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Keywords:

  • female;
  • workforce;
  • education;
  • income

Abstract

  1. Top of page
  2. Abstract
  3. 1 Introduction
  4. 2 Empirical Estimates
  5. 3 Conclusion
  6. Acknowledgements
  7. References

The U-shaped relationship between economic development and female work force participation rate may be explained at the household level in terms of the interaction between social factors and the income of the household. The social attitude and income are likely to be influenced by education, which augments the income on the one hand and on the other shifts women from stigmatised jobs to non-stigmatised jobs and also reduces the adverse social response towards women participation in the labour market. The shift across sectors of employment is also motivated by education, implying positive associations between education and high productivity jobs. Copyright © 2014 John Wiley & Sons, Ltd.

1 Introduction

  1. Top of page
  2. Abstract
  3. 1 Introduction
  4. 2 Empirical Estimates
  5. 3 Conclusion
  6. Acknowledgements
  7. References

Female work force participation rate and its determinants have drawn considerable attention in the literature. Among various supply and demand side factors economic growth is seen to have an impact on female work force participation (see Mathur, 1994; Agarwal, 1985; Durand, 1975; Sinha, 1965): initially it is found to be negative but at higher levels of growth it tends to increase, thus, giving rise to a U-shaped relationship. Cagatay and Ozler (1995) also suggest the possibility of a U-shaped relationship between long-term development and women's share of the labour force. Even the historical record of the developed countries indicates such a relationship between economic development and women's labour force participation rate (Goldin, 1994).1 With urbanisation and industrialization, female-dominated home-based production is expected to decline, as it would be largely replaced by male-dominated factory production (Boserup, 1970). This falling part of the U-shape curve corroborates Boserup's analysis of women's contribution. However, with further economic development, women's labour force participation rate is expected to increase as enhanced industrialization, more education for women, commodification of domestic labour and falling fertility rates help women workers participate in the labour market more explicitly (Oppenheimer, 1970; Boserup, 1970). Also, as per the neoclassical approach with economic growth gender inequalities in terms of access to employment opportunities, work conditions, nature of work and earnings tend to decline (Forsythe, Korzeniewicz & Durrant, 2000). This implies an increase in women's work participation rate because discouraged dropouts tend to decline: with improved and equal status in the job market, women get encouraged to participate in the labour market (Mitra, 2005). Mathur (1994) identified a declining trend in participation rates of both genders for most of the present century, but the trend reversed since 1971 in the case of females (Mathur, 1994: p. 472).

In studying gender issues often, the developed western societies and the developing societies are kept at two extremes assuming that there is a linear relationship between economic development and reduction in gender inequalities. However, Boserup (1970) argues that there is a curvilinear relationship between economic growth and status of women. In the initial stages as growth takes place, the males may benefit at a higher pace compared with the females because of their greater initial endowments. Thus, the gender gap may actually increase notwithstanding economic growth. Also, greater advantages accruing to males in the process of growth may force women to withdraw from the labour market given the conservative attitude of the society towards women and women's work outside home, which is unlikely to undergo any significant change in the short run. However, sustained growth, particularly at very high levels of growth, may impact positively on several socio-economic development indicators including those that define the status of women, implying fall in gender gap at higher stages of growth. Dutta and Panda (2000) noted that gender inequality gets manifested not only in terms of work and income but also in education, nutritional support and health care. Jalan (2000) supports a ‘Gender Kuznets Hypothesis’ over a cross-section of developed and developing countries: for a number of health and education indicators, the gender gap shows a tendency to increase with economic development up to a threshold level, and then decreases with further economic progress, exhibiting an inverted U-pattern. However, two gender-related indicators—the Gender-Related Development Index (GDI) and the Gender Empowerment Measure—introduced by the United Nations Development Programme have serious conceptual and empirical problems which limit the usefulness of these composite indicators, as pointed out by Klasen (2006) and Klasen and Schuler (2011). Interestingly, Seguino (2007) refers to a two-way causation between gender equality and growth, and argues that different gender equalities may have different outcomes in terms of growth, i.e., gender equality in education may stimulate growth while gender equality in wages may not be growth-conducive.

Some of the recent evidence also suggests that even higher human development index (HDI), let alone growth, does not necessarily ensure gender equality in terms of GDI: in the Asia-Pacific context, Japan and Korea have the highest HDI-GDI gap, whereas Thailand and China, whose HDI and GDI are both lower in absolute terms than Japan and Korea, demonstrate lower gender gaps (Murayama, 2005). Gender norms and systems vary widely across cultures but they shape people's lives and interactions in all societies (Hayase, 2005). In general, as women's educational level improves, gender inequality declines (UN, 2001). In other words, with improved levels of education labour market, participation of women in high income jobs is expected to rise. Keeping in view a long-term perspective the ‘Gender Kuznets Curve’ and the U-shaped relationship between women work participation rate and development are mutually consistent.

A variety of factors have been considered as determinants of female labour force participation rate. These include opportunities for informal employment, which tend to decline with development (Bharadwaj, 1989), technological and structural change, spouse's income (Sen, 1981),2 the conflict between housework (including child care) and earning opportunities in the labour market. Among various socio-economic factors, fertility, cross-regional cultural norms, attitude towards manual work, the relative incidence of low caste and tribal population, the size of the agricultural sector, cultivation techniques, crop patterns, poverty and technology are some of the determinants of female work participation rate (see Agarwal 1988). Also, there can be a positive association between work participation rate and the percentage of workers engaged in the tertiary sector as activities in this sector provide greater employment opportunities for women and teenage workers. However, low productivity activities are mostly concentrated in the tertiary sector, and hence, as the share of the tertiary sector in total employment increases, dropouts from the labour market are expected to be high, thus reducing the work participation rate (Nord, 1989)

Women's decision, particularly in low-income households, to participate in the job market is not only constrained by the reproductive activity and the management of the household work, but also by their poor human capital formation. Besides social norms, which vary across regions impinge on women's participation in the labour market (Agarwal, 1994). Given these constraints, women workers get residually absorbed in activities available in the periphery of their residence, often on part-time basis, which then restrict their bargaining strength in the labour market and skill formation required for upward mobility. Thus, they get trapped in the cycles of gender inequality in the labour market, which by resulting in poor human capital formation accentuates inequality in other areas, too (Mitra, 2005). On the whole, women labour/work participation rate is an outcome of numerous socio-cultural and economic factors, which are not always easy to decipher.

Besides, there are problems relating to data as they tend to undercount women workers grossly (see Agarwal, 1985; Sen, 1985). Often, household activities, which enable the male members of the households to participate in income-earning jobs, are carried out by women, and ironically, these activities are not treated as economic activities for not being able to earn a direct income. Besides, women helping the male members of the households in own account enterprises or functioning as home-based workers, tend to get excluded from the set of job market participation. Time use surveys, enterprise surveys and other primary surveys have brought out the fact that women pursue a great deal of household work as well as remunerative work concurrently, which do not get captured by the standard labour force surveys (Hirway, 1987, 2005, 2010; Mitra, 2005).

The present paper reinforces the view that social norms and attitudes towards women labour market participation vary considerably across socio-economic classes, regions and states. The probability of stigma attached to women's work is likely to be greater, the lower is the family income and the lower is the woman's wage (Goldin's (1994). The stigma factor is seen to be a consequence of social norms. As Goldin (1994) points out, when women are poorly educated, their wage labour is mostly in manual work, against which a strong social stigma exists. On the other hand, when women are educated, they enter white-collar jobs, against which no social stigma exists. So education and economic participation enable women to enter activities, against which social stigma is less prevalent or in other words, labour market participation with education help women shift to less stigmatised activities. However, women with a higher level of education may not get compensated with higher wages always given widespread labour-market imperfections, as pointed out by Seguino (2007). Increased education levels raising labour productivity but not leading to higher wages are indicative of the greater exploitation of women (Seguino, 2007). In such situations, we may not expect a positive relationship between education and income and hence, the women's participation in the decision-making process and their ability to change the gendered social norms may not actually materialise. However, we may note that education itself irrespective of whether it can result in enhanced income may bring in changes in gendered social norms as it improves awareness and helps voice one's rights. In general, as women's education level improves, gender systems become more egalitarian (UN, 2001).

Not only between education and income, as mentioned earlier, but also between education and labour market participation, the relationship is not always linear. Women's labour market participation with no or low and high levels of education is expected to have very different implications. Though in both the situations, the participation may turn out to be high, in terms of the nature of activities that they get absorbed in and the incomes they are able to access, there are significant differences (Klasen, 2006). With higher levels of human capital endowment, the demand-induced jobs become accessible, which generate higher incomes, and in this process, women can be engaged directly in the development process and they can experience the benefits of growth more equitably (Behrman & Zhang 1995). Such an outcome can be termed as productive because participation in the labour market results in incomes that not only enhance the individual resource base and help meet the basic requirements in terms of food, shelter, health and education for children but also enable the individual to take a lead role in policy making (Dutta & Panda, 2000). Further, it has been observed that women leaders invest more in infrastructure that is directly relevant to their own gender (Chattopadhyay & Duflo, 2004).

On the other hand, participation in the labour market without any skill may lead to a residual absorption, which is mainly a supply-push phenomenon (ILO, 2002). In other words, labour tends to pick up marginal activities for which demand is highly limited relative to the supplies, resulting in meagre earnings. Because the rural women are usually not endowed with higher levels of skill and at the same time cannot afford to stay outside the labour market because of poor household incomes, they tend to participate in large number in low-income jobs (Jeffery & Jeffery, 1997). On the other hand, higher educational attainments of the urban women enable them to get absorbed in jobs for which substantial demand exists and thus results in better labour market outcomes and provides a sense of dignity (UN, 2001). This in turn again motivates women to join the labour market (Mitra, 2010) in spite of a reasonably high income of the spouse, which is at times taken to cause withdrawal of women from the labour market, that is the backward sloping supply curve, as widely known in the literature (Dasgupta & Goldar, 2006). With this perspective, the present paper tries to explain the effect of education on female labour supply in the Indian context.

2 Empirical Estimates

  1. Top of page
  2. Abstract
  3. 1 Introduction
  4. 2 Empirical Estimates
  5. 3 Conclusion
  6. Acknowledgements
  7. References

The possibility of a U-shaped relationship between the educational level of the head of the household and the female work force participation rate is tested on the basis of the MIMAP-India Survey data set for 3364 rural and 1492 urban households for the year 1996 (see Pradhan & Roy, 2003 for details). However, before turning to these estimates, an attempt is made based on the population census (2001) data at the district level to examine the relationship between female work participation rate on the one hand, female literacy and male work participation rate on the other.

2.1 District Level Analysis

Although in relation to literacy the female work participation rate is expected to rise, it may also fall if school enrolment implies withdrawal from the labour market. On the other hand, with a higher male work participation rate (R/UMWPR), which may mean higher levels of household income, female work participation rate may decline if women decide to focus on non-economic aspects of the household welfare and, thus, withdraw from the labour market (Sen, 1981). However, the relationship can be positive also, that is, districts with higher levels of development and growth may generate demand for both male and female workers, thus pulling up the participation rates for both the sexes. The expansion in demand for labour may be met initially by the supplies of more male labour than women but after a certain stage both are expected to rise. The positive association between the two could very well be a reflection of a positive response of women work participation rate to development.

In explaining the variations in the rural (urban) work participation rates of the females (R/UFWFPR)—R represents rural and U urban—the other variables, which have been controlled for are the following: the percentage of scheduled caste in the total female population in the rural (urban) areas (R/UFSC), the percentage of rural female workers engaged as agricultural labour (RFAGLAB), the level of urbanisation (URBN), and child-woman ratio in the rural (urban) areas (R/UCWR).

Given the dominance of the caste effect in the rural areas, and given the phenomenon of caste-based activities and the prevalence of low caste population being engaged in low productivity activities, a higher incidence of low caste population tends to reduce the work participation rate as a consequence of discouraged dropouts. In the urban areas, however, this could be just the opposite as in the organised sector the caste-based reservation system prevails.

As the number of children per women (R/UCWRATIO) increases, women's responsibilities increase and hence, their explicit participation in the job market drops. Because the category of agricultural labour (RFAGLAB) constitutes largely, the casual wage earners, districts with higher levels of casual employment are expected to report lower incidence of participation rates in the rural areas. Rural women are, on the other hand, more likely to be engaged as self-employed workers in agriculture—the category that the population census classifies as cultivators.

With the level of urbanisation, rural women work participation rate may rise as the migration of the rural males to the urban areas may compel the women members of the households to engage themselves as full-time workers and not just as helpers. Also, faster urbanisation (URBPOP) means extension of urban-based activities to the rural areas implying an increase in rural non-farm sector employment in which women may find lucrative employment opportunities. With respect to urbanisation, women work participation rate in the urban areas is also expected to vary positively as socio-economic development takes place in the process. The rise in the share of ‘other activities’ (UFOT) predominated by trade and services in the urban areas may bring the participation rate down as many of these activities are pursued by women members of the households on part-time basis and/or as helpers.

Empirical findings suggest that (Table 1) in the rural areas, the percentage of female scheduled caste population is linked inversely with the female work participation rate, whereas in the urban areas, it has a positive relationship with participation. This could be because of the fact that in the urban labour market, the caste-based reservation policy is applicable as far as the organised or formal sector is concerned. And even in the urban informal sector, the influence of the caste factor tends to get diluted in the face of anonymity, allowing women from low caste households to work in the urban labour market. On the other hand, in the rural areas, low caste women are engaged in petty and marginal activities to earn their livelihood, which is carried out as an extension of household work, resulting in gross underestimation of their work participation. Besides, their absorption even in response to demand is not independent of caste background in the rural context.

Table 1. Regression results of female work participation rate: 2001 Census District Level Data
Rural areasUrban areas
VariablesCoefficientt-ratioVariablesCoefficientt-ratio
  • *

    denotes significance at 5 per cent level.

  • Source: Based on data from population census, 2001.

RFSC−0.34−6.37*UFSC0.1023.64*
RFLIT−0.29−4.53*UFLIT0.0973.09*
RMWPR1.6911.97*UMWPR0.6013.26*
RAGLAB−0.04−1.43UFOT−0.18−17.40*
URBPOP−0.016−0.49URBPOP0.0050.51
RCWRATIO−0.09−0.94UCWRATIO−0.14−3.15*
Intercept−34.45−2.73*Intercept−8.30−1.86
Adj. R20.46 Adj. R20.63 

Regarding literacy, differences are again seen between rural and urban areas: in the rural areas it leads to a withdrawal from the labour market, whereas in the urban areas, it raises the participation. The male work participation rate seems to have a positive effect on its women counterpart in both the rural and urban areas tending to refute the view that higher male earnings and subsequently higher household income would lead to women's withdrawal from the labour market. The child-woman ratio reduces participation in the urban areas but not in the rural areas. The percentage of women workers in trade and service activities has a negative effect on the work participation possibly because these activities do not offer high productivity jobs and are thus characterised by low incomes.

2.2 The Participation Rate, Household Income and Level of Education: Estimates Based on MIMAP-India Survey (1996)

In this part of the paper, the female work force participation is defined as the number of female earners divided by the total number of females from all households at a particular educational level of the head. A variation tried was the average of the ratio of female earners to total females in each household at a given educational level. The results were not very different from each other.

Both the linear and quadratic relationship between female work force participation rate and the educational level of the head were tried. The relationship was examined for the age-groups 6-60 years. In general, it is to be expected that with the rise in educational and income level of the family, there would be a withdrawal of child labour from the labour force. Hence, the same relationship was tried for the adults in the age group of 18-60 years (regression result presented in Table 2).

Table 2. Results of cross-section analysis of educational level of head as a determinant of labour force participation rate (age group 18-60 years)
Independent variableDependent variable: female labour force participation rate
  • Note: Figures in brackets show the t-statistic.

  • *

    represents significance at 5 per cent level.

  • Source: MIMAP-India Survey Data

Linear form: 
Constant0.22 (7.13)*
V20 (educational level of the head = years of schooling in completed grades)−0.04 (−1.33)
R-squared (adj. R-square)0.0999 (0.0437)
Quadratic form: 
Constant0.30 (8.82)
V20 (educational level of the head = years of schooling in completed grades)−0.034 (−3.55)*
V20-square0.002 (3.32)*
R-squared (adj. R-square)0.4818 (0.4127)
 Dependent variable: male labour force participation rate
Linear form: 
Constant0.89 (35.5)*
V20 (educational level of the head = years of schooling in completed grades)−0.007 (−2.89)*
R-squared (adj. R-square)0.3423 (0.3021)
Quadratic form: 
Constant0.93 (27.2)*
V20 (educational level of the head = years of schooling in completed grades)−0.02 (−2.2)*
V20-square0.0008 (1.45)
R-squared (adj. R-square)0.4244 (0.3477)

From the linear fit, it is noted that the observed values lie above the fitted line on the left portion, below the line in the middle portion and then above the line in the right. This suggested that the quadratic form is more suited and the observed values are seen to be much better distributed on both sides of the U-shaped curve2. This relationship is much weaker in the case of male workers for both, the linear and the quadratic form. The shape of the curve is a well-defined U for the female work force participation rate as can be seen from Figure 1.

image

Figure 1. Female labour force participation rate with education of the head of the household: age group 18-60 years (quadratic form)

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However, the rising portion is absent in the case of males (Figure 2). The quadratic relationship is much stronger in the case of the female work force participation rate. From the tables and the fitted lines, we can assert that the level of education of the head of the household is an important determinant of the labour force participation rate. Secondly, although the relationship is U-shaped in the case of females, there is a steady decline in the case of males.

image

Figure 2. Male labour force participation rate with education of the head of the household: age group 18-60 years (quadratic form)

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Another relationship that needs to be empirically tested at this point is the relationship between income and the educational level of the head of the household, which is expected to be positive. The results are presented in Table 3. Average household income at each educational level of the head is calculated, and this is regressed on the level of education of the head of the household. The fitted curve (not presented here to save space) shows a slight dip initially, which might be related to withdrawal of the women from the work force, after which there is continuous increase in income. Thus, household income does vary positively with the level of education of the head. The increase at a faster rate at higher levels of education could be because of the rapid increase in income from ‘other’ sources (male labour and capital income, i.e. income apart from the contribution of the females).

Table 3. Relationship between household income and level of education of the head.
Independent variableDependent variable: household income
  • Note: Figures in brackets show the t-statistic.

  • *

    represents significance at 5 per cent level.

  • Source: MIMAP-India Survey Data

Linear form: 
Constant−2.06 (−0.011)
V20 (educational level of the head = years of schooling in completed grades)−124.17 (6.83)*
R-squared (adj. R-square)0.7447 (0.7288)
Quadratic form: 
Constant542.35 (3.197)*
V20(educational level of the head = years of schooling in completed grades)−79.99 (−1.73)
V20-square12.01 (4.571)*
R-squared (adj. R-square)0.8933 (0.8791)

We have also used the individual and household level information to test whether education remains an important determinant of female work force participation rate after controlling for other factors. Table 4 presents the probit estimates for participation of the female and male members of age greater than 18 years in the work force. Whereas education of the head of the household is highly significant in explaining the participation of women in work force, it is insignificant in the case of males. This tends to imply that male work participation is more of compulsion in a patriarchal system where males are expected to provide for themselves and women and children within the household. Irrespective of whether the household head is educated or not, it is natural for the males to join the labour market, particularly after a certain age, across all types of households and all income categories. However, in the case of women, the household head's educational level determines his/her attitude towards women and their work outside home; although in very low-income households, women tend to participate in the job market even when the household head is illiterate (Mitra, 2005).

Table 4. Probit estimates for work force participation of females and males aged 18 years and older
Independent variablesFemale workerMale worker
  • **

    Note:significant at 1 per cent;

  • *

    significant at 5 per cent level.

  • Figures in the parenthesis show the standard errors

Head's education−0.0746696** (0.0115)−.0015118 (0.0123373)
Head's education square0.0024961** (0.0008998)−.0015699 (0.0009314)
Age0.0772719** (0.007869)0.1190752** (0.0069374)
Age square-0.0009595** (0.0000924)−0.00152** (0.000073)
SC/ST0.088499* (0.0381295)−0.0291071 (0.0430538)
Number of children−0.0141443 (0.016998)−0.0134235 (0.0189269)
Married−0.3564775** (0.0489792)1.014673** (0.0486221)
Constant−1.721522** (0.150675)−1.465987** (0.1321654)
Pseudo R-squared0.04630.2473
LR340.341882.71
Number of observations75028230

The number of children in the age group 0 to 5 years is insignificant in explaining the participation of women as well as men. Interestingly, marriage reduces the probability of participation of women in work force, whereas the probability of male participation goes up with marriage. These results tend to suggest that men are supposed to participate in work force under all circumstances, whereas women's participation is dependent on numerous factors, mainly those related to social norms. Education allows the possibility of women breaking the social norms. However, after marriage, women are socially required to take care of the household and not to bother about economic status of the household. It is generally suggested in India that women should be happy with whatever the husband can provide and should run the household well within the means available to them.

The dummy for Scheduled Caste/Scheduled Tribes is positive and significant in the case of women, whereas it is insignificant in the case of males. Again, this suggests that social status has an important bearing on women's participation in work force, whereas this is not important in the case of males. The results are the same when we introduce household per capita income instead of the level of education of the head. But introducing it simultaneously with the education of the head of the household changes the signs and significance of the income variable. This is due to multicollinearity problem: education and income are highly correlated, implying that the income effect is captured by the education variable. Note that as already seen in Table 2, education of the head of the household is an important determinant of the income of the household. Further, education of the female within the household is determined by both, the level of education of the head and the income of the household. This is shown in Table 5. All these variables are highly significant and together explain nearly 44% of the variation in the education of the female members of the household.

Table 5. Ordinary least squares estimates for level of education of the females aged 18 years or more
Independent variablesFemale worker
  1. Note: Figures in parenthesis show the t-value

Head's education0.1867623** (6.59)
Head's education square0.0273244** (12.28)
Per capita income of household0.0008688** (18.24)
Constant0.678401** (9.83)
Adjusted R-squared0.4392
Number of observations7502

3 Conclusion

  1. Top of page
  2. Abstract
  3. 1 Introduction
  4. 2 Empirical Estimates
  5. 3 Conclusion
  6. Acknowledgements
  7. References

Based on the unit level data, we have noted a U-shaped pattern of female work force participation rate with respect to the level of education of the household head. The time spent on extended-System of National Accounts (SNA) activities (work at home) does not change much with the level of education. Thus, the major trade-off in the allocation of time of female members of the household is not between work at home and income generating activity, rather it is between leisure and income-earning possibility.

The U-shaped female labour supply function appears to be explainable in terms of the adverse attitude towards female participation in the labour market. The households at very low levels of education and income show very high rates of participation for women because they are in dire need of meeting the minimum consumption requirements. Households belonging to the middle strata are likely to signal their higher social status through non-participation of women in the labour force. At very high levels of education and income, women enjoy greater freedom and shift to activities, against which no stigma exists. With higher educational attainments, they are able to participate in skilled and professional jobs, which results in greater empowerment and participation in the decision making process both at the household level and the societal level.

Based on the census data at the district level, the rural areas are indicative of a negative relationship between the female work participation rate and the literacy rate, whereas in the urban areas, the positive association is quite distinct. All this is indicative of a predominant attitudinal factor in the rural society that cannot change simply with an improvement in literacy. Higher levels of educational attainments are expected to usher in changes in gendered social norms and/or shift women to activities, which are less stigmatized. Higher skills are required to secure non-manual jobs yielding higher incomes and thus education motivates higher labour market participation as the probability of securing high-income jobs increases. In the rural areas, the extent of skill formation is nominal as reflected in the lower educational attainment levels, and thus a mere increase in the literacy rate cannot shift women to jobs yielding higher incomes and/or are less stigamatized.

As incomes rise, the self-employed female workers tend to withdraw from the agriculture sector as social stigma is attached to manual jobs. But at subsequent stages, as the levels of education rise, women workers have the option of accessing employment in the services sector as salaried workers, thus, raising the participation rates. Casual workers are likely to be trapped in a low-level equilibrium situation with low education and low income and therefore may not show much changes in the participation rate over time. The stigma factor can also explain the puzzling feature as to why women have not been able to secure a larger share of the expanding wage jobs in the manufacturing sector (Schultz, 1990). As observed by a number of studies, wage jobs for women in the manufacturing sector are not seen as ‘dignified jobs’.

It is not our intention to imply that other factors are not relevant in explaining the U-shaped participation curve. In fact, a number of factors including some of the demand side rigidities, shifts in the production structure and employment pattern of different sectors, and the substitution and income effects of a rise in wage rates jointly determine the female participation rate in the labour market. As we observed based on the district level data, both male and female work participation rates are positively associated, implying that an overall increase in labour demand can raise both male and female work participation rates. The rise in household income because of the rise in male work participation rate need not necessarily reduce the women work participation rate if the work available for women results from a rapid expansion in economic activities and faster growth.

The policy implication of the study lies in enhancing skill formation and educational attainment levels on the one hand and on the other creating jobs specifically for women. All this is expected to usher in attitudinal changes towards women working outside home, and thus greater gender equality can be achieved through improved participation of women in the labour market. Even if education does not succeed immediately in changing the gendered social norms, it can at least help women shift from more stigmatised to less stigmatised jobs. We believe that accessibility of women to high productivity jobs contribute to their empowerment in the long run.

Acknowledgements

  1. Top of page
  2. Abstract
  3. 1 Introduction
  4. 2 Empirical Estimates
  5. 3 Conclusion
  6. Acknowledgements
  7. References

This paper forms part of the MIMAP-India project (funded by IDRC, Ottawa) in which the first two authors were involved at the National Council of Applied Economic Research, Delhi. The authors would like to thank Professors Bina Agarwal, Andrew Foster and Chris Scott for valuable comments and discussions.

  1. 1

    Goldin (1994) found this association for women aged 45 to 59 years for cross-section of countries using GDP per capita as an index of development.

  2. 2

    Sen (1981) in the case of Indian agriculture showed that women withdraw from the labour market as male income increases.

References

  1. Top of page
  2. Abstract
  3. 1 Introduction
  4. 2 Empirical Estimates
  5. 3 Conclusion
  6. Acknowledgements
  7. References
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