Category Archives: Conflict



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.







A. Abadie & J. Gardeazabal

The American Economic Review, Vol. 93, No. 1 (2003) pp. 113-32

Principal Research Question and Key Result Did the conflict in the Basque country affect the economy? The results suggest a 10% loss of GDP due to the terrorism.
Theory Terrorism could affect GDP in various ways. The most important is likely to be investment. If earning a return on investment becomes uncertain because either the return may be extorted or the entrepreneur killed then this acts as a random yet significant tax on investment. Under such a circumstance investment is depressed and this will affect output and hence GDP. Additionally foreign investment in the affected region could be reduced if conducting business in that region is thought to be risky, although the mechanism is exactly the same, although it operates on international rather than domestic actors.


Motivation Political instability is often said to have strong effects on economic prosperity. However, studies to date have largely been cross country studies which suffer from comparability issues (as conflicts are rarely similar). This study seeks to explain how the richest region in Spain subsequently dropped to the 6th position in terms of GDP per capita. As it is focused on only one such conflict the heterogeneity issues outlined above are circumvented to a certain extent (although, as ever at the expense of external validity).


Data They have panel data for 1968-1997 which includes variables on deaths and killings, as well as GDP and other variables that can be thought to determine GDP such as investment ration, and human capital measures.


Strategy They exploit the fact that ETA was created in 1959 but did not implement large scale terror operations until the mid-70s. Additionally in 1998 a ceasefire was declared which was subsequently cancelled, and this provides testing ground for looking at how economic outcomes varied during both the scale up of violence (largely killings and kidnappings), and the cease fire.

They essentially do a DID, however they cannot simply compare the Basque country to another region, as there was no comparable region – the Basque country was the richest, most industrialized etc. So they construct a synthetic control group. They do so by identifying a list of variables that are thought be drivers of economic growth (agriculture share etc. table III) and assign weights to the other possible control regions such that when aggregated the weighted averages of the variables resemble the observed variables for the Basque country subject to the constraint that the variable that should best be reproduced is the GDP per capita for the Basque country in the 1960s. When this is done, they end up with a synthetic control group that is comprised of 85% Catalonia and 15% Madrid.

During the ceasefire they look at the cumulative abnormal returns of stocks listed as Basque stocks, relative to other stocks on the Spanish market. Asset prices should reflect all available information, and if instability is important then Basque stocks should have performed better when the ceasefire was announced and became credible, and worse as the cease fire broke down. They categorized the stocks using market professionals.


Results They plot the GDP growth of the synthetic control and the Basque country and they follow each other closely until the mid-70s, when the Basque country falls behind. This suggests a loss of 10% of GDP due to the terror. The gap in the GDPs of these regions seems to spike at the same time as the deaths from terrorism in the Basque country. 

The results of the ceasefire study are that the good news dummy coefficients are significant and positive for Basque stocks and negative for Bad news.

Robustness The do a placebo study, by comparing Catalonia and a synthetic Catalonia (constructed as above but excluding the Basque country as a possibility) and find that there is no significant gap in GDP, although the real Catalonia did outperform the synthetic one by 4% around the time of the Barcelona Olympics.


  • The synthetic control is made up almost exclusively of Catalonia, thus it is not very balanced, or impervious to idiosyncratic shocks in that region. Additionally, it is not clear that selecting weights so that GDP is matched is the best strategy, as similar GDP levels in the 60s does not guarantee that what is salient in terms of future growth has been captured.
  • They do not actually estimate the DID using regression techniques as far as I can see, so we have no idea what the standard errors are, or what the other significant factors were in determining outcomes in the Basque country. This does not allow us to verify how important e.g. industrial decline was in explaining GDP in the Basque. Without such results it cannot be said conclusively that the higher industrial share in the Basque country pre-terror was not driving lower GDP in the face of industrial decline post-terror.
  • The authors state that Catalonia and the Basque country were both highly industrialized regions. If one of the effects of terror was to incentivize entrepreneurs or businesses to move away from the Basque country due to instability, the chances are they would relocate to somewhere that was similar to the Basque country, which surely would be Catalonia. As the synthetic control is made up predominantly of Catalonia, any significant movement of human capital from the Basque to Catalonia could have affected GDP outcomes in Catalonia, and hence this would tend to overstate the results.
  • It is not clear that they have isolated anything to do with property rights as such.


Implications Conflict can harm the economy. This is not a new idea. Not sure what the policy implications are, other than avoid civil conflict if possible.





O. Dube & J Vargas

Mimeo MYC (2011)

Principal Research Question and Key Result How do income shocks affect conflict in Columbia? Specifically they examine how conflict is affected by different types of shock. The key result is that negative price shocks in labour intensive  industries such as agriculture increase the incidence of conflict in areas that are more intensely defined by that industry. Positive price shocks in capital intensive industries on the other hand increase the incidence of conflict in those regions more dependent on that type of industry (i.e. the effects are opposites).


Theory Two theoretical mechanisms linking price shocks and conflict are identified.

  1. Opportunity cost: a rise in workers’ wages (due to price shocks) increases  the opportunity cost of participating in conflict if workers decide between working in agriculture or receiving the wages paid by paramilitary type groups. This means that in industries that are labour intensive (e.g. agriculture) a fall in wages may incentivize some of those workers to move into conflict participation. Thus areas that are more dependent on agriculture will see a differential rise in the incidence of violence as opposed to regions that are less dependent.
  2. Rapacity:                  a rise in price of commodities produced using capital intensive methods (such as oil) increases the amount of contestable wealth within a region and thus the returns to predation/conflict rise. This predicts that a positive oil price shock will be associated with a rise in violence in areas that are more reliant on the oil industry as opposed to labour intensive industries.


Motivation Other papers have looked at income shocks on conflict and found positive results. In the Miguel paper (to be summarized shortly) he instruments for GDP using rainfall, and shows that negative shocks to income affect the incidence of violence. This paper aims to go one further by identifying different channels through which income can affect conflict.

The use of panel data is important here. Cross country data do not allow for controlling for fixed/time effects, and data may not be comparable across countries. However, even panel data cannot take account of time varying and region specific fixed effects, and also cross country results are more easily generalizable due to high external validity.


  • Data are from Colombia. 21,000 war related episodes in 950 municipalities from 1988 to 2005. The conflict data separated into Guerilla attacks, Paramilitary attacks, clashes and casualties.
  • Commodity intensity is drawn from land use survey 1997 for coffee production. For oil the data are barrels per day in 1988 and the length of pipeline in 2000. Prices are taken from international statistics, and internal statistics.
  • Colombia makes a good case study as data are available in panel format, and there is lots of variation in violence experienced. Additionally, oil and coffee are different types of industry and both are major contributors to national income.


Strategy In order to get at the two theoretical channels they look at two commodities, coffee (which experienced a major price slump in the period – thus affecting wages) and oil (which experienced a major price rise – thus affecting contestable income as municipalities that produce and transport oil receive royalties from its sale). They interact the price of those commodities with a measure of intensity at the municipal level in order to get at the differential effects of the shocks by municipality.

yit = αj + βt + γ(OilProdj * OPt)+ δ(OilPipej * OPt) + θ(Cofj * CPt) + λXjt + εjt


y are conflict outcomes in municipality j in time t. α and β are municipality and time fixed effects. OilProd measures oil production in municipality j in 1998 and OilPipe is length of pipe in municipality j in 2000. OP is the natural log of the international oil price. Cof is the number of hectares devoted to coffee production in 1997, and CP is natural log of internal coffee price. X is a vector of control variables. γ and δ capture the extent to which the oil price induces a differential change in violence in municipalities that produce or transport oil more intensely and θ shows the extent to which price shocks for coffee affect violence differentially in coffee producing regions. 

Whilst oil price may plausibly be exogenous (as Colombia only produces 1% of world oil so is a price taker), the coffee price is unlikely to be so given that Colombia is a major exporter. If violence restricted production and this increased price, or similar, then this could confound the results. So they instrument for the coffee price using the log of foreign coffee exports. This seems to be fairly plausible strategy.


However, endogeneity concerns still exist as the level of coffee produced is not fixed, and farmers may substitute into and out of production based on prices, conflict etc. Thus they instrument for (Cofj * CPt) using a composite instrument constructed using data on rainfall, temperature and slope of the land, which determine the possibility to produce coffee in a given municipality. They use a topographical instrument to determine exogenous possibilities for a municipality hosting an oil pipeline.


Results θ  is negative and significant for all types of violence indicating that as coffee prices fell, municipalities that are coffee intensive witnessed a differential increase in all types of violence. γ and δ are positive but only significant for paramilitary attacks. The instrumental variable strategy results are similar (although less significant for coffee) although the signs and the significance are pretty varied on the oil related coefficients which may be indication of a more spurious link between the oil price and conflict.


  • They present graphs of attacks by two types of region, those that are coffee intensive, and those that are oil intensive. Before the years of the price shocks, the different types of violence seem to be moving more or less parallel to each other in the coffee/non-coffee municipalities, and then there is divergence which lends some credibility to the results. There is some parallelism in the oil/non-oil states, but to a far lesser degree, and this is less convincing.
  • They include wage levels as the dependent variable to show that price changes of coffee (but not oil) did affect agricultural wages. They do the same for local gov revenue which was affected by oil price changes but not coffee price changes. This means there are probably no spillover effects between the industries.
  • They show similar effects for other agricultural commodities (although with a more limited data set) which adds credibility to the opportunity cost story. They do the same for gold and coal and the results are similar but more mixed in terms of which types of violence are affected. As there is little theoretical justification for these different effects the results are hard to interpret.
  • As coca production could be a substitute for coffee production, and could attract more violence, then there could be bias in the estimates. To counter this notion, they include area under coca cultivation as the dependent variable and find no significant link between coffee price and coca cultivation. They also remove all coca producing municipalities from the sample and still find similar effects.


Problems Whilst they deal with the endogeneity between price and conflict quite convincingly there are still serious endogeneity concerns. In particular there could be variables that determine what crops are produced and also determine the possibility for conflict. For example local institutions may mediate conflict and provide stable investment environment for the introduction of certain crops such as coffee.  Geographical/topographical conditions may also determine choice of crop, and potential for violence.  This instrumentation strategy they use is not convincing, as if geographical factors were the cause of the endogeneity concern, they cannot then be used as instruments as the exclusion restriction is violated (in particular slope has been shown to influence conflict and it will determine agricultural production possibilities) . The same goes for the pipeline instrument. The F-stat is 5.5 in the first stage so the instruments are weak, and as there are quite a few of them, this increases the difficulties with introducing bias due to weak instruments. Lastly the structure of the instruments is extremely opaque – there is no discussion of why the instruments are constructed in the way that they are, and it is not clear therefore for what subset of the sample we are in fact estimating the LATE for. Generally there is no discussion of the exclusion restriction and thus the results without instrumentation may be subject to endogeneity bias, and the result with instrumentation should be taken with a big pinch of salt due to violation of the exclusion restriction, and the problem of weak instruments.

There is no clear reason why oil prices should only increase paramilitary attacks. The authors state that oil production is densely located and thus there is only room for one type of criminal organization to operate. This anecdotal evidence is not empirically supported, and it remains unclear why casualties should not be significant if attacks increase.

Since they have wage data at the municipal level (even if it is only individual level data) it is unclear why they do not test it directly. The show that wages were affected by the fall in price, and that the fall in price is linked to increased violence, but there is no necessary causal link between these two pieces of evidence. They could have instrumented wages using the export values of the foreign producers (or similar) and then used those predicted values to estimate the effect on conflict. There would have be some level of aggregation and there is a chance the wage sample is not representative, but it would have been useful to include some discussion.


Implications This is an interesting paper that advances the theory between income shocks and conflict. However, the identification strategy is not very clean especially in relation to the use of geographic instruments which cast doubts on the results. In general the opportunity cost story seems to be better supported than the rapacity story.

If the opportunity cost story is believed, then support for those dependent upon agriculture in times of falling prices could be a policy used to prevent the outbreak or perpetuation of armed conflict.