Africa Scramble Syracuse

7/23/2019 Africa Scramble Syracuse

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The Long-Run Eects of the Scramble for Africa


Stelios Michalopoulos and Elias Papaioannou

Brown and NBER and CEPR, London Business School and NBER and CEPR

Syracuse University

April 2015
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Introduction

Research Question

I Whose country is this ag?

I What does the ve-pointed star symbolize?
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Introduction

Research Question

The Scramble for Africa: Consequences for the Somali

Somalis split in:

Somaliland

Ethiopia

Kenya

Somalia

Djibouti
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Introduction

Research Question

Questions?. Generalization?

I Has border articiality (w.r.t. ethnic partitioning) contributedto conict in Africa?

I roughly 40% of African population comes from ethnic groups thathave been systematically split across national (colonial) borders

I 220 230 out of 825 ethnic groups were systematically split by

colonial border design (that endured after independence)

I Are there spillovers of ethnic partitioning to nearby groupsand countries (e.g., Ethiopia and Kenya)?
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Introduction

Research Question

Examine the Long-run Eects of the Scramble for Africa

I Border articiality (w.r.t. ethnic partitioning)

I

Ethnic partitioning and type of civil conict (battles, violenceagainst civilians, state-driven conict), ethnic political power andaccess to public goods

I using four distinct georeferenced databases

I Further Evidence - Channels

I political discrimination (direct and indirect evidence)

I foreign interventions (direct evidence)
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Introduction

Historical Overview

Ethnic Homelands before Colonization - Murdock (1959)
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Introduction

Historical Overview

Berlin Conference - The Scramble for Africa
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Introduction

Historical Overview

The Design of African Borders

I Berlin Conference 1884/1885 and subsequent treaties among Europeans in

1890s

I Colonizers had not even explored most of Africa when borders were agreed.

I Drawing involved protectorates, large (free-trade) areas, colonies, and

spheres of inuence rather than potential states. None could foresee at thetime independence

I No major changes of borders during colonization or after independence


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Introduction

Historical Overview

Current National Boundaries and Ethnic Homelands

At independence borders

were respected creating

boundaries of African states

Th L R E f h S bl f Af i
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Introduction

Historical Overview

A European Randomization...

I "we have been engaged in drawing lines upon maps where no whitemans feet have ever tord; we have been giving away mountains and

rivers and lakes to each other, only hindered by the smallimpediment that we never knew exactly where the mountains and

rivers and lakes were." Lord Salisbury (British PM)

I "the study of European archives supports the accidental rather thana conspiratorial theory of the marking of African boundaries."Asiwaju

(1985)

Th L R E t f th S bl f Af i
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Introduction

Historical Overview

Partitioning of Tribal Areas - Binary Classication

Ethnic Homelands

and National Borders

National Boundaries

Non-Partitioned Groups

Partitioned Groups

The Long R n Eects of the Scramble for Africa
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Introduction

Historical Overview

Population Belonging to Partitioned Ethnicities

.2

.4

.6

.8

1

Density

0 .5 1% of a Country's Population Belonging to Partitioned Ethnicities in 2000

kernel = epanechnikov, bandwidth = 0.1332

Kernel density estimate

The Long Run Eects of the Scramble for Africa
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Introduction

Literature

Deep Determinants of African Development

1. Importance of National Contemporary Institutions(Acemoglu et al. (2001, 2002, 2005); La Porta et al. (1998, 1999); Glaeser et al.

(2004); Michalopoulos and Papaioannou (2014))

I Colonization (type or/and identity)

2. Pre-colonial Institutions, Slavery, and Other Ethnic Traits(Herbst (2000); Gennaioli and Rainer (2006, 2007); Michalopoulos and Papaioannou

(2013), Nunn (2008), Nunn and Wantchekon (2011), Fenske (2014))

I Limited penetration of nationwide institutions (poor infrastructure,peculiar country shapes, Michalopoulos and Papaioannou (2014))

I Colonization limited (in duration and location)

I Weak state capacity (Migdal 1988, Besley and Persson, 2010)



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Introduction

Literature

This Paper

..focuses on the long-run eects of the Scramble for Africa

I African historiography (e.g., Dowden (2008); Englebert (2010))

I so far have been neglected in economics

I Border Design - State Articiality(Englebert, Tarrango, and Carter (2002); Alesina, Easterly, and Matuszeski (2011))

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The Long Run Eects of the Scramble for Africa

Introduction

Literature

This Paper

..focuses on the long-run eects of the Scramble for Africa

I African historiography (e.g., Dowden (2008); Englebert (2010))

I so far have been neglected in economics

I Border Design - State Articiality(Englebert, Tarrango, and Carter (2002); Alesina, Easterly, and Matuszeski (2011))

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o g o S o

Introduction

Literature

Related Works

I Ethnic Fragmentation/Polarization and Conict/Development(e.g., Easterly and Levine (1997), Alesina et al. (2003), Fearon ( 2003), Collier and

Hoeer (2007), Montalvo and Reynal-Querol (2005a,b), Esteban, Ray, and Mayoral

(2012), Esteban and Ray (2007, 2011), Alesina, Michalopoulos, and Papaioannou

(2015a,b))

I Origins of Civil War and Conict (e.g., Herge and Sambanis (2006); Kalyvas(2007); reviews Blattman and Miguel (2010) and Collier and Sambanis (2005))

I Civil Conict in Africa - Geo-referenced Data (Harari and La Ferrara(2014); Besley and Reynal-Querol (2014); Berman et al. (2014))

I Long-run consequences of historical events (e.g., Guiso et al. 2008; Huillery,2009; Banerjee and Iyer, 2005; Iyer, 2010; Dell, 2011; Acemoglu et al. 2008; Jedwab

and Moradi, 2012; reviews Nunn (2015) and Fuchs-SchundelnandHassan(2015))

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g

Introduction

Literature

Our Study: Shift the Agenda to the Scramble for Africa

I Exploiting as a quasi-natural experiment the random drawing of political

boundaries during the Scramble for Africa

I African historiography extensively argues that the borders were randomly drawn

(at least w.r.t. to the groups on the ground)

I Establish border arbitrariness (useful for future research)

I Exploiting regional variation (straightforward econometric benets)

I Unobserved country characteristics (colonization, national policies, state capacity)

I Information at the time of colonization (before independence)

I Control for local factors (e.g., natural resources, malaria)

I Studying the eect of partitioning on conict

I This has been hypothesized to be the main channel.

I Important from policy standpoint (autonomy, regionalism).

I Analysis of mechanisms (state-driven conict, interventions fromnearbycountries)

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Introduction

Mechanisms

Channels Linking Partitioning to Civil War

I Unlike non-split groups, partitioned ethnicities have the advantage of

getting assistance from their peers on the other side of the border as well

and nd a safe heaven when in need to retreat and regroup.

I Somali tribes in Ethiopia

I Neighboring governments exploit the presence of co-ethnics across the

border to intervene to stage interventions.

I Casamance region in Southern Senegal, where the partitioned ethnic groups Diola

and the Banyun reside

I Partitioned groups are discriminated and often excluded from the political

sphere. Partitioned ethnicities react to their marginalization by

participating in coups, rebellions against the government.

I Ewe in Ghana; Alur in DRC.

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Introduction

Mechanisms

Channels Linking Partitioning to Civil War (cont.)

I Irredentist demands, as ethnicities that are minority groups in a country

want to unify with their peers across the border

I 3 civil wars in the homeland of the Somalis partitioned between Somalia, Djibouti,

Kenya, Ethiopia

I Partitioned ethnicities ght to secede or obtain more autonomy

I Diolas in the Casamance region in Southern Senegal

I Border disputes due to the poor demarcation of borders particularly in caseof partitioned groups

I ambiguity of the tripartite treaty between Britain, Italy and Ethiopia of 1902 has

also played a role in the Eritrea-Ethiopia devastating war

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Introduction

Mechanisms

Consequences of the Scramble Besides Partitioning

I African border design determined all country-wideaspects related to

I geography, i.e., landlocked, country size, ruggedness, shape, diseaseenvironment etc.

I societal structure, i.e., ethnic fractionalization, polarization,inequality, etc.

I Do not quantify their inuence on conict but account for their

importance with the inclusion of country xed eects.

I Explore how these dierent nationwide by-products of the Scramble for

Africa interact with ethnic partitioning in shaping local conict (tentative).

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Introduction

Roadmap

Presentation Structure

I Border Articiality (split vs. non-split)

I Ethnic Partitioning and Civil Conict (ACLED)

I baseline estimatesI

conict types (battles, violence against civilians, riots) and conictactors (government, rebels-militias, external)I sensitivity analysisI further evidence with alternative conict data (UCDP-GED)

I Ethnic Partitioning and Political Representation (EPR)

I Political Discrimination and Ethnic Wars

I Ethnic Partitioning and Public Goods (DHS)

I Identity and Location (further look at discrimination)

I Summary-Conclusion

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Border Articiality

Objective

I African historiographers argue that Europeans did not take intoaccount local (geographic, political) conditions when delineatingborders

I

Yet in some cases they didI some (few) border realignments

I Approach 1: Balancedness Tests ("similarity regression tests")

I compare split and non-split ethnic homelands across many observable

features (tests of means, medians, distribution)

I Approach 2: Examine whether some specic observablefeatures correlate with ethnic partitioning

I similar to approach 1, but more intuitive given the nature of the

experiment

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Border Articiality

Empirical Specication


SPLITi =a+GEOi+HISTi+PRECOLONIALi+ar+"i

I SPLITi : indicator whether the historical homeland of ethnic group iis partitioned by national boundaries

I 10% thresholdI Other thresholds (and continuous measures)

I GEOi : vector of geographic variables (at the ethnicity level).

I HISTi : vector of historical characteristics (at the ethnicity level).

I PRECOLONIALi : pre-colonial ethnic-specic traits.

I ar : region xed eects (North, South, Central, East, West Africa)


B d A i i li
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Border Articiality

Results

Border Articiality I. Geography(1) (2) (3) (4) (5)

Log Land Area 0.05 56 ** * 0.05 29 ** * 0.06 18 ** * 0.05 54 ** * 0.04 89 ** *(0.0 15 3) (0.0 15 9) (0.0 175 ) (0.01 86 ) (0.01 50 )

Lake Indicator 0.0961* 0.0963 0.0965* 0.0971*(0.0 56 5) (0.0 645 ) (0.05 67 ) (0.05 58 )

River Indicator -0.0053 -0.0164 -0.0092 -0.0065

(0.0 33 7) (0.0 324 ) (0.03 25 ) (0.03 38 )

Elevation -0.0411(0.0709)

Suitability for Agriculture 0.1239(0.0974)

Malaria Stability Index 0.0195

(0.0982)

Distance to the Coast 0.0000(0.0001)

Diamond Mine Indicator 0.0359(0.0651)

Oil Indicator 0.0285

(0.0565)

-


B d A ti i lit
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Border Articiality

Results

Border Articiality II. Pre-colonial History(1) (3) (3) (4) (5) (6) (7)

Pre-colonial Conict Indicator -0.0663(0.0733)

Distance to Pre-colonial Conict -0.0444(0.0839)

Slave Trades Indicator 0.0045

(0.0322)

Log Number of Slaves 0.0063(normalized by land area) (0.0080)

Pre-colonial Kingdom Indicator 0.0466(0.0469)

Distance to Pre-colonial Kingdom 0.0009

(0.1235)

Major City in 1400AD 0.0233(0.0652)

Adjusted R-square 0.056 0.056 0.055 0.056 0.057 0.055 0.055Size, River and Lake Indicators Yes Yes Yes Yes Yes Yes YesRegion Fixed Eects Yes Yes Yes Yes Yes Yes Yes

Observations 825 825 825 825 825 825 825


Border Articialit


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Border Articiality

Results

Border Articiality III. Pre-colonial Ethnic Traits

(1) (2) (3) (4) (5) (6) (7)

Complex Settlement Patterns -0.0004(0.0538)

Share of Agriculture 0.0015(0.0112)

Animal Husbandry (0.0088)(0.0108)

Elections -0.0459(0.0848)

Inheritance Rule for Property 0.0040(0.0891)

Political Centralization -0.0719(0.0482)

Class Stratication -0.0674(0.0444)

adjusted R-squared 0.081 0.081 0.082 0.095 0.0790 0.0830 0.098Size, River and Lake Indicators Yes Yes Yes Yes Yes Yes YesRegion Fixed Eects Yes Yes Yes Yes Yes Yes YesObservations 451 451 437 437 394 394 487


Border Articiality
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Border Articiality

Results

Results Summary: Border Articiality

I among numerous geographic, ecological, and local conditions variables only

land area and area under water (lakes) correlate with partitioning

I out of various pre-colonial ethnic-specic institutional, cultural, economic,

and social variables (reported in George Peter Murdocks Ethnolinguistic

Atlas), none correlates with border drawing

I these simple results (though do not prove by themselves the random border

design) consistent with the vast literature on ethnic partitioning during the

Scramble for Africa arguing that Europeans paid little attention to local

conditions when drawing political boundaries (Asiwaju, 1985)

I at the time no prospect of independenceI implicit understanding that people could move across borders


Border Articiality
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Border Articiality

Results

Further Evidence: Balancedness Tests

I Estimate "similarity regressions" (with country xed eects)associating various geographic, location, natural resource andecology variables with the ethnic partitioning index.

I full sample (1212 country-ethnic homelands)

I border sample (606 country-ethnic homelands)

I No major (within country) dierences in observable characteristics

between split and non-split ethnic homelands

I unobservable features should be way more important than observables(Altonji et al. (2004), Oster (2014))


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Border Articiality

Results

Similarity Regressions

Log Land

Area

Lake

Indicator

River

Indicator

Mean

Elevation

Land

Suitability

Malaria

Stability

Diamond

Indicator

Petroleum

Indicator

Major City

in 1400

Coastal

Indicator

Capital

Indicator

Distance

Sea

Distance

Border

Distance

Capital

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14)

SPLIT -0.1702 -0.0027 0.0023 0.0165 0.0009 -0.0092 -0.0132 -0.0212 -0.0005 -0.0173 -0.024 0.0219 100.8410**0.1059***(0.1045) (0.0225) (0.0333) (0.0365) (0.0193) (0.0218) (0.0189) (0.0220) (0.0094) (0.0274) (0.0159) (0.0316) (13.3237) (0.0276)

marginal R2 0.003 0.000 0.000 0.001 0.000 0.000 0.001 0.001 0.000 0.001 0.003 0.000 0.154 0.017

Mean Dep. Var 2.071 0.108 0.504 0.617 0.407 0.717 0.093 0.084 0.026 0.129 0.039 0.600 107.412 0.505

Observations 1212 1212 1212 1212 1212 1212 1212 1212 1212 1212 1212 1212 1212 1212

Country FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

SPLIT 0.7236*** -0.0191 0.0864* 0.0010 0.0243 -0.0090 0.0123 0.0095 0.0057 0.0475 0.0118 -0.0268 -1.9752 -0.0128

(0.1762) (0.0294) (0.0399) (0.0326) (0.0154) (0.0214) (0.0250) (0.0212) (0.0045) (0.0311) (0.0094) (0.0303) (2.8585) (0.0370)

marginal R2 0.052 0.001 0.005 0.000 0.002 0.000 0.001 0.001 0.002 0.005 0.001 0.001 0.002 0.000

Mean Dep. Var 1.2276 0.0941 0.4620 0.5928 0.4314 0.7714 0.0594 0.0413 0.0033 0.1089 0.0215 0.5855 24.8881 0.5163

Observations 606 606 606 606 606 606 606 606 606 606 606 606 606 606

Country FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Dependent variable is:

Panel A: All Country-Ethnic Homelands

Panel B: Country-Ethnic Homelands close to the National Border


Border Articiality
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o de t c a ty

Results

Border Articiality. A Caveat

I almost impossible to establish randomization BUT

I empirical results in line with African historiography on the limited (atbest) knowledge of colonizers at the time

I It has to be that the factors that somehow aected partitioning atthe time when no-one could foresee independence matter crucially

for contemporary conict (quite unlikely)


Conict Data; Patterns
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;

Types of Conict

Main Dataset

I Source: Armed Conict Location and Events Dataset,ACLED. (vintage 4)

I Raleigh, Linke, Hegre, and Karlsen (2010); Raleigh, Linke, and Down(2014)

I Period: 1997 2013

I Sample: All African countries (but some small islands)

I Original Sources: Reuters, BBC, Associated Press, Agence FrancePress, United Nations Reports, NGOs, etc.


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Types of Conict

Classication. 4 Categories

I Battles, mostly between government forces, rebels, and militias.(23; 381 events, 9; 596 deadly).

I Violence against civilians, by militias (40%), government troops(25%) and rebels (25%). (20; 409 events, 8; 777 deadly).

I Riots and protests. mostly in capitals. (16; 147 events, 1; 040

deadly).I Non-violent activities by conict actors. (4; 727 events, 42 deadly).


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Types of Conict

Civil Conict. ACLED 1997-2013

Type of Violent

Events in ACLED

African Boundaries

Battle

Non-violent activity

Riots/Protests

Violence against civilians


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Types of Conict

Conict Examples

I Battles:I September 1999 the Ugandan army killed 42 Pian warriors, coming from the

Karamojong group split between Uganda, Sudan, and Kenya;

I August 1997 government forces of the Congo DRC retook control of the town of

Watsa, close to the border with Uganda, where the partitioned Alur reside. 800

casualties;

I Violence Against Civilians:

I raids of the Janjaweed militias against civilian population in the Darfur region;

I the assaults and tortures by Presidents Mugabes Central Intelligence

Organization in Zimbabwe;I burning of churches, hostage-taking and child-soldiering raids by rebels in Nigeria

and in Sierra Leone

I Riots and Protests

I Arab Spring

I Marikana events in South Africa


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Types of Conict

Classication. 8 Conict Actors

I Government Forces

I Rebel Forces (political organizations whose goal is to counter an established nationalgoverning regime by violent acts)

I Ethnic and Political Militias (created for a specic purpose or during a specictime period; oering protection/policing, involved in communal violence, etc.)

I Rioters

I Protesters

I Civilians

I Outside/external forces

I intervention by nearby countriesI peace-keeping (UN, African Union)and outside military involvement

(NATO, France)


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Types of Conict

Conict Events by Actor

Actors

African Boundaries

Government Forces

Rebel Forces

Political Militias

Ethnic Militias

Rioters

Protesters

Civilians

External Forces


Specication
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Unit of Analysis: Country-Ethnic Homelands

Conict across Country-Ethnic Homelands

Violent Events Across

Ethnic Homelands 97-13

African Boundaries

ACLED: Violent Events

0

1 - 2

3 - 6

7 - 13

14 - 31

32 - 89

90 - 5423

Fatal Violent Events in

Ethnic Homelands 97-13

African Boundaries

ACLED: Fatal Violent Events

0

1

2 - 3

4 - 7

8 - 15

16 - 35

36 - 2299


Specication

E i i
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Estimation


yi;c =a+SPLITi+SPILi;c+X0

i;c +ac+"i;c

I yi;c : civil conict at the homeland of ethnicity i in country c.

I SPLITi;c : Binary ethnic partitioning index. [518 partitioned, 694 non-split]

I SPILi;c : Share of adjacent split groups (spillovers). [mean 0.41]

I Xi;c : vector of control variables (at the country-ethnicity level).

I including distance to the national borderI including log land area, water indicators, and log population at

independence (simple controls)

I ac : country xed eects [national politics, type of colonization, state capacity,

ethnic composition, etc.]


Specication

E ti ti
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Estimation

Empirical Specication, cont.

I to minimize concerns that the partitioning indicator is capturing anoverall border eect, we also restrict estimation to ethnic areas nearthe national border

yi;c =a+SPLITi+SPILi;c+X0

i;c +ac+"i;c

if BDISTi;

c


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Estimation

Rich Set of Control Variables at a Fine Grid

I suitability for agriculture, elevation

I natural resources (diamond mines, oil/petroleum elds)

I malaria stability index (from Columbias Earth Institute)

I distance to border (and higher order polynomial terms)

I distance to the sea coast

I distance to the capital city

I capital city indicator

I coastal indicator

I more


Specication

Estimation
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Estimation

Technical Issues

I Count data

I Signicant fraction of the observations on regional civil war takes onthe value of zero (25%).

I Some extreme observations in the right tail of the distribution (e.g.,all conict events mean=53:4; p50= 4; p99= 102; max=5; 423)

Approach

I

Negative Binomial ML estimation with country constantsI Linear Probability Model (LPM) estimates (extensive margin)

I Poisson ML model estimation dropping outliers (top 5%, top 10%)

I Conditional Negative Binomial ML estimation (Hahn, Hausman, andGriliches, 1984)


Specication

Estimation
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Estimation

Alternative Aspects of Civil Conict

I Deadly conict incidents

I NB-ML with country constants

I LPM (OLS) with country xed eects

I Conditional NB modelsI Country xed-eects Poisson (no outliers)

I Fatalities (NB-ML with country constants)

I Duration in years (NB-ML and Poisson-ML with countryxed-eects)

I all incidents

I deadly incidents


Specication

Estimation
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Estimation

Inference

I "Standard" heteroskedasticity-adjusted standard errors unreliable.

I Report double clustered s.e. with clustering at the country-level and

at the ethnic family level (Cameron, Gelbach, and Miller (2011))

1. Account for (i) within country correlation; (ii) correlation of ethniccultural, political, economic traits within ethnic families.

2. Account for spatial correlation.

I Conleys (1999) method yields similar (and if anything somewhatsmaller) standard errors.


Ethnic Partitioning and Civil Conict. Results
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Results Preview

I Preliminary Evidence

I Tests of Means and Medians (Full Sample and Border Sample)

I Regression Analysis (Full Sample and Border Sample)

I Conict Events (number and likelihood)I Intensity (Duration, Fatalities, Deadly Events)I Type of Conict (battles, civilian violence, riots-protests)I Sensitivity Analysis

I Digging Deeper.

I Conict Actor (government troops, rebels, militias, foreigninterventions)

I Further evidence of actor-based analysis

I Taking Stock



Results. Preliminary Evidence
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y

Tests of Means and Medians. All Events

Indicator

Likelihood

mean mean median mean median mean median

(1) (2) (3) (4) (5) (6) (7)

non-partitioned ethnic groups (N=596) 0.815 47.616 3.000 30.495 3.000 30.464 3.000

partitioned ethnic groups (N=229) 0.878 65.031 13.000 47.872 10.000 52.526 13.000

difference 0.062 17.415 10.000 17.377 7.000 22.062 10.000

difference (p-value) (0.07) (0.42) (0.00) (0.05) (0.09) (0.02) (0.01)



difference 0.148 33.667 11.000 17.978 9.000 27.820 11.000


Panel A: All Ethnic Homelands

Panel B: Ethnic Homelands close to the National Border

Number of

Incidents - All

Excluding

Capitals

Excluding

Outliers (top 1%)



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Tests of Means and Medians. Battles

Indicator

Likelihood


(1) (2) (3) (4) (5) (6) (7)



difference 0.076 10.215 2.000 8.231 1.000 8.646 1.000




difference 0.164 14.530 3.000 8.305 2.000 10.840 3.000


Number of

Incidents - All

Excluding

Capitals

Excluding

Outliers (top 1%)



The Long-Run Eects of the Scramble for AfricaEthnic Partitioning and Civil Conict. Results

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Tests of Means and Medians. Civilian Violence

Indicator

Likelihood


(1) (2) (3) (4) (5) (6) (7)



difference 0.094 5.610 2.000 6.257 1.000 7.073 2.000




difference 0.150 12.643 3.000 6.133 2.000 9.868 3.000


Number of

Incidents - All

Excluding

Capitals

Excluding

Outliers (top 1%)




Regression Analysis. ACLED-Benchmark Estimates


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Ethnic Partitioning and Civil Conict. NB-ML Estimates.

Full Sample

All Observations Excl. Out. Excl. Cap.(1) (2) (3) (4) (5) (6)

SPLIT (Partitioning) 0.4513*** 0.3329** 0.4495*** 0.4626*** 0.4494*** 0.4565***

Double-clustered s.e. (0.1611) (0.1851) (0.1254) (0.1201) (0.1172) (0.1236)

SPIL (Adjacent Split) 0.0481 0.391 0.4619* 0.4920* 0.4834* 0.4256*Double-clustered s.e. (0.2789) (0.3430) (0.2626) (0.2628) (0.2686) (0.2760)

Log Likeliho od -4506.794 -4280.17 -4119.95 -4108.72 -3993.15 -3781.29Observations 1212 1212 1212 1212 1199 1165

Simple Controls Yes Yes Yes Yes Yes YesLocation Controls No No Yes Yes Yes YesGeographic Controls No No No Yes Yes YesCountry Fixed Eects No Yes Yes Yes Yes Yes


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Ethnic Partitioning and Civil Conict. NB ML Estimates.

Close to the National Border


SPLIT (Partitioning) 0.9247*** 0.8050*** 0.6014*** 0.5906*** 0.5906*** 0.5806***Double-clustered s.e. (0.1704) (0.2372) (0.2226) (0.2176) (0.2176) (0.2146)

SPIL (Adjacent Split) 0.0879 0.5679 0.4328 0.4514 0.4514 0.3928Double-clustered s.e. (0.5748) (0.4733) (0.3818) (0.3565) (0.3565) (0.3640)

Log Likeliho od -1697.469 -1561.61 -1518 -1510.73 -1510.733 -1445.62Observations 579 579 579 579 579 568



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Ethnic Partitioning and Civil Conict. LPM Estimates.

Full Sample


SPLIT (Partitioning) 0.0562** 0.0660*** 0.0783*** 0.0819*** 0.0839*** 0.0789***


SPIL (Adjacent Split) 0.0571 0.1146*** 0.1284*** 0.1443*** 0.1487*** 0.1468***Double-clustered s.e. (0.0486) (0.0394) (0.0397) (0.0408) (0.0402) (0.0408)

Log Likelihood 0.304 0.43 0.44 0.445 0.446 0.446Observations 1212 1212 1212 1212 1199 1162



http://find/


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Ethnic Partitioning and Civil Conict. LPM Estimates.

Close to the National Border


SPLIT (Partitioning) 0.0874** 0.0835* 0.0821 0.0903** 0.0903** 0.0893*Double-clustered s.e. (0.0399) (0.0484) (0.0523) (0.0457) (0.0457) (0.0461)

SPIL (Adjacent Split) 0.1787*** 0.2246*** 0.2297*** 0.2444*** 0.2444*** 0.2347***Double-clustered s.e. (0.0594) (0.0604) (0.0631) (0.0562) (0.0562) (0.0575)

Log Likelihood 0.315 0.463 0.475 0.489 0.489 0.486

Observations 579 579 579 579 579 568





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Ethnic Partitioning and Civil Conict Intensity. Full Sample

Deadly Incidents Deadly Indicator Casualties Dur. All Dur. DeadlyNB-ML LPM NB-ML Poisson Poisson

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

SPLIT (Partitioning) 0.3356** 0.0599** 0.4843*** 0.2015*** 0.1658**

Double-clustered s.e. (0.1357) (0.0287) (0.1651) (0.0622) (0.0740)

SPIL (Adjacent Split) 0.3948 0.1461*** 0.1161 0.2478** 0.3648**Double-clustered s.e. (0.2465) (0.0463) (0.3121) (0.1174) (0.1629)

Log Likelihood -2910.906 -4516.44 -2759.21 -2232.74adjusted R-square 0.411

Simple Controls Yes Yes Yes Yes YesLocation Controls Yes No Yes Yes YesGeographic Controls Yes No No Yes YesCountry Fixed Eects Yes Yes Yes Yes YesObservations 1212 1212 1199 1212 1212


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Ethnic Partitioning and Civil Conict Intensity. Close to

the National Border

Deadly Incidents Deadly Indicator Casualties Dur. All Dur. DeadlyNB-ML LPM NB-ML Poisson Poisson

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

SPLIT (Partitioning) 0.4651** 0.0820* 0.8489*** 0.2784** 0.4350***Double-clustered s.e. (0.2037) (0.0428) (0.3787) (0.1216) (0.1521)

SPIL (Adjacent Split) 0.2745 0.2378*** 0.3573 0.3731** 0.4674*Double-clustered s.e. (0.3110) (0.0611) (0.5155) (0.1804) (0.2415)

Log Likelihood -1028.821 -1657.27 -1057.16 -805.15adjusted R-square 0.449

Simple Controls Yes Yes Yes Yes YesLocation Controls Yes Yes Yes Yes YesGeographic Controls Yes Yes Yes Yes YesCountry Fixed Eects Yes Yes Yes Yes Yes

Observations 579 579 575 579 579


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Ethnic Partitioning and Main Civil Conict Categories. Full

SampleBattles Civilian Violence Riots - Protests

NB-ML LPM NB-ML LPM NB-ML LPM(1) (2) (3) (4) (5) (6)

SPLIT (Partitioning) 0.4428*** 0.0912** 0.4328*** 0.0517 0.0747 0.0193Double-clustered s.e. (0.1489) (0.0375) (0.1229) (0.0320) (0.1526) (0.0305)

SPIL (Adjacent Split) 0.4846 0.0631 0.3816 0.1749*** 0.4119 0.0773Double-clustered s.e. (0.3060) (0.0442) (0.3523) (0.0577) (0.2574) (0.0533)

Log Likelihood -2918.506 -2876.56 -2203.732

Observations 0.465 0.422 0.439

Simple Controls Yes Yes Yes Yes Yes YesLocation Controls Yes Yes Yes Yes Yes YesGeographic Controls Yes Yes Yes Yes Yes YesCountry Fixed Eects Yes Yes Yes Yes Yes YesObservations 1212 1212 1212 1212 1212 1212


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Summary Economic Magntitudes

I Number of Events (intensity): approx. 60% 70%(mean 53 events)

I Deadly Events: approx. 50% 70% (mean 16 events)

I Conict Likelihood: 5% 8: 5% (mean 73%)

I Duration: 25% (mean 4 5 years, median 2:5 years)

I

Duration Deadly: 30%

40% (mean 2:7 years, median 1 year)I Casualties: 100% 180% (mean 317 deaths)


Results. ACLED-Further Evidence
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Digging Deeper

I Ethnic-based discrimination, suocation from government andopposition [rebels, militias]

I Interventions from nearby countries [shelter, regrouping]; "proxy"wars ["internationalized internal" civil wars, PRIO]

I In contrast, no arguments linking partitioning with riots and

protests; mostly urban phenomena (capitals and big cities)


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State Conict (Government Forces, Rebels and Militias)

Actors Involved

in Violence

African Boundaries

Government

Actors Involved

in Violence

African Boundaries

Rebels

Actors Involved

in Violence

African Boundaries

Militias


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Violence against Civilians and Riots-Protests

Actors Involved

in Violence

African Boundaries

Civilians

Actors Involved

in Violence

African Boundaries

Riots and Protests

Actors Involved

in Violence

African Boundaries

Neighboring Governments


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Neighboring Countries Interventions. Examples

I 18/4/1997: "Sudanese government military repulsed an attack by

Ugandan military which began with 4 days of shelling the area -more than

300 Ugandan soldiers were reportedly killed." (Madi homeland in Sudan)

I

4/12/2013 "Ethiopian forces kill a civilian man in Somalia and a memberof the local government administration after he criticized their presence in

Somalia. Two other civilians were injured." (Shebelle homeland in

Somalia).

I

05/07/2007: "Sudan occupies territory of Alur in the Democratic Republicof Congo for administrative reasons." (Lugbara homeland in DRC)

I 26/5/2001: "Guinean military attacks Liberian army in support of Liberian

dissidents" - (Toma homeland in Liberia)


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Ethnic Partitioning and Main Civil Conict Actors. Full

Sample

All Homelands Homelands close to the BorderGovernm. Reb.-Mil. Neighbor External Governm. Reb.-Mil. Neighbor External

(1) (2) (3) (4) (5) (6) (7) (8)

SPLIT (Partitioning) 0.5247*** 0.4908*** 1.1280*** 0.244 0.1089*** 0.0663** 0.0658** 0.0065Double-clustered s.e. (0 .13 94 ) (0 .13 81 ) (0 .25 77 ) (0.2 53 4) (0.0 28 1) (0.0 32 7) (0.0 32 5) (0.0 228 )

SPIL (Adjacent Split) 0.496 0.3258 0.1629 -0.519 0.1300** 0.1059** 0.0737 -0.009Double-clustered s.e. (0 .31 08 ) (0 .30 89 ) (0 .43 27 ) (0.4 76 5) (0.0 53 0) (0.0 48 2) (0.0 46 6) (0.0 292 )

Log Likelihood -3213.3 -3538.276 -1088.254 -571.588 0.453 0.472 0.345 0.378Observations 1212 1212 1178 682 1212 1212 1212 1212

Simple Controls Yes Yes Yes Yes Yes Yes Yes YesLocation Controls Yes Yes Yes Yes Yes Yes Yes YesGeographic Controls Yes Yes Yes Yes Yes Yes Yes YesCountry Fixed Eects Yes Yes Yes Yes Yes Yes Yes YesObservations 1212 1212 1178 682 1212 1212 1212 1212


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Ethnic Partitioning and Main Civil Conict Actors. Full

Sample

All Homelands Homelands close to the BorderGovernm. Reb.-Mil. Neighbor External Governm. Reb.-Mil. Neighbor External

(1) (2) (3) (4) (5) (6) (7) (8)

SPLIT (Partitioning) 0.1089*** 0.0663** 0.0658** 0.0065 0.1240*** 0.0861* 0.0693** 0.0349Double-clustered s.e. (0.0281) (0.0327) (0.0325) (0.0228) (0.0426) (0.0497) (0.0342) (0.0298)

SPIL (Adjacent Split) 0.1 30 0* * 0.1 05 9* * 0.07 37 -0 .0 09 0.1 90 5** * 0 .1 671 ** * 0.0 07 4 -0.0 62 5Double-clustered s.e. (0.0530) (0.0482) (0.0466) (0.0292) (0.0625) (0.0619) (0.0487) (0.0415)

Log Likelihood 0.453 0.472 0.345 0.378 0.467 0.485 0.384 0.425Observations 1212 1212 1212 1212 579 579 579 579

Simple Controls Yes Yes Yes Yes Yes Yes Yes YesLocation Controls Yes Yes Yes Yes Yes Yes Yes YesGeographic Controls Yes Yes Yes Yes Yes Yes Yes YesCountry Fixed Eects Yes Yes Yes Yes Yes Yes Yes Yes
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UCDP-Based Results


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A Closer Look

I Results (so far):

I ethnic partitioning and state conict (battles between governmenttroops and rebels)

I ethnic partitioning and violence against civiliansI ethnic partitioning and foreign interventions by neighboring countries

I Robustness???

I Further Evidence???

I UCDP-GED database


UCDP-Based Results
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A Closer Look

I Results (so far):

I ethnic partitioning and state conict (battles between governmenttroops and rebels)

I ethnic partitioning and violence against civiliansI ethnic partitioning and foreign interventions by neighboring countries

I Robustness???

I Further Evidence???

I UCDP-GED database


UCDP-Based Results
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Data

I Source: Sundberg and Erik Melander (Journal of Peace Research,2013); Sundberg, Lindgren and Padskocimaite (2010)

I Coverage: 1989 2010. 11; 418 fatal incidents.

I

Event denition the incidence of the use of armed force by anorganized actor against another organized actor, or against civilians,resulting in at least 1 direct death in either the best, low or high

estimate categories at a specic location and for a specic temporal

duration

I state-driven conict: 7:332 (64:25%)

I non-state conict: 1:283 (11:25%)

I one-sided violence: 2:803 (24:5%)


UCDP-Based Results
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UCDP Conict Events by Type

Type of Violent Events UCDP 1989-2010

African Boundaries

Type of Violence

State Violence

Non-State Violence

One-Sided Violence


UCDP-Based Results
http://find/


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Ethnic Partitioning and State Conict

All Homelands Homelands close to the BorderAll Events Indicator Duration All Events Indicator Duration

NB-ML LPM NB-ML NB-ML LPM NB-ML(1) (2) (3) (4) (5) (6)

SPLIT (Partitioning) 0.4978** 0.0487* 0.3390** 0.8053*** 0.0799** 0.5469**


SPIL (Adjacent Split) 1.1577*** 0.0902* 0.6868** 0.434 0.0424 0.255Double-clustered s.e. (0.4761) (0.0518) (0.2964) (0.5468) (0.0534) (0.3744)

Log Likelihood -1453.054 -1046.922 -528.002 -383.392Observations 0.471 0.441

1212 1212 1212 579 579 579Simple Controls Yes Yes Yes Yes Yes YesLocation Controls Yes Yes Yes Yes Yes YesGeographic Controls Yes Yes Yes Yes Yes YesCountry Fixed Eects Yes Yes Yes Yes Yes Yes


UCDP-Based Results
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Ethnic Partitioning and Non-State Conict



SPLIT (Partitioning) -0.2087 -0.0459 0.026 -0.4122 -0.0351 -0.1797


SPIL (Adjacent Split) -0.8703 -0.0344 -0.5089 -0.6593 -0.0268 -0.6964Double-clustered s.e. (0.7193) (0.0302) (0.4423) (0.8728) (0.0415) (0.7086)




UCDP-Based Results

E h i P i i i d O Sid d Vi l


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Ethnic Partitioning and One-Sided Violence



SPLIT (Partitioning) 0.3468 0.0269 0.2750* 0.3288 0.0331 0.2925


SPIL (Adjacent Split) 0.4708 0.0829* 0.4935** 0.0901 0.0161 0.1659Double-clustered s.e. (0.4549) (0.0481) (0.2277) (0.6886) (0.0626) (0.3416)




UCDP-Based Results

T ki S k
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Taking Stock

I Strong link between ethnic partitioning and main aspects of civilconict (incidents, likelihood, duration, and fatalities).

I Predominant type of conict: government forces ghting againstrebels and militias (ACLED and UCDP).

I Strong link between ethnic partitioning and military interventions byneighboring countries (ACLED).

I Positive link between ethnic partitioning and one-sided violenceagainst civilians. (ACLED and UCDP).

I No link between ethnic partitioning and riots and protests.(ACLED).

I No link between ethnic partitioning and conict between non-stateactors. (UCDP).


Ethnic Partitioning and Ethnic Power (EPR)

EPR Database

E h i P R l i (EPR) D b
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Ethnic Power Relations (EPR) Database

I Source: Wimmer, Cederman and Min (Ethnic Politics and Armed Conict,American Sociological Review, 2009)

I

The Ethnic Power Relations (EPR) dataset identies all "politicallyrelevant ethnic groups" and records the level of access to statepower by their representatives in all countries of the world from1946 2010 (vintage 3).

I

Ethnic-based Armed ConictI Political discrimination



EPR Database

EPR D t b O t V i bl
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EPR Database. Outcome Variables

I Ethnic Armed Conict: "Ethnic wars typically involve conicts over ethnonationalself-determination, the ethnic balance of power in government, ethnoregional autonomy,

ethnic and racial discrimination (whether alleged or real), and language and other

cultural rights."

I Political discrimination: "Group members are subjected to active, intentional,and targeted discrimination with the intent of excluding them from both regional and

national power. Such active discrimination can be either formal or informal. Formal

discrimination legally limits access to government positions to citizens who speak a

certain mother tongue, display certain phenotypical features, or are members of certain

religious groups. Informal discrimination actively and intentionally inhibits individuals

with certain ethnic backgrounds from rising within the ranks of government."



EPR Database

Eth i P R l ti (EPR) D t b t C t
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Ethnic Power Relations (EPR) Database, cont. Caveats

I Manually matched 547 groups from EPR to Murdock (1959)

I Caveats:

I Matching is not straightforward as the EPR often quotes coalitions ofgroups

I EPR includes only politically relevant groups (may be a plus)

I An ethnic category is politically relevant if at least one signicantpolitical actor claims to represent the interests of that group in the

national political arena, or if members of an ethnic category aresystematically and intentionally discriminated against in the domainof public politics.



EPR Database

Ethnic Partitioning and Political Power
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Ethnic Partitioning and Political Power

Observ. Ethnic Wars Discrimination(1) (2) (3)

Partitioned Ethnicities 234 (0.395) 72 (0.31) 58 (0.248)

Non-Partitioned Ethnicities 359 (0.605) 69 (0.19) 52 (0.145)

Total 593 141 (0.238) 110 (0.185)



EPR Database

Examples
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Examples

Politically discriminated partitioned ethnic groups

I Tigrinya in Ethiopia

I

Asben in NigerI Ambo and Chokwe in Angola

I Zaghawa and Bideyat in Chad

I Ndau in Zimbabwe

I

Alur in UgandaI Yakoma in Central African Republic
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EPR Results

Ethnic Partitioning and Ethnic Political Discrimination


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Ethnic Partitioning and Ethnic Political Discrimination

LPM LPM LPM LPM(1) (2) (3) (4)

SPLIT (Partitioning) 0.1030*** 0.0778*** 0.0766*** 0.0718**Double-clustered s.e. (0.0342) (0.0276) (0.0289) (0.0282)

SPIL (Adjacent Split) 0.0169Double-clustered s.e. (0.0764)

adjusted R-square 0.018 0.475 0.495 0.521Observations 593 593 593 593Countries 40 40 40 40

Country Fixed Eects No Yes Yes YesSimple Controls No No Yes YesLocation Controls No No No YesGeographic Controls No No No Yes



EPR Results

EPR Analysis Results Summary
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EPR Analysis. Results Summary

I Direct evidence linking ethnic partitioning with discrimination fromthe national government

I

Link between ethnic partitioning and major ethnic-based civil warsover full post-independence period (further evidence on conict)

I The link between ethnic partitioning and ethnic wars is attenuatedwhen accounting for discrimination (though still signicant

conditional correlation)I SPLIT coef. (s.e.): 0:066 (0:035)


Ethnic Partitioning, Discrimination and Public Goods Provision (DHS)

DHS Data

Individual-Level Data
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Individual-Level Data

I 24 Sub-Saharan African countries; 300 ethnic groups in the DHS

I 1; 000 to 6; 000 respondents in each country. Out of 112; 737 males88; 171 were matched to 190 pre-colonial groups (Murdock, 1959).

I georeferenced data; at the enumeration area (village/town) level =>

match to ethnic homelandsI information on the ethnic location

I Focus on living conditions (including access to public goods)

I DHS composite wealth index (1 5): information on household

wealth; quantiles of wealth distribution within a countryI Education (0 5): "no education", "incomplete primary", "complete

primary", "incomplete secondary", "complete secondary", and"higher".
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DHS Data

Identifying wiith Split Groups and Residing in Split
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Identifying wiith Split Groups and Residing in Split

Homelands

Non-Split Homeland Split Homeland Total

Non-Partitioned Ethnic Identity 36,694 13,256 49,950

Partitioned Ethnic Identity 12,590 25,631 38,221

Total 49,287 38,887 88,171



DHS Baseline Results

Ethnic Partitioning and Individual Well-Being, DHS
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Ethnic Partitioning and Individual Well Being, DHS

Composite Wealth Index

All Observations (Individuals) Observations close to the Border(1) (2) (3) (4) (5) (6)

Ethnic Partitioning -0.3853*** -0.3227*** -0.1978** -0.2818*** -0.2679*** -0.2248**Double-clustered s.e. (0.1129) (0.0987) (0.0919) (0.1018) (0.0961) (0.0791)

Non-Indigenous 0.1936** 0.1942** 0.1825*** 0.1359* 0.1397* 0.1942**Double-clustered s.e. (0.0887) (0.0844) (0.0690) (0.0783) (0.0741) (0.0688)

Adjusted R-squared 0.028 0.072 0.167 0.053 0.090 0.162

Observations 88171 88171 88171 44090 44090 44090

Country Fixed Eects Yes Yes Yes Yes Yes YesIndividual Controls No Yes Yes No Yes YesLocation Controls No No Yes No No Yes




Ethnic Partitioning and Individual Education
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Ethnic Partitioning and Individual Education

All Observations (Individuals) Observations close to the Border(1) (2) (3) (4) (5) (6)

Ethnic Partitioning -0.3166** -0.2238** -0.1482* -0.1726* -0.1493* -0.1242


Non-Indigenous Indicator 0.1082 0.1163 0.1024 0.0568 0.0587 0.0813Double-clustered s.e. (0.0838) (0.0766) (0.0704) (0.0691) (0.0632) (0.0549)

Adjusted R-squared 0.151 0.222 0.255 0.132 0.188 0.210Observations 88171 88171 88171 44090 44090 44090

Country Fixed Eects Yes Yes Yes Yes Yes YesIndividual Controls No Yes Yes No Yes YesLocation Controls No No Yes No No Yes




Ethnic Partitioning and Wealth: Identity versus Location
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g y

All Observations Border Observations(1) (2) (3) (4)

Partitioning - Identity -0.3122** -0.2078** -0.2893*** -0.2440***Double-clustered s.e. (0.1236) (0.0939) (0.1058) (0.0797)

Partitioning - Location -0.2275** 0.041 0.0346 0.0983Double-clustered s.e. (0.1134) (0.0811) (0.1364) (0.0815)

Non-ndigenous Indicator 0.1894** 0.1837*** 0.1371* 0.2013***Double-clustered s.e. (0.0911) (0.0684) (0.0771) (0.0671)

Adjusted R-square 0.032 0.167 0.053 0.162

Observations 88171 88171 44090 44090

Country Fixed Eects Yes Yes Yes YesIndividual Controls No Yes No YesLocation Controls No Yes No Yes




Ethnic Partitioning and Education: Identity versus Location
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g y

All Observations Border Observations(1) (2) (3) (4)

Partitioning - Identity -0.2832** -0.1659* -0.1922* -0.1484*Double-clustered s.e. (0.1340) (0.0853) (0.1007) (0.0773)

Partitioning - Location -0.1039 0.0729 0.0904 0.1241**Double-clustered s.e. (0.0784) (0.0628) (0.0992) (0.0630)

Indigenous Indicator 0.1062 0.1047 0.0601 0.0903*Double-clustered s.e. (0.0852) (0.0696) (0.0665) (0.0526)

Adjusted R-squared 0.151 0.255 0.133 0.214

Observations 88171 88171 44090 44090

Country Fixed Eects Yes Yes Yes YesIndividual Controls No Yes No YesLocation Controls No Yes No Yes



DHS Further Evidence

Ethnic Partitioning and Wealth Within-Village Variation
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g g

All Observations (Individuals) Observations close to the BorderComposite Wealth Index Education Composite Wealth Index Education

(1) (2) (3) (4) (5) (6) (7) (8)

Ethnic Partitioning -0.0665*** -0.0644*** -0.0856** -0.0799** -0.0541** -0.0506** -0.0680* -0.0582D ouble-clustere d s.e. (0.0196) (0.0196) (0.0364) (0.0347) (0.0237) (0.0243) (0.0407) (0.0396)

Non-Indigenous 0.0761*** 0.0729*** 0.039 0.0328 0.1027*** 0.1022*** 0.0212 0.0164D ouble-clustere d s.e. (0.0208) (0.0205) (0.0341) (0.0321) (0.0243) (0.0238) (0.0460) (0.0430)

Adjusted R-squared 0.694 0.696 0.463 0.482 0.656 0.658 0.416 0.435Observations 88171 88171 88171 88171 44090 44090 44090 44090

Village Fixed Eects Yes Yes Yes Yes Yes Yes Yes YesIndividual Controls No Yes No Yes No Yes No YesLocation Controls No Yes No Yes No Yes No Yes




Ethnic Partitioning Wealth and Education. Non-Indigenous
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g g

All Movers Non-Split Homelands Split Homelands(1) (2) (3) (4) (5) (6)

Partitioning - Identity -0.2897* -0.1887** -0.4192** -0.1950* -0.1764** -0.1496**


Partitioning - Location -0.1582* 0.0021Double-clustered s.e. (0.0833) (0.0675)

Adjusted R-squared 0.135 0.251 0.137 0.290 0.152 0.221Observations 53992 53992 30606 30606 23386 23386

Country Fixed Eects Yes Yes Yes Yes Yes YesIndividual Controls No Yes No Yes No YesLocation Controls No Yes No Yes No Yes




DHS Analysis. Results Summary
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I Individuals identifying with partitioned groups have worse economicconditions and are less educated

I

even when we look across individuals residing in the same village/townI even when we look at "movers" (both in partitioned homelands and

non-split homelands)

I Location (ethnic partitioning) matters but not that much.

I Results apply to both "young" and "old" respondents (persistence)


Conclusion

Research Question and Approach

Main Contributions
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I Vast literature on the eects of colonization(type/identity/investments/infrastructure) on African(under)development.

I Recent works on the eects of the slave trades (Nunn (2008))

I Some work on the long-lasting of ethnic institutions (Michalopoulos andPapaioannou (2013))and other ethnic traits (Fenske (2013))and the legacyof historical conict (Besley and Reynal-Querol (2014))

I This paper: Focuses on the long-run eects of the Scramblefor Africa

I African historiography (e.g., Dowden (2008), Asiwaju (1985))

I so far have been neglected in economics (exception: Alesina et al. (2011))


Conclusion

Research Question and Approach

Main Contributions
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I Vast literature on the eects of colonization(type/identity/investments/infrastructure) on African(under)development.

I Recent works on the eects of the slave trades (Nunn (2008))

I Some work on the long-lasting of ethnic institutions (Michalopoulos andPapaioannou (2013))and other ethnic traits (Fenske (2013))and the legacyof historical conict (Besley and Reynal-Querol (2014))

I This paper: Focuses on the long-run eects of the Scramblefor Africa

I African historiography (e.g., Dowden (2008), Asiwaju (1985))

I so far have been neglected in economics (exception: Alesina et al. (2011))


Conclusion

Results Overview

Results Summary
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1. In the eve of colonization and African independence partitionedethnicities compared to non-partitioned groups

I were of similar levels of economic development

I

did not dier in pre-colonial institutional traitsI shared similar geographical features, except that the partitioned

groups had larger land and water areas

2. Since African independence partitioned ethnicities

I have experienced signicantly more

I civil conict

I violence towards civilians


Conclusion

Results Overview

Results Summary
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1. In the eve of colonization and African independence partitionedethnicities compared to non-partitioned groups

I were of similar levels of economic development

I

did not dier in pre-colonial institutional traitsI shared similar geographical features, except that the partitioned

groups had larger land and water areas

2. Since African independence partitioned ethnicities

I have experienced signicantly more

I civil conict

I violence towards civilians


Conclusion

Results Overview

Further Evidence. Channels
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I Interference by nearby countries mostly takes place in homelands ofpartitioned groups

I State-driven conict (government forces against rebels)

I Partitioning => political discrimination of split groups.

I Partitioning is associated with worse living conditions (access to

public goods) both for partitioned individuals irrespective of theirlocation


Conclusion

Results Overview

Future Research
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I Theoretical reconciliation (Esteban and Ray, 2011, 2012)

I Further understand the mechanisms (case studies, other outcomes,etc.)

I Focus on other regions, such as the Middle East (ISIS spread?)

I

Examine other aspects of state articiality (in Africa and elsewhere)
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Documents

Africa Scramble Syracuse