Population, Technology, and Economic Development in the African Past Javier A. Birchenall

Population, Technology, and Economic Development in the
African Past
Javier A. Birchenall
University of California at Santa Barbara
April 20, 2008
Abstract
From the colonial era to the present, economic progress in sub-Saharan
Africa has been slow. This paper argues that Africa’s stagnation started long
before the European expansion and that Africa, instead of being at a disadvantage in terms of population size, had a population advantage over South
and Central America, a comparable region. Using urbanization as a proxy for
economic progress in pre-modern times, this paper finds that before the 1500s,
large- and medium-sized cities were far less prevalent in African than in South
and Central America. The aggregate findings are verified using disaggregate
measures drawn from the Standard Cross-Cultural Sample (SCCS) which includes 186 pre-modern societies around the world. Our examination suggests
a ‘persistence of misfortunes’ for sub-Saharan Africa and some continuity from
the pre-colonial period. The paper also studies African demography during
the long pre-agricultural period and suggests that a technological ‘leapfrogging’ may explain why Africa is lagging behind.
JEL classification: J11; O11; O33.
Communications to: Javier A. Birchenall
Department of Economics, 2127 North Hall
University of California, Santa Barbara CA 93106
Phone/fax: (805) 893-5275
E-mail: [email protected]
1
Introduction
There are large differences in economic development and income levels in the world but
from the colonial era to the present economic progress in sub-Saharan Africa has been
relatively slow. Currently, sub-Saharan Africa faces severe problems of poverty, high rates
of child and adult mortality, illiteracy, civil war, and political instability among many
others. Although no single factor is likely to have been responsible for Africa’s situation,
common reasons for Africa’s underdevelopment are modern aspects such as the Atlantic
slave trade (1400-1800) and colonialism (1800-1950).1 The purpose of this paper is not
to deny the significance of these modern aspects in Africa’s development but to study the
economic performance of Africa in the very long run, essentially before modern times.
The central argument of this paper is that sub-Saharan Africa lagged behind the rest
of the world long before the European expansion of the 1500s and that Africa, instead
of being at a disadvantage in terms of population size, had a population advantage over
comparable regions. In exploring Africa’s economic development and the role of population
in the very long run we focus on the post-agricultural development of sub-Saharan Africa
and the Americas. We do so because the disappearance of the land bridge that connected
the Americas with Asia has been widely perceived as a ‘natural experiment’ in geographical
isolation (see, e.g., Diamond [17]; Kremer [39]; Mann [46]). Whereas Africa, Asia, and
Europe have continuous land boundaries between them, populations in the Americas lived
in comparative isolation until the European expansion.
While the pre-modern development of the Americas was compared to that of the Old
World by Kremer [39], we argue here that a comparison between sub-Saharan Africa and
1
There is a large literature that studies Africa’s long run economic development. Nunn [53] provides
important empirical evidence of the long-term effects of slavery in Africa. Acemoglu et al. [1] emphasize
colonial policies and institutions. Pre-colonial influences have been stressed by Herbst [30] and Gennaioli
and Rainer [23]. Studies of the economic history of Africa include Austen [2], Austen and Headrick [3],
and Hopkins [32]. While closely related to our analysis of diversity, a link that we do not explore directly
is that of ethnic fractionalization and long-term economic growth which has been studied by Easterly and
Levine [18].
1
the Americas is more likely to capture the long term influence of population on economic
development because it eliminates important confounding factors. Africa and the Americas
are similar in terms of their geography, endowments and climate, and Africa had repeated
contact with the rest of the Old World (see, e.g., Austen [2]; Austen and Headrick [3];
Diamond [17]; Hopkins [32]; Smith [69]). Moreover, current estimates suggest important
differences in past population size and population growth between Africa and the Americas
prior to the 1500s.2
Following Acemoglu et al. [1], we use aggregate measures of urbanization before 1500
as a proxy for economic progress in pre-modern times. Our measures of urbanization are
based on the number of medium- and large-sized cities from Chandler [11], a source also
employed by Acemoglu et al. [1]. (Acemoglu et al. [1], however, omitted this region from
their study of urbanization for reasons that we will describe later on.) Chandler’s [11]
inventory of cities indicate that sub-Saharan Africa was less developed than South and
Central America. For example, while there were no large cities south of the Sahara at the
time of the European expansion, Teothihuacán (currently Mexico city) was among the ten
largest cities of the world in the year 400 A.D., Chandler ([11], 464).
In this paper we also examine the long pre-agricultural period.
As in the post-
agricultural case, we first focus on African demography and propose a measure of relative
population size using the pattern of genetic diversity within human populations. Such
an estimate is possible because genetic changes that lead to current diversity appear in
proportion to past population size (see, e.g., Jobling et al. [33], Relethford [58], Rogers
[64], and the Appendix to this paper). Because populations in Africa are very diverse
2
The Sahara offered a barrier to human passage especially since after 4000 B.C., or some 6 thousand
years ago (KYA), see Fagan ([21], 152). Even if one ignores Eurasia’s influence, population in Africa was
larger than in the Americas according to Biraben [4] and McEvedy and Jones [47]. Throughout the paper
we treat North Africa (including ancient Egypt) as part of Eurasia because biogeographically it is closer to
Eurasia than to sub-Saharan Africa (Diamond [17], 161). We focus on South and Central America because
in terms of biogeography these regions and the West Indies conform a single Neotropical region. (North
America is part of the Nearctic zone.) Communication between South and Central America was far more
common than between Central and North America by the Mexican desserts. For instance, Mexican corn
reached the current US territories only at around 900 A.D., Diamond ([17], 109).
2
(genetically and in many other biological characteristics discussed later on), the genetic
analysis of the human population suggests that populations in sub-Saharan Africa were
larger than populations in other regions of the world or that they originated at an earlier
date and hence had more time to accumulate genetic variations (see, e.g., Cavalli-Sforza
et al. [10]; Jorde et al. [37]; Relethford [60]).3
We cannot determine if the population advantage in Africa translated into technological improvements in the past although archeological and anthropological research indicates
that hunter-gatherers in Africa had important technological advances early on (see, e.g.,
Kusimba [41]; Mellars [48]). However, if any, an initial advantage in pre-agricultural
technologies and population size or time of origin seems to have had little impact on the
timing of the emergence of settled agriculture. Agriculture appeared in sub-Saharan Africa
around 4KYA whereas in the Near East and China agriculture originated some 10KYA
and 8KYA (Smith [69]). In the case of Africa, if correct, an initial advantage in population
and pre-agricultural technologies instead of fostering development delayed agriculture and
city formation.
To complement our aggregate analysis, we study the role of population in technological
sophistication using Murdock and White’s [52] Standard Cross Cultural Sample (SCCS).
The SCCS consists of disaggregate data from 186 ethnographically well-described societies
with different subsistence strategies. The SCCS was designed to be representative of all
the pre-industrial societies in the world and it was constructed to maximize independence
in terms of cultural and historical origin. Past societies are described from historic and
ethnographic literature at the time coinciding with or just after contact with western
cultures; thus, the SCCS can be taken as a representative sample of independent pre3
It is very important to avoid misunderstandings in the study of genetic homogeneity between populations. Genetic homogeneity is neither good nor bad and it provides no indication of genetic inferiority. On
one hand, homogeneity means that the population lacks deleterious genes. On the other hand, homogeneity increases susceptibility to disease. For example, while Africans (whose genetic diversity is the largest)
have some resistance to malaria by the sickle cell trait, the Americas before Columbus were apparently
free of a number of genetically transmitted diseases, see Mann ([46], 103-105).
3
modern economies.
Some variables in the SCCS are designed to measure the complexity of various aspects
of pre-modern economies such as the existence of large buildings, social stratification, specialization and the division of labor, monetary exchange, and political autonomy among
many others. We use some of these measures as indices of sophistication and estimate regional differences between Africa, the Americas, and the rest of the world. In measures of
urbanization and political sophistication, the ‘African dummy’ is negative and statistically
different from zero (even different from an ‘American dummy’). More surprising, perhaps,
controls for latitude, altitude, potential for agriculture, malaria exposure, and for demographics such as community size and population density are still unable to account for the
relatively low sophistication in pre-modern Africa. Our additional disaggregate estimates
confirm that the prevailing economic and institutional conditions in Africa at the time of
the European expansion were less developed than those in the Americas. Moreover, we are
able to confirm with disaggregate data that Africa’s underdevelopment was not associated
with low population densities.
While our focus is on the mutual interaction between population and economy, our
results also provide conclusions that are important for comparative development and for
the ongoing debate on the fundamental causes of world income inequality. Since Africa
was relatively poor even before the European expansion, we argue that Africa’s current
situation is not associated with a ‘reversal of fortune’ as the one described in Acemoglu et
al. [1] for the European colonies in America. Whereas North America was relatively poor
in 1500 and relatively rich today (compared to South and Central America), compared
to North Africa or even to South and Central America, sub-Saharan Africa has been
relatively poor since before modern times. Such a ‘persistence of misfortunes’ suggests
some continuity in the economic conditions currently observed in Africa.4
4
Herbst [30] also argues for continuity in African development from the pre-colonial period based on
institutional views. (See also Robinson [62] for a review of Herbst [30]. Gennaioli and Rainer [23] also stress
4
It is perhaps important to acknowledge that with discussions about economics and
demography in the very long run one can always be skeptical about data quality and
measurement. For example, Acemoglu et al. [1] did not use Chandler’s [11] estimates
of city formation in sub-Saharan Africa arguing for measurement problems (although
they used Chandler [11] for other regions of the world).5 Also, to keep the paper within
reasonable length we have focused on macroeconomic aspects and have abstracted from
several important microeconomic and institutional aspects. Throughout the paper we
provide additional inferences to support our main arguments and discuss some of the
ways in which the conclusions of the present descriptive analysis could change as more
evidence from anthropology, archeology, biology, and demography is accumulated.
Subject to the limited availability of data, and with the caveats of data quality very
much in mind, our analysis suggests that population is only weakly associated with radical
technological changes in the very long run. Instead, agriculture and industrialization seem
to be better described through a pattern of technological leapfrogging as the one studied
by Brezis et al. [6]. While Africa was relatively populated before agriculture, agriculture
originated first in Eurasia and more specifically in Asia, i.e., the Near East and China.
continuity in Africa.) According to Herbst [30], a relative low population density explains the absence of
state-building institutions in pre-colonial Africa since low densities made state control more difficult and
competition for space less attractive. Moreover, the absence of state-building institutions during the precolonial period explains current institutional failures. The comparison in Herbst [30] is between Africa and
Europe which, as we have noted, are in general very different. A comparison between pre-colonial Africa
and the Americas suggests a weaker link between state formation and population density because the
Americas had lower population densities but the independent development of pre-Columbian states in the
Americas resembles to an astonishing degree those patterns seen in the earliest states of Eurasia. It may be
important to notice that the persistence in Africa is not necessarily inconsistent with the economic reversal
across all European colonies described by Acemoglu et al. [1] as Africa was relatively more urbanized than
North America and Australia before the 1500s.
5
Acemoglu et al ([1], Appendix A) argue that “data on urbanization in sub-Saharan Africa is fragmentary, of dubious value and almost certainly underestimated with biases that are hard to assess.” Our claim
of low levels of urbanization remains valid to the to the extent that we cannot nowadays point to a large
sub-Saharan city in pre-modern times or to evidence of large public works typical of urban life. Obviously,
large cities may have existed in sub-Saharan Africa but none has been found yet or maybe cities existed in
the past but are now lost, see Davidson [14]. Acemoglu et al. [1] also considered cities with more than 5
thousand inhabitants whereas we look at cities with more than 20 and 40 thousand inhabitants. Evidence
on the existence of small cities is more difficult to find for the tropics even in Asia where Acemoglu et
al. [1] used extrapolations. To the extent that climate has some effect on the disappearance of cities, a
comparison between Africa and South and Central America is preferable to a more general comparison.
5
Similarly, within Eurasia, industrialization first occurred in Europe rather than in Asia.
Out of all regions of Eurasia, Europe was the most backward in the past because Europe
had no independent origin of agriculture. Europe, instead, was a recipient of developments
from the agricultural centers in the Near East and China. Finally, industrialization diffused
first into regions where agriculture did not flourished or originated independently, North
America and Australia. These changes in technological leadership are not consistent with
the persistence typical of endogenous growth models.
The rest of the paper proceeds as follows. Section 2 discusses some related research
and background. Section 3 studies population in the very long run. Section 4 compares
the conditions available in the Americas and sub-Saharan Africa before the European
expansion and shows that the Americas had higher levels of urbanization than sub-Saharan
Africa. Section 4 also presents an econometric analysis of the SCCS. Section 5 considers
some possible explanations and Section 6 offers additional remarks on the technological
leapfrogging described in the paper. Section 7 concludes this paper.
2
Related research and background
Population and development: Understanding the relationship between population and
economic development is an old theme in social science but assessments of the consequences
of population growth still vary between Malthusian (pessimist) and Boserupian (optimist)
extremes. Kremer [39] proposed a synthesis of Malthusian and Boserupian views and a
very useful way of organizing economic-demographic interactions. Following Kremer [39],
let A(t) and N (t) represent the level of technology and population size at date t ≥ t0 and
assume technology changes according to:
dA(t)
= θA(t)φ N γ (t),
dt
6
(1)
with φ, γ, and θ strictly positive.
The previous parameters can be interpreted as follows. The role of the existing level
of technology A(t) is captured by φ. Since φ > 0, technological sophistication facilitates
the creation of new technologies. Because γ > 0, population size is a positive influence in
technology creation (the Boserupian side). Finally, the influence of exogenous variables,
or variables other than A(t) and N (t), is captured by θ.
On the Malthusian side, any change in technology translates into higher population
and not into higher income per capita. This implies, when φ = 1, that the dynamics of
population are simply given by:6
dN (t)
= θN (t)α , with α := 1 + γ > 1.
dt
(2)
A key testable prediction of Kremer’ [39] synthesis is that the human population should
experience increasingly increasing growth rates or hyperbolic growth. In modern times,
the evidence confirms this prediction. It took almost all of human’s history up to 1800
to reach 1 billion people. The second billion took 125 years, the third 35 years, the
fourth 15 years, the fifth 10 years and the sixth less than 10 years (Kremer [39], Table 1).
This pattern of population change is inconsistent with exponential growth which predicts
constant not decreasing doubling times.
There are some very obvious limitations with past demography but two concerns are
important in the time series tests. First, Deevey [15], the source used by Kremer [39] for
the very long run, assumed that the area populated by humans has constantly increased
since 1 million years ago (MYA) and that the density of the population in any given area
has also increased since 1MYA. These assumptions generate a form of increasing returns
6
Kremer [39] considered a much richer set of possibilites for technological change that includes φ < 1.
(See also Jones [34] for a related analysis.) Variations in population densities have been considered by
Klasen and Nestmann [38]. In the generalizations, population growth is still a function of population as
in equation (2).
7
responsible for increasingly increasing growth.7 Second, and more importantly, time series
support for hyperbolic growth is very sensitive to the exclusion of modern data points.
If one relates the level of population to its growth rate, as suggested by equation (2), a
strong positive association is observable only when the modern demographic transition is
included. There is no systematic association between population levels and growth rates
in Figure 1 which excludes the last 500 years from Figure 1 in Kremer [39].
Kremer [39] also tested the role of population in economic development with crosssectional tests that serve as the basis for this paper. As in Solow’s growth model, equation
(2) is a Bernoulli differential equation whose solution is N (t)1−α = θ(1 − α)t + N (t0 )1−α .
Alternatively,
ln [N (t)] =
ln[θ(1 − α)t + N (t0 )1−α ]
, for N (t0 ) > 0 given.
1−α
(3)
A useful way to interpret equation (3) is the following. Because population varies
in proportion to technology, population and technological sophistication at date t are
increasing functions of the initial population size, N (t0 ), time since origin, t − t0 , and
exogenous influences in technology creation, θ. Equation (3) thus makes possible to use
the melting of the ice caps that divided the continents as a ‘natural experiment.’ Because
the land bridges that connected the Americas with Asia and New Guinea with Australia
(also Tasmania and Flinders Island) disappeared some 12KYA, some populations became
geographically isolated. In other words, geographic isolation induced exogenous differences
in initial population size N (t0 ).8
Assuming that populations were randomly allocated among the continents, and that
7
Deevey himself remarked: “my own treatment of this, published some years ago in Scientific American,
was not very professional,” Deevey ([16], 248). Estimates of past populations are also more likely to give
rise to expansions because available methods in archeology are better suited to find explosions rather than
population crashes (Schacht [66] and Petersen [54]). Since there is more food available in tropical forests
and grasslands, i.e., Hassan ([27], Table 12.5), expansions into temperate areas were probably associated
with a decline in population density. For the hunter-gatherers in the SCCS data set, an increase in distance
from the Equator is associated with a lower population density and a smaller community size. For evidence
in favor of higher densities and a range expansion during the middle Paleolithic in Africa see Hawks et al.
8
Population growth rate (in percent)
0.3
0.2
0.1
0.0
-0.1
0
100
200
300
400
Population (in millions)
Figure 1: − Population levels and population growth in pre-modern samples, 1MYA to 1400.
Data from Kremer ([39], Table 1). Statistically, there is no relationship between population size
and its growth rate.
the initial population densities were the same in the Americas, Australia, and the Old
World some 12KYA, one would predict that the Old World’s size advantage (or population advantage as all areas are assumed to have the same density) would generate a
technological advantage compared to the Americas or Australia, see Kremer ([39], Table
7).
The melting of the ice caps, however, requires a suitable control group to minimize
confounding problems formally represented by θ in equation (3). Even if one assumes
that population densities before agriculture were close in all regions of the world, and
([28], 12). See also Hassan ([27], chapter 2).
8
The tests are a direct application of (3), see for example equation (16) in Kremer [39]. A more general
version of Kremer’s [39] model is available in Klasen and Nestmann [38] where densities are different
between populations. Differences in densities would be part of θ in equation (3). Since ln [N(t)] is roughly a
linear function of log-time in equation (3), hyperbolic growth can be detected through a double logarithmic
plot between population and time. Under hyperbolic growth, log-population and log-time should lie on a
straight line with slope (1 − α)−1 . When α tends to one, population growth becomes exponential instead
of hyperbolic so a direct test for hyperbolic growth is to test whether or not α = 1 in estimates of equation
(3). Those tests (available upon request) also reject hyperbolic growth in pre-modern samples.
9
that populations were randomly divided, factor endowments were not equally distributed
in the different areas (see, e.g., Diamond [17]). The importance assigned to the Old
World’s population size, essentially Eurasia’s, could be in part attributed to advantages
in endowments or geography such as a large number of domesticable species or Eurasia’s
East-West orientation which, according to Diamond [17], favored the origin of agriculture
and the diffusion of post-agricultural technologies such as metallurgy and weaponry. As
we have noted in the introduction, although in the same spirit, our post-agricultural tests
differ from Kremer’s [39] because we evaluate the isolation of the Americas with respect
to Africa rather than with Eurasia or the Old World.
Human genetic history: Human demography often estimates past population size using
ecological approaches that relate occupied area (archeologically determined) and a potential density per area based on observations of current hunter-gatherers, see, e.g., Deevey
[15]; Hassan ([27], chapter 12). As an alternative, past demography can be studied by
examining the patterns of genetic diversity in current populations because larger populations in the past are predicted to be more genetically diverse today (see, e.g., Jobling et
al. [33]; Relethford [60]; Cavalli-Sforza and Bodmer [9]; Cavalli-Sforza et al. [10]).9 Recent molecular and genetic studies suggest that humans are very homogeneous and that
diversity is allocated mostly within populations (Relethford [60]). More importantly for
the purpose of this paper, African populations are the most genetically diverse group and
interpopulation genetic distances cluster in African and non-African groups.
According to Cann et al. [8], Africa’s high genetic diversity is an expected consequence
of an exclusive African origin of the human population. Genetic diversity is higher within
Africa because African populations are older than non-Africans. Also, genetic distances
9
Since genetic changes such as mutations are random events more commonly observed in larger populations, the association between diversity and past population size is reminiscent of Kuznets [42] and Simon
[68] views in which technological innovations take place randomly within a population (see also Kremer
[39]). In such a view, a higher population size increases the likelihood of new ideas and better technologies
by a “law of large numbers.” In biological terms, a large population increases the likelihood of mutations
and for that reason diversity provides a reliable signal to make inferences of human demography in the
past.
10
cluster around African and non-African groups because anatomically and genetically modern humans evolved exclusively in sub-Saharan Africa.10 If correct, an exclusive African
origin of the human population suggests that populations living outside of Africa before
150KYA were not ancestral to living humans and that ecological estimates of the population, such as Deevey [15] and Hassan ([27], chapter 12), are inaccurate descriptions of
past human demography.
An alternative interpretation to the observed African diversity is possible not through
‘molecular time’ but through ‘effective size.’ That is, the association between genetic
diversity and time of origin suggested by Cann et al. [8] assumes that population age is
the only determinant of genetic variations. However, differences in effective population size
could also account for the observed pattern of genetic diversity (Relethford [58], Relethford
[59], and Relethford and Harpending [56]). Fortunately for this paper, regardless of the
extent of time or size differentials in explaining why the descendents of African populations
are genetically diverse, human genetic history suggest a population advantage for Africa.
Either sub-Saharan Africa hosted the only population of modern humans for a long period
of time, or the largest population of modern humans in the past.11
Economic literature: By the use of genetic information to address economic problems,
this paper is related to Spolaore and Wacziarg [70] who have documented and discussed the
relationship between genetic distance and differences in income per capita across countries.
Spolaore and Wacziarg [70] considered genetic diversity as a measure of elapsed time
10
The debate on human origins is not yet resolved in anthropology but the most central question nowadays seems to be the mode of dispersal of modern humans. The dispersal out of Africa could have generated
a full replacement of archaic humans (as suggested by Cann et al. [8]) or an admixture out of Africa, see
Eswaran et al. [20]. See also Templeton [71] for an attempt to date movements in and out of Africa
through molecular markers.
11
The earliest demographic expansion in the human population took place among the ancestors of
contemporary sub-Saharan Africans some 150KYA according to Harpending et al. [26]. Additional analyses
of human population expansions based on molecular data include Rogers and Harpending [63], Excoffier
and Schneider [19], and Sherry et al. [67]. See also Hawks et al. [28] and Eswaran et al. [20]. Fossil records
indicate the presence of modern humans in the Middle East at about 90KYA, East Asia at 40KYA and
Europe at 30KYA, see Jobling et al. ([33], Section 8.2.4). As Jobling et al. ([33], 245) argue: “it is clear
that modern human morphology appeared considerably earlier in Africa than elsewhere.”
11
between populations and not as an indication of size differentials between past populations.
Since current methods cannot separate time and size influences in genetic diversity, a
clear differentiation between both sources of variation is not yet available. (Shortcomings
originated on the failure to recognize differences in population size as a factor that affects
genetic differences are discussed in detail in Relethford [58] and Relethford and Harpending
[56].)
The patterns we describe are consistent with Cavalli-Sforza et al. [10], the main
source in Spolaore and Wacziarg [70], so this paper and Spolaore and Wacziarg’s [70]
are better seen as complements. An important difference with Spolaore and Wacziarg
[70] lies in the interpretation of the association between genetic diversity and economic
performance. Spolaore and Wacziarg [70] assumed that genetic homogeneity facilitates
technological diffusion through cultural transmission while we consider technology creation
and assigned no specific content to the nature of genetic diversity other than as a proxy
for past demography. This difference is important.
Consider industrialization. The Industrial revolution originated in Europe despite an
Asian advantage in population and technology (granted by an early onset of agriculture
in China and the Near East). Genetically, Europe was more homogenous than Asia since
Europe had a relatively low population level compared to Asia. Also, European industrialization first diffused into North America and Australia. At the time of the European
expansion, both regions were very homogeneous because their populations were small (by
a primitive agriculture and the lack of it). An analysis of diffusion would thus suggest
that homogeneity is the cause of the rapid spread of industrialization into both regions.
Our findings suggest that homogeneity also contributed to the origin of industrialization
although not as the result of cultural transmitted attributes but because homogeneity is
a reflection of small population size in the past and technological backwardness.12
12
Spolaore and Wacziarg [70] considered a wide variety of geographic and cultural factors that could
account for the effect of genetic distance on economic variables. Despite the controls, genetic distance
12
3
Test I: Agriculture in Africa
The purpose of this section is to provide evidence in support of a long run African population advantage and to show that despite this advantage, Africa was already behind
other regions of the world some 10KYA when agriculture first originated. In particular,
we show that Africa’s independent transition from a hunter-gather economy to agriculture
took place much later than in other areas in the world. We later argue that this transition
reflects some exogenous differences in endowments that must be taken into account to
properly determine the role of population in long-run development.
Demography: Support for a population advantage in the long run comes mainly from
measures of genetic diversity in the human population. As the Appendix shows in greater
detail, current genetic diversity can be used to estimate relative differences in past populations.
Measures of diversity, some presented in the Appendix to this paper, are robust in
showing that populations in sub-Saharan African exhibit higher levels of genetic variation
compared to any non-African population (see, e.g., Cavalli-Sforza et al. [10]; Jorde et
al. [37]; Relethford [60]). Africa’s diversity is observed in multiple genetic markers but
also in many other biological characteristics such as craniometry, see, e.g., Relethford and
Harpending [56]. High African diversity is also featured in body size variation because
Central Africa hosts one of the tallest and the shortest populations of the world: the
Tutsi and Pygmy tribes. Other measures of quantitative traits such as skin color also
lend support to higher African diversity, Relethford ([59], Figure 5.4). Language and
genetic maps show considerable concordance in human populations (see Cavalli-Sforza et
remains important in explaining income differences today. They also studied the diffusion of industrialization within Europe and showed that genetic homogeneity also mattered within Europe. Genetic differences
also had predictive power for understanding income differences in 1500. Our interpretation of the available
genetic evidence differs from Spolaore and Wacziarg [70]. We argue that genetic differences are related
to past population size and that current income is related to genetic diversity because areas with low
populations in 1500 are relatively wealthy today (Europe, North America, and Australia) whereas poor
regions today (essentially Africa) had larger populations in the past. Within Europe, relatively populous
areas in the past are relatively poor today as well.
13
al. [10]). Linguistically, Africa is also the most diverse continent. Sub-Saharan Africa,
a relatively restricted geographical area, contains four distinct and very diverse language
families whereas in North Africa, Europe, and Asia there is a single Eurasic family. In the
Americas, there is also a single linguistic family, the Amerind, see Ruhlen ([65], Map 8).
Agriculture: To the best of our knowledge, there has been no systematic attempt to
measure economic progress in pre-agricultural societies so we take no position on whether
Africa was more or less developed than non-African regions in the past. While some
archeological evidence suggests that the population movements out of Africa that started
about 80KYA were in part the consequence of a technological advantage, Mellars [48],
the timing of the emergence of settled agriculture suggests that some 10KYA, Africa was
already behind some non-African regions.
Table 1. First domestication of plants and animals, population in 400 B.C., and large cities.
Time of origin
(KYA)
Population
in 400 B.C.
(in millions)
Number of large cities
1000 B.C to 1000 A.D.
(in 1000 A.D. only)
10.00
42
31 (10)
8.50
7.75
)
Region
Near East (Fertile crescent)
China
South (Yangtze river)
North (Yellow river)
The Americas
Central Mexico
South Central Andes
Eastern United States
)
4.75
4.50
4.50
19
)
58 (6)
7
)
8 (1)
1
0
Sub-Saharan Africa
4.00
7
0
Source: Time of origin is taken from Smith ([69], 13). Population size in millions from Biraben
([4], Table 2). McEvedy and Jones [47], suggest higher population for China (42 millions in
200 B.C.), lower population in the Near East (about 20 millions in A.D.) and slightly smaller
populations in the other regions. The number of large cities is cumulative and include cities with
more than 100,000 inhabitants. In parentheses is the number of cities in 1000 A.D. only. The
Mediterranean had 58 (4) cities and Southeast Asia 31 (4), see Modelski ([49], Table 11).
Table 1 reports the time of origin of agriculture in several regions with independent
14
origin. The table shows that agriculture originated independently in seven widely separated places on the world. As Table 2 shows, agriculture started in Africa much later
than in the Near East or China. Table 1 also reports population size in 400 B.C. and
the number of cities with more than 100,000 inhabitants between the years 1000 B.C. and
1000.13 As it is well known, agriculture increases population size and population densities.
Agriculture in sub-Saharan Africa, however, was not associated with the development of
urban systems such as those in the Near East, China and South and Central America.14
Before the European expansion, there were no large cities in North America, Australia,
or south of the Sahara (Modelski [49] and Chandler [11]).
Another important aspect that perhaps has not been stressed enough in the current
literature in long term development is that agriculture was not independently developed in
Europe (Australia or California), see Table 1. As in many similar instances, an expansion
of farming in the Near East has been proposed to understand the population of Europe
and the replacement of the existing hunter-gatherers although the contribution of Neolithic
farmers to the gene pool of modern Europeans is still debated.15
That agriculture failed to originate first in sub-Saharan Africa is not entirelly surprising
because the role of population stress in agricultural origins has been less consistently
argued (Harlan [25]). While there is no general aggreed view on the causal mechanisms
that gave origin to agriculture, the relatively large degree of synchronization suggests that
13
An independent origin of agriculture in the Fertile Crescent, China, Mesoamerica, and the Andes is well
established but the case of Ethiopia, New Guinea, and North America is more problematic as diffusion
rather than innovation could have taken place, see Smith [69]. An independent origin in New Guinea
is particularly interesting because despite the advantage in size and trade with New Guinea, Australia
remained a land of hunter-gatherers until European contact.
14
Population explosions are well documented in all agricultural transitions, even modern hunter-gatherers
with fairly recent settlements, Livi-Bacci ([43], 45). Archaeological material, beginning at least since 6KYA,
shows considerable diversity and several episodes of population declines. Population change in the Egyptian
Nile valley, the Tigris-Euphrates lowlands, the Basin of Mexico, and the central Mayan lowlands of Mexico
and Guatemala has been studied by Whitmore et al. [73].
15
The contribution varies from around a quarter or less to more than half in regions closer to the Near
East (see Relethford [60] and Cavalli-Sforza et al. [10]). Richards [61] provides a detailed review of genetic
traces of population movements in Europe which suggests that movement of ideas were more important
than movements of people.
15
a global factor that made hunter-gathering less attractive played some role (Fagan [21])
while the difference in timing associated with favorable endowments and initial conditions
suggests that geography also played a role (Diamond [17]). Some of these influences are
perhaps better seen as exogenous changes in θ in the model presented above so this is
one of the reasons for why a comparison of post-agricultural societies needs to control for
differences in endowments.
4
Test II: Cities in Africa
In this section we compare the post-agricultural development of Africa and the Americas
using the geographical isolation of the Americas as a ‘natural experiment.’ As in Kremer
[39], we assume that the spread of modern humans across the Bering land bridge was essentially random and consider the development of the isolated populations as independent
of the development of the Old World.16 We evaluate the isolation of the Americas with
respect to Africa since a control region should have the same “pre-treatment” characteristics as the Americas. A comparison between the Old World and the Americas would
most likely reflect influences not related to population but to geography or endowments
that favored an early onset of agriculture in Asia (see, e.g., Diamond [17]).
The main advantage of a comparison between Africa and the Americas is that both
have many similarities. In both, axes run mostly from North to South, Diamond ([17],
177). Also, South and Central America have an area that is somewhat comparable to subSaharan Africa’s (Table 3) and, natural conditions and climate variety are similar as both
continents cross the Equator. Agriculture originated in both regions roughly at the same
16
Only hunter-gatherers settled on the Americas so the first settlers had no major technological advantage. We will later on evaluate the possibility of selection effects using a sample of hunter-gatherers in
Africa and the Americas from the SCCS. It may be possible that the availability of large mammals in
North America played a role but this seems unlikely. The debate on the role of modern humans in the
extinction of large mammals in the Americas is not yet resolved. Diamond [17] cites evidence in favor of
the “overkill” hypothesis. Fagan ([21], 35-40) argues that human hunters had a very minor role in the
extinction since most species were extinct before modern humans populated the New World.
16
time (Table 2), both had similar population sizes in 400 B.C. (Table 3), and in neither of
them there were many domesticable animals. (In the Americas there was the llama while
there were no mammalian candidates for domestication in sub-Saharan Africa, Diamond
([17], Table 9.2).) The number of large-seeded grass species in sub-Saharan Africa (4) is
closer to South and Central America (with 2 and 5 respectively) than to Eurasia (with
more than 30), see Diamond ([17], Table 8.1).
The comparisons we offer next are based on aggregate evidence of past population size
and city formation using sources similar to those employed by Kremer [39] and Acemoglu
et al. [1] and from disaggregate data for a cross-section of 186 known pre-modern societies
around the world, the SCCS sample of Murdock and White [52].
4.1
Aggregate evidence
Demography: Our aggregate analysis proceeds in two steps. First, we document a population advantage in Africa and second, we evaluate if such an advantage generated significant
differences in economic development measured by the number and size of cities before the
European expansion.
To estimate the role of population in economic development one would ideally like to
know the size of the population prior to the isolation of the Americas and around 1500
when the European expansion integrated the isolated areas once again. While there are
several estimates in 1500, estimates prior isolation are not available. This sub-section
relies on modern estimates of past populations beginning in 400 B.C.. Naturally, evidence
on the size of the population in America before Columbus varies substantially.17 For tax
17
Although some earlier estimates are based on an “extraordinary amount of material,” they are either
informed guesses based on travelers’ observations or impressions based on relative densities, Caldwell and
Schidlmayr ([7], 188). Caldwell and Schidlmayr ([7], Table 2) present additional estimates of regional
populations circa 1650. In all estimates but in Riccioli’s 1661 figure, Africa is more populous than the
Americas. As Livi-Bacci [44] notes, a large population in the Americas is used mostly to give credit to
“germs” as an important factor in the population decline after the Conquest. Livi-Bacci [44] also argues
that the new pathologies were important in the depopulation of the Americas but additional factors related
to violence, civil conflicts, famine and hunger, confiscation of labor, and economic and social disruptions
17
purposes, Spanish authorities in 1574 reported a total of about 8 to 10 million inhabitants
in Hispanic America although there are much higher estimates, Livi-Bacci ([44], Table 1).
Numbers in Africa also vary widely but early estimates suggested an African ‘consensus’
around 100 million inhabitants, see, e.g., Caldwell and Schidlmayr [7].
Table 2. Estimated population in Africa and the Americas.
Region
Africa
North
Sub-Saharan
The Americas
North
South and Central
Indian subcontinent
Area
Biraben [4]
400 B.C. A.D. 1000
1500
McEvedy and Jones [47]
A.D.
1000 1500
2
25
10
7
14
12
9
30
9
78
8
8
11
22
8
38
20
20
5
1
7
30
2
10
46
2
16
40
3
39
95
0.4
4
34
0.7
8
77
1.3
13
100
World population
153
252
253
461
170
265
425
Notes: Population in millions. Area (mill. km2 ) from McEvedy and Jones [47]. North Africa
includes the Maghreb, Libya and Egypt. The area in North Africa does not include the Sahara.
North America includes the US, Canada, and the Caribbean.
In addition to the estimates at the time of the European expansion, one would like
to know the time path of population change in Africa and the Americas. Beginning
with Colin Clark’s [12], there have been some attempts to provide longitudinal estimates
of regional populations in post-agricultural times. The estimates from Biraben [4] and
McEvedy and Jones [47] summarized in Table 2 are useful for a first comparison because
both are independent sources (see, e.g., Caldwell and Schidlmayr [7]). From Table 2 is
evident that while the estimates of population levels and increase from Biraben [4] and
McEvedy and Jones [47] differ, both share a common feature: sub-Saharan Africa had a
were also powerful factors in the decline.
18
large population size and the fastest population growth in the world in the years between
400 B.C. (or A.D.) and 1500 (or 1000).18
The first estimate of a population difference is the cross-sectional difference in population levels between Africa and the Americas in 1500, ∆D = NtAf rica −NtAmericas . As Table
2 shows, population in sub-Saharan Africa was about twice as large as the population in
the Americas so ∆D would give an advantage in population to Africa. A second estimate
rica
− nAmerica
, with nt as
is a double difference or a difference in growth rates, ∆DD = nAf
t
t
the population growth rate. (Ideally, the growth rate in ∆DD would be the difference in
populations pre- and post-isolation but those estimates are not available.) As population
in Africa increased between four- and ten-fold, much faster than the populations in the
Americas, a difference in growth rates also suggest an advantage for sub-Saharan Africa.
Given our data, this is the case because the initial populations are similar. Because population size is often estimated based on area, the data of population may be just a reflection
of area differences early on. We will return to this point later on.
Finally, the previous comparisons are constructed as if Africa had no contact with
Eurasia so they represent a lower bound for the influence of population. Contact with
Eurasia existed through the Nile River because of ancient Egypt, thought the Sahara by
the Arab trade that started during the seventh century A.D., and by the East African
trade through the Indian ocean in medieval times. By its population size, one would
expect that ancient Egypt had a large influence on sub-Saharan Africa. In a long-run perspective, however, Egyptian influences in sub-Saharan Africa seem small although definite
assessments are not yet available.19
18
Rapid population growth in sub-Saharan Africa is associated with a series of geographic expansions
of the Bantu-speaking agricultural populations (see Connah [13] and Austen [2]). The rapid growth in
population, beginning as early as 3KYA, has been documented through linguistic, archeological, and even
genetic basis. Signatures of the Bantu expansion exist for mtDNA and Y-chromosome data, see Tishkoff
and Verrelli ([72], 309). Connections with Eurasia also provided an inflow of plants and seeds such as the
“Asian yams, cocoyams [taro], bananas and plantains.” Those crops were introduced between the first and
the eight centuries A.D., Hopkins ([32], 30).
19
For example, cities appeared first in Nubia (or the middle Nile) due to the immediate proximity to
19
Table 3. Population growth in Africa and the Americas.
McEvedy and
Biraben [4]
Jones [47]
400 B.C. to
A.D. to
A.D. to
1000 1500
1000 1500
1000
1500
Baseline population increase
A. Sub-Saharan Africa
B. The Americas
B1 . North
B2 . South and Central
C1 (=A-B1 ). North America
C2 (=A-B2 ). South and Central
Population increase in
D1 . Indian subcontinent
D2 . Eurasia
3.3
10.1
1.5
5.5
3.5
1.6
1.0
1.3
2.0
0.0
0.5
2.3
0.8
4.6
0.6
2.9
2.0
1.0
Difference with the Americas, ∆DD
2.3
8.1
1.5
5.0
1.2
0.8
2.0
5.5
0.9
2.6
1.5
0.6
Controls for Eurasian influence over Africa
0.7
2.2
0.0
1.1
1.9
0.6
0.0
1.4
0.0
0.4
1.3
0.0
Double difference with the Americas, ∆DDD
With respect to Indian subcontinent
E1 (=C1 -D1 ). North America
1.6
6.0
1.5
3.9
-0.7
0.3
E2 (=C2 -D1 ). South and Central
1.3
3.4
0.9
1.5
-0.4
0.1
With respect to Eurasia
F1 (=C1 -D2 ). North America
2.3
6.7
1.5
4.6
0.0
0.9
F2 (=C2 -D2 ). South and Central
2.0
4.2
0.9
2.2
0.2
0.6
Notes: Data from Table 2. Population increase is not taken on uniform time units but simple
normalizations will make rates comparable between periods.
One way to determine Eurasia’s influence in our sample is though a double difference
rica
− nEurasia
) − nAmerica
, with nEurasia
representing a control
given by: ∆DDD = (nAf
t
t
t
t
for the contact of Africa and Eurasia. The difference in growth rates is useful because
discounting the growth rate of a control region would eliminate factors attributed to
ancient Egypt. Meroë, is perhaps the best known city in tropical Africa. In 430 B.C. the population of
Meroë was about 20,000 inhabitants (Chandler [11], 461). Gold, ivory, slaves, and other mineral, animal
and vegetable products were traded with Eurasia through the Nubian corridor that connected tropical
Africa with Egypt. Alternative routes through the Red Sea and the Sahara were secondary early on,
Connah ([13], 18-19). Despite the trade stimulus, it has been suggested that the Nubian corridor was a
“cultural cul-de-sac,” see Connah ([13], 19).
20
Eurasian influences. For instance, if the Eurasian influence is large, we should observe
∆DDD near zero.
Table 3 includes estimates of African population growth that discount the population
increase in the Indian subcontinent (which also has a North-South axis) and in Eurasia.
An alternative would be to use North Africa directly but North Africa’s population growth
was negative in most of the years considered in Table 3. As the table shows, even after
the growth rate of Eurasian is substracted, population growth in Africa was faster than in
the Americas. The only instance in which there is no advantage is for the first millennia
in the population data from McEvedy and Jones [47].20
Cities: There is no standard measure of development or technological sophistication in
pre-modern societies but we consider city formation as a proxy for differences in economic
prosperity during pre-industrial periods.21 As a measure of sophistication, urbanization
offers many advantages since cities are a complex form of organization. Cities are more
complex than movable agricultural settlements and they exhibit a significant degree of division of labor. Cities often result from advances in agricultural productivity or incentives
given by external or internal trade and physical evidence on the existence of cities tends to
be well preserved. See Acemoglu et al. ([1], Section 2) for a related discussion in support
of this view.
20
Hopkins ([32], 121) suggests an estimate of 25 million in West Africa alone during 1700. Caldwell and
Schindlmayr [7] suggest an estimate of the population in Africa around 50 million inhabitants in 1500.
Note also that about 10 million slaves were transported mostly from West Africa during the Atlantic slave
trade, Fogel and Engerman ([22], Figure 2). The demographic impact of slavery in Africa seems small in
part because slavery was already practiced in smaller scale in Africa “long before the rise of the Atlantic
trade,” see Hopkins ([32], 23, and 120-122). It is not possible to draw demographic inferences from the
Atlantic slave trade because population in the Americas declined after European contact and slaves tried
to substitute for indigenous populations. Nonetheless, migrants from Europe and slaves did not locate in
densely populated areas where most of the population losses took place.
21
Africa seems to have experienced an independent origin of iron work often cited as being part of the
advancements spread with the Bantu expansions. However, “iron apparently made no dramatic impact
upon early African agriculture,” Austen ([2], 14). Cattle domestication also seems to have had an independent origin, see Austen ([2], chapter 1). Important independent achievements in mathematics and
science also took place in America, see Mann ([46], 16-20 and 63-65). Some specific aspects associated
with modernization will be discussed later on using the SCCS data set.
21
In Table 4 we compare the number of cities in sub-Saharan Africa to those in the
Americas. The inventory of cities with sizes over 20 and 40 thousand inhabitants is
from Chandler [11]. (The inventory in Chandler [11], according to Connah [13], provided
accurate patterns of city formation in Africa, see also Davidson [14]. Chandler [11] is also
an important source in Acemoglu et al. [1].) Table 4 reports different time periods as the
influence of Eurasia differed over time and divides sub-Saharan Africa in three sub-regions.
The cities in regions with high Arab influence are coded as Muslims while the Middle Nile
and Ethiopia are regions with influence from trade through the Indian ocean and North
Africa. The rest of sub-Saharan Africa can be consider as indigenous formation.
Table 4. Cities in Africa and the Americas.
Sub-Saharan Africa
South
Year North
Middle Nile
Rest
North
and Central
Africa Muslims and Ethiopia (indigenous) Total America
America
A. Number of cities with populations over 20,000 inhabitants
800
10
0
2
3
5
0
10
1000
13
0
1
4
5
0
9
1200
18
6
2
4
12
0
10
1300
18
8
2
5
15
0
11
1400
18
8
2
9
19
0
18
1500
19
13
3
8
24
1
16
B. Number of cities with populations over 40,000 inhabitants
800
4
0
0
1
1
0
2
1500
7
4
0
2
6
0
6
Source: Chandler ([11], 39-57). The size of cities in the Americas in Modelski [49] is slightly
smaller but there are no African cities for a comparison because the size cut-off is larger in Modelski
[49]. The indigenous cities in sub-Saharan Africa cover mostly Ghana, Zimbabwe and the Bantus.
The middle Nile corresponds to Dongola (modern Sudan) and Kaffa. North Africa includes cities
in the Mediterranean (i.e., Arabian, Egypt, Spanish Africa, and Aloa) and the Maghreb.
Up until 1460, when the Portuguese traveled down the coast of West Africa, the Islamic
world was the main Eurasian influence in sub-Saharan Africa. The number of cities with
22
more than 20 thousand inhabitants in the Middle Nile and Ethiopia, a region adjacent to
ancient Egypt, was very small (3 at most).22 Around the time Islam spread into Africa,
after the seventh and eighth centuries, there was a total of 5 cities with more than 20
thousand inhabitants in Africa. In the Americas, in 800 A.D., there were already twice as
many cities, 10. In the Americas, in fact, the number of cities was as large as the number
of cities in North Africa, see Table 4. In 800 A.D., the number of large cities, cities with
more than 40 thousand inhabitants, was also twice as large in the Americas.
After the first millennia, the Arab influence in Africa increased substantially. In 1500
there were 13 cities in regions with Arab influence. And although the number of non-Arab
cities in sub-Saharan Africa increased from 5 to 11 between 800 and 1500, the number
of cities with more than 20 thousand inhabitants in South and Central America was still
larger in 1500. Including the medium-sized Arab cities as part of sub-Saharan Africa would
suggest that Africa was more urbanized than the Americas. However, in 1500, the total
number of cities with more than 40 thousand inhabitants was the same in sub-Saharan
Africa as in the Americas. Of the 6 cities with more than 40 thousand inhabitants in
sub-Saharan Africa, only 2 have an African origin. In the Americas, there were 6 large
indigenous cities.23
In summary, during the post-agricultural period, sub-Saharan Africa had contact with
Eurasia and a larger population size compared to the Americas. Both advantages, however,
22
Cities in the Americas originated independently but there is some disagreement on the nature of
African civilizations. It is often argued that outside stimuli was mainly responsible for African cities
although some cities seem to be an indigenous African development (Davidson [14] and Connah [13]).
Pre-colonial cities can be found in the West African savannah, the West African forest, the middle Nile,
the Ethiopian highlands, Nubia, the East African coast, and Zimbabwe.
23
It is possible that some geographical influences are still unaccounted for in our previous comparison
although the civilizations of the Americas originated in tropical rainforests similar to the African rainforest.
None of the major cities in the Americas or Africa were coastal and in terms of latitude all tropical cities
were close. The latitude of the main cities of the Inca empire was about -13◦ and most Mayan and Aztec
cities were located at latitudes near 20◦ to 22◦ . The ruins of the Great Zimbabwe are located at a latitude
of -20◦ while Kumbi Saleh, the capital of the Ghana empire, at a latitude of 15◦ . The latitudes of Meroë
and Aksum (in East Africa) were 16◦ and 14◦ . The next sub-section verifies that differences in geography
are not sufficiently important to explain the absence of cities in Africa.
23
failed to translate into economic sophistication as measured by rates of urbanization. Cities
in sub-Saharan Africa were scarcer and less densely populated than those in the Americas
or in North Africa. As we have relied on measures of urbanization to proxy for economic
progress in pre-modern times, this suggests that Africa was less developed than North
Africa, South and Central America, and Eurasia at the time of the European expansion.
As today a similar ranking in economic conditions prevails, the aggregate evidence we have
presented thus far suggests a ‘persistence of misfortunes’ in Africa. Within the Americas
and Europe, there has been an overall reversal in the geographic patterns in economic
leadership.24
4.2
Disaggregate evidence
Data: The results thus far must be tempered by the fact that there are important measurement problems in past population and urbanization. This sub-section addresses some
of these concerns by looking at disaggregated data from the Standard Cross-Cultural
Sample (SCCS). The SCCS includes 186 pre-industrial societies with various subsistence
strategies, including hunter-gatherers, fishers, pastoralists, horticulturalists and agriculturalists. The SCCS provides extensive coded data for a large number of cultural variables
constructed from historical records and published field research by ethnographers. The
SCCS also includes a series of measures of technological, economic, and political sophistication. As with many of the variables in this data set, the measures of sophistication are
organized as an ordinal scale.
The societies included in the sample are distributed relatively equally among the major
regions of the world. The geographic composition of our sample is as follows: 32 societies
24
In contrast to Africa’s case, a ‘reversal of fortune’ characterizes the New World and Europe because
North America, which was less urbanized in the 1500s than South and Central America (Table 5), is now
a relatively developed region. Similarly, within Europe, Spain, Portugal, and Italy were among the most
developed nations in Europe during the 1500s whereas in the 1800s Britain, the Netherlands, and France
were the most developed regions.
24
are in sub-Saharan Africa, 35 in South and Central America, 24 in West Eurasia, 34
in East Eurasia, 31 in the insular Pacific and 30 in North America. The geographical
distribution of societies is different from the one in the SCCS (v200) because we treat
South and Central America as a single unit. We also treat some African societies south
of the Sahara as part of sub-Saharan Africa whereas in the SCCS some are seen as part
of Eurasia. (Later on and in the Appendix to this paper we provide a series of robustness
checks.)
Because the aggregate analysis of the previous sub-section studied medium- and largesized cities, our main variable of interest is the existence of large or impressive structures
(v66 in SCCS). This variable is coded as follows: 1 = None (96 cases), 2 = Residences
of influential individuals (24), 3 = Secular or public buildings (31), 4 = Religious or
ceremonial buildings (27), 5 = Military structures (4), 6 = Economic or industrial buildings
(4). Later on we will consider measures of political organization (v81) in which higher
values are also an indication of higher complexity.
Results: Table 5 examines the relationship between the existence of large structures
and geographic region in pre-modern economies. We consider a sample of 131 agricultural
societies and, for robustness, we later use a sample of 55 hunter-gathers (those in which
the contribution of agriculture to the local food supply is less than 10 percent in v3). We
estimate OLS equations of the form:
Yi = α + δ A Africai + δ S Americai + Xi Θ + εi ,
with Yi as the measure of economic and political complexity in society i, Americai as a
dummy variable for South and Central America, and Africai as a dummy for sub-Saharan
Africa. Consequently, the coefficients on Africa and America capture the difference between each region and the rest of the world (mainly Eurasia). As in the aggregate analysis,
25
our primary emphasis is on the difference between both regions.
The vector of controls included in the specification is denoted by Xi . Some controls
are numerical measures such as the distance to the Equator and altitude, while others
are defined in an ordinal scale such as community size, and population density (defined
as number of people per square mile). With community size (v63) and population density (v64), however, the ordinal scale from 1 to 7 is essentially the logarithm of size and
population density respectively. We also consider a measure of the agricultural potential
in the region where the societies are located as an index based on land slope, soil quality, and climate (v921). Other controls are measures typically associated with economic
complexity or modernization such as the existence of writing and records (v149), the fixity of residence (v150), technological specialization (i.e., presence of pottery, metal work,
and loom weaving, v153), land transport (v154), monetary exchange (v155), and social
stratification (including egalitarian societies, castes, and slavery, v158). Finally, a control
for malaria (v1255) is also part of Xi in some specifications. The indicator of malaria
describes severity as an ordinal scale for the cases in which the presence of the disease has
been recorded (v1255>1). All of the controls included in the regression are taken from
the SCCS.
Column (1) in Table 5 includes the African and American indicator variables, not
including any covariates. These results simply reflect mean differences between both areas
and Eurasia. As expected, large urban centers were less prevalent in Africa and South and
Central America. This shows that in terms of urbanization Africa lagged behind the other
regions of the Old World before the 1500s. Table 5 also includes a F-test for differences
between Africa and South and Central America for all specifications.25 Columns (2),
(5), (7), and (9) include only one additional control and the other columns include an
25
There are two versions of the test because one is based on the OLS results reported in the table while
the other uses an ordered probit estimates. The probit uses the ordinal variables as dependent variables.
The estimates of the ordered probit are available upon request but they look similar to the reported OLS.
26
increasing number of additional controls. Column (10) includes controls that seek to
capture differences in demography, technology, and geography.
Columns (2) and (5) include measures of population density and community size. In
both columns, the value of the African dummy remains unchanged while the value of
the American dummy declines and even becomes non-significant. If a low population
density was the main disadvantage in Africa, the value of the African dummy should be
drastically lowered when densities are included. Similarly, if community size in Africa
differs from size in the Americas (i.e., if there was a difference in terms of small cities),
size differentials will reduce the African dummy. In fact, what these specifications show is
that the Americas had lower densities and community sizes and still were more urbanized
than Africa as our previous aggregate analysis suggest. In both specifications, the African
dummy is statistically different (and lower) from the American dummy. Even more, in
column (2), there is no statistical difference between the America and Eurasia in terms of
city formation so the relative low urbanization in South and Central America was in part
associated with their relatively low densities.
Column (3) includes an interaction between the regional indicators and population
density. The results provide additional evidence that the relationship between population
and city formation in Africa is not typical of agricultural societies. Whereas larger densities
in the Americas represented more urbanization, an increase in population densities in
Africa is not associated with higher urbanization (the total effect is negative, 0.21-0.54).
Column (3) thus suggests that, at least for Africa, larger population densities were not
associated with city formation.26 This result is also well in line with our aggregate analyses
26
This point is important because Acemoglu et al. [1] estimated economic conditions in 1500 using
measures of population density as well as measures of urbanization. The estimates of population densities
employed by Acemoglu et al. [1] would suggest a reversal of fortune even within the tropics because Africa
had higher populations in 1500 and similar areas, see Table 2. While Acemoglu et al. [1] report some
measures of urbanization for Africa, they do not report econometric estimates using urbanization for this
region. Because higher population densities were not associated with city formation in Africa, measures
population density are poor proxies for city formation in Africa.
27
of the previous sub-section.
The main weakness of the cross-sectional regressions in Table 5 is that a significant
African dummy may simply be a consequence of an omitted variables problem. If there
are variables that describe why Africa’s development responds differently to variables that
explain city formation elsewhere, their inclusion as controls should render the African
dummy insignificant. The purpose of specifications (4)-(10) is to include additional aspects
often considered as responsible for Africa’s performance. In particular, column (4) includes
measures of geography given by latitude, altitude, and agricultural potential. Additional
variables are added later on. Geographic variables sometimes have negative effects in city
formation but the effects are non-statistically different from zero. The value of the African
dummy is somewhat reduced but still different from zero and smaller than for the dummy
for the Americas.
Column (6) includes our index of economic complexity described above. The results
show that there were differences in the complexity of the societies in sub-Saharan Africa
and the Americas (and between Africa and the rest of the world) but that the differences
are not sufficiently important to explain the absence of large cities. While the African
dummy declines from 1.32 in column (2) to 1.18 in column (7), there are still significant
differences between Africa and the Americas. Column (8) includes controls for economic
complexity, geography and demography. In column (8), the African dummy remains
relatively unchanged compared to specification (7).
The last two specifications include measures of the presence of malaria. We have
only considered cases in which the disease is present but there is no indication of severity
(v1255=2) and cases in which the disease is present and severe (v1255=3). While malaria
has an overall negative effect, the inclusion of our malaria measure in column (9) does not
fully account for the absence of large urban complexs in sub-Saharan Africa. Even when
all the controls we have considered so far are included, column (10), the African dummy
28
is still significant and different from the dummy variable for the Americas.
Table 5. Urbanization, demography, and geography in pre-industrial societies.
Dependent variable: Presence of large buildings and structures
(1)
(2)
(3)
(4)
(5)
∗
∗
∗
∗∗
Intercept
2.82
1.81
1.75
1.64
1.78∗
(0.14) (0.34) (0.39) (0.89) (0.26)
Africa
-1.32∗ -1.30∗
1.31
-1.18∗ -1.24∗
(0.25) (0.26) (1.44) (0.31) (0.26)
America
-0.78∗ -0.40 -0.93∗∗
-0.38
-0.60∗
(0.30) (0.31) (0.54) (0.35) (0.27)
Population density
0.20∗
0.21∗
0.18∗
(0.06) (0.07) (0.06)
Community size
0.24∗
(0.05)
Economic complexity
Africa×Pop. density
America×Pop. density
(6)
1.50∗
(0.85)
-1.16∗
(0.32)
-0.40
(0.35)
0.09
(0.08)
0.18∗
(0.07)
(7)
1.18∗
(0.45)
-1.18∗
(0.25)
-0.40
(0.29)
0.06∗
(0.01)
(8)
1.25
(0.85)
-1.14∗
(0.33)
-0.35
(0.34)
0.01
(0.08)
0.14∗∗
(0.07)
0.05∗
(0.02)
(9)
4.28∗
(0.96)
-1.26∗
(0.28)
-0.68∗
(0.33)
(10)
2.63∗∗
(1.55)
-1.08∗
(0.35)
-0.31
(0.42)
-0.02
(0.10)
0.16∗∗
(0.08)
0.04
(0.03)
-0.54∗
(0.26)
0.16
(0.14)
Distance to Equator
0.01
(0.01)
-0.02
(0.07)
0.01
(0.05)
Log-altitude
Agricultural potential
-0.00
(0.01)
-0.01
(0.07)
0.01
(0.05)
-0.00
-0.01
(0.01)
-0.03
(0.07)
-0.01
(0.05)
Malaria presence
-0.50
(0.34)
(0.01)
-0.00
(0.08)
-0.03
(0.06)
-0.26
(0.31)
2.64∗∗
2.41
0.18
110
3.15∗∗
2.72∗∗
0.25
104
F-test for sub-Saharan Africa=South and Central America
2.54
5.49∗
6.45∗
3.73∗
3.91∗ 3.43∗∗ 5.45∗
3.88∗
2.20
4.15∗
5.79∗
3.17∗∗ 2.69∗∗ 2.73∗∗ 3.81∗ 3.05∗∗
R2
0.15
0.20
0.25
0.18
0.23
0.21
0.24
0.24
N. Obs.
130
128
129
125
130
124
131
124
∗
∗∗
Source: In parentheses are standard errors. ( ) denote statistical significance at the 5 and 10
percent level. The F-test in all specifications but (3) measures differences between the indicators
for Africa and South and Central America. In specification (3), the test is based on differences in
the interaction terms. The F-tests are based on the OLS regression reported and on an ordered
probit available upon request. Data definitions in the text.
OLS
Ordered probit
29
The SCCS contains many alternative measures of economic sophistication and political
organization. In Table 6 we provide an alternative estimation for political autonomy
(v81).27 (As we are interested in the African and the American dummy, the estimates
for the covariates in Table 6 have been omitted.) Overall the results in Table 6 are very
similar to those of Table 5. The African dummy is statistically different from zero in
specifications (1)-(8) but becomes non-significant when malaria is included in columns (9)
and (10). Notice however, that despite the low value for the African dummy in (9) and
(10), the region dummies are statistically different. That is, while Africa’s dummy may
be negative and small, Table 6 suggests larger average levels of political complexity in the
Americas.
Table 6. Autonomy, demography, and geography in pre-industrial societies.
Dependent variable: Political autonomy
(1)
(2)
(3)a
∗
∗
Africa
-0.87
-0.88
-0.50∗
(0.37) (0.38) (0.29)
America
-0.21
-0.03
0.18
(0.36) (0.42) (0.22)
(4)
-0.84∗∗
(0.47)
0.16
(0.45)
(5)
-0.79∗
(0.37)
-0.01
(0.36)
(6)
-0.86∗
(0.44)
0.08
(0.45)
(7)
-0.66∗∗
(0.38)
0.37
(0.36)
(8)
-0.82∗∗
(0.44)
0.18
(0.43)
(9)
-0.40
(0.42)
0.08
(0.42)
(10)
-0.46
(0.48)
0.61
(0.44)
1.13
1.13
0.07
108
4.25∗
4.53∗
0.28
103
F-test for sub-Saharan Africa=South and Central America
2.22
3.08∗
4.61∗
3.92∗
3.16∗∗ 3.62∗∗
5.87∗
4.32∗
2.17
2.92∗∗ 2.91∗∗
3.66∗
3.08∗∗ 3.49∗∗
5.86∗
4.31∗
R2
0.03
0.05
0.08
0.16
0.11
0.19
0.17
0.25
N. Obs.
129
127
127
123
129
123
129
123
a
Notes:
The results in specification (3) are for the interaction terms. In parentheses are
standard errors. ∗ (∗∗ ) denote statistical significance at the 5 and 10 percent level. The results
include the covariates that correspond to each specification in Table 5.
OLS
Ordered probit
The facts about Africa’s pre-colonial development that emerge from the previous tables
are entirely consistent with our aggregate analysis and historical evidence. While Africa
27
Political autonomy is coded as follows: 1 = Dependent totally (16), 2 = Semi-autonomous (41), 3 =
Tribute paid (4), 4 = De facto autonomy (78), 5 = Equal status in pluralistic society (16), and 6 = Fully
autonomous (29). As in all the measures of the SCCS, the order in political autonomy is increasing in
complexity.
30
was less densely populated than Eurasia, the population density of Africa was larger
than the densities of the Americas before the 1500s. Also, measures of urbanization and
political complexity suggest important differences even within the tropics. Africa had
lower levels of urbanization and less politically complex societies than South and Central
America. Finally, the results from the previous tables indicate much lower differentials
between the Americas and Eurasia in terms of city formation and political organization.
These findings thus suggest that isolation was no major obstacle for the development of
pre-modern societies in the Americas.
For robustness, we present an additional set of estimates in the Appendix and report
here on the main results. Perhaps the most important concern is the one of selection
typical of modern migrations. As we have noted before, only hunter-gatherers populated
the Americas. But since most of the population movements in the long pre-agricultural
period have been out of Africa, the sample of societies in Africa may be a negatively
selected sample of hunter-gatherers whereas the hunter-gatherers that migrated into the
Americas had positive attributes that later conduced to faster development. To study
systematic differences between hunter-gatherers in Africa and the Americas, we estimated
a set of regressions for the variables in Tables 5 and 6 for hunter-gatherers. While the
sample size is reduced to 55 societies, there are no predictable differences between the
hunter-gatherers in Africa and those in the Americas. In fact, the hunter-gatherers in
Africa have marginally higher population densities and better geographic locations.
The Appendix also includes results for an alternative classification of societies in Africa
based on the SCCS. As we have considered all agricultural societies in sub-Saharan Africa
whereas the SCCS assumes some belong to Eurasia, we expect differences between the
samples. However, because the societies near the Sahara were more developed, the results
do not suggest smaller differences than those predicted in the text. Additional regressions in which measures of population density are not based on indirect inferences are
31
also included in the Appendix. When high quality measures of population densities are
included (v1311=0), the results are also stronger than those reported in the text. Finally,
we include measures of pathogen loads beyond malaria. Even when these covariates are
included, there is a negative ‘African dummy’ and significant differences between Africa
and the Americas.
5
Some proposed explanations
There are many explanations for the economic backwardness of Africa in the pre-colonial
period often based on a primitive view of Africa. Pre-colonial backwardness has been
associated with anticapitalist value systems or culture, low population densities and inadequate resource endowments, and unfavorable geography or climate among many others,
see, e.g., Austen and Headrick [3]; Hopkins ([32], 9-11).
In terms of differentials in values, Hopkins ([32], chapter 2) and Austen ([2], chapter 1)
have shown that Africans were expert farm managers and that their response to economic
incentives was a typical one for traditional agriculture. (See also Austen and Headrick
[3].) For example, it is known that African farmers did not employ the European plow
despite knowing of its existence. Hopkins ([32], 36) argues that the plow was not an
appropriate technology for West Africa.28 Differences in endowments and productive
factors also have difficulties explaining Africa’s underdevelopment. Compared to Eurasia,
Africa’s pre-colonial situation can be seen as the result of lower population levels and
densities that prevented specialization and the division of labor as argued by Boserup [5]
28
Africa did not adopt the European plow. As Hopkins ([32], 37) notes, soils were not heavy and could
be easily cleared by fire. Also, “pre-colonial West Africa, developed a relatively simple technology, but one
that was well suited to its requirements.” Austen ([2], 13) also notes that “within the West African forest,
it is also impossible to cultivate millet or sorghum related plants,” although Asian and South American
crops are more appropriate. Draught animals were also needed for plowing but they could not survive
in the West African forest. It is also known that Africa south of the Sahara never invented the wheel,
Hopkins ([32], 71) but this innovations was not essential in the tropics; the Aztecs invented the wheel but
it was never employed in transportation because it was not an appropriate technology.
32
or state formation as Herbst [30] suggested. As we have argued throughout, compared
to the Americas, such an explanation is less satisfactory because the Americas was the
last continent ever to be populated by modern humans and it had lower populations in
pre-colonial times. Africa was also clearly connected to Eurasia whereas Eurasia only had
neglible influence in the Americas during the Norse voyages and maybe in some Alaskan
communities.
Similarly, land quality cannot easily explain African underdevelopment because nonindigenous crops were adopted by African farmers and today they are considered as “typical West African agriculture.” After the European arrival, a number of South American
crops such as “maize, cassava, groundnuts, tobacco and later cocoa, as well as a variety if
fruits,” were introduced and adopted, see Hopkins ([32], 30) and Austen ([2], 15-16). In
fact, today, the majority of the food eaten in sub-Saharan Africa is non-indigenous whereas
in Asia, America, and the temperate areas, diets are still based on the crops domesticated
during the agricultural revolution; rice, corn, and wheat (Caldwell and Schindlmayr [7],
195).
While different from Eurasia’s, climate and the geographical conditions for farming
were similar between Africa and the Americas. Disease environments differed and they
seem to be important in Africa’s underdevelopment but we do not seek to explain Africa’s
economic stagnation solely as a consequence of ecology because the impact of pathogenic
loads can be somewhat minimized by establishing settlements in highlands and because
genetic mutations such as the sickle cell trait, common in sub-Saharan Africa, are helpful
in preventing malaria.
There are two aspects that this paper considers important in Africa’s pre-colonial development; technology and institutions. On some level, they cannot be separated because
societies can accumulate institutional rigidities in ways similar to the accumulation of
technological innovations. Moreover, the social organization of production depends on
33
both technological and institutional factors. We emphasize institutional and technological
differences because the organization of societies in Africa and the Americas differed at
the time of the European expansion. Some indigenous empires existed in Africa but most
African societies were organized around tribes or lineage groups while a large part of the
Americas had a social order typical of large tributary empires. (In fact, in the Americas
several the capital cities of the current states were already important cities in pre-colonial
times.) The empires in the Americas, notably the Aztec and Inca empires, were taken so
quickly by the Europeans precisely because of their political structure and concentration
of power. This concentration of power was not present in Africa; in Africa, pre-colonial
states were already “weak,” see, e.g., Herbst [30]. Perhaps the absence of pre-colonial
states helps explain why sub-Saharan Africa, discovered before the Americas, and despite
its proximity to Europe, was only colonized by Europeans in the late 1800s.
Our views agree in part with Herbst [30] who emphasizes the difficulties of statebuilding in Africa although we do not consider the role of population pressure in political
and economic development and instead argue for important institutional and technological rigidities associated with the organization of production in pre-modern societies. In
particular, based on the nature of technological change in the very long run, the next section seeks to answer Austen and Headrick’s ([3], 163) question of “why are certain kinds
of technology which spread throughout Europe and Asia not found south of the Sahara
before the colonial era?”
6
Leapfrogging and technological leadership
Africa’s pre-colonial development is puzzling from a Boserupian point of view because
compared to the Americas, Africa’s population advantage did not conduce to technological and political sophistication. Contact with the outside world was also sufficient to
34
expose African societies to Eurasian technologies but agriculture and post-agricultural
developments were less prevalent in Africa.
A similar and best-known puzzle to Boserupian views is the development of China.
Chinese achievements are well documented in the post-agricultural period and some of
the technologies that made industrialization in Europe possible were available many centuries before in China (Mokyr [50]). As we noted before, Europe was not a populous or
technologically advanced region before or at the time of the Industrial revolution because
Europe did not experience an independent origin of agriculture. Europe, instead, was a
recipient of developments from the Near East and China. Because China was a leader
in agricultural technologies, it is not difficult to imagine reasons for why China would
lock-in technologically. These barriers to adoption and innovation would not be in place
in Europe. Similarly, North America and Australia had no agricultural advantage in precolonial times. For Europe, North America, and Australia, an agricultural disadvantage
paid off in terms of industrialization.
The pre-colonial situation in Africa also seems to resemble a lock-in in pre-agricultural
technologies. We have argued before that populations in Africa had an earlier origin or
a larger size during the long pre-agricultural period. Judging by living hunter-gatherers,
these societies must have had a social, political, and economic structure that differs from
typical agricultural societies in many aspects such as more equal social hierarchies, higher
mobility, and the absence of property rights on land. Whether Africa’s pre-colonial institutional and economic failures are due to advantages in hunter-gathering is of course not
easy to verify.29 However, because of a population advantage in size or time of origin, the
time needed for technological improvements in hunter-gathering as well as the time needed
29
Mellars ([48], 9383) argues, based on archeological findings, that Africa had important technological
advances early on: “the increased levels of technological efficiency and economic productivity in one small
region of Africa could have allowed a rapid expansion of these populations [some 90-40KYA] to other regions
and an associated competitive replacement (or absorption) of the earlier, technologically less “advanced,”
populations in these regions.” See also Kusimba [41] for technological advances in hunter-gathers in Africa.
35
for institutional barriers to arise in Africa was larger than in any other region populated
by hunter-gatherers.30
As we have argued that leadership in a given technology tends to delay the adoption
or the creation of radical innovations, our views are closely related to the economic and
technological leapfrogging model of Brezis et al. [6]. A leapfrogging view differs from
endogenous growth models such as Kremer [39] and Jones [34] because endogenous growth
assumes that past technologies complement future technologies and reinforce patterns of
technological leadership.31
To highlight the difference with the conventional view of endogenous growth models,
consider again equation (1). An essential property of models of endogenous growth is the
complementarity between population or experience and past technology in the production
of new technologies. Integrating equation (1), also a Bernoulli differential equation, gives:
1/(1−φ)
A(t|t0 ) = A(t0 ) ((1 − φ)θ)
∙Z
t
t0
¸1/(1−φ)
N (s) ds
.
γ
(4)
One way to understand the previous equation is the following. A(t|t0 ) represents the
level of technology at date t ≥ t0 for a technology introduced at date t0 . Besides the
role of exogenous factors θ, technology A(t|t0 ) in equation (4) is determined by A(t0 ) and
30
No other technological revolution has received more attention than agriculture and some inferences
can be drawn from current hunter-gatherers. Whether current hunter-gatherers are representative of past
populations is much debated issue in anthropology (Excoffier and Schneider [19]) but it is clear that presentday hunter-gatherers know how to cultivate crops. Agricultural systems, however, require more work for
a unit of food and “neither agricultural nor industrial man has anything like the leisure time of hunters
and gatherers,” see Harlan ([25], 40-43). One can argue that current hunter-gatherers have survived in the
semi-tropical areas of Africa and the Americas because of their food procurement skills. Their accumulated
experience, however, would make technologies for permanent farming much less attractive. Mokyr ([51],
chapter 6) presents an additional discussion of resistance to technology based on rigidities in the economic
system, cultural, religious, and political factors. Abundant examples of barriers of adoption and innovation
in economic history are also discussed in detail by Mokyr ([51], chapter 6).
31
A well-known study is Alexander Gerschenkron’s [24] theory of relative backwardness. Gerschenkron
[24] argued that economic backwardness before industrialization in Europe (i.e., in Russia, Italy, and
Germany) made their post-industrial growth faster. Theoretical models of experience-based overtaking
and vested interests include Jovanovic and Nyarko [35] and Krusell and Ríos-Rull [40].
36
Z
t
N (s)γ ds. The first term can be understood as an initial advantage while the second as
t0
a measure of experience with this technology.
In models of endogenous growth, having and initial advantage or more experience
reinforce leadership (as long as φ < 1). A leapfrogging view considers that past technologies and experience rather than generating and advantage, are a disadvantage when
a major technological change takes place. The main departure from endogenous growth
models in the leapfrogging views of Brezis et al. [6] is that technological change is assumed of two kinds. A ‘normal’ technical change that evolves as predicted by models of
endogenous growth, equation (1), and a ‘radical’ technical change represented by “major
breakthroughs that change the nature of technology fundamentally.32 ” When new tech-
nologies are introduced, according to Brezis et al. ([6], 1212), their advantages over old
technologies are not evident so leadership in a established technology creates an incentive
for incremental investments in ‘normal’ technical change but a disincentive for ‘radical’
changes. Overtaking in technological leadership will take place because ‘radical’ technological changes are more likely to arise in backward regions or in regions without much
accumulated experience in ‘normal’ technologies.
A simple way to organize the role of technological backwardness and leapfrogging is
to assume that a new technology is made available at time T > t0 . Productivity in the
old technology is given by A(T |t0 ) in equation (4). Productivity in the new technology
is Ã(T |T ) as there is no experience in such a technology. As in Brezis et al. [6], there
are advantages in Ã(T ) because Ã(t|T ) > A(t|T ), for t > T . That is, if both technologies
had the same experience, Ã(t) would clearly dominate. These advantages, however, are
32
The idea of multiple kinds of technical change and drastic innovations is often associated with general
purpose technologies (GPTs), see for example Helpman [29]. The examples more frequently discussed
represent modern innovations such as the steam engine, electricity, and the computer although writing
and tool-making technologies also represent GPTs, see Helpman ([29], chapter 1). A dichotomy between
different types of innovations is also featured in Mokyr [50] and [51], see also Helpman ([29], chapter 2). In
Mokyr’s [50] views, microinventions refer to improvements on existing techniques while macroinventions
are radical changes in technology.
37
not internalized and so if a comparative advantage guides the adoption or the allocation
of time between both technologies, backward economies or economies without much experience in A(t) will first adopt or invest in technology Ã(t) and hence surpass the current
technological leaders.
7
Concluding remarks
This paper studied the role of population in Africa’s long run economic development. In
this paper, we argued that Africa lagged behind the rest of the world long before the
European expansion and that Africa has experienced a ‘persistence of misfortunes.’ That
is, while the geographic pattern of economic leadership has been reversed within Europe
and the Americas during the last 500 years, see, e.g., Acemoglu et al. [1], sub-Saharan
Africa was less developed than North Africa and South and Central America before the
European expansion of the 1500s. The paper also argued that African populations in the
past were larger than populations in comparable regions. In particular, inspired by Kremer
[39], we provided a series of cross-sectional comparisons between Africa and South and
Central America using the geographic isolation of the Americas as a ‘natural experiment.’
Our aggregate cross-sectional analyses are relatively narrow in geographic terms because
there are important influences that could contaminate a comparison between Africa and
Eurasia or between the Americas and Eurasia. For example, geography differed between
regions and the distribution of domesticable species was not randomly allocated between
the continents, see, e.g., Diamond [17].
Following Acemoglu et al. [1], we employed measures of urbanization as a proxy
for economic progress in pre-modern times. Using aggregate measures of city formation
from Chandler [11], we find that the number and size of cities in sub-Saharan Africa
was smaller than in the Americas. We also examined disaggregate data from Murdock
38
and White’s [52] Standard Cross-Cultural Sample, SCCS. The SCCS contains detailed
and extensive coded data for 186 pre-industrial societies in the world. As in modern
cross-country growth regressions, we find a negative and statistically significant ‘African
dummy’ in measures of urbanization and political complexity in agricultural societies. In
other words, we find that Africa lagged from the rest of the world, even the Americas, in
city and state formation during pre-modern times (see also Herbst [30]). The finding of
a negative African dummy is robust to the inclusion of controls for population density,
geography, technological sophistication, and pathogenic loads and to a series of checks
detailed in the Appendix to this paper.
Using information from Biraben [4] and McEvedy and Jones [47], our cross sectional
study also revealed that in the 1500s, populations in Africa were larger than in the Americas, had contact with Eurasia, and grew at rates faster than in any other region of the
world after 400 B.C.. Overall, because urbanization and state-building institutions in the
Americas were more common than in Africa, our assessment of the relationship between
population and development finds a weak support for the Boserupian (or optimistic) side.
Our cross-sectional comparisons suggests that the geographic isolation of the Americas
was not a major barrier for their economic development in the past.
In the paper we also argued that populations in Africa were larger than in Eurasia
through out most of human history. As there are no reliable sources capable of providing
an accurate estimate of the human population in the past, we argue for a pre-agricultural
population advantage in Africa based on genetic comparisons of living humans. We show
that Africans are very diverse from a genetic point of view and that the high African
diversity can be seen as a reliable signal of a population advantage. Despite a relatively
larger population size or earlier time of origin, settled agriculture originated in sub-Saharan
Africa much later than in non-African regions.
Since Deevey [15], changes in human population growth in the very long run have
39
been related to changes in three fundamental technologies: tool making, agriculture, and
industrialization. These technological revolutions are not exogenous or random although
they do not reinforce leadership. In this paper we argue that the changes in technological
leadership are better described as part of a leapfrogging pattern in which “success breeds
failure,” see, e.g., Brezis et al. [6]. For example, Africa’s current and pre-modern underdevelopment can be seen in part as a consequence of Africa’s initial success and a lock-in that
prevented Africa from taking the lead in agriculture or post-agricultural developments.
Reversals in technological leadership are also important in other instances especially
for industrialization. The Industrial revolution originated in Europe and not in Asia, a
leader in agricultural technologies. Industrialization also diffused first into North America
and Australia. Since agriculture did not originated independently in Europe or Australia,
and North America had only an incipient agriculture since no cereal was domesticated
indigenously, the modern rise of these regions cannot be easily associated with past technological leadership.33
8
Appendix
8.1
Genetic diversity and past population size
To describe how genetic diversity serves to estimate past population and to describe population
dynamics in the past consider a constant population of size N e and assume individuals mate
randomly within such population. These assumptions imply that the genetics of the next generation
can be viewed as a sampling process with replacement. (See Rogers [64] and Relethford [59] for
further modelling aspects.34 )
We follow Cavalli-Sforza and Bodmer ([9], 504-505). Let Ft represent the fraction of the
33
Our post-agricultural comparison between Africa and the Americas relies on the geographical isolation
of the Americas but one can employ alternative ‘natural experiments’ to understand the consequences of
population growth on economic development. In an interesting example, Holland [31] considered the fate
of Europe if Ogadai Khan had not died on the eve of the Mongol siege of Vienna in 1242 and estimated
the impact using Bagdad as a ‘control.’ According to Holland [31], European cities would have replaced
learning with religious prejudice and would have fallen into the fundamentalism that the Islamic world
experienced after the Mongols swept through Bagdad. Because the Black Death visited only a century
after the Mongol’s retreat, Europe also faced a severe population reduction so the advantage given by the
Mongol’s retreat could not be easily seen as the cause behind Europe’s rise.
34
For example, the Y-chromosome is transmitted only by fathers while mtDNA is only transmitted by
mothers. Nuclear DNA is inherited from both parents. Generalizations that allow for differences in sex
ratios or population growth are discussed in Cavalli-Sforza and Bodmer ([9], chapter 8).
40
population that possess two identical forms of a given gene with each form (or allele) inherited
from one parent. Ft defines homozygosity or identity by descent. Two randomly drawn genes
from the offspring generation t + 1 will share the same form if they are copies of the same gene in
generation t or if they are copies of different but already identical genes in t. Because there are N e
parents (and 2N e genes), the probability of carrying identical genes is 1/2N e . The second event
takes place with a probability (1 − 1/2N e ) Ft . This generates the following recurrence:35
µ
¶
1
1
Ft+1 =
+ 1−
Ft .
2N e
2N e
In the absence of mutations, all genetic diversity is lost in the steady-state (H ∗ := 1 − F ∗ = 0).
Assuming that all types of neutral mutations µ are equivalent and produce genes that never existed
before (the infinite-sites model), two genes will have the same form if there is no mutation in the
path that connects them. Thus,
µ
∙
¶ ¸
1
1
2
Ft+1 = (1 − µ)
+ 1−
Ft ,
(5)
2N e
2N e
with (1 − µ)2 representing the probability that neither of the two genes has mutated in the past
generation. Ignoring small terms, the steady-state probability that two individuals share the same
gene can be approximated by:
1
F∗ '
,
1 + 4µN e
and the amount of genetic diversity is:
H ∗ := 1 − F ∗ =
4µN e
' 4µN e ,
1 + 4µN e
(6)
an increasing function of effective population size and the mutation rate.36
Two important implications from genetic diversity for the understanding of the human population are the following. First, larger populations are expected to have higher genetic diversity. If
populations are very large (as microbial organisms) or if mutation rates are high, heterozygosity
will approach one. And second, a transitory reduction in the effective population size reduces the
amount of genetic diversity in the population. Diversity will reach H ∗ again because Ft is a stable
difference equation.
A measure of gene diversity or heterozygosity that corresponds to the theoretical notion derived
P
in (6) counts the differences in sites between any two DNA sequences by H = 1 − i (ni /n)2 ,
P
in which (ni /n) represents the frequency of copies of type i and n =
n ni is the number of
sites.37 For example, if a sequence has ten sites and one differs between the two sequences,
35
One way to understand the previous equation is to relate genetic changes to the transmission of
surnames in most societies. Two individuals share the same surname if they have the same father or if
they have different fathers, but those fathers already have the same surname.
36
The association between population and diversity assumes neutral mutations or changes not driven by
natural selection. In general, it is difficult to differentiate selective from demographic factors but evidence
for selection is not definite. A size or an age advantage in Africa is observed in selective-neutral sites
and an African advantage matches fossil and archeological records from anatomically modern humans in
Africa, see, e.g., Hawks et al. [28]; Eswaran et al. [20]; Cavalli-Sforza et al. [10]; Tishkoff and Verrelli [72];
Mellars [48].
37
DNA is a molecule genetically transmitted and composed of combinations of four chemical units or
41
H = 1 − (0.9)2 = 0.19. When more than two sequences are being compared, genetic diversity
employs mean pairwise differences. An example of these measures of diversity is included in Table
A1. In that case, if there are m sequences, there will be m(m − 1)/2 potential comparisons. The
mean pairwise difference is represented by:
³n ´ ³n ´
X
i
j
π = m(m − 1)
dij
,
n
n
i<j
with dij as the proportion of sites that differ between the i-th and j-th sequences, see Rogers
[64] and Relethford [59]. Additional measures of diversity tend to correlate well with previous
measures.
Table A1. Genetic diversity between populations in Africa, Europe, and Asia.
Mitochondrial DNA
Nuclear DNA
Region
(a)
(b)
(c)
(d)
Africa
0.022
0.030
0.076
0.085
Europe
0.009
0.010
0.045
0.077
Asia
0.015
0.011
0.047
0.075
Source: Tishkoff and Verrelli ([72], Table 2). (a)-(d) denote different coding regions of DNA,
(a) refers to marker system HVS-I, (b) to HVS-II, (c) to (1q24), and (d) to marker system (22q11).
Diversity is measured by mean pairwise differences.
Table A1 presents a selection taken from Tishkoff and Verrelli [72] for two of the most common
genetic locus: mitochondrial DNA and nuclear DNA (see also Relethford [59], Figure 5.2). In many
other DNA locus, as the ones studied by Reich and Goldstein [55], Tishkoff and Verrelli [72], Jorde
et al. [36], and Jorde et al. [37], the diversity in Africa is also larger than in any other geographic
region. (A survey of the geographical distribution of genetic diversity is available in Jobling et al.
([33], section 8.5.2).)
One could estimate effective population size directly from Table A1 but the estimates would
be biased for comparisons between regions as they would fail to account for migrations between
Africa and non-Africa regions. However, when migrations are allowed, the estimates of relative
size indicate that the population in sub-Saharan Africa should have been 4 or more times larger
than any of the populations in Europe, Australasia, and the Far East. According to Relethford
and Jorde [57], in terms of effective population size, out of 100 humans, 73 should have lived in
Africa.38
bases: A (adenine), T (thymine), G (guanine), and C (cytosine). A DNA sequence is a succession of letters
that represent the structure of the DNA molecule or strand. DNA sequences are very long so analyses
break sequences into small pieces or locus such as mitochondrial DNA (mtDNA) and nuclear DNA.
38
The relevant concept in genetic analyses is that of ‘effective population size’ and not census size.
Effective population size varies with the number and gender ratio of parents (among other attributes).
Measures of effective population size do not correspond exactly to census estimates of population levels
(see Hawks et al. [28]). Nonetheless, variations predicted by effective size are not inconsistent with the
idea of larger African populations.
42
8.2
Additional estimates
Here we provide additional results from the SCCS sample. We should note that the SCCS contains
over 1800 variables for the 186 societies considered so one can almost surely find a combination of
variables that can make the difference in urbanization between Africa and the Americas in Table 5
disappear. The purpose of this Appendix is not to pursue that objective. Here, instead, we focus
on selection and measurement aspects for the variables we considered in the text.
Table A2. Autonomy, demography, and geography in hunter-gatherer societies.
Dependent variable:
Africa
America
Pop.
density
-0.05
(0.40)
-0.60∗∗
(0.26)
Distance
to Eq.
-28.91∗
(5.19)
-16.32∗
(5.93)
Logaltitude
2.71∗
(0.43)
0.79
(0.64)
Agric.
potential
2.50∗∗
(1.50)
2.39∗∗
(1.28)
Economic
complex.
-4.15∗
(1.05)
-3.10∗
(1.05)
Malaria
presence
0.22
(0.14)
-0.06
(0.23)
Political
autonomy
0.38
(0.21)
0.32
(0.26)
F-test (Africa=America)
2.11
3.86∗
12.21∗
0.01
0.70
2.40
2
R
0.03
0.23
0.13
0.06
0.19
0.07
N. Obs.
55
55
55
55
55
21
Source: In parentheses are standard errors. ∗ (∗∗ ) denote statistical significance at the 5 and
10 percent level. The measures of urbanization and community size are identical for the huntergatherers in Africa and the Americas so the results are not included.
0.04
0.03
55
Table A3. Alternative sample selection for Africa using the SCCS classification.
Dependent variable: Presence of large buildings and
(1)
(2)
(3)a
Africa
-1.30∗ -1.28∗ -0.60∗
(0.27) (0.29) (0.27)
America
-0.74∗ -0.36
0.16
(0.30) (0.31) (0.14)
structures
(4)
(5)
∗
-1.10
-1.20∗
(0.35) (0.28)
-0.31
-0.56
(0.35) (0.27)
(6)
-1.05∗
(0.35)
-0.32
(0.34)
(7)
-1.11∗
(0.27)
-0.36
(0.30)
(8)
-0.99∗
(0.36)
-0.26
(0.34)
F-test (Africa=America)
2.25
5.15∗
7.07∗ 3.17∗∗ 3.50∗∗ 2.79∗∗ 4.62∗ 2.87∗∗
R2
0.13
0.19
0.24
0.16
0.21
0.19
0.22
0.22
N. Obs.
131
129
129
125
130
124
131
124
a
Notes:
The results in specification (3) are for the interaction terms. In parentheses are
standard errors. ∗ (∗∗ ) denote statistical significance at the 5 and 10 percent level. The results
include the covariates that correspond to each specification in Table 5.
First we show that there are no systematic difference between current hunter-gatherers in
Africa and the Americas in the dimensions we have considered in the text. This suggests that
selection is not a first order factor in the regressions of Tables 5 and 6. In fact, as Table A2 shows,
hunter-gatherer societies in Africa are located at higher altitudes, have slightly more potential for
43
(9)
-1.22∗
(0.29)
-0.62∗∗
(0.33)
(10)
-0.91∗
(0.38)
-0.19
(0.42)
2.57
0.17
110
2.42
0.22
104
agriculture, are more densely populated than the Americas, and are located further away from the
Equator. These aspects suggest a marginal advantage for these African societies.
Second, we consider a sample composed by the African societies described in the SCCS. The
difference with the sample in the text is that we have considered the following societies as part of
sub-Saharan Africa: Wolof (#21), Songhai (#24), Pastoral Fulani (#25), and Hausa (#26). The
results in Table A3 treats them as part of Eurasia. Perhaps it is important to notice that we have
excluded Madagascar from our sub-Saharan sample. Despite the proximity to East Africa, the
first human settlements of Madagascar came from Asia around 500 A.D.. In the Americas, only in
Haiti (#160) there is some possible African influence but excluding this one society has no effects
in the estimation results.
Next we consider a quality adjustment for the measures of population density we employed in
the text. Table A4 uses a sample in which measures of density are not inferential. Because an
adequate measure of density may capture more general measurement problems, we consider the
whole set of specifications of Table 5 for this sub-sample. As the table shows, improved measures
suggest no difference between the Americas and Eurasia and even larger differences with Africa.
Table A4. Non-inferential measures of population density.
Dependent variable: Presence of large buildings and
(1)
(2)
(3)a
Africa
-1.35∗ -1.28∗ -0.55∗
(0.29) (0.30) (0.27)
America
-0.38
-0.17
0.11
(0.44) (0.45) (0.30)
structures
(4)
(5)
∗
-1.01
-1.27∗
(0.39) (0.30)
-0.07
-0.34
(0.47) (0.39)
(6)
-1.02∗
(0.38)
-0.12
(0.45)
(7)
-1.17∗
(0.29)
-0.16
(0.41)
(8)
-0.99∗
(0.39)
-0.05
(0.45)
(9)
-1.10∗
(0.32)
-0.07
(0.47)
(10)
-0.96∗
(0.42)
0.11
(0.52)
F-test (Africa=America) 3.85∗
4.94∗ 2.91∗∗ 2.91∗∗ 4.32∗ 2.75∗∗ 4.98∗ 3.20∗∗
R2
0.13
0.15
0.20
0.13
0.18
0.16
0.19
0.19
N. Obs.
100
98
98
95
100
95
100
95
a
Notes:
The results in specification (3) are for the interaction terms. In parentheses are
standard errors. ∗ (∗∗ ) denote statistical significance at the 5 and 10 percent level. The results
include the covariates that correspond to each specification in Table 5.
4.48∗
0.16
87
3.94∗
0.19
82
Finally, Table A5 considers a more general measure of pathogen stress (v1260). Pathogen
stress was obtained from medical and public health sources on the latitude and longitude
of the sample societies, using data as close as possible to the defined dates for the sample
societies’ SCCS data. A total of seven pathogens (leishmanias, trypanosomes, malaria,
schistosomes, filariae, spirochetes, and leprosy, v1253-1259) were each rated on a 3-point
scale for frequency; the individual scores were summed for a total pathogen stress score.
While there are several societies in which the diseases are absent or not recorded, we considered the overall measure of disease loads. Overall, pathogen stress reduce the size and
significance of the African dummy from about -1.32 to -0.84 although the dummy is still
statistically different from zero. In specifications (9)0 and (10)0 the total set of pathogens
is included for cases where not all diseases are absent so it is more selected than (9) and
(10) but still there is a negative African dummy.
44
Table A5. Total pathogen stress.
Dependent variable: Presence of large buildings and structures
(9)
(10)
(9)0
(10)0
Africa
-1.23∗ -0.84∗∗ -1.07∗
-0.76∗∗
(0.35) (0.39) (0.36)
(0.40)
America
-0.75∗
-0.26
-0.79∗
-0.37
(0.31) (0.34) (0.31)
(0.36)
∗∗
Total pathogen stress
-0.01 -0.07
-0.07
-0.10∗∗
(0.04) (0.04) (0.05)
(0.04)
F-test (Africa=America)
1.48
1.77
0.49
0.69
2
R
0.15
0.26
0.19
0.29
N. Obs.
131
124
125
118
Notes: a The results in specification (3) are for the interaction terms. In parentheses are
standard errors. ∗ (∗∗ ) denote statistical significance at the 5 and 10 percent level. The results
include the covariates that correspond to each specification in Table 5 but instead of the presence
of malaria, a measure of total pathogen stress is included.
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