Research

Experimenters often run pilots, but how much a small pilot should shape the main-wave design has no settled answer. This paper shows how pilot evidence should guide treatment assignment probabilities in two-wave experiments. Two canonical rules mark the extremes. Balanced assignment guards against worst cases but ignores evidence that one arm is noisier. Feasible Neyman allocation adapts, but with a finite pilot it can overreact to noise, producing arbitrarily large precision losses. This paper proposes a Conditional Minimax Regret (CMR) rule that minimizes worst-case regret over a finite-sample confidence set for the treatment and control variances. CMR retains balance’s worst-case protection with high probability, converges to the Neyman allocation as the pilot grows, and attains the minimax-regret rate up to constants. It extends to multi-arm and stratified designs, and simulations calibrated to four field experiments show it avoids feasible Neyman’s severe small-pilot losses while capturing most of its large-pilot gains.

Researchers estimate two-way effects models to decompose outcomes into unit and cluster components, such as workers and firms, teachers and schools, or individuals and regions. Existing empirical Bayes approaches for this setting rely on parametric prior assumptions. We develop a nonparametric empirical Bayes framework that allows the distribution of unit effects to vary with latent cluster effects, so unit and cluster components need not be independent and units within a cluster can be correlated. We propose a feasible estimation procedure and characterize the resulting shrinkage rules. Simulations show mean squared error close to an oracle benchmark and improvements over i.i.d.-based methods.

We examine how subnational partisan alignment influences corruption in clientelistic environments, focusing on the fabrication of “ghost” students to inflate education transfers to local governments in Colombia. Using a Regression Discontinuity Design, we find that partisan alignment between municipal mayors and departmental governors increases ghost students by 0.3 standard deviations, without improving genuine enrollment or student performance. Alignment also leads to more discretionary hiring, patronage-based outsourcing, and increased electoral fraud risk. The effects are strongest in municipalities with weaker institutions and entrenched clientelism. Alignment also raises the likelihood that mayors’ relatives are appointed to departmental posts and governors’ relatives to municipal posts, consistent with reciprocal patronage. These findings support the view that resource diversion benefits politicians with few benefits for local constituencies. Aligned politicians also experience better future electoral prospects, suggesting a breakdown in accountability. Our results highlight how clientelistic networks distort public service delivery, reinforcing the persistence of political corruption.