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Across the social sciences, lagged explanatory variables are a common strategy to confront challenges to causal identification using observational data. We show that "lag identification"-the use of lagged explanatory variables to solve endogeneity problems-is an illusion: lagging independent variables merely moves the channel through which endogeneity biases causal estimates, replacing a "selection on observables" assumption with an equally untestable "no dynamics among unobservables" assumption. We build our argument intuitively using directed acyclic graphs, then provide analytical results on the bias resulting from lag identification in a simple linear regression framework. We then present simulation results that characterize how, even under favorable conditions, lag identification leads to incorrect inferences. These findings have important implications for current practice among applied researchers in political science, economics, and related disciplines. We conclude by specifying the conditions under which lagged explanatory variables are appropriate for identifying causal effects.

In a recent issue of Political Analysis, Grant and Lebo authored two articles that forcefully argue against the use of the general error correction model (GECM) in nearly all time series applications of political data. We reconsider Grant and Lebo’s simulation results based on five common time series data scenarios. We show that Grant and Lebo’s simulations (as well as our own additional simulations) suggest the GECM performs quite well across these five data scenarios common in political science. The evidence shows that the problems Grant and Lebo highlight are almost exclusively the result of either incorrect applications of the GECM or the incorrect interpretation of results. Based on the prevailing evidence, we contend the GECM will often be a suitable model choice if implemented properly, and we offer practical advice on its use in applied settings.

Abstract:This article utilizes a newly available dataset on the geographical distribution of development projects in Zambia to test whether electoral incentives shape aid allocation at the subnational level. Based on this dataset, it argues that when political elites have limited information to target distributive goods specifically to swing voters, they allocate more donor projects to districts where opposition to the incumbent is strong, as opposed to districts where the incumbent enjoys greater popularity.