the long-run e⁄ects of the scramble for africa - dartmouth …elias/scramble_nov19th_pres.pdf ·...

The Long-Run E¤ects of the Scramble for Africa


Stelios Michalopoulos and Elias Papaioannou

Brown University and Tufts and Dartmouth College and CEPR and NBER

January 2012

ASSA Meetings, Chicago


Introduction

African History

Research Question

I What are the Consequences of the Scramble for Africa?


Introduction

African History

Ethnic Homelands before Colonization - Murdock (1959)


Introduction

African History

Berlin Conference - The Scramble for Africa


Introduction

African History

The Design of African Borders

I Berlin Conference 1884/1885 and subsequent treaties among Europeans in1890s

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

I Drawing involved protectorates, large (free-trade) areas, colonies, andspheres of in�uence rather than potential states.

I no major changes of borders during colonization or after independence


Introduction

African History

Current National Boundaries and Ethnic Homelands

At independence borders

were respected creating the

boundaries of African states


Introduction

African History

A European Randomization...

I "we have been engaged in drawing lines upon maps where no whiteman�s feet have ever tord; we have been giving away mountains andrivers and lakes to each other, only hindered by the smallimpediment that we never knew exactly where the mountains andrivers 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)


Introduction

African History

Partitioning of Tribal Areas

Ü

Ethnic Homelandsand National Borders

National Boundaries

NonBorder Groups

Border Groups

Partitioned Groups >10%


Introduction

African History

Partitioning of Tribal Areas

Ü

Partitioning ofEthnic Homelands

National Boundaries

Continuous Partitioning0.00

0.01 0.08

0.09 0.29

0.30 0.44

0.45 0.77


Introduction

African History

Partitioned Population Per Country.2

.4.6

.81

Den

sity

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

kernel = epanechnikov, bandwidth = 0.1332

Kernel density estimate


Introduction

Research Question

What Are the Consequences of the Scramble for Africa?

1. Was border drawing Random?

2. Did partitioning cause Civil War?


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 (2010))

I Colonization (type or/and identity)

I Persistence

2. Fundamental Role of Local Precolonial Institutions(Herbst (2000); Gennaioli and Rainer (2006, 2007); Michalopoulos and Papaioannou

(2010))

I Limited penetration of nationwide institutions (poor infrastructure)

I Colonization limited (in duration and area)


Introduction

Literature

Other Explanations on Deep Determinants of AfricanDeveleopment

I Slavery (Nunn, 2008)

I Geography-Health (Je¤ Sachs)

I Weak versus Strong States (Migdal 1988, Acemoglu, 2005; Besley and Persson,2008, 2009, 2010)


Introduction

Literature

This Paper

..focuses on the long-run e¤ects 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 Arti�ciality(Englebert, Tarrango, and Carter (2002); Alesina, Easterly, and Matuszeski (2011))


Introduction

Literature

Related Works

I Fragmentation and Development (e.g. Easterly and Levine (1997); Alesina etal. (2003), etc.)

I Fragmentation and Civil Con�ict (e.g. Fearon ( 2003); Collier and Hoe¤er(2007), Matuszeski and Schneider (2006), etc)

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

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

Soares, and Assunção, 2009, Ashraf and Galor (2011))


Introduction

Literature

Our Study: Shift the Agenda to the Scramble for Africa

I Exploiting as a quasi-natural experiment the random drawing of politicalboundaries during the Scramble for Africa

I African historiography convincingly argues that the borders were randomly drawnI We establish border arbitrariness (useful for future research)

I Exploiting regional variation (straightforward econometric bene�ts)

I Unobserved country characteristicsI Information at the time of colonizationI Control for local factors (e.g. natural resources)I Account for ethnic family characteristics (culture; institutions, etc.).

I Studying the e¤ect of partitioning on civil con�ict

I This has been hypothesized to be the main channel.I Important for policy standpoint (autonomy, regionalism).


Introduction

Mechanisms

Channels Linking Partitioning to Civil War

I Irredentist demands, as ethnicities that are minority groups in a countrywant 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 Casamance region in Southern Senegal, where the partitioned ethnic groups Diola

and the Malinke reside

I Partitioned ethnicities react to their marginalization by participating incoups, rebellions against the government

I Ewe in Ghana


Introduction

Mechanisms

Channels Linking Partitioning to Civil War (cont.)

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

I Patronage politics ethnic groups discriminate against minority groups,leading to the marginalization of certain under-privileged ethnicities

I The Alur split between Congo and Uganda. During Mobutu Sesse Soko regime

Alur from Congo moved to the Ugandan side. This generated opposition from the

dominant Buganda group, because of the alteration in the relative group size

I Border arti�ciality may interact with natural resources.

I Con�ict in the Cabinda enclave is driven by the interaction between the arti�cial

border design, the vast oil �elds, and the partitioning of the Bakongo people.


Are Borders Random?

Empirical Speci�cation

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

I SPLITi : indicator whether the historical homeland of ethnic group iis partitioned by national boundaries (using di¤erent thresholds)

I 10% thresholdI Continuous measure of partitioning

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

I HIST : historical ethnic traits.

I PRECOLONIAL : precolonial ethnic institutional/economic traits.

I PDi ;c : population density at independence (in 1960).

I ar : region �xed e¤ects (North, South, Central, East, West Africa)


Are Borders Random?

Partitioning and Geography, Ecology, Natural ResourcesSPLIT FRAC SPLIT FRAC SPLIT FRAC SPLIT FRAC

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

Land Area under Water 0.3209*** 0.0612*** 0.3743*** 0.0668*** 0.3417*** 0.0665*** 0.3353*** 0.0624*** (0.0759) (0.0125) (0.0822) (0.0122) (0.0716) (0.0122) (0.0810) (0.0134)

Land Area 0.0912 0.0154* 0.1184** 0.0187** 0.0979* 0.0172** 0.0745 0.0137* (0.0524) (0.0080) (0.0540) (0.0082) (0.0559) (0.0084) (0.0479) (0.0071)

Elevation 0.0628 0.0181 (0.2532) (0.0304)

Suitability for Agriculture 0.5788** 0.0732 (0.3362) (0.0464)

Malaria Stability Index 0.1215 0.0284 (0.3204) (0.0405)

Distance to the Coast 0.0001 0.0001 (0.0002) (0.0000)

Diamond Mine Indicator 0.1598 0.0176 (0.1796) (0.0289)

Oil Indicator 0.004 0.0009 (0.1709) (0.0352)

Region FE Yes Yes Yes Yes Yes Yes Yes Yes

Pseudo Rsquared 0.051 __ 0.057 __ 0.052 __ 0.052 __Adjusted Rsquared __ 0.084 __ 0.091 __ 0.090 __ 0.085Observations 834 834 834 834 834 834 834 834


Are Borders Random?

What Predicts Partitioning: Historical FeaturesSPLIT FRAC SPLIT FRAC SPLIT FRAC SPLIT FRAC

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

Land Area under Water 0.3287*** 0.0624*** 0.3202*** 0.0613*** 0.3157*** 0.0605*** 0.3223*** 0.0612*** (0.0747) (0.0123) (0.0766) (0.0124) (0.0819) (0.0130) (0.0755) (0.0125)

Land Area 0.0879 0.0148* 0.0903 0.0155* 0.0867 0.0148* 0.0981* 0.0152* (0.0511) (0.0079) (0.0519) (0.0079) (0.0522) (0.0080) (0.0566) (0.0084)

Slave Exports 0.0227 0.0034 (0.0238) (0.0036)

Major City in 1400AD 0.0437 0.0057 (0.1981) (0.0351)

0.0004 0.0001 (0.0003) (0.0000)

Population Density 1960 0.0213 0.0006 (0.0638) (0.0079)

Region FE Yes Yes Yes Yes Yes Yes Yes Yes

Pseudo Rsquared 0.053 __ 0.051 __ 0.054 __ 0.051 __Adjusted Rsquared __ 0.086 __ 0.084 __ 0.087 __ 0.084Observations 834 834 834 834 834 834 834 834

Distance to Explorer'sRoutes


Are Borders Random?

Pre-Colonial Institutions across Ethnicities


Are Borders Random?

What Ethnic Traits Predict Partitioning?SPLIT FRAC SPLIT FRAC SPLIT FRAC SPLIT FRAC

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

Land Area under Water 0.2452** 0.0465*** 0.3029*** 0.0575*** 0.2803** 0.0542*** 0.2546** 0.0501*** (0.0976) (0.0167) (0.0924) (0.0164) (0.0952) (0.0166) (0.0943) (0.0160)

Land Area 0.1672** 0.0262*** 0.1659** 0.0235** 0.1721** 0.0259*** 0.1662** 0.0251*** (0.0637) (0.0094) (0.0677) (0.0102) (0.0598) (0.0083) (0.0586) (0.0083)

Political Centralization 0.1865 0.0307 (0.1668) (0.0210)

Class Stratification 0.0237 0.0037 (0.0474) (0.0072)

Share of Agriculture 0.028 0.0041 (0.0328) (0.0054)

Animal Husbandry 0.0014 0.0003 (0.0350) (0.0055)

Region FixedEffects Yes Yes Yes Yes Yes Yes Yes Yes

pseudo Rsquared 0.069 __ 0.079 __ 0.068 __ 0.067 __adjusted Rsquared __ 0.106 __ 0.123 __ 0.107 __ 0.106Observations 440 440 396 396 490 490 490 490


Are Borders Random?

Con�ict Data

Source of Civil War Data

I Source: Uppsala Con�ict Data Program (UCDP)/InternationalPeace Research Institute, Oslo (PRIO),

I Armed Con�ict Dataset, Version 4-2006

I Armed Con�ict is de�ned as �a contested incompatibility thatconcerns government and/or territory where the use of armed forcebetween two parties, of which at least one is the government of astate, results in at least 25 battle-related deaths.�

I We use the dataset of Raleigh et al. (2006) to obtain information onthe spatial extent of each civil war. This dataset assigns to eachcon�ict a centroid with a corresponding radius in kilometers.


Are Borders Random?

Con�ict Data

Measures of Civil War

I Number of Civil Con�icts per Ethnic Area 1970-2005

I post 1970�s majority of African states is independent, results similarusing 1970-2005


Are Borders Random?

Con�ict Data


Ü

Number of Civil WarsBetween 19702005

National Boundaries

0

1

2

3

4

5


Are Borders Random?

Con�ict Data


I Number of Con�ict Zones per Ethnic Area


Are Borders Random?

Con�ict Data


Ü

Number of Conflict Zones Between 19702005

National Boundaries

0

1

2

3

4

5

6

7

8


Are Borders Random?

Con�ict Data


I Number of Civil War Casualties per Ethnic Area 1970-2005


Are Borders Random?

Con�ict Data


Ü

Number of Casualties Between 19702005

National Boundaries

0

1 700

701 2910

2911 5701

5702 9252

9253 16736

16737 29742


Are Borders Random?

Con�ict Data


I Duration of Civil Con�ict per Ethnic Area 1970-2005


Are Borders Random?

Con�ict Data


Ü

Duration of Civil Conflict Between 19702005

National Boundaries

0

1 5

6 10

11 15

16 19

20 24

25 29

30 34

35 47


Are Borders Random?

Con�ict Data

Correlation Matrix of Civil War Indicators and Luminosity

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

# of Civil Con�icts 1.000

# of Con�ict Zones 0.848 1.000

Civil Con�ict Duration 0.641 0.710 1.000

Casualties (Best Est.) 0.367 0.585 0.545 1.000

Casualties (Low Est.) 0.338 0.556 0.539 0.979 1.000

Casualties (High Est.) 0.392 0.590 0.508 0.978 0.934 1.000


Are Borders Random?

Estimation

Empirical Speci�cation

y f ;ci = a0 + SPLITi + X 0i�+ f (LOCUSi ) + ac + af + "f ;ci

I y f ;ci : civil war measure of ethnic group i

I f (LOCUSi ) : Cubic polynomial in latitude and longitude (Dell, 2011).

I Xi : vector of control variables (at the ethnicity level).

I ac : country �xed e¤ects

I af : ethnic-family �xed e¤ects


Are Borders Random?

Estimation

Rich Set of Control Variables at a Fine Grid

I suitability for agriculture, mean elevation

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

I malaria stability index (from Columbia�s Earth Institute)

I slavery (from Nunn, 2008)

I distance to border (similar results with a �exible polynomial)

I distance to the capital city

I distance to the sea coast

I presence of a large city in 1400 AD (before the slave trades)


Are Borders Random?

Estimation

Technical Issues

I Signi�cant fraction of the observations on regional civil war takes onthe value of zero.

I Some extreme observations in the right tail of the distribution

Approach

I Poisson/Negative Binomial ML estimation

I OLS estimation using ln(1+ x) as dep. variable

I Focus only on intensive margin of wars - Normally Distributed

I Tobit yields similar (and if anything stronger) results


Are Borders Random?

Estimation

Inference

I "Standard" heteroskedasticity-adjusted standard errors unreliable.

I Report double clustered s.e. with clustering at the country level andat the ethnic-family level (Cameron, Gelbach, and Miller (2006))

1. Account for (i) within country correlation; (ii) correlation ofprecolonial institutions and other cultural traits within ethnic families.

2. Account for spatial correlation.

I Conley�s (1999) method yields similar (and if anything somewhatsmaller) standard errors.


Are Borders Random?

Estimation

Externalities

I In the (likely) case of spillovers, the coe¢ cient on SPLIT is going tounder-estimate (lower bound) the local e¤ect of ethnic partitioning(Miguel and Kremer, 2004; see also Bosker and de Ree, 2010).

I higher con�ict in nearby adjacent areas where non-partitioned ethnicgroups reside

I higher con�ict in the "control" group


Are Borders Random?

Estimation

Externalities

I Experimented with

I Controlling for total casualties at the ethnic-family level (netting outcasualties in each ethnic homeland) and also at the country-level.

I Spatial lag models that explicitly model spillovers

I Present results controlling for the number of adjacent groups whichare partitioned

I Present results controlling for the number of adjacent groups


Main Results

Partitioning and the Incidence of Civil Con�ict

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

SPLIT Partitioning 0.2680*** 0.2660*** 0.1687* 0.1656* 0.2039*** 0.1472*** (0.0593) (0.0596) (0.0914) (0.0916) (0.0525) (0.0498)

Distance to the Border 0.0007 0.0008 0.0002 0.0001 (0.0006) (0.0006) (0.0003) (0.0003)

Log Likelihood 1036.88 997.67 970.54 965.08 855.59 819.32

Controls & Region FE Yes Yes Yes Yes Yes YesPolynomial Lat. & Long. No Yes Yes Yes Yes YesLocation Controls No No Yes Yes Yes YesAdditional Controls No No No Yes Yes YesEthnic Family FE No No No No Yes YesCountry FE No No No No No YesObservations 834 834 834 834 834 830

Panel A Dependent Variable: Number of Civil Conflicts


Main Results

Partitioning and the Incidence of Civil Con�ict

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

SPLIT Partitioning 0.3063*** 0.3061*** 0.1691* 0.1864** 0.2266*** 0.1512*** (0.0730) (0.0689) (0.0961) (0.0881) (0.0580) (0.0516)


Log Likelihood 1281.79 1229.68 1176.51 1165.10 969.96 918.956


Panel B Dependent Variable: Number of Conflict Zones


Main Results

Partitioning and Civil Con�ict Casualties(1) (2) (3) (4) (5) (6)

SPLIT Partitioning 0.4429*** 0.3805*** 0.2532** 0.3141** 0.4381*** 0.4113** (0.1915) (0.1093) (0.1178) (0.1399) (0.1516) (0.1969)


Log Likelihood 3196.85 3004.05 2992.58 2960.79 2699.69 2535.66


Panel A: Negative Binomial ML


Main Results

Partitioning and Civil Con�ict Duration(1) (2) (3) (4) (5) (6)

SPLIT Partitioning 0.2907** 0.3474** 0.3236** 0.3202** 0.2750** 0.2333* (0.1449) (0.1302) (0.1289) (0.1320) (0.1115) (0.1321)


Log Likelihood 2377.90 2281.71 2252.00 2236.48 1912.89 1752.21

Controls & Region FE Yes Yes Yes Yes Yes YesPolynomial Lat. & Long. No Yes Yes Yes Yes YesLocation No No Yes Yes Yes YesAdditional Controls No No No Yes Yes YesEthnic Family FE No No No No Yes YesCountry FE No No No No No YesObservations 834 834 834 834 834 830

Panel A: Negative Binomial ML


Main Results

Robustness Checks

I Use continuous measure of partitioning

I Exclude Capital Cities from the analysis

I Exclude South, North, East, West, Central Africa iteratively

I Modeling Spillovers

I Who is your Neighbor Group?

I Focus only on Border Ethnic Groups


Main Results

Accounting for SpilloversI Who is your Neighbor?

I y f ;ci = a0 + SPLITi + �ADJi + �SPLIT_ADJi + X 0i�+f (LOCUSi ) + ac + af + "

f ;ci

I y f ;ci : civil war measure of ethnic group i

I ADJi : # number of adjacent ethnic groups (captures local ethnic diversity)

I SPLIT_ADJi : # number of adjacent partitioned ethnic groups

I f (LOCUSi ) : Cubic polynomial in latitude and longitude

I Xi : vector of control variables (at the ethnicity level).

I ac : country �xed e¤ects

I af : ethnic-family �xed e¤ects


Main Results

Accounting for Spillovers

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

SPLIT Partitioning 0.1788* 0.1832*** 0.2095** 0.2017*** (0.0934) (0.0552) (0.0895) (0.0593)

ADJ Neighbors 0.0141 0.0101 0.0321** 0.0222 (0.0153) (0.0116) (0.0147) (0.0131)

0.0562** 0.0483*** 0.0843*** 0.0655*** (0.0221) (0.0140) (0.0275) (0.0163)


Log Likelihood 962.962 817.787 1157.943 914.438

Region FE Yes No Yes NoPolynomial Lat. & Long. Yes Yes Yes YesRich Conditioning Set Yes Yes Yes YesEthnic Family FE No Yes No YesCountry FE No Yes No YesObservations 834 830 834 830

SPLITADJ PartitionedNeighbors

Incidents Locations


Main Results

Accounting for Spillovers

(5) (6) (7) (8)

SPLIT Partitioning 0.3385** 0.4510*** 0.3317** 0.2817* (0.1385) (0.2133) (0.1415) (0.1643)

ADJ Neighbors 0.0749 0.0447* 0.0468 0.0051 (0.0514) (0.0288) (0.0315) (0.0148)

0.1638** 0.0733 0.1088* 0.0777* (0.0627) (0.0570) (0.0614) (0.0432)


Log Likelihood 2955.756 2533.713 2232.082 1746.042Region FE Yes No Yes NoPolynomial Lat. & Long. Yes Yes Yes YesRich Conditioning Set Yes Yes Yes YesEthnic Family FE No Yes No YesCountry FE No Yes No YesObservations 834 830 834 830


Casualties Duration


Main Results

Partitioning and Con�ict in Border Areas

I Focus on Border Areas Only


Main Results


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

SPLIT Partitioning 0.1833*** 0.1454*** 0.2404*** 0.1845*** (0.0475) (0.0469) (0.0671) (0.0473)

ADJ Neighbors 0.0347 0.0021 0.0652*** 0.0105 (0.0251) (0.0182) (0.0224) (0.0167)

0.0860** 0.0541*** 0.1278*** 0.0751*** (0.0407) (0.0173) (0.0425) (0.0260)

Log Likelihood 485.06 407.00 577.83 451.80



Incidents Locations


Main Results


(5) (6) (7) (8)

SPLIT Partitioning 0.4743*** 0.2959* 0.3622*** 0.1744 (0.1436) (0.1807) (0.1338) (0.1144)

ADJ Neighbors 0.0687 0.0810* 0.0806** 0.0115 (0.0619) (0.0455) (0.0387) (0.0166)

0.2318*** 0.0807 0.1811*** 0.1004** (0.0768) (0.0997) (0.0786) (0.0467)

Log Likelihood 1462.82 1250.86 1068.82 791.84



Casualties Duration


Main Results

Partitioning and Con�ict in Presence of Natural Resources

I Partitioning and Natural Resources

I Prominent Example

I armed con�ict in the Cabinda enclave on the border of Angola withZaire.

I Homeland of the Bakongo partitioned between Angola and Zaire

I Rich in oil �ends


Main Results

Partitioning and Con�ict in Presence of Natural Resources

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

SPLIT Partitioning 0.1633*** 0.1624*** 0.2142*** 0.1677*** (0.0510) (0.0481) (0.0617) (0.0520)

0.0390 0.0446 0.1066 0.0323 (0.0668) (0.0557) (0.0770) (0.0527)

0.2583** 0.1902** 0.1537 0.159 (0.1147) (0.0958) (0.1070) (0.1047)

ADJ Neighbors 0.0255* 0.0011 0.0408***0.0112 (0.0171) (0.0121) (0.0173) (0.0127)

0.0691*** 0.0621*** 0.0965*** 0.0784*** (0.0220) (0.0139) (0.0299) (0.0186)

Log Likelihood 991.23 819.79 1215.02 920.49

Region FE Yes No Yes NoPolynomial Lat. & Long. Yes Yes Yes YesEthnic Family FE No Yes No YesCountry FE No Yes No YesObservations 834 830 834 830


SPLITNR Partitioned& Natural Resources

NR Natural ResourcesIndicator

Incidents Locations


Conclusion

Main Contributions

I Vast literature on the e¤ects of colonization (type/identity) onAfrican development.

I Recent works on the e¤ects of the slave trades (Nunn (2008))

I Some work on the long-lasting of ethnic institutions (Gennaioli andRainer (2006, 2007); Michalopoulos and Papaioannou (2010))

I This paper: Focuses on the long-run e¤ects of the scramblefor Africa

I African historiography (e.g. Wesseling (1996), Asiwaju (1984))

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


Conclusion

Results Summary

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 di¤er in precolonial institutional traits

I shared similar ecological, geographical features,I except that the partitioned groups had larger land and water areas

2. Since African independence partitioned ethnicities

I have experienced signi�cantly more, longer and deadlier incidents ofcivil con�ict


Conclusion

Appendix

The Case of Mende

Ü

Number of Civil WarsBetween 19702005in Mende Homeland

National Boundaries

Part Affected by Civil War 187

Part Affected by Civil War 146

Part Affected by Civil Wars 146, 187

Ü

Number of Conflict Zones Between 19702005 in Mende Homeland

National Boundaries

Affected by Civil War 187

Affected by Civil War 187 and Zone 1 of Civil War 146

Affected by Zones 1, 2 of Civil War 146

Affected by Civil War 187 and Zone 2 of Civil War 146

Affected by Civil War 187 and Zones 1, 2 of Civil War 146


Conclusion

Appendix

OLS Estimation of Casualties and Partitioning(1) (2) (3) (4) (5) (6)

SPLIT Partitioning 0.4854** 0.4336*** 0.3723** 0.4229*** 0.4213*** 0.3149* (0.2120) (0.1574) (0.1511) (0.1497) (0.1485) (0.1701)


Adjusted Rsquared 0.214 0.413 0.486 0.513 0.757 0.839


Panel B: Log Linear OLS


Conclusion

Appendix

OLS Estimation of Duration and Partitioning

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

SPLIT Partitioning 0.2484** 0.2828*** 0.2638** 0.2542** 0.2516** 0.2317** (0.1148) (0.1041) (0.1110) (0.1124) (0.0988) (0.1134)


adjusted Rsquared 0.231 0.391 0.483 0.513 0.774 0.846

Controls & Region FE Yes Yes Yes Yes Yes YesPolynomial Lat. & Long. No Yes Yes Yes Yes YesLocation No No Yes Yes Yes YesAdditional Controls No No No Yes Yes YesEthnic Family FE No No No No Yes YesCountry FE No No No No No YesObservations 834 834 834 834 834 830

Panel B: Log Linear OLS


Conclusion

Appendix

Continuous Measure of Partitioning

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

0.5179** 0.5268*** 0.4882** 0.4698*** 0.7787** 0.8500* 0.7390** 0.6852** (0.2524) (0.1490) (0.2288) (0.1537) (0.4445) (0.4575) (0.3661) (0.2798)

ADJ Neighbors 0.0165 0.0077 0.0338** 0.0218 0.0774 0.0437 0.0469 0.0058 (0.0149) (0.0114) (0.0143) (0.0132) (0.0515) (0.0299) (0.0310) (0.0156)

0.0540*** 0.0473*** 0.0814*** 0.0609*** 0.1592*** 0.0582 0.1041* 0.0719* (0.0212) (0.0136) (0.0267) (0.0163) (0.0619) (0.0546) (0.0597) (0.0371)

Distance to the Border 0.0004 0.0002 0.0006 0.0002 0.0006 0.0006 0.0002 0.0001 (0.0007) (0.0004) (0.0007) (0.0004) (0.0010) (0.0008) (0.0010) (0.0006)

Log Likelihood 962.264 817.307 1158.484 914.767 2956.208 2537.114 2233.127 1746.839Region FE Yes No Yes No Yes No Yes NoPolynomial Lat. & Long. Yes Yes Yes Yes Yes Yes Yes YesRich Conditioning Set Yes Yes Yes Yes Yes Yes Yes YesEthnic Family FE No Yes No Yes No Yes No YesCountry FE No Yes No Yes No Yes No YesObservations 834 830 834 830 834 830 834 830


FRAC ContinuousPartitioning

Incidents Locations Casualties Duration


Conclusion

Appendix

Excluding Capital Cities

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

SPLIT Partitioning 0.1489 0.1765*** 0.2218*** 0.2025*** 0.3105** 0.4404*** 0.3003*** 0.2586*** (0.0999) (0.0604) (0.0927) (0.0638) (0.1311) (0.1634) (0.1125) (0.1109)

ADJ Neighbors 0.0151 0.0027 0.0279* 0.0174 0.0686* 0.0544** 0.0334 0.0107 (0.0157) (0.0116) (0.0142) (0.0130) (0.0453) (0.0279) (0.0308) (0.0181)

0.0599*** 0.0500*** 0.0879*** 0.0694*** 0.1682*** 0.0812 0.0960* 0.0846** (0.0256) (0.0162) (0.0305) (0.0178) (0.0657) (0.0557) (0.0515) (0.0419)

Distance to the Border 0.0005 0.0002 0.0006 0.0003 0.0005 0.0005 0.0001 0.0002 (0.0007) (0.0004) (0.0007) (0.0004) (0.0007) (0.0007) (0.0006) (0.0004)

Log Likelihood 902.723 767.185 1085.483 858.857 2754.551 2388.294 2100.799 1655.854

Region FE Yes No Yes No Yes No Yes NoPolynomial Lat. & Long. Yes Yes Yes Yes Yes Yes Yes YesRich Conditioning Set Yes Yes Yes Yes Yes Yes Yes YesEthnic Family FE No Yes No Yes No Yes No YesCountry FE No Yes No Yes No Yes No YesObservations 787 787 787 787 787 787 787 787


Incidents Locations Casualties Duration

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