Treatment effects at the margin: Everyone is marginal
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866916925267771392 |
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| author | Deng, Haotian |
| author_facet | Deng, Haotian |
| contents | This paper develops a framework for identifying treatment effects when a policy simultaneously alters both the incentive to participate and the outcome of interest -- such as hiring decisions and wages in response to employment subsidies; or working decisions and wages in response to job trainings. This framework was inspired by my PhD project on a Belgian reform that subsidised first-time hiring, inducing entry by marginal firms yet meanwhile changing the wages they pay. Standard methods addressing selection-into-treatment concepts (like Heckman selection equations and local average treatment effects), or before-after comparisons (including simple DiD or RDD), cannot isolate effects at this shifting margin where treatment defines who is observed. I introduce marginality-weighted estimands that recover causal effects among policy-induced entrants, offering a policy-relevant alternative in settings with endogenous selection. This method can thus be applied widely to understanding the economic impacts of public programmes, especially in fields largely relying on reduced-form causal inference estimation (e.g. labour economics, development economics, health economics). |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2508_21583 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Treatment effects at the margin: Everyone is marginal Deng, Haotian Econometrics Methodology This paper develops a framework for identifying treatment effects when a policy simultaneously alters both the incentive to participate and the outcome of interest -- such as hiring decisions and wages in response to employment subsidies; or working decisions and wages in response to job trainings. This framework was inspired by my PhD project on a Belgian reform that subsidised first-time hiring, inducing entry by marginal firms yet meanwhile changing the wages they pay. Standard methods addressing selection-into-treatment concepts (like Heckman selection equations and local average treatment effects), or before-after comparisons (including simple DiD or RDD), cannot isolate effects at this shifting margin where treatment defines who is observed. I introduce marginality-weighted estimands that recover causal effects among policy-induced entrants, offering a policy-relevant alternative in settings with endogenous selection. This method can thus be applied widely to understanding the economic impacts of public programmes, especially in fields largely relying on reduced-form causal inference estimation (e.g. labour economics, development economics, health economics). |
| title | Treatment effects at the margin: Everyone is marginal |
| topic | Econometrics Methodology |
| url | https://arxiv.org/abs/2508.21583 |