Richard A. Nielsen

Foreign Aid Shocks as a Cause of Violent Armed Conflict

In this study we resolve part of the confusion over how foreign aid affects armed conflict. We argue that aid shocks – severe decreases in aid revenues – inadvertently shift the domestic balance of power and potentially induce violence. During aid shocks, potential rebels gain bargaining strength vis-à-vis the government. To appease the rebels, the government must promise future resource transfers, but the government has no incentive to continue its promised transfers if the aid shock proves to be temporary. With the government unable to credibly commit to future resource transfers, violence breaks out. Using AidData’s comprehensive dataset of bilateral and multilateral aid from 1981-2005, we evaluate the effects of foreign aid on violent armed conflict. In addition to rare-event logit analysis, we employ matching methods to account for the possibility that aid donors anticipate conflict. The results show that negative aid shocks significantly increase the probability of armed conflict onset.

Case Selection via Matching

This article shows how statistical matching methods can be used to select “most similar” cases for qualitative analysis. I first offer a methodological justification for research designs based on selecting most similar cases. I then discuss the applicability of existing matching methods to the task of selecting most similar cases and propose adaptations to meet the unique requirements of qualitative analysis. Through several applications, I show that matching methods have advantages over traditional selection in “most similar” case designs: They ensure that most similar cases are in fact most similar; they make scope conditions, assumptions, and measurement explicit; and they make case selection transparent and replicable.

Avoiding Randomization Failure in Program Evaluation, with Application to the Medicare Health Support Program

We highlight common problems in the application of random treatment assignment in large-scale program evaluation. Random assignment is the defining feature of modern experimental design, yet errors in design, implementation, and analysis often result in real-world applications not benefiting from its advantages. The errors discussed here cover the control of variability, levels of randomization, size of treatment arms, and power to detect causal effects, as well as the many problems that commonly lead to post-treatment bias. We illustrate these issues by identifying numerous serious errors in the Medicare Health Support evaluation and offering recommendations to improve the design and analysis of this and other large-scale randomized experiments.