What Does Willingness-to-Pay Reveal About Hospital Market Power in Merger Cases?

Fournier, Gary M.; Gai, Yunwei

doi:10.2139/ssrn.993213

Cited by 10 publications

(16 citation statements)

References 16 publications

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“…Payment is calculated according to the formula [(work RVU) * (work GPCI) + (Practice Expense RVU) * (Practice Expense GPCI) + (Malpractice RVU) * (Malpractice GPCI)] * [Conversion factor] where the conversion factor is a dollar amount used to convert RVUs to physician payment. 10 Furthermore, we note that this assumption is consistent with subsequent studies that make us of the Capps et al (2003) model (Fournier and Gai 2007;Farrell et al 2011) and that Gaynor et al (2013) find that mergers estimated using the assumptions of Capps et al (2003) produce estimates of price increases due to merger that are close to those that would be implied using other economic models of hospital competition. 11 It is important to note that a limitation of this approach is that it assumes that the WTP-per-quantity for Medicare patients is a reasonable proxy for the WTP-per-quantity unit for private patients.…”

Section: Datasupporting

confidence: 77%

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Market power and provider consolidation in physician markets

Kleiner

White

Lyons

2015

Int J Health Econ Manag.

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Physician services comprise a substantial share of total health care spending, and the price of health care services has been cited as a key contributor to the disproportionately high rate of health care spending in the US. However, despite a large literature analyzing market power in the hospital and insurance industries, less is known about the extent to which physicians exercise market power. In this study we make use of a private health insurance claims data set to analyze physician market power for two specialties within three mid-sized US metropolitan areas. Using a method developed for hospital competition analysis, we estimate measures of consumer willingness-to-pay for physician practices within each of these markets and relate these to the prices paid to these practices for a set of physician services. Our results are suggestive of the presence of market power in the markets that we analyze. We simulate physician practice mergers for the two largest practices in each market for each specialty analyzed. Results suggest that practice mergers could result in price increases deemed significant by antitrust authorities in some markets but not in others.

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Section: Datasupporting

confidence: 77%

“…The Herfindahl-Hirschman Index (HHI) is defined as the sum of the squared market shares times 10,000.2 Fournier and Gai (2007) and AkosaAntwi et al (2009) find that these models are reasonable predictors of the price increases that occur for actual hospital mergers.…”

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confidence: 99%

Market power and provider consolidation in physician markets

Kleiner

White

Lyons

2015

Int J Health Econ Manag.

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“…Brand and Balan () conduct a Monte Carlo‐like exercise in which they compare the predictions of various merger screens (including WTP) against data produced by a bargaining model of the negotiations between hospitals and health insurance companies, and find that diversion ratios, WTP changes, and merger simulation produce accurate predictions of post‐merger price changes simulated by the bargaining model. Although this finding implies the new hospital merger screening tools are theoretically sound, the evidence from Fournier and Gai () and May and Noether () comparing the predictions of the WTP screen against actual post‐merger price changes is mixed and limited to a meta‐sample of only three mergers. Apart from May and Noether () and Fournier and Gai (), there has been little research on the accuracy and reliability of WTP, LOCI, UPP, diversion ratios, and hospital merger simulation in predicting the price effects of actual hospital mergers…”

Section: Literature and Case Reviewmentioning

confidence: 98%

“…Although this finding implies the new hospital merger screening tools are theoretically sound, the evidence from Fournier and Gai () and May and Noether () comparing the predictions of the WTP screen against actual post‐merger price changes is mixed and limited to a meta‐sample of only three mergers. Apart from May and Noether () and Fournier and Gai (), there has been little research on the accuracy and reliability of WTP, LOCI, UPP, diversion ratios, and hospital merger simulation in predicting the price effects of actual hospital mergers…”

Section: Literature and Case Reviewmentioning

confidence: 98%

“…Four recent articles have explored the accuracy of the most widely used of the new hospital merger screening tools, willingness‐to‐pay (WTP). Fournier and Gai () find that post‐merger WTP changes estimated using pre‐merger data are accurate predictors of actual post‐merger WTP changes that occurred after two hospital mergers. For one of these mergers, they also find that the price change implied by a WTP‐based merger simulation using pre‐merger data produced a conservative estimate of the actual post‐merger price change.…”

Section: Literature and Case Reviewmentioning

confidence: 99%

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The accuracy of hospital merger screening methods

Garmon

2017

The RAND J of Economics

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This article analyzes the accuracy of various prospective hospital merger screening methods used by antitrust agencies and the courts. The predictions of the screening methods calculated with pre‐merger data are compared with the actual post‐merger price changes of 28 hospital mergers measured relative to controls. The evaluated screening methods include traditional structural measures (e.g., Herfindahl‐Hirschman Index), measures derived from hospital competition models (e.g., diversion ratios, willingness‐to‐pay, and upward pricing pressure), and hospital merger simulation. Willingness‐to‐pay and upward pricing pressure are found to be more accurate at flagging potentially anticompetitive mergers for further investigation than traditional methods.

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Is There a Medicaid Penalty? The Effect of Hospitals' Medicaid Population on Their Private Payer Market Share

Sfekas

2012

Health Economics

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This study examines whether privately insured patients avoid hospitals with large Medicaid populations. I use a conditional logit model of hospital choice to determine whether the size of a hospital's Medicaid population affects the probability that a privately insured patient will choose that hospital. I focus on the metropolitan area of Tampa, Florida, in the years 1994-1996. I control for hospital fixed effects, hospital-specific time trends, patients' driving time to the hospital, and interactions between patient and hospital characteristics. I also instrument for the Medicaid population using the predicted Medicaid population. The results show that privately insured patients are less likely to choose a hospital if it served a larger number of Medicaid patients who were admitted through the emergency department in the previous 6 months. The effect persists over time-an additional 6-month lag cuts the effect in half. Capacity constraints do not seem to be the reason for the effect. I show that the Medicaid effect size could have a moderate effect on the profits of some hospitals. Although limited in scope, this study suggests that hospitals may experience a negative effect on their private revenues when they admit a large population of Medicaid patients.

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What Does Willingness-to-Pay Reveal About Hospital Market Power in Merger Cases?

Cited by 10 publications

References 16 publications

Market power and provider consolidation in physician markets

Market power and provider consolidation in physician markets

The accuracy of hospital merger screening methods

Is There a Medicaid Penalty? The Effect of Hospitals' Medicaid Population on Their Private Payer Market Share

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