My da|ra Login

Detailed view

metadata language: English

Data and Code for: Administration above Administrators: The Changing Technology of Healthcare Management

Version
1
Resource Type
Dataset
Creator
  • Dunn, Abe (Bureau of Economic Analysis)
  • Gottlieb, Joshua D. (University of Chicago. Harris School of Public Policy)
  • Shapiro, Adam H. (Federal Reserve Bank of San Francisco)
Publication Date
2020-05-15
Free Keywords
health care costs; health expenditures; administrative costs
Description
  • Abstract

    This paper measure the costs and types of administrative inputs in health care. We use data on labor and non-labor inputs by industry and categorize them as administrative or not. We find that non-labor inputs are a critical part of administrative spending, over and above labor inputs. Trends in non-labor administrative input spending have differed dramatically from that of labor input spending for hospitals over the last 20 years. Hospitals have substituted away from office workers, and towards externally purchased inputs. The share of managers and technical workers in administration has grown. The technology of healthcare administration is changing.
Temporal Coverage
  • 1997-01-01 / 2017-12-31
    Time Period: Wed Jan 01 00:00:00 EST 1997--Sun Dec 31 00:00:00 EST 2017
Geographic Coverage
  • United States
Availability
Download
Relations
  • Is supplement to
    DOI: 10.1257/pandp.20201031 (Text)
Publications
  • Dunn, Abe, Joshua D. Gottlieb, and Adam Hale Shapiro. “Administration above Administrators: The Changing Technology of Health Care Management.” AEA Papers and Proceedings 110 (May 2020): 274–78. https://doi.org/10.1257/pandp.20201031.
    • ID: 10.1257/pandp.20201031 (DOI)

Update Metadata: 2020-05-18 | Issue Number: 2 | Registration Date: 2020-05-15

Dunn, Abe; Gottlieb, Joshua D.; Shapiro, Adam H. (2020): Data and Code for: Administration above Administrators: The Changing Technology of Healthcare Management. Version: 1. ICPSR - Interuniversity Consortium for Political and Social Research. Dataset. https://doi.org/10.3886/E117801V1