Seeds of distrust: conflict in Uganda

Abstract

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.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3

Notes

  1. 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. 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. 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. 4.

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

  5. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 28.

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

  29. 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. 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. 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.

References

  1. ACLED. (2011). Armed Conflict Location and Event Data. Dataset, www.acleddata.com.

  2. Afrobarometer. (2000). Round 1 Afrobarometer Survey in Uganda. Dataset, www.afrobarometer.org.

  3. Afrobarometer. (2008). Round 4 Afrobarometer Survey in Uganda. Dataset, www.afrobarometer.org.

  4. Akresh, R., & de Walque, D. (2010). Armed Conflict and Schooling: Evidence from the 1994 Rwandan Genocide. Mimeo, University of Illinois at Urbana-Champaign.

  5. Alesina, A., & La Ferrara, E. (2000). Participation in heterogeneous communities. Quarterly Journal of Economics, 115, 847–904.

    Article  Google Scholar 

  6. Alesina, A., & La Ferrara, E. (2002). Who trusts others? Journal of Public Economics, 85, 207–234.

    Article  Google Scholar 

  7. Algan, Y., & Cahuc, P. (2010). Inherited trust and growth. American Economic Review, 100, 2060–2092.

    Article  Google Scholar 

  8. Altonji, J., Elder, T., & Taber, C. (2005). Selection on observed and unobserved variables: Assessing the effectiveness of catholic schools. Journal of Political Economy, 113, 151–184.

    Article  Google Scholar 

  9. Angrist, J., & Pischke, j. (2009). Mostly harmless econometrics: An empiricist’s companion. Princeton, NJ: Princeton University Press.

    Google Scholar 

  10. Ashraf, Q., & Galor, O. (2011). Cultural diversity, geographical isolation, and the origin of the wealth of nations. Mimeo, Williams College and Brown University.

  11. Ashraf, Q., & Galor, O. (2013). The ‘out of Africa’ hypothesis, human genetic diversity, and comparative economic development. American Economic Review, 103, 1–46.

    Article  Google Scholar 

  12. Balcells, L. (2012). The consequences of victimization on political identities: Evidence from Spain. Politics and Society, 40, 309–345.

    Google Scholar 

  13. Barenbaum, J., Ruchkin, V., & Schwab-Stone, M. (2004). The psychological aspects of children exposed to war: Practice and policy initiatives. Journal of Child Psychology and Psychiatry, 45, 41–62.

    Article  Google Scholar 

  14. Bellows, J., & Miguel, E. (2009). War and local collective action in Sierra Leone. Journal of Public Economics, 93, 1144–1157.

    Article  Google Scholar 

  15. Besley, T., & Persson, T. (2011). The logic of political violence. Quarterly Journal of Economics, 126, 1411–1445.

    Article  Google Scholar 

  16. Besley, T., & Reynal-Querol, M. (2012). The legacy of historical conflict: Evidence from Africa. Mimeo, London School of Economics.

  17. Blattman, C. (2009). From violence to voting: War and political participation in Uganda. American Political Science Review, 103, 231–247.

    Article  Google Scholar 

  18. Blattman, C., & Annan, J. (2010). The consequences of child soldiering. Review of Economics and Statistics, 92, 882–898.

    Article  Google Scholar 

  19. Bowles, S., & Gintis, H. (2004). Persistent parochialism: Trust and exclusion in ethnic networks. Journal of Economic Behavior and Organization, 55, 1–23.

    Google Scholar 

  20. Bozzoli, C., Brueck, T., & Muhumuza, T. (2011). Does war influence individual expectations? Economic Letters, 113, 288–291.

    Article  Google Scholar 

  21. Bun, M., & de Haan, M. (2010). Weak instruments and the first stage F-statistic in IV models with a nonscalar error covariance structure. Mimeo, University of Amsterdam.

  22. Caselli, F., & Coleman II, W. J. (2013). On the theory of ethnic conflict. Journal of the European Economic Association, 11, 161–192.

    Article  Google Scholar 

  23. Cassar, A., Grosjean, P., & Whitt, S. (2013) Legacies of violence: Trust and market development. Journal of Economic Growth (forthcoming).

  24. Choi, J.-K., & Bowles, S. (2007). The coevolution of parochial altruism and war. Science, 318, 636–640.

    Article  Google Scholar 

  25. Collier, P., & Hoeffler, A. (2004). Greed and grievance in civil war. Oxford Economic Papers, 56, 563–595.

    Article  Google Scholar 

  26. Collier, P., Hoeffler, A., & Rohner, D. (2009). Beyond greed and grievance: Feasibility and civil war. Oxford Economic Papers, 61, 1–27.

    Article  Google Scholar 

  27. Collier, P., & Rohner, D. (2008). Democracy, development, and conflict. Journal of the European Economic Association, 6, 531–540.

    Article  Google Scholar 

  28. Deininger, K. (2003). Causes and consequences of civil strife: Micro-level evidence from Uganda. Oxford Economic Papers, 55, 579–606.

    Article  Google Scholar 

  29. DellaVigna, S., Enikolopov, R., Mironova, V., Petrova, M., & Zhuravskaya, E. (2011). Unintended media effects in a conflict environment: Serbian radio and Croatian nationalism. NBER Working Paper No. 16989.

  30. De Luca, G., & Verpoorten, M. (2011). From vice to virtue? Civil war and social capital in Uganda. HiCN Working Paper 111, Katholieke Universiteit Leuven.

  31. Derluyn, I., Broekaert, E., Schuyten, G., & De Temmerman, E. (2004). Post-traumatic stress in former Ugandan child soldiers. Lancet, 363, 861–863.

    Article  Google Scholar 

  32. Doepke, M., & Zilibotti, F. (2013). Culture, entrepreneurship and growth. In P. Aghion & S. Duralauf (Eds.), Handbook of economic growth (Vol. 2). North Holland (forthcoming).

  33. Dolan, C. (2009). Social torture: The case of Northern Uganda, 1986–2006. New York: Berghahn Books.

    Google Scholar 

  34. Dyregrov, A., Gupta, L., Gjestad, R., & Mukanoheli, E. (2000). Trauma exposure and psychological reactions to genocide among Rwandan children. Journal of Traumatic Stress, 13, 3–21.

    Article  Google Scholar 

  35. Eichengreen, B. (2008). The European Economy since 1945: Coordinated capitalism and beyond. Princeton, NJ: Princeton University Press.

    Google Scholar 

  36. Esteban, J., Massimo, M., & Dominic, R. (2011). Strategic mass killings. Mimeo, IAE, Columbia University and University of Zurich.

  37. Esteban, J., Mayoral, L., & Ray, D. (2012). Ethnicity and conflict: An empirical study. American Economic Review, 102, 1310–1342.

    Article  Google Scholar 

  38. Esteban, J., & Ray, D. (2011). Linking conflict to inequality and polarization. American Economic Review, 101, 1345–1374.

    Article  Google Scholar 

  39. Fafchamps, M. (2000). Ethnicity and credit in African manufacturing. Journal of Development Economics, 61, 205–235.

    Article  Google Scholar 

  40. Fearon, J., Humphreys, M., & Weinstein, J. (2009). Can development aid contribute to social cohesion after civil war? Evidence from a field experiment in post-conflict Liberia. American Economic Review, 99, 287–291.

    Article  Google Scholar 

  41. Fearon, J., & Laitin, D. (2003). Ethnicity, insurgency, and civil war. American Political Science Review, 97, 75–90.

    Article  Google Scholar 

  42. Fehr, E. (2009). On the economics and biology of trust. Journal of the European Economic Association, 7, 235–266.

    Article  Google Scholar 

  43. Fiala, N. (2013). Economic consequences of forced displacement. Mimeo, German Institute for Economic Research (DIW).

  44. Finnström, S. (2008). Living with bad surroundings: War, history, and everyday moments in Northern Uganda. Durham: Duke University Press.

    Google Scholar 

  45. Fisman, R. (2003). Ethnic ties and the provision of credit: Relationship-level evidence from African firms. Advances in Economic Analysis & Policy 3, Article 4.

  46. Fryer, R., & Levitt, S. (2004). The causes and consequences of distinctively black names. Quarterly Journal of Economics, 119, 767–805.

    Article  Google Scholar 

  47. Gilligan, M., Benjamin, P., & Cyrus, S. (2010). Civil war and social capital: Behavioral-game evidence from Nepal. Mimeo, NYU and Columbia University.

  48. Giuliano, P., & Spilimbergo, A. (2009). Growing up in a recession: Beliefs and the macroeconomy. NBER Working Paper 15321.

  49. Guiso, L., Sapienza, P., & Zingales, L. (2006). Does culture affect economic outcomes? Journal of Economic Perspectives, 20, 23–48.

    Article  Google Scholar 

  50. Guiso, L., Sapienza, P., & Zingales, L. (2009). Cultural biases and economic exchange. Quarterly Journal of Economics, 124, 1095–1131.

    Article  Google Scholar 

  51. Henderson, V., Storeygard, A., & Weil, D. (2012). Measuring economic growth from outer space. American Economic Review, 102, 994–1028.

    Article  Google Scholar 

  52. Hodler, R., & Raschky, P. (2011). Foreign aid and enlightened leaders. Mimeo, Study Center Gerzensee and Monash University.

  53. Horowitz, D. (2000). Ethnic groups in conflict (2nd ed.). Berkeley, CA: University of California Press.

  54. Humphreys, M., & Weinstein, J. (2007). Demobilization and reintegration. Journal of Conflict Resolution, 51, 531–567.

    Article  Google Scholar 

  55. Leon, G. (2012). Civil conflict and human capital accumulation: The long term effects of political violence in Peru. Journal of Human Resources, 47, 991–1022.

    Google Scholar 

  56. Lewis, M. P. (Ed.). (2009). Ethnologue: Languages of the world (16th ed.). Dallas, TX: SIL International. Online version for Languages of Uganda. http://www.ethnologue.com/show_country.asp?name=UG.

  57. Médecins sans frontières. (2004). Life in Northern Uganda: All shades of grief and fear. Report.

  58. Michalopoulos, S., & Papaioannou, E. (2013). Pre-colonial ethnic institutions and contemporary African development. Econometrica, 81, 113–152.

    Article  Google Scholar 

  59. Miguel, E., Saiegh, S., & Satyanath, S. (2011). Civil war exposure and violence. Economics and Politics, 23, 59–73.

    Article  Google Scholar 

  60. Montalvo, J., & Reynal-Querol, M. (2005). Ethnic polarization, potential conflict, and civil wars. American Economic Review, 95, 796–816.

    Article  Google Scholar 

  61. Murdock, G. P. (1967). Ethnographic atlas. Pittsburgh, PA: University of Pittsburgh Press.

    Google Scholar 

  62. Nannyonjo, J. (2005). Conflicts, poverty and human development in Northern Uganda. The Round Table, 94, 473–488.

    Article  Google Scholar 

  63. National Oceanic and Atmospheric Administration. (2010). Version 4: DMSP-OLS Nighttime Lights Time Series. Dataset, http://www.ngdc.noaa.gov/dmsp/downloadV4composites.html#AXP.

  64. Neu, J. (2002). Restoring relations between Uganda and Sudan: The Carter Center process Conciliation Resources. http://www.c-r.org/our-work/accord/northern-uganda/carter-center.php.

  65. Nunn, N., & Wantchekon, L. (2011). The slave trade and the origins of mistrust in Africa. American Economic Review, 101, 3221–3325.

    Article  Google Scholar 

  66. Osafo-Kwaako, P., & Robinson, J. (2013). Political centralization in pre-colonial Africa. Journal of Comparative Economics, 41, 6–21.

    Article  Google Scholar 

  67. Posner, D. (2004). The political salience of cultural difference: Why Chewas and Tumbukas are allies in Zambia and adversaries in Malawi. American Political Science Review, 98, 529–545.

    Article  Google Scholar 

  68. Rohner, D. (2011). Reputation, group structure and social tensions. Journal of Development Economics, 96, 188–199.

    Article  Google Scholar 

  69. Rohner, D., Thoenig, M., & Zilibotti, F. (2012). Seeds of distrust: Conflict in Uganda. CEPR Discussion Paper No. DP8741.

  70. Rohner, D., Thoenig, M., & Zilibotti, F. (2013). War signals: A theory of trade, trust and conflict. Review of Economic Studies (forthcoming).

  71. Shemyakina, G. (2011). The effect of armed conflict on accumulation of schooling: Results from Tajikistan. Journal of Development Economics, 95, 186–200.

    Article  Google Scholar 

  72. Sobel, J. (2002). Can we trust social capital? Journal of Economic Literature, XL, 139–154.

  73. Strömberg, D. (2004). Radio’s impact on public spending. Quarterly Journal of Economics, 119, 189–221.

    Article  Google Scholar 

  74. Swee, E. L. (2008). On war and Schooling attainment: The case of Bosnia and Herzegovina. Mimeo, University of Toronto.

  75. Tilly, C. (1975). The formation of national states in Western Europe. Princeton, NJ: Princeton University Press.

    Google Scholar 

  76. Ugandan Bureau of Statistics. (2002). Census 2002. Dataset, http://www.ubos.org.

  77. UN. (2009). United Nations’ Peace Building and Recovery Assistance Programme For Northern Uganda 2009–2011 (UNPRAP). Report.

  78. UNHCR. (2006). Annual Statistic Report on Uganda. Dataset (obtained by direct correspondence).

  79. UNHCR. (2010). Statistical Online Population Database. Dataset, www.unhcr.org.

  80. UNOCHA. (2002). Pushing the envelope: Moving beyond ‘protected villages’ in Northern Uganda. Report.

  81. Vargas Hill, R., Bernard T., & Dewina, R. (2008). Cooperative behaviour in rural Uganda: Evidence from the Uganda National Household Survey 2005. International Food Policy Research Institute Background Paper.

  82. Voors, M., Nillesen, E., Verwimp, P., Bulte, E., & Lensink, R. (2012). Violent conflict and behavior: A field experiment in Burundi. American Economic Review, 102, 941–964.

    Article  Google Scholar 

  83. Weidmann, N. B., Rød, J. K., & Cederman, L.-E. (2010). Representing ethnic groups in space: A new dataset. Journal of Peace Research, 47, 491–499.

    Article  Google Scholar 

  84. Whitt, S., & Rick, W. (2007). The dictator game, fairness and ethnicity in postwar Bosnia. American Journal of Political Science, 51, 655–668.

    Article  Google Scholar 

  85. Women’s Commission. (2001). Against all odds: Surviving the war on adolescents. Research study, http://www.womenscommission.org/.

  86. World Values Survey. (2009). World Values Survey. Dataset, http://www.worldvaluessurvey.org/index_html.

  87. Yanagizawa-Drott, D. (2012). Propaganda and conflict: Theory and evidence from the Rwandan genocide. CID Working Paper 257, Harvard University.

Download references

Acknowledgments

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.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Fabrizio Zilibotti.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (pdf 340 KB)

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Rohner, D., Thoenig, M. & Zilibotti, F. Seeds of distrust: conflict in Uganda. J Econ Growth 18, 217–252 (2013). https://doi.org/10.1007/s10887-013-9093-1

Download citation

Keywords

  • Acholi
  • Afrobarometer
  • Causal effects of conflict
  • Civil war
  • Ethnic conflict
  • Identity
  • Satellite light
  • Trust
  • Uganda

JEL Classification

  • C31
  • C36
  • H56
  • N47
  • O55
  • Z10