13th Chapter Final

8/4/2019 13th Chapter Final

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Social Exclusion and Earnings

Balwant Singh Mehta

1. Background

The sixty-two year journey of post-colonial India presents a mixed picture. On the one

hand, the country has substantial achievement in some of the sectors and dimensions of

economic development. On the other hand, it has failed more or less in addressing the

problems of poor, marginalized and deprived groups and communities, who have

remained excluded from the fruits of high economic growth. There is an ongoing debate

among the researchers, whether the recent economic development ha sd benefited only a

small section of the Indian population and whether it has led to increasing disparities and

inequalities among different groups and communities over the years.

Several research studies in the past have highlighted the close link between

inequality and social exclusion, wherein unequal societies certain groups and

communities are discriminated (Gore, 1994; dDe Haan, 1995; Nayak, 1995; Nayak,

1994; ILO, 1996 ; Mishra, 1999 ; Atkinson, 1998; Bhalla & and Lapeyre, 1999 ; Mishra,

1999 ; Kabeer, 2000; Majumdar, 2007; Carr &and Chen, 2004 ; Buvinic, 2005 QUERY:

Not included in the reference list.; Mutatkar, 2005; Borooah et al. , 2005; Mutatkar, 2005;Majumdar, 2007; TakahiroI toLO , 2007; Thorat et al , 2008 ; Buvinic 2005 ). This

summari zses the meaning of social exclusion as the inability of an individual to

participate in basic, political, economic and social functioning of society, and goes on to

add that social exclusion is the denial of equal access to opportunities imposed by certain

groups of society upon others. This definition captures the three distinguishable features

of social exclusion: firstly , the effects on culturally defined groups; secondly, that it is

embedded in the social relations (the processes through which individuals or groups are

wholly or partially excluded from full participation in the society in which they live) , and

finally, that it delineates its outcome in terms of low income and high degree of poverty

among the excluded groups (Hann, 1997 ; QUERY: Not included in the reference list.;

Sen 2000 QUERY: Not included in the reference list. ).

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In the context of labour market , exclusion may operate along a number of socio-

economic attributes -- social groups, religion, age -- which effectively reduce the

opportunity for such groups to gain access to social services and limit their participation

in the labour market. In the labour market , exclusion can occur in hiring, for instance,

when two persons with similar education qualification and experience apply for

employment they face discrimination because of some non-economic (social origins like

such as caste, race, ethnicity and religious background) characteristics of an individual.

This kind of discrimination in the labour market can not be ignored, mainly because of its

adverse consequences on access to employment, earnings and working conditions

(Thorat, 2008). The pertinent questions arises that at till what extent inequalities in

employment and earnings occur s due to this discrimination. It is important to de-mystify

labour market discrimination in the context of inter-group inequalities in employment andwage earnings.

This kind of discrimination and disparity has been practiced in India since time

immemorial. The Schedule Caste (SCs) and Schedule Tribes (STs) has been pariah in the

development process of India for a quite a long time. Affirmative actions in the form of

reservation in education and employment were taken after Independence to provide them

space in the mainstream and trigger self - sustaining growth of these groups. In recent

years , the issue has again come to centre -stage in view of the debate between pro- and

anti-reservation lobbies with a point that of exclusion of Other Backward CastesGroup

(OBCs) in social category and Muslims in religious groups from main-stream

development (Majumdar, 2007). Therefore, how various measures for the welfare of

such deprived sections of the Indian society have helped in the labour market would be

worth to be examin eding . To unearth whether such disparities exist and at what extent,

there is an urgent need to address this issue. An attempt has been made in this paper

chapter to address some of these key questions surrounding on the labour market

discrimination with the following objectives/hypothesis.

2. Objectives/Hypothesis

2.1 ObjectivesThe main objectives of the paper are outlined as follows:

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(a). Exploring the trend and pattern in wage/earning differential in the labour market in

terms of access to employment and wage earnings among various socio-religious groups .

( b) . Determining the inter-group inequality in employment and earning among various

socio-religious groups.

(c). Exploring the contribution of other factors including socio-religious groups to

earning inequality in labour market.

2.2 Hypothesis

Following are some of the hypotheses that need to be examined with the above

objectives:(a) There is a selective inclusion or hiring with unequal wages , i.e. lower than those

posited in the market or the wages received by higher socio-religious groups.

( b) The quality of work, i.e. more number of excluded groups in casual or contractual

work than other socio-economic categories or exclusion from regular or permanent work

with higher wages.

(c) Exclusion in certain categories of jobs and mainly involved in the menial occupations

(Thorat , et al., 2003 QUERY: Not included in the reference list. ) (mainly involved in asHair Dressers, Barbers, Sweepers, Cleaners and Related Workers , Sweepers, etc. ).

The paper chapter is divided into seven main sections, Section I 1 presents the

background detail, Section II 2 outline s the objectives and Section III 3 tells about data

and methodology. Section 4IV highlights the current situation of socio-religious groups

in terms of employment and earnings and the fifth section presents a detail picture of

wage/earnings inequality among these socio-religious groups. Section VI explores the

contribution of the various other excluded groups including socio-religious groups

towards explaining earnings or wages inequality. The last section concludes the pch apter

with some major policy remarks.

3. Data and Methodology

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The data for this pch apter is have been taken from two rounds of National Sample Survey

(NSS), i.e. the 50th round (1993 --94) and the 61st round (2004 -- 05), which is available at

individual level with different socio-religious groups. Six socio-religious categories

namely Others, Schedule caste (Dalit), Adivasi (ST), Other Backward Class (OBC) of

Hindus, Muslims and other religious groups are included for the analysis. The data for the

OBC category w ereas not available in 1993 --94. Therefore for the comparison of data

over the time, OBC has been combined with others. Advanced statistical techniques such

as Gini coefficients, General Entropy Measures, Kernel Density Function and

decomposition analysis have been used to analy sze the extent of inequality among the

socio-religious groups.

The data at the disaggregated level permit s us to see the changes and progressmade by specific groups over time. This information also draws attention to exclusion,

strengthening influencing strategies. This also raises the profile and visibility of excluded

groups , which is an important input for researchers and policy - makers.

4. Disparities Aa cross Various Socio-Religious Groups

A brief look at the disparities in the employment opportunities of different socio-religious

groups would be appropriate before analy szing the differential in their labour markets

outcome in terms of employment and earnings. Table 13.11 shows share of population,employment and earnings by socio-religious groups.

Table 13.1: Share in Population, Employment and Earnings by Socio-Religious Groupsin India - ( 1993 -- 2004 ) (15-- 59 Age Groups)

Share inPopulation

Share inEmployment

Share in Earning (constant

1993 prices)1993 2004 1993 2004 1993 2004

Schedule d Tribes 8 7 10 9 6 5Schedule d Caste 18 18 18 18 16 16Others* 58 56 58 57 63 63Muslim 11 13 9 10 7 8Other Religion 5 5 5 5 8 8Total 100 100 100 100 100 100Source: Data calculated from unit level data CD of various NSS rounds .

*Includes OBC .

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The results indicate that the share of population, employment and earning of the

Excluded Groups (Muslims, SCs, STs: Henceforth EGs) , is lower than that of the

Included Group (Upper Caste and OBC Hindu: Henceforth IGs). In wage employment

and earnings , the share of EGs is lower than their corresponding share in population.

However, a contrary trend has been observed in the case of IGs. The gap between IGs

and EGs has been increasing over the period, from 1993 to 2004. It is quite clear from the

above analysis that the EGs are marginalized in the job market , and employment

opportunities are restricted for them. The share of EGs in the population increased with

the decline in the share of employment and earnings indicating discrimination in the

labour market.

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Table 13.2: Distribution by Status of Wage Workers (15 -- 59 Age Groups)

Regular Casual

1993 2004 1993 2004Schedule d Tribes 12.00 13.06 88.00 86.94Schedule d Caste 15.16 20.87 84.84 79.13Others* 41.26 43.32 58.74 56.68Muslim 31.03 33.54 68.97 66.46Other Religion 38.22 44.20 61.78 55.80Total 30.46 33.87 69.54 66.13

Source: Data calculated from unit level data CD of various NSS rounds .

*Includes OBC.

The tT able 13.2 presents the distribution of wage workers by their status of employment.

It is observed that the workers belonging to IGs are proportionately more in regular jobs

with secured wages , while the EGs are largely employed into casual jobs , which does not

have any surety regarding availability of jobs , and hence suffer from uncertainty

regarding earnings too. Since total earning depends both on rate of wages and job

availability, those with casual jobs earn much less because of non-availability of jobs for

a major part of the week/month/year. This indicates that the predominance of cCasual

workers among the EGs with poor quality of work is the main reasons for the existing

disparity in earning. As discrimination leads to disparities in capability formation and

ownership of assets, the EGs are unable to participate in the growing economic affluence

and are being increasingly marginalized (Mazumdar, 2008 QUERY: Not included in thereference list. ).

For examining this phenomenon further , Table 13.3 presents per day wage s and weekly

earnings of workers by their socio-religious groups and stats of employment. As

explained earlier, the shares of EGs in employment are lower than their share in

population - - their shares in total earnings are even further lower. This implies that even

when they are getting jobs, they earn relatively less than the IGs. The disparity is also

alarmingly high as earning per day of EGs is far lower than IGs. This clearly indicates

significant disparity and discrimination in the wage market resulting in further

deprivation of the EGs (Table 13.3).

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Table 3: Earnings by Social Groups for Regular and Casual Workers in India(15-- 59 Age Groups in RsQUERY: Please insert rupee symbol & and at constant

1993 prices)

Daily Earning Weekly EarningRegular Casual Regular Casual

1993 2004 1993 2004 1993 2004 1993 2004Schedule d Tribes 54 77 54 77 367 535 111 138Schedule d Caste 52 67 52 67 351 470 121 159Others* 73 100 73 100 497 701 126 166Muslim 54 72 54 72 368 503 148 185Other Religion 74 103 74 103 503 719 164 198Total 68 92 68 92 464 643 127 164

Source: Data calculated from unit level data CD of various NSS rounds .

*Includes OBC.

It also emerges clearly from Table 13.3 that among regular workers, average daily

wage and weekly earning of EGs ha ves been far lower than the IGs. The gap in the

regular earrings between IGs and EGs has also been widening over the period, from 1993

to 2004. However, among the casual workers , the wage per day and earning differences

between EGs and IGs is far lower in comparison to regular workers. There are more

disparities in regular workers than casual wage workers among different socio-religious

groups. The reasons may be the variety of occupations they involve s.

The reasons for these glaring disparities need to be further explored. One

important reason could be the type of occupations ion which workers of such groups are

engaged. It is seen that the occupational distribution is highly skewed with very few from

the EGs present in the decent jobs (professional, technical, administrators , and managers ,

etc.) An overwhelming majority of the workers from EGs are in occupations like such as

services workers, farming, sales and labours. Since the wages in these occupations are

relatively much lower . (Table 13.4).

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Table 13.4: Occupational Distribution of Wage Workers bySocio-Religion in India (15 -- 59 Age Groups)

Earners 1993 2004ST SC Muslim Others Other

Religion

ST SC Muslim Others Other Religi

onProfessional,technical andrelated workers

3.11 6.50 6.14 75.24 9.01 3.7 9.9 8.3 68.7 9.5

Administrative, eExecutiveandmM anagerialwW orkers

1.15 3.46 2.41 83.44 9.53 1.9 4.3 4.3 80.4 9.1

Clerical andrelated workers

3.10 9.72 5.43 74.14 7.62 3.2 12.1 6.0 70.8 7.9

SaleswW orkers

1.61 6.75 10.82 74.75 6.06 2.3 11.7 14.1 67.3 4.6

ServicewW orkers

4.85 23.38 6.50 57.23 8.04 5.0 28.1 7.6 52.4 6.8

Farmer,fFishermen,hHunter,lLogger andrR elatedworkers

5.43 14.89 10.43 63.12 6.13 10.9 27.8 9.5 46.3 5.5

Workers notclassified

5.20 14.61 7.69 68.11 4.39 4.1 30.7 17.5 45.3 2.4

Total 4.13 12.51 7.90 68.20 7.26 9.0 24.4 9.2 51.4 6.0Source: Data calculated from unit level data CD of various NSS rounds .

Over the period among the IGs , the proportion of service s workers (workers

involved in low - paid job and menial jobs ( mainly involved asin Hair Dressers, Barbers ,

Sweepers, Cleaners and Related Workers , Launderers, Dry-cleaners and, Pressers , etc .)

has gone up.

The distribution of wage by occupations also shows that the wage/earning of

service workers, farming and labo urers is significantly lower than other occupations

(Table 13.56 ). The occupational distribution of wage workers somewhat holds up the

hypothesis that workers belonging to EGs are mostly working in menial jobs as service

workers, farm worker s and labour ers , which are low paid (Table 613.5 ).

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Table 613.5 : Distribution of Wage pP er dDay by Occupation (15 -- 59 Age Groups)Earners 1993 2004

ST SC Muslim Others Other Religio

n

ST SC Muslim Others Other Religio

nProfessional,

technical andrelated workers

77 78 88 105 91 119 114 112 150 135

Administrative,Eexecutive andMm anagerialWw orkers

126 153 123 167 188 296 165 198 281 293

Clerical andrelated workers

70 70 72 83 85 110 98 107 118 126

Sales Ww orkers 28 27 31 40 50 33 36 46 51 61ServicewW orkers

45 39 39 45 46 43 48 50 56 59

Farmer,fFishermen,

hHunter,lLogger andrR elated workers

21 24 30 30 35 26 32 38 39 45

Workers notclassified

36 28 33 42 47 47 26 35 48 93

Total 24 27 36 46 49 33 39 48 65 69Source: Data calculated from unit level data CD of various NSS rounds .

Lastly, whether the government s affirmative actions ha ves been benefited theto

EGs , this can be seen by the employment pattern of regular workers by enterprise type in

2004 --05 (Table 713.6 ).

Table 713.6 : Distribution of Regular Workers in Government/Public Sector Jobs

Govt./Public Sector Others TotalSchedule d Tribes 42.8 57.2 100.0Schedule d Caste 35.3 64.7 100.0Muslim 23.4 76.6 100.0Others* 32.8 67.2 100.0Other Religion 33.4 66.6 100.0Total 32.8 67.2 100.0Source: Data calculated from unit level data CD of NSS 61st round .

Note: In 1993, data w ereas not available by enterprise type.

*Includes OBC.

The tT able 7 13.6 clearly indicate s that how the reservation policy of government has

benefited the socially EGs. In the government and public sector , the proportion of total

wage workers within EGs isare substantially higher than others. Within EGs, among

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schedule d tribes the proportion of government/public sector is highest followed by

schedule d caste s.

5. Earning Inequality aA mong Socio-Religious Group s

The Gini coefficient presented in Table 13.77 shows that there is a high incidence of

disparities in earning among IGs in comparison to EGs. More importantly, the inequality

has also been increased over the period among all the groups. The high increase in

inequality among IGs indicates that wage/earning of higher wage groups has gone up

faster than EGs . The overall income inequality shows continuous increase in all

measures (G (0) to G (2)) over the years. The increase in inequality is sharper in GE (1)

and GE (2), pointed that higher income inequality in upper distribution of earnings of

IGs. Table 13.7: Generalized Entropy Measures for Regular and Casual Workers by Ssocio-

Rr eligious Gg roups ( ( 15-- 59 Age Groups)

Gini GE(0) GE(1) GE(2)

1993 2004 1993 2004 1993 2004 1993 2004Schedule d Tribes 0.40 0.45 0.27 0.35 0.32 0.45 0.56 0.94Schedule d Caste 0.42 0.45 0.30 0.35 0.32 0.40 0.48 0.67Others* 0.42 0.47 0.30 0.38 0.31 0.43 0.42 0.75Muslim 0.51 0.55 0.47 0.55 0.45 0.56 0.63 0.90Other Religion 0.47 0.53 0.41 0.51 0.39 0.52 0.54 0.86All 0.50 0.54 0.43 0.51 0.44 0.55 0.67 0.97Source: Data calculated from unit level data CD of various NSS rounds .

Note: GE classes of measurements are Generali zsed Entropy measures. GE(0) gives more weights to lower tail of the income distribution, GE(1) gives equal weights and GE(2) gives more weights to the upper tail of income distribution .

*Includes OBC.

Further , this shows that workers earnings inequality is increasing with income

distribution. The inequality of casual workers has declined over the years for EGs , which

is sharper in the upper part of the distribution, which shows that more concentration of

EGs in the lower income distribution. SecondAlso , inequality of regular wage workers

has increased over the period and substantially rises in the upper income distribution.

Inequality among regular wage is consistently higher for all socio-religious groups than

casual wage in both the years (Annex Table A2.3) .

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Figure 13.1: Kernel Density Graph of Regular and Casual Workers by Socio-ReligiousGroups in 2004 -- 05

The inequality among the socio-religious groups has been further explored by KDF

(Kernel Density Function) distribution of earning over time for wage workers. This type

of graph gives a visual idea about the nature of inequality. The KDF distribution may be

viewed as histograms that have been smoothened to iron out minor irregularity in the

observed data (Deaton, 1997 QUERY: Not included in the reference list. ) and it draws the

eye to the essential features of the distribution. The weekly earnings of workers have

been taken because this is a better indicator for wage/earning (Figure 13.1).

The KDF graph s shows that workers of EGs have higher concentration at lower level of

earnings and IGs are significantly present at middle and higher level s of earnings. This

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reveals that workers of excluded groups are involved in low - paid job in comparison to

others. Over the period, the mode of EGs at the lower end has gone up , which explains

theat higher concentration of low - paid workers. The KDF shows that over the period,

the distance between EG and IG has increased showing that the concentration of EG has

gone up at lower level of earnings and decline d at middle and higher level s of earnings

(Figure 13.1).

6. Decomposition Analysis

In the earlier sections , details of earnings inequality among wage earners of different

socio-religious categories have been discussed. Further , apart from socio-religious groups

various other factors like such as educational level, employment status, settlement (rural

or urban), industry groups, gender, days of work, age, etc . have also contributed toearning of wage workers. In order to understand the contribution of each of these

attributes , a decomposition analysis has been performed by using Field approach. Field

(200 32) developed a new approach that considers simultaneously the impact of several

characteristic of earnings and allows distinguishing contribution of each of these

characteristics. The approach is useful as it helps to know the contribution of various

factors including categorical factors that enter as a string of dummy variables ( Uma Rani,

2008).

The fF igure 13.2 presents the decomposition of factors (excluding residuals) that

contribute to earnings disparity of all wage earners. Two major factors that contributed to

the differences in earnings are educational level and intensity of work (total days of

work). Regular workers generally get paid for all days in a week whether they work or

are on leave , but casual workers are paid only for the days they actually work. Apart from

the daily wage rate, the earnings of the casual workers directly get affected by the number

of days work and this factor had turned out to be the second most important factor

contributing to earnings inequality. The level of education emerged as the most dominant

factor contributing to the level of inequality in the earnings of wage workers. The

employment status (regular or casual) was the third - most important factor. It showed that

even after controlling days of work, daily wage differential between regular and casual

wage workers was substantial. Interestingly, the relative importance of these factors in

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explaining part of contribution to earning inequality has not changed much over the

period. The socio-religious group contribution is only 2 two per cent, lowest among all

the factors and has not changed over the period. This is possibly due to two factors, one

is high presence of EGs in regular jobs, which are the consequences of affirmative

actions taken by the government (reservation policy -- Table 713.6 ), and another

education level (graduate and above shows very high contribution appendix tT able

A2.1). Further, the detail ed results of regression also show s that the relative contribution

of EGs has gone up over the period, which is possibly again due to the same reasons

elicited above ( appendix tT able A2.1) .

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Figure 13.2: Contribution to Earning of Various Factors of Wage Earners

Figure 2:Contribution to Earnings of Various Factors to Wage/ Earnings

5 48 9

11 11

17 13

20 22

3332

752 2

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%100%

1993 2004Year

C o n

t r i b u

t i o n

Sector Gender Industry Status Education No of days Age Socio-religious gr

7. Conclusions The overall analysis shows that there is a significant presence of social exclusion among

the wage earners of different socio-religious groups. The EGs are getting lower wages in

similar type of jobs and mostly involved in casual wage s. Occupation-wise results also

indicate that significant proportions of EGs are involved in low - paid and menial

occupations. Further , the inequality analysis shows that the inequality among EGs at

lower level of earning is almost similar to IGs, however, at higher level of earning

inequality among EGs is far lower than IGs, indicating concentration of EGs at lower

level. This phenomenon has been further proved through the KDF graph , which clearly

indicates that at lower earning level , the concentration of EGs is substantially higher than

that of IGs ( mM ode of EGs is higher than IGs), which has increased further over the period. This analysis shows that disparity or inequality is increasing between IGs and

EGs. Lastly, the decomposition analysis reveal s that the contribution of the socio-

religious groups in overall earning inequality is not substantial (only 2 per cent) but a

detail ed analysis shows that the contribution of EGs has been increased over the period,

which is possibly due to the reservation policy of the government.

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Therefore, it is necessary to not only provide more jobs to EGs through

reservation policy, but also to make them more employable through imparting quality

education and hands - on skill formation. Unless skills and efficiency among EGs can be

build up , the labour market will continue discriminating them and bereft of earrings, a

vicious cycle of low human capital -, therefore, low earning will continue over

generation s. The human capital formation can not be done alone by reservation policy,

better social infrastructure education and health at their reach is also necessary.

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http://ideas.repec.org/a/tpr/qjecon/v107y1992i1p35-78.htmlhttp://ideas.repec.org/a/tpr/qjecon/v107y1992i1p35-78.htmlhttp://ideas.repec.org/s/tpr/qjecon.htmlhttp://ideas.repec.org/a/tpr/qjecon/v107y1992i1p35-78.htmlhttp://ideas.repec.org/a/tpr/qjecon/v107y1992i1p35-78.htmlhttp://ideas.repec.org/s/tpr/qjecon.html


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Carr, M. & and M. Chen. (2004. ) Globalization, Social Exclusion and Work: withSpecial Reference to Informal Employment and Gender, Working Paper No. 20, PolicyIntegration Department, World Commission on the Social Dimension of Globalization,International Labour Office, Geneva, May 2004.

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de Haan, A. 1995. Bibliographical Review on Social Exclusion in South Asia: AnnotatedIILS / ILO, www.ilo.org/public/english/bureau/inst/papers/1994/dp77/index.htm

de Haan, A. (1999. ) - Social Exclusion: an Alternative Concept for the Study of Deprivation? IDS Bulletin , Vol. 29, No. (1) QUERY: Not cited in the text a nd also

provide page span of the article

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de Haan, A. (1995) Bibliographical Review on Social Exclusion in South Asia:Annotated IILS / ILO,www.ilo.org/public/english/bureau/inst/papers/1994/dp77/index.htm

Dunlop, J.ohn E., and& V.A. Velkoff. (1999. ) - Women and the Economy in India,U.S. Census Bureau, Official Statistics. QUERY: Not cited in the text

Duraisamy, M. & and P. Duraisamy. (1996.) - Sex Discrimination in Indian Labour Markets., Feminist Economics , 2 (2):, pp. 41--61. QUERY: Not cited in the text

Esteve-Volart, B. (2004. ) Gender Discrimination and Growth: Theory and Evidencefrom India., DEDPS 42, Suntory and Toyota International Centres for Economics andRelated Disciplines, London School of Economics and Political Science. QUERY: Notcited in the text

Fields, G. S. (2003. ): Accounting fFor Income Inequality and iIts Changes: A NewMethod with Application to the Distribution of Earnings in the United States.. Research

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As some of the differences in incomes between the different employment statuses

can be attributed to workers educational attainment and to the occupation or industry,

this approach allows us to simultaneously account for these differences. We adopt the

method developed by Fields (2003), which decomposes the contribution of various

explanatory variables to the level and change in inequality within a standard semi -

logarithmic wage (or earning) regression model. The first step in the regression - based

decomposition methodology is the estimation of a semi - logarithmic Mincerian (standard

or augmented) wage/earning function,

Where, ln Y it is the log variance of earnings;

at = [t 1t 2t . . . Jt 1] and Z it = [1 xi1t xi2t . . . xiJt it ] are vectors of coefficients and explanatory variables ,respectively.

A general approach to analy sze household earning inequality would be to regress the log

income on the characteristics of the household head likesuch as , gender, age, socio-

religious category, education, industry , etc. ( Fields, 2003; Gottschalk and Joyce 1995;

Katz and Murphy 1992; Murphy and Welch 1992; Gottschalk and Joyce 1995 ;QUERY:

Not included in the reference list; Fields, 2003 ). However, we have modified this

standard approach in two ways. One, as our interest is to understand the factors that

contribute to inequality at the earning level; we have included the characteristics of the

wage workers in the regression. Two, several other factors like such as days of work and

employment status are also included in the regression to understand the impact of

changing work pattern on inequality.

In the second step, the estimated standard semi-log regression is decomposed to

compute the relative factor inequality weights (i.e. , the percentage of inequality that is

accounted for by the jth factor), which is as follows,

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where, s j (lnY) denotes the share of the log-variance of income that is attributable to the

jth explanatory factor; cov [.] denotes the covariance, cor (.) the correlation coefficient

and (.) the standard deviation . The above decomposition, in other words, computes how

much income inequality is accounted for by each explanatory factor, which is the levels

question. We have excluded the residual and made the total of sub - categories of

explanatory variables 100 and th ean calculate the contribution of each factors and later

combined each attributes and plot graph to show the difference over the period. (The

detail of regression results are following .)

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Appendix 2: TablesTable A2.1: Result of Regression Analysis

Dependent variable: log of weekly earnings

Factors Variables 1993 --94 (Share ) 2004 --05(Share)Sector Rural (reference category)

Urban 0.025 0.025Gender Male 0.037 0.056

Female (reference category)Industry Agriculture (reference category)

Mining & and Quarrying 0.002 0.003Manufacturing 0.004 0.000

Electricity 0.005 0.010Construction --0.001 --0.009

Trade, Hotel and Restaurant 0.000 0.001Transport, Storage and Comm. 0.009 0.008

Finance, Real esS tate, and Business 0.008 0.010Pub Admin, Edu and Health 0.027 0.040

Status of Employment Regular 0.085 0.075Casual (reference category)

Education Illiterate (reference category)Upto primary --0.003 --0.004Upto middle --0.002 --0.005

Upto higher and higher secondary 0.048 0.041Graduate and above 0.114 0.163

Total No . of Days Worked Tot al days 0.099 0.130Age of worker Age 0.024 0.040Social religious category ST (reference category)

SC 0.000 -0.002Muslim 0.000 0.000Other 0.004 0.003

Other Religion 0.005 0.008Residual 0.511 0.407

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Table A2.2: Final contribution of factors to earning inequality (after excluding residual)

Factors 1993 2004Sector 5 4Gender 8 9Industry 11 11Status 17 13Education 32 33

No . of days 20 22Age 5 7Socio-religious group 2 2All 100 100.00

Table A2.3: Generalized Entropy Measures for Regular and Casual Workers by socio-religious groups

GE(0) GE(1) GE(2) Gini

1993 2004 1993 2004 1993 2004 1993 2004 Regular

Schedule d Tribes 0.31 0.42 0.28 0.41 0.31 0.55 0.41 0.49Schedule d Caste 0.31 0.37 0.26 0.35 0.30 0.44 0.40 0.46Others* 0.27 0.38 0.24 0.38 0.28 0.56 0.39 0.47Muslim 0.30 0.43 0.26 0.40 0.30 0.53 0.40 0.48Other Religion 0.27 0.42 0.25 0.38 0.30 0.54 0.38 0.47All 0.31 0.42 0.27 0.40 0.31 0.55 0.40 0.48

Casual Schedule d Tribes 0.13 0.15 0.14 0.14 0.25 0.15 0.27 0.29Schedule d Caste 0.18 0.18 0.17 0.17 0.20 0.19 0.32 0.32Others* 0.36 0.18 0.33 0.18 0.44 0.21 0.43 0.32

Muslim 0.19 0.19 0.19 0.19 0.36 0.23 0.33 0.33Other Religion 0.23 0.22 0.20 0.21 0.22 0.25 0.36 0.35All 0.19 0.19 0.18 0.18 0.28 0.22 0.33 0.33Source: Data calculated from unit level data CD of various NSS rounds .

*Includes OBC.

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