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The effect of female education on marital matches and child health in Bangladesh

Abstract

This paper examines the effects of female education on marriage outcomes by exploiting the exogenous variation generated by the Female Secondary School Stipend Program in Bangladesh, which made secondary education free for rural girls. Our findings show that an additional year of female education leads to an increase in 0.72 years of husband’s education and that better educated women pair with spouses who have better occupations and are closer in age to their own, suggesting assortative mating. Those educated women appear to experience greater autonomy in making decisions on receiving their own health care and visiting their family. Furthermore, educated women have lower fertility and use more maternal health care, and their children have better health outcomes than those of less-educated women. Overall, our results suggest that the marriage market is one of the channels through which women’s education affects their life outcomes.

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Fig. 1

Notes

  1. Boulier and Rosenzweig (1984) and Fafchamps and Quisumbing (2005) examine the positive assortative matching in rural Ethiopia and in the Philippines, respectively. Boulier and Rosenzweig (1984) use father’s education, female unemployment rate, and infant mortality as instruments for female education. Fafchamps and Quisumbing (2005) examine the correlations between characteristics of husbands and wives at the time of marriage.

  2. Source: Bangladesh Bureau of Statistics and UNICEF Bangladesh 2014.

  3. Gross enrolment rate is the ratio of total enrolment, regardless of age, to the total population of the age group that officially corresponds to the level of education. Source: United Nations Educational, Scientific, and Cultural Organization (UNESCO) Institute for Statistics.

  4. Source: UNESCO Institute for Statistics.

  5. Source: Bangladesh Bureau of Statistics, 2012b.

  6. The exclusion restriction requires that the stipend program affect marriage outcomes only through women’s education. We believe that the program satisfies the exclusion restriction for an instrumental variable for the following reasons. First, the proposal for the Female Secondary School Assistance Project (World Bank Report No. P-5945-BD, source: http://documents.worldbank.org/curated/en/512471468200338698/pdf/multi-page.pdf) states that the main objective of the project was to increase the pool of educated women who can participate in further development of the country. In addition, the vast majority of the total project cost (about 88% of the total cost excluding the cost for implementation and monitoring) was directly related to education, such as the stipend program (70%) and a teacher enhancement program and a female education awareness program (18%).

  7. Subscripts indicating survey year and geographic area are omitted for simplicity.

  8. We do not have information where they had lived during their secondary school years. The internal migration rate in Bangladesh, however, is quite low. The migration rate from rural to urban areas was 4.29 whereas in the other direction, the rate was 0.36% (Bangladesh Bureau of Statistics, 2012a). The low urban-to-rural migration rate indicates that to the extent that urban women in our data in fact participated in the stipend program, our first-stage results are likely to show the lower bound of possible effects of the program on education.

  9. Students in grade 7 in 1994 and students in grade 8 in 1995 did not receive a stipend, but they received it for 2 consecutive years in 1996 and 1997 (in grades 9 and 10). Students in grade 8 in 1994 did not receive a stipend but received it for 2 years in 1995 and 1996 (in grades 9 and 10). Students in grade 9 in 1994 received a stipend for 2 years in 1994 and 1995.

  10. As a further check, we provide a falsification test using only non-eligible cohorts (i.e., those who were born between 1965 and 1978) in Table 9. Placebo cohort 1 is those born between 1973 and 1978, while placebo cohort 2 is those born between 1970 and 1972; they are meant to resemble the structure of our original cohort 1 and cohort 2. Using these non-eligible cohorts, we find no significant coefficients on the interaction of placebo cohorts and a rural dummy, and if anything, the coefficient estimates are negative.

  11. In the 2007 DHS, there are 134 urban areas and 227 the rural areas, while in the 2011 and 2014 DHS, there are 207 urban areas and 393 the rural areas.

  12. The informal sector includes semi-skilled labor such as rickshaw drivers, carpenters, domestic servants, and factory workers, while the formal sector includes skilled employment including doctors, lawyers, accountants, entrepreneurs, traders, religious leaders, and factory workers who are skilled and trained.

  13. The statistics look similar when we keep all children under age 5.

  14. Our sample is restricted to married women, and if educated women tend to marry later, younger women, in particular, in the 2007 data, might drop out of the sample. Thus, our results might underestimate the effects of education by excluding young girls. Given that 97% of women in our sample married before they turned 23, if we use the 2011 and 2014 data only, most women (aged 23 to 43) in the sample had already married, and thus, we can partially address the sample selection issue. When we use the 2011 and 2014 data, the results do not change significantly.

  15. Although not reported, we find no statistically significant effect of education on (1) whether a woman can go out without telling her husband, (2) whether a beating is justified if the wife argues with her husband, or (3) whether a beating is justified if the wife refuses to have sex.

  16. The results do not change much when we include all children under age 5 (which increases the sample size about 20%) instead of including only the oldest child under age 5. The effect on height, however, decreases slightly and is no longer statistically significant at the 10% level (p value = 0.17) when the sample of all children under 5 years old is used.

  17. Our estimates are lower than those of Güneş (2015). Note that the estimates of Güneş (2015) are based on the effect of primary schooling completion. In developing countries, returns to education are highest for primary education and the returns decline by the level of schooling (Colclough 1982; Psacharopoulos 1985, 1994; Schultz 1988).

  18. Using randomized control trials in Rwanda, however, Okeke and Chari (2015) show that without addressing supply-side constraints (e.g., quality of service delivery), program-induced increases in the rate of institutional delivery do not necessarily reduce the newborn mortality.

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Acknowledgements

We thank Julie Cullen, Asadul Islam, Booyuel Kim, Do Won Kwak, Pushkar Maitra, Debdulal Mallick, Russell Smyth, Haishan Yuan, the editor, and two anonymous referees, as well as seminar participants at Monash University, University of Queensland, Korea Development Institute, Chung-Ang University, and the 2016 Korea Economic Association Conference for helpful comments.

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Correspondence to Hee-Seung Yang.

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Responsible editor: Junsen Zhang

Appendix

Appendix

Table 9 Effect of the FSSSP on women’s education using non-eligible cohorts

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Hahn, Y., Nuzhat, K. & Yang, HS. The effect of female education on marital matches and child health in Bangladesh. J Popul Econ 31, 915–936 (2018). https://doi.org/10.1007/s00148-017-0673-9

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Keywords

  • Female education
  • School stipend program
  • Assortative mating
  • Spouse characteristics
  • Child health
  • Bangladesh

JEL classification

  • I15
  • I21
  • I25
  • J12
  • J13
  • O12