The Origins of Colonial Investments

Joan Ricart-Huguet∗

September 14, 2016

Abstract

Recent literature has documented the economic and political consequences of colonial-

ism, but we know less about the origins of colonial investments. I present evidence that

they were very unequally distributed within 16 French and British African colonies, even

when adjusted for population. How did colonial powers allocate their investments? Eco-

nomic history emphasizes the role of natural resources while recent literature in political

economy argues that settlers explain the origin of colonial institutions. I argue that observ-

able geographic features—locational fundamentals—led some locations to become centers of

pre-colonial trade, which in turn increased colonial settlement and investments not only in

infrastructure but also in health and education. Disparities did not diminish during the colo-

nial period because those locations became centers of colonial economic activity and benefited

from complementarities between investments, consistent with a logic of increasing returns.

JEL: F63, H50, N37, N57

∗Department of Politics, Princeton University, Princeton, NJ 08540. Email: jricart@princeton.edu. I thankCarles Boix, Evan Lieberman, Marc Ratkovic and Leonard Wantchekon, Jennifer Widner, Tsering Wangyal Shawa,Bill Guthe, Torben Behmer, Brandon Miller de la Cuesta, Costantino Pischedda and seminar participants at theContemporary African Political Economy Research Seminar (CAPERS), the Politics and History Network andPrinceton University for useful comments. I am grateful to Elise Huillery and Bob Woodberry for sharing therelevant data. Jeremy Darrington and Elizabeth Bennett helped me locate multiple data sources. Seth MerkinMorokoff, Luise Zhong and Bruno Schaffa provided excellent research assistance.

1

1 Introduction

The last two decades have seen a surge in the literature examining the economic and political con-

sequences of colonialism, notably in the Americas, Africa and South Asia.1 Existing claims on the

effects of colonial institutions, investments and practices are wide-ranging.2 Some of this literature

uses arbitrary or “as-if” random variation in a particular aspect of colonial rule to identify those

effects, such as spatial discontinuities in agricultural revenue collection systems in India (Banerjee

and Iyer, 2005), in extractive forced labor in Peru (Dell, 2010), or in missionary school location

(Wantchekon and Garcıa-Ponce, 2015). However, colonial investments are generally not random

and they are important (i) to better understand the impact of colonialism on subsequent political

and economic development, (ii) because they may be mechanisms linking deep pre-colonial explana-

tions to current outcomes, and (iii) because they fundamentally affected welfare and development

during colonial rule.

I present novel evidence showing that colonial investments were very unequally distributed

across and within 16 British and French colonies in East and West Africa.3 This paper focuses on

the large within-colony variation in investments among the over 300 districts in those 16 colonies.

One important advantage of the within-colony focus is that it allows me to hold institutions

constant, since they hardly varied across districts within the same colony. Investments in some

districts were orders of magnitude larger than in others within the same colony, even taking

population into account, as Figure 1 shows. This highly unequal distribution applies to each of the

three main types of investments in both empires: education, infrastructure, and health (Figures

13-18 in the Appendix). Further, these inequalities persisted during the colonial period under

study: districts receiving higher investments in the early 1900s continued to receive more decades

later.

Why did Europeans invest so much more in some districts than in others? It is difficult to

answer this question beyond particular cases by examining colonial documents because these lack

a systematic investment strategy (Section 3). Scholars have long argued and provided evidence

for the role of natural resources. Curtin et al. (1995, p. 447) argue that “European capital was

invested where exploitable resources promised the most extractive returns” (Huillery, 2010, p. 271),

notably the natural resources of settler colonies such as South Africa and in extractive East and

West African colonies such as Guinea and Ghana. It is no coincidence that Ghanaian gold and

1See Nunn (2009) for a concise review of the economic literature on the topic.2For example, see Acemoglu et al. (2001) and Mahoney (2010) on the effects of colonial institutions; Huillery

(2009), Ricart-Huguet (2015), and Cage and Rueda (2016) on investments; and Guardado (2014) and Wilkinson(2015) on colonial practices.

3The eight French West African (AOF) colonies are Benin (formerly Dahomey), Burkina Faso (Upper Volta),Cote d’Ivoire, Guinea, Mali (French Soudan), Mauritania, Niger and Senegal. The eight British colonies are themain territories under the British Colonial Office (CO): Ghana (Gold Coast), Kenya, Malawi (Nyasaland), Nigeria,Tanzania (Tanganyika), Sierra Leone, Uganda and Zambia (Northern Rhodesia).

2

Figure 1: Public health and education by district (1910-1939)0

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Sierra Leonean diamonds ended up in British hands. “With a great sense of the practical, the

British had for a long time been snapping up the best coastal sectors. [...] Britain took possession

of the territories with the richest resources and best future, although without any geographical

ties: the Niger delta, basis of future Nigeria; the Gambia estuary, Sierra Leone and the Gold

Coast” (Chi-Bonnardel, 1973, p. 50). More recently, Wantchekon and Stanig (2015, p.5) show that

“colonial infrastructure can be predicted [...] by the presence of extractive resources (mines and

quarries) but not by soil quality. This is in line with conventional wisdom in economic history:

colonial powers were not after farmland but minerals.”

Natural resources are a plausible explanation for colonial investments in infrastructure. How-

ever, their role in non-extractive investments such as health and education is less clear. In fact,

why did the colonial state invest in health and education in these extractive colonies at all? There

are several reasons. First, the 1885 Berlin Conference declared the principle of Effective Occupa-

tion, by which claims to the territory depended on actual presence (Young, 1994, p. 100). These

claims gain veracity if the conqueror builds facilities such as schools or infirmaries. Second, public

health investments have positive externalities that reduce the risk of contagious disease among

3

Africans and Europeans. Third, the French created a class of “evolues” (evolved) just like the

British created a class of “Europeanised”—two racist terms with similar meanings—who would

become colonial civil servants thanks to their European education. Some attribute these expen-

ditures partly to the “white man’s burden”—the presumed European responsibility to educate

at least some among the colonized (Lugard (1922), Suret-Canale (1971)). A more prosaic reason

was the need to reduce the colonial payroll and maximize revenue by having Africans rather than

Europeans occupy junior civil service positions.

The most influential argument to explain variation in non-extractive colonial investments such

as public health and education focuses on the size of settler communities: more settlers led to

more inclusive colonial institutions (Acemoglu et al., 2001) and higher levels of district colonial

investment (Huillery, 2010), both of which rely on partly exogenous variation in settlement size.

The former article argues that European settler mortality rate was lower where disease environment

was more favorable, while the latter argues that the number of settlers was higher in areas where

local chiefs offered less resistance or were less hostile. Indeed, settlers not only had a voice in the

colonial government but sometimes even acted as a lobby, affecting the decisions of the colonial

administration (Gardner, 2012).

The settlers and natural resources explanations are not without problems, however. Settlers can

hardly be a fundamental explanation for investments since most arrived in East and West Africa

in the late 19th and 20th century, centuries after European exploration and conquest had begun.

There were very few settlers pre-colonially, unlike in neo-Europes and in many Latin American

colonies, and settlement was very low even in the 20th century, with all 16 colonies except Kenya

populated by less than 10,000 settlers prior to 1940. Their late arrival means that colonial settlers

influenced but also responded to levels of investments, with causality running both ways. Data

show that a couple of districts in each colony attracted the lion’s share of Europeans; an important

question is why Europeans settled where they did within each colony.

The natural resources explanation also has limitations. Natural resources sometimes drove

initial interest in a colony, but that is different from showing that they drove district-level invest-

ments in infrastructure. The answer to that more precise question remains unclear because much

of the prominent work on colonialism is cross-national (e.g. Engerman and Sokoloff (2012), Ma-

honey (2010)) or uses current rather than historical data on natural resources (e.g. Acemoglu et al.

(2001), Wantchekon and Stanig (2015)). Using disaggregated historical data, this paper shows that

natural resources have a modest influence on within-colony investments, even in infrastructure.

Instead, I show that investments in infrastructure, education and health have a common ori-

gin. Key geographic features led some places to be centers of pre-colonial trade, mostly focused

4

on slavery, which in turn explains colonial settlements and investments.4 Early explorers and

traders had very limited information about the territory, so locational fundamentals (Davis and

Weinstein, 2002)—notably natural harbors and capes—influenced where they landed and therefore

the location of initial trading posts between the 16th century and the 19th century, the period of

slavery and the Triangle trade.5 This is unlike in South America and South Asia, where primary

commodities dominated international trade since the 1500s. And yet, although the slave trade was

illegal and residual in British and French African colonies by the late 19th century, those centers

of pre-colonial trade had already started developing before colonialism by selling Africans, usually

from places further inland, to European traders.

Pre-colonial trading centers were usually located on the coast in a natural harbor (e.g. Mombasa

in Kenya) or in a cape (e.g. Dakar in Senegal), or were aided by the presence of a navigable river or

a river basin (e.g. Bamako in Mali).6 Hence, I estimate the long-term causal effect of pre-colonial

trade on investments, using locational fundamentals as an instrumental variable and find that

trade is a primary cause of investments, both because it is a root cause as well as a quantitatively

important one. Further, I show that the relevance of pre-colonial commerce is not restricted to these

early “colonial development hubs”: distance from early trading centers helps explain the limited

diffusion of investments we observe within each colony. The diffusion is limited because colonialism

in most of East and West Africa was shorter-lived and shallower than in other regions such as South

Africa and certainly South Asia, where trading companies were more important and long-lasting

and colonial infrastructure more developed (Jha (2012), Gaikwad (2014)).7 Hence, investments

in East and West African colonies remain highly concentrated in a few districts throughout the

colonial period (1890s-1950s).

The effect of pre-colonial trade is larger than that of natural resources or post-treatment colonial

socioeconomic variables. The advantage of pre-colonial trading centers persisted into the colonial

period because they became centers of economic activity and benefited from complementarities

between investments in infrastructure, education and health. The pattern is consistent with a

logic of increasing returns (Krugman, 1991b) and in particular with agglomeration economies

in urban areas (Krugman (1991a), Rosenthal and Strange (2004)), although in a very different

context: instead of competitive agents—firms—in a private market, these are monopolistic public

agents—colonial state administrators—allocating public finances. In this light, settlers and colonial

administrators become an important mechanism rather than a root cause to explain why districts

4In most of East and West Africa, “pre-colonial” refers to the period starting in the 1500s up to the 1885 BerlinConference, and “colonial” refers to the period between 1885 and 1960.

5In the Triangular trade, Europeans sold manufactured goods to Africa in exchange for slaves that were sold inthe Americas. The Americas, in turn, provided cotton, sugar and other primary commodities to Europe.

6Jha (2012) first made this case for natural harbors in South Asia.7The famous British East India Company lasted almost three centuries while the British Royal Africa Company

only lasted one. French chartered companies in Africa such as the French West India Company or the SenegalCompany were not prominent either.

5

close to early trading centers received much higher investments during the colonial period: they

were attractive to Europeans because they already had basic infrastructure in place. I show that

the size of the settler community in each district was an important short-term factor affecting

within-colony investment allocations that reinforced this feedback loop between early trade and

colonial investments.

In sum, this paper makes three contributions. One concerns the data collection effort de-

scribed in more detail in Section 4. Second, I show the fundamental role of pre-colonial trade,

as opposed to natural resources or settlers, in explaining the loci of colonial state activity. It

is fundamental because pre-colonial trade is a primary cause as well as a quantitatively impor-

tant one, increasing average district colonial investments by an order of magnitude, from roughly

7,000FRA to 70,000FRA (1910 constant francs). Instrumenting for natural harbors and capes in-

creases confidence in the causality of the finding, while colony fixed effects control for institutions

and other-colony level unobserved factors. This result adds to two existing papers that instru-

ment pre-colonial trade with natural harbors in a different context—India—to explain different

outcomes—tolerance (Jha, 2012) and economic development (Gaikwad, 2014).8 Further, I show

that colonial investment diffusion is partly a result of early trade, thus showing that its importance

extends beyond pre-colonial development hubs. Finally, I apply the logic of increasing returns to a

new context—the public finances of the colonial state—and suggest that settlers are an important

mechanism for the observed historical persistence.

The rest of the paper proceeds as follows. Section 2 develops the argument and section 3

provides the relevant historical background and evidence. Sections 4 and 5, respectively, detail

the data sources and present descriptive results. Sections 6 and 7 present the econometric results.

Section 8 concludes.

2 Locational fundamentals and pre-colonial trade

“It is not an exaggeration that between 1550 and 1800 Europeans learned virtually

nothing new about the lands beyond the African coastline. [...] By 1875, in fact,

European possessions in Africa still only comprised the coastal forts and trading

stations and a few tiny colonies.”

Foster (1967, p. 45, 51)

Locational fundamentals are “crucial [geographic] characteristics” of a territory that “change

little over time—even if their economic meaning may have evolved. For example, there are advan-

tages of being near a river [or] on the coast, on a plain instead of a mountain or desert, etc.” (Davis

8In another paper, Jia (2014) shows that historical treaty port cities in China are more developed today.

6

and Weinstein, 2002, p. 1270). “The central question in [economic geography] is how to explain

the distribution of economic activity across space—across countries of the world, across regions

within a country, and across cities,” as Davis and Weinstein (2002, p. 1269) explain.9 Locational

fundamentals are useful in that endeavor especially in a pre-industrial context where geography af-

fects and restricts mobility and economic activity (Diamond, 2005) more than in the industrial era.

Geographic characteristics that change little over time are also useful because of their exogeneity

in two senses. First, they are physical factors originating independently of the social world. In the

last century, engineering has greatly affected geography, and yet we can still distinguish natural

lakes, such as Lakes Baikal and Titicaca, from man-made ones, such as Lakes Volta, Nasser and

reservoirs more generally. Second, locational fundamentals are exogenous because they can affect

colonial investments without being affected by them—at least without modern engineering. For

example, Koromojo in Kenya and Felou in Mali were the only dams in East and West Africa until

the 1950s.

I argue that the main determinant for early European trade was the presence of observable

geographic features—natural harbors, capes and rivers—in a few locations along the East and West

African coast. The early exploration of the Americas, Africa and Asia was an imperial enterprise

with a clear economic motivation. As early as in the 1600s, Africa became a key component of

the Triangle trade and of the Atlantic slave trade in particular.10 How did Europeans decide

where to dock in order to trade in the absence of man-made docks? Inductively, we observe that

the Portuguese and later the French first landed in three places as they descended the Northwest

African coast. One was Ras Nouadhibou (Cap Blanc), currently divided between Western Sahara

and Mauritania. They also established trade in what would become the cities of Saint Louis and

Dakar in Senegal because the former is a natural harbor formed by the Senegal river mouth and

the latter is located in a cape called Cap-Vert.

In other words, Europeans landed where coastal geography was favorable. For one, naviga-

tion technology during the Age of Sail depended much more on environmental factors and wind

patterns than later in the Age of Steam (Feyrer and Sacerdote, 2009).11 Europeans observed vari-

ation mostly in coastal characteristics because knowledge about the socioeconomic and geological

characteristics of the territory was very limited until the 19th century. While natural harbors,

capes and coastal rivers were not the only factors affecting sail or location of pre-colonial trade,

their presence did make setting sail and hence pre-colonial trade easier. Prior to colonization, only

9The authors consider the three leading theories in economic geography: locational fundamentals, increasingreturns, and random growth theory, and test the extent to which each of them is consistent with the distributionof population in Japanese cities from the Stone Age to the present. See paper and sources therein for a detailedexplanation.

10Young (1994, p. 103) provides a brief discussion of chartered companies in Africa and their relation to thecolonial state.

11Choice was restricted even along that geographic dimension, since African coastal geography is more even andterrain less rugged than in other regions such as the South America or South Asia.

7

areas that had long extracted minerals from known deposits influenced trading locations. The

most famous cases in the colonies under study are perhaps the diamond mines of Sierra Leone and

the Gold Coast (current Ghana), where the British disrupted a Trans-Saharan gold trade that had

existed for centuries (Young, 1994, p. 134). Even then we can partly see a geographic logic: two

key locations where the British landed were the natural harbor of Tagrin Bay in Freetown and

Cape Coast in Ghana, a country without natural harbors. East Africa has a longer history of nav-

igation than West Africa largely because of Arab explorations and the Arab slave trade (Hourani

and Carswell, 1995, p. 83). However, an equivalent logic applies. Mombasa, a natural harbor, and

Zanzibar, an island, became centers of Arab-African trade centuries before the Europeans arrived

in the 1500s. Locational fundamentals such as natural harbors and capes along the coast and

inland rivers matter, especially in a pre-industrial context, because they help us understand early

trade patterns.

2.1 Early trade and colonial investments

Did the initial differences between trading and non-trading locations persist into and during the

colonial period? The Age of Steam in the 19th century drastically reduced the sailboat’s depen-

dence on the topograhy of a territory, and railroads provided an alternative to rivers for hinterland

penetration. Inland areas in a colony developed in a few instances, notably in Kenya’s Rift Valley.

The particular locations where pre-colonial trade was established might have been no more relevant

than other similar locations had they not influenced later colonial settlement and investments. In

fact, that is precisely what happened. Proximity to trading centers became even more advanta-

geous during the colonial period, starting in the late 19th century. They allowed stronger linkages

to the international economy, as Laitin (1982) explains in the case of Western Nigeria.

Some of the early trading centers, such as Abidjan, Freetown and Lagos became small colonies

and then colonial capitals, further establishing their preeminence within the colony. As Horowitz

(1985, p. 151) argues, “groups located near the colonial capital, near a rail line or port, or near

some center of colonial commerce—the sitting of which was usually determined by capricious

factors, such as a harbor or a natural resource to be exploited—were well situated to take up

opportunities as they arose.” Capitals were rarely chosen for their central geographic location

within the colony, unlike in today’s Nigeria (Abuja) or Cote d’Ivoire (Yamoussoukro). Colonial

state borders were not even defined by the time the capital was chosen, which restricted the set

of available localities. The persistent concentration of economic activity in a few places in each

colony had spillover effects on other colonial investments, notably in hospitals and schools such as

the Ecole William Ponty in Goree (Dakar) and Fourah Bay College in Freetown. In other words,

investments in education and health partly followed early investments in basic infrastructure. It

8

Figure 2: Timeline of major historical periods in East and West Africa

Early exploration

1450 1600

Pre-colonial Triangle trade

1600 1850

1885Berlin Conference

SettlementColonial period

1890 1960

Note: Dates are approximate. Pre-colonial trade and colonization periods vary between colonies.

was cheaper for settlers and colonial officials to establish schools and hospitals in places where

basic infrastructure was already in place and hence with lower initial costs.

Similar patterns of concentration and persistence have been theorized elsewhere, originally in

Krugman’s application of increasing returns theory to economic geography to explain why countries

develop an “industrialized core” while the rest remains an “agricultural periphery.” Krugman’s

(1991b, p. 483) key insight is that, “in order to realize scale economies while minimizing transport

costs, manufacturing firms tend to locate in the region with larger demand, but the location of

demand itself depends on the distribution of manufacturing.” This logic can help explain patterns

of public investments in a very different context: the early stages of the pre-industrial colonial

state. Instead of competitive agents—firms—in a private market, we have monopolistic public

agents—colonial state administrators—deciding on the allocation of public finances. And yet, as

Pierson (2000, p. 254) argues, some characteristics of increasing returns are particularly intense

in the political decision-making process, notably (i) reduced competition in the public sector,

(ii) an actor’s shorter time horizon and (iii) institutional status quo biases. The first and third

reason apply to colonial officials. Colonial administrators did not face competition in allocating

public finances and their limited spatial choice and knowledge likely reinforced their biases towards

investing in “known territory,” especially in the early stages in the colonial state—from 1821 for

Ghana to 1922 for Niger, depending on the timing of colonization. Each particular colony had very

few locations with basic infrastructure—locations analogous to Krugman’s industrial core, with

lower fixed costs—while all other districts were part of a periphery that was agricultural, pastoral

or nomadic. Transportation costs were also very high unless basic transportation infrastructure

was in place.

In sum, many colonies in East and West Africa were very far from Weberian states. They

were ruled on the cheap and with thin administration on the ground, so European officials faced

severe informational and budget constraints (Herbst, 2000). Given that large nominally colonized

areas in the hinterland remained mostly unexplored for some time even after colonial borders were

defined, initial fixed costs of investment in a new location were compounded by uncertainty about

the future profitability of investments. In these circumstances, administrators, notably the colonial

9

governor, usually preferred low-risk investments in the core of the colony to high-risk investments

in the periphery.12

3 Historical context

The ‘command and control’ of this [British] empire was always ramshackle and

quite often chaotic. To suppose that an order uttered in London was obeyed round

the world by zealous proconsuls is an historical fantasy (although a popular one).

Darwin (2012, p. xii)

The allocation of investments across districts did not follow a simple decision rule. If it did,

investment strategies contained in the colonial documents and records would be perhaps sufficient

to explain variation in colonial investments. Investments were not proportional to district pop-

ulation as the needs of the native population were not a major concern: colonial officials on the

ground responded to their superiors (upward accountability), not to the colonized (downward ac-

countability).13 Investments were somewhat more responsive to European settlers. The interests

of the settlers, however, did not necessarily coincide with those of the British Colonial Office or

the French Ministry of the Colonies, or with those of other pressure groups such as missionaries

and traders (Darwin, 2012). Again, officials were accountable to their superiors and not to the

European minority.14 In fact, settlers in Kenya went as far as to create the European Taxpayer’s

Protection League to shift the tax burden away from Europeans to the native population (Gard-

ner, 2012, p. 98); they wanted to receive public investments but not pay for their cost. In sum,

investments responded to, but were not simply a function of, the settler population. And even if

investments partly responded to settlers, this paper investigates why a district was more heavily

settled or chosen as the colonial capital in the first place.

3.1 Administration and budgets

While there was no simple decision rule to allocate investments, examining British and French

colonial institutional structures provides us with some interesting insights on the public finances of

the colonial state and on the similarities between empires. These similarities are not emphasized

in the literature because much historical work on colonial policy focuses on how French ideas of

assimilation and direct rule differed from British ideas of association and indirect rule (Crowder

12Economic models on the temporal persistence of inequality address a similar type of question, with individualsdeciding between consumption and investment of assets (Boix, 2010, p. 492).

13In that case, the per capita plots in Figure 1 would be mostly flat. French West Africa did establish districtquotas based on population for military conscription, but these were systematically violated (Echenberg, 1991).

14That started to change after World War II with the introduction of Legislative Councils in multiple colonies.

10

(1964), Sharkey (2013), Strang (1994)) and because studies of colonial finance and administration

focus on one empire or the other (Delavignette (1968), Suret-Canale (1971), Gardner (2012),

Constantine (1984)).15 At the top of the colonial hierarchy there were the Ministry of the Colonies

in Paris and the Colonial Office in London. These ministries sent a Governor (Lieutenant Governor

in French West Africa) to the colony that acted as the main link between the metropole and the civil

servants residing in the colony, which included administrators, teachers, judges, engineers, doctors

and nurses. Each district (cercle in French West Africa) was led by a district head formally called

District Commissioner (Commandant de Cercle).

District heads stationed far from the colonial core had much latitude in local policy and imple-

mentation because of “physical distances and no means of communication. [...] The administrative

organization [in French West Africa] was officially centralized but effectively decentralized,” making

disrict heads “the real chiefs of the French empire” (Huillery (2009, p. 181), Delavignette (1968)).

District heads in both empires were in charge of administration, taxation, justice and other public

services (Suret-Canale, 1971, p. 72). They were also in charge of relations with village chiefs, and

district heads partly relied on them for policy implementation and revenue collection, often using

translators.16 Local chiefs in French West African villages were a useful but certainly subordinate

figure whose “influence [was limited] to small areas” (Huillery, 2009, p. 181). Similarly, “local

chiefs [in British colonies] had a guise of autonomy in their local jurisdictions, but they were actu-

ally guided and supervised by the British colonial administrators” (Strang, 1994, p. 149). Lugard’s

(1922) promotion of indirect rule is well-known for some British colonies, but its practice was not

unique to them. Sharkey (2013) considers the difference in that regard between the two empires a

matter of degree, consistent with the move by Gerring et al. (2011, p. 378) “to understand systems

of [direct and indirect] rule along a continuum that reflects the degree of central control.”17

The most interesting comparison for the purpose of this article concerns the sources of revenue

and budget allocation procedures. London and Paris paid for military expenditures, and even

then soldiers were African to minimize costs.18 The main difference is that the British Colonial

15There are historical studies spanning both empires on other issues, notably economic history (Hopkins, 1973)and labor policy (Cooper, 1996).

16For a collection of essays on the importance of translators and interpreters in French and British colonial Africa,see Lawrance et al. (2006).

17The administrative organization of the French and British colonies also presented some differences. Therewere two layers of hierarchy between the district heads and the metropolitan ministry, but these were differentbetween empires. French West Africa was a political federation under the Governor General, based in Senegal,that commanded and coordinated the eight aforementioned Lieutenant Governors in each colony. The eight Britishcolonies were the main eight territories in the continent controlled by the Colonial Office. However, they did nothave the equivalent figure of the Governor General. Instead, Governors were directly under the Secretary of State forthe Colonies. Another difference is that most British colonies were divided in provinces, while French West Africapresented no administrative layers between the colony and the districts. The British Provincial Commissioner wasthe layer between the Governor and the District Commissioner.

18In West Africa, colonial army soldiers were called Senegalese tirailleurs (Echenberg, 1991), a misnomer sincethey were recruited throughout the French West African territory. The equivalent British army was called the Royal

11

Office did not fully pay for defense costs, and hence some revenue had to be raised locally, whereas

the French Ministry of the Colonies covered defense expenditures with metropolitan taxes. One

reason Britain and its colonies divided military expenditures is that the Colonial Office was often

pressured by the Treasury to contribute more and spend less (Stammer, 1967, p. 194).

The most important fact is that revenue for public investments in both empires was raised

domestically from export tariffs and a variety of other taxes. Resource constraints led both em-

pires to tax African subjects as well as settlers: “the cost of colonization was endured by local

populations rather than French taxpayers and, more precisely, mostly by households rather than

firms” (Huillery, 2009, p. 182). The British poll and hut taxes on Africans and income tax on

Asian and European settlers served a similar purpose (Gardner, 2012, p. 8). These sources of in-

ternal revenue were limited, however, and they help explain the difficulties many colonies endured

building adequate infrastructure beyond the colonial core. Given that revenue was raised domes-

tically, differences in public investments between colonies resulted partly from the fact that some

colonies raised much more revenue than others for a variety of reasons, including revenue collection

capacity and volume of trade in commodities. British and French colonies had to apply for grants

in aid to the respective ministries to complement colonial tax and tariff revenues, especially if they

wanted to conduct large infrastructure expenditures. In any case, colonial budgets had to pay for

public investments in both empires. Similar to the British, only “in the aftermath of World War

II France abandoned the requirement that colonies should pay for themselves and began assuming

the cost of some public programs” (Lawrence, 2013, p. 118).19

Before then, the existence of a federal budget in French West Africa was intended partly to

reduce those limited sources of internal revenue in poorer colonies. The federal budget was mostly

raised from custom duties and used to pay for the cost of the federal government and fund large

public works (Huillery (2009), Newbury (1960)).20 British colonies also charged tariffs of course,

but tariff revenues remained in the colony given the lack of federal structures. This partly explains

why public investments were more unequal in British colonies (Figure 4 on page 17): higher

inequality was in part a result of institutional design.

The lack of a systematic investment strategy in British colonies is perhaps the most surprising

aspect of colonial investment policy, especially if we use current Weberian states as our reference

categories. It is less surprising if we consider that communications and knowledge of the territory

West African Frontier Force (Haywood, 1964); and in East Africa they were called King’s African Rifles (Parsons,1999).

19It is important to distinguish between the period up to 1939 and the post-World War II period. “One of thestriking aspects of the economy of French West Africa (FWA) is the extent of its development since the end ofWorld War II. Between the turn of the century, when settled administration began in most parts of the area, and1939, changes in the economic and social structure of the country were few.” (Berg, 1960, p. 392)

20As Newbury explains, it was mainly a redistributive institution. The socialization of tariffs from more econom-ically dynamic colonies like Cote d’Ivoire allowed poorer colonies like Niger to undertake some public works, and italso avoided the creation of a financial burden on France.

12

were also limited, which made policy coordination difficult between the core and the periphery:

“the ‘command and control’ of this [British] empire was always ramshackle and quite often chaotic”

(Darwin (2012, p. xii), see also Delavignette (1968, p. 63)). Further, cost minimization was a first-

order objective. “The concerns of British parliamentarians that the Empire would become a drain

on the British Treasury” led “the imperial government to delegate the costs and financial risks

of imperial expansion whenever possible” (Gardner, 2012, p. 18). As already mentioned, the

metropole constantly strove to reduce the quantity of funds—especially grants-in-aid—available

to colonial governments (Constantine (1984, p. 14, 84), Gardner (2012, p. 9)). Given meager

budgets and the mostly military role of metropolitan taxes, and especially given the tentative and

improvised nature of colonial rule (Darwin, 2012), there was little room for long-term and detailed

investment strategies.

While expenditures presumably responded to particular needs, “no explicit investment strategy

can be found in [French colonial] local budgets. Motivations reported at the beginning of each local

budget explain the general level of annual resources but do not motivate the spatial distribution of

public goods provision” (Huillery, 2009, p. 181). British local budgets present a remarkably similar

focus on detailed descriptions and administration rather than policy: “colonial tax and spending

patterns did not follow a similar logic throughout British Africa” (Frankema, 2011, p. 147) because

“[Britain] did not strive to apply a common financial policy to the various dependencies” beyond

“general instructions [...] from the Secretary of State for the Colonies” (Stammer, 1967, p. 194). In

sum, while colonies strived to increase sources of revenue from cash crops, mineral resources, local

taxes and trade tariffs, investments did not follow simple decision rules because their rationales

were ill-defined.

4 Data sources

I combine various historical data sources to uncover the determinants of investment levels. To

the best of my knowledge, this paper presents the most extensive data on colonial investments at

the colonial district level for French West Africa—collected by Huillery (2009)—and for the main

eight British colonies under the Colonial Office—original data collection: Benin (formerly Da-

homey), Burkina Faso (Upper Volta), Cote d’Ivoire, Ghana (Gold Coast), Guinea, Kenya, Malawi

(Nyasaland), Mali (French Soudan), Mauritania, Niger, Nigeria, Senegal, Sierra Leone, Tanzania

(Tanganyika), Uganda, Zambia (Northern Rhodesia). One important advantage of focusing on

these colonies is their rather homogeneous colonial institutional structure within each empire, as

explained in Section 3. French West Africa was a federation, and these eight British colonies had

a much more similar colonial structure compared to other territories such as Egypt and South

Africa. Unlike French Algeria or British Southern Africa, which had been colonized before 1900,

13

most of East and West Africa was not even adequately mapped before 1850 and was not integrated

into the French and British empires until late in the 19th century.21 Perhaps due in part to their

similar colonial experience, all of these 16 colonies became independent around 1960.

Another important advantage is that record-keeping procedures were very similar within each

empire, owing again to the fact that French West Africa was a federation and that British colonies

all reported directly to the Colonial Office. The yearly Comptes Definitifs (Final Budgets) and

the Blue Books, as the Colonial Office called its yearly reports, were standardized across colonies

for the purposes of accountability.22 This comparability does not directly extend to Sudan, for

instance, because it was under the control of the Foreign Office. The British National Archives and

the Archives Nationales d’Outre-Mer keep most of the original records. Huillery (2009) collected

the original French records for selected years in the 1910-1939 period.23 I collected British colonial

records from 1915, 1920, 1927, 1928 and 1938 as a function of availability in various libraries.24

British and French records are often organized or can be grouped at the level of districts. While

the colonial records have many gaps, they often contain detailed information on demographics,

education, infrastructure investments and other activities.25

The colonial records are complemented with a set of sources that provide information on physi-

cal and geographic characteristics of the territory. Many of these sources contain data I georeference

and analyze using ArcGIS, such as colonial maps with district boundaries published by the Colo-

nial Office (Figure 20 in Appendix).26 Huillery (2010) collected several of those physical and

geographic attributes for French West Africa, such as distance between the district capital and the

coast, altitude, the presence of navigable rivers—which I complemented with a map by C.S. Ham-

mond (1921)—and pre-colonial trading posts. I extend those variables to include British colonies

and collect others not included by Huillery. The sources for pre-colonial trading posts in British

colonies come from Curtin et al. (1995) and Slave Voyages (2013). For natural harbors and capes

most data come from Ramsar (2016) and Ports.com (2016).27 Altitude is a rough proxy for disease

environment, notably for malaria (World Health Organization, 2016), but to better capture disease

21There are exceptions. Part of the hinterland in Senegal and Ghana had been colonized much earlier. Also,Zambia was incorporated as Northern Rhodesia into the Colonial Office only in 1924. Tanzania, then Tanganyika,was under German occupation prior to World War I and incorporated to the British Empire as a League of NationsMandate only in 1922.

22They were not the only empires to keep such records. The Portuguese and the Belgian records I examined aresimilarly detailed.

23Technically the panel extends to 1956, but data are mostly missing post-1939.24The main difficulty is to make the data usable for statistical purposes, which took close to two years for each

author. Figure 19 is a page of a British Blue Book.25While not the topic of this paper, in a related paper I am combining the British and French colonial data with

recent sources such as the Demographic and Health Surveys (DHS) to explain subnational variation in post-coloniallevels of public goods provision.

26Interestingly, colonial district boundaries are relevant today. With some exceptions, many have changed littleover time and around 80% of them remain in place in the 21st century. Districts today are often partitions of alarger colonial district.

27Tables 21 and 22 in the Appendix provide tests of covariate balance for both indicator variables.

14

environment I use a geocoded map of malaria prevalence around 1900 (Lysenko and Semashko,

1968) and FAO tse-tse fly data (Alsan, 2015). Those data are important in tropical Africa, “often

referred to as ‘the white man’s grave”’ (Darwin, 2012, p. 138). I also georeference several historical

maps of natural resources. One is the map by Hubert (1922) presented in Figure 21, likely the

most comprehensive on the issue for pre-1940 West Africa. The other is a detailed worldwide map

by Kuhne (1927). I complement these two main sources with an early publication by the United

States Geological Services (USGS, 1921) that also has world coverage but is much less detailed.

Large plantations like cotton or crops were important in some colonies, and investments could

partly be a function of crop development. Because they are often an endogenous choice by the

colonizer, I use geological soil characteristics such as nutrient and oxygen availability (FAO/IIASA,

2012). These are current measures due to lack of detailed historical data and hence far from ideal,

but they are useful to the extent soil quality does not change dramatically over time (Wantchekon

and Stanig, 2015, p. 27). Otherwise, the paper purposely avoids contemporary databases on natural

resources and diseases that are commonly used in some well-known studies (e.g. see Parker (1997)

in Acemoglu et al. (2002)) to avoid obvious reverse causality.

Finally, the paper includes data on relevant colonial socioeconomic characteristics, such as a

historical map on the presence of Islam across the continent (Bartholomew, 1913). For some other

pre-colonial characteristics, notably whether the district was located in a pre-colonial kingdom

or in an acephalous society, I extend Huillery’s variables to include British colonies. I also draw

from the Murdock (1959) dataset on pre-colonial ethnic group characteristics because it provides

useful proxies of pre-colonial economic and political development, such as intensity of agriculture,

settlement patterns, the size of local communities and level of political centralization.

5 Descriptive results

Figure 3 presents levels of infrastructure investments by colony. When we adjust investments per

capita, Ghana’s levels of investments are similar to Guinea’s and much higher than those of any

other British colony. The colonies received little help from the metropole, so this difference is largely

due to Ghana’s colonial government raising more revenue locally and hence having much larger

budgets than the other British colonies (Hopkins, 1973, p. 190). Figure 4 quantifies inequality

in infrastructure investments by computing Gini indices by colony. The indices are calculated

using district expenditures, and they are above 0.7 in the average British colony and around 0.6

in the average French colony.28 Figures 5 and 6 show that Gini indices are usually above 0.4 for

educational and health investments, and inequality is especially high in British colonies. Figures

28Typically, Gini indices or coefficients use individual income data to measure economic inequality, where 0 meansperfect equality and 1 perfect inequality.

15

13-18 in the Appendix present the district expenditures from which Gini indices are calculated.

The figures show that inequality was pervasive across colonies even if we examine per capita

investments.29 British colonies, at least measured by public investments, were even more unequal

than French ones. This could partly be the result of the redistributive federal budgets in French

West Africa, but between-colony variation could be the result of many colony and empire-specific

factors that are not the focus of this paper. Figure 7 presents the variation in a map along with

the presence of the most valued natural resources by district. The correlation pattern varies by

colony: it appears to be quite strong in Ghana but nonexistent in Nigeria, and overall it might be

weaker than expected.

Figure 3: Infrastructure expenditures in British and French colonies (1910-1939, in 1910 FRA)

02.

0e+

064.

0e+

06

Mala

wi

Kenya

Zambia

Sierra

Leo

ne

Ugand

a

Tanza

nia

Nigeria

Ghana

Total

01.

0e+

062.

0e+

06

Mala

wi

Kenya

Zambia

Sierra

Leo

ne

Nigeria

Tanza

nia

Ugand

a

Ghana

Per 1 million people

02.

0e+

064.

0e+

06

Niger

Burkin

a Fas

o

Mau

ritan

iaBen

inM

ali

Cote

d'Ivo

ire

Seneg

al

Guinea

Total

01.

0e+

062.

0e+

06

Niger

Burkin

a Fas

oM

ali

Mau

ritan

ia

Cote

d'Ivo

ireBen

in

Seneg

al

Guinea

Per 1 million people

Tables 4, 5 and 6 in the Appendix present summary statistics of investments in infrastructure.

Table 4 shows that infrastructure expenditures in a given district-year were around 50,000FRA,

in 1910 real French francs, in each empire. They are a bit more evenly spread in French West

Africa. More importantly, French per capita expenditures doubles the British. Table 5 shows that

the difference is driven by low investments in British East African colonies—the French had no

colonies in East Africa. Within West Africa, due to Ghana and Nigeria, we observe higher British

expenditures but similar per capita levels between empires and with very similar spread. Europeans

settled earlier in West Africa and raised higher taxes, on average, hence the higher expenditures.

Finally, Table 6 breaks down the four main types of infrastructure. The average British and French

district receives close to no investments in sewage/water sanitation or in electricity/lighting. There

29The denominator includes the local and settler population.

16

Figure 4: Infrastructure Gini indices by colony

0.2

.4.6

.81

British colonies French colonies

Nigeria

Kenya

Tanza

nia

Mala

wi

Ugand

a

Ghana

Sierra

Leo

ne

Zambia

Benin

Burkin

a Fas

o

Cote

d'Ivo

ire

Seneg

al

Mau

ritan

iaM

ali

Niger

Guinea

Infrastructure expenditures

0.2

.4.6

.81

British colonies French colonies

Ugand

a

Kenya

Ghana

Mala

wi

Tanza

nia

Sierra

Leo

ne

Zambia

Nigeria

Burkin

a Fas

oM

ali

Benin

Cote

d'Ivo

ire

Guinea

Seneg

al

Niger

Mau

ritan

ia

Infrastructure expenditures per capita

Figure 5: Education Gini indices by colony

0.2

.4.6

.81

British colonies French colonies

Ugand

a

Nigeria

Ghana

Kenya

Tanza

nia

Sierra

Leo

ne

Zambia

Burkin

a Fas

o

Guinea

Cote

d'Ivo

ire Mali

BeninNige

r

Students0

.2.4

.6.8

1

British colonies French colonies

Ugand

a

Ghana

Tanza

nia

Nigeria

Kenya

Sierra

Leo

ne

Zambia

Burkin

a Fas

oM

ali

Guinea

Cote

d'Ivo

ireNige

r

Benin

Students per capita

Figure 6: Health Gini indices by colony

0.2

.4.6

.81

British colonies French colonies

Ugand

a

Tanza

nia

Nigeria

Kenya

Ghana

Zambia

Mala

wi

Sierra

Leo

ne

Mau

ritan

iaNige

r

Cote

d'Ivo

ireBen

in

Seneg

al

Guinea

Burkin

a Fas

oM

ali

Health staff

0.2

.4.6

.81

British colonies French colonies

Ugand

a

Tanza

nia

Ghana

Kenya

Mala

wi

Zambia

Sierra

Leo

ne

Nigeria

Burkin

a Fas

o

Cote

d'Ivo

ireMali

Guinea

Mau

ritan

iaNige

r

Seneg

al

Benin

Health staff per capita

17

Figure 7: Public infrastructure investments by district (1910-1939 average) and location of baseand precious metals (1922)

was no such system in place in most districts because setup was costly. Colonial records show that

most expenditures were concentrated in buildings and premises of various sorts—port authorities,

residences of district officers in core and remote areas—and in transportation—mostly railroads,

roads, bridges and harbors.

6 Results

This section begins by examining what locational fundamentals affected the placement of early pre-

colonial trading posts. Next, I show that the presence of pre-colonial trading posts, instrumented

by coastal geography, increase colonial investments not only in infrastructure but also in education

and health. Settlers help explain this long-term effect. I also present some further results splitting

the sample by empire given the large literature that compares British and French colonialism.30

Models in Table 1 predict whether a colonial district had a pre-colonial trading post as a

function of locational fundamentals. I use both probit (equations 1 and 3) and linear probability

models (equations 2 and 4) because the former adequately models binary variables but the second

30For a recent quantitative comparison using the colonial split of French and British Cameroons, see Lee (2012).

18

is akin to the first stage of the Two-Stage Least Squares (2SLS) models used below.31 Natural

harbors and capes increase the probability of pre-colonial trading centers in coastal districts, as

expected (models 1 and 2), although the relationship is not deterministic—not all trading posts

were in natural harbors and the other way around (see Table 20 in the Appendix). In the case of

inland colonies (models 3 and 4), pre-colonial trade refers to the first trading post established in

what would later become a colony (e.g. in Niger, Niamey equals 1 and the other districts 0). As

expected, coastal distance negatively affects the probability that a particular district was the first

trading post in the colony, whereas the partial correlation with a navigable river is positive. Given

the importance of terrain ruggedness (Nunn and Puga, 2012) and disease environment (Alsan,

2015) for long-term African development, it is interesting that neither affects the location of early

trading posts even in inland colonies—except for the tsetse fly marginally in one model. This

is further evidence that early trading posts were established with very limited knowledge of the

environment.

Table 1: Pre-colonial trade and locational fundamentals

(1) (2) (3) (4)Coastal, probit Coastal, LPM Inland, probit Inland, LPM

Natural harbor or cape indicator 1.40∗∗ 0.40∗∗

(0.50) (0.15)Coastal distance, in 100km -0.47† -0.03∗

(0.24) (0.01)Navigable river indicator -0.25 -0.04 0.94 0.16∗

(0.55) (0.17) (0.71) (0.07)Terrain ruggedness 1.08 0.27 -1.60 -0.07

(0.87) (0.24) (1.48) (0.09)Malaria prevalence index 0.61 0.19 0.27 0.01

(0.58) (0.18) (0.68) (0.04)Tsetsefly index 0.43 0.12 -1.60 -0.09†

(0.57) (0.16) (1.09) (0.05)Constant -2.95 -0.45 2.21 0.39†

(2.21) (0.59) (2.86) (0.21)

Observations 54 56 101 101Adjusted R2 0.08 0.04Pseudo R2 0.26 0.31

Notes: �p < 0.10, * p < 0.05, ** p < 0.01. Models include colony fixed effects.

The two-stage least squares (2SLS) models in Tables 8–12 in the Appendix identify the causal

effect of pre-colonial trade and of some competing explanations, notably natural resources, on

colonial investments. The exclusion restriction claim is that natural harbors and capes affect

31Using a logit or probit for the first stage of 2SLS is Wooldridge’s “forbidden regression” because the standarderrors of the second stage are not estimated consistently. The formal first stage regression is in Table 7 in theAppendix.

19

colonial investments only because they enabled early pre-colonial trade, as argued by Jha (2012),

Gaikwad (2014), and this paper (Section 2). The 2SLS models take the following form:

Tij = β0 + β1Gij + LTβ2k +NTβ3k + STβ4k + ηj + εij (1)

log(Yij) = β0 + βIV Tij + LTβ2k +NTβ3k + STβ4k + ηj + εij, (2)

where Yij is the colonial investment of interest in district i in colony j, G stands for natural harbors

and capes, L stands for other locational fundamentals, N for natural resources and soil quality,

S for colonial district population, area and pre-colonial socioeconomic characteristics, and ηj are

colony fixed effects. Given the highly unequal investment distributions, ordinary least squares

(OLS) models are logged to reduce dependence on extreme observations.32 While all models

include country fixed effects, model 1 in each table includes only L, model 2 adds N and model

4 adds S. Models 1, 2 and 4 purposely omit variables that would suffer from simultaneity, such

as the number of settlers in the district or an indicator for colonial capitals. African population

and district area are two exceptions included in models 4, 5 and 6 to account for explanations

simply based on demographic and spatial size of the district. Models 3 and 5 include the number

of settlers in the district as a mechanism. Model 6 is equivalent to model 4 but subsets the data

to colonies with natural resources. The Wald F-statistic is between 13 and 17 in all models, above

the Stock and Yogo convention of 10 showing that the instrument is not weak. The first stage is

reported in Table 7 in the Appendix.

Figure 8 plots some key coefficients from tables 8-12 to visualize the overall pattern of signifi-

cance. Pre-colonial trade increases all three types of investments but not the likelihood of having a

railroad.33 Navigable rivers were important for exploration and resource extraction in the absence

of a railroad, suggesting they were substitute modes of transportation. It is more surprising that

rivers do not affect health or educational investments. They only increase public investments other

than railroads in some models when the sample is restricted to inland colonies.34 The indicator

for noble metals and diamonds increases infrastructure investments, especially railroads, but not

human capital investments in teachers, students or public health staff. This is an important result:

while the effect of early trade spills over to health and education, the effect of natural resources

does not, consistent with the extractive nature of the latter. Finally, higher malaria prevalence

32Estimates from seemingly unrelated regressions (SUR) would be identical because, although outcome variablesare indeed correlated, the right-hand side of the equation is the same in all models. Also, a linear probability modeland a logit model yield the same results in Table 9, where presence of a railroad is a binary outcome.

33Two thirds of districts with pre-colonial trading posts had a railroad, but the importance of the railroad isprecisely that it facilitated extractive exports even in coastal places without pre-colonial trade.

34This is partly due to the large difference in investment levels between coastal and non-coastal colonies, withthe former dominating the latter.

20

is negatively correlated with all investments and significantly so in railroad and settler models,

suggesting Europeans avoided moving into areas where malaria was meso, hyper or holoendemic.35

Figure 8: Effects of instrumented pre-colonial trade and locational fundamentals on colonial in-vestments

Pre-colonial trading post

Navigable river

Malaria prevalance

Gold, silver or diamonds

Pre-colonial trading post

Navigable river

Malaria prevalance

Gold, silver or diamonds

-2 -1 0 1 2 3 4 -2 -1 0 1 2 3 4

Infrastructure Railroad

Health staff Students

Regression coefficients

Note: These are coefficients from model 3 in Tables 8, 9, 10 and 11. Confidenceintervals shown at the 95% and 90% level.

Figure 9 presents quantitative estimates of the predicted levels of investments in the models

above by switching the variable of interest—pre-colonial trade and natural resources—from 0 to 1.

A coastal district with a pre-colonial trading post receives infrastructure expenditures an order of

magnitude larger than another coastal district without it, from roughly 7,000FRA to 70,000FRA

(1910 prices). The effect is not only statistically significant and causally identified; it is also a very

large marginal effect. For comparison, consider the marginal effect of natural resources. While

they were a motivation for colonial expansion and raised a colony’s overall revenue, the increase

in public investments for districts possessing them were relatively modest. A district with gold,

silver or diamonds almost quadruples the expenditures of one without any of these three resources,

but the difference is between roughly 8,000FRA and 2,000FRA. The effect of pre-colonial trade

is absolutely and relatively larger. Some colonies like Benin and Kenya did not have natural

resources, however, so using the full sample may be underestimating their importance. We can

provide a harder test for pre-colonial trade by restricting the sample to colonies with natural

35Tables 8-12 show that other fundamentals such as terrain ruggedness and soil quality are not significant predic-tors of investments. Other models including individual measures of soil quality such as nutrient availability, oxygenavailability, rooting conditions also show null effects.

21

Figure 9: Marginal effects of pre-colonial trade and natural resources on colonial investments

6,983FRA

67,556FRA

89

1011

12

Logg

ed in

fras

truc

ture

exp

endi

ture

s in

coa

stal

dis

tric

ts(1

910-

1939

), in

191

0 F

RA

0 1Pre-colonial trading post

2,149FRA

7,745FRA

7.5

88.

59

9.5

Logg

ed in

fras

truc

ture

exp

endi

ture

s(1

910-

1939

), in

191

0 F

RA

0 1Gold, silver, diamonds (1920)

Note: These are the marginal effects for model 3 in Table 8. The left plot restricts thesample to coastal districts (n = 57) to increase comparability. Confidence intervalsshown at the 95% level.

resources such as Ghana and Guinea.36 The results of this exercise are presented in Model 6 of

Tables 8-12. Results show that trade is just as significant, statistically and substantively, in this

subset of colonies. Most investments were located in pre-colonial trading posts that were usually

infamous for a past closely connected to the slave trade.

6.1 Settlers and differences by empire

The aggregate or total effect of early trading posts goes through multiple historical channels such as

settler presence and colonial capitals, some of which were already trading posts centuries earlier.

As expected, pre-colonial trading posts correlate with settlers (ρ = 0.22) and colonial capital

indicators (0.35). In turn, settlers correlate with investments in infrastructure (0.41), health (0.48)

and education (0.46)—the respective correlations for colonial capital indicators are 0.21, 0.34 and

0.22. Models 3 and 5 in tables 8-12 of the Appendix show that colonial settlers account for all the

effect of pre-colonial trade on infrastructure investments, about half of the effect on education and

health, and around one third of the effect on missions. In other words, settlers in the 19th and

20th century naturally chose locations that were centers of economic activity, usually of pre-colonial

36Colonies excluded due to lacking gold, silver and diamonds as of 1920: Benin, Kenya, Malawi, Niger andUganda. The other 11 colonies had at least one of the three resources.

22

trade, which compounded the advantages of these particular locations and their surroundings (e.g.

Lagos Colony and the surrounding Southwestern Nigeria) even further.

The second exercise before concluding this section compares the importance of some covariates

in the British and French empires. While this is not the focus of this paper, I provide a brief dis-

cussion in light of the large literature comparing empires. Figure 10 presents coefficients resulting

from the same model (equation 2) as in Figure 8, but now split by empire. The importance of

pre-colonial trade is very similar in magnitude between empires. First, the figure shows that all

investments are positively correlated with population size. Second, the British invested less in ar-

eas with greater Muslim presence, a factor that only marginally affected French public educational

investments.37 The well-known case of low education provision in Muslim Northern Nigeria, ruled

indirectly, vs. higher investments in the non-Muslim South, ruled directly, may apply more broadly

within the British empire in Africa. Colonialists in both empires held many racial prejudices;38

however, British investments seem to discriminate more than the French based on the number of

ethnic groups in the district, pre-colonial intensity of agriculture and pre-colonial political central-

ization. The patterns in Figure 10 are interesting because, albeit correlational, they suggest the

British might have discriminated more based on observable ethnic and socioeconomic traits than

the French.

The estimates on pre-colonial political centralization are particularly interesting because they

are either null or negative whether I use the ordinal measure from Murdock (reported in Figure 10)

or dichotomous measures for kingdoms and acephalous societies, and even bivariate correlations are

around 0. If pre-colonial kingdoms provided better public services today (Gennaioli et al., 2013), it

is likely not because of higher levels of colonial investments. In conclusion, this section estimated

the causal effect of pre-colonial trade on colonial investments and examined correlationally other

possible determinants of public investments. Pre-colonial trade trumps the importance of social

factors (e.g. pre-colonial kingdoms) and of natural resources insofar as public investments are

concerned.

37Islam does not affect mission location in French West Africa but there were few missions to begin with apartfrom Senegal and Benin.

38Examples of racism by British colonial officials abound. Mamdani (1996), among others, highlights that raciststereotyping was pervasive: “The Baganda proper [in developed central Uganda] are eager to become educated [...]with a zest which is almost pathetic” (Herbertson and Howarth, 1914, p. 297). But French colonial officials werefar from race blind: “the Wolof [in developed Western Senegal] was spoiled and had become a terrible snob,” nolonger fit to be a tirailleur [soldier] (Echenberg, 1991, chapter 2), while the Bambara [from Southern Mali] was notvery intelligent but possessed “all the strong warrior’s virtues.”

23

Figure 10: Effects of socioeconomic variables on colonial investments

Colonial district population, logged

Prevalence of Islam (1910)

Pre-col. # of ethnic groups

Pre-col. intensity of agriculture

Pre-col. political centralization

Colonial district population, logged

Prevalence of Islam (1910)

Pre-col. # of ethnic groups

Pre-col. intensity of agriculture

Pre-col. political centralization

-1 0 1 -1 0 1

Infrastructure Students

Health staff Missions

French colonies British colonies

Regression coefficients

Note: These are the coefficients for model 3 in Tables 8, 10, 11 and 12 splitting thesample by empire. Confidence intervals shown at the 95% and 90% level.

7 Diffusion and persistence of colonial investments

In this section, I provide some further evidence for the importance of trade into the colonial period

and for the role of settlers as a investment diffusion mechanism. I also conduct a horse race between

early missions and early trade and show that the latter better explains colonial investments. In the

second half of the section, empirical evidence suggests that inequality between district investments

remained constant and sometimes even increased during colonial times, consistent with a logic of

increasing returns.

7.1 Colonial investment diffusion

Investments are concentrated in a few districts in each colony (Figures 13-18 in Appendix), and

development hubs—Freetown in Sierra Leone, Saint Louis in Senegal—received much higher invest-

ments than other nearby territories. These districts are often colonial capitals and/or pre-colonial

trading posts. However, other districts usually receive a positive, even if small, amount of invest-

ment. Is this limited diffusion of investments throughout the colony partly a function of early

trade? Or is the relevance of early trade circumscribed to these usually coastal “colonial develop-

ment hubs”?

24

There are historical and economic reasons why investment diffusion may not be a function

of pre-colonial trade in the African continent. Historically, the Principle of Effective Occupation

adopted at the Berlin Conference meant that empires needed to develop some presence throughout

the territory. In some colonies like Niger, French occupation was not a commercial but a military

affair. Budget-constrained colonial states may have invested small amounts somewhat uniformly in

inland and remote border districts in order to strengthen their territorial claims. Even without the

Principle, it was usually necessary to build the district officer headquarters and basic infrastructure

such as secondary roads to facilitate travel and revenue collection. Economically, motives to invest

inland were sometimes orthogonal to early trade. Some natural resources were located far from the

coast, as in Guinea and Mauritania, while territory between the coast and those inland resources

was less profitable. The fertile lands of Kenya’s Rift Valley were developed even though they

were 500km from the main trading center, Mombasa, and much of the territory in between was

underdeveloped.

However, we also know that “agglomeration economies attenuate with distance” (Rosenthal

and Strange, 2004, p. 2120). Colonial state expansion was progressive and limited because of

European financial and manpower constraints. In this gradual logic of expansion, pre-colonial

enclaves could have been important departing points since colonial states did not extend randomly

to recently conquered non-coastal lands. Neighboring districts may benefit from proximity to

pre-colonial trading centers if there are spatial investment spillovers, especially after World War

I when economic activity and hence investments increased. I test these competing hypotheses

econometrically with OLS models:

log(Yik) = β0 + β1T + β2DT + β3DC + β4P + β5E + ηk + εik, (3)

where Y is the outcome of interest, T is an indicator for pre-colonial trade post, DT is the distance

between the district capital and the nearest pre-colonial trading post, DC is the distance between

the district capital and the coast, P is logged population, η are country fixed effects and E is the

logged number of Europeans. The pre-colonial trading post indicator and coastal distance control

means that we are examining the within-coastal colony variation among districts without a trading

post that is not already explained by coastal distance. I use geodesic distances between district

capitals advisedly because it eliminates the endogeneity that would be introduced by taking into

account local geography such as hills and rivers and especially man-made infrastructure such as

roads.

Table 2 shows that distance from a pre-colonial trading post reduces all types of investments.

The results suggest that the limited diffusion of colonial investments we observe is partly a function

of early trade, consistent with findings in India (Gaikwad, 2014). The negative effect of DT likely

goes through many channels. Table 3 shows the results of the model in equation 3 that includes

25

European settlers (E) as a mechanism. Interestingly, distance from a pre-colonial post is no

longer significant—albeit it remains positive—for non-extractive public investments (health and

education). However, it remains significant and very similar in size for investments in infrastructure

and missions. Infrastructure investments were largely not driven by settlers because of their

extractive nature which required mostly forced labor. Results are also consistent with missions

operating under colonial state consent but trying to reach areas in which settlers—or even the

colonial state—were not very present.

Table 2: Diffusion of investments (1910-1939) across districts within coastal colonies

(1) (2) (3) (4) (5)Infrastructure Railroads Education Health Missions

Pre-colonial trading post indicator 1.41† 0.03 1.84∗∗ 1.22∗∗ 0.40∗

(0.64) (0.11) (0.31) (0.23) (0.17)Distance from post, in 100km -0.27∗∗ -0.05∗∗ -0.14† -0.06∗∗ -0.05∗

(0.07) (0.02) (0.06) (0.02) (0.02)Constant 13.16∗∗ 2.07† -2.24 -0.55 0.37

(2.59) (1.05) (3.18) (1.16) (0.91)

Observations 211 211 202 210 211Adjusted R2 0.33 0.15 0.36 0.26 0.42

Notes: † p < 0.10, * p < 0.05, ** p < 0.01. Clustered standard errors by colony in parentheses. All models includecolony fixed effects and control for district population and distance from the coast. All outcome variables are loggedfor normality.

Table 3: Settlers as a colonial investment diffusion mechanism

(1) (2) (3) (4) (5)Infrastructure Railroads Education Health Missions

Pre-colonial trading post indicator 0.35 -0.13 1.39∗∗ 0.76∗∗ 0.26(0.68) (0.12) (0.29) (0.14) (0.19)

Distance from post, in 100km -0.21∗∗ -0.04∗∗ -0.10 -0.04 -0.04∗∗

(0.05) (0.01) (0.06) (0.04) (0.01)European population, logged 1.00∗∗ 0.13∗∗ 0.51∗ 0.43∗∗ 0.11†

(0.21) (0.01) (0.17) (0.05) (0.06)Constant 1.82 -0.12 -2.76 -4.67∗∗ -1.92†

(2.18) (0.47) (3.96) (1.03) (0.99)

Observations 200 200 191 199 200Adjusted R2 0.50 0.26 0.44 0.46 0.44

Notes: † p < 0.10, * p < 0.05, ** p < 0.01. Clustered standard errors by colony in parentheses. All models includecolony fixed effects and control for district population and distance from the coast. All outcome variables are loggedfor normality.

26

7.2 Early trade and early missions

Given the recent importance in the literature on missionaries (Woodberry (2012), Cage and Rueda

(2016)), I exploit distance from the initial locations of economic and missionary activity in each

of the 16 colonies, not just coastal ones, to show that the first pre-colonial trading posts are

better predictors of colonial investments than the first pre-colonial missions. In particular, I code

the location of the first trading post and the first mission in each colony as well as the distance

between that location and each district capital in the colony. The models in Tables 13, 14, 15 and

16 in the Appendix follow the specification

log(Yij) = β0 + β1Tij + β2kDTij + β4Mij + β5DMij + ηj + εij, (4)

where Y is the colonial investment of interest in district i in colony j, T (M) is an indicator for

the first trading post (missionary school) in each colony and DT (DM) is the distance between

each district capital and the trading post (missionary school). η are country fixed effects and ε

are clustered standard errors. The results show that an indicator for the initial trading post in

a colony and its distance from other district capitals in the colony are good predictors of 20th

century investments in infrastructure, education and health. The difference is not simply driven

by first trading posts becoming the colonial capital—which occurs in 5 of 16 colonies.39 The models

show a primacy of early trade over early missions—even in public education. Only the number

of missions in a district (Table 16 in the Appendix) is better predicted by distance from the first

school in the colony, which is not surprising since it was invariably a mission.

7.3 Persistence during colonial times

I now provide evidence for the persistence of investments during the colonial period.40 The col-

lected data from British records are a cross-section for health and education in some colonies,

and although we observe infrastructure investments twice or thrice it is too limited for panel data

analysis. The French records collected by Huillery (2009) are also an unbalanced panel with many

gaps, but many observations are repeated over a few years. The gaps arise in part because colonial

record keeping, though extremely extensive and intensive for its time, was not always systematic or

consistent. Within those limitations, I present correlations over time for the three key investments

39As of 1900, these were Mombasa (Kenya), Lagos (Nigeria), Saint Louis (Senegal), Freetown (Sierra Leone) andEntebbe (Uganda). The capitals of Kenya, Senegal and Uganda have changed since then.

40Pre-20th century differences in investments are difficult to measure for several reasons. For one, the timing ofinitial pre-colonial presence varied widely by colony, ranging from the 1500s in some West African coastal locationssuch as the Island of Goree and Lagos to the end of the 19th century in the Niger or Malawi. That helps explainwhy there are no similarly systematic pre-colonial records. Further, initial European presence came invariably inthe form of trading posts or missionary stations, not in the form of public investments since there was no colonialstate.

27

and then I test whether disparities in investments change over time for the case of French West

Africa.

Across years with sufficient data in the 1910-1939 time frame, the number of students and teach-

ers in French West African districts correlates at around 0.8, as shown in Figure 11. The persistence

in health staff is almost just as high, although it varies somewhat from corr(health1928, health1939) =

0.54 to corr(health1915, health1928) = 0.83. The persistence in infrastructure investments is over

0.95 for any of the three pairwise correlations in Figure 11. Districts that received no infrastruc-

ture in 1915 continue to receive none in 1928, and those high receivers in 1915 continue to be

at the top.41 In the case of British colonies, the number of schools, students and health staff all

also correlate at 0.8 or above. For infrastructure investments, the correlation is 0.57 (Figure 12).

The pattern is overall similar to the French data, and it shows that disparities persist during the

interwar period in both empires, even if shocks such as the Great War and the Great Depression

reduced overall levels of revenue collection and therefore investment (Gardner, 2012, p. 6).

Figure 11: Persistence of public investments in French West Africa

1915

1928

1939

0

2

4

0

2

4

Teachers, logged

1913

1933

1936

4

6

8

4

6

8

Students, logged

1915

1916

1928

0

4

8

12

0

4

8

12

Infrastructure investments, logged

1915

1928

1939

0

2

4

6

0

2

4

6

Health personnel, logged

Note: The correlation matrices show continuity in logged levels of publicinvestments over time. Both X and Y axis use the same logged scale.

Since the French data are a less incomplete panel, albeit unbalanced, I can examine whether

investment patterns over time converge or diverge. I use autoregressive models with one lag (AR1)

41Data on infrastructure for the 1930s was too incomplete.

28

to model inequality over time for the three types of investment—infrastructure, education and

health (equation 5). One advantage of AR1 models, as opposed to a simple serial correlation,

is that the constant controls for deterministic trends.42 I also examine whether initial levels of

colonial investments (Iit0) predict subsequent differences (equation 6):

Iit = α + γIit−1 + βIit0 + εit (5)

4Iit = Iit − Iit−k = α + βIit0 + εit (6)

Investments (I) are indexed by district i and year t. Models 1 and 2 in Tables 17, 18 and 19 in the

Appendix present levels of investments, as in the serial correlations of Figure 11. Models 3 and 4

are logged to reduce dependence on outliers. Equation 5 is an AR1 process that includes the initial

value (t0) of I in the time series to adjust for baseline levels of I (models 1 and 3 of Tables 17, 18

and 19 in the Appendix). β > 0 indicates increasing divergence and β < 0 indicates convergence

when compared to the distribution at t0. This approach is more rigorous but the cost is a reduced

sample size. Hence, equation 6 also considers changes in investments over time (models 2 and 4

of Tables 17, 18 and 19). Because the panel is unbalanced and incomplete, the main difference is

that the previous value It−k is not necessarily the previous year but the nearest previous year for

which there is data available, which in some cases may be several years. These models are more

flexible in determining the previous observation and hence the regressions include larger sample

sizes. I report both for completeness because there can be biases in either modeling strategy given

the gaps in the data.

All models except for one indicate either increasing or constant disparities in educational, health

and infrastructure investments. Table 17 in the Appendix shows that teachers per district in 1915

is a good predictor of increase in district teachers later on. Table 18 shows that districts with

high health staff may have more health staff later on (models 1 and 3) or in any case not fewer

(models 2 and 4). The results on infrastructure investments (Table 19) vary depending on the

specification. These mixed findings on infrastructure suggest that increasing returns may apply

more to public services that clearly benefit from complementarities and economies of scale, such as

schools and hospitals, than to basic infrastructure. In sum, models in this section do not show or

disentangle the mechanisms of persistence—future research should. They show that distance from

early trading posts matters for colonial investments; that disparities remain stable or increase,

consistent with increasing returns to investments in the colonial core; and that settlers are likely

an important mechanism in this feedback loop of weakly increasing disparities.

42In other words, the constant would capture a constant increase across districts due to inflation (already ac-counted for by using real 1910FRA), a larger budget, or other factors.

29

8 Conclusions

This paper establishes and quantifies the striking inequality in colonial investments in infrastruc-

ture, education and health in British and French colonies. By using disaggregated data, I can move

beyond the traditional cross-country analysis to find that coastal districts that were pre-colonial

trading centers—instrumented by natural harbors and capes, adapting Jha (2012)—receive around

ten times more investments than coastal districts that were not. Instrumenting for natural harbors

and capes increases confidence in the causality of the finding, while colony fixed effects control for

institutions and other-colony level unobserved factors. Further, and although trade in Africa was

mostly coastal and colonialism short-lived compared to other continents, I show that the rele-

vance of early trade for all three types of investments is not restricted to these usually coastal

colonial development hubs but that it extends to other district of the colony as an inverse func-

tion of geodesic distance from the trading center, consistent with earlier findings showing that

“agglomeration economies attenuate with distance” (Rosenthal and Strange, 2004, p. 2120). The

persistent concentration of economic activity in a few places in each colony had spillover effects on

other colonial investments, notably in hospitals and schools because investments in education and

health were easier in locations where the initial infrastructure was already in place. In particular,

it was cheaper for settlers and colonial officials to establish schools and hospitals in places with

lower initial costs.

Natural resources were a motivation for the colonial enterprise, but they are poor predictors

of within-colony investment allocations or, more simply, of where the money goes. They increase

investments in infrastructure and in railroads in particular, but the effect does not spill over

into education or health. Even in the case of infrastructure expenditures, the increase in district

investments due to natural resources is smaller than that of pre-colonial trading posts relatively

and absolutely (Figure 9). Possible alternative explanations such as disease environment and pre-

colonial political centralization have marginal or null explanatory power, showing that inferences

from particularly well-known cases (e.g. Buganda kingdom in Uganda) may be unwarranted.

There are at least three areas for future research. First, descriptive results show that invest-

ments in infrastructure, education and health in British colonies were even more unequal than

in French. Between-colony differences are due in part to the more redistributive and federal in-

stitutional design of French West Africa. However, British investments are also more unequal

within-colony. Future research could explore those differences. In this paper I only present some

correlational evidence that the British were more discriminatory in their investments insofar as,

unlike the French, they invested less in districts with (i) higher pre-colonial political centralization,

(ii) higher Muslim presence, and (iii) higher ethnic diversity. Those correlational findings are con-

sistent with the notion that British practiced divide and rule to a greater extent than the French

(Wucherpfennig et al., 2015).

30

Second, in a companion paper (Ricart-Huguet, 2016) I study the consequences of colonial in-

vestments for political elite formation by using novel biographical data on over 900 post-colonial

ministers in those former colonies. Current explanations of cabinet formation emphasize short-term

bargaining and Francois et al. (2012) argue that cabinet shares are largely proportional to popula-

tion. Instead, my data reveal that some districts were represented in post-colonial cabinets much

more than others, even adjusting for population. I show that district political returns—proxied

by minister shares—do not derive from colonial investments or levels of development in general

but from educational investments in particular. While the logic applies to both empires, district

returns are type-specific: they derive only from missionary education in British colonies and only

from public education in French colonies. I argue that these political leaders are a byproduct

of the revenue-maximizing strategy of colonial administrators, who recruited civil servants dis-

proportionately from some districts based on numeracy and literacy rather than on some ethnic

group characteristic. Uneven distributions of power after the end of foreign rule have a structural

long-term component—colonial human capital resulting from formal education—and they might

mediate the relationship between colonialism and current political and economic development.

This is precisely the third line of research, in which I examine the long-term impact of colonial

investments on current outcomes such as welfare and literacy, following Huillery (2009), as well as

on political participation. Examining these downstream consequences is a relevant area of research

to understand current disparities in economic and political development in former colonies.

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Appendix

A Tables and figures

Table 4: Infrastructure expenditures (1910-1939 average) in East and West African districts (in1910 FRA)

N mean sd min median max

British coloniesInfrastructure expenditures 200 44,086 153,461 0 2,834 1,551,032Infrastructure exp. per 100,000 people 200 31,633 110,059 0 3,108 1,134,658

French coloniesInfrastructure expenditures 112 51,240 130,562 0 12,112 1,150,341Infrastructure exp. per 100,000 people 112 96,397 276,465 0 14,451 1,940,058

Table 5: Infrastructure expenditures (1910-1939 average) in West African districts (in 1910 FRA)

N mean sd min median max

British coloniesInfrastructure expenditures 66 107,974 249,909 0 20,117 1,551,032Infrastructure exp. per 100,000 people 66 70,282 180,251 0 9,706 1,134,658

French coloniesInfrastructure expenditures 112 51,240 130,562 0 12,112 1,150,341Infrastructure exp. per 100,000 people 112 96,397 276,465 0 14,451 1,940,058

Table 6: Infrastructure expenditures by category (1910-1939 average) in East and West Africandistricts (in 1910 FRA)

N mean sd min median max

British coloniesBuildings 200 18,364 79,394 0 1,175 974,900Transportation 200 10,159 25,484 0 219 168,122Sewage/water 200 8,784 45,625 0 0 487,848Electricity/lighting 200 6,779 41,014 0 0 451,596

French coloniesBuildings 112 22,688 87,264 0 5,854 907,381Transportation 112 20,535 49,458 0 3,423 276,964Sewage/water 112 7,238 30,153 0 409 220,133Electricity/lighting 112 780 3,735 0 0 32,367

36

Table 7: First stage of 2SLS (equation 1)

(1) (2) (3) (4) (5) (6)

Natural harbor or cape indicator 0.49∗∗ 0.49∗∗ 0.48∗∗ 0.46∗∗ 0.46∗∗ 0.50∗∗

(0.10) (0.10) (0.10) (0.10) (0.09) (0.09)Locational fundamentals

Distance from the coast, in 100km -0.02∗ -0.02∗ -0.02† -0.01 -0.01 -0.00(0.01) (0.01) (0.01) (0.01) (0.01) (0.01)

Navigable river indicator (1910) -0.00 -0.01 0.00 -0.01 -0.00 -0.00(0.03) (0.03) (0.03) (0.03) (0.03) (0.04)

Terrain ruggedness -0.01 -0.01 -0.02 -0.03 -0.02 -0.01(0.04) (0.04) (0.04) (0.05) (0.05) (0.09)

Malaria prevalence index (1900) 0.02 0.03 0.03 0.02 0.03† 0.01(0.02) (0.02) (0.01) (0.02) (0.01) (0.02)

Tsetse fly prevalence index (1970) -0.01 -0.01 -0.01 0.01 0.00 -0.01(0.03) (0.03) (0.03) (0.03) (0.03) (0.04)

Natural resources and soil qualityGold, silver or diamonds indicator (1920) -0.03† -0.03† -0.02 -0.02 -0.02

(0.02) (0.02) (0.02) (0.02) (0.02)Base metals indicator (1920) -0.03 -0.01 0.00 0.01 -0.00

(0.03) (0.02) (0.02) (0.02) (0.02)Soil quality index (2000) -0.02 -0.02 -0.02 -0.02 -0.03

(0.03) (0.03) (0.02) (0.02) (0.02)(Pre-)Colonial socioeconomic characteristics

European population, logged 0.01 0.01†

(0.01) (0.00)African population, logged 0.01 0.01 0.02

(0.02) (0.02) (0.03)Area in km2, logged -0.07∗ -0.05∗ -0.08∗

(0.03) (0.02) (0.03)Prevalence of Islam (1910) 0.02 0.02 -0.01

(0.03) (0.03) (0.03)Ethnic Fractionalization Index 0.06 0.04 0.05

(0.07) (0.06) (0.08)Agriculture (none to irrigation) -0.03∗ -0.03∗ -0.03∗

(0.01) (0.01) (0.01)Settlements (nomadic to complex) -0.02† -0.01 -0.01

(0.01) (0.01) (0.01)Pre-colonial political centralization 0.02 0.02 -0.01

(0.02) (0.02) (0.02)Slavery (absence to prevalent) 0.01 0.01 0.02

(0.02) (0.02) (0.02)Constant 0.14 0.28 0.21 0.89∗ 0.63 0.88

(0.09) (0.21) (0.23) (0.41) (0.41) (0.51)

Observations 312 312 301 312 301 228Adjusted R2 0.31 0.31 0.33 0.36 0.36 0.38

Notes: † p < 0.10, * p < 0.05, ** p < 0.01. Clustered standard errors by colony in parentheses. All models includecolony fixed effects The first stage is identical for all second stages, hence only one first stage is printed in thepaper.

37

Table 8: Logged district infrastructure expenditure (1919-1939, in 1910 FRA)

(1) (2) (3) (4) (5) (6)

Pre-colonial trading post indicator 2.97∗∗ 3.03∗∗ -0.70 3.66∗∗ -0.09 3.35∗

(1.02) (1.05) (0.86) (1.34) (1.24) (1.33)European population, logged 1.19∗∗ 1.17∗∗

(0.15) (0.12)Locational fundamentals

Distance from the coast, in 100km -0.11 -0.09 -0.05 -0.24∗ -0.18∗ -0.22∗∗

(0.13) (0.11) (0.08) (0.10) (0.08) (0.05)Navigable river indicator (1910) -0.28 -0.20 -0.03 -0.27 -0.06 -0.34

(0.47) (0.42) (0.35) (0.43) (0.40) (0.45)Terrain ruggedness 0.84∗∗ 0.88∗∗ 0.40 0.81∗∗ 0.48 0.50†

(0.27) (0.28) (0.30) (0.23) (0.52) (0.26)Malaria prevalence index (1900) -0.32 -0.41 0.32 -0.32 0.47 -0.46

(0.35) (0.33) (0.35) (0.30) (0.32) (0.43)Tsetse fly prevalence index (1970) -0.21 -0.20 -0.06 -0.49 -0.20 -0.13

(0.32) (0.32) (0.27) (0.32) (0.26) (0.29)Natural resources and soil quality

Gold, silver or diamonds indicator (1920) 0.73∗ 0.63† 0.48† 0.51† 0.44†

(0.35) (0.37) (0.25) (0.28) (0.24)Base metals indicator (1920) 0.37 -0.06 0.14 -0.20 0.20

(0.33) (0.25) (0.35) (0.33) (0.35)Soil quality index (2000) 0.32 0.14 0.22 0.08 0.21

(0.22) (0.29) (0.24) (0.30) (0.29)(Pre-)Colonial socioeconomic characteristics

African population, logged 0.57∗∗ 0.09 0.52∗

(0.16) (0.24) (0.26)Area in km2, logged 0.67∗∗ 0.62∗ 0.51∗

(0.18) (0.29) (0.25)Prevalence of Islam (1910) -0.63∗ -0.37 -0.38

(0.30) (0.27) (0.25)Ethnic Fractionalization Index -1.33 -0.63 -0.97

(1.29) (0.83) (1.66)Agriculture (none to irrigation) 0.33 0.23 0.68∗∗

(0.29) (0.20) (0.23)Settlements (nomadic to complex) 0.07 -0.03 0.21

(0.19) (0.17) (0.18)Pre-colonial political centralization 0.23 0.24 -0.06

(0.27) (0.25) (0.32)Slavery (absence to prevalent) -0.49 -0.24 -0.90†

(0.41) (0.34) (0.48)Constant 5.44∗∗ 3.46∗ -1.09 -7.83∗ -7.48 -5.94

(1.70) (1.47) (1.91) (3.96) (5.12) (5.63)

Observations 312 312 301 312 301 228Adjusted R2 0.35 0.35 0.53 0.39 0.55 0.41Wald F Statistic 24.88 24.41 25.38 22.93 25.93 28.34

Notes: † p < 0.10, * p < 0.05, ** p < 0.01. Clustered standard errors by colony in parentheses. All models includecolony fixed effects

38

Table 9: Presence of a colonial railroad

(1) (2) (3) (4) (5) (6)

Pre-colonial trading post indicator -0.01 0.03 -0.38 -0.05 -0.45 0.18(0.23) (0.24) (0.28) (0.25) (0.28) (0.18)

European population, logged 0.14∗∗ 0.14∗∗

(0.01) (0.02)Locational fundamentals

Distance from the coast, in 100km -0.04† -0.04† -0.02† -0.04∗ -0.03∗ -0.04∗∗

(0.02) (0.02) (0.01) (0.02) (0.01) (0.02)Navigable river indicator (1910) -0.21∗∗ -0.20∗∗ -0.17∗∗ -0.21∗∗ -0.18∗∗ -0.21∗∗

(0.07) (0.07) (0.05) (0.06) (0.05) (0.07)Terrain ruggedness 0.03 0.05 -0.01 0.02 -0.02 0.05

(0.08) (0.09) (0.09) (0.08) (0.06) (0.15)Malaria prevalence index (1900) -0.08∗ -0.09∗∗ -0.01 -0.10∗∗ -0.02 -0.16∗∗

(0.03) (0.03) (0.03) (0.04) (0.04) (0.05)Tsetse fly prevalence index (1970) -0.03 -0.04 -0.02 -0.06 -0.02 -0.01

(0.05) (0.05) (0.04) (0.05) (0.05) (0.05)Natural resources and soil quality

Gold, silver or diamonds indicator (1920) 0.22∗ 0.21∗ 0.20∗ 0.20∗∗ 0.21∗

(0.09) (0.08) (0.08) (0.08) (0.08)Base metals indicator (1920) -0.01 -0.02 0.00 -0.00 -0.01

(0.06) (0.06) (0.07) (0.06) (0.07)Soil quality index (2000) 0.03 0.01 0.02 0.01 0.00

(0.03) (0.03) (0.03) (0.03) (0.03)(Pre-)Colonial socioeconomic characteristics

African population, logged 0.08∗ 0.02 0.11∗∗

(0.04) (0.03) (0.04)Area in km2, logged -0.04 -0.04 0.03

(0.05) (0.03) (0.03)Prevalence of Islam (1910) -0.02 0.03 0.11†

(0.08) (0.07) (0.06)Ethnic Fractionalization Index -0.26∗ -0.19† -0.40∗∗

(0.12) (0.10) (0.12)Agriculture (none to irrigation) 0.03 0.03 0.03

(0.03) (0.03) (0.04)Settlements (nomadic to complex) 0.01 -0.00 0.04∗

(0.02) (0.02) (0.01)Pre-colonial political centralization 0.04 0.03 0.02

(0.04) (0.03) (0.05)Slavery (absence to prevalent) -0.03 -0.02 -0.03

(0.04) (0.04) (0.04)Constant 0.77∗∗ 0.60∗∗ -0.01 0.19 0.17 -0.64

(0.21) (0.20) (0.21) (0.65) (0.30) (0.55)

Observations 312 312 301 312 301 228Adjusted R2 0.19 0.20 0.33 0.23 0.32 0.23Wald F Statistic 24.88 24.41 25.38 22.93 25.93 28.34

Notes: † p < 0.10, * p < 0.05, ** p < 0.01. Clustered standard errors by colony in parentheses. All models includecolony fixed effects

39

Table 10: Logged district students (1919-1939)

(1) (2) (3) (4) (5) (6)

Pre-colonial trading post indicator 4.46∗∗ 4.38∗∗ 2.25∗ 4.19∗ 2.39† 3.97∗

(1.31) (1.35) (1.02) (1.64) (1.36) (1.65)European population, logged 0.67∗∗ 0.55∗∗

(0.11) (0.14)Locational fundamentals

Distance from the coast, in 100km -0.02 -0.01 -0.00 -0.05 -0.03 -0.14∗∗

(0.10) (0.10) (0.08) (0.07) (0.05) (0.05)Navigable river indicator (1910) -0.27 -0.19 -0.01 -0.21 -0.07 -0.09

(0.23) (0.24) (0.13) (0.22) (0.18) (0.21)Terrain ruggedness 0.51 0.49 0.28 0.38 0.28 0.94†

(0.41) (0.40) (0.20) (0.43) (0.25) (0.48)Malaria prevalence index (1900) 0.02 -0.02 0.40∗∗ -0.16 0.20 -0.22

(0.19) (0.15) (0.14) (0.10) (0.14) (0.16)Tsetse fly prevalence index (1970) 0.16 0.19 0.25 -0.11 0.04 -0.33

(0.31) (0.29) (0.31) (0.23) (0.23) (0.26)Natural resources and soil quality

Gold, silver or diamonds indicator (1920) 0.11 0.07 -0.12 -0.09 0.01(0.30) (0.23) (0.23) (0.19) (0.30)

Base metals indicator (1920) 0.46† 0.16 0.30 0.06 0.13(0.25) (0.18) (0.21) (0.17) (0.19)

Soil quality index (2000) 0.27∗ 0.16 0.13 0.06 0.11(0.13) (0.13) (0.14) (0.12) (0.13)

(Pre-)Colonial socioeconomic characteristicsAfrican population, logged 0.83∗∗ 0.61∗ 0.70∗

(0.21) (0.24) (0.30)Area in km2, logged 0.10 0.01 0.20

(0.18) (0.09) (0.15)Prevalence of Islam (1910) -0.77∗∗ -0.59∗∗ -0.54∗

(0.20) (0.20) (0.27)Ethnic Fractionalization Index -1.00∗ -0.65∗ -1.04∗

(0.39) (0.25) (0.45)Agriculture (none to irrigation) 0.24∗∗ 0.22† 0.11

(0.08) (0.13) (0.10)Settlements (nomadic to complex) 0.16 0.07 0.09

(0.10) (0.12) (0.07)Pre-colonial political centralization -0.16 -0.17 -0.16

(0.16) (0.13) (0.21)Slavery (absence to prevalent) -0.02 0.12 -0.12

(0.13) (0.12) (0.13)Constant 0.97 -0.78 -3.16∗ -9.61∗∗ -8.57∗ -7.16†

(1.38) (1.74) (1.59) (2.95) (3.51) (4.20)

Observations 288 288 277 288 277 219Adjusted R2 0.39 0.40 0.57 0.50 0.61 0.54Wald F Statistic 28.04 26.47 27.22 22.94 26.09 29.25

Notes: † p < 0.10, * p < 0.05, ** p < 0.01. Clustered standard errors by colony in parentheses. All models includecolony fixed effects

40

Table 11: Logged district health staff (1919-1939)

(1) (2) (3) (4) (5) (6)

Pre-colonial trading post indicator 2.66∗∗ 2.64∗∗ 1.34∗ 2.81∗∗ 1.55∗ 3.10∗∗

(0.93) (0.94) (0.63) (0.99) (0.65) (1.07)European population, logged 0.42∗∗ 0.38∗∗

(0.06) (0.06)Locational fundamentals

Distance from the coast, in 100km 0.01 0.00 0.01 -0.04 -0.02 -0.03(0.06) (0.05) (0.03) (0.04) (0.04) (0.04)

Navigable river indicator (1910) 0.08 0.10 0.15 0.05 0.12 0.01(0.17) (0.19) (0.13) (0.16) (0.13) (0.17)

Terrain ruggedness 0.06 0.01 -0.16 -0.02 -0.14 0.12(0.16) (0.15) (0.11) (0.17) (0.13) (0.27)

Malaria prevalence index (1900) -0.10 -0.10 0.16∗ -0.18∗∗ 0.07 -0.24∗∗

(0.07) (0.07) (0.07) (0.07) (0.05) (0.08)Tsetse fly prevalence index (1970) 0.08 0.08 0.13 -0.03 0.07 0.18

(0.18) (0.18) (0.13) (0.20) (0.15) (0.19)Natural resources and soil quality

Gold, silver or diamonds indicator (1920) 0.02 -0.01 -0.07 -0.06 -0.07(0.15) (0.10) (0.16) (0.12) (0.17)

Base metals indicator (1920) 0.30∗ 0.16 0.21 0.12 0.17(0.12) (0.12) (0.13) (0.13) (0.16)

Soil quality index (2000) 0.02 -0.05 -0.01 -0.05 0.03(0.06) (0.05) (0.06) (0.04) (0.06)

(Pre-)Colonial socioeconomic characteristicsAfrican population, logged 0.36∗∗ 0.20∗∗ 0.44∗∗

(0.10) (0.07) (0.13)Area in km2, logged 0.10 0.07 0.12

(0.10) (0.06) (0.11)Prevalence of Islam (1910) -0.17 -0.06 0.01

(0.12) (0.07) (0.12)Ethnic Fractionalization Index -0.39† -0.13 -0.26

(0.23) (0.21) (0.26)Agriculture (none to irrigation) 0.23∗∗ 0.20† 0.14

(0.08) (0.11) (0.09)Settlements (nomadic to complex) 0.05 0.02 0.01

(0.05) (0.05) (0.05)Pre-colonial political centralization -0.09 -0.09 0.02

(0.09) (0.10) (0.06)Slavery (absence to prevalent) -0.13 -0.05 -0.12

(0.11) (0.10) (0.15)Constant 0.46 0.32 -1.30∗∗ -4.06∗ -3.70∗∗ -5.27∗

(0.58) (0.70) (0.47) (2.06) (0.87) (2.11)

Observations 311 311 300 311 300 227Adjusted R2 0.30 0.31 0.56 0.37 0.58 0.26Wald F Statistic 18.05 17.67 19.03 15.96 18.48 19.12

Notes: † p < 0.10, * p < 0.05, ** p < 0.01. Clustered standard errors by colony in parentheses. All models includecolony fixed effects

41

Table 12: Logged number of missions per district (1919-1939)

(1) (2) (3) (4) (5) (6)

Pre-colonial trading post indicator 0.95∗∗ 0.91∗∗ 0.55† 0.90∗∗ 0.64∗ 1.07∗∗

(0.28) (0.29) (0.29) (0.27) (0.28) (0.20)European population, logged 0.12∗∗ 0.08∗∗

(0.03) (0.02)Locational fundamentals

Distance from the coast, in 100km -0.02 -0.02 -0.01 -0.05† -0.04 -0.03(0.03) (0.03) (0.03) (0.03) (0.03) (0.04)

Navigable river indicator (1910) 0.05 0.06 0.07 0.02 0.03 -0.02(0.06) (0.06) (0.05) (0.05) (0.05) (0.05)

Terrain ruggedness 0.21 0.17 0.13 0.14 0.11 0.53∗∗

(0.25) (0.24) (0.19) (0.20) (0.17) (0.19)Malaria prevalence index (1900) -0.07 -0.07 0.00 -0.14∗ -0.09† -0.16∗∗

(0.07) (0.07) (0.07) (0.06) (0.05) (0.04)Tsetse fly prevalence index (1970) 0.15∗ 0.15∗ 0.17∗ 0.07 0.10† 0.08

(0.06) (0.06) (0.07) (0.05) (0.05) (0.07)Natural resources and soil quality

Gold, silver or diamonds indicator (1920) -0.08 -0.09 -0.15 -0.15 -0.11(0.08) (0.08) (0.11) (0.11) (0.10)

Base metals indicator (1920) 0.21∗∗ 0.17∗ 0.15† 0.14 0.05(0.08) (0.08) (0.08) (0.09) (0.08)

Soil quality index (2000) 0.02 0.00 -0.01 -0.02 -0.00(0.04) (0.04) (0.04) (0.04) (0.04)

(Pre-)Colonial socioeconomic characteristicsAfrican population, logged 0.19∗∗ 0.15∗∗ 0.19∗∗

(0.05) (0.05) (0.05)Area in km2, logged 0.04 0.03 -0.00

(0.04) (0.04) (0.03)Prevalence of Islam (1910) -0.15∗∗ -0.13∗ -0.13†

(0.06) (0.06) (0.07)Ethnic Fractionalization Index -0.09 -0.04 -0.07

(0.17) (0.15) (0.17)Agriculture (none to irrigation) 0.17∗∗ 0.16∗∗ 0.15†

(0.06) (0.06) (0.09)Settlements (nomadic to complex) 0.07∗∗ 0.06∗ 0.03

(0.02) (0.02) (0.03)Pre-colonial political centralization -0.01 -0.01 -0.07

(0.06) (0.07) (0.05)Slavery (absence to prevalent) -0.05 -0.04 -0.11

(0.07) (0.07) (0.10)Constant 1.19∗∗ 1.06∗∗ 0.60 -1.45∗ -1.33∗ -0.79

(0.26) (0.36) (0.44) (0.65) (0.67) (0.94)

Observations 312 312 301 312 301 228Adjusted R2 0.38 0.39 0.45 0.49 0.51 0.48Wald F Statistic 24.88 24.41 25.38 22.93 25.93 28.34

Notes: † p < 0.10, * p < 0.05, ** p < 0.01. Clustered standard errors by colony in parentheses. All models includecolony fixed effects

42

Figure 12: Persistence of public investments in British East and West Africa

pre-1930

1938

0

5

Schools, logged

pre-1930

1938

0

5

10

Students, logged

pre-1930

1938

0

5

10

15

Infrastructure investments, logged

pre-1930

1938

0

2

4

6

Health staff, logged

Table 13: Logged district infrastructure expenditure (1919-1939, in 1910 FRA)

(1) (2) (3)

First British/French pre-colonial trading post 1.83∗ 1.76†

(0.69) (0.96)Distance from first British/French trading post, in 100km -0.18† -0.15∗

(0.09) (0.07)First British/French pre-colonial school 1.30 0.10

(0.87) (1.05)Distance from first British/French school, in 100km -0.13 -0.05

(0.11) (0.05)Constant 10.08∗∗ 9.98∗∗ 10.14∗∗

(0.31) (0.36) (0.37)

Observations 312 312 312Adjusted R2 0.37 0.35 0.36

Notes: † p < 0.10, * p < 0.05, ** p < 0.01. Clustered standard errors by colony inparentheses. All models include colony fixed effects

43

Table 14: Logged district students (1919-1939)

(1) (2) (3)

First British/French pre-colonial trading post 1.03† 0.81†

(0.51) (0.42)Distance from first British/French trading post, in 100km -0.18∗ -0.17∗∗

(0.06) (0.05)First British/French pre-colonial school 0.94† 0.35

(0.52) (0.39)Distance from first British/French school, in 100km -0.11 -0.02

(0.08) (0.03)Constant 5.97∗∗ 5.76∗∗ 5.99∗∗

(0.20) (0.26) (0.25)

Observations 288 288 288Adjusted R2 0.43 0.41 0.43

Notes: † p < 0.10, * p < 0.05, ** p < 0.01. Clustered standard errors by colony inparentheses. All models include colony fixed effects

Table 15: Logged district health staff (1919-1939)

(1) (2) (3)

First British/French pre-colonial trading post 0.86∗ 0.71†

(0.35) (0.35)Distance from first British/French trading post, in 100km -0.08∗∗ -0.06∗∗

(0.02) (0.02)First British/French pre-colonial school 0.71† 0.23

(0.35) (0.29)Distance from first British/French school, in 100km -0.07∗ -0.03∗

(0.03) (0.01)Constant 2.22∗∗ 2.18∗∗ 2.25∗∗

(0.08) (0.10) (0.07)

Observations 311 311 311Adjusted R2 0.37 0.35 0.37

Notes: † p < 0.10, * p < 0.05, ** p < 0.01. Clustered standard errors by colony inparentheses. All models include colony fixed effects

44

Table 16: Logged number of missions per district (1919-1939)

(1) (2) (3)

First British/French pre-colonial trading post 0.08 -0.07(0.18) (0.15)

Distance from first British/French trading post, in 100km -0.04† -0.02†

(0.02) (0.01)First British/French pre-colonial school 0.18 0.22

(0.16) (0.15)Distance from first British/French school, in 100km -0.05∗∗ -0.04∗∗

(0.02) (0.01)Constant 0.26∗∗ 0.28∗∗ 0.31∗∗

(0.06) (0.05) (0.06)

Observations 312 312 312Adjusted R2 0.37 0.38 0.38

Notes: † p < 0.10, * p < 0.05, ** p < 0.01. Clustered standard errors by colony inparentheses. All models include colony fixed effects

Table 17: Disparities in educational investments per district (1915-1939)

(1) (2) (3) (4)levels (eq. 5) FD (eq. 6) levels (eq. 5) FD (eq. 6)

Teachers, lagged 0.86∗∗

(0.03)Teachers in 1915 0.17∗∗ 0.19∗∗

(0.04) (0.07)Teachers, logged first lag 0.64∗∗

(0.04)Teachers in 1915, logged 0.31∗∗ -0.01

(0.04) (0.03)Constant -0.04 0.01 0.08∗ 0.10∗

(0.09) (0.31) (0.03) (0.05)

Observations 374 749 374 749

Standard errors in parentheses† p < 0.10, ∗ p < 0.05, ∗∗ p < 0.01

45

Table 18: Disparities in health investments per district (1915-1939)

(1) (2) (3) (4)levels (eq. 5) FD (eq. 6) levels (eq. 5) FD (eq. 6)

Health staff, lagged 0.40∗∗

(0.08)Health staff in 1915 0.82∗∗ 0.04

(0.09) (0.10)Health staff, logged first lag 0.61∗∗

(0.06)Health staff in 1915, logged 0.35∗∗ 0.01

(0.06) (0.04)Constant -0.70 0.08 0.05 -0.01

(0.44) (0.90) (0.06) (0.07)

Observations 178 305 178 305

Standard errors in parentheses† p < 0.10, ∗ p < 0.05, ∗∗ p < 0.01

Table 19: Disparities in infrastructure investments per district (1915-1939, in 1910 FRA)

(1) (2) (3) (4)levels (eq. 5) FD (eq. 6) levels (eq. 5) FD (eq. 6)

Infrastructure investments, lagged 0.32∗∗

(0.07)Infrastructure investments in 1915 0.27∗ -0.45∗∗

(0.13) (0.12)Infrastructure investments, logged first lag 0.67∗∗

(0.07)Infrastructure investments in 1915, logged 0.11 -0.01

(0.07) (0.07)Constant 9770.81∗ 1795.77 1.71∗∗ -0.23

(3991.53) (4288.49) (0.32) (0.38)

Observations 181 223 181 223

Standard errors in parentheses† p < 0.10, ∗ p < 0.05, ∗∗ p < 0.01

Table 20: Average colonial district infrastructure expenditures (in 1910 FRA)

Pre-colonial trading postYes No

Natural harboror cape

Yes357,723FRA 96,441FRA

n = 12 n = 9

No189,235FRA 38,416FRA

n = 10 n = 25

Some districts (e.g. Accra, Ouidah) without natural harbors or capes nonetheless were centers of pre-colonial trade.That became easier with 19th century technological advances in the Age of Steam. While the number of districts ineach cell is small, colonial investments were relatively low in places that did were not centers of pre-colonial tradeeven if they possessed a natural harbor or cape (e.g. Boke).

46

Table 21: Covariate balance between coastal districts with pre-colonial trading posts (Yes) andthose without (No)

(1) (2) (3)No Yes p-value

Natural harbor or cape indicator 0.265 0.545 0.034Number of ethnic groups in the district 4.412 2.909 0.069Gathering 0.202 0.061 0.090Hunting 0.706 0.949 0.102Fishing 1.298 1.267 0.884Intensity of agriculture (none to irrigation) 3.168 2.906 0.261Settlement patterns (nomadic to complex) 6.275 6.565 0.515Political centralization (acephalous to kingdoms) 2.282 2.465 0.476Slavery (absence to prevalent) 2.251 2.287 0.836Prevalence of Islam (1910) 0.618 0.545 0.753Malaria prevalence index (1900) 2.911 3.233 0.134Tsetse fly prevalence index (1970) 1.663 1.858 0.472

N=56. 22 districts had a pre-colonial trading posts; 34 did not.

The table shows balance along a set of mostly pre-colonial covariates except in the instrument, as expected, andmarginally (p < 0.1) in the number of ethnic groups and in the importance of gathering as an economic activity asdefined in Murdock (1959).

Table 22: Covariate balance between coastal districts with natural harbor or capes (Yes) and thosewithout (No)

(1) (2) (3)No Yes p-value

Number of ethnic groups in the district 4.257 3.095 0.167Gathering 0.104 0.218 0.180Hunting 0.869 0.690 0.234Fishing 1.106 1.585 0.022Intensity of agriculture (none to irrigation) 2.936 3.279 0.143Settlement patterns (nomadic to complex) 6.248 6.626 0.400Political centralization (acephalous to kingdoms) 2.343 2.372 0.909Slavery (absence to prevalent) 2.268 2.262 0.972Prevalence of Islam (1910) 0.543 0.667 0.592Malaria prevalence index (1900) 3.104 2.927 0.419Tsetse fly prevalence index (1970) 1.822 1.603 0.420

N=56. 21 districts have a natural harbor or cape; 35 do not.

The table shows balance along a set of mostly pre-colonial covariates except in fishing, measured as percentage ofthe population of the ethnic group engaged in fishing as defined in Murdock (1959).

47

Figure 13: Public expenditures by district in British colonies (1920-1938, in 1910 FRA)

050

0000

1.0e

+06

1.5e

+06

Krach

i

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a

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aw

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010

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020

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arri

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0015

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ro

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00 2000

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mes

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Aberc

orn

Kalom

o

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stone

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ka

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48

Figure 14: Public expenditures by district in French colonies (1910-1939, in 1910 FRA)

050

,000

1000

0015

0000

Allada

Djougo

u

Ataco

ra

Ouidah

Moy

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ono

Coton

ou

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ey

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Benin

05,

000

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,000

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00

Kaya

Koudo

ugou

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Gaoua

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Bobod

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so Dori

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ou

Burkina Faso

050

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m

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Cote d'Ivoire

050

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bia

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Mac

enta

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ou

Kanka

n

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Guinea

050

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2000

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Satad

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010

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ance

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49

Figure 15: Students by district in British colonies (1920-1938))0

1,00

02,

000

3,00

04,

000

Bekwai

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Krach

i

Lawra

Navro

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kavir

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Mac

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Mom

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-11

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Lower

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a

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Note: British colonial records do not provide disaggregated education data for Malawi.

50

Figure 16: Students in French colonies (1910-1939)0

500

1,00

01,

500

Djougo

u

Ataco

ra

Moy

ennig

er

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u

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050

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150

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an

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Bilma

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z

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daBak

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am

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l

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ane

Loug

a

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Thies

Sinesa

loum

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ance

Saintlo

uis

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Senegal

Note: Student data for Mauritania is missing in Huillery (2009)

51

Figure 17: Public health staff by district in British colonies (1920-1938))

050

100

150

Ashan

ti

Krach

i

Mam

pong

Obuas

i

Lawra

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i

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a

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aw

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02

46

810

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Lower

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Malawi

020

4060

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Adam

awa

Bauch

i

Benue

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a

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o

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r

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o

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010

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Kailah

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020

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Mas

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iji

Bagam

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Miki

ndan

i

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hi

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a

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salaa

m

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050

100

150

200

250

Karam

oja

Mub

ende

Budam

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o

Lang

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o

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Ankole

Centra

l

Mas

aka

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Busog

a

Kigezi

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ro

Men

go

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02

46

Aberc

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le

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li

Fortja

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o

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bwa

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u

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Mum

bwa

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ke

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ga

Seren

je

Seshe

ke

Solwez

i

Kasam

a

Mon

gu

Broke

nhill

Living

stone

Lusa

ka

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52

Figure 18: Public health staff in French colonies (1910-1939)

010

2030

40

Allada

Ataco

ra

Borgo

u

Moy

ennig

er

Djougo

u

Savalo

uM

ono

Ouidah

Abom

ey

Coton

ou

Porto

novo

Benin

010

2030

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Fada

Dori

Kaya

Tenko

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Ouahig

ouya Say

Koudo

ugou

Dedou

gou

Gaoua

Bobod

ioulas

so

Ouaga

doug

ou

Burkina Faso

010

2030

40

Guiglo

Tagou

anas

Inde

nie

Segue

la

Odienn

e

Assini

e

Bondo

ukou

Tabou

Agneb

y

Gouro

s

DaloaKon

g

Laho

u

Sassa

ndra

Man

Nzicom

oe

Bassa

m

Baoule

Lagu

nes

Cote d'Ivoire

05

1015

Gueck

edou

Mac

enta

Nzere

kore

PitaBey

la

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fa

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nLa

be

Siguiri

Dabola

Forec

ariah

Mam

ou

Kouro

ussa

Boke

Kindia

Conak

ry

Guinea

020

4060

80

Bafou

labe

Nema

Bougo

uni

Gound

am

Koutia

laKita

Bandia

gara

Gao

Niafun

keNior

o

Segou

Mac

ina San

Gourm

aNar

a

Tombo

ucto

u

Satad

ougo

u

Sikass

oM

opti

Kayes

Bamak

o

Mali

02

46

Guidim

aka

Assab

a

Baiedu

levrie

r

Gorgo

l

Adrar

Brakn

a

Tagan

t

Trarz

a

Mauritania

05

1015

Tahou

a

Goure

Agade

z

Tessa

oua

Bilma

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i

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020

4060

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gam

bieBak

el

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a

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da

Mat

amLo

uga

Baol

Tivaou

ane

Casam

ance

Sinesa

loum

Dakar

Thies

Saintlo

uis

Senegal

53

B Historical materials

Figure 19: Pages of a Blue Book page for Uganda, 1945 (left) and of a Compte Definif for Benin,1928 (right)

COMPTE DÉFINITIF 1928 xxxm

3 W S CRÉDITSS J O

DEPENSESS u «J ' parPu H S NATURE DES DEPENSESOBSERVATIONS<i pi <; AUTORISATIONX <; pq FAITESo < '

des dépensesa.

Cercle de Cotonou.II 17 Entretien des routes et ponts 10.700 » 10.565 70 Entretien des routes.

8 Entretien des marchés et caravansérails ; 3.000 » 2.717 82 Réparation ie la toiture du mar-9 Entretien des immeubles, gîtes d'étapes et : ché de cotonou et uadigconnage.puits 1.250 » 350 50 Réparation do l'école de fiodomey,

, . crépissage des murs et badigeonnage.Totaux. 14.950 » 13 634 02

17 2 2 Autres dépenses imprévues. —Aménagement

d'un champ d'aviation entre Kadjèhoun etGodomey 3 084 » 2 084 » Aménagement d'un champ d'avia-

tion entre Kadjèhoun et (iodomey.Totaux 3.084 » 2.084 »

20 2 1 Construction de ponts et de routes dans lescercles 25.200 » 24.773 50 Grosses réparations a la route de

, Cotonou.Totaux 25.200 » 24.773 50

Cercle de DjougouIII 7 Entretien des routes et ponts 37.000 » 19.608 » Travaux courants d'entretien des

routes Djougou-Sèméré-Djougou-Onklou sur tout leur parcourt).

8 Entretien des mai chés et caravansérails. 1.000 » 020 » Entretien courant des bâtiments dumarché et <lu caravansérail. Réfec-tion de toiture.

10 Entretien des cimetières 125 » »H Réparations et transformation des Tribunaux

indigènes 225 » 200 » Travaux d'entretien divers, maçon-~nerie et toitures.

Totaux 38.350 » 20.428 »

2 3 Construction de bâtiments pour logement defonctionnaires 4.000 » 610 » Construction d'un pavillon de 3

pièces avec vôrandah, toiture chaume.

Totaux 4 000 » 010 »

20 2 l Construction de pnnts et routes nouvelles.' 32.000 » 16.148 » Construction de la route de Djou-, gou a N'Dali.

Totaux 32.000 » 16.148 »

6 3 Travaux divers aux postes médicaux... 5 000 » 4.180 » construction d'un dispensaire ar, Djougou.

Totaux 5.000 » 4.180 »

Cercle du Borgou.11 1 7 PntrptiPii HP« rontP< Pt nnntt; k\ 000 » 49 876 » Entretien et amélioration desroules/ entretien aes roiue» et ponts -il.UUU » i».o/u

,iu cercle. Remise en état de la routeNikki. Kallé, route Guessou, Senendé.Construction d'un pont sur rivièreKala, réfection des ponts du cercle.

8 Entretien des marchés et caravansérails 1.000». 1-009»decgKcu^^en8e^ffr "^tion des toits des caravansérails des

9 Entretien des immeubles, gîtes d'étapes et subdivisions.puits .. . 4 750 » 4.697 » lîntretiendes immeubles du cercle. * '

et creusement des puits du poste.Construction d'un gîte d'étapes â

Bori.10 Fntfpripn HPS pimptipr'PS 250 » 235 » Crépissage et blanchiement des1U entretien aes Cimctieies ~ou » ~o<;

tombes, réfection du mur d'entou-, , „ , . . . . t m •i. raSe â Bem béréké.11 Réparation et transformation des Tribunaux ~, ,. . m .. , ..f.. ,„ . ocn „. -JKO » Transformation du Tribunal deindigènes 3oO » dou

Parakou. Réfection toiture, aména-

fementsalle d'audiences. Crépissage

1V, „ es murs.

54

Figure 20: Colonial map of Nigeria (1948)

Figure 21: Natural resources in West Africa

55