Rural Hospital Closure and Effect on Local Economies

Lead researcher:
Contact:
Project funded:
September 2020
Anticipated completion date:
August 2021

For the communities they serve, rural hospitals are often an anchor institution, providing not only needed health care, but also a significant portion of jobs and billions of revenues in purchasing goods and services from other businesses. As a major employer in rural areas, hospitals and their closures have tremendous impacts on the economies of already vulnerable communities.

The purpose of this study is to quantify the economic impact rural hospital closures have on communities. Total economic effects can be conceptualized as consisting of four distinct "sub- effects."

  • Direct: Hospital employees lose their salary due to the closure (individual income).
  • Indirect: Communities lose hospital purchases of other goods and services from the community (community income).
  • Induced: Former employees lose their salaries and thus do not spend as much in the community (community income).
  • Amenity: Communities lose an attractant for future migration and business development. The presence of the hospital is conducive (or even necessary) for migration and economic development.

The primary data source is the Center's list of rural hospital closures. Although the list began with tracking closures beginning in 2010, the Center has been working backward to expand the list from earlier periods. The Center recently posted closures beginning in 2005 and is building the list of closures back to 1990.

Economic outcomes come from the U.S. Bureau of Labor Statistics (unemployment), U.S. Bureau of Economic Analysis (per-capita income), and the U.S. Census Bureau (population). Data on hospitals and other institutional providers will come from the Centers for Medicare & Medicaid Services' Provider of Services files. The Area Health Resource File will provide data on physicians and other health professionals at the county level. Medicare cost report data will be used to augment key variables likely to modify the effect (e.g., number of employees and/or payroll).