Women, Work, And Employment Outcomes in Rural Ind

8/8/2019 Women, Work, And Employment Outcomes in Rural Ind

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Women, work, and employment outcomes in rural India


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Women, work, and employment outcomes in rural India

Abstract

This paper analyses the trends and pattern of womens employment in rural Indiausing unit data from two types of large scale surveys. It shows that while ruralwomens employment has grown over the decades, there has not been muchimprovement in outcomes. Women are still largely concentrated in agriculture asself-employed or casual labour. Women workers face various forms ofdiscrimination, including job-typing that pushes them in low paying jobs.

The paper analyses the determinants of womens employment through regressionanalysis. The significant predictor variables are social group, age, marital status,landholding, wealth status, and womens autonomy. Results show that higher work

participation per se does not indicate higher welfare; only when accompanied byhigher education, and /or assets does it lead to better employment outcomes. Further,education may not positively influence a womans participation in work, but forwomen who are in the workforce, education is the most important determinant of

better quality non-agricultural work. Along with education, womens autonomy

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N. Srivastavi and R.

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1. Introduction

Employment is critical for poverty reduction and for enhancing womens status.However, it is potentially empowering and liberating only if it provides women anopportunity to improve their well being and enhance their capabilities. On the other

hand, if it is driven by distress and is low-paying then it may only increase awomans drudgery. To understand womens work status in Indias rural areas andto examine the trends and nature of womens employment, this paper analyses thedata from large scale national surveys.

The National Sample Survey (NSS) Organisation carries out quinquennialsurveys on employment and unemployment and covers more than 100,000households and 500,000 individuals throughout the country. The survey covers

socio-economic and demographic characteristics, employment and unemploymentcharacteristics, and provides information on wages. The latest year for which datais available for both surveys is 2004-05. The National Family Health Survey(NFHS) covers households with women in the reproductive age group of 15-49 andin the latest survey carried out in 2005-06; it covered 63,896 women in this agegroup. The authors estimates from the NSS surveys of 1983, 1993-94, and 2004-05 and the NFHS survey of 2005-06 are based on analysis of unit level data. Inaddition, we have also drawn on data on womens control on agricultural holdingsf th i lt l l l d t d b th Mi i t f


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described as being employed in the Subsidiary


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Status. A person employed either in Usual Principal Status (UPS) or UsualSubsidiary Status (SS) is enumerated as being employed in the Usual Status(also UPSS). Unless otherwise stated, the reference is to UPSS employmentthroughout this paper. The industry associated with her employment is the onewhich she is associated for a major part of the employment.1 We focus for the most

part on rural employment, but also provide data on urban employment in order tohighlight the contrasts.

As in most other parts of the world, fewer women participate inemployment in India compared to men. In 2004-05, while in urban areas, 16.6

percent women and 54.9 percent men (of all ages) were employed, in rural areas,these percentages were 32.7 and 54.6 respectively (Table 1). More women(proportionately) are employed only in the subsidiary status, than men, especially in

rural areas. This can be explained by factors from the supply side as well as thedemand side. Taking the former first, the rural economy has been largely stagnantover the years and employment opportunities have not grown. Most women,therefore, are able to get work for only a few months in the year. This keeps thememployed only in the subsidiary status. On the supply side, womens primaryduties are supposed to be in the household. For economic reasons they have towork, but must do so in addition to their domestic responsibilities, and are thereforeonly able to enter the labour force as subsidiary workers.


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Figure 1: Workers per Thousand Persons by sex and residence, 1972-73 to 2004-05

600

A- ~h A-

^

- - -

X----*-- --*-- - - x x--

x-"

. X

1972-73 1977-78 1983 1987-88 1993-94 1999-00 2004-05

RM---B - - RF-

-*- - UM - X -UF

Note: RM Rural Male, RF Rural Female, UM- Urban Male, UF- Urban

F l S NSS E l t U l t S V i Y


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Figure 2: Rural Workforce Participation Rate by Social Group and Sex, 2004-0560.0 I

50.0

ST SC OBC Others TotalSocial Group

Note: ST Scheduled Tribe; SC Scheduled Caste; OBC, Other BackwardCastes Source: Computed from NSS Employment-Unemployment Surveydata, 2004-05

Figure 3: Rural Workforce Participation Rate by Religion and Sex, 2004-05


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appear to explain this pattern. It is interesting that in urban areas by contrast,womens employment goes up at higher educational levels and shows a patternsimilar to that for men showing the narrowing of gender gaps in urban areas.

Table 2 Workforce Participation Rate by Level of Education, 2004-05

Highest Level of Educational Attainment Rural Urban

Male Female Male FemaleIlliterate 50.8 39.2 37.6 20.1

Literate-&-up to-primary 44.9 21.3 42 12

Middle 70.3 31.8 66 13.6

Secondary 72.6 30.3 67 12.2Higher-secondary 70.8 25.1 60.8 12.9

Diploma/certificate-course 81.5 52.2 79.6 48.4Graduates & above 85 34.3 79.5 28.9

All 54.6 32.7 54.9 16.6

Source: Computed From Employment-Unemployment Survey NSSO 2004-05, Unit Level Data.

H d i t t f i fl th i ti i ti i k?


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Figure 4: WFR across MPCE deciles by sector and sex (15-59 years), 2004-05

A A

-* --*

""

X

"""""' -x - - .X.*" ^~ X

1 2 3 4 5 6 7 8 9 10

4 RM -------RF --A UM --X---UF

Note: RM Rural Male, RF Rural Female, UM- Urban Male, UF- Urban Female Source:

Computed from NSS Employment-Unemployment Survey 2004-05, unit data To conclude,womens participation in gainful work is lower compared to men; it is higher forscheduled caste and scheduled tribe women who are less restricted by socialnorms; among religious groups, work participation is lowest for Muslim women;education impacts differentially for men and women, with level of participationincreasing with educational levels for men, but declining for rural women; as


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Table 3. Percentage Distribution of Workers by Employment Status, 2004-05

Rural Urban

Employment Status / Sector Male Female Persons Male Female Persons

Agriculture

Self Employed 63.8 64.5 64.1 70 62.8 66.9Regular/salaried 1.3 0.5 1 5.3 1.9 3.8Casual labour 34.9 35 34.9 24.7 35.3 29.4

Non-agriculture

Self Employed 47 59.6 49.7 43.1 44.4 43.4

Regular/salaried 24.2 19.8 23.2 42.9 43 43

Casual labour 28.9 20.6 27.1 13.9 12.6 13.7All workers

Self Employed 58.1 63.7 60.1 44.8 47.7 45.4

Regular/salaried 9 3.7 7.1 40.6 35.6 39.6Casual labour 32.9 32.6 32.8 14.6 16.7 15

Total 100 100 100 100 100 100

Source: Computed From Employment-Unemployment Survey NSSO 2004-05, Unit Level Data.

What is the significance of this classification and what does it tell us aboutthe nature and disparities in womens employment? This becomes clear from Table

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Table 4. Wages and Percentage Distribution of Workers by Agriculture and Non-agriculture and by Employment Status -2004-05

Casual Labour

Industry % distribution Wages (Rs. Per day) F/M Wages

Male Female Male Female PersonsAgriculture 70.6 89.5 47.9 33.2 42.5 0.69

Non-Agriculture 29.4 10.5 67.5 44 63.8 0.65

Total 100 100 54.6 34.7 48.5 0.64

Regular Workers

% distribution Wages (Rs) per day F/M wages

Industry Male Female Male Female Persons

Agriculture 9.9 11 68.1 53.7 65.2 0.79

Non-Agriculture 90.1 89 151.1 86.3 139.1 0.57

Total 100 100 143 82.9 131.8 0.58

How does economic status relate to the nature of work that men and women do? Fig 5makes this very clear. Along expected lines, the percentage of casual labourers among bothmale and female workers declines sharply with rising household MPCE deciles. The

percentage of self-employed among workers shows an increasing trend with MPCE deciles,

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Although the structure of employment by employment status has been remarkablyconstant across the years, previous NSS surveys (till 1999-00) showed some increasein casual labour in the rural male and female workforce and a decline in the share ofthe self-employed. But during 1999/00-2004/05, there was a change in the trend; theshare of self-employed workers increased among both female and male workers,

while the share of casual work declined. Why this has happened is difficult to say,but it is likely that the overall stagnation in agriculture and the rural economy mayhave led to this shift. The growth rate in agriculture and allied sectors was only littlemore than 2 percent per annum in this period, registering a negative growth in someyears. This may have led to shrinking availability of wage work and compelledworkers to eke out subsistence from self employment.

The next section discusses womens employment in agriculture, while the

subsequent section takes up womens employment in non-agriculture. In each, the threebroad status of employment are analysed.

3.1 Agriculture

In rural areas, nearly 84 percent women workers are engaged in agriculture, either ascultivators or labourers as compared to 67 percent male workers. Table 5 shows thedecline in the proportion of men as well as women in agriculture; but the decline is

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3.1.1 Agriculture: Casual Workers

Compared to 23.2 percent male rural workers, 29.2 percent female rural workers

were engaged as casual agricultural labourers in 2004-05. There is a disproportionateconcentration of the most deprived social groups in this form of labour. Half of thefemale casual labourers and 43 percent of male casual labourers in India belong toSCs and STs, nearly twice their share in population.

Table 6. Percentage Distribution of Male and Female Workers in Rural Areas by Activity, 1999-00

Operation Male Female Total Male Female

Ploughing 91.5 8.5 100 9.4 1.8

Sowing 64.5 35.5 100 3.4 3.8Transplanting 56.4 43.6 100 3.2 5

Weeding 51.7 48.3 100 7.2 13.7

Harvesting 64.5 35.5 100 16.1 18.2Other cultivation activities 70.5 29.5 100 36.8 31.5

Forestry 58.7 41.3 100 0.6 0.8Plantation 69.1 30.9 100 1.7 1.6

Animal husbandry 49.6 50.4 100 5.9 12.3


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per cent for men and 17 per cent for women for 2004-05 by the Current Daily Statuscriterion and this increased over 1993/94-2004/05 (NCEUS, ibid.).

3.1.2 Agriculture: Self Employed Workers (Farmers)

As noted earlier, women workers are increasingly engaged as self-employed inagriculture. There has been a steady increase in the numbers of both women and menfarmers over all years since 1983 except 1999-00. The sharpest increase has taken

place in the recent quinquennium when the share of women farmers increased to41.8 percent (Table 7), the highest in 32 years. These results attest to the large role

played by women farmers although they do not confirm a systematic trend towardsfeminization.

Tabl e 7.Num

1983

ber and Percentage of Farmers among Agricultural Workers

1987-88 1993-1994 | 1999-2000 2004-2005Numbers (Million)

Male 79.5 83.4 88 85.3 96.8Female 52 54.7 55.2 51.9 69.4

Persons 131.5 138 143.2 137.3 166.2


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percent. In large holdings (greater than 10 hectares), the corresponding percentagesdeclined to 5.7 and 5.6 respectively. These figures could partly be explained by the

pattern of outmigration since it is in smaller holdings where male outmigration is alsolikely to be higher. However, cultural and social factors are also very important inexplaining the fact that a miniscule proportion of women have control on this critical

resource.

This is brought out by the regional pattern of womens control over landholdings. The percentage of such holdings was much higher in the more progressiveSouthern states and in some of the North-eastern states. In Kerala women operated 21

percent of the land holdings and 18 percent of area. In Andhra Pradesh thecorresponding figures were 20 percent and 17 percent respectively while in Tamil

Nadu they were 18.1 percent and 15.1 respectively. In the absence of land titles,

women farmers have much smaller access to institutional credit compared to malefarmers, and receive a much lower degree of institutional support.

3.2 Non-Agriculture

3.2.1 Non-Agriculture: Casual Workers


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Table 8. Percentage Distribution of Rural Male and Female Casual Workers andWages by Industry Divisions -2004-05

Industry % distribution Wages (Rs. Per day) F/MWages

Male Female Male Female Persons

Agriculture, Forestry Fishing 70.6 89.5 47.9 33.2 42.5 0.69Mining & Quarrying 1.4 0.8 68.6 45.7 63.9 0.67

Manufacturing 5.8 3.8 63.8 37.6 57.6 0.59

Elec Gas & Water 0 0 77.4 26.4 74.2 0.34Construction 16.7 4.4 69.5 49.8 66.9 0.72

Wholesale & Retail Trade 1.3 0.1 57.6 36.3 57 0.63

Hotels & Restaurant 0.4 0 65.1 46.7 64.4 0.72Transport Storage & Commu 2.5 0.1 70 41.6 69.3 0.59

Financial Intermediation 0 0 144.5 - 144.5Real Estate, Renting, Business 0.1 0 90.2 139.5 90.8 1.55

Public Admin 0.1 0.1 61.3 40.5 56.3 0.66

Education 0 0.1 56.5 48.6 52.5 0.86

Health & Social Work 0 0.1 86.7 52.8 68.5 0.61Community Social & PersonalService

0.5 0.2 56.6 34.9 53.3 0.62

Private Households 0.5 0.9 61.7 40.4 51.3 0.66


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material and sale of finished goods. This dependence on the contractor together withthe isolation undermines their ability to bargain for higher piece-rates, timely

payments or overtime pay. The annual gross value addition of the rural female home-workers is, on average, Rs.5270 ($108, Table 10), much lower than even the Rs 9000($184) that accrues in female OAEs. The average value of fixed assets engaged by

them is also very low at Rs.3800 ($78).Table 10. Gross Value Added per Worker and Fixed Assets per Enterprise in Home-basedEnterprises, 1999-2000

Gross Value Added Per Worker (Rs.) Fixed Assets Per Enterprise (Rs.)

Male Proprietary Female Proprietary MaleProprietary

Female Proprietary

Rural 8826 5270 13917 3800Urban 13409 6343 39131 13914Total 10435 5544 22341 6229

Note: OAEs located at home and working solely/ mainly on contract are considered home basedenterprises

Source:NCEUS (2007).

About 79 per cent of the women and 63 per cent of the male home workers


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Table 11. Percentage Distribution of Regular Workers by Industry and Wages Per Day, 2004-

05

% Share inEmployment

Wages (Rs) per day F/Mwages

Industry Male Female Male Female

Persons

Agriculture, Forestry Fishing 9.9 11 68.1 53.7 65.2 0.79Mining & Quarrying 1.4 0.5 246.1 74.6 230.9 0.3

Manufacturing 20.6 18 118.4 40.8 105.4 0.35Elec Gas & Water 2.4 0.3 242.4 253.9 242.6 1.05

Construction 2.1 0.3 106 92.5 105.6 0.87

Wholesale & Retail Trade 10.7 1.8 72.3 55.6 71.7 0.77

Hotels & Restaurant 1.7 1.1 85.2 41.4 79.3 0.49Transport Storage & Communication 14.5 1.8 126.5 127.5 126.5 1.01

Financial Intermediation 2.2 0.9 257.1 138.2 246.6 0.54

Real Estate, Renting, Business 1.2 0.6 101.9 133.7 105.1 1.31

Public Admin 12.6 5.9 199.6 81.3 187.4 0.41Education 15.4 37.8 222.4 115.4 183.8 0.52

Health & Social Work 2.5 8.9 178.5 123 154.7 0.69Community Social & Personal 1.8 0.9 80.8 53.6 78.1 0.66


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Consumption characteristics and social development levels, of households havebeen divided as follows: 1. Extremely Poor: up to 0.75 PL; 2. Poor: Between 0.75Pl andPL; 3. Marginal Poor: Between PL and 1.25 PL; 4. Vulnerable: between 1.25 PL and 2 PL;5. Middle: Between 2 PL and 4PL; 6. Higher Income: Above 4 PL. The four lowercategories have together been characterised as Poor and Vulnerable.

Table 12. Percentage Distribution of Women Workers by Poverty and Other Correlates, 2004-05

ECONOMIC CATEGORY

Extremelypoor

Poor Marginal Vulnerable Middle Higherincome

All

Activity Status

Self-employed 45.3 50.2 57.0 67.3 76.9 71.0 63.1Regular wage employee 2.5 2.1 2.0 3.4 6.6 22.2 3.8

Casual worker 52.1 47.7 40.9 29.3 16.5 6.7 33.1

Total 100 100 100 100 100 100 100

Industry

Agriculture 84.5 85.0 84.9 83.7 80.2 63.5 83.2

Mining, Manufacturing & 9.8 9.2 8.7 9.2 7.9 7.2 8.9


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Table 12 shows that while the percentage of casual workers declines rapidlywith improving economic status, the percentage of regular workers is only high inthe last category. The Self-employed have a presence in all economic categories,

but are more predominant as economic well being improves. In terms of industrialcomposition, it can be seen that while agricultural workers are present in all

categories in a large proportion, their weight declines in the highest while that oftertiary sector workers increases. It can also be seen that workers with higher levelsof education are almost entirely present in the higher economic categories. Sinceeducation plays a critical role, this has been analysed in greater detail below.

One of the major attributes of women engaged in agriculture is their low levelof educational attainment. With the ongoing commercialisation of agriculture, cropdiversification, introduction of new technologies and the imperative for better

information processing, education has to be reckoned as a key input in any attemptat overall development and modernization of agriculture. However, the grim pictureis that about 86 per cent of female agricultural labourers and 74 per cent of femalefarmers are either illiterate or have education below the primary level (Table 13).Shocking as it may seem, the average education of a female agricultural labourer wasless than one year in 2004-05.

Table 13: Percentage Distribution of Rural Agricultural Workers by Educational


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5.1 Determinants of Participation in Employment using NSS data (2004-05)

The analysis attempts an explanation not only of why women participate in theworkforce, but also why they participate in specific types of employment (ascultivators, casual workers in agriculture, and in various types of employment in

non-agriculture).The independent variables used are: age group, marital status,education status, caste group, religion, presence of children under 5 years, landholding size category, monthly per capita consumption quintile, and region.

As mentioned earlier, Logistic Regression is used since the model does notmake distributional assumptions on the predictors, which can be both continuousand discrete. The results are presented in Appendix Table 1 in the form of OddsRatios and their significance level, and are briefly discussed here:

5.1.1 Agriculture: Determinants of work participation

As one would expect, possession of land has a very important influence on awomans participation in employment. Controlling for land, the householdsconsumption level has a negative influence. Among the individual characteristics, it isseen that compared to women in the age group 15-29, older women have a higher


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b) Agriculture: Self-employed workers

The highest proportion of women workers are engaged as self-employed inagriculture. The probability of a woman worker being self-employed in agricultureis highest for the high age group (45-59) and for currently married women. The odds

ratio are lower for divorced or separated women indicating that these women nolonger have access to land. The odds ratio declines with increasing levels foreducation and is the lowest for women workers who are graduates or diplomaholders. SC women workers who have the lowest access to land also have the lowest

probability of being self-employed in agriculture. Muslim women workers again areless likely to be engaged in farming. As one may expect the probability of engagingin agriculture increases sharply with bigger landholdings and also with higher levelsof household consumption. Women workers n the Northern region have the highest

probability of being engaged in agriculture as self-employed (relative to those in theEast), while women workers in the Southern region have the lowest probability of

being so engaged.

5.1.2 Non-agriculture: Determinants of Work Participation

Since female workers have largely remained confined to agriculture, the


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b) Non-agriculture: Self employed workers

The probability of being self-employed is higher among young women workers andthose who have never married. The odds ratio is significantly lower among currentlymarried and widowed women workers. Education of the worker increases the

probability of taking up self-employment, but the highest odds ratio are for thosewith secondary or higher secondary level of education. All social groups havehigher odds ratio compared to the reference group (ST) and for the Muslim womenworkers, the odds ratio is more than twice as high as Hindu workers. This is

principally due to the hereditary involvement of these workers households inartisanal activities. Odds ratio declines steadily with increasing possession of landand is significantly higher than one only for the second quintile in terms of householdMPCE. Women workers in the Eastern region (the reference group) have the highest

probability of being so employed.

c) Non-agriculture: Regular workers

This is the smallest segment of workers among rural women. Compared to thereference groups, the odds ratio is higher for higher aged women and forwidowed/separated women (it is lower than one for currently married women). Itincreases dramatically with increasingly levels of education. Among social groups it


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The main distinguishing feature of the NFHS, because of which this data sethas been analysed in this paper, is that it collects information on womensautonomy using a number of indicators. Among these indicators, it used a set of threeindicators to capture womens freedom of mobility which in our view is central totheir participation in the labour market. The three indicators of womens mobility are

if they are allowed to go alone to the market/ health facility/ or outside the village orcommunity.

The variables are similar to those used in earlier analysis, but with thefollowing significant differences. First, this data has a younger age cohort. Second,as mentioned earlier, employment has been measured differently in this survey.Third, the NSS does not provide information on how many children a woman has,though it does give the number of children in a household. With NFHS data it is

possible to identify the mothers with young children. Fourthly, this paper uses thesynthetic wealth indicator given by NFHS which is built on a factor analytic score

based on 33 assets. Finally, the NFHS does not allow us to estimate consumptionexpenditure as in the case of the NSSO and hence this variable has been droppedfrom the analysis.

The results of the Logit analysis are summarised in Appendix Table 2. Theseresults are similar to the earlier results in many basic ways and are not discussed here.


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5.3 Determinants of State level Variations in Employment Participation

The large state-wise and regional difference in rural female employmentparticipation in India has been alluded to earlier. The logit regressions in section 5confirm these differences but the NSSO data set gives very limited measures of socio-

cultural differences. We therefore explore the impact of additional variables, gleanedfrom other data sources such as the National Family Health Survey, the AgriculturalCensus, the Central Statistical Organisation, and the National Bank for RuralDevelopment on state-level variations in female employment. A list of the variablesconsidered for this analysis is given in Table 1.

Table 14: List of Variables and Variable DescriptionVariable Description Source

WPR_total Workforce participation rate Rural women 15-59 years NSSO, 2004-05

WPR_nag Workforce participation rate - non agriculture rural womenworkers

Same as above

WPR_naglab Workforce participation rate - non agricultural female labour(Regular+Casual)

Same as above

WPR_RS Workforce participation rate of Regular / salaried ruralfemale workers

Same as above

WPR SE Workforce participation rate of Self Employed non Same as above


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states. They are the lowest in the Eastern states (West Bengal, Assam, Bihar). Thereare important sectoral differences. For example, states such as Kerala, West Bengaland Orissa which have low total WPRs show a high participation of women in non-agriculture and the highest participation rate of women in regular work is in Kerala.The state level values of the other variables, as well the correlation between the

variables is given in Appendix Tables 4 and 5 respectively.

It is anticipated that these differences could be a result of supply relatedcharacteristics such as (i) the percentage of SC/ST households in a state; (ii) meanyears of education of women, (iii) variables which could proxy womens autonomy toundertake economic activity (independent mobility; control of land holdings,

participation in Self-help groups); and (iv) rural wages; or (v) employment demand(mean rural income proxied by per capita consumption expenditure, per capita State

Domestic Product, or expenditure on rural development programmes).

Since our objective is to understand not only total work force participationrates, but also womens participation in specific types of work, especially in non-agriculture, these rates are regressed for 20 states across the above mentionedvariables, selecting only one variable in (iii) and (v) above. The best fits are

presented below. Some of the independent variables that were considered were foundto be highly correlated, in particular with Mean Years of Education and were dropped


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WPR in non-agricultural regular/salaried work. Mean Years of education is a highlysignificant variable; one level increase in education would increase the WPR inregular/salaried work by 0.82. The share of area in holdings operated by women is alsosignificant but only at 10 percent level of significance. The coefficient of this variable isquite low (0.09).

Table 15: Regression Equations for State level Determinants of Workforce Participation ofRural Women

1 WPR_total = 3.848 - 2.299 MYrSch_all + 0.841 Sh_R_STSC*** + 1.436 Sh_Fholdarea*** +0.001 GDP_cap

R2 = 0.495; Adj. R2 = 0.360 F = 3.671**

2 WPR_nag = 0.519 + 1.135 MYrSch_all*** + 1.854 SHG_RuHh*R2 = 0.494 Adj. R2 = 0.431 F=7.811*

3 WPR_SE = 1.214 + 0.106 MYrSch_all + 1.268 SHG_RuHh*

R2 = 0.461 Adj. R2 = 0.394 F = 3.847*

4 WPR_naglab = -0.469 + 0.815 MYrSch_all** + 0.215 Sh_Fholdarea**

R2 = 0.515 Adj. R2 = 0.458 F = 9.037*n


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6. Conclusion and Policy Implications

While women workers in general constitute a marginalised category within the classof workers, rural women workers occupy a lower position compared to their urbancounterparts, and the lowest layer among them is constituted by those belonging to

the bottom strata of the society i.e. SCs and STs.

Womens time use in economic activities that give them a return is limited buttheir participation in household activities that indirectly contribute to the economicoutput of the household (called extended SNA) far exceeds that of men. But womenhave lower work participation rates in SNA activities. For rural women these arehigher than urban women but also closer to men. These rates are also higher forwomen belonging to SCs/STs than the other women. A significant percentage of

rural women workers are engaged in subsidiary status work.

An important argument in this paper is that higher work participation ratesperse do not indicate a higher level of welfare. Only when higher work participationrates are accompanied by higher educational capabilities and/or asset and income,higher work participation rates become meaningful from a welfare and, especially,income point of view. We show that rural women workers are concentrated inagriculture to a much larger extent than men. On the other hand, a much smaller


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promotional nature from different entry points. In this concluding section we drawattention to some of the major issues which emerge from our analysis;

1. A higher level of education and employable skills for women workers is asine qua nom for improving their levels of productivity and enabling them tomove into non-agricultural vocations. The emphasis on universalisingelementary education has undoubtedly narrowed the enrolment gap betweenmen and women, but given the low levels of education and employable skillsand the gap between men and women workers, initiatives should also focuson the exiting workforce. Further, as the results of this paper and theevidence from other studies shows, break point occurs when women and menacquire a higher secondary level of education, enabling them to enter higherquality jobs (Srivastava 2008).

2. Womens autonomy, measured here in terms of access to land and controlover its operation, mobility, and willingness to join self-help groups affectstheir ability to access resources and improve productivity, and also to moveinto non-agricultural vocations. Such autonomy responds to a complex set ofsocial factors. But policy initiatives can move the frontier outwards and canimprove womens access to knowledge, technology and resources,empowering them as economic agents. Fostering a group approach, drawing


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workers also receive abysmally low wages for a variety of reasons. TheNational Rural

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Employment Guarantee Programme which has been initiated in 2006 andwhich has now been extended to all rural areas can play a major role inimproving demand for womens labour, increasing reservation wages, andsetting labour standards in rural areas. While some impact has already beenfelt in a number of areas, much more needs to be done to implement this

scheme effectively and to increase opportunities for quality and decent workin rural areas. In our view this programme constitutes the axis around whichthe employment conditions of the poorest women workers can improve inrural India.


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References

Anker, R. 1998. Gender and Jobs: Sex Segregation of Occupations in the World,International Labour Organisation, Geneva.

National Sample Survey Organisation. 1993-1994. Employment-Unemployment

Situation in India, Round, Round 50th, Report No. 409, National SampleSurvey Organisation, Ministry of Statistics and Programme Implementation,Government of India, New Delhi.

National Sample Survey Organisation. 1999-2000. Employment-UnemploymentSituation in India, Round 55th, Report No. 458 I and II, Ministry of Statisticsand Programme Implementation, Government of India, New Delhi.

National Sample Survey Organisation. 1999-2000.Participation of Indian Women

in Household Work and Other Specified Activities, Round 55th, Report No.465, National Sample Survey Organisation, Ministry of Statistics andProgramme Implementation, Government of India, New Delhi.

National Sample Survey Organisation. 2004-05. Employment-UnemploymentSituation in India, Round 61st, Report No. 515 I and II, National SampleSurvey Organisation, Ministry of Statistics and Programme Implementation,Government of India, New Delhi.


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Singh, N., Kaur, R. & Sapre, M. 2004. Insecurities, vulnerability anddiscrimination: a study of women garment workers, Paper presented at aSeminar on Globalization and Womens Work, March 25-26, Noida, India.V.V. Giri National Labour Institute.

Srivastava, R. 2008. Education, skills, and the emerging labour market in India, The

Indian Journal of Labour Economics, Vol. 51, Number 4, Oct.-Dec., pp. 759-782

Srivastava, R., N. C. Saxena & S. K. Thorat. 2008. Land institutions, policy, andreforms in India in Ashok Gulati and Shenggen Fan (eds.) The Dragon andthe Elephant: Agricultural and Rural Reforms in China and India, pp. 71-96.

New York: John Hopkins Press, & New Delhi: Oxford University Press.

Vijayabhaskar, M. 2002. Garment industry in India, in G. Joshi ed. GarmentIndustry in South Asia: Rags to Riches: Competitiveness, Productivity and Job

Quality in Post MFA Environment, New Delhi, ILO, SAAT.


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APPENDIX

Appendix Table 1: Results (Odds Ratios) of Logistic Regression for Women/Women Workers, 15-59 Years, 2004-5 (NSSO)

1 =

Employed;

0 = Unemp./

Not

in LabourForce

1 =

Agricultu re

Self-

employed; 0

= OtherWorkers

1 =

Agriculture

Wage

Workers;

0 = OtherWorkers)

1 = Non-agriculture; 0= OtherWorkers)

1 = Non-agriculture:Self-employed; 0= OtherWorkers)

1 = Non-agriculture:RegularWorkers; 0 =OtherWorkers)

1 = Non-agriculture:WageWorkers; 0 =OtherWorkers)

1 2 3 4 5 6 7

Age (Ref: 15-29)

30 - 44 1.806* 0.985 0.870* 1.135* 0.965 1.959* 0.873

45 - 59 1.194* 1.340* 0.744* 0.91 0.806* 2.102* 0.581*

Marital Status (Ref: Never Married)

Currently Married 1.479* 1.585* 0.913 0.643* 0.688* 0.730* 0.607*

Widowed 2.233* 0.807* 1.184 1.061 0.739* 2.280* 1.174Divorced/

Separated

3.387* 0.554* 1.28 1.308 0.771 2.740* 1.394

Education (Ref: Illiterate)

Below Primary 0.707* 0.861* 0.908 1.377* 1.338* 2.203* 0.891

Primary & Middle 0.514* 0.951 0.517* 1.850* 1.637* 4.703* 0.791*

Secondary & HS 0.341* 0.430* 0.225* 4.787* 1.740* 32.724* 0.436*

Graduate & Above

(Diploma)

0.588* 0.053* 0.054* 30.184* 1.480* 178.236* 0.279*


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Appendix Table 3: Rural Female Workforce Participation Rate (UPSS), 15-59 Years, 2004-05In: Agriculture In: Non-agriculture

State Total WPR Self

Employed

Regular/

Salaried

Casual

LabourTotal Self

Employed

Regular/

Salaried

Casual

LabourTotal

Andhra Pradesh 70.5 23.0 0.0 32.3 55.3 10.3 2.8 2.0 15.2

Assam 33.1 21.7 2.0 5.6 29.3 1.4 0.9 1.5 3.8

Bihar 23.8 9.5 0.1 10.9 20.5 2.8 0.3 0.2 3.3Chhatisgarh 75.2 38.5 0.1 31.7 70.2 2.2 1.0 1.8 5.1

Gujarat 67.0 36.7 0.0 22.8 59.5 3.1 1.8 2.6 7.4

Haryana 52.2 41.7 0.1 5.6 47.4 2.4 1.2 1.2 4.8

Himachal Pradesh 73.5 65.9 0.0 0.6 66.4 2.0 4.2 0.9 7.1

J&K 40.8 35.3 0.0 0.1 35.3 3.2 1.4 1.0 5.5

Jharkhand 51.2 36.7 0.1 7.0 43.7 3.4 0.9 3.1 7.4

Karnataka 65.9 25.7 0.1 30.6 56.3 6.1 2.0 1.4 9.5

Kerala 36.0 11.8 0.5 5.5 17.8 6.3 7.0 4.8 18.1Madhya Pradesh 60.9 32.1 0.2 21.4 53.6 3.1 1.9 2.3 7.3

Maharashtra 70.7 32.8 0.1 30.9 63.9 3.5 1.8 1.5 6.8

Orissa 48.3 21.0 0.0 15.3 36.3 8.5 1.1 2.5 12.1

Punjab 48.5 40.8 0.1 2.6 43.5 2.3 2.3 0.5 5.0

Rajasthan 67.7 55.8 0.1 4.6 60.4 2.9 0.9 3.5 7.3

Tamilnadu 66.6 20.5 0.1 28.2 48.8 9.8 4.5 3.6 17.8

Uttaranchal 67.3 60.8 0.0 3.5 64.3 0.9 1.3 0.7 3.0

Uttar Pradesh 40 5 30 2 0 1 4 9 35 2 4 0 0 7 0 7 5 3


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Note: Refer Table 15 for Variable Details and Sources


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Appendix Table 5: Correlation MatrixVariable s WPR

tot

WPR_

nag

WPR n

aglab

WPR

RS

WPR

SE

MYrSc h

all

FCwa

ge na

g

Fwage_n

ag

Sh R S

T/SC

Per any

mob

Sh Fhol

darea

SHG R

uHh

avg

mpc

e

Rdexp

_cap

GDP_

cap

WPRjot 1 0.07 0.16 -0.01 -0.03 -0.05 0.18 -0.01 0.35 0.24 0.33 0.29 0.04 0.32 0.38

WPR na g 0.07 1 .83(") .70(** ) .87(** ) 0.31 -0.20 -0.30 -0.19 0.20 .57(**) .60(") 0.12 -0.32 0.23

WPR na

qlab

0.16 .83(**) 1 .88(** ) 45C) .58(**) 0.15 0.03 -0.23 0.43 54(*) 0.31 0.41 -0.35 0.37

WPR R S -0.01 .70(**) .88(**) 1 0.35 .79(**) 0.22 0.27 -0.39 .60(**) 0.42 0.29 .56(* -0.43 0.44

WPR SE

-0.03 .87(") 45(*) 0.35 1 -0.01 -0.44 -.500 -0.10 -0.05 0.44 .68(") 0.16 -0.22 0.05

MYrSch

all

-0.05 0.31 .58(**) .79(** ) -0.01 1 46C) .67(**) -46(*) 53C) 0.21 -0.09 .78(* -.49(*) .65(** )

FCwage

_nag

0.18 -0.20 0.15 0.22 -0.44 46(*) 1 .70(**) -0.31 0.39 -0.07 -0.13 .61 (* 0.13 0.36

Fwage_

nag

-0.01 -0.30 0.03 0.27 50H .67(**) .70(") 1 -0.36 46(*) -0.15 -0.35 .66(* -0.29 0.42

Sh RS

T/SC

0.35 -0.19 -0.23 -0.39 -0.10 -46D -0.31 -0.36 1 -0.40 -0.43 -0.13 .49(*

)50( *) -0.15

Per any

mob

0.24 0.20 0.43 .60(** ) -0.05 53(*) 0.39 46(*) -0.40 1 0.28 0.16 .48(*)

-0.35 50(*)

Sh Fhol

darea

0.33 .57(") 54(*) 0.42 0.44 0.21 -0.07 -0.15 -0.43 0.28 1 0.41 0.12 -0.14 0.33

SHG Ru

Hh

0.29 .60(") 0.31 0.29 .68(** ) -0.09 -0.13 -0.35 -0.13 0.16 0.41 1 0.25 0.22 -0.11

avg

mpce

-0.04 0.12 0.41 .56(** ) -0.16 .78(**) .61 D .66(**) -.49(*) .48(*) 0.12 -0.25 1 -.48(*) .71(" )

Rdexp_c

ap

0.32 -0.32 -0.35 -0.43 -0.22 -.49(*) 0.13 -0.29 50(*) -0.35 -0.14 0.22 .48(*

)1 -0.34

GDP ca p 0.38 0.23 0.37 0.44 0.05 .65(**) 0.36 0.42 -0.15 50H 0.33 -0.11 .71 (* -0.34 1

** Correlation is significant at the 0.01 level (2-tailed). *Correlation is significant at the 0.05 level (2-tailed).Note:Refer Table 15 for Variable Details and Sources

N.SrivastaviandR.Srivastava -Draftfordiscussion- - 37 -


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Notes

NishaSrivastavaiswiththeDepartmentofEconomics,University ofAllahabad,Allahabad211001,India([email protected])Ravi Srivastava is with the Centre for the Study of Regional Development, Jawaharlal Nehru University, New Delhi 110067 ([email protected]). Currently he is with

the National Commission for Enterprises in the Unorganised Sector (NCEUS).Able research support provided by Swati Sachdev and T. Shobha is gratefully acknowledged.1If a person is not in the labour force (neither employed nor employed), she could be engaged in other types of activities including domestic duties. The CentralStatisticalOrganisation of the Government of India provided official visibility to the double burden of work through a pilot study of utilization of time by men and women insixstates in 1998 (CSO 2000). The report classified the activities based on the 1993 System of National Accounts (SNA) into three categories: (i) Economic activities thatareincluded in the SNA; (ii) Activities not currently included in the SNA but are characterised as extended SNA, which include household maintenance and care forthechildren, old and the sick in the household; and (iii) Non-SNA consisting of the social and cultural activities, leisure and personal care. The study confirmed thatwomenspend a disproportionate amount of time in what is called extended SNA. On the other hand men spend a much greater time in SNA activities than women. As fornon-

SNA activities, the difference was not as striking. In short women spent 17 per cent more time in SNA plus extended SNA activities compared to men.2For definitions of these terms see NCEUS (2007).3The currency reference is the US dollar.

N. Srivastavi and R.Srivastava

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