A p p e n d i x

Additional technical 

information on constructing 

a compensation index 

from BLS data

Additional technical infor mation on constructing a compensation index from BLS data | June 2007 

We constructed wage indexes and compensation indexes 

using three sources of data: the Bureau of Labor Statistics 

(BLS) Occupational Employment Statistics (OES) survey, 

the 2000 decennial census data, and data from provider 

cost reports submitted to CMS. 

We start with data from the BLS OES survey, which is 

published each May. For each metropolitan statistical 

area (MSA), state, and the nation, BLS staff estimate 

hourly wages by occupation across all employers in the 

geographic area. At the national level, they also provide 

for each industry an estimate of the share of employment 

in that industry for each occupation. They construct the 

estimates from a sample of 1.2 million establishments 

drawn over three years. Hourly wages in the OES survey 

do not include benefits, and the sample does not include 

self-employed workers. 

At the national level, the share of each of the top 10 

occupations employed in hospitals, nursing homes, and 

home health agencies is shown in Table 6-1. The analysis 

does not include occupations that typically bill Part B for 

their services, such as physicians.

The occupation accounting for the greatest share of 

workers differs by industry. For example, RNs are the most 

common in hospitals, and health care support occupations 

are most common in nursing homes and home health 

agencies. However, there is significant overlap in the 

occupations all three industries employ, which implies 

that they compete for those types of workers. Nonmedical 

workers—for example, office workers—account for a 

large share of hospital payrolls. Presumably, hospitals 

compete with many industries to hire those workers.

To construct the occupational weights used in our hospital 

analysis, we limited our fixed-weight index to the 30 

occupations shown in Table 6-2, which differs from 

Table 6-1 in that the occupations are weighted by the 

share of wages in the industry rather than by the share 

of employees. Higher paid occupations will have a wage 

share higher than their employment share. Further, we 

express the wage share as the percentage of wages that 

occupation accounts for relative to the total wage share 

these 30 occupations represent. For example, RNs have 

higher than average wages and account for 43.22 percent 

of the wages in the hospital industry represented by these 

30 occupations. However, they represent only 28.1 percent 

of hospital employees. Conversely, health care support 

occupations have a wage share of 8.54 percent and an 

employment share of 12.9 percent because their wages are 

lower than the average hospital wage.

Computing relative compensation for each 

MSA and statewide rural wage area

In our first step, we compute compensation index values 

for each market area—the MSA (or divisions of MSAs) 

and the balance of state areas, which are the non-MSA 

counties in the state. We start by finding the relative wage 

for each occupation in each MSA. The relative wage 

for an occupation is the ratio of the mean wage for that 

occupation in the MSA to the mean wage for the same 

occupation nationally. The wages are for all employers 

of the occupation (as stated previously, this reduces the 

circularity of the wage index). In each market, the relative 

wages are then multiplied by the wage share weights for 

the set of 30 occupations shown in Table 6-2. The result 

is the compensation index for the market (wages only, no 

benefits).

BLS computed the MSA and balance of state (statewide 

rural in CMS parlance) wage indexes for us. In some 

areas, the BLS data do not have a value for every 

occupation. In those cases, we asked BLS to compare 

those occupations for which they had data in an area with 

the same occupations at the national level to compute 

the wage index. This is equivalent to the assumption that 

the missing occupations have the same relative wages in 

that area as the occupations with data. In every market, 

BLS has wages for occupations representing more than 

95 percent of hospital payrolls; hence, these missing data 

have little effect on the computed wage indexes. BLS 

does not report some occupation-specific data in markets 

where one dominant employer could be identified. By 

computing the index value and thus combining data for all 

the occupations, BLS was able to include those data and 

preserve confidentiality. 

BLS uses New England city and town areas (NECTAs) 

rather than MSAs in some New England areas. Some 

argue that NECTAs better represent labor markets than 

MSAs or counties in New England. In these cases, we 

use the NECTA as the market and attempt to map it to a 

county. In some cases, counties and NECTAs do not match 

exactly, and we had to assign a county to a particular 

NECTA or an average of two NECTAs that are both in the 

county. If our recommendations are implemented, CMS 

could consider the option of using smoothed NECTAs 

rather than counties. 

Source of benefit data 

The hospital, skilled nursing facility (SNF), and home 

health cost reports provide data on total wages and total 

benefit costs for each facility. The benefits (wage-related 
 June 2007 | Additional technical infor mation on constructing a compensation index from BLS data

costs) and wages are currently reported in two places on 

cost reports: on worksheet S-3 (which is used for the wage 

index) and on worksheet A. Wage-related costs include the 

employers’ share of the Federal Insurance Contributions 

Act (FICA) tax, Medicare taxes, unemployment insurance, 

health insurance, employer 401k contributions, pension 

costs for defined benefit plans, and other smaller categories 

of wage-related costs. Because benefits include payroll 

taxes (FICA, Medicare, unemployment), we know hospitals 

should be reporting benefit costs that are more than 7 

percent of wages. Wage-related costs are reported on line 

5 column 2 of worksheet A. Total wages are also reported 

on worksheet A on line 101. We computed benefits as 

a share of wages using worksheet A, excluding outliers 

(greater than 35 percent or less than 15 percent). When the 

worksheet A data were outliers, we used worksheet S-3. To 

eliminate the need for hospitals to file worksheet S-3 in the 

future, CMS should require that all hospitals state all their 

benefits’ costs on worksheet A. In most cases, worksheet 

A data are exactly or approximately equal to worksheet 

S-3 data. In some cases, they may differ because hospitals 

use generally accepted accounting principles for worksheet 

S-3 and Medicare accounting for worksheet A. However, 

Office of Inspector General (OIG) auditors informed us 

that they believe the data should match and that hospitals 

are required to follow Medicare accounting even on 

worksheet S-3. OIG reported cases in which this difference 

of opinion about accounting standards had a material 

impact on reported benefit expense (OIG 2007). Clarifying 

the reporting rules should resolve this problem. We did not 

obtain critical access hospital (CAH) benefit information 

for this analysis; however, in the future CMS may want to 

include it. 

Computation of area benefit-to-wage ratios

The 2005 BLS survey data are based on surveys of 

establishments in 2003, 2004, and 2005. Therefore, to 

match the data as closely as possible with benefits, we 

compute the mean level of benefits over the same three 

years. We first create three-year averages of the benefit-towage ratios for hospitals, SNFs, and home health agencies 

in each market area. Because BLS wage data come from 

all employers, we create a weighted average of benefits 

to wages for each occupation in the region based on the 

national share of employment in that occupation across 

hospitals, SNFs, and home health agencies. We then create 

a weighted average benefit-to-wage ratio for the type of 

workers hospitals employ, the type of workers home health 

agencies employ, and the type of workers SNFs employ by 

multiplying the estimated benefit-to-wage ratio for each 

occupation by the national wage share of that occupation in 

each industry. The result is that every MSA and statewide 

T A B L E

6 –1 Top 10 occupation categories by sector, share of employees

Occupation Hospitals Nursing facilities Home health agencies

Registered nurses 28.1% 7.6% 14.5%

Office and administrative support occupations 15.5 4.5 8.0

Health care support occupations 12.9 42.3 33.0

Building and grounds cleaning and maintenance occupations 3.8 6.9 0.4

Licensed practical and licensed vocational nurses 3.6 11.8 6.5

Management occupations 3.4 2.8 3.1

Food preparation and serving related occupations 2.9 11.2

Radiologic technologists and technicians 2.3

Medical and clinical laboratory technologists 2.0

Respiratory therapists 1.5

Medical records and health information technicians 0.8 0.5

Physical therapists 0.6 2.0

Occupational therapists 0.7

Personal care and service occupations 2.8 25.0

Total of top 10 occupations 75.7 91.4 93.8

Source:	 MedPAC analysis of Bureau of Labor Statistics May 2005 Occupational Employment Statistics Survey.
Additional technical infor mation on constructing a compensation index from BLS data | June 2007 

rural area has its own benefit share for hospital-type 

workers, home-health-type workers, and SNF-type workers. 

On average, across all markets, hospital-type workers have a 

benefit-to-wage ratio of 24 percent, SNF-type workers have 

a ratio of 22 percent, and home-health-type employees have 

a ratio of 23 percent. Benefits tend to be slightly higher in 

high-wage areas and slightly lower in the South.1

Computation of the benefit-adjusted 

compensation index

The MSA-level compensation index is computed as follows. 

We start with the national occupation weights from BLS, 

national mean hourly wages by occupation for the 30 

occupations we examine, and an MSA’s hourly wages by 

occupation. The data for two occupations are shown in 

Table 6-3 (p. 4). 

For simplification, assume that data were available only 

for these two occupations (in reality BLS provided us with 

data on occupations representing more than 95 percent 

of wages in every market). Also assume that the ratio of 

benefits to wages was 27 percent in the market shown 

compared with a national average of 24 percent. The 

MSA-level benefits’ adjusted wage index (before budgetneutrality adjustments) would then be equal to:

Wage index without benefits

= [(7.45% × 57.12/39.36) + (43.22% × 33.72/27.80)]/

(7.45% + 43.22%) 

= 0.6325/0.5066 

= 1.2485 

Compensation index with benefits

= (wage index without benefits) × (1 + 0.27)/(1 + 0.24) 

= 1.2787 

The value of 1.2787 would not be the final value for 

the MSA-level compensation index. It is adjusted for 

budget neutrality to make the total payments provided to 

all hospitals under the current wage index (without the 

Section 508 adjustment) equal total payments under the 

new compensation index. Total payments are computed 

with an inpatient payment model that takes into account 

hospital-specific factors such as indirect medical 

education payments, disproportionate share payments, 

sole community hospital status, outlier payments, and 

Medicare-dependent hospital status. Instead of modeling 

outpatient effects for budget neutrality, we assumed that 

outpatient shifts would be proportional to inpatient shifts.

Creating county-specific compensation 

indexes

As a second step, we used data from the 2000 census 

to adjust the wages within market areas by county. 

For each county, the Census Bureau provided data on 

wages by occupation and place of employment. The key 

occupations were RNs (census occupation 313), LPNs 

and licensed vocational nurses (census occupation 350), 

T A B L E

6 –2 Share of hospital 

wages by occupation

Key hospital occupations

Share of 

wages

Registered nurses 43.22%

Office and administrative support occupations 11.73

Health care support occupations 8.54

Management occupations 7.45

Licensed practical and licensed vocational nurses 3.27

Radiologic technologists and technicians 2.83

Medical and clinical laboratory technologists 2.54

Pharmacists 2.48

Building and grounds cleaning

and maintenance occupations 2.14

Respiratory therapists 1.88

Food preparation and serving related occupations 1.65

Physical therapists 1.55

Medical and clinical laboratory technicians 1.18

Surgical technologists 1.13

Medical records and health information technicians 0.99

Diagnostic medical sonographers 0.76

Occupational therapists 0.74

Pharmacy technicians 0.73

Cardiovascular technologists and technicians 0.72

Health technologists and technicians, all other 0.72

Emergency medical technicians and paramedics 0.61

Protective service occupations 0.58

Dietitians and nutritionists 0.43

Nuclear medicine technologists 0.41

Respiratory therapy technicians 0.41

Radiation therapists 0.35

Speech–language pathologists 0.34

Personal care and service occupations 0.28

Dietetic technicians 0.17

Psychiatric technicians 0.17

Note:	 Share of wages as percent of share represented by these 30 occupations.

Source:	 MedPAC analysis of Bureau of Labor Statistics May 2005 Occupational

Employment Statistics Survey.
 June 2007 | Additional technical infor mation on constructing a compensation index from BLS data

management (census occupations 001 to 043), and office 

and administrative support (census occupations 500 to 

593). All the occupations in Table 6-2 (p. 3) were matched 

to census categories. We then aggregated county-level 

employment and wages from census data to create data for 

MSAs and statewide rural areas. 

Next we screened and cleaned the county-level data. For 

all occupations except RNs, if there were 30 or fewer 

observations for an occupation in a county we replaced 

the county data with the census MSA or statewide rural 

average wage for that occupation. For RNs, we required 

that 50 respondents reported working in the county in the 

2000 census. We set a higher threshold for RNs because 

of their high weighting in the compensation index. After 

replacing data in counties where the sample size was 

small, we screened for outliers. The purpose of the sample 

size and outlier screens was to acknowledge that the 

census data are imperfect (as are CMS and BLS data) and 

we did not want one or two errant responses to distort a 

county’s compensation index. Because of this replacement, 

the county-level adjustment has a significant effect only 

for counties with a significant number of health care 

workers. Counties with few health care workers will be 

assigned the market-area wage level for most occupations. 

The county-level wages are then weighted based on the 

weights in Table 6-2 to create a weighted average wage for 

each county and for each MSA or statewide rural area.

Next, the ratio of the county-level weighted average 

wage to the market-level (i.e., MSA, statewide rural area) 

weighted average wage was computed. For example, if the 

weighted county wage was 110 percent of the average for 

the counties in the MSA, the ratio would be 1.1. We then 

took the compensation index for that market area computed 

from BLS data and adjusted it by the county wage ratio 

computed above. This is the county-specific portion of 

the compensation index. To compute the compensation 

index for a county, we weighted the county-specific wage 

by 50 percent and the original market-level compensation 

index by 50 percent. (If some of the county-level data were 

replaced with MSA-level data as part of our screening 

of the county-level data, then the MSA-level data have a 

weight of more than 50 percent.) For example, if the census 

indicates that county A has wages 10 percent above the 

average for the market area (after replacing missing data), 

we elevated that county’s compensation index 5 percent 

above the compensation index for the market area. 

We are implicitly saying that MSA-wide conditions affect 

the wages a hospital has to pay its workers, but countyspecific conditions also affect those wages. We used a 

weighting of 50 percent at the MSA level and 50 percent at 

the county level, although other weightings could be used. 

In computing county-specific compensation indexes, we 

limited the total adjustment to a maximum of 5 percent 

above or below the market-area value. An example is that 

Manhattan wages will affect overall wage patterns in the 

MSA, but counties on the fringe of the New York MSA 

may be able to pay a slightly lower wage than Manhattan 

hospitals because their workers have lower commuting 

costs. The difference between Manhattan and the county 

with the lowest compensation index in the MSA could 

be up to 10 percent, with the lowest wage index county 

being up to 5 percent below the mean, and the Manhattan 

compensation index being 5 percent above the mean. One 

may have to pay workers up to a 10 percent premium ($20 

per day for a worker at $25 per hour) to commute from the 

lower wage counties of an MSA to the core of the city.

The compensation index for a high-wage county in an 

MSA would be computed as follows:

T A B L E

6 –3 Applying BLS data to hospital industry at a national and MSA level, 2005

Hospital industry national

Occupation code Job title

Mean hourly 

wage

Share of 

wages

Mean hourly wage 

for MSA

11–1000 Manager $39.36 7.45% $57.12

29–1111 Registered nurse 27.80 43.22 33.72

Note:	 BLS (Bureau of Labor Statistics), MSA (metropolitan statistical area). Data shown are for MSA code 35644 (NY, NY).

Source:	 MedPAC analysis of Bureau of Labor Statistics May 2005 Occupational Employment Statistics Survey.
Additional technical infor mation on constructing a compensation index from BLS data | June 2007

County-to-MSA ratio 

= (census-weighted county wage)/

(census-weighted MSA wage) 

= 1.1

County-specific portion of compensation index

= (BLS index) × (county-to-MSA ratio) 

= BLS index × 1.1

Blended county/MSA compensation index 

= 0.5 (BLS compensation index) +

0.5 (county-specific compensation index) 

= (0.5 × BLS index) + (0.55 × BLS index) 

= 1.05 times the BLS compensation index for the MSA.

Smoothing

After blending the BLS and census data, the third step 

in our calculation is to smooth the county-level blended 

compensation index values to eliminate large differences 

between adjoining counties. We created a data set of 

county pairs. The data set pairs each county with each 

county that adjoins it. The difference in compensation 

indexes for each county pair is then computed and the 

pair with the greatest difference for each county is 

chosen. If that difference is greater than 10 percent of 

the larger compensation index, the county with the lower 

compensation index value is assigned a compensation 

index equal to 90 percent of its highest neighbor. This 

process is followed for each county pair, resulting in a 

new set of compensation indexes. The same algorithm is 

repeated with the new set of compensation indexes until 

no difference greater than 10 percent remains. Because 

compensation indexes are only increased in this process, 

the entire set of compensation indexes must be revalued to 

keep it budget neutral with the original set. 

If we had selected a smoothing threshold other than 10 

percent, results would differ and the number of iterations 

required to satisfy the condition could differ as well. We 

chose 10 percent to illustrate the mechanism and because 

differences of that magnitude between neighbors might be 

tolerable to providers while still accounting for regional 

differences. In addition, a 10 percent differential is in 

the ballpark of what a hospital would have to pay if it 

were recruiting workers from neighboring counties. For 

example, a 10 percent difference for a worker making the 

national mean RN wage of $25.00 an hour would be $2.50 

per hour, or $20.00 for an eight-hour day. A lower or a 

higher bound could be chosen. However a lower bound, 

such as 5 percent, would cause the smoothing effect to 

ripple out long distances, especially in California due to 

the large size of counties. A smoothing bound significantly 

larger than 10 percent may become large enough to give 

the hospital in the higher wage county an opportunity to 

recruit workers from the lower wage county by offering 

them a wage differential that exceeds the financial and 

time costs of commuting. 

The end result of smoothing and limits on differences 

within MSAs is that the compensation indexes for any 

provider will always be at least 90 percent of its neighboring 

provider’s compensation index and 90 percent of the highest 

compensation index in its MSA.

Adjusting for budget neutrality

After each of the three steps, we adjusted the 

compensation index for budget neutrality. To do this, we 

excluded Maryland hospitals because they are not paid 

under the IPPS system. We estimated inpatient payments 

with our hospital payment simulation model and altered 

compensation index values until the estimated payments 

with the new compensation index differed by less than 

0.1 percent from simulated payments using the CMS 

compensation index (without Section 508 adjustments). 

The payment model takes into account hospital-specific 

factors such as indirect medical education payments, 

disproportionate share payments, sole community 

hospital status, outlier payments, and Medicare-dependent 

hospital status. Instead of modeling outpatient effects for 

budget neutrality, we assumed outpatient shifts would be 

proportional to inpatient shifts.

Limitations

One limitation is that the BLS survey is voluntary. When 

data are missing, BLS imputes data for the missing 

provider. The end result is that BLS provided us with 

data representing occupations that receive at least 95 

percent of hospital payrolls in every MSA, NECTA, 

and statewide rural area. There is a concern that some 

providers would not respond to the survey if it were used 

for payment purposes. However, the incentive to do this 

will be mitigated by the fact that BLS imputes the wages 

for nonresponders. The provider would not know if the 

imputed value would be slightly above or slightly below 

actual wages. To check for accuracy of survey responses, 

BLS uses data screens and can cross-check the OES data 

with other sources of employment and payroll data. 
June 2007 | Additional technical infor mation on constructing a compensation index from BLS data

01001 AL Autauga 0.85

01003 AL Baldwin 0.83

01005 AL Barbour 0.83

01007 AL Bibb 0.88

01009 AL Blount 0.87

01011 AL Bullock 0.84

01013 AL Butler 0.84

01015 AL Calhoun 0.92

01017 AL Chambers 0.80

01019 AL Cherokee 0.82

01021 AL Chilton 0.87

01023 AL Choctaw 0.84

01025 AL Clarke 0.86

01027 AL Clay 0.85

01029 AL Cleburne 0.84

01031 AL Coffee 0.80

01033 AL Colbert 0.88

01035 AL Conecuh 0.84

01037 AL Coosa 0.84

01039 AL Covington 0.86

01041 AL Crenshaw 0.87

01043 AL Cullman 0.82

01045 AL Dale 0.83

01047 AL Dallas 0.81

01049 AL DeKalb 0.83

01051 AL Elmore 0.82

01053 AL Escambia 0.82

01055 AL Etowah 0.82

01057 AL Fayette 0.79

01059 AL Franklin 0.81

01061 AL Geneva 0.80

01063 AL Greene 0.84

01065 AL Hale 0.80

01067 AL Henry 0.82

01069 AL Houston 0.83

01071 AL Jackson 0.84

01073 AL Jefferson 0.92

01075 AL Lamar 0.83

01077 AL Lauderdale 0.89

01079 AL Lawrence 0.82

01081 AL Lee 0.84

01083 AL Limestone 0.90

01085 AL Lowndes 0.85

01087 AL Macon 0.87

01089 AL Madison 0.93

01091 AL Marengo 0.87

Note:	 FIPS (Federal Information Processing Standards).

FIPS 

code State County

MedPAC 

index

01093 AL Marion 0.80

01095 AL Marshall 0.84

01097 AL Mobile 0.87

01099 AL Monroe 0.83

01101 AL Montgomery 0.87

01103 AL Morgan 0.84

01105 AL Perry 0.84

01107 AL Pickens 0.80

01109 AL Pike 0.87

01111 AL Randolph 0.84

01113 AL Russell 0.82

01115 AL St. Clair 0.87

01117 AL Shelby 0.90

01119 AL Sumter 0.83

01121 AL Talladega 0.85

01123 AL Tallapoosa 0.78

01125 AL Tuscaloosa 0.84

01127 AL Walker 0.87

01129 AL Washington 0.80

01131 AL Wilcox 0.85

01133 AL Winston 0.78

02013 AK Aleutians East

Borough

1.16

02016 AK Aleutians West

Census Area

1.15

02020 AK Anchorage

Municipality

1.14

02050 AK Bethel Census Area 1.17

02060 AK Bristol Bay Borough 1.17

02068 AK Denali Borough 1.18

02070 AK Dillingham Census

Area

1.18

02090 AK Fairbanks North

Star Borough

1.16

02100 AK Haines Borough 1.16

02110 AK Juneau City and

Borough

1.21

02122 AK Kenai Peninsula

Borough

1.13

02130 AK Ketchikan Gateway

Borough

1.22

02150 AK Kodiak Island

Borough

1.17

02164 AK Lake and Peninsula

Borough

1.18

02170 AK Matanuska-Susitna

Borough

1.12

FIPS 

code State County

MedPAC 

index

FIPS 

code State County

MedPAC 

index

02180 AK Nome Census Area 1.19

02185 AK North Slope

Borough

1.21

02188 AK Northwest Arctic

Borough

1.18

02201 AK Prince of WalesOuter Ketchikan

Census Area

1.15

02220 AK Sitka City and

Borough

1.17

02232 AK Skagway-HoonahAngoon Census

Area

1.16

02240 AK Southeast

Fairbanks Census

Area

1.15

02261 AK Valdez-Cordova

Census Area

1.16

02270 AK Wade Hampton

Census Area

1.15

02280 AK WrangellPetersburg Census

Area

1.16

02282 AK Yakutat City and

Borough

1.17

02290 AK Yukon-Koyukuk

Census Area

1.14

04001 AZ Apache 0.94

04003 AZ Cochise 0.90

04005 AZ Coconino 0.99

04007 AZ Gila 0.94

04009 AZ Graham 0.88

04011 AZ Greenlee 0.94

04012 AZ La Paz 1.02

04013 AZ Maricopa 0.98

04015 AZ Mohave 1.02

04017 AZ Navajo 0.94

04019 AZ Pima 0.94

04021 AZ Pinal 0.96

04023 AZ Santa Cruz 0.88

04025 AZ Yavapai 0.93

04027 AZ Yuma 0.97

05001 AR Arkansas 0.82

05003 AR Ashley 0.79

05005 AR Baxter 0.78

05007 AR Benton 0.84

05009 AR Boone 0.78

05011 AR Bradley 0.76
Additional technical infor mation on constructing a compensation index from BLS data | June 2007 

05013 AR Calhoun 0.82

05015 AR Carroll 0.81

05017 AR Chicot 0.79

05019 AR Clark 0.78

05021 AR Clay 0.78

05023 AR Cleburne 0.80

05025 AR Cleveland 0.84

05027 AR Columbia 0.76

05029 AR Conway 0.83

05031 AR Craighead 0.79

05033 AR Crawford 0.77

05035 AR Crittenden 0.89

05037 AR Cross 0.80

05039 AR Dallas 0.84

05041 AR Desha 0.80

05043 AR Drew 0.80

05045 AR Faulkner 0.88

05047 AR Franklin 0.80

05049 AR Fulton 0.79

05051 AR Garland 0.85

05053 AR Grant 0.93

05055 AR Greene 0.76

05057 AR Hempstead 0.79

05059 AR Hot Spring 0.84

05061 AR Howard 0.75

05063 AR Independence 0.78

05065 AR Izard 0.77

05067 AR Jackson 0.78

05069 AR Jefferson 0.85

05071 AR Johnson 0.78

05073 AR Lafayette 0.80

05075 AR Lawrence 0.80

05077 AR Lee 0.83

05079 AR Lincoln 0.80

05081 AR Little River 0.79

05083 AR Logan 0.80

05085 AR Lonoke 0.91

05087 AR Madison 0.86

05089 AR Marion 0.79

05091 AR Miller 0.88

05093 AR Mississippi 0.85

05095 AR Monroe 0.79

05097 AR Montgomery 0.78

05099 AR Nevada 0.82

05101 AR Newton 0.82

05103 AR Ouachita 0.75

FIPS 

code State County

MedPAC 

index

05105 AR Perry 0.92

05107 AR Phillips 0.83

05109 AR Pike 0.79

05111 AR Poinsett 0.80

05113 AR Polk 0.77

05115 AR Pope 0.82

05117 AR Prairie 0.82

05119 AR Pulaski 0.94

05121 AR Randolph 0.78

05123 AR St. Francis 0.81

05125 AR Saline 0.88

05127 AR Scott 0.79

05129 AR Searcy 0.79

05131 AR Sebastian 0.81

05133 AR Sevier 0.76

05135 AR Sharp 0.78

05137 AR Stone 0.78

05139 AR Union 0.76

05141 AR Van Buren 0.79

05143 AR Washington 0.86

05145 AR White 0.82

05147 AR Woodruff 0.80

05149 AR Yell 0.83

06001 CA Alameda 1.37

06003 CA Alpine 1.19

06005 CA Amador 1.22

06007 CA Butte 1.10

06009 CA Calaveras 1.22

06011 CA Colusa 1.15

06013 CA Contra Costa 1.37

06015 CA Del Norte 1.15

06017 CA El Dorado 1.23

06019 CA Fresno 1.30

06021 CA Glenn 1.11

06023 CA Humboldt 1.09

06025 CA Imperial 1.08

06027 CA Inyo 1.19

06029 CA Kern 1.12

06031 CA Kings 1.24

06033 CA Lake 1.18

06035 CA Lassen 1.14

06037 CA Los Angeles 1.16

06039 CA Madera 1.22

06041 CA Marin 1.41

06043 CA Mariposa 1.22

06045 CA Mendocino 1.14

FIPS 

code State County

MedPAC 

index

FIPS 

code State County

MedPAC 

index

06047 CA Merced 1.35

06049 CA Modoc 1.19

06051 CA Mono 1.18

06053 CA Monterey 1.38

06055 CA Napa 1.24

06057 CA Nevada 1.16

06059 CA Orange 1.17

06061 CA Placer 1.25

06063 CA Plumas 1.13

06065 CA Riverside 1.13

06067 CA Sacramento 1.25

06069 CA San Benito 1.45

06071 CA San Bernardino 1.13

06073 CA San Diego 1.14

06075 CA San Francisco 1.43

06077 CA San Joaquin 1.35

06079 CA San Luis Obispo 1.24

06081 CA San Mateo 1.43

06083 CA Santa Barbara 1.16

06085 CA Santa Clara 1.50

06087 CA Santa Cruz 1.41

06089 CA Shasta 1.07

06091 CA Sierra 1.19

06093 CA Siskiyou 1.08

06095 CA Solano 1.22

06097 CA Sonoma 1.27

06099 CA Stanislaus 1.35

06101 CA Sutter 1.13

06103 CA Tehama 1.01

06105 CA Trinity 1.19

06107 CA Tulare 1.17

06109 CA Tuolumne 1.22

06111 CA Ventura 1.15

06113 CA Yolo 1.26

06115 CA Yuba 1.15

08001 CO Adams 1.08

08003 CO Alamosa 0.91

08005 CO Arapahoe 1.07

08007 CO Archuleta 0.96

08009 CO Baca 0.97

08011 CO Bent 0.95

08013 CO Boulder 1.08

08015 CO Chaffee 0.94

08017 CO Cheyenne 0.97

08019 CO Clear Creek 1.05

08021 CO Conejos 0.98

Note:	 FIPS (Federal Information Processing Standards).
 June 2007 | Additional technical infor mation on constructing a compensation index from BLS data

08023 CO Costilla 0.96

08025 CO Crowley 0.99

08027 CO Custer 0.96

08029 CO Delta 0.98

08031 CO Denver 1.09

08033 CO Dolores 0.98

08035 CO Douglas 1.10

08037 CO Eagle 1.00

08039 CO Elbert 1.07

08041 CO El Paso 1.00

08043 CO Fremont 0.94

08045 CO Garfield 0.96

08047 CO Gilpin 1.05

08049 CO Grand 0.97

08051 CO Gunnison 0.91

08053 CO Hinsdale 0.98

08055 CO Huerfano 0.95

08057 CO Jackson 0.98

08059 CO Jefferson 1.04

08061 CO Kiowa 0.97

08063 CO Kit Carson 0.95

08065 CO Lake 0.96

08067 CO La Plata 0.92

08069 CO Larimer 0.97

08071 CO Las Animas 0.92

08073 CO Lincoln 0.97

08075 CO Logan 0.91

08077 CO Mesa 0.98

08079 CO Mineral 1.00

08081 CO Moffat 1.00

08083 CO Montezuma 0.98

08085 CO Montrose 0.97

08087 CO Morgan 0.97

08089 CO Otero 0.93

08091 CO Ouray 0.98

08093 CO Park 1.04

08095 CO Phillips 0.97

08097 CO Pitkin 1.00

08099 CO Prowers 0.96

08101 CO Pueblo 0.93

08103 CO Rio Blanco 0.99

08105 CO Rio Grande 0.95

08107 CO Routt 0.96

08109 CO Saguache 0.98

08111 CO San Juan 0.99

08113 CO San Miguel 1.00

08115 CO Sedgwick 0.97

08117 CO Summit 0.95

08119 CO Teller 0.99

08121 CO Washington 0.98

08123 CO Weld 0.98

08125 CO Yuma 0.98

09001 CT Fairfield 1.18

09003 CT Hartford 1.16

09005 CT Litchfield 1.16

09007 CT Middlesex 1.16

09009 CT New Haven 1.21

09011 CT New London 1.17

09013 CT Tolland 1.17

09015 CT Windham 1.30

010001 DE Kent 1.04

010003 DE New Castle 1.01

010005 DE Sussex 0.98

011001 DC District of Columbia 1.16

012001 FL Alachua 0.97

012003 FL Baker 0.90

012005 FL Bay 0.92

012007 FL Bradford 0.88

012009 FL Brevard 0.93

012011 FL Broward 0.99

012013 FL Calhoun 0.89

012015 FL Charlotte 0.91

012017 FL Citrus 0.91

012019 FL Clay 0.92

012021 FL Collier 1.02

012023 FL Columbia 0.90

012027 FL DeSoto 0.86

012029 FL Dixie 0.93

012031 FL Duval 0.95

012033 FL Escambia 0.86

012035 FL Flagler 0.88

012037 FL Franklin 0.90

012039 FL Gadsden 0.97

012041 FL Gilchrist 1.00

012043 FL Glades 0.92

012045 FL Gulf 0.86

012047 FL Hamilton 0.92

012049 FL Hardee 0.87

012051 FL Hendry 0.92

012053 FL Hernando 0.90

012055 FL Highlands 0.90

012057 FL Hillsborough 0.97

012059 FL Holmes 0.85

012061 FL Indian River 0.92

012063 FL Jackson 0.89

012065 FL Jefferson 0.99

012067 FL Lafayette 0.91

012069 FL Lake 0.90

012071 FL Lee 0.95

012073 FL Leon 0.99

012075 FL Levy 0.90

012077 FL Liberty 0.93

012079 FL Madison 0.89

012081 FL Manatee 0.92

012083 FL Marion 0.92

012085 FL Martin 0.95

012086 FL Miami-Dade 0.98

012087 FL Monroe 0.93

012089 FL Nassau 0.93

012091 FL Okaloosa 0.86

012093 FL Okeechobee 0.92

012095 FL Orange 0.92

012097 FL Osceola 0.91

012099 FL Palm Beach 1.02

012101 FL Pasco 0.93

012103 FL Pinellas 0.95

012105 FL Polk 0.93

012107 FL Putnam 0.90

012109 FL St. Johns 0.95

012111 FL St. Lucie 0.91

012113 FL Santa Rosa 0.85

012115 FL Sarasota 0.96

012117 FL Seminole 0.91

012119 FL Sumter 0.84

012121 FL Suwannee 0.90

012123 FL Taylor 0.89

012125 FL Union 0.92

012127 FL Volusia 0.92

012129 FL Wakulla 0.97

012131 FL Walton 0.86

012133 FL Washington 0.84

013001 GA Appling 0.90

013003 GA Atkinson 0.92

013005 GA Bacon 0.90

013007 GA Baker 0.83

013009 GA Baldwin 0.87

013011 GA Banks 0.91

013013 GA Barrow 0.95

FIPS 

code State County

MedPAC 

index

FIPS 

code State County

MedPAC 

index

FIPS 

code State County

MedPAC 

index

Note:	 FIPS (Federal Information Processing Standards).
Additional technical infor mation on constructing a compensation index from BLS data | June 2007 

013015 GA Bartow 0.97

013017 GA Ben Hill 0.84

013019 GA Berrien 0.90

013021 GA Bibb 0.98

013023 GA Bleckley 0.91

013025 GA Brantley 0.89

013027 GA Brooks 0.89

013029 GA Bryan 0.83

013031 GA Bulloch 0.88

013033 GA Burke 0.88

013035 GA Butts 0.93

013037 GA Calhoun 0.92

013039 GA Camden 0.88

013043 GA Candler 0.92

013045 GA Carroll 0.93

013047 GA Catoosa 0.87

013049 GA Charlton 0.90

013051 GA Chatham 0.88

013053 GA Chattahoochee 0.85

013055 GA Chattooga 0.84

013057 GA Cherokee 0.95

013059 GA Clarke 0.90

013061 GA Clay 0.90

013063 GA Clayton 0.96

013065 GA Clinch 0.88

013067 GA Cobb 0.97

013069 GA Coffee 0.84

013071 GA Colquitt 0.89

013073 GA Columbia 0.88

013075 GA Cook 0.83

013077 GA Coweta 0.95

013079 GA Crawford 0.95

013081 GA Crisp 0.83

013083 GA Dade 0.87

013085 GA Dawson 0.93

013087 GA Decatur 0.88

013089 GA DeKalb 0.98

013091 GA Dodge 0.83

013093 GA Dooly 0.90

013095 GA Dougherty 0.83

013097 GA Douglas 0.95

013099 GA Early 0.86

013101 GA Echols 0.83

013103 GA Effingham 0.83

013105 GA Elbert 0.93

013107 GA Emanuel 0.92

013109 GA Evans 0.88

013111 GA Fannin 0.88

013113 GA Fayette 0.96

013115 GA Floyd 0.89

013117 GA Forsyth 0.95

013119 GA Franklin 0.84

013121 GA Fulton 1.00

013123 GA Gilmer 0.92

013125 GA Glascock 0.92

013127 GA Glynn 0.90

013129 GA Gordon 0.92

013131 GA Grady 0.89

013133 GA Greene 0.85

013135 GA Gwinnett 0.98

013137 GA Habersham 0.92

013139 GA Hall 0.93

013141 GA Hancock 0.92

013143 GA Haralson 0.93

013145 GA Harris 0.86

013147 GA Hart 0.93

013149 GA Heard 0.94

013151 GA Henry 0.94

013153 GA Houston 0.95

013155 GA Irwin 0.87

013157 GA Jackson 0.91

013159 GA Jasper 0.95

013161 GA Jeff Davis 0.85

013163 GA Jefferson 0.91

013165 GA Jenkins 0.92

013167 GA Johnson 0.89

013169 GA Jones 0.97

013171 GA Lamar 0.93

013173 GA Lanier 0.83

013175 GA Laurens 0.91

013177 GA Lee 0.80

013179 GA Liberty 0.91

013181 GA Lincoln 0.92

013183 GA Long 0.91

013185 GA Lowndes 0.83

013187 GA Lumpkin 0.92

013189 GA McDuffie 0.89

013191 GA McIntosh 0.89

013193 GA Macon 0.86

013195 GA Madison 0.86

013197 GA Marion 0.86

013199 GA Meriwether 0.93

013201 GA Miller 0.91

013205 GA Mitchell 0.85

013207 GA Monroe 0.97

013209 GA Montgomery 0.89

013211 GA Morgan 0.88

013213 GA Murray 0.87

013215 GA Muscogee 0.87

013217 GA Newton 0.98

013219 GA Oconee 0.88

013221 GA Oglethorpe 0.89

013223 GA Paulding 0.94

013225 GA Peach 0.91

013227 GA Pickens 0.93

013229 GA Pierce 0.85

013231 GA Pike 0.94

013233 GA Polk 0.87

013235 GA Pulaski 0.88

013237 GA Putnam 0.92

013239 GA Quitman 0.92

013241 GA Rabun 0.86

013243 GA Randolph 0.88

013245 GA Richmond 0.93

013247 GA Rockdale 0.97

013249 GA Schley 0.92

013251 GA Screven 0.86

013253 GA Seminole 0.91

013255 GA Spalding 0.93

013257 GA Stephens 0.90

013259 GA Stewart 0.91

013261 GA Sumter 0.86

013263 GA Talbot 0.90

013265 GA Taliaferro 0.92

013267 GA Tattnall 0.83

013269 GA Taylor 0.91

013271 GA Telfair 0.83

013273 GA Terrell 0.83

013275 GA Thomas 0.89

013277 GA Tift 0.87

013279 GA Toombs 0.87

013281 GA Towns 0.86

013283 GA Treutlen 0.90

013285 GA Troup 0.88

013287 GA Turner 0.92

013289 GA Twiggs 0.97

013291 GA Union 0.84

013293 GA Upson 0.87

FIPS 

code State County

MedPAC 

index

FIPS 

code State County

MedPAC 

index

FIPS 

code State County

MedPAC 

index

Note:	 FIPS (Federal Information Processing Standards).
10 June 2007 | Additional technical infor mation on constructing a compensation index from BLS data

013295 GA Walker 0.92

013297 GA Walton 0.93

013299 GA Ware 0.88

013301 GA Warren 0.90

013303 GA Washington 0.91

013305 GA Wayne 0.85

013307 GA Webster 0.92

013309 GA Wheeler 0.92

013311 GA White 0.90

013313 GA Whitfield 0.87

013315 GA Wilcox 0.90

013317 GA Wilkes 0.90

013319 GA Wilkinson 0.92

013321 GA Worth 0.83

015001 HI Hawaii 1.11

015003 HI Honolulu 1.15

015005 HI Kalawao 1.14

015007 HI Kauai 1.14

015009 HI Maui 1.15

016001 ID Ada 0.94

016003 ID Adams 0.92

016005 ID Bannock 0.91

016007 ID Bear Lake 0.87

016009 ID Benewah 0.98

016011 ID Bingham 0.86

016013 ID Blaine 0.89

016015 ID Boise 0.93

016017 ID Bonner 0.98

016019 ID Bonneville 0.86

016021 ID Boundary 0.98

016023 ID Butte 0.89

016025 ID Camas 0.87

016027 ID Canyon 0.91

016029 ID Caribou 0.88

016031 ID Cassia 0.93

016033 ID Clark 0.87

016035 ID Clearwater 0.92

016037 ID Custer 0.87

016039 ID Elmore 0.86

016041 ID Franklin 0.89

016043 ID Fremont 0.86

016045 ID Gem 0.92

016047 ID Gooding 0.87

016049 ID Idaho 0.92

016051 ID Jefferson 0.85

016053 ID Jerome 0.84

016055 ID Kootenai 0.91

016057 ID Latah 0.98

016059 ID Lemhi 0.83

016061 ID Lewis 0.92

016063 ID Lincoln 0.87

016065 ID Madison 0.83

016067 ID Minidoka 0.83

016069 ID Nez Perce 1.02

016071 ID Oneida 0.86

016073 ID Owyhee 0.93

016075 ID Payette 0.91

016077 ID Power 0.91

016079 ID Shoshone 0.88

016081 ID Teton 0.89

016083 ID Twin Falls 0.93

016085 ID Valley 0.89

016087 ID Washington 0.92

017001 IL Adams 0.84

017003 IL Alexander 0.87

017005 IL Bond 0.90

017007 IL Boone 0.92

017009 IL Brown 0.87

017011 IL Bureau 0.82

017013 IL Calhoun 0.91

017015 IL Carroll 0.85

017017 IL Cass 0.87

017019 IL Champaign 0.92

017021 IL Christian 0.86

017023 IL Clark 0.82

017025 IL Clay 0.80

017027 IL Clinton 0.89

017029 IL Coles 0.82

017031 IL Cook 1.04

017033 IL Crawford 0.82

017035 IL Cumberland 0.87

017037 IL DeKalb 0.98

017039 IL De Witt 0.87

017041 IL Douglas 0.88

017043 IL DuPage 1.02

017045 IL Edgar 0.88

017047 IL Edwards 0.87

017049 IL Effingham 0.83

017051 IL Fayette 0.87

017053 IL Ford 0.90

017055 IL Franklin 0.87

017057 IL Fulton 0.81

017059 IL Gallatin 0.87

017061 IL Greene 0.86

017063 IL Grundy 0.98

017065 IL Hamilton 0.79

017067 IL Hancock 0.87

017069 IL Hardin 0.87

017071 IL Henderson 0.87

017073 IL Henry 0.83

017075 IL Iroquois 0.90

017077 IL Jackson 0.84

017079 IL Jasper 0.87

017081 IL Jefferson 0.81

017083 IL Jersey 0.89

017085 IL Jo Daviess 0.87

017087 IL Johnson 0.86

017089 IL Kane 1.03

017091 IL Kankakee 1.00

017093 IL Kendall 0.99

017095 IL Knox 0.81

017097 IL Lake 1.05

017099 IL La Salle 0.89

017101 IL Lawrence 0.80

017103 IL Lee 0.88

017105 IL Livingston 0.90

017107 IL Logan 0.86

017109 IL McDonough 0.87

017111 IL McHenry 0.99

017113 IL McLean 0.91

017115 IL Macon 0.90

017117 IL Macoupin 0.89

017119 IL Madison 0.91

017121 IL Marion 0.80

017123 IL Marshall 0.88

017125 IL Mason 0.83

017127 IL Massac 0.87

017129 IL Menard 0.92

017131 IL Mercer 0.80

017133 IL Monroe 0.89

017135 IL Montgomery 0.86

017137 IL Morgan 0.86

017139 IL Moultrie 0.84

017141 IL Ogle 0.88

017143 IL Peoria 0.90

017145 IL Perry 0.84

017147 IL Piatt 0.87

017149 IL Pike 0.87

FIPS 

code State County

MedPAC 

index

FIPS 

code State County

MedPAC 

index

FIPS 

code State County

MedPAC 

index

Note:	 FIPS (Federal Information Processing Standards).
Additional technical infor mation on constructing a compensation index from BLS data | June 2007 11

017151 IL Pope 0.87

017153 IL Pulaski 0.87

017155 IL Putnam 0.87

017157 IL Randolph 0.85

017159 IL Richland 0.83

017161 IL Rock Island 0.85

017163 IL St. Clair 0.91

017165 IL Saline 0.81

017167 IL Sangamon 0.95

017169 IL Schuyler 0.87

017171 IL Scott 0.87

017173 IL Shelby 0.87

017175 IL Stark 0.87

017177 IL Stephenson 0.88

017179 IL Tazewell 0.88

017181 IL Union 0.86

017183 IL Vermilion 0.98

017185 IL Wabash 0.80

017187 IL Warren 0.87

017189 IL Washington 0.87

017191 IL Wayne 0.83

017193 IL White 0.83

017195 IL Whiteside 0.83

017197 IL Will 0.98

017199 IL Williamson 0.85

017201 IL Winnebago 0.98

017203 IL Woodford 0.89

018001 IN Adams 0.88

018003 IN Allen 0.92

018005 IN Bartholomew 0.95

018007 IN Benton 0.93

018009 IN Blackford 0.90

018011 IN Boone 0.94

018013 IN Brown 0.98

018015 IN Carroll 0.92

018017 IN Cass 0.88

018019 IN Clark 0.96

018021 IN Clay 0.84

018023 IN Clinton 0.90

018025 IN Crawford 0.91

018027 IN Daviess 0.90

018029 IN Dearborn 0.95

018031 IN Decatur 0.86

018033 IN DeKalb 0.87

018035 IN Delaware 0.89

018037 IN Dubois 0.90

018039 IN Elkhart 0.99

018041 IN Fayette 0.88

018043 IN Floyd 0.99

018045 IN Fountain 0.88

018047 IN Franklin 0.95

018049 IN Fulton 0.86

018051 IN Gibson 0.87

018053 IN Grant 0.90

018055 IN Greene 0.86

018057 IN Hamilton 0.99

018059 IN Hancock 0.94

018061 IN Harrison 0.92

018063 IN Hendricks 1.00

018065 IN Henry 0.88

018067 IN Howard 0.94

018069 IN Huntington 0.87

018071 IN Jackson 0.92

018073 IN Jasper 0.94

018075 IN Jay 0.86

018077 IN Jefferson 0.88

018079 IN Jennings 0.86

018081 IN Johnson 0.94

018083 IN Knox 0.84

018085 IN Kosciusko 0.91

018087 IN LaGrange 0.92

018089 IN Lake 0.96

018091 IN LaPorte 0.98

018093 IN Lawrence 0.89

018095 IN Madison 0.92

018097 IN Marion 0.99

018099 IN Marshall 0.91

018101 IN Martin 0.93

018103 IN Miami 0.85

018105 IN Monroe 0.90

018107 IN Montgomery 0.90

018109 IN Morgan 0.94

018111 IN Newton 0.91

018113 IN Noble 0.89

018115 IN Ohio 0.98

018117 IN Orange 0.85

018119 IN Owen 0.89

018121 IN Parke 0.87

018123 IN Perry 0.89

018125 IN Pike 0.91

018127 IN Porter 0.95

018129 IN Posey 0.92

018131 IN Pulaski 0.87

018133 IN Putnam 0.94

018135 IN Randolph 0.86

018137 IN Ripley 0.92

018139 IN Rush 0.88

018141 IN St. Joseph 0.96

018143 IN Scott 0.88

018145 IN Shelby 0.95

018147 IN Spencer 0.89

018149 IN Starke 0.88

018151 IN Steuben 0.91

018153 IN Sullivan 0.83

018155 IN Switzerland 0.90

018157 IN Tippecanoe 0.95

018159 IN Tipton 0.92

018161 IN Union 0.91

018163 IN Vanderburgh 0.90

018165 IN Vermillion 0.88

018167 IN Vigo 0.87

018169 IN Wabash 0.85

018171 IN Warren 0.91

018173 IN Warrick 0.87

018175 IN Washington 0.92

018177 IN Wayne 0.89

018179 IN Wells 0.89

018181 IN White 0.85

018183 IN Whitley 0.90

019001 IA Adair 0.85

019003 IA Adams 0.85

019005 IA Allamakee 0.88

019007 IA Appanoose 0.84

019009 IA Audubon 0.82

019011 IA Benton 0.88

019013 IA Black Hawk 0.86

019015 IA Boone 0.87

019017 IA Bremer 0.81

019019 IA Buchanan 0.87

019021 IA Buena Vista 0.82

019023 IA Butler 0.81

019025 IA Calhoun 0.80

019027 IA Carroll 0.84

019029 IA Cass 0.82

019031 IA Cedar 0.88

019033 IA Cerro Gordo 0.85

019035 IA Cherokee 0.86

019037 IA Chickasaw 0.85

FIPS 

code State County

MedPAC 

index

FIPS 

code State County

MedPAC 

index

FIPS 

code State County

MedPAC 

index

Note:	 FIPS (Federal Information Processing Standards).
12 June 2007 | Additional technical infor mation on constructing a compensation index from BLS data

019039 IA Clarke 0.85

019041 IA Clay 0.81

019043 IA Clayton 0.85

019045 IA Clinton 0.87

019047 IA Crawford 0.80

019049 IA Dallas 0.89

019051 IA Davis 0.79

019053 IA Decatur 0.82

019055 IA Delaware 0.82

019057 IA Des Moines 0.83

019059 IA Dickinson 0.87

019061 IA Dubuque 0.87

019063 IA Emmet 0.87

019065 IA Fayette 0.85

019067 IA Floyd 0.85

019069 IA Franklin 0.81

019071 IA Fremont 0.85

019073 IA Greene 0.83

019075 IA Grundy 0.86

019077 IA Guthrie 0.91

019079 IA Hamilton 0.83

019081 IA Hancock 0.85

019083 IA Hardin 0.82

019085 IA Harrison 0.89

019087 IA Henry 0.87

019089 IA Howard 0.95

019091 IA Humboldt 0.79

019093 IA Ida 0.79

019095 IA Iowa 0.88

019097 IA Jackson 0.85

019099 IA Jasper 0.84

019101 IA Jefferson 0.84

019103 IA Johnson 0.97

019105 IA Jones 0.86

019107 IA Keokuk 0.86

019109 IA Kossuth 0.83

019111 IA Lee 0.82

019113 IA Linn 0.88

019115 IA Louisa 0.88

019117 IA Lucas 0.83

019119 IA Lyon 0.84

019121 IA Madison 0.91

019123 IA Mahaska 0.87

019125 IA Marion 0.87

019127 IA Marshall 0.85

019129 IA Mills 0.89

019131 IA Mitchell 0.95

019133 IA Monona 0.80

019135 IA Monroe 0.85

019137 IA Montgomery 0.85

019139 IA Muscatine 0.88

019141 IA O’Brien 0.81

019143 IA Osceola 0.87

019145 IA Page 0.85

019147 IA Palo Alto 0.80

019149 IA Plymouth 0.84

019151 IA Pocahontas 0.83

019153 IA Polk 0.94

019155 IA Pottawattamie 0.91

019157 IA Poweshiek 0.83

019159 IA Ringgold 0.81

019161 IA Sac 0.83

019163 IA Scott 0.85

019165 IA Shelby 0.82

019167 IA Sioux 0.84

019169 IA Story 0.90

019171 IA Tama 0.85

019173 IA Taylor 0.85

019175 IA Union 0.82

019177 IA Van Buren 0.81

019179 IA Wapello 0.82

019181 IA Warren 0.89

019183 IA Washington 0.92

019185 IA Wayne 0.79

019187 IA Webster 0.83

019189 IA Winnebago 0.89

019191 IA Winneshiek 0.95

019193 IA Woodbury 0.84

019195 IA Worth 0.95

019197 IA Wright 0.84

020001 KS Allen 0.87

020003 KS Anderson 0.87

020005 KS Atchison 0.87

020007 KS Barber 0.77

020009 KS Barton 0.82

020011 KS Bourbon 0.87

020013 KS Brown 0.84

020015 KS Butler 0.81

020017 KS Chase 0.82

020019 KS Chautauqua 0.82

020021 KS Cherokee 0.78

020023 KS Cheyenne 0.88

020025 KS Clark 0.82

020027 KS Clay 0.76

020029 KS Cloud 0.76

020031 KS Coffey 0.85

020033 KS Comanche 0.82

020035 KS Cowley 0.82

020037 KS Crawford 0.79

020039 KS Decatur 0.81

020041 KS Dickinson 0.81

020043 KS Doniphan 0.93

020045 KS Douglas 0.89

020047 KS Edwards 0.82

020049 KS Elk 0.82

020051 KS Ellis 0.78

020053 KS Ellsworth 0.80

020055 KS Finney 0.81

020057 KS Ford 0.81

020059 KS Franklin 0.94

020061 KS Geary 0.83

020063 KS Gove 0.83

020065 KS Graham 0.81

020067 KS Grant 0.82

020069 KS Gray 0.82

020071 KS Greeley 0.88

020073 KS Greenwood 0.81

020075 KS Hamilton 0.87

020077 KS Harper 0.81

020079 KS Harvey 0.81

020081 KS Haskell 0.83

020083 KS Hodgeman 0.83

020085 KS Jackson 0.86

020087 KS Jefferson 0.87

020089 KS Jewell 0.81

020091 KS Johnson 0.99

020093 KS Kearny 0.82

020095 KS Kingman 0.78

020097 KS Kiowa 0.82

020099 KS Labette 0.77

020101 KS Lane 0.83

020103 KS Leavenworth 0.96

020105 KS Lincoln 0.82

020107 KS Linn 0.96

020109 KS Logan 0.81

020111 KS Lyon 0.80

020113 KS McPherson 0.76

020115 KS Marion 0.78

FIPS 

code State County

MedPAC 

index

FIPS 

code State County

MedPAC 

index

FIPS 

code State County

MedPAC 

index

Note:	 FIPS (Federal Information Processing Standards).
Additional technical infor mation on constructing a compensation index from BLS data | June 2007 13

020117 KS Marshall 0.80

020119 KS Meade 0.82

020121 KS Miami 0.97

020123 KS Mitchell 0.82

020125 KS Montgomery 0.77

020127 KS Morris 0.82

020129 KS Morton 0.87

020131 KS Nemaha 0.83

020133 KS Neosho 0.78

020135 KS Ness 0.81

020137 KS Norton 0.82

020139 KS Osage 0.85

020141 KS Osborne 0.81

020143 KS Ottawa 0.82

020145 KS Pawnee 0.82

020147 KS Phillips 0.79

020149 KS Pottawatomie 0.78

020151 KS Pratt 0.80

020153 KS Rawlins 0.82

020155 KS Reno 0.81

020157 KS Republic 0.81

020159 KS Rice 0.81

020161 KS Riley 0.81

020163 KS Rooks 0.82

020165 KS Rush 0.82

020167 KS Russell 0.77

020169 KS Saline 0.82

020171 KS Scott 0.81

020173 KS Sedgwick 0.86

020175 KS Seward 0.79

020177 KS Shawnee 0.87

020179 KS Sheridan 0.81

020181 KS Sherman 0.85

020183 KS Smith 0.82

020185 KS Stafford 0.83

020187 KS Stanton 0.87

020189 KS Stevens 0.82

020191 KS Sumner 0.83

020193 KS Thomas 0.83

020195 KS Trego 0.82

020197 KS Wabaunsee 0.86

020199 KS Wallace 0.87

020201 KS Washington 0.83

020203 KS Wichita 0.83

020205 KS Wilson 0.78

020207 KS Woodson 0.83

020209 KS Wyandotte 0.99

021001 KY Adair 0.81

021003 KY Allen 0.91

021005 KY Anderson 0.87

021007 KY Ballard 0.88

021009 KY Barren 0.83

021011 KY Bath 0.86

021013 KY Bell 0.85

021015 KY Boone 0.96

021017 KY Bourbon 0.89

021019 KY Boyd 0.99

021021 KY Boyle 0.87

021023 KY Bracken 0.99

021025 KY Breathitt 0.84

021027 KY Breckinridge 0.89

021029 KY Bullitt 0.92

021031 KY Butler 0.87

021033 KY Caldwell 0.86

021035 KY Calloway 0.85

021037 KY Campbell 0.96

021039 KY Carlisle 0.88

021041 KY Carroll 0.89

021043 KY Carter 0.89

021045 KY Casey 0.81

021047 KY Christian 0.87

021049 KY Clark 0.92

021051 KY Clay 0.83

021053 KY Clinton 0.85

021055 KY Crittenden 0.82

021057 KY Cumberland 0.84

021059 KY Daviess 0.88

021061 KY Edmonson 0.87

021063 KY Elliott 0.86

021065 KY Estill 0.85

021067 KY Fayette 0.93

021069 KY Fleming 0.84

021071 KY Floyd 0.86

021073 KY Franklin 0.88

021075 KY Fulton 0.86

021077 KY Gallatin 0.98

021079 KY Garrard 0.83

021081 KY Grant 0.96

021083 KY Graves 0.84

021085 KY Grayson 0.86

021087 KY Green 0.86

021089 KY Greenup 0.97

021091 KY Hancock 0.87

021093 KY Hardin 0.95

021095 KY Harlan 0.86

021097 KY Harrison 0.89

021099 KY Hart 0.87

021101 KY Henderson 0.86

021103 KY Henry 0.95

021105 KY Hickman 0.86

021107 KY Hopkins 0.87

021109 KY Jackson 0.86

021111 KY Jefferson 0.97

021113 KY Jessamine 0.89

021115 KY Johnson 0.86

021117 KY Kenton 0.99

021119 KY Knott 0.86

021121 KY Knox 0.85

021123 KY Larue 0.95

021125 KY Laurel 0.85

021127 KY Lawrence 0.91

021129 KY Lee 0.87

021131 KY Leslie 0.83

021133 KY Letcher 0.81

021135 KY Lewis 0.88

021137 KY Lincoln 0.83

021139 KY Livingston 0.82

021141 KY Logan 0.89

021143 KY Lyon 0.85

021145 KY McCracken 0.88

021147 KY McCreary 0.86

021149 KY McLean 0.87

021151 KY Madison 0.84

021153 KY Magoffin 0.83

021155 KY Marion 0.86

021157 KY Marshall 0.85

021159 KY Martin 0.91

021161 KY Mason 0.89

021163 KY Meade 0.93

021165 KY Menifee 0.87

021167 KY Mercer 0.85

021169 KY Metcalfe 0.87

021171 KY Monroe 0.88

021173 KY Montgomery 0.85

021175 KY Morgan 0.87

021177 KY Muhlenberg 0.85

021179 KY Nelson 0.94

021181 KY Nicholas 0.87

FIPS 

code State County

MedPAC 

index

FIPS 

code State County

MedPAC 

index

FIPS 

code State County

MedPAC 

index

Note:	 FIPS (Federal Information Processing Standards).
14 June 2007 | Additional technical infor mation on constructing a compensation index from BLS data

021183 KY Ohio 0.83

021185 KY Oldham 0.95

021187 KY Owen 0.89

021189 KY Owsley 0.84

021191 KY Pendleton 0.98

021193 KY Perry 0.86

021195 KY Pike 0.85

021197 KY Powell 0.89

021199 KY Pulaski 0.84

021201 KY Robertson 0.89

021203 KY Rockcastle 0.82

021205 KY Rowan 0.87

021207 KY Russell 0.85

021209 KY Scott 0.91

021211 KY Shelby 0.93

021213 KY Simpson 0.91

021215 KY Spencer 0.93

021217 KY Taylor 0.86

021219 KY Todd 0.87

021221 KY Trigg 0.89

021223 KY Trimble 0.97

021225 KY Union 0.86

021227 KY Warren 0.88

021229 KY Washington 0.84

021231 KY Wayne 0.89

021233 KY Webster 0.88

021235 KY Whitley 0.83

021237 KY Wolfe 0.87

021239 KY Woodford 0.88

022001 LA Acadia Parish 0.77

022003 LA Allen Parish 0.77

022005 LA Ascension Parish 0.84

022007 LA Assumption Parish 0.84

022009 LA Avoyelles Parish 0.83

022011 LA Beauregard Parish 0.78

022013 LA Bienville Parish 0.83

022015 LA Bossier Parish 0.80

022017 LA Caddo Parish 0.85

022019 LA Calcasieu Parish 0.85

022021 LA Caldwell Parish 0.83

022023 LA Cameron Parish 0.84

022025 LA Catahoula Parish 0.84

022027 LA Claiborne Parish 0.80

022029 LA Concordia Parish 0.81

022031 LA De Soto Parish 0.84

022033 LA East Baton Rouge

Parish

0.85

022035 LA East Carroll Parish 0.84

022037 LA East Feliciana

Parish

0.87

022039 LA Evangeline Parish 0.81

022041 LA Franklin Parish 0.77

022043 LA Grant Parish 0.84

022045 LA Iberia Parish 0.84

022047 LA Iberville Parish 0.89

022049 LA Jackson Parish 0.78

022051 LA Jefferson Parish 0.92

022053 LA Jefferson Davis

Parish

0.84

022055 LA Lafayette Parish 0.83

022057 LA Lafourche Parish 0.87

022059 LA La Salle Parish 0.83

022061 LA Lincoln Parish 0.79

022063 LA Livingston Parish 0.82

022065 LA Madison Parish 0.78

022067 LA Morehouse Parish 0.77

022069 LA Natchitoches Parish 0.80

022071 LA Orleans Parish 0.93

022073 LA Ouachita Parish 0.85

022075 LA Plaquemines Parish 0.94

022077 LA Pointe Coupee

Parish

0.81

022079 LA Rapides Parish 0.84

022081 LA Red River Parish 0.82

022083 LA Richland Parish 0.79

022085 LA Sabine Parish 0.80

022087 LA St. Bernard Parish 0.88

022089 LA St. Charles Parish 0.95

022091 LA St. Helena Parish 0.82

022093 LA St. James Parish 0.83

022095 LA St. John the Baptist

Parish

0.89

022097 LA St. Landry Parish 0.81

022099 LA St. Martin Parish 0.85

022101 LA St. Mary Parish 0.80

022103 LA St. Tammany Parish 0.92

022105 LA Tangipahoa Parish 0.85

022107 LA Tensas Parish 0.85

022109 LA Terrebonne Parish 0.84

022111 LA Union Parish 0.81

022113 LA Vermilion Parish 0.83

022115 LA Vernon Parish 0.80

022117 LA Washington Parish 0.83

022119 LA Webster Parish 0.81

022121 LA West Baton Rouge

Parish

0.86

022123 LA West Carroll Parish 0.85

022125 LA West Feliciana

Parish

0.89

022127 LA Winn Parish 0.80

023001 ME Androscoggin 0.94

023003 ME Aroostook 0.89

023005 ME Cumberland 1.01

023007 ME Franklin 0.90

023009 ME Hancock 0.94

023011 ME Kennebec 0.94

023013 ME Knox 0.92

023015 ME Lincoln 0.95

023017 ME Oxford 0.91

023019 ME Penobscot 0.99

023021 ME Piscataquis 0.91

023023 ME Sagadahoc 1.01

023025 ME Somerset 0.92

023027 ME Waldo 0.90

023029 ME Washington 0.90

023031 ME York 1.01

024001 MD Allegany 0.94

024003 MD Anne Arundel 1.16

024005 MD Baltimore 1.15

024009 MD Calvert 1.07

024011 MD Caroline 0.99

024013 MD Carroll 1.11

024015 MD Cecil 1.04

024017 MD Charles 1.07

024019 MD Dorchester 1.00

024021 MD Frederick 1.12

024023 MD Garrett 0.95

024025 MD Harford 1.12

024027 MD Howard 1.15

024029 MD Kent 0.99

024031 MD Montgomery 1.20

024033 MD Prince George’s 1.01

024035 MD Queen Anne’s 1.10

024037 MD St. Mary’s 1.02

024039 MD Somerset 0.97

024041 MD Talbot 1.02

024043 MD Washington 1.00

024045 MD Wicomico 0.99

FIPS 

code State County

MedPAC 

index

FIPS 

code State County

MedPAC 

index

FIPS 

code State County

MedPAC 

index

Note:	 FIPS (Federal Information Processing Standards).
Additional technical infor mation on constructing a compensation index from BLS data | June 2007 15

024047 MD Worcester 0.98

024510 MD Baltimore city 1.17

025001 MA Barnstable 1.18

025003 MA Berkshire 1.05

025005 MA Bristol 1.10

025007 MA Dukes 1.05

025009 MA Essex 1.09

025011 MA Franklin 1.07

025013 MA Hampden 1.07

025015 MA Hampshire 1.07

025017 MA Middlesex 1.15

025019 MA Nantucket 1.11

025021 MA Norfolk 1.22

025023 MA Plymouth 1.11

025025 MA Suffolk 1.22

025027 MA Worcester 1.17

026001 MI Alcona 0.99

026003 MI Alger 1.00

026005 MI Allegan 0.98

026007 MI Alpena 0.97

026009 MI Antrim 0.92

026011 MI Arenac 0.94

026013 MI Baraga 0.91

026015 MI Barry 0.98

026017 MI Bay 0.98

026019 MI Benzie 0.93

026021 MI Berrien 0.96

026023 MI Branch 0.94

026025 MI Calhoun 1.05

026027 MI Cass 0.93

026029 MI Charlevoix 0.97

026031 MI Cheboygan 0.90

026033 MI Chippewa 0.94

026035 MI Clare 0.92

026037 MI Clinton 1.01

026039 MI Crawford 0.90

026041 MI Delta 0.98

026043 MI Dickinson 0.97

026045 MI Eaton 1.01

026047 MI Emmet 0.94

026049 MI Genesee 1.03

026051 MI Gladwin 0.92

026053 MI Gogebic 0.92

026055 MI Grand Traverse 1.00

026057 MI Gratiot 0.92

026059 MI Hillsdale 0.96

026061 MI Houghton 0.92

026063 MI Huron 0.96

026065 MI Ingham 1.08

026067 MI Ionia 0.97

026069 MI Iosco 0.92

026071 MI Iron 0.96

026073 MI Isabella 0.97

026075 MI Jackson 1.05

026077 MI Kalamazoo 0.99

026079 MI Kalkaska 1.00

026081 MI Kent 1.01

026083 MI Keweenaw 0.98

026085 MI Lake 0.99

026087 MI Lapeer 1.05

026089 MI Leelanau 0.98

026091 MI Lenawee 1.00

026093 MI Livingston 1.07

026095 MI Luce 0.90

026097 MI Mackinac 0.90

026099 MI Macomb 1.08

026101 MI Manistee 0.91

026103 MI Marquette 0.98

026105 MI Mason 0.90

026107 MI Mecosta 0.91

026109 MI Menominee 0.93

026111 MI Midland 0.98

026113 MI Missaukee 0.98

026115 MI Monroe 1.01

026117 MI Montcalm 0.94

026119 MI Montmorency 1.00

026121 MI Muskegon 0.99

026123 MI Newaygo 0.96

026125 MI Oakland 1.13

026127 MI Oceana 0.91

026129 MI Ogemaw 0.93

026131 MI Ontonagon 0.98

026133 MI Osceola 0.94

026135 MI Oscoda 0.97

026137 MI Otsego 0.91

026139 MI Ottawa 0.97

026141 MI Presque Isle 0.98

026143 MI Roscommon 0.90

026145 MI Saginaw 0.98

026147 MI St. Clair 1.05

026149 MI St. Joseph 0.96

026151 MI Sanilac 0.94

026153 MI Schoolcraft 0.95

026155 MI Shiawassee 0.97

026157 MI Tuscola 1.00

026159 MI Van Buren 0.94

026161 MI Washtenaw 1.11

026163 MI Wayne 1.01

026165 MI Wexford 0.90

027001 MN Aitkin 1.00

027003 MN Anoka 1.12

027005 MN Becker 0.92

027007 MN Beltrami 0.94

027009 MN Benton 1.05

027011 MN Big Stone 1.00

027013 MN Blue Earth 0.96

027015 MN Brown 0.97

027017 MN Carlton 1.04

027019 MN Carver 1.10

027021 MN Cass 0.95

027023 MN Chippewa 0.97

027025 MN Chisago 1.06

027027 MN Clay 0.90

027029 MN Clearwater 0.91

027031 MN Cook 1.00

027033 MN Cottonwood 1.00

027035 MN Crow Wing 0.98

027037 MN Dakota 1.08

027039 MN Dodge 1.13

027041 MN Douglas 1.00

027043 MN Faribault 0.91

027045 MN Fillmore 1.05

027047 MN Freeborn 0.99

027049 MN Goodhue 1.05

027051 MN Grant 1.00

027053 MN Hennepin 1.13

027055 MN Houston 0.97

027057 MN Hubbard 0.99

027059 MN Isanti 1.06

027061 MN Itasca 0.94

027063 MN Jackson 0.97

027065 MN Kanabec 0.96

027067 MN Kandiyohi 1.00

027069 MN Kittson 1.00

027071 MN Koochiching 0.94

027073 MN Lac qui Parle 0.97

027075 MN Lake 0.99

027077 MN Lake of the Woods 1.00

FIPS 

code State County

MedPAC 

index

FIPS 

code State County

MedPAC 

index

FIPS 

code State County

MedPAC 

index

Note:	 FIPS (Federal Information Processing Standards).
16 June 2007 | Additional technical infor mation on constructing a compensation index from BLS data

027079 MN Le Sueur 0.99

027081 MN Lincoln 0.91

027083 MN Lyon 0.92

027085 MN McLeod 0.99

027087 MN Mahnomen 1.00

027089 MN Marshall 1.00

027091 MN Martin 0.92

027093 MN Meeker 1.00

027095 MN Mille Lacs 1.00

027097 MN Morrison 1.00

027099 MN Mower 1.05

027101 MN Murray 1.00

027103 MN Nicollet 0.95

027105 MN Nobles 0.93

027107 MN Norman 1.00

027109 MN Olmsted 1.17

027111 MN Otter Tail 1.00

027113 MN Pennington 0.94

027115 MN Pine 0.96

027117 MN Pipestone 0.91

027119 MN Polk 0.90

027121 MN Pope 1.00

027123 MN Ramsey 1.13

027125 MN Red Lake 1.00

027127 MN Redwood 0.91

027129 MN Renville 0.94

027131 MN Rice 1.02

027133 MN Rock 0.92

027135 MN Roseau 0.93

027137 MN St. Louis 1.04

027139 MN Scott 1.10

027141 MN Sherburne 1.06

027143 MN Sibley 0.99

027145 MN Stearns 1.11

027147 MN Steele 1.02

027149 MN Stevens 0.95

027151 MN Swift 0.92

027153 MN Todd 1.00

027155 MN Traverse 1.00

027157 MN Wabasha 1.11

027159 MN Wadena 0.94

027161 MN Waseca 1.00

027163 MN Washington 1.11

027165 MN Watonwan 0.91

027167 MN Wilkin 0.96

027169 MN Winona 1.05

027171 MN Wright 1.06

027173 MN Yellow Medicine 0.91

028001 MS Adams 0.86

028003 MS Alcorn 0.79

028005 MS Amite 0.84

028007 MS Attala 0.81

028009 MS Benton 0.85

028011 MS Bolivar 0.81

028013 MS Calhoun 0.83

028015 MS Carroll 0.85

028017 MS Chickasaw 0.85

028019 MS Choctaw 0.84

028021 MS Claiborne 0.86

028023 MS Clarke 0.86

028025 MS Clay 0.83

028027 MS Coahoma 0.83

028029 MS Copiah 0.83

028031 MS Covington 0.83

028033 MS DeSoto 0.89

028035 MS Forrest 0.84

028037 MS Franklin 0.78

028039 MS George 0.78

028041 MS Greene 0.83

028043 MS Grenada 0.80

028045 MS Hancock 0.93

028047 MS Harrison 0.88

028049 MS Hinds 0.88

028051 MS Holmes 0.82

028053 MS Humphreys 0.83

028055 MS Issaquena 0.86

028057 MS Itawamba 0.84

028059 MS Jackson 0.81

028061 MS Jasper 0.79

028063 MS Jefferson 0.83

028065 MS Jefferson Davis 0.82

028067 MS Jones 0.82

028069 MS Kemper 0.81

028071 MS Lafayette 0.85

028073 MS Lamar 0.81

028075 MS Lauderdale 0.83

028077 MS Lawrence 0.84

028079 MS Leake 0.84

028081 MS Lee 0.84

028083 MS Leflore 0.85

028085 MS Lincoln 0.85

028087 MS Lowndes 0.80

028089 MS Madison 0.90

028091 MS Marion 0.80

028093 MS Marshall 0.95

028095 MS Monroe 0.81

028097 MS Montgomery 0.82

028099 MS Neshoba 0.79

028101 MS Newton 0.83

028103 MS Noxubee 0.82

028105 MS Oktibbeha 0.80

028107 MS Panola 0.83

028109 MS Pearl River 0.84

028111 MS Perry 0.85

028113 MS Pike 0.80

028115 MS Pontotoc 0.81

028117 MS Prentiss 0.78

028119 MS Quitman 0.83

028121 MS Rankin 0.86

028123 MS Scott 0.81

028125 MS Sharkey 0.82

028127 MS Simpson 0.83

028129 MS Smith 0.84

028131 MS Stone 0.84

028133 MS Sunflower 0.81

028135 MS Tallahatchie 0.83

028137 MS Tate 0.92

028139 MS Tippah 0.80

028141 MS Tishomingo 0.80

028143 MS Tunica 0.92

028145 MS Union 0.85

028147 MS Walthall 0.78

028149 MS Warren 0.85

028151 MS Washington 0.82

028153 MS Wayne 0.78

028155 MS Webster 0.81

028157 MS Wilkinson 0.82

028159 MS Winston 0.82

028161 MS Yalobusha 0.83

028163 MS Yazoo 0.86

029001 MO Adair 0.82

029003 MO Andrew 0.93

029005 MO Atchison 0.87

029007 MO Audrain 0.85

029009 MO Barry 0.81

029011 MO Barton 0.87

029013 MO Bates 0.95

029015 MO Benton 0.87

FIPS 

code State County

MedPAC 

index

FIPS 

code State County

MedPAC 

index

FIPS 

code State County

MedPAC 

index

Note:	 FIPS (Federal Information Processing Standards).
Additional technical infor mation on constructing a compensation index from BLS data | June 2007 17

029017 MO Bollinger 0.86

029019 MO Boone 0.90

029021 MO Buchanan 0.95

029023 MO Butler 0.81

029025 MO Caldwell 0.96

029027 MO Callaway 0.88

029029 MO Camden 0.87

029031 MO Cape Girardeau 0.86

029033 MO Carroll 0.87

029035 MO Carter 0.86

029037 MO Cass 0.94

029039 MO Cedar 0.83

029041 MO Chariton 0.87

029043 MO Christian 0.93

029045 MO Clark 0.86

029047 MO Clay 0.98

029049 MO Clinton 0.94

029051 MO Cole 0.88

029053 MO Cooper 0.87

029055 MO Crawford 0.99

029057 MO Dade 0.87

029059 MO Dallas 0.86

029061 MO Daviess 0.87

029063 MO DeKalb 0.94

029065 MO Dent 0.89

029067 MO Douglas 0.87

029069 MO Dunklin 0.85

029071 MO Franklin 0.89

029073 MO Gasconade 0.89

029075 MO Gentry 0.87

029077 MO Greene 0.88

029079 MO Grundy 0.86

029081 MO Harrison 0.86

029083 MO Henry 0.86

029085 MO Hickory 0.86

029087 MO Holt 0.85

029089 MO Howard 0.88

029091 MO Howell 0.79

029093 MO Iron 0.89

029095 MO Jackson 1.00

029097 MO Jasper 0.85

029099 MO Jefferson 0.89

029101 MO Johnson 0.90

029103 MO Knox 0.87

029105 MO Laclede 0.83

029107 MO Lafayette 0.97

029109 MO Lawrence 0.87

029111 MO Lewis 0.85

029113 MO Lincoln 0.91

029115 MO Linn 0.85

029117 MO Livingston 0.87

029119 MO McDonald 0.84

029121 MO Macon 0.81

029123 MO Madison 0.85

029125 MO Maries 0.87

029127 MO Marion 0.85

029129 MO Mercer 0.87

029131 MO Miller 0.87

029133 MO Mississippi 0.86

029135 MO Moniteau 0.86

029137 MO Monroe 0.87

029139 MO Montgomery 0.87

029141 MO Morgan 0.86

029143 MO New Madrid 0.85

029145 MO Newton 0.85

029147 MO Nodaway 0.86

029149 MO Oregon 0.86

029151 MO Osage 0.87

029153 MO Ozark 0.86

029155 MO Pemiscot 0.87

029157 MO Perry 0.87

029159 MO Pettis 0.87

029161 MO Phelps 0.89

029163 MO Pike 0.82

029165 MO Platte 0.94

029167 MO Polk 0.84

029169 MO Pulaski 0.82

029171 MO Putnam 0.86

029173 MO Ralls 0.87

029175 MO Randolph 0.83

029177 MO Ray 0.94

029179 MO Reynolds 0.86

029181 MO Ripley 0.79

029183 MO St. Charles 0.94

029185 MO St. Clair 0.86

029186 MO Ste. Genevieve 0.84

029187 MO St. Francois 0.84

029189 MO St. Louis 0.95

029195 MO Saline 0.87

029197 MO Schuyler 0.87

029199 MO Scotland 0.87

029201 MO Scott 0.84

029203 MO Shannon 0.87

029205 MO Shelby 0.87

029207 MO Stoddard 0.80

029209 MO Stone 0.84

029211 MO Sullivan 0.87

029213 MO Taney 0.83

029215 MO Texas 0.80

029217 MO Vernon 0.87

029219 MO Warren 0.91

029221 MO Washington 0.89

029223 MO Wayne 0.80

029225 MO Webster 0.88

029227 MO Worth 0.87

029229 MO Wright 0.86

029510 MO St. Louis city 0.96

030001 MT Beaverhead 0.86

030003 MT Big Horn 0.89

030005 MT Blaine 0.90

030007 MT Broadwater 0.85

030009 MT Carbon 0.89

030011 MT Carter 0.85

030013 MT Cascade 0.88

030015 MT Chouteau 0.86

030017 MT Custer 0.82

030019 MT Daniels 0.86

030021 MT Dawson 0.87

030023 MT Deer Lodge 0.81

030025 MT Fallon 0.86

030027 MT Fergus 0.82

030029 MT Flathead 0.86

030031 MT Gallatin 0.84

030033 MT Garfield 0.86

030035 MT Glacier 0.88

030037 MT Golden Valley 0.85

030039 MT Granite 0.86

030041 MT Hill 0.81

030043 MT Jefferson 0.87

030045 MT Judith Basin 0.86

030047 MT Lake 0.82

030049 MT Lewis and Clark 0.90

030051 MT Liberty 0.86

030053 MT Lincoln 0.90

030055 MT McCone 0.86

030057 MT Madison 0.86

030059 MT Meagher 0.86

030061 MT Mineral 0.87

FIPS 

code State County

MedPAC 

index

FIPS 

code State County

MedPAC 

index

FIPS 

code State County

MedPAC 

index

Note:	 FIPS (Federal Information Processing Standards).
18 June 2007 | Additional technical infor mation on constructing a compensation index from BLS data

030063 MT Missoula 0.89

030065 MT Musselshell 0.85

030067 MT Park 0.85

030069 MT Petroleum 0.87

030071 MT Phillips 0.86

030073 MT Pondera 0.86

030075 MT Powder River 0.86

030077 MT Powell 0.86

030079 MT Prairie 0.86

030081 MT Ravalli 0.84

030083 MT Richland 0.86

030085 MT Roosevelt 0.86

030087 MT Rosebud 0.90

030089 MT Sanders 0.88

030091 MT Sheridan 0.86

030093 MT Silver Bow 0.84

030095 MT Stillwater 0.86

030097 MT Sweet Grass 0.87

030099 MT Teton 0.87

030101 MT Toole 0.86

030103 MT Treasure 0.86

030105 MT Valley 0.81

030107 MT Wheatland 0.86

030109 MT Wibaux 0.87

030111 MT Yellowstone 0.90

031001 NE Adams 0.88

031003 NE Antelope 0.81

031005 NE Arthur 0.89

031007 NE Banner 0.88

031009 NE Blaine 0.88

031011 NE Boone 0.87

031013 NE Box Butte 0.87

031015 NE Boyd 0.90

031017 NE Brown 0.87

031019 NE Buffalo 0.88

031021 NE Burt 0.87

031023 NE Butler 0.87

031025 NE Cass 0.89

031027 NE Cedar 0.87

031029 NE Chase 0.88

031031 NE Cherry 0.88

031033 NE Cheyenne 0.87

031035 NE Clay 0.86

031037 NE Colfax 0.87

031039 NE Cuming 0.84

031041 NE Custer 0.90

031043 NE Dakota 0.80

031045 NE Dawes 0.87

031047 NE Dawson 0.88

031049 NE Deuel 0.88

031051 NE Dixon 0.84

031053 NE Dodge 0.86

031055 NE Douglas 0.95

031057 NE Dundy 0.88

031059 NE Fillmore 0.87

031061 NE Franklin 0.86

031063 NE Frontier 0.87

031065 NE Furnas 0.87

031067 NE Gage 0.87

031069 NE Garden 0.87

031071 NE Garfield 0.87

031073 NE Gosper 0.88

031075 NE Grant 0.88

031077 NE Greeley 0.87

031079 NE Hall 0.90

031081 NE Hamilton 0.86

031083 NE Harlan 0.87

031085 NE Hayes 0.88

031087 NE Hitchcock 0.87

031089 NE Holt 0.90

031091 NE Hooker 0.87

031093 NE Howard 0.87

031095 NE Jefferson 0.89

031097 NE Johnson 0.88

031099 NE Kearney 0.88

031101 NE Keith 0.87

031103 NE Keya Paha 0.88

031105 NE Kimball 0.88

031107 NE Knox 0.83

031109 NE Lancaster 0.96

031111 NE Lincoln 0.85

031113 NE Logan 0.88

031115 NE Loup 0.88

031117 NE McPherson 0.89

031119 NE Madison 0.85

031121 NE Merrick 0.88

031123 NE Morrill 0.87

031125 NE Nance 0.87

031127 NE Nemaha 0.88

031129 NE Nuckolls 0.85

031131 NE Otoe 0.88

031133 NE Pawnee 0.87

031135 NE Perkins 0.88

031137 NE Phelps 0.83

031139 NE Pierce 0.86

031141 NE Platte 0.83

031143 NE Polk 0.87

031145 NE Red Willow 0.86

031147 NE Richardson 0.84

031149 NE Rock 0.87

031151 NE Saline 0.87

031153 NE Sarpy 0.93

031155 NE Saunders 0.89

031157 NE Scotts Bluff 0.83

031159 NE Seward 0.95

031161 NE Sheridan 0.87

031163 NE Sherman 0.87

031165 NE Sioux 0.87

031167 NE Stanton 0.88

031169 NE Thayer 0.88

031171 NE Thomas 0.87

031173 NE Thurston 0.85

031175 NE Valley 0.85

031177 NE Washington 0.90

031179 NE Wayne 0.87

031181 NE Webster 0.86

031183 NE Wheeler 0.87

031185 NE York 0.85

032001 NV Churchill 1.01

032003 NV Clark 1.07

032005 NV Douglas 1.13

032007 NV Elko 1.03

032009 NV Esmeralda 1.07

032011 NV Eureka 1.04

032013 NV Humboldt 1.01

032015 NV Lander 1.01

032017 NV Lincoln 1.02

032019 NV Lyon 1.06

032021 NV Mineral 1.06

032023 NV Nye 1.07

032027 NV Pershing 1.03

032029 NV Storey 1.05

032031 NV Washoe 1.13

032033 NV White Pine 1.03

032510 NV Carson City 1.13

033001 NH Belknap 1.00

033003 NH Carroll 0.98

033005 NH Cheshire 1.05

FIPS 

code State County

MedPAC 

index

FIPS 

code State County

MedPAC 

index

FIPS 

code State County

MedPAC 

index

Note:	 FIPS (Federal Information Processing Standards).
Additional technical infor mation on constructing a compensation index from BLS data | June 2007 19

033007 NH Coos 0.96

033009 NH Grafton 1.04

033011 NH Hillsborough 1.05

033013 NH Merrimack 1.03

033015 NH Rockingham 1.09

033017 NH Strafford 1.09

033019 NH Sullivan 0.97

034001 NJ Atlantic 1.11

034003 NJ Bergen 1.24

034005 NJ Burlington 1.01

034007 NJ Camden 1.08

034009 NJ Cape May 1.09

034011 NJ Cumberland 1.13

034013 NJ Essex 1.20

034015 NJ Gloucester 1.06

034017 NJ Hudson 1.22

034019 NJ Hunterdon 1.17

034021 NJ Mercer 1.15

034023 NJ Middlesex 1.19

034025 NJ Monmouth 1.15

034027 NJ Morris 1.19

034029 NJ Ocean 1.13

034031 NJ Passaic 1.21

034033 NJ Salem 1.08

034035 NJ Somerset 1.18

034037 NJ Sussex 1.13

034039 NJ Union 1.21

034041 NJ Warren 1.08

035001 NM Bernalillo 0.94

035003 NM Catron 0.93

035005 NM Chaves 0.88

035006 NM Cibola 0.89

035007 NM Colfax 0.87

035009 NM Curry 0.87

035011 NM De Baca 0.92

035013 NM Dona Ana 0.85

035015 NM Eddy 0.90

035017 NM Grant 0.91

035019 NM Guadalupe 0.92

035021 NM Harding 0.93

035023 NM Hidalgo 0.93

035025 NM Lea 0.92

035027 NM Lincoln 0.90

035028 NM Los Alamos 0.95

035029 NM Luna 0.95

035031 NM McKinley 0.92

035033 NM Mora 0.92

035035 NM Otero 0.91

035037 NM Quay 0.93

035039 NM Rio Arriba 0.92

035041 NM Roosevelt 0.91

035043 NM Sandoval 0.92

035045 NM San Juan 0.94

035047 NM San Miguel 0.86

035049 NM Santa Fe 0.95

035051 NM Sierra 0.86

035053 NM Socorro 0.89

035055 NM Taos 0.95

035057 NM Torrance 0.92

035059 NM Union 0.91

035061 NM Valencia 0.89

036001 NY Albany 0.97

036003 NY Allegany 0.87

036005 NY Bronx 1.26

036007 NY Broome 0.92

036009 NY Cattaraugus 0.90

036011 NY Cayuga 0.92

036013 NY Chautauqua 0.92

036015 NY Chemung 0.89

036017 NY Chenango 0.93

036019 NY Clinton 0.92

036021 NY Columbia 0.99

036023 NY Cortland 0.90

036025 NY Delaware 0.95

036027 NY Dutchess 1.01

036029 NY Erie 0.99

036031 NY Essex 0.89

036033 NY Franklin 0.96

036035 NY Fulton 0.91

036037 NY Genesee 0.91

036039 NY Greene 0.94

036041 NY Hamilton 0.96

036043 NY Herkimer 0.89

036045 NY Jefferson 0.96

036047 NY Kings 1.24

036049 NY Lewis 0.96

036051 NY Livingston 0.96

036053 NY Madison 0.94

036055 NY Monroe 0.95

036057 NY Montgomery 0.93

036059 NY Nassau 1.25

036061 NY New York 1.32

036063 NY Niagara 0.95

036065 NY Oneida 0.91

036067 NY Onondaga 0.99

036069 NY Ontario 0.94

036071 NY Orange 1.16

036073 NY Orleans 0.89

036075 NY Oswego 0.97

036077 NY Otsego 0.96

036079 NY Putnam 1.21

036081 NY Queens 1.24

036083 NY Rensselaer 0.94

036085 NY Richmond 1.23

036087 NY Rockland 1.21

036089 NY St. Lawrence 0.93

036091 NY Saratoga 0.95

036093 NY Schenectady 0.96

036095 NY Schoharie 0.91

036097 NY Schuyler 0.89

036099 NY Seneca 0.93

036101 NY Steuben 0.92

036103 NY Suffolk 1.21

036105 NY Sullivan 1.05

036107 NY Tioga 0.89

036109 NY Tompkins 0.99

036111 NY Ulster 1.05

036113 NY Warren 0.96

036115 NY Washington 0.96

036117 NY Wayne 0.89

036119 NY Westchester 1.27

036121 NY Wyoming 0.94

036123 NY Yates 0.95

037001 NC Alamance 0.93

037003 NC Alexander 0.94

037005 NC Alleghany 0.87

037007 NC Anson 1.05

037009 NC Ashe 0.86

037011 NC Avery 0.86

037013 NC Beaufort 0.85

037015 NC Bertie 0.89

037017 NC Bladen 0.91

037019 NC Brunswick 0.89

037021 NC Buncombe 0.95

037023 NC Burke 0.92

037025 NC Cabarrus 0.97

037027 NC Caldwell 0.95

037029 NC Camden 0.90

FIPS 

code State County

MedPAC 

index

FIPS 

code State County

MedPAC 

index

FIPS 

code State County

MedPAC 

index

Note:	 FIPS (Federal Information Processing Standards).
20 June 2007 | Additional technical infor mation on constructing a compensation index from BLS data

037031 NC Carteret 0.90

037033 NC Caswell 0.97

037035 NC Catawba 0.94

037037 NC Chatham 0.97

037039 NC Cherokee 0.85

037041 NC Chowan 0.88

037043 NC Clay 0.93

037045 NC Cleveland 0.89

037047 NC Columbus 0.94

037049 NC Craven 0.93

037051 NC Cumberland 0.91

037053 NC Currituck 0.92

037055 NC Dare 0.86

037057 NC Davidson 0.91

037059 NC Davie 0.89

037061 NC Duplin 0.85

037063 NC Durham 1.04

037065 NC Edgecombe 0.94

037067 NC Forsyth 0.95

037069 NC Franklin 0.92

037071 NC Gaston 0.96

037073 NC Gates 0.89

037075 NC Graham 0.90

037077 NC Granville 0.97

037079 NC Greene 0.93

037081 NC Guilford 0.95

037083 NC Halifax 0.87

037085 NC Harnett 0.88

037087 NC Haywood 0.91

037089 NC Henderson 0.93

037091 NC Hertford 0.86

037093 NC Hoke 0.89

037095 NC Hyde 0.91

037097 NC Iredell 0.92

037099 NC Jackson 0.85

037101 NC Johnston 0.92

037103 NC Jones 0.91

037105 NC Lee 0.94

037107 NC Lenoir 0.88

037109 NC Lincoln 0.92

037111 NC McDowell 0.87

037113 NC Macon 0.86

037115 NC Madison 0.93

037117 NC Martin 0.94

037119 NC Mecklenburg 1.02

037121 NC Mitchell 0.85

037123 NC Montgomery 0.95

037125 NC Moore 0.93

037127 NC Nash 0.94

037129 NC New Hanover 0.95

037131 NC Northampton 0.88

037133 NC Onslow 0.89

037135 NC Orange 1.00

037137 NC Pamlico 0.89

037139 NC Pasquotank 0.86

037141 NC Pender 0.89

037143 NC Perquimans 0.93

037145 NC Person 1.08

037147 NC Pitt 0.94

037149 NC Polk 0.94

037151 NC Randolph 0.89

037153 NC Richmond 0.95

037155 NC Robeson 0.89

037157 NC Rockingham 0.91

037159 NC Rowan 0.90

037161 NC Rutherford 0.88

037163 NC Sampson 0.89

037165 NC Scotland 0.92

037167 NC Stanly 0.95

037169 NC Stokes 0.89

037171 NC Surry 0.86

037173 NC Swain 0.86

037175 NC Transylvania 0.90

037177 NC Tyrrell 0.90

037179 NC Union 1.01

037181 NC Vance 0.91

037183 NC Wake 0.97

037185 NC Warren 0.90

037187 NC Washington 0.89

037189 NC Watauga 0.91

037191 NC Wayne 0.94

037193 NC Wilkes 0.86

037195 NC Wilson 0.88

037197 NC Yadkin 0.91

037199 NC Yancey 0.91

038001 ND Adams 0.82

038003 ND Barnes 0.83

038005 ND Benson 0.82

038007 ND Billings 0.83

038009 ND Bottineau 0.82

038011 ND Bowman 0.82

038013 ND Burke 0.82

038015 ND Burleigh 0.86

038017 ND Cass 0.92

038019 ND Cavalier 0.82

038021 ND Dickey 0.81

038023 ND Divide 0.81

038025 ND Dunn 0.82

038027 ND Eddy 0.82

038029 ND Emmons 0.84

038031 ND Foster 0.81

038033 ND Golden Valley 0.83

038035 ND Grand Forks 0.90

038037 ND Grant 0.82

038039 ND Griggs 0.82

038041 ND Hettinger 0.82

038043 ND Kidder 0.82

038045 ND LaMoure 0.82

038047 ND Logan 0.83

038049 ND McHenry 0.82

038051 ND McIntosh 0.82

038053 ND McKenzie 0.82

038055 ND McLean 0.77

038057 ND Mercer 0.77

038059 ND Morton 0.82

038061 ND Mountrail 0.82

038063 ND Nelson 0.82

038065 ND Oliver 0.83

038067 ND Pembina 0.90

038069 ND Pierce 0.82

038071 ND Ramsey 0.81

038073 ND Ransom 0.83

038075 ND Renville 0.82

038077 ND Richland 0.90

038079 ND Rolette 0.85

038081 ND Sargent 0.83

038083 ND Sheridan 0.82

038085 ND Sioux 0.82

038087 ND Slope 0.82

038089 ND Stark 0.82

038091 ND Steele 0.83

038093 ND Stutsman 0.81

038095 ND Towner 0.82

038097 ND Traill 0.90

038099 ND Walsh 0.90

038101 ND Ward 0.81

038103 ND Wells 0.81

038105 ND Williams 0.81

FIPS 

code State County

MedPAC 

index

FIPS 

code State County

MedPAC 

index

FIPS 

code State County

MedPAC 

index

Note:	 FIPS (Federal Information Processing Standards).
Additional technical infor mation on constructing a compensation index from BLS data | June 2007 21

039001 OH Adams 0.92

039003 OH Allen 0.95

039005 OH Ashland 0.90

039007 OH Ashtabula 0.93

039009 OH Athens 0.91

039011 OH Auglaize 0.93

039013 OH Belmont 0.82

039015 OH Brown 0.95

039017 OH Butler 0.99

039019 OH Carroll 0.89

039021 OH Champaign 0.94

039023 OH Clark 0.94

039025 OH Clermont 0.97

039027 OH Clinton 0.96

039029 OH Columbiana 0.92

039031 OH Coshocton 0.91

039033 OH Crawford 0.91

039035 OH Cuyahoga 1.05

039037 OH Darke 0.96

039039 OH Defiance 0.94

039041 OH Delaware 1.02

039043 OH Erie 0.98

039045 OH Fairfield 0.97

039047 OH Fayette 0.94

039049 OH Franklin 1.03

039051 OH Fulton 0.92

039053 OH Gallia 0.90

039055 OH Geauga 1.01

039057 OH Greene 0.95

039059 OH Guernsey 0.91

039061 OH Hamilton 1.02

039063 OH Hancock 0.94

039065 OH Hardin 0.93

039067 OH Harrison 0.88

039069 OH Henry 0.94

039071 OH Highland 0.90

039073 OH Hocking 0.91

039075 OH Holmes 0.93

039077 OH Huron 0.90

039079 OH Jackson 0.90

039081 OH Jefferson 0.86

039083 OH Knox 0.92

039085 OH Lake 0.99

039087 OH Lawrence 0.94

039089 OH Licking 0.98

039091 OH Logan 0.92

039093 OH Lorain 1.00

039095 OH Lucas 0.97

039097 OH Madison 1.04

039099 OH Mahoning 0.94

039101 OH Marion 0.97

039103 OH Medina 0.98

039105 OH Meigs 0.94

039107 OH Mercer 0.92

039109 OH Miami 0.93

039111 OH Monroe 0.88

039113 OH Montgomery 0.98

039115 OH Morgan 0.95

039117 OH Morrow 0.97

039119 OH Muskingum 0.90

039121 OH Noble 0.92

039123 OH Ottawa 0.96

039125 OH Paulding 0.91

039127 OH Perry 0.88

039129 OH Pickaway 0.97

039131 OH Pike 0.97

039133 OH Portage 0.95

039135 OH Preble 0.92

039137 OH Putnam 0.91

039139 OH Richland 0.98

039141 OH Ross 0.97

039143 OH Sandusky 0.93

039145 OH Scioto 0.92

039147 OH Seneca 0.96

039149 OH Shelby 0.94

039151 OH Stark 0.93

039153 OH Summit 0.97

039155 OH Trumbull 0.97

039157 OH Tuscarawas 0.89

039159 OH Union 1.02

039161 OH Van Wert 0.89

039163 OH Vinton 0.97

039165 OH Warren 0.99

039167 OH Washington 0.86

039169 OH Wayne 0.93

039171 OH Williams 0.91

039173 OH Wood 0.94

039175 OH Wyandot 0.93

040001 OK Adair 0.82

040003 OK Alfalfa 0.83

040005 OK Atoka 0.81

040007 OK Beaver 0.83

040009 OK Beckham 0.81

040011 OK Blaine 0.82

040013 OK Bryan 0.82

040015 OK Caddo 0.80

040017 OK Canadian 0.87

040019 OK Carter 0.81

040021 OK Cherokee 0.85

040023 OK Choctaw 0.79

040025 OK Cimarron 0.87

040027 OK Cleveland 0.87

040029 OK Coal 0.82

040031 OK Comanche 0.82

040033 OK Cotton 0.82

040035 OK Craig 0.80

040037 OK Creek 0.85

040039 OK Custer 0.80

040041 OK Delaware 0.79

040043 OK Dewey 0.82

040045 OK Ellis 0.83

040047 OK Garfield 0.82

040049 OK Garvin 0.82

040051 OK Grady 0.83

040053 OK Grant 0.82

040055 OK Greer 0.81

040057 OK Harmon 0.82

040059 OK Harper 0.81

040061 OK Haskell 0.83

040063 OK Hughes 0.79

040065 OK Jackson 0.85

040067 OK Jefferson 0.85

040069 OK Johnston 0.84

040071 OK Kay 0.80

040073 OK Kingfisher 0.82

040075 OK Kiowa 0.83

040077 OK Latimer 0.82

040079 OK Le Flore 0.80

040081 OK Lincoln 0.86

040083 OK Logan 0.87

040085 OK Love 0.82

040087 OK McClain 0.86

040089 OK McCurtain 0.85

040091 OK McIntosh 0.80

040093 OK Major 0.83

040095 OK Marshall 0.82

040097 OK Mayes 0.85

040099 OK Murray 0.80

FIPS 

code State County

MedPAC 

index

FIPS 

code State County

MedPAC 

index

FIPS 

code State County

MedPAC 

index

Note:	 FIPS (Federal Information Processing Standards).
22 June 2007 | Additional technical infor mation on constructing a compensation index from BLS data

040101 OK Muskogee 0.84

040103 OK Noble 0.83

040105 OK Nowata 0.82

040107 OK Okfuskee 0.83

040109 OK Oklahoma 0.88

040111 OK Okmulgee 0.83

040113 OK Osage 0.84

040115 OK Ottawa 0.77

040117 OK Pawnee 0.85

040119 OK Payne 0.81

040121 OK Pittsburg 0.80

040123 OK Pontotoc 0.79

040125 OK Pottawatomie 0.82

040127 OK Pushmataha 0.81

040129 OK Roger Mills 0.82

040131 OK Rogers 0.86

040133 OK Seminole 0.77

040135 OK Sequoyah 0.79

040137 OK Stephens 0.80

040139 OK Texas 0.80

040141 OK Tillman 0.85

040143 OK Tulsa 0.87

040145 OK Wagoner 0.90

040147 OK Washington 0.82

040149 OK Washita 0.82

040151 OK Woods 0.79

040153 OK Woodward 0.84

041001 OR Baker 1.02

041003 OR Benton 1.07

041005 OR Clackamas 1.15

041007 OR Clatsop 1.05

041009 OR Columbia 1.12

041011 OR Coos 0.97

041013 OR Crook 1.04

041015 OR Curry 1.05

041017 OR Deschutes 1.06

041019 OR Douglas 1.00

041021 OR Gilliam 1.03

041023 OR Grant 1.02

041025 OR Harney 1.01

041027 OR Hood River 1.04

041029 OR Jackson 1.08

041031 OR Jefferson 1.03

041033 OR Josephine 1.03

041035 OR Klamath 1.07

041037 OR Lake 1.07

041039 OR Lane 1.05

041041 OR Lincoln 1.02

041043 OR Linn 1.01

041045 OR Malheur 1.02

041047 OR Marion 1.03

041049 OR Morrow 1.02

041051 OR Multnomah 1.15

041053 OR Polk 1.00

041055 OR Sherman 1.03

041057 OR Tillamook 1.03

041059 OR Umatilla 0.98

041061 OR Union 1.01

041063 OR Wallowa 0.98

041065 OR Wasco 1.03

041067 OR Washington 1.15

041069 OR Wheeler 1.04

041071 OR Yamhill 1.09

042001 PA Adams 1.00

042003 PA Allegheny 0.95

042005 PA Armstrong 0.88

042007 PA Beaver 0.92

042009 PA Bedford 0.88

042011 PA Berks 1.01

042013 PA Blair 0.88

042015 PA Bradford 0.91

042017 PA Bucks 1.05

042019 PA Butler 0.89

042021 PA Cambria 0.85

042023 PA Cameron 0.93

042025 PA Carbon 0.98

042027 PA Centre 0.87

042029 PA Chester 1.07

042031 PA Clarion 0.87

042033 PA Clearfield 0.87

042035 PA Clinton 0.86

042037 PA Columbia 0.88

042039 PA Crawford 0.89

042041 PA Cumberland 0.97

042043 PA Dauphin 0.98

042045 PA Delaware 1.08

042047 PA Elk 0.85

042049 PA Erie 0.92

042051 PA Fayette 0.88

042053 PA Forest 0.93

042055 PA Franklin 1.00

042057 PA Fulton 0.91

042059 PA Greene 0.90

042061 PA Huntingdon 0.91

042063 PA Indiana 0.87

042065 PA Jefferson 0.85

042067 PA Juniata 0.91

042069 PA Lackawanna 0.92

042071 PA Lancaster 1.00

042073 PA Lawrence 0.89

042075 PA Lebanon 0.95

042077 PA Lehigh 1.04

042079 PA Luzerne 0.92

042081 PA Lycoming 0.92

042083 PA McKean 0.86

042085 PA Mercer 0.91

042087 PA Mifflin 0.87

042089 PA Monroe 1.02

042091 PA Montgomery 1.08

042093 PA Montour 0.92

042095 PA Northampton 1.01

042097 PA Northumberland 0.93

042099 PA Perry 0.94

042101 PA Philadelphia 1.08

042103 PA Pike 1.13

042105 PA Potter 0.86

042107 PA Schuylkill 0.93

042109 PA Snyder 0.89

042111 PA Somerset 0.86

042113 PA Sullivan 0.93

042115 PA Susquehanna 0.92

042117 PA Tioga 0.87

042119 PA Union 0.90

042121 PA Venango 0.89

042123 PA Warren 0.87

042125 PA Washington 0.92

042127 PA Wayne 1.02

042129 PA Westmoreland 0.89

042131 PA Wyoming 0.85

042133 PA York 1.04

044001 RI Bristol 1.10

044003 RI Kent 1.17

044005 RI Newport 1.10

044007 RI Providence 1.17

044009 RI Washington 1.10

045001 SC Abbeville 0.93

045003 SC Aiken 0.94

045005 SC Allendale 0.95

FIPS 

code State County

MedPAC 

index

FIPS 

code State County

MedPAC 

index

FIPS 

code State County

MedPAC 

index

Note:	 FIPS (Federal Information Processing Standards).
Additional technical infor mation on constructing a compensation index from BLS data | June 2007 23

045007 SC Anderson 1.03

045009 SC Bamberg 0.89

045011 SC Barnwell 0.89

045013 SC Beaufort 0.94

045015 SC Berkeley 0.86

045017 SC Calhoun 0.93

045019 SC Charleston 0.91

045021 SC Cherokee 0.88

045023 SC Chester 0.91

045025 SC Chesterfield 0.95

045027 SC Clarendon 0.88

045029 SC Colleton 0.92

045031 SC Darlington 0.85

045033 SC Dillon 0.93

045035 SC Dorchester 0.87

045037 SC Edgefield 0.88

045039 SC Fairfield 0.96

045041 SC Florence 0.89

045043 SC Georgetown 0.91

045045 SC Greenville 0.98

045047 SC Greenwood 0.90

045049 SC Hampton 0.88

045051 SC Horry 0.89

045053 SC Jasper 0.88

045055 SC Kershaw 0.89

045057 SC Lancaster 0.92

045059 SC Laurens 0.93

045061 SC Lee 0.87

045063 SC Lexington 0.92

045065 SC McCormick 0.90

045067 SC Marion 0.91

045069 SC Marlboro 0.95

045071 SC Newberry 0.91

045073 SC Oconee 0.93

045075 SC Orangeburg 0.92

045077 SC Pickens 0.94

045079 SC Richland 0.93

045081 SC Saluda 0.88

045083 SC Spartanburg 0.95

045085 SC Sumter 0.92

045087 SC Union 0.88

045089 SC Williamsburg 0.89

045091 SC York 0.97

046003 SD Aurora 0.81

046005 SD Beadle 0.78

046007 SD Bennett 0.80

046009 SD Bon Homme 0.77

046011 SD Brookings 0.82

046013 SD Brown 0.80

046015 SD Brule 0.77

046017 SD Buffalo 0.81

046019 SD Butte 0.84

046021 SD Campbell 0.82

046023 SD Charles Mix 0.81

046025 SD Clark 0.81

046027 SD Clay 0.79

046029 SD Codington 0.81

046031 SD Corson 0.81

046033 SD Custer 0.82

046035 SD Davison 0.77

046037 SD Day 0.81

046039 SD Deuel 0.87

046041 SD Dewey 0.81

046043 SD Douglas 0.80

046045 SD Edmunds 0.81

046047 SD Fall River 0.82

046049 SD Faulk 0.81

046051 SD Grant 0.90

046053 SD Gregory 0.81

046055 SD Haakon 0.84

046057 SD Hamlin 0.80

046059 SD Hand 0.82

046061 SD Hanson 0.81

046063 SD Harding 0.81

046065 SD Hughes 0.79

046067 SD Hutchinson 0.85

046069 SD Hyde 0.81

046071 SD Jackson 0.82

046073 SD Jerauld 0.81

046075 SD Jones 0.81

046077 SD Kingsbury 0.81

046079 SD Lake 0.82

046081 SD Lawrence 0.84

046083 SD Lincoln 0.86

046085 SD Lyman 0.81

046087 SD McCook 0.90

046089 SD McPherson 0.80

046091 SD Marshall 0.81

046093 SD Meade 0.94

046095 SD Mellette 0.80

046097 SD Miner 0.81

046099 SD Minnehaha 0.91

046101 SD Moody 0.83

046103 SD Pennington 0.91

046105 SD Perkins 0.84

046107 SD Potter 0.80

046109 SD Roberts 0.90

046111 SD Sanborn 0.82

046113 SD Shannon 0.82

046115 SD Spink 0.80

046117 SD Stanley 0.82

046119 SD Sully 0.81

046121 SD Todd 0.85

046123 SD Tripp 0.80

046125 SD Turner 0.86

046127 SD Union 0.88

046129 SD Walworth 0.81

046135 SD Yankton 0.82

046137 SD Ziebach 0.84

047001 TN Anderson 0.91

047003 TN Bedford 0.86

047005 TN Benton 0.85

047007 TN Bledsoe 0.87

047009 TN Blount 0.83

047011 TN Bradley 0.85

047013 TN Campbell 0.82

047015 TN Cannon 0.99

047017 TN Carroll 0.81

047019 TN Carter 0.83

047021 TN Cheatham 0.98

047023 TN Chester 0.88

047025 TN Claiborne 0.85

047027 TN Clay 0.88

047029 TN Cocke 0.84

047031 TN Coffee 0.89

047033 TN Crockett 0.86

047035 TN Cumberland 0.89

047037 TN Davidson 1.00

047039 TN Decatur 0.86

047041 TN DeKalb 0.90

047043 TN Dickson 0.95

047045 TN Dyer 0.88

047047 TN Fayette 0.89

047049 TN Fentress 0.81

047051 TN Franklin 0.84

047053 TN Gibson 0.87

047055 TN Giles 0.83

047057 TN Grainger 0.80

FIPS 

code State County

MedPAC 

index

FIPS 

code State County

MedPAC 

index

FIPS 

code State County

MedPAC 

index

Note:	 FIPS (Federal Information Processing Standards).
24 June 2007 | Additional technical infor mation on constructing a compensation index from BLS data

047059 TN Greene 0.86

047061 TN Grundy 0.87

047063 TN Hamblen 0.81

047065 TN Hamilton 0.89

047067 TN Hancock 0.88

047069 TN Hardeman 0.85

047071 TN Hardin 0.87

047073 TN Hawkins 0.82

047075 TN Haywood 0.88

047077 TN Henderson 0.90

047079 TN Henry 0.86

047081 TN Hickman 0.98

047083 TN Houston 0.85

047085 TN Humphreys 0.88

047087 TN Jackson 0.88

047089 TN Jefferson 0.81

047091 TN Johnson 0.87

047093 TN Knox 0.86

047095 TN Lake 0.82

047097 TN Lauderdale 0.84

047099 TN Lawrence 0.84

047101 TN Lewis 0.89

047103 TN Lincoln 0.84

047105 TN Loudon 0.83

047107 TN McMinn 0.83

047109 TN McNairy 0.88

047111 TN Macon 0.98

047113 TN Madison 0.90

047115 TN Marion 0.90

047117 TN Marshall 0.93

047119 TN Maury 0.93

047121 TN Meigs 0.87

047123 TN Monroe 0.84

047125 TN Montgomery 0.91

047127 TN Moore 0.88

047129 TN Morgan 0.82

047131 TN Obion 0.81

047133 TN Overton 0.83

047135 TN Perry 0.89

047137 TN Pickett 0.89

047139 TN Polk 0.80

047141 TN Putnam 0.85

047143 TN Rhea 0.85

047145 TN Roane 0.87

047147 TN Robertson 0.95

047149 TN Rutherford 0.95

047151 TN Scott 0.82

047153 TN Sequatchie 0.85

047155 TN Sevier 0.87

047157 TN Shelby 0.94

047159 TN Smith 0.95

047161 TN Stewart 0.89

047163 TN Sullivan 0.89

047165 TN Sumner 1.01

047167 TN Tipton 0.89

047169 TN Trousdale 0.99

047171 TN Unicoi 0.84

047173 TN Union 0.85

047175 TN Van Buren 0.88

047177 TN Warren 0.89

047179 TN Washington 0.80

047181 TN Wayne 0.90

047183 TN Weakley 0.82

047185 TN White 0.90

047187 TN Williamson 1.03

047189 TN Wilson 0.98

048001 TX Anderson 0.84

048003 TX Andrews 0.87

048005 TX Angelina 0.86

048007 TX Aransas 0.83

048009 TX Archer 0.79

048011 TX Armstrong 0.87

048013 TX Atascosa 0.91

048015 TX Austin 0.95

048017 TX Bailey 0.87

048019 TX Bandera 0.92

048021 TX Bastrop 0.91

048023 TX Baylor 0.87

048025 TX Bee 0.81

048027 TX Bell 0.91

048029 TX Bexar 0.93

048031 TX Blanco 0.87

048033 TX Borden 0.87

048035 TX Bosque 0.84

048037 TX Bowie 0.87

048039 TX Brazoria 0.95

048041 TX Brazos 0.82

048043 TX Brewster 0.88

048045 TX Briscoe 0.88

048047 TX Brooks 0.88

048049 TX Brown 0.80

048051 TX Burleson 0.82

048053 TX Burnet 0.87

048055 TX Caldwell 1.00

048057 TX Calhoun 0.88

048059 TX Callahan 0.83

048061 TX Cameron 0.96

048063 TX Camp 0.84

048065 TX Carson 0.88

048067 TX Cass 0.89

048069 TX Castro 0.87

048071 TX Chambers 0.97

048073 TX Cherokee 0.85

048075 TX Childress 0.85

048077 TX Clay 0.79

048079 TX Cochran 0.87

048081 TX Coke 0.87

048083 TX Coleman 0.87

048085 TX Collin 1.01

048087 TX Collingsworth 0.87

048089 TX Colorado 0.87

048091 TX Comal 0.91

048093 TX Comanche 0.81

048095 TX Concho 0.89

048097 TX Cooke 0.89

048099 TX Coryell 0.90

048101 TX Cottle 0.88

048103 TX Crane 0.88

048105 TX Crockett 0.87

048107 TX Crosby 0.85

048109 TX Culberson 0.88

048111 TX Dallam 0.85

048113 TX Dallas 1.01

048115 TX Dawson 0.86

048117 TX Deaf Smith 0.84

048119 TX Delta 0.97

048121 TX Denton 0.99

048123 TX DeWitt 0.84

048125 TX Dickens 0.88

048127 TX Dimmit 0.87

048129 TX Donley 0.88

048131 TX Duval 0.87

048133 TX Eastland 0.83

048135 TX Ector 0.97

048137 TX Edwards 0.88

048139 TX Ellis 0.98

048141 TX El Paso 0.92

048143 TX Erath 0.86

FIPS 

code State County

MedPAC 

index

FIPS 

code State County

MedPAC 

index

FIPS 

code State County

MedPAC 

index

Note:	 FIPS (Federal Information Processing Standards).
Additional technical infor mation on constructing a compensation index from BLS data | June 2007 25

048145 TX Falls 0.85

048147 TX Fannin 0.91

048149 TX Fayette 0.90

048151 TX Fisher 0.86

048153 TX Floyd 0.86

048155 TX Foard 0.88

048157 TX Fort Bend 0.97

048159 TX Franklin 0.88

048161 TX Freestone 0.84

048163 TX Frio 0.86

048165 TX Gaines 0.85

048167 TX Galveston 0.95

048169 TX Garza 0.85

048171 TX Gillespie 0.86

048173 TX Glasscock 0.87

048175 TX Goliad 0.83

048177 TX Gonzales 0.90

048179 TX Gray 0.82

048181 TX Grayson 0.91

048183 TX Gregg 0.84

048185 TX Grimes 0.88

048187 TX Guadalupe 0.90

048189 TX Hale 0.85

048191 TX Hall 0.89

048193 TX Hamilton 0.88

048195 TX Hansford 0.85

048197 TX Hardeman 0.88

048199 TX Hardin 0.85

048201 TX Harris 1.00

048203 TX Harrison 0.87

048205 TX Hartley 0.87

048207 TX Haskell 0.87

048209 TX Hays 0.91

048211 TX Hemphill 0.87

048213 TX Henderson 0.88

048215 TX Hidalgo 0.97

048217 TX Hill 0.88

048219 TX Hockley 0.87

048221 TX Hood 0.84

048223 TX Hopkins 0.91

048225 TX Houston 0.84

048227 TX Howard 0.87

048229 TX Hudspeth 0.88

048231 TX Hunt 1.01

048233 TX Hutchinson 0.80

048235 TX Irion 0.83

048237 TX Jack 0.86

048239 TX Jackson 0.86

048241 TX Jasper 0.83

048243 TX Jeff Davis 0.87

048245 TX Jefferson 0.87

048247 TX Jim Hogg 0.87

048249 TX Jim Wells 0.82

048251 TX Johnson 0.93

048253 TX Jones 0.82

048255 TX Karnes 0.87

048257 TX Kaufman 0.96

048259 TX Kendall 0.91

048261 TX Kenedy 0.87

048263 TX Kent 0.87

048265 TX Kerr 0.85

048267 TX Kimble 0.88

048269 TX King 0.89

048271 TX Kinney 0.88

048273 TX Kleberg 0.83

048275 TX Knox 0.88

048277 TX Lamar 0.88

048279 TX Lamb 0.83

048281 TX Lampasas 0.86

048283 TX La Salle 0.88

048285 TX Lavaca 0.81

048287 TX Lee 0.89

048289 TX Leon 0.87

048291 TX Liberty 0.95

048293 TX Limestone 0.80

048295 TX Lipscomb 0.88

048297 TX Live Oak 0.88

048299 TX Llano 0.80

048301 TX Loving 0.89

048303 TX Lubbock 0.84

048305 TX Lynn 0.86

048307 TX McCulloch 0.87

048309 TX McLennan 0.87

048311 TX McMullen 0.88

048313 TX Madison 0.89

048315 TX Marion 0.82

048317 TX Martin 0.87

048319 TX Mason 0.86

048321 TX Matagorda 0.89

048323 TX Maverick 0.85

048325 TX Medina 0.88

048327 TX Menard 0.88

048329 TX Midland 0.91

048331 TX Milam 0.85

048333 TX Mills 0.87

048335 TX Mitchell 0.82

048337 TX Montague 0.84

048339 TX Montgomery 0.97

048341 TX Moore 0.82

048343 TX Morris 0.86

048345 TX Motley 0.87

048347 TX Nacogdoches 0.85

048349 TX Navarro 0.88

048351 TX Newton 0.87

048353 TX Nolan 0.84

048355 TX Nueces 0.84

048357 TX Ochiltree 0.82

048359 TX Oldham 0.88

048361 TX Orange 0.83

048363 TX Palo Pinto 0.84

048365 TX Panola 0.81

048367 TX Parker 0.93

048369 TX Parmer 0.86

048371 TX Pecos 0.80

048373 TX Polk 0.89

048375 TX Potter 0.87

048377 TX Presidio 0.87

048379 TX Rains 0.91

048381 TX Randall 0.88

048383 TX Reagan 0.88

048385 TX Real 0.88

048387 TX Red River 0.88

048389 TX Reeves 0.85

048391 TX Refugio 0.86

048393 TX Roberts 0.86

048395 TX Robertson 0.81

048397 TX Rockwall 1.00

048399 TX Runnels 0.82

048401 TX Rusk 0.86

048403 TX Sabine 0.85

048405 TX San Augustine 0.89

048407 TX San Jacinto 0.98

048409 TX San Patricio 0.81

048411 TX San Saba 0.87

048413 TX Schleicher 0.88

048415 TX Scurry 0.89

048417 TX Shackelford 0.87

048419 TX Shelby 0.85

FIPS 

code State County

MedPAC 

index

FIPS 

code State County

MedPAC 

index

FIPS 

code State County

MedPAC 

index

Note:	 FIPS (Federal Information Processing Standards).
26 June 2007 | Additional technical infor mation on constructing a compensation index from BLS data

048421 TX Sherman 0.89

048423 TX Smith 0.92

048425 TX Somervell 0.89

048427 TX Starr 0.87

048429 TX Stephens 0.83

048431 TX Sterling 0.89

048433 TX Stonewall 0.87

048435 TX Sutton 0.87

048437 TX Swisher 0.88

048439 TX Tarrant 0.99

048441 TX Taylor 0.84

048443 TX Terrell 0.87

048445 TX Terry 0.85

048447 TX Throckmorton 0.87

048449 TX Titus 0.85

048451 TX Tom Green 0.84

048453 TX Travis 0.96

048455 TX Trinity 0.89

048457 TX Tyler 0.85

048459 TX Upshur 0.83

048461 TX Upton 0.88

048463 TX Uvalde 0.82

048465 TX Val Verde 0.83

048467 TX Van Zandt 0.91

048469 TX Victoria 0.83

048471 TX Walker 0.89

048473 TX Waller 0.95

048475 TX Ward 0.87

048477 TX Washington 0.85

048479 TX Webb 0.86

048481 TX Wharton 0.87

048483 TX Wheeler 0.86

048485 TX Wichita 0.80

048487 TX Wilbarger 0.80

048489 TX Willacy 0.87

048491 TX Williamson 0.93

048493 TX Wilson 0.90

048495 TX Winkler 0.87

048497 TX Wise 0.93

048499 TX Wood 0.89

048501 TX Yoakum 0.86

048503 TX Young 0.80

048505 TX Zapata 0.89

048507 TX Zavala 0.88

049001 UT Beaver 0.92

049003 UT Box Elder 0.93

049005 UT Cache 0.88

049007 UT Carbon 0.84

049009 UT Daggett 0.90

049011 UT Davis 0.86

049013 UT Duchesne 0.89

049015 UT Emery 0.89

049017 UT Garfield 0.89

049019 UT Grand 0.89

049021 UT Iron 0.92

049023 UT Juab 0.93

049025 UT Kane 0.91

049027 UT Millard 0.93

049029 UT Morgan 0.89

049031 UT Piute 0.89

049033 UT Rich 0.89

049035 UT Salt Lake 0.95

049037 UT San Juan 0.90

049039 UT Sanpete 0.84

049041 UT Sevier 0.89

049043 UT Summit 0.99

049045 UT Tooele 0.93

049047 UT Uintah 0.90

049049 UT Utah 0.90

049051 UT Wasatch 0.89

049053 UT Washington 0.92

049055 UT Wayne 0.89

049057 UT Weber 0.85

050001 VT Addison 0.95

050003 VT Bennington 0.97

050005 VT Caledonia 0.95

050007 VT Chittenden 0.97

050009 VT Essex 0.98

050011 VT Franklin 0.97

050013 VT Grand Isle 0.97

050015 VT Lamoille 0.98

050017 VT Orange 0.95

050019 VT Orleans 0.94

050021 VT Rutland 0.99

050023 VT Washington 1.03

050025 VT Windham 1.01

050027 VT Windsor 0.98

051001 VA Accomack 0.88

051003 VA Albemarle 1.02

051005 VA Alleghany 0.85

051007 VA Amelia 0.94

051009 VA Amherst 0.90

051011 VA Appomattox 0.92

051013 VA Arlington 1.14

051015 VA Augusta 0.92

051017 VA Bath 0.90

051019 VA Bedford 0.90

051021 VA Bland 0.90

051023 VA Botetourt 0.91

051025 VA Brunswick 0.89

051027 VA Buchanan 0.83

051029 VA Buckingham 0.92

051031 VA Campbell 0.90

051033 VA Caroline 0.96

051035 VA Carroll 0.87

051036 VA Charles City 0.98

051037 VA Charlotte 0.91

051041 VA Chesterfield 0.97

051043 VA Clarke 1.07

051045 VA Craig 0.91

051047 VA Culpeper 0.96

051049 VA Cumberland 0.96

051051 VA Dickenson 0.87

051053 VA Dinwiddie 0.93

051057 VA Essex 0.87

051059 VA Fairfax 1.13

051061 VA Fauquier 1.07

051063 VA Floyd 0.91

051065 VA Fluvanna 1.02

051067 VA Franklin 0.88

051069 VA Frederick 0.96

051071 VA Giles 0.84

051073 VA Gloucester 0.89

051075 VA Goochland 0.98

051077 VA Grayson 0.90

051079 VA Greene 1.01

051081 VA Greensville 0.89

051083 VA Halifax 0.97

051085 VA Hanover 0.96

051087 VA Henrico 1.00

051089 VA Henry 0.90

051091 VA Highland 0.91

051093 VA Isle of Wight 0.90

051095 VA James City 0.94

051097 VA King and Queen 0.94

051099 VA King George 0.96

051101 VA King William 0.94

051103 VA Lancaster 0.83

FIPS 

code State County

MedPAC 

index

FIPS 

code State County

MedPAC 

index

FIPS 

code State County

MedPAC 

index

Note:	 FIPS (Federal Information Processing Standards).
Additional technical infor mation on constructing a compensation index from BLS data | June 2007 27

051105 VA Lee 0.91

051107 VA Loudoun 1.08

051109 VA Louisa 0.96

051111 VA Lunenburg 0.91

051113 VA Madison 0.91

051115 VA Mathews 0.93

051117 VA Mecklenburg 0.87

051119 VA Middlesex 0.89

051121 VA Montgomery 0.82

051125 VA Nelson 1.00

051127 VA New Kent 0.95

051131 VA Northampton 0.86

051133 VA Northumberland 0.90

051135 VA Nottoway 0.89

051137 VA Orange 0.96

051139 VA Page 0.96

051141 VA Patrick 0.86

051143 VA Pittsylvania 0.87

051145 VA Powhatan 0.97

051147 VA Prince Edward 0.87

051149 VA Prince George 0.94

051153 VA Prince William 1.07

051155 VA Pulaski 0.83

051157 VA Rappahannock 0.96

051159 VA Richmond 0.90

051161 VA Roanoke 0.87

051163 VA Rockbridge 0.90

051165 VA Rockingham 0.92

051167 VA Russell 0.83

051169 VA Scott 0.83

051171 VA Shenandoah 0.96

051173 VA Smyth 0.84

051175 VA Southampton 0.88

051177 VA Spotsylvania 1.07

051179 VA Stafford 1.07

051181 VA Surry 0.93

051183 VA Sussex 0.93

051185 VA Tazewell 0.85

051187 VA Warren 1.07

051191 VA Washington 0.82

051193 VA Westmoreland 0.88

051195 VA Wise 0.88

051197 VA Wythe 0.91

051199 VA York 0.94

051510 VA Alexandria city 1.10

051515 VA Bedford city 0.91

051520 VA Bristol city 0.87

051530 VA Buena Vista city 0.89

051540 VA Charlottesville city 1.02

051550 VA Chesapeake city 0.94

051560 VA Clifton Forge city 0.83

051570 VA Colonial Heights

city

0.96

051580 VA Covington city 0.91

051590 VA Danville city 0.87

051595 VA Emporia city 0.84

051600 VA Fairfax city 1.08

051610 VA Falls Church city 1.11

051620 VA Franklin city 0.89

051630 VA Fredericksburg city 1.07

051640 VA Galax city 0.90

051650 VA Hampton city 0.94

051660 VA Harrisonburg city 0.92

051670 VA Hopewell city 0.98

051678 VA Lexington city 0.90

051680 VA Lynchburg city 0.93

051683 VA Manassas city 1.07

051685 VA Manassas Park city 1.09

051690 VA Martinsville city 0.89

051700 VA Newport News city 0.93

051710 VA Norfolk city 0.95

051720 VA Norton city 0.87

051730 VA Petersburg city 0.93

051735 VA Poquoson city 0.93

051740 VA Portsmouth city 0.94

051750 VA Radford city 0.78

051760 VA Richmond city 0.98

051770 VA Roanoke city 0.92

051775 VA Salem city 0.95

051790 VA Staunton city 0.88

051800 VA Suffolk city 0.98

051810 VA Virginia Beach city 0.92

051820 VA Waynesboro city 0.85

051830 VA Williamsburg city 0.93

051840 VA Winchester city 0.96

053001 WA Adams 1.00

053003 WA Asotin 0.98

053005 WA Benton 1.04

053007 WA Chelan 1.11

053009 WA Clallam 1.05

053011 WA Clark 1.12

053013 WA Columbia 1.09

053015 WA Cowlitz 1.03

053017 WA Douglas 1.01

053019 WA Ferry 1.09

053021 WA Franklin 0.98

053023 WA Garfield 1.09

053025 WA Grant 1.01

053027 WA Grays Harbor 1.06

053029 WA Island 1.08

053031 WA Jefferson 1.05

053033 WA King 1.23

053035 WA Kitsap 1.12

053037 WA Kittitas 1.11

053039 WA Klickitat 1.05

053041 WA Lewis 1.04

053043 WA Lincoln 1.06

053045 WA Mason 1.09

053047 WA Okanogan 1.02

053049 WA Pacific 1.02

053051 WA Pend Oreille 1.09

053053 WA Pierce 1.15

053055 WA San Juan 1.09

053057 WA Skagit 1.08

053059 WA Skamania 1.01

053061 WA Snohomish 1.19

053063 WA Spokane 1.01

053065 WA Stevens 1.05

053067 WA Thurston 1.13

053069 WA Wahkiakum 1.09

053071 WA Walla Walla 1.02

053073 WA Whatcom 0.99

053075 WA Whitman 1.09

053077 WA Yakima 1.11

054001 WV Barbour 0.84

054003 WV Berkeley 0.96

054005 WV Boone 0.84

054007 WV Braxton 0.84

054009 WV Brooke 0.83

054011 WV Cabell 1.00

054013 WV Calhoun 0.86

054015 WV Clay 0.83

054017 WV Doddridge 0.86

054019 WV Fayette 0.83

054021 WV Gilmer 0.86

054023 WV Grant 0.86

054025 WV Greenbrier 0.84

054027 WV Hampshire 0.94

FIPS 

code State County

MedPAC 

index

FIPS 

code State County

MedPAC 

index

FIPS 

code State County

MedPAC 

index

Note:	 FIPS (Federal Information Processing Standards).
28 June 2007 | Additional technical infor mation on constructing a compensation index from BLS data

054029 WV Hancock 0.83

054031 WV Hardy 0.86

054033 WV Harrison 0.89

054035 WV Jackson 0.84

054037 WV Jefferson 1.07

054039 WV Kanawha 0.86

054041 WV Lewis 0.84

054043 WV Lincoln 0.91

054045 WV Logan 0.85

054047 WV McDowell 0.85

054049 WV Marion 0.84

054051 WV Marshall 0.83

054053 WV Mason 0.90

054055 WV Mercer 0.85

054057 WV Mineral 0.86

054059 WV Mingo 0.91

054061 WV Monongalia 0.93

054063 WV Monroe 0.89

054065 WV Morgan 1.04

054067 WV Nicholas 0.83

054069 WV Ohio 0.83

054071 WV Pendleton 0.87

054073 WV Pleasants 0.87

054075 WV Pocahontas 0.88

054077 WV Preston 0.92

054079 WV Putnam 0.91

054081 WV Raleigh 0.88

054083 WV Randolph 0.82

054085 WV Ritchie 0.85

054087 WV Roane 0.81

054089 WV Summers 0.86

054091 WV Taylor 0.84

054093 WV Tucker 0.85

054095 WV Tyler 0.89

054097 WV Upshur 0.87

054099 WV Wayne 1.01

054101 WV Webster 0.84

054103 WV Wetzel 0.84

054105 WV Wirt 0.85

054107 WV Wood 0.86

054109 WV Wyoming 0.87

055001 WI Adams 0.98

055003 WI Ashland 0.97

055005 WI Barron 0.99

055007 WI Bayfield 0.99

055009 WI Brown 1.05

055011 WI Buffalo 1.00

055013 WI Burnett 0.97

055015 WI Calumet 0.99

055017 WI Chippewa 1.02

055019 WI Clark 0.96

055021 WI Columbia 1.01

055023 WI Crawford 0.92

055025 WI Dane 1.07

055027 WI Dodge 0.97

055029 WI Door 0.96

055031 WI Douglas 0.99

055033 WI Dunn 0.99

055035 WI Eau Claire 0.99

055037 WI Florence 0.99

055039 WI Fond du Lac 0.99

055041 WI Forest 0.97

055043 WI Grant 0.94

055045 WI Green 0.98

055047 WI Green Lake 0.98

055049 WI Iowa 1.01

055051 WI Iron 0.99

055053 WI Jackson 0.97

055055 WI Jefferson 0.99

055057 WI Juneau 1.00

055059 WI Kenosha 0.99

055061 WI Kewaunee 1.04

055063 WI La Crosse 1.01

055065 WI Lafayette 0.97

055067 WI Langlade 0.97

055069 WI Lincoln 0.96

055071 WI Manitowoc 0.98

055073 WI Marathon 1.07

055075 WI Marinette 1.00

055077 WI Marquette 1.00

055078 WI Menominee 0.98

055079 WI Milwaukee 1.08

055081 WI Monroe 0.97

055083 WI Oconto 1.00

055085 WI Oneida 0.95

055087 WI Outagamie 1.04

055089 WI Ozaukee 1.03

055091 WI Pepin 1.00

055093 WI Pierce 1.06

055095 WI Polk 1.00

055097 WI Portage 0.97

055099 WI Price 1.00

055101 WI Racine 1.03

055103 WI Richland 0.92

055105 WI Rock 0.99

055107 WI Rusk 0.96

055109 WI St. Croix 1.10

055111 WI Sauk 0.96

055113 WI Sawyer 1.02

055115 WI Shawano 0.97

055117 WI Sheboygan 1.03

055119 WI Taylor 0.97

055121 WI Trempealeau 0.95

055123 WI Vernon 0.92

055125 WI Vilas 0.93

055127 WI Walworth 0.97

055129 WI Washburn 0.95

055131 WI Washington 1.04

055133 WI Waukesha 1.07

055135 WI Waupaca 0.96

055137 WI Waushara 0.99

055139 WI Winnebago 1.02

055141 WI Wood 1.00

056001 WY Albany 0.89

056003 WY Big Horn 0.84

056005 WY Campbell 0.92

056007 WY Carbon 0.90

056009 WY Converse 0.87

056011 WY Crook 0.88

056013 WY Fremont 0.85

056015 WY Goshen 0.84

056017 WY Hot Springs 0.87

056019 WY Johnson 0.88

056021 WY Laramie 0.90

056023 WY Lincoln 0.86

056025 WY Natrona 0.92

056027 WY Niobrara 0.87

056029 WY Park 0.89

056031 WY Platte 0.85

056033 WY Sheridan 0.88

056035 WY Sublette 0.89

056037 WY Sweetwater 0.90

056039 WY Teton 0.93

056041 WY Uinta 0.89

056043 WY Washakie 0.93

056045 WY Weston 0.87

FIPS 

code State County

MedPAC 

index

FIPS 

code State County

MedPAC 

index

FIPS 

code State County

MedPAC 

index

Note:	 FIPS (Federal Information Processing Standards).
Additional technical infor mation on constructing a compensation index from BLS data | June 2007 29

1 Our estimates of hospital benefits relative to hospital wages 

had a national mean almost exactly equal to the national ratio 

of hospital benefits to hospital wages that BLS reported. We 

had to use cost report benefit data because the industry-level 

BLS benefit data are available only on a national basis and not 

on a market-by-market basis. 

Endnotes
30 June 2007 | Additional technical infor mation on constructing a compensation index from BLS data

Office of Inspector General, Department of Health and Human 

Services. 2007. Review of hospital wage index data used to 

calculate inpatient prospective payment system wage indexes. 

A–01–05–00504. Washington, DC: OIG. 

References