Female secondary school attendance has recently increased in sub-Saharan Africa, and so has the risk of becoming pregnant while attending school. We analyze the impact of teenage pregnancy on young women’s human capital using longitudinal data in Madagascar that capture the transition from adolescence to adulthood for a cohort aged 21–24 in 2012, first interviewed in 2004. We find that early childbearing increases the likelihood of dropping out of school and decreases the chances of completing secondary school. This pregnancy-related school dropout also has a detrimental impact on standardized test scores in math and French. We instrument early pregnancy with the young woman’s community-level access and her exposure to condoms since age 15 after controlling for pre-fertility socioeconomic conditions. Our results are robust to different specifications that address potential endogeneity of program placement and instrument validity.
This is a preview of subscription content, access via your institution.
Complications during pregnancy and childbirth are the leading cause of death for girls aged 15–19 in low- and middle-income countries (World Health Organization 2011).
Modern methods include oral contraceptives (“the pill”), female and male sterilization, IUD, injectable contraceptives, implants, male and female condoms, diaphragm, and emergency contraception (INSTAT and ICF Macro 2010).
Abortion is illegal, and estimates put abortion rates at 1 per 10 live births. Abortion complications are one of the major contributors to maternal death in Madagascar (Sharp and Kruse 2011).
In a related study, Field and Ambrus (2008) found a negative effect of adolescent marriage on schooling in Bangladesh. In this context, female schooling is restricted by marriage; pregnancy comes after marriage. This is different from African countries, where out-of-wedlock pregnancy is common.
The 2004 survey defined a community as the catchment area for a primary school. These communities were chosen from a national school-based sampling frame (see Glick et al. 2009). The data are not strictly nationally representative of the entire population, but they closely reflect the main demographic characteristics of our cohort members.
Comparisons between female attritors and nonattritors in 2004 on socioeconomic characteristics used to predict early childbearing show that attrition is not a source of bias in our IV results. Results are available upon request.
In spite of the activities of the 1996 National Action Plan to encourage single mothers to resume education, government school rules in Madagascar stipulate that pregnant girls be expelled and not allowed back to school after childbirth (United Nations Economic Commission for Africa 2009). This evidence is consistent with our interviews with community-level various stakeholders, who acknowledged that school girls who get pregnant are socially pressured, often by the school principal, to leave the school to reduce reputational costs for the school.
The exact question is, “Can the residents obtain condoms in the community? Since when (year) were these available?”
In all of the communities with condom availability, community leaders report that condoms are available “at all times,” suggesting that stockouts might not be an issue.
We lack information on the age of sexual initiation in our surveys.
As a robustness check, we estimate our IV models with different measures of exposure: since age 10, and since the young woman’s birth year. We find that these instruments have lower correlation with ever-mother. The F statistic of exposure to condoms since age 15 is twice as large as the F statistic of these instruments.
The 2 SLS IV models that use exposure to condoms are available upon request. Results are similar to the IV probit models.
Using a dummy variable for whether the parents were alive when a young woman was 15 does not change the results.
Our IV results are robust to the estimation of the models including the young women who dropped out before age 13. Results using the full sample are available upon request.
The stable unit treatment value assumption (SUTVA) in our IV model would imply that there are no plausible social network or spillover effects resulting from community-level condom access. To the extent that young women who live in areas without condom access do benefit from condom availability, our first stage might be weakened. However, we do not expect that such spillovers would bias our main results because our IV estimates explore only differences in community-level access to condoms, which will still be positive even in the presence of such spillovers; it is very unlikely that condom distribution points benefit equally the communities that have no access to condoms.
Distance is indicated as a separate reason for not using modern contraception (INSTAT and ICF Macro 2010).
Despite the low HIV/AIDS prevalence in Madagascar, it remains a serious public health concern (Sharp and Kruse 2011).
From the 2007 commune census, information on the number of births and number of women who died during or after delivery in 2006 is available for only 68 and 66 of our 73 communities, respectively.
Only 71 of our 73 sample communities were included in the 2001 commune census.
Meekers et al. (2006) showed that young women aged 15–24 who self-reported condom access (defined as knowing a condom source within 10 min walking) are 1.8 times more likely than others to have ever used condoms.
Our IV results are qualitatively the same when we exclude the community-level controls. Results are available upon request.
Results using access to condoms are qualitatively similar. We keep the specifications with exposure to condoms.
We fail to reject the null hypothesis of exogeneity under the Haussmann and Durbin Watson test using access to condoms as an instrument.
Based on the Haussmann and Durbin Watson test, we reject the null hypothesis of exogeneity for both math and French standardized tests scores at the 5 % significance level, using access and exposure to condoms as IVs.
Angeles, G., Guilkey, D. K., & Mroz, T. A. (1998). Purposive program placement and the estimation of family planning program effects in Tanzania. Journal of the American Statistical Association, 93, 884–899.
Angeles, G., Guilkey, D. K., & Mroz, T. A. (2005). The determinants of fertility in rural Peru: Program effects in the early years of the National Family Planning Program. Journal of Population Economics, 18, 367–389.
Angrist, J. D., & Pischke, J. S. (2009). Mostly harmless econometrics: An empiricist’s companion. Princeton, NJ: Princeton University Press.
Arceo-Gomez, E. O., & Campos-Vazquez, R. M. (2014). Teenage pregnancy in Mexico: Evolution and consequences. Latin American Journal of Economics, 51, 109–146.
Ardington, C., Menendez, A., & Mutevedzi, T. (2015). Early childbearing, human capital attainment and mortality risk: Evidence from a longitudinal demographic surveillance area in rural KwaZulu-Natal, South Africa. Economic Development and Cultural Change, 63, 281–317.
Ashcraft, A., Fernández-Val, I., & Lang, K. (2013). The consequences of teenage childbearing: Consistent estimates when abortion makes miscarriage non-random. Economic Journal, 123, 875–905.
Ashcraft, A., & Lang, K. (2006). The consequences of teenage childbearing (NBER Working Paper No. 12485). Cambridge, MA: National Bureau of Economic Research.
Azevedo, J. P., Lopez-Calva, L. F., & Perova, E. (2012). Is the baby to blame? An inquiry into the consequences of early childbearing (World Bank Policy Research Working Paper No. 6074). Washington, DC: World Bank Group.
Baird, S., McIntosh, C., & Özler, B. (2011). Cash or condition? Evidence from a cash transfer experiment. Quarterly Journal of Economics, 126, 1709–1753.
Bandiera, O., Buehren, N., Burgess, R., Goldstein, M., Gulesci, S., Rasul, I., & Sulaiman, M. (2015). Women’s empowerment in action: Evidence from a randomized control trial in Africa. Retrieved from https://www.povertyactionlab.org/sites/default/files/publications/111%20Womens%20Empowerment%20June%202015.pdf
Behrman, J. R., Murphy, A., Quisumbing, A. R., & Yount, K. M. (2009). Are returns to mothers’ human capital realized in the next generation? The impact of mothers’ intellectual human capital and long-run nutritional status on children’s human capital in Guatemala (IFPRI Discussion Paper No. 850). Washington, DC: International Food Policy Research Institute.
Bongaarts, J. (1994). The impact of population policies: Comment. Population and Development Review, 20, 616–620.
Branson, N., & Byker, T. (2018). Causes and consequences of teen childbearing: Evidence from a reproductive health intervention in South Africa. Journal of Health Economics, 57, 221–235.
Breierova, L., & Duflo, E. (2004). The impact of education on fertility and child mortality: Do fathers really matter less than mothers? (NBER Working Paper No. 10513). Cambridge, MA: National Bureau of Economic Research.
Buckles, K. S., & Hungerman, D. M. (2016). The incidental fertility effects of school condom distribution programs (NBER Working Paper No. w22322). Cambridge, MA: National Bureau of Economic Research.
Canning, D., & Schultz, T. P. (2012). The economic consequences of reproductive health and family planning. Lancet, 380, 165–171.
Chandra-Mouli, V., McCarraher, D. R., Phillips, S. J., Williamson, N. E., & Hainsworth, G. (2014). Contraception for adolescents in low and middle income countries: Needs, barriers, and access. Reproductive Health, 11. https://doi.org/10.1186/1742-4755-11-1
Chong, A., Gonzalez-Navarro, M., Karlan, D., & Valdivia, M. (2013). Effectiveness and spillovers of online sex education: Evidence from a randomized evaluation in Colombian public schools (NBER Working Paper No. w18776). Cambridge, MA: National Bureau of Economic Research.
Conley, T. G., Hansen, C. B., & Rossi, P. E. (2012). Plausibly exogenous. Review of Economics and Statistics, 94, 260–272.
Diaz, C. J., & Fiel, J. E. (2016). The effect(s) of teen pregnancy: Reconciling theory, methods, and findings. Demography, 53, 85–116.
Duflo, E., Dupas, P., & Kremer, M. (2015). Education, HIV, and early fertility: Experimental evidence from Kenya. American Economic Review, 105, 2757–2797.
Dupas, P. (2011). Do teenagers respond to HIV risk information? Evidence from a field experiment in Kenya. American Economic Journal: Applied Economics, 3(1), 1–34.
Field, E., & Ambrus, A. (2008). Early marriage, age of menarche, and female schooling attainment in Bangladesh. Journal of Political Economy, 116, 881–930.
Fletcher, J. M., & Wolfe, B. L. (2009). Education and labor market consequences of teenage childbearing evidence using the timing of pregnancy outcomes and community fixed effects. Journal of Human Resources, 44, 303–325.
Free, C., Roberts, I. G., Abramsky, T., Fitzgerald, M., & Wensley, F. (2011). A systematic review of randomised controlled trials of interventions promoting effective condom use. Journal of Epidemiology & Community Health, 65, 100–110.
Friedman, W. (2015, May). Antiretroviral drug access and behavior change. Paper presented at the 7th Annual Meeting on the Economics of Risky Behaviors, Balçova, İzmir, Turkey.
Geronimus, A. T., & Korenman, S. (1992). The socioeconomic consequences of teen childbearing reconsidered. Quarterly Journal of Economics, 107, 1187–1214.
Glick, P., Randriamamonjy, J., & Sahn, D. E. (2009). Determinants of HIV knowledge and condom use among women in Madagascar: An analysis using matched household and community data. African Development Review, 21, 147–179.
Glick, P., Randrianarisoa, J. C., & Sahn, D. E. (2011). Family background, school characteristics, and children’s cognitive achievement in Madagascar. Education Economics, 19, 363–396.
Glick, P. J., Sahn, D. E., & Walker, T. F. (2016). Household shocks and education investments in Madagascar. Oxford Bulletin of Economics and Statistics, 78, 792–813.
Grant, M. J., & Hallman, K. K. (2008). Pregnancy-related school dropout and prior school performance in KwaZulu-Natal, South Africa. Studies in Family Planning, 39, 369–382.
Güneş, P. M. (2015). The role of maternal education in child health: Evidence from a compulsory schooling law. Economics of Education Review, 47, 1–16.
Hotz, V. J., McElroy, S. W., & Sanders, S. G. (2005). Teenage childbearing and its life cycle consequences exploiting a natural experiment. Journal of Human Resources, 40, 683–715.
Imbens, G. W., & Angrist, J. D. (1994). Identification and estimation of local average treatment effects. Econometrica, 62, 467–475.
Institut National de la Statistique (INSTAT), & ICF Macro. (2010). Demographic and Health Surveys of Madagascar: 2008–2009. Antananarivo, Madagascar: INSTAT and ICF Macro. Retrieved from http://dhsprogram.com/pubs/pdf/FR236/FR236.pdf
Joshi, S., & Schultz, T. P. (2013). Family planning and women’s and children’s health: Long-term consequences of an outreach program in Matlab, Bangladesh. Demography, 50, 149–180.
Kane, J. B., Morgan, S. P., Harris, K. M., & Guilkey, D. K. (2013). The educational consequences of teen childbearing. Demography, 50, 2129–2150.
Klepinger, D., Lundberg, S., & Plotnick, R. (1999). How does adolescent fertility affect the human capital and wages of young women? Journal of Human Resources, 34, 421–448.
Lee, D. (2010). The early socioeconomic effects of teenage childbearing: A propensity score matching approach. Demographic Research, 23(article 25), 697–736. https://doi.org/10.4054/DemRes.2010.23.25
Levine, D. I., & Painter, G. (2003). The schooling costs of teenage out-of-wedlock childbearing: Analysis with a within-school propensity-score-matching estimator. Review of Economics and Statistics, 85, 884–900.
Lloyd, C. B., & Mensch, B. S. (2008). Marriage and childbirth as factors in dropping out from school: An analysis of DHS data from sub-Saharan Africa. Population Studies, 62, 1–13.
Marteleto, L., Lam, D., & Ranchhod, V. (2008). Sexual behavior, pregnancy, and schooling among young people in urban South Africa. Studies in Family Planning, 39, 351–368.
McQueston, K., Silverman, R., & Glassman, A. (2013). The efficacy of interventions to reduce adolescent childbearing in low- and middle-income countries: A systematic review. Studies in Family Planning, 44, 369–388.
Meekers, D., Agha, S., & Klein, M. (2005). The impact on condom use of the “100% Jeune” social marketing program in Cameroon. Journal of Adolescent Health, 36, 529–530.
Meekers, D., Silva, M., & Klein, M. (2006). Determinants of condom use among youth in Madagascar. Journal of Biosocial Science, 38, 365–380.
Miller, G. (2010). Contraception as development? New evidence from family planning in Colombia. Economic Journal, 120, 709–736.
Miller, G., & Babiarz, K. S. (2016). Family planning program effects: Evidence from Microdata. Population and Development Review, 42, 7–26.
Mmari, K., & Sabherwal, S. (2013). A review of risk and protective factors for adolescent sexual and reproductive health in developing countries: An update. Journal of Adolescent Health, 53, 562–572.
Molyneaux, J. W., & Gertler, P. J. (2000). The impact of targeted family planning programs in Indonesia. Population and Development Review, 26(Suppl.), 61–85.
Ozier, O. (2018). The impact of secondary schooling in Kenya: A regression discontinuity analysis. Journal of Human Resources, 53, 157–188.
Paton, D. (2002). The economics of family planning and underage conceptions. Journal of Health Economics, 21, 207–225.
Pinchoff, J., Boyer, C. B., Mutombo, N., Chowdhuri, R. N., & Ngo, T. D. (2017). Why don’t urban youth in Zambia use condoms? The influence of gender and marriage on non-use of male condoms among young adults. PLoS One, 12(3). https://doi.org/10.1371/journal.pone.0172062
Pitt, M. M., Rosenzweig, M. R., & Gibbons, D. M. (1993). The determinants and consequences of the placement of government programs in Indonesia. World Bank Economic Review, 7, 319–348.
Pörtner, C. C., Beegle, K., & Christiaensen, L. (2011). Family planning and fertility: Estimating program effects using cross-sectional data (World Bank Policy Research Working Paper No. WPS5812). Washington, DC: World Bank Group.
Pritchett, L. H. (1994). Desired fertility and the impact of population policies. Population and Development Review, 20, 1–55.
Raharinjatovo, J. A. (2014). Madagascar (2008): Family planning TRaC study evaluating the condom use and pill use as contraceptive methods among young females 15–24 years—Round three (PSI TRAC Summary report). Washington, DC: Population Services International. https://doi.org/10.7910/DVN/27620
Ranchhod, V., Lam, D., Leibbrandt, M., & Marteleto, L. (2011, January). Estimating the effect of adolescent fertility on educational attainment in Cape Town using a propensity score weighted regression. Paper presented at the fifth annual PopPov Conference on Population, Reproductive Health, and Economic Development, Marseille, France.
Ribar, D. C. (1994). Teenage fertility and high school completion. Review of Economics and Statistics, 76, 413–424.
Rindfuss, R. R., Bumpass, L., & St. John, C. (1980). Education and fertility: Implications for the roles women occupy. American Sociological Review, 45, 431–447.
Schultz, T. P. (2007). Population policies, fertility, women’s human capital, and child quality. Handbook of Development Economics, 4, 3249–3303.
Sharp, M., & Kruse, I. (2011). Health, nutrition, and population in Madagascar, 2000–09 (World Bank Working Paper No. 216). Washington, DC: World Bank Publications.
Staiger, D., & Stock, J. (1997). Instrumental variables regression with weak instruments. Econometrica, 65, 557–586.
Stock, J. H., & Watson, M. W. (2007). Introduction to econometrics (2nd ed.). London, UK: Pearson.
Strauss, J., & Thomas, D. (1995). Human resources: Empirical modeling of household and family decisions. Handbook of Development Economics, 3, 1883–2023.
Strauss, J., & Thomas, D. (2007). Health over the life course. In T. P. Schultz & J. Strauss (Eds.), Handbook of development economics (pp. 3375–3474). Amsterdam, the Netherlands: Elsevier.
Sweat, M. D., Denison, J., Kennedy, C., Tedrow, V., & O’Reilly, K. (2012). Effects of condom social marketing on condom use in developing countries: A systematic review and meta-analysis, 1990–2010. Bulletin of the World Health Organization, 90, 613–622A.
Thomas, D. (1999). Fertility, education and resources in South Africa. In C. H. Bledsoe, J. B. Casterline, J. A. Johnson-Kuhn, & J. G. Haaga (Eds.), Critical perspectives on schooling and fertility in the developing world (pp. 138–180). Washington, DC: National Academy Press.
Todd, P. E., & Wolpin, K. I. (2003). On the specification and estimation of the production function for cognitive achievement. Economic Journal, 113(485), F3–F33.
United Nations Economic Commission for Africa. (2009). African women’s report: Measuring gender inequality in Africa: Experiences and lessons from the African Gender and Development Index. Addis Ababa, Ethiopia: United Nations Economic Commission for Africa. Retrieved from https://www.uneca.org/sites/default/files/PublicationFiles/awr09_fin.pdf
Urdinola, B. P., & Ospino, C. G. (2015). Long-term consequences of adolescent fertility: The Colombian case. Demographic Research, 32(article 55), 487–1518. https://doi.org/10.4054/DemRes.2015.32.55
Williamson, N. E. (2013). Motherhood in childhood: Facing the challenge of adolescent pregnancy. New York, NY: Information and External Relations Division of the United Nations Population Fund.
World Bank. (2013). World Development Indicators. Retrieved from http://data.worldbank.org/data-catalog/world-development-indicators
World Health Organization (WHO). (2011). WHO guidelines on preventing early pregnancy and poor reproductive outcomes among adolescents in developing countries. Geneva, Switzerland: WHO. Retrieved from http://apps.who.int/iris/bitstream/10665/44691/1/9789241502214_eng.pdf
The authors thank Günther Fink, George Jakubson, Ravi Kanbur, David Lam, Paul Schultz, anonymous reviewers and seminar participants at the 2013 Cornell Economics Seminar, the 2013 Population Association of America Conference, the 2013 Northeast Universities Development Consortium Conference-NEUDC, the 2014 Population and Reproductive Health Conference, and the 2015 Harvard Population Center seminar series for helpful comments and discussions. This study was funded by the IZA/DFID GLM | LIC Program under Grant Agreement GA-C1-RA4-067. This document is an output from a project funded by the UK Department for International Development (DFID) and the Institute for the Study of Labor (IZA) for the benefit of developing countries. The views expressed are not necessarily those of DFID or IZA. Herrera is very grateful for the support from the Hewlett Foundation/(IIE) Doctoral Dissertation Fellowship. The authors declare that they have no conflict of interest. Any errors are solely the responsibility of the authors.
Electronic supplementary material
About this article
Cite this article
Herrera Almanza, C., Sahn, D.E. Early Childbearing, School Attainment, and Cognitive Skills: Evidence From Madagascar. Demography 55, 643–668 (2018). https://doi.org/10.1007/s13524-018-0664-9
- Early childbearing
- Female education
- Cognitive skills
- Family planning