Did Massachusetts Health Care Reform Lower Mortality? No According to Randomization Inference

References

• Abadie, A., Diamond, A., and Hainmueller, J. (2010), “Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California's Tobacco Control Program,” Journal of the American Statistical Association, 105, 493–505.
   Web of Science ®Google Scholar
• Bertrand, M., Duflo, E., and Mullainathan, S. (2004), “How Much Should We Trust Differences-In-Differences Estimates?” The Quarterly Journal of Economics, 119, 249–275.
   Web of Science ®Google Scholar
• Bloom, N., Eifert, B., Mahajan, A., McKenzie, D., and Roberts, J. (2013), “Does Management Matter? Evidence from India,” The Quarterly Journal of Economics, 128, 1–51.
   Web of Science ®Google Scholar
• Braun, T.M., and Feng, Z. (2001), “Optimal Permutation Tests for the Analysis of Group Randomized Trials,” Journal of the American Statistical Association, 96, 1424–1432.
   Web of Science ®Google Scholar
• Cameron, A.C., Gelbach, J.B., and Miller, D.L. (2008), “Bootstrap-Based Improvements for Inference With Clustered Errors,” The Review of Economics and Statistics, 90, 414–427.
   Web of Science ®Google Scholar
• Cameron, A.C., and Miller, D.L. (in press), “A Practitioner's Guide to Cluster-Robust Inference,” Journal of Human Resources.
   Web of Science ®Google Scholar
• Cohen, J., and Dupas, P. (2010), “Free Distribution or Cost-Sharing? Evidence from a Randomized Malaria Prevention Experiment,” The Quarterly Journal of Economics, 125, 1–45.
   Web of Science ®Google Scholar
• Conley, T.G., and Taber, C.R. (2011), “Inferences with ‘Difference in Differences’ with a Small Number of Policy Changes,”The Review of Economics and Statistics, 93, 113–125.
   Web of Science ®Google Scholar
• Courtemanche, C.J., and Zapata, D. (2014), “Does Universal Coverage Improve Health? The Massachusetts Experience,” Journal of Policy Analysis and Management, 33, 36–69.
   PubMed Web of Science ®Google Scholar
• Donald, S.G., and Lang, K. (2007), “Inference With Difference-in-Differences and Other Panel Data,” The Review of Economics and Statistics, 89, 221–233.
   Web of Science ®Google Scholar
• Fisher, R.A. (1935), The Design of Experiments, Edinburgh, London: Oliver and Boyd. Available at http://catalog.hathitrust.org/Record/001306228
   Google Scholar
• Gail, M.H., Mark, S.D., Carroll, R.J., Green, S.B., and Pee, D. (1996), “On Design Considerations and Randomization-Based Inference for Community Intervention Trials,” Statistics in Medicine, 15, 1069– 1092.
   PubMed Web of Science ®Google Scholar
• Gail, M.H., Tan, W.Y., and Piantadosi, S. (1988), “Tests for No Treatment Effect in Randomized Clinical Trials,” Biometrika, 75, 57–64.
   Web of Science ®Google Scholar
• Good, P.I. (2000), Permutation Tests: A Practical Guide to Resampling Methods for Testing Hypotheses, New York: Springer.
   Google Scholar
• Good, P.I. (2005), Permutation, Parametric, and Bootstrap Tests of Hypotheses (3rd ed.), New York: Springer.
   Google Scholar
• Ho, D.E., and Imai, K. (2006), “Randomization Inference With Natural Experiments: An Analysis of Ballot Effects in the 2003 California Recall Election,” Journal of the American Statistical Association, 101, 888–900.
   Web of Science ®Google Scholar
• Imbens, G.W., and Rosenbaum, P.R. (2005), “Robust, Accurate Confidence Intervals With a Weak Instrument: Quarter of Birth and Education,” Journal of the Royal Statistical Society, Series A, 168, 109– 126.
   Web of Science ®Google Scholar
• Long, S.K. (2008), “On the Road to Universal Coverage: Impacts of Reform in Massachusetts at One Year,” Health Affairs, 27, w270–w284.
   Web of Science ®Google Scholar
• Long, S.K., Stockley, K., and Yemane, A. (2009), “Another Look at the Impacts of Health Reform in Massachusetts: Evidence Using New Data and a Stronger Model,” American Economic Review, Papers and Proceedings, 99, 508–511.
   PubMed Web of Science ®Google Scholar
• MacKinnon, J.G., and Webb, M.D. (2014), ``Wild Bootstrap Inference for Wildly Different Cluster Sizes,’’ Working Paper 1314, Department of Economics, Queen's University. Available at https://ideas. repec.org/p/qed/wpaper/1314.html
   Google Scholar
• Moulton, B.R. (1990), “An Illustration of a Pitfall in Estimating the Effects of Aggregate Variables on Micro Unit,” The Review of Economics and Statistics, 72, 334–338.
   Web of Science ®Google Scholar
• Meyer, B. (1995), “Natural and Quasi-Experiments in Economics,” Journal of Business and Economic Statistics, 12, 151–161.
   Web of Science ®Google Scholar
• Rosenbaum, P.R. (2002), “Covariance Adjustment in Randomized Experiments and Observational Studies,” Statistical Science, 17, 286–327.
   Web of Science ®Google Scholar
• Ryan A. M., Burgess J. F., and Dimick J. B. (2015), “Why We Should Not Be Indifferent to Specification Choices for Difference-in-Differences,” Health Services Research, 50, 1211–35.
   Google Scholar
• Small, D.S, Ten Have, T.R., and Rosenbaum, P.R. (2008), “Randomization Inference in a Group–Randomized Trial of Treatments for Depression: Covariate Adjustment, Noncompliance, and Quantile Effects,” Journal of the American Statistical Association, 103, 271–279.
   Web of Science ®Google Scholar
• Sommers, B.D., Long, S.K., and Baicker, K. (2014), “Changes in Mortality After Massachusetts Health Care Reform: A Quasi-Experimental Study,” Annals of Internal Medicine, 160, 585–593.
   PubMed Web of Science ®Google Scholar
• Webb, M.D. (2014), “Reworking Wild Bootstrap Based Inference for Clustered Errors,” Working Paper 1315, Department of Economics, Queen's University. Available at https://ideas.repec.org/p/qed/wpaper/1315. html
   Google Scholar