Technical information: (202) 691-6567 USDL 07-1119 http://www.bls.gov/cew/ For release: 10:00 A.M. EDT Media contact: 691-5902 Wednesday, July 25, 2007 (NOTE: Tables 1 and 2 in the HTML and text versions of this news release were corrected online on July 27, 2007. The PDF and print versions were correct as originally issued.) COUNTY EMPLOYMENT AND WAGES: FOURTH QUARTER 2006 As of December 2006, three counties heavily affected by Hurricane Katrina had recovered some of the job losses caused by the storm. Harrison County, Miss., had the largest over-the-year percentage in- crease in employment among the largest counties in the U.S., accord- ing to preliminary data released today by the Bureau of Labor Statis- tics of the U.S. Department of Labor. Harrison County, which includes the cities of Gulfport and Biloxi, experienced an over-the-year em- ployment gain of 18.7 percent compared with national job growth of 1.6 percent. Orleans and Jefferson counties in Louisiana had over-the- year gains of 12.2 and 10.5 percent, respectively. Employment gains in these counties reflected a partial employment recovery following substantial job losses that occurred in 2005 due to Hurricane Katrina. The U.S. average weekly wage rose by 4.2 percent from fourth quarter 2005 to fourth quarter 2006. Among the largest counties, Rockingham, N.H., had the greatest gain over the same time span with an increase of 18.0 percent. Of the 325 largest counties in the United States, as measured by 2005 annual average employment, 135 had over-the-year percentage growth in employment above the national average (1.6 percent) in De- cember 2006 and 179 experienced changes below the national average. The percent change in average weekly wages was higher than the nation- al average (4.2 percent) in 122 of the largest U.S. counties, but was below the national average in 185 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 8.9 million employer reports cover 135.9 million full- and part-time workers. The attached tables contain data for the na- tion and for the 325 U.S. counties with annual average employment le- vels of 75,000 or more in 2005. December 2006 employment and 2006 fourth-quarter average weekly wages for all states are provided in ta- ble 4 of this release. Final data for all states, metropolitan sta- tistical areas, counties, and the nation through the fourth quarter of 2005 are available on the BLS Web site at http://www.bls.gov/cew/. Preliminary data for fourth quarter 2006, along with updated data for the first, second, and third quarters of 2006, will be available later in July on the BLS Web site. Large County Employment In December 2006, national employment, as measured by the QCEW pro- gram, was 135.9 million, up by 1.6 percent from December 2005. The 325 U.S. counties with 75,000 or more employees accounted for 71.0 percent of total U.S. covered employment and 77.1 percent of total covered wages. These 325 counties had a net job gain of 1,409,950 over the year, accounting for 66.8 percent of the overall U.S. employment in- crease. Employment rose in 270 of the large counties from December 2005 to December 2006. Harrison County, Miss., had the largest over-the-year percentage increase in employment (18.7 percent). Orleans, La., had the next largest increase, 12.2 percent, followed by the counties of Jeffer- son, La. (10.5 percent), Williamson, Texas (7.7 percent), and Utah, Utah (6.8 percent). The large employment gains in Harrison, Orleans, and Jef- ferson counties reflected significant recovery from depressed employment levels in December 2005, which were related to Hurricane Katrina. (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 fourth 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 December 2006 employment, December 2005-06 employment growth, and December 2005-06 percent growth in employment ------------------------------------------------------------------------------------ Employment in large counties ------------------------------------------------------------------------------------ | | December 2006 employment | Growth in employment, | Percent growth (thousands) | December 2005-06 | in employment, | (thousands) | December 2005-06 ------------------------------------------------------------------------------------ | | United States ..... 135,933.2| United States ..... 2,110.6| United States ...... 1.6 -----------------------------|----------------------------|------------------------- | | Los Angeles, Calif. . 4,242.5| Harris, Texas ........ 76.3| Harrison, Miss. ... 18.7 Cook, Ill. .......... 2,569.9| Maricopa, Ariz. ...... 68.5| Orleans, La. ...... 12.2 New York, N.Y. ...... 2,359.8| New York, N.Y. ....... 43.9| Jefferson, La. .... 10.5 Harris, Texas ....... 1,993.9| Dallas, Texas ........ 42.6| Williamson, Texas . 7.7 Maricopa, Ariz. ..... 1,854.5| King, Wash. .......... 34.2| Utah, Utah ........ 6.8 Orange, Calif. ...... 1,519.1| Bexar, Texas ......... 26.6| Horry, S.C. ....... 6.6 Dallas, Texas ....... 1,490.2| Salt Lake, Utah ...... 26.5| Collin, Texas ..... 6.5 San Diego, Calif. ... 1,335.2| Travis, Texas ........ 25.7| Montgomery, Texas . 6.3 King, Wash. ......... 1,173.0| Clark, Nev. .......... 24.3| Fort Bend, Texas .. 5.6 Miami-Dade, Fla. .... 1,032.7| Mecklenburg, N.C. .... 24.1| Wake, N.C. ........ 5.1 | | ------------------------------------------------------------------------------------ Employment declined in 41 counties from December 2005 to December 2006. The largest percentage decline in employment was in Trumbull County, Ohio (-4.7 percent). Elkhart, Ind., had the next largest employment decline (-3.3 percent), followed by the counties of Wayne, Mich. (-3.1 percent), Oakland, Mich. (-2.7 percent), and Genesee, Mich. (-2.4 percent). In each of these five counties, the greatest number of jobs lost occurred in the manufacturing industry. The largest gains in the level of employment from December 2005 to De- cember 2006 were recorded in the counties of Harris, Texas (76,300), Mar- icopa, Ariz. (68,500), New York, N.Y. (43,900), Dallas, Texas (42,600), and King, Wash. (34,200). (See table A.) The largest declines in employment levels occurred in Wayne, Mich. (-25,100), followed by the counties of Oakland, Mich. (-19,800), Montgom- ery, Ohio (-5,200), and Elkhart, Ind., and Monroe, N.Y. (-4,200 each). Large County Average Weekly Wages The national average weekly wage in the fourth quarter of 2006 was $861. Average weekly wages were higher than the national average in 105 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 $1,781. Santa Clara, Calif., was second with an average weekly wage of $1,569, followed by Fairfield, Conn. ($1,515), Suffolk, Mass. ($1,481), and San Francisco, Calif. ($1,460). (See table B.) - 3 - Table B. Top 10 large counties ranked by fourth quarter 2006 average weekly wages, fourth quarter 2005-06 growth in average weekly wages, and fourth 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 fourth quarter 2006 | wage, fourth quarter | average weekly wage, | 2005-06 | fourth quarter 2005-06 ------------------------------------------------------------------------------------ | | United States ......... $861| United States ........ $35| United States ....... 4.2 ------------------------------------------------------------------------------------ | | New York, N.Y. ...... $1,781| Rockingham, N.H. .... $155| Rockingham, N.H. .... 18.0 Santa Clara, Calif. . 1,569| Sedgwick, Kan. ...... 104| Sedgwick, Kan. ...... 14.0 Fairfield, Conn. .... 1,515| Travis, Texas ....... 102| Trumbull, Ohio ...... 14.0 Suffolk, Mass. ...... 1,481| Trumbull, Ohio ...... 100| Travis, Texas ....... 10.9 San Francisco, Calif. 1,460| New York, N.Y. ...... 96| Waukesha, Wis. ...... 10.4 Washington, D.C. .... 1,424| Rock Island, Ill. ... 86| Santa Cruz, Calif. .. 10.1 Arlington, Va. ...... 1,419| Waukesha, Wis. ...... 86| Rock Island, Ill. ... 9.5 San Mateo, Calif. ... 1,402| San Francisco, Calif. 83| Ada, Idaho .......... 8.9 Somerset, N.J. ...... 1,373| Santa Clara, Calif. . 76| Miami-Dade, Fla. .... 8.1 Fairfax, Va. ........ 1,297| Santa Cruz, Calif. .. 75| East Baton Rouge, La. 8.1 | | Lafayette, La. ...... 8.1 | | Utah, Utah .......... 8.1 ------------------------------------------------------------------------------------ There were 219 counties with an average weekly wage below the nation- al average in the fourth quarter of 2006. The lowest average weekly wages were reported in Cameron County, Texas ($527), followed by the coun- ties of Hidalgo, Texas ($542), Yakima, Wash. ($570), Webb, Texas ($571), and Horry, S.C. ($578). (See table 1.) Over the year, the national average weekly wage rose by 4.2 percent. Among the largest counties, Rockingham, N.H., led the nation in average weekly wage growth with an increase of 18.0 percent from the fourth quarter of 2005. Sedgwick, Kan., and Trumbull, Ohio, were second in wage growth (14.0 percent each), followed by the counties of Travis, Texas (10.9 percent) and Waukesha, Wis. (10.4 percent). Eight counties experienced over-the-year declines in average weekly wages. New Castle, Del., had the largest decrease (-5.7 percent), followed by the counties of Elkhart, Ind. (-5.3 percent), Orleans, La. (-4.4 per- cent), York, Pa. (-4.3 percent), and Harrison, Miss. (-2.4 percent). Ten Largest U.S. Counties Each of the 10 largest counties (based on 2005 annual average employment levels) reported increases in employment from December 2005 to December 2006. Harris, Texas, experienced the largest percent increase in employment among the largest counties (4.0 percent). Within Harris County, employment rose in every industry group. The largest percent gains were in natural resources and mining (12.2 percent), followed by construction (6.8 percent). Maricopa, Ariz., had the next largest percent increase in employment (3.8 percent), followed by King, Wash. (3.0 percent). The smallest percent increases in em- ployment occurred in Los Angeles, Calif. (0.5 percent), Orange, Calif. (0.7 percent), and San Diego, Calif. (0.8 percent). (See table 2.) Each of the 10 largest U.S. counties saw over-the-year increases in average weekly wages. Miami-Dade, Fla., had the fastest growth in wages among the 10 largest counties with a gain of 8.1 percent. Within Miami- Dade County, average weekly wages increased the most in professional and business services (18.7 percent), followed by financial activities (9.0 percent). Harris, Texas, was second in wage growth with a gain of 7.2 percent, followed by King, Wash. (5.8 percent). The smallest wage gains among the 10 largest counties occurred in Orange, Calif. (2.7 percent), followed by Dallas, Texas (3.3 percent) and San Diego, Calif. (3.6 per- cent). - 4 - Largest County by State Table 3 shows December 2006 employment and the 2006 fourth quarter average weekly wage in the largest county in each state, which is based on 2005 annual average employment levels. (This table includes two coun- ties--Yellowstone, Mont., and Laramie, Wyo.--that had employment levels below 75,000.) The employment levels in the counties in table 3 in De- cember 2006 ranged from approximately 4.2 million in Los Angeles County, Calif., to 42,200 in Laramie County, Wyo. The highest average weekly wage of these counties was in New York, N.Y. ($1,781), while the lowest average weekly wage was in Laramie, Wyo. ($682). 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. 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 first quarter 2007 is sche- duled to be released on Thursday, October 18. ___________________________________________________________________ | | | Upcoming Changes to Quarterly Census of Employment and Wages | | Data | | | | Data for 2006 will be the last from the Quarterly Census of | | Employment and Wages (QCEW) program using the 2002 version of the | | North American Industry Classification System (NAICS). With the | | release of first quarter 2007 data, scheduled for October 18, the | | QCEW program will switch to the 2007 NAICS as the basis for the | | assignment and tabulation of economic data by industry. | |___________________________________________________________________| - 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 8.9 | 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 is the first to include the data on a CD for enhanced access and usability. As a result of this change, the printed booklet contains only selected graphic representations of QCEW data; the data tables themselves are published exclusively in electronic formats as PDF and fixed-width text files. Employment and Wages Annual Averages, 2005 is 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, fourth quarter 2006(2) Employment Average weekly wage(5) Establishments, County(3) fourth quarter Percent Ranking Percent Ranking 2006 December change, by Average change, by (thousands) 2006 December percent weekly fourth percent (thousands) 2005-06(4) change wage quarter change 2005-06(4) United States(6)......... 8,929.5 135,933.2 1.6 - $861 4.2 - Jefferson, AL............ 18.8 378.1 1.0 180 868 4.1 137 Madison, AL.............. 8.5 176.1 2.2 87 893 5.8 35 Mobile, AL............... 9.9 172.7 1.1 168 756 6.2 31 Montgomery, AL........... 6.6 139.0 0.3 249 764 5.5 50 Tuscaloosa, AL........... 4.3 85.9 3.1 44 759 2.8 248 Anchorage Borough, AK.... 8.1 145.2 1.0 180 879 4.9 75 Maricopa, AZ............. 94.1 1,854.5 3.8 28 856 4.6 101 Pima, AZ................. 20.1 377.2 3.1 44 743 3.6 183 Benton, AR............... 5.4 95.6 4.0 22 752 1.2 303 Pulaski, AR.............. 14.4 249.5 0.7 212 782 4.3 114 Washington, AR........... 5.6 93.9 1.5 141 716 5.0 71 Alameda, CA.............. 50.1 686.3 0.0 271 1,106 5.3 54 Contra Costa, CA......... 28.5 350.0 0.8 199 1,057 4.8 83 Fresno, CA............... 29.6 352.8 2.1 95 688 3.6 183 Kern, CA................. 17.5 282.9 2.4 76 721 5.1 64 Los Angeles, CA.......... 400.2 4,242.5 0.5 232 1,011 4.3 114 Marin, CA................ 11.8 111.7 1.1 168 1,148 0.5 308 Monterey, CA............. 12.3 149.5 -1.3 305 764 6.1 32 Orange, CA............... 96.6 1,519.1 0.7 212 994 2.7 255 Placer, CA............... 10.6 136.4 -0.1 279 866 7.0 16 Riverside, CA............ 43.9 637.0 1.3 156 711 4.4 108 Sacramento, CA........... 51.4 631.6 0.1 266 929 4.3 114 San Bernardino, CA....... 46.7 666.6 1.0 180 747 4.0 145 San Diego, CA............ 93.8 1,335.2 0.8 199 922 3.6 183 San Francisco, CA........ 44.9 547.8 2.2 87 1,460 6.0 33 San Joaquin, CA.......... 17.3 221.5 0.6 224 744 3.3 212 San Luis Obispo, CA...... 9.2 104.1 2.6 66 727 3.9 160 San Mateo, CA............ 23.3 343.3 2.4 76 1,402 2.9 241 Santa Barbara, CA........ 13.8 182.3 2.3 80 810 1.3 301 Santa Clara, CA.......... 56.7 898.3 2.1 95 1,569 5.1 64 Santa Cruz, CA........... 8.8 92.3 0.3 249 818 10.1 6 Solano, CA............... 10.0 129.3 -2.2 314 809 5.3 54 Sonoma, CA............... 18.0 193.0 0.5 232 841 2.3 275 Stanislaus, CA........... 14.2 174.4 0.7 212 708 2.2 281 Tulare, CA............... 9.0 149.0 4.0 22 593 2.4 269 Ventura, CA.............. 22.0 319.6 0.0 271 948 6.4 25 Yolo, CA................. 5.5 98.6 1.2 163 763 4.8 83 Adams, CO................ 9.3 153.4 1.8 124 785 3.0 235 Arapahoe, CO............. 19.8 280.3 1.9 115 1,022 4.8 83 Boulder, CO.............. 12.6 160.8 3.5 33 1,026 5.4 51 Denver, CO............... 25.4 439.7 2.2 87 1,069 6.3 28 Douglas, CO.............. 9.1 89.8 3.5 33 859 2.1 285 El Paso, CO.............. 17.5 247.2 1.0 180 774 3.2 220 Jefferson, CO............ 18.8 209.2 0.0 271 852 4.4 108 Larimer, CO.............. 10.1 128.2 2.3 80 784 3.7 176 Weld, CO................. 5.9 81.8 3.4 37 711 5.0 71 Fairfield, CT............ 32.8 428.5 1.9 115 1,515 1.2 303 Hartford, CT............. 25.1 507.6 2.7 60 1,045 1.2 303 New Haven, CT............ 22.4 374.8 2.2 87 911 2.2 281 New London, CT........... 6.8 130.5 0.0 271 869 3.2 220 New Castle, DE........... 19.6 289.1 0.3 249 1,004 -5.7 321 Washington, DC........... 32.7 675.0 0.4 238 1,424 5.0 71 Alachua, FL.............. 6.5 125.7 0.4 238 691 5.7 39 Brevard, FL.............. 14.6 207.7 -0.3 289 807 3.5 196 Broward, FL.............. 63.8 762.9 1.6 136 861 5.6 46 Collier, FL.............. 12.4 140.4 3.1 44 818 1.6 297 Duval, FL................ 25.7 470.9 2.0 104 849 4.0 145 Escambia, FL............. 7.9 131.1 -0.3 289 687 2.5 262 Hillsborough, FL......... 36.2 656.1 2.1 95 815 4.2 123 Lake, FL................. 6.9 85.8 3.0 50 693 3.6 183 Lee, FL.................. 18.9 229.6 3.1 44 749 2.2 281 Leon, FL................. 8.0 149.6 1.6 136 730 2.7 255 Manatee, FL.............. 8.9 132.3 1.8 124 674 3.4 204 Marion, FL............... 8.1 104.9 2.7 60 636 3.8 168 Miami-Dade, FL........... 84.9 1,032.7 1.3 156 898 8.1 9 Okaloosa, FL............. 6.0 83.8 1.9 115 691 3.6 183 Orange, FL............... 34.9 693.7 3.3 38 786 3.1 227 Palm Beach, FL........... 49.5 578.8 2.5 71 873 5.7 39 Pasco, FL................ 9.5 102.0 2.5 71 630 5.0 71 Pinellas, FL............. 31.0 448.1 0.4 238 765 4.7 88 Polk, FL................. 12.5 212.6 0.8 199 675 2.1 285 Sarasota, FL............. 15.1 161.7 1.4 146 766 3.1 227 Seminole, FL............. 14.7 180.1 1.4 146 784 1.8 293 Volusia, FL.............. 13.9 167.5 0.7 212 645 5.6 46 Bibb, GA................. 4.7 85.5 -1.3 305 695 2.8 248 Chatham, GA.............. 7.5 138.7 4.0 22 738 3.2 220 Clayton, GA.............. 4.4 109.6 -0.4 291 751 0.3 311 Cobb, GA................. 20.2 313.7 1.5 141 917 4.1 137 De Kalb, GA.............. 15.9 284.2 0.1 266 900 4.8 83 Fulton, GA............... 40.2 790.0 1.8 124 1,120 -2.0 316 Gwinnett, GA............. 23.3 333.5 4.2 16 910 4.6 101 Muscogee, GA............. 4.9 97.4 -1.3 305 671 3.7 176 Richmond, GA............. 4.8 104.1 (7) - 711 5.3 54 Honolulu, HI............. 24.1 460.2 1.6 136 787 3.1 227 Ada, ID.................. 15.1 211.7 3.6 31 818 8.9 8 Champaign, IL............ 4.1 91.8 0.6 224 706 4.1 137 Cook, IL................. 136.4 2,569.9 0.9 189 1,051 5.1 64 Du Page, IL.............. 35.0 601.8 1.1 168 1,021 4.9 75 Kane, IL................. 12.3 210.4 1.1 168 803 3.1 227 Lake, IL................. 20.5 329.8 0.9 189 1,081 5.3 54 McHenry, IL.............. 8.2 102.3 3.2 41 771 3.2 220 McLean, IL............... 3.6 86.0 1.0 180 795 4.3 114 Madison, IL.............. 5.9 95.2 0.4 238 713 -1.2 315 Peoria, IL............... 4.7 104.1 2.5 71 818 2.3 275 Rock Island, IL.......... 3.5 78.6 -0.8 297 996 9.5 7 St. Clair, IL............ 5.3 96.2 1.3 156 691 3.6 183 Sangamon, IL............. 5.2 130.6 0.0 271 823 4.6 101 Will, IL................. 12.7 183.8 3.5 33 788 2.5 262 Winnebago, IL............ 6.8 138.4 0.9 189 729 3.0 235 Allen, IN................ 8.9 186.4 1.4 146 723 2.6 258 Elkhart, IN.............. 4.8 123.8 -3.3 318 691 -5.3 320 Hamilton, IN............. 7.1 101.3 2.1 95 840 2.1 285 Lake, IN................. 10.0 196.9 -0.1 279 738 2.9 241 Marion, IN............... 23.6 589.6 1.1 168 867 3.6 183 St. Joseph, IN........... 6.0 126.9 0.2 259 706 2.5 262 Vanderburgh, IN.......... 4.8 108.2 -2.0 313 708 4.0 145 Linn, IA................. 6.3 122.8 2.8 55 829 3.4 204 Polk, IA................. 14.6 274.6 2.9 52 853 3.1 227 Scott, IA................ 5.2 90.1 0.2 259 705 3.2 220 Johnson, KS.............. 20.1 315.1 3.6 31 881 3.6 183 Sedgwick, KS............. 12.2 257.0 3.8 28 848 14.0 2 Shawnee, KS.............. 4.9 93.0 -1.3 305 716 3.8 168 Wyandotte, KS............ 3.3 81.8 4.4 15 818 4.9 75 Boone, KY................ 3.5 76.9 0.9 189 783 4.0 145 Fayette, KY.............. 9.3 178.7 (7) - 778 2.9 241 Jefferson, KY............ 22.8 439.0 2.1 95 830 4.0 145 Caddo, LA................ 7.4 125.4 -0.6 293 716 3.9 160 Calcasieu, LA............ 4.9 86.2 2.1 95 726 4.3 114 East Baton Rouge, LA..... 14.0 263.4 0.7 212 771 8.1 9 Jefferson, LA............ 14.5 198.1 10.5 3 817 2.4 269 Lafayette, LA............ 8.4 132.7 3.2 41 818 8.1 9 Orleans, LA.............. 11.8 162.5 12.2 2 941 -4.4 319 Cumberland, ME........... 12.1 175.1 0.6 224 769 2.5 262 Anne Arundel, MD......... 14.3 230.7 2.3 80 888 3.3 212 Baltimore, MD............ 21.7 381.6 0.3 249 919 5.4 51 Frederick, MD............ 5.9 93.9 1.7 131 805 3.2 220 Harford, MD.............. 5.6 83.1 0.9 189 771 3.9 160 Howard, MD............... 8.4 145.6 1.9 115 995 3.6 183 Montgomery, MD........... 32.7 472.8 1.4 146 1,136 2.4 269 Prince Georges, MD....... 15.7 317.6 0.0 271 934 4.2 123 Baltimore City, MD....... 14.1 355.0 0.6 224 1,013 2.2 281 Barnstable, MA........... 9.2 87.3 -1.2 304 759 3.8 168 Bristol, MA.............. 15.7 224.2 0.4 238 769 3.8 168 Essex, MA................ 20.7 300.5 0.7 212 916 3.4 204 Hampden, MA.............. 14.1 202.1 0.2 259 789 4.1 137 Middlesex, MA............ 47.1 815.6 1.4 146 1,209 4.3 114 Norfolk, MA.............. 21.5 325.6 0.3 249 1,060 4.2 123 Plymouth, MA............. 13.8 179.4 0.3 249 834 4.0 145 Suffolk, MA.............. 21.7 584.8 2.2 87 1,481 4.9 75 Worcester, MA............ 20.6 325.0 0.7 212 858 3.5 196 Genesee, MI.............. 8.4 147.7 -2.4 315 782 (7) - Ingham, MI............... 7.1 162.3 0.0 271 824 4.2 123 Kalamazoo, MI............ 5.7 117.2 -0.7 296 769 3.5 196 Kent, MI................. 14.7 342.8 -1.0 299 793 3.0 235 Macomb, MI............... 18.5 320.5 (7) - 889 -0.1 314 Oakland, MI.............. 40.7 701.7 -2.7 316 1,030 1.9 290 Ottawa, MI............... 5.9 110.0 -1.8 311 758 2.6 258 Saginaw, MI.............. 4.5 89.5 -0.2 286 759 1.6 297 Washtenaw, MI............ 8.3 196.2 -1.1 300 924 1.3 301 Wayne, MI................ 33.9 771.4 -3.1 317 969 1.8 293 Anoka, MN................ 7.9 116.8 -0.1 279 811 4.8 83 Dakota, MN............... 10.5 175.5 0.4 238 832 3.0 235 Hennepin, MN............. 42.4 851.5 0.1 266 1,052 3.8 168 Olmsted, MN.............. 3.6 90.7 0.8 199 843 4.1 137 Ramsey, MN............... 15.6 333.6 -0.2 286 907 3.3 212 St. Louis, MN............ 5.9 96.5 0.8 199 696 3.1 227 Stearns, MN.............. 4.5 81.3 2.0 104 667 4.2 123 Harrison, MS............. 4.4 86.0 18.7 1 663 -2.4 317 Hinds, MS................ 6.5 129.4 0.6 224 759 3.3 212 Boone, MO................ 4.5 82.8 1.4 146 647 3.4 204 Clay, MO................. 5.1 88.7 0.8 199 774 2.4 269 Greene, MO............... 8.2 155.8 1.7 131 632 1.0 306 Jackson, MO.............. 18.8 371.0 0.9 189 863 2.9 241 St. Charles, MO.......... 8.0 123.8 3.2 41 714 0.8 307 St. Louis, MO............ 34.1 635.1 1.4 146 907 2.4 269 St. Louis City, MO....... 8.1 220.5 -1.1 300 936 4.1 137 Douglas, NE.............. 15.6 318.4 1.2 163 814 3.0 235 Lancaster, NE............ 8.0 155.9 1.0 180 677 2.4 269 Clark, NV................ 47.6 921.1 2.7 60 815 5.6 46 Washoe, NV............... 14.3 221.8 2.0 104 821 4.9 75 Hillsborough, NH......... 12.6 200.8 -0.1 279 994 4.7 88 Rockingham, NH........... 11.0 140.5 1.0 180 1,015 18.0 1 Atlantic, NJ............. 7.0 148.0 1.6 136 783 4.5 107 Bergen, NJ............... 35.1 460.7 1.1 168 1,114 4.0 145 Burlington, NJ........... 11.7 205.7 0.6 224 906 3.3 212 Camden, NJ............... 13.9 214.4 0.8 199 926 3.7 176 Essex, NJ................ 22.0 365.6 0.3 249 1,111 3.7 176 Gloucester, NJ........... 6.5 107.4 1.5 141 793 3.4 204 Hudson, NJ............... 14.4 239.6 0.2 259 1,119 4.9 75 Mercer, NJ............... 11.3 231.5 1.1 168 1,118 3.6 183 Middlesex, NJ............ 21.6 405.9 1.4 146 1,101 5.1 64 Monmouth, NJ............. 21.1 259.4 0.2 259 954 3.0 235 Morris, NJ............... 18.5 297.8 2.3 80 1,284 3.6 183 Ocean, NJ................ 12.3 148.4 0.2 259 762 2.8 248 Passaic, NJ.............. 12.9 181.3 -0.1 279 929 3.9 160 Somerset, NJ............. 10.4 176.5 1.0 180 1,373 4.9 75 Union, NJ................ 15.3 233.9 0.6 224 1,118 4.0 145 Bernalillo, NM........... 17.3 335.7 2.7 60 760 4.0 145 Albany, NY............... 9.9 231.7 0.7 212 901 (7) - Bronx, NY................ 15.7 224.7 0.1 266 828 5.7 39 Broome, NY............... 4.5 96.3 0.5 232 663 4.7 88 Dutchess, NY............. 8.3 120.2 0.4 238 853 3.9 160 Erie, NY................. 23.3 461.7 0.0 271 757 6.9 17 Kings, NY................ 44.1 474.7 2.1 95 771 4.0 145 Monroe, NY............... 17.8 385.0 -1.1 300 809 2.8 248 Nassau, NY............... 52.2 616.6 1.1 168 980 3.7 176 New York, NY............. 116.4 2,359.8 1.9 115 1,781 5.7 39 Oneida, NY............... 5.3 112.4 2.3 80 664 4.7 88 Onondaga, NY............. 12.7 253.0 -0.1 279 801 4.0 145 Orange, NY............... 9.9 132.1 0.9 189 722 2.3 275 Queens, NY............... 42.1 497.2 1.9 115 853 3.9 160 Richmond, NY............. 8.5 94.3 2.7 60 764 3.5 196 Rockland, NY............. 9.7 117.1 2.0 104 906 4.0 145 Suffolk, NY.............. 49.6 628.8 1.1 168 953 6.5 21 Westchester, NY.......... 36.3 425.6 1.1 168 1,211 2.9 241 Buncombe, NC............. 7.5 114.7 3.1 44 691 4.7 88 Catawba, NC.............. 4.5 89.6 2.4 76 673 5.2 59 Cumberland, NC........... 6.0 118.3 1.2 163 634 2.3 275 Durham, NC............... 6.5 182.2 (7) - 1,072 5.7 39 Forsyth, NC.............. 8.9 184.6 0.8 199 791 4.6 101 Guilford, NC............. 14.1 283.7 2.8 55 765 3.9 160 Mecklenburg, NC.......... 29.7 557.7 4.5 14 973 4.4 108 New Hanover, NC.......... 7.1 102.1 3.3 38 708 4.1 137 Wake, NC................. 25.9 440.4 5.1 10 866 4.3 114 Cass, ND................. 5.7 96.1 3.5 33 724 4.6 101 Butler, OH............... 7.3 146.3 0.8 199 744 0.5 308 Cuyahoga, OH............. 38.3 755.6 -0.5 292 874 2.1 285 Franklin, OH............. 29.4 692.9 0.7 212 835 3.3 212 Hamilton, OH............. 24.2 530.3 -0.6 293 915 2.0 289 Lake, OH................. 6.9 101.5 0.4 238 720 3.6 183 Lorain, OH............... 6.3 100.8 -1.6 310 703 0.3 311 Lucas, OH................ 10.9 225.7 -1.1 300 750 2.9 241 Mahoning, OH............. 6.3 104.8 0.8 199 627 1.8 293 Montgomery, OH........... 13.0 272.7 -1.9 312 828 6.4 25 Stark, OH................ 9.1 162.4 -1.5 309 670 4.0 145 Summit, OH............... 14.9 275.4 0.1 266 788 3.1 227 Trumbull, OH............. 4.8 83.0 -4.7 319 814 14.0 2 Oklahoma, OK............. 23.2 426.2 0.9 189 759 6.5 21 Tulsa, OK................ 19.3 348.9 2.8 55 776 4.3 114 Clackamas, OR............ 12.5 149.9 1.8 124 794 4.1 137 Jackson, OR.............. 6.7 86.5 1.5 141 626 3.8 168 Lane, OR................. 10.9 151.9 1.7 131 672 2.6 258 Marion, OR............... 9.2 137.1 2.4 76 669 4.9 75 Multnomah, OR............ 27.0 449.6 2.8 55 868 5.6 46 Washington, OR........... 15.9 250.7 2.2 87 948 5.1 64 Allegheny, PA............ 35.1 690.6 0.7 212 912 6.4 25 Berks, PA................ 9.1 172.0 2.3 80 774 6.3 28 Bucks, PA................ 20.2 266.7 0.8 199 849 4.7 88 Butler, PA............... 4.7 78.4 2.6 66 723 5.2 59 Chester, PA.............. 14.9 239.5 2.0 104 1,107 4.2 123 Cumberland, PA........... 5.9 127.9 2.0 104 773 2.5 262 Dauphin, PA.............. 7.3 182.9 2.0 104 827 4.7 88 Delaware, PA............. 13.6 212.0 0.5 232 924 3.5 196 Erie, PA................. 7.3 128.4 0.4 238 671 3.5 196 Lackawanna, PA........... 5.8 103.2 1.7 131 662 2.5 262 Lancaster, PA............ 12.1 231.1 0.7 212 733 3.1 227 Lehigh, PA............... 8.7 179.3 2.6 66 860 5.4 51 Luzerne, PA.............. 7.9 144.2 0.5 232 652 0.3 311 Montgomery, PA........... 27.5 493.7 0.4 238 1,094 6.5 21 Northampton, PA.......... 6.4 99.2 1.7 131 765 4.7 88 Philadelphia, PA......... 29.2 638.9 -0.2 286 1,009 4.2 123 Washington, PA........... 5.3 78.6 1.8 124 725 2.8 248 Westmoreland, PA......... 9.5 138.2 -0.8 297 665 4.6 101 York, PA................. 8.9 177.3 0.9 189 750 -4.3 318 Kent, RI................. 5.8 84.8 1.5 141 753 1.9 290 Providence, RI........... 18.3 292.9 0.8 199 847 4.2 123 Charleston, SC........... 14.2 208.4 4.6 13 728 3.4 204 Greenville, SC........... 14.1 236.2 2.0 104 748 3.2 220 Horry, SC................ 9.8 112.5 6.6 6 578 1.9 290 Lexington, SC............ 6.5 94.7 4.0 22 646 2.9 241 Richland, SC............. 10.9 218.5 0.4 238 737 5.1 64 Spartanburg, SC.......... 7.0 119.4 2.6 66 724 3.4 204 Minnehaha, SD............ 6.3 115.0 1.9 115 705 4.3 114 Davidson, TN............. 18.3 455.8 1.4 146 888 5.3 54 Hamilton, TN............. 8.5 196.0 0.5 232 765 5.8 35 Knox, TN................. 10.8 228.7 3.3 38 762 4.4 108 Rutherford, TN........... 4.0 100.4 2.2 87 790 7.8 13 Shelby, TN............... 20.0 522.4 2.0 104 877 3.4 204 Bell, TX................. 4.4 97.6 2.5 71 643 4.7 88 Bexar, TX................ 31.3 716.4 3.9 27 760 2.3 275 Brazoria, TX............. 4.4 84.8 4.1 20 801 6.5 21 Brazos, TX............... 3.7 85.4 1.9 115 615 4.4 108 Cameron, TX.............. 6.4 123.8 4.2 16 527 4.2 123 Collin, TX............... 15.5 274.8 6.5 7 986 2.5 262 Dallas, TX............... 67.4 1,490.2 2.9 52 1,069 3.3 212 Denton, TX............... 9.8 161.1 (7) - 768 (7) - El Paso, TX.............. 13.1 267.5 1.4 146 602 5.2 59 Fort Bend, TX............ 7.7 120.7 5.6 9 912 6.8 19 Galveston, TX............ 5.2 93.5 (7) - 772 (7) - Harris, TX............... 93.6 1,993.9 4.0 22 1,087 7.2 15 Hidalgo, TX.............. 10.2 213.2 4.2 16 542 5.7 39 Jefferson, TX............ 5.8 124.2 2.1 95 836 2.8 248 Lubbock, TX.............. 6.6 124.1 2.6 66 644 3.5 196 McLennan, TX............. 4.8 103.5 1.3 156 677 4.2 123 Montgomery, TX........... 7.5 114.5 6.3 8 823 5.2 59 Nueces, TX............... 8.0 151.1 1.8 124 734 7.3 14 Smith, TX................ 5.1 92.1 1.1 168 739 2.6 258 Tarrant, TX.............. 35.8 755.9 2.9 52 874 5.7 39 Travis, TX............... 27.0 562.8 4.8 11 1,038 10.9 4 Webb, TX................. 4.6 87.0 3.1 44 571 4.0 145 Williamson, TX........... 6.4 112.4 7.7 4 819 1.4 299 Davis, UT................ 7.2 101.7 4.1 20 709 5.8 35 Salt Lake, UT............ 39.4 584.4 4.8 11 803 4.4 108 Utah, UT................. 13.2 172.7 6.8 5 680 8.1 9 Weber, UT................ 5.8 92.2 2.8 55 650 6.9 17 Chittenden, VT........... 5.8 96.4 0.6 224 825 4.7 88 Arlington, VA............ 7.4 160.7 2.0 104 1,419 4.2 123 Chesterfield, VA......... 7.1 120.7 2.5 71 776 3.6 183 Fairfax, VA.............. 32.0 585.5 1.6 136 1,297 4.0 145 Henrico, VA.............. 8.8 179.3 2.1 95 897 1.4 299 Loudoun, VA.............. 7.7 127.9 1.0 180 1,064 0.4 310 Prince William, VA....... 6.6 105.2 1.3 156 766 2.3 275 Alexandria City, VA...... 6.0 95.6 1.2 163 1,123 4.7 88 Chesapeake City, VA...... 5.4 101.5 2.3 80 681 3.7 176 Newport News City, VA.... 3.9 100.7 1.8 124 765 4.2 123 Norfolk City, VA......... 5.7 144.2 0.3 249 834 3.5 196 Richmond City, VA........ 7.0 163.2 0.9 189 968 2.8 248 Virginia Beach City, VA.. 11.4 179.8 1.2 163 689 4.7 88 Clark, WA................ 11.7 131.3 1.9 115 765 3.9 160 King, WA................. 77.4 1,173.0 3.0 50 1,043 5.8 35 Kitsap, WA............... 6.6 84.8 0.7 212 760 3.8 168 Pierce, WA............... 20.4 269.8 2.0 104 741 4.2 123 Snohomish, WA............ 17.4 238.4 3.8 28 850 4.7 88 Spokane, WA.............. 15.1 208.0 2.7 60 677 5.1 64 Thurston, WA............. 6.7 97.6 2.2 87 750 5.9 34 Whatcom, WA.............. 6.9 80.6 1.1 168 646 5.2 59 Yakima, WA............... 8.0 91.3 4.2 16 570 3.3 212 Kanawha, WV.............. 6.1 109.2 0.8 199 724 3.7 176 Brown, WI................ 6.6 149.5 -0.1 279 759 3.8 168 Dane, WI................. 13.7 301.3 -0.6 293 813 6.8 19 Milwaukee, WI............ 21.0 500.8 0.3 249 866 4.2 123 Outagamie, WI............ 5.0 103.8 1.3 156 732 1.7 296 Racine, WI............... 4.1 77.3 0.2 259 830 6.3 28 Waukesha, WI............. 13.1 238.0 1.3 156 913 10.4 5 Winnebago, WI............ 3.7 88.8 0.3 249 796 2.7 255 San Juan, PR............. 14.9 310.4 -5.7 (8) 577 6.1 (8) 1 Includes workers covered by Unemployment Insurance (UI) and Unemployment Compensation for Federal Employees (UCFE) programs. These 325 U.S. counties comprise 71.0 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, fourth quarter 2006(2) Employment Average weekly wage(4) Establishments, fourth quarter County by NAICS supersector 2006 Percent Percent (thousands) December change, Average change, 2006 December weekly fourth (thousands) 2005-06(3) wage quarter 2005-06(3) United States(5)............................. 8,929.5 135,933.2 1.6 $861 4.2 Private industry........................... 8,649.9 114,287.7 1.7 866 4.5 Natural resources and mining............. 125.0 1,723.6 4.2 872 8.9 Construction............................. 890.1 7,534.7 1.7 949 6.4 Manufacturing............................ 363.7 14,039.7 -1.1 1,036 4.5 Trade, transportation, and utilities..... 1,909.3 27,038.2 1.2 733 3.7 Information.............................. 145.5 3,068.8 -0.3 1,290 3.4 Financial activities..................... 860.9 8,222.7 0.7 1,346 5.1 Professional and business services....... 1,456.8 17,785.7 3.0 1,093 5.1 Education and health services............ 806.5 17,228.1 2.8 811 3.7 Leisure and hospitality.................. 717.2 12,939.4 3.1 368 4.2 Other services........................... 1,151.2 4,391.6 1.1 546 4.0 Government................................. 279.6 21,645.5 1.1 837 3.7 Los Angeles, CA.............................. 400.2 4,242.5 0.5 1,011 4.3 Private industry........................... 396.5 3,652.2 0.5 1,008 3.8 Natural resources and mining............. 0.5 11.2 8.5 992 8.5 Construction............................. 14.3 157.4 1.2 1,033 7.2 Manufacturing............................ 15.8 458.5 -2.2 1,019 5.4 Trade, transportation, and utilities..... 56.1 843.8 0.5 828 6.2 Information.............................. 9.1 213.4 0.5 1,793 1.0 Financial activities..................... 25.4 249.8 -0.6 1,486 4.9 Professional and business services....... 43.9 608.2 1.2 1,185 3.2 Education and health services............ 28.3 475.9 1.0 925 4.4 Leisure and hospitality.................. 27.5 394.2 1.9 821 0.9 Other services........................... 175.3 239.9 0.6 440 4.5 Government................................. 3.7 590.3 0.8 1,032 7.5 Cook, IL..................................... 136.4 2,569.9 0.9 1,051 5.1 Private industry........................... 135.2 2,258.7 1.1 1,059 5.0 Natural resources and mining............. 0.1 1.4 0.6 1,127 8.0 Construction............................. 11.9 94.5 2.2 1,323 6.6 Manufacturing............................ 7.2 245.6 -1.6 1,072 4.3 Trade, transportation, and utilities..... 27.5 496.7 0.1 826 3.4 Information.............................. 2.5 59.1 -2.5 1,412 5.1 Financial activities..................... 15.7 219.9 -0.2 1,748 6.5 Professional and business services....... 27.9 445.1 2.8 1,395 5.0 Education and health services............ 13.3 366.4 1.6 902 6.4 Leisure and hospitality.................. 11.4 230.3 3.3 421 3.7 Other services........................... 13.5 95.1 -0.5 722 4.0 Government................................. 1.2 (6) (6) (6) (6) New York, NY................................. 116.4 2,359.8 1.9 1,781 5.7 Private industry........................... 116.2 1,909.3 2.3 1,959 6.5 Natural resources and mining............. 0.0 0.1 -5.0 1,442 -62.8 Construction............................. 2.2 32.3 6.0 1,783 9.8 Manufacturing............................ 3.0 38.0 -7.1 1,386 0.0 Trade, transportation, and utilities..... 21.3 257.0 1.1 1,277 3.8 Information.............................. 4.1 132.0 -0.8 2,062 6.1 Financial activities..................... 17.8 374.0 2.8 3,922 7.5 Professional and business services....... 23.3 478.7 3.0 2,017 5.6 Education and health services............ 8.3 289.1 1.5 1,021 2.8 Leisure and hospitality.................. 10.7 208.2 3.2 935 13.1 Other services........................... 16.9 87.1 0.5 997 5.8 Government................................. 0.2 450.5 0.1 1,025 (6) Harris, TX................................... 93.6 1,993.9 4.0 1,087 7.2 Private industry........................... 93.1 1,740.9 4.4 1,117 7.4 Natural resources and mining............. 1.4 76.1 12.2 2,722 1.2 Construction............................. 6.4 144.7 6.8 1,094 12.8 Manufacturing............................ 4.6 180.1 5.5 1,357 8.1 Trade, transportation, and utilities..... 21.2 425.6 2.1 953 7.4 Information.............................. 1.3 32.8 3.5 1,220 2.3 Financial activities..................... 10.1 118.5 1.1 1,390 4.7 Professional and business services....... 18.3 325.3 4.7 1,335 10.0 Education and health services............ 9.7 206.3 3.7 901 3.4 Leisure and hospitality.................. 7.0 169.9 5.1 377 3.6 Other services........................... 10.8 56.3 2.1 612 5.7 Government................................. 0.4 253.0 1.1 887 5.6 Maricopa, AZ................................. 94.1 1,854.5 3.8 856 4.6 Private industry........................... 93.5 1,636.8 4.0 858 4.5 Natural resources and mining............. 0.5 9.5 5.8 776 10.2 Construction............................. 9.9 171.0 -0.5 924 7.4 Manufacturing............................ 3.4 136.0 0.8 1,229 11.4 Trade, transportation, and utilities..... 20.1 386.1 4.3 786 2.7 Information.............................. 1.6 33.0 0.3 1,058 12.4 Financial activities..................... 11.8 152.5 2.0 1,105 1.8 Professional and business services....... 20.6 321.9 5.9 870 2.8 Education and health services............ 9.1 193.2 6.8 931 4.3 Leisure and hospitality.................. 6.7 179.6 4.7 405 6.0 Other services........................... 6.7 49.1 5.3 565 0.7 Government................................. 0.6 217.7 2.3 842 5.5 Orange, CA................................... 96.6 1,519.1 0.7 994 2.7 Private industry........................... 95.3 1,388.4 0.9 998 2.5 Natural resources and mining............. 0.2 4.9 -13.4 650 6.7 Construction............................. 7.2 106.5 -0.2 1,122 7.5 Manufacturing............................ 5.6 182.7 0.2 1,178 5.3 Trade, transportation, and utilities..... 18.0 286.5 0.6 893 4.7 Information.............................. 1.4 31.2 -1.8 1,364 9.6 Financial activities..................... 11.6 136.9 -6.4 1,594 -2.9 Professional and business services....... 19.6 280.4 4.2 1,096 2.3 Education and health services............ 9.9 139.3 4.0 919 2.5 Leisure and hospitality.................. 7.1 172.0 3.6 386 1.8 Other services........................... 14.6 48.0 -2.8 583 2.8 Government................................. 1.4 130.7 -1.4 961 6.1 Dallas, TX................................... 67.4 1,490.2 2.9 1,069 3.3 Private industry........................... 67.0 1,328.5 3.1 1,088 2.9 Natural resources and mining............. 0.6 7.5 2.1 3,254 2.9 Construction............................. 4.3 80.4 4.4 1,012 5.7 Manufacturing............................ 3.2 146.9 0.3 1,145 3.5 Trade, transportation, and utilities..... 14.8 315.6 1.6 971 -0.1 Information.............................. 1.7 52.9 -0.9 1,371 4.1 Financial activities..................... 8.6 143.4 3.5 1,491 9.1 Professional and business services....... 14.1 269.5 4.2 1,287 1.7 Education and health services............ 6.4 142.5 5.5 994 1.8 Leisure and hospitality.................. 5.2 126.7 4.0 483 2.5 Other services........................... 6.4 39.5 1.5 669 4.9 Government................................. 0.4 161.7 1.7 911 5.3 San Diego, CA................................ 93.8 1,335.2 0.8 922 3.6 Private industry........................... 92.3 1,110.5 0.5 910 3.2 Natural resources and mining............. 0.8 10.1 -9.1 591 4.2 Construction............................. 7.4 90.5 -2.8 1,020 6.1 Manufacturing............................ 3.3 102.9 -0.6 1,211 4.4 Trade, transportation, and utilities..... 14.8 232.8 0.7 725 5.1 Information.............................. 1.3 37.7 0.5 1,696 -14.6 Financial activities..................... 10.2 83.7 -2.0 1,167 -4.1 Professional and business services....... 16.8 215.1 0.3 1,184 9.8 Education and health services............ 8.1 125.4 2.2 898 5.2 Leisure and hospitality.................. 6.9 155.8 3.5 396 4.2 Other services........................... 22.8 56.3 1.2 492 4.2 Government................................. 1.5 224.8 2.2 978 5.2 King, WA..................................... 77.4 1,173.0 3.0 1,043 5.8 Private industry........................... 76.9 1,020.8 3.5 1,054 5.8 Natural resources and mining............. 0.4 2.7 -6.8 1,275 4.6 Construction............................. 6.8 68.7 9.4 1,032 6.4 Manufacturing............................ 2.5 112.6 3.7 1,371 6.6 Trade, transportation, and utilities..... 15.0 229.5 2.4 907 5.7 Information.............................. 1.8 74.8 7.4 1,872 4.2 Financial activities..................... 6.9 76.0 -1.0 1,351 9.3 Professional and business services....... 12.8 184.2 5.4 1,235 4.7 Education and health services............ 6.4 119.4 2.9 817 4.3 Leisure and hospitality.................. 6.0 108.8 2.7 427 3.4 Other services........................... 18.3 44.1 -1.8 561 5.3 Government................................. 0.5 152.2 0.0 968 4.5 Miami-Dade, FL............................... 84.9 1,032.7 1.3 898 8.1 Private industry........................... 84.6 880.0 1.6 888 9.0 Natural resources and mining............. 0.5 10.5 6.5 477 2.1 Construction............................. 5.9 54.1 13.3 922 2.7 Manufacturing............................ 2.6 47.3 -2.6 805 7.9 Trade, transportation, and utilities..... 22.9 257.8 1.8 816 7.8 Information.............................. 1.6 21.7 -3.4 1,194 1.2 Financial activities..................... 10.3 72.4 2.8 1,331 9.0 Professional and business services....... 17.2 141.2 -5.5 1,207 18.7 Education and health services............ 8.7 134.9 4.4 854 6.2 Leisure and hospitality.................. 5.6 101.7 1.7 482 7.6 Other services........................... 7.6 35.3 2.4 519 7.2 Government................................. 0.3 152.7 -0.2 959 3.7 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, fourth quarter 2006(2) Employment Average weekly wage(5) Establishments, fourth quarter County(3) 2006 Percent Percent (thousands) December change, Average change, 2006 December weekly fourth (thousands) 2005-06(4) wage quarter 2005-06(4) United States(6)......... 8,929.5 135,933.2 1.6 $861 4.2 Jefferson, AL............ 18.8 378.1 1.0 868 4.1 Anchorage Borough, AK.... 8.1 145.2 1.0 879 4.9 Maricopa, AZ............. 94.1 1,854.5 3.8 856 4.6 Pulaski, AR.............. 14.4 249.5 0.7 782 4.3 Los Angeles, CA.......... 400.2 4,242.5 0.5 1,011 4.3 Denver, CO............... 25.4 439.7 2.2 1,069 6.3 Hartford, CT............. 25.1 507.6 2.7 1,045 1.2 New Castle, DE........... 19.6 289.1 0.3 1,004 -5.7 Washington, DC........... 32.7 675.0 0.4 1,424 5.0 Miami-Dade, FL........... 84.9 1,032.7 1.3 898 8.1 Fulton, GA............... 40.2 790.0 1.8 1,120 -2.0 Honolulu, HI............. 24.1 460.2 1.6 787 3.1 Ada, ID.................. 15.1 211.7 3.6 818 8.9 Cook, IL................. 136.4 2,569.9 0.9 1,051 5.1 Marion, IN............... 23.6 589.6 1.1 867 3.6 Polk, IA................. 14.6 274.6 2.9 853 3.1 Johnson, KS.............. 20.1 315.1 3.6 881 3.6 Jefferson, KY............ 22.8 439.0 2.1 830 4.0 East Baton Rouge, LA..... 14.0 263.4 0.7 771 8.1 Cumberland, ME........... 12.1 175.1 0.6 769 2.5 Montgomery, MD........... 32.7 472.8 1.4 1,136 2.4 Middlesex, MA............ 47.1 815.6 1.4 1,209 4.3 Wayne, MI................ 33.9 771.4 -3.1 969 1.8 Hennepin, MN............. 42.4 851.5 0.1 1,052 3.8 Hinds, MS................ 6.5 129.4 0.6 759 3.3 St. Louis, MO............ 34.1 635.1 1.4 907 2.4 Yellowstone, MT.......... 5.5 75.1 1.8 688 6.2 Douglas, NE.............. 15.6 318.4 1.2 814 3.0 Clark, NV................ 47.6 921.1 2.7 815 5.6 Hillsborough, NH......... 12.6 200.8 -0.1 994 4.7 Bergen, NJ............... 35.1 460.7 1.1 1,114 4.0 Bernalillo, NM........... 17.3 335.7 2.7 760 4.0 New York, NY............. 116.4 2,359.8 1.9 1,781 5.7 Mecklenburg, NC.......... 29.7 557.7 4.5 973 4.4 Cass, ND................. 5.7 96.1 3.5 724 4.6 Cuyahoga, OH............. 38.3 755.6 -0.5 874 2.1 Oklahoma, OK............. 23.2 426.2 0.9 759 6.5 Multnomah, OR............ 27.0 449.6 2.8 868 5.6 Allegheny, PA............ 35.1 690.6 0.7 912 6.4 Providence, RI........... 18.3 292.9 0.8 847 4.2 Greenville, SC........... 14.1 236.2 2.0 748 3.2 Minnehaha, SD............ 6.3 115.0 1.9 705 4.3 Shelby, TN............... 20.0 522.4 2.0 877 3.4 Harris, TX............... 93.6 1,993.9 4.0 1,087 7.2 Salt Lake, UT............ 39.4 584.4 4.8 803 4.4 Chittenden, VT........... 5.8 96.4 0.6 825 4.7 Fairfax, VA.............. 32.0 585.5 1.6 1,297 4.0 King, WA................. 77.4 1,173.0 3.0 1,043 5.8 Kanawha, WV.............. 6.1 109.2 0.8 724 3.7 Milwaukee, WI............ 21.0 500.8 0.3 866 4.2 Laramie, WY.............. 3.1 42.2 2.3 682 5.4 San Juan, PR............. 14.9 310.4 -5.7 577 6.1 St. Thomas, VI........... 1.8 23.5 0.9 682 8.8 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. Table 4. Covered(1) establishments, employment, and wages by state, fourth quarter 2006(2) Employment Average weekly wage(3) Establishments, fourth quarter State 2006 Percent Percent (thousands) December change, Average change, 2006 December weekly fourth (thousands) 2005-06 wage quarter 2005-06 United States(4)......... 8,929.5 135,933.2 1.6 $861 4.2 Alabama.................. 118.3 1,948.9 1.0 737 4.4 Alaska................... 21.0 296.2 1.7 837 5.3 Arizona.................. 152.9 2,693.3 3.5 805 4.7 Arkansas................. 82.1 1,179.3 1.0 652 2.8 California............... 1,300.2 15,672.1 1.1 987 4.4 Colorado................. 175.8 2,283.3 2.2 877 5.0 Connecticut.............. 112.1 1,706.3 2.0 1,101 2.0 Delaware................. 30.2 427.5 0.5 896 -4.1 District of Columbia..... 32.7 675.0 0.4 1,424 5.0 Florida.................. 593.5 8,126.2 1.7 788 4.6 Georgia.................. 267.7 4,090.4 2.2 812 2.1 Hawaii................... 37.5 632.3 1.9 762 3.5 Idaho.................... 56.2 649.8 4.0 672 7.0 Illinois................. 353.6 5,899.5 1.3 928 4.6 Indiana.................. 155.4 2,924.3 0.6 723 2.6 Iowa..................... 93.3 1,486.3 1.4 697 3.7 Kansas................... 85.6 1,358.9 2.6 725 6.5 Kentucky................. 112.1 1,815.4 1.7 708 3.8 Louisiana................ 123.7 1,855.1 4.3 748 5.1 Maine.................... 49.6 603.4 0.7 679 2.7 Maryland................. 163.2 2,570.5 1.2 941 3.4 Massachusetts............ 209.3 3,244.5 1.1 1,072 4.5 Michigan................. 265.4 4,242.5 -1.9 852 2.2 Minnesota................ 167.0 2,683.1 -0.2 840 4.0 Mississippi.............. 69.4 1,140.3 2.3 630 2.6 Missouri................. 174.0 2,737.5 1.4 741 2.3 Montana.................. 41.5 431.6 3.0 625 5.8 Nebraska................. 58.3 912.2 1.3 687 3.6 Nevada................... 74.2 1,285.8 2.6 817 5.4 New Hampshire............ 49.2 636.9 0.6 917 8.1 New Jersey............... 283.1 4,023.6 0.9 1,055 4.4 New Mexico............... 53.3 823.2 3.7 705 7.1 New York................. 573.2 8,643.1 1.3 1,104 5.3 North Carolina........... 251.5 4,054.0 3.2 751 4.6 North Dakota............. 24.6 341.0 2.5 643 4.7 Ohio..................... 292.5 5,346.2 -0.3 774 3.1 Oklahoma................. 98.0 1,536.4 2.0 679 5.8 Oregon................... 129.3 1,723.9 2.3 763 4.8 Pennsylvania............. 336.1 5,680.8 1.1 837 4.4 Rhode Island............. 36.1 488.4 1.0 817 3.8 South Carolina........... 135.6 1,886.8 3.0 688 3.3 South Dakota............. 29.9 387.1 2.2 614 4.2 Tennessee................ 137.8 2,785.2 1.7 773 4.6 Texas.................... 540.5 10,164.2 3.5 871 5.8 Utah..................... 88.4 1,208.0 5.1 725 5.5 Vermont.................. 24.8 308.7 0.2 707 3.4 Virginia................. 220.5 3,682.9 1.3 887 3.7 Washington............... 219.2 2,863.7 2.5 846 5.2 West Virginia............ 48.2 714.3 1.4 656 4.6 Wisconsin................ 157.9 2,792.4 0.6 746 4.5 Wyoming.................. 24.0 270.9 5.4 759 11.3 Puerto Rico.............. 61.5 1,062.8 -3.0 494 4.7 Virgin Islands........... 3.5 45.5 1.2 711 7.2 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.