The African Enigma: The mystery of tall African adults despite low national incomes revisited
Kalle Hirvonen (IFPRI) and Alexander Moradi (University of Sussex, CSAE and Stellenbosch)
Abstract
African adult populations are remarkably tall for the low income levels that prevail at the country
level. The average African woman is about 158.5 cm tall, whereas the low Gross Domestic Product
per capita would let us expect a mean height more similar to the shortest populations in the world,
about 4 cm shorter. This is the case in spite of the fact that indicators of socioeconomic status and
height are positively correlated within each country. We also show that the physical stature of
African children fit well into the global income-height relationship. Hence, we conclude that the
anomaly in the income-height nexus at country level appears to originate between childhood and
adulthood. We present evidence for considerable catch-up growth involving the entire populations.
We discuss possible reasons for this catch-up growth including genetics, and, above all, better
nutrition and health conditions during adolescence.
Keywords: nutritional status; heights; income; catch-up growth; Africa
1
1. The African Enigma
The nexus between income and height underlies much of the economics and human biology
literature. Typically, income is positively correlated with the proximate determinants of height,
nutrition and health, and thereby height itself. The ‘antebellum puzzle’ is the best-known and a wellresearched anomaly: while incomes were growing in antebellum America 1800-1860, heights of
native born whites were decreasing (Fogel et al. 1982; Komlos 1987, 1996; Haines, Craig, and Weiss
2003). Cross-country evidence from Africa provides another anomaly. Deaton (2007) observed that
adult Africans are tall despite of the very low income levels that prevail in Africa. What is more,
African children grow up under a very severe disease environment and poor health; and that is also at
odds with the observation of tall adults. This is true for levels as well as trends. Inasmuch as infant
mortality rates in sub-Saharan Africa 1950-1980 were declining, heights have not been increasing
consistently (Akachi and Canning 2010). Scatter plots display these anomalies (Figure 1 and Figure
2). Heights of African populations are clustered far above the line of best fit based on data for nonAfrican countries, indicating that Africa significantly deviates from the global pattern between
income, mortality and height.
Figure 1: Tall adult Africans despite low incomes
2
Notes: Each dot represents a country-wide average. Heights of women born 1971-80 were derived from Demographic
and Health Surveys and the 2005 Eurobarometer. Gross Domestic Product (GDP) per capita (PPP, 2005 US dollars) from
Penn World Tables 8.0 are averaged over 1971-80.
Figure 2: Tall adult Africans despite high infant mortality rates
Notes: Each dot represents a country-wide average. Heights of women born 1971-80 were derived from Demographic
and Health Surveys and the 2005 Eurobarometer. Infant mortality rates (number of deaths of children under one year old
per 1,000 live births) from 2014 World Development Indicators are averaged over 1971-80.
Bozzoli, Deaton, and Quintana-Domeque (2009) proposed a solution to this puzzle pointing to the
scarring and selection effects of childhood diseases. Scarring is the long-term negative effect of
diseases on survivors. Those who survive the diseases are, on average, shorter than they would have
been if they had not contracted the disease in early childhood. Selection, on the other hand, removes
the least healthy (shorter) members of the population by mortality, so that the survivors are healthier
(taller). In such cases, only the surviving (taller) population is included in the sample, whereas those
(short) individuals who have passed away are not. Bozzoli, Deaton, and Quintana-Domeque (2009)
suggested that for Africa, precisely because of the high mortality environment, the selection effect
would dominate the scarring effect. The higher mortality risk of shorter than average children is
indeed well-documented for Africa (Billewicz and McGregor 1982; Boerma, Sommerfelt, and
3
Bicego 1992; Rouanet 2011). Moradi (2010b), however, showed in simulations that the selection
effect is too small to account for the observed size of the anomaly in the heights of African adults.
In this article we discuss the income height nexus for African populations. We begin by narrowing
down possible explanations. We find that
•
Income does correlate positively with height within any one country in Africa.
•
Heights of African children do fit into the global income-height pattern.
These two findings let us conclude that the anomaly at the continental level must arise during the
period between childhood and adulthood. We present evidence for substantial catch-up growth
involving almost the entire population. This catch-up growth leaves the income-height relationship
within and between African countries largely intact, but it has the effect of raising the adult height of
Africans. We discuss possible reasons for this catch-up growth including genetics, and, above all,
better nutrition and health conditions during adolescence.
2. Income-height nexus at the micro-level: A meta-review
One of the most consistent findings in the height literature is a positive relationship between height
and socioeconomic status within a country. Does this positive correlation between income and height
at the individual level also hold within African countries?
We conducted a meta–analysis reviewing studies that took individual children or adults as unit of
observation (micro-level studies).1 The articles differ widely in their methodologies. Many studies
show correlations. The choice of model specification as well as the set of control variables varies.
1
We searched three databases for articles published before 20th of June 2014: PubMed, Scopus and Web of Science. To
restrict the search to height in African countries, we used “Height” and “Africa*” as keywords that both had to be
mentioned at least once anywhere in the title, abstract or body text of the article. The asterisk refers to a wildcard: it
considers all words that start with “Africa”; e.g. Africa, African, Africans. In addition, we used the three keywords
“Income”, “Socioeconomic” and “Wealth”, of which at least one had to be mentioned in the article. The search led to the
identification of 876 distinct articles. After a first screening of title and abstract, we excluded 768 articles that did not
study the height-income relationship or did not use data from the African continent. Out of 108 articles 20 articles were
not accessible and 11 articles did not study or report height-income or height-wealth associations. Six articles studied
mean heights at the country (macro) level. This left us with 71 articles for the final review. They are listed at the end of
this article.
4
Some studies use instrumental variable techniques.2 The more convincing studies use natural
experiments or interventions. Here, we do not distinguish between methodologies or quality of
analyses. Our aim is only to see whether micro studies find a positive income-height correlation
within African populations.3 Results are summarised in Table 1.
The overwhelming majority of studies found a statistically significant and positive correlation
between income and height (e.g. Sahn 1994; Thomas, Lavy, and Strauss 1996; Alderman,
Hoogeveen, and Rossi 2006; Cogneau and Jedwab 2012). Only one study, Kebede (2005), found a
correlation that was not statistically significant. While this study did document a significant and
positive coefficient in Ordinary Least Squares (OLS) regression, when proxying income with
household consumption expenditures the coefficient turns insignificant in subsequent models that
exploit individual fixed or random effects. Such models take a large toll on degrees of freedom.
Therefore, finding an insignificant coefficient on a household consumption variable (that is typically
considered as a proxy for permanent income rather than transient income) is not surprising. Kebede
(2005) also attempted to address the endogeneity of income using a not entirely convincing
instrumental variable approach, which again led to a positive but insignificant coefficient.4 This is
also expected as instrumental variable methods are well-known for producing larger standard errors
than OLS. Overall, we can conclude that income is a robust predictor of attained height in micro
studies.
Table 1: Findings of micro-level studies on the height-income relationship in Africa
Height determinant:
Positive effect
Non-significant
Negative effect
Income
Assets
18
1
0
18
2
1
Education
of mother
of father
5
23
2
9
1
1
Urban
17
2
0
Notes: Total number of articles reviewed is 71. Articles did not necessarily test the impact of all indicators. Studies that
analysed more than one country and tested determinants measured at the micro-level under country fixed effects
2
We are very critical about the use of instrumental variable regressions in the height literature. While in theory using
instruments are a sound way to account for endogeneity, in practice it is very difficult for any instrument to pass the
exclusion restriction for height, a variable that is demonstrably influenced by so many channels.
3
We did not distinguish between child and adult populations as findings did not appear to differ between the two groups.
4
Kebede (2005) instruments household expenditures with per capita land size cultivated by the household and the size of
land owned by the parents of spouses. This strategy is valid only if land sizes do not exert an independent impact on
children’s HAZ (other than through household incomes). This is unlikely to be the case as, for example, larger land also
allows the cultivation of a wider range of food products that may in turn contribute positively to children’s diet diversity,
and in turn to their HAZ.
5
specifications are included under micro level as the identifying variation comes from within country differences. Studies
in the meta-review are listed at the end of the article.
Income is difficult to measure in developing countries and is rarely collected in surveys.5 A common
approach is to use asset indices instead to proxy for household wealth. Components of the asset index
frequently include housing quality indicators such as type of toilet facility, source of drinking water
or roof and wall type of the dwelling (5 studies), consumer durables such as owning a radio,
television, refrigerator (5 studies), or both (8 studies) and to a smaller degree livestock, land and
agricultural tools (3 studies). In Fawzi et al. (1998) the level of household wealth was assessed
subjectively by the interviewer. Similar to the reviewed studies using income, a positive correlation
between wealth and height is documented in the overwhelming majority of cases: 18 out of 22
studies find a statistically significant and positive relationship between height and assets. In two
studies, the coefficient on the asset variable was not statistically different from zero. Using a sample
of 888 children from the Soweto neighbourhood in Johannesburg, South-Africa, Sheppard et al.
(2009) regressed Height-for-Age (HAZ) scores on individual asset categories (TV, car, education,
access to health care). While the coefficients of untreated variables were positive but insignificant,
after collapsing them into different asset indices, they found 4 out of 7 indices positively and
significantly associated with HAZ-scores. Mamabolo et al. (2005) used data for 162 children from
Limpopo province in South-Africa and found no statistically significant association between housing
type (brick vs other) and stunting probability at age 3 years. Only Pierre-Louis et al. (2007) found a
negative correlation between children’s HAZ and household per capita livestock assets in Mali but
the sample was small (N=49) and non-representative.
Education often serves as a proxy of socioeconomic status. In Africa, a wage premium is typically
associated with secondary schooling and above (Schultz 1999, 2003). Hence, education is likely to
be positively correlated with wage income. In addition, many analysts expect significant benefits
beyond monetary channels, for example, through better child care. Most studies tested the correlation
with mother’s education (26 studies), father’s education (1) or education of both parents (7 studies).
While the correlations for education are less clear-cut than for assets or income, the meta-review
largely agrees with Alderman and Headey (2014) in that there is a positive association between
5
Measurement problems also exist for the GDP per capita estimates of African countries (Jerven 2013). Note however
that African countries would only fit into the international income–height relationship, as indicated by the regression line
in Figure 1, if GDP per capita were around $5,000-10,000. This is highly implausible.
6
education of parents and children’s physical development, generally only with secondary education,
and that maternal education yields larger returns than paternal education.
Finally, a significant number of studies highlighted the urban-rural dichotomy in living standards.
Urban wages are, on average, higher and city dwellers typically have better access to health care and
cities. A large majority of studies found that living in cities is associated with a significant height
advantage (e.g. see Paciorek et al. 2013).6
From this meta-review we conclude that the puzzle does not exist at the micro-level. Income and
proxies of income, wealth and socioeconomic status are positively correlated with heights. The
puzzle is therefore at the macro-level; originating from studies that compare countries and
populations to each other.
3. Income-height relationship: From children to adults
The African Enigma has not been identified in studies of child malnutrition (Klasen 2008). Figure 3
shows that the income-height puzzle does not exist in child anthropometry: African children aged 0-5
years are shorter than their counterparts from other regions, as indicated by lower HAZ-scores, in
line with the lower incomes prevailing in Africa.7 In contrast to the case of adults in Figure 1,
considering the African countries only results in a nearly identical line of best fit. Hence, the same
height-income pattern in children holds when considering African countries only.
6
The urban ‘dividend’ is largely a 20th century phenomenon. Pre-20th century urban dwellers in Europe, for example,
were found to be shorter than their rural counterparts.
7
Height-for-Age Z-scores measure the distance to the median height of a healthy and well-nourished reference
population of equal sex and age in terms of standard deviations of that same reference population. For instance, a HAZscore of -2 indicates that the child (population) is two standard deviations shorter than the median child (population) of
the reference population. Median and standard deviation vary by age and gender. We used the US 2000 NCHS/CDC as
reference population because it allows the calculation of HAZ scores from birth to 20 years of age, and is constructed
using the same reference throughout the entire growth period (Kuczmarski et al. 2002). Height-for-age Z-scores were
calculated using the zanthro command in Stata 13.
7
Figure 3: Height-income relationship in children (0-5 years)
Notes: See Figure 1. Data from Demographic and Health Surveys conducted around 2005 only. No data for European
countries.
African children fit the global height-income relationship, but African adults do not. This may
suggest that the anomaly in the height-income relationship in African adults originates after early
childhood. We explore this idea further by showing the HAZ-scores and income levels prevailing
around birth for both adults and children in one graph. We connect the child-adult outcomes for each
country by a line indicating the movement in GDP/c and HAZ. Figure 4 shows these values for
African countries. First, for all but one country HAZ-scores of women exceed the ones of children.
Second, as indicated by the overwhelmingly north-south direction of the lines this is despite the fact
that incomes have not changed much between 1970-1980 and 2000-2005. Hence, the higher HAZscores of adult women cannot be explained by higher incomes at birth. This pattern does not exist in
other regions of the world. Figure 5 shows non-African countries. Here, as indicated by the
predominantly southwest-northeast direction of the lines, incomes and HAZ-scores both improved
when moving from the adult cohort to the child cohort. The slope of the lines fit very much the crosssectional pattern: there is only one income-height relationship that holds in the cross-section and over
8
birth/ age cohorts. This is not the case in African countries. Figure 4 rather indicates that there are
two income-height relationships for African countries: one for adult women and one for children.
Figures 6 and 7 further confirm this interpretation.
Figure 4: Correlation between height of children and women and GDP/c (PPP) at birth:
African countries only
Notes: The graph show HAZ-scores of children born 2000-2005, HAZ-scores of women born 1971-1980 and their
respective GDP per capita at birth. The HAZ-score of women is connected to the HAZ-score of children of the same
country. Hence, vertical lines from bottom (children) to top (women) indicate higher HAZ-scores for adults despite of the
same GDP per capita. MLI refers to Mali, BFA to Burkina Faso, NER to Niger, TCD to Chad, TZA to Tanzania, GHA to
Ghana, and ETH to Ethiopia.
Figure 5: Correlation between height of children and women and GDP/c at birth: Non-African
countries
9
Notes: The graph show HAZ-scores of children born 2000-2005, HAZ-scores of women born 1971-1980 and their
respective GDP per capita at birth. The origin of the arrow indicates the HAZ of children, whereas the end point of the
arrow indicates HAZ of women. Hence, arrows from bottom left to top right indicate higher HAZ-scores for adults
despite of a lower GDP per capita. EGY refers to Egypt.
Figure 6 compares the HAZ-income relationship between African children and adults.8 We excluded
the Sahel countries (Burkina Faso, Chad, Mali, Niger, Senegal) when computing the line of best fit.
Sahel countries are outliers insofar as adult women are extraordinarily tall despite of low incomes
prevailing around the time of their birth.9 Moradi (2012) argued that in the Sahel countries GDP per
capita in the 1970s did not reflect nutritional intakes. The Sahel countries are beyond the fringe of
trypanosomiasis endemic areas. Cattle holdings per capita are high compared to the African countries
in the tropical forests; and cattle provide protein in form of milk and meat, and are associated with
better HAZ-scores of children and adults (Moradi and Baten 2005; Hoddinott, Headey, and Dereje
8
The data are based on adult women and children who are less than 5 years of age. The observed patterns are not due to
gender differences in early childhood: using data for female children produces a nearly identical graph.
9
The mean heights of women are as follows: Burkina Faso (161.7 cm), Chad (162.5), Mali (162.1), Niger (160.6) and
Senegal (163.1). Overall, women in the five Sahel countries are about 0.7 HAZ-scores (4.7 cm) taller than African
populations with the same GDP/c.
10
2015).10 The 1970s saw droughts and famines, and nutrition deteriorated to levels more in line with
the low GDP per capita. As a consequence, the outlier status of Sahel countries disappears in children
born in 2000-2005 (Moradi 2012). Figure 6 confirms the pattern of two income-height relationships
for African countries. The regression lines for children and adults are almost parallel, but the child
data lie well below the adult data. Figure 7 shows that such differences do not exist in data from nonAfrican countries. Thus, if income remained constant over time, we would still find that short
children become tall adults in Africa. In other words, African children catch up after early childhood.
For any income level, African adults lie on a 0.5-0.6 standard deviation higher HAZ curve than
African children. In adult heights 0.5 HAZ-scores corresponds to about 3.4-4.1 cm.
Figure 6: Height income nexus in children (0-5 years) and adult women: African countries
Notes: Line of best fit excludes Sahel states (Burkina Faso, Chad, Mali, Niger, and Senegal).
10
The significant height advantage of African pastoral populations is in line with this observation (Little et al., 1989;
Little et al., 2001). Even though poor in income terms, their diets are high in proteins, e.g. protein intakes of Kenyan
Maasai and Senegalese Fulani were reported to exceed 200% of recommended daily intakes (Benefice et al., 1984;
Nestel, 1986). Caloric intakes, in contrast, are low.
11
Figure 7: Height income nexus in children (0-5 years) and adult women: Non-African countries
Multivariate regression analysis confirms this finding. We regressed HAZ-scores on GDP per capita,
the infant mortality rate (IMR) and an Africa dummy. The latter indicates the size of the African
Enigma: by how much are African populations taller given their income and mortality rates. Column
(1) of Table 2 shows the estimates for children based on the Demographic and Health Survey (DHS)
data for developing and emerging countries. The coefficient on national income is highly significant
and positive, whereas the IMR coefficient is negative but insignificant. Note that income and infant
mortality rates are collinear: the correlation coefficient equals -0.6 in the children’s sample and -0.9
in the adults’ sample.11 The Africa dummy coefficient, however, is close to zero and insignificant
indicating that the HAZ-scores of African children correspond well with their low incomes and high
mortality rates. Column (2) of Table 2 shows the estimates for adults based on DHS for developing
or emerging countries and Eurobarometer data for OECD countries. The coefficients for income and
IMR do not change much, but the coefficient on the Africa dummy variable does. African adults are,
on average, 0.52 HAZ-scores taller than one would expect from their income and IMR. Column (3)
11
The higher correlation in adults is due to the inclusion of developed countries in the adult sample.
12
of Table 2 shows the estimates of the pooled regression. The slope parameters for GDP per capita
and IMR are stable across generations. Interestingly, after controlling for national income and infant
mortality rate, adults seem to have generally somewhat higher HAZ-scores in general than children
(by 0.22 HAZ), but not significantly so at the 5% level. Most importantly, relative to their national
income and IMR, African child populations are not taller than others (the Africa dummy variable is
only 0.02). The height anomaly only exists in African adults who are taller. Overall, Africans adults
have higher HAZ-scores than African children by 0.7 HAZ units (0.48+0.22). This is in line with
African children experiencing considerable catch-up growth after early childhood. Column 4 restricts
the data to countries for which we have data on both child and adult heights. Dropping the OECD
countries from the adult sample renders the coefficient on the adult dummy insignificant. The
coefficient on the Africa-adult interaction term remains highly significant, and the overall magnitude
of the difference is similar to the one obtained in Column 3. According to Column 4, African adults
are on average 0.6 HAZ units (-0.16+0.80) taller than African children.
Table 2: Multivariate regression analysis of the income height relationship at the macro level
Children
(1)
Adults
(2)
Pooled
(3)
Pooled: DHS only
(4)
ln GDP per capita
0.24***
(5.03)
0.22***
(3.01)
0.24***
(5.25)
0.18***
(4.13)
Infant Mortality Rate
-0.004
(-1.38)
-0.007***
(-2.68)
-0.006***
(-3.47)
-0.003*
(-1.74)
0.22*
(1.79)
-0.21
(-1.19)
0.02
(0.16)
-0.16
(-1.49)
0.48***
(3.35)
0.80***
(4.63)
Adults
Africa dummy
-0.08
(-0.59)
0.52***
(3.22)
Africa x Adults
Data source
2
R -adjusted
Number of observations
DHS
0.56
58
DHS +
Eurobarometer
0.61
69
DHS +
Eurobarometer
0.66
127
DHS
0.43
100
Notes: See Figure 1. OLS estimator. All regressions include a constant; Robust t-statistics in parentheses.
Significance denoted at *** p<0.01, ** p<0.05, * p<0.1.
13
At what age does this catch-up growth occur? The analysis is made difficult by the fact that most
household surveys in developing countries, including DHS, do not collect anthropometric data for
children older than five years. The few surveys that do contain anthropometric data for a
representative sample of all ages support the view that African children’s growth catches up
considerably during puberty. Figure 8 shows growth curves for three data sets. In Figure 8, the HAZscores of Ethiopian and Tanzanian children remain relatively stable in children aged 5-10 years.
HAZ-scores then decrease at ages of around 12-14 years. This decrease is due to the fact that at this
age the reference population enters the adolescent growth spurt and their growth velocity
significantly increases, whereas the growth velocity of the study population remains stable because
the growth spurt is delayed in malnourished and stunted populations (Eveleth and Tanner 1990).
However, when Ethiopian and Tanzanian teenagers enter puberty, they experience remarkable catchup growth leading them to a HAZ-score of attained adult height which exceeds the one before
puberty by approximately 0.5 units – roughly the same size as we found in Table 2. Ghanaian
children, although starting from a higher level, also recover from the pre-pubertal growth faltering
during puberty.12
Figure 8: Catch-up growth in Ghana, Tanzania and Ethiopia: Age 2-24 years, males and females
12
There are gender differences in the growth trajectories not shown in Figure 8: girls begin their pubertal growth spurt
earlier. In Tanzania and Ghana the gender-difference in catch-up growth between age 2-5 years and 20-24 years is
negligible, whereas in Ethiopia girls catch-up more than boys, by about 0.31 HAZ.
The average HAZ-score of adult women in the African DHS surveys is -0.72.
14
Figure 9: Catch-up growth in Ghana, Tanzania and Ethiopia: Age 25-50 years, males and
females
Notes: Kernel-weighted local polynomial regression. HAZ based on CDC-2000 growth standard.
Source: All data sets are longitudinal and contain more than one height observations per individual. The data for Ghana is
based on the Ghana Living Standard Measurement Study surveys 1988/1989 (total number of height observations;
N=25,349). The data for Kagera (Tanzania) is based on Kagera Health and Development Survey 1991/94 (N=14,228).
Using the information from the follow-up surveys administered 10-16 years later, individuals who did not survive their
18th birthday were removed from the sample. The data for Ethiopia is based on Ethiopia Rural Household Survey 199597 (N=23,340). The 95% confidence intervals (not reported) are small, around 0.1 HAZ.
We assumed that the observed patterns in the growth curves of Figure 8 are mainly due to age
effects. In other words, we assumed that children follow the same HAZ-age trajectory as they
become older. However, since the underlying data are cross-sectional, a valid objection to this
assumption is that the HAZ differences at certain ages could reflect birth-cohort effects as well:
children’s heights could differ because they were born under different economic conditions. This is
unlikely to be the case, though. The income data do not support the notion that economic conditions
improved substantially just when these children were adolescent. In fact Tanzanian GDP per capita
stagnated between 1973 and 1980 (the birth period of those Tanzanians aged 13 to 20 years in the
sample) and Ethiopian GDP per capita was only $100 higher in 1976-1980 compared to 1991-1996
(the birth periods of those aged 16-20 and 0-5 respectively).13 Moreover, in Figure 9 we do not
13
Because Ethiopia was very poor – GDP per capita was only $600 in the1976-1980 period – the $100 imply a rather
large difference in relative terms of 20% percentage points. But again, this variation in GDP per capita is small compared
15
observe similar substantial variation in adult heights: HAZ curves between 25 and 50 ages are stable
compared to Figure 8. Figure 9 also confirms the lack of upward secular trend in adult heights in
African countries.
Longitudinal studies that follow children from early childhood to adulthood enable us to eliminate
the role of the birth-cohort effects. Evidence from three such longitudinal studies confirm our
previous results. Coly et al. (2006) tracked 2,900 Senegalese children for two decades and
documented nearly complete catch-up growth in puberty. Prentice et al. (2013) provided similar
evidence for Gambia, exploiting longitudinal data of 160 children aged 8 to 24 years and finding
nearly complete catch-up growth that occurred in puberty. Using 19-year tracking data from the
Kagera region in Tanzania, Hirvonen (2014) found that the mean HAZ-score in the cohort of 540
children improved from -1.86 in early childhood to -1.20 in adulthood; cross-sectional evidence then
suggested that most of this catch-up growth took place in puberty. Finally, economic historians have
documented similar growth patterns among African Americans (Steckel 1987; Komlos 1992).
4. Possible explanations for the African Enigma
We have argued that the explanation for tall African adults lies in catch-up growth. But what triggers
this catch-up growth? Evidence is limited. Using a longitudinal survey from Tanzania, Hirvonen
(2014) observed that the HAZ distribution of the whole adolescent population shifts upwards. With
almost universal catch-up growth, even cohort studies are unable to shed light on the determinants of
catch-up growth. This is because if the whole population catches up, there is limited variation in
catch-up growth between individuals. Therefore, individual level differences cannot account for the
larger universal experience.14 This then also suggests that ‘something’ at the national (or population)
level is causing the catch-up growth. Therefore, the explanation of this finding must remain for
future researchers to explore.
We cannot rule out the genetic argument. While a large fraction of height differences between
individuals can be explained by genetics (Silventoinen 2003), it is typically assumed that populations
of different ethnic backgrounds have the same genetic potential throughout the growing years. In
to the variation experienced in the 1950-1980 period, when the survey’s adults aged 25-50 years grew up, see Panel B
Figure 5.
14
Econometrically speaking, catch-up growth would then be included in the constant.
16
other words, it is expected that populations growing up under the same nutrition and health
conditions attain the same mean height.15 A large number of studies have documented how wellnourished and healthy children from different ethnic backgrounds attain very similar mean heights
(Habicht et al. 1974; Bhandari et al. 2002; WHO Multicentre Growth Reference Study Group and de
Onis 2006).16 Therefore, for genetics to explain tall African adults, its impact must be somehow
switched on during puberty. Pradhan, Sahn, and Younger (2003, 277), for example, allude to this
when they justify the use of children’s height as health indicator but “reject the use of heights of
adults because of genetic variability” although without providing any evidence or citation for this
presumption.17 Overall, the genetics of the African population has not been explored sufficiently and
is a topic for further research.
We can nevertheless provide ‘soft evidence’ against genetics as an explanation using data for African
Americans. It can be assumed that African Americans share approximately the same genetic pool as
modern-day Africans but are exposed to a very different disease and socio-economic environment
(Eltis 1982). In Figure 5 we use data from the latest available National Health and Nutrition
Examination Survey (NHANES) round and construct HAZ-scores for African Americans. The HAZ
curve is relatively flat hovering around the zero line implying that the growth curve of an average
African American child approximately follows the growth trajectory of the median child in the
American reference population. This suggests that environmental factors are more likely candidates
to explain the catch-up growth, rather than genetics. Steckel (1987, 2000), for example, found
considerable catch-up growth among African American slaves in 1820–1860 when they came of
working age and slave owners markedly improved their diet to maintain the slaves’ labour
productivity. Moradi (2010a) found that economic growth measured around age of puberty strongly
predicts final attained height of African women: the birth cohorts born in the 1970s were so much
affected by the economic crisis of the 1980s (the lost decade), that they attained a height shorter than
1960 birth cohorts even though conditions at birth were similar or better. In a similar vein, Akresh et
15
There is a long lasting debate whether height differences across populations are due to genetics or other factors (diets,
environment, and income), see in particular the ‘small but healthy’ debate by Seckler (1982, 1984), Messer (1986) and
Beaton (1989); for a more recent version of the genetics debate, see Panagariya (2013), Coffey et al. (2013) and Gillespie
(2013).
16
Adoption studies from non-African countries also support this view (Graham and Adrianzen 1971; Winick, Meyer, and
Harris 1975).
17
In height regressions genetics is picked up by the residual that captures height differences that cannot be explained by
environmental conditions. The Africa dummy can be claimed to be genetics, but it can also represent environmental
conditions at puberty, that are rather similar across African countries and that previous models have omitted so far.
17
al. (2012) found adult height of women of ethnic groups exposed to the Nigerian civil war at age 1316 years to be more affected than women exposed to the war at younger ages.
Figure 10: HAZ of African Americans 2012
Notes: HAZ based on CDC-2000 growth standard. Data drawn from NHANES 2011-12. Kernel-weighted local
polynomial regression.
Moradi (2010b) put forward two hypotheses which did not have a genetic basis. First, the incidence
of diseases is much less severe during puberty compared to early childhood. Age-specific mortality
rates underline this. Most of the deaths are concentrated in infancy and childhood, not among
adolescents. This can be due to a less harsh age-specific morbidity as well as an acquired immunity
at puberty. Better health conditions then translate into considerable catch-up growth, when children
go through the adolescent growth spurt. Second, puberty is also the age when adolescents start to
contribute more and more to the household income. This may improve their intra-household
bargaining power leading to increased food shares within the household or can supplement their food
portions from their own income. While plausible, there is no strong evidence for these hypotheses as
of yet.
18
5. More research is needed on African heights
Is a catch-up growth that results in an adult height anomaly of 3 or 4 cm substantial enough to merit
attention? In most countries, we observe a secular trend in adult height (Cole 2003). In the US and
Western Europe between 1880 and 1980, the secular trend in mean male heights reached about 0.9
cm per decade (Costa and Steckel 1997; Baten and Blum 2012). During the same period of time,
Latin American and East Asian heights increased by about 0.4 cm and 0.5 cm per decade
respectively (Baten and Blum 2012). Hence, 3 to 4 cm difference would correspond to an
improvement in nutrition and health status of about four to eight decades. We therefore think this
height anomaly is substantial enough to merit further attention.
Heights have been shown to correlate with earnings, productivity, cognitive abilities, educational
outcomes, lower morbidity and mortality in later life (e.g. Steckel 2009). Most studies that
investigate adult outcomes such as wages or cognitive test scores use adult height, to a lesser degree
height of children, but not both.18 Indeed if HAZ-scores are more or less stable from childhood to
adulthood, it will be difficult to distinguish between the two and the approach is justified. The
important implication of the African Enigma is that this is not the case for African countries. The
damage during early childhood – as reflected in stunted height – is partly reversed during puberty. It
may well be that adult height rather than childhood height is more important for certain outcomes
(such as physical strength, labour productivity and earnings). Putting the entire focus on childhood
may therefore be misleading. This calls for future research. In particular, emphasis should be given to
a better understanding of what happens to Africans during puberty and how the experience during
this specific period affects later outcomes.
18
We are not aware of a single study that disentangles the two.
19
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