Most inpatient and emergency health care services in the U.S. are delivered by non-profit organizations. To understand the impact of policies that are designed to affect competitive outcomes in hospital markets, it’s important to understand whether the “non-profit” structure changes the behavior and competitive conduct of firms. Given the complexity of the product space within which hospitals operate, we focus on more easily interpreted decisions within the hospital market: entry and exit. Using comprehensive administrative data for the universe of California hospitals from 1980 to 2013, we document the observed entry and exit behavior. We estimate flexible exit policy functions and demonstrate a difference in behavior between for-profit and non-profit firms that exists after accounting for several observable characteristics of hospitals. We find differences in observed behavior: this is a finding that strongly suggests that there are differences in the underlying objective function of the various firms.
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Hospitals that change ownership may submit multiple reports in a year—we combine them into a single observation. Therefore, our analysis does not consider firms that merge to be exiters; as long as the physical facility is used as a hospital in subsequent years, it is considered to be a continuing incumbent.
For readability, we report this value in levels throughout our tables. We use the natural logarithm of this value in our regression analysis.
In some cases, the addresses in the data are no longer valid. When this occurs (most often due to ZIP code changes), we manually set the locations with the use of a combination of internet resources, including Google Maps and archived news with regards to those hospitals and locations.
Some measurement error is introduced during this process as historical street network data are not readily available; the distances were calculated according to California’s street network (as mapped by Open Street Map contributors) as of November 2016.
It’s important to note that more granular data (e.g., city-level) may not be useful here. Among other reasons, hospitals tend to draw patients from beyond their immediate physical location, particularly in rural areas. Additionally, many federal policies that influence hospital cash flows (such as Medicare reimbursement rates) are set at the county level.
Discharges are observed in the data, but the span of the data (i.e., the number of days that the discharge variable represents) differs across observations. To resolve this issue, we divided the number of discharges observed by the number of days represented in the data and multiplied by 365 to obtain an annualized rate for each hospital-year observation.
Data entry issues cause the values for some hospitals to be unavailable or unable to be interpreted—particularly for the total value of property, plant, and equipment variable. The results presented in this section are the most conservative treatment of the missing data: All observations with any missing values are dropped. Our results are qualitatively robust to alternative treatments: either removing the affected variables from the analysis, or imputing missing values from observables.
There are a few outliers in the data seen in Table 5—a facility with six beds, a facility with an extremely low discharge rate, and one with implausibly low physical capital. We have estimated the model reported later with these outliers removed and found the qualitative results are numerically similar and qualitatively the same.
A Chow test (Chow 1960) is not immediately applicable here as we are performing a logit regression instead of a standard linear regression. We perform an analogous test by pooling the observations, estimating a fully-interacted model, and testing the interaction terms against the null hypothesis. When we do so, we reject the null hypothesis with a probability of 93.5%.
It is possible that the difference could come in part through a stochastic channel; non-profit firms may receive cost or scrap value shocks drawn from a different distribution than do for-profit firms. This explanation would still require a source for the alternative distributions.
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The authors gratefully acknowledge the staff of the California Office of Statewide Health Planning and Development for their help in procuring and organizing the data, as well as one anonymous referee and the editors Chris Snyder and Larry White, who provided very helpful comments on an earlier draft.
Appendix: Correlation Coefficients
Appendix: Correlation Coefficients
In this appendix, we present correlation coefficients for the observations that are used in our primary specification. Table 9 reports the matrix of correlations for the observations of for-profit hospitals, and Table 10 reports the matrix of correlations for the non-profit hospital observations. Across samples, the direction of correlations are consistent with common sense priors of the hospital industry. Furthermore, few variables are very highly correlated. The highest coefficients stem from comparing either competitive environment variables, such as the number of hospitals within 50 miles and the population density, or different measures of hospital size, such as the number of licensed beds and the total value of hospital capital.
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Miller, K.S., Wilson, W.W. Governance Structure and Exit: Evidence from California Hospitals. Rev Ind Organ 53, 31–55 (2018). https://doi.org/10.1007/s11151-017-9595-7
- Governance structure
- Policy functions