Tag Archives: Conflict



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.