arXiv 2 Aug 2024 · Econometrics
arXiv:2408.01208 · PDF · DOI · OpenAlex · Extracted main text
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.
appendix boundary found by appendix_command · 83% of the source is main text. Read the extracted text to check this.
The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | E. Maasoumi and L. Wang (2019) The gender gap between earnings distributions | 1.000 | 11 | 3 | 100% |
| 2 | S. Firpo (2007) Efficient semiparametric estimation of quantile treatment effects | 1.000 | 9 | 4 | 100% |
| 3 | S. Bonhomme and U. Sauder (2011) Recovering distributions in difference-in-differences models: A comparison of selective and comprehensive schooling | 1.000 | 9 | 3 | 100% |
| 4 | Y. Fan and Z. Yu (2012) Partial identification of distributional and quantile treatment effects in difference-in-differences models | 1.000 | 6 | 3 | 100% |
| 5 | B. I. Miller (2007) Doubly-robust quantile treatment effect estimation | 1.000 | 6 | 3 | 100% |
| 6 | L. Sun and S. Abraham (2021) Estimating dynamic treatment effects in event studies with heterogeneous treatment effects | 1.000 | 6 | 3 | 100% |
| 7 | S. Athey and G. W. Imbens (2006) Identification and inference in nonlinear difference-in-differences models | 1.000 | 5 | 3 | 100% |
| 8 | J. M. Wooldridge (2021) Two-Way Fixed Effects, the Two-Way Mundlak Regression, and Difference-in-Differences Estimators | 1.000 | 5 | 3 | 100% |
| 9 | B. Callaway and P. H. Sant'Anna (2021) Difference-in-differences with multiple time periods | 0.974 | 39 | 7 | 92% |
| 10 | B. Callaway and T. Li Quantile treatment effects in difference in differences models with panel data | 0.961 | 44 | 7 | 89% |
Showing the top 10 of 43 scored citations.