Cluster-Robust Variance Estimation for Dyadic Data

Dyadic data are common in the social sciences, although inference for such settings involves accounting for a complex clustering structure. Many analyses in the social sciences fail to account for the fact that multiple dyads share a member, and that errors are thus likely correlated across these dyads. We propose a nonparametric sandwich-type robust variance estimator for linear regression to account for such clustering in dyadic data. We enumerate conditions for estimator consistency. We also extend our results to repeated and weighted observations, including directed dyads and longitudinal data, and provide an implementation for generalized linear models such as logistic regression. We examine empirical performance with simulations and applications to international relations and speed dating

Can intergroup contact affect ingroup dynamics? Insights from a field study with Jewish and Arab-Palestinian youth in Israel

How can intergroup contact programs affect conflict-ridden communities besides improving the outgroup attitudes of participating individuals? We address this question by examining the effects of an intergroup contact intervention on ingroup dynamics that may mitigate intergroup conflict. We also examine how outgroup attitudes and psychological resources mediate such effects. We present the results from a difference-in-differences design with 149 Jewish and Arab-Palestinian youth, some of whom participated in an intergroup contact and sports program operated by a nongovernmental organizations in Israel. Our main outcome is one’s tendency to censure ingroup members’ provocations toward the outgroup. As expected, we find a positive impact of the program on ingroup censuring. However, this result is only marginally significant. We find a positive effect of program participation on outgroup attitudes among Jewish youth as expected. To our surprise, among Arab-Palestinian youth, we find a negative effect on outgroup attitudes. Exploring the underlying processes and group-based differences further, we find that outgroup regard mediates the effect of intergroup contact on ingroup censuring for Jewish youth. We find no evidence for mediation among Arab-Palestinian youth but a positive association between ingroup censuring and psychological resources. These results suggest that the psychological conditions of ingroup censuring may differ by group. We discuss implications for peace-building interventions in societies with groups in conflict

Microdynamics of War-to-Peace Transitions: Evidence from Burundi

In these three essays, I study important facets of the transition to peace after Burundi's 1993-2005 civil war. The first essay studies the effects of quota-based ethnic integration within Burundi's army on expressions of prejudice and ethnic salience among soldiers. Exploiting a natural experiment within the army, I find that exposure to quota-based integration reduced prejudice and had no effect on ethnic salience, countering a prevailing view in the literature that quota-based integration is likely to exacerbate ethnic tensions. The second essay studies individuals' preferences over transitional justice alternatives. I find that support for punishment and truth-seeking is more tepid than the advocacy literature has suggested, that ethno-political motivations seem to dominate expressed preferences for punishment and truth-seeking, and, using a persuasion experiment, that simple forms of deliberation may actually polarize people. The third essay, co-authored with Michael Gilligan and Eric Mvukiyehe, examines the impact of Burundi's ex-combatant reintegration program on the economic and political reintegration of demobilized rebels. Exploiting another natural experiment, we find that the program provided substantial economic benefits, but that these economic benefits did not seem to contribute to political integration, at least in the short-run. The essays enrich our understanding of Burundi's difficult transition to peace. They also essays just how one may bring high scientific standards to study policies in the otherwise challenging context of post-conflict transitions

From local to global: extrapolating experiments

The use of randomised control trials (RCTs) in evaluating the design and efficacy of policies has exploded in the last decade. New papers appear every week. But while RCTs are quickly becoming the gold standard for impact evaluations in international development and aid interventions, questions persist about what the results of an RCT in one context can tell us about the probable results of similar programme implemented in another context. Indeed, such questions are not unique to RCT’s but apply to the full set of empirical tools that economists apply in estimating policy impacts and outcomes

Design-Based Inference for Spatial Experiments with Interference

We consider design-based causal inference in settings where randomized treatments have effects that bleed out into space in complex ways that overlap and in violation of the standard "no interference" assumption for many causal inference methods. We define a spatial "average marginalized response," which characterizes how, in expectation, units of observation that are a specified distance from an intervention point are affected by treatments at that point, averaging over effects emanating from other intervention points. We establish conditions for non-parametric identification, asymptotic distributions of estimators, and recovery of structural effects. We propose methods for both sample-theoretic and permutation-based inference. We provide illustrations using randomized field experiments on forest conservation and health

Generalizing Trimming Bounds for Endogenously Missing Outcome Data Using Random Forests

In many experimental or quasi-experimental studies, outcomes of interest are only observed for subjects who select (or are selected) to engage in the activity generating the outcome. Outcome data is thus endogenously missing for units who do not engage, in which case random or conditionally random treatment assignment prior to such choices is insufficient to point identify treatment effects. Non-parametric partial identification bounds are a way to address endogenous missingness without having to make disputable parametric assumptions. Basic bounding approaches often yield bounds that are very wide and therefore minimally informative. We present methods for narrowing non-parametric bounds on treatment effects by adjusting for potentially large numbers of covariates, working with generalized random forests. Our approach allows for agnosticism about the data-generating process and honest inference. We use a simulation study and two replication exercises to demonstrate the benefits of our approach