Technical information: (202) 691-6567 USDL 07-0525 http://www.bls.gov/cew/ For release: 10:00 A.M. EDT Media contact: 691-5902 Wednesday, April 11, 2007 COUNTY EMPLOYMENT AND WAGES: THIRD QUARTER 2006 In September 2006, Jefferson County, La., 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. Jefferson County, a New Or- leans suburb, experienced an over-the-year employment gain of 22.4 percent, compared with national job growth of 1.5 percent. Employment gains in Jef- ferson County reflected significant recovery from substantial job losses that occurred in September 2005 due to Hurricane Katrina. In contrast, Or- leans County, which also was affected by Hurricane Katrina, continued to show an over-the-year employment decline (-12.3 percent). Kent County, R.I., had the largest over-the-year gain in average weekly wages in the third quarter of 2006, with an increase of 18.4 percent. The U.S. average weekly wage rose by 0.9 percent over the same time span. Of the 325 largest counties in the United States, as measured by 2005 annual average employment, 130 had over-the-year percentage growth in em- ployment above the national average (1.5 percent) in September 2006, and 187 experienced changes below the national average. The percent change in average weekly wages was higher than the national average (0.9 percent) in 133 of the largest U.S. counties, but was below the national average in 184 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 8.8 million em- ployer reports cover 135.0 million full- and part-time workers. The attached tables contain data for the nation and for the 325 U.S. counties with annual average employment levels of 75,000 or more in 2005. September 2006 employment and 2006 third-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 2005 are avail- able on the BLS Web site at http://www.bls.gov/cew/. Preliminary data for third quarter 2006, along with updated data for the first and second quarters of 2006, will be available later in April on the BLS Web site. Large County Employment In September 2006, national employment, as measured by the QCEW program, was 135.0 million, up by 1.5 percent from September 2005. The 325 U.S. counties with 75,000 or more employees accounted for 70.7 percent of total U.S. covered employment and 76.5 percent of total covered wages. These 325 counties had a net job gain of 1,328,166 over the year, accounting for 66.0 percent of the overall U.S. employment increase. Employment rose in 256 of the large counties from September 2005 to September 2006. Jefferson County, La., had the largest over-the-year percentage increase in employment (22.4 percent). Snohomish, Wash., had the next largest increase, 8.2 percent, followed by the counties of Collin, Texas (7.2 percent), Harrison, Miss. (6.8 percent), and Montgomery, Texas (5.7 percent). The large employment gains in Jefferson County reflected significant recovery from the substan- tial job losses in September 2005, which were related to Hurricane Katrina. Strong employment growth in Harrison County, which also was impacted by this hurricane, showed that the county had begun to rebound from job losses in 2005. (See table 1.) ------------------------------------------------------------------- | Hurricane Katrina | | | | The employment and wages reported in this news release re- | | flect the impact of Hurricane Katrina and ongoing labor market | | trends in certain counties. The effects of Hurricane Katrina, | | which hit the Gulf Coast on August 29, 2005, were first apparent | | in the September QCEW employment counts and in the wage totals | | for the third quarter of 2005. This catastrophic storm contin- | | ued to affect monthly employment and quarterly wage totals in | | parts of Louisiana and Mississippi in the third quarter of 2006. | | For more information, see the QCEW section of the Katrina cover- | | age on the BLS Web site at http://www.bls.gov/katrina/qcewques- | | tions.htm. | ------------------------------------------------------------------- - 2 - Table A. Top 10 large counties ranked by September 2006 employment, September 2005-06 employment growth, and September 2005-06 percent growth in employment ---------------------------------------------------------------------------------- Employment in large counties ---------------------------------------------------------------------------------- | | September 2006 employment| Growth in employment, | Percent growth (thousands) | September 2005-06 | in employment, | (thousands) | September 2005-06 ---------------------------------------------------------------------------------- | | United States .... 134,988.9| United States ..... 2,013.1| United States ..... 1.5 ----------------------------|----------------------------|------------------------ | | Los Angeles, Calif. 4,161.2| Harris, Texas ........ 79.4| Jefferson, La. ... 22.4 Cook, Ill. ......... 2,553.4| Maricopa, Ariz. ...... 76.2| Snohomish, Wash. .. 8.2 New York, N.Y. ..... 2,292.3| New York, N.Y. ....... 42.0| Collin, Texas ..... 7.2 Harris, Texas ...... 1,959.1| King, Wash. .......... 40.6| Harrison, Miss. ... 6.8 Maricopa, Ariz. .... 1,819.1| Clark, Nev. .......... 39.1| Montgomery, Texas . 5.7 Orange, Calif. ..... 1,517.9| Dallas, Texas ........ 38.3| Lake, Fla. ........ 5.5 Dallas, Texas ...... 1,466.0| Jefferson, La. ....... 35.5| Williamson, Texas . 5.5 San Diego, Calif. .. 1,321.7| Los Angeles, Calif. .. 29.2| Utah, Utah ........ 5.5 King, Wash. ........ 1,167.1| Salt Lake, Utah ...... 25.4| Douglas, Colo. .... 4.6 Miami-Dade, Fla. ... 1,008.4| Bexar, Texas ......... 24.4| Horry, S.C. ....... 4.6 | | Salt Lake, Utah ... 4.6 | | ---------------------------------------------------------------------------------- Employment declined in 62 counties from September 2005 to September 2006. The largest percentage decline in employment was in Orleans County, La. (-12.3 percent). Employment losses in Orleans County reflected the deva- station caused by Hurricane Katrina. Trumbull, Ohio, had the next largest employment decline (-4.5 percent), followed by the counties of Macomb, Mich. (-4.0 percent), Oakland, Mich. (-3.5 percent), and Rock Island, Ill. (-3.0 percent). The largest gains in the level of employment from September 2005 to September 2006 were recorded in the counties of Harris, Texas (79,400), Maricopa, Ariz. (76,200), New York, N.Y. (42,000), King, Wash. (40,600), and Clark, Nev. (39,100). (See table A.) The largest declines in employment levels occurred in Oakland, Mich. (-25,200), followed by the counties of Orleans, La. (-21,600), Wayne, Mich. (-20,500), Macomb, Mich. (-13,400), and Kent, Mich. (-5,500). Large County Average Weekly Wages The national average weekly wage in the third quarter of 2006 was $784. Average weekly wages were higher than the national average in 111 of the largest 325 U.S. counties. New York County, N.Y., held the top position among the highest-paid large counties with an average weekly wage of $1,421. Santa Clara, Calif., was second with an average weekly wage of $1,414, fol- lowed by Arlington, Va. ($1,323), Washington, D.C. ($1,307), and San Mateo, Calif. ($1,278). (See table B.) - 3 - Table B. Top 10 large counties ranked by third quarter 2006 average weekly wages, third quarter 2005-06 growth in average weekly wages, and third quarter 2005-06 percent growth in average weekly wages ----------------------------------------------------------------------------------- Average weekly wage in large counties ----------------------------------------------------------------------------------- Average weekly wage, | Growth in average weekly | Percent growth in third quarter 2006 | wage, third quarter | average weekly wage, | 2005-06 | third quarter 2005-06 ----------------------------------------------------------------------------------- | | United States ......... $784| United States ........ $7| United States ........ 0.9 ----------------------------------------------------------------------------------- | | New York, N.Y. ...... $1,421| Kent, R.I. ......... $132| Kent, R.I. .......... 18.4 Santa Clara, Calif. .. 1,414| Orleans, La. ........ 121| Orleans, La. ........ 16.2 Arlington, Va. ....... 1,323| Trumbull, Ohio ....... 85| Trumbull, Ohio ...... 12.3 Washington, D.C. ..... 1,307| Jefferson, Texas ..... 74| Jefferson, La. ...... 10.5 San Mateo, Calif. .... 1,278| Jefferson, La. ....... 69| Jefferson, Texas .... 10.5 San Francisco, Calif. 1,246| Lafayette, La. ....... 56| Mobile, Ala. ......... 8.6 Suffolk, Mass. ....... 1,208| Mobile, Ala. ......... 55| Lafayette, La. ....... 8.2 Fairfield, Conn. ..... 1,191| Ingham, Mich. ........ 52| East Baton Rouge, La. 7.4 Fairfax, Va. ......... 1,179| Morris, N.J. ......... 49| Harrison, Miss. ...... 7.2 Somerset, N.J. ....... 1,165| Vanderburgh, Ind. .... 48| Vanderburgh, Ind. .... 7.1 | East Baton Rouge, La. 48| Ingham, Mich. ........ 7.1 | Galveston, Texas ..... 48| Galveston, Texas ..... 7.1 | | ----------------------------------------------------------------------------------- There were 212 counties with an average weekly wage below the national average in the third quarter of 2006. The lowest average weekly wages were reported in Cameron County, Texas ($493), followed by the counties of Hidalgo, Texas ($514), Horry, S.C. ($517), Webb, Texas ($525), and Yakima, Wash. ($537). (See table 1.) Over the year, the national average weekly wage rose by 0.9 percent. Among the largest counties, Kent, R.I., led the nation in growth in aver- age weekly wages, with an increase of 18.4 percent from the third quarter of 2005. Orleans, La., was second with growth of 16.2 percent, followed by the counties of Trumbull, Ohio (12.3 percent), and Jefferson, La., and Jefferson, Texas (10.5 percent each). The high average weekly wage growth rate for Orleans County was related to the disproportionate job losses in lower-paid industries due to the impact of Hurricane Katrina. That is, the loss of low paid jobs due to the storm boosted average wages in Orleans County. One hundred and twelve counties experienced over-the-year declines in average weekly wages. Passaic, N.J., had the largest decrease, -10.2 per- cent, followed by the counties of Williamson, Texas (-5.7 percent), Fort Bend, Texas (-5.0 percent), Loudoun, Va. (-4.2 percent), and Ventura, Calif. (-4.0 percent). Ten Largest U.S. Counties Each of the 10 largest counties (based on 2005 annual average employment levels) reported increases in employment from September 2005 to September 2006. Maricopa County, Ariz., experienced the largest percent increase in employment among the largest counties with a 4.4 percent increase. Within Maricopa County, employment rose in every industry group except information. The largest gains were in education and health services (6.2 percent), fol- lowed by construction (5.9 percent). Harris, Texas, had the next largest increase in employment, 4.2 percent, followed by King, Wash. (3.6 percent). The smallest percent increase in employment occurred in Miami-Dade, Fla. (0.6 percent), followed by Cook, Ill., and Los Angeles, Calif. (0.7 percent each). (See table 2.) Eight of the 10 largest U.S. counties saw over-the-year increases in average weekly wages. King County, Wash., had the fastest growth in wages among the 10 largest counties, with a gain of 4.7 percent. Within King County, Wash., average weekly wages increased the most in information (19.4 percent), followed by natural resources and mining (17.4 percent). Dallas, Texas, was second in wage growth with a gain of 2.2 percent, followed by Harris, Texas (2.0 percent). The smallest wage gains among the 10 largest counties occurred in New York, N.Y. (0.3 percent). San Diego, Calif. (-0.7 percent) and Orange, Calif. (-1.1 percent) experienced declines in average weekly wages. - 4 - Largest County by State Table 3 shows September 2006 employment and the 2006 third quarter average weekly wage in the largest county in each state, which is based on 2005 annual average employment levels. (This table includes two counties--Yellowstone, Mont., and Laramie, Wyo.--that had employment levels below 75,000.) The employment lev- els in the counties in table 3 in September 2006 ranged from approximately 4.2 mil- lion in Los Angeles County, Calif., to 42,100 in Laramie County, Wyo. The highest average weekly wage of these counties was in New York, N.Y. ($1,421), while the low- est average weekly wage was in Yellowstone, Mont. ($637). 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/. Ad- ditional information about the QCEW data also may be obtained by e-mailing QCEWinfo@ bls.gov or by calling (202) 691-6567. 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. ______________________________ The County Employment and Wages release for fourth quarter 2006 is scheduled to be released on Wednesday, July 25. - 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 2006 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 326 counties presented in this release were derived using 2005 preliminary annual averages of employment. For 2006 data, four counties have been added to the publication tables: Douglas, Colo., Weld, Colo., Boone, Ky., and Butler, Pa. These counties will be included in all 2006 quarterly releases. One county, Potter, Texas, which was published in the 2005 releases, no longer has an employment level of 75,000 or more and will be excluded in the 2006 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 8.8 | ministrative records| ments | million establish- | submitted by 6.8 | | 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 | by NAICS supersec- | | industry | tors and by size of | | | firm | | |--Future expansions | | | will include data at| | | the county, MSA, and| | | state level | -----------|---------------------|----------------------|-------------------------- 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 | -An analysis of em- | cators | surveys | ployment expansion | | | and contraction by | | | size of firm | -----------|---------------------|----------------------|-------------------------- 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 nearly 9 million employer reports of employment 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 2005, UI and UCFE programs covered workers in 131.6 million jobs. The estimated 126.7 million workers in these jobs (after adjust- ment for multiple jobholders) represented 96.6 percent of civilian wage and salary employment. Covered workers received $5.352 trillion in pay, representing 94.5 percent of the wage and salary component of personal income and 43.0 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. 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. Over-the-year comparisons of average weekly wages may reflect fluctuations in average monthly employment and/or total quarterly wages between the current quarter and prior year levels. 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. For instance, the average weekly wage of the work force could increase significantly when there is a large decline in the number of employees that had been receiving below-average wages. Wages may include payments to workers not present in the employment counts because they did not work during the pay period in- cluding the 12th of the month. When comparing average weekly wage levels between industries, states, or quarters, these factors should be taken into consideration. - 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, fed- eral wages contain payments for six pay periods, while in other quarters their wages include payments for seven pay periods. Over-the-year com- parisons 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 ef- fect on over-the-year pay comparisons can be pronounced in federal govern- ment 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 concentrations 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 estab- lishments that exist in a county or industry at a point in time. Estab- lishments can move in or out of a county or industry for a number of rea- sons--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 calculated using an adjusted version of the final 2005 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 owner- ship information of their establishments. The most common adjustments for administrative change are the result of updated information about the county location of individual establishments. Included in these adjust- ments are administrative changes involving the classification of establish- ments that were previously reported in the unknown or statewide county or unknown industry categories. The adjusted data do not account for adminis- trative changes caused by multi-unit employers who start reporting for each individual establishment rather than as a single entity. - 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 2005 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 2005 version of this news release. This edition will also be the first to include the data on a CD for enhanced access and usability. As a result of this change, the printed booklet will contain only selected graphic representations of QCEW data; the data tables themselves will be published exclusively in electronic formats as PDF and fixed-width text files. Employment and Wages Annual Averages, 2005 will soon be available for sale from the United States Government Printing 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 2005 bulletin is available in a portable document format (PDF) on the BLS Web site at http://www.bls.gov/cew/cewbultn05.htm. News releases on quarterly measures of gross job flows also are available upon request from the Division of Administrative Statistics and Labor Turn- over (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 326 largest counties, third quarter 2006 (2) Employment Average weekly wage(5) Establishments, County (3) third quarter Percent Ranking Percent Ranking 2006 September change, by Average change, by (thousands) 2006 September percent weekly third percent (thousands) 2005-06(4) change wage quarter change 2005-06 (4) United States (6)........ 8,841.2 134,988.9 1.5 - $784 0.9 - Jefferson, AL............ 18.6 374.6 0.8 187 803 1.0 127 Madison, AL.............. 8.3 174.2 2.3 89 863 1.8 72 Mobile, AL............... 9.8 171.5 2.8 60 692 8.6 6 Montgomery, AL........... 6.6 138.5 1.0 170 669 2.5 49 Tuscaloosa, AL........... 4.3 84.5 2.9 56 673 2.7 42 Anchorage Borough, AK.... 8.2 148.8 .5 213 849 -.7 257 Maricopa, AZ............. 92.3 1,819.1 4.4 16 792 .5 165 Pima, AZ................. 19.8 368.6 2.6 68 708 1.9 70 Benton, AR............... 5.3 94.3 3.7 30 686 1.5 86 Pulaski, AR.............. 14.2 249.9 1.9 111 717 1.3 102 Washington, AR........... 5.7 93.6 2.1 99 639 1.3 102 Alameda, CA.............. 49.4 691.1 .7 192 1,054 .3 182 Contra Costa, CA......... 28.2 348.6 .7 192 979 .8 140 Fresno, CA............... 29.1 366.2 2.9 56 619 .5 165 Kern, CA................. 17.3 287.8 2.6 68 676 2.3 55 Los Angeles, CA.......... 392.8 4,161.2 .7 192 894 1.7 79 Marin, CA................ 11.8 110.4 1.1 161 985 -.5 241 Monterey, CA............. 12.2 180.4 .3 230 695 1.3 102 Orange, CA............... 95.9 1,517.9 1.1 161 897 -1.1 277 Placer, CA............... 10.5 137.8 .3 230 780 -.6 249 Riverside, CA............ 43.1 635.4 3.6 33 678 .0 203 Sacramento, CA........... 50.5 640.5 1.1 161 871 -.6 249 San Bernardino, CA....... 45.8 654.2 1.9 111 702 .6 158 San Diego, CA............ 92.5 1,321.7 .9 178 850 -.7 257 San Francisco, CA........ 44.2 537.0 1.8 116 1,246 2.6 44 San Joaquin, CA.......... 17.0 227.7 .9 178 685 -.3 225 San Luis Obispo, CA...... 9.1 105.7 .2 240 664 1.4 94 San Mateo, CA............ 23.2 337.3 1.8 116 1,278 .9 134 Santa Barbara, CA........ 13.7 186.4 .9 178 751 -.3 225 Santa Clara, CA.......... 55.9 884.9 2.3 89 1,414 . 8 140 Santa Cruz, CA........... 8.7 102.2 1.3 147 772 2.5 49 Solano, CA............... 9.9 133.1 -.1 261 752 1.1 120 Sonoma, CA............... 17.8 194.6 .4 220 785 1.2 113 Stanislaus, CA........... 13.9 179.7 -.4 277 677 1.5 86 Tulare, CA............... 8.9 152.4 2.8 60 560 1.4 94 Ventura, CA.............. 21.8 317.5 1.4 136 826 -4.0 319 Yolo, CA................. 5.4 102.2 2.3 89 760 -2.6 304 Adams, CO................ 9.4 155.2 3.1 53 744 -.8 264 Arapahoe, CO............. 19.9 277.0 1.4 136 955 2.0 65 Boulder, CO.............. 12.7 158.4 2.5 73 955 -3.6 316 Denver, CO............... 25.5 436.3 1.7 118 988 4.1 22 Douglas, CO.............. 9.0 88.5 4.6 9 779 -3.7 317 El Paso, CO.............. 17.6 245.8 1.5 131 733 -1.1 277 Jefferson, CO............ 19.0 208.7 .2 240 809 .0 203 Larimer, CO.............. 10.2 129.6 1.7 118 727 .7 154 Weld, CO................. 6.0 81.8 3.8 28 671 1.7 79 Fairfield, CT............ 32.7 418.9 1.3 147 1,191 -.5 241 Hartford, CT............. 25.0 500.4 2.3 89 945 -2.3 301 New Haven, CT............ 22.3 367.8 1.7 118 835 -1.4 288 New London, CT........... 6.8 130.4 -.2 266 810 -.2 219 New Castle, DE........... 19.6 282.8 .2 240 957 4.0 25 Washington, DC........... 32.0 674.2 .7 192 1,307 3.6 28 Alachua, FL.............. 6.4 126.5 1.6 126 679 2.4 52 Brevard, FL.............. 14.5 206.8 .0 257 738 -1.1 277 Broward, FL.............. 63.0 746.0 1.3 147 754 .9 134 Collier, FL.............. 12.3 130.7 4.3 20 721 .1 198 Duval, FL................ 25.6 463.7 2.4 83 784 2.6 44 Escambia, FL............. 7.9 130.1 .7 192 626 -.6 249 Hillsborough, FL......... 35.8 638.0 2.4 83 757 1.1 120 Lake, FL................. 6.9 84.4 5.5 6 589 -1.0 273 Lee, FL.................. 18.6 222.5 4.1 25 689 .7 154 Leon, FL................. 8.0 146.8 .6 204 694 1.5 86 Manatee, FL.............. 8.9 126.9 4.1 25 634 .3 182 Marion, FL............... 8.0 103.6 4.5 12 584 .5 165 Miami-Dade, FL........... 84.1 1,008.4 .6 204 792 1.5 86 Okaloosa, FL............. 6.0 84.4 2.9 56 642 .0 203 Orange, FL............... 34.5 682.2 3.2 51 728 .0 203 Palm Beach, FL........... 48.9 554.3 1.7 118 756 -1.6 296 Pasco, FL................ 9.4 100.4 3.5 37 589 4.2 19 Pinellas, FL............. 30.8 445.3 .3 230 682 -.4 234 Polk, FL................. 12.4 206.3 2.1 99 644 2.4 52 Sarasota, FL............. 14.9 158.9 3.5 37 675 -1.0 273 Seminole, FL............. 14.5 177.8 2.4 83 696 .9 134 Volusia, FL.............. 13.9 167.4 2.0 105 580 1.6 82 Bibb, GA................. 4.7 84.3 -1.2 306 644 -1.4 288 Chatham, GA.............. 7.4 135.3 1.9 111 676 .3 182 Clayton, GA.............. 4.4 108.7 -.4 277 738 -2.9 311 Cobb, GA................. 20.0 312.4 2.8 60 864 1.3 102 De Kalb, GA.............. 15.8 277.2 -1.1 302 854 1.8 72 Fulton, GA............... 39.6 777.7 1.3 147 1,016 1.0 127 Gwinnett, GA............. 23.0 327.2 3.3 48 830 -.4 234 Muscogee, GA............. 4.8 96.6 -2.5 315 625 -.5 241 Richmond, GA............. 4.8 103.1 -1.9 313 680 2.1 62 Honolulu, HI............. 24.0 452.2 2.3 89 744 .5 165 Ada, ID.................. 14.7 210.7 4.4 16 727 1.1 120 Champaign, IL............ 4.1 91.6 .8 187 676 .6 158 Cook, IL................. 135.0 2,553.4 .7 192 928 1.0 127 Du Page, IL.............. 34.6 597.4 .4 220 927 1.1 120 Kane, IL................. 12.1 212.5 2.1 99 718 -1.8 299 Lake, IL................. 20.3 333.8 .8 187 936 2.6 44 McHenry, IL.............. 8.1 103.0 3.4 43 693 -.3 225 McLean, IL............... 3.6 85.8 .4 220 766 .8 140 Madison, IL.............. 5.9 95.5 .3 230 651 .5 165 Peoria, IL............... 4.7 103.3 2.5 73 749 -.8 264 Rock Island, IL.......... 3.4 77.5 -3.0 318 756 .1 198 St. Clair, IL............ 5.3 95.7 1.2 156 642 .0 203 Sangamon, IL............. 5.2 130.5 -1.1 302 783 2.0 65 Will, IL................. 12.5 183.5 4.5 12 717 -1.1 277 Winnebago, IL............ 6.8 136.6 .1 252 695 1.5 86 Allen, IN................ 8.9 185.5 1.4 136 681 -.3 225 Elkhart, IN.............. 4.8 126.9 .1 252 667 -3.1 313 Hamilton, IN............. 7.0 101.6 3.7 30 767 -3.4 315 Lake, IN................. 10.0 195.6 -.4 277 704 .7 154 Marion, IN............... 23.6 583.0 .2 240 814 -.5 241 St. Joseph, IN........... 6.0 125.3 -1.1 302 667 .2 194 Vanderburgh, IN.......... 4.8 108.9 .2 240 723 7.1 10 Linn, IA................. 6.2 121.0 2.1 99 745 -2.6 304 Polk, IA................. 14.4 271.3 2.4 83 783 -1.0 273 Scott, IA................ 5.2 89.3 -.8 295 649 1.4 94 Johnson, KS.............. 20.0 312.0 3.4 43 812 -1.6 296 Sedgwick, KS............. 12.2 252.4 3.3 48 729 1.5 86 Shawnee, KS.............. 4.8 93.2 -.6 288 675 .4 173 Wyandotte, KS............ 3.2 81.1 2.9 56 770 .8 140 Boone, KY................ 3.4 74.8 -2.6 316 712 -3.0 312 Fayette, KY.............. 9.2 173.4 (7) - 715 .7 154 Jefferson, KY............ 22.5 433.2 1.7 118 775 (7) - Caddo, LA................ 7.3 125.7 1.0 170 666 3.3 29 Calcasieu, LA............ 4.9 85.5 .6 204 654 .3 182 East Baton Rouge, LA..... 13.8 262.2 2.5 73 698 7.4 8 Jefferson, LA............ 14.4 194.2 22.4 1 727 10.5 4 Lafayette, LA............ 8.2 131.2 4.5 12 737 8.2 7 Orleans, LA.............. 11.7 154.8 -12.3 322 870 16.2 2 Cumberland, ME........... 12.0 172.6 .7 192 711 .3 182 Anne Arundel, MD......... 14.2 228.4 2.4 83 835 1.0 127 Baltimore, MD............ 21.5 374.2 -.7 289 809 .2 194 Frederick, MD............ 5.8 92.2 -.2 266 752 .4 173 Harford, MD.............. 5.5 82.2 1.4 136 759 .8 140 Howard, MD............... 8.4 143.5 1.2 156 908 -1.2 283 Montgomery, MD........... 32.4 467.1 1.3 147 1,034 .6 158 Prince Georges, MD....... 15.5 315.2 .0 257 867 -.1 212 Baltimore City, MD....... 14.1 350.5 -.4 277 911 .3 182 Barnstable, MA........... 9.3 97.8 -1.6 310 667 .6 158 Bristol, MA.............. 15.6 221.7 .0 257 693 -.4 234 Essex, MA................ 20.6 301.2 1.1 161 844 -.2 219 Hampden, MA.............. 14.1 201.7 -.1 261 733 .4 173 Middlesex, MA............ 47.1 804.6 1.6 126 1,108 -.3 225 Norfolk, MA.............. 21.5 321.6 .2 240 943 2.2 59 Plymouth, MA............. 13.8 179.6 .5 213 742 -.7 257 Suffolk, MA.............. 21.5 575.5 1.5 131 1,208 .8 140 Worcester, MA............ 20.5 322.3 .9 178 792 -.8 264 Genesee, MI.............. 8.3 146.3 -2.4 314 769 5.6 14 Ingham, MI............... 7.1 162.4 -.3 274 787 7.1 10 Kalamazoo, MI............ 5.6 116.2 -1.2 306 711 .3 182 Kent, MI................. 14.6 341.8 -1.6 310 730 1.2 113 Macomb, MI............... 18.3 322.7 -4.0 320 839 -1.1 277 Oakland, MI.............. 40.4 697.4 -3.5 319 931 .1 198 Ottawa, MI............... 5.8 115.2 .2 240 696 -.9 270 Saginaw, MI.............. 4.5 89.2 -1.5 309 722 4.6 16 Washtenaw, MI............ 8.2 195.2 -.8 295 913 1.6 82 Wayne, MI................ 33.6 769.1 -2.6 316 905 -1.5 291 Anoka, MN................ 7.9 115.7 -.9 299 748 -.5 241 Dakota, MN............... 10.4 173.4 .3 230 755 -2.6 304 Hennepin, MN............. 41.9 841.4 .2 240 982 -.9 270 Olmsted, MN.............. 3.6 90.7 .9 178 880 2.7 42 Ramsey, MN............... 15.4 333.3 -.4 277 851 -1.2 283 St. Louis, MN............ 5.8 96.3 .9 178 641 -2.4 302 Stearns, MN.............. 4.5 80.3 1.4 136 632 1.1 120 Harrison, MS............. 4.3 84.8 6.8 4 628 7.2 9 Hinds, MS................ 6.5 128.5 1.3 147 697 1.3 102 Boone, MO................ 4.5 82.6 1.6 126 620 .8 140 Clay, MO................. 5.0 87.3 -.7 289 747 -1.8 299 Greene, MO............... 8.1 154.4 2.4 83 615 -1.3 286 Jackson, MO.............. 18.6 367.8 1.0 170 799 .5 165 St. Charles, MO.......... 7.9 122.8 2.5 73 679 -.6 249 St. Louis, MO............ 33.7 625.8 .7 192 825 -.2 219 St. Louis City, MO....... 8.0 223.6 -.1 261 869 -.1 212 Douglas, NE.............. 15.4 314.5 1.2 156 734 -.9 270 Lancaster, NE............ 7.9 154.8 .6 204 649 -.6 249 Clark, NV................ 46.2 922.5 4.4 16 751 -.3 225 Washoe, NV............... 14.0 221.3 2.0 105 749 .1 198 Hillsborough, NH......... 12.5 196.8 -.3 274 861 1.1 120 Rockingham, NH........... 11.0 140.9 1.4 136 764 -2.7 308 Atlantic, NJ............. 6.9 152.5 1.4 136 694 -.3 225 Bergen, NJ............... 34.7 450.7 .6 204 969 .3 182 Burlington, NJ........... 11.6 202.0 .4 220 843 -.6 249 Camden, NJ............... 13.8 213.3 1.1 161 794 -1.5 291 Essex, NJ................ 21.7 360.1 .4 220 990 -1.1 277 Gloucester, NJ........... 6.5 104.7 .2 240 714 -.4 234 Hudson, NJ............... 14.2 236.1 -.8 295 1,061 2.9 36 Mercer, NJ............... 11.1 227.7 1.1 161 980 -.4 234 Middlesex, NJ............ 21.3 396.4 .2 240 996 3.2 30 Monmouth, NJ............. 20.8 259.2 .3 230 830 -.2 219 Morris, NJ............... 18.3 288.6 1.3 147 1,136 4.5 17 Ocean, NJ................ 12.1 152.4 .3 230 669 -.1 212 Passaic, NJ.............. 12.8 177.3 -.2 266 835 -10.2 323 Somerset, NJ............. 10.2 173.1 1.5 131 1,165 1.0 127 Union, NJ................ 15.1 229.6 .3 230 967 -.7 257 Bernalillo, NM........... 17.0 335.0 3.4 43 709 .4 173 Albany, NY............... 9.9 227.7 -.1 261 801 -.5 241 Bronx, NY................ 15.8 221.8 .6 204 789 1.8 72 Broome, NY............... 4.5 94.4 -.2 266 641 2.6 44 Dutchess, NY............. 8.3 118.4 .4 220 814 4.1 22 Erie, NY................. 23.4 454.1 -.9 299 689 .4 173 Kings, NY................ 44.0 462.9 1.0 170 691 .9 134 Monroe, NY............... 17.8 380.3 -.4 277 782 1.8 72 Nassau, NY............... 52.2 600.1 1.0 170 867 1.4 94 New York, NY............. 116.2 2,292.3 1.9 111 1,421 .3 182 Oneida, NY............... 5.3 110.1 1.3 147 605 -1.3 286 Onondaga, NY............. 12.8 250.6 -.5 285 734 2.1 62 Orange, NY............... 9.8 130.0 .3 230 676 1.2 113 Queens, NY............... 41.9 489.6 1.1 161 782 -1.4 288 Richmond, NY............. 8.5 91.3 1.9 111 711 .0 203 Rockland, NY............. 9.6 113.1 .6 204 831 2.8 38 Suffolk, NY.............. 49.6 617.2 .5 213 850 1.8 72 Westchester, NY.......... 36.3 413.9 .5 213 1,029 1.8 72 Buncombe, NC............. 7.3 112.9 2.2 96 629 2.4 52 Catawba, NC.............. 4.4 87.6 1.7 118 617 .8 140 Cumberland, NC........... 5.9 116.7 -.7 289 605 -1.5 291 Durham, NC............... 6.3 177.1 3.8 28 1,037 1.5 86 Forsyth, NC.............. 8.6 180.7 .8 187 762 -.1 212 Guilford, NC............. 13.8 275.1 .5 213 714 1.1 120 Mecklenburg, NC.......... 28.7 544.4 3.5 37 922 3.1 34 New Hanover, NC.......... 6.9 101.9 3.5 37 644 1.4 94 Wake, NC................. 25.0 426.7 3.6 33 789 1.3 102 Cass, ND................. 5.7 96.2 3.4 43 649 .2 194 Butler, OH............... 7.3 146.0 1.0 170 694 -2.4 302 Cuyahoga, OH............. 38.1 757.1 -.4 277 800 -.6 249 Franklin, OH............. 29.3 683.2 .4 220 805 .4 173 Hamilton, OH............. 24.1 525.5 -.7 289 871 .8 140 Lake, OH................. 6.9 100.5 -.4 277 646 -2.7 308 Lorain, OH............... 6.3 102.3 -.2 266 672 -3.7 317 Lucas, OH................ 10.9 225.5 -.7 289 720 1.3 102 Mahoning, OH............. 6.3 105.4 .6 204 584 .0 203 Montgomery, OH........... 13.0 273.5 -1.8 312 777 3.7 27 Stark, OH................ 9.1 162.9 -.7 289 633 .6 158 Summit, OH............... 14.9 274.8 .3 230 715 -1.7 298 Trumbull, OH............. 4.8 83.1 -4.5 321 777 12.3 3 Oklahoma, OK............. 23.0 424.0 1.5 131 708 3.2 30 Tulsa, OK................ 19.1 342.8 2.6 68 705 .3 182 Clackamas, OR............ 12.4 148.1 2.0 105 740 -.5 241 Jackson, OR.............. 6.7 85.9 1.6 126 599 -.3 225 Lane, OR................. 10.8 150.6 2.3 89 634 .3 182 Marion, OR............... 9.2 142.3 2.1 99 638 4.1 22 Multnomah, OR............ 26.8 442.5 3.3 48 803 .5 165 Washington, OR........... 15.8 247.7 3.2 51 925 -1.5 291 Allegheny, PA............ 35.3 683.8 .4 220 823 1.5 86 Berks, PA................ 9.1 169.8 2.2 96 716 1.3 102 Bucks, PA................ 20.0 264.6 1.1 161 766 .8 140 Butler, PA............... 4.7 77.6 2.5 73 668 1.2 113 Chester, PA.............. 14.9 236.0 1.4 136 983 -.2 219 Cumberland, PA........... 5.9 126.7 .5 213 733 -2.7 308 Dauphin, PA.............. 7.3 183.0 2.5 73 766 -1.5 291 Delaware, PA............. 13.6 209.7 .8 187 826 1.6 82 Erie, PA................. 7.2 129.0 -.5 285 632 .8 140 Lackawanna, PA........... 5.7 101.8 .9 178 613 -.5 241 Lancaster, PA............ 12.1 229.6 .1 252 687 -1.2 283 Lehigh, PA............... 8.4 178.3 2.5 73 781 2.9 36 Luzerne, PA.............. 7.9 143.3 -1.0 301 623 -.6 249 Montgomery, PA........... 27.5 484.6 .2 240 964 .6 158 Northampton, PA.......... 6.4 99.0 1.2 156 701 -.4 234 Philadelphia, PA......... 29.2 632.9 -.3 274 929 .8 140 Washington, PA........... 5.3 79.0 2.0 105 715 4.5 17 Westmoreland, PA......... 9.5 138.8 -1.4 308 652 2.0 65 York, PA................. 8.9 175.5 1.3 147 697 -.7 257 Kent, RI................. 5.7 83.5 .4 220 849 18.4 1 Providence, RI........... 18.2 291.1 .4 220 754 .8 140 Charleston, SC........... 13.8 203.7 2.6 68 671 -.3 225 Greenville, SC........... 13.8 231.6 1.6 126 684 -.1 212 Horry, SC................ 9.6 117.4 4.6 9 517 2.8 38 Lexington, SC............ 6.4 92.9 4.2 22 613 1.0 127 Richland, SC............. 10.7 212.7 -.8 295 705 2.3 55 Spartanburg, SC.......... 6.8 116.7 .7 192 698 2.2 59 Minnehaha, SD............ 6.3 113.4 2.0 105 668 .6 158 Davidson, TN............. 18.2 451.4 1.4 136 792 2.5 49 Hamilton, TN............. 8.5 195.0 .7 192 685 -.1 212 Knox, TN................. 10.7 226.7 3.0 54 670 .3 182 Rutherford, TN........... 4.0 99.8 3.5 37 711 6.0 13 Shelby, TN............... 20.1 509.4 .2 240 814 .0 203 Bell, TX................. 4.4 95.5 .9 178 615 3.2 30 Bexar, TX................ 31.1 704.2 3.6 33 696 3.0 35 Brazoria, TX............. 4.4 83.2 4.3 20 748 4.2 19 Brazos, TX............... 3.7 84.3 1.4 136 558 1.3 102 Cameron, TX.............. 6.3 121.4 4.1 25 493 1.4 94 Collin, TX............... 15.3 270.0 7.2 3 921 .9 134 Dallas, TX............... 67.0 1,466.0 2.7 65 961 2.2 59 Denton, TX............... 9.7 157.1 (7) - 693 (7) - El Paso, TX.............. 13.0 264.1 1.4 136 570 2.3 55 Fort Bend, TX............ 7.5 116.4 4.4 16 820 -5.0 321 Galveston, TX............ 5.1 93.6 (7) - 723 7.1 10 Harris, TX............... 92.7 1,959.1 4.2 22 950 2.0 65 Hidalgo, TX.............. 10.1 203.7 3.7 30 514 2.8 38 Jefferson, TX............ 5.8 121.7 2.7 65 781 10.5 4 Lubbock, TX.............. 6.6 122.9 2.5 73 594 .5 165 McLennan, TX............. 4.8 102.9 1.2 156 633 1.3 102 Montgomery, TX........... 7.3 111.6 5.7 5 723 -1.0 273 Nueces, TX............... 8.0 150.0 2.1 99 671 2.6 44 Smith, TX................ 5.1 91.5 2.5 73 691 1.9 70 Tarrant, TX.............. 35.4 744.7 2.8 60 814 3.2 30 Travis, TX............... 26.6 553.0 4.5 12 883 -.1 212 Webb, TX................. 4.6 85.3 3.0 54 525 .2 194 Williamson, TX........... 6.3 108.6 5.5 6 742 -5.7 322 Davis, UT................ 7.3 101.6 4.2 22 635 -.2 219 Salt Lake, UT............ 39.4 572.1 4.6 9 729 1.4 94 Utah, UT................. 13.0 169.2 5.5 6 617 4.2 19 Weber, UT................ 5.8 91.8 2.5 73 593 1.7 79 Chittenden, VT........... 5.8 96.6 1.1 161 778 1.8 72 Arlington, VA............ 7.4 157.4 .9 178 1,323 1.2 113 Chesterfield, VA......... 7.1 118.3 2.2 96 719 2.0 65 Fairfax, VA.............. 31.8 576.3 1.7 118 1,179 -.8 264 Henrico, VA.............. 8.8 175.1 .7 192 809 -.7 257 Loudoun, VA.............. 7.6 126.4 1.0 170 966 -4.2 320 Prince William, VA....... 6.6 104.0 1.7 118 714 -.8 264 Alexandria City, VA...... 5.9 94.8 1.0 170 1,025 1.2 113 Chesapeake City, VA...... 5.4 98.9 1.5 131 639 1.6 82 Newport News City, VA.... 3.9 97.9 .1 252 711 -.4 234 Norfolk City, VA......... 5.7 141.4 -1.1 302 757 -.8 264 Richmond City, VA........ 7.0 161.5 .6 204 890 1.0 127 Virginia Beach City, VA.. 11.3 178.8 -.2 266 627 1.3 102 Clark, WA................ 11.4 131.4 2.3 89 723 .4 173 King, WA................. 75.6 1,167.1 3.6 33 1,044 4.7 15 Kitsap, WA............... 6.5 84.5 2.0 105 709 -3.3 314 Pierce, WA............... 20.0 269.4 2.6 68 716 .1 198 Snohomish, WA............ 16.9 235.3 8.2 2 798 -.7 257 Spokane, WA.............. 14.8 206.9 3.4 43 651 .9 134 Thurston, WA............. 6.6 96.9 3.5 37 733 2.8 38 Whatcom, WA.............. 6.7 80.8 2.7 65 632 3.8 26 Yakima, WA............... 7.8 108.5 2.8 60 537 2.1 62 Kanawha, WV.............. 6.1 108.1 .7 192 676 1.2 113 Brown, WI................ 6.7 149.2 -.1 261 707 2.3 55 Dane, WI................. 13.9 299.4 -.5 285 784 .8 140 Milwaukee, WI............ 21.4 497.2 .1 252 783 .4 173 Outagamie, WI............ 5.0 102.5 -.2 266 680 .4 173 Racine, WI............... 4.2 76.9 .0 257 715 -2.6 304 Waukesha, WI............. 13.3 235.8 .5 213 790 1.4 94 Winnebago, WI............ 3.8 89.1 -.2 266 737 .0 203 San Juan, PR............. 14.8 299.0 -4.3 (8) 514 1.6 (8) 1 Includes workers covered by Unemployment Insurance (UI) and Unemployment Compensation for Federal Employees (UCFE) programs. These 325 U.S. counties comprise 70.7 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, third quarter 2006 (2) Employment Average weekly wage (4) Establishments, third quarter County by NAICS supersector 2006 Percent Percent (thousands) September change, Average change, 2006 September weekly third (thousands) 2005-06 (3) wage quarter 2005-06 (3) United States (5)............................ 8,841.2 134,988.9 1.5 $784 0.9 Private industry........................... 8,562.2 113,752.0 1.7 776 .8 Natural resources and mining............. 124.0 1,895.7 3.3 761 3.7 Construction............................. 882.5 7,852.5 3.2 829 1.7 Manufacturing............................ 363.4 14,152.6 -0 5 947 .1 Trade, transportation, and utilities..... 1,899.4 25,982.1 1.1 685 .4 Information.............................. 144.9 3,034.8 - 7 1,217 .7 Financial activities..................... 852.0 8,175.1 1.0 1,133 1.9 Professional and business services....... 1,437.6 17,684.7 3.1 938 1.0 Education and health services............ 799.9 16,992.1 2.6 748 .4 Leisure and hospitality.................. 711.4 13,290.1 2.0 334 .9 Other services........................... 1,128.5 4,373.4 .8 510 1.0 Government................................. 279.0 21,236.9 .8 832 1.7 Los Angeles, CA.............................. 392.8 4,161.2 .7 894 1.7 Private industry........................... 389.1 3,608.2 .8 872 1.2 Natural resources and mining............. 0.6 12.2 7.4 1,184 -1.9 Construction............................. 14.2 160.0 2.8 896 1.8 Manufacturing............................ 15.9 463.8 -1.7 937 3.3 Trade, transportation, and utilities..... 55.6 807.9 .8 750 .8 Information.............................. 9.0 206.4 -1.6 1,486 1.3 Financial activities..................... 25.2 247.2 -.2 1,440 3.0 Professional and business services....... 43.4 603.5 1.4 978 -1.4 Education and health services............ 28.2 469.4 1.7 834 2.2 Leisure and hospitality.................. 27.1 392.5 1.9 513 2.8 Other services........................... 169.9 245.1 1.9 413 2.2 Government................................. 3.7 553.0 .2 1,038 4.6 Cook, IL..................................... 135.0 2,553.4 .7 928 1.0 Private industry........................... 133.8 2,241.8 .9 925 1.3 Natural resources and mining............. .1 1.6 -.9 1,036 7.2 Construction............................. 11.8 100.6 3.1 1,147 3.1 Manufacturing............................ 7.2 245.6 -1.8 956 -.1 Trade, transportation, and utilities..... 27.5 477.6 .3 784 3.3 Information.............................. 2.5 58.6 -3.0 1,275 -2.8 Financial activities..................... 15.5 219.5 .4 1,433 2.9 Professional and business services....... 27.6 441.4 2.5 1,135 -.1 Education and health services............ 13.2 363.4 1.8 813 1.0 Leisure and hospitality.................. 11.3 236.1 2.0 411 2.2 Other services........................... 13.4 93.8 -1.9 670 1.1 Government................................. 1.2 311.5 -.8 (6) (6) New York, NY................................. 116.2 2,292.3 1.9 1,421 .3 Private industry........................... 115.9 1,852.5 2.4 1,519 .9 Natural resources and mining............. .0 0.1 -7.3 1,571 15.5 Construction............................. 2.2 32.4 5.1 1,395 2.0 Manufacturing............................ 3.0 38.9 -7.5 1,105 2.2 Trade, transportation, and utilities..... 21.3 241.0 1.2 1,081 1.1 Information.............................. 4.2 132.4 .5 1,825 2.9 Financial activities..................... 17.8 369.7 3.2 2,619 .7 Professional and business services....... 23.2 464.3 2.9 1,637 .7 Education and health services............ 8.3 276.2 1.5 967 -.9 Leisure and hospitality.................. 10.7 198.8 2.1 685 -.3 Other services........................... 16.8 85.3 1.2 855 4.3 Government................................. .2 439.9 -.5 1,010 -4.6 Harris, TX................................... 92.7 1,959.1 4.2 950 2.0 Private industry........................... 92.3 1,708.2 4.5 960 1.6 Natural resources and mining............. 1.4 73.7 10.7 2,286 -6.3 Construction............................. 6.3 142.0 7.1 917 6.3 Manufacturing............................ 4.6 178.4 5.5 1,204 1.4 Trade, transportation, and utilities..... 21.2 409.4 3.4 846 1.7 Information.............................. 1.3 31.9 .7 1,169 1.0 Financial activities..................... 10.1 117.4 .2 1,182 5.2 Professional and business services....... 18.0 320.2 5.1 1,074 1.4 Education and health services............ 9.7 204.0 3.6 812 .9 Leisure and hospitality.................. 7.0 170.1 4.3 358 .6 Other services........................... 10.6 56.0 1.4 551 .7 Government................................. .4 250.9 2.1 878 4.9 Maricopa, AZ................................. 92.3 1,819.1 4.4 792 .5 Private industry........................... 91.7 1,605.4 4.8 779 -.4 Natural resources and mining............. .5 8.1 2.2 682 12.9 Construction............................. 9.5 177.8 5.9 804 1.4 Manufacturing............................ 3.4 136.9 2.3 1,082 .6 Trade, transportation, and utilities..... 19.7 366.7 4.1 750 -1.8 Information.............................. 1.5 31.3 -1.3 1,024 3.7 Financial activities..................... 11.3 150.3 2.7 1,027 -.1 Professional and business services....... 19.9 316.8 5.8 756 -.4 Education and health services............ 8.9 188.6 6.2 835 -.4 Leisure and hospitality.................. 6.4 174.0 4.2 368 -1.6 Other services........................... 6.4 47.8 3.0 550 .5 Government................................. .6 213.7 1.2 897 7.3 Orange, CA................................... 95.9 1,517.9 1.1 897 -1.1 Private industry........................... 94.5 1,378.8 1.2 893 -1.0 Natural resources and mining............. .2 5.1 -16.5 636 1.4 Construction............................. 7.1 111.0 3.7 972 1.1 Manufacturing............................ 5.6 183.4 .5 1,083 2.4 Trade, transportation, and utilities..... 17.9 271.2 .2 826 .2 Information.............................. 1.4 31.1 -2.3 1,199 -3.5 Financial activities..................... 11.5 137.0 -5.1 1,381 -5.9 Professional and business services....... 19.4 280.4 3.7 931 .1 Education and health services............ 9.9 138.9 4.8 849 .4 Leisure and hospitality.................. 7.1 172.2 3.0 387 .0 Other services........................... 14.4 48.5 -1.7 549 .5 Government................................. 1.4 139.0 .3 938 -1.6 Dallas, TX................................... 67.0 1,466.0 2.7 961 2.2 Private industry........................... 66.5 1,306.9 3.0 969 2.1 Natural resources and mining............. .6 7.4 3.4 3,640 48.6 Construction............................. 4.3 80.4 2.4 877 2.5 Manufacturing............................ 3.2 148.8 2.0 1,099 -3.9 Trade, transportation, and utilities..... 14.8 303.9 1.4 907 1.8 Information.............................. 1.7 52.7 -2.0 1,300 2.9 Financial activities..................... 8.5 140.8 3.3 1,285 6.4 Professional and business services....... 14.0 263.3 4.4 1,050 2.2 Education and health services............ 6.4 139.2 4.1 876 -1.9 Leisure and hospitality.................. 5.1 128.1 4.6 436 3.1 Other services........................... 6.4 38.9 1.2 608 .7 Government................................. .4 159.1 .3 894 3.4 San Diego, CA................................ 92.5 1,321.7 .9 850 -.7 Private industry........................... 91.0 1,106.4 .9 832 -.8 Natural resources and mining............. .8 11.6 -1.6 527 .6 Construction............................. 7.3 95.0 .7 877 -1.7 Manufacturing............................ 3.3 103.6 -.7 1,112 1.6 Trade, transportation, and utilities..... 14.6 220.1 .4 695 -.3 Information.............................. 1.3 37.1 -.7 1,554 -19.2 Financial activities..................... 10.1 83.8 -.8 1,041 -3.5 Professional and business services....... 16.6 215.6 1.2 1,052 4.9 Education and health services............ 8.0 123.5 1.3 816 1.6 Leisure and hospitality.................. 6.8 160.0 3.5 397 -.3 Other services........................... 22.0 56.0 1.2 479 1.3 Government................................. 1.5 215.3 1.2 944 -.1 King, WA..................................... 75.6 1,167.1 3.6 1,044 4.7 Private industry........................... 75.2 1,015.2 4.2 1,052 4.6 Natural resources and mining............. .4 3.1 -3.7 1,193 17.4 Construction............................. 6.6 70.5 11.0 954 .1 Manufacturing............................ 2.5 112.4 11.5 1,198 -3.5 Trade, transportation, and utilities..... 14.7 221.2 1.9 876 2.8 Information.............................. 1.7 74.0 5.2 2,812 19.4 Financial activities..................... 6.8 76.0 -.4 1,247 6.5 Professional and business services....... 12.4 183.7 5.7 1,095 .3 Education and health services............ 6.3 118.2 2.3 796 .8 Leisure and hospitality.................. 5.9 110.8 2.6 423 2.4 Other services........................... 17.8 45.2 .0 537 2.7 Government................................. .5 151.9 -.4 984 4.5 Miami-Dade, FL............................... 84.1 1,008.4 .6 792 1.5 Private industry........................... 83.8 858.2 1.0 760 1.7 Natural resources and mining............. .5 8.4 -2.6 487 4.1 Construction............................. 5.8 53.2 13.6 795 -.9 Manufacturing............................ 2.6 47.5 -3.2 700 -2.2 Trade, transportation, and utilities..... 22.9 249.0 1.7 705 -.8 Information.............................. 1.6 21.4 -5.4 1,139 3.5 Financial activities..................... 10.1 71.3 3.4 1,085 .3 Professional and business services....... 16.9 138.2 -5.7 943 7.8 Education and health services............ 8.6 133.1 3.4 763 1.6 Leisure and hospitality.................. 5.6 98.4 -.3 450 (6) Other services........................... 7.5 34.5 1.9 490 2.3 Government................................. .3 150.2 -1.4 988 1.6 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, third quarter 2006 (2) Employment Average weekly wage (5) Establishments, third quarter County (3) 2006 Percent Percent (thousands) September change, Average change, 2006 September weekly third (thousands) 2005-06 (4) wage quarter 2005-06 (4) United States (6)........ 8,841.2 134,988.9 1.5 $784 0.9 Jefferson, AL............ 18.6 374.6 0.8 803 1.0 Anchorage Borough, AK.... 8.2 148.8 .5 849 -.7 Maricopa, AZ............. 92.3 1,819.1 4.4 792 .5 Pulaski, AR.............. 14.2 249.9 1.9 717 1.3 Los Angeles, CA.......... 392.8 4,161.2 .7 894 1.7 Denver, CO............... 25.5 436.3 1.7 988 4.1 Hartford, CT............. 25.0 500.4 2.3 945 -2.3 New Castle, DE........... 19.6 282.8 .2 957 4.0 Washington, DC........... 32.0 674.2 .7 1,307 3.6 Miami-Dade, FL........... 84.1 1,008.4 .6 792 1.5 Fulton, GA............... 39.6 777.7 1.3 1,016 1.0 Honolulu, HI............. 24.0 452.2 2.3 744 .5 Ada, ID.................. 14.7 210.7 4.4 727 1.1 Cook, IL................. 135.0 2,553.4 .7 928 1.0 Marion, IN............... 23.6 583.0 .2 814 -.5 Polk, IA................. 14.4 271.3 2.4 783 -1.0 Johnson, KS.............. 20.0 312.0 3.4 812 -1.6 Jefferson, KY............ 22.5 433.2 1.7 775 (7) East Baton Rouge, LA..... 13.8 262.2 2.5 698 7.4 Cumberland, ME........... 12.0 172.6 .7 711 .3 Montgomery, MD........... 32.4 467.1 1.3 1,034 .6 Middlesex, MA............ 47.1 804.6 1.6 1,108 -.3 Wayne, MI................ 33.6 769.1 -2.6 905 -1.5 Hennepin, MN............. 41.9 841.4 .2 982 -.9 Hinds, MS................ 6.5 128.5 1.3 697 1.3 St. Louis, MO............ 33.7 625.8 .7 825 -.2 Yellowstone, MT.......... 5.5 74.8 1.6 637 3.1 Douglas, NE.............. 15.4 314.5 1.2 734 -.9 Clark, NV................ 46.2 922.5 4.4 751 -.3 Hillsborough, NH......... 12.5 196.8 -.3 861 1.1 Bergen, NJ............... 34.7 450.7 .6 969 .3 Bernalillo, NM........... 17.0 335.0 3.4 709 .4 New York, NY............. 116.2 2,292.3 1.9 1,421 .3 Mecklenburg, NC.......... 28.7 544.4 3.5 922 3.1 Cass, ND................. 5.7 96.2 3.4 649 .2 Cuyahoga, OH............. 38.1 757.1 -.4 800 -.6 Oklahoma, OK............. 23.0 424.0 1.5 708 3.2 Multnomah, OR............ 26.8 442.5 3.3 803 .5 Allegheny, PA............ 35.3 683.8 .4 823 1.5 Providence, RI........... 18.2 291.1 .4 754 .8 Greenville, SC........... 13.8 231.6 1.6 684 -.1 Minnehaha, SD............ 6.3 113.4 2.0 668 .6 Shelby, TN............... 20.1 509.4 .2 814 .0 Harris, TX............... 92.7 1,959.1 4.2 950 2.0 Salt Lake, UT............ 39.4 572.1 4.6 729 1.4 Chittenden, VT........... 5.8 96.6 1.1 778 1.8 Fairfax, VA.............. 31.8 576.3 1.7 1,179 -.8 King, WA................. 75.6 1,167.1 3.6 1,044 4.7 Kanawha, WV.............. 6.1 108.1 .7 676 1.2 Milwaukee, WI............ 21.4 497.2 .1 783 .4 Laramie, WY.............. 3.1 42.1 2.5 757 19.4 San Juan, PR............. 14.8 299.0 -4.3 514 1.6 St. Thomas, VI........... 1.8 22.0 -2.6 644 12.0 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, third quarter 2006 (2) Employment Average weekly wage (3) Establishments, third quarter State 2006 Percent Percent (thousands) September change, Average change, 2006 September weekly third (thousands) 2005-06 wage quarter 2005-06 United States(4)......... 8,841.2 134,988.9 1.5 $784 0.9 Alabama.................. 117.3 1,938.9 1.6 682 1.9 Alaska................... 21.1 324.8 1.4 798 .1 Arizona.................. 150.6 2,629.0 4.2 753 1.1 Arkansas................. 81.9 1,183.9 1.5 603 .7 California............... 1,270.4 15,655.0 1.5 892 .6 Colorado................. 176.9 2,260.1 2.2 819 1.4 Connecticut.............. 111.9 1,680.7 1.6 957 -.9 Delaware................. 30.2 424.6 0.5 850 3.4 District of Columbia..... 32.0 674.2 .7 1,307 3.6 Florida.................. 588.1 7,941.7 1.9 713 .7 Georgia.................. 264.5 4,039.3 2.0 752 .5 Hawaii................... 37.4 621.2 2.3 722 1.1 Idaho.................... 55.3 661.2 4.1 613 1.3 Illinois................. 350.2 5,883.6 1.1 831 .7 Indiana.................. 155.4 2,922.7 .3 687 -.3 Iowa..................... 92.8 1,480.7 1.2 641 .0 Kansas................... 85.6 1,347.3 2.4 662 .6 Kentucky................. 110.7 1,795.1 .9 656 .6 Louisiana................ 122.5 1,835.7 3.7 683 7.1 Maine.................... 49.4 610.2 .6 636 .8 Maryland................. 161.5 2,545.0 .7 858 .5 Massachusetts............ 208.8 3,228.1 .9 950 .3 Michigan................. 261.0 4,278.9 -1.8 790 .3 Minnesota................ 165.5 2,685.1 .0 784 -.6 Mississippi.............. 69.1 1,134.3 2.9 585 2.1 Missouri................. 172.1 2,725.1 1.1 691 .0 Montana.................. 41.4 434.4 2.3 581 3.0 Nebraska................. 57.8 906.9 1.1 633 .0 Nevada................... 72.4 1,287.6 3.7 751 .0 New Hampshire............ 48.9 634.9 .6 774 .3 New Jersey............... 279.8 3,984.7 .7 931 .3 New Mexico............... 52.6 826.1 4.4 654 4.0 New York................. 573.2 8,471.7 .8 950 1.1 North Carolina........... 241.5 3,982.6 1.8 700 1.6 North Dakota............. 24.7 342.2 2.0 589 1.4 Ohio..................... 291.7 5,350.9 -.1 725 .3 Oklahoma................. 97.3 1,517.6 2.2 633 3.3 Oregon................... 128.6 1,729.2 2.7 719 .7 Pennsylvania............. 335.9 5,644.8 .8 768 .5 Rhode Island............. 36.0 490.8 .8 763 3.7 South Carolina........... 132.4 1,866.0 1.8 642 1.1 South Dakota............. 29.8 389.6 2.1 571 .7 Tennessee................ 137.1 2,761.1 1.4 698 1.2 Texas.................... 536.7 10,019.0 3.6 786 2.5 Utah..................... 88.1 1,188.7 4.8 660 2.0 Vermont.................. 24.7 305.8 .6 672 1.4 Virginia................. 220.0 3,649.5 1.0 815 -.1 Washington............... 214.5 2,911.9 3.3 823 2.7 West Virginia............ 48.2 711.8 1.2 599 1.7 Wisconsin................ 161.8 2,800.8 .5 687 .1 Wyoming.................. 24.1 274.1 4.6 706 10.0 Puerto Rico.............. 60.6 1,020.9 -1.9 439 1.2 Virgin Islands........... 3.4 43.2 -2.0 692 12.5 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.