The cost of running a hospital in New York City is much higher than running a hospital in Bozeman, Montana. To take into account these cost differences, the Centers for Medicare and Medicaid Services (CMS) has created a wage index to adjust the inpatient prospective payment system (IPPS) for differences in labor costs.
However, the U.S. isn’t the only country where public health agencies adjust payments based on labor costs. For the past 30 years, England’s Staff Market Forces Factor (MFF) adjusts National Health Service (NHS) payments for medical care. The MFF’s origin began in a1976 report from the Resource Allocation Working Party (RAWP). Although the goal of the MFF is to control for geographic variation in input costs, labor costs make up 65% of these input costs. Although drug and equipment costs also make up 26% of input costs, the prices of these goods are fairly constant across all English regions. A paper by Elliot et al. (2010) investigates the construction of the labor portion of MFF in more detail.
The MFF is calculated based on standardized spatial wage differentials (SSWDs). These SSWDs in essence calculates the difference in labor input costs for each region compared to the national average. The paper divides the country into regions through three different mechanisms: a region in one of three ways: 303 primary-care trusts (PCTs), 354 local authority districts (LADs) and 207 travel-to-work areas (TTWAs). LADs and PCTs are administrative areas while TTWAs are intended to constitute largely self-contained labor markets based on commuting patterns. Using these three definitions, the authors calculate the SSWD from the Annual Survey of Hours and Earnings (ASHE) as:
- ln(wij)=xijβprivate + vjprivate + εij
- ln(wij)=xijβNHS + vjNHS + εij
The first equation is used to measure wage differentials for a variety of workers whereas the last only examines NHS nurses. The variable xij contains information on age, age-squared, gender, year dummies, industry dummies and occupational dummies. The fixed effect variable vj measures the difference in log wages from in region j from the national mean. In the case of the NHS regression, year and occupational dummies are removed because nurses constitute working in a single industry.
To calculate the MFF for area j, the authors impose a log-to-level wage transformation for the variable vj and normalize this differential based on the national mean.
- MFFj=100*exp[vjprivate]/exp[J-1 (Σj vjprivate)]
The authors also conduct estimate regional variation in labor costs for doctors. Because the ASHE sample of NHS doctors is too small to estimate robust SSWDs, the authors instead obtain data on the annual financial returns of NHS trusts through the Department of Health.
How well are these adjustments working? To answer this question, the authors examine how the differential between private and NHS pay affect the vacancy rate for NHS positions for doctors and nurses. When private pay is higher than NHS pay, the authors find that the nurse vacancy rate increases. This makes sense since when the private sector pays more, nurses will be more likely to take jobs outside the NHS. On the other hand, when private sector pay for doctors is higher, the NHS vacancy rate for physicians is lower. This seems counterintuitive that physicians would be attracted to lower paying NHS areas. One explanation is that areas with relatively less generous NHS pay have higher private sector pay. Thus, these physicians can take the NHS job, but also spend part of his time working for higher private-sector pay. Using this information, the authors conclude that “The case for additional funding in high-cost low-amenity areas to employ doctors is not supported by this analysis. The MFF adjustment in the NHS funding formula should be amended to reflect this.”
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