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International Journal of Health Economics and Management

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23.11.2017 | Research Article

Medicare hospital payment adjustments and nursing wages

verfasst von: Peter McHenry, Jennifer Mellor

Erschienen in: International Journal of Health Economics and Management | Ausgabe 2/2018

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Abstract

Despite the importance of the nursing profession for healthcare delivery, costs, and quality, there is relatively little research on how provider payments to hospitals affect the labor market for nurses. This study deals with the hospital wage index (HWI) adjustment to Medicare hospital payments, an area-level adjustment intended to compensate hospitals in high-cost labor markets. Since the HWI adjustment is based on hospital-reported labor costs, some argue that it incentivizes hospitals in concentrated markets to pay higher wages to nurses and other workers (the “circularity” critique). We investigate this critique using market-level data on the relative wages reported by nurses and hospital-level data on the average hourly wage for healthcare workers. For identification, we exploit a 2005 change in the geographic area used to define labor markets, which resulted in exogenous changes in the ability of some hospitals to influence their area’s wage index. We find that worker-reported relative nurse wages and hospital-reported healthcare worker wages are higher in some locations where hospitals experienced increased opportunities to game the circularity of the wage index, but these effects appear to be driven by pre-existing wage growth. Medicare’s HWI adjustment method does not appear to suffer from inefficiency due to circularity.

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Some related evidence comes from a study of 21 hospitals selected for an audit because they were large employers within their CBSAs, among other factors. Most hospitals overstated average hourly wages on their Medicare cost reports, some by as much as 21% (OIG 2007, p. 4).

See, for example, Nicholson and Song (2001), Lindrooth et al. (2006), Acemoglu and Finkelstein (2008), and Kaestner and Guardado (2008) on the effects of Medicare payment on hospitals’ use of nurse labor, among other outcomes. Relatedly, Konetzka et al. (2004) examine nurse staffing in skilled nursing facilities.

Hospitals typically treat a mix of Medicare and non-Medicare patients; care provided to non-Medicare patients may be reimbursed by other payers (i.e., Medicaid, private insurance), or paid out-of-pocket by some uninsured patients, or uncompensated in certain cases (charity care and bad debt).

Some hospitals are exempted from the IPPS. For example, Maryland hospitals are paid under that state’s all-payer rate setting system. Hospitals designated as “Critical Access Hospitals” are reimbursed based on the costs of the care they provide to Medicare patients.

Medicare may adjust the HWI for certain groups of hospitals (IOM 2012, p. 86). The most common adjustment is reclassification of a hospital to a different labor market area with a higher HWI. Such adjustments are made at the request of an individual hospital and are reviewed and approved by the Medicare Geographic Classification Review Board (MGCRB). We discuss the potential impact of reclassification in the description of our empirical methods.

Authors’ calculations from the 2004 and 2005 CMS Impact Files; counts exclude Puerto Rico.

Clemens and Gottlieb (2014) uses the price shock created by Medicare’s consolidation of physician payment areas in 1997 to identify the effect of physician payment on treatment decisions.

Some studies find that nurse wages are higher in more concentrated hospital markets, which is consistent with classic monopsony (Hurd 1973; Link and Landon 1975; Feldman and Scheffler 1982; Bruggink et al. 1985). However, alternative empirical approaches yield differing degrees of support for monopsony power (Adamache and Sloan 1982; Hirsch and Schumacher 1995, 2005; Currie et al. 2005; Staiger et al. 2010). It is possible that gaming of the HWI adjustment process contributes to the weak evidence about monopsony power in the nurse labor market. Since hospitals in highly concentrated markets have a greater ability to influence the area-level HWI, market concentration might induce upward pressure on nurse wages, and prior studies thus may yield attenuated estimates of monopsony power.

First, we measure HHI among the subset of IPPS hospitals, since only their wage data are used to construct the HWI. Second, we use two-stage least squares models to identify changes in HHI driven by the change from MSAs to CBSAs. Third, we interact changes in the HHI with a measure of Medicare’s importance to hospitals. We expect that if the wage index process creates circularity, then changes in the HHI will have a larger effect on wages for markets or hospitals where Medicare is more important to revenues.

In the pre-period, therefore, we must assign MSA-level HHI values to each CBSA-level relative nurse wage; this is described below.

ACS data from 2006 are too close to the policy change given that wage data are retrospective, and the 2005 ACS is the first to include the PUMA (location) variable required for our analysis.

8.5% of the nurses in our sample are male; we include these observations to increase the number of nurses and the precision of our estimates of \(\varphi _{k}\). We control for sex in Eq. (3).

Healthcare-related occupations are defined as: medical scientists; physicians; dentists; optometrists; podiatrists; other health and therapy; pharmacists; dietitians and nutritionists; respiratory therapists; occupational therapists; physical therapists; speech therapists; therapists, not elsewhere classified; physicians’ assistants; psychologists; social workers; clinical laboratory technologists and technicians; dental hygienists; health record tech specialists; radiologic tech specialists; licensed practical nurses; health technologists and technicians, not elsewhere classified; biological technicians; private household cleaners and servants; guards, watchmen, doorkeepers; protective services, not elsewhere classified; dental assistants; health aides, except nursing; nursing aides, orderlies, and attendants; dental laboratory and medical appliance technicians.

A PUMA is a place (often following county or Census-defined “place” borders) including at least 100,000 residents.

The annual survey by the American Hospital Association (AHA) does not collect data on nurse wages, only employment. The Occupational Mix Survey, a survey that most hospitals are required to complete every three years, does not contain wage data prior to 2006, and our identification strategy requires data before 2005. Finally, the HWI itself is a poor choice for a dependent variable since, by design, the HWI will vary with hospital market composition. Consider a hospital in a small urbanized area that is defined as part of the large MSA in 2000 and then as a small CBSA in 2007. In both 2000 and 2007, wages in the hospital’s immediate urbanized area are higher than those in the rest of the MSA. The hospital’s HHI and HWI would both clearly increase between 2000 and 2007. However, this mechanical correlation over time has nothing to do with circularity (the hospital raising wages in response to the increased HHI).

We include the CBSA-level shares of residents who are black, Hispanic, American Indian/Alaskan native, Asian and/or Pacific Islander, or another race. The omitted category is the share of residents who are white non-Hispanic. For education, we include the CBSA-level shares of residents that have some college education, an associate’s degree, a bachelor’s degree, and a master’s degree or more. The omitted category is the share of residents whose highest level of education is high school or less. In the case of marital status, we include the CBSA-level shares of residents who are: (1) divorced, separated, or widowed; and (2) never married. The omitted category is the share of residents who are married.

The AHA data are proprietary and we have access only to the 1999 and 2009 data for this project. In the 2009 data, we identify CAHs using the last four digits of the hospital’s Medicare Provider Number or MPN, following ResDAC (2016). In the 1999 data, we identify CAH hospitals using a list obtained from the Flex Monitoring Team, a university consortium that studies issues affecting rural hospitals. This list includes Critical Access Hospitals as of January 2004, the earliest date available.

We believe that the ability to request reclassification does not remove the incentive for hospitals in highly-concentrated markets to increase nurse wages. There is uncertainty about whether such requests will be approved or denied. A hospital’s application must demonstrate that its wages exceed those paid by other hospitals in the market to which it was geographically assigned and are comparable to the higher-paying hospitals in the market to which it seeks assignment. Using data from fiscal year 2007, which is within the time period of our analysis, Dalton et al. (2007) calculate that 23% of all IPPS hospitals experienced labor market reclassifications (p. 17).

Of these 419 CBSAs, 320 experienced increases in HHI; the mean and maximum increases are 0.184 units and 0.98 units respectively. By comparison, 79 CBSAs experienced decreases in HHI, and the mean and maximum decreases are \(-\,0.044\) units and \(-\,0.415\) units respectively. The remaining 20 unchanged CBSAs had an HHI of 1 in both periods.

As described in section “Assigning MSA-level HHI measures to 2000 CBSA-level relative nurse wages” of the “Data Appendix”, when assigning values of the MSA-level HHI to each CBSA-level relative nurse wage in 2000, we use a crosswalk between a CBSA and a single MSA. In each pairing of CBSA to MSA, we determine the fraction of the CBSA population that resided in the single MSA to which it is matched. The column 3 models exclude a small number of CBSAs that matched to MSAs where less than 75% of the population resided.

These are calculated using the same Impact File data used to calculate the HHI measures, and in the same way—using the MSA in 2000 (matched to CBSA-level nurse wages based on population overlaps) and the CBSA in 2008.

The median is 0.2065 which is near the classification of a “highly concentrated” market in horizontal merger considerations (U.S. Department of Justice and FTC 2010).

Appendix Table 7 reports sample means. The first-stage results from 2SLS models are reported in Appendix Table 8.

Results are available upon request.

Such perceptions are illustrated by the case of a Nantucket hospital and the IPPS “rural floor” provision. Each hospital’s HWI must be at least as high as the HWI for rural hospitals in the same state (the rural floor). The Nantucket hospital’s conversion to the IPPS system increased hospital reimbursements elsewhere in Massachusetts by raising the state’s rural floor. Since the HWI adjustment is budget-neutral, the windfall to Massachusetts’ hospitals reduced reimbursements to hospitals in other states, which then advocated for policy changes.

Specifically, we have \(940-574+48-11+29-5+2=429\) CBSAs.

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Titel: Medicare hospital payment adjustments and nursing wages
verfasst von: Peter McHenry
Jennifer Mellor
Publikationsdatum: 23.11.2017
Verlag: Springer US
Erschienen in: International Journal of Health Economics and Management / Ausgabe 2/2018
Print ISSN: 2199-9023
Elektronische ISSN: 2199-9031
DOI: https://doi.org/10.1007/s10754-017-9232-x

Springer Medizin

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