Oversubscribed treatments are often allocated using randomized waiting lists. Applicants are ranked randomly, and treatment offers are made following that ranking until all seats are filled. To estimate causal effects, researchers often compare applicants getting and not getting an offer. We show that those two groups are not statistically comparable. Therefore, the estimator arising from that comparison is inconsistent when the number of waitlists goes to infinity. We propose a new estimator, and show that it is consistent, provided the waitlists have at least two seats. Finally, we revisit an application, and we show that using our estimator can lead to a statistically significant difference with respect to the results obtained using the commonly used estimator.

Social and emotional learning (SEL) programs that target disruptive students aim to improve their classroom behavior. Small-scale programs in high-income countries have demonstrated positive effects. Using a randomized experiment, we show that a nationwide SEL program in Chile has no effect. Very disruptive students seem to reduce the program’s effectiveness. With attention deficit hyperactivity disorder being more prevalent in middle- than high-income countries, very disruptive students may be more present there, which could diminish the effectiveness of SEL programs. Moreover, implementation fidelity seems lower in this program than in the small-scale ones considered earlier, which could also explain the program’s null effect.

We study treatment-effect estimation using panel data. The treatment may be nonbinary, nonabsorbing, and the outcome may be affected by treatment lags. We make a parallel-trends assumption and propose event-study estimators of the effect of being exposed to a weakly higher treatment dose for ℓ periods. We also propose normalized estimators that estimate a weighted average of the effects of the current treatment and its lags. We also analyze commonly used two-way, fixed-effects regressions. Unlike our estimators, they can be biased in the presence of heterogeneous treatment effects. A local-projection version of those regressions is biased even with homogeneous effects.

Linear regressions with period and group fixed effects are widely used to estimate treatment effects. We show that they estimate weighted sums of the average treatment effects (ATE ) in each group and period, with weights that may be negative. Due to the negative weights, the linear regression coefficient may for instance be negative while all the ATEs are positive. We propose another estimator that solves this issue. In the two applications we revisit, it is significantly different from the linear regression estimator.