Bureau of Labor Statistics Washington, D.C. 20212 

Technical Contact: USDL: 06-1680 

(202) 691-6199 NCSinfo@bls.gov 

Media Contact: FOR RELEASE: 10:00 A.M. EDT 

(202) 691-5902 THURSDAY, SEPTEMBER 28, 2006 

Internet address: 

http://www.bls.gov/ncs/ocs/home.htm 

(This news release was reissued on Wednesday, May 26, 2010, to remove table asterisks that have 

incorrectly indicated statistically significant differences between some estimates. News release text 

references to statistical significance have also been removed. Pay relative estimates have not changed. 

For more information, see http://www.bls.gov/ncs.)

OCCUPATIONAL PAY RELATIVES, 2005 

The Bureau of Labor Statistics (BLS) of the U.S. Department of Labor produces relative occupational 

pay comparisons between metropolitan areas and the United States as a whole. Using data from the National 

Compensation Survey (NCS), pay relatives—a means of assessing relative pay differences—have been 

prepared for 2005 for each of the 9 major occupational groups within 78 Metropolitan Statistical Areas (MSAs), 

as well as averaged across all occupations for each area. (See table 1.) In addition, for the first time, similar 

area-to-area comparisons have been calculated for all 78 areas and soon will be available on the BLS website at 

http://www.bls.gov/ncs/ocs/payrel.htm. 

The pay relative in 2005 for workers in construction and extraction occupations in the San Francisco 

MSA was 123, meaning the pay in San Francisco in that occupational group averaged 23 percent more than the 

national average pay for that occupational group. The pay relative averaged across all occupations in the San 

Francisco MSA was 117, meaning that pay on average was 17 percent above the national average. By contrast, 

the pay relative for workers in construction and extraction occupations in the Brownsville, Texas MSA was 72, 

meaning pay for workers in those occupations averaged 28 percent less than the national average. Pay averaged 

across all occupations in the Brownsville area was 19 percent below the national average. 

The National Compensation Survey (NCS), introduced in 1997, collects earnings and other data on 

employee compensation covering over 820 detailed occupations in 152 metropolitan and non-metropolitan 

areas. Average occupational earnings from the NCS are published annually for more than 80 metropolitan areas 

and for the United States as a whole. 

What is a pay relative? 

A pay relative is a calculation of pay—wages, salaries, commissions, and production bonuses—for a 

given metropolitan area relative to the nation as a whole. The calculation controls for differences among areas 

United States 

Department News of Labor 
2

in occupational composition, establishment and occupational characteristics, and the fact that data are 

collected for areas at different times during the year. 

Metropolitan areas differ greatly in the types of occupations that are available to the local workforce. 

For example, in Brownsville, Texas, the ratio of workers in the high-paying management, business, and 

financial occupational group to the number of workers in all occupations is approximately 5 percent, whereas 

nationally this ratio is nearly 9 percent.1 Similarly, the composition of establishment and occupational 

characteristics varies by area. In addition to these factors, the NCS collects compensation data for metropolitan 

areas at different times during the year. Payroll reference dates differ between areas which makes direct 

comparisons between areas difficult. 

The pay relative approach controls for these differences to isolate the geographic effect on wage 

determination. To illustrate the importance of controlling for these effects, consider the following example. 

The average pay for professional workers in San Francisco is $39.41 and the average pay for professional 

workers in the entire United States is $30.24.2 A simple pay comparison can be calculated from the ratio of the 

two average pay levels, multiplied by 100 to express the comparison as a percentage. The pay comparison in 

the example is calculated as: 

   13010024.30$41.39$

However, this comparison does not control for the interarea difference in occupational composition. 

Some of the 30 percent pay premium in San Francisco relative to the nation as a whole is due to the higher 

concentration of highly compensated professional workers in San Francisco. A more accurate estimate of the 

geographic effect on wage determination in San Francisco can be obtained by taking into account this and other 

differences. Controlling for the differences in occupational composition, establishment and occupational 

characteristics, and the payroll reference date in San Francisco relative to the nation as the whole, the pay 

relative for professional and related occupations in San Francisco is equal to 117. 

Due to sample variation from year to year, data users are cautioned about inferring that there have been 

actual changes in underlying economic conditions from changes in the estimated pay relatives between 2004 

and 2005. This caution applies even more strongly to estimates by occupational group. 

1

 Data for this example are based on the May 2005 Occupational Employment and Wage Estimates, 

http://www.bls.gov/oes/current/oessrcma.htm. 2

 Average pay for professional workers in San Francisco and for the United States are based on wage estimates published in the San 

Francisco–Oakland–San Jose, CA National Compensation Survey, March 2005 and the National Compensation Survey: Occupational 

Wages in the United States, June 2005, http://www.bls.gov/ncs/ocs/compub.htm.
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Table 1. Pay relatives for major occupational groups in metropolitan areas, National Compensation Survey, 2005(Average pay nationally for all occupations and for each occupational group shown = 100.)

Metropolitan Area1 All occupations Management, business, and financial Professional and related Service Sales and related Office and administrative support Construction and extraction Installation, maintenance, and repair Production Transportation and material moving

United States .................................................... 100 100 100 100 100 100 100 100 100 100

Amarillo, TX ...................................................... 89 78 87 86 91 89 85 84 94 94

Anchorage, AK ................................................. 110 112 103 121 106 107 123 108 116 114

Atlanta, GA ....................................................... 103 105 101 97 101 106 103 105 99 112

Augusta-Aiken, GA-SC ..................................... 97 90 101 91 84 99 83 100 102 93Austin-San Marcos, TX ..................................... 96 91 93 94 101 98 95 98 98 93

Birmingham, AL ................................................ 95 91 92 100 97 97 84 90 92 96Bloomington, IN ................................................ 94 97 90 93 94 90 83 98 98 108

Boston-Worcester-Lawrence, MA-NH-ME-CT .. 112 110 107 113 113 116 118 112 109 114

Brownsville-Harlingen-San Benito, TX ............. 81 82 97 77 86 78 72 80 73 79

Buffalo-Niagara Falls, NY ................................. 103 87 93 109 104 99 108 98 110 106Charleston-North Charleston, SC ..................... 92 89 94 85 100 96 82 87 93 108

Charlotte-Gastonia-Rock Hill, NC-SC ............... 97 93 93 98 85 99 96 98 100 104

Chicago-Gary-Kenosha, IL-IN-WI ..................... 108 107 107 106 108 111 124 111 107 105

Cincinnati-Hamilton, OH-KY-IN ........................ 100 97 99 101 108 102 87 99 102 101Cleveland-Akron, OH ........................................ 100 91 99 98 98 102 102 108 108 104Columbus, OH .................................................. 101 107 96 98 117 100 102 98 98 99

Corpus Christi, TX ............................................ 87 87 92 83 92 86 78 80 88 88

Dallas-Fort Worth, TX ....................................... 99 101 102 95 107 99 89 95 89 102

Dayton-Springfield, OH ..................................... 98 100 95 96 99 92 107 106 109 97

Denver-Boulder-Greeley, CO ........................... 101 94 103 98 103 104 95 110 99 105Detroit-Ann Arbor-Flint, MI ................................ 106 101 107 103 102 104 112 103 118 105

Elkhart-Goshen, IN ........................................... 98 101 100 93 97 92 100 87 98 113

Fort Collins-Loveland, CO ................................ 96 90 94 91 99 96 98 102 101 104Grand Rapids-Muskegon-Holland, MI .............. 103 98 97 105 112 101 122 96 107 103Great Falls, MT ................................................. 90 90 84 93 90 82 125 103 100 86

Greensboro-Winston Salem-High Point, NC .... 99 102 93 98 104 100 96 98 101 105

Greenville-Spartanburg-Anderson, SC ............. 97 99 92 95 91 98 81 87 104 101

Hartford, CT ...................................................... 112 106 109 121 113 112 118 107 108 112

Hickory-Morganton-Lenoir, NC ......................... 97 90 96 92 97 100 83 90 100 107Honolulu, HI ...................................................... 103 99 108 106 100 99 107 110 96 101

Houston-Galveston-Brazoria, TX ...................... 97 98 101 87 98 98 91 97 99 94

Huntsville, AL .................................................... 95 98 96 92 95 96 97 90 97 94

Indianapolis, IN ................................................. 99 92 98 101 96 98 96 102 106 100

Iowa City, IA ..................................................... 101 94 100 106 95 103 104 91 95 103Johnstown, PA .................................................. 89 90 89 91 89 84 91 98 88 83

Kansas City, MO-KS ......................................... 97 94 92 95 96 100 96 100 104 98

Knoxville, TN .................................................... 92 96 91 90 100 92 84 89 89 102

Lincoln, NE ....................................................... 89 91 87 90 81 88 85 100 89 91Los Angeles-Riverside-Orange County, CA ..... 105 104 109 108 108 107 113 107 99 97

Louisville, KY-IN ............................................... 99 101 99 101 94 97 106 99 98 94Melbourne-Titusville-Palm Bay, FL ................... 91 95 83 93 94 87 93 100 94 101

See footnotes at end of table.
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Table 1. Pay relatives for major occupational groups in metropolitan areas, National Compensation Survey, 2005 — Continued(Average pay nationally for all occupations and for each occupational group shown = 100.)

Metropolitan Area1 All occupations Management, business, and financial Professional and related Service Sales and related Office and administrative support Construction and extraction Installation, maintenance, and repair Production Transportation and material moving

Memphis, TN-AR-MS ....................................... 94 92 88 88 97 96 89 106 97 95Miami-Fort Lauderdale, FL ............................... 95 97 94 92 94 97 84 101 97 96Milwaukee-Racine, WI ...................................... 101 102 94 104 110 102 110 101 103 104Minneapolis-St. Paul, MN-WI ........................... 108 106 103 120 109 106 110 109 113 110

Mobile, AL ......................................................... 94 103 92 86 103 94 98 93 94 95

New Orleans, LA .............................................. 93 100 97 86 106 92 84 92 91 91

New York-Northern New Jersey- Long Island,

NY-NJ-CT-PA ................................................. 112 113 114 114 108 113 128 112 103 113

Norfolk-VA Beach-Newport News, VA-NC ....... 93 92 94 92 94 95 87 95 88 94

Ocala, FL .......................................................... 90 91 86 91 93 92 79 88 88 100

Oklahoma City, OK ........................................... 91 97 86 90 87 87 91 99 94 98

Orlando, FL ....................................................... 93 93 91 88 100 93 87 97 91 100

Philadelphia-Wilmington-Atlantic City,

PA-NJ-DE-MD ................................................ 106 109 109 107 99 108 107 106 105 108

Phoenix-Mesa, AZ ............................................ 100 95 102 97 107 103 89 99 95 103

Pittsburgh, PA ................................................... 96 94 95 97 93 98 97 94 95 99

Portland-Salem, OR-WA .................................. 101 101 94 112 106 100 119 104 95 98Providence-Fall River-Warwick, RI-MA ............ 108 115 110 116 104 108 103 95 104 107

Reading, PA ..................................................... 103 122 98 100 102 99 108 101 103 105Reno, NV .......................................................... 97 93 95 99 101 94 92 106 95 100Richland-Kennewick-Pasco, WA ...................... 102 100 96 112 107 98 106 91 94 97Richmond-Petersburg, VA ................................ 98 99 97 98 97 99 87 101 96 106Rochester, NY .................................................. 98 97 98 105 93 97 90 85 98 102Rockford, IL ...................................................... 101 88 99 101 100 93 112 103 106 104Sacramento-Yolo, CA ....................................... 107 107 106 116 108 105 100 114 104 113

Salinas, CA ....................................................... 113 115 122 117 119 105 119 117 103 96

San Antonio, TX ............................................... 90 92 93 84 98 90 89 89 98 90

San Diego, CA .................................................. 105 100 110 114 107 103 102 103 102 97

San Francisco-Oakland-San Jose, CA ............. 117 112 117 123 113 121 123 112 109 112

Seattle-Tacoma-Bremerton, WA ...................... 107 96 100 121 108 108 113 105 112 106

Springfield, MA ................................................. 97 105 112 108 108 112 110 108 116 73

Springfield, MO ................................................. 90 86 92 87 89 87 85 96 95 92

St. Louis, MO-IL ................................................ 100 99 96 96 98 100 117 101 102 112Tallahassee, FL ................................................ 87 76 88 89 92 88 90 85 90 105

Tampa-St. Petersburg-Clearwater, FL ............. 93 93 90 91 89 98 96 94 92 98

Visalia-Tulare-Porterville, CA ........................... 95 86 98 100 90 95 92 94 99 95Washington-Baltimore, DC-MD-VA-WV ........... 106 101 109 107 100 112 102 111 113 99

York, PA ........................................................... 97 104 99 99 94 95 93 99 92 99

Youngstown-Warren, OH ................................. 95 86 94 87 97 87 96 96 103 110

1 A metropolitan area can be a Metropolitan Statistical Area (MSA) or Consolidated Metropolitan Statistical Area (CMSA) as defined by the Office of Management and Budget, 1994.
5

Technical Note 

The pay relatives in this release, as with estimates from any sample survey, are subject to sampling and 

non-sampling errors. Sampling errors are differences that occur between the pay relatives estimated from the 

sample and the true pay relatives derived from the population. Pay relatives are also subject to a variety of nonsampling errors that can influence the estimates. The NCS may be unable to obtain information for some 

establishments; there may be difficulties with survey definitions; respondents may be unable to provide correct 

information, or mistakes in recording or coding the data may occur. Non-sampling errors of these kinds were 

not specifically measured. However, they are expected to be minimal due to the extensive training of the field 

economists who gathered the survey data, computer edits of the data, and detailed data review. 

Historical pay relative data are available for 1992-1996, 1998, 2002, and 2004. There are several 

differences between the recent pay relatives and the pay relatives for earlier years, including different industry 

and occupation classification systems, varying methodology, and different survey designs. These differences 

limit comparability. The pay relatives for 2004 and 2005 were calculated using the same industry and 

occupation classification systems, methodology, and survey design. Nonetheless, comparisons between the 

estimates for the two years should be made only with a high degree of caution. 

Pay relatives were estimated using a multivariate regression technique methodology to control for 

interarea differences. This technique controls for the following ten characteristics: 

 Occupational type 

 Industry type 

 Work level 

 Full-time / part-time status 

 Time / incentive status 

 Union / nonunion status 

 Ownership type 

 Profit / non-profit status 

 Establishment employment 

 Payroll reference date 

Even accounting for the characteristics used in the current regression analysis, there is still wage 

variation across the areas. The variation is due to differences in wage determinants that were not included in 

the model. Examples of these determinants include price levels, environmental amenities such as a pleasant 

climate, and cultural amenities. 

For more details, see Maury B. Gittleman, "Pay Relatives for Metropolitan Areas in the NCS" Monthly 

Labor Review, March 2005, pp. 46-53, and Parastou Karen Shahpoori, "Pay Relatives for Major Metropolitan 

Areas," Compensation and Working Conditions Online, April 28, 2003.