Distributional Difference-in-Differences Models with Multiple Time Periods
[Draft available at http://arxiv.org/abs/2408.01208]
Abstract: Researchers are often interested in evaluating the impact of a policy on the entire (or specific parts of the) distribution of the outcome of interest. In this paper, I provide a method to recover the whole distribution of the untreated potential outcome for the treated group in non-experimental settings with staggered treatment adoption by generalizing the existing quantile treatment effects on the treated (QTT) estimator proposed by Callaway and Li (2019). Besides the QTT, I consider different approaches that anonymously summarize the quantiles of the distribution of the outcome of interest (such as tests for stochastic dominance rankings) without relying on rank invariance assumptions. The finite-sample properties of the estimator proposed are analyzed via different Monte Carlo simulations. Despite being slightly biased for relatively small sample sizes, the proposed method’s performance increases substantially when the sample size increases.
[Draft available at Department of Economics, Ca' Foscari University of Venice]
Abstract: This paper provides novel evidence on the impact of a cost-containment measure first introduced in Italy in 2007 – Piani di Rientro sanitari (PdRs) – on the quality and efficiency of Regional Health Services (RHSs). Thus far, ten out of twenty-one RHSs have undergone at least one round of PdRs – three managed to exit, but seven are still treated – raising the question of whether cost reduction has had any unintended negative effect on the quality of treated RHSs. I answer this question using the Two-way Mundlak approach. Compared to the classic Two-way Fixed Effects, this method explicitly models the staggered nature of the policy by allowing me to analyze how the treatment effect varies along different dimensions. Further, it allows the estimation of the long-run impact of PdRs. Overall, I find that Piani di Rientro managed to reduce costs. However, cost reduction was not followed by a boost in the efficiency of RHSs and the appropriateness of care provided, as expected by the policymaker. Conversely, reduced budgets made available to regions only resulted in an unintended deterioration in the quality of healthcare services. Results also hold in the long run and are robust to a set of bounded-variations assumptions.🏥