Abstract
I use a 2010 non-profit hospital merger in Ohio to study the effect of market concentration on market outcomes. Using the Synthetic Control Method and Truven MarketScan data, I document three findings. First, courts are lenient to non-profit mergers, and I cast doubt on this practice by showing that the studied merger led to a 123% increase in the payments for inpatient childbirth services. Second, I provide the first empirical evidence for the conjecture that mergers increase out-of-pocket payments and reduce the utilization of care. Last, I show that the effect of market power on market outcomes is asymmetric: the increase in payments and welfare loss created by a merger persist after the merger is rescinded. Thus, even successful FTC challenges may not revert the effect of harmful mergers, and it is essential to deny such mergers before they proceed.
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Truven Health Analytics is a confidential proprietary dataset and cannot be made available.
Code availability
Readers may contact the author at maysamq@usf.edu to receive the codes.
Notes
In this paper, I study childbirth services, and I use the term payment instead of price. Price is the amount billed for one item on a medical bill, while payment is the total amount transacted for a childbirth case. One childbirth case often includes tens of services that are priced and claimed separately. What influences patients is how much it costs to give birth (payment) and not the price of each item billed.
Truven Health MarketScan® Research Databases. MarketScan is a registered trademark of Truven Health Analytics, part of the IBM Watson Health business.
Primary, secondary, and tertiary services refer to the level of referral that is required before a patient receives a particular medical service. Primary care physicians may refer patients to secondary care (specialists), and they may further refer patients to tertiary care. Tertiary care is often more advanced, complicated, and costly.
The court documents do not disclose the exact timing of the negotiations.
The Consolidated Omnibus Budget Reconciliation Act of 1985 or COBRA allows individuals to continue insurance coverage after leaving employment.
According to census data documentation, each year’s data are surveyed uniformly throughout the year. So, if annual data are to be assigned to a specific point in time, it must be the middle of the year. That said, I use annual data 2009–12 to interpolate the monthly values for January 2010 through December 2011: annual data 2009–10 to interpolate January–June 2010, annual data 2010–11 for July 2010 through June 2011, and annual data 2011–12 for July-December 2011.
The literature uses simple averages and this paper follows the same practice.
General acute care includes all hospital services except chronic and long-term care.
The probability of an Ohioan patient giving birth to multiple newborns is 3.56%, and this number has been stable over the past decade (Statista, 2018a, 2018b). In MarketScan data, this probability is 2.21% in Toledo. Four of the 20 cases in April 2011 are multi-birth. The probability of observing four or more multi-birth cases in a sample of 20 cases is less than 0.005. Multi-birth cases are more complicated and substantially more expensive than single-birth cases. It explains the increase in April 2011. Note that there are no multi-birth cases in the post-merger period. Also, multi-birth cases are unlikely to influence Panel A because childbirth cases usually exceed the out-of-pocket maximum, and patients are insulated from the remaining variations in payments.
It is the increase in hospital revenues that is observed in MarketScan data. Since the data only captures a fraction of the market, the actual effect is expected to be substantially larger. “Conclusions” section estimates the overall market effect.
Although demand and utilization are distinct concepts, the predicted decline in the demand provides an upper-bound for the predicted decline in utilization, assuming an upward-sloped supply curve.
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Acknowledgements
I thank Gabriel Picone, Andrei Barbos, Padmaja Ayyagari, Haiyan Liu, Abolfazl Saghafi, Art Goldsmith, Patralekha Ukil, and the seminar participants at the Southern Economic Association, University of South Florida, and University of Alaska Anchorage. I am also grateful to editor Guy David for his support, and to him and two anonymous referees for constructive comments that substantially improved this work.
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Appendices
Appendix 1: alternative Wilcoxon test
See Table
8.
Appendix 2: normalized trends of utilization
The monthly number of births depends on the number of days in the month. Further, the birth rate is seasonal throughout the year. Figure
Normalized utilization trends adjusted for seasonality and the number of days in the month
9 is the equivalent of Fig. 3 Panel C that is normalized with respect to the number of days in each month (by scaling all months to 30 days) and further normalized to even out the seasonality.
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Rabbani, M. Non-profit hospital mergers: the effect on healthcare costs and utilization. Int J Health Econ Manag. 21, 427–455 (2021). https://doi.org/10.1007/s10754-021-09303-8
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DOI: https://doi.org/10.1007/s10754-021-09303-8
Keywords
- Mergers and acquisitions
- Non-profit hospital
- Healthcare cost and utilization
- Federal Trade Commission
- Market power