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

Figures & data

Figure 1. Stylised education production function following Glewwe and Kremer (2006). Authors’ own representation.

Figure 2. Data availability for ABCC calculations (a) and height measures (b). Authors’ own representation.

Figure A.1. Relationship of average years of schooling and numeracy (ABCC Index). Data from IPUMS, MICS and Afrobarometer. Authors’ own representation.

Figure 3. ABCC Index per admin I level for each birth decade between 1950 and 1990. Data from IPUMS, MICS and Afrobarometer. Authors’ own representation.

Figure 4. Schooling efficiency per admin I level for each birth decade between 1950 and 1990. Data from IPUMS, MICS, Afrobarometer and DHS. Authors’ own representation.

Table 1. Baseline regression – correlation of schooling efficiency and height

	(1)	(2)	(3)	(4)
	ln(Education efficiency)
Height	0.070***	0.078***	0.058***	0.061***
	(0.012)	(0.013)	(0.013)	(0.014)
Constant	−10.925***	−12.011***	−5.643***	−8.799***
	(1.942)	(2.047)	(2.063)	(2.309)
Birth decade FE	No	Yes	Yes	Yes
Regional FE	No	Yes	Yes	Yes
Sociodemographic controls	No	No	Yes	Yes
Geographic controls	No	No	No	Yes
Observations	1,234	1,115	1,111	969
R-squared	0.065	0.117	0.333	0.481

Notes: These regressions show our baseline model which estimates the relationship between adult height (a proxy for health during childhood), and schooling efficiency. The underlying dataset is a pooled panel of African admin I regions for birth decades 1950 to 1999. Due to the modest change over time in our outcome variable, we opted to use our data in a pooled panel format rather than using the panel structure to estimate fixed effects, for example. The fixed effects model would have too little variation over time. Column 1 shows the raw correlation, column 2 adds birth decade and regional fixed effects, column 3 sociodemographic controls and column 4 geographical controls. The sociodemographic controls include age at marriage, share of Muslims, and religious fractionalization. The geographical controls include the length of colonization, colonial railways, ancient trade routes, diamond mines, explorer routes, missions, nutrient availability, soil workability, malaria ecology index, petroleum sites, ruggedness, tsetse fly suitability, the ratio of the suitability for nomadic pastoralism to sedentary agriculture, distance to capital, distance to coast, population density and area. Standard errors in parentheses are clustered at the admin I level. Asterisks denote significance levels at ***p < 0.01.

Table 2. Instrumental variable regression – the impact of nutrition on schooling efficiency

	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
	ln (Education efficiency)
	1st Stage	2nd Stage	1st Stage	2nd Stage	1st Stage	2nd Stage	1st Stage	2nd Stage
Height		0.181***		0.179***		0.169***		0.258***
		(0.041)		(0.042)		(0.042)		(0.091)
Rainfall shock	−0.002***		−0.002***		−0.002***		−0.001***
	(0.000)		(0.000)		(0.000)		(0.000)
Constant	154.92***	−24.30**	155.75***	−37.16**	151.49***	−26.12**	149.51***	−53.60***
	(0.751)	(11.414)	(0.731)	(14.517)	(1.883)	(10.246)	(2.427)	(17.639)
Birth decade FE	No		Yes		Yes		Yes
Regional FE	No		Yes		Yes		Yes
Sociodemographic controls	No		No		Yes		Yes
Geographic controls	No		No		No		Yes
Observations	1,225	1,225	1,106	1,106	1,102	1,102	960	960
R-squared	0.095		0.126		0.262	0.204	0.524	0.277
F-statistic (1st stage)	66.79		15.29		18.58		16.67

Notes: These regressions show our IV model which estimates the relationship between adult height (a proxy for health during childhood), and schooling efficiency. Height is instrumented by rainfall during pregnancy and early childhood. The underlying dataset is a panel of African admin I regions for birth decades 1950–1999. The first set of regressions is without further controls, the second adds birth decade and regional fixed effects, the third sociodemographic controls and the fourth geographical controls. The sociodemographic controls include age at marriage, share of Muslims, and religious fractionalization. The geographical controls include the length of colonization, colonial railways, ancient trade routes, diamond mines, explorer routes, missions, nutrient availability, soil workability, malaria ecology index, petroleum sites, ruggedness, tsetse fly suitability, the ratio of the suitability for nomadic pastoralism to sedentary agriculture, distance to capital, distance to coast, population density and area. Standard errors are in parentheses and clustered at the admin I level. Asterisks denote significance levels at ***p < 0.01; **p < 0.05.

Table 3. Robustness checks – exclusion of the low or high extreme rain values and by rural/urban areas

	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
	Exclude top & bottom 10 % of rain distribution		Exclude top & bottom 20% of rain distribution		Exclude areas more than 50% urban		Exclude areas more than 50% rural
	1st Stage	2nd Stage	1st Stage	2nd Stage	1st Stage	2nd Stage	1st Stage	2nd Stage
Height		0.327***		0.359***		0.367**		0.136*
		(0.123)		(0.137)		(0.166)		(0.079)
Rainfall shock	−0.001***		−0.001***		−0.001***		−0.001**
	(0.000)		(0.000)		(0.000)		(0.000)
Constant	156.758***	−50.280***	157.028***	−55.187**	155.576***	−56.258**	154.793***	−20.199*
	(2.100)	(19.188)	(2.178)	(21.437)	(2.205)	(25.682)	(3.700)	(11.959)
Birth decade FE	Yes		Yes		Yes		Yes
Regional FE	Yes		Yes		Yes		Yes
Sociodem. controls	Yes		Yes		Yes		Yes
Geographic controls	Yes		Yes		Yes		Yes
Observations	782	782	615	615	778	778	182	182
R-squared	0.554	0.247	0.577	0.310	0.576	0.090	0.599	0.242
F-statistic (1st stage)	14.03		13.76		16.42		43.60

Notes: These regressions show robustness checks for the IV model of Table 2 (see notes to Table 2). The first set of regressions excludes the 10 % rainiest and driest regions and the second set the 20 % rainiest and driest. All regressions include birth decade and regional fixed effects, sociodemographic controls and geographical controls. The sociodemographic controls include age at marriage, share of Muslims and religious fractionalization. The geographical controls include the length of colonization, colonial railways, ancient trade routes, diamond mines, explorer routes, missions, nutrient availability, soil workability, malaria ecology index, petroleum sites, ruggedness, tsetse fly suitability, the ratio of the suitability for nomadic pastoralism to sedentary agriculture, distance to capital, distance to coast, population density and area. Standard errors are in parentheses and clustered at the admin I level. Asterisks denote significance levels at ***p < 0.01; **p < 0.05; *p < 0.1.

Table A.1. Data set construction overview

Variable	Primary source	Secondary source	Tertiary source
ABCC	IPUMS	MICS	Afrobarometer
School years	DHS	MICS	IPUMS
Height	DHS
Age of cohabitation	DHS	MICS	IPUMS
Share of Muslims	DHS	MICS	IPUMS
Rel. fractionalization	DHS	MICS	IPUMS

Table A.2. Geographic control variables

Variable	Source	Comments	Access
Length of colonisation	Henderson and Whatley (2014)	Length of colonization of a country in years	Henderson, M., & Whatley, W. (2014), Pacification and Gender in Colonial Africa: Evidence from the Ethnographic Atlas. MPRA Paper No. 61203.
Colonial railways	Nunn (2011)	Dummy if colonial railway in admin I	Nunn, N. (2011), The Slave Trade and the Origins of Mistrust in Africa. American Economic Review, 101 (7), 3221–3252.
Diamond mines	Peace Research Institute Oslo (PRIO)	Calculated number of diamond mines per admin I area	https://www.prio.org/Data/Geographical-and-Resource-Datasets/Diamond-Resources/.
Explorer routes	Nunn (2011)	Dummy if pre-colonial explorer route in admin I	Nunn, N. (2011), The Slave Trade and the Origins of Mistrust in Africa. American Economic Review, 101 (7), 3221–3252.
Missions	Cagé and Rueda (2016); Cagé and Rueda (2020), Nunn (2010)	Dummy if Christian mission in admin I	https://rdmc.nottingham.ac.uk/handle/internal/8312. https://scholar.harvard.edu/nunn/pages/data-0.
Traderoutes	OWTRAD	Dummy if ancient trade route in admin I	http://www.ciolek.com/OWTRAD/DATA/tmcDZm0500.html.
Global agro-ecological zones (GAEZ)	Fischer et al. (2008)	Mean value of nutrient availability and soil workability per adminI area	http://www.fao.org/soils-portal/data-hub/soil-maps-and-databases/harmonized-world-soil-database-v12/en/.
Malaria Ecology Index	Kiszewski (2004)	Mean value per admin I	https://sites.google.com/site/gordoncmccord/datasets.
Petrolium	Peace Research Institute Oslo (PRIO)	Calculated a 0.2 degree (about 20 kilometres at the equator) buffer around each polygon for on- and offshore deposits and the estimated the overlap with admin I areas	https://www.prio.org/Data/Geographical-and-Resource-Datasets/Petroleum-Dataset/Petroleum-Dataset-v-12/.
Ruggedness	Nunn and Puga (2012)	Mean value per adminI	Nunn, N., & Puga, D. (2012), Ruggedness: The blessing of bad geography in Africa. Review of Economics and Statistics, 94(1), 20–36.
TseTse Fly suitability	FAO	Mean value of tsetse fly index for morsitans and palpalis per admin I	https://data.apps.fao.org/map/catalog/srv/eng/catalog.search#/metadata/f8a4e330-88fd-11da-a88f-000d939bc5d8.
Ratio nomadic pastoralism to sedentary agriculture	Beck and Sieber (2010)	Logarithm of the suitability for nomadic pastoralism to the suitability for sedentary agriculture based on a maximum entropy model	Beck, J., & Sieber, A. (2010). Is the spatial distribution of mankind’s most basic economic traits determined by climate and soil alone? PLOS ONE, 5(5), 2–10.
Population density	HYDE 3.2	Population density per birth decade for each admin I area	Klein Goldewijk, K., A. Beusen, J.Doelman and E. Stehfest (2017), Anthropogenic land use estimates for the Holocene; HYDE 3.2, Earth System Science Data, 9, 927–953.
Distance coast	n.a.	distance from centroid of admin I to coast
Distance capital	n.a.	Distance from centroid of admin I to capital
Area	n.a.	Area of admin I region in square kilometers

Table A.3. Summary statistics

	Obs.	Mean	Std.Dev.	Min	Max
ABCC Index	1749	83.369	14.019	29.870	100
Education Efficiency	1544	0.226	0.672	−2.266	3.701
Years of schooling	2103	5.028	2.879	0.014	12.453
Height	1722	158.709	2.685	141	166.795
Age at marriage	2007	20.401	2.275	15.033	37.551
Share Muslim	1971	0.252	0.349	0	1
Rel. Fract.	2103	0.274	0.281	0	1

Figure A.2. Development of numeracy over time for selected countries – Benin, Niger, Kenya, and Zimbabwe. The average refers to all Sub-Saharan African countries included in the data set. Source: IPUMS, MICS and Afrobarometer. Authors’ own representation.

Glewwe, P., & Kremer, M. (2006). Schools, teachers, and education outcomes in developing countries. In E. Hanushek & F. Welch (Eds.), Handbook of the Economics of Education (vol. 2, pp. 945–1017). Oxford and Amsterdam: Elsevier.

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