Data and Code for: Detecting Potential Overbilling in Medicare Reimbursement via Hours Worked: Reply

Version
V0
Resource Type
Dataset
Creator
  • Fang, Hanming (University of Pennsylvania. Department of Economics)
  • Gong, Qing (University of North Carolina-Chapel Hill)
Publication Date
2020-11-23
Description
  • Abstract

    Matsumoto (forthcoming) pointed out data and coding errors in Fang and Gong (2017). We show that these errors have limited impacts: all qualitative findings remain after correcting them. Matsumoto also discussed potential service over-counting in the aggregated utilization data we used to illustrate our method, and then quantified the extent of over-counting with a sample of Medicare claims. We acknowledge the issue but discuss the noise and the bias in his quantification. Overall, our proposed method remains useful, as regulators who are interested in applying the method are unlikely to be subject to the data limitations.
Availability
Download
This study is freely available to the general public via web download.
Relations
  • Has version
    DOI: 10.3886/E119192V1
Publications
  • Fang, Hanming, and Qing Gong. “Detecting Potential Overbilling in Medicare Reimbursement via Hours Worked: Reply.” American Economic Review, n.d.

Update Metadata: 2020-11-23 | Issue Number: 1 | Registration Date: 2020-11-23