Seeds of distrust: conflict in Uganda

We study the effect of civil conflict on social capital, focusing on Uganda’s experience during the last decade. Using individual and county-level data, we document large causal effects on trust and ethnic identity of an exogenous outburst of ethnic conflicts in 2002–2005. We exploit two waves of survey data from Afrobarometer (Round 4 Afrobarometer Survey in Uganda, 2000, 2008), including information on socioeconomic characteristics at the individual level, and geo-referenced measures of fighting events from ACLED. Our identification strategy exploits variations in the both the spatial and ethnic intensity of fighting. We find that more intense fighting decreases generalized trust and increases ethnic identity. The effects are quantitatively large and robust to a number of control variables, alternative measures of violence, and different statistical techniques involving ethnic and spatial fixed effects and instrumental variables. Controlling for the intensity of violence during the conflict, we also document that post-conflict economic recovery is slower in ethnically fractionalized counties. Our findings are consistent with the existence of a self-reinforcing process between conflicts and ethnic cleavages.

1. Bellows and Miguel (2009) use a household survey to analyze whether people who have been victimized in the civil war in Sierra Leone are affected in their post-war behavior. In particular, they find that more victimized people are more likely to “attend community meetings”, and to “join social and political groups”.

2. An example of this strategy is the Amnesty Act of 2000, by which the Government of Uganda granted amnesty to all rebels who would abandon violence, renouncing to criminal prosecution or punishment for offenses related to the insurgency.

3. Although Afrobarometer also ran a survey in 2005, we decided to use the 2008 data for a variety of reasons. First, the number of conflicts was still large in 2005 (see Fig. 1). Second, we are interested in persistent effects of conflict on trust rather than in emotional reactions that may arise while the conflict is still ongoing. Last but not least important, there were still many refugees in 2005. This raises two issues. On the one hand, poor living conditions in refugee camps may affect trust reported by respondents. On the other hand, many people could be living in camps outside of their counties, rendering our identification strategy invalid.

4. The district of the respondent is the most disaggregated geographical information provided by the 2000 Afrobarometer.

5. Although this instrument is time invariant, our identification relies on the fact that such geographical characteristics affected the intensity of fighting after the September 11, 2001 shock. So, in a sense, our instrument captures an interaction between the political shock and the geographic characteristic.

6. In this sense our paper is related to a recent literature studying endogenous ethnic and political identity in various contexts (see Balcells 2012; Caselli and Coleman 2013; Choi and Bowles 2007; Fryer and Levitt 2004; Posner 2004).

7. See Fearon and Laitin (2003), Collier and Hoeffler (2004), Collier and Rohner (2008), Collier et al. (2009), Montalvo and Reynal-Querol (2005) and Esteban et al. (2012).

8. For a general discussion of the origins and effects of trust and social capital on economic development, see the survey articles of Doepke and Zilibotti (2013), Fehr (2009), Guiso et al. (2006), and Sobel (2002).

9. This study uses a different econometric specification that does not control for past trust (which play a key role in our identification), nor does it consider ethnic identity. It is based on Afrobarometer 2005, whereas we prefer to use Afrobarometer (2008) for reasons explained in detail below. Finally it emphasizes different outcome variables, and does not link fighting events to specific ethnic groups.

10. According to Finnström (2008), the Museveni government has tried hard to frame the LRA as non-politically motivated criminals who attack their own people. In particular, “the rhetoric of a local northern conflict in which Acholi kill fellow Acholi like cannibalistic grasshoppers, reflects a more general Ugandan conception of the Acholi as violent and war-prone” (Finnström 2008, p. 107).

11. “The conduct of the Museveni’s troops (...) soon deteriorated. Killings, rape, and other forms of physical abuse aimed at noncombatants became the order of the day soon after the soldiers established themselves in Acholiland, which was foreign territory for them” (Finnström 2008, p. 71).

12. Afrobarometer selects samples in the following way: “The sample is designed as a representative cross-section of all citizens of voting age in a given country. The goal is to give every adult citizen an equal and known chance of selection for interview. We strive to reach this objective by (a) strictly applying random selection methods at every stage of sampling and by (b) applying sampling with probability proportionate to population size wherever possible (...). The sample is stratified by key social characteristics in the population such as sub-national area (e.g. region/province) and residential locality (urban or rural)” (Afrobarometer 2008).

13. Examples of violence against civilians in the ACLED database for Uganda include e.g. different ethnic clans attacking each other in cattle raids, rebel ambushes of passenger vehicles, or rebel raids against villages supposed to support the enemy.

14. We construct this variable by computing with ArcGIS the minimum distance between the geo-referenced border of a given county and the geo-referenced border of Sudan.

15. If we had a longer span of data and a full dynamic model, the instrument would be the interaction between September 11 and “distance to Sudan”. Note that “distance to Sudan” could have a direct permanent effect on trust (if, e.g., Acholi people trust the Kampala government less than do people in the rest of Uganda). However, this effect is filtered out by TRUST \(_{d}^{00}.\) See the discussion below.

16. The coefficient of Slavery in columns (2) and (5) is, as expected, consistently negative: individuals belonging to groups highly exposed to enslavement in the eighteenth century report a lower Generalized trust in 2008, ceteris paribus. The point estimates range between \(-\)0.65 and \(-\)0.66, being on the margin of standard levels of statistical significance (the p-values range between 0.116 and 0.128 across the different specifications). The fact that the effect of slavery is smaller than in Nunn and Wantchekon (2011) is not surprising, since our regressions control for trust in 2000 which filters out most of the long-term variation. Consistent with this interpretation, Slavery becomes statistically significant if we omit \(\mathbf{TRUST}_{d}^{\mathbf{00}}\).

17. We include IDP for two reasons: First, they are a proxy of fighting intensity. Second, forced displacements can be viewed as a deliberate military strategy in conflict (cf. Esteban et al. 2011). Indeed, some authors see the protected villages for IDP in Uganda as part of an aggressive military strategy pursued by the Museveni government to control and oppress the civilian population in the North (Finnström 2008; Dolan 2009).

18. Consistent with this interpretation, the bias of the OLS coefficient is smaller when we measure violence by the number of fatalities than when we use the number of fighting episodes, see Panel b of Table 2. The reason is that fatalities is a better (albeit imperfect) measure of the intensity of violence.

19. We run two regressions: one with a restricted set of control variables and one with a full set of controls. The restricted set of controls consists of the primary controls, \(\mathbf{TRUST}_{d}^{\mathbf{00}}\) and \(\mathbf{ETHNIC}_{e}\) (i.e., we exclude \(\mathbf{X}_{i}\) and \(\mathbf{Z}_{d}\) in Eq. 1)—both are essential constituents of our econometric specification. Then, we calculate the ratio \(\left| \hat{a}_{1}\right| /\left( \left| \hat{a}_{1}^{R} \right| -\left| \hat{a}_{1}\right| \right) \), where \(\hat{a}_{1}\) is the estimated coefficient with the full set of controls and the alternative options for \(\mathbf{ETHNIC}_{e}\) (columns 1–3 in Table 2), while \(\hat{a}_{1}^{R}\) is the estimated coefficient with the restricted set of controls. In absence of ethnic controls we obtain \(\hat{a}_{1}^{R}=-1.02\), implying that \(\left| \hat{a}_{1}^{R}\right| <\left| \hat{a}_{1} \right| \) (since \(\hat{a}_{1}=-2.10\)). With ethnic covariates we get \(\hat{a}_{1}^{R}=-0.73\;(\hat{a}_{1}=-1.12)\) and with ethnic fixed effects \(\hat{a}_{1}^{R}=-0.45\;(\hat{a}_{1}=-0.94)\). In none of the three cases is the point estimate attenuated by the inclusion of the full set of controls. In fact, such inclusion increases the absolute value of the point estimate.

    Note that the power of this robustness test depends on the explanatory power of the observable characteristics that are included. In our case, 17 out of the 34 additional control variables are significant at the 5 % level and their inclusion increases the \(R^2\) by 0.04 (with small variations across the alternative options for \(\mathbf{ETHNIC}_{e}\)).

20. We repeated the Altonji et al. (2005) procedure to detect problems of selection on unobservables. The restricted regression yields with no ethnic control \(\hat{a}_{1}^{R}=0.33\;(\hat{a}_{1}=0.74\) in column (1) of Table 4), with ethnic covariates \(\hat{a}_{1}^{R}=0.35\) (with \(\hat{a}_{1}=0.43\) in col. 2), and with ethnic fixed effects, \(\hat{a} _{1}^{R}=0.25\) (with \(\hat{a}_{1}=0.49\) in col. 3). Thus, again, selection on unobservables does not appear to drive our results.

21. In the Appendix Table 15 we also report the benchmark IV estimates of Generalized trust (Panel A of Table 2) and Ethnic identity (Panel A of Table 4)—with and without ethnic fixed effects—using IV-Probit, which leads to very similar results as in Tables 2 and 4.

    Finally, our main results also hold when the generalized trust variable is not coded as a binary variable, but left in its original ordinal scale. In this case, one can use an Ordered Probit estimator. However, the results of this specification are not robust to the inclusion of ethnic fixed effects.

22. In all the tables, the fighting variables have been rescaled by a factor \(10^{3}\) in order to improve readability of their estimated coefficients.

23. These figures correspond to the average percentage of respondents answering “Most people can be trusted” to the World Values Survey Question A165 “Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?”. We use the average scores over the first three waves of the World Values Survey (2009).

24. In particular, this dummy codes as one all counties where Acholis are the largest ethnic group everywhere in the territory according to GREG.

25. We also find that “Trust in known people” is more negatively affected in ethnically diverse areas. In particular, in OLS regressions we find that, when we split the sample, in low-fractionalization counties the relationship between trust and fighting is insignificant, whereas in highly fractionalized areas it is negative and highly significant. This is consistent with a large proportion of known people being from other ethnic groups in fractionalized areas. However, these results are not robust to TSLS where, due to very large standard errors, the differences between high- and low-fractionalization areas are insignificant. Since these results (which are available upon request) are not robust, we do not emphasize them.

26. We have followed a conservative matching strategy, only linking events that can be attributed with a very high confidence to particular groups. The results are similar when a more aggressive matching strategy is used, or when particular rebel groups are removed. The matching table is available from the authors upon publication.

27. The main effects of Fight(Eth) and Fight(Cou) are now absorbed by the county and ethnic fixed effects and cannot be estimated separately. If we omit the fixed effects, the estimated coefficients of Fight(Eth) and Fight(Cou) are negative and significant at the 95 % level (\(-\)1.27, s.e. 0.50, and \(-\)0.12, s.e. 0.06, respectively) in the case of general trust, and positive but insignificant (0.55, s.e. 0.34, and 0.03, s.e. 0.04) in the case of ethnic identity. If one adds the interaction term Fight(Eth)*Fight(Cou) to this specification without fixed effects, the estimated main effects Fight(Eth) and Fight(Cou) remain negative and significant (positive and insignificant) for the case of general trust (ethnic identity), while the interaction coefficient is in both cases insignificant.

28. Note that in this regression we cannot control for ethnic fixed effects, since the dependent variable is measured at the county level.

29. The small sample size in the split sample reduces the power of the first-stage regression. The Kleibergen–Paap F-stats are well below 10, raising a concern of a weak-instrument bias.

30. The results are very similar if one controls for the district-averages of our past trust and ethnic identity variables from the 2000 Afrobarometer survey.

31. In particular, fighting affects negatively living standards in ethnically fractionalized counties. In contrast, violence has no effect in non-fractionalized counties. When ethnic fixed effects are included, all interaction effects have the expected sign, but most are statistically insignificant. The fact that the specification using the subjective measure of living standards yields less robust results is not surprising, given the noisier nature of this variable.

An earlier version of this paper (with date April 2011) was circulated and presented under the title “Seeds of Distrust? Conflict in Uganda”. We thank three anonymous referees, Jody Ono, Sebastian Ottinger, David Schö nholzer and Nathan Zorzi for excellent assistance, and are grateful for comments to Erwin Bulte, Stefano Della Vigna, Oeindrila Dube, Ernst Fehr, Oded Galor, Pauline Grosjean, Andreas Itten, Peter Jensen, Hannes Mü ller, Eleonora Nillesen, Nathan Nunn, Florian Pelgrin, Torsten Persson, David Strömberg, Jakob Svensson, Marie-Anne Valfort, Leonard Wantchekon, and to seminar participants at the Annual Meeting of the Society of Economic Dynamics in Ghent, “Concentration on Conflict” meeting in Barcelona, “First Meeting on Institutions and Political Economy” in Lisbon, IIES-Stockholm University, Keio University, Namur Workshop on the “Political Economy of Governance and Conflicts”, Royal Economic Society Annual Meeting, CEPR Workshop on the “Political Economy of Development and Conflict” at CREi Barcelona, Tilburg Development Economics Workshop, Università di Bologna, University of Gothenburg, University of Neuchâtel, University of Paris 1 Panthéon-Sorbonne, and University of Southern Denmark. We also thank Henrik Pilgaard from UNHCR for sharing with us data on IDP in Uganda. Dominic Rohner acknowledges financial support from the Swiss National Science Foundation (grant no. 100014-122636). Mathias Thoenig acknowledges financial support from the ERC Starting Grant GRIEVANCES-313327. Fabrizio Zilibotti acknowledges financial support from the ERC Advanced Grant IPCDP-229883.

Authors and Affiliations

1. Department of Economics, University of Lausanne, Lausanne, Switzerland

   Dominic Rohner & Mathias Thoenig

2. Department of Economics, University of Zurich, Zurich, Switzerland

   Fabrizio Zilibotti

Corresponding author

Correspondence to Fabrizio Zilibotti.

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