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News Bureau of Labor Statistics United States Department of Labor Washington, D.C. 20212 Technical Contact: (202) 691-6199 NCSinfo@bls.gov Media Contact: (202) 691-5902 Internet address: http://www.bls.gov/ncs/ocs/home.htm USDL: 07-1455 FOR RELEASE: 10:00 A.M. EDT WEDNESDAY, SEPTEMBER 26, 2007 (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 COMPARISONS AMONG METROPOLITAN AREAS, 2006 Average pay in the San Francisco metropolitan area was 19 percent above the national average in 2006, the highest among the 78 metropolitan areas studied by the National Compensation Survey (NCS), the Bureau of Labor Statistics of the U.S. Department of Labor reported today. In contrast, pay was lowest in the Brownsville, Texas metropolitan area with a pay relative of 78, meaning Brownsville workers earned an average of 78 cents for every dollar earned by workers nationwide. Using data from the NCS, pay relatives—a means of assessing pay differences—are available for each of the 9 major occupational groups within 78 metropolitan areas, as well as averaged across all occupations for each area. (See table 1.) Table A below lists the five highest and five lowest paying metropolitan areas among those studied in the NCS. In addition, similar area-to-area comparisons have been calculated for all 78 areas and will soon be available on the BLS website at http://www.bls.gov/ncs/ocs/payrel.htm. Table A. Highest and lowest metropolitan area pay relative rankings (of 78 metropolitan areas surveyed) Rank 1. 2. 3. 4. 74. 78. Metropolitan Area San Francisco-Oakland-San Jose, CA New York-Northern New Jersey-Long Island, NY-NJ-CT-PA Salinas, CA Boston-Worcester-Lawrence, MA-NH-ME-CT Hartford, CT Corpus Christi, TX Great Falls, MT Johnstown, PA Springfield, MO Brownsville-Harlingen-San Benito, TX Pay Relative 119 114 113 112 112 87 87 87 87 78 2 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 in occupational composition, establishment and occupational characteristics, and the fact that data are collected for areas at different times during the year. Simple pay comparisons calculating the ratio of the average pay for an area versus the entire United States in percentage terms would not control for interarea differences in occupational composition and other factors, which may have an effect on pay relatives. More information on pay relative controls and calculations are available in the Technical Note. The pay relative in 2006 for workers in construction and extraction occupations in the San Francisco area was 122, meaning the pay in San Francisco for that occupational group averaged 22 percent more than the national average pay for that occupational group. By contrast, the pay relative for workers in construction and extraction occupations in the Brownsville, Texas area was 67, meaning pay for workers in those occupations averaged 33 percent less than the national average. The National Compensation Survey (NCS), introduced in 1997, collects earnings and other data on employee compensation covering over 800 detailed occupations in 152 metropolitan and nonmetropolitan areas. Average occupational earnings from the NCS are published annually for 78 metropolitan areas and for the United States as a whole. Beginning in 2006, the NCS implemented a number of significant survey changes including imputing for temporary non-response situations and benchmarking estimated employment. For more details on these changes, see the article at http://www.bls.gov/opub/cwc/cm20070122ar01p1.htm. Data users are cautioned not to use yearly differences in area and occupational pay group differences in pay relatives to infer changes in underlying economic conditions. 88 109 102 94 95 94 90 112 78 100 93 101 108 98 100 100 87 98 98 102 106 96 100 101 87 95 94 112 95 105 96 96 97 98 87 99 92 89 107 96 93 95 Amarillo, TX ...................................................... Anchorage, AK ................................................. Atlanta, GA ....................................................... Augusta-Aiken, GA-SC ..................................... Austin-San Marcos, TX ..................................... Birmingham, AL ................................................ Bloomington, IN ................................................ Boston-Worcester-Lawrence, MA-NH-ME-CT .. Brownsville-Harlingen-San Benito, TX ............. Buffalo-Niagara Falls, NY ................................. Charleston-North Charleston, SC ..................... Charlotte-Gastonia-Rock Hill, NC-SC ............... Chicago-Gary-Kenosha, IL-IN-WI ..................... Cincinnati-Hamilton, OH-KY-IN ........................ Cleveland-Akron, OH ........................................ Columbus, OH .................................................. Corpus Christi, TX ............................................ Dallas-Fort Worth, TX ....................................... Dayton-Springfield, OH ..................................... Denver-Boulder-Greeley, CO ........................... Detroit-Ann Arbor-Flint, MI ................................ Elkhart-Goshen, IN ........................................... Fort Collins-Loveland, CO ................................ Grand Rapids-Muskegon-Holland, MI .............. Great Falls, MT ................................................. Greensboro-Winston Salem-High Point, NC .... Greenville-Spartanburg-Anderson, SC ............. Hartford, CT ...................................................... Hickory-Morganton-Lenoir, NC ......................... Honolulu, HI ...................................................... Houston-Galveston-Brazoria, TX ...................... Huntsville, AL .................................................... Indianapolis, IN ................................................. Iowa City, IA ..................................................... Johnstown, PA .................................................. Kansas City, MO-KS ......................................... Knoxville, TN .................................................... Lincoln, NE ....................................................... Los Angeles-Riverside-Orange County, CA ..... Louisville, KY-IN ............................................... Melbourne-Titusville-Palm Bay, FL ................... Memphis, TN-AR-MS ....................................... See footnotes at end of table. 100 All occupations United States .................................................... Metropolitan Area1 103 85 106 91 85 96 100 94 82 95 86 93 85 91 95 108 89 105 99 99 97 96 94 93 104 93 96 100 92 100 89 110 73 91 98 99 93 104 102 89 91 92 100 Management, business, and financial 98 88 109 96 81 92 98 96 97 95 91 95 76 94 90 108 89 104 92 102 104 94 95 97 107 99 101 92 94 101 97 108 95 91 94 93 85 100 102 100 95 95 100 Professional and related (Average pay nationally for all occupations and for each occupational group shown = 100.) 84 91 109 103 101 91 87 95 97 102 89 95 94 95 97 119 90 110 96 99 101 96 94 104 107 103 98 100 83 96 90 113 76 106 86 98 87 121 97 89 93 101 100 Service 98 85 115 99 96 104 95 100 95 92 86 96 88 86 91 108 91 105 97 104 101 95 106 109 106 94 95 105 88 103 78 106 75 105 101 109 90 108 97 83 100 94 100 Sales and related 95 87 106 98 89 98 98 96 98 101 84 100 80 97 93 112 95 99 92 102 105 92 101 100 109 98 101 99 84 99 88 113 77 99 93 102 87 106 104 96 97 96 100 Office and administrative support Table 1. Pay relatives for major occupational groups in metropolitan areas, National Compensation Survey, June 2006 85 85 110 110 92 94 91 91 93 101 96 108 118 91 82 114 98 113 100 91 110 109 99 106 125 90 101 98 92 90 78 124 67 111 80 92 83 125 93 95 88 84 100 Construction and extraction 87 93 106 94 101 98 95 90 95 102 88 104 100 98 84 108 91 115 106 105 97 92 104 96 114 98 102 100 81 95 85 115 78 100 82 98 81 111 102 97 100 99 100 Installation, maintenance, and repair 89 91 99 99 105 97 100 100 108 101 84 104 96 101 105 111 100 106 107 103 118 98 95 102 104 100 105 96 89 91 97 108 76 111 101 104 89 115 103 98 94 87 100 Production 96 91 102 94 103 96 95 93 99 96 82 97 92 104 95 107 102 104 105 98 108 101 111 101 106 99 106 101 86 99 104 111 76 101 104 99 92 110 107 96 91 98 100 Transportation and material moving 3 106 99 105 97 96 104 108 102 98 103 98 97 100 106 113 89 108 119 109 109 87 101 91 95 99 107 97 96 Pittsburgh, PA ................................................... Portland-Salem, OR-WA .................................. Providence-Fall River-Warwick, RI-MA ............ Reading, PA ..................................................... Reno, NV .......................................................... Richland-Kennewick-Pasco, WA ...................... Richmond-Petersburg, VA ................................ Rochester, NY .................................................. Rockford, IL ...................................................... Sacramento-Yolo, CA ....................................... Salinas, CA ....................................................... San Antonio, TX ............................................... San Diego, CA .................................................. San Francisco-Oakland-San Jose, CA ............. Seattle-Tacoma-Bremerton, WA ...................... Springfield, MA ................................................. Springfield, MO ................................................. St. Louis, MO-IL ................................................ Tallahassee, FL ................................................ Tampa-St. Petersburg-Clearwater, FL ............. Visalia-Tulare-Porterville, CA ........................... Washington-Baltimore, DC-MD-VA-WV ........... York, PA ........................................................... Youngstown-Warren, OH ................................. 90 91 98 107 98 93 110 117 99 109 88 99 95 97 97 107 121 91 97 95 111 94 95 95 107 101 90 88 85 88 116 95 97 104 85 96 Professional and related 96 95 101 106 98 91 112 123 119 105 84 94 98 104 99 111 115 87 98 112 112 101 99 113 105 96 95 92 91 94 114 96 100 118 84 91 Service 92 98 102 106 91 92 106 124 111 113 91 100 99 96 100 102 129 84 89 110 103 106 101 105 100 107 94 93 93 101 112 95 102 109 95 101 Sales and related 91 100 97 111 95 92 105 122 108 110 86 99 99 98 97 104 111 91 97 105 106 102 99 100 106 101 91 88 89 90 114 96 104 103 90 96 88 97 92 100 102 99 107 122 111 105 77 117 92 94 111 103 132 97 96 114 104 102 96 111 106 82 87 83 101 91 128 91 110 113 94 90 87 94 93 116 99 95 107 117 106 110 91 107 99 89 104 118 110 97 96 110 108 100 111 93 112 100 94 106 108 93 114 93 101 107 98 91 Office and Installation, Construction administrative maintenance, and extraction support and repair 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. 81 89 101 103 102 96 106 114 103 104 82 97 98 89 90 102 115 93 91 104 109 105 97 96 87 88 87 94 114 114 91 90 91 93 107 100 101 81 90 Management, business, and financial 96 102 108 88 95 All occupations Norfolk-VA Beach-Newport News, VA-NC ....... Ocala, FL .......................................................... Oklahoma City, OK ........................................... Orlando, FL ....................................................... Philadelphia-Wilmington-Atlantic City, PA-NJ-DE-MD ................................................ Phoenix-Mesa, AZ ............................................ Miami-Fort Lauderdale, FL ............................... Milwaukee-Racine, WI ...................................... Minneapolis-St. Paul, MN-WI ........................... Mobile, AL ......................................................... New Orleans, LA .............................................. New York-Northern New Jersey- Long Island, NY-NJ-CT-PA ................................................. Metropolitan Area1 (Average pay nationally for all occupations and for each occupational group shown = 100.) Table 1. Pay relatives for major occupational groups in metropolitan areas, National Compensation Survey, June 2006 — Continued 95 93 105 108 96 102 104 108 117 108 94 104 102 100 103 111 95 96 99 101 112 99 98 98 100 96 93 95 89 85 105 97 106 117 96 95 Production 93 103 97 106 103 110 101 108 113 115 89 108 100 100 104 108 104 86 96 100 105 102 101 100 108 100 89 103 87 106 110 98 107 107 91 100 Transportation and material moving 4 5 Technical Note Pay relative controls and calculations Pay relatives control for differences among areas in occupational composition as well as establishment and occupational characteristics. Metropolitan areas often differ greatly in the composition of establishments and 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 under 6 percent, whereas nationally this ratio is over 8 percent.1 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 and related workers in San Francisco is $37.57 and the average pay for professional and related workers in the entire United States is $29.76.2 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: ($37.57 $29.76) 100 126 This comparison does not control for differences between San Francisco and the nation in the mix of occupations, industries, and other factors. A more accurate estimate of the geographic effect of wages in San Francisco can be obtained by taking these differences into account. Controlling for 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. Historical data Historical pay relative data are available for 1992-1996, 1998, 2002, 2004, and 2005. 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, 2005, and 2006 were calculated using the same industry and occupation classification systems, methodology, and survey design. Nonetheless, comparisons between the estimates for these years should be made only with a high degree of caution. Survey methodology 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 6 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. 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. For more details, see Maury B. Gittleman, "Pay Relatives for Metropolitan Areas in the U.S." Monthly Labor Review, March 2005, pp. 46-53, and Parastou Karen Shahpoori, "Pay Relatives for Major Metropolitan Areas," Compensation and Working Conditions, Spring 2003. 1 Data for this example are based on the May 2006 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 2006 and the National Compensation Survey: Occupational Wages in the United States, June 2006, http://www.bls.gov/ncs/ocs/compub.htm.