arXiv 24 Dec 2025 · Econometrics
arXiv:2512.20918 · PDF · DOI · OpenAlex · Extracted main text
This paper proposes a framewrok for analyzing how the welfare effects of policy interventions are distributed across individuals when those effects are unobserved. Rather than focusing solely on average outcomes, the approach uses readily available information on average welfare responses to uncover meaningful patterns in how gains and losses are distributed across different populations. The framework is built around the concept of superquantile and applies to a broad class of models with unobserved individual heterogeneity. It enables policymakers to identify which groups are most adversely affected by a policy and to evaluate trade-offs between efficiency and equity. We illustrate the approach in three widely studied economic settings: price changes and compensated variation, treatment allocation with self-selection, and the cost-benefit analysis of social programs. In this latter application, we show how standard tools from the marginal treatment effect and generalized Roy model literature are useful for implementing our bounds for both the overall population and for individuals who participate in the program.
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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 | Bhattacharya, Debopam (2024) Nonparametric approaches to empirical welfare analysis | 1.000 | 6 | 3 | 100% |
| 2 | D. McFadden (1981) Structural Analysis of Discrete Data with Econometric Applications | 1.000 | 5 | 4 | 100% |
| 3 | Allen, Roy and Rehbeck, John (2019) Identification With Additively Separable Heterogeneity | 0.976 | 14 | 6 | 93% |
| 4 | Berry, Steven T. and Haile, Philip A (2014) Identification in Differentiated Products Markets Using Market Level Data | 0.956 | 8 | 5 | 88% |
| 5 | James J. Heckman and Edward J. Vytlacil (2007) Chapter 70 Econometric Evaluation of Social Programs, Part I: Causal Models, Structural Models and Econometric Policy Evaluation | 0.928 | 4 | 3 | 100% |
| 6 | Philipp Eisenhauer and James J. Heckman and Edward Vytlacil (2015) The Generalized Roy Model and the Cost-Benefit Analysis of Social Programs | 0.874 | 13 | 2 | 100% |
| 7 | Sasaki, Yuya and Ura, Takuya (2024) Welfare Analysis via Marginal Treatment Effects | 0.874 | 5 | 2 | 100% |
| 8 | McFadden, Daniel L and Fosgerau, Mogens (2012) A theory of the perturbed consumer with general budgets | 0.843 | 3 | 3 | 100% |
| 9 | R.Tyrrell Rockafellar and Stanislav Uryasev (2002) Conditional value-at-risk for general loss distributions | 0.737 | 3 | 3 | 67% |
| 10 | Rockafellar, R.T. and Royset, J.O (2014) Random variables, monotone relations, and convex analysis | 0.737 | 3 | 3 | 67% |
Showing the top 10 of 48 scored citations.