References
- Abadie, A., and G. W. Imbens. 2006. “Large Sample Properties of Matching Estimators for Average Treatment Effects.” Econometrica 74 (1): 235–267. doi:https://doi.org/10.1111/j.1468-0262.2006.00655.x.
- Angrist, J. D., and J.-S. Pischke. 2009. Mostly Harmless Econometrics: An Empiricist’s Companion. Princeton: Princeton University Press.
- Brunt, C. S. 2015. “Medicare Part B Intensity and Volume Offset.” Health Economics 24 (8): 1009–1026. doi:https://doi.org/10.1002/hec.3081.
- Chen, A. J., A. J. Graves, M. J. Resnick, and M. R. Richards. 2018. “Does Spending More Get More? Health Care Delivery and Fiscal Implications from a Medicare Fee Bump.” Journal of Policy Analysis and Management 37 (4): 706–731. doi:https://doi.org/10.1002/pam.22084.
- Christensen, S. 1992. “Volume Responses to Exogenous Changes in Medicare’s Payment Policies.” Health Services Research 27 (1): 65–79. April.
- Clemens, J., and J. D. Gottlieb. 2014. “Do Physicians’ Financial Incentives Affect Medical Treatment and Patient Health?” The American Economic Review 104 (4): 1320. doi:https://doi.org/10.1257/aer.104.4.1320.
- Diamond, A., and J. S. Sekhon. 2013. “Genetic Matching for Estimating Causal Effects: A General Multivariate Matching Method for Achieving Balance in Observational Studies.” Review of Economics and Statistics 95 (3): 932–945. doi:https://doi.org/10.1162/REST_a_00318.
- Hadley, J., J. Reschovsky, C. Corey, and S. Zuckerman. 2009. “Medicare Fees and the Volume of Physicians’ Services.” Inquiry 46 (4): 372–390. doi:https://doi.org/10.5034/inquiryjrnl_46.4.372.
- Hadley, J., and J. D. Reschovsky. 2006. “Medicare Fees and Physicians’ Medicare Service Volume: Beneficiaries Treated and Services per Beneficiary.” International Journal of Health Care Finance and Economics 6 (2): 131–150. June. doi:https://doi.org/10.1007/s10754-006-8143-z.
- Kaestner, R., B. Garrett, J. Chen, A. Gangopadhyaya, and C. Fleming. 2017. “Effects of Aca Medicaid Expansions on Health Insurance Coverage and Labor Supply.” Journal of Policy Analysis and Management 36 (3): 608–642. doi:https://doi.org/10.1002/pam.21993.
- Linden, A., and J. L. Adams. 2011. “Applying a Propensity Score-based Weighting Model to Interrupted Time Series Data: Improving Causal Inference in Programme Evaluation.” Journal of Evaluation in Clinical Practice 17 (6): 1231–1238. doi:https://doi.org/10.1111/j.1365-2753.2010.01504.x.
- McGuire, T. G., and M. V. Pauly. 1991. “Physician Response to Fee Changes with Multiple Payers.” Journal of Health Economics 10 (4): 385–410. doi:https://doi.org/10.1016/0167-6296(91)90022-F.
- Medicare Board of Trustees. 2012. Review of assumptions and methods of the Medicare trustees’ financial projections, December. Accessed 20 April 2019. https://aspe.hhs.gov/system/files/pdf/180601/TechnicalPanelReport2010-2011.pdf
- Mitchell, J. B., and J. Cromwell. 1995. “Impact of Medicare Payment Reductions on Access to Surgical Services.” Health Services Research 30 (5): 637–655. December.
- Mitchell, J. M., J. Hadley, and D. J. Gaskin. 2002. “Spillover Effects of Medicare Fee Reductions: Evidence from Ophthalmology.” International Journal of Health Care Finance and Economics 2 (3): 171–188. September. doi:https://doi.org/10.1023/A:1020436509217.
- Nguyen, N. X., and F. W. Derrick. 1997. “Physician Behavioral Response to a Medicare Price Reduction.” Health Services Research 32 (3): 283–298. August.
- Rice, T. H. 1983. “The Impact of Changing Medicare Reimbursement Rates on Physician-induced Demand.” Medical Care 21 (8): 803–815. August. doi:https://doi.org/10.1097/00005650-198308000-00004.
- Rice, T. H., and N. McCall. 1982. “Changes in Medicare Reimbursement in Colorado: Impact on Physicians’ Economic Behavior.” Health Care Financing Review 3 (4): 67–85. June.
- Ryan, A. M. 2018. “Well-balanced or Too Matchy-matchy? the Controversy over Matching in Difference-in-differences.” Health Services Research 53 (6): 4106–4110. doi:https://doi.org/10.1111/1475-6773.13015.
- Ryan, A. M., J. F. Burgess, and J. B. Dimick. 2015. “Why We Should Not Be Indifferent to Specification Choices for Difference-in-differences.” Health Services Research 50 (4): 1211–1235. doi:https://doi.org/10.1111/1475-6773.12270.
- Ryan, A. M., E. Kontopantelis, A. Linden, and J. F. Burgess Jr. 2019. “Now Trending: Coping with Non-parallel Trends in Difference-in-differences Analysis.” Statistical Methods in Medical Research 28 (12): 3697–3711. doi:https://doi.org/10.1177/0962280218814570.
- Sekhon, J. 2011. “Multivariate and Propensity Score Matching Software with Automated Balance Optimization: The Matching Package for R.” Journal of Statistical Software, Articles 42 (7): 1–52.
- Stuart, E. A., H. A. Huskamp, K. Duckworth, J. Simmons, Z. Song, M. E. Chernew, and C. L. Barry. 2014. “Using Propensity Scores in Difference-in-differences Models to Estimate the Effects of a Policy Change.” Health Services & Outcomes Research Methodology 14 (4): 166–182. doi:https://doi.org/10.1007/s10742-014-0123-z.
- Volume-and-Intensity Response Team, Office of the Actuary. 1998. Physician volume and intensity response, August. Accessed 20 April 2019. https://www.cms.gov/Research-Statistics-Data-and-Systems/Research/ActuarialStudies/downloads/PhysicianResponse.pdf
- Yip, W. C. 1998. “Physician Response to Medicare Fee Reductions: Changes in the Volume of Coronary Artery Bypass Graft (Cabg) Surgeries in the Medicare and Private Sectors.” Journal of Health Economics 17 (6): 675–699. December. doi:https://doi.org/10.1016/S0167-6296(98)00024-1.
- Zuckerman, S., S. A. Norton, and D. Verrilli. 1998. “Price Controls and Medicare Spending: Assessing the Volume Offset Assumption.” Medical Care Research and Review 55 (4): 457–478. discussion 479–83. doi:https://doi.org/10.1177/107755879805500404.
- Zuckerman, S., L. Skopec, and M. Epstein. 2017. Medicaid Physician Fees after the Aca Primary Care Fee Bump. Washington, DC: Urban Institute.