Rohini Pande, Harvard University and EPoD - Global interventions to improve women's and girls' wellbeing and implications for India
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Presentation made at an IFPRI event on "What Lies Beneath: Women’s and Girls’ Wellbeing as a Critical Underpinning of India’s Nutritional Challenge" on December 10, 2018, in New Delhi
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Rohini Pande, Harvard University and EPoD - Global interventions to improve women's and girls' wellbeing and implications for India
- 1. Valuing women and girls Rohini Pande 1
- 2. Puzzle 1: Stunting in India v/s Africa
- 3. Puzzle 2: Women’s work in India v/s rest of the world
- 4. Today’s talk ● Linking son preference and child stunting ● The economic rationale for son preference: ○ role of gender norms around women’s work ● Changing power dynamics through public policy: ○ Political quotas ○ Financial independence 4
- 5. Birth order and child height Joint with Seema Jayachandran
- 6. Jayachandran Pande (2017) ● 27 African DHS surveys and 2005-6 Indian DHS (NFHS) ● Focus on height for age z-score: deviation from reference median divided by reference standard deviation ( a z-score of - 1.0 means child's height is 1.0 s.d below median for reference group) ● Indian height deficit driven by higher-parity children. ○ Provides bound on how much genetics can matter ○ Parents' allocation across children integral to height puzzle ○ Diminishing returns to inputs means that unequal allocation across children reduces average height 6
- 7. Original finding in JP (2017) on birth order gradient 7
- 8. Updated: 30 SSA DHS surveys after 2010 and NFHS-4 8
- 9. Ten years of change… 9 ● Average stunting in both India and Sub Saharan Africa reduced. In India from 40% to 35% ● The average India-Africa height gap is reduced (from 0.08 to 0.02, on average) but remains significant and negative. ● This birth order effect continues to be driven by birth order 3+ children.. ● The nature of which health inputs are linked as contributory factors changing
- 10. Birth order effects in health inputs 10 ● In JP 2016: birth order effects in pre- and post-natal inputs ● Between 2005 and 2014: India’s Total child vaccinations and institutional delivery rates have risen (from 6.6 to 7.2 and 0.45 to 0.75) ● Implication: No significant birth order difference in vaccination rates/institutional delivery. ● But no significant change in average number of pre- natal visits in India significant birth order differences across India and Africa persist ● Some birth order effects in taking iron supplements
- 11. Our explanation for the Africa Indian birth order gradient: First-son preference ● Cultural preference in India for eldest sons ○ Norms of patrilocality, patrilineality ● Affects resource allocation among children ○ More resources given to eldest son than to other children ○ Affects fertility decisions because keep having children until you have a son 11
- 12. How eldest son preference disadvantages girls ● Even if parents only preferred their eldest son -- and treated other sons just like daughters -- girls would be disadvantaged, relative to boys ● Girls are in larger families, on average, because families keep having children (Clark 2000, Jensen 2003) ● In JP 2017 we found that the height girl exists only for girls 12
- 13. In our updated sample, the India-Africa height gap continues to exist only for girls ● The HFA z-score for Indian girls relative to African counterparts is -0.08 z-score lower ● For WFA z-score the gap is -0.06 z-score ● More concerningly: birth order gradient is now steeper among girls ● We continue to see that girls without an elder brother do worse ● Economic growth is not changing the gradient, significantly 13
- 14. Within India, son preference strong predictor of girls’ height 14
- 15. Summarizing the findings ● India has grown at over 7% per annum since early 1990s ● Economic growth – and investments in health care systems – have improved child nutrition and lowered stunting ● However, India still lags poorer countries in Sub-Saharan Africa ● Son preference – which influences fertility behavior and health behavior – important driver of health outcomes ● Economic growth is not enough to change gender norms.. 15
- 16. What are norms? ● “The standards describing typical or desirable behaviour.” (Tankard & Palluck, 2016) ● “The set of ‘social sanctions or rewards’ that incentivize a certain behavior.” Benabou & Tirole (2011) ● Social norms as “behaviors and opinions [that] are socially desirable, while others are stigmatized”. manifests in “social pressure or concerns about image”. Bursztyn & Jensen (2016) 16
- 17. The Indian Work Puzzle 17
- 18. Female LFP in India is low and declining… ● 23% of India’s working age women were in the labour force in 2015. ● Current estimates from 12% to 27% for 2017. ● In the best-case scenario, India ranks 121 out of 131 countries on female LFP.
- 19. India’s macroeconomic trends challenge the U- shape hypothesis… GDP is growing quickly Female education is rapidly increasing Fertility has fallen significantly 19
- 20. Source: Bhargava (2018) ● Strong income effect reducing female LFP among poor as incomes increased over time. ● But no evidence of rising female LFP among the wealthy, as predicted by U-shape hypothesis. 20
- 21. In contrast to rich countries, a key problem in India is lack of entry rather than exit… 21
- 22. Latent demand for work among Indian women, especially the poor 22 Rural Urban ● Among women occupied with domestic duties, around a third in both urban and rural areas say they would accept paid work, if available. ● Willingness to work higher among poorer marginalized caste groups.
- 23. So, what constrains Indian women who want to work? 23
- 24. A range of inter-linked norms constrain women at the household level, suppressing female LFP 24 Gender Norms Gender Norms Restrict women’s opportunities Restrict women’s behavior Restrict women’s movement Influence marital norms Interact with caste norms
- 25. Men assign a higher social cost to husbands of working women than the women themselves 25
- 26. Safety and reputational concerns restrict women’s mobility and work 26 Very few women are working Safety concerns are higher Women are discouraged to work Workplace is male-dominated ● Relations with men outside the family can damage a woman’s reputation and marriage prospects. ● Cyclical reasoning could entrench women’s sectoral marginalization. ● Safety concerns contribute to restricted mobility, both for everyday transport and migration for work(Borker 2018).
- 27. Implications for women’s well-being ● Parents in South Asia value children providing old-age support ● Patrilocality/Patrilineality implies that the presumption is that sons will provide this support ● If, in addition, daughters are less likely to enter the labor force then son preference can be magnified by economic concerns ● Jensen (2012) finds that providing villagers information about job market opportunities influences young women’s labor market outcomes and health investments in young girls 27
- 29. Gender Quotas in Politics ● Numerical targets that stipulate the number or percentage of women that must be included in a candidate list or the number of seats to be allocated to women in a legislature. ● By providing women political positions it can transfer power. ● Exposure to powerful leaders can change beliefs and aspirations ● Women leaders may alter spending decisions in favor of women and girls 29
- 30. Changes policies with implications for health ● Chattopadhyay-Duflo use variation in political reservation in Gram Panchayat leadership in India to show that women leaders are more likely to invest in drinking water in West Bengal and Rajasthan; Beaman et al replicate for all India ● Bhalotra &Clots-Figueras use close elections in state legislatures to show that a 10 percentage point increase in women’s representation results in a 2.1 percentage point reduction in neonatal mortality ● Find that women politicians are more likely to invest in village health infrastructure 30
- 31. Quotas also improved future election outcomes for female candidates (Beaman et al 2010) 31
- 32. Reservations reduce gender gap in aspirations (survey) 32
- 33. Extend affirmative action? ● Affirmative action in employment could mimic success of reservation policies for caste and women in politics ○ Reservation policies increased SC/ST representation in regular salaried employment by around 5 percentage points (Borooah et al., 2007). ● However, better monitoring and complementary policies to skill women in required fields are necessary. ● A survey of vocational trainees in 2016 founds that female quotas in training and jobs can act as ceilings to FLFP - important to align preferences of those responsible for implementation (Artiz Prillaman et al., 2017). 33
- 34. Enable financial independence for women ● Alleviating the impact of norms requires intervening within the household. ● Can strengthening women’s control over earnings strengthen their bargaining position when it comes to work? ● And if it does increase work then does this influence norms in the longer run? 34
- 35. A field experiment on Female bank accounts with linked earnings (FBA) ● Work with married couples in rural Madhya Pradesh who were potential beneficiaries of India’s federal workfare program ● Status quo at the time – when women work, their wages paid into husband’s bank account ● This study aims to discern the impacts of ensuring women receive wages for work in federal program into own account with basic financial training (accounts plus linking) ● Distinguish impact from just providing women accounts (without or with training) .. 35
- 36. Who should be more influenced by intervention? Even in rural India, significant variation in propensity to work - We may expect relatively larger impacts among women who are more constrained to start with Separate impacts for women who did or did not report having worked at baseline 36
- 37. Did women’s decision to work change? For women not already working, yes! Women entered the labor force 37
- 38. Did work change beliefs? It did for women who hadn’t worked in the baseline... 38
- 39. We see positive trends for women’s empowerment among those that did not work in the baseline 39
- 40. Gender based violence unchanged but some deterioration in mental health 40
- 41. Women’s economic empowerment and well-being ● Gender norms that influence women’s work can distort the allocation of talent and lead to intergenerational persistence of health disadvantages for women ● Economic growth and direct health interventions can reduce some of this disadvantage ● But it is also important to directly influence women’s economic empowerment – especially in settings where women state a desire to work ● Understanding how norms influence the allocation of labor market returns will be critical to designing effective policies. 41
Editor's Notes
- Our next question is - can we isolate the reasons for declining female LFP in a developing setting and use policy to stop / reverse the trend? We will use the context of India to answer this question.
- Labor economics traditionally argued for U-shape in female labor force participation as economies developed. India is below trend for female LFP.
- Early optimism: as countries grow, women will re-enter the labor force - leading to the U-shaped trend (Goldin, 1994). Will occur earlier for unmarried women, relative to married women. This re-entry would reflect: Increasing education of women allowing for non-manufacturing labour opportunities. Preferences / possibilities that are better realized with dual household incomes. Expectation of traditional roles (in childcare and housework) prevent married women from entering the formal labour force. In wealthier countries there was a post-WWII rise for in female LFP. Specific shocks mattered e.g. demand for men in the armed forces, availability of contraception (Goldin and Katz). Many developed countries enacted generous childcare, education and parental leave policies, with a small positive impact on female LFP (Olivetti & Petrongolo, 2017). Growth in the service sector was an important contributor, increasing women’s relative wages and market hours (Ngai & Petrongolo, 2017). Add graph(?) from Goldin (1983) on US LFP over time for women
- Early optimism: as countries grow, women will re-enter the labor force - leading to the U-shaped trend (Goldin, 1994). Will occur earlier for unmarried women, relative to married women. This re-entry would reflect: Increasing education of women allowing for non-manufacturing labour opportunities. Preferences / possibilities that are better realized with dual household incomes. Expectation of traditional roles (in childcare and housework) prevent married women from entering the formal labour force. In wealthier countries there was a post-WWII rise for in female LFP. Specific shocks mattered e.g. demand for men in the armed forces, availability of contraception (Goldin and Katz). Many developed countries enacted generous childcare, education and parental leave policies, with a small positive impact on female LFP (Olivetti & Petrongolo, 2017). Growth in the service sector was an important contributor, increasing women’s relative wages and market hours (Ngai & Petrongolo, 2017). Add graph(?) from Goldin (1983) on US LFP over time for women
- Contrasting view: Boserup (1970) argues women are marginalized during structural transformation that sees them exit the labour force, making re-entry difficult. This is evidenced by concentration of women in informal and tertiary sector. For example, in India women are disproportionately concentrated in work that is an extension of traditional household responsibilities (e.g. domestic work or childcare). Women are 16% of all service sector workers but 60% of domestic workers (Basole et al., 2018). By contrast, manufacturing industries are 80% male. [RP: graph we can use?] This sectoral inequality has proved hard to tackle.
- Contrasting view: Boserup (1970) argues women are marginalized during structural transformation that sees them exit the labour force, making re-entry difficult. This is evidenced by concentration of women in informal and tertiary sector. For example, in India women are disproportionately concentrated in work that is an extension of traditional household responsibilities (e.g. domestic work or childcare). Women are 16% of all service sector workers but 60% of domestic workers (Basole et al., 2018). By contrast, manufacturing industries are 80% male. [RP: graph we can use?] This sectoral inequality has proved hard to tackle.
- Our next question is - can we isolate the reasons for declining female LFP in a developing setting and use policy to stop / reverse the trend? We will use the context of India to answer this question.
- Female LFP is low and declining… According to the Labour Bureau (LB), 23% of working age women were in the labour force in 2015. More recent estimates are highly variable. The number according to Centre for Monitoring the Indian Economy (CMIE) in 2017 was 12%. The ILO estimated the female LFP at 27% in 2017, ranking 121 out of 131 countries and making India’s comparable peers Saudi Arabia and Pakistan.
- This is despite macroeconomic trends that, according to the traditional U-shape hypothesis, would suggest otherwise. (1) Good macroeconomic growth - India’s GDP has been growing at ~7% for the past decade (2) Rising education of women. N.B. The original U-shape theory of female LFP argued that there was a stigma against women’s manual labour. But, as education rose, women would access secondary / tertiary sector work and re-enter the labour force (Goldin, 1994). This has not happened in India. In fact, we often see returns showing up in dowries, not on the labour market (Dalmia and Lawrence, 2005; Deolalikar and Rao, 1998; Ashraf et al., 2015). (3) Lower fertility - Indian women have gone from an average of 4 children in 1990 to just over 2 today.
- Our next question is - can we isolate the reasons for declining female LFP in a developing setting and use policy to stop / reverse the trend? We will use the context of India to answer this question.
- The explanation behind this puzzles? Constraints placed on women - particularly those driven by restrictive gender norms. Critically, these norms restrict women’s: Movement Behaviour Social networks Many are difficult to tackle, as they are perpetuated in the private sphere (household).
- Safety concerns - particularly regarding sexual violence - in women’s work lead to movement and labour restrictions. These are strong both because of genuine safety worries, but also because any sexual relations with men significantly damage a woman’s reputation and marriage prospects. The #MeToo movement is nascent in India and many public accusers have already met with backlash. There are multiple equilibria in this context, and the current one disadvantages women There are very few women working, so the workplace is likely to be male dominated, safety concerns are therefore higher, so fewer women work… and so on. Note, as detailed earlier, that there exist female-dominated industries. These are usually low-paid, low-skilled and extend women’s traditional gender roles. Safety concerns (by the reasoning above) could perpetuate this (c.f. Boserup, 1970).
- Looking forward, then, what policies could be tested or enacted to loosen the constraints of norms? Now we look at a three-pronged approach to improving policy with a view to generating discussion around gender in the Indian elections come 2019.
- Affirmative action in employment could mimic the success of reservation policies for caste and women in politics, but better monitoring is required Indian state and central government often use reservation policies in public administration and politics to increase representation. Reservation policies increased SC/ST representation in regular salaried employment by around 5 percentage points (Borooah, Dubey, and Iyer 2007). Fletcher, Moore and Pande (2018) note the significant rise in female employment in education after Operation Blackboard effectively reserved teacher positions for women. Evidence from Panchayats, where reservation for women has existed for significant periods, suggests that quotas can also reduce bias against women (Beaman et al., 2009). Some states are experimenting with broad-based gender quotas for public sector employment (e.g. Rajasthan introduced a 30% reservation for women in government jobs in 2010 including a 5% reservation for widows) and other states have targeted quotas in specific sectors (e.g. police in Gujarat). However, more attention needs to be given to monitoring of whether these quotas are filled and necessary complementary policies to skill women in required fields. Further, a survey of vocational trainees in 2016 founds that female quotas in training and jobs can act as ceilings to FLFP in some cases, indicating the policy may not reduce bias long-term (Artiz Prillaman et al., 2017).
- Redesigning policy strategy Increase social programs that target individuals rather than households, and ensure effective implementation. The social causes of gender discrimination are insidious. The most challenging include the stigma against women working (particularly when married and/or with children) and migrating (except for marriage). A woman working outside the home drains household status in an extremely patriarchal society (Bernhardt et al., 2018). In patriarchal societies, household work is deemed the domain of women. This affects women throughout their lives - as daughters and eventually as wives. Unsurprisingly, marriage and having children is particularly highly correlated with women leaving the workforce (Jayachandran, 2015). Changing norms thus requires intervening within the household. Direct benefit transfers are an increasingly important part of India’s social sector infrastructure. However, discussion of conditional cash transfers and, specifically, whether transfers should be targeted to specific individuals within the household is relatively limited. Relatedly, better access to earnings for women is key to ensure their labour decisions, and the economic power that comes with working, are their own. [FBA study] Generally, improved social safety facilities could help reduce reliance on informal social networks and accountability to peers’ norms.
- The general lesson for policy? Consideration of norms (both gender, caste and others) and offsetting these should be an indispensable component of all other policies (e.g. labour, healthcare, investment). Policies to increase FLFP are often considered separately from those of job creation and skilling. This can mean FLFP programs fail to level the playing field. For example, skilling programs in India are usually blind to the different safety and mobility concerns of men and women. Gender quotas in programs like this are valuable, but likely to fail if the program does not account for gender-based labor market barriers (Artiz Prillaman et al., 2017).