Nutrition Matters: Numeracy, Child Nutrition and Schooling Efficiency in Sub-Saharan Africa in the Long Run

Abstract

School enrolment has increased at an unprecedented scale in Sub-Saharan Africa but learning and the associated education efficiency have not. Given that resources are limited, the efficient use of inputs is of utmost importance for sustainable development. Hence, we investigate whether improvements in children’s nutrition can improve learning and hence efficiency. To assess this relationship, we employ average female height as our proxy for nutrition during childhood. For learning, we estimate numeracy and efficiency using a linearized version of the Whipple Index. Our data is at the subnational level focusing on the birth decades from 1950 to 1999. To deal with the endogeneity of nutrition, we use an instrumental variable approach. Our instrument is negative rainfall shocks during childhood which can adversely affect nutrition. We find that better nutrition increases education efficiency. Therefore, investments in nutrition can advance self-sustaining long-term growth based on human capital in Sub-Saharan Africa.

Keywords:

• Numeracy
• school efficiency
• height
• Africa

Acknowledgments

We thank Elias Papaioannou, and the participants at AEHN (Lund), WEHC (Paris), EHES (Groningen) and workshop participants in Tuebingen and Valencia for their comments.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 The concept was first used by demographers to assess data quality (Bachi, 1951), It is now commonly used to estimate basic numeracy (Crayen & Baten, 2010; Hippe & Baten, 2012; Stolz, Baten, & Botelho, 2013).

2 This holds true if survivor bias is not substantial. Earlier studies found that survivor bias does not invalidate the results (Crayen & Baten, 2010).

3 Height evidence is from www.clio-infra.eu, reporting male height for 32 Sub-Saharan African countries for the 1960–69 and 34 in the 1980–89 birth periods (based on age groups 20–50). Similarly, the NCD Risk Factor Collaboration (NCD-RisC) (2016) study arrives at 169.7 cm for 45 Sub-Saharan African countries both for 1960–69 and 169.1 cm for the 1980–89 birth periods (including the 17-year age group and no upper limit). For the 1990s, the trend continued down to 167.8 cm. From the birth decade of the 1950s to the 1960s, an increase from 169.0–169.4 to 169.7–169.9 can be observed.

4 The demography literature provides a few contributions using the age-heaping approach to evaluate the quality of survey data (Bwalya, Phiri, & Mwansa, 2015; Fayehun, Ajayi, Onuegbu, & Egerson, 2020; Lyons-Amos & Stones, 2017). Yet, while these studies employ age heaping, their methodology is not completely comparable, and they do not link their estimates to the numerical abilities of respondents.

5 Named after A’Hearn, Baten, and Crayen who published this transformation in 2009, and Greg Clark who suggested it in a comment on their paper.

6 In earlier studies the age heaping of individuals aged 17–22 was characterized as being impossible to compare with the age heaping of older individuals (Crayen & Baten, 2010). At such young age, just having finished stature growth a few years ago, some individuals tend to round to even numbers (such as 18 or 22), others to 20, others not at all, even if they are not numerate enough to provide an exact age. Prayon (2014) compared this age group (17–22) to other age groups and found a much lower signal-to-noise ratio. This probably depends on the frequency of important life events at this age, such as military service, marriage, and similar events. For all these reasons, a consensus in the literature has emerged to concentrate on the age groups 23–32, 33–42, and so on, as the age heaping-education relationship was much closer for these age groups. This does not imply that rounding on age 20 was negligible, but rounding on 18 and 22 was also very substantial, and the shifts to these ages have not been modelled yet in a way to obtain a reliable indicator. Also taking all round ages such as 18 or 22 as “heaped” does not result in a reliable proxy, that can be compared with other age groups.

7 Crayen and Baten (2010) have studied a large, global sample for the birth decades of the 1870s to 1940s using a country-decade panel of 1549 observations to identify to which degree individuals of age group 23–32 rounded less on multiples of five, compared to later age groups who were born in the same birth decade, but were interviewed in later censuses. For example, those born in the 1880s, age 23–32 in 1910, were compared to the same persons born in the 1880s, but interviewed in the 1920s, when they were 33–42. Crayen and Baten (2010) estimated an adjustment of the ABCC by −25% for the age groups 23–32. This resulted in a quite similar numeracy level for the same birth cohorts, independent of their age during the census. Clearly, the correct adjustment might be −24% or −26% in some cases, but we cannot identify the subtle differences. The average reduction of 25 percent moves the estimates for this age group closer to the true value.

8 We acknowledge that years of schooling as our educational input may not fully capture the educational input in a nonformal environment (i.e., the household). However, the years a child ideally spends in a school are the same in which learning in a nonformal environment needs to take place. Thus, we believe that using years of schooling is a good approximation for the time a child spends learning, whether in a school or a nonformal environment. Moreover, the average years of schooling in a region naturally declines the more people never attend school, thus, reflecting the overall availability of schooling and attendance at schools, which are crucial inputs at the early stages of educational expansion.

9 The DHS has good coverage of height data for women, but only very few surveys have data on male heights. Therefore, we opted to only use the female height data to ensure comparability between samples. Moreover, given that women and girls are the more marginalized group compared to their male counterparts, using female height data as a proxy for children’s undernutrition might even be the more reliable indicator for undernutrition. If food becomes scarce, it is often first redistributed within the household from females to males (Doss, 2013). Hence, female height might be more ‘sensitive’ to periods of malnutrition during childhood.

10 We include age at first marriage as a proxy for female empowerment (Baten & de Pleijt, 2022). We include the share of Muslims as a control variable as the average educational attainment of Muslims is on average lower than that of Christians. Similarly, we include religious fractionalization to control for the homogeneity of the religious community and religious competition has been linked to higher educational outcomes (Gallego & Woodberry, 2010). To calculate religious fractionalization, we follow Alesina, Devleeschauwer, Easterly, Kurlat, and Wacziarg (2003).

11 We acknowledge that individuals may have moved over the course of their lives such that the conditions in the district of residence might not resemble conditions in the actual birth district. However, there is only limited information about the district of birth for a small subset of respondents. Thus, we can only calculate the rainfall shock for the current district of residence.

Additional information

Funding

This work was supported by German Research Foundation (DFG) grant “Collaborative Research Centre 1070 ‘ResourceCultures’”, project number 215859406.