STATUS, CASTE AND TIME ALLOCATION OF WOMEN IN INDIABharat Ramaswami

Indian Statistical Institute

South Asia Development Workshop, University of New South Wales, September 15-16, 2011

CO-AUTHORS

Mukesh Eswaran, University of British Columbia

Wilima Wadhwa, ASER Centre, Delhi

SOME WELL KNOWN TRENDS

Increasing spread of education among women

Declining fertility rates

TOTAL FERTILITY RATE (WORLD BANK INDICATORS)

Source: http://www.google.co.in/publicdata/explore?ds=d5bncppjof8f9_&ctype=l&strail=false&nselm=h&met_y=sp_dyn_tfrt_in&hl=en&dl=en#ctype=l&strail=false&nselm=h&met_y=sp_dyn_tfrt_in&scale_y=lin&ind_y=false&rdim=country&idim=country:IND&ifdim=country&hl=en&dl=en

A PUZZLE (MAITREYI DAS, 2006)

Yet, work participation rates of women low in India (South Asia generally) – around 35%.

Over time (1983-99), this rate has not moved – in fact, a marginal decline of about 2% percentage points over so.

Incomes have increased; so is it all because of an income effect?

CROSS-COUNTRY EVIDENCE ON LABOR FORCE PARTICIPATION AND GDP (GOLDIN1995)

CULTURE AND STIGMA – GOLDIN (1995) Cross- country data shows that relation between

women’s work participation and GDP is U shaped. Declining part of curve is because of income

effect, absence of a substitution effect (from higher relative wages of women), decline in home production and family stigma attached to married women working at manual labour (outside the home).

These get reversed in the rising part of the curve – strong substitution effect (due to spread of education) and access to `stigma-free’ white collar jobs.

OTHER COUNTRY EVIDENCE

Goldin: Economic history of the United States in the early 20th century.

Humphries (1987): Women’s labor supply response to the factory system in England in the 19th century.

Cameron, Worswick and Dowling (2001): Using data from 5 developing countries, they show that education increases workforce participation in only some countries but not all. Is a stigma effect working here?

INDIA EVIDENCE: STIGMA AND STATUS

So is India in the falling segment of the U curve?

Is stigma part of the story here? Das (2006): Workforce participation

(dominated by agriculture) negatively correlated with education for women.

Variation in participation rates across groups and regions.

Castes ranked higher in the traditional heirarchy have lower participation rates.

Sociological evidence: Status concerns serious for the `upper castes’.

INDIA EVIDENCE…2

`Lower’ castes may not exhibit the same concerns but may imitate upper caste social norms when they acquire other markers of status (e.g., education, land, income) – Sanskritization hypothesis.

Tribals outside the traditional caste hierarchy and status concerns are not expected to govern female labour supply.

Female work participation higher in South and Central India which have higher proportions of lower castes and tribals.

BOSERUP (1970): ORIGINS OF STATUS

Lower castes and tribals have traditions of `female farming’.

Wives from upper castes avoided work in fields to differentiate themselves from “the despised and hard-working female labourers even if this means that she must live in poverty.”

Parallel in Rwanda,Burundi: Hutu (female farming tradition) and Tutsi (land-owning class)

OUR WORK

Set out a simple household model of family status

Family status is a public good produced by married women.

Derive comparative statics about how time devoted to status and time devoted to market work respond to wealth, education and membership of social group (caste).

Examine these effects in two data sets The effect on female labour supply in a data set

on work participation and employment. The effect on female labour supply and on status

activities in a data set of time use.

OUR WORK

A household comprises a couple. They consume 3 goods: a consumption good

C, status good Z and leisure, R. Market good is jointly consumed. Leisure is private Status good is a household public good

STONE-GEARY UTILITY FUNCTION

0 , , , , ) ( ) ( ) , , , (2 1 2 1 r r z c r r z c U

STATUS CONCERNS

Beta is the `status’ parameter. How is status produced? It is the amount of market work (time) that

the wife relinquishes. Consumption good is the numeraire Each person is endowed with one unit of

time. There is non-labour income (wealth) denoted

by A.

0,,,,)()(),,,( 2121 rrzcrrzcU

.)1()1(..),,,(max 221121,,,

21

cArwzrwtsrrzcUrrzc

RESULTS: HIGHER WEALTH

Higher wealth increases consumption of all three goods: market, status and leisure

But it decreases female labour supply more than male labour supply because status is a normal good and supplied only by women.

Therefore, higher wealth decreases the ratio of female labour supply to male labour supply.

APPLICATION OF THIS RESULT

Main wealth variable in rural data set is land. Should we see the ratio of female to male

labour supply decline for larger landowning households?

Not necessarily: family labour is preferred to hired labour because of supervision costs associated with the latter.

Therefore as land ownership increases, family labour will be first exploited. So this will lead female to male labour supply to increase with land.

Net effect??

HIGHER BETA (STATUS COEFFICIENT)

Higher `beta’ Increases female time to status Reduces her work and leisure time Increases male work time and reduces male

leisure time. Therefore, reduces the ratio of female to male

labour supply.

HYPOTHESES: CASTE AND EDUCATION

`Beta’ is a function of status markers – caste and female education.

Then female labour supply (relative to male supply) should be lower for higher castes and for households with more educated women.

Note in the absence of status effects, female education should increase relative wages and increase female labour supply (relative to males) – the substitution effect.

Therefore, a contrary finding is strong evidence of status effects.

INTERACTION AMONG STATUS VARIABLES

Status effects would be magnified if status markers reinforce each other (wealth and caste, wealth and education, education and caste – interaction effects). For e.g., status effects of caste is greater in a

household with more land and in a household with greater levels of female education.

Competing hypothesis: `Sanskritization’ Households from lower castes that aspire to

higher levels of social status emulate the status norms of higher castes. In this case, the interaction effects of caste

would be in the opposite direction.

DATA

Test our hypotheses using two different cross-sectional data sets – (a) data set of employment and work participation and (b) data set of time allocation between work and non-work (including status activities).

EMPLOYMENT DATA REGRESSION, 2004/05

Dependent variable: Ratio of labor supplied by female members of household to labor supplied by male members of household.

Status variables: Caste, land, female education and their interactions.

Control variables: male education, children Village fixed effects: controls for relative

wages Regression therefore uses only within village

variation in status variables.

EMPIRICAL MODEL

TABLE 3: RESULTS FROM EMPLOYMENT DATA – RURAL SECTOR DEPENDENT VARIABLE: RATIO OF FEMALE TO MALE LABOR SUPPLY

Excludes ST observations Includes ST observationsCoefficient Std. Err. Coefficient Std. Err.

ST 0.0685 *** 0.0139OBC -0.0068 0.0087 -0.0084 0.0086Other castes -0.0404 *** 0.0107 -0.0387 *** 0.0105Proportion of females with at least primary education -0.0324 ** 0.0143 -0.0304 ** 0.0141Land 0.0016 0.0049 0.0016 0.0048ST × Land 0.0062 0.0063OBC × Land 0.0064 0.0050 0.0065 0.0049Other Castes × Land 0.0001 0.0050 0.0004 0.0049

ST × Proportion of females with at least primary education 0.0021 0.0252

OBC × Proportion of females with at least primary education -0.0208 0.0163 -0.0223 0.0161Other Castes × Proportion of females with at least primary education -0.0105 0.0173 -0.0141 0.0171

Land × Proportion of females with at least primary education -8.39E-06 *** 2.89E-06 -8.68E-06 *** 2.77E-06

Proportion of males with at least primary education -0.0833 *** 0.0064 -0.0825 *** 0.0060# of children below the age of 5 0.0010 0.0024 0.0013 0.0023# of children between 6 and 14 0.0262 *** 0.0019 0.0263 *** 0.0018Constant 0.4311 *** 0.0078 0.4345 *** 0.0076

Religion dummies Yes Yes

Village Fixed Effects Yes Yes

Observations 49086 54598

R2(within group) 0.023 0.021

*** significant at 1%, ** significant at 5%, * significant at 10%,

FINDINGS (RURAL WOMEN) Negative and significant at 5% (or 1%) level: caste

and female education. Land is not significant. Is it the opposing effects

of status and the superiority of family labour? Among interaction variables, the interaction of

female education with land is significant. Others are not.

URBAN SECTOR REGRESSIONS

Urban sector data lacks a wealth variable. To include compensating controls, regression

includes dummies for the occupation of head of the household – white collar jobs, service sector jobs (in trade, hotels and personal services) and blue collar jobs (manual labour in agriculture and industry).

TABLE 4: RESULTS FROM EMPLOYMENT DATA – URBAN SECTOR DEPENDENT VARIABLE: RATIO OF FEMALE TO MALE LABOR SUPPLY

Coefficient Std. Err. Coefficient Std. Err.

OBC -0.0451 *** 0.0142 -0.0452 *** 0.0142

Other castes-0.0827 *** 0.0160 -0.0803 *** 0.0161

Proportion of females with at least primary education -0.0354 * 0.0184 0.0213 0.0248

Service sector dummy 0.0132 0.0087 0.0860 *** 0.0183

Manual labor dummy 0.0205 ** 0.0084 0.0628 *** 0.0170

OBC × Proportion of females with at least primary education -0.0163 0.0209 -0.0164 0.0209Other Castes × Proportion of females with at least primary education 0.0033 0.02177 0.0003 0.0219

Servicesector dummy × Proportion of females with at least primary education -0.1022 *** 0.0220

Manual labor dummy × Proportion of females with at least primary education -0.0494 ** 0.0204

Proportion of males with at least primary education -0.1031 *** 0.0095 -0.1006 *** 0.0096

# of children below the age of 5-0.0140 *** 0.0036 -0.0140 *** 0.0036

# of children between 6 and 140.0225 *** 0.0027 0.0225 *** 0.0027

Constant 0.3676 *** 0.0149 0.3200 *** 0.0203

Religion dummies Yes YesUrban Block Fixed Effects Yes YesObservations 24662 24662R2(within group) 0.026 0.027

FINDINGS

Caste effects even stronger than rural regression. Female education has a negative effect Blue collar households have the highest female

labor supply and the white collar households the least. The occupation dummies are likely to be correlated with wealth and caste and therefore these are also likely to be status effects.

In the interaction specification, female education is not significant but the interaction with occupation dummies is negative and significant.

The negative effect of female education is seen in blue collar and service sector households – not in white collar households – consistent with the Goldin hypothesis.

TIME USE DATA

Time use survey of individuals above the age of 5 in 6 states of India.

Our data set is for rural sector. From the data, we can compute the time devoted

to economic activities and the time devoted to leisure.

We also define status activities as time spent in social and cultural activities. This includes participation in social events, community

functions, religious activities, socializing, arts & music, games & sports, reading and watching TV.

Does not include household chores, learning, care of others, and personal care (including sleeping, doing nothing).

TIME USE DATA, 1998/99

Here we do three regressions. A regression with the ratio of female work time

to male work time as dependent variable (with the same specification as earlier).

A regression with female status time as dependent variable

A regression with female leisure time as dependent variable

TABLE 6: RESULTS FROM TIME USE DATA

Dependent Variable Ratio of women’s work time to men’s work time

Proportion of women’s time spent in status activities

Proportion of women’s time spent in status activities + personal care

and maintenance

Coefficient Std. Err. Coefficient Std. Err. Coefficient Std. Err.

Castes other than SC -0.2429 *** 0.0762 0.0066 *** 0.0014 0.0149 *** 0.0031Land -0.0634 0.1951 0.0044 *** 0.0008 0.0138 *** 0.0017

Proportion of females with at least primary education -0.2550 ** 0.1234 0.0075 *** 0.0013 -0.0008 0.0028Other castes × Land 0.0573 0.1949

Other castes × Proportion of females with at least primary education 0.2389 * 0.1331

Land × Proportion of females with at least primary education 0.0000 0.0001

Proportion of males with at least primary education-0.0722 0.0542 0.0028 ** 0.0012 0.0037 0.0026

House of permanent materials -0.0833 0.0669 0.0130 *** 0.0014 0.0159 *** 0.0032Children below the age of 15 -0.0156 0.0148 -0.0003 0.0003 -0.0054 *** 0.0007Constant 0.8515 *** 0.0700 0.0067 *** 0.0014 0.1665 *** 0.0031

Religion dummies Yes Yes Yes

Village Fixed Effects Yes Yes Yes

Observations 5855 5984 5984

R2(within group) 0.005 0.061 0.044

*** significant at 1%, ** significant at 5%, * significant at 10%,

LABOUR SUPPLY REGRESSIONS

Caste and female education have negative and significant effects.

Interaction between female education and caste is positive and significant – sign implies that the withdrawal of educated females from the work force is sizeable for the `low’ caste group – Sanskritization effect?

STATUS AND LEISURE REGRESSIONS

In status regression, wealth variables, `higher’ castes, female and male education have positive and significant impacts.

In leisure regression, education variables (male and female) are not significant.

CONCLUSIONS

Female withdrawal from the workforce means that measured poverty does not fall as fast as it might in the absence of such responses – yet households would deem themselves considerably better off.

Could it be that economic progress from low levels of income reinforces rather than undermine the traditional gendered division of labour?

Female autonomy and family status may move in opposite directions.

Current work: Looks at how status variables could be used as instruments for female labour supply and therefore examine the effects of exogenous variation in work participation on female and male wages.