Technical information: (202) 691-6567 USDL 06-1275 http://www.bls.gov/cew/ For release: 10:00 A.M. EDT Media contact: 691-5902 Wednesday, July 26, 2006 COUNTY EMPLOYMENT AND WAGES: FOURTH QUARTER 2005 In December 2005, Lee County, Fla., had the largest over-the-year percentage increase in employment among the largest counties in the U.S., according to preliminary data released today by the Bureau of Labor Statistics of the U.S. Department of Labor. Lee County, which includes Fort Myers, experienced an over-the-year employment gain of 9.2 percent, compared with national job growth of 1.7 percent. Orleans County (New Orleans), La., had the largest over-the-year gain in average weekly wages in the fourth quarter of 2005, with an increase of 28.7 percent. The in- crease in Orleans County was related to the effects of Hurricane Katrina, discussed in some detail below. The U.S. average weekly wage increased by 1.5 percent over the same time span. Of the 322 largest counties in the United States, as measured by 2004 annual average employment, 133 had over-the-year percentage growth in em- ployment above the national average in December 2005, and 176 experienced changes below the national average. Average weekly wages grew faster than the national average in 127 of the largest U.S. counties, while the percent change in average weekly wages was below the national average in 183 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 8.7 million em- ployer reports cover 133.8 million full- and part-time workers. The at- tached tables contain data for the nation and for the 322 U.S. counties with annual average employment levels of 75,000 or more in 2004. December 2005 employment and 2005 fourth-quarter average weekly wages for all states are provided in table 4 of this release. Final data for all states, metro- politan statistical areas, counties, and the nation through the fourth quar- ter of 2004 are available on the BLS Web site at http://www.bls.gov/cew/. Preliminary data for the fourth quarter of 2005 and revised data for the first, second, and third quarters of 2005 will be available in July on the BLS Web site. ------------------------------------------------------------------- | Hurricanes Katrina and Rita | | | | The measures of employment and wages reported in this news | | release reflect the impact of both Hurricanes Katrina and Rita and| | ongoing labor market trends. The effects of Hurricane Katrina, | | which hit the Gulf Coast on August 29, 2005, were first reflected | | in the September QCEW employment counts and the wage totals for | | the third quarter of 2005. The impact of this catastrophic storm | | in parts of Louisiana and Mississippi continue to be reflected | | in monthly employment and quarterly wage totals in the fourth | | quarter of 2005. Modifications to QCEW nonresponse adjustment | | methods were made for both the third and fourth quarters of 2005 | | to better reflect the impact of the hurricane in parts of | | Louisiana and Mississippi. Hurricane Rita made landfall on | | September 24, after the September reference period. Nonresponse | | adjustment methods were modified for the fourth quarter of 2005 | | to reflect the impact of this hurricane in parts of Louisiana. | | For more information, see the QCEW section of the Katrina | | coverage on the BLS Web site at http://www.bls.gov/katrina/ | | qcewquestions.htm. | ------------------------------------------------------------------- - 2 - Table A. Top 10 large counties ranked by December 2005 employment, December 2004-05 employment growth, and December 2004-05 percent growth in employment ------------------------------------------------------------------------------------ Employment in large counties ------------------------------------------------------------------------------------ | | December 2005 employment | Growth in employment, | Percent growth (thousands) | December 2004-05 | in employment, | (thousands) | December 2004-05 ---------------------------|----------------------------|--------------------------- U.S. 133,834.6|U.S. 2,275.4|U.S. 1.7 ---------------------------|----------------------------|--------------------------- Los Angeles, Calif. 4,196.5|Maricopa, Ariz. 97.4|Lee, Fla. 9.2 Cook, Ill. 2,547.4|Los Angeles, Calif. 73.4|Kern, Calif. 8.6 New York, N.Y. 2,310.7|Harris, Texas 69.7|Pasco, Fla. 7.9 Harris, Texas 1,919.8|Clark, Nev. 56.2|Seminole, Fla. 7.8 Maricopa, Ariz. 1,784.8|New York, N.Y. 44.2|Clark, Nev. 6.7 Orange, Calif. 1,507.7|Dallas, Texas 39.3|Montgomery, Texas 6.6 Dallas, Texas 1,457.5|King, Wash. 37.5|Lake, Fla. 6.5 San Diego, Calif. 1,315.8|Broward, Fla. 30.7|Maricopa, Ariz. 5.8 King, Wash. 1,145.1|Orange, Fla. 28.9|Webb, Texas 5.7 Miami-Dade, Fla. 1,022.1|Orange, Calif. 28.4|Collier, Fla. 5.6 | |East Baton Rouge, La. 5.6 ------------------------------------------------------------------------------------ Large County Employment In December 2005, national employment, as measured by the QCEW pro- gram, was 133.8 million, up by 1.7 percent from December 2004. The 322 U.S. counties with 75,000 or more employees accounted for 70.8 percent of total U.S. covered employment and 76.9 percent of total covered wages. These 322 counties had a net job gain of 1,500,400 over the year, accounting for 65.9 percent of the U.S. employment increase. Employment increased in 250 of the large counties from December 2004 to December 2005. Lee, Fla., had the largest over-the-year percentage increase in employment (9.2 percent). Kern, Calif., had the next largest increase, 8.6 percent, followed by the coun- ties of Pasco, Fla. (7.9 percent), Seminole, Fla. (7.8 percent), and Clark, Nev. (6.7 percent). (See table 1.) Employment declined in 56 counties from December 2004 to December 2005. The largest percentage decline in employment was in Orleans County, La. (-39.3 percent), followed by the counties of Harrison, Miss. (-20.2 percent), and Jefferson, La. (-17.0 percent). Employment losses in these three Gulf Coast counties reflected the devastation caused by Hurricane Katrina. Stark, Ohio, had the next largest employment decline (-1.8 percent), followed by Saginaw, Mich. (-1.5 percent). The largest gains in employment from December 2004 to December 2005 were recorded in the counties of Maricopa, Ariz. (97,400), Los Angeles, Calif. (73,400), Harris, Texas (69,700), Clark, Nev. (56,200), and New York, N.Y. (44,200). (See table A.) The largest declines in employment occurred in the Katrina-affected counties of Orleans, La. (-96,800), Jefferson, La. (-36,900), and Harrison, Miss. (-18,200), followed by the counties of Wayne, Mich. (-11,400), and Oakland, Mich. (-5,700). - 3 - Table B. Top 10 large counties ranked by fourth quarter 2005 average weekly wages, fourth quarter 2004-05 growth in average weekly wages, and fourth quarter 2004-05 percent growth in average weekly wages ------------------------------------------------------------------------------ Average weekly wage in large counties ------------------------------------------------------------------------------ | | Average weekly wage, | Growth in average weekly| Percent growth in fourth quarter 2005 | wage, fourth quarter | average weekly wage, | 2004-05 | fourth quarter 2004-05 ------------------------------------------------------------------------------ U.S. $825|U.S. $12|U.S. 1.5 ---------------------------|-------------------------|------------------------ New York, N.Y. $1,684|Orleans, La. $216|Orleans, La. 28.7 Fairfield, Conn. 1,496|Jefferson, La. 113|Harrison, Miss. 18.9 Santa Clara, Calif. 1,490|Harrison, Miss. 108|Jefferson, La. 16.2 Suffolk, Mass. 1,412|San Francisco, Calif. 96|York, Pa. 10.8 San Francisco, Calif. 1,378|New Castle, Del. 88|New Castle, Del. 9.0 San Mateo, Calif. 1,365|Fulton, Ga. 80|Fulton, Ga. 7.6 Washington, D.C. 1,354|York, Pa. 76|San Francisco, Calif.7.5 Arlington, Va. 1,345|Marin, Calif. 71|Collier, Fla. 7.4 Somerset, N.J. 1,296|New York, N.Y. 69|Baltimore City, Md. 7.1 Fairfax, Va. 1,247|Baltimore City, Md. 66|Marin, Calif. 6.7 | |Lake, Fla. 6.7 ------------------------------------------------------------------------------ Hurricane Rita affected the Texas-Louisiana border counties. However, the damage was not as extensive as with Hurricane Katrina. Despite the effects of Hurricane Rita, Calcasieu County, La., posted a small over-the- year gain in employment (1,125) in December 2005--1.3 percent. Prior to Hurricane Rita, Calcasieu had posted strong over-the-year employment growth in both June and September, 5.2 and 4.7 percent, respectively. The evacuation, due to Hurricane Katrina, of a large part of the New Orleans area population to other parts of the state likely led to changes in the employ- ment situation in those counties in the third and fourth quarters of 2005. For example, East Baton Rouge County, La., saw a significant employment gain of 5.6 percent over the year ending in December, which followed a year-over-year gain of 4.8 percent in September, as contrasted with over-the-year growth of 2.0 per- cent in June 2005, 3 months before the hurricane. The pattern of job growth was similar in Lafayette, La., which posted an over-the-year gain of 5.5 percent in December and 6.2 percent in September, after a gain of only 2.6 percent in June 2005. Large County Average Weekly Wages The national average weekly wage in the fourth quarter of 2005 was $825. Average weekly wages were higher than the national average in 105 of the largest 322 U.S. counties. New York County, N.Y., held the top position among the highest-paid large counties with an average weekly wage of $1,684. Fairfield, Conn., was second with an average weekly wage of $1,496, followed by Santa Clara, Calif. ($1,490), Suffolk, Mass. ($1,412), and San Francisco, Calif. ($1,378). (See table B.) There were 217 counties with an average weekly wage below the national average in the fourth quarter of 2005. The lowest average weekly wages were reported in Cameron County, Texas ($506), followed by the counties of Hidalgo, Texas ($512), Webb, Texas ($548), Yakima, Wash. ($552), and Horry, S.C. ($556). (See table 1.) - 4 - Over the year, the national average weekly wage rose by 1.5 percent. Among the largest counties, Orleans, La., led the nation in growth in average weekly wages, with an increase of 28.7 percent from the fourth quarter of 2004. Harrison, Miss., was second with 18.9 percent growth, followed by the counties of Jefferson, La. (16.2 percent), York, Pa. (10.8 percent), and New Castle, Del. (9.0 percent). The high average weekly wage growth rates for Orleans, Harrison, and Jefferson Counties were related to the disproportionate job and pay losses in lower-paid industries due to the impact of Hurricane Katrina. Seventy-two counties experienced over-the-year declines in average weekly wages. Clayton, Ga., and Williamson, Texas, had the largest de- crease, -8.6 percent each, followed by the counties of Trumbull, Ohio (-5.8 percent), Brown, Wis. (-5.1 percent), and Anoka, Minn. (-4.7 per- cent). Ten Largest U.S. Counties Of the 10 largest U.S. counties (based on 2004 annual average employ- ment levels), all reported increases in employment from December 2004 to December 2005. Maricopa County, Ariz., experienced the fastest growth in employment among the largest counties, with a 5.8 percent increase. Within Maricopa County, employment rose in every industry group except natural resources and mining. The largest gains were in construction (13.6 percent) and financial activities (7.3 percent). Harris, Texas, had the next largest increase in employment, 3.8 percent, followed by King, Wash. (3.4 percent). The smallest employment gains occurred in Cook County, Ill., and San Diego, Calif. (0.9 percent each), followed by Los Angeles, Calif. (1.8 percent). (See table 2.) All of the 10 largest U.S. counties saw over-the-year increases in average weekly wages. New York, N.Y., had the fastest growth in wages among the 10 largest counties, increasing by 4.3 percent. Within New York County, average weekly wages increased the most in natural resources and mining (175.4 percent), a very small sector. Increases in professional and business services (4.8 percent), however, had a larger impact on the county's wage growth. Harris, Texas, was second in wage growth, increasing by 3.8 per- cent, followed by Dallas County, Texas (3.4 percent). The smallest wage gains among the 10 largest counties occurred in Los Angeles, Calif. (0.3 percent), followed by King, Wash. (1.1 percent), and Cook County, Ill. (1.4 percent). Largest County by State Table 3 shows December 2005 employment and the 2005 fourth quarter average weekly wage in the largest county in each state, which is based on 2004 annual average employment levels. (This table includes two counties--Yellowstone, Mont., and Laramie, Wyo.--that have employment levels below 75,000.) The employment levels in these counties in December 2005 ranged from approximately 4.2 million in Los Angeles County, Calif., to 41,300 in Laramie County, Wyo. The highest average weekly wage of these counties was in New York, N.Y. ($1,684), while the lowest average weekly wage was in Laramie, Wyo. ($647). - 5 - 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. ------------------------------------------------------------------ | Regional Quarterly Census of Employment and Wages News Releases | | | | Several BLS regional offices are issuing QCEW news releases | | targeted to local data users. For links to these releases, see | | http://www.bls.gov/cew/cewregional.htm. | ------------------------------------------------------------------ - 6 - Technical Note These data are the product of a federal-state cooperative program, the Quarterly Census of Employment and Wages (QCEW) program, also known as the ES-202 program. The data are derived from summaries of employment and to- tal pay of workers covered by state and federal unemployment insurance (UI) legislation and provided by State Workforce Agencies (SWAs). The summaries are a result of the administration of state unemployment insurance programs that require most employers to pay quarterly taxes based on the employment and wages of workers covered by UI. Data for 2005 are preliminary and sub- ject to revision. For purposes of this release, large counties are defined as having em- ployment levels of 75,000 or greater. In addition, data for San Juan, Puerto Rico, are provided, but not used in calculating U.S. averages, rankings, or in the analysis in the text. Each year, these large counties are selected on the basis of the preliminary annual average of employment for the previous year. The 323 counties presented in this release were derived using 2004 preliminary annual averages of employment. All of the 318 counties that were published in the 2004 releases are included in the 2005 releases. The following counties grew enough in 2004 to be included in the 2005 releases: Lake, Fla., Wyandotte, Kan., Harford, Md., Washington, Pa., and Whatcom, Wash. These counties will be included in all 2005 quar- terly releases. The counties in table 2 are selected and sorted each year based on the annual average employment from the preceding year. The preliminary QCEW data presented in this release may differ from data released by the individual states. These potential differences result from the states' continuing receipt of UI data over time and ongoing review and editing. The individual states determine their data release timetables. Differences between QCEW, BED, and CES employment measures The Bureau publishes three different establishment-based employment measures for any given quarter. Each of these measures--QCEW, Business Employment Dynamics (BED), and Current Employment Statistics (CES)--makes use of the quarterly UI employment reports in producing data; however, each measure has a somewhat different universe coverage, estimation pro- cedure, and publication product. Differences in coverage and estimation methods can result in somewhat different measures of employment change over time. It is important to understand program differences and the intended uses of the program pro- ducts. (See table below.) Additional information on each program can be obtained from the program Web sites shown in the table below. - 7 - Summary of Major Differences between QCEW, BED, and CES Employment Measures -------------------------------------------------------------------------------- | QCEW | BED | CES -----------|---------------------|----------------------|----------------------- Source |--Count of UI admini-|--Count of longitudi- |--Sample survey: | strative records | nally-linked UI ad- | 400,000 establish- | submitted by 8.6 | ministrative records| ments | million establish- | submitted by 6.7 | | 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 | -Analysis of employ-| cators | surveys | ment expansion and | | | contraction by size| | | of firm | -----------|---------------------|----------------------|-------------------------- Program |--www.bls.gov/cew/ |--www.bls.gov/bdm/ |--www.bls.gov/ces/ Web sites | | | ----------------------------------------------------------------------------------- - 8 - Coverage Employment and wage data for workers covered by state UI laws are compiled from quarterly contribution reports submitted to the SWAs by employers. For federal civilian workers covered by the Unemployment Compensation for Federal Employees (UCFE) program, employment and wage data are compiled from quarterly reports that are sent to the appropriate SWA by the specific federal agency. In addition to the quarterly contribution reports, employers who operate multiple establishments within a state com- plete a questionnaire, called the "Multiple Worksite Report," which provides detailed information on the location and industry of each of their establish- ments. The employment and wage data included in this release are derived from microdata summaries of more than 8 million employer reports of employ- ment and wages submitted by states to the BLS. These reports are based on place of employment rather than place of residence. UI and UCFE coverage is broad and basically comparable from state to state. In 2004, UI and UCFE programs covered workers in 129.3 million jobs. The estimated 124.4 million workers in these jobs (after adjust- ment for multiple jobholders) represented 96.6 percent of civilian wage and salary employment. Covered workers received $5.088 trillion in pay, representing 94.4 percent of the wage and salary component of personal income and 43.4 percent of the gross domestic product. Major exclusions from UI coverage include self-employed workers, most agricultural workers on small farms, all members of the Armed Forces, elected officials in most states, most employees of railroads, some domes- tic workers, most student workers at schools, and employees of certain small nonprofit organizations. State and federal UI laws change periodically. These changes may have an impact on the employment and wages reported by employers covered under the UI program. Coverage changes may affect the over-the-year comparisons presented in this news release. Beginning with the first quarter of 2005, Oregon implemented a change in their state UI laws. This change extended UI coverage to providers of home care for the elderly. These providers are now considered state workers for purposes of UI benefits. Concepts and methodology Monthly employment is based on the number of workers who worked during or received pay for the pay period including the 12th of the month. With few exceptions, all employees of covered firms are reported, including pro- duction and sales workers, corporation officials, executives, supervisory personnel, and clerical workers. Workers on paid vacations and part-time workers also are included. Average weekly wage values are calculated by dividing quarterly total wages by the average of the three monthly employment levels (all employees, as described above) and dividing the result by 13, for the 13 weeks in the quarter. These calculations are made using unrounded employment and wage values. The average wage values that can be calculated using rounded data from the BLS database may differ from the averages reported. Included in the quarterly wage data are non-wage cash payments such as bonuses, the cash value of meals and lodging when supplied, tips and other gratuities, and, in some states, employer contributions to certain deferred compensation plans such as 401(k) plans and stock options. 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. - 9 - Federal government pay levels are subject to periodic, sometimes large, fluctuations due to a calendar effect that consists of some quarters having more pay periods than others. Most federal employees are paid on a bi- weekly pay schedule. As a result of this schedule, in some quarters, federal wages contain payments for six pay periods, while in other quarters their wages include payments for seven pay periods. Over-the-year comparisons of average weekly wages may reflect this calendar effect. Higher growth in average weekly wages may be attributed, in part, to a comparison of quarterly wages for the current year, which include seven pay periods, with year-ago wages that reflect only six pay periods. An opposite effect will occur when wages in the current period, which contain six pay periods, are compared with year-ago wages that include seven pay periods. The effect on over-the-year pay comparisons can be pronounced in federal government due to the uniform nature of federal payroll processing. This pattern may exist in private sector pay, however, because there are more pay period types (weekly, biweekly, semimonthly, monthly) it is less pronounced. The effect is most visible in counties with large concentra- tions of federal employment. In order to ensure the highest possible quality of data, states verify with employers and update, if necessary, the industry, location, and own- ership classification of all establishments on a 3-year cycle. Changes in establishment classification codes resulting from this process are in- troduced with the data reported for the first quarter of the year. Changes resulting from improved employer reporting also are introduced in the first quarter. QCEW data are not designed as a time series. QCEW data are simply the sums of individual establishment records and reflect the number of establishments that exist in a county or industry at a point in time. Establishments can move in or out of a county or industry for a number of reasons--some reflecting economic events, others reflecting administrative changes. For example, economic change would come from a firm relocating into the county; administrative change would come from a company correcting its county designation. The over-the-year changes of employment and wages presented in this release have been adjusted to account for most of the administrative corrections made to the underlying establishment reports. This is done by modifying the prior-year levels used to calculate the over-the-year changes. Percent changes are calcuated using an adjusted version of the final 2004 quarterly data as the base data. The adjusted prior-year levels used to calculate the over-the-year percent change in employment and wages are not published. These adjusted prior-year levels do not match the unadjusted data maintained on the BLS Web site. Over-the-year change calculations based on data from the Web site, or from data published in prior BLS news releases, may differ substantially from the over-the-year changes presented in this news release. The adjusted data used to calculate the over-the-year change measures presented in this release account for most of the administrative changes-- those occurring when employers update the industry, location, and 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. - 10 - The adjusted data used to calculate the over-the-year change measures presented in any County Employment and Wages news release are valid for comparisons between the starting and ending points (a 12-month period) used in that particular release. Comparisons may not be valid for any time period other than the one featured in a release even if the changes were calculated using adjusted data. County definitions are assigned according to Federal Information Processing Standards Publications (FIPS PUBS) as issued by the National Institute of Standards and Technology, after approval by the Secretary of Commerce pursuant to Section 5131 of the Information Technology Management Reform Act of 1996 and the Computer Security Act of 1987, Public Law 104- 106. Areas shown as counties include those designated as independent cities in some jurisdictions and, in Alaska, those designated as census areas where counties have not been created. County data also are presented for the New England states for comparative purposes even though townships are the more common designation used in New England (and New Jersey). The regions re- ferred to in this release are defined as census regions. Additional statistics and other information An annual bulletin, Employment and Wages, features comprehensive infor- mation by detailed industry on establishments, employment, and wages for the nation and all states. The 2004 edition of this bulletin contains selected data produced by Business Employment Dynamics (BED) on job gains and losses, as well as selected data from the fourth quarter 2004 version of this news release. Employment and Wages Annual Averages, 2004 is now available for sale from the United States Government Printing Office, Superintendent of Documents, P.O. Box 371954, Pittsburgh, PA 15250, tele- phone 866-512-1800, outside of Washington, D.C. Within Washington, D.C., the telephone number is 202-512-1800. The fax number is 202-512-2104. Also, the 2004 bulletin is available in a portable document format (PDF) on the BLS Web site at http://www.bls.gov/cew/cewbultn04.htm. News releases on quarterly measures of gross job flows also are available upon request from the Division of Administrative Statistics and Labor Turnover (Business Employment Dynamics), telephone 202-691-6467; (http://www.bls.gov/bdm/); (e-mail: BDMInfo@bls.gov). Information in this release will be made available to sensory impaired individuals upon request. Voice phone: 202-691-5200; TDD message referral phone number: 1-800-877-8339. Table 1. Covered(1) establishments, employment, and wages in the 323 largest counties, fourth quarter 2005(2) Employment Average weekly wage(5) Establishments, County(3) fourth quarter Percent Ranking Percent Ranking 2005 December change, by Average change, by (thousands) 2005 December percent weekly fourth percent (thousands) 2004-05(4) change wage quarter change 2004-05(4) United States(6)......... 8,690.4 133,834.6 1.7 - $825 1.5 - Jefferson, AL............ 18.9 375.8 0.8 202 836 0.5 221 Madison, AL.............. 8.3 172.2 2.8 81 844 -0.4 260 Mobile, AL............... 9.9 171.1 3.4 61 712 6.6 12 Montgomery, AL........... 6.7 138.5 4.0 49 725 0.6 209 Tuscaloosa, AL........... 4.3 82.9 4.2 43 740 3.9 32 Anchorage Borough, AK.... 7.9 144.0 1.1 170 837 0.8 189 Maricopa, AZ............. 86.1 1,784.8 5.8 8 818 2.1 94 Pima, AZ................. 18.7 364.2 3.1 70 717 2.4 80 Benton, AR............... 4.9 91.5 4.7 26 743 2.3 87 Pulaski, AR.............. 13.7 248.7 1.2 164 749 -1.1 284 Washington, AR........... 5.4 91.9 4.4 29 685 2.4 80 Alameda, CA.............. 48.0 683.1 0.5 222 1,052 -0.5 264 Contra Costa, CA......... 27.5 344.5 -0.4 282 1,010 0.0 248 Fresno, CA............... 29.1 345.3 4.0 49 662 1.5 128 Kern, CA................. 16.7 275.1 8.6 2 683 -0.1 249 Los Angeles, CA.......... 378.7 4,196.5 1.8 128 966 0.3 234 Marin, CA................ 11.6 111.4 -0.1 260 1,133 6.7 10 Monterey, CA............. 11.9 150.9 0.2 235 720 2.9 59 Orange, CA............... 91.7 1,507.7 1.9 123 964 2.6 68 Placer, CA............... 9.9 134.7 2.4 99 810 1.8 107 Riverside, CA............ 40.7 625.2 4.3 35 683 0.9 179 Sacramento, CA........... 48.6 629.0 2.4 99 894 3.5 42 San Bernardino, CA....... 44.0 654.3 4.4 29 718 0.8 189 San Diego, CA............ 88.9 1,315.8 0.9 192 890 1.8 107 San Francisco, CA........ 43.5 538.1 1.9 123 1,378 7.5 7 San Joaquin, CA.......... 16.4 218.5 1.0 180 718 1.6 119 San Luis Obispo, CA...... 8.7 100.4 1.9 123 690 1.5 128 San Mateo, CA............ 22.7 333.1 -0.4 282 1,365 3.5 42 Santa Barbara, CA........ 13.2 179.0 3.3 65 798 1.4 138 Santa Clara, CA.......... 53.7 877.2 1.7 134 1,490 1.8 107 Santa Cruz, CA........... 8.5 91.9 0.1 241 738 -4.4 314 Solano, CA............... 9.6 131.2 2.7 86 765 1.5 128 Sonoma, CA............... 17.4 191.4 0.4 225 821 2.8 61 Stanislaus, CA........... 13.5 172.6 2.5 95 691 3.1 53 Tulare, CA............... 8.6 140.6 4.7 26 583 1.6 119 Ventura, CA.............. 20.9 318.3 2.7 86 892 -1.5 288 Yolo, CA................. 5.3 98.4 1.8 128 729 -4.3 313 Adams, CO................ 9.0 150.5 4.0 49 776 -0.4 260 Arapahoe, CO............. 19.3 274.7 1.4 152 974 -0.9 280 Boulder, CO.............. 12.3 156.5 1.3 156 971 1.8 107 Denver, CO............... 24.9 431.6 1.8 128 1,003 0.9 179 El Paso, CO.............. 16.7 244.3 2.3 104 750 1.4 138 Jefferson, CO............ 18.5 209.0 1.1 170 817 -0.8 275 Larimer, CO.............. 9.8 125.1 1.6 140 755 3.0 55 Fairfield, CT............ 32.3 420.6 0.9 192 1,496 4.3 25 Hartford, CT............. 24.6 493.5 1.3 156 1,033 1.5 128 New Haven, CT............ 22.1 366.4 -0.4 282 895 1.0 170 New London, CT........... 6.7 130.1 0.1 241 842 -0.7 271 New Castle, DE........... 19.5 286.6 0.0 251 1,068 9.0 5 Washington, DC........... 30.9 673.5 1.0 180 1,354 4.9 17 Alachua, FL.............. 6.3 125.1 (7) - 656 (7) - Brevard, FL.............. 14.0 207.3 5.0 18 777 0.5 221 Broward, FL.............. 62.3 752.1 4.3 35 813 1.0 170 Collier, FL.............. 12.0 136.8 5.6 10 801 7.4 8 Duval, FL................ 24.9 462.3 4.2 43 816 1.2 154 Escambia, FL............. 7.7 128.8 2.4 99 675 3.7 36 Hillsborough, FL......... 35.1 643.8 2.7 86 781 0.6 209 Lake, FL................. 6.5 83.4 6.5 7 669 6.7 10 Lee, FL.................. 17.9 223.6 9.2 1 733 2.8 61 Leon, FL................. 7.8 147.6 -0.7 297 708 0.9 179 Manatee, FL.............. 8.3 130.8 1.1 170 642 2.4 80 Marion, FL............... 7.6 101.5 (7) - 613 1.7 114 Miami-Dade, FL........... 84.4 1,022.1 2.1 113 833 1.5 128 Okaloosa, FL............. 6.1 83.6 (7) - 666 (7) - Orange, FL............... 33.4 675.4 4.5 28 766 0.9 179 Palm Beach, FL........... 47.9 565.1 3.1 70 826 0.9 179 Pasco, FL................ 8.8 96.5 7.9 3 600 -0.2 251 Pinellas, FL............. 30.9 446.6 0.6 216 730 1.1 162 Polk, FL................. 12.0 211.8 5.0 18 660 2.6 68 Sarasota, FL............. 14.9 164.3 2.6 91 740 4.2 27 Seminole, FL............. 13.8 174.3 7.8 4 777 3.7 36 Volusia, FL.............. 13.5 166.0 4.8 22 611 -1.6 291 Bibb, GA................. 4.8 86.6 -1.3 308 682 1.8 107 Chatham, GA.............. 7.3 132.6 0.6 216 718 4.7 19 Clayton, GA.............. 4.4 111.0 3.0 77 758 -8.6 319 Cobb, GA................. 20.3 318.9 4.2 43 885 0.6 209 De Kalb, GA.............. 17.0 295.2 0.5 222 864 -0.3 253 Fulton, GA............... 38.3 757.1 1.7 134 1,139 7.6 6 Gwinnett, GA............. 22.5 323.5 3.4 61 862 0.9 179 Muscogee, GA............. 4.8 98.7 0.7 210 647 3.7 36 Richmond, GA............. 4.9 107.0 1.2 164 677 0.4 228 Honolulu, HI............. 23.9 452.5 2.3 104 763 0.9 179 Ada, ID.................. 14.2 203.5 5.4 14 751 1.2 154 Champaign, IL............ 4.0 91.6 0.8 202 679 0.6 209 Cook, IL................. 132.3 2,547.4 0.9 192 1,000 1.4 138 Du Page, IL.............. 34.0 591.4 0.6 216 967 -0.7 271 Kane, IL................. 11.8 207.6 1.6 140 776 1.7 114 Lake, IL................. 19.8 325.3 0.0 251 1,025 -0.6 267 McHenry, IL.............. 7.9 98.9 2.2 109 746 1.8 107 McLean, IL............... 3.5 85.6 2.5 95 761 -4.4 314 Madison, IL.............. 5.8 93.8 -0.3 273 718 3.3 49 Peoria, IL............... 4.6 100.9 1.2 164 800 1.4 138 Rock Island, IL.......... 3.4 79.4 1.6 140 908 0.6 209 St. Clair, IL............ 5.2 95.0 1.0 180 664 2.8 61 Sangamon, IL............. 5.2 131.5 0.7 210 788 2.1 94 Will, IL................. 11.9 173.7 4.3 35 773 0.5 221 Winnebago, IL............ 6.8 137.1 -1.0 302 707 1.0 170 Allen, IN................ 8.8 183.7 1.2 164 704 -0.8 275 Elkhart, IN.............. 4.8 127.8 3.1 70 730 2.1 94 Hamilton, IN............. 6.8 98.0 4.8 22 823 -2.0 298 Lake, IN................. 10.0 196.1 0.9 192 716 -2.3 301 Marion, IN............... 23.5 585.3 0.1 241 839 0.8 189 St. Joseph, IN........... 6.0 126.8 -0.1 260 689 0.6 209 Vanderburgh, IN.......... 4.8 110.1 1.5 145 682 -0.3 253 Linn, IA................. 6.1 119.6 0.8 202 802 1.5 128 Polk, IA................. 14.2 267.6 1.7 134 828 1.2 154 Scott, IA................ 5.1 89.7 2.3 104 684 0.1 239 Johnson, KS.............. 19.6 306.7 2.3 104 846 0.5 221 Sedgwick, KS............. 12.0 247.9 0.8 202 746 2.6 68 Shawnee, KS.............. 4.8 94.4 -0.6 291 691 1.0 170 Wyandotte, KS............ 3.2 78.1 1.4 152 782 -0.5 264 Fayette, KY.............. 8.9 172.3 1.7 134 757 1.3 147 Jefferson, KY............ 22.1 429.5 0.8 202 798 -1.5 288 Caddo, LA................ 7.2 126.2 3.2 69 689 1.6 119 Calcasieu, LA............ 4.8 84.7 1.3 156 697 4.7 19 East Baton Rouge, LA..... 13.5 262.2 5.6 10 711 3.5 42 Jefferson, LA............ 14.3 179.5 -17.0 313 812 16.2 3 Lafayette, LA............ 8.0 127.7 5.5 12 754 4.7 19 Orleans, LA.............. 12.6 149.5 -39.3 315 968 28.7 1 Cumberland, ME........... 11.8 173.7 -0.3 273 749 -2.3 301 Anne Arundel, MD......... 14.2 227.0 2.0 116 853 2.4 80 Baltimore, MD............ 21.3 378.9 1.5 145 872 1.3 147 Frederick, MD............ 5.8 92.5 0.3 233 779 1.6 119 Harford, MD.............. 5.5 82.7 2.2 109 742 -0.8 275 Howard, MD............... 8.3 141.3 1.7 134 962 0.1 239 Montgomery, MD........... 32.5 467.9 1.6 140 1,109 4.0 31 Prince Georges, MD....... 15.5 318.2 -0.1 260 897 3.8 33 Baltimore City, MD....... 14.0 353.0 -0.7 297 992 7.1 9 Barnstable, MA........... 9.2 88.4 0.0 251 730 0.8 189 Bristol, MA.............. 15.5 223.1 0.1 241 737 1.4 138 Essex, MA................ 20.5 298.4 0.8 202 885 2.3 87 Hampden, MA.............. 14.2 201.3 -0.4 282 758 1.2 154 Middlesex, MA............ 47.3 803.7 1.3 156 1,158 1.0 170 Norfolk, MA.............. 21.6 323.7 0.9 192 1,013 0.6 209 Plymouth, MA............. 13.7 178.6 0.7 210 804 0.2 235 Suffolk, MA.............. 21.7 572.6 1.3 156 1,412 3.7 36 Worcester, MA............ 20.5 322.1 0.1 241 831 0.5 221 Genesee, MI.............. 8.3 151.1 (7) - 771 -3.3 309 Ingham, MI............... 7.1 162.3 (7) - 789 2.9 59 Kalamazoo, MI............ 5.5 117.8 1.0 180 741 0.8 189 Kent, MI................. 14.5 346.1 1.8 128 770 -1.2 286 Macomb, MI............... 18.1 332.1 0.4 225 890 -0.6 267 Oakland, MI.............. 40.5 721.7 -0.8 299 1,018 0.8 189 Ottawa, MI............... 5.8 112.6 1.1 170 744 0.8 189 Saginaw, MI.............. 4.5 89.4 -1.5 311 747 -0.8 275 Washtenaw, MI............ 8.1 197.0 -1.1 304 915 1.1 162 Wayne, MI................ 34.0 793.6 -1.4 309 951 0.2 235 Anoka, MN................ 8.0 116.7 1.8 128 774 -4.7 316 Dakota, MN............... 10.6 174.2 1.1 170 807 -1.8 295 Hennepin, MN............. 42.9 850.6 1.3 156 1,013 -3.4 310 Olmsted, MN.............. 3.6 89.4 1.2 164 811 1.0 170 Ramsey, MN............... 15.8 334.5 1.1 170 884 -3.5 311 St. Louis, MN............ 6.0 96.0 1.3 156 674 -2.3 301 Stearns, MN.............. 4.5 79.6 1.9 123 640 -3.2 308 Harrison, MS............. 4.5 72.2 -20.2 314 680 18.9 2 Hinds, MS................ 6.5 128.6 -0.6 291 736 3.2 51 Boone, MO................ 4.4 81.7 3.0 77 626 -0.3 253 Clay, MO................. 5.0 88.0 1.5 145 755 0.1 239 Greene, MO............... 8.1 153.3 3.5 60 626 1.5 128 Jackson, MO.............. 18.8 367.3 0.7 210 839 2.6 68 St. Charles, MO.......... 7.7 119.1 2.0 116 706 1.1 162 St. Louis, MO............ 34.0 627.5 0.3 233 887 3.0 55 St. Louis City, MO....... 8.1 223.2 0.5 222 897 1.6 119 Douglas, NE.............. 15.4 314.7 1.0 180 789 4.2 27 Lancaster, NE............ 7.8 154.5 0.9 192 662 1.1 162 Clark, NV................ 43.3 895.6 6.7 5 771 0.1 239 Washoe, NV............... 13.5 217.6 2.9 79 782 1.7 114 Hillsborough, NH......... 12.3 200.3 1.0 180 949 1.9 103 Rockingham, NH........... 10.9 138.6 1.1 170 860 -1.5 288 Atlantic, NJ............. 6.7 145.5 -0.3 273 749 0.7 203 Bergen, NJ............... 34.3 456.2 0.1 241 1,072 0.5 221 Burlington, NJ........... 11.3 203.7 0.1 241 878 1.3 147 Camden, NJ............... 13.5 212.9 -0.2 269 892 3.1 53 Essex, NJ................ 21.4 364.8 0.0 251 1,069 -1.7 293 Gloucester, NJ........... 6.3 106.9 3.3 65 766 0.9 179 Hudson, NJ............... 14.1 239.0 -0.2 269 1,068 0.8 189 Mercer, NJ............... 10.9 227.6 2.4 99 1,085 1.9 103 Middlesex, NJ............ 20.9 396.8 -0.3 273 1,043 -0.6 267 Monmouth, NJ............. 20.3 258.1 0.4 225 925 1.2 154 Morris, NJ............... 17.9 288.0 -0.1 260 1,239 1.7 114 Ocean, NJ................ 11.7 146.9 2.1 113 745 0.7 203 Passaic, NJ.............. 12.5 180.2 0.9 192 893 -0.4 260 Somerset, NJ............. 10.1 174.8 3.3 65 1,296 4.5 22 Union, NJ................ 14.8 229.5 (7) - 1,078 0.7 203 Bernalillo, NM........... 16.7 326.5 2.3 104 731 0.4 228 Albany, NY............... 9.7 231.6 -0.5 288 826 0.6 209 Bronx, NY................ 15.7 224.5 1.7 134 780 0.9 179 Broome, NY............... 4.5 96.1 -0.1 260 631 1.1 162 Dutchess, NY............. 8.1 119.4 -0.9 300 823 1.6 119 Erie, NY................. 23.3 461.3 -1.0 302 708 -0.7 271 Kings, NY................ 43.0 464.1 1.1 170 741 0.8 189 Monroe, NY............... 17.7 390.5 0.8 202 785 0.8 189 Nassau, NY............... 51.7 613.4 0.1 241 953 3.0 55 New York, NY............. 114.8 2,310.7 2.0 116 1,684 4.3 25 Oneida, NY............... 5.3 110.0 (7) - 633 0.6 209 Onondaga, NY............. 12.7 253.1 0.2 235 770 0.8 189 Orange, NY............... 9.6 130.9 -0.1 260 708 0.9 179 Queens, NY............... 41.1 487.0 1.0 180 822 0.7 203 Richmond, NY............. 8.3 91.6 -0.3 273 738 0.4 228 Rockland, NY............. 9.5 115.0 -0.1 260 873 1.3 147 Suffolk, NY.............. 48.8 617.5 0.2 235 894 1.5 128 Westchester, NY.......... 35.9 420.7 -0.3 273 1,173 5.4 14 Buncombe, NC............. 7.2 110.6 0.4 225 658 1.2 154 Catawba, NC.............. 4.4 87.6 -0.6 291 638 -0.3 253 Cumberland, NC........... 5.8 116.6 1.6 140 620 2.5 75 Durham, NC............... 6.3 172.3 1.5 145 1,015 -1.2 286 Forsyth, NC.............. 8.6 184.0 2.2 109 754 -2.2 300 Guilford, NC............. 13.7 275.5 1.1 170 743 0.4 228 Mecklenburg, NC.......... 28.2 533.2 2.5 95 932 -1.6 291 New Hanover, NC.......... 6.7 98.5 5.1 17 683 4.4 23 Wake, NC................. 24.4 416.9 4.3 35 828 1.5 128 Cass, ND................. 5.8 92.9 2.6 91 691 0.1 239 Butler, OH............... 7.0 139.5 2.2 109 734 -0.9 280 Cuyahoga, OH............. 38.0 756.8 -0.6 291 855 -0.6 267 Franklin, OH............. 29.1 693.9 0.4 225 806 0.1 239 Hamilton, OH............. 24.5 547.2 -0.1 260 895 2.8 61 Lake, OH................. 6.9 100.8 0.4 225 692 -0.1 249 Lorain, OH............... 6.2 101.8 -1.1 304 702 -1.8 295 Lucas, OH................ 10.9 229.2 -0.2 269 730 -2.0 298 Mahoning, OH............. 6.4 107.3 0.7 210 624 0.2 235 Montgomery, OH........... 13.1 280.8 -1.4 309 777 -0.5 264 Stark, OH................ 9.2 166.5 -1.8 312 649 -0.3 253 Summit, OH............... 14.9 274.6 1.0 180 767 -2.5 305 Trumbull, OH............. 4.8 84.3 -0.5 288 704 -5.8 318 Oklahoma, OK............. 22.6 420.6 1.4 152 713 2.3 87 Tulsa, OK................ 18.7 339.4 3.9 52 744 1.6 119 Clackamas, OR............ 12.2 147.4 4.4 29 761 1.2 154 Jackson, OR.............. 6.5 85.5 3.9 52 602 1.2 154 Lane, OR................. 10.7 149.7 4.4 29 656 2.2 90 Marion, OR............... 9.0 134.6 3.4 61 638 1.4 138 Multnomah, OR............ 26.4 438.8 2.5 95 822 1.0 170 Washington, OR........... 15.4 242.7 4.3 35 904 -0.8 275 Allegheny, PA............ 34.8 687.7 0.0 251 858 0.8 189 Berks, PA................ 9.0 167.4 1.3 156 725 -1.0 283 Bucks, PA................ 19.7 264.5 1.4 152 807 0.7 203 Chester, PA.............. 15.0 235.3 1.9 123 1,062 0.5 221 Cumberland, PA........... 5.8 125.9 -0.6 291 754 0.1 239 Dauphin, PA.............. 7.1 177.9 0.8 202 785 -3.1 307 Delaware, PA............. 13.7 211.7 0.0 251 892 2.2 90 Erie, PA................. 7.1 127.9 -0.3 273 646 0.2 235 Lackawanna, PA........... 5.7 101.3 0.9 192 648 1.7 114 Lancaster, PA............ 12.0 229.8 0.6 216 712 0.1 239 Lehigh, PA............... 8.2 176.2 0.7 210 820 -0.7 271 Luzerne, PA.............. 7.9 142.9 -0.2 269 650 0.8 189 Montgomery, PA........... 27.7 491.3 1.5 145 1,024 1.1 162 Northampton, PA.......... 6.2 95.2 1.5 145 723 0.4 228 Philadelphia, PA......... 29.0 640.6 0.2 235 962 -0.9 280 Washington, PA........... 5.3 75.9 0.0 251 700 1.0 170 Westmoreland, PA......... 9.5 139.4 0.4 225 635 -1.9 297 York, PA................. 8.8 175.5 2.0 116 781 10.8 4 Kent, RI................. 5.7 83.5 0.0 251 735 1.4 138 Providence, RI........... 18.2 289.9 0.1 241 813 3.4 46 Charleston, SC........... 12.6 199.5 1.8 128 703 5.2 16 Greenville, SC........... 12.7 229.8 1.0 180 725 0.8 189 Horry, SC................ 8.6 105.6 3.7 57 556 -0.2 251 Lexington, SC............ 5.9 90.2 2.0 116 628 -1.1 284 Richland, SC............. 9.8 210.1 0.0 251 696 -0.3 253 Spartanburg, SC.......... 6.4 116.1 -1.1 304 700 1.3 147 Minnehaha, SD............ 6.1 113.0 3.1 70 678 2.4 80 Davidson, TN............. 18.0 453.2 2.9 79 835 0.7 203 Hamilton, TN............. 8.4 194.6 0.1 241 724 2.5 75 Knox, TN................. 10.5 222.2 1.0 180 729 3.0 55 Rutherford, TN........... 3.8 97.4 3.6 59 730 -2.4 304 Shelby, TN............... 19.8 509.2 0.9 192 848 1.8 107 Bell, TX................. 4.3 94.9 2.7 86 616 2.7 66 Bexar, TX................ 30.5 687.8 4.2 43 744 3.6 40 Brazoria, TX............. 4.3 81.8 4.4 29 757 2.2 90 Brazos, TX............... 3.6 83.4 4.4 29 588 2.6 68 Cameron, TX.............. 6.2 118.5 2.0 116 506 1.4 138 Collin, TX............... 14.6 255.2 5.4 14 964 1.4 138 Dallas, TX............... 66.5 1,457.5 2.8 81 1,033 3.4 46 Denton, TX............... 9.4 154.3 5.3 16 725 2.0 99 El Paso, TX.............. 12.9 263.7 2.8 81 575 -0.3 253 Fort Bend, TX............ 7.3 114.1 4.8 22 854 2.2 90 Galveston, TX............ 4.9 89.2 3.1 70 723 4.9 17 Harris, TX............... 91.1 1,919.8 3.8 55 1,014 3.8 33 Hidalgo, TX.............. 9.8 204.3 4.2 43 512 1.6 119 Jefferson, TX............ 5.8 120.5 4.3 35 815 5.4 14 Lubbock, TX.............. 6.5 121.3 2.1 113 619 2.5 75 McLennan, TX............. 4.8 102.7 1.1 170 647 0.6 209 Montgomery, TX........... 7.1 107.4 6.6 6 781 4.4 23 Nueces, TX............... 8.1 148.3 2.0 116 687 1.5 128 Potter, TX............... 3.7 71.9 -0.3 273 667 3.3 49 Smith, TX................ 5.0 91.7 2.8 81 723 3.6 40 Tarrant, TX.............. 34.9 730.8 2.8 81 827 1.6 119 Travis, TX............... 25.7 538.9 3.7 57 935 0.6 209 Webb, TX................. 4.5 84.3 5.7 9 548 1.9 103 Williamson, TX........... 6.0 104.4 5.5 12 807 -8.6 319 Davis, UT................ 6.9 97.8 3.8 55 670 4.2 27 Salt Lake, UT............ 37.7 558.0 4.3 35 769 3.4 46 Utah, UT................. 12.3 161.9 4.9 20 629 2.1 94 Weber, UT................ 5.6 89.5 1.0 180 607 2.4 80 Chittenden, VT........... 5.8 95.8 -1.1 304 789 1.0 170 Arlington, VA............ 7.3 156.5 -0.1 260 1,345 4.2 27 Chesterfield, VA......... 6.9 117.7 2.6 91 753 1.3 147 Fairfax, VA.............. 31.3 578.9 3.1 70 1,247 0.4 228 Henrico, VA.............. 8.6 174.2 0.4 225 887 2.8 61 Loudoun, VA.............. 7.2 124.5 4.1 48 1,059 2.7 66 Prince William, VA....... 6.4 103.6 4.3 35 749 2.6 68 Alexandria City, VA...... 5.9 94.9 0.2 235 1,077 2.4 80 Chesapeake City, VA...... 5.2 97.6 2.6 91 660 2.6 68 Newport News City, VA.... 3.9 99.5 0.6 216 737 -0.4 260 Norfolk City, VA......... 5.7 144.5 -0.9 300 807 3.5 42 Richmond City, VA........ 7.0 162.0 1.2 164 940 3.2 51 Virginia Beach City, VA.. 11.1 178.0 1.5 145 658 2.0 99 Clark, WA................ 11.0 128.8 4.9 20 734 2.5 75 King, WA................. 76.3 1,145.1 3.4 61 985 1.1 162 Kitsap, WA............... 6.4 83.7 2.4 99 733 6.4 13 Pierce, WA............... 19.8 264.3 3.3 65 711 2.0 99 Snohomish, WA............ 16.5 227.5 4.8 22 815 3.8 33 Spokane, WA.............. 14.7 201.3 2.7 86 643 1.1 162 Thurston, WA............. 6.4 95.1 3.1 70 709 1.9 103 Whatcom, WA.............. 6.6 79.9 3.9 52 613 2.5 75 Yakima, WA............... 7.8 87.2 -0.5 288 552 2.0 99 Kanawha, WV.............. 6.1 108.2 -0.6 291 699 1.3 147 Brown, WI................ 6.8 149.2 0.9 192 732 -5.1 317 Dane, WI................. 14.2 302.8 1.0 180 756 -1.7 293 Milwaukee, WI............ 21.9 497.8 -0.4 282 833 0.6 209 Outagamie, WI............ 5.0 101.2 -0.3 273 722 2.1 94 Racine, WI............... 4.3 76.6 -0.4 282 783 -3.9 312 Waukesha, WI............. 13.5 233.2 0.2 235 826 0.1 239 Winnebago, WI............ 3.9 88.5 0.6 216 776 -2.9 306 San Juan, PR............. 14.5 330.1 -2.0 (8) 549 3.0 (8) 1 Includes workers covered by Unemployment Insurance (UI) and Unemployment Compensation for Federal Employees (UCFE) programs. These 322 U.S. counties comprise 70.8 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 2005(2) Employment Average weekly wage(4) Establishments, fourth quarter County by NAICS supersector 2005 Percent Percent (thousands) December change, Average change, 2005 December weekly fourth (thousands) 2004-05(3) wage quarter 2004-05(3) United States(5)............................. 8,690.4 133,834.6 1.7 $825 1.5 Private industry........................... 8,413.4 112,417.0 1.9 829 1.6 Natural resources and mining............. 123.4 1,650.4 3.7 802 5.7 Construction............................. 857.7 7,396.7 5.4 892 3.2 Manufacturing............................ 365.1 14,199.1 -0.6 991 0.6 Trade, transportation, and utilities..... 1,876.6 26,693.7 1.4 707 0.4 Information.............................. 142.6 3,077.0 -0.6 1,246 0.5 Financial activities..................... 825.4 8,155.9 2.1 1,280 3.3 Professional and business services....... 1,390.3 17,256.9 3.5 1,039 2.1 Education and health services............ 775.0 16,754.9 2.3 782 1.0 Leisure and hospitality.................. 697.1 12,547.1 1.4 353 2.0 Other services........................... 1,121.1 4,336.1 0.9 525 1.4 Government................................. 277.1 21,417.6 0.7 807 1.5 Los Angeles, CA.............................. 378.7 4,196.5 1.8 966 0.3 Private industry........................... 374.9 3,609.5 1.9 967 0.4 Natural resources and mining............. 0.5 10.4 -2.0 911 -29.0 Construction............................. 13.5 154.5 8.7 961 2.6 Manufacturing............................ 16.1 466.4 -2.3 966 0.9 Trade, transportation, and utilities..... 53.1 833.5 2.5 781 -0.3 Information.............................. 8.7 211.8 -2.6 1,765 -0.3 Financial activities..................... 23.7 248.8 2.8 1,412 3.2 Professional and business services....... 40.5 592.5 2.9 1,141 0.6 Education and health services............ 27.4 466.7 0.3 885 1.7 Leisure and hospitality.................. 26.1 385.1 2.0 799 1.9 Other services........................... 165.0 239.2 9.1 427 -3.0 Government................................. 3.8 587.1 0.9 964 0.3 Cook, IL..................................... 132.3 2,547.4 0.9 1,000 1.4 Private industry........................... 131.1 2,234.1 1.1 1,009 1.7 Natural resources and mining............. 0.1 1.4 9.4 1,091 1.9 Construction............................. 11.4 92.5 -1.0 1,238 4.0 Manufacturing............................ 7.5 252.1 -1.9 1,029 -0.6 Trade, transportation, and utilities..... 27.2 494.5 0.2 802 -0.1 Information.............................. 2.5 60.2 -2.8 1,341 0.8 Financial activities..................... 14.8 220.2 1.5 1,639 3.8 Professional and business services....... 27.2 430.3 3.9 1,328 1.2 Education and health services............ 13.0 361.1 1.1 849 1.8 Leisure and hospitality.................. 11.1 221.9 2.5 406 5.2 Other services........................... 13.2 95.2 -0.5 696 1.6 Government................................. 1.2 313.3 -0.7 936 -0.3 New York, NY................................. 114.8 2,310.7 2.0 1,684 4.3 Private industry........................... 114.6 1,860.6 2.3 1,840 4.2 Natural resources and mining............. 0.0 0.1 0.0 4,005 175.4 Construction............................. 2.1 29.9 2.5 1,621 2.5 Manufacturing............................ 3.1 41.7 -6.7 1,393 -2.4 Trade, transportation, and utilities..... 21.3 253.5 1.8 1,234 4.6 Information.............................. 4.1 131.4 1.2 1,947 4.5 Financial activities..................... 17.3 365.3 2.9 3,632 4.7 Professional and business services....... 22.6 459.4 1.9 1,910 4.8 Education and health services............ 8.1 281.6 2.2 997 3.2 Leisure and hospitality.................. 10.4 200.3 1.5 826 -0.8 Other services........................... 16.5 85.8 2.1 944 4.2 Government................................. 0.2 450.1 0.7 1,044 4.1 Harris, TX................................... 91.1 1,919.8 3.8 1,014 3.8 Private industry........................... 90.7 1,669.7 4.0 1,040 4.2 Natural resources and mining............. 1.3 68.0 5.5 2,693 13.3 Construction............................. 6.2 135.5 3.9 974 6.4 Manufacturing............................ 4.5 170.4 4.0 1,262 2.8 Trade, transportation, and utilities..... 21.0 416.5 3.3 885 1.8 Information.............................. 1.3 31.8 -4.3 1,191 3.9 Financial activities..................... 9.9 117.8 2.4 1,323 5.3 Professional and business services....... 17.7 309.8 7.2 1,213 3.6 Education and health services............ 9.4 199.4 3.4 869 -1.4 Leisure and hospitality.................. 6.8 160.9 1.7 365 2.2 Other services........................... 10.6 55.2 0.9 579 3.0 Government................................. 0.4 250.1 2.5 840 0.7 Maricopa, AZ................................. 86.1 1,784.8 5.8 818 2.1 Private industry........................... 85.5 1,571.9 6.5 821 2.6 Natural resources and mining............. 0.5 8.9 -1.7 704 6.2 Construction............................. 8.8 170.7 13.6 860 5.1 Manufacturing............................ 3.3 134.5 2.6 1,103 3.4 Trade, transportation, and utilities..... 18.9 370.4 5.6 765 1.2 Information.............................. 1.4 32.9 0.8 939 -3.2 Financial activities..................... 10.4 150.0 7.3 1,087 6.3 Professional and business services....... 18.6 300.6 6.4 845 1.6 Education and health services............ 8.3 180.9 5.1 893 1.6 Leisure and hospitality.................. 6.1 171.0 5.4 383 3.2 Other services........................... 6.0 46.4 3.3 563 3.7 Government................................. 0.6 212.8 0.9 796 -1.6 Orange, CA................................... 91.7 1,507.7 1.9 964 2.6 Private industry........................... 90.3 1,375.1 2.2 971 2.8 Natural resources and mining............. 0.2 5.2 8.7 612 -6.6 Construction............................. 6.8 105.8 11.4 1,042 2.5 Manufacturing............................ 5.7 181.8 -1.7 1,123 1.5 Trade, transportation, and utilities..... 17.2 285.5 1.2 856 -0.2 Information.............................. 1.4 32.1 -2.6 1,248 0.9 Financial activities..................... 10.5 144.7 2.9 1,641 8.0 Professional and business services....... 18.0 272.3 3.5 1,050 2.0 Education and health services............ 9.4 132.2 1.6 894 1.6 Leisure and hospitality.................. 6.8 166.5 1.5 379 4.4 Other services........................... 14.3 48.8 2.6 574 4.4 Government................................. 1.4 132.6 -0.8 899 1.1 Dallas, TX................................... 66.5 1,457.5 2.8 1,033 3.4 Private industry........................... 66.1 1,296.2 2.9 1,053 3.3 Natural resources and mining............. 0.5 7.5 7.1 3,177 14.3 Construction............................. 4.3 77.3 5.1 969 6.6 Manufacturing............................ 3.3 147.7 1.7 1,101 -2.6 Trade, transportation, and utilities..... 14.8 312.1 1.3 1,001 8.2 Information.............................. 1.7 53.8 -0.2 1,311 -1.9 Financial activities..................... 8.4 138.6 2.4 1,368 3.4 Professional and business services....... 13.8 259.7 7.4 1,209 0.8 Education and health services............ 6.2 134.9 1.5 980 2.9 Leisure and hospitality.................. 5.1 122.3 0.2 473 2.6 Other services........................... 6.5 39.0 0.1 638 3.2 Government................................. 0.4 161.3 1.9 868 4.2 San Diego, CA................................ 88.9 1,315.8 0.9 890 1.8 Private industry........................... 87.5 1,096.1 1.1 881 1.5 Natural resources and mining............. 0.8 11.2 3.2 567 4.0 Construction............................. 6.9 92.9 3.3 960 0.6 Manufacturing............................ 3.4 103.2 -1.0 1,169 3.5 Trade, transportation, and utilities..... 14.2 230.2 2.1 686 -1.0 Information.............................. 1.3 37.3 -0.5 1,990 4.7 Financial activities..................... 9.4 84.5 1.2 1,211 5.4 Professional and business services....... 15.2 209.7 -0.6 1,081 0.7 Education and health services............ 7.7 121.8 0.1 854 2.4 Leisure and hospitality.................. 6.7 149.6 2.8 382 3.5 Other services........................... 21.8 55.6 1.5 474 0.2 Government................................. 1.4 219.7 0.2 931 2.6 King, WA..................................... 76.3 1,145.1 3.4 985 1.1 Private industry........................... 75.8 993.2 4.0 994 0.8 Natural resources and mining............. 0.4 2.9 7.0 1,200 -0.5 Construction............................. 6.5 63.1 11.8 970 1.7 Manufacturing............................ 2.6 108.6 5.7 1,297 8.9 Trade, transportation, and utilities..... 14.8 227.2 1.3 853 -0.2 Information.............................. 1.6 70.8 2.6 1,775 -8.0 Financial activities..................... 6.5 76.1 0.8 1,236 7.3 Professional and business services....... 12.2 175.4 7.4 1,182 -1.3 Education and health services............ 6.2 117.2 3.3 782 0.3 Leisure and hospitality.................. 5.8 106.6 4.8 413 -0.7 Other services........................... 19.2 45.2 -1.3 534 5.3 Government................................. 0.5 152.0 -0.5 925 2.4 Miami-Dade, FL............................... 84.4 1,022.1 2.1 833 1.5 Private industry........................... 84.1 869.1 2.5 817 2.6 Natural resources and mining............. 0.5 9.6 -7.9 482 6.9 Construction............................. 5.6 47.5 10.8 901 7.0 Manufacturing............................ 2.7 48.3 -2.3 747 3.3 Trade, transportation, and utilities..... 23.6 253.0 1.4 761 2.0 Information.............................. 1.8 22.9 (6) 1,190 (6) Financial activities..................... 9.7 71.0 4.2 1,212 3.9 Professional and business services....... 16.8 149.3 5.9 1,018 0.9 Education and health services............ 8.5 128.8 2.2 807 1.5 Leisure and hospitality.................. 5.7 99.0 0.3 450 0.0 Other services........................... 7.7 35.7 1.4 482 0.6 Government................................. 0.3 153.0 0.1 927 -3.0 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 2005(2) Employment Average weekly wage(5) Establishments, fourth quarter County(3) 2005 Percent Percent (thousands) December change, Average change, 2005 December weekly fourth (thousands) 2004-05(4) wage quarter 2004-05(4) United States(6)......... 8,690.4 133,834.6 1.7 $825 1.5 Jefferson, AL............ 18.9 375.8 0.8 836 0.5 Anchorage Borough, AK.... 7.9 144.0 1.1 837 0.8 Maricopa, AZ............. 86.1 1,784.8 5.8 818 2.1 Pulaski, AR.............. 13.7 248.7 1.2 749 -1.1 Los Angeles, CA.......... 378.7 4,196.5 1.8 966 0.3 Denver, CO............... 24.9 431.6 1.8 1,003 0.9 Hartford, CT............. 24.6 493.5 1.3 1,033 1.5 New Castle, DE........... 19.5 286.6 0.0 1,068 9.0 Washington, DC........... 30.9 673.5 1.0 1,354 4.9 Miami-Dade, FL........... 84.4 1,022.1 2.1 833 1.5 Fulton, GA............... 38.3 757.1 1.7 1,139 7.6 Honolulu, HI............. 23.9 452.5 2.3 763 0.9 Ada, ID.................. 14.2 203.5 5.4 751 1.2 Cook, IL................. 132.3 2,547.4 0.9 1,000 1.4 Marion, IN............... 23.5 585.3 0.1 839 0.8 Polk, IA................. 14.2 267.6 1.7 828 1.2 Johnson, KS.............. 19.6 306.7 2.3 846 0.5 Jefferson, KY............ 22.1 429.5 0.8 798 -1.5 Orleans, LA.............. 12.6 149.5 -39.3 968 28.7 Cumberland, ME........... 11.8 173.7 -0.3 749 -2.3 Montgomery, MD........... 32.5 467.9 1.6 1,109 4.0 Middlesex, MA............ 47.3 803.7 1.3 1,158 1.0 Wayne, MI................ 34.0 793.6 -1.4 951 0.2 Hennepin, MN............. 42.9 850.6 1.3 1,013 -3.4 Hinds, MS................ 6.5 128.6 -0.6 736 3.2 St. Louis, MO............ 34.0 627.5 0.3 887 3.0 Yellowstone, MT.......... 5.4 73.8 2.4 650 1.9 Douglas, NE.............. 15.4 314.7 1.0 789 4.2 Clark, NV................ 43.3 895.6 6.7 771 0.1 Hillsborough, NH......... 12.3 200.3 1.0 949 1.9 Bergen, NJ............... 34.3 456.2 0.1 1,072 0.5 Bernalillo, NM........... 16.7 326.5 2.3 731 0.4 New York, NY............. 114.8 2,310.7 2.0 1,684 4.3 Mecklenburg, NC.......... 28.2 533.2 2.5 932 -1.6 Cass, ND................. 5.8 92.9 2.6 691 0.1 Cuyahoga, OH............. 38.0 756.8 -0.6 855 -0.6 Oklahoma, OK............. 22.6 420.6 1.4 713 2.3 Multnomah, OR............ 26.4 438.8 2.5 822 1.0 Allegheny, PA............ 34.8 687.7 0.0 858 0.8 Providence, RI........... 18.2 289.9 0.1 813 3.4 Greenville, SC........... 12.7 229.8 1.0 725 0.8 Minnehaha, SD............ 6.1 113.0 3.1 678 2.4 Shelby, TN............... 19.8 509.2 0.9 848 1.8 Harris, TX............... 91.1 1,919.8 3.8 1,014 3.8 Salt Lake, UT............ 37.7 558.0 4.3 769 3.4 Chittenden, VT........... 5.8 95.8 -1.1 789 1.0 Fairfax, VA.............. 31.3 578.9 3.1 1,247 0.4 King, WA................. 76.3 1,145.1 3.4 985 1.1 Kanawha, WV.............. 6.1 108.2 -0.6 699 1.3 Milwaukee, WI............ 21.9 497.8 -0.4 833 0.6 Laramie, WY.............. 3.0 41.3 4.0 647 2.7 San Juan, PR............. 14.5 330.1 -2.0 549 3.0 St. Thomas, VI........... 1.8 23.5 1.6 625 -1.3 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 2005(2) Employment Average weekly wage(3) Establishments, fourth quarter State 2005 Percent Percent (thousands) December change, Average change, 2005 December weekly fourth (thousands) 2004-05 wage quarter 2004-05 United States(4)......... 8,690.4 133,834.6 1.7 $825 1.5 Alabama.................. 117.9 1,929.6 2.5 706 1.6 Alaska................... 20.7 291.8 1.3 793 1.5 Arizona.................. 139.3 2,596.6 5.4 769 2.5 Arkansas................. 78.3 1,168.4 1.8 633 1.6 California............... 1,261.8 15,515.7 2.4 944 1.6 Colorado................. 170.1 2,234.8 2.4 835 0.5 Connecticut.............. 110.8 1,671.0 0.6 1,080 2.2 Delaware................. 29.6 422.9 1.1 937 6.2 District of Columbia..... 30.9 673.5 1.0 1,354 4.9 Florida.................. 573.6 7,999.0 3.5 752 1.9 Georgia.................. 258.9 4,007.3 2.5 794 2.7 Hawaii................... 36.8 619.6 2.7 736 1.8 Idaho.................... 53.1 625.5 4.6 628 1.6 Illinois................. 342.9 5,830.1 1.0 887 1.1 Indiana.................. 154.2 2,906.4 0.8 705 -0.1 Iowa..................... 91.8 1,465.0 1.6 672 0.7 Kansas................... 84.1 1,325.6 0.7 680 1.6 Kentucky................. 107.3 1,783.6 1.2 682 0.1 Louisiana................ 120.9 1,783.8 -5.5 710 7.4 Maine.................... 48.7 598.2 -0.6 662 0.3 Maryland................. 160.1 2,540.2 1.3 910 3.5 Massachusetts............ 208.4 3,206.4 0.9 1,026 2.0 Michigan................. 255.9 4,320.9 -0.6 835 0.0 Minnesota................ 168.5 2,687.5 1.8 808 -3.2 Mississippi.............. 68.2 1,114.5 -0.2 614 4.6 Missouri................. 171.6 2,700.9 1.2 723 2.0 Montana.................. 40.6 418.9 2.4 591 3.3 Nebraska................. 57.4 900.2 0.9 663 2.3 Nevada................... 68.8 1,253.2 5.7 775 0.9 New Hampshire............ 48.1 630.8 1.0 848 1.1 New Jersey............... 274.0 3,988.9 0.9 1,011 0.8 New Mexico............... 51.1 793.2 2.5 658 2.0 New York................. 564.7 8,531.8 0.8 1,048 2.9 North Carolina........... 236.1 3,916.7 1.7 718 0.4 North Dakota............. 25.1 332.7 2.0 614 2.3 Ohio..................... 289.9 5,359.4 0.2 751 -0.4 Oklahoma................. 94.9 1,502.5 2.8 642 2.4 Oregon................... 125.6 1,686.0 3.8 728 1.3 Pennsylvania............. 332.4 5,619.5 0.8 801 0.6 Rhode Island............. 35.7 483.6 0.3 787 2.7 South Carolina........... 120.9 1,830.0 1.0 666 1.7 South Dakota............. 29.4 378.6 2.0 589 1.4 Tennessee................ 133.4 2,742.6 1.5 736 1.2 Texas.................... 525.4 9,821.7 3.5 823 3.0 Utah..................... 84.0 1,149.3 4.4 687 3.5 Vermont.................. 24.7 308.1 1.1 684 1.0 Virginia................. 216.0 3,637.5 1.9 855 1.7 Washington............... 213.4 2,794.2 3.0 804 1.8 West Virginia............ 48.0 704.3 1.2 627 1.1 Wisconsin................ 163.1 2,773.4 0.6 713 -0.8 Wyoming.................. 23.2 258.4 4.1 678 5.8 Puerto Rico.............. 57.8 1,093.7 -0.5 474 1.7 Virgin Islands........... 3.5 44.9 2.0 664 0.5 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.