E. Miguel, S. Satyanath & E. Sergenti

Journal of Political Economy Vol. 122, No. 4 (2004) pp. 725-53

Principal Research Question and Key Result Do economic shocks increase the incidence of civil conflict in sub-Saharan Africa? A 5% drop in economic growth in the previous year is associated with an increased probability of 12% of a civil conflict (at least 25 dead) the following year which is a more than one half increase in likelihood.
Theory Collier and Hoeffler claim that the gap between the returns to economic activities relative to the taking up of arms is what causes low incomes to be a determinant of civil conflict. Others argue that low incomes mean that military and transport infrastructure is low, and this means that governments are less able to repress insurgents, and this is the mechanism at work.


Motivation To address the endogeneity problems in much of the recent research that has linked economic conditions and civil conflict by using instrumental variables. Previous research was aware of endogeneity concerns and tried to solve them by using lagged right hand side variables. However, this approach assumed that economic actors did not anticipate the incidence of civil conflict and adjust their behavior accordingly, which is a very strong assumption. This paper makes the first attempt to find a decent instrument. A further benefit of the approach is that they are able to deal with the measurement error in reported national income figures that come out of Africa.


Data Armed conflict data, based on minimum 25 deaths per year from infraction involving armed force between government and other parties. They also use an alternate measure of 1000 deaths from another data source.

Rainfall data are monthly estimates for various points within the country taken from the Global Precipitation Climatology Project. The principal measure of rainfall shock in the proportional change in rainfall from the previous year.

Strategy They instrument per capita economic growth in the first stage with current and lagged rainfall growth along with other country characteristics.

In the second stage they include country fixed effects in some specifications.

Weather shocks are plausible instruments for economic outcomes in economies that are agriculture dependent and are largely not irrigated, as in the case of SSA.

In the Second stage they use instrumented values of growth in the current period and the previous period


Results In the reduced form higher rainfall is associated significantly with less conflict (for both 25 and 1000 deaths).


The “SLS estimate with controls for ethnolinguistic and religious, and oil exporting, population etc. etc. is significant and negative for lagged growth, but not current growth (although they are jointly significant at the 90% level). The other controls which have been suggested by the recent conflict literature are small and insignificant, indicating that incidence of civil wars is influenced by economic shocks rather than other political style determinants. In fact a 1% decline in GDP growth is associated with a 2% rise in the chance of conflict. That the results are so much bigger than the OLS results indicates the problems of measurement error associated with using African national income data. These results hold for the 25 and 1000 death definitions (although smaller for 1000 death – and current growth is more important than lagged growth for the 1000 death data).  NB this is about growth, not absolute levels of GDP. The level measure does not come out significant.


They interact econ growth with democracy and other potential determinants and find no significant relationships. In other words there is little heterogeneity of effects across SSA, and countries are not differentially affected based on their institutions, or ethnic makeup, or oil exporting…. Etc. etc. This would seem to indicate that economic concerns trump other factors in determining the incidence of civil war – social and institutional factors seem to be of little importance (however, this could also be being driven by limited variation in those other variables).


They restrict the sample to cases of conflict where there had been no conflict the year previously to see how growth affects the onset of conflict and get similar results.

Robustness Robust to dropping one country at a time.

They use alternative measures of rainfall.

They investigate potential violations of the exclusion restriction. 1. Rainfall affects government budgets and spending through taxation – not the case, there is no association in the data between rainfall and tax revenues. 2. High rainfall may destroy roads etc. that makes it more costly for the government to repress insurgents – this flows the wrong way as the results show that more rainfall is associated with less conflict. Perhaps this same mechanism makes it harder for people to engage in conflict, but there is no association between rainfall and the extent of usable road in the country.

  • The paper focusses on short term triggers not long term determinants.
  • External validity low, and method not applicable elsewhere where less agriculturally dependent.
  • The paper cannot identify the mechanism at work. Whilst they claim that the results are consistent both with the weak state (as background conditions) and the opportunity costs (that trigger the conflict), they cannot disentangle the effects. For that we need the Columbia paper summarized above. They do not have reliable data on inequality, as this is another potential mechanism (heightens tensions across nations), so they cannot rule this mechanism out either, although they do test with proxies for inequality and do not find any compelling associations. 
  • There is no sub national level data, and as rainfall is at a very specific location and has to be aggregated up to national level there could be spurious correlation as rainfall could be falling in one region and the conflict going on in a totally different region unaffected by the weather/income shock. This is unfortunate.
  • Much violence in SSA does not involve the state, but other parties, and this will not be captured by the Armed Conflict Data.
  • The rainfall instrument is surprisingly weak, with an F-Stat of only 4.5. This can cause problems for efficiency but also consistency is there is any measurement error. They do a false experiment whereby they use future rainfall in the first stage and find no relationship at all which is encouraging.
Implications Economic variables are more important determinants of civil war than measures of objective political grievances. This could indicate that a way to reduce the incidence of conflict is to better able individuals to smooth away weather related income shocks.  This may be possible using informal institutions at the village level (Townsend) but if the weather shock is aggregate chances are that there will be a lack of insurance as there is little evidence of across village insurance, and even if it existed the shock may be so aggregate that the insurance mechanism does not function. In this event formal state sponsored insurance, or income transfers should be made available conditional upon remaining in agriculture, such that the opportunity cost of working in agriculture does not get so high that people are incentivized to take up arms.





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