Onil Boussim
arXiv 13 Oct 2025 · Econometrics
arXiv:2510.11659 · PDF · Extracted main text
In difference-in-differences (DiD) settings with categorical outcomes, treatment effects often operate on both total quantities (e.g., voter turnout) and category shares (e.g., vote distribution across parties). In this context, linear DiD models can be problematic: they suffer from scale dependence, may produce negative counterfactual quantities, and are inconsistent with discrete choice theory. We propose compositional DiD (CoDiD), a new method that identifies counterfactual categorical quantities, and thus total levels and shares, under a parallel growths assumption. The assumption states that, absent treatment, each category's size grows or shrinks at the same proportional rate in treated and control groups. In a random utility framework, we show that this implies parallel evolution of relative preferences between any pair of categories. Analytically, we show that it also means the shares are reallocated in the same way in both groups in the absence of treatment. Finally, geometrically, it corresponds to parallel trajectories (or movements) of probability mass functions of the two groups in the probability simplex under Aitchison geometry. We extend CoDiD to i) derive bounds under relaxed assumptions, ii) handle staggered adoption, and iii) propose a synthetic DiD analog. We illustrate the method's empirical relevance through two applications: first, we examine how early voting reforms affect voter choice in U.S. presidential elections; second, we analyze how the Regional Greenhouse Gas Initiative (RGGI) affected the composition of electricity generation across sources such as coal, natural gas, nuclear, and renewables.
appendix boundary found by appendix_command · 77% 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 | Aitchison, John (1982) The statistical analysis of compositional data | 0.843 | 3 | 3 | 100% |
| 2 | Ban, Kyunghoon and Kédagni, Désiré (2022) Robust Difference-in-differences Models | 0.843 | 3 | 3 | 100% |
| 3 | Egozcue, Juan José and Pawlowsky-Glahn, Vera and Mateu-Figueras, Glò… (2003) Isometric logratio transformations for compositional data analysis | 0.843 | 3 | 3 | 100% |
| 4 | Aitchison, John (2002) Simplicial inference | 0.644 | 2 | 2 | 100% |
| 5 | Michael Lechner (2011) The Estimation of Causal Effects by Difference-in-Difference Methods | 0.644 | 2 | 2 | 100% |
| 6 | Athey, Susan and Imbens, Guido W (2006) Identification and inference in nonlinear difference-in-differences models | 0.644 | 2 | 2 | 100% |
| 7 | Zhou, Yidong and Kurisu, Daisuke and Otsu, Taisuke and Müller, Hans-… (2025) Geodesic Difference-in-Differences | 0.644 | 2 | 2 | 100% |
| 8 | McFadden, Daniel (1977) Modelling the choice of residential location | 0.511 | 2 | 2 | 50% |
| 9 | Ahn, Young and Kasahara, Hiroyuki (2025) Event-Study Designs for Discrete Outcomes under Transition Independence | 0.511 | 2 | 1 | 100% |
| 10 | Berry, Steven T and Cox, Christian and Haile, Philip (2025) Selective Turnout, Voting Policy, and Partisan Bias: Evidence from Multi-Level Data | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 38 scored citations.