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Fuzzy Difference-in-Discontinuities: Identification Theory and Application to the Affordable Care Act

Hector Galindo-Silva, Nibene Habib Some, Guy Tchuente

arXiv 16 Dec 2018 · Econometrics · 2 citations (OpenAlex)

arXiv:1812.06537 · PDF · DOI · OpenAlex · Extracted main text

Abstract

This paper explores the use of a fuzzy regression discontinuity design where multiple treatments are applied at the threshold. The identification results show that, under the very strong assumption that the change in the probability of treatment at the cutoff is equal across treatments, a difference-in-discontinuities estimator identifies the treatment effect of interest. The point estimates of the treatment effect using a simple fuzzy difference-in-discontinuities design are biased if the change in the probability of a treatment applying at the cutoff differs across treatments. Modifications of the fuzzy difference-in-discontinuities approach that rely on milder assumptions are also proposed. Our results suggest caution is needed when applying before-and-after methods in the presence of fuzzy discontinuities. Using data from the National Health Interview Survey, we apply this new identification strategy to evaluate the causal effect of the Affordable Care Act (ACA) on older Americans' health care access and utilization.

Citation extraction

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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
1Grembi, V., Nannicini, T. and Troiano, U (2016) Do fiscal rules matter?0.87492100%
2Eggers, A. C., Freier, R., Grembi, V. and Nannicini, T (2018) Regression discontinuity designs based on population thresholds: Pitfalls and solutions0.87462100%
3Card, D., Dobkin, C. and Maestas, N (2008) The impact of nearly universal insurance coverage on health care utilization: evidence from medicare0.87452100%
4Hahn, J., Todd, P. and Van der Klaauw, W (2001) Identification and estimation of treatment effects with a regression-discontinuity design0.87452100%
5Courtemanche, C., Friedson, A., Koller, A. P. and Rees, D. I (2017) The Affordable Care Act and Ambulance Response Times0.73732100%
6Sommers, B., Blendon, R., Orav, E. and Epstein, A (2016) Changes in utilization and health among low-income adults after medicaid expansion or expanded private insurance0.73732100%
7Obama, B (2016) United states health care reform progress to date and next steps0.51121100%
8Calonico, S., Cattaneo, M. D. and Titiunik, R (2014) Robust nonparametric confidence intervals for regression-discontinuity designs0.51121100%
9Hull, P (2018) Isolateing: Identifying counterfactual-specific treatment effects with cross-stratum comparisons0.51121100%
10Imbens, G. and Kalyanaraman, K (2012) Optimal bandwidth choice for the regression discontinuity estimator0.51121100%

Showing the top 10 of 30 scored citations.

Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

Citing paperIntensityMentionsSections
1navyblue Geographic Difference-in-Discontinuities0.40511
2Difference-in-Discontinuities: Estimation, Inference and Validity Tests0.40511
3Correcting invalid regression discontinuity designs with multiple time period data0.40511
4Triple Difference Designs with Heterogeneous Treatment Effects0.40511
5Difference-in-differences with as few as two cross-sectional units – A new perspective to the democracy–growth debate0.00021