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Family size and schooling in sub-Saharan Africa: testing the quantity-quality trade-off

Journal of Population Economics volume 32, pages 1353–1399 (2019)Cite this article

Abstract

Many family planning programs are based on the idea that small families lead to improved development outcomes, such as more schooling for children. Because of endogeneity issues, this idea is however difficult to verify. A handful of studies have made use of twin birth to deal with the endogeneity of family size. We do so for sub-Saharan African countries. In a compilation of 86 survey rounds from 34 countries, we exploit the birth of twins to study the effect of a quasi-exogenous increase in family size on the schooling of children at the first, second and third birth order. Our findings do not support the generally assumed negative effect of family size on schooling.

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Notes

  1. This text featured on a 2-rupee coin issued by India in 1993, to promote family planning.

  2. According to Smits and Monden (2011), twinning is associated with maternal age, maternal height, smoking, oral contraceptive use, race and ethnicity.

  3. Rosenzweig and Zhang (2009) point out that twins have lower-average birth weight than singletons, and perhaps worse health or cognitive achievement later on, and this may threaten the exclusion restriction if parents therefore allocate resources away from twins, towards older singleton-birth children.

  4. In the first study using twins as an instrument, Rosenzweig and Wolpin (1980) find that schooling levels of Indian children decrease with exogenous increases in fertility. Consecutive studies using twins find mixed results. Cáceres-Delpiano (2006), relying on US Census data, finds a negative effect of family size on the probability of enrolment in private schooling, Black et al. (2010) find that family size negatively affects the IQ of younger cohorts in Norway and Li et al. (2008), relying on Chinese twins for identification, find a negative effect of sibship on schooling attainments. However, family size is found to have no effect on children’s educational attainment in Norway (Black et al. 2005) and in Israel (Angrist et al. 2010). Furthermore, Marteleto and de Souza (2012), studying the effect of family size on adolescents’ schooling in Brazil, find a positive effect in periods and regions in the earlier stages of socioeconomic development, but these effects disappear for recent periods “when the opportunities for child farm work have declined, education has expanded, and fertility has declined to below-replacement levels” (p. 1473).

  5. Bhalotra and Clarke (2016) tested the quantity-quality trade-off in a large sample, with data from 72 countries among which many sub-Saharan African countries, but they did not specifically focus on SSA.

  6. Data on 46 sub-Saharan African countries reveal an average total fertility rate (TFR) of 4.97 in 2014 (World Bank 2016) and a gross school enrolment (GSE) rate of 98.96 in 2013 (UIS Stat. UNESCO, 2016). The 2014 TFR is markedly lower than that in 1985 (6.62) but still much higher than the 2014 TFR in South Asia (2.56) and in Latin America (2.11). Similarly, the 2013 GSE has greatly improved from its level in 1985 (79.24) but still compares unfavourably with the 2013 GSE in Southern Asia (109.06) and in Latin America and the Caribbean (109.03).

  7. Since the variation in completed grades across country and by child age is so large, it is unlikely to be completely captured by country and age fixed effects. We therefore use age-standardized z-score for which the reference group comprises children in the same country and birth cohort. In the robustness section, we show that our results remain stable when using completed grades as outcome variable.

  8. Mother’s ethnicity is country specific and generated as follows: (country code × 1000) + ethnic group code. The insertion of the complete set of ethnicity fixed effects (as dummy variables) makes country fixed effects superfluous. In some DHS rounds, ethnicity is omitted (e.g. Rwanda, Burundi). These rounds are omitted from our baseline results but included in a robustness check that uses region of residence of the household as a proxy for ethnicity.

  9. Infant deaths proxy among others the reproductive health of the mother.

  10. For each round, the DHS separates all interviewed households into five wealth quintiles based on their wealth index. The wealth index is a composite measure of a household’s cumulative living standard. It is calculated using principal component analysis on easy-to-collect data on a household’s ownership of selected assets, such as televisions and bicycles; materials used for housing construction; and types of water access and sanitation facilities (Standard Recode Manual for DHS 6, 2013).

  11. The results are robust to clustering at the ethnicity or region level. The number of countries is insufficient to get a robust covariance matrix when clustering at the country level.

  12. For instance, in the 3+ sample, the indicator variable Twinh is equal to 1 if the third birth is a multiple birth and 0 otherwise.

  13. In the period 1990–2014, 86 DHS rounds were administered in 34 sub-Saharan African countries. For the following surveys, we could not construct and merge the key variables because of issues with the unique identifiers of surveyed individuals: 1991 and 1998 in Cameroon, 1996–1997 in Chad, 1998–1999 in Cote d’Ivoire, 2001 in Mali and 1992 in Niger.

  14. A household is defined as one person or a group of persons who usually live and eat together. This is not the same as a family. A family includes people who are related, but a household includes any people who live together, whether or not they are related (DHS Interviewer’s Manual, October 2012).

  15. The coefficients are multiplied by the standard deviation of completed years of education in sample I (see Table 1) and interpreted with reference to the average age of children in sample I.

  16. For instance, Bhalotra and Clarke (2016), Marteleto and de Souza (2012), Angrist et al. (2010) and Black et al. (2005)

  17. To measure access to prenatal care, we use the percentage of births (occurring within 5 years prior to the survey) with prenatal care in the DHS cluster. Access to a doctor or nurse is proxied by the DHS cluster-level percentage of births with prenatal care given by a doctor or nurse.

  18. We do not formally verify this intuition, since the formalized extension by Oster (2017) only applies to OLS estimates.

  19. Note that our prior value on γ is more conservative than the 0.004 used in Bhalotra and Clarke (2016). Our results hold even with γ set at 0.008.

  20. We control for several characteristics likely to affect birth weight such as a mother’s age, education, body mass, age at first birth and ethnicity; the child’s sex, preceding birth interval and year of birth; and the household’s residence and wealth quintile. All error terms are clustered at the DHS cluster level.

  21. We regress BMI and BMI percentile on the twin indicator, controlling for the factors mentioned in the previous footnote, as well as for birth weight, child age, the number of months of breastfeeding, the number of under-5-year-old children in the households and whether or not the mother lives with her husband.

  22. The difference in BMI is measured in terms of percentiles. A child is considered underweighted if its BMI is below the 10th percentile of the World Health Organization’s reference BMI distribution (see Cole et al. 2007). According to this definition, 13.75% of twins are underweighted compared to only 10% of singletons. But, as shown in Table 3, on average, twins and singletons belong to the same decile.

  23. Burkina Faso, Burundi, Congo DR, Guinea, Malawi, Mali, Niger, Nigeria and Chad (see Lesthaeghe 2014, p. 2)

  24. Ghana, Lesotho, Liberia, Madagascar, Namibia, Rwanda, Senegal, South Africa, Swaziland and Uganda (see Lesthaeghe 2014, p. 2)

  25. The initiative aimed at a global commitment to provide quality basic education for all children, youth and adults and was first launched in 1990 (http://www.worldbank.org/en/topic/education/brief/education-for-all). Admittedly, children born in 2000 reached schooling age only in 2006. On the other hand, the initiative’s adoption itself is unlikely to have had an immediate impact on the ground. In any case, any cut-off year would be somewhat arbitrary. Choosing slightly different cut-off years (1998 and 1999 which give almost balanced samples across cohorts) yields similar results.

  26. Family size might have a different impact on girls’ schooling (no impact or negative impact) because of time spent in household chores by older daughters (Mueller 1984; Tiefenthaler,1997) which might negatively affect their educational achievements.

  27. Also following Angrist et al. (2010), we try out a second instrument, i.e. sibship sex composition. This alternative instrument relies on the idea that parents prefer to have both boys and girls rather than only children of the same sex. Hence, in the latter case, they may be more likely to have an additional child. However, in our case, only twinning performs well in the first stage. For instance, where the F-statistic surpasses 100 for twinning, it barely reaches 20 for the sex composition instrument (results not reported, but available on request). We therefore focus on twinning in our analysis.

  28. We censor the completed years of education to the child’s age minus 6, assuming that schooling can start only from 6 years onwards since children are admitted to primary school at 6 in most of the countries (from http://data.worldbank.org/indicator). For instance, if a 12-year-old child in our sample is reported to have completed 9 years of schooling, we set years of schooling to 6.

  29. This yields a more balanced sample between West and Central Africa (57.80%) and East and Southern Africa (42.20%).

  30. In the case of the 4+ sample, we obtain a negative and slightly significant (at 10%) coefficient in the subsample of non-poor households when using censored education z-scores (panel C of Table 20) and when using the household’s region of residence instead of mother’s ethnicity (panel C of Table 22).

  31. This result remains significant but is lower when considering an age difference of ≤ 4 years, but it loses significance when the age difference is 2 years or less, both in the 3+ and 2+ samples.

  32. Enrolment, school starting age and dropout of firstborn in the 4+ sample are not affected by family size (results not reported).

  33. When sharing books, tutoring or transporting to school, the per-child cost of schooling declines when two or more children can be sent to school simultaneously (Qian 2009).

  34. In our analysis, child labour is broadly defined as the sum of time spent in household chores, the number of working hours for a family member (including himself/herself) or for someone outside the household. In an alternative definition, we exclude household chores; our findings remain robust to this change.

  35. In 21 surveys of 15 countries, information on children’s time allocation is provided (the rounds and countries are listed in Table 11 in the Appendix). The average total of hours worked by a child, including time spent on household chores, is 9.78 per week and is significantly larger in poor families in a sample mean t test (11.73 h) compared to 7.59 in richer ones.

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Acknowledgements

We received much appreciated comments from Elena Briones Alonso, Philip H. Ross, Pieter Serneels, NikStoop and the participants at seminars, conferences and workshops in Leuven (LICOS-KU Leuven seminar),Antwerp (Doctoral Day) and Oxford (2017 CSAE conference), as well as from the Editor and two anonymousreferees of this journal.

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Authors and Affiliations

  1. IOB–Institute of Development Policy and Management, University of Antwerp, Antwerp, Belgium

    Sahawal Alidou & Marijke Verpoorten

  2. LICOS–Centre for Institutions and Economic Performance, KU Leuven, Leuven, Belgium

    Sahawal Alidou & Marijke Verpoorten

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  1. Sahawal Alidou
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  2. Marijke Verpoorten
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Correspondence to Marijke Verpoorten.

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We received much appreciated comments from Elena Briones Alonso, Philip H. Ross, Pieter Serneels, Nik Stoop and the participants at seminars, conferences and workshops in Leuven (LICOS-KU Leuven seminar), Antwerp (Doctoral Day) and Oxford (2017 CSAE conference), as well as from the Editor and two anonymous referees of this journal.

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Appendix

Appendix

Table 11 Cross-country summary statistics based on sample I
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Table 12 Summary statistics of variables
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Table 13 Estimate of the direct effect of twinning on children’s schooling (γ)
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Table 14 Testing twin instrument validity using data on children under 5
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Table 15 Robustness check IV estimates of the effect of family size on children schooling accounting for heterogeneity of twin effect on family size
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Table 16 Robustness check IV estimates of the effect of family size on children schooling using partially missing instruments and an unrestricted specification
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Table 17 Robustness check using samples including children with school-aged siblings living outside the household
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Table 18 Regression results for decentralized level of decision (mother level analysis – with number of children defined as the mother’s total number of sons and daughters living in the household) using decentralized samples
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Table 19 Robustness check with number of children defined as the total number of births given by the household head’s wives
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Table 20 Robustness check using censored education z-score
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Table 21 Robustness check with years of education as outcome variable
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Table 22 Robustness check with a control for the household’s region of residence instead of mother’s ethnicity
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Table 23 Robustness check with a control for country-urban by rural FE instead of mother’s ethnicity
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Table 24 Robustness check with an alternative definition of poverty
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Table 25 Exploring the proximate cause underlying the zero-effect of family size on schooling using data on first-born in 2+Sample
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Table 26 Exploring the proximate cause underlying the positive effect of family size on schooling, across first and second born in 3+Sample
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Table 27 Exploring the proximate cause underlying the positive effect of family size on schooling in 3+Sample, across first and second born of poor households using 3+Sample
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Table 28 Exploring the proximate cause underlying the positive effect of family size on schooling in 3+Sample, across first and second born of non-poor households using 3+Sample
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Table 29 Exploring the proximate cause underlying the positive effect of family size on schooling in 4+Sample, across the second born and third born
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Table 30 Exploring the proximate cause underlying the positive effect of family size on schooling in 4+Sample, across the second born and third born of poor households
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Table 31 Exploring the proximate cause underlying the positive effect of family size on schooling in 4+Sample, across the second born and third born of non-poor households
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Table 32 Formal tests of child labour mechanism
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Alidou, S., Verpoorten, M. Family size and schooling in sub-Saharan Africa: testing the quantity-quality trade-off. J Popul Econ 32, 1353–1399 (2019). https://doi.org/10.1007/s00148-019-00730-z

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Keywords

  • Family size
  • Schooling
  • Quantity-quality trade-off
  • Sub-Saharan Africa

JEL classification

  • D1
  • O1