Estimating Treatment Effects in Panel Data Without Parallel Trends
Fuente:
arXiv
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866909988716281856 |
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| author | Ishimaru, Shoya |
| author_facet | Ishimaru, Shoya |
| contents | This paper proposes a novel approach for estimating treatment effects in panel data settings, addressing key limitations of the standard difference-in-differences (DID) approach. The standard approach relies on the parallel trends assumption, implicitly requiring that unobservable factors correlated with treatment assignment be unidimensional, time-invariant, and affect untreated potential outcomes in an additively separable manner. This paper introduces a more flexible framework that allows for multidimensional unobservables and non-additive separability, and provides sufficient conditions for identifying the average treatment effect on the treated. An empirical application to job displacement reveals substantially smaller long-run earnings losses compared to the standard DID approach, demonstrating the framework's ability to account for unobserved heterogeneity that manifests as differential outcome trajectories between treated and control groups. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_08281 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Estimating Treatment Effects in Panel Data Without Parallel Trends Ishimaru, Shoya Econometrics This paper proposes a novel approach for estimating treatment effects in panel data settings, addressing key limitations of the standard difference-in-differences (DID) approach. The standard approach relies on the parallel trends assumption, implicitly requiring that unobservable factors correlated with treatment assignment be unidimensional, time-invariant, and affect untreated potential outcomes in an additively separable manner. This paper introduces a more flexible framework that allows for multidimensional unobservables and non-additive separability, and provides sufficient conditions for identifying the average treatment effect on the treated. An empirical application to job displacement reveals substantially smaller long-run earnings losses compared to the standard DID approach, demonstrating the framework's ability to account for unobserved heterogeneity that manifests as differential outcome trajectories between treated and control groups. |
| title | Estimating Treatment Effects in Panel Data Without Parallel Trends |
| topic | Econometrics |
| url | https://arxiv.org/abs/2601.08281 |