On the Persistent Eﬀects of the Slave Trade on
Postcolonial Politics in Africa∗
Gaku Ito†‡
April 14, 2023

Abstract
How does the disruption of traditional institutions shape modern political outcomes?
I argue that the demographic shock to indigenous societies induced by Africa’s slave
trade inﬂuences postcolonial politics by tragically empowering ethnic institutions andleadership, thereby aﬀecting the coup-civil war trap and the underlying commitment
problems. The empirical analysis leverages the soil suitability for cassava as an instrument to exploit plausibly exogenous variation in the ethnic group-level exposure to theslave trade. The ﬁndings are four-fold: Ethnic groups with greater slave trade exposure are (1) less likely to experience battle incidents within their traditional homelands,(2) less likely to ﬁght civil wars against the central government, (3) more likely to be
included in state power-sharing schemes, and (4) more likely to stage coups in postcolonial states. A series of falsiﬁcation tests exploiting the timing of cassava’s arrivalin Africa and additional sources of variation lends further credibility to the ﬁndings.
Word Count
10, 996 words
Keywords
armed conﬂict, coups, historical legacies, power sharing, slave trade∗

I thank Kentaro Fukumoto, Cullen S. Hendrix, Yukari Iwanami, Motoshi Suzuki, Atsushi Tago, Yuichiro
Yoshida, session participants of the 2020 Annual Meeting of the Japanese Political Science Association andthe 2021 Annual Convention of the International Studies Association, and seminar participants at Hiroshima
University, Keio University, and Osaka University for valuable comments and suggestions. Financial support
from the Japan Society for the Promotion of Science is gratefully acknowledged (Grant Numbers 18KT0054,
19K21681, and 20K01464). All remaining errors are mine.
†
Associate Professor, Department of Economics, Osaka Metropolitan University. 3–3–138 Sugimoto,Sumiyoshi, Osaka 558–8585, Japan. Email: gaku@omu.ac.jp. URL: https://gaku-ito.github.io.
‡
Research Fellow, Network for Education and Research on Peace and Sustainability, Hiroshima University.
1–3–1 Kagamiyama, Higashihiroshima, Hiroshima 739–8530, Japan.

1Introduction

How do historical events and institutions persistently aﬀect modern political outcomes?
Previous literature highlights three pathways. First, institutions impose constraints on individual behavior and strategic interactions. Historical events shape contemporary economic
performance and armed conﬂicts by inﬂuencing institutional constraints, collective action
capacity, and bargaining leverages (e.g., Acemoglu et al., 2001, 2014; Michalopoulos & Papaioannou, 2013; Wig, 2016). Second, the breakdown of historical institutions also mattersin shaping future economic development, altruistic tendencies, and political attitudes andbehavior. Such legacies can be generated and transmitted across generations by, for example,
the fall of traditional polities (e.g., Lowes et al., 2017) as well as the damages inﬂicted byforms of political violence (e.g., Bellows & Miguel, 2006; Rozenas & Zhukov, 2019). Third,
and relatedly, shocks facilitate institutional change. Such shocks can arise from, for example, technological innovations (e.g., Acemoglu et al., 2005) along with forms of political andeconomic events such as foreign occupation (e.g., Acemoglu et al., 2011) and the slave trade(e.g., Whatley, 2014, 2022).
This article expands these historical institutionalist perspectives by exploring the longrun eﬀects of the transatlantic and Indian Ocean slave trades on power sharing, rebellion,
and coups d’état in postcolonial Africa. I argue that descendant ethnic groups with greater
exposure to the slave trade are less likely to engage in armed conﬂicts while more likely to
be included in the state power-sharing schemes in postcolonial politics due to the tragically
improved group-level institutions and leadership authority, with a by-product of the increased
risks of insider coups. Existing literature suggests that the slave-trade shock improved thepolitical authority of local chiefs (Whatley, 2014). Similar to the logic of war and statemaking (Tilly, 1975), the intensiﬁed competition for survival and the beneﬁts from slaveraids also facilitate local chiefs’ investments into ethnic institutions to better mobilize groupmembers and prevent free-riding. The similarly increased demands of group members forprotection from slave raiding by outsiders and institutional constraints to prevent intra1

group predating are also attuned to enhanced leadership authority and ethnic institutions.
The improved institutions and leadership authority enable an ethnic group to make credible
threats and promises, which in turn alleviates war-causing commitment problems.
Empirically, this article focuses on the later half of the slave trade during the eighteenth to
the nineteenth centuries and leverages the soil agricultural suitability for cassava (manioc),
a New World crop introduced to Africa by the Portuguese, as an instrument to exploit
plausibly exogenous variation in the ethnic group-level exposure to the slave trade. While
known as a major staple crop today, cassava did not exist in Africa before the Columbian
Exchange and was ﬁrst introduced to the continent by the Portuguese in the middle ofthe slave trade period. The ecological features of cassava include its tolerance to stressfulenvironments, high energy yields, and less input-demanding nature (El-Sharkawy, 2004), as
well as the suitability for lengthy travels. The introduction of cassava increased land potential
to sustain a larger population and additional food surplus in regions with both high and low
suitability for other crops while magnifying slave raiders’ incentives and capabilities for slave
capture in the cassava-suitable regions.
The ﬁrst-stage results conﬁrm the discernible association between cassava suitability andgroup-level exposure to the transatlantic and Indian Ocean slave trades during the 1700–1900(post-cassava arrival) period. The second-stage estimates then reveal four empirical patterns
consistent with the advanced argument: Ethnic groups with greater historical exposure to
the slave trade are (1) less likely to experience battle incidents between armed forces within
their traditional homelands, (2) less likely to ﬁght civil wars against the central government,(3) more likely to be included in state power-sharing schemes, and (4) more likely to stage
coups in postcolonial states. The main falsiﬁcation test exploits the arbitrary timing of
cassava’s arrival in Africa in the middle of the slave trade, and lends further credibility to
the ﬁndings by demonstrating a systematic ﬁrst-stage association after, but not before, thearrival of the New World crop.
This article contributes to three bodies of literature. First, it speaks to the growing

2literature on the persistent eﬀects of historical events on modern outcomes, particularly thestudies on the historical roots of contemporary civil conﬂicts. Some historical events such as
ethnic partitioning by modern border design escalate postcolonial conﬂicts (Michalopoulos& Papaioannou, 2016), while other institutions including precolonial political centralization
have a pacifying eﬀect (Wig, 2016). In addition to highlighting the long-run eﬀects, this article traces out how traditional ethnic institutions have been aﬀected by prior historical shocks
to persistently inﬂuence subsequent political outcomes. Second, the ﬁndings deepen our understanding of the persistent legacies of the slave trade. Previous literature demonstrates
the impacts of the slave trade on, for example, economic growth (Nunn, 2008), traditional
political institutions (Whatley, 2014), trust attitudes (Nunn & Wantchekon, 2011), and precolonial conﬂicts in Africa (Fenske & Kala, 2017). What remains less clear is the legacies ofthe slave trade on postcolonial politics, which this article investigates. Finally, this article
carries implications to broader political science literature. Uncovering the deep historicalroots, the ﬁndings directly speak to the ongoing debate over the causes and consequences ofpower sharing (e.g., Cederman et al., 2010; Paine, 2021, 2022; Roessler, 2011, 2016; Roessler& Ohls, 2018). These long-run consequences of the slave trade shock also provide anotherpiece of insight into how violence and institutions interact to generate political outcomes.
The remainder of this article proceeds as follows. The next section reviews existing
literature, followed by the argument and testable predictions in Section 3. Section 4 describes
the identiﬁcation strategy. Sections 5 and 6 present the main ﬁndings and falsiﬁcation tests.
Section 7 concludes by highlighting broader implications and pathways for future research.

2

Historical Legacies and the Coup-Civil War Trap

Scholars increasingly investigate the legacies of Africa’s slave trade as well as the institutional
determinants of postcolonial politics. This section reviews the insights of related literature,
followed by the mechanisms that might link the slave trade and postcolonial politics.

3

2.1

Slave Trade Legacies

One of the most prominent aspects of Africa’s slave trades is the demographic shock to local
societies. During the ﬁfteenth to nineteenth centuries, more than 10 million Africans wereenslaved by the transatlantic slave trade alone, inﬂicting a historically rare demographic
shock to the exposed communities (Curtin, 1969; Eltis et al., 1999; Manning, 1990).
The slave trade generated “tragically interconnected transformations” into African society (Manning, 1990, 147) and thereby left lasting legacies. Since the seminal data construction by Eltis et al. (1999), Nunn (2008), and Nunn & Wantchekon (2011), the empiricalliterature has increasingly explored the persistent eﬀects of the shock-induced transformations on economic, political, and cultural outcomes. Nunn & Wantchekon (2011) demonstrate how the historical exposure to the slave trade undermined the interpersonal trust
such that individuals with ancestor ethnic groups heavily exposed to the slave raids are lesstrusting of others and political authority. Dalton & Leung (2014) and Teso (2019) leverage
the abnormal distortions in sex ratios induced by the slave traders’ preference for males inthe transatlantic slave trade. The shortage of males facilitated the spread of polygyny andgender equality, or norms and informal institutions that have persisted to the present day.
This article is closely related to Whatley (2014) on the impact of the slave trade on
indigenous political authority in West Africa. Using the port-level records of slave exports,
Whatley (2014) highlights a slave trade-induced transformation of political authority bydemonstrating that regions with greater exposure to the transatlantic slave trade see anincreased proportion of ethnic groups with absolutist authority structure in the succession of
local headman or chiefs, measured as the patrilineal heir or matrilineal heir of local politicalauthority. As in conﬂict situations, “individuals subject to slave capture will pay more forprotection, including relinquishing freedoms and rights that might otherwise be cherishedin times of peace” (Whatley, 2014, 471). Whatley (2014) further uncovers that absolutist
authority survived the colonial periods, as the colonial authorities relied on the existing local
authority structure to govern and extract resources in West Africa (see also, Whatley, 2022).
4

While previous studies explore the inﬂuence of the slave trade on historical conﬂicts(Fenske & Kala, 2017), the slave trade legacies on postcolonial politics remain relatively
under-studied. As discussed below, the primary methodological challenge stems from thenonrandom assignment of the slave trade exposure and the correlates of political and economic outcomes in Africa. Since the work of Nunn & Wantchekon (2011), previous studies
often employ coast distance to instrument the slave trade exposure. However, the established correlations between coast distance and economic development and between economic
development and armed conﬂict in Africa discourage the use of the coast distance-based
identiﬁcation strategy to examine the slave trade legacies on, for example, contemporary
conﬂicts due to the concern for the probable exclusion restriction violations.

2.2

Bargaining in the Shadow of History and Violence

Another body of literature related to this article focuses on the link between ethnic institutions and postcolonial politics. For example, Depetris-Chauvin (2016) and Wig (2016),
respectively, demonstrate how historical statehood experiences and centralized traditional institutions enable ethnic groups to make credible commitments and decrease the risks of armed
conﬂicts in postcolonial Africa. In contrast, Paine (2019) highlights the conﬂict-escalatingeﬀect of traditional institutions, such that groups with precolonial state-like institutions
exacerbate civil war and coup risks by increasing interethnic tensions in host countries.
Behind these claims is the now-dominant view of postcolonial politics in Africa as theoutcomes of strategic interactions between ethnic groups in the shadow of violence (Francois
et al., 2015; Roessler, 2011, 2016; Roessler & Ohls, 2018).1 Rather than a single “big man”
dominating the political realm, distinct ethnic groups with diﬀering interests and accessto state power bargain over and compete for the ruling coalition with the threat of armed
1

Admittedly, ethnic cleavage is a major but not the sole driver of Africa’s postcolonial politics. The
political salience of ethnic cleavages depends on, for example, relative group (demographic) size within thedomestic arena and does not necessarily matter for politics in all the circumstances (Posner, 2004). Yet rebel
and political entrepreneurs often have strategic incentives to exploit observable and less-manipulable ethnic
traits to facilitate mobilization under informational asymmetry (Hale, 2008; Roessler, 2011) while limiting
access to the spoils (Fearon, 1999).

5

uprising from outside; rulers and ruling groups strategically allocate rents and access to state
power to buy oﬀ potential rivals and thereby prevent an outside rebellion at the increased costof insider coups and other forms of defection from inside. 2 Both sustained power sharing andfailure of such peace arrangements into ineﬃcient ﬁghting emerge as equilibrium outcomesfrom these strategic interactions.
Existing literature conceives the strategic situation as the coup-civil war trap (Francois
et al., 2015; Paine, 2021; Roessler, 2011, 2016). On the one hand, rulers need to acquire support from a broader population by assuring rewards to consolidate the regime. Excluding
relevant groups from state power risks an outsider rebellion, while granting power-sharing
spoils not only mitigates the imminent threats but also contributes to the government’s counterinsurgency capacities. On the other hand, having potential rivals within the regime entails
increased risks of coups d’état from inside. Rival groups make demands of political power byposing the twofold threats of inside coups and outside rebellions, and the ever-present threats
incentivize rulers to oﬀer rents and power-sharing spoils in order to secure their survival inoﬃce. The resultant power-sharing deals between the ruler and the rival groups, however,often remain vulnerable given weak state institutions, as each side’s capability and incentives
to acquire more power at the expense of others generate persistent commitment problems.
In the absence of commitment devices or the presence of rapid power shifts, the underlying
commitment problems can in turn invite a breakdown of power-sharing deals into ineﬃcientﬁghting (Fearon, 1995; Powell, 2006).
While insightful, it remains less clear how group-level institutions aﬀect the general coupcivil war trade-oﬀ. If traditional institutions shape ethnic groups’ abilities to make credible
commitments, group-level institutions and the transformations thereof should also inﬂuence
the severity of the general trade-oﬀ and the prospects for domestic peace.

2

Besides coups, another threat option available to an insider group is to “defect when facing outsidepressure, which entails disobeying orders to repress urban protesters or shirking in their eﬀort at counterinsurgency” (Paine, 2022, 1426, emphasis in the original). In any case, the rulers weigh the threats of outsider
rebellion and the risks of insider disloyalty options, which can be imposed by either coups and defections.

6

3

Slave Trade, Institutions, and Postcolonial Politics

I argue that descendant ethnic groups with severe exposure to the slave trade are less likely
to engage in armed conﬂicts while more likely to be included in the state power sharing
schemes in postcolonial politics due to the ironically strengthened, group-level institutions
and leadership authority. Yet the increased chance of power-sharing deals is coupled with
the increased risks of insider coups. This section lays out the two building blocks of theargument: The ﬁrst component is the institutional change induced by the slave trade, andthe second is the institutional devices shaping group mobilization and bargaining.

3.1

Slave Trade and Institutional Change

As Whatley (2014) demonstrates, one dimension of the slave trade-induced institutionalchange is an absolutist authority structure in the succession of local headmen and chiefs.
Another key dimension is the increased investments in institutional devices or political centralization, with the resultant institutional change involving enhanced leadership authority
combined with improved institutional constraints. 3 To illustrate, some ethnic groups such as
the Yoruba in Nigeria and the Bisa in Zambia, having suﬀered severely from the slave trade,have absolutist authority and court- and legislature-like institutions. Other groups such as
the Teke in DR Congo and the Goroa in Tanzania suﬀered less from the slave trade while
lacking absolutist political structure and developed institutions.
The institutional change emerges from the incentives and strategic interactions of chiefs
and group members. A major source of the incentives for absolutist chiefs to make costly
investments into group institutions is their long time horizons and the ways of leadership
appointments. Somewhat paradoxically, once established, chiefs in the communities witha less competitive, absolutist political structure would have increased incentives to invest
in group-level institutions to organize collective actions and deliver local public goods due
3

Using the instrumental variable design below, Appendix C reveals a positive association between thegroup-level slave trade exposure and the prevalence of absolutist authority and institutional constraints.

7

to longer time horizons (Baldwin, 2016). Expecting to “rule for life,” chiefs with longer
time horizons have “more incentive than elected politicians to make up-front investments ininstitutions that will improve the ability of their communities to act collectively over the longrun” (Baldwin, 2016, 10). In other words, because they are unelected and lack alternative
commitment devices, chiefs have incentives to invest in institutions to tie their own hands
to assure group members not to exploit their power at the expense of group members,
thereby consolidating and securing their authority and long-run payoﬀs. Consistent with thereasoning, a Ghanaian local expresses, traditional chiefs often lack formal political power
and thus “have to earn trust” of the local population to sustain their authority. 4
Another set of institution-building incentives for local chiefs stems from the competition
for the survival of polities in the face of external insecurity. On the one hand, the logic
of war and state-making failed to contribute to the emergence of territorial states in Africa(Herbst, 2000; Tilly, 1975). On the other hand, warfare to capture scarce people, rather thanabundant lands, and the competition for survival has been a key driver of the continent’s
history (Dincecco et al., 2019; Herbst, 2000). The slave trade played an important role insuch historical trajectories by reducing the incentives to build large-scale territorial states
while intensifying the incentives to raid outsiders for slaves (Whatley & Gillezeau, 2011).
The intensiﬁed insecurity and the increased proﬁts from raiding outsiders can generateinstitution-building incentives for local leaders to better mobilize group members for groupsurvival and collective beneﬁts. As defense from outside threats and the beneﬁts from raiding outsiders constitute public goods, group members have the incentives for free-riding onothers’ eﬀorts. Institutional devices combined with uncontested authority mitigate the collective action challenge by improving the credibility of selective rewards and punishments.
Survival and protection of group beneﬁts in an increasingly competitive environment also
contribute to chiefs’ payoﬀs and generate incentives for local chiefs to invest in ethnic institu4

Quoted in “Chiefs in command: Africa’s chiefs are more trusted than its politicians.” Economist,
https://amp.economist.com/middle-east-and-africa/2017/12/19/
December 19, 2017.
Available at:africas-chiefs-are-more-trusted-than-its-politicians, accessed September 30, 2020.

8

tions. Turning to empirical patterns, Bates (2014) documents suggestive covariation of war,state formation-destruction, and institutional development in Africa during the 1400–1900
period. Acemoglu et al. (2014) and Bellows & Miguel (2006) uncover positive associations
between internally less-competitive chiefdoms and enhanced local collective action capacities,
and between exposure to wartime violence and political mobilization in Sierra Leone.
Group members’ incentives and fear for enslavement are also attuned to the developmentof ethnic institutions. The threats of enslavement stem not only from outside but also frominside. Indeed, intra-group kidnapping and trickery, along with inter-group warfare and raids,
accounted for a major volume of enslavement in Africa (Lovejoy, 2000, 3–4). One the one
hand, the increased demands for protection from external threats incentivize group members
to support authority to coordinate group defense (Whatley, 2014, 471). On the other hand,the strategy itself does not prevent the authority from predating group members for private
advantages and thereby intensify the insecurity from inside. Even if the leadership commits
to group protection, group members have reasons to expect the same leadership to renege onprior commitments once the external threats are mitigated or given the increasing beneﬁts
from slave-raiding group members.5 Without institutional constraints that tie their own
hands, uncontested, absolutist authority would fail to resolve the intra-group commitment
and agency problems. Here, group-level institutions serve as commitment devices that help
the leadership assure and mobilize group members and thereby survive the local competition.

3.2

Ethnic Institutions and the Coup-Civil War Trap

The improved group-level institutions and leadership authority, or centralized absolutist
political structure, inﬂuence the general coup-civil war trade-oﬀ by altering the credibilityof two distinct forms of commitments: threats and promises. First, institutional constraints
enable an ethnic group to make a credible threat of an outside rebellion against political5

The slave trade-induced, deteriorated interpersonal trust (Nunn & Wantchekon, 2011) does not necessarily contradict with improved ethnic institutions. Rather, the undermined trust and informal institutional
constraints can facilitate investments in costly and otherwise unnecessary institutional devices.

9exclusion from state power. As Roessler (2016) and Roessler & Ohls (2018) argue, a key
determinant of the ruler’s decision to oﬀer a power-sharing deal is the capabilities of outsidergroups to credibly threaten and violently overthrow the regime. Political exclusion andinclusion constitute rulers’ strategies of consolidating state power; rulers have little incentive
to grant power-sharing spoils to outsider groups lacking the coercive capacity to challenge
and recapture sovereign authority.
In emphasizing coercive capacity, this article is not suggesting that the demographic sizeof an outsider group is the sole determinant of the credibility of civil war threats. Besides
mobilization potential, the insights of Olson (1965) suggest that collective action problems
also play a key role in altering the credibility of the threat of an outsider rebellion. In
principle, higher mobilizational potential, boosts a group’s ability to challenge the central
government with force (Roessler, 2016; Roessler & Ohls, 2018). The same mobilizational
potential or group size, however, exacerbates the collective action problem and thereby
impedes the group’s actual mobilizational capacity and threat credibility (Ito, 2021). To
translate its mobilizational potential into actual mobilizational capacity, a group leadershipneeds some device or endowments to better mobilize and coordinate group members.
Here, institutional devices and uncontested leadership matter in mitigating the collective
action challenge and shaping the credibility of threats. The underlying idea is that theacute collective action problem for a demographically large group can exacerbate information
problem arising from the uncertainty over the actual mobilizational capacity (Ito, 2021, 989–
990). With large demographic size, not only a leadership with high actual mobilization
capacity but also a leadership without such capacity would have strategic incentives tomake larger demands to gain concessions from the rulers, which gives rise to the classic
risk-return trade-oﬀ (Powell, 1999). While a large concession can buy oﬀ even an outsidergroup with high mobilizational capacity, such an oﬀer undermines the rulers’ own payoﬀ
when the outsider group turns out to be “weak” with low mobilization capacity. The rulers’
screening incentives, in turn, invite the risks of bargaining breakdown into ineﬃcient civil

10

war. Observable, improved institutional devices and uncontested leadership help mitigate
the strategic problem invited by uncertainty. In other words, when conjoined with improved
collective action capacity, mobilizational potential permits a credible threat of an outside
rebellion gives rise to rulers’ incentives to grant war-avoiding, suﬃcient power-sharing spoils.
Second, institutions and eﬀective leadership authority allow an ethnic group to makecredible promises to follow through power-sharing deals and not to stage an insider coupor defect in the face of popular uprising against the ruling coalition. The inclusion of apotential rival in the state power-sharing schemes entails an increased risk of coups frominside, and the coup risk is partly a function of the rival’s coercive capacity and leadershipeﬀectiveness. Indeed, the coercive capacity to stage an outside armed uprising also enhances
the feasibility of an insider coup (Paine, 2021, 512). To circumvent the side-eﬀect of coercive capacities, therefore, an ethnic group needs to credibly promise to follow through thepower-sharing deals and reassure the ruling groups. Yet a contested leadership with limited
capabilities to monitor and control group members undercuts the group’s ability to makecredible commitments. In the absence of eﬀective group authority or the presence of internal
fractionalization, current leadership’s promises not to stage an insider coup and other formsof future behavior would remain incredible (Cunningham, 2013). Just as traditional ethnic institutions (Wig, 2016), absolutist authority creates leadership continuity and thereby
mitigates the challenge of internal fractionalization.
For a power-sharing deal to be self-enforcing, a ruler, as well as an outsider group, must
credibly commit to the political bargain. Here, rulers not only decide whether or not toinclude an outsider group but also strategically choose the amount of power-sharing spoils
distributed to the group (Paine, 2021, 511–512). A possible strategy for a ruler to makecredible commitments to share power is to grant valuable pieces of state power that allow
the outsider groups for eﬀective resistance against the rulers, such as military organizations.
Nonetheless, the spoils of key state institutions also contribute to the coup technologies,
and the strategy can in turn result in higher risks of insider coups by boosting a group’s

11capability to stage coup attempts (Paine, 2021; Roessler, 2016; Roessler & Ohls, 2018).
In sum, the tragically improved institutions and leadership shape postcolonial politics byinﬂuencing the coup-civil war trade-oﬀ. Institutional constraints contribute to the group’s
ability to make credible threats for improved collective action capacity, and less-contested
leadership to monitor and control group members allows for making credible promises tofollow through power-sharing deals. If ethnic groups with greater exposure to the slave tradeare better equipped with such institutional devices and leadership authority, the historicaltreatment leads to not only fewer armed conﬂicts but also higher chances of political inclusion
in postcolonial states. A probable by-product of the increased power-sharing chance is theincreased risks of coups, as increased power-sharing spoils enhance coup technologies.

4

Research Design

The key features of the empirical strategy are twofold. First, to unpack the causal eﬀects,
it adopts an instrumental variable (IV) approach leveraging the soil suitability for cassava
cultivation along with the arbitrary timing of its arrival in Africa. Second, to investigate
the underlying mechanisms and validate the IV design, it employs location- and grouplevel outcome variables reﬂecting postcolonial politics as well as an intermediate outcome
reﬂecting traditional ethnic institutions, followed by a causal mediation analysis and auxiliary
reduced-form regressions reported in the Appendix.

4.1

Data and Measurement

The unit of analysis is ethnic groups nested by host countries, which follows previous studies(e.g., Michalopoulos & Papaioannou, 2013, 2016). The sample ethnolinguistic groups andsettlement areas build upon the map of Murdock (1959) digitized by Nunn (2008). As
described in detail below, the primary treatment indicator of slavery exposure is measured
at Murdock ethnic group level. The list-wise deletion due to missing values in the variables

12(a) Slave Trade Exports, 1700–1900(b) Population Density, 1500(c) Per Capita Slave Exports, 1700–1900(d) Battle Incidents, 1997–2020

Figure 1: Slave Trade Exposure, Precolonial Demography, and Postcolonial Battles
Notes: Thin (solid) segments represent group boundaries (international borders as of 2000).

below leaves 824 unique groups and 1,282 country-group observations nested by 48 hostcountries. Figure 1 depicts the group settlement pattern along with the key variables.
The group-level records of the slave trade come from Nunn & Wantchekon (2011). The
dataset covers the group-level number of slave exports in the transatlantic and Indian
Ocean slave trades, but not the trans-Saharan and Red Sea slave trades. The scope ofthe following analysis is thus limited to the former two slave trades. 6 The key treatment
6

One might wonder how historical group boundaries documented in Murdock (1959) overlap contemporary

13

(
)
Slave Export ivariable, Slavepc
=
ln
0.01
+
, is the population-normalized slave trade exi
Populationi,1500posure, with Slave Export i reﬂecting the number of slave exports of group i during the
1700–1900 (post-cassava arrival) period and Population i,1500 measuring the estimated group
population in 1500 (HYDE data, Kees et al., 2011). As an initial robustness check against
Areameasurement error in Slavepc=
i , I also construct an area-normalized measure, Slavei()
Export iln 0.01 + SlaveArea, with Area i indicating the area in km2 of a group’s homeland.
i

The empirical analysis employs two series of outcome variables, each capturing distinct
aspects of the slave trade legacies. First, to capture the local dynamics of armed conﬂicts, I
utilize the Armed Conﬂict Location and Event Dataset (ACLED, Raleigh et al., 2010). Its
African subset contains 216,808 geocoded violent and non-violent events from January 1997
to July 2020 (version August 1, 2020). The current version of the dataset distinguishes threemajor event categories: “Violent events,” “demonstrations,” and “non-violent actions.” As
the analysis primarily concerns armed conﬂicts, I use the 59,121 records of battle events (asub-category of “violent events” category). Of the 59,121 records, 2,684 entries (4.5%) arecoded with provincial capitals (without precise locations) and excluded from the analysis. I
then overlay the battle locations onto Murdock’s (1959) map to count the number of events
falling into each group’s settlement area to construct Battle (Figure 1(d)).
Second, to construct the group-level outcome variables, I ﬁrst match Murdock ethnic
groups to the ethnic groups in the Ethnic Power Relations (EPR) dataset (Cederman et al.,2010; Vogt et al., 2015), using the Linking Ethnic Data from Africa (LEDA) database andalgorithm (Müller-Crepon et al., 2022). The LEDA algorithm identiﬁes links between ethnic
groups in diﬀerent datasets based on the linguistic tree of the Ethnologue database andlinguistic distances between distinct groups.7 The algorithm eﬀectively connects 939 out ofboundaries. Nunn & Wantchekon (2011) reveal a systematic association between the two slave export
measures, one based on reported ethnicities in the 2005 Afrobarometer survey (ethnicity-based measure)and another based on the locations where the respondents reside today (geography-based measure). The
two measures take the same values for 55 percent of the respondents in their sample (Nunn & Wantchekon,
2011, 3248). Relatedly, Bahrami-Rad et al. (2021) empirically validate the database of Murdock (1959, 1967)
using contemporary survey data of the Standard Demographic and Health Surveys (DHS).
7
I employ the linguistic distance-based matching algorithm of R-package LEDA, with a linguistic distance(ranging from 0 to 1) threshold of 0.2 to generate the Murdock-EPR links. The partial correlation estimates in14

1,282 country-group observations to the EPR groups.
The coding of the group-level outcomes follows previous studies on the coup-civil war
trade-oﬀ (Roessler, 2011, 2016; Roessler & Ohls, 2018). Power Sharing measures the fractionof years in which an ethnic group is included in the national power sharing schemes or coded
as “monopoly,” “dominant,” “senior partner,” or “junior partner” in the EPR dataset. Power
Sharing is normalized by the total years during which a group is available in the dataset
because groups diﬀer in the period in which they are available in the EPR data, and ranges
from 0 (fully excluded) to 1 (fully included). Rebel and Coup are constructed from the
EPR-compatible dataset of Roessler & Ohls (2018). Rebel (Coup) is a dummy variable thattakes a value of 1 if members of an ethnic group launched a rebellion (coup attempt) and 0
otherwise, during the 1946–2013 period.
As explained below, this article leverages soil suitability for cassava cultivation to instrument historical exposure to the slave trade at the ethnic group level. To construct the
instrument, Cassava, I rely on the suitability index developed by the Global Assessment ofLand Use Dynamics, Greenhouse Gas Emissions and Ecosystem Services (GLUES) Project(Zabel et al., 2014).8 The GLUES data provide suitability indexes for 16 crops and generalagriculture, ranging from 0 (least suitable) to 100 (most suitable), based on the climatic,
soil, and topographic conditions at a resolution of 30 arc-second (≈ 1 km). As in Figure 2,
I measure group-level average suitability measures for cassava and overall agriculture basedon the estimates for the baseline 1961–1990 period.
Broadly following previous studies (e.g., Nunn & Wantchekon, 2011), I measure threesets of covariates to facilitate the econometric analysis. To proxy precolonial political andeconomic geographies, the ﬁrst set of covariates includes logged population density in 1500,
logged percentages of cropland and grassland in 1500 (Kees et al., 2011), and ecological
Appendix A reveal little covariate imbalance between matched and non-matched group across the covariates
listed below. As reported in Appendix B, alternative threshold values produce qualitatively similar results.
8
I use the baseline estimates for the 1961–1990 period to construct soil suitability measures. The GLUES
data provide geographically ﬁner information compared to a 10 km-resolution of the Food and Agriculture
Organization (FAO) Global Agro-Ecological Zones database (GAEZ). See Zabel et al. (2014) for a focusedcomparison.

15

(a) Cassava Suitability Index(b) Group-Level Mean Suitability

Figure 2: Soil Suitability for Cassava Cultivation

diversity index (Fenske, 2014). The ﬁrst set also includes dummy variables for the presenceof cities with more than 20,000 inhabitants in 1500 (Reba et al., 2016), node cities in thetrans-Saharan and North African trade networks (Ciolek, 1999), precolonial kingdoms, andprecolonial conﬂicts in a settlement area (1400–1700, Besley & Reynal-Querol, 2014; takenfrom the dataset of Michalopoulos & Papaioannou, 2016).
The second set measures geographic attributes, including logged mean elevation andruggedness (Shaver et al., 2019; USGS 1996), the presence of capital cities and water body,the average temperature during the 1901–1910 period (Harris et al., 2020), logged meanmalaria suitability index (Kiszewski et al., 2004), geographical areas of total homelands inlogged km2 , the share of country-group settlement areas relative to total traditional homelands, population density in 1960, and a dummy variable for partitioning by international
borders. I also include the distances in logged kilometers from settlement centroids to capitals, borders, equator, and coastlines, and the GLUES overall agricultural suitability index.
The third set includes three dummy variables that proxy European inﬂuence during
the colonial period. The dummies are equal to 1 if a settlement area contains colonial
railways, European explorer routes, or missions (Nunn & Wantchekon, 2011). To mitigate
posttreatment bias, these covariates are included for a robustness check purpose.
16

4.2

Soil Suitability for Cassava Cultivation as an Instrument

Any investigation into long-run causal eﬀects encounters the challenges of confounding bias
and measurement error. For example, ethnic groups less able to resist outside violence might
have suﬀered more from the slave trade and experience fewer conﬂicts today, generating a
spurious negative correlation. Probable measurement errors in slave export records invite
another concern for an attenuation bias and further discourage naı̈ve comparisons.
To address these methodological challenges, I rely on an IV approach. Motivated byprevious studies (Cherniwchan & Moreno-Cruz, 2019; Lowes & Montero, 2021; Nunn & Qian,2011), the IV design leverages the variation in land soil suitability for cassava cultivation,rather than overall agricultural suitability or crop yields, as an instrument. In additionto (conditional) independence, a valid instrument in the current context needs to fulﬁll
two conditions: A valid instrument (1) must be strongly correlated with the group-level
exposure to the slave trade (instrument relevance) while (2) not aﬀecting contemporary
political outcomes through any path other than the slave trade (exclusion restriction). 9
Besides the rich variation in Figure 2, there are three reasons to leverage cassava suitability as an instrument. First, unlike crop yields, soil suitability is mainly a function of thetime-constant or slow-moving climatic and topographic conditions exogenous to human activities, which alleviates the concerns for instrument independence. Second, as reported below,
cassava suitability is strongly correlated with group-level slave trade exposure. Cassava did
not exist in Africa before its travel in the sixteenth to seventeenth centuries and gradually
spread across the continent toward the nineteenth century (Alpern, 1992, 24–26; Crosby,
1972, 185–188). With its tolerance to adverse environments and high energy yields, cassava
improved land potential to provide food supplies and sustain a larger population. Whileother New World crops such as maize also contributed to population growth (Cherniwchan& Moreno-Cruz, 2019), another distinctive feature of cassava is its less input-demanding
and capital-intensive nature (El-Sharkawy, 2004, 482). As such, cassava contributes to both
9

Appendix A examines covariate balance across the instrument variable distribution.

17

intercropping in regions with higher agricultural suitability and the production of otherwiseabsent food surplus in less suitable regions. The improved land potential raised the abilities of groups in cassava-suitable areas to engage in warfare while amplifying the slave raid
incentives due to increased supplies of slaves and foodstuﬀ to sustain their travel. Thesepathways also mirror the insights of historical studies, for example, that “the exports of anindividual region responded far more to local supply conditions than they did to the demandof European traders” (Curtin, 1969, 226, emphasis added; see also, Crosby, 1972, 188).
Third, cassava suitability circumvents the concern for the exclusion restriction, andits timing of arrival permits a unique falsiﬁcation test. Originally proposed by Nunn &Wantchekon (2011), a common strategy in previous literature is to instrument slave exportsby the geodesic distance between group locations and coastlines. While the coast-distancedesign is valid in some circumstances, the exclusion restriction is less likely to hold in thecurrent context. For example, the established correlation between economic performance andconﬂict risks, combined with the poor economic performance of landlocked regions, would
violate the assumption by opening up a pathway from coastline distance to postcolonial
conﬂicts. By contrast, as reported later, cassava suitability is not systematically associatedwith established correlates of armed conﬂicts and power sharing, regional development andpopulation size (e.g., Cederman et al., 2010; Fearon & Laitin, 2003; Francois et al., 2015).
A potential pathway that violates the exclusion restriction is that cassava suitability
aﬀects modern outcomes through the slave trade before, as well as after, cassava’s travel toAfrica. While soil suitability is largely time-constant, the current IV strategy implies that
before cassava’s arrival, which altered the role of soil suitability for cassava in shaping theslave trade, we have little reason to see a systematic association between cassava suitability
and slave exports. Unlike maize, which traveled from the New World in the early sixteenth
century and diﬀused rapidly, cassava’s delayed arrival left the earlier parts of the slave trade
unaﬀected while opening up the cassava suitability-slave trade pathway in the later periods.
A “fake” ﬁrst-stage regression with the pre-arrival slave exports as the outcome thus serves18

as a plausible falsiﬁcation test for the suitability-based IV design.
Another concern for the exclusion restriction is that cassava suitability aﬀects modern
political outcomes by directly improving group institutions and leadership rather than exclusively through the slave trade. Mayshar et al. (2017), however, reveal the noticeable roleof cereal cultivation (e.g., maize), rather than perennial root cultivation (e.g., cassava), in
facilitating institution building. Their ﬁndings not only support the current IV design but
also caution against using cereal suitability as an instrument for the slave trade exposure,
as it violates the exclusion restriction by directly aﬀecting ethnic institutions. 10

4.3

Model Speciﬁcation

The IV estimation builds on the following two-stage speciﬁcation:
Slavei = αc + γCassavai + Xi⊤ β + Xic⊤ η + Mic⊤ θ + f1 (sic ) + eic ,(1)\i + Xi⊤ ζ + Xic⊤ ϕ + Mic⊤ λ + f2 (sic ) + uic ,Yic = µc + τIV Slave(2)

where i and c index ethnic groups and host countries, respectively. Yic is one of the outcome\i is the correvariables, Slavei is population- or area-normalized slave trade exposure, Slavesponding ﬁtted values from the ﬁrst stage, and Cassavai is logged cassava suitability index.
Xi and Xic denote the vectors of group-level and country group-level covariates, f1 (sic )and f2 (sic ), with sic = (Longitudeic , Latitudeic ), are two-dimensional cubic polynomials of
settlement centroids to screen out regional heterogeneity, and Mic is a vector of synthetic
covariates representing the Moran eigenvectors to absorb residual spatial autocorrelations(spatial ﬁltering, Griﬃth & Peres-Neto, 2006).11 Country ﬁxed eﬀects αc and µc subsume
cross-country heterogeneity including colonizer policies and (post-)colonial institutions.
10

Appendix D.1 presents a focused analysis and discussion on cassava and non-cassava crop suitability.
Xic includes share of contemporary settlement areas relative to traditional homelands, border distance,
capital distance, capital presence, border partition, and population density (1960). Xi includes the remaining
covariates. In the sample, 42.4% of the country-group observations are partitioned by modern borders. The
spatial ﬁltering approach follows Rozenas & Zhukov (2019). I rely on a distance-based spatial weight matrix
with a 5◦ (≈ 550 km) threshold to construct the synthetic variables.
11

19

The parameter of interest is τIV , which captures the local average treatment eﬀect (LATE)
of the group-level exposure to the slave trade. Following the recommendations of Angrist& Pischke (2008, 197–205), I rely on two-stage least square (2SLS) models throughout theanalysis, rather than nonlinear models that require additional assumptions. To account forpossible error dependence, I report heteroskedasticity and autocorrelation consistent (HAC)
standard errors adjusted for spatial clustering with an exponential kernel (Kelly, 2020).

4.4

Covariate Balance

Table A.1 in the Appendix reports descriptive statistics of the variables, and Figures A.1 to
A.3 present partial correlations to examine covariate balance. Consistent with the instrument(conditional) independence assumption, the covariates are roughly balanced, with exceptions
including overall agricultural suitability and precolonial grassland proportion. To prevent theremaining covariate imbalance from plaguing the estimates, the regression models reported
below always adjust for these two covariates along with precolonial population density andsettlement area as a restricted set of controls.

5

Results

This section reports the main empirical results in three steps. First, I present the ﬁrst-stage
estimates to examine the relevance of the cassava suitability instrument. Second, I report thesecond-stage results on the slave trade-battle association with the location-level outcome. I
then turn to the group-level outcomes to further test the advanced argument, followed by aseries of investigations into the underlying mechanisms and robustness checks.

5.1

Instrument Relevance

Table 1 reports the ﬁrst-stage estimates, along with the coeﬃcients on coast distance, anestablished predictor of the slave trade exposure. Consistent with the current IV strategy,
20

cassava suitability is positively associated with group-level slave exports, with F -statistics
passing Stock & Yogo’s (2005) critical value of 16.38 against weak instruments in justidentiﬁed models, regardless of model speciﬁcations and (sub)samples. Panels A and B
display the ﬁrst-stage results with the population-normalized (columns 1–3) and the areanormalized slave export measures (columns 4–6) for the full 1,282 country-group observations
and the subsample of the EPR-connected observations, respectively. Columns 1 and 4 adjust
for the two covariates with relative imbalance, along with precolonial population density andsettlement area (restricted controls). Columns 2–3 and 5–6 further adjust for the remaining
geographic and precolonial controls and the dummies for European inﬂuence. The Moran’s
I statistics for regression residuals fail to retain statistical signiﬁcance, suggesting that thecovariate adjustments and the spatial ﬁltering approach eﬀectively address spatial autocorrelations. I revisit the validity of the remaining identiﬁcation assumptions in Section 6.

5.2

Results I: Battle Exposure

Table 2 reports the IV-2SLS estimates for battle incidents (columns 2–3 and 5–6), alongwith the uninstrumented ordinary least squares (OLS) results (columns 1 and 4). Despite
the negligible coeﬃcient estimates in the OLS models, the coeﬃcients on the slave export
intensity retains statistical signiﬁcance once instrumented, and remain stable across model
speciﬁcations, which provides initial support for the advanced argument. In addition to its
persistence, the negative association is sizable. Given the log-log speciﬁcation, the coeﬃcientscan readily be interpreted as elasticity. Substantively, even after centuries, the coeﬃcient of−0.955 (column 2) translates into that a 1 percent increase in the slave trade exposure isfollowed by a roughly 0.96 percent decrease in battles in a group’s traditional homeland.
Besides the diﬀerence between the average treatment eﬀect (ATE) and the LATE, the
OLS-IV discrepancy may reﬂect the bias remaining in the OLS estimates, IV-2SLS estimates,or both. First, the OLS estimates might suﬀer from confounding bias in addition to a classical form of measurement error, such that unobserved group-level propensity to engage in21

Table 1: First-Stage Estimates for the Group-Level Slave Trade Exports
Panel A: All Observations
pc

SlaveArea(area-normalized slave
exports, 1700–1900)

Slave(population-normalized
slave exports, 1700–1900)(1)
Cassava Suitability

(2)
∗∗

(4)
∗∗

(5)
∗∗

(6)

0.161
(0.034)0.194(0.037)−0.308∗∗(0.081)

0.197(0.038)−0.293∗∗(0.083)0.197(0.047)0.245(0.049)−0.520∗∗(0.111)0.247∗∗(0.050)−0.503∗∗(0.111)

1,282
0.400
22.227−1.1841,282
0.420
27.223−1.416

1,282
0.421
26.673−1.0791,282
0.420
17.634−0.3981,282
0.449
24.642−0.8751,282
0.451
24.679−0.853

Coast DistanceObservations
Adjusted R2
F-statistic (weak instrument)
Residual Moran’s I (std. deviate)

(3)
∗∗

∗∗

Panel B: LEDA-Matched Observations(1)
Cassava Suitability

Country FE
Lon-Lat polynomial
Moran eigenvectors
Restricted controls
Precolonial controls
Geographic controls
European inﬂuence indicators

(3)
∗∗

(4)
∗∗

(5)
∗∗

(6)

0.199
(0.041)

0.198(0.044)−0.294∗∗(0.088)0.201(0.044)−0.279∗∗(0.090)0.236(0.055)0.239(0.058)−0.499∗∗(0.116)

0.245∗∗(0.059)−0.486∗∗(0.118)939
0.416
23.241
−1.27

939
0.441
20.468−1.129939
0.444
21.015−1.054939
0.430
18.302−1.182939
0.468
16.725−0.564

939
0.470
17.5
−0.84

✓
✓
✓
✓

✓
✓
✓
✓
✓
✓

✓
✓
✓
✓
✓
✓
✓

✓
✓
✓
✓

✓
✓
✓
✓
✓
✓

✓
✓
✓
✓
✓
✓
✓

Coast DistanceObservations
Adjusted R2
F-statistic (weak instrument)
Residual Moran’s I (std. deviate)

(2)
∗∗

∗∗

Notes: + p < 0.1; ∗ p < 0.05; ∗∗ p < 0.01. Kelly’s (2020) heteroskedasticity and autocorrelation consistent(HAC) standard errors adjusted for spatial clustering with an exponential kernel are in parentheses. Restricted controls: Area, Grassland in 1500, Overall Suitability, and Population Density in 1500. Precolonial
controls: Coast Distance, Cropland in 1500, Ecological Diversity Index, Malaria Suitability Index, Trade
Route Cities, Cities in 1500, Precolonial Conﬂict, and Precolonial Kingdom. Geographic controls: Area
Share, Average Temperature (1901–1910), Equator Distance, Elevation, Ruggedness, Partition, Capital(dummy), Capital Distance, Border Distance, Population Density in 1960, and Water Body. European
inﬂuence indicators: Colonial Railway, Explore Routes, and Missions. See Tables A.1 and A.2 for a detailed description of the covariates and descriptive statistics. Moran eigenvectors in Panel B are based on
the second-stage estimates with Power Sharing as the outcome.

ﬁghting is positively associated with the slave trade exposure and modern battle events. For
example, war-prone groups might have been heavily exposed to slave exports due to the increased prisoners of war. A probable result is a downward bias toward zero, which masks theunderlying negative slave trade-battle association. Second, despite the instrument relevance,
22

Table 2: Slave Export Intensity and Battle Events, 1997–2020
Dependent variable: ln(1 + Battle)

Slavepc

OLS(1)

IV-2SLS(2)

IV-2SLS(3)0.001(0.032)−0.955∗(0.459)−0.991∗(0.436)

SlaveArea
Observations
Adjusted R2
F-statistic (weak instrument)
Residual Moran’s I (std. deviate)
Country FE
Lon-Lat polynomial
Moran eigenvectors
Restricted controls
Precolonial controls
Geographic controls
European inﬂuence indicators

1,282
0.559

1,282

1,282

−0.42227.223−0.31326.673−0.512

✓
✓
✓
✓
✓
✓

✓
✓
✓
✓
✓
✓

✓
✓
✓
✓
✓
✓
✓

OLS
(4)

IV-2SLS(5)

IV-2SLS(6)0.019(0.026)−0.753∗(0.372)−0.788∗(0.365)

1,282
0.559

1,282

1,282

24.642−0.67724.679−0.557

✓
✓
✓
✓
✓
✓

✓
✓
✓
✓
✓
✓
✓

−0.7
✓
✓
✓
✓
✓
✓

Notes: + p < 0.1; ∗ p < 0.05; ∗∗ p < 0.01. Kelly’s (2020) spatial HAC standard errors are in parentheses.
See notes in Table 1 for a description of the covariates.

the IV-2SLS estimates might suﬀer from the exclusion restriction violations and unadjusted
spatial trends. The falsiﬁcation tests reported below, however, fail to invalidate the current
IV strategy and thereby support the causal interpretation of the IV-2SLS estimates.

5.3

Results II: Coups, Rebel, and Power Sharing

Turning to the group-level outcomes, the results again lend empirical support to the advanced
institutional change mechanism. Table 3 reports the IV-2SLS and uninstrumented OLS
estimates for Power Sharing, Rebel, and Coup. The IV-2SLS results in columns (2)–(3) and(5)–(6) indicate that groups with higher exposure to the slave trade are more likely to be
included in national power-sharing schemes (panel A), less likely to ﬁght civil wars against thecentral government (panel B), and more likely to stage coup attempts in postcolonial states(panel C). Substantively, the estimates in column (2) indicate that a 10 percent increase in
the slave trade exposure is followed by a τIV × ln(1.1) ≈ 0.018 increase in power sharing
prevalence, a 1.87 percent point decrease in the rebel risk, and a 3.83 percent point increase23

Table 3: Slave Export Intensity and Power Sharing, Rebel, and Coup, 1946–2013
Panel A. Dependent variable: Power Sharing

Slavepc

OLS(1)

IV-2SLS(2)

IV-2SLS(3)−0.003(0.005)0.192∗(0.077)0.186∗(0.076)

SlaveArea
Observations
Adjusted R2
F-statistic (weak instrument)
Residual Moran’s I (std. deviate)

939
0.721

939

939

−0.23920.468−0.56121.015−0.657OLS(1)

IV-2SLS(2)

IV-2SLS(3)0.007(0.005)−0.196∗(0.077)−0.195∗(0.077)OLS(4)

IV-2SLS(5)

IV-2SLS(6)−0.005(0.004)0.159∗(0.067)0.152∗(0.064)

939
0.719

939

939

16.725−0.56717.5−0.568−0.266

Panel B. Dependent variable: Rebel

Slavepc
SlaveArea
Observations
Adjusted R2
F-statistic (weak instrument)
Residual Moran’s I (std. deviate)

939
0.721

939

939

−1.03520.676−0.57420.647−0.393OLS(1)

IV-2SLS(2)

IV-2SLS(3)

0.008(0.007)0.402∗∗(0.137)0.395∗∗(0.138)OLS(4)

IV-2SLS(5)

IV-2SLS(6)0.008+(0.004)−0.163∗(0.066)−0.160∗(0.066)

939
0.719

939

939

16.96
−0.66

17.041−0.558−1.205

Panel C. Dependent variable: Coup

Slavepc
SlaveArea
Observations
Adjusted R2
F-statistic (weak instrument)
Residual Moran’s I (std. deviate)
Country FE
Lon-Lat polynomial
Moran eigenvectors
Restricted controls
Precolonial controls
Geographic controls
European inﬂuence indicators

939
0.721

939

939

0.301

20.4960.802

21.474
0.528

✓
✓
✓
✓
✓
✓

✓
✓
✓
✓
✓
✓

✓
✓
✓
✓
✓
✓
✓

OLS
(4)

IV-2SLS(5)

IV-2SLS(6)0.006(0.007)0.333∗∗(0.118)0.324∗∗(0.115)

939
0.719

939

939

0.068

17.2080.633

17.525
0.338

✓
✓
✓
✓
✓
✓

✓
✓
✓
✓
✓
✓

✓
✓
✓
✓
✓
✓
✓

Notes: + p < 0.1; ∗ p < 0.05; ∗∗ p < 0.01. Kelly’s (2020) spatial HAC standard errors are in parentheses.
See notes in Table 1 for a description of the covariates.

in the coup risk, respectively, highlighting the political legacy of the slave trade.
Figure 3 displays the partial correlations between the (instrumented) slave exports on24

the horizontal axis and each of the four outcomes on the vertical axis, with 100 randomly
chosen observations. Rather than outlier-driven variations, the estimates capture the overall
patterns visible among the sample observations. The partial correlation plots also allow forcase-based interpretations of the regression results. The Bemba and the Bisa in Zambia,
for example, suﬀered severely from the slave trade while granted power sharing spoils andinvolving coup attempts in the postcolonial era. By contrast, the Tama in Chad and western
Sudan have been exposed to conﬂict-related violence while suﬀering less from the slave trade.

5.4

Results III: Mechanisms

While revealing the empirical patterns consistent with the advanced institutionalist argument, the above results speak little about the underlying mechanisms. To complement themain ﬁndings, Appendix C takes a closer look the causal mechanisms in two ways, both
directly and indirectly. First, and directly, I explicitly examine the association between theslave trade exposure and the prevalence of group leadership conjoined with institutions, theprimary force mediating the slave trade and contemporary political outcomes in the presented argument. As explained in detail in Appendix C, I use “Succession to the Oﬃce ofLocal Headman” variable (Column 72) and “Jurisdictional Hierarchy Beyond Local Community” (Column 33) in the dataset of Murdock (1967) to construct a binary indicator of
absolutist authority with enhanced institutions as an outcome variable for this exercise. Consistent with the argument, using the cassava suitability IV design, Table C.1 and Figure C.1
reveal a positive association between the severity of the slave trade exposure and absolutist
authority with enhanced institutions.
Second, and indirectly, to rule out alternative causal pathways, I use a mediation analysis
approach of the sequential g-estimator (Acharya et al., 2016) with the established correlatesof coup-civil war trap, contemporary population size and economic development, as mediators. As Figure C.2 shows, the (controlled) direct eﬀect of the slave trade net contemporary
population size and economic development remains almost identical to the IV estimates. Sug25

Bondjo (DRC)

3

Mayogo, Badjo (Ne) (DRC)
Assini (CDI)
Diola (SEN)

Sonjo (TAZ)

2Tama (SUD)

e(Power Sharing|X)e(Battle|X)

1

Mijertein (SOM)

Shebelle (ETH)0
Kreda (CHA)
Manga (NIR)−1

Warjawa (NIG)

0.3

Masai (KEN)

Kota (CON)

−2

0.0

Sapo (LBR)

Gula (CHA)

Barea (ERI)
Ngwato (SAF)

Katla (SUD)

Tunisians (TUN)

Tama (SUD)

Ohekwe (BOT)
Shluh (MOR)−0.3

Somrai (CHA)

Gule (SUD)

Asben (ALG)

Azjer (NIR)
Maba (SUD)

Mubi (CHA)

Tigre (ERI) Koba (NAM)

Sanga (CON)

Budja (DRC)

Gisiga (CAO) Azjer (NIR)

Berabish (MLI)

Zenega (MLI)

Runga (CEN)
Wurkum (NIG)

Tuburi (CAO)

Gude (CAO)

Luo (KEN)

−3
−0.50

−0.25

0.00

0.25

−0.4

−0.2

e(Slave hat|X)

0.0(a) Battle (τ̂IV = −0.955)(b) Power Sharing (τ̂IV = 0.192)
Kare (DRC)

Ibo (NIG)

Kotopo (CAO)

Lisi (CHA)
Zenega (MLI)

Mende (SIE)

0.4
Guin (CDI)

Berti (SUD)

Nzankara (CEN)

Gule (SUD)

Yombe (DRC) Fali (CAO)

Ngbandi (CEN)

0.5

0.2

e(Slave hat|X)

Hemat (CHA)

Gadames (ALG)
Sokoto (NIR)

Bashi (DRC)e(Coups|X)

Berabish (MLI)e(Rebel|X)

Nyoro (UGA)
Mayogo, Badjo (Ne) (DRC)
Budja (DRC)
Barea (ERI)
Tama (SUD)

Ababda (EGY) Mubi (CHA)
Azjer (NIR)
Samburu (KEN)
Wanga (UGA)

0.0

Deforo (BFO)

Ngwato (BOT)
Nusan (NAM) Afar (ETH)

Gula (CHA)

0.0
Ngwato (SAF)

Arusi (ETH)−0.2

Bideyat (SUD)
Lele (DRC)

Mao (ETH)

−0.8
0.0

Yoruba (NIG)

Antessar (MLI)

Mossi (MLI)

Assini (CDI)−0.4

Wallo (ETH)
Nkundo (DRC)−0.4

Katla (SUD)
Luo (KEN)

Kgalagadi (BOT)

0.2

Zenega (MLI)−0.4e(Slave hat|X)

Igala (NIG)

−0.2

0.0

0.2

e(Slave hat|X)(c) Rebel (τ̂IV = −0.196)(d) Coups (τ̂IV = 0.402)

Figure 3: Partial Correlations Between Per Capita Slave Exports and the Main Outcomes
Notes: The slopes correspond to the coeﬃcient estimates in Column (2) in Tables 2 and 3 for the outcome
variable on the vertical axis. Dots and labels represent 100 randomly chosen observations, with labels
denoting group names with host country name abbreviations in parentheses.

gesting limited roles of these alternative mediating forces in generating the political legaciesof the slave trade, the mediation analysis also lends support for the advanced argument.

5.5

Robustness Checks

Appendix B addresses robustness concerns using (1) the post-cassava arrival slave tradeintensity relative to the pre-arrival intensity (treatment measurement), (2) an alternative
26

battle intensity measure (outcome measurement), (3) alternative linguistic distance thresholds for the LEDA algorithm, and (4) the LEDA-generated matching pair counts as regression weights (group-matching algorithm). Reassuringly, the results remain qualitatively
unchanged in these alternative speciﬁcations.

6

Falsiﬁcation Tests

Thus far, I have interpreted the IV estimates as the causal eﬀects of the slave trade on postcolonial politics. Primary threats to the current IV-based identiﬁcation include exclusion
restriction violations along with unadjusted spatial trends correlated with the instrument
assignment. To address these identiﬁcation concerns, this section presents multifaceted falsiﬁcation tests while relegating the details to Appendix D.

6.1

Placebo Treatment: Pre-Cassava Slave Trade Exposure

The ﬁrst test exploits the timing of cassava’s arrival in Africa in the sixteenth to seventeenth
centuries. Recall that the current design hinges on the ideas that cassava’s arrival generated
exogenous ﬂuctuations in the slave trade exposure, and that the cassava suitability aﬀects
the modern political outcomes exclusively through the slave trade after cassava’s arrival. A
systematic ﬁrst-stage association in the pre-arrival period invalidates the IV design by implying an unblocked IV-outcome pathway through the slave trade exposure before cassava’s
arrival.12 For this exercise, I construct placebo treatment variables using the 1400–1599 period records, and then reestimate the ﬁrst-stage regressions with the pre-arrival measures. 13
I drop the seventeenth-century records as the slave exports during this period may have been
12

Regardless of the underlying mechanisms, (1) a systematic ﬁrst-stage association in the pre-arrival periodand (2) a systematic association between the pre-cassava slave trade exposure and the main outcomes would
jointly violate the exclusion restriction assumption. One might reasonably expect that the ﬁrst association
holds due to, for example, unobservable ecological features or selective migration into cassava-suitable regions
correlated with the slave trade exposure. The absence of the pre-cassava ﬁrst-stage association falsiﬁes thepossible violation even in the presence of the second association.
13
I use the 1400–1599 (instead of the 1400–1500) period records due to dataset availability. The slave
export dataset of Nunn & Wantchekon (2011) aggregates the export records during the ﬁrst 200 years.

27

Table 4: False First-Stage Estimates for Group-Level Slave Exports, 1400–1599
Slavepc
1400–1599(population-normalized
slave exports, 1400–1599)
Cassava Suitability
Coast DistanceObservations
Adjusted R2
F-statistic (weak instrument)
Residual Moran’s I (standard deviate)

SlaveArea
1400–1599(area-normalized slave
exports, 1400–1599)

(1)

(2)

(3)

(4)

0.017
(0.015)−0.138∗∗(0.049)0.018(0.019)−0.105+(0.056)0.026(0.018)−0.172∗∗(0.050)0.028(0.023)−0.131∗∗(0.048)

1,282
0.406
1.222
−0.437

939
0.409
0.858
−0.631

1,282
0.438
2.029
−0.269

939
0.451
1.499
−0.337

Country FE
Lon-Lat polynomial
Moran eigenvectors
Restricted controls
Precolonial controls
Geographic controls
LEDA-connected sample

✓
✓
✓
✓
✓
✓

✓
✓
✓
✓
✓
✓
✓

✓
✓
✓
✓
✓
✓

✓
✓
✓
✓
✓
✓
✓

Notes: + p < 0.1; ∗ p < 0.05; ∗∗ p < 0.01. Kelly’s (2020) spatial HAC standard errors are in parentheses.
See notes in Table 1 for a description of the covariates.

partly (un)aﬀected by cassava suitability.
Table 4 reports the “false” ﬁrst-stage estimates. Consistent with the current IV design,
the cassava-slave trade association turns out to be substantially and statistically insigniﬁcant
once the treatment variables are replaced by the pre-arrival measures. By contrast, regardless of the slave trade measures and the sample, the coeﬃcients on coast distance remain
negative and statistically signiﬁcant in the pre-arrival estimates, thereby underlining thetime-invariant nature of cost distance in shaping the slave trade intensity. The discernible
negative association also suggests that this exercise is not just picking up the demographicallyless severe nature of the slave trade during the pre-cassava period.

6.2

Additional Tests

Appendix D reports four additional falsiﬁcation tests. The ﬁrst additional exercise is motivated by the “false experiment” speciﬁcation of Miguel et al. (2004, 736) and leverages soil
suitability for non-cassava crops as placebo instruments. If the current IV design is valid,28we have little reason to see systematic ﬁrst-stage and second-stage associations once cassava suitability is replaced with placebo instruments, or soil suitability for non-cassava crops(see also, Lowes & Montero, 2021). The second exercise examines reduced-form associations
with contemporary nightlight intensity (as a proxy of regional development) and population
density as auxiliary outcomes, which might mediate the reduced-form association between
the cassava instrument and contemporary political outcomes. Not to violate the exclusion
restriction assumption the cassava instrument should remain unassociated with these correlates of domestic ﬁghting and power sharing. The third exercise uses the ethnic groups intoday’s North and South African states that have barely been exposed to the slave trade asa placebo sample. Because the slave trade pathway is virtually absent in the placebo subsample, we should not see systematic reduced-form associations between cassava suitability
and the outcomes. The last exercise follows the diagnostic procedure proposed by Kelly(2019) and uses spatially autocorrelated artiﬁcial noise as placebo treatment and outcome.
Any systematic association between the artiﬁcial noise and the treatment (outcome) warns
spatial curve-ﬁtting plaguing the analysis. Reassuringly, these additional tests also fail toinvalidate the proposed IV design and thereby lend further credibility to the main ﬁndings.

7

Conclusion

Historical institutions matter in shaping modern political outcomes. This article exploits
African slave trades to unpack the persistent eﬀects of the external shock to local societieson postcolonial politics in Africa. The historically rare shock to local communities induced by
the slave trade left lasting legacies on postcolonial politics by enhancing ethnic institutions
and leadership authority and thereby easing the general coup-civil war trap and commitment
problems. The empirical analysis reveals that ethnic groups with greater historical exposure
to the slave trade are (1) more likely to be included in state power-sharing schemes, (2) lesslikely to experience battle incidents within their traditional homelands, and (3) less likely to29

ﬁght civil wars, while (4) more likely to stage coups in postcolonial states.
These ﬁndings carry implications for broader literature. First, the persistent eﬀects ofthe slave trade on postcolonial politics highlight the importance of historical confounders forinvestigations into the coup-civil war trap. Although limited to the context of postcolonial
Africa, the ﬁndings suggest that the historical exposure to the slave trade and induced shock
systematically alter the chances of power sharing, civil wars, and coups d’état in the modern
era. A related implication follows that when omitted, the group-level slave trade exposure
would invite confounding bias in the investigations into the power sharing-conﬂict link. Related literature now pays careful attention to the underlying selection process and strategicinteractions, yet the historically deeper roots and historical confounders also warrant further
attention when unpacking the determinants of contemporary political outcomes.
Second, the ﬁndings underline how the interactions between historical events and institutional change shape subsequent political outcomes. As ﬁrst suggested by Whatley (2014), theslave trade shock facilitated institutional change and empowered local chiefs. The externallyinvited institutional change, in turn, inﬂuences power sharing, outsider rebellion, and insider
coups in postcolonial African states by altering the institutional constraints on the strategicinteractions. More generally, this article traces out how outside-in shocks to local societies
foster institutional transformations and thereby inﬂuence subsequent trajectories, which inturn generates persistent legacies of historical events.

30

References
Acemoglu, Daron; Davide Cantoni; Simon Johnson & James A Robinson (2011) The consequences of radical reform: The French revolution. American Economic Review 101(7):3286–3307.
Acemoglu, Daron; Simon Johnson & James A Robinson (2001) The colonial origins of comparative development: An empirical investigation. American Economic Review 91(5):1369–1401.
Acemoglu, Daron; Simon Johnson & James A Robinson (2005) The Rise of Europe: Atlantic
Trade, Institutional Change, and Economic Growth. American Economic Review 95(3):546–579.
Acemoglu, Daron; Tristan Reed & James A Robinson (2014) Chiefs: Economic Development
and Elite Control of Civil Society in Sierra Leone. Journal of Political Economy 122(2):319–368.
Acharya, Avidit; Matthew Blackwell & Maya Sen (2016) Explaining causal ﬁndings withoutbias: Detecting and assessing direct eﬀects. American Political Science Review 110(3):512–529.
Alpern, Stanley B (1992) The European Introduction of Crops into West Africa in Precolonial
Times. History in Africa 19: 13–43.
Angrist, Jpshua D & J.-S Pischke (2008) Mostly Harmless Econometrics: An Empiricist’sCompanion. Princeton, NJ: Princeton University Press.
Bahrami-Rad, Duman; Anke Becker & Joseph Henrich (2021) Tabulated nonsense? Testing
the validity of the Ethnographic Atlas. Economics Letters 204: 109880.
Baldwin, Kate (2016) The paradox of traditional chiefs in democratic Africa. Cambridge:
Cambridge University Press.
Bates, Robert H (2014) The Imperial Peace. In: Emmanuel Akyeampong; Robert H. Bates;
Nathan Nunn & James Robinson (eds.) African Development in Historical Perspective.
Cambridge: Cambridge University Press chapter 13.
31

Bellows, John & Edward Miguel (2006) War and institutions: New evidence from Sierra
Leone. American Economic Review 96(2): 394–399.
Besley, Timothy & Marta Reynal-Querol (2014) The Legacy of Historical Conﬂict: Evidence
from Africa. American Political Science Review 108(2): 319–336.
Cederman, L.-E; Andreas Wimmer & Brian Min (2010) Why Do Ethnic Groups Rebel? New
Data and Analysis. World Politics 62(1): 87–119.
Cherniwchan, Jevan & Juan Moreno-Cruz (2019) Maize and precolonial Africa. Journal of
Development Economics 136(March 2018): 137–150.
Ciolek, Matthew T (1999). Old World Trade Routes (OWTRAD) Project Demographic
Digital Data Archives. Available at: www.ciolek.com/OWTRAD/DATA/oddda.html.
Crosby, Alfred (1972) The Columbian Exchange: Biological and Cultural Consequences of1492. Westport, CT: Praeger.
Cunningham, Kathleen G (2013) Actor fragmentation and civil war bargaining: How internal
divisions generate civil conﬂict. American Journal of Political Science 57(3): 659–672.
Curtin, Phillip D (1969) The Atlantic Slave Trade: A Census. Madison: University of
Wisconsin Press.
Dalton, John T & Tin C Leung (2014) Why is polygyny more prevalent in Western Africa?
An African slave trade perspective. Economic Development and Cultural Change 62(4):599–632.
Depetris-Chauvin, Emilio (2016). State history and contemporary conﬂict: Evidence fromsub-saharan africa. Working Paper.
Dincecco, Mark; James Fenske & Massimiliano G Onorato (2019) Is Africa Diﬀerent? Historical Conﬂict and State Development. Economic History of Developing Regions 34(2):209–250.
El-Sharkawy, Mabrouk A (2004) Cassava biology and physiology. Plant Molecular Biology
56(4): 481–501.
Eltis, David; Stephen D Behrendt; David Richardson & Herbert S Klein (1999) The Trans-

32

Atlantic Slave Trade: A Database on CD-Rom. New York: Cambridge University Press.
Fearon, James D (1995) Rationalist Explanations for War. International Organization 49(3):379–414.
Fearon, James D (1999) Why Ethnic Politics and “Pork” Tend to Go Together. Working
paper.
Fearon, James D & David D Laitin (2003) Ethnicity, Insurgency, and Civil War. American
Political Science Review 97(1): 75–90.
Fenske, James (2014) Ecology, trade, and states in pre-colonial africa. Journal of the European Economic Association 12(3): 612–640.
Fenske, James & Namrata Kala (2017) 1807: Economic shocks, conﬂict and the slave trade.
Journal of Development Economics 126(May): 66–76.
Francois, Patrick; Ilia Rainer & Francesco Trebbi (2015) How Is Power Shared in Africa?
Econometrica 83(2): 465–503.
Griﬃth, Daniel A & Pedro R Peres-Neto (2006) Spatial modeling in ecology: The ﬂexibilityof eigenfunction spatial analyses. Ecology 87(10): 2603–2613.
Hale, Henry E (2008) The Foundations of Ethnic Politics: Separatism of States and Nations
in Eurasia and the World. Cambridge: Cambridge University Press.
Harris, Ian; Timothy J Osborn; Phil Jones & David Lister (2020) Version 4 of the CRU TS
monthly high-resolution gridded multivariate climate dataset. Scientiﬁc Data 7(1): 1–18.
Herbst, Jeﬀrey (2000) States and power in Africa: Comparative lessons in authority andcontrol. Princeton, NJ: Princeton University Press.
Ito, Gaku (2021) Why Does Ethnic Partition Foster Violence? Unpacking the Deep Historical
Roots of Civil Conﬂicts. Journal of Peace Research 58(5): 986–1003.
Kees, Klein G; Arthur Beusen; Gerard Van Drecht & Martine De Vos (2011) The HYDE 3.1
spatially explicit database of human-induced global land-use change over the past 12,000
years. Global Ecology and Biogeography 20(1): 73–86.
Kelly, Morgan (2019). The Standard Errors of Persistence. Working Paper.

33

Kelly, Morgan (2020). Understanding persistence. Working Paper.
Kiszewski, Anthony; Andrew Mellinger; Andrew Spielman; Pia Malaney; Sonia E Sachs &Jeﬀrey Sachs (2004) A global index representing the stability of malaria transmission.
American Journal of Tropical Medicine and Hygiene 70(5): 486–498.
Lovejoy, Paul E (2000) Transformations in Slavery: A History of Slavery in Africa (2 ed.).
Cambridge: Cambridge University Press.
Lowes, Sara & Eduardo Montero (2021) The Legacy of Colonial Medicine in Central Africa.
American Economic Review 111(4): 1284–1314.
Lowes, Sara; Nathan Nunn; James A Robinson & Jonathan L Weigel (2017) The Evolutionof Culture and Institutions: Evidence From the Kuba Kingdom. Econometrica 85(4):1065–1091.
Manning, Patrick (1990) Slavery and African Life: Occidental, Oriental, and African Slave
Trades. Cambridge: Cambridge University Press.
Mayshar, Joram; Omer Moav; Zvika Neeman & Luigi Pascali (2017). Cereals, Appropriability and Hierarchy.
Michalopoulos, Stelios & Elias Papaioannou (2013) Pre-Colonial Ethnic Institutions andContemporary African Development. Econometrica 81(1): 113–152.
Michalopoulos, Stelios & Elias Papaioannou (2016) The Long-Run Eﬀects of the Scramble
for Africa. American Economic Review 106(7): 1802–1848.
Miguel, Edward; Shanker Satyanath & Ernst Sergenti (2004) Economic Shocks and Civil
Conﬂict: An Instrumental Variables Approach. Journal of Political Economy 112(4):725–753.
Müller-Crepon, Carl; Yannick I Pengl & N.-C Bormann (2022) Linking Ethnic Data fromAfrica. Journal of Peace Research 59(3): 425–435.
Murdock, George P (1959) Africa: Its Peoples and their Culture History.

New York:

McGraw-Hill.
Murdock, George P (1967) Ethnographic Atlas: A Summary. Ethnology 6(2): 109–236.

34

Nunn, Nathan (2008) The Long-Term Eﬀects of Africa’s Slave Trades. Quarterly Journal of
Economics 123(1): 139–176.
Nunn, Nathan & Nancy Qian (2011) The potato’s contribution to population and urbanization: Evidence from a historical experiment. Quarterly Journal of Economics 126(2):593–650.
Nunn, Nathan & Leonard Wantchekon (2011) The slave trade and the origins of Mistrust in
Africa. American Economic Review 101(7): 3221–3252.
Olson, Mancur (1965) The logic of collective action: Public goods and the theory of groups.
Cambridge, MA: Harvard University Press.
Paine, Jack (2019) Ethnic Violence in Africa: Destructive Legacies of Pre-Colonial States.
International Organization 73(3): 645–683.
Paine, Jack (2021) The Dictator’s Power-Sharing Dilemma: Countering Dual Outsider
Threats. American Journal of Political Science 65(2): 510–527.
Paine, Jack (2022) Reframing the Guardianship Dilemma: How the Military’s Dual Disloyalty Options Imperil Dictators. American Political Science Review 116(4): 1425–1442.
Posner, Daniel N (2004) The Political Salience of Cultural Diﬀerence: Why Chewas andTumbukas Are Allies in Zambia and Adversaries in Malawi. American Political Science
Review 98(4): 529–545.
Powell, Robert (1999) In the Shadow of Power: States and Strategies in International Politics. Princeton, NJ: Princeton University Press.
Powell, Robert (2006) War as a Commitment Problem. International Organization 60(1):169–203.
Raleigh, Clionadh; Andrew M Linke; Havard Hegre & Joakim Karlsen (2010) Introducing
ACLED: An armed conﬂict location and event dataset. Journal of Peace Research 47(5):651–660.
Reba, Meredith; Femke Reitsma & Karen C Seto (2016) Spatializing 6,000 years of global
urbanization from 3700 BC to AD 2000. Scientiﬁc Data 3(160034).

35

Roessler, Philip (2011) The enemy within: Personal rule, coups, and civil war in Africa.
World Politics 63(2): 300–346.
Roessler, Philip (2016) Ethnic politics and state power in Africa: The logic of the coup-civil
war trap. Cambridge: Cambridge University Press.
Roessler, Philip & David Ohls (2018) Self-Enforcing Power Sharing in Weak States. International Organization 72(2): 423–454.
Rozenas, Arturas & Yuri M Zhukov (2019) Mass repression and political loyalty: Evidence
from Stalin’s ‘terror by hunger’. American Political Science Review 113(2): 569–583.
Shaver, Andrew; David B Carter & Tsering W Shawa (2019) Terrain Ruggedness and Land
Cover: Improved Data for All Research Designs. Conﬂict Management and Peace Science
36(2): 191–218.
Stock, James H & Motohiro Yogo (2005) Testing for Weak Instruments in Linear IV Regression. In: James H. Stock & Donald W.K. Andrews (eds.) Identiﬁcation and Inference
for Econometric Models: Essays in Honor of Thomas Rothenberg. New York: Cambridge
University Press.
Teso, Edoardo (2019) The long-term eﬀect of demographic shocks on the evolution of genderroles: Evidence from the transatlantic slave trade. Journal of the European Economic
Association 17(2): 497–534.
Tilly, Charles (1975) Reﬂections on the History of State Making. In: Charles Tilly (ed.) The
Formation of National States in Western Europe. Princeton, NJ: Princeton University
Press chapter 1.
U.S. Geological Survey (1996). Global 30-Arc-Second Elevation Data, GTOPO30.
Vogt, M.; N.-C Bormann; Seraina Ruegger; L.-E Cederman; Philipp Hunziker & Luc Girardin (2015) Integrating Data on Ethnicity, Geography, and Conﬂict: The Ethnic Power
Relations Data Set Family. Journal of Conﬂict Resolution 59(7): 1327–1342.
Whatley, Warren C (2014) The transatlantic slave trade and the evolution of political authority in West Africa. In: Emmanuel Akyeampong; Robert H. Bates; Nathan Nunn & James

36

Robinson (eds.) Africa’s Development in Historical Perspective. Cambridge: Cambridge
University Press chapter 15, , 460–488.
Whatley, Warren C (2022) How the International Slave Trades Underdeveloped Africa. Journal of Economic History 82(2): 403–441.
Whatley, Warren C & Rob Gillezeau (2011) The Fundamental Impact of the Slave Trade on
African Economies. In: P. Rhode; J. Rosenbloom & D. Weiman (eds.) Economic Evolution
and Revolution in Historical Time. Stanford , 86–110.
Wig, Tore (2016) Peace from the Past: Pre-colonial Political Institutions and Contemporary
Ethnic Civil Wars in Africa. Journal of Peace Research 53(4): 509–524.
Zabel, Florian; Birgitta Putzenlechner & Wolfram Mauser (2014) Global agricultural land
resources: A high resolution suitability evaluation and its perspectives until 2100 underclimate change conditions. PLoS ONE 9(9): 1–12.

37

Online Appendix for“On the Persistent Effects of the Slave
Trade on Postcolonial Politics in Africa”
April 14, 2023

Contents
Introduction

A1

A Covariate Balance and Model Dependence
A1
A.1 Covariate Balance Across Cassava Suitability . . . . . . . . . . . . . . . . . . A1
A.2 Covariate Balance Across Group Links . . . . . . . . . . . . . . . . . . . . . A4
A.3 Model Dependence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A9
B Robustness Checks
B.1 Alternative Slave Trade Exposure Measures . . . . . . . . . . . . . . . . . .
B.2 Alternative Battle Incident Measure . . . . . . . . . . . . . . . . . . . . . . .
B.3 Alternative Murdock-EPR Group-Matching Threshold . . . . . . . . . . . .
B.4 Group-Level Regressions with the LEDA Matching-Pair Weights . . . . . . .

A9
A12
A13
A16
A18

C Mechanisms and Alternative Mediating Forces
A18
C.1 Intermediate Outcome: Traditional Ethnic Institutions . . . . . . . . . . . . A20
C.2 Alternative Mediating Forces: Contemporary Population Size and Economic
Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A23
D Falsiﬁcation Tests
A26
D.1 Placebo Instrument: Soil Suitability for Non-Cassava Crops . . . . . . . . . A27
D.2 Non-Treatment Pathways: Slave Trade and the Correlates of the Coup-Civil
War Trap . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A30
D.3 Placebo Sample: Groups in North and South African States . . . . . . . . . A32
D.4 Spatial Noise Placebo Test . . . . . . . . . . . . . . . . . . . . . . . . . . . . A33
References

A37

Introduction
This Online Appendix presents a series of descriptive statistics and regression estimates tosupplement the empirical analysis reported in “On the Persistent Eﬀects of the Slave Tradeon Postcolonial Politics in Africa.” Section A presents summary statistics and examines
covariate balance and possible model dependence. Section B presents the details of the robustness checks, and Section C further investigates the underlying mechanisms by examining
the shorter-run association between the slave trade and ethnic institutions and the roles ofalternative mediating forces. Section D reports the results of the falsiﬁcation tests.
The empirical analysis was conducted in R 4.1.0 for macOS. I primarily rely on sf and sp
packages for geoprocessing (Bivand et al., 2013; Pebesma, 2018; Pebesma & Bivand, 2005),
LEDA package for the Murdock-EPR group matching (Müller-Crepon et al., 2022), and lfe
and spatialreg packages for regression analysis (Bivand & Piras, 2015; Gaure, 2013). I use
Kelly’s (2020) method and the accompanying R-scripts (https://github.com/morganwkelly/
persistence) to compute the heteroskedasticity and autocorrelation consistent (HAC) standard errors adjusted for spatial clustering reported in the regression tables.

A

Covariate Balance and Model Dependence

Table A.1 reports the descriptive statistics for the baseline and additional posttreatment
covariates in the empirical analysis. Table A.2 summarizes the sources of the key variables,
including the variables for the robustness checks and falsiﬁcation tests. The remainder ofthis section examines covariate balance and possible model dependence.

A.1

Covariate Balance Across Cassava Suitability

Figure A.1 displays the partial correlation between the cassava suitability index and each of
the main covariates to examine covariate balance. For each of the covariates, the instrument
and the covariate on the vertical axis are partialled out by the remaining baseline covariates,
A1

Table A.1: Descriptive Statistics
Observations

Mean

SD

MedianIQR

Panel A: Dependent Variables
BattlePower Sharing
Rebel
Coup

1, 282
939
939
939

1.765
0.434
0.371
0.477

1.775
0.355
0.483
0.500

1.386
0.310
0.000
0.000

3.045
0.571
1.000
1.000

Panel B: Treatment and Instrument
pc

Slave (per capita slave exports)
SlaveArea (per area slave exports)
Cassava Suitability−3.958−3.6982.849

1, 282
1, 282
1, 282

1.334
1.737
1.264

−4.605−4.605
3.411

0.453
0.975
0.843

Panel C: Covariates
Restricted controls
Area
Grassland in 1500
Overall Suitability
Population Density in 1500

1, 282
1, 282
1, 282
1, 282

9.801
1.314
3.497
0.578

1.321
0.647
1.033
1.539

9.716
1.366
3.820
0.659

1.904
0.859
0.451
1.789

Precolonial controls
Coast Distance
Cropland in 1500
Ecological Diversity Index
Malaria Suitability Index
Trade Route Cities
Cities in 1500
Precolonial Conﬂict
Precolonial Kingdom

1, 282
1, 282
1, 282
1, 282
1, 282
1, 282
1, 282
1, 282

5.978
0.708
0.279
2.320
0.055
0.024
0.054
0.414

1.159
0.724
0.229
1.053
0.227
0.154
0.226
0.493

6.300
0.473
0.292
2.731
0.000
0.000
0.000
0.000

1.456
0.804
0.474
1.247
0.000
0.000
0.000
1.000

Geographic controls
Area Share
Avg. Temperature, 1901–1910
Equator Distance
Elevation
Ruggedness
Partition
Capital (dummy)
Capital DistanceBorder Distance
Population Density in 1960
Water Body

1, 282
1, 282
1, 282
1, 282
1, 282
1, 282
1, 282
1, 282
1, 282
1, 282
1, 2820.642
24.220
6.817
6.160
3.438
0.424
0.034
5.970
3.928
1.984
0.596

0.390
3.257
0.928
0.859
1.062
0.494
0.180
0.811
1.410
1.737
0.491

0.837
24.918
6.980
6.205
3.478
0.000
0.000
6.029
4.067
2.165
1.000

0.766
4.865
0.929
1.163
1.507
1.000
0.000
0.929
2.156
1.858
1.000

European inﬂuence
Colonial Railway
1, 2820.145
Explore Routes
1, 282
0.408
Missions
1, 282
0.379
Notes: SD = standard deviation, IQR = interquartile range.

0.352
0.492
0.485

0.000
0.000
0.000

0.000
1.000
1.000

group homeland location polynomial, and country ﬁxed eﬀects. Solid segments represent
linear regression ﬁts, and the texts denote the partial correlation estimates.
Speciﬁcally, for each of the baseline covariates, I estimate the following linear regressions
A2

Table A.2: Sources of the Key Variables
Variable
Dependent Variables
BattlePower Sharing
Rebel
Coup
Treatment and Instrument
Slavepc
SlaveArea
Cassava Suitability
Covariates
Overall Agricultural Suitability
Area
Population Density in 1500
Cropland in 1500
Grassland in 1500
Average Temperature, 1901–1910
Trade Route Cities
Elevation
Ruggedness
Equator Distance
Ecological Diversity Index
Malaria Suitability Index
Water Body
Precolonial Kingdom
Precolonial Conﬂict
Cities in 1500
Area ShareBorder Distance
Capital (dummy)
Capital Distance
Coast DistancePartition

Population Density in 1960
Colonial Railway
Explore Routes
Missions
Robustness checks
and Falsiﬁcation Tests∆Slavepc∆SlaveArea
xSub Battle
Nightlight in 2010
Population Density in 2010
Non-Cassava Crop Suitability

Source and Description
ACLED data (Raleigh et al., 2010)
Roessler & Ohls (2018)
Roessler & Ohls (2018)
Roessler & Ohls (2018)
Per capita slave exports, 1700–1900 (Nunn & Wantchekon, 2011), group population
counts are computed using the HYDE data (Kees et al., 2011)
Per area slave exports, 1700–1900 (Nunn & Wantchekon, 2011), settlement areas arecomputed based on Murdock (1959), digitized by Nunn (2008)
GLUES data (Zabel et al., 2014)
GLUES data (Zabel et al., 2014)
Murdock’s (1959) map digitized in shapeﬁle-format by Nunn (2008)
HYDE data, version 3.1 (Kees et al., 2011)
HYDE data, version 3.1 (Kees et al., 2011)
HYDE data, version 3.1 (Kees et al., 2011)
CRU TS, version 4 (Harris et al., 2020)OWTRAD data (Ciolek, 1999)
USGS (1996)
Shaver et al. (2019)
Natural Earth (https://www.naturalearthdata.com/110m-physical-vectors/)
White’s (1983) map provided in shapeﬁle-format by Fenske (2014)
Kiszewski et al. (2004)
Natural Earth (https://www.naturalearthdata.com/10m-physical-vectors/)
Besley & Persson (2011), taken from the replication data of Michalopoulos & Papaioannou (2016)
Besley & Persson (2011), taken from the replication data of Michalopoulos & Papaioannou (2016)
Historical Urban Population data (Reba et al., 2016)
Murdock’s (1959) map digitized in shapeﬁle-format by Nunn (2008) and cshapes data
(Weidmann et al., 2010)
cshapes data (Weidmann et al., 2010)
cshapes data (Weidmann et al., 2010)
cshapes data (Weidmann et al., 2010)
cshapes data (Weidmann et al., 2010)
Murdock’s (1959) map digitized in shapeﬁle-format by Nunn (2008) and cshapes data
(Weidmann et al., 2010). Following Michalopoulos & Papaioannou (2016), Partition
is coded 0 if more than 90% of a historical homeland falls into a single country.
HYDE data, version 3.1 (Kees et al., 2011)
Nunn & Wantchekon (2011)
Nunn & Wantchekon (2011)
Nunn & Wantchekon (2011)

Per capita increase in slave exports in 1700–1900 relative to the 1400–1599 slave exports(Nunn & Wantchekon, 2011)
Per area increase in slave exports in 1700–1900 relative to the 1400–1599 slave exports(Nunn & Wantchekon, 2011).
xSub data (Zhukov et al., 2019)
DMSP-OLS (NSDC 2014)
WorldPop (2016)
GLUES data (Zabel et al., 2014)

with the instrument (cassava suitability) and the kth covariate as the dependent variables:′
Cassavai = αc + Xic,−k
β + g(sic ) + eic ,

(A.1)

′
η + h(sic ) + uic ,Xic,k = µc + Xic,−k

(A.2)

A3

where the right-hand-side variables and coeﬃcients are deﬁned analogously to the two-stage
speciﬁcation in the main text.1 Since the cassava-covariate association attributable to othercovariates, X−k , is partialled out, the remaining covariance in each panel represents theconditional correlation between the cassava suitability index on the horizontal axis and thecovariate on the vertical axis. All variables are standardized for a comparative purpose.
Reassuringly, the broadly ﬂat regression slopes in Figure A.1 indicate a fair balance formost of the covariates. Exceptions include overall agricultural suitability and grassland
proportion (panels (a) and (b)) out of the 23 baseline covariates, and to a lesser extent,
ecological diversity index and precolonial conﬂict presence (panels (c) and (e)). The absenceof systematic associations in the remaining panels suggests that, as far as other covariates areadjusted for, these covariates remain incapable of inducing confounding bias. Adjusting forthese covariates increases the accuracy of the model if they are associated with the outcomes.
To address the remaining imbalance, the estimations in the main text always adjust for thesecovariates with relative imbalance across the levels of cassava suitability.
Figure A.2 displays the corresponding partial correlations for the observations connectedto the EPR groups by the LEDA algorithm, and the results remain qualitatively similar to thefull sample estimates in Figure A.1. An exception is the stronger partial correlation between
cassava suitability and water body presence in the LEDA-connected sample (panel (h)).

A.2

Covariate Balance Across Group Links

One might wonder if the group matching procedure with the LEDA algorithm induces sample selection (collider) bias in the group-level outcome results by omitting the groups without
the Murdock-EPR links. To address the concern, Figure A.3 examines the partial correlations for the covariate imbalance between the LEDA-matched and unmatched observations.
Speciﬁcally, I replace the dependent variable in equation (A.1) with a dummy variable thattakes the value of 1 if a country-group (Murdock group partitioned by contemporary borders)
1

The baseline covariates refer to the covariates excepting the three indicator variables for European inﬂuence. The results remain qualitatively unchanged regardless of the adjustments for the indicator variables.

A4

(a) Overall Suitability(b) Grassland (1500)(c) Precol. Conﬂict(d) Precol. Kingdom(e) Ecol. Diversity Index(f) Area(g) Area Share(h) Water Body(i) Pop. Density (1500)(j) Cropland (1500)(k) Elevation(l) Ruggedness

Figure A.1: Partial Correlations between Cassava Suitability and the Baseline Covariates, All
Observations (Nobs = 1, 282). Continued on next page.
Notes: Each panel displays standardized partial correlation between the cassava suitability index(instrument) and one of the baseline covariates. The cassava suitability index on the horizontal axis ispartialled out by the baseline covariates excepting for the vertical axis variable, location polynomial, andcountry ﬁxed eﬀects, while the covariate on the vertical axis is partialled out by the baseline covariates
excepting itself, location polynomial, and country ﬁxed eﬀects, as in equations (A.1) and (A.2). Solidsegments represent linear regression ﬁts, and the text denotes the corresponding regression slope estimates.

A5(m) Malaria Suit. Index(n) Coast Distance(o) Equator Distance(p) Temp. (1901–1910)(q) Cities (1500)(r) Trade Route Cities(s) Capital (dummy)(t) Capital Distance(u) Border Distance(v) Partition(w) Pop. Density (1960)

Figure A.1 (contd.): Partial Correlations between Cassava Suitability and the Baseline Covariates,
All Observations (Nobs = 1, 282)

A6

(a) Overall Suitability(b) Grassland (1500)(c) Precol. Conﬂict(d) Precol. Kingdom(e) Ecol. Diversity Index(f) Area(g) Area Share(h) Water Body(i) Pop. Density (1500)(j) Cropland (1500)(k) Elevation(l) Ruggedness

Figure A.2: Partial Correlations between Cassava Suitability and the Baseline Covariates, LEDAConnected Observations (Nobs = 939). Continued on next page.
Notes: See notes in Figure A.1.

A7

(m) Malaria Suit. Index(n) Coast Distance(o) Equator Distance(p) Temp. (1901–1910)(q) Cities (1500)(r) Trade Route Cities(s) Capital (dummy)(t) Capital Distance(u) Border Distance(v) Partition(w) Pop. Density (1960)

Figure A.2 (contd.): Partial Correlations between Cassava Suitability and the Baseline Covariates,
LEDA-Connected Observations (Nobs = 939)

A8

observation is linked to EPR groups, and 0 otherwise, and add the cassava suitability index
to covariate vector X in equations (A.1) and (A.2). I then subsequently estimate the partial
correlations between the LEDA link dummy variable and the covariates (panels (a)–(w))
along with the cassava suitability index (panel (x)). The partial correlation estimates arelargely negligible and reveal fair balance across the covariates and, importantly, the cassava
instrument, inviting little concern for arbitrary sample selection.

A.3

Model Dependence

One might wonder if covariate imbalance and possible model dependence invite any bias
and robustness concerns. To guard against arbitrary picking of model speciﬁcations, I follow
Ho et al. (2007) and replicate the ﬁrst-stage estimate for each of 2 19 = 524, 288 model
speciﬁcations with diﬀerent combinations of 19 covariates, along with the two covariateswith relative imbalance (overall suitability and grassland proportion), precolonial population
density and settlement area, location polynomial, and country ﬁxed eﬀects.
Figure A.4 shows the empirical distribution of the coeﬃcient estimates obtained fromthe 524, 288 model speciﬁcations, for each of the population-normalized and area-normalized
slave export index. The baseline point estimates in Table 1 are close to the mean of therespective empirical distribution of coeﬃcient estimates. The roughly normally distributed
coeﬃcient estimates suggest that the inclusion or exclusion of the remaining covariates areunlikely to invite systematic bias into the ﬁrst-stage estimates beyond random noise.

B

Robustness Checks

This section addresses the remaining robustness concerns. Speciﬁcally, the following sections
present a series of robustness checks using (1) an alternative slavery exposure measure,the post-cassava arrival slave trade exposure relative to the pre-arrival intensity, (2) analternative battle event measure, and (3) alternative linguistic distance thresholds for theA9(a) Overall Suitability(b) Grassland (1500)(c) Precol. Conﬂict(d) Precol. Kingdom(e) Ecol. Diversity Index(f) Area(g) Area Share(h) Water Body(i) Pop. Density (1500)(j) Cropland (1500)(k) Elevation(l) Ruggedness

Figure A.3: Partial Correlations between the LEDA Group Link and the Baseline Covariates(Nobs = 1, 282). Continued on next page.
Notes: See notes in Figure A.1. The LEDA link dummy variable is coded using the LEDA-based matchingprocedure with the linguistic distance threshold of 0.2 as in the main text.

A10

(m) Malaria Suit. Index(n) Coast Distance(o) Equator Distance(p) Temp. (1901–1910)(q) Cities (1500)(r) Trade Route Cities(s) Capital (dummy)(t) Capital Distance(u) Border Distance(v) Partition(w) Pop. Density (1960) (x) Cassava Suitability

Figure A.3 (contd.): Partial Correlations between the LEDA Group Link and the Baseline Covariates (Nobs = 1, 282)
Notes: See notes in Figure A.1.

A11

Mean of 524288 specifications(=0.1883)

0.14

0.16

0.18

Point estimate in the Baseline Model(=0.1935)

0.20

0.22

Mean of 524288 specifications(=0.2368)

0.18

0.20

0.22

Point estimate in the Baseline Model(=0.2454)

0.24

0.26

0.28

First−Stage Coefficient (gamma)

First−Stage Coefficient (gamma)(a) Population-Normalized Slave Trade Export(b) Area-Normalized Slave Trade Export

Figure A.4: Empirical Distributions of the First-Stage Cassava-Slave Trade Association
Notes: Empirical distributions of the ﬁrst-stage coeﬃcients on cassava suitability across 2 19 = 524, 288
model speciﬁcations, with (a) population-normalized and (b) area-normalized slavery exposure measures as
the ﬁrst-stage outcomes. Blue vertical segments indicate the baseline estimates reported in Table 1 in themain text. Magenta vertical segments represent the mean values of the empirical distributions, and therange between dashed vertical segments cover the corresponding 95% conﬁdence intervals.

LEDA algorithm. I also report (4) the group-level results with regression weights accounting
for the multiple matching pairs generated by the LEDA algorithm.

B.1

Alternative Slave Trade Exposure Measures

The current IV strategy implies that ethnic groups with higher cassava soil suitability shouldhave experienced increased slave trade exposure after cassava’s arrival in Africa (1700–1900)
relative to the pre-arrival exposure (1400–1599). A possible alternative treatment index thus
measures the diﬀerence between the slave trade intensity in the pre-cassava and the postarrival periods. To empirically examine this observable implication, I employ ﬁrst-diﬀerence)(∆Slave iand ∆SlaveArea=
equivalent slave export measures, ∆Slavepc
=
ln
0.01
+
i
i
Population i,1500()
iln 0.01 + ∆Slave, with ∆Slave i = Slave Export i,1700–1900 − Slave Export i,1400–1599 + s andArea is = |min(Slave Export i,1700–1900 − Slave Export i,1400–1599 )|.
Tables B.1 and B.2 reestimate the baseline regressions in Tables 2 and 3 in the main
text with the alternative population-normalized (∆Slavepci ) and area-normalized measures(∆SlaveArea). While the relatively weaker ﬁrst-stage associations caution against the interi
pretation of the second-stage estimates as consistent estimates, the coeﬃcient signs remainunchanged for all outcomes and provide additional support for the main ﬁndings.

A12

Table B.1: Slave Trade Intensity and Battle Events, 1997–2020, Alternative Slave Trade Measures
Dependent variable: ln(1 + Battle)∆Slavepc

OLS(1)

IV-2SLS(2)

IV-2SLS(3)

0.081(0.078)−2.925∗(1.445)−3.162∗(1.379)∆SlaveArea
Country FE
Lon-Lat polynomial
Moran eigenvectors
Restricted controls
Precolonial controls
Geographic controls
European inﬂuence indicators
Observations
Adjusted R2
F-statistic (weak instrument)
Residual Moran’s I (std. deviate)

OLS
(4)

IV-2SLS(5)

IV-2SLS(6)

0.078(0.104)−3.431∗(1.645)−3.623∗(1.689)

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1,282

1,282

1,282
0.557

1,282

1,282

27.223−0.11726.673−0.33924.642−0.01624.679−0.774−0.879−0.09

Notes: + p < 0.1; ∗ p < 0.05; ∗∗ p < 0.01. Kelly’s (2020) standard errors adjusted for spatial clusteringwith an exponential kernel are in parentheses. Restricted controls: Area, Grassland in 1500, Overall
Suitability, and Population Density in 1500. Precolonial controls: Coast Distance, Cropland in 1500,
Ecological Diversity Index, Malaria Suitability Index, Trade Route Cities, Cities in 1500, Precolonial
Conﬂict, and Precolonial Kingdom. Geographic controls: Area Share, Average Temperature (1901–
1910), Equator Distance, Elevation, Ruggedness, Partition, Capital (dummy), Capital Distance, BorderDistance, Population Density in 1960, and Water Body. European inﬂuence indicators: Colonial Railway, Explore Routes, and Missions. See Tables A.1 and A.2 for a detailed description of the covariates.

B.2

Alternative Battle Incident Measure

Another remaining concern is the reliance on the ACLED data to construct the battle exposure in the empirical analysis. Reasonably, the exclusive reliance on a single database invites
a robustness concern such that the ﬁndings might depend on the use of the ACLED data.
To address this robustness concern, I employ the Cross-National Data on Sub-National
Violence (xSub) database to construct an alternative battle incident measure (Zhukov et al.,2019). The xSub database hosts diﬀerent datasets with diﬀerent spatio-temporal coverage,
including the ACLED data and the UCDP Georeferenced Event Data (GED, Sundberg &Melander, 2013), which are widely used in previous studies. The xSub database also allows
for combining the entries in diﬀerent sources into a single dataset based on the Matching
Event Data by Location, Time, and Type (MELTT) algorithm (Donnay et al., 2019). For

A13

Table B.2: Slave Trade Intensity and Power Sharing, Rebel, and Coup, 1946–2013, Alternative
Slave Trade Measures
Panel A. Dependent variable: Power Sharing∆Slavepc

OLS(1)

IV-2SLS(2)

IV-2SLS(3)−0.016(0.012)0.617∗(0.269)0.585∗(0.255)

939
0.729

939

939

14.978−0.52614.551−0.556∆SlaveArea
Observations
Adjusted R2
F-statistic (weak instrument)
Residual Moran’s I (std. deviate)−0.124OLS(4)IV-2SLS(5)

IV-2SLS(6)−0.037∗(0.014)

0.673∗(0.295)0.647∗(0.289)

939
0.73

939

939

13.9690.108

14.2320.349−0.168Panel B. Dependent variable: Rebel∆Slavepc

OLS(1)

IV-2SLS(2)

IV-2SLS(3)

0.013(0.011)−0.631∗(0.252)−0.615∗(0.250)∆SlaveArea
Observations
Adjusted R2
F-statistic (weak instrument)
Residual Moran’s I (std. deviate)

939
0.824
−0.808939

939

15.586−0.27614.938−0.624OLS(4)IV-2SLS(5)

IV-2SLS(6)0.009(0.023)−0.687∗(0.274)−0.680∗(0.275)

939
0.821

939

939

13.8350.007

14.2770.128−0.641Panel C. Dependent variable: Coup∆Slavepc

OLS(1)

IV-2SLS(2)

IV-2SLS(3)0.026(0.016)

1.291∗∗(0.480)1.243∗∗(0.459)∆SlaveArea
Observations
Adjusted R2
F-statistic (weak instrument)
Residual Moran’s I (std. deviate)
Country FE
Lon-Lat polynomial
Moran eigenvectors
Restricted controls
Precolonial controls
Geographic controls
European inﬂuence indicators

939
0.72

939

939

0.305

15.511
1.232

14.95
0.375

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IV-2SLS(5)

IV-2SLS(6)0.026(0.028)1.407∗∗(0.536)1.374∗∗(0.504)

939
0.718

939

939

0.789

14.0370.969

14.544
0.392

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Notes: + p < 0.1; ∗ p < 0.05; ∗∗ p < 0.01. Kelly’s (2020) standard errors adjusted for spatial clusteringwith an exponential kernel are in parentheses. Restricted controls: Area, Grassland in 1500, Overall
Suitability, and Population Density in 1500. Precolonial controls: Coast Distance, Cropland in 1500,
Ecological Diversity Index, Malaria Suitability Index, Trade Route Cities, Cities in 1500, Precolonial
Conﬂict, and Precolonial Kingdom. Geographic controls: Area Share, Average Temperature (1901–
1910), Equator Distance, Elevation, Ruggedness, Partition, Capital (dummy), Capital Distance, BorderDistance, Population Density in 1960, and Water Body. European inﬂuence indicators: Colonial Railway, Explore Routes, and Missions. See Tables A.1 and A.2 for a detailed description of the covariates.

A14

Figure B.1: xSub Battle Events, 1997–2019
Notes: Darker shades represent greater prevalence of battle events. Thin (solid) segments represent groupboundaries (international borders as of 2000). Settlement areas with missing values and the areas outsideof the study region are left blank.

consistency, the analysis restricts its temporal scope to the 1997–2019 period as the ACLED
data, one of the major sources of MELTT-integrated xSub event dataset for the study
region, covers the period after 1997 and the temporal coverage of the xSub database islimited to the pre-2019 period. I rely on the xSub-MELTT algorithm with a 1-km-by-1-day
spatiotemporal window to extract the multi-source event dataset for each of the 48 countries
in the study region. This procedure leaves 79,985 records of battle incidents (“DYAD A B,”
government-opposition interactions, Zhukov et al., 2019) in the study region during the 1997–2019 period. I then aggregate the xSub battle incidents at the country-group level following
the geoprocessing procedure described in the main text to construct the alternative battle
exposure measure, xSub Battle, depicted in Figure B.1.
Table B.3 reestimates the OLS and IV-2SLS regressions in Table 2 in the main text withln(1 + xSub Battle) as the second-stage outcome variable. The IV-2SLS estimates remain
remarkably stable with the alternative battle incident measure, with the coeﬃcient signs andsizes remaining almost unchanged compared to the baseline of Table 2. The results suggestthat the ﬁnding is unlikely to be a product of the reliance on a speciﬁc dataset.
A15

Table B.3: Slave Trade Intensity and xSub Battle Events, 1997–2019
Dependent variable: ln(1 + xSub Battle)

Slavepc

OLS(1)

IV-2SLS(2)

IV-2SLS(3)

0.029(0.035)−0.916∗(0.413)−1.013∗(0.417)

SlaveArea
Observations
Adjusted R2
F-statistic (weak instrument)
Residual Moran’s I (std. deviate)
Country FE
Lon-Lat polynomial
Moran eigenvectors
Restricted controls
Precolonial controls
Geographic controls
European inﬂuence indicators

1,282
0.567

1,282

1,282

−0.59726.685−1.661+26.413
−1.47

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IV-2SLS(5)

IV-2SLS(6)0.049+(0.029)−0.723∗(0.340)−0.806∗(0.346)

1,282
0.568

1,282

1,282

−1.06124.497−1.27824.539−1.67+

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Notes: + p < 0.1; ∗ p < 0.05; ∗∗ p < 0.01. Kelly’s (2020) standard errors adjusted for spatial clusteringwith an exponential kernel are in parentheses. Restricted controls: Area, Grassland in 1500, Overall
Suitability, and Population Density in 1500. Precolonial controls: Coast Distance, Cropland in 1500,
Ecological Diversity Index, Malaria Suitability Index, Trade Route Cities, Cities in 1500, Precolonial
Conﬂict, and Precolonial Kingdom. Geographic controls: Area Share, Average Temperature (1901–
1910), Equator Distance, Elevation, Ruggedness, Partition, Capital (dummy), Capital Distance, BorderDistance, Population Density in 1960, and Water Body. European inﬂuence indicators: Colonial Railway, Explore Routes, and Missions. See Tables A.1 and A.2 for a detailed description of the covariates.

B.3

Alternative Murdock-EPR Group-Matching Threshold

The main analysis matches the ethnic groups in Murdock (1959) to the groups in the Ethnic
Power Relations (EPR) dataset (Cederman et al., 2010; Vogt et al., 2015) using the Linking
Ethnic Data from Africa (LEDA) algorithm (Müller-Crepon et al., 2022). Speciﬁcally, it
employs the linguistic distance algorithm in LEDA package for R, with the language distance (ranging from 0 to 1) threshold of 0.2. Although the partial correlation estimates in
Figure A.3 fail to reveal noticeable covariate imbalance between the LEDA-matched and nonmatched groups, the threshold setting drops roughly 26.8% (343) of the 1,282 country-groupobservations from the analysis and might invite a concern for arbitrary sample selection.
To address this concern, Table B.4 reestimates the analysis of Table 3 in the main text
with an alternative threshold value set to the sample median of 0.423 for the LEDA-Murdock

A16

Table B.4: Slave Trade Intensity and Power Sharing, Rebel, and Coup, 1946–2013, with an Alternative LEDA Linguistic Distance Threshold Set to the Sample Median
Panel A. Dependent variable: Power Sharing

Slavepc

OLS(1)

IV-2SLS(2)

IV-2SLS(3)

0.002(0.004)0.167∗∗(0.073)0.155∗∗(0.073)

SlaveArea
Observations
Adjusted R2
F-statistic (weak instrument)
Residual Moran’s I (std. deviate)

1,155
0.763
−0.517

1,155

1,155

24.498−0.88223.979−0.582OLS(4)

IV-2SLS(5)

IV-2SLS(6)−0.001(0.003)0.135∗∗(0.060)0.126∗∗(0.061)

1,155
0.763

1,155

1,155

21.104−0.82219.823−0.657−0.573

Panel B. Dependent variable: Rebel

Slavepc

OLS(1)

IV-2SLS(2)

IV-2SLS(3)−0.003(0.004)−0.201∗∗(0.080)−0.208∗∗(0.082)

SlaveArea
Observations
Adjusted R2
F-statistic (weak instrument)
Residual Moran’s I (std. deviate)

1,155
0.819
−0.311

1,155

1,155

25.312−0.57525.463−0.262OLS(4)

IV-2SLS(5)

IV-2SLS(6)−0.002(0.004)−0.162∗∗(0.066)−0.168∗∗(0.068)

1,155
0.818

1,155

1,155

20.744−0.634

20.639−0.416−0.205

Panel C. Dependent variable: Coup

Slavepc

OLS(1)

IV-2SLS(2)

IV-2SLS(3)−0.005(0.005)0.303∗∗(0.124)0.299∗∗(0.124)

SlaveArea
Observations
Adjusted R2
F-statistic (weak instrument)
Residual Moran’s I (std. deviate)

1,155
0.769

1,155

1,155

0.232

24.492−0.21124.990−0.194

LEDA language dist. threshold
Country FE
Lon-Lat polynomial
Moran eigenvectors
Restricted controls
Precolonial controls
Geographic controls
European inﬂuence indicators

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IV-2SLS(5)

IV-2SLS(6)−0.007(0.005)0.245∗∗(0.102)0.243∗∗(0.104)

1,155
0.770

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Notes: + p < 0.1; ∗ p < 0.05; ∗∗ p < 0.01. Kelly’s (2020) standard errors adjusted for spatial clusteringwith an exponential kernel are in parentheses. Restricted controls: Area, Grassland in 1500, Overall
Suitability, and Population Density in 1500. Precolonial controls: Coast Distance, Cropland in 1500,
Ecological Diversity Index, Malaria Suitability Index, Trade Route Cities, Cities in 1500, Precolonial
Conﬂict, and Precolonial Kingdom. Geographic controls: Area Share, Average Temperature (1901–
1910), Equator Distance, Elevation, Ruggedness, Partition, Capital (dummy), Capital Distance, BorderDistance, Population Density in 1960, and Water Body. European inﬂuence indicators: Colonial Railway, Explore Routes, and Missions. See Tables A.1 and A.2 for a detailed description of the covariates.

A17

matching. The results remain qualitatively unchanged regardless of the threshold values forthe group-matching procedure and the resultant (increased) sample size.

B.4

Group-Level Regressions with the LEDA Matching-Pair Weights

Relatedly, the group-level regressions and the Murdock-EPR matching procedure permit
multiple matches such that the LEDA algorithm generates, when available, multiple matching pairs for a single EPR (Murdock) group with equal language distances. 2 For example,
the matching rule allows a single instance of outside rebellion (recorded at the EPR-grouplevel) to be counted for more than one time when the corresponding EPR group is matchedwith more than one Murdock group. This matching rule might invite another concern forthe possible inﬂation in the group-level outcome variables.
To address this concern, Table B.5 reestimates the group-level regressions with the number of matched pairs as regression weights. Although the coeﬃcient sizes vary, the coeﬃcientsigns remain unchanged from the baseline estimates reported in Table 3 in the main text. As
in the baseline results, the coeﬃcients of the slave trade measures fail to retain substantial
and statistical signiﬁcance in the uninstrumented OLS estimates (columns 1 and 4). By contrast, once instrumented, the slave trade exposure measures are positively associated with
Power Sharing and Coup while negatively associated with Rebel (columns 2–3 and 4–5).

C

Mechanisms and Alternative Mediating Forces

An untested observable implication of the advanced institutional mechanism is the positive
association between slavery exposure and improved ethnic institutions. The argument also
implies that traditional institutions play a substantial role in generating the long-run eﬀect,
and, indirectly, the treatment eﬀect should remain stable regardless of the adjustments foralternative mediating forces. This section empirically examines these additional implications.
2

The number of matching pairs aggregated at Murdock-group level ranges from 1 to 10, with the sample
median (mean) of 2 (2.959).

A18

Table B.5: Slave Trade Intensity and Power Sharing, Rebel, and Coup, 1946–2013, with the LEDAMatching Weights
Panel A. Dependent variable: Power Sharing

Slavepc

OLS(1)

IV-2SLS(2)

IV-2SLS(3)−0.001(0.003)0.109∗(0.053)0.104∗(0.051)

SlaveArea
Observations
Adjusted R2
F-statistic (weak instrument)
Residual Moran’s I (std. deviate)

939
0.854
1.429

939

939

14.756
1.183

14.921
0.182

OLS
(4)

IV-2SLS(5)

IV-2SLS(6)−0.002(0.003)0.105∗(0.053)0.100+(0.053)

939
0.854

939

939

11.3391.431

11.032
0.327

1.502

Panel B. Dependent variable: Rebel

Slavepc

OLS(1)

IV-2SLS(2)

IV-2SLS(3)0.004(0.003)−0.114∗(0.055)−0.095+(0.053)

SlaveArea
Observations
Adjusted R2
F-statistic (weak instrument)
Residual Moran’s I (std. deviate)

939
0.898
0.249

939

939

14.759−0.50216.369−0.653OLS(4)

IV-2SLS(5)

IV-2SLS(6)0.005+(0.003)−0.115∗(0.056)−0.102∗(0.051)

939
0.898

939

939

11.684−0.531

13.625
−0.75

0.323

Panel C. Dependent variable: Coup

Slavepc

OLS(1)

IV-2SLS(2)

IV-2SLS(3)0.001(0.005)0.311∗(0.128)0.277∗∗(0.103)

SlaveArea
Observations
Adjusted R2
F-statistic (weak instrument)
Residual Moran’s I (std. deviate)
LEDA matched-pair weights
Country FE
Lon-Lat polynomial
Moran eigenvectors
Restricted controls
Precolonial controls
Geographic controls
European inﬂuence indicators

939
0.832

939

939

1.4

13.4220.312

18.842
0.478

✓
✓
✓
✓
✓
✓
✓

✓
✓
✓
✓
✓
✓
✓

✓
✓
✓
✓
✓
✓
✓
✓

OLS
(4)

IV-2SLS(5)

IV-2SLS(6)−0.0005(0.005)0.271∗(0.110)0.265∗∗(0.103)

939
0.833

939

939

1.164

12.4520.988

15.406
0.883

✓
✓
✓
✓
✓
✓
✓

✓
✓
✓
✓
✓
✓
✓

✓
✓
✓
✓
✓
✓
✓
✓

Notes: + p < 0.1; ∗ p < 0.05; ∗∗ p < 0.01. Kelly’s (2020) standard errors adjusted for spatial clusteringwith an exponential kernel are in parentheses. Restricted controls: Area, Grassland in 1500, Overall
Suitability, and Population Density in 1500. Precolonial controls: Coast Distance, Cropland in 1500,
Ecological Diversity Index, Malaria Suitability Index, Trade Route Cities, Cities in 1500, Precolonial
Conﬂict, and Precolonial Kingdom. Geographic controls: Area Share, Average Temperature (1901–
1910), Equator Distance, Elevation, Ruggedness, Partition, Capital (dummy), Capital Distance, BorderDistance, Population Density in 1960, and Water Body. European inﬂuence indicators: Colonial Railway, Explore Routes, and Missions. See Tables A.1 and A.2 for a detailed description of the covariates.

A19

C.1

Intermediate Outcome: Traditional Ethnic Institutions

The advanced institutional change mechanism emphasizes traditional ethnic institutions asa primary mediating force to generate the slave trade legacies. Although Whatley (2014)
reveals a positive association between slave trade exposure and absolutist political authority
in West Africa, it warrants a focused examination given the proposed IV design, additional
emphasis on institutional constraints, and the expanded sample.
Recall that this article emphasizes institutional development or the combination of absolutist political authority and institutional constraints, in addition to the ways of succession
or appointing local headmen (Whatley, 2014). I construct the dependent variable for the
additional analysis, Institutionalized Absolutist, to measure the characteristics of traditional
ethnic institutions along with the two aspects: absolutist authority and political institutions. First, to measure the degree of absolutist authority, I follow the absolutism index of
Whatley (2014) based on the dataset of Murdock (1967) Ethnographic Atlas. Speciﬁcally,
the absolutism indicator, Absolutist, takes the value of 1 if a group is coded as “patrilineal heir” or “matrilineal heir” in “Succession to the Oﬃce of Local Headman” variable(Column 72), and 0 otherwise.3 Second, I use variable “Jurisdictional Hierarchy Beyond
Local Community” (Column 33) in Ethnographic Atlas to measure group-level institutional
development. The variable counts the number of a group’s political organizations or jurisdictional levels, and ranges from 0 (no levels of jurisdictional hierarchy, or “stateless societies”),
through 1–2 (“petty and larger paramount chiefdoms or their equivalent”), to 3–4 (“large
states,” Murdock, 1967, 160). Previous studies use the variable to proxy the degree of grouplevel institutions and political centralization (e.g., Fenske, 2014; Gennaioli & Rainer, 2007;
Michalopoulos & Papaioannou, 2013; Wig, 2016). Third, I interact the absolutist dummywith the jurisdictional hierarchy variable to construct the institutionalized absolutism index.
The resultant index is coded in a categorical scale, with a higher score indicating an ethnic

3

I rely on the digitized version of the Ethnographic Atlas of Giuliano & Nunn (2018) to construct thevariables that measure the aspects of traditional institutions.

A20

Table C.1: Slave Trade Intensity and Traditional Ethnic Institutions
Dependent Variable:
Slavepc
First stage(1)
Cassava Suitability

Institutionalized
Absolutist
OLS
IV-2SLS(2)(3)

0.598∗∗(0.101)0.464∗(0.233)−0.004(0.046)

Slavepc

SlaveArea
First stage(4)
0.721∗∗(0.119)SlaveArea
Observations
Adjusted R2
F-statistic (weak instr.)
Residual Moran’s I(standard deviate)
Region FE
Moran eigenvectors
Lon-Lat polynomial
Precolonial controls
Geographic controls

429
0.352
35.265−0.098429
0.104−0.378

✓
✓
✓
✓
✓

✓
✓
✓
✓
✓

429

Institutionalized
Absolutist
OLS
IV-2SLS
(5)
(6)

0.003
(0.038)0.384∗(0.195)

429
0.104

429

35.265−0.257429
0.404
36.7210.135−0.38136.721−0.616

✓
✓
✓
✓
✓

✓
✓
✓
✓
✓

✓
✓
✓
✓
✓

✓
✓
✓
✓
✓

Notes: + p < 0.1; ∗ p < 0.05; ∗∗ p < 0.01. Kelly’s (2020) spatial HAC standard errors with an exponential
kernel are in parentheses. Precolonial controls: Coast Distance, Cropland in 1500, Ecological Diversity
Index, Malaria Suitability Index, Trade Route Cities, Cities in 1500, Precolonial Conﬂict, Precolonial
Kingdom, and Population Density in 1500. Geographic controls: Area, Grassland in 1500, Overall Suitability, Average Temperature (1901–1910), Equator Distance, Elevation, Ruggedness, and Water Body.

group with absolutist political authority and developed institutions.
I then regress Institutionalized Absolutist on the slave trade exposure measures to examine
the intermediate institutional legacies of the slave trade. The model speciﬁcation follows thebaseline two-stage IV speciﬁcation, with the spatial polynomial term is speciﬁed as a linear
polynomial given the reduced sample size. As the analysis concerns the pre-independence
periods, I use Murdock ethnic group (not split by contemporary borders) as the unit ofanalysis and replace the country ﬁxed eﬀects with region ﬁxed eﬀects. I also exclude thecovariates that reﬂect modern international borders and capital locations. 4
Table C.1 reports the slave trade-customary institution association along with the ﬁrststage cassava-slavery association, and Figure C.1 displays the partial correlations between
4

Excluded covariates are Area Share, Border Distance, Capital (dummy), Capital Distance, Partition,
and Population Density (1960). The regression models adjust for the remaining baseline covariates.

A21

4

e(Institutionalized Absolutist|X)

Nyoro
Fur
Suku

2

YorubaBunda

Nyaneka
Antaisaka

Lamba

Swazi
Mao

Kukuruku
ThongaKikuyu0

Lotuko

Lala
Bogo

Koma
Lango

KarekareAbarambo

Kentu

Malinke
Fali
Banda

Drawa
KagoroAlagya

Gusii

Fanti

Yeke

−2

−1.0

−0.5

0.0

0.5

e(Slave hat|X)

Figure C.1: Partial Correlation Between Per Capita Slave Exports and Ethnic Institutions
Notes: The partial correlation estimate is based on Column (2) in Table C.1. Black dots and labels
indicate the names of 30 randomly chosen ethnic groups in Murdock (1967).

the slavery exposure (instrumented by cassava suitability) and the centralized absolutist
index. Although the results remain suggestive given possible non-random attrition in thelist of Murdock (1967), the intermediate-outcome regressions reveal the patterns consistent
with the advanced institutional mechanism. The ﬁrst-stage estimates in columns (1) and(4) reveal a positive association between cassava suitability and the slave trade exposure,with the F-statistics passing the critical value of 16.38 for both population-normalized andarea-normalized slave export measures. The second-stage results in columns (3) and (6)reveal a positive association between slavery exposure and centralized absolutist politicalstructure. The coeﬃcients of the slave trade measures are positive and retain statisticalsigniﬁcance at the conventional 5% level in the IV-2SLS estimates (columns 3 and 6) while
the uninstrumented OLS estimates remain indeterminate (columns 2 and 5).
Admittedly, the cross-sectional nature of Ethnographic Atlas does not allow us to trace thetemporal variations of ethnic institutions during the slave trade period. Indeed, Ethnographic
Atlas is derived from the ethnographic works documented during the colonial period, andthe list provides a cross-sectional snapshot of ethnic groups as of the period. In other words,
the positive association is obtained from the sample of survivor ethnic groups in the colonial
age is likely to be driven by the combination of the institutional adoption and the sample
A22

selection process (see also, Tilly, 1975, 14–15; King et al., 1994, 136). Closely related to thisperspective, Bates (2014) points out (431–432, emphasis added):“While scanty and highly imperfect, the data suggest that the competition among thestates in pre-colonial Africa may have given rise to a cross-section of polities in whichstate-formation and economic development co-varied. Either because they responded
to external threats by seeking to strengthen their economic base, or because interstatecompetition winnowed from the sample polities that were weak and poor, we ﬁnd thatsocieties that were ‘state-like’ also appear to have been developmental.”

Unfortunately, the limited temporal coverage of the available records does not allow further
investigation into the magnitude of possible sample selection bias.
Despite the limitation, the estimates suggest that greater exposure to the slave trade wasindeed followed by an increased prevalence of absolutist political authority and improved
institutions. These results provide additional evidence in support of the institutional changemechanism by highlighting the shorter-run political legacies of the slave trade.

C.2

Alternative Mediating Forces: Contemporary Population Sizeand Economic Development

A possible strategy to examine the role of precolonial customary institutions in generating
the political legacies of the slave trade is to regress modern political outcomes on indigenous
ethnic institutions. Yet such a naive strategy invites another set of methodological concerns
including the probable non-random assignment of ethnic institutions as well as the nonrandom sample attrition in the list of the Ethnographic Atlas.
However, it is still possible to further explore the causal pathways indirectly by investigating the roles of alternative mediating forces. The intuition follows that if the slave tradelegacies operate through alternative mediating forces rather than precolonial customary institutions, then the claimed treatment eﬀect should be attenuated away once we adjust for
the alternative mediators. Possible mediating forces include contemporary population size
A23

and regional development, or two of the established correlates of power sharing and domestic ﬁghting (e.g., Cederman et al., 2010; Collier & Hoeﬄer, 2004; Fearon & Laitin, 2003;
Francois et al., 2015). Previous studies also uncover a negative association between the slave
trade exposure and contemporary economic development (Nunn, 2008). If, on the one hand,contemporary population size and economic development play a major role in generating theslave trade legacies, then the unblocked causal pathways would undermine the validity of
the advanced institutional mechanism. If, on the other hand, the reported treatment eﬀects
remain robust to additional adjustments for these alternative mediating forces, then theanalysis provides indirect but additional support for the advanced institutional mechanism.
I present two series of estimations to examine how these alternative mediating forces
participate in the underlying causal pathways. First, I simply add the group-level logged
population density (WorldPop, 2016) and logged nightlight intensity (as a proxy of economic
performance, NSDC 2014) in 2010 to the main IV-2SLS speciﬁcations (columns 2 and 5 inTables 2 and 3 in the main text) and reestimate the regressions. If the slave trade legacies
primarily operate through the contemporary population and regional development channels,
then the LATE estimate should be attenuated away once these pathways are blocked.
Second, to account for possible posttreatment bias, I use the sequential g-estimator toquantify the roles of contemporary population density and regional development in the underlying causal pathways (Acharya et al., 2016a; Vansteelandt, 2009). The quantity of interestin the sequential g-estimator is the average controlled direct eﬀect (ACDE), which measuresthe direct treatment eﬀect with the mediators ﬁxed at some arbitrary value for all units. 5
The ACDE estimate should be substantially smaller than the baseline treatment eﬀect estimate if contemporary population size and economic development play a major role in the5

I recenter the two mediators, population density and nightlight intensity, to respective sample meansin the sequential g-estimations so that the ACDE estimates measure the direct eﬀect with the mediators
ﬁxed at the mean values for all observations. Note that the results of the g-estimation should be interpretedwith caution due to potential violations of the sequential unconfoundedness assumption: (1) the treatment(slave trade exposure) is conditionally ignorable given the pretreatment confounders, and (2) the mediators(population density and nightlight intensity) are conditionally ignorable given the treatment, pretreatment
confounders, and intermediate confounders (Acharya et al., 2016a, 519).

A24

Per capita slave trade

Per capita slave trade(1) IV−2SLS adjusting for mediators (tau=−0.844)(1) IV−2SLS adjusting for mediators (tau=0.195)(2) g−estimator (tau=−0.992)(2) g−estimator (tau=0.205)

Per area slave trade

Per area slave trade(3) IV−2SLS adjusting for mediators (tau=−0.674)(3) IV−2SLS adjusting for mediators (tau=0.164)(4) g−estimator (tau=−0.783)

−2.2

−2

−1.8

−1.5

−1.2

−1

−0.8

−0.5

(4) g−estimator (tau=0.17)

−0.2

0

0.2

0.4

−0.1

0

0.1

0.3

0.4

0.5

Coefficient on Slave Trade Exposure(a) Dependent variable: Battle(b) Dependent variable: Power Sharing

Per capita slave trade

Per capita slave trade(1) IV−2SLS adjusting for mediators (tau=−0.199)(1) IV−2SLS adjusting for mediators (tau=0.414)(2) g−estimator (tau=−0.195)(2) g−estimator (tau=0.429)

Per area slave trade

−0.5

0.2

Coefficient on Slave Trade Exposure−0.4

Per area slave trade(3) IV−2SLS adjusting for mediators (tau=−0.167)(3) IV−2SLS adjusting for mediators (tau=0.348)(4) g−estimator (tau=−0.163)(4) g−estimator (tau=0.358)

−0.3

−0.2

−0.1

0

0.1

−0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

Coefficient on Slave Trade Exposure

Coefficient on Slave Trade Exposure(c) Dependent variable: Rebel(d) Dependent variable: Coup

0.7

0.8

Figure C.2: IV-2SLS and Sequential G-Estimates Adjusting for Population Density and NightlightIntensity in 2010
Notes: Dots indicate the point estimates with per capita (solid) and per area (hollow) slave trade exposure.
Segments represent the corresponding 95% conﬁdence intervals based on Kelly’s (2020) spatial HAC
standard errors with an exponential kernel (IV-2SLS) and Acharya et al.’s (2016a) consistent varianceestimator (sequential g-estimator, implemented in DirectEﬀect package for R).

underlying causal pathways. By contrast, a nonzero ACDE implies the presence of causal
channels not captured by the adjusted mediators (Acharya et al., 2016a, 517). To keep theestimates comparable, I plug the ﬁtted values of the slave trade measures (instrumented
by the cassava suitability) into the sequential g-estimation. In the sequential g-estimations,some of the group-level covariates are included as intermediate confounders, or posttreatment
variables aﬀected by the treatment while confounding the mediator-outcome association. 6
Figure C.2 reports the results of the IV-2SLS regressions and sequential g-estimations
adjusting for contemporary population density and nightlight intensity for each of the main
outcome variables, with the per capita and per area slave trade measures. Consistent with theadvanced institutional change mechanism, the estimates reveal limited roles of contemporary
population size and regional development in linking the slave trade exposure and political
6

The set of intermediate confounders includes Border Distance, Capital (dummy), Capital Distance,
Partition, and Population Density (1960). The ﬁrst-stage regressions do not adjust for these covariates topredict the slave trade exposure passed to the sequential g-estimator.

A25

outcomes in postcolonial states. Regardless of model speciﬁcations, the treatment eﬀect
estimate remains stable and statistically signiﬁcant, with the mediator-adjusted estimatesremaining almost identical to the unadjusted estimates reported in the main text.
Also note that the association between precolonial centralization and contemporary development (e.g., Gennaioli & Rainer, 2007; Michalopoulos & Papaioannou, 2013) does notnecessarily undermine the claimed political legacies of the slave trade. The suitability-based
IV strategy remains valid as far as cassava suitability aﬀects contemporary political outcomes and their correlates exclusively through the slave trade. Admittedly, the exclusion
restriction assumption would be violated if cassava suitability instrument aﬀects the main
political outcomes through non-slave trade pathways. However, and reassuringly, a seriesof falsiﬁcation tests reported in the next section also fail to detect such unblocked directchannels between slave trade exposure and modern political outcomes.

D

Falsiﬁcation Tests

The current IV design yields several testable implications for falsiﬁcation tests: (1) thetreatment take-up attributable to the instrument (ﬁrst-stage association) should be absent in
the pre-cassava arrival period; (2) the treatment take-up should be absent or weaker for noncassava crop suitability; (3) the cassava instrument should not be systematically associatedwith the correlates of contemporary armed conﬂicts and power sharing; (4) the cassava
instrument should not be systematically associated with the modern political outcomes inthe subsample without the slave trade treatment; and (5) the main ﬁndings should disappear
once we replace the treatment or the outcomes with artiﬁcial spatial noise. The ﬁrst two
implications correspond to the key identiﬁcation idea that cassava’s travel from the New
World generated exogenous ﬂuctuation in the endogenous treatment; and the third andfourth implications inform us about the validity of the exclusion restriction assumption. 7
7

Note that the ﬁrst exercise can also inform us about the validity of the exclusion restriction assumption.
The current IV design hinges on the assumption that cassava suitability aﬀects the modern political outcomes

A26

The last implication addresses the concern for spatial curve-ﬁtting recently highlighted by
Kelly (2019, 2020) with a particular focus on persistence studies.
To empirically test these implications, this article leverages, respectively, (1) the arbitrary
timing of cassava’s arrival in Africa and the slave exports during the pre-arrival period(placebo treatment), (2) soil suitability measures for non-cassava crops (placebo instrument),(3) auxiliary reduced-form regressions, (4) North and South African observations barely
exposed to the slave trade (placebo subsample), and (5) spatially autocorrelated artiﬁcial
noise (placebo treatment and outcome). The main text reports the ﬁrst exercise, and theremainder of this section reports the second to ﬁfth tests.

D.1

Placebo Instrument: Soil Suitability for Non-Cassava Crops

Motivated by the approaches of Lowes & Montero (2021) and Miguel et al. (2004), the second
falsiﬁcation test focuses on the ﬁrst stage and exploits the land soil suitability for non-cassava
crops as placebo instruments.8 The advanced IV design implies that the strong ﬁrst-stage
association should be speciﬁc to cassava. If cassava suitability, rather than omitted heterogeneity, inﬂuenced the group-level exposure to the slave trade, we have little reason to see
systematic ﬁrst-stage associations for the suitability measures for non-cassava crops. 9 Discernible ﬁrst-stage associations for the placebo instruments thus invalidate the proposed IV
strategy by suggesting that the cassava-slave export association reﬂects unadjusted heterogeneity correlated with the soil suitability for cassava as well as non-cassava crops.
exclusively through the slave trade exposure after cassava’s arrival in Africa. Therefore, any systematic prearrival ﬁrst-stage association would violate the exclusion restriction assumption by suggesting instrumentoutcome pathways other than the treatment, the exposure to the slave trade in the post-arrival period.
8
Miguel et al. (2004, 736) present an identiﬁcation check of a “false experiment” by regressing economic
growth (endogenous treatment) on future rainfall shock (false instrument) instead of past rainfall shock (trueinstrument). Lowes & Montero (2021) estimate a series of reduced-form regressions with placebo instruments,
non-cassava crop suitability relative to millet suitability, instead of their main instrument, cassava suitability
relative to millet suitability.
9
The analysis focuses on the ﬁrst-stage associations between non-cassava crop suitability and the slave
trade exposure, rather than reduced-form associations. Even if the suitability measure for a non-cassava
crop is systematically associated with political outcomes, the lack of ﬁrst-stage associations undermines thecrop’s ability to serve as an instrument for the slave trade. Even in the absence of a ﬁrst-stage association,
it is still possible that the non-cassava crop inﬂuences political outcomes through non-slave trade pathways.

A27

0

3

6

9

12 15 18 21 24 27 30 33 −3 −2 −1
F=27.223

0

1

2

3

0

3

6

9

12 15 18 21 24 27 30 33 −3 −2 −1

t=−2.082

F=24.642

Cassava

0

1

2

3

t=−2.027

Cassava
F=13.255

t=1.386

F=13.46Maize

t=1.411

Maize
F=10.304

t=1.912

F=10.1

Groundnut

t=1.282

MilletF=9.652

t=1.265

F=10.05

Millett=0.678

Sunflower
F=8.539t=−0.693

Winter Wheat

F=9.083

t=1.9

F=8.477

t=2.002

Groundnut
F=8.264t=1.061

Rice

PotatoF=6.541t=1.678

F=5.426

Sorghumt=−0.687

Winter Wheat
F=6.232

t=0.661

F=4.814

Sunflower

t=1.027

Rice
F=5.866

t=1.649

F=4.668

Soy

t=1.61Soy
F=4.733t=−0.021

F=4.546

Sugarcane

t=1.058

Summer Wheat
F=4.558t=−0.518

F=4.457

Oilpalmt=−0.517

Oilpalm
F=3.187t=0.984

F=4.337

Summer Wheatt=1.583

Sorghum
F=2.673

t=1.421

F=4.222

Potatot=−0.021

Sugarcane
F=1.42t=0.858

F=1.123

t=0.981F=2.253

Rapeseed

t=1.293

BarleyBarley0

36

9

t=0.055

Weak instrument
critical value (=16.38)

F=1.205

Rapeseed

12 15 18 21 24 27 30 33 −3 −2 −1First Stage F−statistict=−1.301

Rye

Weak instrument
critical value (=16.38)

F=0.003

Rye

F=2.212

0

1

2

3

0

Second Stage t−value

3

6

9

12 15 18 21 24 27 30 33 −3 −2 −1First Stage F−statistic(a) Population-Normalized Slave Trade Export

t=0.860

1

2

3

Second Stage t−value(b) Area-Normalized Slave Trade Export

Figure D.1: IV-2SLS Regressions with Cassava Suitability and Placebo Instruments
Notes: First-stage F-statistics with (a) population-normalized (solid) and (b) area-normalized (hollow)
slave trade exposure measures as the ﬁrst-stage outcomes (in the left panels), along with the corresponding
second-stage t-values for (instrumented) per capita or per area slave trade exposure, with the soil
suitability measures for cassava and non-cassava crops as (placebo) instruments. The model speciﬁcation
follows column (2) of Table 1 in the main text. Each dot in the left (right) panel indicates the ﬁrst-stage
F-statistic (second-stage t-value) for the crop on the vertical axis. The reported F-statistics and t-values
are based on Kelly’s (2020) spatial HAC standard errors. Solid vertical segments indicate the critical valueof 16.38 (Stock & Yogo, 2005) against weak instruments in the left panels, and the critical values for thestatistical signiﬁcance at the 5% level in the right panels (|t| = 1.96).

To investigate this concern, I subsequently replace the cassava suitability index withthe placebo suitability measure for one of the 15 crops available in the GLUES data andreestimate the IV-2SLS regressions. The left panes of Figures D.1 and D.2 plot the Fstatistics for the placebo instruments and the cassava suitability measure (true instrument),
with population-normalized and area-normalized slavery measures as the ﬁrst-stage outcome
for all country-group observations (Figure D.1) and the LEDA-connected observations (Figure D.2). The model speciﬁcation follows column (2) of Table 2 in the main text. Eachdot represents the ﬁrst-stage F-statistics for each crop suitability, and the solid vertical segments indicate the critical value of 16.38 for weak instruments. The right panels display

A28

0

3

6

9

12 15 18 21 24 27 30 33 −3 −2 −1

0

1

2

F=20.468

3

0

3

6

9

12 15 18 21 24 27 30 33 −3 −2 −1

t=2.48401

2

F=16.725

Cassava3

t=2.39Cassava
F=15.374

t=1.984

F=15.198

Maize

t=1.985

Maize
F=13.294

t=1.973

F=12.46

t=2.207

Groundnut

F=12.131

t=2.19F=11.409

t=1.931

MilletMilletGroundnut
F=10.991

t=1.576

F=10.577

t=1.738

F=10.774

Soy

t=1.278

Sunflower
F=8.86

Sorghum

t=1.54Soy
F=8.482

t=1.257

F=7.743

Sunflower

t=1.679

Sorghum
F=7.961

t=0.192

F=6.769

Rice

t=0.191

Rice
F=6.122t=1.095

F=5.544

Summer Wheatt=1.527

Potato
F=5.9

t=1.776

F=5.691t=1.737

F=5.498

Winter Wheatt=1.085

Summer Wheat
F=4.573

Rapeseed

t=0.817

BarleyF=4.514t=−0.662

F=4.331

Sugarcane

t=1.642

Rapeseed
F=4.313t=−0.499

F=4.063

Oilpalm

t=1.578

Winter Wheat
F=2.673

t=0.776

F=3.538

Barleyt=−0.648

Sugarcane
F=1.517t=1.053

PotatoF=1.375

Rye
0

3

6

9

t=0.793t=−0.495

Weak instrument
critical value (=16.38)

F=0

Rye

12 15 18 21 24 27 30 33 −3 −2 −1First Stage F−statistic

F=3.537

Oilpalm

Weak instrument
critical value (=16.38)

0

1

2

3

0

Second Stage t−value

3

6

9

12 15 18 21 24 27 30 33 −3 −2 −1First Stage F−statistic(a) Population-Normalized Slave Trade Export

t=0.018

0

1

2

3

Second Stage t−value(b) Area-Normalized Slave Trade Export

Figure D.2: IV-2SLS Regressions with Cassava Suitability and Placebo Instruments, with the
LEDA-Connected Observations
Notes: See notes in Figure D.1. The LEDA-connected sample includes 939 country-group observations
with the LEDA linguistic distance threshold of 0.2, as in the baseline setting. The t-values in the rightpanels are computed based on the second-stage estimates with Power Sharing as the dependent variable.

the corresponding second-stage t-values for per capita and per area slave trade measures
instrumented by the cassava and non-cassava crop suitability indexes.
For all of the 15 non-cassava crops, the ﬁrst-stage F-statistics fail to pass the critical valueof 16.38 to reject the null hypothesis that the instrument is weak. Somewhat consistent withthe country-level ﬁndings of Cherniwchan & Moreno-Cruz (2019), among the 15 non-cassava
crops, maize suitability is most strongly associated with the slave trade exposure. However,regardless of the slavery measures and (sub)samples, the maize-slave trade association remains weaker at the ethnic-group level than at the country level and fails to pass the criticalvalue of 16.38. These weaker ﬁrst-stage associations for placebo instruments underline thedistinctive role of soil suitability for cassava, rather than other Old World and New World
crops, in shaping the group-level exposure to the slave trade.
Similarly, the second-stage t-values of the “fake” IV regressions with non-cassava instruA29

ments almost always fail to retain statistical signiﬁcance at the conventional 5% level, andthe corresponding second-stage explanatory power (measured by t-values) are consistently
weaker than the cassava-based estimates. Admittedly, it is possible that non-cassava crop
suitability aﬀects contemporary political outcomes through pathways other than the slave
trade. For example, Mayshar et al. (2017) reveal the positive eﬀect of cereal cultivation
on group-level institution-building by highlighting that “productivity advantage of cereals
over roots and tubers, unlike absolute land productivity, is the source of hierarchy” (p.7).
An important implication of their earlier ﬁndings is that using the suitability for a cereal
grain (e.g., maize), rather than a perennial root (e.g., cassava), as an instrument for theslave trade exposure would severely violate the exclusion restriction by opening up non-slave
trade pathways toward the advanced mediator (i.e., traditional institutions) and modern
political outcomes. Although the current analysis tells us little about the plausibility of suchnon-slave trade pathways, the false IV estimates fail to support the validity of non-cassava
crop suitability as an instrument to investigate the political legacies of the slave trade.

D.2

Non-Treatment Pathways: Slave Trade and the Correlates of
the Coup-Civil War Trap

Third, reduced-form regressions with additional outcome variables also inform us about
potential exclusion restriction violation induced by cassava-outcome pathways not throughslave trade exposure. For example, a combination of systematic associations (1) between
cassava suitability and current population density in group homelands and (2) between current population density and battle activities opens up a direct instrument-outcome pathway.
Such potential pathways are consistent with the insights of previous studies (e.g., Cederman
et al., 2010; Collier & Hoeﬄer, 2004; Fearon & Laitin, 2003; Francois et al., 2015), and canseverely violate the instrument exclusion restriction. Not to invite such concerns for the exclusion restriction violations, the instrument should not be associated with the (unadjusted)correlates of civil war or power sharing.
A30

Table D.1: Reduced-Form Regressions with the Main Outcomes and the Correlates of the CoupCivil War Trap
Main Outcomesln(1 + Battle)
Cassava Suitability (ζ)
Overall Suitability
Coast DistanceObservations
Adjusted R2
Residual Moran’s I(standard deviate)
Country FE
Moran eigenvectors
Lon-Lat polynomial
Restricted controls
Precolonial controls
Geographic controls
LEDA-connected sample

Power
Sharing

Potential Correlates

Rebel

Coup

Nightlight

Population
Density

(1)

(2)

(3)

(4)

(5)

(6)

−0.185∗(0.086)0.113(0.102)−0.018(0.100)0.038∗∗(0.013)−0.016(0.024)0.042∗(0.018)−0.039∗∗(0.012)0.036∗(0.016)0.013(0.015)0.080∗∗(0.023)−0.013(0.022)0.089∗∗(0.030)−0.004(0.061)−0.136+(0.081)−0.457∗∗(0.081)−0.062(0.046)
0.347∗∗(0.063)−0.038(0.064)

1,282
0.561
−0.019

939
0.731
0.109

939
0.819
0.004

939
0.723
0.962

1,282
0.606
0.693

1,282
0.787
−1.123

✓
✓
✓
✓
✓
✓

✓
✓
✓
✓
✓
✓
✓

✓
✓
✓
✓
✓
✓
✓

✓
✓
✓
✓
✓
✓
✓

✓
✓
✓
✓
✓
✓

✓
✓
✓
✓
✓
✓

Notes: + p < 0.1; ∗ p < 0.05; ∗∗ p < 0.01. Kelly’s (2020) standard errors adjusted for spatial clustering with an
exponential kernel are in parentheses. Restricted controls: Area, Grassland in 1500, Overall Suitability, and Population Density in 1500. Precolonial controls: Coast Distance, Cropland in 1500, Ecological Diversity Index, Malaria
Suitability Index, Trade Route Cities, Cities in 1500, Precolonial Conﬂict, and Precolonial Kingdom. Geographic controls: Area Share, Average Temperature (1901–1910), Equator Distance, Elevation, Ruggedness, Partition, Capital(dummy), Capital Distance, Border Distance, Population Density in 1960, and Water Body. See Tables A.1 and A.2
for a detailed description of the covariates.

To investigate the concern, I estimate the following reduced-form model:
YicRF = κc + ζCassavai + Xi′ λ + Xic′ ϕ + Eic′ ψ + f (sic ) + vic ,(D.3)

where the right-hand-side variables and parameters are deﬁned analogously to the two-stage
speciﬁcation in the main text, with Eic denoting the Moran eigenvectors. YicRF is one of loggednightlight intensity, Nightlight, logged population density in 2010, Population Density, andthe main outcome variables, Battle, Power Sharing, Rebel, and Coup.
The reduced-form regressions inform us about the instrument validity in two ways. First,
to be a relevant instrument, cassava suitability should predict the main outcomes (ζ ̸= 0).
Second, not to violate the exclusion restriction, the instrument should not be associated withthe correlates of the coup-civil war trap, Nightlight and Population Density (ζ = 0), which
would open the instrument-outcome pathways not through the slave trade.

A31

Table D.1 reports auxiliary reduced-form estimates. Reassuringly, we see no systematic
associations between cassava suitability and nightlight intensity or population density. Also
note that overall agricultural suitability and coastline distance are systematically associatedwith the key correlates of domestic ﬁghting and power sharing. These results reveal no clear
signs of the exclusion restriction violations in the suitability-based IV design and thereby
provide additional credibility to the main ﬁndings while discouraging the use of alternative
instrument candidates in the current analysis.

D.3

Placebo Sample: Groups in North and South African States

The fourth exercise utilizes a negative control (placebo) sample with “a similar confounding
structure as the population of interest but was not exposed to the treatment of interest”(Davies et al., 2017, 2069). In the current context, ethnic groups in today’s North and South
African states have barely been exposed to the slave trade and constitute a plausible negative control subsample. Because the slavery pathway (“cassava suitability → slave trade →outcome variables”) is absent in the subsample, we should not see any reduced-form association between cassava suitability and the outcomes (Davies et al., 2017, 2070). 10 Systematic
instrument-outcome associations in the negative control subsample suggest the presence of
unblocked direct pathways linking the instrument and the outcomes, which violates the assumption of exclusion restriction. Based on the United Nations region code, 248 out of 1,282
country-group observations (19.34%) for the full sample and 197 out of 939 observations(20.98%) in the LEDA-connected subsample are located in North and South African states.
Figure D.2 depicts the distribution of the negative control observations.
Table D.2 reports the subsample reduced-form estimates with the model speciﬁcation ofequation (D.3), with f (sic ) speciﬁed as a linear polynomial of settlement coordinates given
10

Note that the IV estimator can be written as the ratio of the reduced-form instrument-outcome association relative to the ﬁrst-stage instrument-treatment association (Angrist & Pischke, 2008, 120–121). A zero
reduced-form association thus immediately indicates a zero second-stage association. Nunn & Wantchekon(2011) adopt a similar reduced-form strategy with a placebo sample as a falsiﬁcation test by leveraging nonAfrican observations immune to African slave trades. Acharya et al. (2016b) also employ a similar subsample
approach in the context of the slavery legacies in the United States.

A32

Figure D.3: Country-Group Observations (Not) Falling into North and South African States
Notes: Darker shades represent the settlement areas of the country-group observations in North and South
African states based on the United Nations region code.

the reduced sample size. Consistent with the IV strategy, the coeﬃcient estimates remain
statistically and substantially indistinguishable from zero. An exception is the marginallysigniﬁcant association between cassava suitability and battle exposure in column (1), with thecorresponding t value of t = 0.234≈ 1.756. However, the reduced-form coeﬃcient is positive
0.133
and inconsistent with the main ﬁnding of the negative slave trade-battle association, which
again fails to invalidate the IV design.

D.4

Spatial Noise Placebo Test

Finally, a remaining but important concern stems from the spatial nature of historical phenomena rather than the IV design, such that the regression estimates reﬂect spatial trendsor heterogeneity rather than the legacies of the slave trade. Kelly (2019) proposes a two-step
diagnosis procedure to guard against the methodological concern of spatial curve-ﬁtting. The
ﬁrst diagnosis procedure is to simply report the Moran’s I statistic of spatial autocorrelation
for regression residuals. A statistically signiﬁcant Moran’s I statistic in regression residuals
warns the danger of curve-ﬁtting. The second procedure is to replace the key treatment
A33

Table D.2: Reduced-Form Regressions for the Negative Control Observationsln(1 + Battle)(1)

Power Sharing
(2)

Rebel
(3)

Coup
(4)

Cassava Suitability (ζ)

0.234+(0.133)0.006(0.026)−0.001(0.013)−0.011(0.027)

Observations
Adjusted R2
Residual Moran’s I (std. deviate)

248
0.608
0.258

197
0.381
−1.724+

197
0.943
0.337

197
0.774
0.387

Country FE
Lon-Lat polynomial (linear)
Moran eigenvectors
Restricted controls
Precolonial controls
Geographic controls
LEDA-connected sample

✓
✓
✓
✓
✓
✓

✓
✓
✓
✓
✓
✓
✓

✓
✓
✓
✓
✓
✓
✓

✓
✓
✓
✓
✓
✓
✓

Notes: + p < 0.1; ∗ p < 0.05; ∗∗ p < 0.01. Kelly’s (2020) standard errors adjusted for spatial
clustering with an exponential kernel are in parentheses. Restricted controls: Area, Grassland in
1500, Overall Suitability, and Population Density in 1500. Precolonial controls: Coast Distance,
Cropland in 1500, Ecological Diversity Index, Malaria Suitability Index, Trade Route Cities,
Cities in 1500, Precolonial Conﬂict, and Precolonial Kingdom. Geographic controls: Area Share,
Average Temperature (1901–1910), Equator Distance, Elevation, Ruggedness, Partition, Capital(dummy), Capital Distance, Border Distance, Population Density in 1960, and Water Body.
European inﬂuence indicators: Colonial Railway, Explore Routes, and Missions. See Tables A.1
and A.2 for a detailed description of the covariates.

and outcome variables with artiﬁcial spatial noise. The empirical ﬁndings will be invalidated (1) if spatially autocorrelated noise explains the outcome variable (spatial noise as aplacebo treatment) and (2) if the key treatment explains the artiﬁcial noise (spatial noise
as a placebo outcome). While the Moran’s I statistics fail to retain statistical signiﬁcanceat the conventional 5% level in all the regressions reported in this article, the concern for
spatial curve-ﬁtting warrants a further investigation through a spatial noise placebo test.
The spatial noise simulation involves two steps. I ﬁrst generate spatially autocorrelated
random variables with the Moran’s I statistics similar to the I statistics obtained from theobserved slave export and outcome variables.11 I then reestimate the main IV-2SLS regression speciﬁed by equations (1) and (2) in the main text with either the treatment (Slave)11

For a comparative purpose, I follow the procedure of Bivand et al. (2013, 257–258) to generate spatially
autocorrelated random variables measured at the original unit of analysis (country-group) using a distancebased spatial weight matrix (SWM) with a 5◦ (≈ 555 km) cutoﬀ as in the main analysis. The univariate
distribution of the noise variable follows the standard normal distribution. As reported below, the SWMbased procedure ensures that the artiﬁcial variables are measured at the same level as the observed variables.

A34

Table D.3: Spatial Noise Simulation% Statistically Signiﬁcant at:ρ

Mean
Moran’s I

0.8

0.185

Spatial Noise Replaces:p = 0.05

p = 0.01

p = 0.001% Spatial Noise
Outperforms

Treatment (spatial noise
as a placebo treatment)
Outcome (spatial noise asa placebo outcome)

2.51%

0.00%

0.00%

0.24%

9.24%

1.86%

0.07%

0.85

0.254

Treatment
Outcome3.53%
11.55%

0.00%
2.66%

0.00%
0.08%

0.39%

0.9

0.368

Treatment
Outcome5.33%
14.34%

0.00%
4.25%

0.00%
0.21%

0.81%

0.925

0.458

Treatment
Outcome7.21%
17.61%

0.00%
5.12%

0.00%
0.4%

1.13%

Notes: Spatial autoregressive parameter ρ (in the ﬁrst column) controls the degree of spatial autocorrelation in the artiﬁcial noise. The fourth to sixth columns report the proportions of simulation
runs where the noise variable (as a placebo treatment) or the treatment (with the noise variable as aplacebo outcome) retains statistical signiﬁcance at level p. The last column reports the proportion ofsimulations where the spatial noise variable outperforms the original treatment (Slave pc ) in explanatory power measured by the second-stage t-value, which can roughly be interpreted as the empiricalsigniﬁcance of the treatment variable (Kelly, 2019, 16). The Moran’s I statistics obtained from theobserved treatment and outcome variables are I Slave(pc) = 0.33, I Slave(Area) = 0.319, I Battle = 0.195,
I Power Sharing = 0.304, I Rebel = 0.477, and I Coup = 0.38.

or the outcome (Y ) replaced with the noise variable. I repeat the two-step procedure 10,000
times for each of the spatial autocorrelation settings parameterized by spatial autoregressive
parameter ρ to obtain the empirical distributions of the key statistics. 12
Table D.3 summarizes the simulation results with diﬀerent levels of spatial autocorrelation. The second column reports the mean value of the Moran’s I statistics of spatial
correlation obtained from simulations with diﬀerent values of ρ. The fourth to sixth columns
summarize the proportions of artiﬁcial regressions out of 10,000 simulation runs in which (1)the spatial noise variable (as a placebo treatment) retains statistical signiﬁcance in the second
stage (with Battle as the second-stage outcome variable) and (2) the treatment (Slavepc ) issystematically associated with the spatial noise (as a placebo outcome) with the statisticalsigniﬁcance at p level denoted in the table header. The last column reports the proportion of
12

To speed up the simulations, I ﬁt the IV-2SLS estimations without the Moran eigenvector spatial ﬁltering.
The t-values and statistical signiﬁcance reported below are based on the Huber-White robust standard errors.
For a comparative purpose, I also reestimate the baseline (“true”) IV-2SLS regression without the Moraneigenvectors and obtain the t-value based on robust standard errors.

A35

the simulation runs where the noise variable outperforms the original treatment in explanatory power measured by the second-stage t-values. The noise outperformance proportion
serves as a rough proxy of the empirical signiﬁcance level of the treatment (Kelly, 2019, 16).
The spatial noise simulation yields four patterns and provides additional conﬁdence inthe main ﬁndings. First, the spatial noise variable rarely explains the original outcomes. For
example, with ρ = 0.8 and at the conventional 5% signiﬁcance level, the noise variable issystematically associated with Battle in only 251 (2.51%) out of 10,000 runs. Regardless of
the autoregressive parameter settings, the explanatory power of the noise variable remains
similar to or lower than random chance. Second, Slavepc rarely explains the noise variable.
As a reference, although the proportion is higher compared to the ﬁrst exercise, Slavepc issystematically associated with spatial noise at the 5% level in only 942 (9.42%) of simulation
runs with ρ = 0.8. Also note that the Moran’s I statistic of one of the original outcome
variables, Battle, is 0.195 and located close to the mean of the Moran’s I statistics, 0.185,
obtained from the noise simulations with ρ = 0.8. In other words, despite the similar degreesof spatial autocorrelation, the explanatory power of Slavepc turns out to be negligible once
we replace the outcome with the noise variable. Third, the simulations yield a positive
association between the degree of spatial correlation of the artiﬁcial noise (parametrizedby ρ) and the proportions of simulation runs where Slavepc explains spatial noise or thenoise explains the original outcome. This positive association is consistent with the original
simulation experiments by Kelly (2019) across major previous works in the persistent-eﬀect
literature. Finally, as reported in the seventh column, the noise variable rarely outperforms
the original treatment. The empirical signiﬁcance measure ranges from 0.24% (with ρ = 0.8)
to 1.13% (with ρ = 0.925), and is generally similar to or lower than the nominal signiﬁcancebased on Kelly’s (2020) spatial HAC standard errors reported in the main regression tables.
Combined, these simulation results fail to falsify the main ﬁndings. If anything, the noise
simulation reveals only a weak caution against spatial curve-ﬁtting.

A36

References
Acharya, Avidit; Matthew Blackwell & Maya Sen (2016a) Explaining causal ﬁndings withoutbias: Detecting and assessing direct eﬀects. American Political Science Review 110(3):512–529.
Acharya, Avidit; Matthew Blackwell & Maya Sen (2016b) The Political Legacy of American
Slavery. Journal of Politics 78(3): 621–641.
Angrist, Jpshua D & J.-S Pischke (2008) Mostly Harmless Econometrics: An Empiricist’sCompanion. Princeton, NJ: Princeton University Press.
Bates, Robert H (2014) The Imperial Peace. In: Emmanuel Akyeampong; Robert H. Bates;
Nathan Nunn & James Robinson (eds.) African Development in Historical Perspective.
Cambridge: Cambridge University Press chapter 13.
Besley, Timothy & Torsten Persson (2011) The logic of political violence. Quarterly Journalof Economics 126(3): 1411–1445.
Bivand, Roger & Gianfranco Piras (2015) Comparing Implementations of Estimation Methods for Spatial Econometrics. Journal of Statistical Software 63(18).
Bivand, Roger S; Edzer Pebesma & Virgilio Gomez-Rubio (2013) Applied spatial data analysis with R volume 2nd edition. New York: Springer.
Cederman, L.-E; Andreas Wimmer & Brian Min (2010) Why Do Ethnic Groups Rebel? New
Data and Analysis. World Politics 62(1): 87–119.
Cherniwchan, Jevan & Juan Moreno-Cruz (2019) Maize and precolonial Africa. Journal of
Development Economics 136(March 2018): 137–150.
Ciolek, Matthew T (1999). Old World Trade Routes (OWTRAD) Project Demographic
Digital Data Archives. Available at: www.ciolek.com/OWTRAD/DATA/oddda.html.
Collier, Paul & Anke Hoeﬄer (2004) Greed and grievance in civil war. Oxford Economic
Papers 56(4): 563–595.
Davies, Neil M; Kyla H Thomas; Amy E Taylor; Gemma M Taylor; Richard M Martin;
Marcus R Munafò & Frank Windmeijer (2017) How to compare instrumental variable
A37and conventional regression analyses using negative controls and bias plots. International
Journal of Epidemiology 46(6): 2067–2077.
Donnay, Karsten; Eric T Dunford; Erin C McGrath; David Backer & David E Cunningham(2019) Integrating Conﬂict Event Data. Journal of Conﬂict Resolution 63(5): 1337–1364.
Fearon, James D & David D Laitin (2003) Ethnicity, Insurgency, and Civil War. American
Political Science Review 97(1): 75–90.
Fenske, James (2014) Ecology, trade, and states in pre-colonial africa. Journal of the European Economic Association 12(3): 612–640.
Francois, Patrick; Ilia Rainer & Francesco Trebbi (2015) How Is Power Shared in Africa?
Econometrica 83(2): 465–503.
Gaure, Simen (2013) lfe: Linear group ﬁxed eﬀects. The R Journal 5(2): 104–116.
Gennaioli, Nicola & Ilia Rainer (2007) The modern impact of precolonial centralization in
Africa. Journal of Economic Growth 12(3): 185–234.
Giuliano, Paola & Nathan Nunn (2018) Ancestral Characteristics of Modern Populations.
Economic History of Developing Regions 33(1): 1–17.
Harris, Ian; Timothy J Osborn; Phil Jones & David Lister (2020) Version 4 of the CRU TS
monthly high-resolution gridded multivariate climate dataset. Scientiﬁc Data 7(1): 1–18.
Ho, Daniel E; Kosuke Imai; Gary King & Elizabeth A Stuart (2007) Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference.
Political Analysis 15(3): 199–236.
Kees, Klein G; Arthur Beusen; Gerard Van Drecht & Martine De Vos (2011) The HYDE 3.1
spatially explicit database of human-induced global land-use change over the past 12,000
years. Global Ecology and Biogeography 20(1): 73–86.
Kelly, Morgan (2019). The Standard Errors of Persistence. Working Paper.
Kelly, Morgan (2020). Understanding persistence. Working Paper.
King, Gary; Robert O Keohane & Sidney Verba (1994) Designing Social Inquiry: ScientiﬁcInference in Qualitative Research. Princeton, NJ: Princeton University Press.

A38

Kiszewski, Anthony; Andrew Mellinger; Andrew Spielman; Pia Malaney; Sonia E Sachs &Jeﬀrey Sachs (2004) A global index representing the stability of malaria transmission.
American Journal of Tropical Medicine and Hygiene 70(5): 486–498.
Lowes, Sara & Eduardo Montero (2021) The Legacy of Colonial Medicine in Central Africa.
American Economic Review 111(4): 1284–1314.
Mayshar, Joram; Omer Moav; Zvika Neeman & Luigi Pascali (2017). Cereals, Appropriability and Hierarchy.
Michalopoulos, Stelios & Elias Papaioannou (2013) Pre-Colonial Ethnic Institutions andContemporary African Development. Econometrica 81(1): 113–152.
Michalopoulos, Stelios & Elias Papaioannou (2016) The Long-Run Eﬀects of the Scramble
for Africa. American Economic Review 106(7): 1802–1848.
Miguel, Edward; Shanker Satyanath & Ernst Sergenti (2004) Economic Shocks and Civil
Conﬂict: An Instrumental Variables Approach. Journal of Political Economy 112(4):725–753.
Müller-Crepon, Carl; Yannick I Pengl & N.-C Bormann (2022) Linking Ethnic Data fromAfrica. Journal of Peace Research 59(3): 425–435.
Murdock, George P (1959) Africa: Its Peoples and their Culture History.

New York:

McGraw-Hill.
Murdock, George P (1967) Ethnographic Atlas: A Summary. Ethnology 6(2): 109–236.
National Geophysical Data Center (2014). Dmsp-ols nighttime lights time series, version 4.
Available at: http://ngdc.noaa.gov/eog/dmsp/downloadV4composites.html.
Nunn, Nathan (2008) The Long-Term Eﬀects of Africa’s Slave Trades. Quarterly Journal of
Economics 123(1): 139–176.
Nunn, Nathan & Leonard Wantchekon (2011) The slave trade and the origins of Mistrust in
Africa. American Economic Review 101(7): 3221–3252.
Pebesma, Edzer (2018) Simple features for R: Standardized support for spatial vector data.
R Journal 10(1): 439–446.

A39

Pebesma, Edzer J & Roger S Bivand (2005) Classes and methods for spatial data in R. R
News 5(2): 9–13.
Raleigh, Clionadh; Andrew M Linke; Havard Hegre & Joakim Karlsen (2010) Introducing
ACLED: An armed conﬂict location and event dataset. Journal of Peace Research 47(5):651–660.
Reba, Meredith; Femke Reitsma & Karen C Seto (2016) Spatializing 6,000 years of global
urbanization from 3700 BC to AD 2000. Scientiﬁc Data 3(160034).
Roessler, Philip & David Ohls (2018) Self-Enforcing Power Sharing in Weak States. International Organization 72(2): 423–454.
Shaver, Andrew; David B Carter & Tsering W Shawa (2019) Terrain Ruggedness and Land
Cover: Improved Data for All Research Designs. Conﬂict Management and Peace Science
36(2): 191–218.
Stock, James H & Motohiro Yogo (2005) Testing for Weak Instruments in Linear IV Regression. In: James H. Stock & Donald W.K. Andrews (eds.) Identiﬁcation and Inference
for Econometric Models: Essays in Honor of Thomas Rothenberg. New York: Cambridge
University Press.
Sundberg, Ralph & Erik Melander (2013) Introducing the UCDP Georeferenced Event
Dataset. Journal of Peace Research 50(4): 523–532.
Tilly, Charles (1975) Reﬂections on the History of State Making. In: Charles Tilly (ed.) The
Formation of National States in Western Europe. Princeton, NJ: Princeton University
Press chapter 1.
U.S. Geological Survey (1996). Global 30-Arc-Second Elevation Data, GTOPO30.
Vansteelandt, Stijn (2009) Estimating direct eﬀects in cohort and case-control studies. Epidemiology 20(6): 851–860.
Vogt, M.; N.-C Bormann; Seraina Ruegger; L.-E Cederman; Philipp Hunziker & Luc Girardin (2015) Integrating Data on Ethnicity, Geography, and Conﬂict: The Ethnic Power
Relations Data Set Family. Journal of Conﬂict Resolution 59(7): 1327–1342.

A40

Weidmann, Nils B; Doreen Kuse & Kristian S Gleditsch (2010) The geography of the international system: The CShapes dataset. International Interactions 36(1): 86–106.
Whatley, Warren C (2014) The transatlantic slave trade and the evolution of political authority in West Africa. In: Emmanuel Akyeampong; Robert H. Bates; Nathan Nunn & James
Robinson (eds.) Africa’s Development in Historical Perspective. Cambridge: Cambridge
University Press chapter 15, , 460–488.
White, Frank (1983) The Vegetation of Africa: A Descriptive Memoir to Accompany the
UNESCO/AETFAT/UNSO Vegetation Map of Africa. Natural Resources Research 20.
Wig, Tore (2016) Peace from the Past: Pre-colonial Political Institutions and Contemporary
Ethnic Civil Wars in Africa. Journal of Peace Research 53(4): 509–524.
WorldPop (2016). Africa Continental Population Datasets (2000-2020). Available at: https://www.worldpop.org.
Zabel, Florian; Birgitta Putzenlechner & Wolfram Mauser (2014) Global agricultural land
resources: A high resolution suitability evaluation and its perspectives until 2100 underclimate change conditions. PLoS ONE 9(9): 1–12.
Zhukov, Yuri M; Christian Davenport & Nadiya Kostyuk (2019) Introducing xSub: A New
Portal for Cross-National Data on Sub-National Violence. Journal of Peace Research
56(4): 604–614.

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