Colonialism and Its Legacies:
A Comprehensive Historical Dataset
A PROJECT FUNDED BY THE NATIONAL SCIENCE FOUNDATION
(PROPOSALS 0648292 and 0647921)
Principal investigators:
John Gerring, Department of Political Science, Boston University
James Mahoney, Department of Sociology and Political Science, Northwestern University
Collaborators:
Paul Barclay, Department of History, Lafayette College
Neil Englehart, Department of Political Science, Bowling Green State University
Andrew Harris, Department of Government, Harvard University
Charles Kurzman, Department of Sociology, University of North Carolina
James Robinson, Department of Government, Harvard University
Nicolas van de Walle, Department of Government, Cornell University
The current era of globalization began in 1415 with the Portuguese conquest of Ceuta (off the coast
of Morocco), the first enduring overseas European colony. In subsequent centuries, AngloEuropeans and Japanese managed to control, at various times, virtually the entire inhabitable planet.
Only a few nations escaped direct control of these colonial powers. None escaped their spheres of
influence.
It has become commonplace to observe that the colonial experience shaped the modern era
in profound ways. Colonial policies and practices are widely blamed for the underdevelopment of
the South, the absence of significant industrialization, ethnic strife, weak state capacity, authoritarian
rule, weak national identity, diffuse and porous borders, hunger, illiteracy, and corruption.
Interestingly, colonialism is also sometimes praised for furthering social, political, and economic
development in the South. Indeed, it is a central issue of dispute in the scholarly community
whether colonialism fostered, or delayed, the development of the regions that it touched.
Africa provides a stunning example of these directly contradictory arguments.
Conventionally, Africa’s developmental prospects were thought to have been hindered by colonial
interference (e.g., Young 1994). Yet, the striking fact is that Africa experienced considerably less
colonial intervention than most parts of the world. This has led some writers to claim, at least
implicitly, that Africa’s problems at the present time are attributable to insufficient colonial influence
(Herbst 2000; Mamdani 1996). In short, while there is general agreement that “colonialism
mattered,” it is less clear what the long-term effects of this traumatic intervention actually have been.
Indeed, the virulence of scholarly and popular opinions about colonialism is matched only by the
inconclusiveness of current research (contrast Alam 2000 and Grier 1999).
Given that colonialism is a complex subject and evokes strong feelings it is perhaps not
surprising that the extensive study devoted to this weathered subject across the fields of the social
sciences has not rendered a clear verdict on its legacy. Contributing to the inconclusiveness of this
research are certain persistent methodological problems associated with the two dominant strategies
of research – the case study (area study) and the global study (crossnational study) – which we now
briefly review.
Usually, work on colonialism follows a case study approach that involves the intensive study
of a particular country or region of the world (e.g., Brown 2000; Young 1994), or a particular
colonial power (e.g., Armitage 2000). Alternatively, an author or a group of authors may cover the
globe but do so with a series of case study analyses (e.g., Chamberlain 1998; Kohli 2004). While
there are advantages to this way of approaching the subject – and our own approach builds selfconsciously on precisely this sort of work – case study work is not designed to estimate typical
causal effects for large populations. Rather, the goal of this literature is to understand the causes and
consequences of colonialism in delimited contexts, leaving questions about general causal effects for
other types of research. Thus, while case studies have taught us a great deal about the effects of
colonialism in particular places and at particular times, they have not told us whether colonialism
overall had a positive or negative impact on the non-western world, or in what specific ways it
affected that world as a whole. Given that many writers do presume general effects, not simply
contextually specific effects, there is a prima facie case for a general (global) approach to the
problem of colonialism. In any case, to learn whether colonialism left behind general effects cannot
be determined by mere assertion; it requires comprehensive evidence in the form of a global dataset.
The same may be said for the claim of historical specificity; “different effects in different places” is
not a hypothesis that can be proven without systematic testing across all the cases (or a significant
sub-sample thereof).
The opposite difficulty is posed by the (rather few) studies of colonialism that are truly
global in scope – where a general hypothesis is tested across all developing countries. The problem
here is that the subject of interest, colonialism, is usually reduced to a single dimension, e.g., a) a
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dummy variable registering the predominant colonizer of a country (La Porta et al. 1999), b) a
measure of “settler” versus “extractive” colonialism (Acemoglu, Johnson & Robinson 2001), or c) a
variable measuring the number of years a country was under colonial control (Grier 1999). These
sorts of studies are useful, though preliminary, attempts to systematize hypotheses drawn from the
case study literature. However, they scarcely exhaust the topic. Indeed, the true effects of
colonialism may not be apparent from an approach that reduces the topic to one or two dimensions.
Consider the debate over the relative importance of colonial institutions (Acemoglu,
Johnson & Robinson 2001, 2002; North 1981) and geography (Diamond 1997; Olsson & Hibbs
2000; Sachs & Warner 1997) in structuring long-run economic development. While the
measurement of geographic factors has advanced to include a host of highly differentiated variables
(e.g., climate, soil, native perennial wild grasses, disease vectors, domesticable mammals, continental
axis, latitude), the measurement of colonialism has been stuck at one or two (as listed above).
Consequently, it has been impossible to provide a fine-grained test of the colonialism hypothesis.
Likewise, students of political regimes have long suspected that patterns of colonialism may
strongly shape national prospects to establish and maintain democracy. For example, cross-national
statistical research has found a relationship between colonial status and subsequent regime history
(e.g., Bernhard, Reenock & Nordstrom 2004; Bollen 1979; Bollen & Jackman 1985). However,
these findings are based on the use of one or two variables: dummies for the identity of the colonial
power and (in some studies) a variable measuring the number of years under colonial rule. These are
not unsophisticated studies in other respects, but their measurement of the key hypothesis is strictly
circumscribed.
A survey of the immense literature on colonialism thus reveals two truisms: 1) the case
study literature is informative but also un-systematic and 2) the crossnational literature is systematic
but not very discriminating. As a consequence, the topic of colonialism suffers from simultaneous
promiscuity and neglect. Although the subject is ubiquitous in the contemporary fields of
anthropology, economics, history, political science, and sociology, it is rarely studied in a detailed and
systematic manner.
Our ambition is to marry the virtues of case study and crossnational approaches so that the
influence of colonialism on the modern world (whatever that may be) can be measured in ways that
are satisfying to scholars working with in-depth historical studies as well as global datasets.
Specifically, we propose to develop a comprehensive dataset on colonialism, a cross-national timeseries study that will stimulate future research concerning the causes and effects of colonialism by
scholars in all fields of the social sciences -- regardless of method, theoretical framework, or
substantive area of interest.
Our focus is on Anglo-European and Japanese overseas colonialism since the fifteenth
century. We intend to measure the type and degree of involvement of various colonial powers
across a variety of dimensions that may be expected to influence subsequent development
trajectories. We suggest that good data on these dimensions alone would go a long way toward
enabling scholars to test key hypotheses related to colonialism.
In addition to activities directly tied to colonialism, we seek to put together data on other key
historical variables that might be relevant to scholars who study colonialism and long-term patterns
of development. These ancillary topics fall into five broad categories – geography, economics and
demography, human development, the state, and the nation – described at length in the appendix to
this document.
Without such a comprehensive historical dataset we lack the means to adjudicate among
rival causal hypotheses. What has colonialism wrought? Under what circumstances might
colonialism leave favorable or unfavorable legacies? Is the causal effect of colonialism to be
discovered in the immense variety of colonial experiences? If so, how shall we understand these
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experiences, and judge their effects? How might the various eras of globalization be compared with
each other? Was colonialism in the eighteenth century, for example, significantly different from
colonialism in the nineteenth and twentieth centuries? These kinds of questions motivate the data
collection effort of this study. We hope not only to elucidate the fraught subject of colonialism but
also to shed light on long-term patterns of development, a subject usually hostage to late-twentieth
century datasets.
The study also has an implicit methodological goal. It is often noted that the field of
comparative politics is rent by a central cleavage separating cross-national statistical researchers, who
work with global datasets drawn from the postwar era, and historical institutionalists, who work with
historical materials drawn from a single region or a small set of countries (Pierson & Skocpol 2002).
The project at hand attempts to bridge these two camps, integrating the salient features of in-depth
historical accounts into a single global dataset that stretches back over the centuries. As such, we
hope it will provide a new way of doing business in the social sciences, one that is acceptable and
accessible to both qualitative and quantitative researchers.
The Dataset Problem
Given that the work of social science is increasingly global in scope it is not surprising that global
datasets have played an increasingly important role in the disciplines of political science, sociology,
and economics. A short list of the most important and most frequently utilized datasets in these
fields would include the following: Correlates of War, Cross-National Time-Series Data Archive,
Penn World Tables, Polity IV, State Failure Task Force, and World Development Indicators (see
Table 1). Scholars rely on these datasets -- and many others -- for a wide range of tasks. They
perform roughly the same role for comparativists that standard surveys such as the National
Election Study and the General Social Survey perform for Americanists.
Yet, despite the prominence of crossnational data in contemporary research, existing datasets
suffer from three generic problems. First, they are limited in temporal scope. Few global datasets
reach back before 1950, and only three extend to the early nineteenth century. We have not found a
single widely used global dataset that extends into the eighteenth century. Given that work in the
social sciences increasingly deals with causal and descriptive propositions that extend back to the
Enlightenment or to earlier historical eras, this lack of historical coverage may be regarded as a
monumental lacuna.
Second, existing datasets often suffer from ambiguity about their sources, coding
procedures, and the methods of aggregation. Thus, although crossnational datasets have become
staples of scholarly research, it is with considerable unease that scholars employ their variables.
While some of these faults are inherent to the enterprise – collecting data globally is, after all, a
daunting task – others may be corrected through careful attention to coding decisions, the use of
multiple sources, the recording of data in disaggregated form, detailed recording of procedures, and
– perhaps most important of all -- reliance on the expertise of country specialists. These
methodological issues are discussed at length below.
Third, existing datasets do not address the issue of colonialism in any detail. At best, a single
dichotomous variable for principal colonizer is included (e.g., British colonial origin). Thus, our
dataset would constitute the first attempt to systematically examine and record the imprint of
colonialism on the modern world.
The need for a comprehensive historical dataset that stretches back to the pre-modern era,
and treats colonialism in a more differentiated fashion, seems clear.
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Table 1:
A Sample of Extant Global Datasets
Dataset
Subjects
Years
Source, Location
Correlates of War
International
relations
1815-
Singer, Diehl (1990)
www.umich.edu/~cowproj/
Cross-National Time-Series
Data Archive
Comparative
politics
1815-
Banks (1994)
http://www.databanks.sitehosting.net/
Penn World Tables (PWT)
Economics
1950-
Heston, Summers (1991)
www.bized.ac.uk/dataserv/penndata/pennhome.htm
Polity IV
Democracy,
governance
1800-
Marshall, Jaggers (2002)
www.cidcm.umd.edu/inscr/polity/
State Failure Task Force
International
relations
1955-
Goldstone et al. (2000)
www.cidcm.umd.edu/inscr/stfail/
World Development
Indicators (WDI)
Economics,
demography
1960-
World Bank (2003)
www.worldbank.org/data/wdi2002/
Hypotheses
In order to direct the data-collection process it is necessary to establish priorities. Which descriptive
patterns and causal relationships warrant attention? What sorts of evidence can be coded
numerically or in natural-language categories such that broader features of social, political, and
cultural development across the world can be better understood?
Data collection is always, at least implicitly, motivated by theory. Even so, we are wary of
macro-theoretical frameworks that might limit the utility of the resulting dataset for scholars
working in other schools and genres. To this end, we wish to avoid an overly “theoretical”
vocabulary that would identify this as a project emanating from Marxist, world-systems, Weberian,
neoclassical, or some other theoretical framework. In this light, our approach is fairly close-to-theground.
It should be clear that the purpose of this investigation is not solely to explore causal
relationships, but also descriptive patterns. Colonialism is of great intrinsic significance, influencing
our views on a wide range of present-day phenomena, e.g., globalization, North/South relations,
slavery, development, and what some have called “neo-imperalism.” Many of the assertions at issue
in these contemporary debates concern what? questions, rather than (or in addition to) why?
questions. Thus, our initial hypotheses, listed in Table 2 (below), include both causal and descriptive
inferences.
It is our hope that, once completed, the data included in this project will generate new
hypotheses. This, in turn, will undoubtedly stimulate further collection of data (which we expect will
5
be integrated into the dataset). Social science is a dynamic process. But one must start somewhere.
We offer the following list of hypotheses in an open-ended spirit, as a point of departure for future
work on colonialism and long-term development.
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Table 2:
Hypotheses
COLONIALISM
British/other
Japan/other
Africa/other
Property rights
Population
density
Extractable
resources
British rule was different – more decentralized, more indirect, more democracy, and/or better
governance.
Japanese rule was different from European rule – more interventionist, more developmental.
Africa was less intensively colonized than Latin America, South and Southeast Asia.
Property rights were more likely to be established in colonies that attracted large numbers of settlers
(AJR 2001, 2002).
Densely settled indigenous areas were less likely (AJR), or more likely (Sokoloff & Engerman 2000),
to be targets of settlement by Europeans.
Regions with readily extractable resources (e.g., gold) were subject to more European settlers (a
common assumption in the literature on Latin America).
DEVELOPMENT
British rule
Territorial
continuity
Colonial
intervention
Type of
colonization
Property rights
Property rights
and conflict
British colonialism, by virtue of its greater local democracy, indirect rule, and/or effective civil
service, leads to greater development.
Continuity of borders, or at least the endurance of a “core” region within the colony, allows for a
more successful transition during the post-independence era, and hence to greater development.
Greater colonial intervention causes greater (Alam 2000; Grier 1999), or lesser (Young 1994),
development.
Directly-ruled settler colonies have the strongest developmental performance; indirectly-ruled nonsettler colonies have the worst.
Areas with well established property rights experienced greater subsequent development (AJR 2001,
2002; North 1981).
Reification of customary norms governing access to land and property in colonial law generated
conflict over interpretation and enforcement of such laws (Chanock 1998, Colson 1974).
DEMOCRACY
British rule
Direct/indirect
rule
Colonial
settlement
Pre-colonial
legislatures
British rule encouraged local- and national-level democracy, thus establishing norms and procedures
that would help democracy survive in the post-independence era (Bernhard, Reenock & Nordstrom
2004; Bollen and Jackman 1985; Lipset et al., 1993; Weiner 1987).
Directly ruled colonies are more democratic later on because direct rule destroys traditional (and
often undemocratic) power-holders (e.g., chiefs).
European settlement produces democracy.
Experience with democratic procedures through pre-colonial legislatures helps to establish and
protect democratic norms in the post-independence era.
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Constructing the Dataset
There is no simple recipe for designing and pursuing a successful data collection project. Existing
crossnational datasets, discussed above, provide both exemplary models and cautionary tales. They
are exemplary insofar as they manage to capture, in quantitative form, a variety of indispensable
concepts commonly used in comparative analysis. They are worrisome insofar as they have often
failed to provide adequate explanation of their coding procedures and are subject to important
measurement errors (see, e.g., Munck and Verkuilen 2002).
We aim to provide a more careful and thorough – and consequently a more useful – dataset
that includes a detailed codebook with all concepts, definitions, and primary and secondary sources
employed. Our intention is to remain as close to the ground as possible in our coding decisions,
which is to say that aggregated concepts will be employed only in conjunction with their component
(disaggregated) parts, so that future scholars can re-visit the ground that we cover. In this manner,
most coding decisions can be easily revised.
Variables
Our hope is to identify dimensions of politics, economy, and society that are valid across time and
across regions. Ideally, these measures would also be applicable to a variety of political units
including empires, nation-states, city-states, colonies, and so forth. Of course, we do not imagine
that data will be equally available, or equally informative, for these diverse units. The point, rather, is
that the coding categories should be valid.
Variables are divided into six general categories: 1) colonial rule, 2) politics, 3) geography, 4)
economics and demography, 5) human development, and 6) society. Granted, these categories are
somewhat arbitrary. Indeed, future researchers may choose to divide up the subject in quite
different ways. Fortunately, the six-part division of subjects does not affect the substantive goals of
the project in any way.
A complete list of variables, along with their definitions and potential sources, can be found
in the appendix. Note that most variables could be conceptualized alternately as explanatory
variables, control variables, or outcome variables, depending upon one’s theoretical proposition.
Only a few, such as several of the geographic variables, are entirely exogenous.
Note also that some variables are invariant, or occur only at one period of time (e.g., at the
moment of the initial colonial encounter). These will be static (identical in all years of the timeseries dataset). Most variables of interest experience some change over time and are thus properly
coded in a time-series format. Some time-series variables, such as GDP, are available only for
contemporary years (Angus Maddison’s estimates extend back to 1800; solid annual data for a broad
cross-section of countries begins in 1950). We choose to include such variables, even though they
cannot be extended over the entirety of our chosen time-period. (This means that the resulting
panel will be “unbalanced.”) A final category of variables will be composed from various time-series
indicators to allow for a summary measure of a country’s overall colonial or development
experience, as discussed below.
Coding
Our intention is to collect data on “natural” units of analysis, as defined by primary and secondary
sources, leaving the task of aggregation for a later stage. This means that we must deal with a wide
variety of units of analysis -- empires, continents, cultural zones, nation-states, subnational regions,
cities, and so forth. These units overlap and, in many instances, change over time. The British
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Empire in 1700 evidently refers to a very different geographic entity than the British Empire in
1800.
In order to incorporate this complexity into the basic architecture of the dataset we plan to
employ GIS mapping software. Evidently, we need to find a data structure that can preserve the
original units of analysis (as drawn from primary sources) while offering the possibility of multiple
aggregation techniques. For example, we need to be able to reconstruct the history of present-day
nation-states, whatever their previous geo-political identities might have been.
Temporal units of analysis are also complicated. Evidently, data for many of our variables
are available only at very irregular intervals. At the same time, a few variables are much more
precisely dated. In many cases, for example, we know the exact year and day that colonial
administrators assumed office (Henige 1970). Again, the purpose of the dataset is to preserve as
much precision in the original data as possible. Thus, we are hoping to find an architecture that
allows a precise dating of particular days (June 10, 1555) as well as larger, less precise temporal units
(the eighteenth century).
Some data will be available in convenient numerical form (e.g., number of colonial settlers in
a region). Other information will have to be estimated, on the basis of historical accounts or by
expert coders (e.g., theory of colonial rule). We distinguish between “primary” variables (requiring
relatively judgment on the part of the coder) and “secondary” variables (more highly aggregated, and
relying on information from primary variables). The dataset thus represents a mix of “objective”
and “subjective” codings, and quantitative and qualitative data. Resulting variables are of all sorts:
string (ordinary language), nominal, ordinal, and interval.
For each data point (cell), the dataset will note the following: variable (substantive
information), location (i.e., country, colony, region, city, or town), year, source(s), and additional
notes. The latter is an all-purpose field allowing us to comment on the viability of the source,
disagreements among sources or coders, special coding rules, or any other facet of the data point
that might be relevant. This cell-by-cell information system should make the task of any future recoding immeasurably easier and allows for a full reporting of the procedures employed.
Imputation and Aggregation
While the primary purpose of the dataset is to provide a centralized primary-data source for
scholars, we also intend to apply imputation and aggregation techniques so that the data is rendered
in a more usable form. The problem of aggregation besets variable-centered research. Evidently,
the way in which indicators of a central concept are aggregated (e.g., additive; weighted-additive;
necessary/sufficient approaches) can have important implications for final result. All aggregations
are, in some sense, research interventions. In turn, these aggregation issues depend in part on how
the specific indicators are measured themselves (e.g., at a nominal, ordinal, or interval level). Our
effort will be to preserve the most basic indicators used in all aggregative concepts, wherever
possible, so that our choices of aggregation can be revisited by future scholars, perhaps with
different hypotheses in mind. Here, we describe a few of the techniques that may be employed for
these secondary datasets.
Where several sources are employed to measure the same concept, or very similar concepts,
we may choose the “best” (most reliable, most consistent, most comprehensive) of these variables to
report in a final, secondary dataset. Alternatively, we may combine these multiple variables into a
single variable, using an averaging or imputation technique (to fill in missing data).
When a single variable lacks temporal data, and where this data is highly trended, simple
techniques of linear interpolation may be employed.
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A more complicated problem arises with missing spatial data (data unavailable for particular
units), or temporal data that is not highly trended. Here, multiple-imputation techniques may be
employed (e.g., King et al. 2001).
In arriving at a final, “complete” dataset, we are cognizant that different temporal units may
be advisable. For some variables, an annual coding scheme may be possible. For others, decadal or
century-long intervals may be more appropriate.
The objective, ultimately, would be to provide a reasonably complete dataset for the world
over the past six centuries across chosen dimensions. The purpose of “complete-ness” in this
context is to provide a dataset that can be used for varied analyses -- descriptive, causal, and
predictive – without biasing results by over-representing those parts of the world where richer data
is available. At the same time, any techniques of aggregation that involve imputation of missing data
should be reflected in a corresponding measure of uncertainty for that variable. Measures of
uncertainty will therefore be reported along with each variable in the final product.
Most important, the raw data – as collected from numerous sources – will be preserved so as
to be available to future scholars, who may wish to apply different techniques of aggregation. Thus,
at the end of the project we envision several datasets, as follows: 1) a Primary dataset, with raw data,
along with any new variables that we decide to code; 2) a Completed dataset, with selected variables
for key concepts interpolated and imputed so that all units and time-periods are covered across the
entire six centuries; 3) a Cross-sectional dataset, centered on the year 2010, summarizing the
temporal data for each contemporary nation-state in a static cross-country format. The latter will be
based on cumulative totals and/or averages. For a subset of variables, this dataset will provide
information in four time-periods: a) prior to colonization, b) at the height of the colonial period
(during the decade of greatest colonial influence), c) at independence (the approximate year in which
a country attains formal sovereignty), and d) at present (the most recent year in the dataset). Thus,
once the data collection phase of the project is complete, a set of subsidiary variables and datasets
will be constructed from the underlying (raw) data.
Operationalizing Key Concepts
While the utility and conceptual validity of most of the variables appearing in the appendix will be
readily apparent to the reader, some of them raise complicated issues of conceptualization and
measurement and thus deserve more extended commentary. In the following sections we discuss
measures of Colonialism, Economic development, Human development, and Cultural
transformation.
Colonialism
Measuring colonial rule requires a viable definition of colonialism, a term that has been variously
understood (Abernethy 2000: 19-21; Esherick et al. 2006). Our definition, a fairly conventional one,
highlights three elements: 1) a colonizer (the metropole) makes a successful claim of sovereignty
over an overseas territory; 2) it exerts influence over the occupied territory through the creation of
an administrative structure that extracts resources and enforces regulations; and 3) it perceives the
indigenous population as different and usually inferior in culture (as defined by race, ethnicity,
religion, customs, and/or language) and denies this population full citizenship rights. Accordingly,
we include in the analysis all of the European colonizers as well as Japan. Excluded from our
purview are land empires such as the Qing, Ottoman, and Romanov Empires.
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To address questions about colonialism and its legacies, four variables seem especially
important: 1) presence of colonial rule; 2) identity of primary colonizer; 3) type of colonialism; and
4) level of colonialism. We discuss these in turn.
First, we code the presence of colonial rule using three categories: sovereign territory, partial
colony, and colony. We include the second category of partial colony to accommodate borderline
cases such as India from the mid-1700s until the formal establishment of colonialism in 1857 and
Sierra Leone during the 1787-1896 period. Scholars who have a more expansive definition of
colonialism may choose to treat these borderline cases as colonies, whereas those with a more
restrictive definition may choose to treat them as periods of sovereignty. The number of countryyears that will be coded in the borderline category is modest, corresponding mostly to territories
where the beginning or end of colonialism is ambiguous over several decades.
Second, for country-years where colonialism is fully or partially present, we code the identity
of the primary colonizer (i.e., England, Spain, Portugal, and so on). Again, in most cases this coding
is straight-forward, but occasionally territories are subject to multiple colonizers. For example,
portions of the southern United States were colonized by Spain and France, and subsequently by
England. In this regard, it bears emphasis that our GIS-based approach to coding takes account of
(estimates of) the actual spatial control of different colonizers, as judged by historical maps. GIS
software also allow us to arrive at aggregate measures of colonial rule over the longue duree, using
different measures of colonial control (e.g., percent of territory now part of the United States
controlled by Metropole A, multiplied by the number of years it was in control of that territory).
Third, we propose to code cases in light of four types of colonialism that are of central
importance in the literature: direct settler, indirect settler, direct non-settler, and indirect non-settler
colonialism. Direct settler colonies refer to those in which the colonial settler population represents the
majority of the total population and permanently resides in the colony; these colonies are marked by
direct rule by the settler population (e.g., Pearson 2001). Classic examples include the British settler
colonies of Canada, the United States, Australia, and New Zealand. Indirect settler colonies refer to
colonies in which the colonial settler population represents a substantial portion of the total
population (though less than half) and this population permanently resides in the colony; however,
these colonies feature indirect rule in which indigenous elites are responsible for governance outside
of the central bureaucracy. Examples of indirect settler colonies include South Africa, Zimbabwe,
and the Spanish colonial empire for much of its history.
Both direct and indirect non-settler colonies refer to colonies in which the population from
the colonizing nation does not permanently settle the colonial territory. The two types are
distinguished by the presence of direct or indirect rule. Direct rule occurs when the colonial state is
unified and bureaucratically organized. Indirect rule, by contrast, consists of a bifurcated state in
which the central administration is bureaucratically organized and the peripheral administration –
which is supposed to be based on pre-colonial institutions and is run by indigenous intermediaries
who collaborate with the colonial authorities – is organized along patrimonial lines. The uneasy
combination of central bureaucratization and peripheral patrimonialism is therefore the hallmark of
indirect rule.
For some cases, more than one type may be applicable. For example, India featured both
pockets of direct rule and pockets of indirect rule. For these cases, we will code both the dominant
type of colonialism and the secondary type of colonialism. If there is real ambiguity concerning
which type is dominant, the case will be coded as a hybrid.
Finally, perhaps our most important new variable is “level of colonialism.” An adequate
measure of this variable is central to testing to whether, ceteris paribus, more colonialism or less
colonialism is associated with better post-colonial development performance. Level of colonialism
is a multi-dimensional category that aggregates several important dimensions. The overarching
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variable of level of colonialism will be especially useful for scholars who seek to introduce a basic
control for colonialism in their studies. By contrast, scholars who seek to explore the impact of
specific features of colonialism will likely find the codes for the underlying dimensions of this
concept to be more useful.
We define level of colonialism as the extent to which the economic, sociocultural, and
political institutions of a metropole are imposed upon a colonized region. Thus, we are concerned
with the extent to which a broad array of institutions is transferred – in whole or in part and possibly
with substantial modification – from a colonizing nation to a colonized territory. We operationalize
the concept using three measures: economic institutional transformation, sociocultural institutional
transformation, and political institutional transformation. Because we will systematically gather data
on each of these dimensions, the three measures themselves could be used as variables for analysis.
With economic institutions, we are centrally concerned with the extent to which the
colonizing power shapes methods of production and trade relations within the colonized territory.
In some cases, colonizers introduced entirely new modes of production into their occupied
territories (e.g., the Spanish in the Andes). In other cases, colonizers left indigenous modes of
production at least partly intact (e.g., British colonialism in parts of East Africa). Likewise, the
extent to which internal and external trade relations were controlled and regulated by colonial
authorities varied across regions and time (compare, for example, the restrictions of Spanish
mercantilism with the more liberal orientation of British and Japanese colonizers). We draw on
these observable differences to assess the extent to which economic institutions were implanted into
the different colonies.
In the course of coding the economic institutional measure, we will be able to gather data on
additional variables related to the economy (see appendix). For example, the variables of colonial
economic activity (extractive, mixed, extensive), level of colonial investment, and type of labor force
can be coded in conjunction with scoring the measure of economic institutional transformation.
With sociocultural institutions, we are concerned with the extent to which the colonizing
power implanted new cultural practices and new styles of life into colonial territories. For empirical
purposes, we especially focus on cultural institutions, such as religious institutions, civic associations,
and educational facilities. In some places and at some times, colonizers implanted almost none of
these institutions, whereas in other places and at other times, colonizers vigorously pursued the
cultural transformation of the colonized territory. We seek to measure these variations.
Again, in the course of scoring cases for the measure of sociocultural transformation, we will
be in a position to gather data relevant to the coding of several other variables. These variables
include those related to missionary activity, religion, and language (see appendix).
With political institutions, we focus on the extent to which colonizers brought bureaucratic
and governmental structures into the colonies. In some colonies, the colonial state represented no
more than a couple hundred bureaucrats and soldiers located in a distant capital; no effort was made
to impose a formal government (e.g., much of Africa). In other colonies, the colonial state was
more developed, perhaps featuring a national government in which indigenous leaders collaborated
with colonial authorities (e.g., the West Indies in the 20th century). In still other colonies, the
colonial power imposed a large, centrally-controlled bureaucratic, legal, military, and government
apparatus that extended deeply into society (e.g., parts of East Asia).
When coding political institutional transformation, we will also seek to gather data for
several other related variables, including those related to military presence, legal penetration, and a
range of variables that we list under “Politics” in the appendix.
Economic development
12
We rely centrally on demographic variables to measure the developmental capacity of societies prior
to the modern era. Two demographic variables, urbanization and population of a state’s largest city,
are looked upon as proxies for aggregate societal wealth and civilizational development (including
technology, the division of labor, and the development of advanced forms of social and political
organization) in periods prior to the demographic revolution (Acemoglu et al. 2002; Bairoch 1988).
These are also periods in which the calculation of a gross domestic product is virtually impossible,
since there was no formal economy to speak of (and, to make matters even more complicated, no
common measures by which purchasing power parity could be observed crossnationally). This
makes demographic variables all the more essential. Fortuitously, most civilizations that we are
aware of – and certainly all modern civilizations -- were based on urban agglomerations.1 Cities and
civilization went hand in hand (Bairoch 1988; Chandler 1987; Childe 1950; Modelski 2000).
In the twentieth century, and perhaps even the nineteenth, it becomes possible to arrive at
reasonably good estimates of GDP per capita (Maddison 2001), which can be combined with
demographic data from earlier periods to arrive at a comprehensive accounting of societal wealth
throughout the world during the period covered in this survey (1400-the present).
Another variable that may be helpful in charting economic growth within colonies and
nation-states prior to the mid-twentieth century (when GDP statistics become widely available and
reliable) is export revenue per capita (e.g., Manning 1982: 4).
Human development
It is important to stress that the foregoing measures are regarded as proxies for aggregate wealth and
civilizational development, not human development. Various privations associated with social
inequality and oppression, of which slavery is the most egregious example, are entirely excluded
from such demographic and economic variables. Indeed, it is likely that urbanization was associated
with an increase in mortality rates prior to the twentieth century (Bairoch 1988). It would be folly, in
any case, to equate the size of a political unit’s largest city, its level of urbanization, or its export
revenue with the quality of life enjoyed by its inhabitants.
Measurements of human development are more problematic since estimates of mortality -the most common indicator -- are available on a global scale only from the mid-twentieth century.
Other indicators of health and literacy are even more limited in historical and geographic range.
For a small set of regions an available proxy for human physical wellbeing exists in the form
of human stature. Stature, understood here as the average height of mature members of a specific
human community, is the best measure – indeed, virtually the only measure – of human
development prior to the tabulation of mortality rates (Bogin 1988; Bogin & Keep 1999; Eveleth &
Tanner 1991; Komlos & Baten 1998; Steckel 1995; Steckel & Rose 2002; Steckel & Floud 1997).
Human stature is highly sensitive to nutritional intake, particularly during childhood, and to
insecurities in food provision that might disrupt nutritional intake. Since we have accurate
measurements of stature potential – drawn from healthy specimens of present-day populations with
similar genetic composition – biologists can easily calculate the degree to which previous
populations achieved this potential. Particularly revelatory is the degree to which stature has varied
over time in human populations around the world. Strong evidence suggests, for example, that
stature declined in the initial aftermath of colonial interventions in Latin America. Granted, stature
is not quite the same concept as overall mortality since, in principle, a population of tall adults might
co-exist with high infant and child mortality rates (if children are dying of diseases that are not
1 Partial exception might be made for the Egyptian empire (Modelski 2000: 25-6) and the Roman Empire in its
later stages, when wealth migrated from Rome to large latifundia-style estates situated in rural regions around the empire.
However, the fact that this movement was associated with the empire’s decline is not coincidental.
13
nutritionally related). However, prior to recent discoveries about disease, sanitation, and medicine
we can expect that adult stature corresponded with mortality rates. Thus, we propose that stature is
a useful proxy for human development through most eras covered in this study. As data on
historical stature in different parts of the world becomes available, it will be integrated into the
dataset and may provide a good measure of human development over the long run.
Cultural Transformation
Constructing accurate and sensitive measures of cultural change is perhaps the most daunting task of
all. One set of proxies involves linguistic and religious practices. If these change – if, for example, a
region adopts the language and/or religion of its principal colonizer – there is strong reason to
suppose that a wide-ranging cultural transformation has occurred. The speed and thoroughness of
this transformation can presumably be tracked by the rate and extent to which indigenous practices
disappear. Thus, the variables measuring linguistic and religious practices (see appendix) offer a
crude tracking of broader societal transformations.
A related approach looks to changes in the racial complexion of a population as a clue to the
cultural transformation of indigenous peoples and – equally important -- how integrated/segregated
these societies were, overall. Presumably, where the color line was shifting and indistinct, fewer
barriers separated colonial and indigenous peoples.
These are all outcome-based approaches, to be sure. In order to get a sense of the inputs –
i.e., the extent of direct cultural intervention on the part of a colonizing power – one may attempt to
estimate the number of schools run by, or established by, the colonizer and the principal language of
instruction in that school system(s).
Methods of Analysis
Before concluding, we wish to pay explicit attention to the methods by which the data resulting
from this study might be analyzed. The first anticipated use is purely descriptive, i.e., to show how
countries and/or colonies fared at various points in their historical trajectory. Indeed, the most
important use of this dataset may be primarily descriptive, allowing researchers to make better and
broader comparisons through time and across space. This, in turn, may provide the point of
departure for focused case studies.
A second use of the dataset is to provide direct evidence of causal relationships. Our point
of departure is the global cross-country research design, usually focused on the postwar decades
(1950-). Although sometimes approached in a pooled time-series format (Gerring, Thacker &
Moreno 2005), there is usually relatively little variation in key variables through this time period, and
such variation as exists is manifestly non-experimental (and thus correlated with the error term).
Complicating matters further is the extreme heterogeneity across units (nation-states). It is no
wonder that the format has been strongly criticized in recent years (e.g., Kittel 2006; Rodrik 2005).
Even so, for many questions of interest to scholars the crossnational regression remains among the
best of all bad options.
While we take no position in this ongoing debate it is worth noting that whatever confidence
one might have in cross-sectional models depends largely upon the problem of adequately specifying
the model. This, in turn, rests upon the intensity and variety of specification tests that a writer is
able to apply to a given hypothesis (since “correct” benchmark models are virtually impossible to
identify). Only if a result is robust in the face of many plausible specifications can it be regarded as
providing strong evidence of a causal hypothesis. Such specification tests evidently depend upon
14
the prior existence of a large set of correctly measured and crossnationally valid indicators – to be
used as controls or as alternate measures of the key concept of interest. In short, crossnational
regressions depend upon specification tests, and specification tests depend upon data. Once again,
the crucial importance of the present project becomes apparent if we are to sustain the viability of
this common mode of crossnational analysis.
At the same time, one of the anticipated benefits of the Colonial Legacies project is to open
up new methodological approaches. In particular, we hope to offer scholars the possibility of
exploiting useful variation over time. Note that the projected dataset will collect information on key
variables at annual, decadal, or centennial intervals (depending upon data availability). This means
that the resulting data may be examined in a panel format over a much longer period of time, and this
opens the way for what may prove, in some circumstances, a more productive use of the panel
format.
Since the project will collect some data for spatial units much smaller than the contemporary
nation-state (e.g., cities and regions) it may also be possible to perform analyses that are much more
disaggregated than the typical cross-country regression. These analyses might center on spatial units
chosen according to available GIS formats, e.g., geographically circumscribed areas or hectares. The
possibilities for new spatial units of analysis are, in principle, unlimited, and may greatly change our
capacity to model causal relations through time.
An additional approach, also relying on temporal variation, focuses on the histories of
territories whose colonial ruler changed – from Dutch to British in New York and in South Africa,
for example. These cases, which are quite numerous throughout the world, offer critical evidence
for any hypothesis concerning the effects of colonial rulership, for the ceteris paribus assumption of
causal analysis is likely to be satisfied if the comparison is restricted to periods just prior to, and after,
the changeover in control.
A final methodology compares the performance of colonies in decades just prior to, and
after, the achievement of independence. This approach regards independence as an exogenous
“treatment,” allowing for pre- and post-tests along various dimensions of development (political,
social, and economic). Of particular importance in this sort of analysis is the construction of
appropriate controls for regional and global trends that might otherwise produce spurious findings.
Also important is a wide-angle focus on either side of the independence divide so that temporary
effects associated with this unique political rupture are neutralized. (Alternatively, one might simply
ignore the decadal period surrounding the date of independence. In any case, territories achieving
independence in the very recent past [e.g., East Timor] or perhaps not at all [e.g., Puerto Rico]
would be excluded from this species of analysis.)
Advancing Historical Research in the Social Sciences
Social science theories increasingly recognize that historical events and processes (i.e., events lying in
the distant past) are critical for the explanation of contemporary outcomes. They are also
increasingly inclined to seek global answers for significant questions and problems. Global history is
here to stay. Indeed, it is increasingly recognized that most of the various processes attendant upon
“globalization” are by no means novel to the twentieth century.
However, researchers in the social sciences are often unable to devise adequate empirical
tests for propositions rooted in the distant past. This is not primarily the fault of the researchers.
The problem is that data for prior historical periods is limited, and that which exists often suspect,
requiring a deep and nuanced knowledge of a particular time-period and region. Evidence
15
constructed in this painstaking fashion seems to resist all but the most anodyne generalizations and
hence provides rather unpropitious ground for social-scientific theory-making.
We are well aware that data recovery for the fifteenth century will never match the quality
and quantity of data available for the twentieth century. Yet, we are equally convinced that much
more can be done to collect the data that lies out there already (in the form of secondary sources
and specialized datasets), to provide new codings of substantively important topics, and to make this
information more widely available to scholars. We regard this project as an important step in this
direction, and one that will greatly enhance the ability of social scientists to test the propositions
suggested by their increasingly historically-oriented theories. In much the same way that Polity IV,
Correlates of War, and the World Development Indicators now function as standard references for
the study of nation-states in the modern era, we anticipate that this new dataset may serve as the
leading source of quantitative and qualitative information for those investigating periods prior to the
nineteenth century – a jumping-off place for research on all conceivable topics.
Plan of Action
The project is funded for three years, beginning in Summer, 2007. Over this period, we anticipate
three phases of activity. In the first phase, we plan to incorporate all existing historical data relevant
to colonialism and development that is relatively easy to collect, e.g., information can be drawn from
existing datasets or printed sources. In the second phase, we will begin coding original data for
additional variables, or adding additional data to provide more complete or more reliable coverage
for existing variables. Decisions on which topics (i.e., variables) to address will be based on three
general criteria: a) ease of collection, b) data reliability, and c) expected theoretical yield. In the third
and final phase we will aggregate the raw data into a series of aggregate variables and sub-set
datasets, as described above.
A project this expansive has no definitive point of completion. No matter how long we
labor, there will always remain significant shortcomings -- in data coverage, data reliability, and
theoretical scope. This is true, naturally, of all projects. But it is particularly true of a project that
aims to discover patterns on a global scale and in the distant past. Our long-term objective,
therefore, is to ensure that this project will be maintained – amended, emended, perhaps even
fundamentally reconceptualized -- into the future. Just as other datasets (WDI, PWT, State Failure,
Polity) have endured, so, we imagine, the Colonial Legacies dataset might endure. To this end, we
hope to create a community of scholars who are sufficiently committed to the project that they will
lend their expertise, and their time, to ensure its future.
16
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20
Appendix:
VARIABLES AND SOURCES
Sources listed below refer to primary sources of data (i.e., published work or publicly available datasets) or, in
some cases, to works that offer discussions or examples of the hypothesis that a variable represents. In
addition, we wish to acknowledge certain general sources, of use for a wide range of indicators. These
include: Beach & Fahs (1925), Benjamin (2006), Boswell (1989), Carlson (2005), Chase-Dunn & Reifer (2003),
Clark (1936), Correlates of War dataset (Singer & Diehl 1990), Cross-National Time-Series Data Archive
(Banks 1994), Eisenstadt & Rokkan (1973), Etemad (2000), Frankema (2006), Hailey (1945), Henige (1970;
see dataset employed in Strang 1990), Hensel (various years), Jacobson (1968), Kuczynski (1953), Mitchell
(1998a, 1998b, 1998c), Penn World Tables (Heston & Summers 1991), Polity IV dataset (Marshall & Jaggers
2002), State Failure Task Force dataset (Goldstone et al. 2000), Statesman’s Yearbook (various years), Stewart
(1996, 1999), Wilkinson (forthcoming), Woodberry (2004), World Development Indicators (“WDI” [various
years]). These will be carefully culled for additional data.
For each coding, it will be necessary to assign a spatial unit – empire, country, colony, region, city,
and so forth. This should follow the designation of the original source as closely as possible, unless there are
reasons to assign a different coding (e.g., to retain consistent usage in the meaning of a place-name or to
conform to a more reliable spatial designation than is contained in the original source).
The principal temporal unit of analysis is the territory-year. However, more precise dates (e.g., for an
election) should also be noted, wherever available.
Note that most questions can be applied to all regions, while some pertain only to colonies.
I. Colonial rule
Primary variables
Arrival of first explorers. Coding: date.
Arrival of first missionaries. Coding: date.
Arrival of first traders. Coding: date.
Arrival of first permanent officials from the metropole. Coding: date.
Colonial status. Coding: 1) sovereign territory, 2) partial colony, 3) full colony. Sources: Abernethy (2000:
appendix), Clark (1936), Putterman (2003), Strang (1991).
Identity of colonizer. Coding: string. Includes countries that are coded as colony or partial colony. Sources:
Clark (1936), Strang (1990, 1991).
Distance from metropole. Applicable only with reference to colonies. Coding: miles from colonial capital.
Travel time from metropole. Applicable only with reference to colonies. Coding: shortest time required to travel
from the metropole to the colony.
Distance from trade centers. A measure of peripheral status relative to the cores of the world economy in the
modern era. Coding: miles from London or Japan, whichever is closest (from the country’s center).
21
Type of military presence. Coding: 1) no foreign military presence except that which may be voluntarily
negotiated (e.g., NATO troops in Europe), 2) temporary interventions (or threat of same), 3) permanent
stationing of foreign troops.
Number of permanent military. Coding: number of troops from the metropole (or in the service of the
metropole).
Status within the empire. Coding: 1) colonial center, 2) semi-periphery, 3) periphery. Sources: Mahoney (2003).
Settler mortality. Sources: Acemoglu, et al. (2001, 2002), Curtin (1989).
 Antonio McDaniel, Swing Low Sweet Chariot, the Mortality Cost of Colonizing Liberia in the 19th
Century, 1995. Re: Liberia, 1800-1900. Location: Mugar HB 1497.2 A3 M35 1995
 R.R. Kuczynski, Colonial Population, 1937. Re: Global, 1900-1937. Location:
Mugar HB 885 K8 1969
Colonials as percent of native population. Sources: Clark (1936), Kuczynski (1953).
 Antonio McDaniel, Swing Low Sweet Chariot, the Mortality Cost of Colonizing Liberia in the 19th
Century, 1995. Re: Liberia, 1800-1900. Location: Mugar HB 1497.2 A3 M35 1995
 R.R. Kuczynski, Colonial Population, 1937. Re: Global, 1900-1937. Location:
Mugar HB 885 K8 1969
Europeans/Japanese as percent of population. Sources: Acemoglu et al. (2002), Kuczynski (1953), McEvedy and
Jones (1978).
 R.R. Kuczynski, Colonial Population, 1937. Re: Global, 1900-1937. Location:
Mugar HB 885 K8 1969
Territory under foreign control. Coding: percent of territory under foreign rule. Sources: Putterman (2003).
Legal penetration. Number of customary court cases/total number of court cases/total population at
independence. Sources: Lange (2003).
Open door treaty. Coding: 1) no open door treaty, 2) open door treaty.
Theory of colonial rule. Refers to the ideology of colonialism not to facts on the ground. Coding: 1) direct, 2)
mixed, 3) indirect.
 Antonio McDaniel, Swing Low Sweet Chariot, the Mortality Cost of Colonizing Liberia in the 19th
Century, 1995. Re: Liberia, 1800-1900. Location: Mugar HB 1497.2 A3 M35 1995
Practice of colonial rule. Refers to facts on the ground, as near as they can be ascertained, rather than the official
dogma professed by leaders of the colonial power. Coding: percent of country ruled directly (i.e., under the
direct administrative authority of the metropole or those appointed and responsible to the metropole) and
indirectly.
Colonial administrators. Coding: number of colonial administrators. Sources: British Blue books.
Colonial administrators (%). Coding: number of colonial administrators as percent of total administration (see
below).
Profits provided by colony. Net profits/losses from all sources.
22
Cost of colony. Costs, including infrastructure, military, social policies, and administration.
Net Cost/Profit of colony. Profits – costs.
Independence movement. The “strength” of an independence movement understood in terms of the size and
coherence of the formal organization, the size of its (passive) support among the populace, its commitment
to independence as a goal, and actions taken to achieve that end. Coding: 1) weak, 2) medium-strength, 3)
strong.
Achieving independence. 1) won by force of arms, 2) won by some combination of force and politics, 3) granted
freely by the colonist without military threat.
Colonial taxation. A measure of the proportion of colonial revenues derived from “native” hut, poll, and
income taxes.
Agricultural policy and institutions. 1) A measure of the export tax on primary commodities of the colony. 2) An
indicator noting the presence of government monopsony over a primary export commodity.
“Extractiveness” of colonial activity. The value of tax revenues and exports minus administrative expenditures.
Colonial land policy. A measure of colonial policy towards indigenous land tenure systems.
Colonial regulation of indigenous political activity. A set of variables recording colonial policy on native
representation and political organization.
Secondary variables
Level of colonialism. Discussed in text. [Fill in details here.]
Type of colonialism. Coding: 1) direct settler, 2) indirect settler, 3) direct non-settler colonialism, 4) indirect nonsettler colonialism. For a slightly more disaggregated typology see Fieldhouse (1966; reported in Abernethy
2000: 55-6).
Colonial economic activity (predominant type). Coding: 1) extractive, 2) mixed, 3) extensive. [Is this codable?]
II. Politics
Primary variables
Predominant political unit. Coding: 1) bands, clans, or tribes (nothing above the tribal level), 2) kingdom
(hereditary), 3) empire, 4) nation-state. Sources: Cohen and Service (1978), Putterman (2003).
Democracy (Polity). The Polity IV dataset codes sovereign countries with populations of at least half a million
on a twenty-one point scale from 1800 to the present. Sources: the “Polity2” variable (Polity IV).
Elections. Coding: 1) no election, 2) an election. Sources: Wilkinson (forthcoming).
Type of election. Coding: 1) metropole (election in colony for an office in the metropole), 2) national or colonywide, 3) subnational (regional or local).
Suffrage. Coding: constitutional rule or norm, e.g., white males only or citizens of the metropole only.
23
Suffrage (%). Coding: percent of permanent residents who are permitted to vote.
Turnout. Coding: percent of permanent residents who vote.
Decentralization. Autonomy of local political units relative to central authority. Coding 1) low, 2) moderate, 3)
high. [Note: this variable needs more work; not sure how to clarify.]
Monopoly of physical force. Concerns the degree to which the “official” political unit said to be in charge of a
territory manages (by force or persuasion) to suppress other conflicts. Specifically, are there civil wars,
regions of self-proclaimed autonomy, or areas where government officials venture with trepidation? Coding:
1) very little control over the use of physical force, 2) partial monopoly of physical force, 3) total monopoly of
physical force.
Revenue. Coding: central government tax revenue as share of GDP or (prior to the availability of GDP
measures) population. Sources: Mitchell (1998a, 1998b, 1998c), WDI.
 G.B. Kay, Political Economy of Colonialism in Ghana, a Collection of Documents and Statistics
1900-1960, 1972. Re: Ghana, 1900-1960. Location: Widener Afr 6193.82
Expenditure. Coding: central government expenditure as share of GDP or (prior to the availability of GDP
measures) population. Sources: Mitchell (1998a, 1998b, 1998c), WDI.
 G.B. Kay, Political Economy of Colonialism in Ghana, a Collection of Documents and Statistics
1900-1960, 1972. Re: Ghana, 1900-1960. Location: Widener Afr 6193.82
Bureaucracy. Size of bureaucracy measured as the number of persons in regular employ, military and nonmilitary.
Bureaucracy (non-military). Size of bureaucracy measured as the number of persons in regular employ, nonmilitary only.
Indigenization of post-colonial bureaucracy. Coding: 1) gradual, 2) moderate, 3) fast and thorough.
Military (number). Coding: size of military as share of population.
Military (character). Coding: 1) separate from colonizers’ armed forces, 2) many, incorporated into colonizers’
armed forces.
Legal system. Coding: 1) common law, 2) mixed, 3) civil law, 4) indigenous system of law, 5) no formal system
of law.
III. Geography
Primary variables
Total land area. Coding: square kilometers. Sources: WDI.
Latitude. Coding: absolute value of latitude (natural logarithm). Sources: La Porta et al. (1998).
Continental axis. Coding: the distance in longitudinal degrees between the eastern and westernmost points of
each continent, divided by the distance in latitudinal degrees between the northernmost and southernmost
24
points. A value of 2, for instance, indicates that the landmasss in question is about two times more East-West
oriented than North-South oriented. Sources: Diamond (1997), Olsson and Hibbs (2000).
Isolation. Coding: 1) on a north-south land-mass (North and South America, Africa) or isolated island (e.g.,
Australia), 2) on an east-west landmass (Asia). Sources: Diamond (1997), Olsson and Hibbs (2000).
Frost. Coding: Proportion of land with more than 5 frost-days per month in winter. Sources: Masters and
McMillan (2000).
Climate. Coding: 1) unfavorable to agriculture, 2) moderately favorable, 3) favorable to agriculture. Sources:
Olsson and Hibbs (2000).
Mineral resources. Judged according to their availability, ease of extraction, and recognized value at the time.
Coding: 1) poor, 2) moderate, 3) plentiful.
Natural infrastructure. Includes harbors, waterways, sea access, and terrain suitable for roads. Coding: 1)
poor, 2) moderate, 3) plentiful.
Secondary variables
Border consistency. Coding: how many years has the current border been maintained (with only minor
revisions)?
Homeland duration. Coding: how many years has the territory of the current state been together in the same
unit?
IV. Economics and Demography
Primary variables
GDP per capita. Sources: Maddison (2001), Mitchell (1998a, 1998b, 1998c), Thorp (1998), WDI.
Trade routes. Coding: List any direct trade contacts, i.e., the city or state which is directly linked by a trade
route (a trade route is the route taken by an overland caravan, ship, or plane, and may include several stops).
Trade travel time. Coding: List travel time in number of days to each direct trade contact.
Trade openness (quantitative). Refers to foreign trade, i.e., trade outside the official borders of a political unit
(whether a sovereign state or colony). Trade between the metropole and its colony is understood as foreign
trade for both units. Coding: Imports and exports as share of GDP. Sources: Clark (1936), Mitchell (1998a,
1998b, 1998c), WDI.
Export revenue. Coding: export revenue per capita. Sources: Manning (1982: 4).
 G.B. Kay, Political Economy of Colonialism in Ghana, a Collection of Documents and Statistics
1900-1960, 1972. Re: Ghana, 1900-1960. Location: Widener Afr 6193.82
Trade openness (qualitative). Coding: 1) all foreign trade expressly forbidden and strictly enforced, 2) trade
permitted only through a few entrepot trading centers and strictly limited, 3) trade within the confines of the
empire, strictly enforced, 4) trade within the confines of an empire, not strictly enforced, 5) trade with all
parties allowed.
25
Currency. Coding: 1) no currency of any sort, 2) primitive monies (not used as units of account and not fully
fungible; restricted to special purposes), 3) a variety of currencies that compete with one another, 4) a
uniform currency enforced by national authorities.
Roads -- Length. Coding: length of all major highways (kilometers). Sources: Herbst (2000: 84-), WDI.
 G.B. Kay, Political Economy of Colonialism in Ghana, a Collection of Documents and Statistics
1900-1960, 1972. Re: Ghana, 1900-1960. Location: Widener Afr 6193.82
Railroads -- Length. Coding: length of railway line open (kilometers). Sources: Mitchell (1998a, 1998b, 1998c).
 G.B. Kay, Political Economy of Colonialism in Ghana, a Collection of Documents and Statistics
1900-1960, 1972. Re: Ghana, 1900-1960. Location: Widener Afr 6193.82
Railroads -- Freight. Coding: freight traffic on railways (thousand metric tons). Sources: Mitchell (1998a,
1998b, 1998c).
 G.B. Kay, Political Economy of Colonialism in Ghana, a Collection of Documents and Statistics
1900-1960, 1972. Re: Ghana, 1900-1960. Location: Widener Afr 6193.82
Railroads – Passenger traffic. Coding: passenger traffic on railways (thousands). Sources: Mitchell (1998a,
1998b, 1998c).
Connectedness. Coding: % of population who are one day’s journey away or less (using usual means of
transport) from a navigable river, airport, or ocean port.
Postal traffic. Coding: mail items (millions). Sources: Mitchell (1998a, 1998b, 1998c).
 G.B. Kay, Political Economy of Colonialism in Ghana, a Collection of Documents and Statistics
1900-1960, 1972. Re: Ghana, 1900-1960. Location: Widener Afr 6193.8
Telegraph traffic. Coding: telegrams (millions). Sources: Mitchell (1998a, 1998b, 1998c).
Telephones. Coding: telephones in use (Mitchell) or telephone mainlines (telephone lines connecting a
customer’s equipment to the public switched telephone network per 1,000 people; WDI). Sources: Mitchell
(1998a, 1998b, 1998c), WDI.
Waterways. Percent of territory reachable by navigable waterway. [Clarification needed.]
Property rights. Coding: 1) communal property or no property rights, 2) mix of communal and private
property but not all property is trade-able, 3) private property is recognized and freely tradable.
Labor force, Subsistence. Coding: percent of labor force engaged in hunting, gathering and planting for
consumption only (non-commercial).
Labor force, Plantation. Coding: percent of labor force engaged in plantation agriculture (commercial).
Labor force, Agriculture. Coding: percent of labor force engaged in agriculture (any variety).
Agriculture as % of GDP. Sources: Mitchell (1998a, 1998b, 1998c), WDI.
Mineral extraction. Coding: percent of formal economy (GDP or equivalent) derived from mineral extraction.
 G.B. Kay, Political Economy of Colonialism in Ghana, a Collection of Documents and Statistics
1900-1960, 1972. Re: Ghana, 1900-1960. Location: Widener Afr 6193.82
26
Population. Sources: Clark (1936), Kuczynski (1953), Maddison (2001), McEvedy and Jones (1978), Mitchell
(1998a, 1998b, 1998c), WDI.
 Antonio McDaniel, Swing Low Sweet Chariot, the Mortality Cost of Colonizing Liberia in the 19th
Century, 1995. Re: Liberia, 1800-1900. Location: Mugar HB 1497.2 A3 M35 1995.
 Bruce Fetter, Demography from Scanty Evidence, Central Africa in the Colonial Era, 1990. Re:
Africa, 1800s-1970. Location: Widener HB 3664.3 A3 D46 1990
 R.R. Kuczynski, Colonial Population, 1937. Re: Global, 1900-1937. Location:
Mugar HB 885 K8 1969; R.R. Kuzynski, The Cameroons and Togoland: A Demographic Study,
1939. “Collection of population statistics and to the demographic situation of an African area from
the beginning of its colonization up to the present time.”
 James D. Tarver, The Demography of Africa, 1996. Re: Africa, 1950-1996 (AD14-1996). Location:
Widener HB 3661 A3 T37 1996
Urbanization. Coding: percent of population living in urban areas. Sources: Acemoglu et al. (2002a), Bairoch
(1988), Eggimann (1999), Maddison (2001), Mitchell (1998a, 1998b, 1998c), WDI.
Nomadic. Coding: percent of population who are not sedentary.
Population density. Sources: Kuczynski (1953), WDI.
 Bruce Fetter, Demography from Scanty Evidence, Central Africa in the Colonial Era, 1990. Re:
Africa, 1800s-1970. Location: Widener HB 3664.3 A3 D46 1990
 G.B. Kay, Political Economy of Colonialism in Ghana, a Collection of Documents and Statistics
1900-1960, 1972. Re: Ghana, 1900-1960. Location: Widener Afr 6193.82
City population. Main sources: Chandler (1987), Modelski (2000). Additional sources: Maddison (2001),
WDI.
Largest city. Largest city within larger unit (e.g., nation-state, colony, empire). Derived from City population
variable (above).
Compactness of human settlement. Coding: 1) dispersed (multiple population centers), 2) moderately dispersed, 3)
compact (a single population center). Sources: Englebert (2000).
Ethnic cleansing. Forcible displacement of population groups, either within-country or emigration, of groups
equaling at least 3% of national population. Coding: 1) none, 2) ethnic cleansing in progress.
V. Human Development
Primary variables
Stature. Discussed in text. Sources: Bogin (1988), Bogin and Keep (1999), Eveleth and Tanner (1991),
Komlos and Baten 1998), Steckel (1995), Steckel and Rose (2002), Steckel and Floud (1997).
Birth rate. Sources: Kuczynski (1953), Maddison (2001), Mitchell (1998a, 1998b, 1998c), WDI.
 Antonio McDaniel, Swing Low Sweet Chariot, the Mortality Cost of Colonizing Liberia in the 19th
Century, 1995. Re: Liberia, 1800-1900. Location: Mugar HB 1497.2 A3 M35 1995
 Bruce Fetter, Demography from Scanty Evidence, Central Africa in the Colonial Era, 1990. Re:
Africa, 1800s-1970. Location: Widener HB 3664.3 A3 D46 1990
27

James D. Tarver, The Demography of Africa, 1996. Re: Africa, 1950-1996 (AD14-1996). Location:
Widener HB 3661 A3 T37 1996
Infant mortality. Sources: Kuczynski (1953), Mitchell (1998a, 1998b, 1998c), WDI.
 Bruce Fetter, Demography from Scanty Evidence, Central Africa in the Colonial Era, 1990. Re:
Africa, 1800s-1970. Location: Widener HB 3664.3 A3 D46 1990
 R.R. Kuczynski, Colonial Population, 1937. Re: Global, 1900-1937. Location:
Mugar HB 885 K8 1969
 James D. Tarver, The Demography of Africa, 1996. Re: Africa, 1950-1996 (AD14-1996). Location:
Widener HB 3661 A3 T37 1996
Mortality rate (crude). Sources: Kuczynski (1953), Maddison (2001), Mitchell (1998a, 1998b, 1998c), WDI.
 Antonio McDaniel, Swing Low Sweet Chariot, the Mortality Cost of Colonizing Liberia in the 19th
Century, 1995. Re: Liberia, 1800-1900. Location: Mugar HB 1497.2 A3 M35 1995
 Bruce Fetter, Demography from Scanty Evidence, Central Africa in the Colonial Era, 1990. Re:
Africa, 1800s-1970. Location: Widener HB 3664.3 A3 D46 1990
 R.R. Kuczynski, Colonial Population, 1937. Re: Global, 1900-1937. Location:
Mugar HB 885 K8 1969
 James D. Tarver, The Demography of Africa, 1996. Re: Africa, 1950-1996 (AD14-1996). Location:
Widener HB 3661 A3 T37 1996
Life expectancy. Sources: Maddison (2001), WDI.
 Antonio McDaniel, Swing Low Sweet Chariot, the Mortality Cost of Colonizing Liberia in the 19th
Century, 1995. Re: Liberia, 1800-1900. Location: Mugar HB 1497.2 A3 M35 1995
Literacy. Coding: the percentage of people aged 15+ who can, with understanding, both read and write a
short, simple statement on their everyday life. (Coded as zero if there is no generally recognized written
language.) Sources: Eisenstadt and Rokkan (1973: 245-47), United Nations Educational, Social and Cultural
Organization (1957), WDI.
Schooling – Primary and Secondary. Coding: the number of children in school as percent of school-age
population (or, where unavailable, of general population). Sources: Mitchell (1998a, 1998b, 1998c), PWT,
WDI.
 Bruce Fetter, Demography from Scanty Evidence, Central Africa in the Colonial Era, 1990. Re:
Africa, 1800s-1970. Location: Widener HB 3664.3 A3 D46 1990
Schooling – University. Coding: the number of university students as percent of total population. Sources:
Mitchell (1998a, 1998b, 1998c), PWT, WDI.
Schooling – Overseas. Coding: the number of persons educated (for some period of time) in a university
located in a metropole. Sources: Kurzman & Leahey (2004).
Schooling – system type. Coding: public secular (%), public denominational (%), private secular (%), private
denominational (%).
Schooling – language of instruction. Coding: language of instruction in each of the following: public schools,
private secular schools, private denominational schools.
28
VI. Society
Primary variables
Colonial genocide: Percent of indigenous population felled by disease or other causes as a direct result of
colonizer’s presence during the initial colonial encounter.
Inter-breeding. Inter-breeding between colonials and indigenes. Coding: 1) low, 2) medium, 3) high. Sources:
Kuczynski (1953).
Missionary religion(s). Coding: list each missionary group and, if available, the approximate number of
missionaries that they had in the field. Sources: Beach & Fahs (1925), Woodberry (2004).
 Bruce Fetter, Demography from Scanty Evidence, Central Africa in the Colonial Era, 1990. Re:
Africa, 1800s-1970. Location: Widener HB 3664.3 A3 D46 1990
Religion (members). Coding: list all major religions and the approximate percent of the population who adhered
to, or were born into, each.
 Bruce Fetter, Demography from Scanty Evidence, Central Africa in the Colonial Era, 1990. Re:
Africa, 1800s-1970. Location: Widener HB 3664.3 A3 D46 1990
Religion (geography). Coding: show all major religions and the territory in which their adherents lived.
Religious homogeneity. Percent of population in a given territory who are members, or are born into, the largest
religion. Sources: Posner (1999, 2000).
Language. Coding: list all major languages and the approximate percent of the population who are speakers of
each. Numbers may exceed 100, since some will have multiple competencies.
Language (geography). Coding: show all major languages and the territory in which their speakers lived.
“First” language. Coding: list all major “first” (native) languages and the approximate percent of the
population who are speakers of each.
Linguistic homogeneity. Coding: percent who are fluent in the most commonly spoken tongue (even if not a
‘first’ language for most users).
Linguistic distance. Coding: distance between major “first” languages, as understood by linguists.
Ethnicity (geography). Coding: show all major ethnicities and the territory in which their adherents lived.
Cultural composition. Culture is understood here in the most encompassing sense, i.e., including whatever racial,
religious, linguistic, and ethnic cues help to identify ‘us’ from ‘them’ in a given context. (Sometimes the term
“ethnic” is used in this sense.) Coding: 1) unipolar (more than 90% of the population belong to a single
cultural group), 2) bipolar (more than 90% of the population belong to two main cultural groups), 3)
multipolar (no two cultural groups together comprise 90% of the population). Sources: Brown (1997),
Kuczynski (1953), Posner (1999, 2000).
Cross-border cultural affinities. Coding: 1) main ethnic groups are not found in neighboring countries, 2) at least
one major ethnic group has significant numbers in an adjacent country. Sources: Englebert (2000).
29
Cultural stratification. Concerns the degree of separateness characterizing the most salient cultural cleavages;
the degree to which cultural divisions are invidious. Excludes consideration of very small groups (e.g., Jews
in most European countries, the Burakumin in Japan). Coding: 1) low, 2) moderate, 3) high (e.g., caste or
racial apartheid).
Cultural conflict. Level of conflict among non-colonial (indigenous) groups. Coding: 1) low, 2) moderate, 3)
high.
Cultural geography. Coding: 1) none (no important cultural cleavage), 2) dispersed (cultural groups
intermingle), 3) regionally concentrated (cultural groups do not intermingle). Sources: Brown (1997).
Cultural cleavages. Coding (narrative): describe how linguistic, ethnic, and religious cleavages overlap or
reinforce.
Slavery. Coding: 1) no slavery or slavetrading, 2) extensive slave-trading, 3) extensive slave population.
 Antonio McDaniel, Swing Low Sweet Chariot, the Mortality Cost of Colonizing Liberia in the 19th
Century, 1995. Re: Liberia, 1800-1900. Location: Mugar HB 1497.2 A3 M35 1995
Economic equality. Coding: Gini coefficient of family income. Sources: WDI.
30