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Economic Shocks and Civil Conflict: An Instrumental Variables Approach
- Journal of Political Economy
, 2004
"... Estimating the impact of economic conditions on the likelihood of civil conflict is difficult because of endogeneity and omitted variable bias. We use rainfall variation as an instrumental variable for economic growth in 41 African countries during 1981–99. Growth is strongly negatively related to c ..."
Abstract
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Cited by 66 (1 self)
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Estimating the impact of economic conditions on the likelihood of civil conflict is difficult because of endogeneity and omitted variable bias. We use rainfall variation as an instrumental variable for economic growth in 41 African countries during 1981–99. Growth is strongly negatively related to civil conflict: a negative growth shock of five percentage points increases the likelihood of conflict by one-half the following year. We attempt to rule out other channels through which rainfall may affect conflict. Surprisingly, the impact of growth shocks on conflict is not significantly different in richer, more democratic, or more ethnically diverse countries. I.
Genocide and Land Scarcity: Can Rwandan Rural Households Manage?
"... During the nineties, Rwandan households had to cope with severe shocks of war and genocide. In addition, two major structural problems in Rwanda, land scarcity and declining soil fertility, remain unsolved. How do Rwandan households manage? This is an important question from a development perspectiv ..."
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Cited by 1 (0 self)
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During the nineties, Rwandan households had to cope with severe shocks of war and genocide. In addition, two major structural problems in Rwanda, land scarcity and declining soil fertility, remain unsolved. How do Rwandan households manage? This is an important question from a development perspective, but also from a security perspective, because uneven development increases the risk of peace collapse. To find an answer to our question, we study welfare gains and losses of a sample of 189 Rwandan rural households over the period 1990-2002. In our sample, many households were severely affected by the genocide. In addition, poverty and inequality increased. Moreover, we observe a lot of income mobility. Only one quarter of the households remained in the same income quintile over time. Especially the households headed by widows and prisoner's wives moved downward in the income distribution. Households who reduced their dependence on subsistence agriculture moved upward. We are grateful to MINAGRI and USAID for the use of the pre-war dataset, and to Philip Verwimp for the tracing of the households in 1999/2000 and for collaboration in the early stages of this research. Field research for the post-war dataset was made possible by funding from DGIS and VLIR. We received useful comments on an earlier draft presented at the EUDN workshop for doctoral students in Bonn and at a seminar at the CSAE in Oxford. We especially acknowledge helpful comments of Stefan Dercon and Andy McKay. The authors are exclusively responsible for all errors and omissions. All views and opinions are theirs alone.

