UC Davis Agricultural and Resource Economics

Chris Barrett, Cornell University

Revisiting the Effect of Food Aid on Conflict: A Methodological Caution

Date and Location

Monday, May 15, 2017, 4:10 PM - 5:30 PM
ARE Library, 4101 Social Sciences and Humanities


An increasingly popular panel data estimation strategy uses an instrumental variable (IV) constructed by interacting an exogenous time series variable with a potentially endogenous cross-sectional exposure variable so as to generate a continuous difference-in-differences (DID) estimator that identifies causal effects from inter-temporal variation in the IV differentially scaled by cross-sectional exposure. This strategy is, however, susceptible to violations of the exclusion restriction arising from heterogeneous nonlinear trends analogous to violations of the parallel trends assumption in standard DID estimation. Using that strategy, Nunn and Qian (2014) claim to identify a causal effect of food aid shipments on conflict in recipient countries. We show in a series of failed placebo tests that heterogeneous nonlinear trends confound their causal interpretation of the estimated relationship. Indeed, in Monte Carlo analysis we show that their IV results could suffer upward bias and are consistent with a data generating process in which US food aid does not influence (or even prevents) conflict. These findings not only serve to correct an erroneous–and widely publicized–policy implication; more generally, they serve as a cautionary tale for authors using similar identification strategies based on DID-type IV estimators in panel data.

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