Technical information: (202) 691-6567 USDL 06-40 http://www.bls.gov/cew/ For release: 10:00 A.M. EST Media contact: 691-5902 Wednesday, January 11, 2006 COUNTY EMPLOYMENT AND WAGES: SECOND QUARTER 2005 In June 2005, Pasco County, Fla., had the largest over-the-year percentage increase in employment among the largest counties in the U.S., according to preliminary data released today by the Bureau of Labor Statistics of the U.S. Department of Labor. Pasco County (north of Tampa) experienced an over-the-year employment gain of 9.5 percent, compared with national job growth of 1.7 percent. Webb County, Texas (which includes Laredo) had the largest over-the-year gain in average weekly wages in the second quarter of 2005, with an increase of 11.3 percent. The U.S. average weekly wage increased by 3.9 percent over the same time span. Of the 322 largest counties in the United States, as measured by 2004 annual average employment, 131 had over-the-year percentage growth in employment above the national average in June 2005, and 176 experienced changes below the national average. Average weekly wages grew faster than the national average in 128 of the largest U.S. counties, while the percent change in average weekly wages was below the national average in 174 counties. The employment and average weekly wage data by county are compiled under the Quarterly Census of Employment and Wages (QCEW) program, also known as the ES-202 program. The data are derived from reports submitted by every employer subject to unemployment insurance (UI) laws. The 8.6 million employer reports cover 132.8 million full- and part-time workers. The attached tables and charts contain data for the nation and for the 322 U.S. counties with annual average employment levels of 75,000 or more in 2004. June 2005 employment and 2005 second-quarter average weekly wages for all states are provided in table 4 of this release. Final data for all states, metropolitan statistical areas, counties, and the nation through the fourth quarter of 2004 are available on the BLS Web site at http://www.bls.gov/cew/. Preliminary data for the first and second quarters of 2005 will be available later in January on the BLS Web site. ------------------------------------------------------------------ | Regional Quarterly Census of Employment and Wages News Releases | | | | Several BLS regional offices are issuing QCEW news releases | | targeted to local data users. For links to these releases, see | | http://www.bls.gov/cew/cewregional.htm. | ------------------------------------------------------------------ - 2 - Table A. Top 10 counties ranked by June 2005 employment, June 2004-05 employment change, and June 2004-05 percent change in employment --------------------------------------------------------------------------------- Employment in large counties --------------------------------------------------------------------------------- | | June 2005 employment | Net change in employment, | Percent change (thousands) | June 2004-05 | in employment, | (thousands) | June 2004-05 ---------------------------|----------------------------|------------------------ U.S. 132,808.3|U.S. 2,210.2|U.S. 1.7 ---------------------------|----------------------------|------------------------ Los Angeles, Calif. 4,089|Maricopa, Ariz. 88.0|Pasco, Fla. 9.5 Cook, Ill. 2,528|Clark, Nev. 60.2|Lee, Fla. 9.3 New York, N.Y. 2,257|Harris, Texas 47.0|Clark, Nev. 7.5 Harris, Texas 1,867|Orange, Fla. 32.9|Seminole, Fla. 7.4 Maricopa, Ariz. 1,677|Orange, Calif. 32.5|Kern, Calif. 6.6 Orange, Calif. 1,503|Los Angeles, Calif. 28.3|Collier, Fla. 6.6 Dallas, Texas 1,417|Palm Beach, Fla. 28.3|Montgomery, Texas 6.1 San Diego, Calif. 1,301|Riverside, Calif. 28.2|Okaloosa, Fla. 6.0 King, Wash. 1,120|San Bernardino, Calif. 27.7|Williamson, Texas 5.9 Miami-Dade, Fla. 999|New York, N.Y. 25.7|Lake, Fla. 5.8 --------------------------------------------------------------------------------- Large County Employment In June 2005, national employment, as measured by the QCEW program, was 132.8 million, up by 1.7 percent from June 2004. The 322 U.S. counties with 75,000 or more employees accounted for 70.6 percent of total U.S. covered employment and 76.3 percent of total covered wages. These 322 counties had a net job gain of 1,494,600 over the year, accounting for 67.6 percent of the U.S. employment increase. Employment increased in 272 of the large counties from June 2004 to June 2005. Pasco County, Fla., had the largest over-the- year percentage increase in employment (9.5 percent). Lee, Fla., had the next largest increase, 9.3 percent, followed by the counties of Clark, Nev. (7.5 percent), Seminole, Fla. (7.4 percent), and Kern, Calif., and Collier, Fla. (6.6 percent each). (See table 1.) Employment declined in 39 large counties from June 2004 to June 2005. The largest percentage decline in employment was in Hinds County, Miss. (-2.2 percent), followed by the counties of Shawnee, Kan. (-1.8 percent), Lorain, Ohio (-1.5 percent), Orleans, La. (-1.2 percent), and Lucas, Ohio (-1.1 percent). The largest gains in employment from June 2004 to June 2005 were recorded in the counties of Maricopa, Ariz. (88,000), Clark, Nev. (60,200), Harris, Texas (47,000), Orange, Fla. (32,900), and Orange, Calif. (32,500). (See table A.) The largest decline in employment occurred in Allegheny County, Pa. (-6,500), followed by the counties of Erie, N.Y. (-3,400), Orleans, La. (-3,100), Hinds, Miss. (-2,800), and Hennepin, Minn. (-2,700). Large County Average Weekly Wages The national average weekly wage in the second quarter of 2005 was $751. Average weekly wages were higher than the national average in 116 of the largest 322 U.S. counties. New York County, N.Y., held the top position among the highest-paid large counties with an average weekly wage of $1,350. Santa Clara, Calif., was second with an average weekly wage of $1,316, followed by San Mateo, Calif. ($1,267), Arlington, Va. ($1,257), and Washington, D.C. ($1,236). (See table B.) - 3 - Table B. Top 10 counties ranked by second quarter 2005 average weekly wages, second quarter 2004-05 change in average weekly wages, and second quarter 2004-05 percent change in average weekly wages ------------------------------------------------------------------------------ Average weekly wage in large counties ------------------------------------------------------------------------------ | | Average weekly wage, | Change in average weekly| Percent change in second quarter 2005 | wage, second quarter | average weekly wage, | 2004-05 | second quarter 2004-05 ---------------------------|-------------------------|------------------------ U.S. $751|U.S. $28|U.S. 3.9 ---------------------------|-------------------------|------------------------ New York, N.Y. $1,350|San Mateo, Calif. $121|Webb, Texas 11.3 Santa Clara, Calif. 1,316|Fairfax, Va. 88|San Mateo, Calif. 10.6 San Mateo, Calif. 1,267|Arlington, Va. 86|Clark, Nev. 9.4 Arlington, Va. . 1,257|Marin, Calif. 68|Collier, Fla. 8.4 Washington, D.C. 1,236|Clark, Nev. 64|Fairfax, Va. 8.1 Fairfax, Va. 1,177|Durham, N.C. 63|Rockingham, N.H. 7.6 Suffolk, Mass. 1,170|San Francisco, Calif. 56|Henrico, Va. 7.5 Fairfield Conn. 1,169|Henrico, Va. 56|Marin, Calif. 7.4 San Francisco, Calif. 1,162|Fairfield, Conn. 55|Lake, Fla. 7.3 Somerset, N.J. 1,127|Collier, Fla. 55|Arlington, Va. 7.3 | | |Rockingham, N.H. 55| |Webb, Texas 55| ------------------------------------------------------------------------------ There were 206 counties with an average weekly wage below the national average in the second quarter of 2005. The lowest average weekly wages were reported in Cameron County, Texas ($463), followed by the counties of Hidalgo, Texas ($473), Horry, S.C. ($499), Yakima, Wash. ($509), and Tulare, Calif. ($532). (See table 1.) Over the year, the national average weekly wage rose by 3.9 percent. Among the largest counties, Webb, Texas, led the nation in growth in average weekly wages, with an increase of 11.3 percent from the second quarter of 2004. San Mateo, Calif., was second with 10.6 percent growth, followed by the counties of Clark, Nev. (9.4 percent), Collier, Fla. (8.4 percent), and Fairfax, Va. (8.1 percent). Six counties experienced over-the-year declines in average weekly wages. Pierce County, Wash., had the largest decrease, -7.9 percent, followed by the counties of Clayton, Ga. (-6.3 percent), Rock Island, Ill. (-2.9 per- cent), Spartanburg, S.C. (-2.3 percent), and Trumbull, Ohio (-1.3 percent). Ten Largest U.S. Counties Of the 10 largest U.S. counties (based on 2004 annual average employment levels), all reported increases in employment from June 2004 to June 2005. Maricopa County, Ariz., experienced the fastest growth in employment among the largest counties, with a 5.5 percent increase. Within Maricopa County, employment rose in every industry group except two--natural resources and mining and information. The largest gains were in construction (15.5 per- cent) and professional and business services (7.2 percent). (See table 2.) Harris, Texas, had the next largest increase in employment, 2.6 per- cent, followed by Miami-Dade, Fla. (2.4 percent). The smallest employment gain occurred in Cook County, Ill. (0.2 percent). - 4 - All of the 10 largest U.S. counties also saw over-the-year increases in average weekly wages. Miami-Dade, Fla., had the fastest growth in wages among the 10 largest counties, with an increase of 6.1 percent. Within Miami-Dade County, wages increased the most in natural resources and mining (15.8 percent) and professional and business services (10.6 percent). Harris, Texas, was second in wage growth, with an increase of 5.1 percent. The smallest wage gains among the 10 largest counties occurred in San Diego County, Calif., and Dallas, Texas (3.2 percent each). Largest County by State Table 3 shows June 2005 employment and the 2005 second quarter average weekly wage in the largest county in each state. (This table includes two counties--Yellowstone, Mont., and Laramie, Wyo.--that have employment levels below 75,000.) The employment levels in these counties in June 2005 ranged from approximately 4.1 million in Los Angeles County, Calif., to 41,300 in Laramie County, Wyo. The highest average weekly wage of these counties was in New York, N.Y. ($1,350), while the lowest average weekly wage was in Laramie County, Wyo. ($594). For More Information For additional information about the quarterly employment and wages data, please read the Technical Note or visit the QCEW Web site at http://www.bls.gov/cew/. Additional information about the QCEW data also may be obtained by e-mailing QCEWinfo@bls.gov or by calling (202) 691-6567. - 5 - Technical Note These data are the product of a federal-state cooperative program, the Quarterly Census of Employment and Wages (QCEW) program, also known as the ES-202 program. The data are derived from summaries of employment and to- tal pay of workers covered by state and federal unemployment insurance (UI) legislation and provided by State Workforce Agencies (SWAs). The summaries are a result of the administration of state unemployment insurance programs that require most employers to pay quarterly taxes based on the employment and wages of workers covered by UI. Data for 2005 are preliminary and sub- ject to revision. For purposes of this release, large counties are defined as having em- ployment levels of 75,000 or greater. In addition, data for San Juan, Puerto Rico, are provided, but not used in calculating U.S. averages, rankings, or in the analysis in the text. Each year, these large counties are selected on the basis of the preliminary annual average of employment for the previous year. The 323 counties presented in this release were derived using 2004 preliminary annual averages of employment. All of the 318 counties that were published in the 2004 releases are included in the 2005 releases. The following counties grew enough in 2004 to be included in the 2005 releases: Lake, Fla., Wyandotte, Kan., Harford, Md., Washington, Pa., and Whatcom, Wash. These counties will be included in all 2005 quar- terly releases. The counties in table 2 are selected and sorted each year based on the annual average employment from the preceding year. The preliminary QCEW data presented in this release may differ from data released by the individual states. These potential differences result from the states' continuing receipt of UI data over time and ongoing review and editing. The individual states determine their data release timetables. Differences between QCEW, BED, and CES employment measures The Bureau publishes three different establishment-based employment measures for any given quarter. Each of these measures--QCEW, Business Employment Dynamics (BED), and Current Employment Statistics (CES)--makes use of the quarterly UI employment reports in producing data; however, each measure has a somewhat different universe coverage, estimation pro- cedure, and publication product. Differences in coverage and estimation methods can result in somewhat different measures of employment change over time. It is important to understand program differences and the intended uses of the program pro- ducts. (See table below.) Additional information on each program can be obtained from the program Web sites shown in the table below. - 6 - Summary of Major Differences between QCEW, BED, and CES Employment Measures -------------------------------------------------------------------------------- | QCEW | BED | CES -----------|---------------------|----------------------|----------------------- Source |--Count of UI admini-|--Count of longitudi- |--Sample survey: | strative records | nally-linked UI ad- | 400,000 establish- | submitted by 8.6 | ministrative records| ments | million establish- | submitted by 6.6 | | ments | million private-sec-| | | tor employers | -----------|---------------------|----------------------|----------------------- Coverage |--UI and UCFE cover- |--UI coverage, exclud-|Nonfarm wage and sal- | age, including all | ing government, pri-| ary jobs: | employers subject | vate households, and|--UI coverage, exclud- | to state and feder-| establishments with | ing agriculture, pri- | al UI laws | zero employment | vate households, and | | | self-employed workers | | |--Other employment, in- | | | cluding railroads, | | | religious organiza- | | | tions, and other non- | | | UI-covered jobs -----------|---------------------|----------------------|----------------------- Publication|--Quarterly |--Quarterly |--Monthly frequency | -7 months after the| -8 months after the | -Usually first Friday | end of each quar- | end of each quarter| of following month | ter | | -----------|---------------------|----------------------|----------------------- Use of UI |--Directly summarizes|--Links each new UI |--Uses UI file as a sam- file | and publishes each | quarter to longitu- | pling frame and annu- | new quarter of UI | dinal database and | ally realigns (bench- | data | directly summarizes | marks) sample esti- | | gross job gains and | mates to first quar- | | losses | ter UI levels -----------|---------------------|----------------------|------------------------ Principal |--Provides a quarter-|--Provides quarterly |--Provides current month- products | ly and annual uni- | employer dynamics | ly estimates of employ- | verse count of es- | data on establish- | ment, hours, and earn- | tablishments, em- | ment openings, clos-| ings at the MSA, state, | ployment, and wages| ings, expansions, | and national level by | at the county, MSA,| and contractions at | industry | state, and national| the national level | | levels by detailed |--Future expansions | | industry | will include data at| | | the county, MSA, and| | | state level and by | | | size of establish- | | | ment | -----------|---------------------|----------------------|-------------------------- Principal |--Major uses include:|--Major uses include: |--Major uses include: uses | -Detailed locality | -Business cycle | -Principal national | data | analysis | economic indicator | -Periodic universe | -Analysis of employ-| -Official time series | counts for bench- | er dynamics under- | for employment change | marking sample | lying economic ex- | measures | survey estimates | pansions and con- | -Input into other ma- | -Sample frame for | tractions | jor economic indi- | BLS establishment | -Future: Employment| cators | surveys | expansion and con- | | | traction by size of| | | establishment | -----------|---------------------|----------------------|-------------------------- Program |--www.bls.gov/cew/ |--www.bls.gov/bdm/ |--www.bls.gov/ces/ Web sites | | | ----------------------------------------------------------------------------------- - 7 - Coverage Employment and wage data for workers covered by state UI laws are compiled from quarterly contribution reports submitted to the SWAs by employers. For federal civilian workers covered by the Unemployment Compensation for Federal Employees (UCFE) program, employment and wage data are compiled from quarterly reports that are sent to the appropriate SWA by the specific federal agency. In addition to the quarterly contribution reports, employers who operate multiple establishments within a state com- plete a questionnaire, called the "Multiple Worksite Report," which provides detailed information on the location and industry of each of their establish- ments. The employment and wage data included in this release are derived from microdata summaries of more than 8 million employer reports of employ- ment and wages submitted by states to the BLS. These reports are based on place of employment rather than place of residence. UI and UCFE coverage is broad and basically comparable from state to state. In 2004, UI and UCFE programs covered workers in 129.3 million jobs. The estimated 124.4 million workers in these jobs (after adjust- ment for multiple jobholders) represented 96.6 percent of civilian wage and salary employment. Covered workers received $5.088 trillion in pay, representing 94.4 percent of the wage and salary component of personal income and 43.4 percent of the gross domestic product. Major exclusions from UI coverage include self-employed workers, most agricultural workers on small farms, all members of the Armed Forces, elected officials in most states, most employees of railroads, some domes- tic workers, most student workers at schools, and employees of certain small nonprofit organizations. State and federal UI laws change periodically. These changes may have an impact on the employment and wages reported by employers covered under the UI program. Coverage changes may affect the over-the-year comparisons presented in this news release. Beginning with the first quarter of 2005, Oregon implemented a change in their state UI laws. This change extended UI coverage to providers of home care for the elderly. These providers are now considered state workers for purposes of UI benefits. Concepts and methodology Monthly employment is based on the number of workers who worked during or received pay for the pay period including the 12th of the month. With few exceptions, all employees of covered firms are reported, including pro- duction and sales workers, corporation officials, executives, supervisory personnel, and clerical workers. Workers on paid vacations and part-time workers also are included. Average weekly wage values are calculated by dividing quarterly total wages by the average of the three monthly employment levels (all employees, as described above) and dividing the result by 13, for the 13 weeks in the quarter. These calculations are made using unrounded employment and wage values. The average wage values that can be calculated using rounded data from the BLS database may differ from the averages reported. Included in the quarterly wage data are non-wage cash payments such as bonuses, the cash value of meals and lodging when supplied, tips and other gratuities, and, in some states, employer contributions to certain deferred compensation plans such as 401(k) plans and stock options. Average weekly wages are affected by the ratio of full-time to part-time workers as well as the number of individuals in high-paying and low-paying occupations and the incidence of pay periods within a quarter. When compar- ing average weekly wage levels between industries and/or states, these fac- tors should be taken into consideration. Wages may include payments to workers not present in the employment counts because they did not work dur- ing the pay period including the 12th of the month. - 8 - Federal government pay levels are subject to periodic, sometimes large, fluctuations due to a calendar effect that consists of some quarters having more pay periods than others. Most federal employees are paid on a bi- weekly pay schedule. As a result of this schedule, in some quarters, federal wages contain payments for six pay periods, while in other quarters their wages include payments for seven pay periods. Over-the-year comparisons of average weekly wages may reflect this calendar effect. Higher growth in average weekly wages may be attributed, in part, to a comparison of quarterly wages for the current year, which include seven pay periods, with year-ago wages that reflect only six pay periods. An opposite effect will occur when wages in the current period, which contain six pay periods, are compared with year-ago wages that include seven pay periods. The effect on over-the-year pay comparisons can be pronounced in federal government due to the uniform nature of federal payroll processing. This pattern may exist in private sector pay, however, because there are more pay period types (weekly, biweekly, semimonthly, monthly) it is less pronounced. The effect is most visible in counties with large concentra- tions of federal employment. In order to ensure the highest possible quality of data, states verify with employers and update, if necessary, the industry, location, and own- ership classification of all establishments on a 3-year cycle. Changes in establishment classification codes resulting from this process are in- troduced with the data reported for the first quarter of the year. Changes resulting from improved employer reporting also are introduced in the first quarter. QCEW data are not designed as a time series. QCEW data are simply the sums of individual establishment records and reflect the number of establishments that exist in a county or industry at a point in time. Establishments can move in or out of a county or industry for a number of reasons--some reflecting economic events, others reflecting administrative changes. For example, economic change would come from a firm relocating into the county; administrative change would come from a company correcting its county designation. The over-the-year changes of employment and wages presented in this release have been adjusted to account for most of the administrative corrections made to the underlying establishment reports. This is done by modifying the prior-year levels used to calculate the over-the-year changes. Percent changes are calcuated using an adjusted version of the final 2004 quarterly data as the base data. The adjusted prior-year levels used to calculate the over-the-year percent change in employment and wages are not published. These adjusted prior-year levels do not match the unadjusted data maintained on the BLS Web site. Over-the-year change calculations based on data from the Web site, or from data published in prior BLS news releases, may differ substantially from the over-the-year changes presented in this news release. The adjusted data used to calculate the over-the-year change measures presented in this release account for most of the administrative changes-- those occurring when employers update the industry, location, and ownership information of their establishments. The most common adjustments for administrative change are the result of updated information about the county location of individual establishments. The adjusted data do not account for administrative changes caused by (1) multi-unit employers who start reporting for each individual estab- lishment rather than as a single entity and (2) the classification of establishments previously reported in the unknown county or unknown in- dustry categories. - 9 - The adjusted data used to calculate the over-the-year change measures presented in any County Employment and Wages news release are valid for comparisons between the starting and ending points (a 12-month period) used in that particular release. Comparisons may not be valid for any time period other than the one featured in a release even if the changes were calculated using adjusted data. County definitions are assigned according to Federal Information Processing Standards Publications (FIPS PUBS) as issued by the National Institute of Standards and Technology, after approval by the Secretary of Commerce pursuant to Section 5131 of the Information Technology Management Reform Act of 1996 and the Computer Security Act of 1987, Public Law 104- 106. Areas shown as counties include those designated as independent cities in some jurisdictions and, in Alaska, those designated as census areas where counties have not been created. County data also are presented for the New England states for comparative purposes even though townships are the more common designation used in New England (and New Jersey). The regions re- ferred to in this release are defined as census regions. Additional statistics and other information An annual bulletin, Employment and Wages, features comprehensive infor- mation by detailed industry on establishments, employment, and wages for the nation and all states. The 2004 edition of this bulletin contains selected data produced by Business Employment Dynamics (BED) on job gains and losses, as well as selected data from the fourth quarter 2004 version of this news release. Employment and Wages Annual Averages, 2004 will be available for sale in January 2006 from the United States Government Print- ing Office, Superintendent of Documents, P.O. Box 371954, Pittsburgh, PA 15250, telephone 866-512-1800, outside of Washington, D.C. Within Washington, D.C., the telephone number is 202-512-1800. The fax number is 202-512-2104. Also, the 2004 bulletin is available in a portable document format (PDF) on the BLS Web site at http://www.bls.gov/cew/cewbultn04.htm. News releases on quarterly measures of gross job flows also are available upon request from the Division of Administrative Statistics and Labor Turnover (Business Employment Dynamics), telephone 202-691-6467; (http://www.bls.gov/bdm/); (e-mail: BDMInfo@bls.gov). Information in this release will be made available to sensory impaired individuals upon request. Voice phone: 202-691-5200; TDD message referral phone number: 1-800-877-8339. Table 1. Covered(1) establishments, employment, and wages in the 323 largest counties, second quarter 2005(2) Employment Average weekly wage(5) Establishments, County(3) second quarter Percent Ranking Percent Ranking 2005 June change, by Average change, by (thousands) 2005 June percent weekly second percent (thousands) 2004-05(4) change wage quarter change 2004-05(4) United States(6)......... 8,562.0 132,808.3 1.7 - $751 3.9 - Jefferson, AL............ 18.8 371.4 0.2 257 763 6.0 25 Madison, AL.............. 8.1 168.5 2.1 109 795 0.9 300 Mobile, AL............... 9.8 167.2 3.2 56 620 4.7 76 Montgomery, AL........... 6.7 135.8 2.7 74 643 2.1 255 Tuscaloosa, AL........... 4.2 81.3 5.0 21 648 6.4 16 Anchorage Borough, AK.... 7.9 144.2 2.2 100 813 3.0 207 Maricopa, AZ............. 82.9 1,677.4 5.5 12 761 4.1 112 Pima, AZ................. 18.1 344.4 2.8 71 684 5.1 47 Benton, AR............... 4.8 90.2 5.7 11 697 5.1 47 Pulaski, AR.............. 13.4 245.5 1.1 184 689 3.9 129 Washington, AR........... 5.3 90.4 4.4 30 619 5.5 34 Alameda, CA.............. 47.0 679.2 -0.4 289 988 2.9 215 Contra Costa, CA......... 26.8 343.9 0.1 267 957 5.0 55 Fresno, CA............... 28.1 348.2 2.2 100 601 3.6 161 Kern, CA................. 16.0 270.1 6.6 5 636 (7) - Los Angeles, CA.......... 363.1 4,089.4 0.7 217 852 4.4 90 Marin, CA................ 11.5 110.1 0.3 249 985 7.4 8 Monterey, CA............. 11.7 183.3 2.0 116 673 3.9 129 Orange, CA............... 89.7 1,503.3 2.2 100 861 3.6 161 Placer, CA............... 9.5 135.5 3.1 61 758 3.7 153 Riverside, CA............ 39.0 612.3 4.8 24 654 0.9 300 Sacramento, CA........... 46.6 623.5 1.5 153 816 1.2 294 San Bernardino, CA....... 42.1 636.5 4.6 27 674 3.9 129 San Diego, CA............ 86.4 1,301.4 1.5 153 812 3.2 191 San Francisco, CA........ 42.3 524.8 1.1 184 1,162 5.1 47 San Joaquin, CA.......... 15.8 223.4 1.6 141 650 3.2 191 San Luis Obispo, CA...... 8.6 103.8 1.4 161 616 -0.2 312 San Mateo, CA............ 22.2 328.0 0.0 273 1,267 10.6 2 Santa Barbara, CA........ 13.0 191.6 3.0 65 721 4.8 68 Santa Clara, CA.......... 52.0 860.3 0.5 236 1,316 1.8 275 Santa Cruz, CA........... 8.3 101.5 0.2 257 709 3.5 168 Solano, CA............... 9.3 129.7 0.7 217 720 4.0 120 Sonoma, CA............... 16.9 192.9 -0.2 282 747 3.3 181 Stanislaus, CA........... 13.1 177.4 2.2 100 643 3.9 129 Tulare, CA............... 8.3 144.0 3.4 47 532 3.1 199 Ventura, CA.............. 20.5 316.3 1.3 167 818 4.7 76 Yolo, CA................. 5.1 101.0 2.2 100 715 3.9 129 Adams, CO................ 9.0 149.4 2.5 87 708 1.3 289 Arapahoe, CO............. 19.3 275.5 1.3 167 891 3.4 178 Boulder, CO.............. 12.2 156.2 2.5 87 898 1.9 268 Denver, CO............... 24.8 425.7 0.9 199 923 4.1 112 El Paso, CO.............. 16.6 242.6 1.6 141 701 2.9 215 Jefferson, CO............ 18.5 209.4 1.4 161 770 4.1 112 Larimer, CO.............. 9.6 128.1 1.5 153 666 2.8 221 Fairfield, CT............ 31.9 420.0 1.1 184 1,169 4.9 62 Hartford, CT............. 24.5 494.9 1.7 132 945 5.4 35 New Haven, CT............ 22.1 368.0 0.6 224 826 2.7 229 New London, CT........... 6.7 130.5 0.6 224 804 2.0 264 New Castle, DE........... 19.7 280.9 -0.7 302 890 3.5 168 Washington, DC........... 30.5 675.1 1.5 153 1,236 4.1 112 Alachua, FL.............. 6.2 120.9 (7) - 639 (7) - Brevard, FL.............. 13.7 203.9 3.4 47 728 2.0 264 Broward, FL.............. 61.1 718.1 2.5 87 743 5.1 47 Collier, FL.............. 11.6 120.6 6.6 5 713 8.4 4 Duval, FL................ 24.4 446.9 3.6 40 735 3.8 144 Escambia, FL............. 7.6 124.1 1.1 184 610 5.4 35 Hillsborough, FL......... 33.9 617.3 3.6 40 713 2.9 215 Lake, FL................. 6.2 77.1 5.8 10 572 7.3 9 Lee, FL.................. 17.2 207.9 9.3 2 675 3.7 153 Leon, FL................. 7.7 144.4 2.8 71 647 1.3 289 Manatee, FL.............. 8.1 120.5 4.5 28 611 4.3 99 Marion, FL............... 7.3 97.3 (7) - 573 4.0 120 Miami-Dade, FL........... 84.9 999.0 2.4 93 760 6.1 21 Okaloosa, FL............. 6.0 82.2 6.0 8 633 (7) - Orange, FL............... 32.1 655.9 5.3 15 698 4.2 104 Palm Beach, FL........... 46.7 542.5 5.5 12 745 2.6 237 Pasco, FL................ 8.5 87.3 9.5 1 579 2.7 229 Pinellas, FL............. 30.3 434.8 -0.2 282 666 4.7 76 Polk, FL................. 11.5 196.7 5.2 18 607 3.8 144 Sarasota, FL............. 14.5 154.1 3.1 61 666 5.0 55 Seminole, FL............. 13.3 166.1 7.4 4 696 5.0 55 Volusia, FL.............. 13.2 159.2 4.8 24 573 4.8 68 Bibb, GA................. 4.7 87.5 0.1 267 635 4.1 112 Chatham, GA.............. 7.2 131.6 3.4 47 634 3.9 129 Clayton, GA.............. 4.4 107.4 1.8 128 754 -6.3 316 Cobb, GA................. 20.2 306.2 2.9 66 826 2.2 254 De Kalb, GA.............. 17.0 291.0 0.5 236 823 3.1 199 Fulton, GA............... 37.9 741.3 2.9 66 975 3.6 161 Gwinnett, GA............. 22.0 314.7 3.1 61 789 3.1 199 Muscogee, GA............. 4.8 96.8 0.4 245 607 5.0 55 Richmond, GA............. 4.8 106.0 1.2 175 635 2.1 255 Honolulu, HI............. 23.6 441.9 3.2 56 700 3.7 153 Ada, ID.................. 13.6 198.4 4.4 30 693 3.6 161 Champaign, IL............ 4.0 91.2 1.1 184 642 4.2 104 Cook, IL................. 129.8 2,527.8 0.2 257 902 4.9 62 Du Page, IL.............. 33.5 589.1 0.6 224 878 2.5 243 Kane, IL................. 11.6 208.4 1.2 175 693 4.1 112 Lake, IL................. 19.5 332.1 0.8 208 896 3.2 191 McHenry, IL.............. 7.8 101.1 2.8 71 670 4.0 120 McLean, IL............... 3.5 84.5 0.8 208 798 5.7 31 Madison, IL.............. 5.7 94.6 -0.5 293 635 2.1 255 Peoria, IL............... 4.6 100.7 2.0 116 710 3.0 207 Rock Island, IL.......... 3.4 79.7 1.3 167 704 -2.9 315 St. Clair, IL............ 5.2 94.3 1.0 192 610 3.2 191 Sangamon, IL............. 5.1 132.6 -0.5 293 734 2.1 255 Will, IL................. 11.4 171.1 2.5 87 704 4.1 112 Winnebago, IL............ 6.7 138.1 -0.6 299 656 4.0 120 Allen, IN................ 8.8 178.2 -0.1 278 667 3.1 199 Elkhart, IN.............. 4.8 126.5 -0.3 285 680 2.7 229 Hamilton, IN............. 6.6 97.1 5.0 21 739 1.7 282 Lake, IN................. 10.0 193.4 0.7 217 689 5.0 55 Marion, IN............... 23.5 581.0 0.2 257 783 1.8 275 St. Joseph, IN........... 6.0 125.9 -0.5 293 654 5.1 47 Vanderburgh, IN.......... 4.8 107.7 0.5 236 633 1.4 286 Linn, IA................. 6.1 120.0 2.1 109 711 3.5 168 Polk, IA................. 14.2 267.0 1.3 167 740 4.2 104 Scott, IA................ 5.1 89.5 1.6 141 627 5.4 35 Johnson, KS.............. 19.1 304.3 1.8 128 778 3.6 161 Sedgwick, KS............. 11.8 244.4 1.7 132 705 5.4 35 Shawnee, KS.............. 4.7 94.5 -1.8 315 663 5.1 47 Wyandotte, KS............ 3.2 76.6 0.2 257 747 3.8 144 Fayette, KY.............. 8.7 170.4 2.6 80 690 2.4 247 Jefferson, KY............ 21.2 425.1 1.1 184 748 3.7 153 Caddo, LA................ 7.1 123.5 1.3 167 638 0.8 302 Calcasieu, LA............ 4.7 86.4 5.2 18 607 3.8 144 East Baton Rouge, LA..... 13.2 251.7 2.0 116 642 4.9 62 Jefferson, LA............ 14.3 217.0 0.7 217 624 3.0 207 Lafayette, LA............ 7.8 121.5 2.6 80 658 4.4 90 Orleans, LA.............. 12.9 245.7 -1.2 313 691 (7) - Cumberland, ME........... 11.6 172.8 -0.6 299 684 3.0 207 Anne Arundel, MD......... 14.1 222.8 1.7 132 788 4.2 104 Baltimore, MD............ 21.2 374.7 1.1 184 769 3.9 129 Frederick, MD............ 5.8 93.0 2.6 80 715 1.4 286 Harford, MD.............. 5.5 81.8 1.7 132 705 0.6 306 Howard, MD............... 8.2 140.4 2.5 87 872 4.2 104 Montgomery, MD........... 32.4 464.9 2.1 109 995 4.8 68 Prince Georges, MD....... 15.6 313.6 -0.1 278 827 3.9 129 Baltimore City, MD....... 14.1 351.9 -0.6 299 871 3.8 144 Barnstable, MA........... 9.5 101.2 -0.5 293 654 1.9 268 Bristol, MA.............. 15.8 223.9 0.1 267 686 1.8 275 Essex, MA................ 21.3 299.0 -0.7 302 807 3.5 168 Hampden, MA.............. 14.6 202.4 0.1 267 689 2.1 255 Middlesex, MA............ 49.3 798.8 1.0 192 1,062 1.8 275 Norfolk, MA.............. 22.5 322.7 0.2 257 899 1.9 268 Plymouth, MA............. 14.2 181.3 2.7 74 747 1.9 268 Suffolk, MA.............. 22.8 566.1 0.8 208 1,170 0.7 304 Worcester, MA............ 20.9 322.1 -0.4 289 778 2.1 255 Genesee, MI.............. 8.5 150.4 (7) - 705 1.4 286 Ingham, MI............... 7.1 159.9 (7) - 729 4.1 112 Kalamazoo, MI............ 5.6 117.2 0.5 236 683 0.4 308 Kent, MI................. 14.6 341.0 1.2 175 711 5.3 41 Macomb, MI............... 18.2 333.6 0.9 199 830 3.1 199 Oakland, MI.............. 41.1 729.9 0.5 236 913 2.6 237 Ottawa, MI............... 5.8 115.4 2.0 116 671 1.1 297 Saginaw, MI.............. 4.6 90.0 -0.8 307 673 0.7 304 Washtenaw, MI............ 8.3 193.5 0.4 245 856 2.3 252 Wayne, MI................ 34.4 797.2 0.0 273 894 4.4 90 Anoka, MN................ 7.6 114.8 0.0 273 777 6.9 11 Dakota, MN............... 10.0 172.2 -0.9 309 757 2.9 215 Hennepin, MN............. 41.1 831.3 -0.3 285 941 2.8 221 Olmsted, MN.............. 3.4 90.0 0.4 245 785 2.5 243 Ramsey, MN............... 15.2 329.5 -0.7 302 849 0.2 310 St. Louis, MN............ 5.8 96.4 0.5 236 613 0.5 307 Stearns, MN.............. 4.3 77.8 0.8 208 602 1.2 294 Harrison, MS............. 4.6 91.7 1.3 167 558 6.3 17 Hinds, MS................ 6.5 127.6 -2.2 316 656 3.3 181 Boone, MO................ 4.3 80.9 3.5 45 613 4.6 81 Clay, MO................. 5.0 89.5 1.7 132 694 2.5 243 Greene, MO............... 8.0 149.8 3.2 56 592 2.6 237 Jackson, MO.............. 18.6 367.4 0.6 224 775 3.9 129 St. Charles, MO.......... 7.5 119.0 2.9 66 670 4.4 90 St. Louis, MO............ 33.6 626.0 0.8 208 818 4.9 62 St. Louis City, MO....... 8.1 224.2 0.6 224 856 6.5 14 Douglas, NE.............. 15.1 312.5 0.3 249 691 3.3 181 Lancaster, NE............ 7.7 155.0 1.3 167 608 1.2 294 Clark, NV................ 41.5 867.2 7.5 3 748 9.4 3 Washoe, NV............... 13.2 212.2 3.3 53 720 4.8 68 Hillsborough, NH......... 12.2 197.9 1.2 175 836 5.7 31 Rockingham, NH........... 10.8 139.5 1.6 141 776 7.6 6 Atlantic, NJ............. 6.7 153.1 1.7 132 692 3.9 129 Bergen, NJ............... 34.2 453.5 0.3 249 950 4.3 99 Burlington, NJ........... 11.3 205.0 1.5 153 809 3.3 181 Camden, NJ............... 13.5 212.7 0.1 267 779 3.7 153 Essex, NJ................ 21.2 361.5 0.3 249 965 3.9 129 Gloucester, NJ........... 6.2 105.4 3.2 56 697 3.6 161 Hudson, NJ............... 14.0 237.8 1.6 141 982 2.8 221 Mercer, NJ............... 10.8 224.4 2.7 74 939 4.7 76 Middlesex, NJ............ 20.8 396.9 0.5 236 921 0.8 302 Monmouth, NJ............. 20.1 264.1 1.5 153 809 1.3 289 Morris, NJ............... 17.8 290.5 0.6 224 1,094 5.2 44 Ocean, NJ................ 11.6 154.5 1.6 141 659 3.8 144 Passaic, NJ.............. 12.5 180.5 1.4 161 839 5.4 35 Somerset, NJ............. 10.1 174.1 3.6 40 1,127 2.7 229 Union, NJ................ 14.9 231.3 (7) - 945 (7) - Bernalillo, NM........... 16.5 320.9 1.4 161 685 5.4 35 Albany, NY............... 9.7 230.7 0.2 257 779 2.4 247 Bronx, NY................ 15.7 222.9 2.4 93 733 1.8 275 Broome, NY............... 4.5 95.5 -1.0 311 625 6.3 17 Dutchess, NY............. 8.0 119.5 1.4 161 785 6.2 19 Erie, NY................. 23.4 457.2 -0.7 302 671 2.8 221 Kings, NY................ 42.6 456.3 1.7 132 671 3.2 191 Monroe, NY............... 17.7 388.7 0.8 208 783 6.5 14 Nassau, NY............... 51.3 606.9 0.2 257 863 4.6 81 New York, NY............. 113.9 2,256.6 1.2 175 1,350 4.2 104 Oneida, NY............... 5.3 110.8 0.0 273 596 3.5 168 Onondaga, NY............. 12.8 252.9 1.0 192 700 1.6 285 Orange, NY............... 9.5 130.9 1.2 175 680 4.5 85 Queens, NY............... 40.7 481.7 1.6 141 755 3.0 207 Richmond, NY............. 8.2 91.0 1.1 184 694 3.6 161 Rockland, NY............. 9.5 114.9 1.3 167 826 3.9 129 Suffolk, NY.............. 48.6 621.9 0.3 249 814 3.8 144 Westchester, NY.......... 35.8 419.5 0.7 217 1,001 2.4 247 Buncombe, NC............. 7.0 108.8 1.9 124 599 5.3 41 Catawba, NC.............. 4.3 86.1 -0.8 307 593 3.3 181 Cumberland, NC........... 5.7 115.8 3.4 47 574 1.8 275 Durham, NC............... 6.2 168.6 1.2 175 982 6.9 11 Forsyth, NC.............. 8.5 178.7 2.2 100 702 3.4 178 Guilford, NC............. 13.6 272.8 0.8 208 683 3.2 191 Mecklenburg, NC.......... 27.6 520.7 3.1 61 879 4.9 62 New Hanover, NC.......... 6.6 94.9 5.2 18 613 4.4 90 Wake, NC................. 23.9 406.4 4.3 32 753 2.7 229 Cass, ND................. 5.7 92.3 3.2 56 613 3.9 129 Butler, OH............... 7.0 136.4 1.0 192 680 0.4 308 Cuyahoga, OH............. 38.1 758.5 -0.1 278 778 2.8 221 Franklin, OH............. 29.2 684.8 0.9 199 759 3.7 153 Hamilton, OH............. 24.6 545.8 -0.1 278 804 2.9 215 Lake, OH................. 6.9 102.2 0.3 249 628 3.5 168 Lorain, OH............... 6.3 102.8 -1.5 314 650 2.0 264 Lucas, OH................ 10.9 227.1 -1.1 312 689 3.1 199 Mahoning, OH............. 6.5 108.1 0.9 199 563 3.5 168 Montgomery, OH........... 13.2 283.6 -0.4 289 717 2.7 229 Stark, OH................ 9.3 168.1 0.2 257 602 3.1 199 Summit, OH............... 15.0 271.1 0.6 224 726 5.8 28 Trumbull, OH............. 4.8 85.2 -0.3 285 663 -1.3 313 Oklahoma, OK............. 22.4 411.5 1.6 141 644 1.1 297 Tulsa, OK................ 18.5 330.9 3.6 40 673 3.7 153 Clackamas, OR............ 11.8 145.7 2.7 74 710 3.5 168 Jackson, OR.............. 6.5 83.1 3.4 47 581 2.1 255 Lane, OR................. 10.5 147.1 4.1 34 608 2.0 264 Marion, OR............... 8.8 140.3 2.6 80 602 2.6 237 Multnomah, OR............ 25.8 429.5 1.7 132 772 2.9 215 Washington, OR........... 15.0 237.5 3.8 39 857 1.3 289 Allegheny, PA............ 35.2 689.1 -0.9 309 793 4.2 104 Berks, PA................ 9.0 165.5 1.6 141 685 2.1 255 Bucks, PA................ 20.3 266.5 1.4 161 743 2.6 237 Chester, PA.............. 15.1 233.6 2.6 80 982 4.0 120 Cumberland, PA........... 5.8 126.0 -0.5 293 709 0.0 311 Dauphin, PA.............. 7.1 179.8 1.6 141 740 1.9 268 Delaware, PA............. 13.7 210.9 0.8 208 793 4.5 85 Erie, PA................. 7.2 131.1 2.4 93 603 3.4 178 Lackawanna, PA........... 5.8 100.6 1.9 124 595 5.1 47 Lancaster, PA............ 11.9 230.7 0.9 199 662 4.3 99 Lehigh, PA............... 8.4 176.7 0.6 224 753 4.0 120 Luzerne, PA.............. 8.1 144.3 0.5 236 605 2.7 229 Montgomery, PA........... 27.9 485.9 0.7 217 929 5.2 44 Northampton, PA.......... 6.3 95.6 2.4 93 673 3.2 191 Philadelphia, PA......... 29.1 630.4 0.6 224 865 3.3 181 Washington, PA........... 5.3 77.2 0.1 267 649 1.7 282 Westmoreland, PA......... 9.6 140.7 0.9 199 603 2.7 229 York, PA................. 8.8 172.7 2.6 80 672 4.0 120 Kent, RI................. 5.6 83.5 1.2 175 683 1.3 289 Providence, RI........... 18.0 286.9 -0.7 302 736 3.1 199 Charleston, SC........... 12.3 200.1 2.7 74 634 6.0 25 Greenville, SC........... 12.6 225.1 0.6 224 679 5.8 28 Horry, SC................ 8.3 115.1 3.4 47 499 5.7 31 Lexington, SC............ 5.8 88.5 3.9 37 598 6.2 19 Richland, SC............. 9.7 203.2 0.6 224 650 3.7 153 Spartanburg, SC.......... 6.4 115.3 -0.2 282 670 -2.3 314 Minnehaha, SD............ 6.0 112.4 2.0 116 624 3.0 207 Davidson, TN............. 17.9 433.4 2.0 116 749 3.5 168 Hamilton, TN............. 8.4 189.0 1.0 192 657 2.8 221 Knox, TN................. 10.4 218.7 1.6 141 659 5.3 41 Rutherford, TN........... 3.8 94.7 5.3 15 692 1.9 268 Shelby, TN............... 19.8 500.8 0.9 199 752 1.8 275 Bell, TX................. 4.2 94.3 (7) - 578 4.7 76 Bexar, TX................ 30.1 677.1 2.9 66 655 3.0 207 Brazoria, TX............. 4.3 78.2 1.2 175 709 4.4 90 Brazos, TX............... 3.6 79.2 3.3 53 562 6.6 13 Cameron, TX.............. 6.2 116.1 0.3 249 463 2.4 247 Collin, TX............... 14.1 243.1 5.0 21 909 6.1 21 Dallas, TX............... 65.8 1,416.8 1.5 153 909 3.2 191 Denton, TX............... 9.2 146.6 4.2 33 657 5.0 55 El Paso, TX.............. 12.6 256.1 2.2 100 540 3.3 181 Fort Bend, TX............ 7.1 110.5 2.4 93 767 5.8 28 Galveston, TX............ 4.9 88.2 2.2 100 657 3.8 144 Harris, TX............... 90.0 1,866.9 2.6 80 892 5.1 47 Hidalgo, TX.............. 9.7 197.9 4.8 24 473 3.3 181 Jefferson, TX............ 5.8 118.3 2.7 74 683 4.4 90 Lubbock, TX.............. 6.5 118.7 0.9 199 563 5.2 44 McLennan, TX............. 4.7 101.7 0.6 224 604 4.3 99 Montgomery, TX........... 7.0 103.5 6.1 7 683 4.9 62 Nueces, TX............... 8.0 147.7 1.9 124 615 4.8 68 Potter, TX............... 3.7 71.8 0.0 273 595 4.6 81 Smith, TX................ 5.0 89.4 1.7 132 636 2.1 255 Tarrant, TX.............. 34.6 716.3 2.1 109 770 5.0 55 Travis, TX............... 25.1 531.7 4.0 36 840 4.5 85 Webb, TX................. 4.4 80.4 2.4 93 543 11.3 1 Williamson, TX........... 5.9 101.7 5.9 9 763 6.1 21 Davis, UT................ 6.6 97.8 2.1 109 600 1.7 282 Salt Lake, UT............ 36.2 539.7 3.5 45 688 2.5 243 Utah, UT................. 11.7 156.8 3.6 40 570 3.8 144 Weber, UT................ 5.5 89.0 1.8 128 564 2.4 247 Chittenden, VT........... 5.7 96.3 -0.4 289 740 2.6 237 Arlington, VA............ 7.2 155.1 0.2 257 1,257 7.3 9 Chesterfield, VA......... 6.8 116.1 2.1 109 683 4.8 68 Fairfax, VA.............. 30.6 571.1 3.9 37 1,177 8.1 5 Henrico, VA.............. 8.5 172.8 1.8 128 802 7.5 7 Loudoun, VA.............. 6.9 121.4 5.3 15 950 2.8 221 Prince William, VA....... 6.3 102.4 3.3 53 678 4.5 85 Alexandria City, VA...... 5.8 94.4 1.5 153 968 4.4 90 Chesapeake City, VA...... 5.0 95.4 1.6 141 602 4.5 85 Newport News City, VA.... 3.8 99.4 1.0 192 694 4.4 90 Norfolk City, VA......... 5.6 146.5 0.8 208 725 4.0 120 Richmond City, VA........ 7.0 161.1 1.9 124 845 1.9 268 Virginia Beach City, VA.. 10.9 179.1 2.3 99 591 4.8 68 Clark, WA................ 10.4 126.1 4.1 34 690 3.3 181 King, WA................. 74.0 1,119.5 2.1 109 933 4.8 68 Kitsap, WA............... 6.1 83.0 2.9 66 681 2.3 252 Pierce, WA............... 19.2 259.0 2.5 87 676 -7.9 317 Snohomish, WA............ 16.0 224.2 5.4 14 775 6.0 25 Spokane, WA.............. 14.2 199.0 2.0 116 617 3.9 129 Thurston, WA............. 6.2 94.2 2.2 100 689 4.2 104 Whatcom, WA.............. 6.4 80.3 4.5 28 592 3.3 181 Yakima, WA............... 7.5 107.7 0.5 236 509 3.9 129 Kanawha, WV.............. 6.2 108.8 -0.5 293 674 4.3 99 Brown, WI................ 6.7 148.7 0.3 249 680 3.0 207 Dane, WI................. 13.9 296.7 2.0 116 729 1.1 297 Milwaukee, WI............ 21.6 494.4 -0.3 285 753 4.0 120 Outagamie, WI............ 4.9 103.5 1.0 192 659 3.5 168 Racine, WI............... 4.3 77.5 0.9 199 703 4.6 81 Waukesha, WI............. 13.4 233.6 0.4 245 759 2.8 221 Winnebago, WI............ 3.9 88.8 0.7 217 725 6.1 21 San Juan, PR............. 14.1 315.7 -1.2 (8) 490 4.3 (8) 1 Includes workers covered by Unemployment Insurance (UI) and Unemployment Compensation for Federal Employees (UCFE) programs. These 322 U.S. counties comprise 70.6 percent of the total covered workers in the U.S. 2 Data are preliminary. 3 Includes areas not officially designated as counties. See Technical Note. 4 Percent changes were computed from quarterly employment and pay data adjusted for noneconomic county reclassifications. See Technical Note. 5 Average weekly wages were calculated using unrounded data. 6 Totals for the United States do not include data for Puerto Rico or the Virgin Islands. 7 Data do not meet BLS or State agency disclosure standards. 8 This county was not included in the U.S. rankings. Table 2. Covered(1) establishments, employment, and wages in the ten largest counties, second quarter 2005(2) Employment Average weekly wage(4) Establishments, second quarter County by NAICS supersector 2005 Percent Percent (thousands) June change, Average change, 2005 June weekly second (thousands) 2004-05(3) wage quarter 2004-05(3) United States(5)............................. 8,562.0 132,808.3 1.7 $751 3.9 Private industry........................... 8,285.5 111,711.2 1.9 740 3.9 Natural resources and mining............. 122.7 1,849.4 2.1 697 7.4 Construction............................. 843.5 7,445.2 4.7 775 3.6 Manufacturing............................ 366.5 14,270.1 -0.6 913 3.4 Trade, transportation, and utilities..... 1,869.1 25,635.7 1.3 656 3.6 Information.............................. 142.0 3,062.2 -1.4 1,137 4.2 Financial activities..................... 810.8 8,064.3 1.7 1,083 4.0 Professional and business services....... 1,367.5 16,929.2 3.1 905 5.4 Education and health services............ 764.2 16,430.5 2.4 704 4.1 Leisure and hospitality.................. 690.0 13,292.2 2.4 315 2.3 Other services........................... 1,086.5 4,387.6 0.7 488 3.6 Government................................. 276.5 21,097.1 0.8 808 3.3 Los Angeles, CA.............................. 363.1 4,089.4 0.7 852 4.4 Private industry........................... 359.3 3,500.5 0.8 830 4.0 Natural resources and mining............. 0.5 11.5 -3.5 1,088 25.2 Construction............................. 13.1 148.9 6.4 841 5.8 Manufacturing............................ 16.1 468.8 -3.5 884 6.0 Trade, transportation, and utilities..... 52.3 781.7 0.7 718 2.6 Information.............................. 8.4 196.9 -4.9 1,484 5.5 Financial activities..................... 23.0 242.4 1.7 1,302 6.4 Professional and business services....... 39.4 576.2 2.5 960 4.8 Education and health services............ 27.0 453.1 0.0 774 5.4 Leisure and hospitality.................. 25.3 382.4 2.3 467 -1.7 Other services........................... 153.8 237.9 6.4 402 1.5 Government................................. 3.8 589.0 -0.1 988 7.0 Cook, IL..................................... 129.8 2,527.8 0.2 902 4.9 Private industry........................... 128.5 2,208.2 0.4 892 5.1 Natural resources and mining............. 0.1 1.5 7.5 950 5.7 Construction............................. 11.0 95.6 -1.5 1,081 3.4 Manufacturing............................ 7.5 253.8 -1.7 912 2.8 Trade, transportation, and utilities..... 27.0 476.0 -0.5 737 3.8 Information.............................. 2.5 61.1 -2.2 1,224 3.4 Financial activities..................... 14.5 217.4 -0.2 1,378 2.8 Professional and business services....... 26.7 417.7 2.1 1,165 11.6 Education and health services............ 12.8 355.7 2.3 761 3.4 Leisure and hospitality.................. 10.9 229.6 0.6 384 4.6 Other services........................... 13.0 96.0 -1.2 637 1.8 Government................................. 1.2 319.6 -1.0 972 4.0 New York, NY................................. 113.9 2,256.6 1.2 1,350 4.2 Private industry........................... 113.6 1,803.3 1.3 1,452 4.7 Natural resources and mining............. 0.0 0.1 -2.2 1,177 -1.2 Construction............................. 2.1 28.9 -0.2 1,288 -0.7 Manufacturing............................ 3.2 42.8 -7.0 1,092 8.7 Trade, transportation, and utilities..... 21.5 236.9 0.8 1,035 3.6 Information.............................. 4.1 129.2 0.7 1,714 2.9 Financial activities..................... 17.2 356.8 1.2 2,538 5.9 Professional and business services....... 22.5 447.6 1.8 1,593 5.3 Education and health services............ 8.0 272.0 0.6 904 4.0 Leisure and hospitality.................. 10.4 193.8 0.7 670 2.8 Other services........................... 16.4 84.0 1.2 820 4.9 Government................................. 0.2 453.3 0.4 944 0.6 Harris, TX................................... 90.0 1,866.9 2.6 892 5.1 Private industry........................... 89.5 1,622.7 2.8 907 5.3 Natural resources and mining............. 1.3 65.9 5.7 2,284 8.7 Construction............................. 6.2 130.6 0.8 848 3.5 Manufacturing............................ 4.5 167.1 2.3 1,134 5.3 Trade, transportation, and utilities..... 21.0 393.0 2.1 817 4.1 Information.............................. 1.3 32.4 -2.9 1,100 4.3 Financial activities..................... 9.8 115.4 0.6 1,100 5.4 Professional and business services....... 17.4 297.0 4.5 1,008 6.8 Education and health services............ 9.2 194.6 4.1 769 3.2 Leisure and hospitality.................. 6.8 166.8 2.8 336 4.7 Other services........................... 10.5 56.1 0.4 530 4.7 Government................................. 0.4 244.2 1.3 792 3.3 Maricopa, AZ................................. 82.9 1,677.4 5.5 761 4.1 Private industry........................... 82.3 1,498.7 5.9 744 4.2 Natural resources and mining............. 0.5 10.2 -8.3 544 14.5 Construction............................. 8.5 160.5 15.5 761 5.0 Manufacturing............................ 3.3 132.7 2.8 1,017 3.1 Trade, transportation, and utilities..... 18.5 345.7 5.2 736 4.0 Information.............................. 1.4 32.6 -5.8 912 5.1 Financial activities..................... 10.1 143.0 5.1 987 9.5 Professional and business services....... 18.1 287.6 7.2 734 2.5 Education and health services............ 8.1 171.5 3.8 776 4.2 Leisure and hospitality.................. 5.9 165.1 5.0 350 1.4 Other services........................... 5.7 45.8 0.1 520 5.5 Government................................. 0.6 178.7 2.7 889 3.7 Orange, CA................................... 89.7 1,503.3 2.2 861 3.6 Private industry........................... 88.3 1,352.6 2.5 854 3.9 Natural resources and mining............. 0.2 6.4 -16.4 576 9.7 Construction............................. 6.6 102.9 7.1 900 4.3 Manufacturing............................ 5.7 183.5 0.3 1,017 3.7 Trade, transportation, and utilities..... 17.0 269.2 0.6 818 4.3 Information.............................. 1.4 32.4 -1.9 1,181 7.1 Financial activities..................... 10.2 140.1 3.8 1,327 2.4 Professional and business services....... 17.6 268.8 5.9 881 2.9 Education and health services............ 9.3 131.8 1.8 774 4.3 Leisure and hospitality.................. 6.7 168.6 1.5 372 3.3 Other services........................... 13.5 48.7 1.4 522 5.0 Government................................. 1.4 150.7 -0.2 919 1.4 Dallas, TX................................... 65.8 1,416.8 1.5 909 3.2 Private industry........................... 65.3 1,259.1 1.4 918 3.0 Natural resources and mining............. 0.5 7.3 6.3 2,065 -9.5 Construction............................. 4.3 78.0 6.7 849 4.4 Manufacturing............................ 3.2 145.8 0.9 1,042 3.7 Trade, transportation, and utilities..... 14.8 297.4 0.4 885 5.9 Information.............................. 1.7 53.9 -2.5 1,211 -1.9 Financial activities..................... 8.4 135.1 1.6 1,186 3.9 Professional and business services....... 13.6 242.9 2.3 1,018 0.5 Education and health services............ 6.2 130.4 0.2 849 5.6 Leisure and hospitality.................. 5.0 125.2 1.2 411 0.7 Other services........................... 6.5 40.0 -0.6 571 4.0 Government................................. 0.5 157.6 1.8 839 4.6 San Diego, CA................................ 86.4 1,301.4 1.5 812 3.2 Private industry........................... 85.0 1,080.7 1.5 796 3.8 Natural resources and mining............. 0.8 11.7 -1.3 528 4.3 Construction............................. 6.8 92.7 5.0 831 3.6 Manufacturing............................ 3.4 104.6 -0.1 1,070 3.4 Trade, transportation, and utilities..... 14.0 213.9 0.4 677 3.2 Information.............................. 1.3 37.5 2.5 1,538 1.1 Financial activities..................... 9.3 83.1 1.3 1,044 0.9 Professional and business services....... 14.8 211.1 3.2 967 4.3 Education and health services............ 7.7 120.2 -0.8 750 5.6 Leisure and hospitality.................. 6.5 150.8 1.6 365 6.7 Other services........................... 20.3 54.8 2.1 457 2.2 Government................................. 1.4 220.7 1.2 891 1.4 King, WA..................................... 74.0 1,119.5 2.1 933 4.8 Private industry........................... 73.5 966.2 2.6 935 4.9 Natural resources and mining............. 0.4 3.3 0.4 1,099 8.5 Construction............................. 6.3 59.2 6.1 890 1.8 Manufacturing............................ 2.6 106.2 4.4 1,259 10.5 Trade, transportation, and utilities..... 14.5 216.8 0.7 822 3.4 Information.............................. 1.6 69.4 1.3 1,674 8.6 Financial activities..................... 6.3 74.7 -0.1 1,155 7.5 Professional and business services....... 12.0 168.7 6.2 1,065 0.3 Education and health services............ 6.1 115.2 3.3 750 3.6 Leisure and hospitality.................. 5.6 107.0 1.8 396 -0.8 Other services........................... 18.2 45.5 -3.3 505 8.1 Government................................. 0.5 153.3 -0.5 919 4.0 Miami-Dade, FL............................... 84.9 999.0 2.4 760 6.1 Private industry........................... 84.7 847.6 3.1 729 6.7 Natural resources and mining............. 0.5 8.8 -2.4 476 15.8 Construction............................. 5.5 46.2 14.0 793 7.2 Manufacturing............................ 2.8 48.9 -3.6 675 -1.5 Trade, transportation, and utilities..... 24.1 242.5 2.0 702 7.8 Information.............................. 1.9 23.7 (6) 1,047 (6) Financial activities..................... 9.3 69.2 5.6 1,051 7.0 Professional and business services....... 16.8 143.0 6.6 868 10.6 Education and health services............ 8.4 127.3 1.6 726 4.0 Leisure and hospitality.................. 5.8 99.9 3.1 417 4.0 Other services........................... 7.8 34.8 -0.1 483 10.3 Government................................. 0.3 151.4 -1.4 935 4.9 1 Includes workers covered by Unemployment Insurance (UI) and Unemployment Compensation for Federal Employees (UCFE) programs. 2 Data are preliminary. 3 Percent changes were computed from quarterly employment and pay data adjusted for noneconomic county reclassifications. See Technical Note. 4 Average weekly wages were calculated using unrounded data. 5 Totals for the United States do not include data for Puerto Rico or the Virgin Islands. 6 Data do not meet BLS or State agency disclosure standards. Table 3. Covered(1) establishments, employment, and wages in the largest county by state, second quarter 2005(2) Employment Average weekly wage(5) Establishments, second quarter County(3) 2005 Percent Percent (thousands) June change, Average change, 2005 June weekly second (thousands) 2004-05(4) wage quarter 2004-05(4) United States(6)......... 8,562.0 132,808.3 1.7 $751 3.9 Jefferson, AL............ 18.8 371.4 0.2 763 6.0 Anchorage Borough, AK.... 7.9 144.2 2.2 813 3.0 Maricopa, AZ............. 82.9 1,677.4 5.5 761 4.1 Pulaski, AR.............. 13.4 245.5 1.1 689 3.9 Los Angeles, CA.......... 363.1 4,089.4 0.7 852 4.4 Denver, CO............... 24.8 425.7 0.9 923 4.1 Hartford, CT............. 24.5 494.9 1.7 945 5.4 New Castle, DE........... 19.7 280.9 -0.7 890 3.5 Washington, DC........... 30.5 675.1 1.5 1,236 4.1 Miami-Dade, FL........... 84.9 999.0 2.4 760 6.1 Fulton, GA............... 37.9 741.3 2.9 975 3.6 Honolulu, HI............. 23.6 441.9 3.2 700 3.7 Ada, ID.................. 13.6 198.4 4.4 693 3.6 Cook, IL................. 129.8 2,527.8 0.2 902 4.9 Marion, IN............... 23.5 581.0 0.2 783 1.8 Polk, IA................. 14.2 267.0 1.3 740 4.2 Johnson, KS.............. 19.1 304.3 1.8 778 3.6 Jefferson, KY............ 21.2 425.1 1.1 748 3.7 Orleans, LA.............. 12.9 245.7 -1.2 691 (7) Cumberland, ME........... 11.6 172.8 -0.6 684 3.0 Montgomery, MD........... 32.4 464.9 2.1 995 4.8 Middlesex, MA............ 49.3 798.8 1.0 1,062 1.8 Wayne, MI................ 34.4 797.2 0.0 894 4.4 Hennepin, MN............. 41.1 831.3 -0.3 941 2.8 Hinds, MS................ 6.5 127.6 -2.2 656 3.3 St. Louis, MO............ 33.6 626.0 0.8 818 4.9 Yellowstone, MT.......... 5.4 73.0 1.3 606 4.5 Douglas, NE.............. 15.1 312.5 0.3 691 3.3 Clark, NV................ 41.5 867.2 7.5 748 9.4 Hillsborough, NH......... 12.2 197.9 1.2 836 5.7 Bergen, NJ............... 34.2 453.5 0.3 950 4.3 Bernalillo, NM........... 16.5 320.9 1.4 685 5.4 New York, NY............. 113.9 2,256.6 1.2 1,350 4.2 Mecklenburg, NC.......... 27.6 520.7 3.1 $879 4.9 Cass, ND................. 5.7 92.3 3.2 613 3.9 Cuyahoga, OH............. 38.1 758.5 -0.1 778 2.8 Oklahoma, OK............. 22.4 411.5 1.6 644 1.1 Multnomah, OR............ 25.8 429.5 1.7 772 2.9 Allegheny, PA............ 35.2 689.1 -0.9 793 4.2 Providence, RI........... 18.0 286.9 -0.7 736 3.1 Greenville, SC........... 12.6 225.1 0.6 679 5.8 Minnehaha, SD............ 6.0 112.4 2.0 624 3.0 Shelby, TN............... 19.8 500.8 0.9 752 1.8 Harris, TX............... 90.0 1,866.9 2.6 892 5.1 Salt Lake, UT............ 36.2 539.7 3.5 688 2.5 Chittenden, VT........... 5.7 96.3 -0.4 740 2.6 Fairfax, VA.............. 30.6 571.1 3.9 1,177 8.1 King, WA................. 74.0 1,119.5 2.1 933 4.8 Kanawha, WV.............. 6.2 108.8 -0.5 674 4.3 Milwaukee, WI............ 21.6 494.4 -0.3 753 4.0 Laramie, WY.............. 3.0 41.3 1.6 594 3.7 San Juan, PR............. 14.1 315.7 -1.2 490 4.3 St. Thomas, VI........... 1.8 23.0 0.3 626 6.6 1 Includes workers covered by Unemployment Insurance (UI) and Unemployment Compensation for Federal Employees (UCFE) programs. 2 Data are preliminary. 3 Includes areas not officially designated as counties. See Technical Note. 4 Percent changes were computed from quarterly employment and pay data adjusted for noneconomic county reclassifications. See Technical Note. 5 Average weekly wages were calculated using unrounded data. 6 Totals for the United States do not include data for Puerto Rico or the Virgin Islands. 7 Data do not meet BLS or State agency disclosure standards. Table 4. Covered(1) establishments, employment, and wages by state, second quarter 2005(2) Employment Average weekly wage(3) Establishments, second quarter State 2005 Percent Percent (thousands) June change, Average change, 2005 June weekly second (thousands) 2004-05 wage quarter 2004-05 United States(4)......... 8,562.0 132,808.3 1.7 $751 3.9 Alabama.................. 116.8 1,900.6 2.2 644 3.9 Alaska................... 20.6 315.1 2.7 759 3.3 Arizona.................. 133.1 2,429.7 5.3 723 4.3 Arkansas................. 77.4 1,158.2 1.8 592 4.2 California............... 1,213.5 15,387.2 2.0 849 3.5 Colorado................. 169.3 2,215.9 2.0 769 3.4 Connecticut.............. 110.2 1,676.5 1.1 946 4.3 Delaware................. 29.9 421.3 0.7 797 3.1 District of Columbia..... 30.5 675.1 1.5 1,236 4.1 Florida.................. 558.8 7,656.1 3.4 689 5.2 Georgia.................. 256.6 3,937.6 2.7 722 3.1 Hawaii................... 36.1 605.9 3.4 678 4.0 Idaho.................... 51.6 628.5 3.5 574 3.4 Illinois................. 336.6 5,816.8 0.6 803 4.2 Indiana.................. 153.4 2,889.9 0.6 664 2.8 Iowa..................... 91.8 1,475.0 1.7 614 3.9 Kansas................... 83.0 1,323.6 0.6 636 4.6 Kentucky................. 105.5 1,772.9 1.8 651 3.8 Louisiana................ 119.5 1,909.2 1.5 616 4.1 Maine.................... 48.2 610.7 -0.6 609 3.7 Maryland................. 160.0 2,527.3 1.4 818 4.1 Massachusetts............ 217.1 3,219.6 0.6 916 2.1 Michigan................. 257.2 4,366.7 0.1 768 3.4 Minnesota................ 161.4 2,664.7 0.0 760 2.3 Mississippi.............. 67.5 1,117.3 0.7 556 4.1 Missouri................. 169.6 2,702.2 1.3 678 4.1 Montana.................. 40.2 424.9 1.6 553 4.7 Nebraska................. 56.4 905.4 1.0 598 3.3 Nevada................... 66.8 1,220.7 6.4 738 7.7 New Hampshire............ 47.4 631.7 1.1 754 5.2 New Jersey............... 270.8 4,012.7 1.4 901 3.4 New Mexico............... 50.5 784.8 1.9 624 4.5 New York................. 562.1 8,471.1 0.9 913 4.1 North Carolina........... 233.1 3,855.7 1.7 665 4.1 North Dakota............. 24.7 333.2 2.0 561 4.1 Ohio..................... 292.0 5,376.0 0.4 693 3.1 Oklahoma................. 94.6 1,465.3 2.7 594 2.8 Oregon................... 122.8 1,683.2 2.9 687 2.5 Pennsylvania............. 335.4 5,620.2 0.9 737 3.8 Rhode Island............. 35.6 487.7 0.4 720 3.4 South Carolina........... 118.0 1,823.5 0.7 621 4.4 South Dakota............. 29.0 387.4 1.5 543 3.4 Tennessee................ 132.4 2,695.7 1.6 670 3.4 Texas.................... 519.1 9,592.4 2.6 738 4.5 Utah..................... 80.5 1,120.9 3.7 622 3.2 Vermont.................. 24.5 304.1 0.5 644 1.6 Virginia................. 212.1 3,618.9 2.2 787 5.5 Washington............... 206.9 2,825.2 2.4 761 3.4 West Virginia............ 48.0 703.0 1.3 612 3.9 Wisconsin................ 160.6 2,794.2 0.9 663 3.1 Wyoming.................. 23.0 267.0 2.9 616 5.1 Puerto Rico.............. 56.3 1,039.3 -0.5 418 2.7 Virgin Islands........... 3.5 44.3 3.8 639 3.7 1 Includes workers covered by Unemployment Insurance (UI) and Unemployment Compensation for Federal Employees (UCFE) programs. 2 Data are preliminary. 3 Average weekly wages were calculated using unrounded data. 4 Totals for the United States do not include data for Puerto Rico or the Virgin Islands.