03 0.78 0 0.17

Other transport equipment 0 0.26 0.34 0 0 0.13

Furniture 1.12 1.32 0.35 0 5.31 0.26

Other manufacturing

0

2.9

3.9

5.94

2.65

4.79


0

0.4

0

0.26

0

0.13

Total

100

100

100

100

100

100

Table 6: Wage Ratio of religious groups by usual status in manufacturing industry.(a) Rural

Source: Same as Table 1Note: Same as Table 3 .



1993-94 2011-12 Hindu Muslim Christian Others Hindu Muslim Christian Others

Food Product 0.52 0.44 0.57 n.a 0.66 0.73 0.25 0.55

Beverages 0.16 n.a n.a n.a 0.98 n.a 0.45 n.a

Tobacco 0.51 0.57 n.a 1.89 0.55 0.97 n.a n.a

Textiles 0.25 0.67 0.42 n.a 0.62 0.82 0.86 0.65

Wearing Apparel 0.40 0.57 n.a 0.84 0.84 0.61 n.a 0.85

Leather and related products 1.93 n.a n.a n.a 0.74 0.81 n.a n.a

Wood and products of wood and cork, except furniture 0.19 0.80 0.07 n.a 0.39 0.65 0.28 n.a

Paper and paper products 0.24 n.a n.a n.a 0.42 n.a n.a n.a

Printing and reproduction of recorded media 0.22 1.63 n.a n.a 2.02 n.a n.a n.a

Coke and refined petroleum products 0.36 n.a n.a n.a n.a n.a n.a 0.96

Chemicals and chemical products 0.25 n.a 0.12 n.a 0.44 0.58 0.62 n.a

Pharmaceuticals, medicinal chemical and botanical products 0.71 n.a n.a n.a 0.57 n.a n.a n.a

Rubber and plastics products 0.40 n.a 0.30 n.a 0.76 1.26 0.45 n.a

Other non-metallic mineral products 0.43 0.63 n.a 0.36 0.74 0.51 0.88 0.68

Basic metals 0.36 n.a n.a n.a 0.37 n.a n.a n.a

Fabricated metal products, except machinery and equipment 0.31




Motor vehicles, trailers and semi-trailers 0.48

Other transport equipment 0.14

Furniture 0.42

Other manufacturing


Total Manufacturing

1.14

0.45

0.31

0.85

n.a

n.a

0.34

n.a

n.a

n.a

n.a

n.a

n.a

0.38

n.a

n.a

0.40

n.a

n.a

n.a

n.a

n.a

n.a

0.92

n.a

n.a

0.21

n.a

n.a

n.a

n.a

n.a

n.a

n.a

n.a

n.a

0.27

0.42

0.38

0.33

1.34

0.23

0.42

0.68

0.79

n.a

0.54

n.a

n.a

n.a

n.a

n.a

n.a

n.a

n.a

n.a

0.59

n.a

n.a

n.a

n.a

n.a

n.a

5.54

n.a

0.53

n.a

n.a

n.a

n.a

n.a

n.a

n.a

n.a

n.a

0.57

(b) Urban

Source: Same as Table 1Note: Same as Table 3.


Divisions of Manufacturing Industry 1993-94 2011-12 Hindu Muslim Christian Others Hindu Muslim Christian Others

Food Product 0.27 0.29 0.53 0.14 0.44 0.27 0.10 0.29

Beverages 0.03 n.a 0.09 n.a 0.38 n.a 1.00 n.a

Tobacco 0.38 0.48 n.a n.a 0.87 0.41 n.a n.a

Textiles 0.29 0.26 0.53 2.22 0.56 0.44 1.99 0.29

Wearing Apparel 0.33 0.14 0.60 1.99 0.56 0.54 0.65 0.26

Leather and related products 0.44 0.30 0.78 n.a 1.00 0.76 1.20 n.a

Wood and products of wood and cork, except furniture 0.14 0.83 0.04 n.a 0.39 1.82 1.49 n.a

Paper and paper products 0.17 0.08 0.53 0.12 0.71 0.17 n.a n.a

Printing and reproduc tion of recorded media 0.62 n.a 4.69 n.a 0.40 n.a 0.29 n.a

Coke and refined petroleum products 0.88 n.a n.a n.a 0.09 0.37 n.a n.a

Chemicals and chemical products 0.25 0.06 1.14 0.56 0.87 0.36 n.a n.a

Pharmaceuticals, medicinal chemical and botanical products 0.48 0.24 3.77 2.18 0.27 0.13 0.06 n.a

Rubber and plastics products 0.53 0.90 0.29 n.a 0.32 0.42 0.56 n.a

Other non-metallic mineral products 0.29 0.52 0.27 0.33 0.52 n.a 1.61 n.a

Basic metals 0.94 n.a 0.34 n.a 0.58 0.29 0.28 n.a

Fabricated metal products, except machinery and equipment 0.47 0.58 0.26 0.90 0.83 n.a n.a n.a

Computer, electronic and optical products 0.62 0.29 1.38 n.a 0.56 n.a n.a n.a

Electrical equipment 0.67 1.07 0.99 n.a 0.64 n.a n.a n.a

Machinery and equipment n.e.c. 0.60 1.10 0.55 0.82 1.90 n.a n.a n.a

Motor vehicles, trailers and semi-trailers 0.36 n.a n.a n.a 0.50 n.a n.a n.a

Other transport equipment 0.59 1.18 0.82 n.a n.a n.a 0.17 5.86

Furniture 0.04 n.a n.a n.a 1.09 0.39 0.97 0.31

Other manufacturing 0.36 0.09 0.36 n.a 1.80 n.a n.a n.a

Repair and installation of machinery and equipment 1.00 n.a n.a n.a n.a n.a n.a n.a

Total Manufacturing 0.30 0.23 0.85 0.70 0.49 0.46 0.38 0.26

Table 7: Wage Ratio (Female to Male) of social groups by usual status inmanufacturing industry.




1993-94 2011-12


Food Product 0.43 0.91 0.33 0.69 0.86 0.60

Beverages 2.60 n.a 0.05 1.16 1.01 0.95

Tobacco 0.57 0.63 0.52 0.86 0.58 0.74

Textiles 0.40 0.48 0.29 0.51 0.80 0.63

Wearing Apparel 0.42 0.32 0.42 1.44 0.77 0.94

Leather and related products n.a n.a 1.61 n.a 1.03 0.62

Wood and products of wood and cork, except furniture 0.26 0.51 0.15 0.61 0.29 0.77

Paper and paper products n.a n.a 0.29 1.37 0.43 0.24

Printing and reproduction of recorded media n.a n.a 0.31 n.a n.a 1.82

Coke and refined petroleum products 0.47 n.a n.a n.a n.a 0.92


Pharmaceuticals, medicinal chemical and botanical products 0.00 0.00 0.74 1.03 0.65 0.85

Rubber and plastics products 0.00 1.02 0.38 0.57 0.98 0.78

Other non-metallic mineral products 0.72 0.59 0.26 0.71 0.78 0.72

Basic metals 0.00 0.00 0.35 0.76 0.42 0.47

Fabricated metal products, except machinery and equipment

n.a n.a 1.39 0.99 0.48 0.37

Computer, electronic and optical products n.a n.a 0.44 n.a 0.30 0.47

Electrical equipment n.a n.a 0.30 0.93 n.a 0.31

Machinery and equipment n.e.c. n.a n.a 0.83 n.a n.a 1.23

Motor vehicles, trailers and semi-trailers n.a n.a 0.57 n.a 1.01 0.22

Other transport equipment 0.02 n.a 0.25 n.a n.a 0.40

Furniture 1.87 n.a 0.42 n.a n.a 0.62

Other manufacturing n.a n.a n.a n.a 0.60 0.87


n.a n.a n.a

n.a n.a n.a

Total Manufacturing 0.41 0.53 0.30 0.68 0.64 0.52

(a) Rural

Table 7: Wage Ratio (Female to Male) of social groups by usual status inmanufacturing industry. (Contd.)

(b) Urban



1993-94Divisions of Manufacturing Industry SC ST Others SC ST Others

Food Product 0.54 0.56 0.26 0.38 0.58 0.37

Beverages n.a 0.13 0.03 n.a 0.22 0.38

Tobacco 1.30 0.57 0.37 n.a 1.98 0.55

Textiles 0.24 0.45 0.26 0.58 0.82 0.55

Wearing Apparel 0.09 0.19 0.33 0.92 0.71 0.56

Leather and related products n.a 0.58 0.42 n.a 0.55 1.35

Wood and products of wood and cork, except furniture

n.a 0.12 0.22 0.40 0.62 0.84

Paper and paper products n.a 0.04 0.19 n.a 0.85 0.63

Printing and reproduction of recorded media 6.74 0.37 1.37 0.88 0.28 0.54

Coke and refined petroleum products 1.54 n.a 0.61 0.06 0.61 0.09

Chemicals and chemical products 0.11 0.37 0.31 n.a 0.97 0.79

Pharmaceuticals, medicinal chemical and botanical products n.a 0.45 0.83

n.a 0.37 0.21

Rubber and plastics products 0.54 0.43 0.58 0.23 0.35 0.35

Other non-metallic mineral products 0.20 0.42 0.30 n.a 4.84 0.45

Basic metals 0.74 0.20 1.32 0.37 0.22 0.39

Fabricated metal products, except machinery and equipment 2.23 n.a 0.47 n.a 0.72 1.09

Computer, electronic and optical products 0.91 0.29 0.67 n.a 2.87 0.41

Electrical equipment n.a 0.80 0.64 n.a 0.63 0.71

Machinery and equipment n.e.c. 0.72 n.a 0.61 n.a 0.94 2.50

Motor vehicles, trailers and semi-trailers n.a n.a 0.39 n.a n.a 0.51

Other transport equipment n.a n.a 0.71 n.a n.a 0.28

Furniture n.a n.a 0.04 0.69 0.66 1.06

Other manufacturing n.a 0.18 0.29 n.a 0.87 1.94


n.a 1.04 n.a

n.a n.a n.a

Total Manufacturing 0.38 0.34 0.30 0.41 0.65 0.47

2011-12



Table 8: Industrial gender segregation in India 1993-94 and 2011-12


Year

Regular salaried employees

Casual wage labourers


(ID) (ID) (ID) (ID)

1993-94 0.43 0.29 0.41 0.38

2011-12 0.38 0.27 0.45 0.40

Table 9: Gender segregation in Manufacturing Industry across major Indian statesfrom 1993-94 to 2011-12

Table 10: Impact of Explanatory Variables on Female Work Participation: Results ofProbit Estimation

Major States 1993-94 2011-12


Andhra Pradesh 0.47 0.47 0.56 0.54

Assam 0.76 0.75 0.67 0.60

Bihar 0.25 0.51 0.43 0.65

Gujarat 0.63 0.50 0.49 0.55

Haryana 0.60 0.65 0.51 0.40

Jammu & Kashmir 0.72 0.58 0.53 0.54

Karnataka 0.49 0.50 0.64 0.58

Kerala 0.39 0.48 0.69 0.52

Madhya Pradesh 0.34 0.50 0.32 0.45

Maharashtra 0.47 0.34 0.52 0.52

Orissa 0.27 0.50 0.52 0.57

Punjab 0.62 0.46 0.71 0.46

Rajasthan 0.32 0.43 1.00 0.37

Tamilnadu 0.26 0.37 0.39 0.32

Tripura 0.28 0.38 0.46 0.68

Uttar Pradesh 0.30 0.40 0.47 0.46

West Bengal 0.29 0.37 0.62 0.36

India 0.42 0.33 0.42 0.33

Variables Coefficients z-statistic P>z _cons 0.25 58.57 0 hhsz -0.08 -132.7 0 DFemale -0.16 7.1 0 DYear 0.00 0.67 0.501 DYear2011_female -0.31 -8.43 0 DFemale_Hindu 0.11 10.13 0


Table 10: Impact of Explanatory Variables on Female Work Participation: Results ofProbit Estimation. (Contd)


Variables Coefficients z-statistic P>z DFemale_Muslim -0.11 -7.06 0 DFemale_ST 0.23 23.51 0 DFemale_SC 0.38 45.13 0 DFemale_belowprimary -0.27 -23.75 0 DFemale_uptomiddle -0.53 -59.69 0 DFemale_secandhs -0.31 -29.87 0 DFemale_techedu -0.82 -39.68 0 DRural -0.26 -82.67 0 DFemale_Hindu_11 -0.15 -7.95 0 DFemale_Muslim_11 -0.24 -8.96 0 DFemale_ST_11 -0.01 -0.69 0.493 DFemale_SC_11 -0.14 -9.49 0 DFemale_belowprimary_11 0.10 4.96 0 DFemale_uptomiddle_11 0.13 8.67 0 DFemale_secandhs_11 -0.22 -13.07 0 DFemale_techedu_11 0.24 7.09 0 Inverse Mill’s Ratio

-0.08 -58.23 0 rho -0.01 sigma 0.92

Table 11: Estimated Coefficients for Men and Women Earnings Function

Explanatory variables Coefficients z-statistic P>z _cons 9.56 386.93 0 age 0.06 54.11 0 age2 0.00 -36.52 0 DFemale -0.31 -10.97 0 DRural -0.52 -107.13 0 DYear -2.67 -565.54 0 DYear2011_female 0.46 10.14 0 Dlit_pri -0.25 -33.85 0 Dpri_middle -0.10 -18.29 0 Dsec_hs 0.26 45.02 0 DFemale_belowprimary 0.23 11.94 0 DFemale_uptomiddle 0.15 8.84 0 DFemale_secandhs 0.51 29.54 0 DFemale_gradandabove 0.00 DFemale_belowprimary_11 -0.35 -10.65 0 DFemale_uptomiddle_11 -0.35 -14.52 0 DFemale_secandhs_11 -0.52 -18.96 0 DTechedu -0.68 -74.68 0


Table 11: Estimated Coefficients for Men and Women Earnings Function (Contd.)Explanatory variables Coefficients z-statistic P>z DFemale_techedu -0.10 -3.93 0 DFemale_techedu_11 -0.20 -5.02 0 DFemale_Hindu -0.26 -16.5 0 DFemale_Muslim -0.41 -16.99 0 DFemale_ST -0.01 -0.92 0.355 DFemale_SC -0.19 -15.6 0 DFemale_Hindu_11 -0.07 -2.36 0.018 DFemale_Muslim_11 0.00 -0.09 0.926 DFemale_ST_11 0.00 0.11 0.909 DFemale_SC_11 -0.06 -2.7 0.007


Source: Same as Table 1Note: a) Positive number indicates advantage to male group; negative number indicates advantage tofemale group. b) The results from decomposition are presented using Blinder’s (1973) original formulationof E, C, U, and D. The endowments (E) component of the decomposition is the sum of (the coefficientvector of the regressors of the male-wage group)times (the difference in group means between the male-wage and female-wage groups for the vector of regressors). The coefficients (C) component of thedecomposition is the sum of the(group means of the female-wage group for the vector of regressors)times (the difference between the regression coefficients of the male-wage group and the female-wagegroup). The unexplained portion of the differential (U) is the difference in constants between the malewage group and the female-wage group. The portion of the differential due to discrimination is C+U. Theraw (or total) differential is E+C+U.

Table 12: Blinder and Oaxaca decomposition methodComponents of Decomposition In %

1993-94 2011-12

Amount attributable: 161.1 74.6

- due to endowments (E): 38.4 13.9

- due to coefficients (C): 122.7 60.8

Shift coefficient (U): -71.7 -37.2

Raw differential (R) {E+C+U}: 89.4 37.4

Adjusted differential (D) {C+U}: 51 23.5

Endowments as % total (E/R): 42.9 37.1

Discrimination as % total (D/R): 57.1 62.9

AppendixA1: Explanations of the variables used in the regression method:


Female Gender dummy variable, equal to 1 for women and 0 for men

Y11 Time dummy variable, equal to 1 if the person comes from the 68th

round (2011 -12) and 0 if he/she comes from the 50th round (1993-94).

Y11_female Gender dummy interacted with time dummy, indicating the change of

the effect of gender dummy variable over the years

female_hindu Gender dummy variable for Hindus, equal to 1 for Hindu women and

0 otherwise

female_muslim Gender dummy variable for Muslims, equal to 1 for Muslim women

and 0 otherwise

female_st Gender dummy variable for STs, equal to 1 for ST women and 0

otherwise

female_sc Gender dummy variable for SCs, equal to 1 for SC women and 0

otherwise

female_Literatebelowprimary Gender dummy variable for literate below primary level of education,

equal to 1 for women with literate below primary level of education

and 0 otherwise female_primary_ middle Gender dummy variable for primary level of education, equal to 1 for

women with primary level of education and 0 otherwise

female_Sec_HS Gender dummy variable for secondary and higher secondary level of

education, equal to 1 for women with secondary and higher secondary

level of education and 0 otherwise

female_graduate_ above Gender dummy variable for graduation and above level of education,

equal to 1 for women with graduation and above level of education

and 0 otherwise

female_tech Gender dummy variable for technical skill, equal to 1 for women with

technical skill and 0 otherwise.

female_hindu_11 Hindu gender dummy interacted with time dummy, indicating the

change of the effect of Hindu women over the years

female_muslim_11 Muslim gender dummy interacted with time dummy, indicating the

change of the effect of Muslim women over the years

female_st_11 ST gender dummy interacted with time dummy, indicating the change

of the effect of ST women over the years

female_sc_11 SC gender dummy interacted with time dummy, indicating the change

of the effect of SC women over the years

AppendixA1: Explanations of the variables used in the regression method: (Contd.)

female_Literatebelowprimary_11

Gender dummy with Literate below primary level of education

interacted with

time dummy, indicating the change of the effect of

women with primary level of over the years.

female_primary_ middle_11

Gender dummy with primary and middle level of education interacted

with time dummy, indicating the change of the effect of women with

above primary level of over the years.

female_Sec_HS_11 Gender dummy with secondary and higher secondary level of

education interacted with time dummy, indicating the change of the

effect of women with secondary and higher secondary level of

education.

female_graduate_ above_11

Gender dummy with graduation and above level of education

interacted with time dummy, indicating the change of the effect of

women with university and higher degree,

female_tech_11

Gender dummy with technical skill interacted with time dummy,

indicating the change of the effect of women with technical skill over

the years.



Export Competitiveness and Intensity of Technology in IndianManufacturing Industries –Analysis with ASI Unit Level Data

Panchanan Das1, University of Calcutta, Kolkata, IndiaAbhishek Halder, University of Calcutta, Kolkata, India

Rahul Dutt, University of Calcutta, Kolkata, India

Abstract

This study looks into export competitiveness of the manufacturing firms in terms of firmspecific characteristics. The pooled unit level data compiled from the Annual Survey ofIndustries (ASI) during the period 2008 -2012 are used for this purpose. A generalisedlinear model with a binomial distribution and a logit link function is used in estimatingthe regression relationship. In the pooled regression model, firm’s age has negative effecton export competitiveness of a firm. Firm size has a significant positive effect on firm’sexport competitiveness. Larger the size of a firm, larger will be the export share to totaloutput. As expected, human capital favourably affects the export competitiveness ofmanufacturing firms. The effect of human capital is, however, less than the effect of firmsize on export share of firm’s production. The capital intensity of a firm, on the other hand,has no significant effect on its export competitiveness. The export performance of the firmsmanufacturing apparels, leather and food products is better than that of the firms in othermanufacturing industries.

1. Introduction

1.1 The manufacturing sector has traditionally been treated as an engine of growth ofany economy (Kaldor, 1966). It enhances growth and productivity, generates employmentand it also strengthens agriculture and service sectors through forward and backwardlinkages. The faster growth in India, as appeared since the late 1980s, however, is notattributed significantly to the industrial sector growth. In the recent past, output from themanufacturing sector grew at a higher rate and the annual growth rate reached a peak at 12percent in 2006-07. Despite the high growth, the share of manufacturing sector in GDP hasremained at around 15 percent even in 2013-14. The manufacturing sector has not contributedperceptibly to tackle the problem of unemployment and underemployment in theunorganised sector (GOI, 2011). The exports from manufacturing sector, however, hasgrown at a compound annual rate of more than 16 percent during 2007-08 to 2013-14contributing over 60 percent to total merchandise exports2. The engineering goods haveemerged as the most contributors sharing roughly 40 percent of total manufacturing exportsfollowed by gems and jewellery (22 percent), and textiles (14 percent) in 2010-11.

1.2 Significant trade liberalisation together with the successive devaluation of domesticcurrency were expected to improve the export competitiveness of Indian firms and lead toincreased contribution of exports to the Indian economy. The significance of exports fromthe manufacturing sector has been increasing steadily in the Indian economy during theperiod of trade liberalisation. The propagation in the use of nontariff measures mainly by

1 e-mail: [email protected] Total merchandise trade in India was recorded at 37 percent of the GDP in 2010-11.

226

the developed countries, in many cases, acts as one of the major constraints in exportcompetitiveness in a country like India. Sanitary and phytosanitary standards and technicalbarriers to trade, for example, creates critical questions in increasing export performance bythe manufacturing firms in India (Kallummal 2006).

1.3 Against this observed facts, this study looks into export competitiveness of themanufacturing firms in terms of firm specific characteristics with unit level data from AnnualSurvey of Industries (ASI) during the period 2008 -2012. In analysing export competitivenessof manufacturing firms in India, the rest of the study is organised in following manner.Section 2 focuses on the determinants of export competitiveness as proposed in this study.Section 3 describes the data, in short, and construction of variables for this study. Section4 deals with econometric methodology applied in empirical exercise. Characteristic featuresof the sample firms are summarised in section 5. Section 6 interprets the empirical findings.Section 7 summarises and concludes.

2. Determinants of export competitiveness

2.1 The export competitiveness and the pattern of trade of a country are analysedconventionally in terms of comparative cost advantage in a macroeconomic framework.The comparative cost advantage of exporting a commodity arises from factor productivitydifferentials and difference in factor endowments between two countries. The notion ofcomparative advantage is extended further in the Heckscher-Ohlin model under theassumption of same production technology, but with different factor endowments3. Lateron, human capital is treated as an additional important factor affecting trade in the neoclassical model. Scale economies and oligopolistic competition have become importantfactors for determining trade patterns in the strategic trade models. Technology has alsobecome a crucial determinant of international trade.

2.2 Traditionally, export competitiveness of a country is measured by its share ofworld markets for its products (Michael et al. 2008). It is the firm that decides ultimately if itshould trade or not. In this study we measure export competitiveness of a firm as its shareof export in total sales. While export competitiveness of a country is related to its comparativeadvantage in producing and selling its products in international markets, tradecompetitiveness of a firm is defined as the ability of the firm to export more in terms of itstotal sales. The capability of a firm to perform in the world market depends largely on itstechnological characteristics along with other micro and macroeconomic factors.Technological progress improves technical efficiency that lowers comparative costs of theexportable. Thus technologically advanced firms are more competitive than the other firms(Posner 1961, Vernon 1966).

2.3 In this study, we have taken firm size, production age of the firm, human capitalemployed, capital intensity and value added along with organisational type and

3 The Heckscher–Ohlin model is a general equilibrium mathematical model of international trade, developedby Eli Heckscher and Bertil Ohlin. One of the theorems of this model states that a country will exportgoods that use its abundant factors intensively, and import goods that use its scarce factors intensively.Bertil Ohlin published the book which first explained the theory in 1933. Heckscher was credited as co-developer of the model, because of his earlier work on the problem, and because many of the ideas in thefinal model came from Ohlin’s doctoral thesis, supervised by Heckscher.

Export Competitiveness and Intensity of Technology in Indian Manufacturing .... 227

manufacturing type of a firm as the major determinant of export competitiveness of thatfirm. Larger firms are generally regarded as more capable of coping with the large investmentsand high risks associated with exporting (Aitken, Hanson and Harrison, 1997). The positiverelation between exports and firm size is expected because of efficiency advantage of largerfirms due to economies of scale. In some studies, however, firm size no longer plays asignificant role beyond a certain threshold level (Wagner, 2001). Firm’s production age isalso significant in analysing exports performance of a firm. Older firm may enjoy betterleverage to compete the world market. But, in some cases, younger firms may be moreflexible, aggressive and proactive when catering to world demand (Lefebvre and Lefebvre,2001). Human capital, measured in this study by the share of engineers and managerialstaffs to total employment, of a firm is expected to have positive effect on exportcompetitiveness. Capital intensity is often treated as a determinant of a firm’s exportperformance. A high capital-intensive firm may have high propensity to export because ofits past innovations. Value added is treated normally as an indicator of productive health ofa firm. More productive firm generates more value added. Thus, a firm with higher valueadded is expected to be more export competitive.

3. Data and construction of variables

3.1 In this study we have used unit level data from the ASI, the main source ofinformation about registered industries, published each year by the Central StatisticalOffice (CSO), Government of India. The CSO has published a separate set of unit levelinformation of ASI compatible for preparing unit level panel and the data are available from1998 to 2012 for each year. However, one has to face a serious problem with this data set inpreparing a balanced panel. The major problem relates to the identification code as recordedin the data set. Some identification code are not matching meaningfully for every rear. Thusthe same factory unit identified by the code does not appear in many years. For this reasonwe have pooled the data by treating individual factories as cross section units. In thisstudy we have pooled the data for four years from 2008 to 2011 because export informationat the factory level are available since 2008. We have kept those factory units which arecurrently operating (status of unit =1, as per the schedule). The number of currently operatingfactory units in the pooled data set are 36020, 39109, 41211 and 41706 for 2008, 2009, 2010and 2011 respectively. Thus, total number of sample observation in our data set is 158047.

3.2 The ex-factory value of output at constant prices is used as a measure of realoutput. ASI reports output data in value terms (Rs. Lakh). The nominal values of output aredeflated by the wholesale price indices at 2004-05 base period for manufactured goods. Wehave used two distinct types of labour inputs: manufacturing workers, and managerial andtechnical employees. Unlike other factors of production, capital is used beyond a singleaccounting period and measuring capital stock is rather problematic. Figures of fixed capitalshown in the ASI schedule include the values of plant and machinery along with othertypes of assets used in production, transportation, living or recreational facilities, hospitals,schools, etc., and are measured in terms of historical prices based on the book value offixed assets. We have used the net closing value of plant and machinery at the end of theyear as capital input. We define relative size of a firm as the total employment in the firmrelative to the total employment in the sector in which the firm lies. Thus, relative size of firmi in textile industry, for example, is total employment in firm i divided by total employment inthe textile sector. Production age of a firm is calculated by taking the difference between the


year of survey and year of initial production. The variable human capital is constructed bytaking the ratio of managerial and technical employees to total number of employees.Capital intensity is defined here as the ratio of values of plant and machinery to total labour.Value added is calculated at the unit level by taking the difference between total sales valueand total input costs. Export share of total sales value, a measure of export competitivenessof a firm, is used as a dependent variable in our estimated model.

4. Econometric methodology

4.1 In exploring the relationship between export competitiveness and the proposeddeterminants, we note that the dependent variable is bounded between zero and one. Thusoften used ordinary least square (OLS) method is not appropriate for assessing thisrelationship. The relationship to be estimated in this study is specified in the followingform:

itq

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(1)

Here, yit = export share to total output of firm i in time t as a proxy for export competitiveness

x1it = relative firm size defined as the number of employees of firm i in time t divided by totalnumber of employee in the sector where firm i belongs.

x2it = human capital in terms of share of engineers and managerial staff to total employee offirm i in time t

x3it = capital intensity measured by capital to labour ratio of firm i in time t

x4it = value added, the difference between total sales value and total value of inputs used,of firm i in time t

x5it = age of firm i in time t, the difference between the survey year and year of initialproduction

D1j = Dummy variable related to year j

D2h = Dummy variable related to organisation type h ,

D3p = Dummy variable related to ownership type p

D4q = Dummy variable related to manufacturing group qit is the random errorAs the variable yit, 10 ity , is to be explained by a set of explanatory variables, thepopulation regression model based on normality assumption as given in (2) rarelyprovides the best description of E(y|x, D).

Export Competitiveness and Intensity of Technology in Indian Manufacturing .... 229

n

qqqp

m

pp

l

hhh

k

jjj DDDD

xxxxxDxyE

143

112

11

55443322110,|

(2)

4.2 The primary reason is that y is bounded between 0 and 1, and so the effect of anyparticular xj cannot be constant throughout the range of x (unless the range of xj is verylimited). The most common alternative to equation (2) has been to model the log-odds ratioas a linear function. If y is strictly between zero and one then a linear model for the log-oddsratio is

q

n

qqp

m

pp

l

hhh

k

jjj DDDD

xxxxxDxyyE

41

311

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(3)

4.3 In equation (3), the log-odds ratio can take on any real value as y varies between0 and 1, so it is natural to model its population regression as a linear function. Nevertheless,there is a potential problem with equation (3). The expected value of log odds as shown inequation (3) cannot be true if y takes on the values 0 or 1 with positive probability.Consequently, given a set of data, if any observation, yi, equals 0 or 1 then an adjustmentmust be made before computing the log-odds ratio. It is possible to estimate E(y|x, D) byassuming beta distribution for y given x and D, and estimating the parameters of theconditional distribution by maximum likelihood. One important limitation of the betadistribution is that it implies that each value in [0,1 ] is taken on with probability zero. Thus,the beta distribution is difficult to justify in applications where at least some portion of thesample is at the extreme values of zero or one.

4.4 To overcome this problem, Papke and Wooldridge (1996) in their seminal paperproposed a fractional response model that extends the generalised linear model with abinomial distribution and a logit link function4 which may be appropriate in fractionaldependent variable. Papke and Wooldridge (2008) developed fractional response modelsfor panel data and use quasi-maximum likelihood estimator (QLME) to obtain a robustmethod for estimating fractional response models without an ad hoc transformation of theboundary values. Hausman and Leonard (1997) applied fractional logit to panel data ontelevision ratings of National Basketball Association games to estimate the effects ofsuperstars on telecast ratings. In using pooled QMLE with panel data, the only extracomplication is in ensuring that the standard errors are robust to arbitrary serial correlation(in addition to misspecification of the conditional variance). Wagner (2003) analyses a largepanel data set of firms to explain the export-sales ratio as a function of firm size byincorporating firm-specific intercepts in the fractional logit model. While including dummiesfor each cross section observation allows unobserved heterogeneity to enter in a flexibleway, it suffers from an incidental parameters problem under random sampling when T (thenumber of time periods) is small and N (the number of cross sectional observations) islarge.

4 The generalized linear model is a flexible generalization of ordinary linear regression that allows forresponse variables that have error distribution models other than a normal distribution.It generalizeslinear regression by allowing the linear model to be related to the response variable via a link function andby allowing the magnitude of the variance of each measurement to be a function of its predicted value.


4.5 In this study we have estimated equation (1) by following Papke and Wooldridge(2008) with pooled data constructed from four independent samples for four different timeperiods (2008, 2009, 2010 and 2011) from the same population. The conditional expectationof the frac-tional response variable is specified as

l

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k

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xxxxxGDxyE

1 1 1432

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55443322110

]

[,|

(4)

In the non-linear fractional response regression, G(.) is specified as a logistic function. Themain advantage of the fractional response model is its ability to capture non-linearity.Typically, G(.) is a distribution function similar to the logistic function -

z

zzGexp1

exp

(5)

The condi-tional likelihood for observation i is

.1log1.log GyGyL ii (6)

The estimated values of the coefficients as shown in equation (1) are obtained bymaximising equation (6).

5. Characteristics of sample firms by industry groups

5.1 Before analysing the export competitiveness of the firms in the registeredmanufacturing sector we need to look at their structural characteristics. Table 1 presentsthe summary view of the major factors likely to explain export competitiveness of the firmsin 2011-12, the terminal year in our sample. The mean export share of the firms in wearingapparel industry was the highest followed by the manufacture of leather products. Theother manufacturing industries exhibiting notable export shares included manufacture ofpharmaceuticals, manufacture of computer, manufacture of textiles and manufacture offabricated metal. Export share was the lowest in beverages, tobacco products and the paperindustry in 2011-12. Thus, the export performance in terms of export shares varied widelyacross the industry groups. The textiles and wearing apparel industries are traditionallyexport led industry in India. In 2006-07 textiles and wearing apparel industries togethercontributed 15 percent to total merchandise exports in India, and the share increased bymore than doubled in 2011-12 (Economic Survey, GOI, 2013). ASI data for 2011-12 alsoindicate that the exports of manufacturing products are led primarily by the private limited,public limited and partnership companies. Moreover, the manufacturing industries runwholly by private ownership exhibited the highest share of exports to their total salesvalue.

5.2 The mean production age of the sample firms varied widely across the differentmanufacturing industry groups. As shown in Table 1, the mean production age in yearswas the highest in tobacco industry exporting only 1 percent of their total sales in 2011-12.

231Export Competitiveness and Intensity of Technology in Indian Manufacturing ....

The average production age of textiles and apparel industries, the leading manufacturingexporters in India, was over 50 years. It is believed that older the manufacturing unit higherwill be its export share because of higher experience in production and innovation. However,the relatively young industries in India, for example leather industry, export higher share oftheir products than tobacco industry. Leather industry exported more than one fourth oftheir products in 2011-12 (Table 1).

5.3 We have calculated relative size of a firm by taking the ratio of firm’s employmentto total employment in the sector in which the lies. The average firm size was the highest inthe repair and installation of machinery, followed by manufactures of furniture and petroleumproducts. Firm size is one of the important factors affecting export performance of the firm.In India, most of the manufacturing firms exporting larger share of their products are smallor moderate in size. Table 1 shows that average size of a firm in textiles, wearing apparelsand leathers was low as compared to many other manufacturing sectors which includerepairing and petroleum. Human capital, measured by the ratio of mangers and technicalpersons to total employee, also varied widely across different manufacturing sectors. Itwas the highest in manufacture of machinery and pharmaceuticals, and the lowest in tobaccoproducts.

5.4 Export intensive industries are normally viewed as technology intensive. Capitalintensity was different in different manufacturing sectors (Table 1). Manufacture ofcomputer, non-metallic products, basic metals, paper, petroleum products, pharmaceuticalsare high capital intensive sectors, while tobacco products, apparel were low capital intensivesectors in registered manufacturing in India. Low or moderate capital intensive sectorsexported relatively larger share of their products as compared to high capital intensivesectors. This kind of findings is consistent with the relative factor abundance in a countrylike India. Labour abundant country enjoys comparative advantage in labour intensivesectors. For this reason industries like wearing apparel and leather are exporting more astheir products are less capital intensive.

6. Empirical results

6.1 We have estimated equation (1) in a framework of the fractional response model asdescribed above. In this model we have considered export share to total output as thedependent variable and firm’s age, firm size, human capital, capital intensity and somedummies as explanatory variables. We have incorporated year dummy yr_11 and yr_12 foryears 2011 and 2012 to look into the time effect compared to the base year 2008 on exportcompetitiveness of manufacturing firms. Export competitiveness may vary across theorganisation type of the firms. In the ASI data, firm’s organisation is of 11 types. We haveregrouped the organisation type to divide them private and public sector organisations.Government organisation is treated as a reference group. We include proprietorship,partnership and limited companies as shown in the ASI schedule in the private sector. Wehave used here three dummies (D_proprietary, D_partnership and D_limited) to capture thedifferences in export performance among firms under private organisation as compared tofirms under government organisation. There are 24 manufacturing groups at the two digitlevel in our data set. We have taken 23 sector dummies corresponding to differentmanufacturing groups to avoid dummy variable trap by treating repairing and installationas a reference industry group.


6.2 The estimated values of the coefficients along with the corresponding odds ratiosof equation (1) are displayed in Table 2. From our regression analysis we can see that firm’sproduction age has negative effect on export competitiveness of a firm. Thus, youngerfirms export larger percentage of their total products as compared to the older firms in theforeign market. This result supports the observed facts as shown in Table 1. Firm’s size hasa significant positive effect on export competitiveness. Larger the size of a firm larger is itsexport share to total output. Large firms enjoy scale effect and thus they have morecomparative cost advantage than the smaller firm. This result supports the findings ofWagner (2003). Human capital of a firm also has a positive effect on its export share asexpected. But, in Indian manufacturing industries, scale effect is higher than the effect ofhuman capital. Capital intensity, on the other hand, of the firm has no significant effect onexport competitiveness of the firm. India is a labour abundant country and the manufacturingfirms of the country exports largely labour intensive commodities by following the rule ofcomparative advantage.

6.3 The year dummies included in the estimated equation capture the time effect onexport competitiveness. The sign of the coefficients for year dummies (yr_11, yr_12) aresignificantly negative implying that the mean export share of the manufacturing firms, othereffects remain zero, declined in 2011 and further to 2012 from its value in 2008. Thus, exportcompetitiveness of the firms in registered manufacturing sector declined in the year of 2011and 2012 as compared to 2008. We also observe that most of the coefficients correspondingto sector dummies are statistically significant. Export performance of the firms in manufactureof apparel, leather and food products performed better than the firms in other sectors inmanufacturing industries. However, the export performance of beverages, tobacco productsand paper was less than even the repairing sector. Textile, wearing apparel and othermanufacturing industries consisting jewellery, sports goods, medical instruments industryperformed better than the firms in repair and installation of machinery industry.

6.4 The estimated values of the odds ratio also provide the similar inferences onexport competitiveness. Odds ratios are greater than 1 for those industries whose coefficientvalues are positive and industries having negative coefficient value, have less than 1 oddsratio as well that seems probability of decreasing export share is higher over probability ofincreasing exporting share.

6.5 The export competitiveness of the manufacturing industries is not affected similarly.To find out the differential effects we have estimated the effects of firm specific factors asmentioned above on export competitiveness for the leading exporting industries by usingthe unit level information only for 2011-12. The selected major exporting industries includemanufacturing of food products, manufacturing of textiles, manufacturing of wearing apparel,manufacturing of leather and related products, manufacturing of chemical and chemicalproducts, manufacturing of pharmaceuticals, manufacturing of fabricated products andmanufacturing of computer, electronic and optical products industry. The estimated resultsare shown in Table 1. We observe that the effects of firms’ age, firm size, human capital andcapital intensity are not similar for all industries.

6.6 Firms’ production age has negative effect on export share in food products andtextile industry, but positive effect in pharmaceuticals. Thus, in the case of both foodproducts and textile industries the export share of older firms is less than the young firms.


But, for pharmaceutical industry the older firms export more than the new firms. However,there is no significant effect of firms’ production age on export shares of the firms in themanufacturing of leather, chemicals, metal products and computer. Size of the firm haspositive and significant impact on export share for all the industries as shown in Table 3.Larger the size, larger is the share of export to firm’s output, but at different rates across theindustry groups. The size effect is the highest in food products industry followed bytextiles and metal products industries. The effect of human capital on firm’s export isstatistically significant in most of the industries. While the effect is favourable to firm’sexport share in textile, apparel and chemical industries, it is negative for leather andpharmaceutical industries. Human capital has no significant role in export performance forfood products industry. Capital intensity has negative significant role in export share at thefirm level in food products and textile industry, but it has a positive role in pharmaceuticalindustry. The firms under individual proprietorship, partnership and limited companiesexported more than the firms in the public sector for the major exporting manufacturinggroups. The share of export has increased more for individual proprietorship, followed bypartnership and limited companies for food products industry, textiles industry and leatherand related product industry.

7. Summary and conclusions

7.1 This study investigates how export competitiveness of the manufacturing firms isaffected by the firm specific characteristics with unit level ASI data during the period 2008-2012. We have taken firm size, production age of the firm, human capital employed, capitalintensity and value added along with organisational type and manufacturing type of a firmas the major determinant of export competitiveness of that firm. Export competitiveness ismeasured by the fraction of total output exported by a firm. The generalised linear modelwith a binomial distribution and a logit link function is used in estimating the regressionrelationship with pooled data constructed from four independent samples for four differenttime periods (2008, 2009, 2010 and 2011) from the same population.

7.2 The average export share of a firm in manufacture of wearing apparel was thehighest followed by the manufacture of leather products. The other sectors withinmanufacturing showing notable export shares at the firm level included manufacture ofpharmaceuticals, manufacture of computer, manufacture of textiles and manufacture offabricated metal. Low or moderate capital intensive sectors exported relatively larger shareof their products as compared to high capital intensive sectors. This kind of findings areconsistent with the relative factor abundance in a country like India. Labour abundantcountry enjoys comparative advantage in labour intensive sectors.

7.3 In the pooled regression model, firm’s production age has negative effect onexport competitiveness of a firm. Thus younger firms performed better than the older firmsin the foreign market. Firm size has a significant positive effect on firm’s exportcompetitiveness. Larger the size of a firm larger will be the export share to total output.Larger firms enjoy scale effect and thus they have more comparative cost advantage thanthe smaller firm. Human capital affected favourably the export competitiveness ofmanufacturing firms as expected. But, the effect of human capital was less than the effect offirm size on export share of firm’s production. Capital intensity, on the other hand, of thefirm has no significant effect on export competitiveness of the firm. Export performance of


the firms in manufacture of apparel, leather and food products performed better than thefirms in other sectors in manufacturing industries.

7.4 This paper also analyses the effects of firm specific factors on the export performanceof firms in selected Indian manufacturing industries. Younger firms in food products andtextiles performed, while older firms in pharmaceuticals exported greater proportion of theirproducts. The size effect was the highest in food products followed by textiles and metalproducts. The firms under individual proprietorship, partnership and limited companiesexported more than the firms in the public sector. The findings of this study are based onoutput share to export by the manufacturing firms at the two digit level. The particularproducts that dominate in merchandise trade need to be studied further in depth at thedisaggregated level to understand some other relevant issues relating to exportcompetitiveness in the context of trade liberalisation in India.

References

Aitken, B., G. Hanson and A. Harrison (1997), Spillovers, foreign investment, and exportbehaviour, Journal of International Economics, 43(1-2): 103-132.

Hausman, J.A., Leonard, G.K., (1997). Superstars in the national basketball association:Economic value and policy, Journal of Labor Economics, 15: 586–624.

Kaldor, N. (1966). Causes of the Slow Rate of Growth in the UK, Cambridge UniversityPress, London reprinted in Targetti and Thirlwall (1989)

Kallummal, Murali (2006), NonAgricultural Market Access Negotiations: Real Concerns ,Social Scientist, 34 (910), SeptemberOctober 2006.

Lefebvre, E. and L. Lefebrve (2001), “Innovative Capabilities as Determinants of ExportPerformance and Behaviour: A longitudinal Study of Manufacturing SMEs” in Innovationand Firm Performance: Econometric Explorations of Survey Data, Palgrave (MacMillanPress) London at Basingstoke.

Michael E. Porter, Christian Ketels and Mercedes Delgado (2008), The MicroeconomicFoundations of Prosperity: Findings from the Business Competitiveness Index, The GlobalCompetitiveness Report 2007- 2008,

http://siteresources.worldbank.org/EXTEXPCOMNET/Resources/24635931213989126859/03_Porter_GCI_ch1&2.pdf.

Papke, L. E. and J. Wooldridge (1996), Econometric Methods for Fractional Response Variablewith an Application to 401 (K) Plan Participation Rates, Journal of Applied Econometrics,11(6): 619-632.

Papke, L. E., and Wooldridge, J. (2008). Panel data methods for fractional response variableswith an application to test pass rates. Journal of Economet-rics, 145(1-2): 121-133.

Posner, M. V. (1961), International Trade and Technical Change, Oxford Economic Paper,13: 323-341.


Vernon, R. (1966), International Investment and International Trade in the Product Cycle,Quarterly Journal of Economics, 80: 190-207.

Wagner, J. (2001), A Note on Firm Size—Export Relationship, Small Business Economics 17:229-237.

Wagner, J., (2003). Unobserved firm heterogeneity and the size-exports nexus: Evidencefrom German panel data, Review of World Economics, 139: 161–172.


Table 1 Characteristics of sample firms by industry groups: 2011-12

Industry group

Fraction of

output exported

Average age of factory units

Average size*

Human capital**

Capital intensity***

Manufacture of food products 0.04 59.31 0.01 0.11 14.08 Manufacture of beverages 0.01 48.03 0.04 0.11 16.02 Manufacture of tobacco products 0.01 78.66 0.04 0.08 3.53 Manufacture of textiles 0.06 53.94 0.01 0.09 10.28 Manufacture of wearing apparel 0.32 50.66 0.01 0.09 3.72 Manufacture of leather products 0.26 39.39 0.03 0.10 6.28 Manufacture of wood 0.02 49.26 0.02 0.13 7.46 Manufacture of paper 0.01 48.92 0.02 0.12 21.63 Manufacture of printing 0.02 65.36 0.03 0.14 7.89 Manufacture of petroleum products 0.02 48.40 0.08 0.15 17.02 Manufacture of chemical products 0.04 51.09 0.01 0.14 15.76 Manufacture of pharmaceuticals 0.08 60.33 0.02 0.16 13.57 Manufacture of rubber 0.03 43.63 0.01 0.13 14.54 Manufacture of non-metallic products 0.02 44.98 0.01 0.11 22.31 Manufacture of basic metals 0.02 47.93 0.01 0.12 21.94 Manufacture of fabricated metal 0.06 50.34 0.01 0.13 9.15 Manufacture of computer 0.07 54.90 0.03 0.15 36.26 Manufacture of electrical equipment 0.03 52.28 0.02 0.14 14.12 Manufacture of machinery 0.04 46.30 0.01 0.16 5.68 Manufacture of motor vehicles 0.04 45.67 0.02 0.12 15.42 Manufacture of transport equipment 0.03 51.40 0.04 0.12 6.07 Manufacture of furniture 0.04 36.24 0.09 0.13 6.94 Other manufacturing 0.21 31.67 0.03 0.12 5.12 Repair and installation of machinery 0.01 34.87 0.12 0.15 3.76

Note: *Relative size of a firm by taking the ratio of firm’s employment to total employment in thesector in which the lies; **Human capital is constructed by taking the ratio of managerial and technicalemployees to total number of employees; ***Capital intensity is defined as the ratio of values of plantand machinery to total labour employment.Source: Authors’ estimation with ASI unit level data for 2011-12


Table 2 QMLE of the fractional response model for all manufacturing units

variables Coefficients Odds ratio

Z statistic P>z

Intercept -4.50 0.01 -8.12 0.00 log (age) -0.11 0.89 -8.73 0.00 log (size) 0.42 1.52 36.14 0.00 log (human capital) 0.08 1.08 4.83 0.00 log (capital intensity) 0.01 1.01 1.17 0.244 yr_11 -0.15 0.86 -4.45 0.00 yr_12 -0.08 0.93 -2.29 0.022 D_proprietary 1.49 4.42 9.34 0.00 D_partnership 1.54 4.65 9.84 0.00 D_limited_company 1.15 3.16 7.47 0.00 D_owner_joint 0.91 2.47 2.51 0.012 D_owner_private 1.43 4.17 4.45 0.00 D_food 2.08 8.02 4.74 0.00 D_beverage -0.95 0.39 -1.8 0.071 D_tobacco -0.29 0.75 -0.56 0.576 D_textile 2.47 11.86 5.64 0.00 D_apparel 4.05 57.27 9.25 0.00 D_leather 3.38 29.35 7.71 0.00 D_wood 0.41 1.51 0.91 0.364 D_paper -0.08 0.92 -0.18 0.858 D_print 0.11 1.12 0.24 0.81 D_petro 0.15 1.16 0.29 0.775 D_chemical 1.68 5.36 3.82 0.00 D_pharma 1.83 6.24 4.16 0.00 D_ruber 1.10 3.02 2.49 0.013 D_nonmetal 0.93 2.54 2.11 0.035 D_basic_metal 1.15 3.17 2.61 0.009 D_fabri_metal 2.04 7.68 4.65 0.00 D_computer 1.67 5.33 3.78 0.00 D_elec_equipment 1.07 2.93 2.42 0.015 D_machinary 1.55 4.71 3.52 0.00 D_motor 1.24 3.47 2.81 0.005 D_transport 0.63 1.88 1.38 0.168 D_furniture 0.40 1.50 0.84 0.399 D_other_manu 3.05 21.03 6.94 0.00

Source: Authors’ estimation with ASI unit level data (2008-2011)


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SECTION II

Page No.

• Selected Economic Indicators of 243Manufacturing Sector of India : Table1

• Employment by Industry Division in 246Manufacturing Sector : Table 2

• Employment by Industry Group in 247Manufacturing Sector : Table 3

• All India ASI Data Based on Units with 249100 and more Employees : Table 4

• 2-digit NIC Division and Description 252

• New Initiatives in Annual Survey of Industries (ASI) 254

SECTION II 241

Table 1: Selected Economic Indicators by 2-digit Industry Div. based on ASI 2012-13and 2013-14

NIC-2008 Description

Value Added per Person Engaged

(Rs. Lakh)

Capital Productivity

Div. 2012-13 2013-14 2012-13 2013-14 01* Crop and animal production, hunting and related

service activities 4.00 8.45 0.84 1.54 08** Other mining and quarrying 3.57 4.09 0.98 1.01 10 Manufacture of food products 3.63 3.79 0.42 0.42 11 Manufacture of beverages 8.66 7.47 0.49 0.44 12 Manufacture of tobacco products 2.87 2.70 2.26 2.16 13 Manufacture of textiles 3.32 3.06 0.39 0.23 14 Manufacture of wearing apparel 1.89 2.31 0.72 1.17 15 Manufacture of leather and related products 1.99 2.45 0.78 0.88 16 Manufacture of wood and of products of wood and

cork, except furniture; manufacture of articles of straw and plaiting materials 2.87 3.05 0.47 0.50

17 Manufacture of paper and paper products 3.19 4.42 0.18 0.23 18 Printing and reproduction of recorded media 4.81 3.62 0.58 0.41 19 Manufacture of coke and refined petroleum products 113.11 92.91 0.70 0.53 20 Manufacture of chemicals and chemical products 12.10 11.20 0.52 0.50 21 Manufacture of basic pharmaceutical products and

pharmaceutical preparations 10.45 10.54 0.80 0.78 22 Manufacture of rubber and plastics products 4.71 6.22 0.42 0.51 23 Manufacture of other non-metallic mineral products 4.83 4.06 0.21 0.27 24 Manufacture of basic metals 7.81 11.90 0.16 0.22 25 Manufacture of fabricated metal products, except

machinery and equipment 5.10 4.40 0.69 0.56 26 Manufacture of computer, electronic and optical

products 8.39 9.92 0.83 0.59 27 Manufacture of electrical equipment 7.20 6.91 0.76 0.77 28 Manufacture of machinery and equipment n.e.c. 8.96 7.52 0.97 0.75 29 Manufacture of motor vehicles, trailers and semi-

trailers 7.46 5.70 0.46 0.31 30 Manufacture of other transport equipment 7.54 7.94 0.50 0.63 31 Manufacture of furniture 4.04 4.00 0.42 0.81 32 Other manufacturing 4.86 4.95 1.24 1.14 33 Repair and installation of machinery and equipment 5.76 6.43 0.57 0.60 38 Waste collection, treatment and disposal activities;

materials recovery 3.13 3.23 0.22 0.23 58 Publishing activities 11.60 11.24 0.74 0.88 Others Other Industries 9.27 9.60 0.13 0.14 Total 6.58 6.61 0.39 0.38

Value Added per Person Engaged: Net Value Added / Total Number of Persons EngagedCapital Productivity: Net Value Added / Fixed Capital*01: Includes only post harvest crop activities (0163) and seed processing for propagation (0164)**08: Includes only the activities of extraction of salt (0893)

Selected Economic Indicators of Manufacturing Sector of India : Table 1 243

All India

Table 1 (cntd.): Selected Economic Indicators by 2-digit Industry Div. based on ASI2012-13 and 2013-14


Ratio of Total Output to Total Input

Output per Person Engaged

(Rs. Lakh) Div. 2012-13 2013-14 2012-13 2013-14 01* Crop and animal production, hunting and related

service activities 1.09 1.12 55.58 86.04 08** Other mining and quarrying 1.98 2.36 7.87 7.84 10 Manufacture of food product s 1.11 1.10 45.13 48.88 11 Manufacture of beverages 1.38 1.31 36.80 38.54 12 Manufacture of tobacco products 1.64 1.57 7.67 7.83 13 Manufacture of textiles 1.23 1.19 22.02 24.90 14 Manufacture of wearing apparel 1.27 1.24 9.80 12.77 15 Manufacture of leather and related products 1.19 1.22 14.46 15.29 16 Manufacture of wood and of products of wood and

cork, except furniture; manufacture of articles of straw and plaiting materials 1.16 1.16 24.23 26.89

17 Manufacture of paper and paper products 1.18 1.21 29.12 33.89 18 Printing and reproduction of recorded media 1.39 1.30 20.34 19.66 19 Manufacture of coke and refined petroleum products 1.14 1.11 1056.73 1013.49 20 Manufacture of chemicals and chemical products 1.23 1.22 74.64 72.54 21 Manufacture of basic pharmaceutical products and

pharmaceutical preparations 1.48 1.51 35.77 34.69 22 Manufacture of rubber and plastics products 1.19 1.25 36.02 36.47 23 Manufacture of other non-metallic mineral products 1.38 1.36 21.90 19.89 24 Manufacture of basic metals 1.15 1.20 75.54 87.89 25 Manufacture of fabricated metal products, except

machinery and equipment 1.29 1.25 25.53 25.39 26 Manufacture of computer, electronic and optical

products 1.25 1.24 48.72 56.16 27 Manufacture of electrical equipment 1.23 1.21 42.60 44.25 28 Manufacture of machinery and equipment n.e.c. 1.35 1.30 37.93 36.42 29 Manufacture of motor vehicles, trailers and semi -

trailers 1.22 1.19 52.07 49.99 30 Manufacture of other transport equipment 1.23 1.23 45.13 48.13 31 Manufacture of furniture 1.25 1.21 22.55 25.32 32 Other manufacturing 1.10 1.09 59.09 64.53 33 Repair and installation of machinery and equipment 1.32 1.53 27.35 21.60 38 Waste collection, treatment and disposal activities;

materials recovery 1.08 1.09 56.29 57.01 58 Publishing activities 1.82 1.84 28.96 27.34 Others Other Industries 1.24 1.24 67.18 66.83 Total 1.20 1.19 46.53 48.42

Ratio of Total Output to Total Input: Total Output / Total InputOutput per Person Engaged: Total Output / Total Number of Persons Engaged*01: Includes only post harvest crop activities (0163) and seed processing for propagation (0164)**08: Includes only the activities of extraction of salt (0893)


All India

Table 1 (cntd.): Selected Economic Indicators by 2-digit Industry Div. based on ASI2012-13 and 2013-14


Wage Rate (Rs.) Direct Workers Contract Workers

Div. 2012-13 2013-14 2012-13 2013-14 01* Crop and animal production, hunting and related

service activities 60156 68488 54079 67195 08** Other mining and quarrying 67816 71085 52098 61694 10 Manufacture of food products 84221 100880 72414 87984 11 Manufacture of beverages 126262 137867 85886 96142 12 Manufacture of tobacco products 60771 35187 27143 33838 13 Manufacture of textiles 90264 102609 84814 92250 14 Manufacture of wearing apparel 82158 87576 74691 95402 15 Manufacture of leather and related products 77329 89654 80167 85055 16 Manufacture of wood and of products of wood and

cork, except furniture; manufac ture of articles of straw and plaiting materials 81951 82894 81879 84114

17 Manufacture of paper and paper products 112952 126302 88591 96810 18 Printing and reproduction of recorded media 138319 144437 89665 109488 19 Manufacture of coke and refined pe troleum products 458864 575551 164672 132431 20 Manufacture of chemicals and chemical products 151922 168179 94856 101720 21 Manufacture of basic pharmaceutical products and

pharmaceutical preparations 164286 163585 92215 106042 22 Manufacture of rubber and plastics products 110322 127009 103320 97045 23 Manufacture of other non-metallic mineral products 101503 107958 65207 69291 24 Manufacture of basic metals 208229 247700 110898 119423 25 Manufacture of fabricated metal products, except

machinery and equipment 131121 137340 93002 98055 26 Manufacture of computer, electronic and optical

products 165576 217647 112676 116739 27 Manufacture of electrical equipment 167342 177993 94626 108464 28 Manufacture of machinery and equipment n.e.c. 172302 179267 115515 116383 29 Manufacture of motor vehicles, trailers and semi -

trailers 190880 207443 97859 110292 30 Manufacture of other transport equipment 160359 187909 109738 120293 31 Manufacture of furniture 122636 122032 94777 106126 32 Other manufacturing 116781 134923 94109 137527 33 Repair and installation of machinery and equipment 248033 282157 116839 115138 38 Waste collection, treatment and disposal activities;

materials recovery 97378 98254 90496 90814 58 Publishing activities 200898 207589 116658 153360 Others Other Industries 122851 132349 101804 126977 Total 123216 132929 85590 97774

Wage Rate (Direct Workers): Wages & Salary to Direct Workers / No. of Direct WorkersWage Rate (Contract Workers): Wages & Salary to Contract Workers / No. of Contract Workers*01: Includes only post harvest crop activities (0163) and seed processing for propagation (0164)**08: Includes only the activities of extraction of salt (0893)

Selected Economic Indicators of Manufacturing Sector of India : Table 1 245

All India

Table 2: Estimates and Proportions of the Employed by Type of Employment at 2-digitIndustry Div. (NIC-2008) based on ASI 2013-14 (All India).

NIC-2008

Factories in

Operation (no.)

Total Persons Engaged

(no.)

Total Workers

(no.)

Directly Emp.

Workers (no.)

Contract Workers

(no.)

Total Workers

(%)

Contract Workers

(%)

(1) (2) (3) (4) (5) (6) (7) (8) 01* 3153 97567 73096 45607 27489 74.92 37.61

08** 160 9072 8072 2525 5548 88.98 68.73 10 30130 1582527 1232679 897515 335165 77.89 27.19 11 1936 158507 121346 59186 62160 76.56 51.23 12 2452 444942 425799 327823 97976 95.70 23.01 13 13379 1496194 1267670 1057406 210264 84.73 16.59 14 7047 978709 823850 720475 103375 84.18 12.55 15 3330 311594 266153 209622 56531 85.42 21.24 16 3714 78981 60034 42704 17330 76.01 28.87 17 5626 248529 193026 134984 58042 77.67 30.07 18 3717 156988 100708 79477 21231 64.15 21.08 19 1327 109964 81641 39863 41778 74.24 51.17 20 9710 708401 494253 296487 197766 69.77 40.01 21 4315 618493 381664 198152 183512 61.71 48.08 22 10447 591001 466790 293938 172852 78.98 37.03 23 22089 970367 788819 308007 480813 81.29 60.95 24 9732 976196 748923 409285 339638 76.72 45.35 25 13685 678906 523947 302043 221904 77.18 42.35 26 2167 222987 152710 100069 52641 68.48 34.47 27 6191 513943 370071 219418 150653 72.01 40.71 28 10062 647199 441733 292959 148773 68.25 33.68 29 4908 792885 604693 343496 261196 76.26 43.19 30 1925 283498 223641 115233 108408 78.89 48.47 31 1212 60368 44394 29243 15151 73.54 34.13 32 2755 274129 212561 171255 41306 77.54 19.43 33 669 34906 24371 16383 7988 69.82 32.78 38 301 14202 10954 6326 4629 77.13 42.26 58 226 24243 11905 7422 4483 49.11 37.66

Others 9325 452816 288900 207319 81582 63.80 28.24 Total 185690 13538114 10444404 6934221 3510184 77.15 33.61

*01: Includes only post harvest crop activities (0163) and seed processing for propagation (0164)**08: Includes only the activities of extraction of salt (0893)


Employment by Industry Division in Manufacturing Sector : Table 3

Table 3: Estimates and Proportions of the Employed by type of Employment at 3-digitIndustry Group (NIC-2008), based on ASI 2013-14 (All India).

247

NIC-2008

Factories in Operation

(no.) Total Persons Engaged (no.)

Total Workers

(no.)

Directly Emp.

Workers (no.)

Contract Workers

(no.)

Total Workers

(%)

Contract Workers (%)

(1) (2) (3) (4) (5) (6) (7) (8) 016 3153 97567 73096 45607 27489 74.92 37.61 089 160 9072 8072 2525 5548 88.98 68.73 101 145 25607 19945 11007 8938 77.89 44.81 102 403 44178 37226 25974 11252 84.26 30.23 103 998 58331 45924 24406 21519 78.73 46.86 104 2654 107623 83115 54988 28127 77.23 33.84 105 1652 145601 107988 63412 44576 74.17 41.28 106 15703 319659 238365 147677 90688 74.57 38.05 107 7828 836742 668302 548845 119457 79.87 17.87 108 748 44786 31814 21205 10609 71.04 33.35 110 1936 158507 121346 59186 62160 76.56 51.23 120 2452 444942 425799 327823 97976 95.70 23.01 131 10060 1236688 1053632 891441 162191 85.20 15.39 139 3320 259506 214037 165965 48072 82.48 22.46 141 4320 713833 598482 510013 88469 83.84 14.78 142 6 615 499 311 188 81.14 37.68 143 2722 264261 224869 210151 14718 85.09 6.55 151 1628 104532 88941 58776 30164 85.08 33.91 152 1703 207062 177213 150846 26367 85.58 14.88 161 1188 8738 6091 5612 479 69.71 7.86 162 2526 70244 53944 37093 16851 76.80 31.24 170 5626 248529 193026 134984 58042 77.67 30.07 181 3699 156430 100441 79210 21231 64.21 21.14 182 18 558 267 267 0 47.85 0.00 191 567 28876 22733 15830 6903 78.73 30.37 192 760 81088 58908 24033 34875 72.65 59.20 201 3766 279851 198967 103404 95563 71.10 48.03 202 5788 399828 272518 174868 97650 68.16 35.83 203 156 28722 22768 18216 4553 79.27 20.00 210 4315 618493 381664 198152 183512 61.71 48.08 221 2230 218754 174510 104351 70159 79.77 40.20 222 8217 372247 292281 189588 102693 78.52 35.14 231 611 61978 50650 27865 22786 81.72 44.99 239 21478 908389 738169 280142 458027 81.26 62.05 241 4768 650680 499885 271286 228599 76.83 45.73 242 1336 107454 82764 39652 43112 77.02 52.09 243 3628 218062 166275 98347 67928 76.25 40.85 251 3843 230178 172830 90558 82272 75.09 47.60 252 75 2153 1426 1131 295 66.23 20.69 259 9768 446576 349691 210355 139337 78.30 39.85 261 974 76619 55266 40503 14763 72.13 26.71

262 113 24594 15515 8834 6682 63.08 43.07263 256 41562 27164 15521 11643 65.36 42.86264 191 26327 19817 10449 9368 75.27 47.27265 484 45892 29183 20792 8392 63.59 28.76266 94 6003 4225 2502 1723 70.38 40.78267 53 1951 1512 1451 61 77.50 4.03268 3 39 28 18 10 71.79 35.71271 2342 198774 132980 84701 48278 66.90 36.30272 373 43871 35374 21589 13786 80.63 38.97

Table 3: Estimates and Proportions of the Employed by type of Employment at 3-digitIndustry Group (NIC-2008), based on ASI 2013-14 (All India).

For NIC 2008 detailed description, may visit the URL given below:http://mospi.nic.in/Mospi_New/site/inner.aspx?status=2&menu_id=129http://www.csoisw.gov.in/CMS/En/1026-national-industrial-activity-classification.aspx


NIC-2008

Factories in

Operation (no.)

Total Persons Engaged

(no.)

Total Workers

(no.)

Directly Emp.

Workers (no.)

Contract Workers

(no.)

Total Workers

(%)

Contract Workers

(%) (1) (2) (3) (4) (5) (6) (7) (8) 273 1219 95439 73573 36696 36876 77.09 50.12 274 416 52601 42682 21677 21005 81.14 49.21 275 845 53623 36427 24143 12284 67.93 33.72 279 996 69635 49036 30612 18424 70.42 37.57 281 4828 338964 235526 154078 81448 69.48 34.58 282 5235 308235 206206 138881 67325 66.90 32.65 291 163 176523 120786 84899 35887 68.43 29.71 292 510 50210 37712 19533 18179 75.11 48.20 293 4235 566153 446194 239064 207130 78.81 46.42 301 106 28018 22963 5623 17340 81.96 75.51 302 289 27252 20578 12420 8158 75.51 39.64 303 85 10864 7335 5197 2138 67.52 29.15 304 36 654 480 214 266 73.39 55.42 309 1409 216710 172287 91781 80506 79.50 46.73 310 1212 60368 44394 29243 15151 73.54 34.13 321 1170 157630 122188 104480 17708 77.52 14.49 322 11 269 214 203 11 79.55 5.14 323 151 8540 6491 6367 124 76.01 1.91 324 52 2162 1727 1396 331 79.88 19.17 325 450 41540 31083 24860 6224 74.83 20.02 329 921 63989 50858 33949 16909 79.48 33.25 331 598 29790 20857 14569 6288 70.01 30.15 332 71 5115 3514 1814 1700 68.70 48.38 381 24 799 550 108 442 68.84 80.36 382 104 7855 5958 2878 3080 75.85 51.70 383 173 5548 4446 3339 1107 80.14 24.90 581 226 24243 11905 7422 4483 49.11 37.66

Others 9325 452816 288900 207319 81582 63.80 28.24 Total 185690 13538114 10444404 6934221 3510184 77.15 33.61

All India ASI Data Based on Units with 100 and more Employees : Table 4 249

Table 4: Selected Characteristics of Factory Sector (100 and more employees) by 2-digit Industry Div. (NIC-2008) for all-India based on ASI 2013-14

(Values in Rs. Lakh unless otherwise mentioned)

Table 4 (cntd.): Selected Characteristics of Factory Sector (100 and more employees)by 2-digit Industry Div. (NIC-2008) for all-India based on ASI 2013-14


Characteristics 2-Digit Industry Div: NIC-2008

14 15 16 17 18 19 20 21 1 Number of Factories (no.) 2722 906 106 486 462 176 1599 1382 2 Fixed Capital 1512157 609303 277319 3780032 863537 18615019 14334216 7478162 3 Physical Working Capital 1445138 642874 237753 1052386 309841 10569476 5710965 3794790 4 Working Capital 1106052 335819 142686 437065 -845582 -327066 4505498 6528515 5 Invested Capital 2957296 1252177 515072 4832419 1173378 29184495 20045180 11272953 6 Rent Paid 80327 28341 2572 10269 18395 25787 71469 91615 7 Outstanding Loan 1024123 181698 172246 1378747 312657 4032360 3642547 2355999 8 Interest Paid 174253 63078 32156 204314 59276 674734 969121 356551 9 Rent Received 5671 2847 193 3120 13278 2337 10593 7806 10 Interest Received 22174 6362 2292 17571 17255 101992 115417 222642 11

Gross Value of Plant & Machinery 895964 408519 249320 4161690 904059 21714769 20443539 6567210

12 Value of Product and By -Product 8487471 3071844 806514 5415134 1174628 1.08E+08 40119388 16655118 13 Total Output 9594126 3412651 1033934 5692067 1957918 1.09E+08 43394210 18847709 14 Fuels Consumed 168733 70932 37270 686603 50471 1564697 3308560 715118

Characteristics Total ASI 2-Digit Industry Div: NIC-2008 All 01 08 10 11 12 13

1 Number of Factories (no.) 224576 30411 194 34 4116 439 423 3079 2 Fixed Capital 237371903 208361832 150111 24732 10650139 2277802 474282 18120322 3 Physical Working Capital 101083632 81019014 372196 8497 11469139 832018 603790 5446129 4 Working Capital 66268577 49251115 280356 7891 5370752 454417 501172 4065888 5 Invested Capital 338455535 289380846 522308 33229 22119278 3109820 1078072 23566451 6 Rent Paid 1527272 1189913 3354 379 91156 20215 5865 29934 7 Outstanding Loan 122209355 99261824 58096 12456 6264305 2438807 157728 6965493 8 Interest Paid 15485061 12142216 14331 3580 1170016 121118 34882 1083152 9 Rent Received 297051 160052 62 1 15075 2177 1741 11559

10 Interest Received 2333143 1924011 2403 222 152578 16621 4165 87427 11 Gross Value of Plant & Machinery 216613848 197718577 120921 18267 9934541 2054142 536456 14478402 12 Value of Product and By-Product 570344878 473444346 1557075 38396 45936682 4748083 2463135 25461418 13 Total Output 655525116 529202509 1768217 45135 52610598 5265185 2627958 29800805 14 Fuels Consumed 29850770 24778180 27502 2532 1364635 217940 27986 2345082 15 Materials Consumed 423046161 346135179 898246 3861 38268828 3084257 1157512 18724824 16 Total Input 549013952 438598334 1170075 18827 47549652 4061648 1529846 24817955 17 Gross Value Added 106511164 90604175 598141 26309 5060946 1203536 1098112 4982850 18 Depreciation 16976977 14384044 17001 1766 838492 222318 52478 1092704 19 Net Value Added 89534187 76220130 581141 24542 4222454 981218 1045634 3890147 20 Net Fixed Capital Formation 18396832 16670420 -2475 -1391 590633 89391 35613 356941 21 Gross Fixed Capital Formation 35373809 31054465 14526 375 1429126 311709 88091 1449645 22 Total workers (no.) 10444404 7864151 22615 5972 854331 102868 395152 1054630 23 Total Persons Engaged (no.) 13538114 10047793 29916 6538 1075519 132841 405786 1220337 24 Wages to Workers 12649644 10406101 17105 3630 943050 124699 133501 1091596 25 Emoluments to Employees 27241503 22564407 42125 5074 1816224 293307 180925 1696404 26 Gross Capital Formation 42184321 36009096 50748 1163 1558456 287260 294312 1766304 27 Income 75152048 64972064 565921 20807 3128935 858683 1010793 2876047 28 Profit 43956552 38932357 520651 15165 1079833 526057 806663 963744



14 15 16 17 18 19 20 21 15 Materials Consumed 4770980 2159394 548893 3445541 1048684 93049596 26909998 8719972 16 Total Input 7644390 2767299 865866 4614398 1488745 97490390 35131691 12191811 17 Gross Value Added 1949736 645352 168068 1077669 469174 11092566 8262519 6655897 18 Depreciation 143759 65821 31960 270087 92843 1173626 1229718 629659 19 Net Value Added 1805978 579531 136108 807582 376331 9918940 7032801 6026239 20 Net Fixed Capital Formation 142026 40323 -7339 199615 3359 2743409 998330 653634 21 Gross Fixed Capital Formation 285784 106144 24621 469702 96202 3917035 2228048 1283293 22 Total workers (no.) 706701 217539 25045 111127 49093 66204 366982 327262 23 Total Persons Engaged (no.) 829035 250524 31516 140204 84068 531500 534997 24 Wages to Workers 624468 196838 25999 159817 80789 587258 460377 25 Emoluments to Employees 1052448 321334 53119 308113 237867 1508995 1611329 26 Gross Capital Formation 431021 177669 43759 628135 150155 2530557 1730207 27 Income 1579243 497321 103865 613689 329193 6118221 5808520 28 Profit 407024 133856 45762 247715 63868

86480 260238 541556

4545229 9322749 8641095 4317906 3978498

Table 4 (cntd.): Selected Characteristics of Factory Sector (100 and more employees)by 2-digit Industry Div. (NIC-2008) for all-India based on ASI 2013-14

Table 4(cntd.): Selected Characteristics of Factory Sector (100 and more employees) by2-digit Industry Div.(NIC-2008) for all-India based on ASI 2013-14



22 23 24 25 26 27 28 29 1 Number of Factories (no.) 1347 2027 1624 1670 505 1190 1399 1551 2 Fixed Capital 5058511 13138496 51314439 3902754 3412570 3837391 5225966 13481274 3 Physical Working Capital 2170564 2649380 12653924 2587973 1717391 3162133 3630699 4026996 4 Working Capital 1877303 1951120 8319590 1180842 2222065 3276626 3419393 -395651 5 Invested Capital 7229075 15787876 63968363 6490727 5129960 6999524 8856665 17508270 6 Rent Paid 32598 67790 63691 21501 117018 62491 76595 88960 7 Outstanding Loan 2364387 5351313 32027904 6063797 1636511 1773528 2263296 5973063 8 Interest Paid 308914 553128 2697954 300859 207062 439908 404602 556136 9 Rent Received 4334 3935 11415 6830 5960 3188 10458 20589 10 Interest Received 32115 63162 340099 42936 111722 59437 99394 156100 11

Gross Value of Plant & Machinery 5459700 14173527 41267382 3311245 2483911 3834864 4710787 14577610

12

Value of Product and By -Product 14439797 14227439 59804584 10219127 7543721 15993952 16908258 34513237

13 Total Output 15274177 15275496 66187068 12012821 10724839 17695748 19196078 37445852 14 Fuels Consumed 677356 3372715 5952457 411287 109686 273125 299947 2244668 15 Materials Consumed 10147691 5862904 39564972 6738834 5719666 11561920 11209685 24939964 16 Total Input 12139754 11064810 53096720 9546536 8678922 14271650 14618946 31659415 17 Gross Value Added 3134423 4210686 13090348 2466285 2045917 3424099 4577132 5786437 18 Depreciation 461113 979768 2258893 347514 219230 387492 515399 1686898 19 Net Value Added 2673310 3230918 10831455 2118771 1826688 3036607 4061732 4099539 20 Net Fixed Capital Formation 353338 648116 5682259 144003 64310 192770 338500 1033542 21 Gross Fixed Capital Formation 814450 1627884 7941152 491517 283540 580262 853900 2720441 22 Total workers (no.) 121019 283216 310464 530221 23 Total Persons Engaged (no.) 176238 390386 451692 693954 24 Wages to Workers 241707 465120 553830 919995 25 Emoluments to Employees 868939 1172172 1602326 2047947 26 Gross Capital Formation 620405 733092 757661 2786478 27 Income 1620290 2596833 3690387 3631132 28 Profit

327978402958418256829694

118371823682471430030

424635525117439944938007

161876926770981596209

614662793539

12992732497918883738184213255449742

343590 433870 444674 927997 588799

1846178 794984 638582 1235329 1816184 1195887

Table 4 (cntd.): Selected Characteristics of Factory Sector (100 and more employees)by 2-digit Industry Div.(NIC-2008) for all-India based on ASI 2013-14



30 31 32 33 38 58 Others 1 Number of Factories (no.) 539 150 752 89 33 75 1336 2 Fixed Capital 3070267 223639 921037 288200 108095 245661 24966400 3 Physical Working Capital 1429338 184443 2410824 89176 31022 50589 1729568 4 Working Capital 782432 927902 2233949 111977 -4275 62405 721972 5 Invested Capital 4499605 408082 3331861 377376 139117 296250 26695968 6 Rent Paid 23807 7493 29271 5558 325 3125 110014 7 Outstanding Loan 1544952 122017 981166 190718 86176 619727 9266007 8 Interest Paid 218823 25354 174845 10698 9726 10606 1263043 9 Rent Received 3097 1965 797 389 164 1293 9178 10 Interest Received 48343 1281 85748 3563 8871 2377 99741 11

Gross Value of Plant & Machinery 2490035 166964 709961 131839 91261 272355 21549336

12

Value of Product and By-Product 11839703 916908 11304826 122834 106510 182788 11724172

13 Total Output 12827799 1065574 14622686 483699 203697 548906 21004599 14 Fuels Consumed 212656 27384 67914 11669 10474 8370 510413 15 Materials Consumed 8826261 642804 10110106 104418 102793 188228 7624346

16 Total Input 10408020 880860 13403097 300263 154432 268927 16763390 17 Gross Value Added 2419779 184714 1219589 183436 49265 279979 4241209 18 Depreciation 261495 19057 90600 27449 14009 24431 1228465 19 Net Value Added 2158284 165657 1128989 155987 35256 255548 3012744 20

Net Fixed Capital Formation -20519 -6610 86455 9178 -5300 4987 2303321

21

Gross Fixed Capital Formation 240976 12447 177055 36628 8709 29418 3531786

22 Total workers (no.) 201605 28069 176010 15501 5509 9202 166946 23 Total Persons Engaged (no.) 252821 38839 222495 21194 6597 19337 259495 24 Wages to Workers 323073 36015 246922 43074 5972 17718 241163 25 Emoluments to Employees 651190 101908 441996 82016 9683 84949 638845 26 Gross Capital Formation 310418 31620 567797 28036 3976 34179 3711791 27 Income 1967095 136056 1011419 143684 34240 245487 1748606 28 Profit 1221432 24439 528080 50902 23511 148843 1030366

All India ASI Data Based on Units with 100 and more Employees : Table 4 251

2-digit NIC Division and DescriptionNIC-2008

Description

01 CROP AND ANIMAL PRODUCTION, HUNTING AND RELATED SERVICE ACTIVITIES 02 FORESTRY AND LOGGING 03 FISHING AND AQUACULTURE 05 MINING OF COAL AND LIGNITE 06 EXTRACTION OF CRUDE PETROLEUM AND NATU RAL GAS 07 MINING OF METAL ORES 08 OTHER MINING AND QUARRYING 09 MINING SUPPORT SERVICE ACTIVITIES 10 MANUFACTURE OF FOOD PRODUCTS 11 MANUFACTURE OF BEVERAGES 12 MANUFACTURE OF TOBACCO PRODUCTS 13 MANUFACTURE OF TEXTILES 14 MANUFACTURE WEARING APPA REL 15 MANUFACTURE LEATHER AND RELATED PRODUCTS 16 MANUFACTURE OF WOOD AND OF PRODUCTS OF WOOD AND CORK, EXCEPT FURNITURE;

ARTICLES OF STRAW AND PLAITING MATERIAL 17 MANUFACTURE OF PAPER AND PAPER PRODUCTS 18 MANUFACTURE OF PRINTING AND REPRODUCTION OF RECORDED MEDIA 19 MANUFACTURE OF COKE AND REFINED PETROLEUM PRODUCTS 20 MANUFACTURE OF CHEMICALS AND CHEMICAL PRODUCTS

21 MANUFACTURE OF BASIC PHARMACEUTICAL PRODUCTS AND PHARMACEUTICAL PREPARATIONS 22 MANUFACTURE OF RUBBER AND PLASTICS PRODUCTS 23 MANUFACTURE OF OTHER NON-METALLIC MINERAL PRODUCTS 24 MANUFACTURE OF BASIC METALS 25 MANUFACTURE OF FABRICATED METAL PRODUCTS, EXCEPT MACHINERY AND EQUIPMENT

26 MANUFACTURE OF COMPUTER, ELECTRONIC AND OPTICAL PRODUCTS 27 MANUFACTURE OF ELECTRICAL EQUIPM ENT 28 MANUFACTURE OF MACHINERY AND EQUIPMENT N.E.C. 29 MANUFACTURE OF MOTOR VEHICLES, TRAILERS AND SEMI -TRAILERS 30 MANUFACTURE OF OTHER TRANSPORT EQUIPMENT 31 MANUFACTURE OF FURNITURE 32 OTHER MANUFACTURING 33 REPAIR AND INSTALLATION OF MACHINERY A ND EQUIPMENT 35 ELECTRICITY, GAS, STEAM AND AIR CONDITIONING SUPPLY 36 WATER COLLECTION, TREATMENT AND SUPPLY 37 SEWERAGE 38 WASTE COLLECTION, TREATMENT AND DISPOSAL ACTIVITIES; MATERIALS RECOVERY 39 REMEDIATION ACTIVITIES AND OTHER WASTE MANAGEMENT S ERVICES 41 CONSTRUCTION OF BUILDINGS 42 CIVIL ENGINEERING 43 SPECIALIZED CONSTRUCTION ACTIVITIES 45 WHOLESALE AND RETAIL TRADE AND REPAIR OF MOTOR VEHICLES AND MOTORCYCLES 46 WHOLESALE TRADE, EXCEPT OF MOTOR VEHICLES AND MOTORCYCLES 47 RETAIL TRADE, EXCEPT OF MOTOR VEHICLES AND MOTORCYCLES 49 LAND TRANSPORT AND TRANSPORT VIA PIPELINES 50 WATER TRANSPORT 51 AIR TRANSPORT 52 WAREHOUSING AND SUPPORT ACTIVITIES FOR TRANSPORTATION 53 POSTAL AND COURIER ACTIVITIES 55 ACCOMMODATION 56 FOOD AND BEVERAGE SERVICE ACTIVITIES 58 PUBLISHING ACTIVITIES 59 MOTION PICTURE, VIDEO AND TELEVISION PROGRAMME PRODUCTION, SOUND RECORDING AND

MUSIC PUBLISHING ACTIVITIES


2-digit NIC Division and Description (Contd.)NIC-2008

Description

60 BROADCASTING AND PROGRA MMING ACTIVITIES 61 TELECOMMUNICATIONS 62 COMPUTER PROGRAMMING, CONSULTANCY AND RELATED ACTIVITIES 63 INFORMATION SERVICE ACTIVITIES 64 FINANCIAL SERVICE ACTIVITIES, EXCEPT INSURANCE AND PENSION FUNDING 65 INSURANCE, REINSURANCE AND PENSION FUNDING, E XCEPT COMPULSORY SOCIAL SECURITY 66 OTHER FINANCIAL ACTIVITIES 68 REAL ESTATE ACTIVITIES 69 LEGAL AND ACCOUNTING ACTIVITIES 70 ACTIVITIES OF HEAD OFFICES; MANAGEMENT CONSULTANCY ACTIVITIES 71 ARCHITECTURE AND ENGINEERING ACTIVITIES; TECHNICAL TESTING AND ANALYSIS 72 SCIENTIFIC RESEARCH AND DEVELOPMENT 73 ADVERTISING AND MARKET RESEARCH 74 OTHER PROFESSIONAL, SCIENTIFIC AND TECHNICAL ACTIVITIES 75 VETERINARY ACTIVITIES 77 RENTAL AND LEASING ACTIVITIES 78 EMPLOYMENT ACTIVITIES 79 TRAVEL AGENCY, TO UR OPERATOR AND OTHER RESERVATION SERVICE ACTIVITIES 80 SECURITY AND INVESTIGATION ACTIVITIES 81 SERVICES TO BUILDINGS AND LANDSCAPE ACTIVITIES 82 OFFICE ADMINISTRATIVE, OFFICE SUPPORT AND OTHER BUSINESS SUPPORT ACTIVITIES 84 PUBLIC ADMINISTRATION AND DEFENCE; COMPULSORY SOCIAL SECURITY 85 EDUCATION 86 HUMAN HEALTH ACTIVITIES 87 RESIDENTIAL CARE ACTIVITIES 88 SOCIAL WORK ACTIVITIES WITHOUT ACCOMMODATION 90 CREATIVE, ARTS AND ENTERTAINMENT ACTIVITIES 91 LIBRARIES, ARCHIVES, MUSEUMS AND OTHER CULTUR AL ACTIVITIES 92 GAMBLING AND BETTING ACTIVITIES 93 SPORTS ACTIVITIES AND AMUSEMENT AND RECREATION ACTIVITIES 94 ACTIVITIES OF MEMBERSHIP ORGANIZATIONS 95 REPAIR OF COMPUTERS AND PERSONAL AND HOUSEHOLD GOODS 96 OTHER PERSONAL SERVICE ACTIVITIES 97 ACTIVITIES OF HOUSEHOLDS AS EMPLOYERS OF DOMESTIC PERSONNEL 98 UNDIFFERENTIATED GOODS - AND SERVICES -PRODUCING ACTIVITIES OF PRIVATE HOUSEHOLDS

FOR OWN USE 99 ACTIVITIES OF EXTRATERRITORIAL ORGANIZATIONS AND BODIES

2-digit NIC Division and Description 253

New Initiatives in Annual Survey of Industries (ASI)Various new initiatives have been taken to improve the coverage and quality of ASI data.Based on the detailed deliberations of the Standing Committee on Industrial Statistics(SCIS), the following decisions were taken:

1. Issue of coverage of frame of Annual Survey of Industries (ASI)

1.1 After detailed discussions relating to the issue of coverage of ASI and alsoinclusion of Company Identification Number (CIN) in ASI schedule and discussions basedon the report of the study of database maintained by M/o Corporate Affairs & the list ofunits available in the frame of the 6th Economic Census, the followings decisions weretaken:

1.2 The coverage of ASI may be extended beyond the purview of the Section 2m (i)and 2m (ii) of the Factories Act, 1948 and the Bidi & Cigar Workers (Conditions ofEmployment) Act, 1966 as recommended by the Sub-Group on Sampling Design of ASI.For this purpose, Business Register of Enterprises(BRE) prepared for the respectivestates and Directory of Establishments based on Sixth Economic Census will be used byCSO (IS Wing). It was decided that initially the coverage should be restricted to unitshaving 20 or more employees not registered under Section 2m (i) and 2m (ii) of the FactoriesAct, 1948 but registered under Companies Act, which is identified and validated in theBusiness Register of Establishments (BRE).

1.2.1 To start with the implementation of the augmented frame, units with 100 or moreemployees not registered under Section 2m (i) and 2m (ii) of the Factories Act, 1948 butincluded in the BRE of the respective states would be included in ASI frame. For this, BREof Andhra Pradesh (AP) readily available with National Accounts Division (NAD) wasincluded in the frame of Andhra Pradesh for ASI 2014-15 after verification of such units byFOD because inclusion of full set of units from the BRE would have suddenly increased thesize of the frame of AP considerably and consequently sample size for AP would have to beincreased appreciably, apart from verification of such units in the field before actual selection.In ASI 2015-16, most of the states had sent the list of units not registered under FactoriesAct, 1948 and also having 100 or more workers and also registered under any of the SevenActs included in BRE for inclusion in ASI 2015-16 Frame. Thus, ASI 2015-16 survey scheduledto be launched from October, 2016 would include units not registered under Factories Act,1948 in the respective States. This is a significant departure from past practices and it is animprovement in coverage of registered manufacturing sector.

2. New Sampling Design of ASI to be implemented from ASI 2015-16

2.1 Report of the Sub-Group of SCIS on Sampling Design of ASI under theChairmanship of Dr. G. C. Manna, the then ADG, ESD (CSO), was placed in the meeting ofthe SCIS for discussion and approval. Detailed discussion was held on various issuesrelated to the existing sampling design of ASI and the recommendations of the Sub-Groupand accordingly, the following decisions were taken:

2.2 It was decided that the existing employment cut-off of 100 employees to defineCensus Sector might be changed to 75 for six (06) States (namely, Jammu & Kashmir,Himachal Pradesh, Rajasthan, Bihar, Chhattisgarh and Kerala) and to 50 for three (03)States/UTs (namely, Chandigarh, Delhi and Puducherry). For other States/UTs, the existing


cut-off of 100 employees may be continued. The recommendation of the Sub-Group relatingto the formation of strata based on District X 3-digit level of NIC was also approved by theSCIS. Considering the above points, the new sampling design of ASI, which would beimplemented from ASI 2015-16, is noted as follows:

2.3 ASI sample is divided into two parts – Central Sample and State Sample. TheCentral Sample consists of two schemes: Census and Sample. Under Census scheme, allthe units are surveyed.

(1) Census Scheme:

(i) All industrial units belonging to the seven less industrially developed States/UTs viz. Arunachal Pradesh, Manipur, Meghalaya, Nagaland, Sikkim, Tripura andAndaman & Nicobar Islands.

(ii) For the States/ UTs other than those mentioned in (i),

(a) units having 75 or more employees from six States, namely, Jammu &Kashmir, Himachal Pradesh, Rajasthan, Bihar, Chhattisgarh and Kerala;

(b) units having 50 or more employees from three States/UTs, namely,Chandigarh, Delhi and Puducherry;

(c) units having 100 or more employees for rest of the States/UTs, notmentioned in (a) and (b) above and;

(d) all factories covered under ‘Joint Return’ (JR), where JR should be allowedwhen the two or more units located in the same State/UT belonging to thesame industry (3-digit level of NIC) under the same management. It may benoted that the principle of JR is applicable only before the selection of unitsbefore the survey and unit(s) belonging to the “Census Scheme” will not bejoint with unit(s) of “Sample Scheme” (defined below) at the field stage or allunits belonging to the “Sample Scheme” should not be joint amongthemselves at the field stage even if the conditions of JR are satisfied.

(iii) After excluding the Census Scheme units, as defined above, all units belongingto the strata (State x District x Sector x 3 digit NIC-2008) having less than orequal to 4 units are also considered under Census Scheme. It may be noted thatstrata are separately formed under three sectors considered as Bidi, Manufacturingand Electricity.

(2) All the remaining units in the frame are considered under Sample Scheme. For all thestates, each stratum is formed on the basis of State x District x Sector x 3-digit NIC-2008.The units are arranged in descending order of their number of employees. Samples aredrawn as per Circular Systematic Sampling technique for this scheme. An even number ofunits with a minimum of 4 units are selected and distributed in four sub-samples. It may benoted that in certain cases each of 4 sub-samples from a particular stratum may not haveequal number of units.

(3) Out of these 4 sub-samples, two pre-assigned sub-samples are given to NSSO (FOD)and the other two subsamples are given to State/UT for data collection.

New Initiatives in Annual Survey of Industries (ASI) 255

(4) The entire census units plus all the units belonging to the two sub-samples given toNSSO (FOD) are treated as the Central Sample.

(5) The entire census units plus all the units belonging to the two sub-samples given toState/UT are treated as the State Sample. Hence, State/UT has to use Census Units (collectedby NSSO (FOD) and processed by CSO (IS Wing)) along with their sub-samples whilederiving estimates for their respective State/UTs based on State Sample.

(6) The entire census units plus all the units belonging to the two sub-samples given toNSSO (FOD) plus all the units belonging to the two sub-samples given to State/UT arerequired for obtaining pooled estimates based on Central Sample and State Sample withconsequent increase in sample size.

2.4 It was decided that the list of units having status code “02: Closed” and “03: Non-Operating” in the ASI Frame might be prepared based on the latest updated frame, whichwere not selected in the current survey of ASI, and may be shared with NSSO (FOD) forverification of the units considering its existence, activities etc., so that, the units, whichare not in existence at all, existing but shifting its line of activities from manufacturing toothers, which are not associated in the line of manufacturing activities, may be deleted fromASI frame by NSSO (FOD) before sample is drawn for the subsequent survey. This isexpected to increase the effective sample size used for estimation.

2.5 It was also decided to exclude Govt. Departmental units from the ASI frame andCSO (IS Wing) were entrusted with the responsibility to identify such units based on thecurrent survey, so that the same may not be selected in the subsequent surveys of ASIstarting from ASI 2015-16

3. Modifications made in ASI schedule for ASI 2015-16

3.1 The schedule for the year 2015-16, as compared to the ASI schedule for the year2014-15, are given in the following table:

Block Row, Column

As per ASI 2014-15

As per ASI 2015-16

Remarks

A No change in ASI 2015-16 B 3 Type of

ownership (code)

Company Identification Number (CIN)

8 Does your unit have computerized accounting system? (yes – 1, no – 2)

Whether the share capital of the company includes share of foreign entities (yes – 1, no – 2)

As the existing item 8 of Block – B has no relevance in the era of advancement of IT and such information is not tabulated, it was decided to replace the existing item with the information related to share capital of foreign entities.

9 Can your unit supply ASI data in computer media? (yes – 1, no – 2)

Any R&D unit in your factory? (yes & registered with DST/DBT – 1, yes & registered with others – 2, no – 3)

This information is considered useful in today’s context.


New Initiatives in Annual Survey of Industries (ASI) 257

No change in ASI 2015-16



Block Row, Column

As per ASI 2014-15

As per ASI 2015-16

Remarks

C, D & E F 7,3 Total expenses

(1 to 6)

Since the “total” is auto calculated field without any s ignificance, for minimal changes in ASI Web Portal, the item “Total expenses (1 to 6)” was replaced with the item “Expenses on Research & Development (R&D)”.

Because of growing importance of units engaged in 'manufac-turing services'(job work), the existing item G1,3 is split into 'manufacturing services' G(item 1, col 3) and 'non-manufacturing services' G(item 2, col 3).

G

1,3

2,3

7,3 Total Receipts

(1 to 6)

H & I J Col. 11

Distributive expenses (Rs.) -Total

)Subsidy (-

K, L, M, N, Part A & B

Part II 11

Type of ownership (code)

N.A.

.

R e c e i p t s f r o m m a n u f a c t u r i n g serv ices( includ ing work done for others on materials supplied by them and sale value of waste left by the party)

Receipts from non- m a n u f a c t u r i n g services (including n o n - i n d u s t r i a l services)

Variation in stock of semi-finished goods (col. 4 minus col. 3 against item 5 in Block D)

Due to incorporation of new column relating to Subsidy, the c o l . r e l a t i ng t o “ To t a l Distributive Expenses” may be replaced with the col. relating to “Subsidy”.

Due to omission of the item, the item description is modified accordingly the position of entering the code is disabled, so that, no code may be recorded. The item may be utilized in future, if new information are required to be collected.

Variation in stock of semi-finished goods (col. 4 minus col. 3 against item 5 in Block D)

I n c o m e f r o m services (industrial/n o n i n d u s t r i a l inc luding work done for others on materials supplied by them and sale value of waste left by the party)

Expenses on Research & D e v e l o p m e n t (R&D)

4. Type of Organisation and Ownership codes used in ASI schedule

4.1 Organisation and Ownership codes used in Block – B of ASI schedule were reviewedand finally it was decided that the “Type of Ownership” code collected in item 3 of Block –B might be excluded from the ASI schedule, as the proposed “Type of Organisation” code,which would be collected in item 2 of Block – B, would take care of the ownership aspect ofthe unit also. The modified codification structure of “Type of Organisation” along with theconcepts, definitions and terminologies to be used is given below:

Block B: item 2: type of organisation: This item is to be recorded in terms of the followingcodes:

Individual Proprietorship -1Partnership -2Limited Liability Partnership -3Government Company-Public -4Government Company-Private -5Non-Government Company-Public -6Non-Government Company-Private -7Co-operative Society -8Others (including Joint Family (HUF),Trusts, Wakf Boards, Handlooms,KVIC, etc.) -9

Type of Organisation: The following characteristics may be noted for recording entries inthis block:Type of Organisation Description

Proprietary

Partnership

Limited Liability Partnership(LLP)

Government Company-Public

Government Company-Private Non-Government Company-Public Non-Government Company-Private

Here, an individual is the sole owner of the enterprise.

It means relation between persons who have agreed to share the profits of a business carried on by all or any one of them acting for all.

A Firm incorporated under the Limited Liability Partnership Act, 2008. LLPs are given by ROC a unique 8-digit identification number called LLPIN.

It is a company where paid-up share capital of the appropriate Government (Central/ State/ Local) is not less than 51% and number of shareholders is at least 7 and no upper limit for number of shareholders. It is a company where paid-up share capital of the appropriate Government (Central/ State/ Local) is not less than 51% and number of shareholders (including the Government) is at least 1 and maximum number of shareholders is 200. It is a company where paid-up share capital of the appropriate Government (Central/ State/ Local) is less than 51% and number of shareholders is at least 7 and no upper limit for number of shareholders.

It is a company where paid-up share capital of the appropriate Government (Central/ State/ Local) is less than 51% and number of shareholders is at least 1 and maximum number of shareholders is 200.


5. Other changes related to conceptual issues in ASI Schedule

5.1 Block C, item 6: Pollution control equipment/environment improvement equipment:This refers to machinery installed for pollution control as well as environment improvement.

5.2 Convertible debentures were included as part of ‘Outstanding Loan’ in Block D.Convertible debentures had been considered as part of outstanding loan or long-term debt,as it remains part of long-term liability of the factory until the date of redemption of thedebenture.

5.3 Omission of Non-operating expenses (Block F, Item 4) in ASI Schedule. Non-operating expenses have been merged in ‘Operating Expenses’ and now all the expenses ofa factory have to be shown as Operating expenses (Item 3 Block F) only.

5.5 Rationalization of reporting of Value of Own Construction (Block G item 4). Thenet balance of capital work-in-progress i.e. (Col. 7 – Col.3) of item 9 in Block C, if positive,would be transferred to Value of Own Construction (Block G item 4). One new item has beenincluded in Block F item 4 as “Expenses on raw materials and other components for ownconstruction” to include the input cost for value of own construction.

5.6 Treatment of Subsidy/Rebate in ASI schedule. A new column has been added asColumn 12 in Block J with heading ‘Subsidy’ under Distributive expenses to include subsidyon product. The value in this column has been added to Gross Sale Value (Col.7) to arriveat the ex-factory value. The production subsidies (i.e. as distinct from ‘Subsidies onProducts’) have been included in existing Block G, Item 12 and hence new item has beendescribed as ‘Other Production Subsidies’ as per guidelines in SNA.

5.7 Specific Reporting of Sale value in Block J. Output value of the entire amount offinished products/by-products have to be calculated at cost price of producing the itemand reported in Block J as ex-factory-value in cases where a factory has not sold anyquantity of a finished product/by-products produced during the current reference period.As an alternative to the above, sale value has not been counted in the current referenceperiod in cases where a factory did not produce any quantity of finished product/by-product during the current reference period and sold only from last year stock.

5.8 Recording of R&D Expenditure in ASI Schedule. A separate item in Block F hasbeen created to report R & D expenditure. One question has been formed in Block B ‘AnyR&D unit in your factory?’ with options 1) ‘Yes & Registered with DST/DBT’ 2) ‘Yes &Registered with Others’ 3) ‘No’.

Type of Organisation Description

Co-operative society

Others

It is a society formed through the co-operation of a number of persons (members of the society) to benefit the members. The funds are raised by members' contributions/ investments, and the members share the profits. The government or government agency can also be a member or shareholder of a registered co-operative society but this fact cannot render the society into a public sector enterprise for the purpose of the survey.

These are the enterprises not falling under any of the above categories.

New Initiatives in Annual Survey of Industries 259

5.9 Reframing the existing questions in Block K: Information and CommunicationTechnology (ICT) block as per UN guidelines.

Block K: Information and Communication technology (ICT) usage Sl. No.

ICT indicator yes-1, no-2

1. Did the factory use computer/s during <reference period>? 2. Did the factory use the Internet during <reference period>? 3. Does the factory have a web site as on the date of survey? 4. Did the factory receive orders via the Internet during <reference period>? 5. Did the factory place orders for business purpose via the Internet during

<reference period>?

6. Did the factory connect to the Internet either by a. Narrowband or b. Fixed broadband or by c. Mobile broadband during <reference period> ?

7. Does the factory have a local area network (LAN) as on the date of survey?

Explanatory Notes

-

A computer refers to a desktop or a laptop computer. It does not include equipment with some embedded computing abilities such as mobile cellular phones, personal digital assistants (PDA) or TV sets.The Internet is a worldwide public computer network. It provides access to a number of communication services including the World Wide Web and carries email, news, entertainment and data files, irrespective of the device used (not assumed to be only via a computer - it may also be by mobile phone, games machine, digital TV, etc.). Access can be via a fixed or mobile network.A web presence includes a website, home page or presence on another entity's website (including a related business). It excludes inclusion in an on-line directory of any other webpages where the business does not have control over the content of the page.

Orders received include orders received via the Internet whether or not payment was made online. They include orders received via websites, specialized Internet marketplaces, extranets, EDI over the Internet, Internet-enabled mobile phones and email. They also include orders received on behalf of other organizations – and orders received by other organizations on behalf of the business. They exclude orders that were cancelled or not completed.

Orders placed include orders placed via the Internet whether or not payment was made online. They include orders placed via websites, specialized Internet marketplaces, extranets, EDI over the Internet, Internet-enabled mobile phones and email. They exclude orders that were cancelled or not completed.Narrowband includes analogue modem (dial-up via standard phone line), Integrated Services Digital Network (ISDN), Digital Subscriber Line (DSL) at speeds below 256 kbit/s, and mobile phone and other forms of access with an advertised download speed of less than 256 kbit/s. Narrowband mobile phone access services include CDMA 1x (Release 0), GPRS, WAP and imodeFixed broadband refers to technologies such as DSL, at speeds of at least 256 kbit/s, cable modem, high speed leased lines, fibre-to-the-home, powerline, satellite, fixed wireless, Wireless Local Area Network (WLAN) and WiMAX.Mobile broadband access services include Wideband CDMA (W-CDMA), known as Universal Mobile Telecommunications System (UMTS) in Europe; High-speed Downlink Packet Access (HSDPA), complemented by High-Speed Uplink Packet Access (HSUPA); CDMA2000 1xEV-DO and DCMA 2000 1xEV-DV. Access can be via any device (mobile, cellular phone, laptop, PDA, etc.)A LAN refers to a network connecting computers within a localized area such as a single building, department or site; it may be wireless.


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