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Non-linear Triple Changes Estimator for Targeted Policies

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  • Sina Akbari
  • Negar Kiyavash

Abstract

The renowned difference-in-differences (DiD) estimator relies on the assumption of 'parallel trends,' which does not hold in many practical applications. To address this issue, the econometrics literature has turned to the triple difference estimator. Both DiD and triple difference are limited to assessing average effects exclusively. An alternative avenue is offered by the changes-in-changes (CiC) estimator, which provides an estimate of the entire counterfactual distribution at the cost of relying on (stronger) distributional assumptions. In this work, we extend the triple difference estimator to accommodate the CiC framework, presenting the `triple changes estimator' and its identification assumptions, thereby expanding the scope of the CiC paradigm. Subsequently, we empirically evaluate the proposed framework and apply it to a study examining the impact of Medicaid expansion on children's preventive care.

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  • Sina Akbari & Negar Kiyavash, 2024. "Non-linear Triple Changes Estimator for Targeted Policies," Papers 2402.12583, arXiv.org.
    • Handle: RePEc:arx:papers:2402.12583
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    References listed on IDEAS

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    6. Jonathan Roth & Pedro H. C. Sant'Anna, 2023. "When Is Parallel Trends Sensitive to Functional Form?," Econometrica, Econometric Society, vol. 91(2), pages 737-747, March.
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