Skip to main content

Advertisement

SpringerLink
Log in

Mothers and fathers: education, co-residence, and child health

  • Original Paper
  • Published:
Journal of Population Economics Aims and scope Submit manuscript

Abstract

This paper evaluates the causal effects of mother’s and father’s education on child-health outcomes in Zimbabwe, exploiting the exogenous variation generated by the 1980 education reform. We use four waves of Demographic and Health Surveys for Zimbabwe and estimate a simultaneous-equation model to take into account possible selection into co-residence between parents and children, endogeneity biases, and parental education sorting. Our results suggest that father’s education affects the health outcomes of under-5 children and matters more than that of the mother. These results continue to hold in a number of robustness checks. Moreover, while there is selection into co-residence with the child, this does not affect the causal effect of education on child health. Last, parental educational sorting is also shown to be important. Our findings suggest that not taking the education of both parents into account simultaneously may yield misleading conclusions.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

Notes

  1. Authors’ calculations from World Health Organization (2020).

  2. Zimbabwe is a low-income country with 16 million inhabitants and GDP per capita of 2,085.7 current international $ in PPP in 2017 (World Bank, World Development Indicators)) located in Southern Africa. The under-5 mortality rate was 86 per 1000 live births in 2010 (World Bank, World Development Indicators). Life expectancy at birth was 61 in 1985, 44 in 2002, and 51 in 2010 (World Bank, World Development Indicators). The large fall at the end of the 1990 s reflects the country’s high rate of HIV prevalence. The HIV prevalence rate in the Demographic and Health Surveys was 21.1% for women aged 15–49, and 14.5% for men in 2005 (vs. figures of 17.7 and 12.3, respectively in 2010/11).

  3. Two additional waves are available: 1988 and 2015. We first cannot use the 1988 survey wave as it is not possible to link the household children to their fathers. Second, we do not use the 2015 survey wave as the parents were born later, so that only few were not exposed to the reform: only 0.2% of the children in this wave were born to unexposed mothers, and 5.5% to unexposed fathers.

  4. In the remainder of the paper, we use the term “children” to refer to the children in this specific age group, and “mothers and fathers” for the parents of the children in this age group.

  5. Only 100 children had mothers born before 1950, and 1004 fathers born before 1950.

  6. Children are stunted if their height-for-age Z-score is over two standard deviations below the reference value and wasted if their weight-for-height Z-score is over two standard deviations below the reference value.

  7. The use of bed-nets was not asked in the 1994 and 1999 survey waves. We do not analyze breastfeeding as 98% of children were breastfed. We are unable to estimate the effect of parental education on child mortality as it is not possible to link dead children to their fathers: the information required to do so is only recorded for children who are alive and living in the sampled households.

  8. Up to 1986, children started primary school at age 7 (World Development Indicators).

  9. Agüero and Bharadwaj (2014) and Grépin and Bharadwaj (2015) restrict their sample to those aged 9–20 in 1980. Agüero and Bharadwaj (2014) define the treatment variable as being 15 or younger in 1980. Grépin and Bharadwaj (2015) consider women who were aged 13 and younger in 1980 to have been fully exposed to the policy, women aged 14 and 15 in 1980 to have been partially exposed, and women aged 16 or older in 1980 as the control group. Croke et al. (2016) and De Neve and Subramanian (2017) do not exclude the partially exposed. The only difference between their two definitions is that those born in 1963 appear in the “partially exposed” group in Croke et al. (2016) and in the control group in De Neve and Subramanian (2017).

  10. These coefficients come from the estimation of a number of linear-probability models separately by gender.

  11. We find the same results in terms of size and significance when estimating these equations on the initial sample (that not restricted to having both the mother and father currently living with the observed child) and larger F-statistics (see Online Appendix Table F1).

  12. Years of education are strictly positive for almost all sample parents: only 3% of fathers and 5% of mothers have no education. This small percentage of zeros justifies our use of OLS regressions in the first stage.

  13. Prior to independence, the health-care system was urban-centered and focused on curative rather than preventive care. The post-independence government adopted a policy of “health-for-all” in order to increase the geographical and financial access to care (Bassett et al. , 1997). It increased the number of rural health centers, midwives and nurses were trained and deployed in previously under-served rural areas, rural water and sanitation were improved, and low-income Zimbabweans were entitled to free health services (Hecht et al. , 1993).

  14. Pilon and Vignikin (2006) note considerable variation in Sub-Saharan Africa: in Namibia, only 26% of children below the age of 15 live with both parents, with figures of 33% in South Africa, 50% in Zimbabwe and Rwanda, 65% in Benin, 71% in Ethiopia, and 78% in Burkina Faso.

  15. We estimate a selection equation to explain why children do or do not currently live with each parent. Only 2% of mothers and 5% of fathers of sampled children are dead. Fathers/mothers who do not live with their child are therefore mainly parents who have decided not to live together: divorcees, migrants who quit the household, and those who have entrusted their child to somebody else’s care. We hypothesize that all of these potential (unobserved) reasons can be summarized in one single selection equation, a hypothesis that is of course debatable. However, we do not impose the same selection for mothers and fathers, and we do indeed see that the proportion of children who live with their mother only is much higher than the proportion of children who live with their father only.

  16. When we add the female and male national AIDS-related mortality rates (from UNAIDS data), averaged between the child birth year and the survey year, only the impact of female AIDS-related mortality is negative and significant in the selection equation of the father (see Online Appendix Table D1), and the second-stage results are robust to this inclusion (see Online Appendix Table G3). These variables account for the incidence of the HIV/AIDS epidemic in Zimbabwe over the sample period. However, the orphanhood due to AIDS-related deaths is unlikely to drive selection into co-residence, as the proportion of orphans is only small in the initial sample. In addition, the exclusion restriction may be violated if more AIDS-related deaths switch financial, material, and human resources away from the delivery and child care units of health facilities.

  17. The number of observations used to calculate each proportion differs slightly depending on the sample. To calculate the proportion of women who gave birth before marriage, the sample is all women who had at least one child. On average, this is calculated for a sample of 14 women per cluster (median of 13, minimum 2, and maximum 47). To calculate the proportion of separated respondents, the sample is all individuals who were ever in a union, with an average of 25.7 respondents per cluster (median=24, minimum=4, and maximum=62). The proportion of polygamous unions is calculated for the sample of married women, distinguishing between monogamous and polygamous husbands. The sample used has an average of 13.9 observations per cluster (median=13, minimum=2, maximum=47).

  18. We are unable to include an interaction between mother’s and father’s education to test for complementarity between the two, as there are no children who are born to an unexposed mother and an exposed father.

  19. In practice, as the first-stage and outcome equations are estimated simultaneously, we have as many first-stage regressions as outcomes. Given that the sample size varies slightly between the outcomes, depending on the number of missing values, the results from the first-stage estimations may also differ. However, this turns out not to be the case: the results are very similar across outcomes and sample sizes. In this section, and in the paper in general, we only show and comment on the first-stage regressions for the analysis sample.

  20. The final specification, described in Section 4.4, also includes the Inverse Mills Ratio to correct for possible selection bias in the outcome equation. These ratios also have to be included as a right-hand side variable in the first-stage equations; there is no correction for selection in Table 3, but the coefficients on the instrumental variables are unchanged in terms of size and significance when this correction is applied (see Benchmark Panel in Online Appendix Table F3).

  21. The pre-reform level of education differs between mothers and fathers: see the descriptive statistics in Table 2 and Fig. 1

  22. Note that the second-stage estimates are unaffected if we use the dummy variable for being exposed to the reform as the sole instrumental variable or if we use a linear spline specification with four segments (see Online Appendix Tables G1 and G2).

  23. Our sample is not restricted to children living in a nuclear family (i.e., with their mother and father and no other adults): the education of other household members (grandparents, uncles and aunts, etc.) could then also affect child-health outcomes, and so the estimated effect of parents’ education on child-health outcomes. Our point estimates are however very similar when we restrict the sample to nuclear families (see Online Appendix Table G5).

  24. The first-stage estimates are very similar to those in Table 3 (see Online Appendix Table F3).

  25. Mothers (fathers) were born up to 31 (28) years after 1965.

  26. We would ideally like to include variables reflecting the economic and social context faced by each parent depending on their birth year. This information is not available for all cohorts, and more importantly, these variables would be collinear with the parents’ birth cohort.

  27. Note that the sample is smaller in column 2, as observing the father’s ideal number of children requires the father to be selected to answer the male questionnaire. The DHS sampling design is such that not all sampled households are eligible for the male questionnaire in the surveys collected in 1994 and 1999.

  28. Note however that we cannot adopt this estimation strategy for fertility behaviors, as these are only observed when the parent lives with the child. As such, we cannot include these variables in the selection equations and calculate the corresponding Inverse Mills Ratios to be included in the rest of the model.

  29. https://data.unicef.org/topic/maternal-health/newborn-care/

References

  • Adda J, Bjorklund A, Holmlund H (2011) The role of mothers and fathers in providing skills: evidence from parental deaths IZA DP No. 5425

  • Agüero J, Bharadwaj P (2014) Do the more educated know more about health? Evidence from schooling and HIV knowledge in Zimbabwe. Econ Dev Cult Change 62(3):489–517

    Google Scholar 

  • Albouy V, Lequien L (2009) Does compulsory education lower mortality? J Health Econ 28:155–168

    Google Scholar 

  • Alderman H, Headey D (2017) How important is parental education for child nutrition? World Dev 94:448–464

    Google Scholar 

  • Apouey B, Geoffard PY (2016) Parents’ education and child body weight in France: the trajectory of the gradient in the early years. Econ Hum Biol 20:70–89

    Google Scholar 

  • Averett S, Gennetian L, Peters E (2005) Paternal child care and children’s development. J Popul Econ 18(3):391–414

    Google Scholar 

  • Azam JP, Djemai E (2019) Matching. Cooperation and HIV in the Couple, Mimeo

    Google Scholar 

  • Baker DP, Leon J, Smith Greenaway EG, John C, Marcela M (2011) The education effect on population health: a reassessment. Popul Dev Rev 37(2):307–332

    Google Scholar 

  • Banerjee A, Duflo E (2011) Poor economics, A radical rethinking of the way to fight global poverty. Public Affairs, New York

    Google Scholar 

  • Bassett M, Biljmakers L, Sanders D (1997) Professionalism, patient satisfaction and quality of health care: experience during Zimbabwe’s structural adjustment programme. Soc Sci Med 45(12):1845–1852

    Google Scholar 

  • Basu A (2002) Why does education lead to lower fertility? A critical review of some of the possibilities. World Dev 30(10):1779–1790

    Google Scholar 

  • Behrman J (2015) Does schooling affect women’s desired fertility? Evidence From Malawi. Uganda and Ethiopia. Demography 52(3):787–809

    Google Scholar 

  • Behrman JR, Rosenzweig MR (2002) Does increasing women’s schooling raise the schooling of the next generation? American Economic Review 92(1):323–334

    Google Scholar 

  • Bhalotra S, Clarke D (2014) Educational attainment and maternal mortality background paper prepared for the Education for All Global Monitoring Report 2013/4

  • Bhalotra S, Rawlings S (2011) Intergenerational persistence in health in developing countries: the penalty of gender inequality? J Public Econ 95(3–4):286–299

    Google Scholar 

  • Breierova L, Duflo E (2004) The impact of education on fertility and child mortality: do fathers really matter more than mothers? NBER Working Paper 10513

  • Card D (1999) Chapter 30 - the causal effect of education on earnings. In: Ashenfelter OC, Card D (eds) Handbook of labor economics, vol 3. Elsevier, pp 1801–1863

    Google Scholar 

  • Carneiro P, Meghir C, Parey M (2013) Maternal education, home environments and the development of children and adolescents. J Eur Econ Assoc 11(S1):123–160

    Google Scholar 

  • Case A, Paxson C (2001) Mothers and others: who invests in children’s health? J Health Econ 20:301–328

    Google Scholar 

  • Chiappori PA, Iyigun M, Weiss Y (2009) Investment in schooling and the marriage market. American Economic Review 99(5):1689–1713

    Google Scholar 

  • Chicoine L (2021) Free primary education, fertility, and women’s access to the labor market: evidence from Ethiopia. The World Bank Economic Review 35(2):480–498

    Google Scholar 

  • Chou SY, Liu JT, Grossman M, Joyce T (2010) Parental education and child health: evidence from a natural experiment in Taiwan. Am Econ J Appl Econ 2(1):33–61

    Google Scholar 

  • Colclough C, Löfstedt J, Manduvi-Moyo J, Maravanyika O, Ngwata W (1990) Education in Zimbabwe. Issues of quality and quantity. Education Division Documents No 50, Swedish International Developement Activity

  • Croke K, Grossman G, Larreguy HA, Marshall J (2016) Deliberate disengagement: how education can decrease political participation in electoral authoritarian regimes. American Political Science Review 110(3):579–600

    Google Scholar 

  • Cutler DM, Lleras-Muney A (2010) Understanding differences in health behaviors by education. J Health Econ 29(1):1–28

    Google Scholar 

  • d’Albis H, Augeraud-Veron E, Djemai E, Ducrot A (2012) The dispersion of age differences between partners and the asymptotic dynamics of the HIV epidemic. J Biol Dyn 6(2):695–717

    Google Scholar 

  • De Neve JW, Subramanian S (2017) Causal effect of parental schooling on early childhood undernutrition - quasi-experimental evidence from Zimbabwe. Am J Epidemiol 187(1):82–93

    Google Scholar 

  • Delesalle E (2021) The effect of the Universal Primary Education program on consumption and on the employment sector: evidence from Tanzania. World Dev 142

  • Dorsey B (1989) Educational development and reform in Zimbabwe. Comp Educ Rev 33(1):40–58

    Google Scholar 

  • Emran S, Greene W, Shilpi F (2018) When measure matters: coresidency, truncation bias, and intergenerational mobility in developing countries. J Hum Resour April 19, 2017(0216-7737R1)

  • Fafchamps M, Shilpi F (2014) Education and household welfare. Econ Dev Cult Change 63(1):73–115

    Google Scholar 

  • Fitzsimons E, Mesnard A (2014) Can conditional cash transfers compensate for a father’s absence? The World Bank Economic Review 28(3):467–491

    Google Scholar 

  • Garfinkel I, Glei D, McLanahan S (2002) Assortative mating among unmarried parents: implications for ability to pay child support. J Popul Econ 15(3):417–432

    Google Scholar 

  • Günes PM (2015) The role of maternal education in child health: evidence from a compulsory schooling law. Econ Educ Rev 47:1–16

    Google Scholar 

  • Grossman M (1972) On the concept of health capital and the demand for health. J Polit Econ 80:223–255

    Google Scholar 

  • Grossman M (2000) The human capital model. Handb Health Econ 1:348–408

    Google Scholar 

  • Grépin K, Bharadwaj P (2015) Maternal education and child mortality in Zimbabwe. J Health Econ 44:97–117

    Google Scholar 

  • Hahn Y, Nuzhat K, Yang HS (2018) The effect of female education on marital matches and child health in Bangladesh. J Popul Econ 31(3):915–936

    Google Scholar 

  • Hecht R, Overholt C, Holmberg H (1993) Improving the implementation of cost recovery for health: lessons from Zimbabwe. Health Policy 25:213–242

    Google Scholar 

  • Jürges H, Kruk E, Reinhold S (2013) The effect of compulsory schooling on health?evidence from biomarkers. J Popul Econ 26(2):645–672

    Google Scholar 

  • Kanyongo G (2005) Zimbabwe’s public education system reforms: successes and challenges. Int Electron J 6(1):65–74

    Google Scholar 

  • Kemptner D, Jurges H, Reinhold S (2011) Changes in compulsory schooling and the causal effect of education on health: evidence from Germany. J Health Econ 30:340–354

    Google Scholar 

  • Lindeboom M, Llena-Nozal A, van der Klaauw B (2009) Parental education and child health: evidence from a schooling reform. J Health Econ 28:109–131

    Google Scholar 

  • Lleras-Muney A (2005) The relationship between education and adult mortality in the U.S. Rev Econ Stud 72(1):189–221

    Google Scholar 

  • Makate M, Makate C (2018) Educated mothers, well-fed and healthy children? Assessing the impact of the 1980 school reform on dietary diversity and nutrition outcomes of Zimbabwean children. The Journal of Development Studies 54(7):1196–1216

    Google Scholar 

  • Masuda K, Yamauchi C (2020) How does female education reduce adolescent pregnancy and improve child health?: evidence from Uganda’s universal primary education for fully treated cohorts. The Journal of Development Studies 56(1):63–86

    Google Scholar 

  • Müller T, Shaikh M (2018) Your retirement and my health behavior: evidence on retirement externalities from a fuzzy regression discontinuity design. J Health Econ 57:45–59

    Google Scholar 

  • Montiel Olea J, Pflueger C (2013) A robust test for weak instruments. Journal of Business & Economic Statistics 31(3):358–369

    Google Scholar 

  • Osili U, Long B (2008) Does female schooling reduce fertility? Evidence from Nigeria. J Dev Econ 87:57–75

    Google Scholar 

  • Oyelere R (2010) Africa’s education enigma? The Nigerian story. J Dev Econ 91:128–139

    Google Scholar 

  • Pilon M, Vignikin K (2006) Ménages et familles en Afrique subsaharienne. Editions des archives contemporaines, Paris

    Google Scholar 

  • Shrestha V, Shrestha R (2020) Multigenerational effects of education reform: mother’s education and children’s human capital in Nepal. The World Bank Economic Review 34(3):698–729

    Google Scholar 

  • Silles M (2009) The causal effect of education on health: evidence from the United Kingdom. Econ Educ Rev 28:122–128

    Google Scholar 

  • Silles M (2011) The effect of schooling on teenage childbearing: evidence using changes in compulsory education laws. J Popul Econ 24:761–777

    Google Scholar 

  • Stancanelli E, Van Soest A (2012) Retirement and home production: a regression discontinuity approach. American Economic Review 102(3):600–605

    Google Scholar 

  • Van Bavel J, Klesment M (2017) Educational pairing, motherhood and women’s relative earnings in Europe. Demography 54:2331–2349

    Google Scholar 

  • de Walque D (2007) How does the impact of an HIV/AIDS information campaign vary with educational attainment? Evidence from rural Uganda. J Dev Econ 84:686–714

    Google Scholar 

  • Wooldridge J (2010) Econometric analysis of cross section and panel data, 2nd edn, The MIT Press, chap 19, pp 777–851

  • World Health Organization (2020) Global Health Estimates 2019: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva

  • Zenebe Gebre T (2020) Free primary education, timing of fertility, and total fertility. The World Bank Economic Review 34(3):730–748

    Google Scholar 

Download references

Acknowledgements

We thank editor Alfonso Flores-Lagunes and the three anonymous referees for their helpful comments. We would like to thank Richard Akresh, Eric Bonsang, Andrew Clark, Brigitte Dormont, Martin Karlsson, Carole Treibich, Jean-Noel Senne, and participants at the LEGOS seminar (Paris, November 2017), the 39th “Journées des Economistes de la Santé Francais” (Marseille, December 2017), the 2nd meeting of the Society of the Economics of the Household (Paris, May 2018), the DIAL-Gretha Workshop (Paris, June 2018), the 4th IRDES Workshop on Applied Health Economics and Policy Evaluation (Paris, June 2018), and the DIAL Development Conference (Paris, July 2019), who provided insightful comments on earlier drafts. The usual disclaimer applies.

Funding

This project has benefited from the financial support of the Health Chair–a joint initiative by PSL, Université Paris-Dauphine, ENSAE, MGEN and ISTYA under the aegis of the Fondation du Risque (FDR).

Author information

Authors and Affiliations

  1. Université Paris-Dauphine, Université PSL, LEDa, CNRS, IRD, 75016, Paris, France

    Elodie Djemai & Yohan Renard

  2. Université Paris Panthéon Assas, LEMMA, 75006, Paris, France

    Anne-Laure Samson

Authors
  1. Elodie Djemai
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yohan Renard
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Anne-Laure Samson
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Elodie Djemai.

Ethics declarations

Conflict of interest

The authors declare no competing interests.

Additional information

Responsible editor: Alfonso Flores-Lagunes

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Previous versions of this paper were circulated under the title “The impact of mother’s and father’s education on child’s health: Evidence from a quasi-experiment in Zimbabwe”.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file 1 (pdf 356 KB)

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Djemai, E., Renard, Y. & Samson, AL. Mothers and fathers: education, co-residence, and child health. J Popul Econ 36, 2609–2653 (2023). https://doi.org/10.1007/s00148-023-00966-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00148-023-00966-w

Keywords

JEL Codes

Search

Navigation