Kathleen Dalton

Gregory C. Pope

Walter Adamache

Brian Dulisse

Nathan West

RTI International

1440 Main Street, Suite 310

Waltham, MA 02451–1623

MedPAC

601 New Jersey Avenue, NW

Suite 9000

Washington, DC 20001

(202) 220-3700

Fax: (202) 220-3759

www.medpac.gov

•

•

The views expressed in this report

are those of the authors.

No endorsement by MedPAC

is intended or should be inferred.

Potential Refinements

to Medicare’s Wage 

Indexes for Hospitals 

A study conducted by staff from RTI International 

for the Medicare Payment Advisory Commission

June 2007 • No. 07–3

and Other Sectors
Potential Refinements to Medicare’s 

Wage Indexes for Hospitals 

and Other Sectors 

Final Report 

Prepared for 

Medicare Payment Advisory Commission 

601 New Jersey Avenue, NW 

Suite 9000 

Washington, DC 20001 

Prepared by 

Kathleen Dalton, Ph.D. 

Gregory C. Pope, M.S. 

Walter Adamache, Ph.D. 

Brian Dulisse, Ph.D. 

Nathan West, M.P.A. 

RTI International 

1440 Main Street, Suite 310 

Waltham, MA 02451–1623 

RTI Project Number 0210572.001 
CONTENTS 

EXECUTIVE SUMMARY .............................................................................................................1 

ES.1 Scope of This Report .....................................................................................................1 

ES.2 Organization of This Report ..........................................................................................1 

ES.3 Summary of Key Findings.............................................................................................2 

1.0 HISTORY OF THE CURRENT CMS INPATIENT PROSPECTIVE PAYMENT 

SYSTEM HOSPITAL WAGE INDEX..................................................................................5 

1.1 Background....................................................................................................................5 

1.2 Hourly Wage Data Sources............................................................................................7 

1.2.1 Original BLS Data ................................................................................................7 

1.2.2 CMS Wage Surveys..............................................................................................8 

1.2.3 Changes in the Computation of PPS Hourly Wage ..............................................9 

1.3 Labor Markets..............................................................................................................10 

1.3.1 Definitions...........................................................................................................10 

1.3.2 Geographic Reclassification ...............................................................................11 

1.4 Occupation Mix Adjustment........................................................................................14 

1.5 Other Adjustments .......................................................................................................14 

1.5.1 Rural Floors ........................................................................................................15 

1.5.2 Out–Migration Adjustments ...............................................................................15 

1.6 Wage Index Implementation as of FY 2007................................................................16 

1.7 Summary Comparison: BLS Versus CMS Source Data..............................................16 

2.0 SELECTED ISSUES IN ALTERNATIVE APPROACHES TO HOSPITAL WAGE 

INDEXES .............................................................................................................................22 

2.1 Theory of Wage Indexes..............................................................................................22 

2.1.1 Hospital Cost Functions and Wage Indexes .......................................................22 

2.1.2 Medicare IPPS Wage Index................................................................................24 

2.2 Hospital–Specific Versus All–Industry Wages ...........................................................26 

2.2.1 Theory of Competitive Wage Setting .................................................................27 

2.2.2 Theory of Inter–Industry Wage Differentials .....................................................28 

2.2.3 Implications for the Hospital Wage Index..........................................................29 

2.2.4 Evidence on Inter–Industry Wage Differentials .................................................30 

2.2.5 Wages by Hospitals and All Industries for Hospital Wage Index 

Occupations.........................................................................................................31 

2.3 Employment Composition of the Hospital Wage Index Occupations.........................34 

2.4 Evidence on Factors Affecting the Hospital Average Wage .......................................36 

2.5 Arguments For and Against the Use of Hospital–Specific Versus All–Industry 

Wage Data....................................................................................................................37 

2.5.1 Arguments for Using Hospital–Specific Wage Data in the IPPS Hospital 

Wage Index .........................................................................................................38 

i 
2.5.2 Arguments for Using All–Industry Wage Data in the IPPS Hospital 

Wage Index .........................................................................................................38 

3.0 AREA WAGE INDEXES IN OTHER MEDICARE PROSPECTIVE PAYMENT 

SYSTEMS.............................................................................................................................40 

3.1 Other PPS Settings Using Hospital Relative Wages....................................................40 

3.2 Labor Market Comparability and Other Issues............................................................41 

4.0 DATA QUALITY AND VOLATILITY ANALYSIS OF CURRENT AND 

ALTERNATIVE WAGE INDEXES....................................................................................42 

4.1 Overview......................................................................................................................42 

4.2 Methods........................................................................................................................42 

4.2.1 Data Sources and Timing....................................................................................42 

4.2.2 Wage Index Areas for Comparison.....................................................................43 

4.2.3 BLS Wage Index Construction ...........................................................................44 

4.2.4 CMS Wage Index Construction..........................................................................46 

4.2.5 Measures of Volatility.........................................................................................47 

4.3 Volatility of the Published CMS Wage Index Versus the BLS Wage Iindex .............47 

4.3.1 Distribution of Index Values for Each Year, 1999–2004 ...................................47 

4.3.2 Distribution of Annual Percent Changes in Index Values, 2000–2004..............49 

4.3.3 Distribution of Annual Absolute Percent Changes in Index Values, 

2000–2004...........................................................................................................51 

4.3.4 Distribution of Average Annual Absolute Percent Changes in Index 

Values, 2000–2004 .............................................................................................53 

4.3.5 Distribution of Standard Deviations of Wage Index Values, 2000–2004 ..........53 

4.4 Sources of Wage Index Volatility and Data Quality Issues.........................................53 

4.4.1 Random Variation and MSA Size.......................................................................57 

4.4.2 Volatility in Wages, Benefits, and Total Compensation from CMS Wage 

Survey Data.........................................................................................................57 

4.4.3 Computational and Data Quality Issues in the BLS Series.................................60 

4.4.4 Is Volatility a Useful Gauge for Data Quality? ..................................................62 

4.5 Variation in Hospital Benefits Over Time and Geography .........................................62 

4.5.1 CMS Benefits Data .............................................................................................63 

4.5.2 Variation in Benefits as Percent of Total Hourly Compensation .......................63 

REFERENCES ..............................................................................................................................68 

ii 
List of Figures

Figure 1 Temporal Trends in Benefits as Reported on the Medicare Cost Report 

Worksheet S–3......................................................................................................... 64 

Figure 2 Geographic Variations in Benefits as Reported on the Medicare Cost Report 

Worksheet S–3 (FY 2004 survey only) .................................................................. 65 

Figure 3 Benefits as a Share of Total Compensation, 1993–2006, According to BLS 

Health Services and Hospital Occupations, National .............................................. 66 

Figure 4 Potential Regional Bias from Excluding Benefits in the Wage Index 

Computations ........................................................................................................... 67 

List of Tables 

Table 1 Legislative History of the Hospital Area Wage Index............................................... 6 

Table 2 Summary of Exceptions and Adjustments to the Hospital Area Wage Index as 

Implemented for FY 2007 IPPS............................................................................... 17 

Table 3 Hospital Area Wage Index Chronology, by Year of Implementation ..................... 18 

Table 4 Summary Differences between BLS and CMS Source Data................................... 21 

Table 5 National Hospital and All–Industry Wages and Employment by MedPAC 

Hospital Wage Index Occupations, May 2005 ........................................................ 32 

Table 6 National Employment and Wages for Selected Industries and Occupations........... 34 

Table 7 Composition of MedPAC's BLS Hospital Wage Index by Industry and 

Occupation Employment Proportions, in Percents .................................................. 36 

Table 8 Use of the Hospital Area Wage Index in Other Medicare Prospective Payment 

Systems .................................................................................................................... 40 

Table 9 General Hospital Employment–Share Weights for Key Occupations, 

1999–2004................................................................................................................ 45 

Table 10 CMS IPPS Area Wage Index Distributions for MSAs with BLS RN Wage Data, 

1999–2004................................................................................................................ 48 

Table 11 BLS Area Wage Index Distributions for MSAs with RN Wage Data, 

1999–2004................................................................................................................ 49 

Table 12 Distribution of Annual Percent Changes in CMS's IPPS Wage Indexes for MSAs 

that have BLS RN Wage Data, 2000–2004 ............................................................. 50 

Table 13 Distribution of Annual Percent Changes in the BLS Area Wage Indexes for 

MSAs That Have RN Wage Data, 2000–2004 ....................................................... 51 

Table 14 Distribution of Annual Absolute Percent Changes in CMS's IPPS Wage Indexes 

and the BLS Wage Index for MSAs with BLS RN Wage Data, 2000–2004 .......... 52 

Table 15 Distribution of Mean Annual Absolute Percent Change in Wage Indexes, for 

MSAs with BLS RN Wage Data, 2000–2004 ......................................................... 54 

Table 16 Distribution of the Standard Deviation of CMS's IPPS Wage Index for MSAs 

with BLS RN Wage Data, 1999–2004..................................................................... 55 

Table 17 Distribution of the Standard Deviation of the BLS Wage Index for MSAs with 

RN Wage Data, 1999–2004 ..................................................................................... 56 

Table 18 Ten MSAs with Largest and Smallest Wage Index Standard Deviations, 

1999–2004................................................................................................................ 58 

Table 19 Characteristics of Ten MSAs with Largest and Smallest Wage Index Standard 

Deviations, 1999–2004 ............................................................................................ 59 

iii 
Table 20 Distribution of annual percent changes in indexes of hospital wages, benefits, 

and total compensation, 2000–2004 ........................................................................ 60 

Table 21 Benefits as Percent of Hospital Total Hourly Compensation, by Year and 

Location, as Reported on the Medicare Cost Reports 

(market–level mean values) ..................................................................................... 64 

iv 
EXECUTIVE SUMMARY 

ES.1 Scope of This Report 

As part of its examination of alternatives to the current Medicare area hospital wage 

index adjustment, the Medicare Payment Advisory Commission (MedPAC) contracted with RTI 

International to analyze certain issues in the current wage index and its proposed alternative 

wage index. RTI's scope of work for this contract included the following: 

• Document the history of the current wage index and the resulting exceptions. 

• Present the theoretical arguments for and against the current wage index and the 

proposed alternative. 

• Document what wage indexes are used in the other Medicare prospective payment 

systems and the advantages and disadvantages of using the pre–floor, pre–

reclassification hospital wage index for other settings. 

• Analyze the quality and characteristics of the data underlying the current and 

alternative wage indexes (including the volatility of both indexes, and the volatility of 

indexes of wages only, benefits only, and total compensation). 

• Identify trends and regional variation in the ratio of benefits to total compensation. 

ES.2 Organization of This Report 

This report is organized in four sections. In Section 1, we provide a review of the 

legislative and regulatory history of wage index. We trace major CMS regulatory and 

congressionally mandated changes that were implemented between 1984 and 2007, including: 

the history of the wage data sources; the processes implemented for assigning hospitals to labor 

markets; attempts to standardize the wage index for occupation mix differences; and remaining 

special exceptions and adjustments that have been imposed over the years. Also in Section 1, we 

use hospital–level data from the FY 2007 final wage index implementation to identify the 

number of facilities eligible and/or covered under initial rules, reclassifications, exceptions and 

other adjustments. 

In Section 2 we discuss selected issues in the two alternative approaches to constructing 

the Medicare hospital wage index, using the CMS survey of hospital wages or BLS all–industry 

wage data. These include how Medicare’s and MedPAC's approaches are related to theoretical 

wage indexes; the theory and empirical evidence on competitive wage setting and inter–industry 

wage differentials, and implications for the hospital wage index; empirical analysis using BLS 

data of national differences in wages by industry; the industry composition of the MedPAC BLS 

wage index; a summary of earlier research RTI staff did for CMS on the factors that impact 

average hospital wages; and arguments for and against the use of hospital–specific versus all–

industry wage data in the hospital wage index. 

1 
In Section 3, we discuss issues with using the area hospital wage index to adjust 

payments in several other institutional payment settings such as skilled nursing facilities and 

inpatient rehabilitation facilities. 

In Section 4, we compare the volatility of wage indexes based on the CMS hospital wage 

survey data and an index constructed from the BLS all–industry wages for hospital occupations. 

We discuss possible sources of volatility and data quality issues, including a comparison of the 

volatility of indexes of wages, benefits, and total compensation. Section 4 also contains results 

on geographic and temporal variation in benefits as a proportion of total compensation. 

ES.3 Summary of Key Findings 

Section 1

• Nearly four of every ten PPS hospitals has some sort of exception. Thirty–nine 

percent of all rural facilities and 17 percent of all urban facilities have some sort of 

reclassification. Ten percent of urban providers are located in MSAs subject to the 

rural floor provisions or to the imputed rural floors. Outmigration adjustments 

increase the index values for two percent of urban PPS providers (133 hospitals) but 

nine percent of rural ones (90 hospitals). The size of the adjustments averages 1.3 

percent across qualifying urban counties but 3.4 percent across qualifying rural ones. 

Section 2

• The use of the Laspeyres (fixed weight) functional form for the hospital wage index is 

reasonable. 

• With competitive labor markets, the area all–industry wage for an occupation should 

accurately represent the area cost of labor to hospitals. 

• Inter–industry wage differentials have been observed, but the reasons for their 

existence, and whether they are consistent with competitive wage setting theories, are 

controversial. 

• On a national basis, hospital wages by occupation are similar to all–industry wages. 

But hospitals pay different wages than some other specific industries nationally. 

• The MedPAC BLS wage index can be interpreted as a blend of a hospital–specific 

wage index (one–third weight) and a non–hospital wage index (two–thirds weight). 

• Many factors have been shown to affect area hospital average wages, including area 

opportunity wages; area hospital–specific wages; hospital size, casemix, occupation 

mix, and unionization; and the competitiveness of the area labor market. 

• The major argument for using hospital–specific wages in the Medicare hospital wage 

index is to reflect appropriate hospital–specific labor–market factors. The major 

arguments for using all–industry wages are to avoid circularity in payment, avoid 

2 
noncompetitive and idiosyncratic factors in hospital wage setting, and measure wages 

more accurately using a broader sample of workers. 

Section 3

• The major drawbacks to using acute care hospital relative wages as adjusters for non–

hospital health care payments are that (1) acute care area wage differences may be 

influenced by differences in occupation mix that are not applicable to other settings; 

(2) there are many markets, including even some state rural markets, where the 

hospital index values are computed from data submitted by only one or two facilities; 

and (3) non–hospital providers to which the hospital wage index is applied may be 

located in counties without hospitals, hence their wage index is based on wage data 

that are not specific to the area in which they are located. 

Section 4

• A wage index constructed from BLS all–industry wage data is somewhat less volatile 

than a published wage index based on the CMS survey of hospital wages. 

– For approximately 250 MSAs from 2000 to 2004, 1.3 percent of MSA/year 

changes in the CMS wage index were 10 percent or greater, compared to 0.9 

percent for the BLS index. Similarly, 8.8 percent of the CMS changes were 5 to 

10 percent, while only 4.8 percent of the BLS changes were as large. Over 80 

percent of the BLS index MSA/year changes were smaller than 2.5 percent, 

compared to less than 65 percent of the CMS index changes. 

– The average annual absolute percentage change for approximately 250 MSAs 

from 2000 to 2004 was 2.3 percent for the CMS index versus 1.7 percent for the 

BLS index. 

– The average standard deviation of MSA wage indexes from 1999 to 2004 was 1.8 

percent for the index based on BLS data versus 2.4 percent for the index based on 

CMS wage survey data. 

• The inclusion of benefits in the CMS wage index does not explain why the CMS 

wage index is more volatile than the BLS wage index (which does not include 

benefits). 

• The hospital Medicare Cost Report data show a clear increase in reported benefits as 

a percentage of total compensation from 1999 to 2004, that occurred in both rural and 

urban areas, and across all regions. Across markets, the mean value of benefits as a 

proportion of total hourly compensation rose from 16.7 percent in 1999 to 19.7 in 

2004. 

• The BLS Employer Cost for Employee Compensation series shows a slight increase 

in benefits on a share of total compensation for hospital workers between 1993 and 

2004. The BLS series also shows an increase in the share of benefits after 2004. 

3 
• In 2004, benefits were a lower percentage of total compensation in the South than in 

other regions according to the Medicare Cost Report hospital survey data. Excluding 

benefits from the Medicare hospital wage index would therefore overstate relative 

total compensation costs in the South relative to other regions (i.e., would benefit the 

South at the expense of other regions). 

4 
5 

1.0 HISTORY OF THE CURRENT CMS INPATIENT PROSPECTIVE PAYMENT 

SYSTEM HOSPITAL WAGE INDEX 

1.1 Background 

The hospital area wage index is used to adjust institutional payment rates for geographic 

variation in labor costs under several of Medicare’s prospective payment systems (PPS). The 

area wage index has been a part of the inpatient PPS rate structure since that payment system 

was first implemented in 1983. Since 1997 the hospital wage index has also been applied to PPS 

payments for hospital outpatient services, home health agencies, skilled nursing facilities, 

inpatient psychiatric facilities, inpatient rehabilitation facilities and long–term acute care 

hospitals. 

Section 1886 (d) (3) (E) of the Social Security Act requires that, as part of the method for 

determining prospective payments to hospitals, the Secretary must adjust standardized payment 

amounts “for area differences in hospital wage levels by a factor reflecting the relative hospital 

wage level in the geographic area of the hospital compared to the national average hospital wage 

level”. Area wage adjustments to prospective rates are required to be budget neutral, such that 

total national payments after applying the wage index are the same as they would be in the 

absence of a wage index. 

Although the Secretary was initially given broad discretion with respect both to the type 

and source of data used for wage standardization and to the definition of geographic areas, 

Congress has intervened on several occasions to direct specific modifications or exceptions to 

computation and application of the wage index. While the data sources have changed several 

times over the past two decades, the geographic areas (or “labor markets”) used in the 

construction of the area wage index have always been defined on the basis of metropolitan 

statistical areas (MSAs) as established by the Office of Management and Budget (OMB), with 

state–level aggregates of non–metropolitan areas used to define rural markets. Over time, 

however, several exceptions have been developed for alternate market assignments. 

Under each prospective payment system the wage index is used to adjust a portion of the 

base payment rate per unit of service that is referred to as the “labor–related share” of payments. 

The labor related share is a sector–specific factor also set by the Secretary and can range from as 

low as 46 percent in ambulatory surgery centers to nearly 80 percent for nursing homes. Over the 

years the wage index values for specific markets have ranged from lows close to 0.70 to highs of 

over 1.60. With such a potentially powerful influence over the distribution of Medicare 

payments, the index came under close scrutiny in the early years following hospital PPS 

implementation, and again in the late 1990s through the early 2000s following PPS expansion 

into other care settings. Several evaluations of the wage index were conducted for and by CMS 

and the Prospective Payment Assessment Commission (ProPAC) (Cromwell, Hendricks and 

Pope 1986; ProPAC 1987, 1991, 1993; Williams, Pettengill, and Lisk 1990; Wright and Marlor 

1990; Williams 1991). The index is perceived as having a powerful effect on the distribution of 

Medicare payments between rural and urban areas, and has also come under close scrutiny by 

rural policy analysts (Dalton and Slifkin, 2002, Wellever 2000). As documented by the 

legislative history in Table 1, Congress has often intervened to alter the area wage adjustments 
under prospective payment, mandating changes in the source data, in the index computation 

methods and in the rules governing labor market assignments. 

Table 1 

Legislative History of the Hospital Area Wage Index 

Legislative Act Section/P.L. No. Effect on Wage Index 

Social Security Act of 1983 P.L. 98–21 

Section 1886(d)(3)(E) 

Initial inpatient PPS legislation. Established a wage index to adjust 

the standardized amounts “for area differences in hospital wage 

levels by a factor (established by the Secretary) reflecting the 

relative hospital wage level in the geographic area of the hospital 

compared to the national average hospital wage level.” 

Title VI of the Social Security 

Amendments of 1983 

Section 601 (H) Legislated a change in the labor–related share of standardized 

payment to be adjusted by wage index, and directed Secretary to 

make periodic updates 

Deficit Reduction Act of 1984 P.L. 98–369 

Section 2316 

Directed Secretary to develop new wage index controlling for full 

and part–time wages (effectively mandating move from BLS to 

CMS wage data–see Table 4 for a comparison of the old 1984 BLS 

survey and the new BLS survey that MedPAC is proposing). 

Consolidated Omnibus Budget 

Reconciliation Act of 1985 

P.L. 99–272 

Section 9103 

Delayed implementation of index from 1982 survey data 

Omnibus Budget Reconciliation 

Act of 1987 (OBRA 1987) 

P.L. 100–203 

Section 4005(a) 

Introduced “deemed urban” counties (new section 1886(d)(8)(B)) 

for counties adjacent to MSAs meeting certain worker commuting 

patterns (“Lugar counties”). 

Technical and Miscellaneous 

Revenue Act of 1988 

P.L. 100–647 

Section 8403(a) 

Directed the wage index for those rural counties with hospitals that 

were deemed urban and that are affected by the recomputation 

from P.L. 100–203 to be calculated separately. (“Deemed urban” 

counties otherwise resulted in the reduction of the wage index 

values for several MSAs and rural areas). 

Omnibus Budget Reconciliation 

Act of 1989 (OBRA 1989) 

P.L. 101–239 

Section 6003(h) 

Created the Medicare Geographic Classification Review Board 

(MGCRB) and set for criteria for reclassification of hospitals. 

Required that wage indexes be updated annually beginning FY 

1994 (after October 1, 1993). 

Limited the potential negative impact from reclassifications to 1 

percent or less, in market to which facilities are reclassified. 

Omnibus Budget Reconciliation 

Act of 1990 (OBRA 1990) 

P.L. 101–508 

Section 4002(h) 

Limited the effects of redesignation on index values for non–

redesignated facilities in urban markets being reclassified into and 

for markets being reclassified out of. Index values for all rural 

markets cannot be reduced by reclassification 

Omnibus Budget Reconciliation 

Act of 1993 

P.L. 103–66 

Section 13501(b) 

Prevented a reduction in the wage index for an urban area where 

index value is below the state rural value 

Medicare, Medicaid, and Children's 

Health Provisions of the Balanced 

Budget Act of 1997 (BBA) 

P.L. 105–33 

Section 4410 

Establishes rural floors (urban index values to be set no lower than 

state rural value) 

Balanced Budget Refinement Act 

of 1999 (BBRA) 

P.L. 106–113 

Section 401 

Permitted an urban hospital to apply to be treated as being located 

in the rural area of a State in which the hospital is located (urban to 

rural reclassification). 

Medicare, Medicaid, and SCHIP 

Benefits Improvement and 

Protection Act of 2000 (BIPA) 

P.L. 106–554 

Section 304 

Mandates occupational mix data collection and implementation of 

adjustment to the hospital wage index beginning October 1, 2004. 

Allows a statewide entity to apply for reclassification for a 

statewide wage index. 

Permits an urban hospital, separate from the MGCRB, to apply to 

be treated as being in the rural area of the State 

Medicare Prescription Drug, 

Improvement, and Modernization 

Act of 2003 (MMA) 

P.L. 108–173 

Section 505; 

Section 508 

“Wage Index Reclassification Reform:” 

508 exemptions; 

Outmigration adjustments; 

One–time appeals process for MGCRB decision 

The Tax Relief and Health Care Act 

of 2006 

P.L. 109–432 

Section 106 

Extended Section 508 reclassifications 

Mandated MedPAC study of alternative wage indexes 

Directed Secretary to implement changes in wage index 

SOURCE: RTI Synopsis of Federal Registers.

6 
In the following sections we provide a review of the legislative and regulatory history of 

wage index. We trace major CMS regulatory and congressionally mandated changes that were 

implemented between 1984 and 2007. At the end of the section, Table 3 also provides a 

chronologic summary in table form. The section is organized as follows: Section 1.2 describes 

the history of the wage data sources; Section 1.3 follows the processes implemented for 

assigning hospitals to labor markets; Section 1.4 discusses attempts to standardize the wage 

index for occupation mix differences; and Section 1.5 describes remaining special exceptions 

and adjustments that have been imposed over the years. Finally in Section 1.6, we use hospital–

level data from the FY 2007 final wage index implementation to place the history of the wage 

index in context by identifying the number of facilities eligible and/or covered under initial rules, 

reclassifications, exceptions and other adjustments. 

1.2 Hourly Wage Data Sources 

1.2.1 Original BLS Data 

The initial hospital wage index was created from data supplied by the Bureau of Labor 

Statistics (BLS) using its 1981 Employment, Wages, and Contribution File (Federal Register, 

1983, p. 39765). The BLS series reported average wages per hospital worker, taken from the 

quarterly reports filed to state employment security agencies by all non–federal hospitals. 

Quarterly data on total covered hospital wages and total covered workers were reported at the 

county level, aggregated to CMS’ defined labor markets and used to compute aggregate average 

wages paid per covered worker. The index was constructed by dividing market aggregate 

average wages per worker by the national mean wage per worker, where the national mean wage 

was an unweighted average across labor markets. This index adjusted hospital prospective 

payments in federal years (FY) 1984 and 1985. 

While the BLS data represented the best national and regional data available at the time, 

technical limitations of this data were recognized from the start. These included the following: 

• The series measures average wage per worker without distinguishing between full–

time and part–time employees. Thus, the average hourly wage rate would be 

underestimated in geographic areas more likely to rely on part–time labor, and vice–

versa. 

• The data were tied to wages and workers covered by unemployment insurance. They 

were also subject to distortion by short–term fluctuations caused by natural disasters, 

strikes or retro–active adjustments. 

• The data measured aggregate wages without adjustment for differences in skill mix. 

Therefore the index values reflected market–level variation in both the price level and 

the mix of occupations. 

• The data did not include benefits or other wage–related costs that are also part of total 

compensation. 

Congress attempted to address some of these problems in the Deficit Reduction Act of 

1984 (P.L. 98–369). Section 2816 of this law required CMS (then HCFA) to develop a new data 

7 
collection tool for use in constructing a hospital wage index, specifically identifying the need to 

control for part–time employment and to explore ways to adjust for occupation mix differences. 

1.2.2 CMS Wage Surveys 

1982 Wage Data

CMS developed a survey for all Medicare participating hospitals that collected data on 

wages and paid hours of employment known as the HCFA Wage Index Survey. The first HCFA 

survey was conducted in 1984 and collected data from FY 1982, to be used for adjusting 

payments in FY 1986. 

The major improvements of the new wage data were that they measured average wage 

per hour rather than per worker, thus controlling for part–time workers and eliminating 

distortions from non–recurring changes in total employment. The index was constructed in a 

similar manner to the construction from the BLS data, using aggregate average hourly wages for 

each labor market divided by a national average hourly wage that was an unweighted mean 

across all labor markets (50 FR 35661, September 3, 1985). 

Implementation of the new index was delayed from October 1, 1985 to May 1, 1986 by 

the Consolidated Omnibus Budget Reconciliation Act of 1985 (P.L. 99–272, Section 9103). The 

index was in effect for the remainder of FY 1986 and all of FY 1987. 

1984 Wage Data

As part of their audit activities in FY 1986, CMS instructed its intermediaries to collect 

the same wage survey data from providers’ 1984 records (51 FR 31499, September 3, 1986) . 

This information was to be used for the index to adjust payments in FY 1988. A separate effort 

was undertaken to gather 1986 wage data by occupational category, but hospitals were given 

very little time to respond to the survey and many claimed they did not have reliable wage data 

by occupation. Only one–third of the hospitals responded to the survey, and collection of 

occupation–specific wage data was subsequently abandoned (Pope and Adamache, 1993). 

To cushion large market–level changes, CMS used a blend of 1982 and 1984 wage data 

to construct the index applicable to payments in FY 1988 and FY 1989 (Federal Registers 

September 1, 1987 and May 8, 1989, p. 19646). Also at that time, the national average wage 

used to normalize the index was changed to the aggregate average (i.e. sum of all wages divided 

by the sum of all hours) rather than the unweighted average across markets.1 An index was 

constructed from 1984 survey data alone was not used for payments in FY 1990 (Federal 

Register September 1, 1989, p. 36475). 

 

1 This change was made because the unweighted national average was sensitive to wage corrections and area redesignations, which tended to make the index unstable. Changing the normalization factor does not alter the 

market relative values but it does alter the actual level of the index; to maintain budget neutrality, the federal 

payment amount had to be re-standardized in FY 1988 (52 FR 22102). 

8 
1988 Wage Data

A new wage survey was fielded in 1990 that collected data from providers’ 1988 records 

to serve as the basis for adjusting payments in FY 1991. The new survey incorporated several 

data changes including the addition of fringe benefits, home office costs and limited types of 

contract labor. It also added lines to identify and exclude wages and hours associated with 

nursing units and other non–hospital activities. Data on contract labor were later excluded from 

the computation due to problems in reporting paid contract hours. Because of the remaining 

changes and the long period elapsed between wage surveys (1984 to 1988), a one year transition 

was initiated for those labor markets where the index value would have changed by more than 

eight percent (Federal Register, September 4 1990 p 36041). However, when Congress delayed 

implementation of the 1988 survey until January 1991, CMS eliminated the one year phase–in 

(Federal Register, January 7, 1991, 562). The wage index from the 1988 survey continued to be 

used for IPPS payments throughout FY 1992 and 1993. 

Medicare Cost Report (MCR) Wage Data

The time lag between data collection and application to payments was thought to present 

problems for hospitals in some regions, particularly those experiencing rapid wage increases for 

clinical personnel (Wellever 2000). Congress addressed the issue of data timeliness in PL 101–

239 (OBRA 1989), by directing CMS to update the index annually beginning in FY 1994. CMS 

incorporated worksheets into the annual Medicare cost report (Worksheet S3 Parts II and III), as 

the source for annual updates on wages and paid hours (Federal Register, September 1, 1992 

p.39746). The first MCR wage data were from the FY 1990 cost reports and were used to 

construct the index for FY 1994 payments. New wage index computations have been made 

annually since that year. Although incorporating the wage data into the cost report solved the 

problem of regularity in the updates, it did not improve the timeliness of the wage index, as the 

four–year lag between the data year and the index year has remained in effect. The lag is due to 

the length of the cost reporting cycle and the need to allow time for review and corrections. 

1.2.3 Changes in the Computation of PPS Hourly Wage 

The rules for computing the average hourly wage applicable to IPPS activities have been 

modified several times since the survey was first incorporated into the annual cost report. After 

having to exclude contract labor as reported in the 1988 survey due to incomplete or inaccurate 

data (Federal Register, September 1, 1992 p.39746), CMS revised and clarified the rules for 

reporting contracted labor for the worksheets in the FY 1990 cost report. An audit of the data 

was conducted and CMS found it to be “reasonable” and the costs for contract services directly 

related to patient care were included in the average hourly wage calculations beginning in FY 

1994 (Federal Register, May 26, 1993 p 30222).2 Benefits—primarily payroll taxes, health 

 

2 Including contract costs was a particular concern of rural providers during this time, because it was thought that 

rural hospitals made a disproportionate use of professional contract labor in response to skilled worker shortages 

(Size, 1992). Excluding higher-paid contract labor would therefore have the effect of artificially lowering 

average hourly wages in rural markets. Also in response to rural concerns, CMS began to include contract costs 

and hours of chief executive officers and other high-level administrators hired under certain management 

contracts, beginning in FY 1995 (Federal Register, May 27, 1994 p27708). 

9 
insurance and pension costs—were incorporated into the 1988 survey but multiple clarifications 

of what constitutes allowable wage related costs have been issued since that time. 

The definition of allowable wages and hours has also been modified to allow the average 

hourly wage to reflect those amounts associated with activities included under IPPS –DRG 

payments only. The 1988 survey first excluded data for SNF and long–term care units. In 

subsequent years exclusions were expanded to other non–IPPS areas (home health agencies, 

inpatient rehabilitation and psychiatric units, federally–qualified clinics) and then incorporated 

lines to track wages and hours for general administrative and hospital support services in order to 

pro–rate these according to the ratio of IPPS and non–IPPS salaries. In 2000, the survey began 

adjusting for wages related to teaching physicians and medical residents, and after a five–year 

phase out, CMS removed these components to the hourly wage computation by 2004 (64 FR 

41508). 

1.3 Labor Markets 

1.3.1 Definitions 

“Area wage variation” is the variation of relative wages across hospital labor markets. 

CMS has always defined labor markets based on metropolitan county classifications of the 

Office of Management and Budget (OMB). From FY 1984 to FY 2004 these were primarily 

based on Metropolitan Statistical Areas (MSAs), with a few instances of MSAs further grouped 

into Primary Metropolitan Statistical Areas (PMSAs) or New England County Metropolitan 

Areas (NECMAs); non–metropolitan counties grouped into single state–level rural labor 

markets. In 2003, OMB used the 2000 census data to revise its metropolitan statistical area 

definitions and announced new Core–Based Statistical Areas (CBSAs). OMB defines a CBSA as 

a county or group of counties that form “a geographic entity associated with at least one core of 

10,000 or more population, plus adjacent territory that has a high degree of social and economic 

integration with the core as measured by commuting ties”. CBSAs identify metropolitan areas 

(still referred to as MSAs) that are similar to the MSAs under the old classification system (at 

least one core of 50,000 or more population), but also identify a new category called 

“micropolitan areas” that include counties or small groups of counties with at least one core 

having 10,000–50,000 population. CMS adopted the new CBSA metropolitan areas as labor 

markets effective with the wage index applicable to FY 2005, but continued to group 

micropolitan counties with other rural counties to form state–wide rural markets 3 (69 FR 

49026). 

Because some counties previously included in MSAs became micropolitan, several 

hospitals that had been classified as urban became rural. CMS provided a “hold harmless’ 

transition period for these facilities that allowed them to maintain their assignment to the MSA 

where they were previously located for three years, from FY 2005 – FY 2007. Beginning in 

2008, these hospitals will receive their statewide rural wage index, although they will be eligible 

to apply for reclassification during or after the transition period (71 FR 48007). Hospitals located 

in all other areas that experienced a reduction in their wage index as a result of the change from 

 

3 In a few very large areas labor markets use a subdivided category called Metropolitan Divisions. 

10 
MSAs to CBSAs were allowed a two–year phase–in of the new index (i.e., a 50–50 blend in 

2005, and the full new index in 2006). Hospitals in areas where CBSA changes caused an 

increase in the index were paid using that increased index. 

1.3.2 Geographic Reclassification 

MSAs are aggregates of counties based primarily on population size and commuting 

patterns. From the outset there were numerous critics of the use of aggregated geo–political units 

to define economic markets, particularly among providers in the state–level rural markets, where 

average wages tended to be low. In the late 1980s some argued in favor of a regional wage index 

to correct for perceived underpayments to rural hospitals located near or adjacent to urban areas, 

but CMS concluded that a regional wage index would not substantively improve these problems 

and would lead to excessive intra–market variation (Federal Register September 1, 1989, p.

36476). ProPAC had studied intra–market variation in wages and confirmed that variation was in 

part due to differences in skill mix, competition, and staffing practices among hospitals (ProPAC 

1986). For labor market boundaries with large differences in index values, there was concern that 

the wage index could unfairly penalize hospitals that competed for workers within the same labor 

pool. In 1987 and 1988 Congress intervened through the Omnibus Budget Reconciliation Act of 

1987 (P.L. 100–203, later amended by P.L. 100–360) by allowing the Secretary to treat some 

counties as urban for wage index purposes if they met certain criteria regarding commuting 

patterns to adjacent MSAs. These exceptions are referred to as “Lugar counties,” and they 

created hospitals that were “deemed urban”, effective October 1988 (FY 1989). However, the 

exceptions created instability in the average wage values for the adjacent MSAs and the original 

rural markets, and subsequent attempts to fix this (P.L. 100–647) inadvertently reduced the 

benefit of reclassification for some of the very counties that the exceptions were intending to 

help. The Omnibus Budget Reconciliation Act of 1989 (P.L. 101–239) addressed some of these 

problems by creating a mid–year change (effective for discharges after April 1, 1990) that 

limited the negative impact of redesignated hospitals or counties on existing MSA index values. 

In P.L. 101–239 Congress also responded to industry requests for a better labor market 

assignments exceptions process by establishing the Medicare Geographic Classification Review 

Board (MGCRB). Interim Final Rules for geographic reclassification were published in 

September of 1990 (55 FR 36754) and finalized in June of 1991 (56 FR 25458). Beginning in FY 

1991, hospitals meeting certain criteria could apply for reclassification from their geographically 

assigned labor market to a neighboring labor market to be effective during the following 

payment year, if the hospital met certain qualifications with respect to geographic proximity and 

contiguity, and wage comparability. In some situations entire counties are allowed to seek re–

designation from one market to another. The rules also limited the impact that reclassifications 

could have on the individual index values for markets to or from which the reclassifications were 

being made (although the overall impact of reclassifications on PPS payments must be made 

budget neutral). 

In its first year of operation, the MGCRB approved 90% of the requests it received, and 

as a result 930 primarily rural hospitals were reclassified in FY 1992, including twenty–eight 

percent of all rural PPS providers. This large number of reclassifications was not expected, and 

to meet statutory budget neutrality requirements the standardized payment amounts for urban 

areas had to be reduced by 1.1 percent. In September 1992 CMS restricted the criteria for 

11 
eligibility in the applications that were to become effective in FY 1994. Under the first year of 

these rules the number of reclassified hospitals declined by approximately 60 percent (Federal 

Register, May 27, 1994). 

While the MGCRB reclassifications benefited some hospitals, ProPAC and some others 

in the provider community argued for an adoption of new labor market area definitions to 

address the fundamental flaws to any system based on geo–political boundary lines. In 1993, 

ProPAC recommended defining hospital–specific labor market areas based on geographic 

proximity by using a “nearest neighbor” principle. This method suggested computing local 

average wages using an average for all facilities located within a certain fixed radius of each PPS 

hospital (ProPAC 1993). CMS conducted its own analysis of the nearest neighbor proposal in 

1994 and requested public comment on the approach in May of 1994 (59 FR 27724), but finding 

no strong support within the industry for the change, subsequently decided to not adopt it (60 FR 

29219). At that time the agency stated its belief that a classification system that combined an 

MSA–based approach with hospital–specific exceptions had “considerable potential for 

improving the wage index”. 

Hospitals may qualify for reclassification to an adjacent labor market area if they meet 

certain criteria relating to geographic proximity and wage cost comparability. Since 2002, these 

have been the following: 

• If the hospital is rural, proximity is defined as location within 35 miles of the border 

of the market to which reclassification is requested. If the hospital is urban, the 

definition is within 15 miles. Alternatively, proximity conditions can be met if 50 

percent of the hospital’s workers live in the market to which reclassification is being 

requested. 

• Hospitals must demonstrate that their hourly wages are comparable to the average 

wages of the market to which they are requesting reclassification. If the requesting 

hospital is rural, comparability is defined as a hospital hourly wage no less than 82 

percent of the hourly wages in the market to which they are requesting 

reclassification, and no less than 106 percent of the average wage of all other 

hospitals in their geographic market. If the requesting hospital is urban, the thresholds 

are 84 percent and 108 percent, respectively. 

• If the hospital is or has ever been classified as a rural referral center (RRC), it is not 

required to meet the 106 percent criterion. 

• If the hospital is a sole community hospital (SCH) or an RRC, it is not required to 

meet the proximity criteria. 

• Further special exceptions are allowed for SCHs in low population density areas to 

reclassify to any MSA in their state. 

Reclassification requests were originally granted for one year only, but began to be 

effective for three year periods in FY 2003. Wage comparability tests since FY 2003 have been 

based on rolling 3–year average wages published by CMS. 

12 
Under certain circumstances county–level or even state–level reclassifications can be 

granted, provided all hospitals in the group agree. Reclassifications are permitted from one rural 

market to another, from one urban market to another, and from a rural market to an urban 

market. Reclassifications from urban markets to rural markets were not originally permitted, but 

were added under Section 401 of the BBRA of 1999, directing the Secretary to treat qualifying 

urban hospitals as though they are located in the rural area of their States (FR 65 47087). Section 

401 hospitals therefore qualify for other PPS payment exceptions applicable to rural facilities, 

including sole community or rural referral center status and special exceptions and hold–

harmless provisions under the hospital outpatient PPS. 

In late 2003, Section 508 of the MMA (P.L.108–173) added a provision that allowed the 

Secretary to exercise discretion in granting one–time special appeals on selected reclassification 

requests that did not otherwise meet MGCRB criteria. Section 508 reclassifications were 

originally applicable to discharges occurring between April 1, 2004 and March 31, 2007, but 

were extended to the close of FY 2007 by the Tax Relief and Health Care Act of 2006. 

Reclassifying hospitals from one market to another alters the average wages of both the 

sending and the receiving markets.4 MGCRB implementing regulations limit the impact of this 

on the final wage index. For markets that hospitals are reclassifying out of

• Index values for rural markets are held harmless; index values are computed using the 

higher of average wages based on all facilities geographically located in that market, 

or all facilities assigned to that market post–reclassification. 

• Index values for urban markets are computed without the data from reclassified 

facilities. However, if all facilities in an urban market are reclassified out of that 

market, the published value for that market is equal to the pre–reclassified index 

value. Index values for urban markets could not be reduced to a value that was lower 

than the index for the state rural market. 

For markets that hospitals are reclassifying in to

• If the index value for the markets declines by less than one percent as a result of 

adding data from the newly reclassified hospitals, then the index is computed 

excluding data from the reclassifying hospitals and the index applies to 

geographically located as well as newly reclassified hospitals. 

• If the index value for the markets declines by one percent or more as a result of 

adding data from the newly reclassified hospitals, then an index value from the new 

combined average wage is computed, but it is applicable only to the reclassifying 

facilities. For hospitals geographically located in that market, the index is based on 

average wages computed from data for facilities geographically located in, and not 

seeking reclassification out of, that market. 

 

4 Reclassifying hospitals tend to be at the upper end of the distribution of average wages in the market they are 

leaving, but at the lower end of the distribution in the market into which they are requesting reclassification. 

Thus in many circumstances, reclassification leads to a reduction in average wages for both markets. 

13 
1.4 Occupation Mix Adjustment 

Following the failure of the 1986 special survey on occupation mix, CMS expressed a 

willingness to revisit the issue at some point in the future to take occupational mix into account 

in calculating the wage index (Federal Register, September 1, 1987, p.33041). Although CMS 

announced its intention to collect occupational–mix adjustment data two years later in the PPS 

Final Rules published in September of 1989, by 1991 occupational–mix adjustments had been 

reviewed again and ruled out “at that time” (Federal Register, June 3, 1991, p. 25192). The 

reasons cited included (1) the administrative burden on hospitals was heavy, (2) the definition of 

the optimal mix of occupations was not known (3) the desirability (or equity impact) of 

redistributing PPS payments was not clear, and (4) that the impact of standardizing relative 

wages for occupation mix differences across markets may have been overstated (Wellever 2000). 

The issue of occupational mix adjustment was then reintroduced by Congress when the 

Benefits Improvement and Protection Act of 2000 (BIPA) was passed (PL 106–554). The 

legislation mandated that the hospital wage index be adjusted to reflect the occupational mix of 

employees, and ordered CMS to collect data on the occupational mix of employees for each 

hospital no less frequently than every three years. 

CMS’ initial occupational mix survey following this legislation occurred in early 2003, 

collecting data also from a period spanning 2001 to 2003. Paid hours were collected on 19 

occupation groups representing just over one–half of hospital employment. These were used to 

construct a set of adjustment factors for covered occupations that would standardize the wage 

index applicable to payments in FY 2005 through FY 2007 (non–covered occupations were 

effectively given an occupation weight of one). Due to a number of concerns about data quality, 

CMS decided to implement a blended index using only 10% occupation mix–adjusted and 90% 

unadjusted values (Federal Register, August 11, 2004). In addition, CMS stated they wanted to 

move cautiously with occupational mix adjustment in recognition of changing trends in the 

hiring of nurses, the largest group in the occupational mix survey. The second “triennial” 

occupation mix survey occurred in 2006, and in this survey CMS expanded data collection for 

additional categories of nursing employment but dropped the data collection for non–nursing 

occupation groups. 

The nursing occupation mix survey was fielded from January to June of 2006, with the 

intention of being used to develop nursing–only occupation mix adjustment factors for the wage 

index applied to payments from FY 2008 through FY 2010. In April 2006, in Bellevue Hosp. 

Center v. Leavitt, the Court of Appeals for the Second Circuit ordered CMS to apply the 

occupational mix adjustment to 100 percent of the wage index effective for FY 2007. Rather than 

use a 100% adjustment from the 2003 survey, CMS chose to use the first 3 months of data 

collected from the 2006 survey to calculate a new “nursing only” occupational mix adjustment 

(71 FR 47870, August 18, 2006). This final index affected payments for discharges starting 

October 1 (71 FR 59886, October 11, 2006). The full 6 months of data will be used to construct 

occupation mix adjustment factors for the FY 2008 wage index. 

1.5 Other Adjustments 

Two other major adjustments to the hospital wage index are allowed under IPPS. One is 

referred to as the “rural floor,” and the other is the “out–migration adjustment.” 

14 
1.5.1 Rural Floors 

Section 4410 of the BBA (P.L. 105–33) provided that for discharges occurring on or after 

October 1, 1997, the area wage index applicable to any hospital that is located in an urban area of 

a state may not be less than the area wage index applicable to hospitals located in rural areas in 

the state (69 FR 49109). Commonly referred to as the “rural floor”, this provision is actually an 

urban floor, in that it sets a minimum index value for any urban labor index that is equal to the 

rural index value for the state where that MSA is located. “Rural floors” are distinguished from 

“urban to rural” reclassifications in that they do not re–group hospitals into different labor 

markets for purposes of re–computing average market wages. Rural floors do not change 

anything about the computation of the wage index, but do alter the assignment of specific wage 

index values. Hospitals benefiting from the rural floor provisions receive the higher rural wage 

index value, but do not alter their status as urban hospitals. 

The rural floor provision could not be applied in the two States where there were no non–

metropolitan areas (New Jersey and Rhode Island). Hospitals in those two States commented that 

the absence of a rural floor disadvantaged them for wage index purposes compared to hospitals 

where the rural floor provision applied (69 FR 28291), contending that they would have higher 

wage indexes if there were a rural area in their State to set a floor under the wage indexes within 

that State. CMS agreed with these hospitals and subsequently developed an “imputed floor” for 

the two all–urban States (based on the ratio of the lowest to highest index values in states that did 

have applicable rural floors). CMS went further with this to also impute a floor for 

Massachusetts, which had geographic rural areas but no IPPS hospitals actually classified as 

rural. Despite some objections in the public comments, CMS adopted a policy of imputing 

hypothetical rural floors for these three states, but limited the effective period to 3 years (FYs 

2005, 2006, and 2007) so that CMS could monitor the policy change (69 FR 49110). 

1.5.2 Out–Migration Adjustments 

Section 505 of the MMA provided for an increase in the wage index for certain hospitals 

located in counties that have a high percentage of residents that commute for hospital 

employment in neighboring higher–wage MSAs. The Secretary set a commuting (or “out–

migration”) threshold of ten percent for counties to qualify for the adjustment, based on 

commuting patterns as identified from decennial census data. In addition, hospitals located in 

qualifying counties must have average wages that are higher than the average for the labor 

market in which they are located. By statute hospitals that are “deemed urban”, hospitals that are 

reclassified through the MGCRB, and hospitals that are reclassified under the terms of Section 

508 of the MMA cannot receive this adjustment. 

Qualifying counties are identified for three–year periods (commuting patterns are 

measured only every ten years, but because qualification is a function of commuting percentages 

and relative wages, a change in the index could trigger a change in eligibility). Qualifying 

hospitals located in qualifying counties receive an increase in the wage index that is equal to an 

average of the difference between the wage index for the resident county and the higher wage 

index for the work area county(s), weighted by the overall percentage of workers who are 

employed in an area with a higher wage index. When this adjustment was first implemented in 

FY 2005, 230 counties met the commuting criteria for the adjustment (69 FR 49064, August 11, 

2004). There were 415 PPS hospitals located within these counties and of these, 181 were not 

15 
eligible because they were already reclassified. Of the remaining 234 hospitals, automatic 

positive adjustments to the wage index were processed for those with hourly wages greater than 

the pre–reclassified market average. 

The out–migration adjustment is unusual in that is not required to be budget neutral. At 

the individual hospital level the adjustments can be substantial, with some counties qualifying for 

increases of more than ten percent. However, due to the relatively small size of the qualifying 

counties and the small number of hospitals that qualify, CMS estimated that the impact on total 

hospital payments would be minimal, with an overall impact of 0.1 percent. 

1.6 Wage Index Implementation as of FY 2007 

Most of the adjustments, exceptions and market reclassification opportunities have been 

added cumulatively to the wage index, such that a substantial portion of PPS facilities operate 

under one or another of them. Table 2 identifies rural and urban providers that are paid under 

IPPS in FY 2007 and are therefore subject to the wage index. Nearly four of every ten PPS 

hospitals has some sort of exception. Thirty–nine percent of all rural facilities and 17 percent of 

all urban facilities have some sort of reclassification. Ten percent of urban providers are located 

in MSAs subject to the rural floor provisions or to the imputed rural floors. Outmigration 

adjustments increase the index values for two percent of urban PPS providers (133 hospitals) but 

nine percent of rural ones (90 hospitals). The size of the adjustments averages 1.3 percent across 

qualifying urban counties but 3.4 percent across qualifying rural ones. 

1.7 Summary Comparison: BLS Versus CMS Source Data 

MedPAC's proposed alternative approach to computing a wage index is recommending 

that CMS return to the BLS as the source for area wage data. However, both the BLS data that 

MedPAC is recommending and its proposed new approach to computing the index are very 

different from the original BLS index used by CMS for the first two years of inpatient PPS. 

Where the original BLS series (as well as the subsequent CMS wage data) were hospital–specific 

averages of wages for all occupations, the Occupation Employment Statistics (OES) data now 

proposed are all–industry measures of average wages by occupation. Further, where the original 

data measured average wages per worker without adjustment for part–time status or occupation 

mix, the proposed BLS data come from surveys of average wages per hour, with adjustment for 

occupation mix based on national employment weights for short–term general hospitals. Table 4

presents the main differences between BLS data as originally used in the first PPS wage indexes, 

the CMS data as developed over the last twenty years under PPS, and the BLS OES data as now 

proposed for an alternative approach to area wage adjustment. 

16 
Table 2 

Summary of Exceptions and Adjustments to the Hospital Area Wage Index as 

Implemented for FY 2007 IPPS 

Provider Counts and Percents 

Urban Rural Total 

Total inpatient PPS providers (FY 2007 Impact File) 2,590 100% 1,005 100% 3,595 100%

Labor market reclassifications: 

 Lugar Counties (“deemed urban”, since 1988) 49 2% 49 1% 

 MGCRB decisions, standard criteria 280 11% 358 36% 638 18% 

 MGCRB decisions (MMA “Section 508” special appeals) 81 3% 27 3% 108 3% 

 Urban to Rural (BIPA “Section 401” providers) 30 1% * 30 1% 

 “Special Exceptions” by the Secretary (see note) 13 1% 5 0.5% 18 1% 

Subtotal, all reclassified providers 453 17% 390 39% 843 23% 

Out–migration adjustments (MMA “Section 505”) 133 2% 91 9% 224 6% 

 Mean value of adjustments: 

 Minimum 

 Maximum 

1.3% 

0.07% 

5.6% 

3.2% 

0.07% 

12.4% 

2.1% 

0.07% 

12.4% 

Rural floor index assigned 

 Original: providers in states with rural markets 216 8% ** 216 6% 

 Imputed: providers in “all urban states 40 2% 40 1% 

“Hold–Harmless” providers from MSA–to–CBSA change 

(now in rural but formerly in metropolitan markets, allowed to 

retain former metropolitan designation for three years, FY 2005–

2007) 

46 5% 46 1% 

Summary Count: 

at least one exception to assigned labor market or computed 

wage index: 842 33% 481 47% 1323 37% 

* Five providers in this category were identified in the CMS impact file as rural (presumed error; placed in urban column) 

** Two providers in this category were identified in the CMS impact file as rural (presumed error; placed in urban column) 

NOTE: “Special exceptions” are reclassifications allowed at the discretion of the Secretary for certain providers that had 

previously qualified under rules for group (county–wide) reclassifications, where statutory changes related to other PPS 

provisions would otherwise have disqualified these providers from reclassification. These exceptions were first implemented in 

FY 2005 (see 69 FR 49104–49105). 

SOURCES: RTI analysis of FY 2007 Impact File as published October 2006, supplemented by Tables 4A–1, 4B–1, 4C–1, 4F, 

4J, 9A, 9B and 9C published in FR 71– 59975 to 60040. Rural floor providers were identified by table forwarded from CMS. 

17 
Table 3 

Hospital Area Wage Index Chronology, by Year of Implementation 

Payment 

Fiscal Year 

Wage Data 

Source 

Data Definitions 

and Adjustments 

Labor Market 

Definitions 

Labor Market 

Exceptions 

Other Adjustments 

and Exceptions 

1984 BLS Market aggregate average 

hospital wage/worker; 

national = unweighted 

average across areas 

MSAs or state 

non–metro 

None None 

1985 BLS Same Same None None 

1986 1982 HCFA Wage 

Index Survey 

(HWIS) 

Market aggregate average 

hospital hourly wage; 

national = unweighted 

average across areas 

Same None None 

1987 50/50 blend of 1982 

and 1984 HWIS1

Market aggregate average 

hospital hourly wage; 

national = aggregate average 

hourly wage 

Same None None 

1988 Blend of 1982 and 

1984 HWIS 

Same Same 

1989 Same Same Same “Lugar” Counties 

(hospitals deemed 

urban based on 

commuting patterns 

to adjacent MSA) 

Index re–computed 

for affected MSAs 

and rural areas as 

though deemed 

hospitals were 

located in adjacent 

MSA 

1990 1984 HWIS Same Same “Lugar” Counties Index assignments 

for “deemed urban” 

counties set at higher 

of county–specific or 

rural rate; rural areas 

held harmless from 

any reduction 

1991 1988 HWIS Excluded data from non–

acute hospital areas; 

included fringe benefits and 

better editing. 

100% 1988 data 

implemented as of 1/1/91 

(proposed phase–in of 1988 

survey data for areas with 

large shifts was dropped 

following mandated delay of 

implementation date). 

Same “Lugar” Counties Limited negative 

impact of 

reclassification (on 

facilities originally 

located in urban 

markets) to 1 percent 

reduction. 

1992 1988 HWIS Same Same “Lugar” Counties 

First year of 

MGCRB 

reclassifications 

implemented for 

rural–to–rural, 

rural–to–urban and 

urban–to–urban 

changes (930 

hospitals) 

Same 

(continued) 

18 
Table 3 (continued) 

Hospital Area Wage Index Chronology, by Year of Implementation 

Payment 

Fiscal Year 

Wage Data 

Source 

Data Definitions 

and Adjustments 

Labor Market 

Definitions 

Labor Market 

Exceptions 

Other Adjustments 

and Exceptions 

1993 1988 HWIS Same Reclassification 

criteria tightened 

Lugar Counties 

2nd year of MGCRB 

reclassifications 

Same 

1994 1990 Medicare Cost 

Reports (MCR) 

Allowed wages and hours for 

professional contract 

services if directly related to 

patient care. 

Same Lugar Counties 

3rd year of MGCRB 

reclassifications; 

number approved 

reduced by 60 

percent under 

revised eligibility 

criteria 

Same 

1995 1991 MCR Allowed wages and hours for 

contracted managers 

Same Same Same 

1996 1992 MCR Same Same Same Same 

1997 1993 MCR Same Same Same Same 

1998 1994 MCR Same Same Same BBA adds “Rural 

floors” where urban 

WI values cannot be 

lower than state rural 

values 

1999 1995 MCR Allowed wages and hours for 

contract physician Part A 

costs 

Same BBRA Section 401 

allows urban–to– 

rural 

reclassifications 

Same 

2000 1996 MCR Began a five–year phase–out 

of wages and hours for 

teaching physicians, medical 

residents and nurse 

anesthetists 

Same 441 total hospitals 

were re–declassified 

hospitals 

Same 

2001 1997 MCR Same Same Same Same 

2002 1998 MCR Same Same Same Same 

2003 1999 MCR 2003 Occupational Mix data 

collected 

Same MGCRB decisions 

become effective for 

three years 

Same 

2004 2000 MCR Same MMA adds “Section 

508” reclassification 

decision appeals 

permitted 

MMA adds “Section 

505” out–migration 

adjustments to wage 

index for qualifying 

counties 

MMA places upper 

limit of 62% on 

labor–related share of 

standardized rate 

topped at 62% for 

markets with WI 

below 1.00. 

(continued) 

19 
Table 3 (continued) 

Hospital Area Wage Index Chronology, by Year of Implementation 

Payment 

Fiscal Year 

Wage Data 

Source 

Data Definitions 

and Adjustments 

Labor Market 

Definitions 

Labor Market 

Exceptions 

Other Adjustments 

and Exceptions 

2005 2001 MCR, 

adjusted by 10% 

2003 Occupation 

Mix data 

Occupation Mix Adjustment 

(OMA) implemented using 

10% of computed adjustment 

factors 

CBSA–based 

definitions of 

metropolitan areas; 

counties newly 

classified as non–

metropolitan retain 

prior metropolitan 

assignment for 3 

years (“hold 

harmless”). 

2–year phase–in for 

any markets with 

reduced wage index 

due to CBSA –

related reassignment. 

“Imputed rural 

floors” added for all–

urban states 

Occupation Mix 

Adjustment (OMA) 

implemented using 

10% of computed 

adjustment factors 

2006 2002 MCR, 

adjusted by 10% 

2003 Occupation 

Mix data 

Continued 10% OMA 

implementation 

Same 

2007 2003 MCR, 

adjusted by 100% 

2006 Occupation 

Mix data 

Court order overrules 10% 

phase–in; CMS implements 

100% OMA from 2006 one 

year ahead of schedule 

Same CMS re–assigns 

hospital wage index 

exceptions and 

reclassifications 

based on highest 

payments after 

occupation–mix 

revision 

SOURCE: RTI analysis of Federal Registers and other sources. 

20 
Table 4 

Summary Differences between BLS and CMS Source Data 

Original BLS wage data 

(used for payments in 

FY 1984 and 1985) 

CMS wage data 

(used for payments in 

FY 1986 and later) Proposed new BLS wage data 

Data: 

Employment Series 202 for SIC 206 (hospitals). 

(Calendar 1981 data series) 

Data: 

HCFA Wage index Surveys (periodic, earliest 

1982 latest 1988), used for payments from FY 

1986 through FY 1993 

 

Medicare cost report Worksheet S–3 (starting 

FY 1990 data), used for payments from FY 

1994 on. 

Data: 

Occupation Employment Statistics (OES); all–

industry average hourly wages by state and/or 

metropolitan area, for key hospital occupations as 

reported in Industry–Specific Occupational 

Employment & Wage Estimates for NAICS 

622100 (General Medical and Surgical Hospitals) 

Source: 

Quarterly tax filings with State Employment 

Security Agencies, from all non–federal 

hospitals 

Source: 

HCFA (CMS) surveys from all PPS hospitals 

(to payment year 1993) 

Annual cost report wage data (Wks S–3) 

beginning FY 1994 

Source: 

BLS employer surveys conducted over rolling 3–

year periods (data contributed by approximately 

70% of all employers). 

Measures: 

County aggregates of total covered wages 

County aggregates of total covered workers 

Measures: 

PPS Hospital average hourly wage (until 1988 

survey) 

PPS Hospital average hourly total 

compensation (after 1988) 

Measures: 

Average hourly wages by occupation, computed 

across all industry settings for that occupation 

Index construction: 

MSA or rural market–level aggregate average 

wage per worker 

 / 

National area–weighted average wage per 

worker 

(= unweighted mean of market average wages, 

computed across all labor markets) 

Index construction: 

MSA or rural market–level aggregate average 

hourly wage 

 / 

National aggregate average hourly 

(note: use of aggregate average in 

denominator was adopted in 1988) 

Index construction: 

MSA or rural market–level weighted sum of 

occupation–specific relative wages, where: 

occupation–specific relative wage = 

occupation hourly wage / national hourly wage 

and 

weights for weighted sum= 

occupation–specific hospital wages / total hospital 

wages 

Comments: 

o No adjustment for part–time workers 

o Influenced by seasonal variation, work 

stoppages and bonus or back pay 

o No data on benefits or contract labor 

o Data suppressed for counties with <3 

reporting entities 

o 3–year lag from data year to payment year 

o No opportunity for occupation mix 

adjustment 

o Average wages reflect all hospital pay, 

including non–PPS 

Comments

o Adjusts for part–time workers 

o Not influenced by variations in total 

employment 

o Surveys gradually added data on benefits 

and contract labor between 1986 and 

1990. Cost report worksheets gradually 

added adjustments to limit reporting to 

IPPS portions of hospital operations. 

o Data suppressed only for hospitals 

reporting below minimum wage or > 2.5 

times national hourly 

o 4–year lag from data year to payment 

year 

o No occupation mix adjustment from S–3; 

additional survey data mandated for 

collection 2003 and again in 2006, 

selected occupations only 

o Hourly wages reflect PPS hospital pay 

only 

Comments:

o Hourly wage corrects for part–time workers 

o Not influenced by variations in total 

employment 

o No data on benefits 

o Contract labor wages are reported by 

contractor 

o Data suppressed for MSA/occupation 

combinations with large sampling error 

o 3– to 5–year lag from data year to payment 

year (i.e. 1⁄3 3–yr + 1⁄3 4–yr +1⁄3 5–yr) 

o Fixed national occupation weights for all 

occupations in the index computation, 

updated annually 

o Hourly wages reflect all–industry averages, to 

extent that specific occupations are found in 

hospital and non–hospital settings 

SOURCE: RTI analysis of BLS and CMS wage data documentation. 

21 
2.0 SELECTED ISSUES IN ALTERNATIVE APPROACHES TO 

HOSPITAL WAGE INDEXES 

This chapter discusses selected issues in two alternative approaches to constructing the 

Medicare inpatient prospective payment system (IPPS) hospital wage index. The alternative 

approaches are Medicare's current index based on its survey of hospital–specific wages and 

compensation and MedPAC's alternative approach based on BLS all–industry wage data. We 

begin the chapter in Section 2.1 with a presentation of the theory of wage indexes, and how 

Medicare's and MedPAC's approaches are related to theoretical wage indexes. Because use of 

hospital–specific versus all–industry wage data is a key difference in the two approaches, we 

discuss the theory and empirical evidence on inter–industry wage differentials, and implications 

for the hospital wage index in Section 2.2. Section 2.2 also contains empirical analysis using 

BLS data of national differences in wages by industry. Section 2.3 evaluates the industry 

composition of the MedPAC BLS wage index. We show that the MedPAC BLS wage index can 

be interpreted as a blend of a hospital–specific wage index (one–third weight) and a non–hospital 

wage index (two–thirds weight). Section 2.4 summarizes earlier research RTI staff did for CMS 

on the factors that impact average hospital wages, which are the current basis of the IPPS wage 

index. Section 2.5 presents arguments for and against the use of hospital–specific versus all–

industry wage data in the IPPS hospital wage index in the context of the previous sections. 

2.1 Theory of Wage Indexes 

2.1.1 Hospital Cost Functions and Wage Indexes 

The IPPS is intended to reimburse hospitals for the cost of providing efficient inpatient 

treatments to patients. A large component of the cost is wages paid to employees, which vary 

across geographic areas. The hospital wage index is intended to adjust for geographic variation 

in wages. 

Economists represent hospital costs by a "cost function" (Varian, 1978), which relates 

total costs C(j) in area j to the "output" Q of the hospital and the vector of input prices (wages) 

w(i, j) it faces in area j for occupations indexed by i: 

C(j) = C(Q, w(i, j)). (1) 

The wage index is a measure of costs in one area relative to another. Typically, the base 

or denominator of the wage index is the nation as a whole, which we denote by N. The ratio of 

costs in area j to the nation N are 

C(j)/C(N) = C(Q, w(i, j))/C(Q, w(i, N)). (2) 

Equation (2) keeps output Q the same in area j as in the nation because the IPPS adjusts 

payments for hospital outputs through its diagnosis–related–group (DRG) casemix index, not the 

wage index. The wage index is a measure of the effect of wage, not output, variation on costs. 

Equation (2) also assumes that the hospital cost function C(Q, w(i, j)) is the same in area j as in 

the nation, that is, that the technology of hospital patient treatment is the same. 

22 
In general, as shown by equation (2), the relative wage index depends on output Q (even 

if it is the same in both areas) and wages in the two areas w(i, j) and w(i, N). However, if the cost 

function C is "multiplicatively separable" in output and wages, the wage index depends only on 

wages: 

C(j)/C(N) = f(Q)c(w(i, j))/f(Q)c(w(i, N)) = c(w(i, j))/c(w(i, N)). (3) 

Multiplicative separability is a property of certain theoretical cost functions often used by 

economists to model real–world cost relationships, such as the Cobb–Douglas cost function. The 

form of the cost function depends on the characteristics of the technology used in hospital 

production. 

If the technology of hospital production exhibits no substitution among alternative types 

of labor (e.g., registered nurses and licensed practical nurses), then the wage index can be 

expressed as a fixed weight "Laspeyres" index. This index can be written as5 

C(j)/C(N) = ∑ es(i, N)*w(i, j)/ Σ es(i, N)*w(i, N), (4) 

where 

es(i, N) = national employment shares of occupation i in hospital employment, 

w(i, j) = the wage of occupation i in area j, and 

w(i, N) = the wage of occupation i nationally. 

Note that the Laspeyres wage index can also be written 

C(j)/C(N) = ∑ [es(i, N)*w(i, N)/Σ (es(i, N)*w(i, N))] * [w(i,j)/w(i, N)] 

= ∑ ws(i, N)* [w(i, j)/w(i, N)], (5) 

where 

ws(i, N) is the national wage bill share of occupation i, the share of occupation i in total wages 

paid to all employees. 

Equation (5) says that the wage index for an area is the sum of the products of the 

national wage bill share of each occupation and each occupation’s wage in the area relative to its 

national average wage. 

The Laspeyres form was chosen by MedPAC for its alternative wage index based on BLS 

data. If there is substitution among occupations, the correct functional form for the wage index 

will not be the fixed–weight Laspeyres. Other, more exotic functional forms (e.g., geometric, 

translog) correspond to different amounts of substitution. Although it seems unlikely that there is 

little or no substitution among hospital occupations, Pope (1989) shows that alternative 

functional forms has only a small empirical impact on wage index values, so that the choice of 

 

5 All summations in equations (4) and (5) are across occupations i. 

23 
functional form––among the range of reasonable possibilities––is not an important one. This 

result is driven by the fact that relative wages for important hospital occupational categories––

especially nurses of different skill levels––do not vary substantially across areas.6 Even if there 

are technical possibilities for substitution, if relative wages are nearly constant across areas, the 

cost–minimizing labor mix will be nearly the same in all areas, and a Laspeyres fixed weight 

index will accurately measure relative labor costs. The fixed weight Laspeyres form is simple, 

and widely used, accepted, and understood, so there would seem to be little reason to deviate 

from it. MedPAC's choice of the Laspeyres index form is reasonable. 

2.1.2 Medicare IPPS Wage Index 

Non–Occupationally–Adjusted Wage Index

The Medicare IPPS wage index is derived from average hourly hospital wages.7 In its 

basic (non–occupationally–adjusted) form, the Medicare index is an area's average hourly 

hospital wage divided by the national average hourly hospital wage. This can be expressed as: 

IPPS wage index for area j = ∑ es(i, j)*w(i, j)/ Σ es(i, N)*w(i, N), (6) 

where es(i,j) = employment shares of labor category i in area j in hospital employment, and 

Σ es(i, N)*w(i, N) = the national average wage (NAW). The denominator of the IPPS wage index 

is the same as the denominator of the Laspeyres index shown in equation (4): both are the 

national average wage. But the numerator of the IPPS index uses area–specific labor category 

employment shares es(i, j) rather than the fixed, national shares es(i, N) used in the Laspeyres 

index. Thus, unlike the Laspeyres index, the IPPS index is influenced by area variations in 

hospital casemix, intensity and quality of care, business decisions on labor mix, and resulting 

occupation– and skill–mix differences (Pope, 1989). These variations should either be paid for 

through other elements of IPPS hospital payment (DRG casemix index, teaching adjustment), or 

payment should be neutral with respect to them (decisions on intensity and business decisions on 

labor mix). 

Occupationally–Adjusted Wage Index

CMS has recently added an occupation–mix adjustment for nursing personnel to the 

Medicare IPPS wage index. In the adjusted index, each area's average hospital wage is replaced 

with an adjusted average hourly wage, defined as 

Adjusted average hourly wage = [(total nursing wages)*(nursing occupational mix adjustment 

factor) + (total non–nursing wages)]/(total hours). (7) 

 

6 In the BLS data analyzed for this report, the three correlations across areas of registered nurses, licensed practical 

nurse, and health support worker wages range from 0.68 to 0.80, a fairly high degree of correlation. 

7 Actually compensation, but for simplicity we will use "wages" here. 

24 
If we decompose total wages into the product of the average hourly wage and hours 

worked for both nurses and other (non–nursing) occupations, and divide by total hours to get an 

employment share, we can express Medicare's occupationally–adjusted IPPS index as 

Occupationally–adjusted IPPS wage index for area j = 

[es(n,j)*w(n,j)*occ(n,j) + es(o,j)*w(o,j)]/NAW, (8) 

where 

es(n,j) = the employment share of nurses in area j; 

w(n,j) = the average hourly nurse wage in area j; 

occ(n,j) = the CMS nursing occupation mix adjustment for area j; 

es(o,j) = the employment share of other (non–nursing) occupations in area j; 

w(o,j) = the average hourly wage of other occupations in area j; and 

NAW = the national average wage. 

Comparing equations (8) and (6), the major difference is the nursing occupation mix 

adjustment occ(n,j). This adjustment is computed as the ratio of the national average nursing 

wage to the area average nursing wage, where the "area average" is computed using area average 

nursing employment proportions, but national average wages by nursing category. So the 

occupation mix adjustment reflects the costliness of an area's nursing labor mix relative to the 

national average mix. If an area's nursing mix is more costly than average, its occupation mix 

adjustment is less than one, and its IPPS wage index value is reduced. 

Although CMS' procedure adjusts for occupation mix within the nursing category, 

variations in labor mix between nursing and non–nursing occupations, and within non–nursing 

occupations, are not adjusted for. Nurse hours make up 43 percent of total hours in the Medicare 

hospital wage survey data according to CMS' proposed rule; while this is the largest component, 

it is still a minority of total hours. Thus, CMS' occupation mix adjustment applies to less than 

half the hospital work force. 

Consider only the nursing portion of CMS' occupationally–adjusted wage index in (8). 

Does it appropriately adjust for nursing occupation mix? The formula (8) is different than the 

theoretically appropriate Laspeyres index formula (4). Are they equivalent? Pope (1989) shows 

that if relative occupational wages are constant across the country, the average hourly hospital 

wage on which the Medicare IPPS index is based can be decomposed as follows: 

Average hourly hospital wage in area j = COL(j)*OCCMIX(j), (9) 

where COL(j) is a "cost of living" factor for area j that adjusts all wages in the area relative to the 

national average; and 

OCCMIX(j) = Σ w(i, N)*es(i,j) is an hospital occupation mix factor for area j that measures the 

costliness of an area's occupation mix using national average wages w(i, N) and area j 

employment proportions es(i,j). 

Pope (1989) also presents evidence that relative hospital wages do not vary much across 

areas, so this decomposition is empirically reasonable. Furthermore, Pope (1989) notes that with 

this assumption, the theoretically appropriate hospital wage index is just COL(j). 

25 
The nursing portion of the occupationally–adjusted IPPS wage index can be written 

occupationally–adjusted IPPS wage index = AHW(n,j)*occ(n,j)/AHW(n, N), (10) 

where AHW(n,j) = the average hourly wage for nursing occupations in area j; 

occ(n,j) = the CMS nursing occupational mix adjustment as described above; and 

AHW(n, N) = the national average hourly hospital nursing wage = Σ w(i, N)*es(i, N). 

Under the assumption in equation (9), equation (10), the occupationally–adjusted IPPS 

wage index, can be written: 

AHW(n,j)*occ(n,j)/AHW(n, N) = COL(j)*OCCMIX(j)*occ(j)/AHW(n, N). (11) 

But 

occ(j) = Σ w(i, N)*es(i, N)/Σw(i, N)*es(i,j), (12) 

so 

occ(j)*OCCMIX(j) = [Σ w(i, N)*es(i, N)/Σw(i, N)*es(i,j)]* Σ w(i, N)*es(i,j) 

 = Σ w(i, N)*es(i, N). (13) 

Hence, the Medicare occupationally–adjusted wage index (11) can be written 

COL(j)* Σ w(i, N)*es(i, N)/AHW(n, N) = COL(j). (14) 

This algebra has proven that under the assumption of constant relative wages, the CMS 

formula for occupationally–adjusting the nursing wage yields the theoretically appropriate 

nursing wage index. Put simply, CMS' formula for occupationally–adjusting nursing wages is 

appropriate, at least under the assumption of constant relative nursing wages across areas. 

Under this assumption (of constant relative wages), the Laspeyres form chosen by 

MedPAC is also appropriate and is in fact simpler, because it directly adjusts for occupation mix 

and does not require a separate occupation mix adjustment. Also, MedPAC’s proposed index 

adjusts for occupation mix among all hospital personnel, not just nurses. 

2.2 Hospital–Specific Versus All–Industry Wages 

Medicare Payment Advisory Commission (MedPAC) staff have explored an alternative 

method of computing the IPPS wage index that relies on U.S. Bureau of Labor Statistics (BLS) 

Occupational Employment Statistic (OES) data, rather than the CMS Hospital Wage Survey data 

(MedPAC, 2007). Probably the most important distinction between the BLS and the CMS data is 

the that the BLS data measure average wages for workers in multiple occupations across all 

industries, whereas the CMS data measure average wages for all occupations in a single industry, 

26 
hospitals. Recently, CMS has also begun collecting hospital occupation–specific wage data for 

selected occupations. 

Which of these two types of data––hospital–specific or all–industry––is a better measure 

of the wage rate input prices faced by hospitals? By "wage rate input prices", we mean the 

minimum wages that must be paid by a hospital to hire workers of specified characteristics 

(skills and productivity). Worker characteristics that could affect productivity include training, 

education, experience, ability, and motivation. 

To address this question, we discuss the theory and evidence on first, in Section 2.2.1, the 

standard competitive wage setting model, and then, in Section 2.2.2, on inter–industry wage 

differentials. In Section 2.2.3, we discuss implications for the hospital wage index. In Section 

2.2.4, we review some of the academic empirical literature on inter–industry wage differences, 

and in Section 2.2.5, we present evidence on inter–industry wage differences for hospital 

occupations from BLS data. 

2.2.1 Theory of Competitive Wage Setting 

The standard economic theory of market wage setting (see e.g., Ehrenberg and Smith, 

1988) assumes perfect competition in both the labor and product markets. There are no 

deviations from perfect competition in either the supply of labor (such as might be caused by 

labor unions) or in the demand for labor (such as might be caused by only a single employer in 

an area with monopsony power). Further, the neoclassical competitive theory assumes that all 

workers within an occupation are identical and are perfect substitutes for each other, and that 

employers maximize profits, and hence hire workers at the lowest wage possible.8 The theory 

also assumes that workers can move costlessly among firms, and that employers can hire 

replacement workers without loss of productivity. 

Under these conditions, a single market wage rate for an occupation is determined by the 

intersection of the market aggregate supply of labor and demand for labor curves. This single 

equilibrium wage rate “clears the market” in the sense that aggregate supply of labor and 

aggregate demand for labor are equal at this, and only this, wage rate. At higher wage rates, the 

number of hours workers wish to work (“supply of labor”) exceeds the hours of labor demanded 

by employers. The excess supply allows firms to hire workers at lower wages, putting 

downwards pressure on wages until they reach the equilibrium level. Analogously, at wages 

lower than the equilibrium wage, firms’ demand for labor is greater than labor hours supplied by 

workers, so wages are bid up by employers until they reach the equilibrium level. 

A corollary of competitive wage setting is that the wage rate paid by different industries 

for the same type of workers (same occupation) is identical. Industries and firms compete for 

workers. If one industry pays less than another, the workers will move to the higher–paying 

industry. Each industry is forced to pay the market wage to retain its work force and be able to 

hire new workers in the labor market. 

 

8 Cost minimization is a necessary condition for profit maximization. To minimize costs, an employer must hire 

workers at the lowest wage possible. 

27 
On the other hand, firms never pay more than the market wage. This would lower their 

profits, and they are assumed to be profit maximizing (this assumption is relaxed later). 

Moreover, paying above–market wages would raise their costs, and hence the price of their final 

product. Since product markets are also assumed to be perfectly competitive, firms paying above 

market wages would not be able to sell their products (because their price is higher than the 

market price), and would go out of business. 

Different industries might still pay the same wage rate to similar workers, even if some of 

the assumptions of the competitive wage setting model are violated. If for example, a dominant 

employer in an area (e.g., a large manufacturing firm) pays an above market wage, other 

industries (e.g., hospitals), in competition with the dominant industry, might have to pay the 

same wage to attract workers to their own industries. The outcome in this case would depend on 

how many workers the dominant industry was willing to hire at an above–market wage. If this 

industry was willing to employ a large fraction of the workers in the area at the higher wage, the 

other industries in the area would have to match the higher wage to retain their workers. The 

dominant industry essentially “pegs” the market wage rate to the level it pays. On the other hand, 

if the higher wage paid by the dominant industry (the large manufacturing firm) reduced its 

demand for labor relative to its demand at the competitive market wage, the residual labor supply 

to other industries (e.g., hospitals) would increase, and the wage rate paid by other industries 

could fall. In this latter case, not only would the wages paid by the non–dominant industries 

(such as hospitals) be lower than the wage paid by the dominant industry, they would also be 

lower than the all–industry competitive wage. 

2.2.2 Theory of Inter–Industry Wage Differentials 

If all industries paid the same wages to workers with similar characteristics, it would not 

matter from a theoretical point of view if industry-specific or all-industry wages were 

measured—they would be the same. Equality of wages for similar workers across industries is 

the outcome that is expected under competitive wage setting and free mobility of labor. But large 

and persistent differences have been found in the wages paid to apparently similar workers 

employed by different industries (Krueger and Summers, 1988). For example, hospitals have 

been found to pay more to nurses than they are paid by non–hospital employers (Schumacher 

and Hirsh, 1997). If wages paid by different industries vary, and if the mix of industries differs 

across areas, relative all–industry wages could differ from relative single–industry wages. 

Inter–industry wage differentials pose a challenge to neoclassical wage setting theories, 

under which they should not exist. Neoclassical theory implies that workers' pay should depend 

only on their productivity (characteristics), not on what industry they work in. If differentials 

were present, workers would move from the lower–paying industry to the higher–paying 

industry until wages equalized. 

Economists have advanced the following theories to explain inter–industry wage 

differentials (Osburn, 2000; Borjas and Ramey, 2000): 

1. Unmeasured differences in worker skills, abilities, or experience. The apparent 

paradox of inter–industry wage differentials is only apparent, not real. When worker 

characteristics are fully controlled for inter–industry differences disappear. 

28 
2. Compensating differentials for job conditions. Some industries pay higher wages than 

others because of more difficult working conditions. For example, hospital nurses 

might face more stress than non–hospital nurses because they treat sicker patients. 

Hospitals have to pay nurses higher wages to compensate for the greater stress and 

responsibility of hospital jobs. 

3. “Efficiency wages.” This theory argues that employers may choose to pay above–

market wages to increase employee productivity, for example, to reduce turnover and 

shirking. Employers are concerned with minimizing total labor costs, which are 

impacted by productivity, turnover, training costs, etc., not just wages paid. 

4. Rent sharing. Profitable firms share some of their profits with workers in the form of 

higher wages. This could also occur in nonprofits, e.g., nonprofit hospitals using 

some of their revenues to pay their employees above–market wages. Employee labor 

unions may seek to obtain a share of firm profits in the form of higher wages and 

benefits, and may be successful. 

5. Firm business decisions (worker sorting). Firms may make business decisions to 

compete on low costs by hiring relatively low–skill and low–wage workers, or on 

high productivity or quality by using higher–skilled workers who earn higher wages. 

The proportion of firms in each industry making such decisions could be influenced 

by the nature of the production process and of technology in the industry. 

2.2.3 Implications for the Hospital Wage Index 

What are the implications of the various theories of wage setting—including the standard 

competitive model and the various theories that attempt to explain inter–industry wage 

differentials—for the choice between hospital–specific and all–industry wages for the IPPS wage 

index? 

The importance if not predominance of competitive wage setting factors is not 

controversial. Firms compete for workers and workers compete for jobs. All industries in a labor 

market area have to respond to the competitive market situation in hiring workers. They have to 

pay at least what other industries pay to attract workers, but benefit from not paying more than 

necessary (i.e., more than other firms) because that keeps their costs competitive. 

To the extent that wage setting conforms to the standard competitive model, the choice 

between measuring hospital–specific or all–industry wages is not important from a theoretical 

point of view because they should be the same. However, as discussed further in Section 2.5, 

there may be empirical and policy reasons to prefer measuring all–industry wages. These include 

the larger and more stable sample of wages available from all industries that is less subject to the 

idiosyncratic peculiarities of measuring wages for only one industry (hospitals). Moreover, 

measuring all–industry wages avoids the circularity/incentive problems of using hospital wages 

to set hospital payments, and is less influenced by any deviations from competitive wage setting 

behavior by hospitals. 

What about the implications of the theories that attempt to explain inter–industry wage 

differentials? 

29 
To the extent that the first two theories––unmeasured differences and compensating 

differentials––are true, all–industry wages may not accurately measure the wage rates faced by 

hospitals and thus the case for using hospital–specific wage data is strengthened. If hospitals are 

employing different types of workers than other industries, even within categories such as 

occupation that can be measured, then all–industry wages will not accurately measure the wages 

hospitals have to pay to attract their workers. For example, hospitals may need to hire more 

skilled or experienced nurses than home health agencies to treat sicker hospital patients. 

Similarly, if hospitals pay higher wages because of more difficult working conditions, hospital–

specific wages are a more accurate measure of labor costs than all–industry wages. Hospital jobs 

may be more stressful or difficult than those in some other industries, so hospitals have to pay a 

premium to hire workers. 

To the extent the latter three theories are correct––efficiency wages, rent–sharing, and 

business decisions––the hospital is making decisions affecting wages that are under its control. 

These theories imply that observed hospital wages may not reflect the market wage rate faced by 

hospitals. If a hospital is paying efficiency wages, it is choosing to pay above–market wages. 

Doing so may minimize its overall labor costs, but it is nevertheless paying more than the market 

wage. A hospital might also share some of its profits with its workers, especially if it is not–for–

profit. If its workers are unionized, the union may be able to extract some of the hospital's profits 

as above–average wages paid to employees. A hospital may make business decisions to employ a 

lower–wage worker mix, or a higher–wage one. These decisions could be related to productivity, 

cost savings, or attempts to enhance quality or patient satisfaction. Some of the variations in 

worker characteristics that impact wages may be measurable––such as occupation––but many 

may not be easily measurable. Hence, observed hospital wages may deviate from market wages 

for workers of average characteristics. 

Of course, the goal of the Medicare hospital wage index is to measure relative wage 

variation across areas. Even if hospital wages differ from the market wage, or if wages vary 

across industries, relative wages may be measured accurately if these factors operate uniformly 

across areas. For example, if hospital wages deviate from the market wage by approximately the 

same proportion in all areas, or if the industry mix is similar in most or all areas. 

2.2.4 Evidence on Inter–Industry Wage Differentials 

What is the evidence on the reasons for inter–industry wage differentials? An early and 

influential study by Krueger and Summers (1988) rejected the unobserved ability and 

compensating differentials hypotheses, and suggested that "workers in high wage industries 

receive noncompetitive rents". Murphy and Topol (1990) conclude that unmeasured individual 

worker characteristics is the primary explanation of inter–industry wage differentials. Gibbons 

and Katz (1992) find that no model of inter–industry wage differences fits all the facts. 

Blackburn and Neumark (1992) conclude that inter–industry wage differentials are not 

attributable to variation in unobserved labor quality or ability. Keane (1993) find that individual 

characteristics explain most inter–industry wage differences. Clearly, there is little agreement in 

the general empirical literature on inter–industry wage differences. 

What about the medical industry specifically? Schumacher and Hirsh (1997) examine 

differences in hospital versus non–hospital registered nurse wages. They find that hospitals pay 

30 
31 

 

RNs roughly 20 percent more. One–third to one–half of this difference is attributable to 

unmeasured worker ability, and the rest is probably reflects compensating differentials for 

hospital disamenities. Supporting these conclusions is evidence that hospital RNs have higher 

cognitive ability and higher–quality job experience than non–hospital RNs, and indications that 

shift work accounts for roughly 10 percent of the hospital premium. Sloan and Steinwald (1980) 

empirically evaluate Feldstein's (1971) hypothesis that hospitals share rents with employees 

(what Feldstein labeled "philanthropic wage setting"). Sloan and Steinwald find little empirical 

support for this hypothesis. 

2.2.5 Wages by Hospitals and All Industries for Hospital Wage Index Occupations 

We used the BLS Occupation Employment Statistics (OES) data to directly examine 

nationwide differences between hospital9 and all–industry wages for the 30 occupations that 

MedPAC has included in its OES–based hospital wage index.10 The results presented in Table 5

show that for most occupations, especially the ones with high weights in the MedPAC index, the 

differences between average hospital and all–industry wages are small.11 For example, 

registered nurses are paid $27.80 per hour on average by hospitals and $27.35 per hour by all 

industries. Even for occupations where hospitals do not dominate the labor market, the wage 

difference is not large. For example, hospitals pay office and administrative support workers 

$13.68 per hour on average versus $14.28 for all industries. Overall, the average hospital and the 

average all–industry wage are nearly identical for the occupations in MedPAC's hospital wage 

index, $20.35 for hospitals versus $20.36 for all industries.12 These similarities lend credence to 

using all–industry wages to measure the wages of specific occupations used in a hospital wage 

index. 

9 By "hospitals" we mean "general medical and surgical hospitals." 

10 See also Osburn (2000) for use of the BLS OES data to examine inter-industry wage differentials. 

11 Hospital wages deviate substantially from all-industry wages for two occupations–protective service occupations 

and food preparation and serving-related occupations. But these occupations have small weights in the hospital 

wage index and are special cases. Protective service occupations includes police officers and firefighters, who 

are well paid, while hospital workers in this category are more likely to be low-paid security guards. Food 

preparation and serving occupations includes low-paid waitresses and fast-food workers, while hospital workers 

are more likely to include higher-paying occupations in this category. 

12 Average wages are weighted by the share of each occupation in MedPAC's index. 
Table 5 

National Hospital and All–Industry Wages and Employment by MedPAC 

Hospital Wage Index Occupations, May 2005 

% Employment 

Weight % Hospit

in MedPAC General Medical and in All– 

Occupation Hospital Surgical Hospitals All Industries Industry 

Code Occupation Title Wage Index Employment Wage($) Employment Wage($) Employme

All1 –– 100.00 4,280,170 20.35 58,768,350 20.36 7.3 

11–0000 Management occupations 3.86 164,950 39.36 5,960,560 42.52 2.8 31–0000 Healthcare support occupations 14.53 622,340 11.95 3,363,800 11.47 18.5 

33–0000 Protective service occupations 0.88 37,560 13.42 3,056,660 17.19 1.2 

35–0000 Food preparation and serving related 

occupations 3.22 138,170 10.37 10,797,700 8.58 1.3 

37–0000 Building and grounds cleaning and 

maintenance occupations 4.23 180,790 10.30 4,342,550 10.55 4.2 

39–0000 Personal care and service occupations 0.51 21,570 11.38 3,188,850 10.67 0.7 43–0000 Office and administrative support 

occupations 

17.44 746,540 13.68 22,784,330 14.28 3.3 

29–1031 Dietitians and nutritionists 0.39 16,840 22.37 48,850 22.09 34.5 29–1051 Pharmacists 1.21 51,430 41.93 229,740 42.62 22.4 29–1111 Registered nurses 31.63 1,354,020 27.80 2,368,070 27.35 57.2 29–1122 Occupational therapists 0.53 22,470 28.64 87,430 28.41 25.7 29–1123 Physical therapists 1.04 44,220 30.59 151,280 31.42 29.2 29–1124 Radiation therapists 0.24 10,080 29.95 14,120 30.59 71.4 29–1126 Respiratory therapists 1.73 73,830 22.15 95,320 22.24 77.5 29–1127 Speech–language pathologists 0.24 10,160 29.15 94,660 27.89 10.7 29–2011 Medical and clinical laboratory 

technologists 2.21 94,690 23.36 155,250 23.37 61.0 

29–2012 Medical and clinical laboratory 

technicians 1.45 62,430 16.53 142,330 15.95 43.9 

29–2031 Cardiovascular technologists and 

technicians 0.76 32,150 19.50 43,560 19.99 73.8 

29–2032 Diagnostic medical sonographers 0.59 25,090 26.34 43,590 26.65 57.6 29–2033 Nuclear medicine technologists 0.29 12,490 28.37 18,280 29.10 68.3 29–2034 Radiologic technologists and 

technicians 2.54 108,790 22.64 184,580 22.60 58.9 

29–2041 Emergency medical technicians and 

paramedics 0.88 37,870 14.05 196,880 13.68 19.2 

32 
Table 5 (continued) 

National Hospital and All–Industry Wages and Employment by MedPAC 

Hospital Wage Index Occupations, May 2005 

33 

% Employment 

Weight % Hospit

in MedPAC General Medical and in All– 

Occupation Hospital Surgical Hospitals All Industries Industry 

Code Occupation Title Wage Index Employment Wage($) Employment Wage($) Employme

29–2051 Dietetic technicians 0.26 11,040 13.20 23,780 12.20 46.4 29–2052 Pharmacy technicians 1.09 46,840 13.61 266,790 12.19 17.6 29–2053 Psychiatric technicians 0.25 10,820 13.90 62,040 14.04 17.4 29–2054 Respiratory therapy technicians 0.44 19,060 18.79 22,060 18.57 86.4 29–2055 Surgical technologists 1.36 58,170 16.96 83,680 17.27 69.5 29–2061 Licensed practical and licensed 

vocational nurses 4.00 171,270 16.65 710,020 17.41 24.1 

29–2071 Medical records and health 

information technicians 1.40 59,870 14.41 160,450 13.81 37.3 

29–2099 Health technologists and technicians, 

all other 0.81 34,620 18.18 71,140 18.04 48.7 

1Wages are an average weighted by each occupation's employment weight in the MedPAC hospital wage index. Employment is a sum of each occupat

and healthcare shares of all industry employment are ratios of total employment. 

SOURCE: U.S. Bureau of Labor Statistics, Occupational Employment Statistics, May 2005, available at http://www.bls.gov/oes/current/oes_nat.htm#

07. 

NOTES: 1. Wages are mean hourly wages. 
However, particular industries do pay differently than hospitals on a national level. 

Wages and employment for hospitals and selected other industries for the three occupations with 

the largest weights in MedPAC's hospital wage index (accounting for 64 percent of the index in 

total) are shown in Table 6. For example, nursing and residential care facilities pay RNs $24.53 

per hour, 12 percent less than hospitals. On the other hand, nursing and residential care facilities 

pay LPNs $17.99 per hour (not shown in Table 6), eight percent more than hospitals. Because of 

these differences, if the mix of industries differs across areas, the all–industry wage could 

diverge from the hospital wage. But national wages may reflect general area wage differentials 

as well as industry differences, so it is not clear if these industry mix issues are ever significant 

empirically. Further investigation of industry mix differences in a few particular market areas 

and the implications for the hospital wage index could be warranted. 

Table 6 

National Employment and Wages for Selected Industries and Occupations 

Healthcare Support 

Occupations 

Office and 

Administrative Support 

Occupations Registered Nurses 

Employment Wage($) Employment Wage($) Employment Wage($) 

General Medical 

and Surgical 

Hospitals 622,340 11.95 746,540 13.68 1,354,020 27.80 

Nursing and 

Residential Care 

Facilities 1,060,230 10.11 144,840 13.17 157,870 24.53 

Offices of 

Physicians 302,960 12.81 790,300 13.39 202,790 27.03 

Educational 

Services 33,180 12.98 1,301,400 13.98 73,850 23.26 

Federal Executive 

Branch 25,900 16.33 299,880 21.99 49,130 32.32 

NOTE: Wages are mean hourly wages. 

SOURCE: RTI analysis of May 2005 BLS Occupational Employment Statistics data. 

2.3 Employment Composition of the Hospital Wage Index Occupations 

We also examined the national industry employment mix of occupations included in 

MedPAC's alternative hospital wage index based on the BLS OES data. The BLS OES data 

report on workers in all industries, including hospitals. Hence, the OES mean wages for an 

occupation are a weighted average of the wages of hospital and non–hospital workers. In some 

health care occupations, most of the workers whose wages are reflected in the OES data are 

employed in hospitals. Table 5 shows the percentage of hospital workers in all–industry 

employment by occupation. For example, 2.8 percent of all "management occupations" workers 

are employed in the hospital industry, but 57.2 percent of registered nurses are hospital workers. 

34 
The MedPAC OES hospital wage index as a whole can be thought of as an average of 

hospital and non–hospital wages. Multiplying the hospital employment share by each 

occupation's employment weight in the MedPAC index, we find that, on average, 32.4 percent of 

the MedPAC index is based on hospital workers. The MedPAC hospital wage index can be 

roughly thought of as one–third consisting of the wages of hospital employees and two–thirds 

consisting of the wages of non–hospital employees. 

Hospitals are a subcategory of the health care industry, which also includes physician 

offices, nursing facilities, etc. Hospital and health care industry wages may be affected by similar 

forces, so it is also relevant to know how much of the MedPAC OES hospital wage index is 

based on employment in health care industries more broadly. Table 5 shows the share of health 

care in all–industry employment.13 For example, 57.2 percent of registered nurses are employed 

in hospitals and 84.1 percent in health care industries. Multiplying the health care employment 

share by each occupation's employment weight in the MedPAC index, we find that, on average, 

60.4 percent of the MedPAC index is based on health care industry employees. Put another way, 

32.4 percent of the index is based on hospital employees and 28.0 percent on non–hospital health 

care industry employees. 

Occupations as well as industries can be classified as health care related or not. Health 

care occupations are classified into the BLS OES major occupation categories 29 Healthcare 

Practitioner and Technical Occupations and 31 Healthcare Support Occupations. Of the 30 

MedPAC hospital wage index occupations, only six––management, protective service, food 

preparation and serving, building and grounds cleaning and maintenance, personal care and 

service, and office and administrative support, are not health care occupations.14 The non–health 

care occupations comprise 30.1 percent, or about one–third, of the index. Office and 

administrative support occupations alone account for over one–half of the non–health care 

occupations, with an employment weight of 17.4 percent in the MedPAC index. In short, health 

care occupations account for roughly two thirds of the MedPAC BLS hospital wage index, and 

non–health care occupations about one third. 

Putting the industry and occupation analyses together, the MedPAC BLS OES hospital 

wage index employment weighting can be decomposed as shown in Table 7: 

• 57.7 percent healthcare industry employees in healthcare occupations (of which 31.5 

percent hospital employees and 26.2 percent other healthcare industry employees); 

• 2.7 percent healthcare industry employees in non–healthcare occupations; 

• 12.2 percent non–healthcare industry employees in healthcare occupations; and 

 

13 We obtained "health care industry" employment from the BLS OES data by subtracting NAICS 624000 Social 

Assistance employment from "Sector 62 Healthcare and Social Assistance" employment. 

14 Table 5 shows that only these six non-health care occupations, and three health care occupations--pharmacists, 

pharmacy technicians, and speech-language pathologists--have less than half of their employment in the health 

care industry. 

35 
• 27.4 percent non–healthcare industry employees in non–healthcare occupations. 

The Medicare IPPS hospital wage index, of course, is drawn totally from the hospitals, 

total occupations category in Table 7, which accounts for only 32.4 percent of the MedPAC/BLS 

index. The MedPAC/BLS index draws from a much broader universe of workers including other 

healthcare industry workers and non–healthcare industry workers, two–thirds of which are 

excluded from the Medicare index. Therefore, the MedPAC index has less of a circularity issue 

than the current Medicare index (see Section 2.5). 

Table 7 

Composition of MedPAC's BLS Hospital Wage Index by Industry and Occupation 

Employment Proportions, in Percents 

Healthcare Industry 

(Hospitals) (Other) 

Non–Healthcare 

Industry 

Total 

Industries 

Healthcare 57.7 

Occupations (31.5) (26.2) 

12.2 69.9 

Non–Healthcare 2.7 

Occupations (0.9) (1.8) 

27.4 30.1 

Total 60.4 

Occupations (32.4) (28.0) 

39.6 100.0 

NOTE: The table analyzes weighted average employment proportions of the 30 occupations 

included in MedPAC's hospital wage index by industry and occupation category. To derive the 

weighted average proportions, each occupation's proportion in a category is weighted by its 

employment share in MedPAC's index. Hospitals are general medical and surgical hospitals. 

SOURCE: RTI analysis of May 2005 BLS Occupational Employment Statistics data. 

Of these four categories of employment in MedPAC's index, it seems reasonable to 

hypothesize that only the last (non–healthcare industry employees in non–healthcare 

occupations) might diverge substantially from the wages paid in hospitals in particular areas. But 

non–healthcare workers in non–healthcare occupations comprise only about one–quarter of the 

MedPAC/BLS hospital wage index. So any deviations that occur will not have a large effect on 

the index. For instance, if wages for this category were 20 percent greater than the wages paid by 

hospitals, the index would be raised only by (20 percent) X (27.4 percent) = 5.5 percent. 

Moreover, it is not clear that if hospital and all–industry wages diverge that it is preferable to use 

hospital wages in the Medicare wage index (see Section 2.5 for discussion). 

2.4 Evidence on Factors Affecting the Hospital Average Wage 

Pope and Adamache (1993) analyzed factors affecting hospital average hourly 

compensation, the basis for Medicare' IPPS hospital wage index. They found that the following 

seven factors explain about 70 percent of the variation in individual hospital average hourly 

compensation: 

36 
• Area opportunity wage (i.e., the amount hospital workers could earn in alternative 

occupations in an area); 

• Area hospital–specific wages; 

• Hospital size; 

• Hospital casemix; 

• Hospital occupation mix; 

• Hospital unionization; and 

• The competitiveness of the area labor market. 

The largest influence on hospital wages was opportunity wages, but the other factors, 

especially considered as a group, had a large impact. An important hospital–specific component 

to wages was found, which does not favor the use of an opportunity wage alone to measure 

hospital wages in different geographic areas. Pope and Adamache argue that the effects of 

occupation mix (and any other worker characteristics), casemix, and hospital size on the wage 

index (which are thought to reflect primarily unmeasured worker characteristics) should be 

removed. This is because the need for different worker characteristics is reimbursed through the 

DRG casemix and teaching adjustments to hospital payments. The effects of competition on the 

index should not be removed, because this is a structural market factor beyond the control of 

hospitals. Whether the effects of unions should be removed is a policy decision. 

It is not clear whether the CMS Hospital Wage Survey data or the BLS OES data best 

reflects the appropriate factors influencing hospital wages, while not reflecting the undesirable 

factors. Both data should measure the area opportunity wage. The CMS survey will better 

measure area hospital–specific labor market factors, although, as argued above, the BLS OES 

data include a significant hospital– and healthcare–industry–specific component. The BLS OES 

data will be better than the CMS hospital wage survey data at avoiding measuring the effects of 

hospital size, casemix, occupation mix, unionization, and area competition for healthcare 

workers. 

2.5 Arguments For and Against the Use of Hospital–Specific Versus All–Industry Wage 

Data 

In this section, we list arguments for and against the use of hospital–specific (CMS 

Hospital Wage Survey data) and all–industry (BLS OES wage data) wages in the IPPS hospital 

wage index. These arguments draw on the discussion in the preceding sections, and on additional 

considerations such as characteristics of the two alternative data sources. These arguments are 

not meant to be comprehensive of all advantages and disadvantages of alternative approaches, 

but are particularly salient points to make in the context of the discussion in this paper. 

In the following discussion, it should be kept in mind that factors that affect absolute

wages do not necessarily affect relative wages. Factors that affect absolute wages will only affect 

37 
relative wages if they operate non–uniformly across market areas. For example, if the industry 

mix varies across areas. Measuring relative wages accurately is the goal of the IPPS wage index. 

2.5.1 Arguments for Using Hospital–Specific Wage Data in the IPPS Hospital Wage 

Index 

This section presents arguments in favor of using hospital–specific wage and 

compensation data, such as data from CMS' Hospital Wage Survey, to construct the IPPS 

hospital wage index. 

1. Supply and Demand Factors Specific to the Market for Hospital Labor are Captured. 

Every industry, even every employer, may face specific, exogenous supply and 

demand factors that affect the wages it has to pay to attract workers. These hospital–

specific factors should ideally be reflected in the hospital wage index. For example, 

hospital nursing jobs might require more skill, or be more demanding or stressful than 

nursing jobs in other industries, and thus hospitals may have to pay a premium 

relative to other industries to employ nurses. Hospitals may require more night shift 

work that may require higher wages as a compensating differential. Hospital–specific 

factors will only be important for a relative wage index if they apply differentially 

across areas. 

2. Factors Specific to Markets for Non–Hospital Labor are Not Reflected. Limiting the 

hospital wage index to hospital wage data avoids "contamination" from factors 

affecting wages paid in other industries. For example, all–industry wages in an area 

with a concentration of large, unionized manufacturing firms may exceed the wages 

hospital need to pay to hire their workers. The above–market wages paid by 

unionized manufacturing firms should not be reflected in the hospital wage index. 

Also, industries that are concentrated in certain areas may employ certain specialized 

types of labor within broader occupational categories whose wages are not 

representative of hospitals or even the occupational category as a whole. In general, 

hospitals compete with other industries for labor and wages should be similar, but 

there may be distortions of the normal competitive market for labor in some 

instances. 

3. The hospital–specific mix of workers is reflected. Hospitals may disproportionately 

employ certain specific types of labor within the broader occupation categories for 

which wages can be measured in available data. These broader labor categories may 

not accurately reflect the worker–mix or wages of hospital employees. Again, even if 

this affects absolute wages, it is not clear that it would affect relative wages. 

2.5.2 Arguments for Using All–Industry Wage Data in the IPPS Hospital Wage 

Index 

This section presents arguments in favor of using all–industry wage data, such as data 

from the BLS OES, to construct the IPPS hospital wage index. Note that, as discussed above, the 

BLS data are really an average of hospital–specific and non–hospital wages. For healthcare 

occupations, the BLS data reflect a large share of hospital–specific data. Over all the 30 

occupations in MedPAC's BLS–based index, about one–third of the wages are based on hospital 

38 
employees. In this section, we focus on the arguments for including the two–thirds of non–

hospital–employee data in the IPPS hospital wage index. 

1. Hospital wages are not fully "passed through" into Medicare payments. With a 

hospital–specific wage index, a hospital's area wage index, and thus its Medicare 

payments, can be influenced by the wages it pays. This may reduce the incentive of 

hospitals to bargain hard with its workers over compensation, and pay the lowest 

wages necessary to attract and retain employees. This incentive problem is 

exacerbated in smaller MSAs with only one or a few hospitals, and for hospitals with 

a larger Medicare share of business. Hospitals are included in an all–industry wage, 

but they are only one component, along with other industries. 

2. Noncompetitive factors in hospital wage setting are avoided. To the extent that 

hospitals pay noncompetitive––above market––wages, a hospital–specific wage index 

will not reflect the true relative cost of labor faced by hospitals. Hospitals may pay 

noncompetitive wages for a variety of reasons, including sharing surpluses with 

employees, unionization of the hospital workforce, misjudging or lack of information 

about the market wage, or business decisions. All–industry data will be less 

influenced by these factors. 

3. Worker skill–mix variations related to differences in hospital outputs or business 

decisions are not reflected. Hospitals that treat particularly difficult cases may require 

highly skilled and specialized workers, even within occupational category, that are 

paid above–average wages. The labor costs associated with a more difficult casemix 

should be reflected in a hospital's DRG casemix, not in its wage index. Also, hospitals 

may make business decisions about their labor skill mix, or to offer specialized 

services or technology requiring highly–paid workers, that affect their average wage. 

The IPPS wage index should ideally be neutral with respect to hospital business 

decisions. An index incorporating non–hospital wage data will be less influenced by 

hospital–specific skill–mix variations. 

4. The broader base of all–industry data makes it less subject to reporting errors, 

idiosyncratic factors, and volatility. By definition, all–industry wage data are 

collected from a larger universe of firms and workers. This broader base makes all–

industry data less subject to reporting errors by individual hospitals, and idiosyncratic 

or nonrepresentative wages paid by hospitals. Also, the all–industry wage data should 

be more stable from year–to–year; it will not be as influenced by discontinuous wage 

changes at hospitals or other one–time changes. 

39 
3.0 AREA WAGE INDEXES IN OTHER MEDICARE PROSPECTIVE PAYMENT 

SYSTEMS 

3.1 Other PPS Settings Using Hospital Relative Wages 

Since 1997 Medicare prospective payment systems have been extended from inpatient 

hospital services to services in several other institutional payment settings. Although setting–

specific wage data are collected for several of these settings, the new PPSs continue to use the 

hospital wage index to adjust payments. For each new payment system CMS sets a specific 

labor–related share of payments to be subject to area wage adjustment. Thus far CMS has 

decided against computing new relative wages using hourly setting–specific wage data. 

In most cases the new PPS use a “pre–floor, pre–reclassification” version of the index 

rather than the published IPPS index (the exception being for hospital outpatient payments). 

Since 2005, the version of the index with partial occupation mix adjustment has been used for 

payments for other specialized hospital services, while the non–adjusted version is used for non–

hospital settings such as skilled nursing, home health and hospice. Specific prospective payment 

settings using the hospital wage index are listed below in Table 8. 

Table 8 

Use of the Hospital Area Wage Index in Other Medicare Prospective Payment Systems 

Health Care Setting Index Description 

Inpatient services: 

Inpatient Rehabilitation Facilities (IRF) Pre–floor, pre–reclassification, occupation–mix 

adjusted index without outmigration adjustment. 

Inpatient Psychiatric Facilities (IPF) Pre–floor, pre–reclassification, occupation–mix 

adjusted index without outmigration adjustment. 

Long Term Acute Care Hospitals (LTCH) Pre–floor, pre–reclassification, occupation–mix 

adjusted index without outmigration adjustment. 

Index was phased in over a 5–year period due to lack 

of evidence on relationship between the index and 

case–mix–adjusted average cost per discharge. 

Inpatient nursing care: 

Skilled Nursing Facilities (SNF) Pre–floor, pre–reclassification index without 

occupation–mix or outmigration adjustments. 

Outpatient and home–based settings

Hospital outpatient services (HOPD) Uses the same index that is applied to the hospital’s 

inpatient services. 

Ambulatory Surgery Centers (ASC) Pre–floor, pre–reclassification index without 

occupation–mix or outmigration adjustments. 

Home Health (HHA) Pre–floor, pre–reclassification index without 

occupation–mix or outmigration adjustments. 

Hospice Pre–floor, pre–reclassification index without 

occupation–mix or outmigration adjustments. 

SOURCE: RTI synopsis of Federal Registers and other sources. 

40 
3.2 Labor Market Comparability and Other Issues 

The major drawbacks to using acute care hospital relative wages as adjusters for non–

hospital health care payments are that (1) acute care area wage differences may be heavily 

influenced by differences in occupation mix that are not applicable to other settings; (2) there are 

many markets, including even some state rural markets, where the hospital index values are 

computed from data submitted by only one or two facilities; and (3) non–hospital providers to 

which the hospital wage index is applied may be located in counties without hospitals, hence 

their wage index is based on wage data that are not specific to the area in which they are located. 

With respect to occupation mix influences, previous work has estimated that as much as 

one–fourth of the rural–urban hourly wage differentials might be due to occupation mix 

differences associated with smaller facilities with less complex case loads (Dalton and Slifkin 

2000). These differentials may not apply to settings with distinctly different employment mixes 

such as nursing homes or home health agencies, where similar urban–rural differences in firm 

size, complexity, or case mix may not apply. The proposed use of BLS all–industry hourly wages 

with separate employment weights by industry setting would address this issue. 

Small market problems stem from the fact that the index uses data only from IPPS 

hospitals. In larger metropolitan areas it is reasonable to assume that relative wages may be 

dominated by the hiring practices of these larger facilities. In smaller markets, however, local 

market conditions may be influenced as much or more by wages paid in nursing homes, critical 

access hospitals, outpatient facilities, specialized hospitals or even federally–owned facilities 

(IHS or VA). Also, some non–hospital providers are located in counties without hospitals, and 

thus there are no hospital wage data available to accurately measure wages in their locale. Thus 

the IPPS relative wages may not reflect the market differences or geographical locations relevant 

to non–hospital providers. The use of the BLS all–industry data would also address these issues. 

Exacerbating these problems is the fact that there are no provisions to allow for 

geographic reclassification for non–IPPS providers. SNF PPS rules call for reclassification to be 

made available whenever a SNF–specific wage index is developed. Although CMS computed a 

SNF wage index for evaluation, they have postponed its use (citing data quality problems) and 

therefore delayed introducing any reclassification procedures for nursing facilities (68 FR 46046, 

August 4, 2003). 

41 
4.0 DATA QUALITY AND VOLATILITY ANALYSIS OF CURRENT AND 

ALTERNATIVE WAGE INDEXES 

4.1 Overview 

This chapter compares the volatility of a wage index constructed from the BLS 

Occupational Employment Statistics (OES) data to CMS hospital wage index values. We 

construct an index using the BLS mean hourly rates from the metropolitan area cross—industry 

wages and the national industry—specific weights applicable to general hospitals. We compare 

volatility in this index over six years to volatility in a published CMS index applicable to 

payments in urban PPS hospitals for this same time period. We also compare volatility for a 

similar time period in wage, benefit, and total compensation indexes constructed from CMS’ 

hospital wage survey. Prior analysis by MedPAC found evidence that a BLS wage index exhibits 

less volatility than the CMS IPPS area wage index. We extend MedPAC’s analysis by using 

more years of data, by utilizing additional measures of volatility, and by exploring the 

contribution of reported benefits to overall volatility in the CMS index. We also use the CMS 

hospital wage survey data to analyze trends and regional variation in the ratio of benefits to total 

compensation. 

Section 4.2 describes our methods, including the composition of the data series, the 

construction of an alternative BLS index, and our approaches to measuring volatility. Results 

from our analysis of volatility in the BLS series compared to the CMS series are presented in 

Section 4.3. Section 4.4 discusses possible sources of volatility and data quality issues, and 

includes a comparison of the volatility of indexes of wages, benefits, and total compensation. 

Section 4.5 contains results on geographic and temporal variation in benefits as a proportion of 

total compensation. 

4.2 Methods 

4.2.1 Data Sources and Timing 

Data sources include the following: 

1. the BLS’ published OES occupation survey data from 1999 through 2004; 

2. published CMS wage index values from payment years FY1999 to FY 2004; and 

3. CMS’ annual hospital wage surveys derived from cost reports filed from FY 1999 

through FY 2004. 

The six payment years for the published CMS wage index correspond to data collection 

from cost reports filed between FY 1996 to FY 2000, and thus only partially overlap the data 

collection periods for the other two wage series. Although published index values from FY 2003 

through FY 2008 would have provided a better overlap with the BLS data collection periods, we 

could not use these because CMS adopted the new CBSAs to define labor markets starting in 

2005. 

42 
The BLS occupational wage series has a three–year survey structure. Each published year 

of data includes results from six surveys taken over three survey years, where the earlier data are 

inflation adjusted to reflect the price levels of the third year. Wage series are published in May of 

the year following the last survey year. For example, what we refer to as the 2004 BLS data were 

released in May of 2005, and were composed of results from two surveys in 2004, two in 2003 

and two in 2002. The timing of the data publication is such that the 2004 BLS data could be used 

to construct an index to adjust PPS payments no earlier than federal FY 2007. 

Although the BLS data series is constructed from a rolling sample design, it should not be 

confused with a standard moving average using repeated employer measures. While the full 

sample covers some 1.2 million establishments employing approximately 70 percent of the 

workforce, in any given year only one–third of the establishments are surveyed.15 Data for prior 

years (e.g., the surveys from 2002 and 2003 that contribute to the “2004” series) are updated to 

reflect 2004 labor dollars using the Employment Cost Index.16

We compare volatility measures with those computed from published CMS index values 

for payment years FY1999 to FY2004 (data years FY1996 to FY2000). We could not 

synchronize the BLS index with the CMS published index—either on the basis of data years or 

payment years—for two reasons. First, we were limited to published index years during which 

CMS used pre–CBSA definitions of metropolitan areas (index years FY 2004 and earlier). 

Second, we were limited to years when BLS national industry–specific hourly wages were 

available for all of the occupation categories (data years 1999 and later). Prior to 1999 the BLS 

did not publish data on aggregate occupational categories (e.g., xx–0000) within specific 

industry sectors, making it infeasible to construct compatible employment weights for pre– and 

post–1999 data. 

We also use CMS wage survey data from cost reports filed FY 1999 through FY 2004 to 

construct a “wages–only” index that excludes the reported benefits and contract labor. This 

constructed CMS index is the closest possible match to the BLS series both from the timing of 

the data collection and the definition of hourly wage. 

4.2.2 Wage Index Areas for Comparison 

The volatility comparisons in this chapter are based on changes in metropolitan markets 

only, because the BLS does not publish occupational wage survey data for state–level aggregates 

of non–metropolitan counties.17 The number of metropolitan markets in our analyses was 

reduced further due to missing hourly wages in the BLS series. BLS suppresses publication of 

 

15 See “Why does the OES program produce estimates from more than one year's data?” at 

http://www.bls.gov/oes/oes_ques.htm

16 See “Permanent features of OES methodology” at http://www.bls.gov/oes/oes_ques.htm

17 Data for state-level aggregates are published, but it would not be appropriate to subtract the employment totals 

for metropolitan areas from those of state areas, because many MSAs cross state boundaries. Although not 

publicly available, BLS can generate special runs to identify state-level non-metropolitan employment and wage 

data appropriate to Medicare’s defined rural markets. 

43 
local wage information on specific occupations if the survey data are based on information from 

fewer than three employers in the MSA (to protect confidentiality of responses), or if the 

standard errors of the estimate are too large (whether due to small response numbers or other 

reasons).18 Missing data were found for several occupation categories over different MSAs and 

data years, and in consultation with the project officer a decision was made to exclude any MSA 

from the volatility analyses that did not have RN wages in all six of the analysis years. For all 

other missing categories, weights were re–normalized to adjust for the missing data. Excluding 

MSAs with missing RN data in the BLS series reduced the number of metropolitan markets in 

our analyses from 318 to 264. 

CMS data were restricted to the same MSA sample as the BLS data. The number of 

metropolitan markets in the CMS data is slightly lower than the number in the BLS series 

because prior to 2005, CMS used NECMAs (New England County Metropolitan Areas) to define 

labor markets in New England. A single NECMA may correspond to multiple MSAs. In the end, 

our analysis was based on 264 wage index areas for the BLS data and 251 comparable wage 

index areas for the CMS data. 

4.2.3 BLS Wage Index Construction 

The BLS data series used here focuses on occupation codes found in the ‘all hospitals’ 

category (NAICS 622000) rather than General Medical and Surgical Hospitals category (NAICS 

622100). The reason for this is that prior to 2002 BLS used the SIC system, which did not break 

out General Medical and Surgical Hospitals from other hospitals. Comparability over the entire 

time period required the use of the more aggregated category.19 

To construct the hospital indexes for these analyses we used twenty–nine of the thirty 

occupational categories used by MedPAC in its proposed BLS index.20 These are occupations 

that are (a) prevalent in general acute care hospital facilities, and (b) included as allowable Part 

A costs under IPPS (occupations that are primarily reimbursed through Medicare Part B rather 

than through the IPPS rates are excluded). The twenty–nine categories combined account for 

approximately 87 percent of total employment in the BLS data for the hospital industry group, 

with specific category weights ranging from 31 percent to 0.2 percent. As shown in Table 9, the 

share of hospital employment by occupation category is fairly stable over time, although changes 

in these shares do contribute to BLS index volatility over time. 

 

18 This restriction affects only our volatility analyses and not the potential to compute a wage index. Data that are 

suppressed to protect employer confidentiality (rather than due to sampling error) can still be provided by BLS 

for purposes of constructing the wage index. The majority of those with missing RN data are in relatively small 

MSAs, and we assume that the data were suppressed to protect confidentiality of responding employers. 

19 Also of note is that in 2002 BLS did not publish the 3-digit NAICS data (e.g., NAICS 622000); as a result, the 

data used here for 2002 was aggregated from the published 4-digit NAICS data (e.g., 622100, 622200, etc.). 

20 Code 29-2099, for “health technologists and technicians, all other”, was published for the first time in 2004. It 

accounted for only 0.72 percent of hospital employment total in 2004, but to maximize consistency over time this 

category was dropped. 

44 
Table 9 

General Hospital Employment–Share Weights for Key Occupations, 1999–2004 

BLS Data Year 

CODE Description 1999 2000 2001 2002 2003 2004 

Detailed occupation categories: 

29–1111 registered nurses 25.7% 25.9% 25.9% 26.3% 26.6% 26.9% 

29–2061 licensed practical and vocational nurses 4.0% 3.9% 3.9% 3.9% 3.8% 3.7% 

29–2034 radiologic technologists and technicians 1.9% 2.0% 1.9% 2.0% 2.0% 2.0% 

29–2011 medical and clinical laboratory technologists 1.8% 1.8% 1.7% 1.8% 1.8% 1.8% 

29–1126 respiratory therapists 1.4% 1.4% 1.4% 1.4% 1.4% 1.4% 

29–2012 medical and clinical laboratory technicians 1.2% 1.3% 1.3% 1.2% 1.3% 1.2% 

29–2071 

medical records and health information 

technicians 1.1% 1.1% 1.1% 1.1% 1.1% 1.2% 

29–2055 surgical technologists 1.0% 1.1% 1.1% 1.1% 1.1% 1.1% 

29–1051 pharmacists 1.0% 1.0% 1.0% 1.0% 1.0% 1.0% 

29–1123 physical therapists 0.9% 0.9% 0.9% 0.9% 0.9% 0.9% 

29–2052 pharmacy technicians 0.8% 0.9% 0.9% 0.9% 0.9% 0.9% 

29–2041 emergency medical technicians and paramedics 0.7% 0.7% 0.7% 0.7% 0.7% 0.7% 

29–2053 psychiatric technicians 0.8% 0.7% 0.9% 0.7% 0.7% 0.7% 

29–2031 cardiovascular technologists and technicians 0.6% 0.6% 0.6% 0.6% 0.6% 0.6% 

29–1122 occupational therapists 0.5% 0.5% 0.5% 0.5% 0.5% 0.5% 

29–2032 diagnostic medical sonographers 0.4% 0.4% 0.4% 0.4% 0.4% 0.5% 

29–2054 respiratory therapy technicians 0.6% 0.5% 0.5% 0.5% 0.5% 0.4% 

29–1031 dietitians and nutritionists 0.3% 0.3% 0.3% 0.3% 0.3% 0.3% 

29–2033 nuclear medicine technologists 0.2% 0.2% 0.2% 0.2% 0.2% 0.2% 

29–2051 dietetic technicians 0.3% 0.3% 0.3% 0.3% 0.2% 0.2% 

29–1127 speech–language pathologists 0.2% 0.2% 0.2% 0.2% 0.2% 0.2% 

29–1124 radiation therapists 0.2% 0.2% 0.2% 0.2% 0.2% 0.2% 

Major (aggregated) occupation categories:

43–0000 office and administrative support occupations 15.0% 15.1% 15.4% 15.3% 15.4% 15.3% 

31–0000 healthcare support occupations (aides) 12.8% 12.7% 12.8% 12.9% 13.1% 13.2% 

37–0000 building & grounds cleaning & maintenance 4.0% 3.9% 3.9% 3.9% 3.9% 3.8% 

11–0000 management occupations 4.1% 3.9% 3.8% 3.7% 3.5% 3.5% 

35–0000 food preparation and serving related occupations 3.1% 3.0% 3.0% 3.0% 3.0% 2.9% 

33–0000 protective service occupations 0.9% 0.9% 0.9% 0.9% 0.9% 0.9% 

39–0000 personal care and service occupations 0.5% 0.5% 0.5% 0.5% 0.5% 0.5% 

Total BLS employment share for included general hospital 

occupation categories 85.9% 85.8% 85.9% 86.3% 86.7% 87.0% 

Total excluded occupation categories 14.1% 14.2% 14.1% 13.7% 13.3% 13.0% 

SOURCE: RTI analysis of BLS Occupational Employment Statistics data. 

45 
The BLS wage index was constructed by dividing an estimate of the MSA–specific 

‘mean wage’ for hospitals with an estimate of the national mean wage for hospitals. The MSA–

specific ‘mean wage’ is a misnomer, in that it does not attempt to estimate the mean wage 

actually paid to hospital employees. First, the estimate is constructed using only the 29 

occupational categories described above. Second, the mean wage rates in these 29 categories are 

weighted by the national (as opposed to local) employment shares of these 29 occupations in the 

hospital industry.21 This approach eliminates differences in index values due to variations in the 

employment shares of the occupations across MSAs. The national mean wage used as a 

denominator for the index is the weighted average mean wage generated using the 29 

occupational categories with national employment weights and national mean wages by 

occupation. 

Many of the 264 MSAs used in our analysis are missing some occupational wage data in 

one or more years. In these cases we proportionally increased the (national) wage bill shares of 

the occupations with reported wage data to equal 100 percent for the area with missing data. This 

procedure implicitly assumes that the relative wages of the missing occupations were the same as 

the average relative wage of the occupations with reported data (weighted by their wage bill 

shares). As a sensitivity analysis, we tried a second normalization for missing data based on 

employment rather than wage shares, but the second normalization had very little impact on the 

final index numbers. 

4.2.4 CMS Wage Index Construction 

MSA Published Values

MSA values for the CMS wage index were taken directly from the CMS’ wage history 

file that accumulates index values by MSA by covered period. We had intended to use a pre–

reclassification, pre–floor index. However, the two indexes available in CMS' wage history file 

are both post–reclassification. We used the one calculated for hospitals that are located in the 

index area and hospitals that were reclassified into the area and had average wages at least as 

high as the average wages of the hospitals located in the area. The MSA value of the wage index 

is based on the average wages of all such hospitals. The other wage index in CMS' file, that we 

did not use, is for hospitals that have been reclassified into the area but whose average wages are 

less than average wages of the hospitals located in the area. Both wage indexes in the CMS file, 

including the one we used, reflect wage floors that might have applied. Where there were 

multiple records in the CMS wage index history file for a given MSA/year (for example, due to 

CMS mid–year updates), we used the latest index values for that year. For the volatility analysis, 

CMS wage index values were used for each urban labor market (or NECMA aggregation) that 

was included in the BLS data. 

 

21 If there are no missing data by occupation, this approach is mathematically equivalent to weighting local relative 

wages (i.e., local wages divided the national average in each category) by the national wage bill share of each 

occupation for the industry. 

46 
Indexes Constructed from Hospital–Level Data

We also constructed indexes of wages, benefits, and total compensation from the 

hospital–level wage and hour data made available on the CMS web site. These data are from the 

IPPS provider cost reports, and are the files used by CMS to construct the hospital wage index 

for payment years 2003 through 2008. The data years are comparable to the data years for the 

BLS series. The downloaded files contain data that have been edited to remove extreme values 

and the wages have been price–adjusted (using the BLS employment cost index) to reflect a 

common accounting period ending date of December 31 for each year. None of the CMS hourly 

wage series are adjusted for occupation mix. 

We grouped provider data by MSA and constructed indexes for PPS relative wages and 

PPS relative total compensation, using measures S3 Part III line 3 (for PPS wages); and S3 Part 

III line 6 (for PPS compensation including contract labor and benefits). Local average wages 

were calculated as the sum of market wages divided by the sum of market hours. Aggregate 

average national hourly wages and hourly total compensation were computed for each year from 

data on all providers in all markets. To be able to compare geographic variation in benefits 

distinct from variation in base wages, we also computed MSA–level measures of average hourly 

benefits and benefits as a percent of total hourly compensation. 

4.2.5 Measures of Volatility 

We explored several measures of volatility both over time and across markets. Range and 

percentile distributions of index values are presented to examine differences in stability across 

the data sources. We also computed the annual percent changes in index values for each MSA 

over the six–year study period, and the distribution of markets by the level of change. For each 

MSA, we computed two study–period summary measures: the first is a mean of the absolute 

values of the five annual change measures, and the second is the MSA–level standard deviation 

(computed over six observations, one for each year). 

4.3 Volatility of the Published CMS Wage Index Versus the BLS Wage Index 

Volatility, or temporal variation in the computed index values, is captured by examining 

both the year–to–year percent change in individual MSA index values over the six years of data, 

and by computing a single measure of dispersion for each labor market, the standard deviation of 

the index across all six years of data. In the results presented below, it was found that the CMS 

wage index usually exhibited more volatility than the BLS wage index. The difference in 

volatility, however, was not dramatic. 

4.3.1 Distribution of Index Values for Each Year, 1999–2004 

First we examined the overall distributions of the CMS and BLS indexes for any 

evidence of volatility. The CMS wage index distribution is stable over time. It has stable 

minimum and maximum values that range each year from about 0.76 to about 1.52 (Table 10). 

Likewise the means (0.97), the medians (0.94), and standard deviations (0.13) were stable, as 

were the other quantile measures such as the first (25%) quartiles and third (75%) quartiles. The 

distributions of the BLS wage indexes (Table 11) were also stable with the exception of a slight 

upward movement of the maximum value from 1.32 in 1999 to 1.41 in 2004. As indicated by its 

47 
larger annual standard deviation and its higher maximum values, the CMS wage distributions are 

slightly more spread out than the BLS wage distributions. The fact the CMS wage indexes were 

based on total compensation (salaries plus fringe benefits) whereas the BLS wage indexes were 

based on just salaries could have contributed to this finding. The overall distributions of the CMS 

and BLS indexes are stable and similar and show little evidence of volatility. 

Table 10 

CMS IPPS Area Wage Index Distributions for MSAs with BLS RN Wage Data, 1999–2004 

 Payment (Data) Year* 

1999 2000 2001 2002 2003 2004 

Quantile (1995) (1996) (1997) (1998) (1999) (2000) 

100% (Maximum) 1.52 1.51 1.50 1.53 1.52 1.51 

99% 1.45 1.45 1.45 1.44 1.44 1.45 

95% 1.21 1.24 1.20 1.21 1.24 1.22 

90% 1.14 1.14 1.13 1.13 1.13 1.13 

75% 1.02 1.02 1.02 1.02 1.04 1.03 

50% (Median) 0.93 0.93 0.94 0.95 0.94 0.94 

25% 0.87 0.87 0.88 0.88 0.89 0.89 

10% 0.83 0.84 0.84 0.84 0.85 0.84 

5% 0.81 0.81 0.82 0.81 0.81 0.82 

1% 0.77 0.78 0.77 0.78 0.79 0.78 

0% (Minimum) 0.76 0.77 0.77 0.74 0.77 0.78 

Mean – unweighted 0.97 0.97 0.97 0.97 0.98 0.97 

Standard deviation 0.13 0.13 0.13 0.13 0.13 0.13 

Number of MSAs 251 251 251 251 251 251 

* The payment year is on top and the data year is in parentheses below the payment year. 

NOTE: Published IPPS area wage indexes (see text). 

SOURCE: RTI analysis of CMS Wage Index History file through Federal Fiscal Year 2004. 

COMPUTER OUTPUT: wa_medpac_r2_march06.log (3–8–07) 

48 
Table 11 

BLS Area Wage Index Distributions for MSAs with RN Wage Data, 1999–2004 

 BLS Data Year 

Quantile 1999 2000 2001 2002 2003 2004 

100% (Maximum) 1.32 1.34 1.35 1.36 1.38 1.41 

99% 1.30 1.32 1.33 1.33 1.33 1.33 

95% 1.17 1.16 1.16 1.16 1.15 1.17 

90% 1.13 1.12 1.13 1.12 1.12 1.13 

75% 1.02 1.02 1.02 1.03 1.03 1.03 

50% (Median) 0.94 0.94 0.94 0.95 0.94 0.94 

25% 0.88 0.88 0.89 0.89 0.89 0.89 

10% 0.84 0.84 0.84 0.85 0.85 0.84 

5% 0.82 0.83 0.83 0.83 0.82 0.83 

1% 0.80 0.78 0.80 0.79 0.79 0.79 

0% (Minimum) 0.78 0.73 0.76 0.76 0.76 0.78 

Mean – unweighted 0.96 0.96 0.96 0.96 0.96 0.97 

Standard deviation 0.11 0.11 0.11 0.11 0.11 0.11 

Number of MSAs 264 264 264 264 264 264 

SOURCE: RTI analysis of BLS Occupational Employment Statistics data. 

COMPUTER OUTPUT: kd_medpac3_fama_march07.log 

4.3.2 Distribution of Annual Percent Changes in Index Values, 2000–2004 

Next, we examined volatility for individual wage index areas by calculating the annual 

percent change in both the CMS and BLS wage indexes for each wage index area (MSA) for 

each year from 2000 to 2005.22 For year 2000 the percent change is from 1999 to 2000 and so 

forth for each subsequent year. The distributions of the annual percent changes for each year are 

shown in Tables 12 and 13. Although the annual wage index changes for most areas are small, 

some areas experience large changes in any year. The largest increases in the CMS wage index 

range from 13.2 to 16.0 percent while largest increases in the BLS wage indexes range from 9.4 

to 20.8 percent. The largest decreases in the CMS wage index range from –7.0 to –14.2 percent 

while the decreases in the BLS wage index range from –4.0 to –14.0 percent. The first and third 

 

22 The annual percent change is equal to the difference between the current year’s wage index and the previous 

year’s index, divided by the previous year’s index and multiplied by 100. 

49 
quartile changes, covering 50 percent of the market areas, were larger in absolute value in the 

CMS wage index than in the BLS wage index. The standard deviations of the changes were also 

larger in the CMS wage index than in the BLS wage index. The CMS wage index exhibited more 

annual volatility than the BLS wage index, but not by a large amount. 

Table 12 

Distribution of Annual Percent Changes in CMS's IPPS Wage Indexes for MSAs that have 

BLS RN Wage Data, 2000–2004 

 Payment (Data) Year* 

2000 2001 2002 2003 2004 

Quantile (1996) (1997) (1998) (1999) (2000) 

100% (Maximum) 15.1% 15.2% 13.2% 14.3% 16.0% 

99% 10.9% 6.9% 11.0% 9.1% 7.4% 

95% 6.2% 5.1% 6.2% 4.7% 3.7% 

90% 4.7% 3.4% 4.2% 3.3% 2.8% 

75% 2.2% 1.8% 2.2% 2.2% 1.4% 

50% (Median) –0.1% 0.2% 0.3% 0.2% –0.1% 

25% –1.9% –1.6% –1.4% –1.4% –1.7% 

10% –4.2% –3.3% –2.9% –3.0% –4.3% 

5% –5.0% –5.0% –4.4% –4.0% –5.5% 

1% –9.5% –6.5% –6.3% –6.4% –7.7% 

0% (Minimum) –11.9% –9.3% –9.4% –7.0% –14.2% 

Mean – unweighted 0.2% 0.1% 0.6% 0.4% –0.3% 

Standard deviation 3.7% 3.0% 3.2% 2.9% 3.1% 

Number of MSAs 251 251 251 251 251 

*The payment year is on top and the data year is in parentheses below the payment year. 

NOTES: Published IPPS area wage indexes (see text). 

SOURCE: RTI analysis of CMS Wage Index History file through Federal Fiscal Year 2004. 

COMPUTER OUTPUT: wa_medpac_r3_updated_march06.log (3–7–07) 

50 
51 

Table 13 

Distribution of Annual Percent Changes in the BLS Area Wage Indexes for MSAs 

That Have RN Wage Data, 2000–2004 

 BLS Data Year 

Quantile 2000 2001 2002 2003 2004 

100% (Maximum) 19.9% 13.9% 20.8% 12.8% 9.4% 

99% 9.5% 7.6% 8.5% 6.0% 8.7% 

95% 4.9% 4.9% 3.1% 2.6% 5.5% 

90% 3.4% 3.3% 1.6% 1.7% 3.5% 

75% 1.9% 1.2% 0.5% 0.7% 1.4% 

50% (Median) 0.0% 0.2% –0.2% –0.2% 0.0% 

25% –2.0% –0.7% –1.0% –0.9% –1.3% 

10% –4.0% –1.5% –1.8% –1.7% –2.4% 

5% –6.0% –2.2% –2.4% –2.0% –3.7% 

1% –10.9% –3.8% –3.6% –5.0% –6.8% 

0% (Minimum) –14.0% –11.6% –4.0% –6.4% –13.3% 

Mean – unweighted –0.1% 0.5% 0.0% –0.1% 0.2% 

Standard deviation 3.6% 2.4% 2.1% 1.7% 2.7% 

Number of MSAs 264 264 264 264 264 

SOURCE: RTI analysis of BLS Occupational Employment Statistics data. 

COMPUTER OUTPUT: kd_medpac3_fama_march07.log 

4.3.3 Distribution of Annual Absolute Percent Changes in Index Values, 2000–2004 

The absolute values of annual percent changes for each MSA were also calculated for 

both the CMS and BLS wage indexes over the period 2000–2004. A frequency distribution by 

size of change is shown in Table 14. Across all years combined, there was a higher percentage of 

larger changes in the CMS index than the BLS index. In the CMS index, 1.3 percent of 

MSA/year changes were 10 percent or greater, compared to 0.9 percent for the BLS index. 

Similarly, 8.8 percent of the CMS changes were 5 to 10 percent, while only 4.8 percent of the 

BLS changes were as large. Over 80 percent of the BLS index MSA/year changes were smaller 

than 2.5 percent, compared to less than 65 percent of the CMS index changes. These statistics 

indicate somewhat greater volatility in the CMS index than in the BLS index. 
52 

Annual CMS Payment (Data) Year* 

Absolute Percent 2000 2001 2002 2003 2004 

Change from (1996) (1997) (1998) (1999) (2000) 

Previous Year Count Percent Count Percent Count Percent Count Percent Count Pe

10% or more 5 2.0 1 0.4 4 1.6 2 0.8 4 5% to 9.99% 32 12.8 24 9.6 22 8.8 15 6.0 17 2.5% to 4.99% 72 28.7 64 25.5 62 24.7 64 25.5 54 0% to 2.49% 142 56.6 162 64.5 163 64.9 170 67.7 176 Number of MSAs 251 100.0 251 100.0 251 100.0 251 100.0 251 

BLS (Data) Year 

2000 2001 2002 2003 2004 

Count Percent Count Percent Count Percent Count Percent Count Pe

10% or more 6 2.3 3 1.1 1 0.4 1 0.4 1 5% to 9.99% 23 8.7 10 3.8 5 1.9 5 1.9 20 2.5% to 4.99% 69 26.1 34 12.9 22 8.3 19 7.2 40 0% to 2.49% 166 62.9 217 82.2 236 89.4 239 90.5 203 Number of MSAs 264 100.0 264 100.0 264 100.0 264 100.0 264 

Table 14 

Distribution of Annual Absolute Percent Changes in CMS's IPPS Wage Indexes and the BLS Wage Index 

RN Wage Data, 2000–2004 

SOURCE: RTI analysis of CMS Wage Index History File through Fiscal Year 2004 and BLS Occupational Emp

data. 

COMPUTER OUTPUT: wa_medpac_r3_updated_march06.log (3–07–07); kd_medpac3_fama_march07.log * The payment year is on top and the data year is in parentheses below the payment year. NOTE: CMS values from published IPPS area wage indexes (see text). 
While a greater share of MSAs experienced larger changes in the CMS wage indexes 

than in the BLS wage indexes, the share of MSAs in the smallest CMS change group (0 to 

2.49%) steadily increased from 57 to 70 percent between 2000 and 2004. In contrast, there were 

large changes in the share of MSAs in the smallest BLS change group. The share in 2000 was 63 

percent and then jumped to 82 percent in 2001, and then again to about 90 percent in 2002 and 

2003, but then declined to 77 percent in 2004. Nevertheless, the BLS share in the smallest 

change group was larger in every year than the CMS share. So while there was more overall 

volatility in the CMS wage index than in the BLS wage index, there was a decline of the 

volatility in the CMS wage index over the period. The BLS wage index exhibited volatility that 

was variable over the period, but always less than the CMS index. 

4.3.4 Distribution of Average Annual Absolute Percent Changes in Index Values, 

2000–2004 

The mean of the annual absolute percent changes was calculated for each MSA over the 

five years 2000 to 2004. That is, for each MSA, five annual percent changes were calculated, 

expressed as absolute values, and then averaged. The results were then grouped into the ranges 

shown in Table 15, where the frequency of each range is shown. No MSA had an average annual 

change of 10 percent or more across the five years. About 5 percent of the MSAs had an average 

annual change in the CMS wage index between 5 and 10 percent while less than 2 percent of the 

MSAs had an average change in the BLS wage index in the same range. Two–thirds of MSAs 

had an average change in the CMS wage index of between 0 and 2.49% while nearly 85 percent 

of MSAs had an average change in the BLS wage index of that amount. The average annual 

change across all MSAs was 2.3 percent for the CMS index versus 1.7 percent for the BLS 

index. On this measure, then, although the average change is modest for both indexes, the CMS 

index was one–third more volatile than the BLS index ((2.3–1.7))/1.7 = 0.35). 

4.3.5 Distribution of Standard Deviations of Wage Index Values, 2000–2004 

Another measure of volatility of the indexes is the standard deviation of the index values 

for each MSA over the period 1999 to 2004. Tables 16 and 17 present the distributions of the 

standard deviations for the CMS and BLS index values respectively; histograms of these 

distributions are also presented. Tables 16 and 17 are consistent with the previous percent change 

tables in suggesting that the variability of the BLS index is somewhat smaller than that of the 

CMS index. The mean and median values of the MSA standard deviations of the BLS index are 

roughly three–quarters of the corresponding values of the CMS index; a comparison of the 

histograms shows that the modal BLS value is lower as well. 

4.4 Sources of Wage Index Volatility and Data Quality Issues 

This section discusses possible sources of volatility in the CMS and BLS wage indexes in 

Sections 4.4.1 and 4.4.2, and computational and data quality issues in the BLS data in Section 

4.4.3. It includes a comparison of the volatility of CMS indexes of wages, benefits, and total 

compensation in Section 4.4.2. Section 4.4.4 concludes with some remarks on the usefulness of 

volatility as a measure of data quality. 

53 
Table 15 

Distribution of Mean Annual Absolute Percent Change in Wage Indexes, for MSAs with 

BLS RN Wage Data, 2000–2004 

CMS Index BLS Index 

Annual Mean Change* Count Percent Count Percent 

10% or more 0 0.0 0 0.0 

5% to 9.99% 13 5.2 5 1.9 

2.5% to 4.99% 70 27.9 36 13.6 

0% to 2.49% 168 66.9 223 84.5 

Mean across all MSAs 2.3% 1.7% 

Number of MSAs 251 100.0 264 100.0 

* For each MSA, average of annual absolute percent changes for each of the 5 years 2000 to 

2004. 

NOTE: CMS values from published IPPS area wage indexes (see text). 

SOURCE: RTI analysis of CMS Wage History File through Fiscal Year 2004 and BLS 

Occupational Employment Statistics data. 

COMPUTER OUTPUT: wa_medpac_r3_updated_march06.log (3–07–07) and 

kd_medpac3_fama_march07.log 

54 
Table 16 

Distribution of the Standard Deviation of CMS’ IPPS Wage Index for MSAs with BLS RN 

Wage Data, 1999–2004 

 Standard 

Quantile Deviation 

100% (maximum) 0.087 

99% 0.069 

95% 0.048 

90% 0.040 

75% 0.029 

50% (median) 0.022 

25% 0.015 

10% 0.011 

5% 0.008 

1% 0.005 

0% (minimum) 0.004 

Mean–unweighted 0.024 

Number of MSAs 251 

NOTE: Published IPPS area wage indexes (see text). 

SOURCE: RTI analysis of CMS Wage Index History file through Federal Fiscal Year 2004. 

COMPUTER OUTPUT: wa_medpac_r3_updated_march06.log (3–7–07) 

55 
Table 17 

Distribution of the Standard Deviation of the BLS Wage Index for MSAs 

with RN Wage Data, 1999–2004 

SOURCE: RTI analysis of BLS Occupational Employment Statistics data. 

COMPUTER OUTPUT: kd_medpac3_fama_march07.log 

 Standard 

Quantile Deviation 

100% (maximum) 0.092 

99% 0.064 

95% 0.039 

90% 0.032 

75% 0.022 

50% (median) 0.016 

25% 0.011 

10% 0.007 

5% 0.005 

1% 0.004 

0% (minimum) 0.002 

Mean–unweighted 0.018 

Number of MSAs 264 

56 
4.4.1 Random Variation and MSA Size 

Table 18 presents the MSAs with the largest and smallest standard deviations in the CMS 

and BLS indexes. If there were a great deal of overlap among these MSAs, that could suggest 

that both indexes are appropriately measuring true wage volatility, or lack of volatility, in these 

MSAs. Alternatively, if there is little overlap in the lists of MSAs between the two indexes, that 

could suggest that random variation plays a larger role than true wage variation in determining 

wage index volatility, or at least that the two indexes are measuring different wage volatility. 

Although two MSAs (Odessa TX and Yolo CA) appear on both lists of MSAs with the largest 

standard deviations, there is no overlap among the MSAs with the smallest standard deviations in 

the two indexes. Of particular interest is that while Tallahassee FL appears on the list of MSAs 

with the smallest standard deviations in the BLS index, it appears on the list of MSAs with the 

largest standard deviations in the CMS index. This suggests that random variation played a 

significant role in determining volatility in the two data series, or that they are measuring 

different wage changes. 

Table 19 compares the largest and smallest standard deviation MSAs on population, the 

total number of hospitals, and the number of short term hospitals in the CMS and BLS indexes. 

The interest here is examining whether the volatility of the index values is a function of MSA 

characteristics, in particular characteristics related to the size of the MSA or of the hospital sector 

in the MSA. The mean population of the least volatile MSAs is larger than the mean population 

of the most volatile MSAs for both indexes. It is not the case, however, that the more volatile 

MSAs are all smaller in population than less volatile ones: the maximum population in the group 

of MSAs with the largest standard deviation is larger than the minimum population in the group 

with the smallest standard deviation, in both indexes. Similarly, the mean number of hospitals is 

higher among the low standard deviation MSAs than among the high standard deviation MSAs, 

but the maximums in the high standard deviation MSAs is larger than the minimum among the 

low standard deviation MSAs. In short, the volatility of wage index values is related to MSA 

size, but MSA size is not the only factor determining volatility. 

4.4.2 Volatility in Wages, Benefits, and Total Compensation from CMS Wage 

Survey Data 

The CMS wage index is composed of both wages and benefits (total compensation), 

whereas the BLS index is based on wages only. It is possible that the greater volatility of the 

CMS index could be due to the inclusion of benefits. We tested this hypothesis by constructing 

geographic (MSA) indexes of wages only, benefits only, and total compensation from the CMS 

hospital wage survey data. Table 20 shows the percentile distribution of annual percent changes 

in the three indexes from 2000 to 2004 (all years pooled). The volatility (change over time) of 

the benefits index is much greater than the volatility of the wage index. However, the volatility 

of the total compensation index (wages plus benefits) is similar to the volatility of the wages only 

index. We conclude that the inclusion of benefits in the CMS wage index does not explain why 

the CMS wage index is more volatile than the BLS wage index (which does not include 

benefits). We discuss and evaluate the benefits data collected as part of the CMS survey further 

below, in Section 4.5. 

57 
Table 18 

Ten MSAs with Largest and Smallest Wage Index Standard Deviations, 1999–2004 

58 

CMS Wage Index BLS Wage Index 

MSA Code MSA Name 

Standard 

Deviation 

MSA Code MSA Name 

MSAs with Largest Standard Deviations

0600 Augusta–Aiken, GA–SC 0.0497 8240 Tallahassee, FL MSA 5800 Odessa–Midland, TX 0.0499 1580 Cheyenne, WY MSA 4600 Lubbock, TX 0.0505 5800 Odessa–Midland, TX MSA 8720 Vallejo–Farifield–Napa, CA 0.0522 9270 Yolo, CA PMSA 9270 Yolo, CA 0.0561 4880 McAllen–Edinburg–Mission, TX MSA 2281 Dutchess County, NY 0.0563 7485 Santa Cruz–Watsonville, CA PMSA 1400 Champaign–Urbana, IL 0.0668 4080 Laredo, TX MSA 1240 Brownsville–Harlingen–San Benit 0.0692 1900 Cumberland, MD–WV MSA 3740 Kankakee, IL 0.0770 8735 Ventura, CA PMSA 5280 Muncie, IN 0.0870 6320 Pittsfield, MA MSA 

MSAs with Smallest Standard Deviations

8680 Utica–Rome, NY 0.0040 7040 St. Louis, MO–IL MSA 2335 Elmira, NY 0.0041 1520 Charlotte–Gastonia–Rock Hill, NC–SC 3610 Jamestown, NY 0.0049 3720 Kalamazoo–Battle Creek, MI MSA 5120 Minneapolis–St. Paul, MN–WI 0.0061 4760 Manchester, NH PMSA 8240 Tallahassee, FL 0.0072 3280 Hartford, CT MSA 6080 Pensacola, FL 0.0072 6760 Richmond–Petersburg, VA MSA 2020 Daytona Beach, FL 0.0072 7600 Seattle–Bellevue–Everett, WA PMSA 9260 Yakima, WA 0.0074 5190 Monmouth–Ocean, NJ PMSA 1640 Cincinnati, OH–KY–IN 0.0075 1720 Colorado Springs, CO MSA 0520 Atlanta, GA 0.0077 4520 Louisville, KY–IN MSA 

SOURCE: RTI analysis of CMS Wage Index History file and BLS Occupational Employment Statistics data. COMPUTER OUTPUT: kd_medpac3_fama_march07.log 
59 

Table 19 

Characteristics of Ten MSAs with Largest and Smallest Wage Index Standard Deviations, 

1999–2004 

 MSAs with Smallest SD MSAs with Largest SD 

Characteristic Mean Minimum Maximum Mean Minimum Maximum 

CMS Wage Index 

Population estimate 2004 758,473 89,984 3,116,206 255,564 107,188 545,309 

Population estimate 2003 750,769 90,413 3,083,637 253,626 105,625 543,943 

Population estimate 2002 745,202 90,614 3,054,637 250,786 104,657 541,340 

Population estimate 2001 737,175 90,675 3,015,573 246,672 104,122 532,091 

Population estimate 1999 708,971 91,738 2,872,109 235,358 102,720 506,685 

Total number hospitals 2003 10.9 3 37 4.7 1 11 

Total number hospitals 2002 10.9 3 37 4.8 1 11 

Total number hospitals 2001 10.8 3 37 4.7 1 11 

Total number hospitals 2000 10.7 3 36 4.8 1 11 

Total number hospitals 1999 10.9 3 36 4.8 1 11 

# short term general hosps 2003 8.6 2 28 3.3 1 7 

# short term general hosps 2002 8.6 2 28 3.4 1 7 

# short term general hosps 2001 8.6 2 28 3.3 1 7 

# short term general hosps 2000 8.3 2 26 3.3 1 7 

# short term general hosps 1999 8.7 2 27 3.6 1 8 

BLS Wage Index 

Population estimate 2004 1,397,527 458,332 2,667,862 314,614 85,296 797,699 

Population estimate 2003 1,381,905 459,174 2,639,978 309,871 84,083 791,130 

Population estimate 2002 1,372,099 457,081 2,633,925 306,013 82,894 783,920 

Population estimate 2001 1,354,734 453,455 2,617,637 299,666 81,958 770,630 

Population estimate 1999 1,300,141 447,164 2,569,029 283,697 78,877 745,063 

Total number hospitals 2003 19.6 5 49 4.4 2 8 

Total number hospitals 2002 19.5 5 48 4.7 2 10 

Total number hospitals 2001 19.0 5 48 4.7 2 10 

Total number hospitals 2000 19.5 5 47 4.7 2 10 

Total number hospitals 1999 20.1 5 49 4.8 2 10 

# short term general hosps 2003 14.5 4 37 3.6 2 7 

# short term general hosps 2002 14.0 4 35 3.6 2 8 

# short term general hosps 2001 14.0 4 37 3.7 2 8 

# short term general hosps 2000 14.5 4 36 3.7 2 8 

# short term general hosps 1999 14.9 4 38 3.7 2 8 

SOURCE: RTI analysis of CMS Wage Index History file, BLS Occupational Employment Statistics data, and Area 

Resource file. 

COMPUTER OUTPUT: kd_medpac3_fama_march07.log 
Table 20 

Distribution of annual percent changes in indexes of hospital wages, benefits, and total 

compensation, 2000–2004 

Wages Benefits Total Compensation 

Percent change from prior year 

Maximum 30.7 175.7 24.3 

90th 3.5 11.5 4.0 

75th 1.6 5.1 1.9 

50th 0.1 0.3 0.1 

25th –1.3 –4.0 –1.5 

10th percentile –2.9 –8.8 –3.4 

Minimum –27.1 –66.6 –25.8 

NOTE: Includes PPS wages, benefits, and total compensation. 

SOURCE: RTI Analysis of FY 1999 through FY 2004 CMS Hospital Wage Survey File. 

4.4.3 Computational and Data Quality Issues in the BLS Series 

1) Effect of Rolling Sample 

There are two primary issues associated with the rolling nature of the BLS process of 

sampling employers from which to collect wages (see Section 4.2.1 for description of the 

BLS sampling and data collection procedures). First, this structure results in year to year 

samples that are not independent: year to year, two–thirds of the data are unchanged 

(ignoring the updating, about which see below). As a result, changes in relative wages in 

an MSA are smoothed, which will tend to reduce the perceived volatility of the data 

series. Second, because of inflation, data from previous years used in a given year must 

be updated into current dollars. The Employment Cost Index (ECI) is used to do this. The 

ECI assumes that the occupational composition in a sector is relatively constant, and that 

local wages in an occupational category change at the same rate as the national average 

for that occupational category. This also tends to smooth out the variability in the sample, 

in that the updating process pulls all MSAs in towards the mean change over time; that is, 

MSAs experiencing larger than average growth in wage rates are not inflated enough, and 

MSAs experiencing smaller than average growth in wage rates are inflated too much. 

2) Series Consistency23

To the extent that specialty hospitals (i.e., those in NAICS categories 622200 and 

622300) employ a different occupational mix or offer different wages in a given 

occupational category than general hospitals (i.e., NAICS 622100), the BLS indexes of 

all hospitals may not accurately reflect changes in relative wages in general medical and 

surgical hospitals. 

 

23 Further details are available at http://www.bls.gov/oes/oes_ques.htm#Ques27. 

60 
3) Full Time Versus Part Time Employees 

The employment data in the BLS OES series is based on total employment rather than 

full time equivalents (FTE); the series does not distinguish between full and part time 

employment. To the extent that there is a difference in hourly wage rates between full and 

part time employees, and to the extent that some occupational categories are more or less 

likely to have full time employment in the hospital sector than other sectors, the BLS data 

series may not yield accurate occupational level mean hourly wages for the hospital 

sector. 

4) Changes in the BLS Series Over Time 

BLS instituted a number of changes to the OES series over the period 1999 – 2004 used 

here. These include: 

• In 2002, BLS switched from defining industry sectors under the Standard Industrial 

Classification (SIC) system to defining them under the North American Industry 

Classification System (NAICS). This primarily affected how ‘auxiliary’ 

establishments are categorized, but also affected the specific jobs listed on the forms 

(and consequently, employment counts in residual categories). 

• When BLS switched to the NAICS in 2002, it did not release data on the 3 digit (e.g., 

622000) classification. In order to maximize consistency, the data used here were 

aggregated from the 4–digit classifications (i.e., 622100, 622200, 622300). The 

aggregation procedure used here may not be equivalent to the aggregation procedure 

used by BLS. 

• In an attempt to reduce seasonal influences on the data series, in 2002 BLS switched 

from collecting data in October – December to collecting data at two points during 

the year, in May and November, and began publishing results twice per year. The 

May series was used in the analyses here (following MedPAC), and so may introduce 

some volatility as a result of the 6 – 9 month difference in the seasonal collection 

pattern. 

• Also in 2002, BLS changed the way the occupational mean wage was calculated for 

categories with highly paid workers (>$70/hr). Note that this occurs if any workers in 

the survey exceeded this threshold, not if the average wage exceeded the threshold. 

• In 2001, BLS changed the data collection method, specifically regarding employment 

in categories not listed on the survey forms. This affects residual categories as well as 

all–sector (e.g., xx–0000) counts. 

5) Missing Data 

As noted above, wage and employment data were not available at the MSA level for all 

occupations in all years. Data on RNs (29–1111) were available in all 6 years for 264 out 

of 318 (83%) MSAs; of these 264 MSAs, only five had complete data in all other 

categories in all years. The distribution of the data not observed may systematically differ 

61 
from the distribution of the data which is observed. For example, it is clear that the more 

hospitals in an MSA, the more likely that the RN data were reported: 70 of 71 MSAs 

with more than nine hospitals reported complete RN data, while only 61 out of 149 

MSAs with fewer than three hospitals reported complete RN data. To the extent that the 

number of hospitals in a labor market affects the demand for RN labor, it is plausible that 

the demand conditions in markets missing the RN data are substantially different than in 

the MSAs with complete data. 

In the absence of outside data, we have to adjust for missing data by making an 

assumption about the missing wages. Here, we have proportionally normalized the 

(national) wage bill shares of the occupations with reported wages to equal 100 percent. 

This assumes that the relative wages (local relative to national) for the missing 

occupations are equal to the average relative wage of occupations reported for an MSA 

(weighted by their national wage bill shares). Our normalization for missing data may 

introduce errors in the MSA–specific index values. 

6) Choice of Occupations for Fixed Weight Index 

Another question of interest is whether broad aggregate categories (i.e., xx–0000) should 

be used in the construction of the index. The alternative would be to decompose the xx–

0000 categories into the finer classification units (e.g., xx–1234), choosing either the 

largest categories, or a sufficient number of categories to account for a threshold 

percentage of employment in the xx–0000 category. The best argument in favor of using 

the aggregated category would be that the standard error of the estimate would likely be 

lower than the standard error of the weighted average of the smaller categories; the 

argument in favor of using the finer categories is that the estimate produced would likely 

better reflect the occupational mix within the hospital sector. It is an empirical question 

whether the reduced variance would compensate for the increased bias. 

4.4.4 Is Volatility a Useful Gauge for Data Quality? 

It should be recognized that not all variability in a wage index is ‘bad.’ The purpose of a 

wage index is to recognize real differences in wages across labor market areas, including 

changes over time in a labor market area’s relative wages. Variability due to real changes is 

desirable; it is variability that does not reflect real changes that should be avoided. Unfortunately, 

there is no definitive way to distinguish the former from the latter within either the CMS or BLS 

data series. It may be possible to infer that changes which are consistent across the two series are 

more likely to be real; or that annual wage changes that are consistent with longer–term trends in 

area wages are more likely to be real than changes that do not exhibit any trend. But there is no 

conclusive test for how much of the variation within either wage series reflects real wage 

changes versus measurement errors. The absence of the definitive ability to distinguish ‘good’ 

variability from ‘bad’ variability limits the usefulness of variability itself as a metric on which to 

evaluate the performance of the alternative wage indexes. 

4.5 Variation in Hospital Benefits Over Time and Geography 

Most of our discussion has focused on wages, but benefits comprise a significant and 

growing share of the total compensation data underlying the current Medicare hospital wage 

62 
index. In this section, we first discuss the definition and reporting of the benefits data in the 

Medicare Cost Reports, in Section 4.5.1. Then we present empirical evidence on trends and 

geographic variation in hospital benefits as a percentage of total compensation in Section 4.5.2. 

If benefits were a constant proportion of total compensation across areas, then it would be 

sufficient to measure wage variation across areas, that is, it would not be necessary to 

incorporate benefits into the Medicare area wage index adjustment. 

4.5.1 CMS Benefits Data 

Our CMS total compensation wage series includes both contract labor and benefits, and 

both of these items are less consistently reported across hospitals than wages. Relative benefits 

are expected to exhibit more variability – across providers, markets and over time – because they 

are not as precisely defined as payroll–related wages. “Wage–related costs” as reported on the 

Worksheet S–3 surveys are intended to capture benefits accumulated in the hospitals’ 

accounting record books in accordance with generally accepted accounting principles (GAAP). 

These types of costs are expected to include items such as payroll taxes, health insurance, 

pensions or other retirement contributions, child care benefits, tuition reimbursement or retiree 

health care costs. The instructions to Worksheet S–3 Part II allow hospitals to record additional 

wage–related costs that may not have been allowed elsewhere on the MCR, provided they follow 

GAAP. Although there is an extensive reconciliation worksheet that providers are asked to file to 

document all of the benefits that they report for wage index purposes (Exhibit 6 of CMS Form 

339, the “Provider Cost Report Reimbursement Questionnaire”), there are many grey areas 

where appropriate measurement may be argued. This is particularly true for capturing current 

period pension expenses, expenses related to self–funded insurance, and the proper handling of 

severance pay. For this reason, it would be difficult to establish how much of the market–level 

variation in relative benefits that we see is due to real variation in this type of compensation, and 

how much is due to variation in accounting practices. Where we see stable differences in relative 

benefits between larger markets, this might be more likely to reflect true regional variation in 

non–wage compensation. In markets with only a few hospitals, total compensation index values 

are sensitive to individual provider decisions on specific accounting issues. 

4.5.2 Variation in Benefits as Percent of Total Hourly Compensation 

Using the hospital Medicare Cost Report data, there is a clear increase in reported 

benefits as a percentage of total compensation over the six years of our study, that occurred in 

both rural and urban areas, and across all regions (Table 21 and Figure 1). Across markets, the 

mean value of benefits as a proportion of total hourly compensation rose from 16.7 percent in 

1999 to 19.7 in 2004. The increase was especially rapid from 2000 to 2003. Outlier markets with 

values below 10 percent occurred in only two years, and very few markets have aggregate 

benefits greater than 25 percent of total compensation (Figure 1). Using FY 2004 data only, 

there is some regional variation in benefits as a percentage of total compensation—the Northeast 

and Midwest are above average, the South is below average, and the West is about average 

(Table 21 and Figure 2). Statistically significant differences are present only between the South 

and the Northeast, and the South and the Midwest. There are no statistically significant 

differences in benefits as a percentage of total compensation between urban and rural markets 

(Figure 2). 

63 
Table 21 

Benefits as Percent of Hospital Total Hourly Compensation, by Year and Location, as 

Reported on the Medicare Cost Reports (market–level mean values) 

Urban markets Northeast Midwest South West All Areas 

1999 17.0% 16.9% 16.5% 16.6% 16.7% 

2000 17.0% 16.7% 16.5% 16.9% 16.7% 

2001 17.5% 17.5% 16.9% 17.2% 17.2% 

2002 18.6% 18.6% 17.8% 18.3% 18.2% 

2003 20.0% 20.3% 18.6% 19.3% 19.4% 

2004 20.2% 20.6% 18.9% 19.5% 19.6% 

Rural markets 

1999 17.9% 16.8% 16.8% 17.2% 17.1% 

2000 17.9% 17.2% 16.9% 17.8% 17.3% 

2001 18.3% 18.1% 17.4% 18.2% 17.9% 

2002 19.4% 19.2% 18.2% 19.2% 18.9% 

2003 21.1% 20.5% 18.9% 19.9% 19.9% 

2004 21.7% 20.2% 19.2% 20.4% 20.1% 

SOURCE: RTI analysis of hospital Medicare Cost Report wage survey data. 

Figure 1 

Temporal Trends in Benefits as Reported on the Medicare Cost Report Worksheet S–3 .05 .1 .15 .2 .25 .3 BEN_PC

1999 2000 2001 2002 2003 2004

Benefits as percent of total hourly compensation

SOURCE: RTI analysis of hospital Medicare Cost Report wage survey data. 

64 
65 

The BLS data Employer Cost for Employee Compensation series does not show any 

trend in benefits as a share of total compensation for all health services workers between 1993 

and 2002, but shows an upward trend from 2002 to 2006 (Figure 3). For hospital workers, the 

share of benefits increased slightly by about 1.5 percentage points between 1993 and 2004. It 

increased again subsequent to 2004 by nearly a full percentage point, possibly due to a surge in 

health insurance premiums. From 1999 to 2004, the increase in the BLS share of benefits is 

clearly smaller than the increase in the Medicare Cost Report share of benefits (1.5 percentage 

points versus 3 percentage points). It is possible that the larger upward trend in the benefit 

percentage reported on the Medicare Cost Reports arises from the incentive for more complete 

reporting of benefits because of the use of these data in the Medicare hospital wage index 

adjustment. It appears that the BLS is including more benefits in its series than CMS is, because 

the BLS benefit percentage is about 30 percent, compared to less than 20 percent for the CMS 

series. Hence, the benefit trends in these two data series may not be directly comparable. 

SOURCE: RTI analysis of hospital Medicare Cost Report wage survey data. 

Figure 2 

Geographic Variations in Benefits as Reported on the Medicare Cost Report Worksheet S–3 

(FY 2004 survey only) 

.1 .15 .2

perc

non-metro metro

excludes outside values

FY 2004 survey data .1 .15 .2 .25 .3 percent .25 .3 ent

NORTHEAST MIDWEST SOUTH WEST

excludes outside values

By region

By rural or urban

Benefits as percent total hourly compensation

for markets grouped by location
27.8 28.0 27.8 27.9

27.1 26.7 26.4 26.6 27.4 27.4 27.5 27.6 27.6 27.9 27.7 27.7 27.8 27.9 28.1 28.1 28.1 28.3 28.4 28.5 28.5

29.1 29.3 28.8 28.9

30.0 30.2 29.8 30.4 29.9

29.3 29.5 29.6 29.9 29.9 29.9 30.0 30.0 30.4 30.5 30.6 30.8 31.0 31.0 31.1 31.1 31.4 31.5 31.6 31.3

32.1 32.2 32.2 32.3

0.05.0

10.015.020.025.030.035.0

1993 1995 1997 1999 20012002-q22002-q42003-q22003-q42004-q22004-q42005-q22005-q42006-q22006-q4

Year and Quarter

Percent H

H

Figure 3 

Benefits as a Share of Total Compensation, 1993–2006, According to BLS 

Health Services and Hospital Occupations, National 

66 

SOURCE: RTI analysis of BLS Employer Cost for Employee Compensation data. NOTE: Civilian workers. 
If reported Medicare Cost Report benefits are accurate, the differences in more recent 

years between markets in the South and markets elsewhere in the country raise the potential that 

any index constructed without data on benefits will overstate relative total compensation costs in 

the South. We computed the ratio of the total compensation index to the wage–only index (minus 

1) as a measure of potential bias. If relative total compensation is higher than relative wages in 

an area, this measure will be positive. The bar graphs in Figure 4 show unweighted mean values 

of this measure across all markets, grouped by region. In 1999 there would been have been little 

regional bias from excluding benefits in the computation, but by 2004, relative total 

compensation in the South is systematically lower than relative wages and relative total 

compensation in other regions, particularly the West, is systematically higher than relative 

wages. Hence, excluding benefits from the hospital wage index calculation would benefit the 

South and disadvantage other regions, particularly the West. 

Figure 4 

Potential Regional Bias from Excluding Benefits in the Wage Index Computations -.01 -.005 0 .005 .01 .015 ratio-1

NORTHEAST MIDWEST SOUTH WEST

FY 1999 Survey Data

-.01 -.005 0 .005 .01 .015

ratio -1

NORTHEAST MIDWEST SOUTH WEST

FY 2004 Survey Data

aggregate averages by region

Differential:

(total compensation index/wage only index)-1

SOURCE: RTI analysis of hospital Medicare Cost Report wage survey data. 

67 
REFERENCES 

Blackburn, M. and D. Neumark, 1992, "Unobserved Ability, Efficiency Wages, and 

Interindustry Wage Differentials", Quarterly Journal of Economics 107:1421–36. 

Borjas, G. and V. Ramey, 2000, "Market Responses to Interindustry Wage Differentials", NBER 

Working Paper # 7799. 

Centers for Medicare and Medicaid Services. FY 2007 HOSPITAL IPPS PROPOSED RULE–

GEOGRAPHIC RECLASSIFICATION. Obtained from the following internet address: 

http://www.cms.hhs.gov/apps/media/press/release.asp?Counter=1837 

Cromwell, J., Henricks, A., and Pope, G.C. Report on Geographic (Urban–Rural) Refinements to 

PPS Payment Adjustment. Report prepared for Health Care Financing Administration, Contract 

No. 500–85–0015. 1986. 

Dalton, K and R. Slifkin. Rural Hospital Wages and the Area Wage Index: 1990–1997, North 

Carolina Rural Health Research and Policy Analysis Center. Chapel Hill, NC. Working Paper 

No. 70, November 2000. 

Ehrenberg, R. and R. Smith, 1988, Modern Labor Economics. Theory and Public Policy. Third 

Edition. Boston: Scott Foresman and Company. 

Feldstein, M., 1971, The Rising Cost of Hospital Care. Washington DC: Information Resources 

Press. 

Gibbons, R. and L. Katz, 1992, "Does Unmeasured Ability Explain Inter–Industry Wage 

Differences?", Review of Economic Studies 59:513–35. 

Keane, M., 1993, "Individual Heterogeneity and Inter–industry Wage Differentials", Journal of 

Human Resources 28(1):134–61. 

Krueger, A. and L. Summers, 1988, "Efficiency Wages and the Inter–Industry Wage Structure", 

Econometrica 56:259–94. 

MedPAC, 2007, "An alternative method to compute the wage index". 

Murphy, K. and R. Topol, 1990, "Efficiency Wages Reconsidered: Theory and Evidence." In 

Advances in the Theory and Measurement of Unemployment, edited by Yoram Weiss and 

George Fishelson, pp. 204–240. London: Macmillan. 

Office of Management and Budget (OMB) 2004. Update of Statistical Area Definitions and 

Additional Guidance on Their Uses. Obtained from the following internet address: 

http://www.whitehouse.gov/omb/bulletins/fy04/b04–03.html. 

Osburn, J., 2000, "Interindustry wage differentials: patterns and possible sources", Monthly 

Labor Review, February 2000. 

68 
Pope, G. Occupational Adjustment of the Prospective Payment System Wage Index. Health Care 

Financing Review 11(1): 49–61, Fall 1989. 

Pope, G. and K. Adamache, 1993, Hospital Wages and the Prospective Payment System Wage 

Index. Report to the Health Care Financing Administration under Cooperative Agreement No. 

17–C–99500/1–01. Waltham, MA: Center for Health Economics Research. 

Pope, G., 1989, "Occupational adjustment of the prospective payment system wage index", 

Health Care Financing Review 11(1):49–61. 

Prospective Payment Assessment Commission: Adjusting a Wage Index for Geographic 

Differences in Occupational Mix. Washington. US Government Printing Office, June 1990. 

Prospective Payment Assessment Commission: Technical Appendices to the Report and 

Recommendations to the Secretary, US Department of Health of Human Services. April 1986. 

Schumacher, E. and B. Hirsh, 1997, "Compensating Differentials and Unmeasured Ability in the 

Labor Market for Nurses: Why Do Hospitals Pay More?", Industrial and Labor Relations 

Review 50(4):557–579. 

Size, T. “A Pause on the Road to Rural Hospital Equity”. Rural Health Care. Nov–Dec, pp. 14–

16. 1992. 

Sloan, F. and B. Steinwald, 1980, Hospital Labor Markets. Lexington, MA: D.C. Heath and 

Company, Lexington Books. 

Varian, H., 1978, Microeconomic Analysis. New York: W.W. Norton. 

VLex.us April 2006. Obtained from the following internet address: 

http://www.vlex.us/caselaw/U–S–Court–Of–Appeals–Second–Circuit/Bellevue–Hospital–

Center–v–Leavitt/2100–20306007,01.html

Wellever, A. Hospital Labor Market Area Definitions Under PPS. Working Paper #7. October 

1997. 

Wellever, A. The Area Wage Index of the Medicare Inpatient Hospital Prospective Payment 

System: Perspectives, Policies and Choices: August 2000. The Rural Policy Research Institute. 

Williams, D. Improving the Wage Index: The Area Wage Index and the Mix of Occupations 

Across Areas. Prospective Payment Assessment Commission. Technical Report I–91–03. 

Washington DC. July 1991. 

Williams, D., Pettengill, J. and Lisk, C. Adjusting a Wage Index for Geographic Differences in 

Occupational Mix. Prospective Payment Assessment Commission. Technical Report I–90–02. 

Washington DC. June 1990. 

Wright, G. and Marlor, F. Alternative Market Area Definitions. Prospective Payment 

Assessment Commission. Technical Report E–90–02. Washington DC. February 1990. 

69