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3. Does inequality lead to civil wars? A global long-term study using

3.4. Conclusions

Several studies have documented the negative effect of economic deprivation on civil war for the last few decades. In most cases, GDP per capita or its growth rate has been used to indicate an economy’s level of development. When assessing the 306 civil wars that took place between 1816 and 1999, we find that anthropometric measures are powerful indicators for explaining the onset of civil war. A negative correlation of absolute welfare levels and the probability of civil war outbreak was confirmed in most of our results, but did not pass the instrumental variables tests. Relative deprivation measured by the height inequality, on the other hand, was positive and significant in all of the models and passed robustness tests.

We also explicitly addressed the issue of endogeneity of absolute and relative deprivation by introducing instrumental variables. We used lactose tolerance as well as climatic and geological factors that are correlated with inequalities. These turned out to be strong instruments. The IV regressions confirmed the robustness of our results regarding the effect of inequality on onset of civil war. The inequality coefficients remained negative and significant throughout every specification, passing robustness tests as well as instrumental variables estimation.

Our study took a long-term view, analysing civil wars over the last two centuries. This was only possible by the use of innovative methods of measuring absolute and relative deprivation. Relative deprivation has not been used in empirical civil war literature as there is a lack of data on inequality in several countries. Our study overcame the problem of data scarcity because of an extensive data base for inequality in well-being. The impact of inequality on civil war probability could be systematically tested and a significant impact is found in our study.

Chapter 3. Does inequality lead to civil wars? A global long-term study using anthropometric indicators (1816-1999).

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Appendix B

Variable definitions

Civil war is coded as a dichotomous variable adopting the value 1 if civil war broke out in given country and five-year period. It is defined as sustained combat with at least 1,000 battle-related deaths per year that takes place between the armed forces of a government and forces of another entity for central control or for local issues. Military and civilian deaths are counted. Sources: Correlates of War Project and Uppsala Conflict Data Project: http://www.correlatesofwar.org/

Colonydummy is coded as a dichotomous variable adopting the value 1 if a country was a colony. Source: Correlates of War 2 Project. Colonial/Dependency Contiguity Data, 1816-2002. Version 3.0

Democracy indicates the openness of democratic institutions in a country and is measured on a scale of -10 (low) to 10 (high). Source: Polity IV Project.

Diamond is coded as a dichotomous variable adopting the value 1 if a country had diamond deposits that could be extracted.

Ethnic fractionalisation: based on a combination of racial and linguistic characteristics and defined as 1 minus the Herfindahl index of group shares of these characteristics. Source: Alesina et al. (2003). Data available at http://www.nsd.uib.no/macrodataguide/set.html?id=16&sub=1.

Height describes the average adult male height in the respective country ten years prior to the time of observation (except for the instrumental variables regressions, where we take current height); measured in centimetres. Sources: Baten and

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Blum (2012), Measure DHS (Demographic and Health Surveys) project and the World Health Organisation’s (WHO) Global Database on Child Growth and Malnutrition.

Height growth is the growth rate of heights between two decades: (heightt – height t-1)/

heightt-1

Inequality is the coefficient of height variation at time t in the case of instrumental variables regressions and t-1 for all other specifications.

Language Fractionalisation is defined as 1- Herfindahl Index of linguistic group shares, which reflects the probability that two randomly selected persons belong do different groups. Source: Alesina et al. (2003). Data available at http://www.nsd.uib.no/macrodataguide/set.html?id=16&sub=1.

Natural resource exports: percentage value of raw materials and mining products, relative to total exports, around 1980. Source: World Bank Data 1999 (CD-Rom)

Religious Fractionalisation is defined as the probability that 2 randomly selected individuals belong to different religious groups. Source: Alesina et al. (2003).

Data available at http://www.nsd.uib.no/macrodataguide/set.html?id=16&sub=1.

Peace duration is the number of decades that country i has not been affected by conflict.

Source: Intra-State war data set (version 4.1, posted in March 2011) from the Correlates of War project

Population (log) is the log of a country’s population at the beginning of a ten-year period. Sources: The World Bank and Maddison (2001).

Warlast counts the number of months a country has experienced internal conflicts during the previous decade and divides them by 12 to get the average number of

Chapter 3. Does inequality lead to civil wars? A global long-term study using anthropometric indicators (1816-1999).

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years. We include all intrastate conflicts (apart from civil wars also regional internal and intercommunal conflicts) as we want to capture the overall dissatisfaction. Source: Intra-State war data set (version 4.1, posted in March 2011) from the Correlates of War project.

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Table B.1: World regions, individual countries, and birth decades: coverage of the data set (grey indicates that real data was available and was accepted for height and civil war)

Chapter 3. Does inequality lead to civil wars? A global long-term study using

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Chapter 3. Does inequality lead to civil wars? A global long-term study using

82 Romania (1). Source: Baten and Blum (2012).”men” denotes countries from the middle East and North Africa. The group “naa” includes North America, Australia and New Zealand.

Chapter 3. Does inequality lead to civil wars? A global long-term study using anthropometric indicators (1816-1999).

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Figure B.1: The probability of onset of civil war subject to democracy

.05 .1.15 .2

Pr(cwon)

-10 -5 0 5 10

democracy

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Table B.2: Multicollinearity statistics. variance inflation factors for the independent variables and correlation between height inequality and lagged average height

Variable VIF 1/VIF

Inequality 1.17 0.855

Lag height 1.45 0.689

Democracy 1.40 0.717

(Democracy)2 1.51 0.663

Population (logs) 1.31 0.765

Ethnic polarisation 1.17 0.856

Colony 1.16 0.862

Diamond mining 1.18 0.845

Mean VIF 1.29

Lag height Inequality

Lag height 1

Inequality -0.045 1

Chapter 3. Does inequality lead to civil wars? A global long-term study using anthropometric indicators (1816-1999).

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Table B.3: Horserace between inequality and lagged inequality (1)

Panel Logit

Inequality 0.041

(0.143)

Inequality t-1 0.040*

(0.094)

Height -0.464

(0.173)

Population (log) 0.013

(0.172)

Peace Duration -0.002

(0.302)

Colony -0.023

(0.431)

Time dummies? Yes

N 454

Chi-squared 101.7

Notes: * significant on the 10% level.

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Table B.4: Within- and between variation of variables

Variable Mean Std. dev

Onset of civil war Overall 0.07 0.25

Between 0.10

Within 0.23

Inequality Overall 3.77 0.37

Between 0.28

Within 0.27

Height Overall 1.68 0.05

Between 0.03

Within 0.03

Democracy Overall -0.01 0.07

Between 0.05

Within 0.05

Democracy2 Overall 0.45 0.35

Between 0.23

Within 0.27

Colony Overall 0.22 0.41

Between 0.26

Within 0.32

Diamond Overall 0.13 0.34

Between 0.34

Within 0.00

Ethn. Fract Overall 0.46 0.26

Between 0.26

Within 0.00

Population (logs) Overall 7.86 1.84

Between 1.67

Within 0.80

Chapter 3. Does inequality lead to civil wars? A global long-term study using anthropometric indicators (1816-1999).

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Table B.5: Robustness test of the results on ethnic fractionalisation.

Comparison of fractionalisation measures.

Notes: This table includes solely our deprivation variables and different fractionalisation measures. As fractionalisation has been mentioned as one of the main drivers of civil wars in the previous literature on civil wars, we introduce ethnic fractionalisation, ethno-linguistic fractionalisation and religious

fractionalisation separately to assess whether perhaps fractionalisation rather than deprivation is the actual driver of a conflict, or whether the deprivation coefficients change dramatically if we include

fractionalisation measures. As a result, fractionalisation does not turn significant in any of the

specifications in our long-run study. Heteroskedasticity-robust clustered standard errors applied in every model. P-values in parentheses, ***, **, * significant on the 1, 5, and 10%-level respectively. Inequality is proxied by height cv, absolute deprivation is proxied by height, both lagged by one decade. Peace duration: sequence of decades where no war has started up to the current period. Other notes: see Table 3.3.

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4. How effective is development aid? Human stature as