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Structural Representations and Identification of Marginal Policy Effects

Zhixin Wang, Yu Zhang, Zhengyu Zhang

arXiv 13 Jun 2025 · Econometrics · publishedOxford Bulletin of Economics and Statistics (2025)

arXiv:2506.11694 · PDF · DOI · OpenAlex · Extracted main text

Abstract

This paper investigates the structural interpretation of the marginal policy effect (MPE) within nonseparable models. We demonstrate that, for a smooth functional of the outcome distribution, the MPE equals its functional derivative evaluated at the outcome-conditioned weighted average structural derivative. This equivalence is definitional rather than identification-based. Building on this theoretical result, we propose an alternative identification strategy for the MPE that complements existing methods.

Citation extraction

24
references
93
in-text mentions
24
distinct cited
2
self-citations
7,572
main-text words

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Most heavily cited references

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.

ReferenceIntensityMentionsSectionsMain text
1Firpo, S., Fortin, N. M., and Lemieux, T (2009) Unconditional quantile regressions1.000215100%
2Martínez-Iriarte J, Montes-Rojas G, Sun Y (2024) Unconditional effects of general policy interventions1.00094100%
3Rothe, C (2010) Identification of unconditional partial effects in nonseparable models0.9568588%
4Rothe, C (2012) Partial distributional policy effects0.95014586%
5Hoderlein, S., and Mammen, E (2007) ”Identification of marginal effects in nonseparable models without monotonicity.”0.89911473%
6van der Vaart, AW (2000) Asymptotic Statistics0.7374350%
7Hoderlein, S., and Mammen, E (2009) Identification and estimation of local average derivatives in nonseparable models without monotonicity0.7373367%
8Alejo, J., Galvao, A. F., Martínez-Iriarte, J., and Montes-Rojas, G (2024) Unconditional quantile partial effects via conditional quantile regression0.73732100%
9Firpo, S., and Pinto, C (2016) Identification and estimation of distributional impacts of interventions using changes in inequality measures0.64422100%
10Imbens, G. W., and Newey, W. K (2009) Identification and estimation of triangular simultaneous equations models without additivity0.64422100%

Showing the top 10 of 24 scored citations.