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The unintended effects of cash transfers on fertility: evidence from the Safe Motherhood Scheme in India

Abstract

India launched the Safe Motherhood Scheme (Janani Suraksha Yojana or JSY) in 2005 in response to persistently high maternal and child mortality rates. JSY provides a cash incentive to socioeconomically disadvantaged women for childbirth at health facilities. This study explores some unintended consequences of JSY. Using data from two large household surveys, we examine a policy variation that exploits the differential incentive structure under JSY across states and population subgroups. We find that JSY may have resulted in a 2.5–3.5 percentage point rise in the probability of childbirth or pregnancy over a 3-year period in states already experiencing high population growth.

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Notes

  1. 1.

    For example, the infant mortality rates of India, Nepal, Bangladesh, and Sri Lanka were 47, 39, 37, and 11 per 1000 live births in 2011, respectively. Source: Level and Trends in Child Mortality. Report 2011. Estimates Developed by the UN Inter-agency Group for Child Estimation (UNICEF, WHO, World Bank, UN DESA, UNPD).

  2. 2.

    Source: MDGMonitor, United Nations Development Programme, http://www.mdgmonitor.org.

  3. 3.

    Assuming US$1 = INR 60, approximately. The annual budget of JSY in 2010–11 was INR 16.18 billion. INR = Indian rupees.

  4. 4.

    Randive et al. (2013) also find a positive association between JSY and institutional deliveries.

  5. 5.

    The phrases “low-performing states” and “high-performing states” are official JSY terms for describing these two groups of states.

  6. 6.

    See Hotz et al. (1993) for a review of the literature on economics of fertility. Del Boca (2002) finds that the availability of publicly funded child care increased fertility in Italy. Milligan (2005) finds that pronatalist tax benefits may have increased childbirth probability in Canada, while Laroque and Salanié (2013) estimate that child benefits might increase total fertility in France. Cohen et al. (2007) and Mörk et al. (2013) find similar effects of fiscal benefits on fertility in Israel and Sweden respectively.

  7. 7.

    The Honduran Programa de Asignación Familiar (PRAF-II) CCT provides two different cash incentives for schooling and health. One of them is a US$48 cash incentive, per person per year, provided for health care and nutrition of pregnant and nursing mothers, and young children (Moore 2008).

  8. 8.

    Except Jammu & Kashmir and Assam, the rest are EAG states.

  9. 9.

    Before 2013, the BPL women in HPS needed to be at least 19 years old to be eligible for JSY. This restriction has been now abolished.

  10. 10.

    In DLHS 2007–2008 data, 41, 12.9, and 19 % of villages in India had a primary health sub-center, a primary health center, and a private clinic, respectively. The availability of other types of facilities, such as community health center or private hospital, was even lower. The average distance from a village to a primary health center and a private clinic was 9 and 10 km respectively (IIPS 2014).

  11. 11.

    The state of Nagaland was not surveyed in DLHS-3.

  12. 12.

    Our results are robust to the inclusion of births during 2004 and 2005 in our analysis, as discussed later (also see Table 8).

  13. 13.

    In the absence of a below-poverty-line indicator in DLHS-2, we use an approximate measure for poverty. First, we use principal-component analysis to create an household-level wealth index following Filmer and Pritchett (2001). Only variables that are common to both DLHS rounds are considered in this index—for example, the possession of a radio, sewing machine, TV, bicycle, car, and so on—and indicators of housing condition such as construction quality, availability of toilets, sources of drinking water, and the type of cooking fuel used. Then, we consider households on or below the 30th percentile of the wealth index distribution, separately in DLHS-2 and DLHS-3, as approximately poor. For comparison, the national poverty headcount rate was 33.8 % in India in 2009 (Source: World Bank data, http://data.worldbank.org/indicator/SI.POV.RUHC?page=1, accessed May 5, 2015)

  14. 14.

    We use maps from the 2001 Indian census, and the GADM (http://gadm.org) database, to identify the districts along state borders. There are several extra districts in the DLHS-3 data. A new district in DLHS-3 was typically created by carving out from a parent district in DLHS-2. In such cases, we mapped the DLHS-3 district back to the parent district. However, one particular border district in DLHS-3, Mewat in the state of Haryana, was created from more than one parent DLHS-2 districts. We exclude these parent districts and Mewat from our analysis. Also excluded from analysis is the state of Nagaland which was surveyed in DLHS-2 but not in DLHS-3. Figures 1 and 2 in the appendix present the district maps.

  15. 15.

    For example, pregnant women may visit or temporarily live at their natal home for the purpose of childbirth. However, DLHS-3 data show that less than 1 % of pregnant women who reported receiving antenatal care did so at their natal homes, and only 3.75 % of the most recent child deliveries took place in natal homes.

  16. 16.

    17 We include 26 indicators of household asset ownership and housing condition in the pre-JSY (2001–2003) propensity score estimation, and 46 such indicators in the post-JSY (2006–2008) propensity score estimation. The difference is a result of DLHS-3 collecting data on more such indicators as compared with DLHS-2.

  17. 17.

    The availability of a dai may be associated with the rollout of JSY in a village. According to official JSY documents, traditional birth attendants are often trained and converted into ASHAs. Anganwadi centers operate under the Integrated Child Development Services (ICDS) of India, a large national program providing supplemental nutrition, immunization, health check-up and education, and preschool education to over 90 million women and children. See http://wcd.nic.in/icds.htm, accessed April 29, 2015.

  18. 18.

    Powell-Jackson et al. (2015) use a different way of estimating the coverage of JSY. They consider the proportion of women giving birth at a public facility who receive the cash benefit as an indicator of coverage. This method ignores births at accredited private health facilities. In addition, the inconsistencies in JSY uptake and usage data at the level of the mother from DLHS-3 have been already pointed out by Das et al. (2011).

  19. 19.

    Past experience from India’s coercive fertility control policy (abolished in 1996) show that minorities and the poor were disproportionately targeted by health workers seeking to meet contraceptive targets mandated by the government (Sangwan and Maru 1999). These groups may be similarly vulnerable to coercion by health workers under JSY.

  20. 20.

    One possible confounding factor for the fertility effect of JSY seen in our results is a change in the family planning policy. If the family planning policy was relaxed in LPS at the same time as the introduction of JSY, the effect of JSY will be overestimated. However, both the National Population Policy of 2000 and the Working Group on Population Stabilization for the Eleventh Year Plan (2007–2012) of India vigorously emphasized the need for family planning particularly in LPS states (GoI 2006). Therefore, any concurrent relaxation of family planning policies is highly unlikely.

  21. 21.

    In additional analyses, we examined the space between two most recent births during the study period for women with multiple births in the data. We do not find any statistically significant effect of JSY on birth spacing. This could be either due to the small sample size of women with multiple births during the 3-year study period, or an indication of JSY’s positive effect on total fertility. Powell-Jackson et al. (2015) also suggest that the fertility effect of JSY may not be temporary, as it increased with the program’s age. However, longer-term data are required for more research on such effect.

References

  1. Abadie A, Imbens GW (2006) Large sample properties of matching estimators for average treatment effects. Econometrica 74:235–267

    Article  Google Scholar 

  2. Arroyo CR, Zhang J (1997) Dynamic microeconomic models of fertility choice: a survey. J Popul Econ 10:23–65

    Article  Google Scholar 

  3. Becker GS, Lewis HG (1973) On the interaction between the quantity and quality of children. J Polit Econ 81:S279–88

    Article  Google Scholar 

  4. Becker GS, Tomes N (1976) Child endowments and the quantity and quality of children. J Polit Econ 84:S143–S162

    Article  Google Scholar 

  5. Becker SO, Caliendo M (2007) Sensitivity analysis for average treatment effects. Stata J 7:71–83

    Google Scholar 

  6. Bhalotra S, van Soest A (2008) Birth-spacing, fertility and neonatal mortality in India: dynamics, frailty, and fecundity. J Econ 143:274–290

    Article  Google Scholar 

  7. Cain M (1981) Risk and insurance: perspectives on fertility and agrarian change in India and Bangladesh. Popul Dev Rev 7:435–474

    Article  Google Scholar 

  8. Cohen A, Dehejia R, Romanov D (2007) Do Financial Incentives Affect Fertility?

  9. Conde-Agudelo A, Rosas-Bermúdez A, Kafury-Goeta AC (2006) Birth spacing and risk of adverse perinatal outcomes. J Am Med Assoc 295:1809–1823

    Article  Google Scholar 

  10. Das A, Rao D, Hagopian A (2011) India’s Janani Suraksha Yojana: further review needed. Lancet 377:295–6

    Article  Google Scholar 

  11. Dehejia RH, Wahba S (1999) Causal effects in nonexperimental studies: reevaluating the evaluation of training programs. J Am Stat Assoc 94:1053–1062

    Article  Google Scholar 

  12. Del Boca D (2002) The effect of child care and part time opportunities on participation and fertility decisions in Italy. J Popul Econ 15:549–573

    Article  Google Scholar 

  13. Dewey KG, Cohen RJ (2007) Does birth spacing affect maternal or child nutritional status? A systematic literature review. Matern Child Nutr 3:151–173

    Article  Google Scholar 

  14. Filmer D, Pritchett LH (2001) Estimating wealth effects without expenditure data-or-tears: an application to educational enrollments in States of India. Demography 38:115–132

    Google Scholar 

  15. Fiszbein A, Schady N, Ferreira FHG et al (2009) Conditional cash transfers: reducing present and future poverty. The World Bank, Washington DC

    Book  Google Scholar 

  16. GoI (2006) Report Of The Working Group On Population Stabilization For The Eleventh Five Year Plan (2007–2012). The Planning Commission, Government of India

  17. Gopalan SS, Durairaj V (2012) Addressing maternal healthcare through demand side financial incentives: experience of Janani Suraksha Yojana program in India. BMC Heal Serv Res. doi:10.1186/1472-6963-12-319

    Google Scholar 

  18. Heckman JJ, Ichimura H, Todd PE (1997) Matching as an econometric evaluation estimator: evidence from evaluating a job training programme. Rev Econ Stud 64:605–654

    Article  Google Scholar 

  19. Heckman JJ, Willis RJ (1976) Estimation of a stochastic model of reproduction an econometric approach. Household Production and Consumption. National Bureau of Economic Research, In, pp 99–146

    Google Scholar 

  20. Hotz VJ, Klerman JA, Willis RJ (1993) The economics of fertility in developed countries. In: Stark O (ed) Rosenzweig MR. Elsevier, Handbook of Population and Family Economics, pp 275–347

    Google Scholar 

  21. Hotz VJ, Miller RA (1984) The Economics of Family Planning. University of Chicago - Population Research Center

  22. IIPS (2014) District Level Household and Facility Survey (DLHS-4), 2012–13. IIPS, International Institute of Population Sciences, Mumbai, India

    Google Scholar 

  23. Laroque G, Salanié B (2013) Identifying the response of fertility to financial incentives. J Appl Econ 29:314–332

    Article  Google Scholar 

  24. Leung SF (1991) A stochastic dynamic analysis of parental sex preferences and fertility. Q J Econ 106:1063–1088

    Article  Google Scholar 

  25. Lim SS, Dandona L, Hoisington JA et al (2010) India’s Janani Suraksha Yojana, a conditional cash transfer programme to increase births in health facilities: an impact evaluation. Lancet 375:2009–2023

    Article  Google Scholar 

  26. Mantel N, Haenszel W (1959) Statistical aspects of the analysis of data from retrospective studies of disease. J Natl Cancer Inst 22:719–784

    Google Scholar 

  27. Milligan K (2005) Subsidizing the stork: new evidence on tax incentives and fertility. Rev Econ Stat 87:1–39

    Article  Google Scholar 

  28. MoHFW (2012) Annual Report 2011–12: Ministry of Health and Family Welfare. Government of India, New Delhi, India

    Google Scholar 

  29. Moore C (2008) Assessing Honduras? CCT Programme PRAF, Programa de Asignación Familiar: Expected and Unexpected Realities. International Policy Centre for Inclusive Growth, Country Study 01/2008

  30. Mörk E, Sjögren A, Svaleryd H (2013) Childcare costs and the demand for children—evidence from a nationwide reform. J Popul Econ 26:33–65

    Article  Google Scholar 

  31. Morris SS, Flores R, Olinto P, Medina JM (2004) Monetary incentives in primary health care and effects on use and coverage of preventive health care interventions in rural Honduras: cluster randomised trial. Lancet 364:2030–7. doi:10.1016/S0140-6736(04)17515-6

    Article  Google Scholar 

  32. MoSPI (2010) Household Consumer Expenditure in India, 2007–08. Government of India, New Delhi, India

    Google Scholar 

  33. Newman JL (1988) Research in Population Economics. In: Schultz TP (ed). JAI Press, London,

  34. Powell-Jackson T, Mazumdar S, Mills A (2015) Financial incentives in health: new evidence from India’s Janani Suraksha Yojana. J Health Econ. doi:10.1016/j.jhealeco.2015.07.001

    Google Scholar 

  35. Randive B, Diwan V, Costa A (2013) india’s conditional cash transfer programme (the JSY) to promote institutional birth: is there an association between institutional birth proportion and maternal mortality? PLoS One 8:e67452

    Article  Google Scholar 

  36. Rosenbaum PR (2002) Observational Studies, 2nd edn. Springer, New York

    Book  Google Scholar 

  37. Rosenbaum PR, Rubin DB (1983) The central role of the propensity score in observational studies for causal effects. Biometrika 70:41–55

    Article  Google Scholar 

  38. Rosenzweig MR, Schultz TP (1985) The demand for and supply of births: fertility and its life cycle consequences. Am Econ Rev 75:992–1015

    Google Scholar 

  39. Rutstein SO (2005) Effects of preceding birth intervals on neonatal, infant and under-five years mortality and nutritional status in developing countries: evidence from the demographic and health surveys. Int J Gynecol Obstet 89:S7–S24

    Article  Google Scholar 

  40. Sangwan N, Maru RM (1999) The target-free approach: an overview. J Health Manag 1:71–96

    Article  Google Scholar 

  41. Schultz TP (1993) Demand for children in low income countries. Handbook of Population and Family Economics. Elsevier, In, pp 349–430

    Google Scholar 

  42. Sianesi B (2004) An evaluation of the swedish system of active labor market programs in the 1990s. Rev Econ Stat 86:133–155

    Article  Google Scholar 

  43. Stecklov G, Winters P, Todd J, Regalia F (2007) Unintended effects of poverty programmes on childbearing in less developed countries: experimental evidence from Latin America. Popul Stud 61:125–140

    Article  Google Scholar 

  44. Todd J, Winters P, Stecklov G (2011) Evaluating the impact of conditional cash transfer programs on fertility: the case of the Red de Proteccion Social in Nicaragua. J Popul Econ 25:267–290

    Article  Google Scholar 

  45. UNFPA (2012) Trends in Maternal Mortality:1990–2010. United Nations Population Fund (with UNICEF, WHO, World Bank)

  46. Wolpin KI (1984) An estimable dynamic stochastic model of fertility and child mortality. J Polit Econ 92:852–874

    Article  Google Scholar 

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Acknowledgments

We thank the two anonymous reviewers for their very helpful comments, and Ashvin Ashok, Nikolay Braykov, and Nestor Mojica for their research assistance.

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Correspondence to Arindam Nandi.

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Responsible editor: Junsen Zhang

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Nandi, A., Laxminarayan, R. The unintended effects of cash transfers on fertility: evidence from the Safe Motherhood Scheme in India. J Popul Econ 29, 457–491 (2016). https://doi.org/10.1007/s00148-015-0576-6

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Keywords

  • Janani Suraksha Yojana
  • JSY
  • Conditional cash transfer
  • Fertility
  • India
  • JEL Codes
  • J1
  • I1
  • O1