Technical information: (202) 691-6567 USDL 06-638 http://www.bls.gov/cew/ For release: 10:00 A.M. EDT Media contact: 691-5902 Wednesday, April 12, 2006 COUNTY EMPLOYMENT AND WAGES: THIRD QUARTER 2005 In September 2005, Lee County, Fla., had the largest over-the-year per- centage increase in employment among the largest counties in the United States, 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 11.4 percent, compared with national job growth of 2.0 percent. Passaic County, N.J., an area north of Newark, had the largest over-the-year gain in average weekly wages in the third quarter of 2005, with an increase of 19.0 percent. The U.S. average weekly wage increased by 6.1 percent over the same time span. Of the 322 largest counties in the U.S., as measured by 2004 annual average employment, 136 had over-the-year percentage growth in employment above the national average in September 2005, and 173 experienced changes below the national average. Average weekly wages grew faster than the national average in 132 of the largest U.S. counties, while the percent change in average weekly wages was below the national average in 173 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 employers subject to unemployment insurance (UI) laws. The 8.6 million employer reports cover 132.9 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. September 2005 employment and 2005 third-quarter average weekly wages for all states are provided in table 4 of this release. Final data for all states, metropoli- tan statistical areas, counties, and the nation through the fourth quarter of 2004 are available on the BLS Web site at http://www.bls.gov/cew/. Pre- liminary data for first, second, and third quarters of 2005 will be available later in April on the BLS Web site. --------------------------------------------------------------------- | Hurricanes Katrina and Rita | | | | The measures of employment and wages reported in this news | | release reflect the impact of Hurricane Katrina and ongoing | | labor market trends. Hurricane Katrina hit the Gulf Coast on | | August 29, 2005, with catastrophic effects in parts of Louisiana, | | Mississippi, and Alabama. This event occurred after the August | | QCEW reference period and before the September period. Its ef- | | fects are first reflected in the September QCEW employment counts | | and the wage totals for the third quarter of 2005. QCEW nonre- | | sponse adjustment methods were modified for September 2005 to | | better reflect the impact of the hurricane in parts of Louisiana | | and Mississippi. For more information, see the QCEW section of | | the Katrina coverage on the BLS Web site (http://www.bls.gov/ | | katrina/qcewquestions.htm). | | | | Hurricane Rita made landfall September 24, after the September | | reference period. The impact of this event did not warrant changes | | to QCEW methodology for the third quarter of 2005. | --------------------------------------------------------------------- - 2 - Table A. Top 10 counties ranked by September 2005 employment, September 2004-05 employment growth, and September 2004-05 percent growth in employment --------------------------------------------------------------------------------- Employment in large counties --------------------------------------------------------------------------------- | | September 2005 employment | Growth in employment, | Percent growth (thousands) | September 2004-05 | in employment, | (thousands) | September 2004-05 ---------------------------|----------------------------|------------------------ U.S. 132,929.3|U.S. 2,614.4|U.S. 2.0 ---------------------------|----------------------------|------------------------ Los Angeles, Calif. 4,105|Maricopa, Ariz. 105.5|Lee, Fla. 11.4 Cook, Ill. 2,529|Los Angeles, Calif. 72.1|Seminole, Fla. 10.7 New York, N.Y. 2,243|Harris, Texas 61.9|Collier, Fla. 8.2 Harris, Texas 1,882|Clark, Nev. 58.7|Kern, Calif. 7.6 Maricopa, Ariz. 1,735|Broward, Fla. 44.9|Lake, Fla. 7.4 Orange, Calif. 1,498|Orange, Fla. 40.0|Volusia, Fla. 7.4 Dallas, Texas 1,431|New York, N.Y. 37.0|Clark, Nev. 7.1 San Diego, Calif. 1,301|Miami-Dade, Fla. 32.0|Polk, Fla. 6.9 King, Wash. 1,129|Orange, Calif. 31.6|Maricopa, Ariz. 6.5 Miami-Dade, Fla. 1,003|San Bernadino, Calif. 31.1|Broward, Fla. 6.5 --------------------------------------------------------------------------------- Large County Employment In September 2005, national employment, as measured by the QCEW program, was 132.9 million, up by 2.0 percent from September 2004. The 322 U.S. counties with 75,000 or more employees accounted for 70.6 percent of total U.S. covered employment and 76.4 percent of total covered wages. These 322 counties had a net job gain of 1,776,000 over the year, accounting for 67.9 percent of the U.S. employment increase. Employment increased in 275 of the large counties from September 2004 to September 2005. Lee County, Fla., had the largest over-the-year percentage increase in employment (11.4 percent). Seminole, Fla., had the next largest increase, 10.7 percent, followed by the counties of Collier, Fla. (8.2 percent), Kern, Calif. (7.6 percent), and Lake and Volusia, Fla. (7.4 percent each). (See table 1.) Employment declined in 35 large counties from September 2004 to September 2005. The largest percentage decline in employment was in Orleans County, La. (-26.3 percent), followed by the counties of Jefferson, La. (-25.6 percent), and Harrison, Miss. (-13.9 percent). Employment losses in these three Gulf Coast counties reflected the devastation caused by Hurricane Katrina. Hinds, Miss., located farther inland and within 100 miles of the path of Katrina, had the next largest employment decline (-2.1 percent). Winnebago, Ill., followed with a 1.4 percent decline. The largest gains in employment from September 2004 to September 2005 were recorded in the counties of Maricopa, Ariz. (105,500), Los Angeles, Calif. (72,100), Harris, Texas (61,900), Clark, Nev. (58,700), and Broward, Fla. (44,900). (See table A.) The largest decline in employment occurred in Orleans County, La. (-63,600), followed by the counties of Jefferson, La. (-54,400), Harrison, Miss. (-12,500), Wayne, Mich. (-9,900), and Hinds, Miss. (-2,700). Large County Average Weekly Wages The national average weekly wage in the third quarter of 2005 was $777. Average weekly wages were higher than the national average in 115 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 $1,419. Santa Clara, Calif., was second with an average weekly wage of $1,403, followed by Arlington, Va. ($1,292), San Mateo, Calif. ($1,268), and Washington, D.C. ($1,265). (See table B.) - 3 - Table B. Top 10 counties ranked by third quarter 2005 average weekly wages, third quarter 2004-05 growth in average weekly wages, and third 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 third quarter 2005 | wage, third quarter | average weekly wage, | 2004-05 | third quarter 2004-05 ------------------------------------------------------------------------------ U.S. $777|U.S. $45|U.S. 6.1 ---------------------------|-------------------------|------------------------ New York, N.Y. $1,419|Passaic, N.J. $148|Passaic, N.J. 19.0 Santa Clara, Calif. 1,403|San Mateo, Calif. 143|Fort Bend, Texas 15.4 Arlington, Va. 1,292|Boulder, Colo. 120|Boulder, Colo. 13.8 San Mateo, Calif. 1,268|Fairfax, Va. 117|San Mateo, Calif. 12.7 Washington, D.C. 1,265|Fort Bend, Texas 115|Harrison, Miss. 12.7 San Francisco, Calif. 1,219|San Francisco, Calif. 108|Fairfax, Va. 10.9 Suffolk, Mass. 1,198|Santa Clara, Calif. 95|Ventura, Calif. 10.7 Fairfield, Conn. 1,197|New York, N.Y. 93|Orleans, La. 10.7 Fairfax, Va. 1,188|Arlington, Va. 92|Montgomery, Texas 10.5 Somerset, N.J. 1,148|Alameda, Calif. 83|Collier, Fla. 10.4 |Marin, Calif. 83| |Ventura, Calif. 83| ------------------------------------------------------------------------------ There were 206 counties with an average weekly wage below the national average in the third quarter of 2005. The lowest average weekly wages were reported in Cameron County, Texas ($486), followed by the counties of Hidalgo, Texas ($499), Horry, S.C. ($505), and Webb, Texas, and Yakima, Wash. ($525 each). (See table 1.) Over the year, the national average weekly wage rose by 6.1 percent. Among the largest counties, Passaic, N.J., led the nation in growth in average weekly wages, with an increase of 19.0 percent from the third quarter of 2004. Fort Bend, Texas, was second with 15.4 percent growth, followed by the counties of Boulder, Colo. (13.8 percent), and San Mateo, Calif., and Harrison, Miss. (12.7 percent each). The average weekly wage growth rate for Harrison, Miss., and the 10.7 percent wage gain for Orleans, La., were boosted as a result of the disproportionate job and pay losses in lower-paid industries following Hurricane Katrina. Five counties experienced over-the-year declines in average weekly wages. Clayton County, Ga., had the largest decrease, -5.1 percent, followed by the counties of Benton, Ark. (-1.2 percent), Trumbull, Ohio (-0.6 percent), Saginaw, Mich. (-0.4 percent), and St. Joseph, Ind. (-0.1 percent). 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 September 2004 to September 2005. Maricopa County, Ariz., experienced the fastest growth in employment among the largest counties, with a 6.5 percent increase. With- in Maricopa County, employment rose in every industry group except informa- tion. The largest gains were in construction (15.0 percent) and professional and business services (8.2 percent). (See table 2.) Harris, Texas, had the next largest increase in employment, 3.4 percent, followed by Miami-Dade, Fla. (3.3 percent). The smallest employment gains occurred in Cook County, Ill. (0.7 percent), followed by the counties of San Diego, Calif. (1.6 percent), and New York, N.Y. (1.7 percent). All 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, 8.9 percent. Within Miami-Dade County, wages increased the most in government (13.2 percent) and manufacturing (11.2 percent). Maricopa, Ariz., was second in wage growth, with a gain of 8.1 percent, followed by Harris County, Texas (7.8 percent). The small- est wage gains among the 10 largest counties occurred in Los Angeles, Calif., and Cook, Ill. (5.5 percent each), followed by Dallas County, Texas (6.6 per- cent). - 4 - Largest County by State Table 3 shows September 2005 employment and the 2005 third-quarter average weekly wage in the largest county in each state (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 September 2005 ranged from approximately 4.1 million in Los Angeles County, Calif., to 41,000 in Laramie County, Wyo. The highest average weekly wage of these counties was in New York, N.Y. ($1,419), while the lowest average weekly wage was in Yellowstone, Mont. ($619). 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 tar- | | geted to local data users. For links to these releases, see http: | | //www.bls.gov/cew/cewregional.htm. | ---------------------------------------------------------------------- - 5 - Technical Note These data are the product of a federal-state cooperative program, the Quarterly Census of Employment and Wages (QCEW) program, also known as the ES-202 program. The data are derived from summaries of employment and to- tal pay of workers covered by state and federal unemployment insurance (UI) legislation and provided by State Workforce Agencies (SWAs). The summaries are a result of the administration of state unemployment insurance programs that require most employers to pay quarterly taxes based on the employment and wages of workers covered by UI. Data for 2005 are preliminary and sub- ject to revision. For purposes of this release, large counties are defined as having em- ployment levels of 75,000 or greater. In addition, data for San Juan, Puerto Rico, are provided, but not used in calculating U.S. averages, rankings, or in the analysis in the text. Each year, these large counties are selected on the basis of the preliminary annual average of employment for the previous year. The 323 counties presented in this release were derived using 2004 preliminary annual averages of employment. All of the 318 counties that were published in the 2004 releases are included in the 2005 releases. The following counties grew enough in 2004 to be included in the 2005 releases: Lake, Fla., Wyandotte, Kan., Harford, Md., Washington, Pa., and Whatcom, Wash. These counties will be included in all 2005 quar- terly releases. The counties in table 2 are selected and sorted each year based on the annual average employment from the preceding year. The preliminary QCEW data presented in this release may differ from data released by the individual states. These potential differences result from the states' continuing receipt of UI data over time and ongoing review and editing. The individual states determine their data release timetables. Differences between QCEW, BED, and CES employment measures The Bureau publishes three different establishment-based employment measures for any given quarter. Each of these measures--QCEW, Business Employment Dynamics (BED), and Current Employment Statistics (CES)--makes use of the quarterly UI employment reports in producing data; however, each measure has a somewhat different universe coverage, estimation pro- cedure, and publication product. Differences in coverage and estimation methods can result in somewhat different measures of employment change over time. It is important to understand program differences and the intended uses of the program pro- ducts. (See table below.) Additional information on each program can be obtained from the program Web sites shown in the table below. - 6 - Summary of Major Differences between QCEW, BED, and CES Employment Measures -------------------------------------------------------------------------------- | QCEW | BED | CES -----------|---------------------|----------------------|----------------------- Source |--Count of UI admini-|--Count of longitudi- |--Sample survey: | strative records | nally-linked UI ad- | 400,000 establish- | submitted by 8.6 | ministrative records| ments | million establish- | submitted by 6.7 | | 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 | | | ----------------------------------------------------------------------------------- - 7 - Coverage Employment and wage data for workers covered by state UI laws are compiled from quarterly contribution reports submitted to the SWAs by employers. For federal civilian workers covered by the Unemployment Compensation for Federal Employees (UCFE) program, employment and wage data are compiled from quarterly reports that are sent to the appropriate SWA by the specific federal agency. In addition to the quarterly contribution reports, employers who operate multiple establishments within a state com- plete a questionnaire, called the "Multiple Worksite Report," which provides detailed information on the location and industry of each of their establish- ments. The employment and wage data included in this release are derived from microdata summaries of more than 8 million employer reports of employ- ment and wages submitted by states to the BLS. These reports are based on place of employment rather than place of residence. UI and UCFE coverage is broad and basically comparable from state to state. In 2004, UI and UCFE programs covered workers in 129.3 million jobs. The estimated 124.4 million workers in these jobs (after adjust- ment for multiple jobholders) represented 96.6 percent of civilian wage and salary employment. Covered workers received $5.088 trillion in pay, representing 94.4 percent of the wage and salary component of personal income and 43.4 percent of the gross domestic product. Major exclusions from UI coverage include self-employed workers, most agricultural workers on small farms, all members of the Armed Forces, elected officials in most states, most employees of railroads, some domes- tic workers, most student workers at schools, and employees of certain small nonprofit organizations. State and federal UI laws change periodically. These changes may have an impact on the employment and wages reported by employers covered under the UI program. Coverage changes may affect the over-the-year comparisons presented in this news release. Beginning with the first quarter of 2005, Oregon implemented a change in their state UI laws. This change extended UI coverage to providers of home care for the elderly. These providers are now considered state workers for purposes of UI benefits. Concepts and methodology Monthly employment is based on the number of workers who worked during or received pay for the pay period including the 12th of the month. With few exceptions, all employees of covered firms are reported, including pro- duction and sales workers, corporation officials, executives, supervisory personnel, and clerical workers. Workers on paid vacations and part-time workers also are included. Average weekly wage values are calculated by dividing quarterly total wages by the average of the three monthly employment levels (all employees, as described above) and dividing the result by 13, for the 13 weeks in the quarter. These calculations are made using unrounded employment and wage values. The average wage values that can be calculated using rounded data from the BLS database may differ from the averages reported. Included in the quarterly wage data are non-wage cash payments such as bonuses, the cash value of meals and lodging when supplied, tips and other gratuities, and, in some states, employer contributions to certain deferred compensation plans such as 401(k) plans and stock options. 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, 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. - 9 - The adjusted data used to calculate the over-the-year change measures presented in any County Employment and Wages news release are valid for comparisons between the starting and ending points (a 12-month period) used in that particular release. Comparisons may not be valid for any time period other than the one featured in a release even if the changes were calculated using adjusted data. County definitions are assigned according to Federal Information Processing Standards Publications (FIPS PUBS) as issued by the National Institute of Standards and Technology, after approval by the Secretary of Commerce pursuant to Section 5131 of the Information Technology Management Reform Act of 1996 and the Computer Security Act of 1987, Public Law 104- 106. Areas shown as counties include those designated as independent cities in some jurisdictions and, in Alaska, those designated as census areas where counties have not been created. County data also are presented for the New England states for comparative purposes even though townships are the more common designation used in New England (and New Jersey). The regions re- ferred to in this release are defined as census regions. Additional statistics and other information An annual bulletin, Employment and Wages, features comprehensive infor- mation by detailed industry on establishments, employment, and wages for the nation and all states. The 2004 edition of this bulletin contains selected data produced by Business Employment Dynamics (BED) on job gains and losses, as well as selected data from the fourth quarter 2004 version of this news release. Employment and Wages Annual Averages, 2004 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, third quarter 2005(2) Employment Average weekly wage(5) Establishments, County(3) third quarter Percent Ranking Percent Ranking 2005 September change, by Average change, by (thousands) 2005 September percent weekly third percent (thousands) 2004-05(4) change wage quarter change 2004-05(4) United States(6)......... 8,634.7 132,929.3 2.0 - $777 6.1 - Jefferson, AL............ 18.8 372.8 1.3 186 796 7.7 51 Madison, AL.............. 8.2 169.6 2.5 103 847 9.3 14 Mobile, AL............... 9.8 166.4 3.2 72 638 6.2 126 Montgomery, AL........... 6.7 136.0 3.3 67 658 6.3 120 Tuscaloosa, AL........... 4.2 81.9 4.4 43 655 6.7 98 Anchorage Borough, AK.... 7.9 148.0 1.9 141 857 5.8 153 Maricopa, AZ............. 84.3 1,735.0 6.5 9 790 8.1 35 Pima, AZ................. 18.3 356.9 3.6 61 697 8.7 23 Benton, AR............... 4.9 90.8 5.3 22 673 -1.2 313 Pulaski, AR.............. 13.5 245.7 1.3 186 709 6.1 133 Washington, AR........... 5.3 91.3 5.0 28 634 5.3 194 Alameda, CA.............. 47.5 681.8 0.3 256 1,053 8.6 26 Contra Costa, CA......... 27.1 343.1 0.5 243 973 5.6 168 Fresno, CA............... 28.6 354.6 1.7 155 616 3.9 266 Kern, CA................. 16.4 280.5 7.6 4 658 (7) - Los Angeles, CA.......... 370.3 4,105.4 1.8 150 879 5.5 174 Marin, CA................ 11.5 110.2 0.6 237 988 9.2 15 Monterey, CA............. 11.8 179.8 0.2 265 687 6.7 98 Orange, CA............... 90.3 1,497.5 2.2 122 907 7.1 71 Placer, CA............... 9.7 134.8 (7) - 783 (7) - Riverside, CA............ 39.9 610.6 5.3 22 680 7.1 71 Sacramento, CA........... 47.6 630.6 2.6 99 878 7.9 42 San Bernardino, CA....... 43.0 637.2 5.1 25 698 6.6 105 San Diego, CA............ 87.6 1,301.3 1.6 164 855 7.3 60 San Francisco, CA........ 42.8 528.9 1.4 176 1,219 9.7 12 San Joaquin, CA.......... 16.1 224.8 1.0 209 686 6.0 142 San Luis Obispo, CA...... 8.7 104.4 1.5 167 655 3.8 271 San Mateo, CA............ 22.4 329.0 0.3 256 1,268 12.7 4 Santa Barbara, CA........ 13.1 186.1 2.3 114 751 7.1 71 Santa Clara, CA.......... 52.7 864.2 1.7 155 1,403 7.3 60 Santa Cruz, CA........... 8.4 100.4 0.1 271 750 9.8 11 Solano, CA............... 9.5 131.7 1.7 155 753 8.7 23 Sonoma, CA............... 17.1 193.4 -0.2 287 772 5.5 174 Stanislaus, CA........... 13.2 177.9 1.5 167 671 5.8 153 Tulare, CA............... 8.4 144.1 5.1 25 556 5.3 194 Ventura, CA.............. 20.7 312.5 2.0 137 861 10.7 7 Yolo, CA................. 5.2 100.7 1.2 192 784 6.7 98 Adams, CO................ 9.1 150.2 3.0 83 755 6.9 86 Arapahoe, CO............. 19.6 273.0 1.6 164 935 7.3 60 Boulder, CO.............. 12.4 155.5 2.1 130 989 13.8 3 Denver, CO............... 25.1 429.4 1.7 155 947 6.6 105 El Paso, CO.............. 16.9 241.9 2.2 122 741 6.5 111 Jefferson, CO............ 18.8 208.0 2.0 137 810 6.0 142 Larimer, CO.............. 9.8 127.3 2.1 130 722 4.6 240 Fairfield, CT............ 32.2 413.7 0.9 221 1,197 5.7 159 Hartford, CT............. 24.6 488.6 1.2 192 968 5.6 168 New Haven, CT............ 22.2 361.9 0.0 276 845 4.2 258 New London, CT........... 6.7 130.6 0.8 227 812 6.6 105 New Castle, DE........... 19.8 281.2 0.3 256 921 7.5 56 Washington, DC........... 30.7 666.4 0.8 227 1,265 4.5 244 Alachua, FL.............. 6.2 124.4 (7) - 665 (7) - Brevard, FL.............. 13.9 204.1 5.4 19 746 2.1 301 Broward, FL.............. 61.6 738.1 6.5 9 746 6.9 86 Collier, FL.............. 11.7 125.1 8.2 3 722 10.4 10 Duval, FL................ 24.6 452.7 4.9 32 765 6.8 93 Escambia, FL............. 7.6 127.2 2.9 86 632 8.2 33 Hillsborough, FL......... 34.5 626.2 4.1 48 745 7.3 60 Lake, FL................. 6.4 80.7 7.4 5 600 8.9 21 Lee, FL.................. 17.4 214.0 11.4 1 685 7.9 42 Leon, FL................. 7.8 148.8 3.2 72 679 7.8 47 Manatee, FL.............. 8.2 122.5 5.6 17 624 9.1 18 Marion, FL............... 7.5 98.7 (7) - 580 (7) - Miami-Dade, FL........... 84.4 1,003.2 3.3 67 781 8.9 21 Okaloosa, FL............. 6.0 83.6 6.0 15 642 9.6 13 Orange, FL............... 32.6 665.8 6.4 11 729 6.9 86 Palm Beach, FL........... 47.2 546.3 (7) - 768 6.7 98 Pasco, FL................ 8.6 94.2 (7) - 565 (7) - Pinellas, FL............. 30.4 441.9 1.5 167 687 7.7 51 Polk, FL................. 11.7 201.8 6.9 8 629 4.8 223 Sarasota, FL............. 14.7 158.9 3.8 54 679 9.2 15 Seminole, FL............. 13.6 171.5 10.7 2 699 7.0 79 Volusia, FL.............. 13.3 164.3 7.4 5 571 3.6 279 Bibb, GA................. 4.7 85.1 -1.2 305 653 4.5 244 Chatham, GA.............. 7.2 132.0 2.6 99 674 7.2 68 Clayton, GA.............. 4.4 109.6 3.3 67 765 -5.1 314 Cobb, GA................. 20.2 313.1 4.5 41 857 7.1 71 De Kalb, GA.............. 17.0 291.5 1.1 202 843 7.3 60 Fulton, GA............... 38.0 744.4 2.2 122 1,007 4.9 218 Gwinnett, GA............. 22.3 319.7 3.6 61 828 6.0 142 Muscogee, GA............. 4.8 98.7 2.3 114 627 6.6 105 Richmond, GA............. 4.8 104.6 1.9 141 667 6.4 115 Honolulu, HI............. 23.8 441.7 3.2 72 740 5.3 194 Ada, ID.................. 13.9 201.2 5.4 19 719 6.4 115 Champaign, IL............ 4.0 91.4 1.2 192 670 3.2 289 Cook, IL................. 131.0 2,529.4 0.7 232 920 5.5 174 Du Page, IL.............. 33.7 587.8 1.5 167 913 7.0 79 Kane, IL................. 11.6 208.0 2.8 88 732 6.1 133 Lake, IL................. 19.6 329.9 0.6 237 909 3.9 266 McHenry, IL.............. 7.8 99.6 2.7 94 694 4.8 223 McLean, IL............... 3.5 85.8 1.9 141 760 8.6 26 Madison, IL.............. 5.8 94.2 0.2 265 648 5.5 174 Peoria, IL............... 4.6 100.6 2.4 109 753 8.5 30 Rock Island, IL.......... 3.4 79.9 1.9 141 755 5.6 168 St. Clair, IL............ 5.2 94.3 0.4 252 641 6.3 120 Sangamon, IL............. 5.2 131.8 0.6 237 767 4.8 223 Will, IL................. 11.7 172.4 3.2 72 727 4.2 258 Winnebago, IL............ 6.7 136.4 -1.4 309 685 (7) - Allen, IN................ 8.7 182.7 0.7 232 683 4.0 263 Elkhart, IN.............. 4.8 126.8 0.3 256 687 4.4 248 Hamilton, IN............. 6.6 96.9 4.3 44 795 4.7 232 Lake, IN................. 10.0 195.6 1.0 209 700 4.5 244 Marion, IN............... 23.5 584.0 0.5 243 819 7.1 71 St. Joseph, IN........... 6.0 126.7 -0.5 297 666 -0.1 310 Vanderburgh, IN.......... 4.8 108.5 1.0 209 676 7.5 56 Linn, IA................. 6.1 118.3 1.7 155 763 8.1 35 Polk, IA................. 14.2 265.6 1.9 141 792 6.9 86 Scott, IA................ 5.1 89.9 3.9 52 641 6.3 120 Johnson, KS.............. 19.4 302.9 1.7 155 823 7.9 42 Sedgwick, KS............. 11.8 244.0 0.7 232 727 5.7 159 Shawnee, KS.............. 4.7 94.0 -0.8 300 673 8.0 39 Wyandotte, KS............ 3.2 78.3 1.2 192 764 2.3 299 Fayette, KY.............. 8.8 169.8 2.4 109 711 3.8 271 Jefferson, KY............ 21.6 425.3 1.4 176 761 5.5 174 Caddo, LA................ 7.2 124.1 2.3 114 647 5.4 188 Calcasieu, LA............ 4.8 85.0 4.7 35 654 9.0 19 East Baton Rouge, LA..... 13.3 256.2 4.8 34 653 5.2 203 Jefferson, LA............ 14.3 158.3 -25.6 312 656 6.0 142 Lafayette, LA............ 7.9 125.1 6.2 13 680 6.8 93 Orleans, LA.............. 12.9 178.2 -26.3 313 742 10.7 7 Cumberland, ME........... 11.7 171.3 -0.1 279 710 6.0 142 Anne Arundel, MD......... 14.0 224.3 2.2 122 817 6.8 93 Baltimore, MD............ 21.0 374.1 1.4 176 808 7.3 60 Frederick, MD............ 5.7 92.5 2.2 122 748 7.0 79 Harford, MD.............. 5.4 81.5 2.5 103 754 9.0 19 Howard, MD............... 8.2 140.0 2.5 103 921 8.0 39 Montgomery, MD........... 32.0 462.4 2.6 99 1,027 8.0 39 Prince Georges, MD....... 15.4 316.0 0.4 252 869 6.2 126 Baltimore City, MD....... 13.9 352.1 0.3 256 908 8.1 35 Barnstable, MA........... 9.6 99.3 0.0 276 664 4.9 218 Bristol, MA.............. 16.0 221.0 0.2 265 695 3.4 285 Essex, MA................ 21.4 296.7 0.1 271 848 7.1 71 Hampden, MA.............. 14.8 201.7 1.4 176 731 3.8 271 Middlesex, MA............ 49.8 791.8 0.9 221 1,110 6.5 111 Norfolk, MA.............. 22.6 319.5 0.6 237 923 4.3 255 Plymouth, MA............. 14.3 178.5 1.4 176 748 3.6 279 Suffolk, MA.............. 22.9 566.3 1.5 167 1,198 1.6 307 Worcester, MA............ 21.1 318.6 -0.5 297 799 2.3 299 Genesee, MI.............. 8.4 149.9 (7) - 736 1.1 308 Ingham, MI............... 7.1 163.8 (7) - 741 2.9 292 Kalamazoo, MI............ 5.5 117.9 0.8 227 708 3.4 285 Kent, MI................. 14.6 347.1 2.9 86 721 2.7 295 Macomb, MI............... 18.2 332.4 1.1 202 849 4.0 263 Oakland, MI.............. 40.8 720.6 -0.1 279 935 4.7 232 Ottawa, MI............... 5.8 115.3 0.2 265 705 4.9 218 Saginaw, MI.............. 4.5 89.8 (7) - 689 -0.4 311 Washtenaw, MI............ 8.2 194.1 -1.0 303 891 5.3 194 Wayne, MI................ 34.2 789.3 -1.2 305 910 4.8 223 Anoka, MN................ 7.8 115.6 1.8 150 751 2.0 302 Dakota, MN............... 10.3 171.8 0.6 237 777 4.4 248 Hennepin, MN............. 41.9 837.8 1.4 176 990 6.1 133 Olmsted, MN.............. 3.5 89.4 1.4 176 854 3.1 291 Ramsey, MN............... 15.5 334.1 1.2 192 864 5.6 168 St. Louis, MN............ 5.9 95.0 0.0 276 655 3.5 283 Stearns, MN.............. 4.4 78.3 1.9 141 628 3.5 283 Harrison, MS............. 4.6 77.8 -13.9 311 586 12.7 4 Hinds, MS................ 6.5 126.9 -2.1 310 689 5.5 174 Boone, MO................ 4.4 81.4 4.2 46 615 5.3 194 Clay, MO................. 5.0 87.9 1.0 209 761 8.6 26 Greene, MO............... 8.0 150.9 3.5 63 623 5.2 203 Jackson, MO.............. 18.7 364.2 0.3 256 794 4.6 240 St. Charles, MO.......... 7.6 118.7 2.8 88 682 5.6 168 St. Louis, MO............ 33.8 622.5 1.0 209 828 6.4 115 St. Louis City, MO....... 8.1 224.2 -0.1 279 869 7.7 51 Douglas, NE.............. 15.2 310.3 0.5 243 741 5.6 168 Lancaster, NE............ 7.8 154.3 0.6 237 653 5.3 194 Clark, NV................ 42.3 883.1 7.1 7 752 7.3 60 Washoe, NV............... 13.3 217.1 3.8 54 747 4.8 223 Hillsborough, NH......... 12.3 197.6 1.7 155 854 3.3 288 Rockingham, NH........... 10.8 138.8 1.3 186 784 6.1 133 Atlantic, NJ............. 6.7 149.3 1.1 202 698 4.5 244 Bergen, NJ............... 34.2 450.2 0.5 243 964 5.9 149 Burlington, NJ........... 11.3 200.5 0.5 243 853 7.8 47 Camden, NJ............... 13.5 210.8 0.5 243 804 7.9 42 Essex, NJ................ 21.3 358.4 0.7 232 998 5.3 194 Gloucester, NJ........... 6.3 105.2 4.0 49 712 4.7 232 Hudson, NJ............... 14.1 239.1 1.5 167 1,024 4.0 263 Mercer, NJ............... 10.9 223.6 2.8 88 987 5.2 203 Middlesex, NJ............ 20.8 392.4 -0.1 279 960 2.0 302 Monmouth, NJ............. 20.2 257.8 1.2 192 828 5.1 209 Morris, NJ............... 17.9 283.7 -0.1 279 1,086 5.2 203 Ocean, NJ................ 11.7 150.4 1.0 209 669 7.7 51 Passaic, NJ.............. 12.6 176.3 -0.2 287 929 19.0 1 Somerset, NJ............. 10.1 171.3 3.2 72 1,148 4.4 248 Union, NJ................ 14.9 226.7 (7) - 977 (7) - Bernalillo, NM........... 16.5 323.4 2.6 99 707 6.2 126 Albany, NY............... 9.7 229.3 0.2 265 804 1.9 305 Bronx, NY................ 15.7 221.2 1.9 141 772 2.8 294 Broome, NY............... 4.5 94.8 0.4 252 623 3.7 275 Dutchess, NY............. 8.1 117.5 -0.1 279 783 5.5 174 Erie, NY................. 23.4 457.2 -0.4 293 690 3.9 266 Kings, NY................ 42.8 457.5 2.2 122 686 3.2 289 Monroe, NY............... 17.8 382.7 0.8 227 766 2.0 302 Nassau, NY............... 51.6 597.8 0.4 252 862 (7) - New York, NY............. 114.3 2,243.4 1.7 155 1,419 7.0 79 Oneida, NY............... 5.4 108.9 0.2 265 613 4.1 260 Onondaga, NY............. 12.8 251.7 1.0 209 718 3.9 266 Orange, NY............... 9.6 129.6 0.9 221 670 5.5 174 Queens, NY............... 40.8 484.6 1.3 186 794 5.7 159 Richmond, NY............. 8.2 89.7 0.8 227 709 2.5 298 Rockland, NY............. 9.5 112.4 1.6 164 806 4.8 223 Suffolk, NY.............. 48.8 609.8 0.3 256 836 5.0 213 Westchester, NY.......... 35.9 411.9 0.3 256 1,005 4.1 260 Buncombe, NC............. 7.1 109.7 2.1 130 613 4.8 223 Catawba, NC.............. 4.3 86.0 -1.1 304 611 3.7 275 Cumberland, NC........... 5.8 117.0 3.2 72 614 5.5 174 Durham, NC............... 6.2 170.1 1.8 150 1,019 6.8 93 Forsyth, NC.............. 8.5 180.3 1.7 155 763 0.7 309 Guilford, NC............. 13.6 272.5 1.2 192 710 5.5 174 Mecklenburg, NC.......... 27.7 527.4 3.7 58 894 6.9 86 New Hanover, NC.......... 6.6 97.7 4.6 37 639 7.0 79 Wake, NC................. 24.0 409.9 4.3 44 778 6.3 120 Cass, ND................. 5.7 93.0 3.3 67 648 6.2 126 Butler, OH............... 7.1 139.0 2.4 109 703 5.7 159 Cuyahoga, OH............. 38.1 755.8 0.1 271 802 3.4 285 Franklin, OH............. 29.2 686.1 1.1 202 802 8.5 30 Hamilton, OH............. 24.5 541.6 -0.4 293 862 7.1 71 Lake, OH................. 6.9 100.8 1.1 202 657 3.8 271 Lorain, OH............... 6.3 102.0 -1.3 307 698 7.9 42 Lucas, OH................ 10.9 227.7 -0.1 279 707 5.7 159 Mahoning, OH............. 6.5 108.4 1.3 186 590 3.7 275 Montgomery, OH........... 13.2 282.3 -0.8 300 747 5.7 159 Stark, OH................ 9.3 167.4 0.1 271 634 6.6 105 Summit, OH............... 15.0 273.9 1.5 167 729 4.4 248 Trumbull, OH............. 4.8 84.0 -0.1 279 684 -0.6 312 Oklahoma, OK............. 22.5 415.7 1.5 167 685 6.4 115 Tulsa, OK................ 18.6 334.0 4.2 46 701 5.7 159 Clackamas, OR............ 12.0 145.1 4.6 37 739 7.3 60 Jackson, OR.............. 6.5 84.8 4.0 49 600 5.1 209 Lane, OR................. 10.6 147.3 3.7 58 633 5.9 149 Marion, OR............... 9.0 139.6 2.8 88 613 5.5 174 Multnomah, OR............ 26.2 430.5 2.4 109 798 5.3 194 Washington, OR........... 15.2 237.7 4.0 49 943 7.2 68 Allegheny, PA............ 35.0 682.9 -0.3 292 811 4.6 240 Berks, PA................ 9.0 165.5 1.8 150 705 5.4 188 Bucks, PA................ 20.5 262.3 1.3 186 757 6.5 111 Chester, PA.............. 15.1 231.7 2.3 114 986 8.2 33 Cumberland, PA........... 5.8 125.8 -0.2 287 748 6.3 120 Dauphin, PA.............. 7.2 177.3 0.9 221 780 5.8 153 Delaware, PA............. 13.6 208.6 0.1 271 813 2.9 292 Erie, PA................. 7.2 129.6 1.2 192 626 5.2 203 Lackawanna, PA........... 5.7 100.6 2.1 130 617 4.9 218 Lancaster, PA............ 11.9 229.7 1.5 167 696 6.1 133 Lehigh, PA............... 8.4 175.5 1.0 209 762 5.0 213 Luzerne, PA.............. 7.9 144.3 1.0 209 627 4.7 232 Montgomery, PA........... 28.0 483.5 1.1 202 956 5.5 174 Northampton, PA.......... 6.2 95.5 3.0 83 696 4.3 255 Philadelphia, PA......... 29.1 633.9 0.9 221 921 6.0 142 Washington, PA........... 5.4 76.3 0.7 232 675 6.5 111 Westmoreland, PA......... 9.6 140.4 1.0 209 642 7.5 56 York, PA................. 8.8 173.4 2.1 130 705 6.0 142 Kent, RI................. 5.7 82.7 1.2 192 715 6.1 133 Providence, RI........... 18.2 290.8 0.9 221 750 2.7 295 Charleston, SC........... 12.5 199.6 2.8 88 674 8.5 30 Greenville, SC........... 12.7 227.4 2.5 103 688 3.9 266 Horry, SC................ 8.5 112.1 4.6 37 505 4.6 240 Lexington, SC............ 5.9 88.3 4.6 37 608 4.3 255 Richland, SC............. 9.8 206.7 -0.4 293 685 6.2 126 Spartanburg, SC.......... 6.4 115.6 -0.2 287 684 5.7 159 Minnehaha, SD............ 6.1 111.3 2.3 114 666 6.7 98 Davidson, TN............. 17.9 446.0 3.2 72 773 5.0 213 Hamilton, TN............. 8.4 193.2 1.0 209 686 6.2 126 Knox, TN................. 10.4 220.6 1.1 202 666 5.5 174 Rutherford, TN........... 3.8 95.9 3.9 52 668 3.6 279 Shelby, TN............... 19.7 505.9 1.9 141 814 3.7 275 Bell, TX................. 4.3 94.4 5.0 28 596 4.4 248 Bexar, TX................ 30.2 677.9 3.2 72 675 5.0 213 Brazoria, TX............. 4.3 80.1 3.2 72 719 4.7 232 Brazos, TX............... 3.6 82.6 3.8 54 551 1.7 306 Cameron, TX.............. 6.2 117.0 1.9 141 486 3.6 279 Collin, TX............... 14.3 250.4 6.3 12 913 6.9 86 Dallas, TX............... 66.1 1,431.1 2.0 137 940 6.6 105 Denton, TX............... 9.3 149.5 4.9 32 687 6.7 98 El Paso, TX.............. 12.8 260.1 2.3 114 558 5.5 174 Fort Bend, TX............ 7.2 111.0 2.7 94 860 15.4 2 Galveston, TX............ 4.9 87.7 3.3 67 671 4.7 232 Harris, TX............... 90.4 1,882.0 3.4 66 930 7.8 47 Hidalgo, TX.............. 9.7 196.2 5.0 28 499 5.1 209 Jefferson, TX............ 5.8 117.7 2.1 130 711 6.9 86 Lubbock, TX.............. 6.5 120.2 2.2 122 590 6.1 133 McLennan, TX............. 4.8 101.9 0.5 243 627 5.4 188 Montgomery, TX........... 7.1 104.8 6.2 13 727 10.5 9 Nueces, TX............... 8.0 147.2 2.5 103 654 7.4 59 Potter, TX............... 3.7 72.0 1.4 176 631 5.3 194 Smith, TX................ 5.0 89.7 2.0 137 680 5.4 188 Tarrant, TX.............. 34.7 719.8 2.3 114 789 5.8 153 Travis, TX............... 25.4 531.0 3.8 54 882 6.7 98 Webb, TX................. 4.4 82.2 5.3 22 525 6.1 133 Williamson, TX........... 6.0 102.0 5.1 25 785 5.9 149 Davis, UT................ 6.8 97.5 3.5 63 636 5.0 213 Salt Lake, UT............ 36.9 547.1 4.5 41 719 7.0 79 Utah, UT................. 11.9 160.8 5.8 16 591 4.8 223 Weber, UT................ 5.6 89.5 2.8 88 583 4.9 218 Chittenden, VT........... 5.7 95.5 -0.9 302 764 5.4 188 Arlington, VA............ 7.2 154.3 0.5 243 1,292 7.7 51 Chesterfield, VA......... 6.8 115.2 3.2 72 710 5.7 159 Fairfax, VA.............. 30.8 569.6 3.7 58 1,188 10.9 6 Henrico, VA.............. 8.6 172.4 2.2 122 816 5.2 203 Loudoun, VA.............. 7.0 123.1 5.5 18 1,008 4.7 232 Prince William, VA....... 6.4 102.0 5.4 19 721 8.7 23 Alexandria City, VA...... 5.8 94.2 1.2 192 1,013 7.1 71 Chesapeake City, VA...... 5.1 96.4 2.5 103 627 8.1 35 Newport News City, VA.... 3.8 98.6 1.4 176 719 6.8 93 Norfolk City, VA......... 5.6 144.4 -0.2 287 761 5.8 153 Richmond City, VA........ 7.0 161.9 2.7 94 876 7.0 79 Virginia Beach City, VA.. 10.9 179.1 3.5 63 619 8.6 26 Clark, WA................ 10.7 128.2 5.0 28 720 5.4 188 King, WA................. 75.2 1,129.1 2.7 94 997 7.2 68 Kitsap, WA............... 6.3 82.6 3.0 83 734 5.8 153 Pierce, WA............... 19.5 262.0 3.1 82 715 6.4 115 Snohomish, WA............ 16.3 217.4 2.1 130 802 5.1 209 Spokane, WA.............. 14.5 199.3 2.7 94 643 6.1 133 Thurston, WA............. 6.3 93.6 2.4 109 714 4.7 232 Whatcom, WA.............. 6.5 78.6 4.7 35 609 7.8 47 Yakima, WA............... 7.7 105.8 1.8 150 525 4.4 248 Kanawha, WV.............. 6.2 107.4 -0.5 297 668 6.2 126 Brown, WI................ 6.8 148.8 1.0 209 695 5.9 149 Dane, WI................. 14.0 300.8 2.3 114 780 9.2 15 Milwaukee, WI............ 21.8 494.8 0.3 256 785 4.8 223 Outagamie, WI............ 5.0 101.3 -0.4 293 678 4.1 260 Racine, WI............... 4.3 76.1 -1.3 307 737 6.3 120 Waukesha, WI............. 13.5 232.5 0.5 243 781 2.6 297 Winnebago, WI............ 3.9 89.3 1.4 176 737 4.4 248 San Juan, PR............. 14.3 313.4 -2.2 (8) 504 4.6 (8) 1 Includes workers covered by Unemployment Insurance (UI) and Unemployment Compensation for Federal Employees (UCFE) programs. These 322 U.S. counties comprise 70.6 percent of the total covered workers in the U.S. 2 Data are preliminary. 3 Includes areas not officially designated as counties. See Technical Note. 4 Percent changes were computed from quarterly employment and pay data adjusted for noneconomic county reclassifications. See Technical Note. 5 Average weekly wages were calculated using unrounded data. 6 Totals for the United States do not include data for Puerto Rico or the Virgin Islands. 7 Data do not meet BLS or State agency disclosure standards. 8 This county was not included in the U.S. rankings. Table 2. Covered(1) establishments, employment, and wages in the ten largest counties, third quarter 2005(2) Employment Average weekly wage(4) Establishments, third quarter County by NAICS supersector 2005 Percent Percent (thousands) September change, Average change, 2005 September weekly third (thousands) 2004-05(3) wage quarter 2004-05(3) United States(5)............................. 8,634.7 132,929.3 2.0 $777 6.1 Private industry........................... 8,357.6 111,846.5 2.2 770 6.4 Natural resources and mining............. 122.9 1,834.7 3.3 732 12.1 Construction............................. 851.0 7,581.2 5.4 816 6.3 Manufacturing............................ 365.9 14,218.1 -0.9 946 5.3 Trade, transportation, and utilities..... 1,873.5 25,666.2 1.6 682 5.2 Information.............................. 142.3 3,057.1 -0.2 1,207 8.3 Financial activities..................... 816.5 8,084.8 2.2 1,113 7.1 Professional and business services....... 1,378.4 17,138.0 4.0 929 8.0 Education and health services............ 769.3 16,557.0 2.7 745 5.8 Leisure and hospitality.................. 693.9 13,006.0 1.8 331 5.4 Other services........................... 1,104.0 4,329.4 0.9 505 5.9 Government................................. 277.1 21,082.9 1.2 817 4.6 Los Angeles, CA.............................. 370.3 4,105.4 1.8 879 5.5 Private industry........................... 366.4 3,554.9 1.9 862 5.9 Natural resources and mining............. 0.5 11.4 -2.4 1,207 17.6 Construction............................. 13.2 154.6 6.6 891 8.9 Manufacturing............................ 16.1 467.5 -3.0 908 4.4 Trade, transportation, and utilities..... 52.6 794.4 1.8 745 5.4 Information.............................. 8.5 211.2 2.4 1,456 5.1 Financial activities..................... 23.2 245.0 2.4 1,409 11.1 Professional and business services....... 39.8 587.0 4.5 987 6.9 Education and health services............ 27.1 459.5 0.4 816 7.7 Leisure and hospitality.................. 25.6 382.8 1.8 498 0.0 Other services........................... 159.5 241.0 6.4 407 1.2 Government................................. 3.9 550.5 0.8 994 4.2 Cook, IL..................................... 131.0 2,529.4 0.7 920 5.5 Private industry........................... 129.7 2,214.7 0.9 915 5.9 Natural resources and mining............. 0.1 1.5 9.7 989 5.0 Construction............................. 11.2 97.4 -0.4 1,110 3.4 Manufacturing............................ 7.5 252.3 -2.1 958 6.0 Trade, transportation, and utilities..... 27.0 475.0 -0.1 759 3.8 Information.............................. 2.5 60.4 -1.4 1,315 8.9 Financial activities..................... 14.6 218.3 0.8 1,394 4.0 Professional and business services....... 26.8 423.8 3.1 1,143 8.3 Education and health services............ 12.9 357.3 2.2 808 6.2 Leisure and hospitality.................. 11.0 229.1 1.3 403 6.9 Other services........................... 13.0 95.0 -0.6 664 6.1 Government................................. 1.2 314.7 -0.7 959 3.3 New York, NY................................. 114.3 2,243.4 1.7 1,419 7.0 Private industry........................... 114.0 1,801.7 1.8 1,507 7.5 Natural resources and mining............. 0.0 0.1 6.3 1,407 26.1 Construction............................. 2.1 29.9 2.1 1,363 4.0 Manufacturing............................ 3.1 42.1 -7.6 1,101 10.2 Trade, transportation, and utilities..... 21.4 237.2 1.2 1,071 7.7 Information.............................. 4.1 130.7 0.8 1,777 3.6 Financial activities..................... 17.3 356.3 2.0 2,605 8.3 Professional and business services....... 22.6 447.6 2.3 1,632 7.9 Education and health services............ 8.0 269.1 0.9 979 6.5 Leisure and hospitality.................. 10.4 192.0 0.7 679 6.8 Other services........................... 16.4 83.6 1.9 822 6.2 Government................................. 0.2 441.7 1.0 1,064 4.0 Harris, TX................................... 90.4 1,882.0 3.4 930 7.8 Private industry........................... 90.0 1,636.5 3.7 944 8.3 Natural resources and mining............. 1.3 67.1 6.7 2,409 18.3 Construction............................. 6.2 132.7 4.0 867 4.7 Manufacturing............................ 4.6 169.9 4.6 1,188 10.2 Trade, transportation, and utilities..... 21.0 396.3 3.0 829 6.3 Information.............................. 1.3 32.1 -2.3 1,152 7.7 Financial activities..................... 9.8 117.1 2.3 1,127 7.0 Professional and business services....... 17.6 302.8 5.5 1,062 7.7 Education and health services............ 9.3 196.4 3.6 807 2.7 Leisure and hospitality.................. 6.8 162.7 1.5 356 9.9 Other services........................... 10.5 55.4 0.4 547 5.8 Government................................. 0.4 245.5 1.5 837 4.6 Maricopa, AZ................................. 84.3 1,735.0 6.5 790 8.1 Private industry........................... 83.7 1,524.0 7.2 785 8.1 Natural resources and mining............. 0.5 7.9 2.5 607 6.5 Construction............................. 8.6 165.9 15.0 798 11.3 Manufacturing............................ 3.3 133.2 2.6 1,077 4.5 Trade, transportation, and utilities..... 18.7 350.4 6.4 765 7.6 Information.............................. 1.4 31.5 -2.1 991 14.4 Financial activities..................... 10.1 146.1 6.9 1,032 14.7 Professional and business services....... 18.3 294.9 8.2 761 5.7 Education and health services............ 8.2 177.1 5.8 841 7.5 Leisure and hospitality.................. 6.0 165.4 6.0 376 7.4 Other services........................... 5.8 45.9 1.6 547 9.2 Government................................. 0.6 211.0 1.7 836 8.2 Orange, CA................................... 90.3 1,497.5 2.2 907 7.1 Private industry........................... 88.9 1,359.2 2.5 903 7.1 Natural resources and mining............. 0.2 5.5 -4.3 629 10.5 Construction............................. 6.7 105.4 6.9 962 9.4 Manufacturing............................ 5.7 182.9 -0.7 1,059 6.6 Trade, transportation, and utilities..... 17.0 270.9 1.1 829 5.5 Information.............................. 1.4 32.2 -0.9 1,250 3.6 Financial activities..................... 10.3 144.1 4.5 1,466 6.5 Professional and business services....... 17.7 271.4 5.7 926 7.8 Education and health services............ 9.4 131.6 1.8 845 8.2 Leisure and hospitality.................. 6.7 166.4 0.6 388 6.0 Other services........................... 13.8 48.7 2.6 550 7.2 Government................................. 1.4 138.3 -1.2 951 7.5 Dallas, TX................................... 66.1 1,431.1 2.0 940 6.6 Private industry........................... 65.6 1,272.6 2.2 949 6.9 Natural resources and mining............. 0.5 7.3 7.8 2,432 10.3 Construction............................. 4.3 78.4 5.0 856 8.4 Manufacturing............................ 3.3 146.0 0.5 1,135 12.3 Trade, transportation, and utilities..... 14.8 301.8 0.9 895 5.2 Information.............................. 1.7 53.8 -0.1 1,257 4.5 Financial activities..................... 8.3 136.0 1.5 1,213 8.3 Professional and business services....... 13.7 250.9 5.4 1,021 5.7 Education and health services............ 6.2 134.0 2.0 901 5.1 Leisure and hospitality.................. 5.0 122.6 0.3 429 5.7 Other services........................... 6.5 38.5 -0.1 606 5.2 Government................................. 0.5 158.5 0.0 867 4.0 San Diego, CA................................ 87.6 1,301.3 1.6 855 7.3 Private industry........................... 86.2 1,088.1 1.6 838 7.7 Natural resources and mining............. 0.8 11.3 -1.8 534 8.1 Construction............................. 6.8 93.7 2.5 897 9.4 Manufacturing............................ 3.4 103.7 -0.6 1,102 4.1 Trade, transportation, and utilities..... 14.1 218.6 2.6 691 6.0 Information.............................. 1.3 37.6 1.4 1,903 12.2 Financial activities..................... 9.3 83.5 1.3 1,077 6.4 Professional and business services....... 15.0 210.8 1.5 999 9.8 Education and health services............ 7.7 120.9 0.6 804 10.1 Leisure and hospitality.................. 6.6 153.0 2.3 399 5.6 Other services........................... 21.1 55.0 1.2 478 6.2 Government................................. 1.4 213.2 1.8 944 4.9 King, WA..................................... 75.2 1,129.1 2.7 997 7.2 Private industry........................... 74.8 976.9 3.2 1,005 7.7 Natural resources and mining............. 0.4 3.2 0.6 1,009 7.5 Construction............................. 6.4 63.6 10.5 954 8.3 Manufacturing............................ 2.6 100.2 -1.9 1,245 3.1 Trade, transportation, and utilities..... 14.6 219.1 1.3 854 4.8 Information.............................. 1.6 70.6 3.5 2,347 18.8 Financial activities..................... 6.4 76.4 1.9 1,166 5.2 Professional and business services....... 12.1 174.6 7.6 1,090 5.1 Education and health services............ 6.1 115.9 4.2 792 9.5 Leisure and hospitality.................. 5.7 108.0 3.3 413 3.3 Other services........................... 18.8 45.2 -2.7 527 9.8 Government................................. 0.5 152.2 -0.3 940 3.6 Miami-Dade, FL............................... 84.4 1,003.2 3.3 781 8.9 Private industry........................... 84.1 850.5 3.5 750 7.9 Natural resources and mining............. 0.5 8.5 6.2 478 8.9 Construction............................. 5.6 46.5 11.6 805 6.8 Manufacturing............................ 2.7 48.6 -2.5 717 11.2 Trade, transportation, and utilities..... 24.0 244.5 2.0 716 7.2 Information.............................. 1.8 23.0 (6) 1,097 (6) Financial activities..................... 9.5 69.8 4.3 1,076 11.0 Professional and business services....... 16.8 144.8 7.5 886 10.2 Education and health services............ 8.4 128.7 2.9 758 4.0 Leisure and hospitality.................. 5.8 97.7 3.4 434 7.7 Other services........................... 7.7 35.0 0.8 476 9.7 Government................................. 0.3 152.7 1.9 961 13.2 1 Includes workers covered by Unemployment Insurance (UI) and Unemployment Compensation for Federal Employees (UCFE) programs. 2 Data are preliminary. 3 Percent changes were computed from quarterly employment and pay data adjusted for noneconomic county reclassifications. See Technical Note. 4 Average weekly wages were calculated using unrounded data. 5 Totals for the United States do not include data for Puerto Rico or the Virgin Islands. 6 Data do not meet BLS or State agency disclosure standards. Table 3. Covered(1) establishments, employment, and wages in the largest county by state, third quarter 2005(2) Employment Average weekly wage(5) Establishments, third quarter County(3) 2005 Percent Percent (thousands) September change, Average change, 2005 September weekly third (thousands) 2004-05(4) wage quarter 2004-05(4) United States(6)......... 8,634.7 132,929.3 2.0 $777 6.1 Jefferson, AL............ 18.8 372.8 1.3 796 7.7 Anchorage Borough, AK.... 7.9 148.0 1.9 857 5.8 Maricopa, AZ............. 84.3 1,735.0 6.5 790 8.1 Pulaski, AR.............. 13.5 245.7 1.3 709 6.1 Los Angeles, CA.......... 370.3 4,105.4 1.8 879 5.5 Denver, CO............... 25.1 429.4 1.7 947 6.6 Hartford, CT............. 24.6 488.6 1.2 968 5.6 New Castle, DE........... 19.8 281.2 0.3 921 7.5 Washington, DC........... 30.7 666.4 0.8 1,265 4.5 Miami-Dade, FL........... 84.4 1,003.2 3.3 781 8.9 Fulton, GA............... 38.0 744.4 2.2 1,007 4.9 Honolulu, HI............. 23.8 441.7 3.2 740 5.3 Ada, ID.................. 13.9 201.2 5.4 719 6.4 Cook, IL................. 131.0 2,529.4 0.7 920 5.5 Marion, IN............... 23.5 584.0 0.5 819 7.1 Polk, IA................. 14.2 265.6 1.9 792 6.9 Johnson, KS.............. 19.4 302.9 1.7 823 7.9 Jefferson, KY............ 21.6 425.3 1.4 761 5.5 Orleans, LA.............. 12.9 178.2 -26.3 742 10.7 Cumberland, ME........... 11.7 171.3 -0.1 710 6.0 Montgomery, MD........... 32.0 462.4 2.6 1,027 8.0 Middlesex, MA............ 49.8 791.8 0.9 1,110 6.5 Wayne, MI................ 34.2 789.3 -1.2 910 4.8 Hennepin, MN............. 41.9 837.8 1.4 990 6.1 Hinds, MS................ 6.5 126.9 -2.1 689 5.5 St. Louis, MO............ 33.8 622.5 1.0 828 6.4 Yellowstone, MT.......... 5.4 73.6 3.0 619 8.2 Douglas, NE.............. 15.2 310.3 0.5 741 5.6 Clark, NV................ 42.3 883.1 7.1 752 7.3 Hillsborough, NH......... 12.3 197.6 1.7 854 3.3 Bergen, NJ............... 34.2 450.2 0.5 964 5.9 Bernalillo, NM........... 16.5 323.4 2.6 707 6.2 New York, NY............. 114.3 2,243.4 1.7 1,419 7.0 Mecklenburg, NC.......... 27.7 527.4 3.7 894 6.9 Cass, ND................. 5.7 93.0 3.3 648 6.2 Cuyahoga, OH............. 38.1 755.8 0.1 802 3.4 Oklahoma, OK............. 22.5 415.7 1.5 685 6.4 Multnomah, OR............ 26.2 430.5 2.4 798 5.3 Allegheny, PA............ 35.0 682.9 -0.3 811 4.6 Providence, RI........... 18.2 290.8 0.9 750 2.7 Greenville, SC........... 12.7 227.4 2.5 688 3.9 Minnehaha, SD............ 6.1 111.3 2.3 666 6.7 Shelby, TN............... 19.7 505.9 1.9 814 3.7 Harris, TX............... 90.4 1,882.0 3.4 930 7.8 Salt Lake, UT............ 36.9 547.1 4.5 719 7.0 Chittenden, VT........... 5.7 95.5 -0.9 764 5.4 Fairfax, VA.............. 30.8 569.6 3.7 1,188 10.9 King, WA................. 75.2 1,129.1 2.7 997 7.2 Kanawha, WV.............. 6.2 107.4 -0.5 668 6.2 Milwaukee, WI............ 21.8 494.8 0.3 785 4.8 Laramie, WY.............. 3.0 41.0 3.1 634 6.6 San Juan, PR............. 14.3 313.4 -2.2 504 4.6 St. Thomas, VI........... 1.8 22.6 0.2 575 1.4 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, third quarter 2005(2) Employment Average weekly wage(3) Establishments, third quarter State 2005 Percent Percent (thousands) September change, Average change, 2005 September weekly third (thousands) 2004-05 wage quarter 2004-05 United States(4)......... 8,634.7 132,929.3 2.0 $777 6.1 Alabama.................. 117.1 1,905.9 2.5 669 6.4 Alaska................... 20.7 320.2 1.8 797 5.6 Arizona.................. 135.9 2,511.8 6.0 748 8.2 Arkansas................. 77.9 1,165.7 1.9 599 4.9 California............... 1,237.6 15,443.3 2.5 887 7.0 Colorado................. 171.8 2,212.1 2.3 808 7.3 Connecticut.............. 110.8 1,655.2 0.8 966 5.3 Delaware................. 30.1 420.1 1.3 823 7.0 District of Columbia..... 30.7 666.4 0.8 1,265 4.5 Florida.................. 564.9 7,801.6 5.5 708 8.1 Georgia.................. 257.3 3,960.8 3.2 748 5.2 Hawaii................... 36.5 606.0 3.2 714 5.8 Idaho.................... 52.4 635.5 4.5 605 6.0 Illinois................. 339.7 5,820.7 1.2 825 5.9 Indiana.................. 153.2 2,916.3 1.0 689 5.2 Iowa..................... 91.8 1,461.1 1.9 641 6.1 Kansas................... 83.1 1,315.3 0.5 659 6.5 Kentucky................. 107.2 1,779.5 1.9 651 5.2 Louisiana................ 120.3 1,770.8 -4.9 637 6.9 Maine.................... 48.5 606.0 -0.5 631 4.6 Maryland................. 158.3 2,526.5 1.9 854 7.6 Massachusetts............ 219.0 3,193.3 0.8 947 4.5 Michigan................. 257.2 4,353.1 -0.1 787 4.1 Minnesota................ 165.1 2,671.9 1.4 790 4.9 Mississippi.............. 68.0 1,098.4 -1.4 573 5.9 Missouri................. 170.9 2,696.2 1.6 691 5.5 Montana.................. 40.5 424.2 2.7 563 7.4 Nebraska................. 56.9 896.7 1.1 633 5.3 Nevada................... 67.7 1,242.5 6.3 750 6.7 New Hampshire............ 47.8 630.7 1.2 772 5.8 New Jersey............... 272.7 3,960.8 1.2 928 5.8 New Mexico............... 50.5 791.0 3.0 629 6.8 New York................. 564.1 8,394.8 0.9 941 5.7 North Carolina........... 234.2 3,903.7 1.9 690 5.7 North Dakota............. 24.9 335.4 2.4 581 6.0 Ohio..................... 291.4 5,360.6 0.5 723 5.5 Oklahoma................. 94.3 1,482.5 2.8 612 5.7 Oregon................... 124.8 1,683.4 3.5 714 5.6 Pennsylvania............. 335.9 5,597.6 1.1 764 5.7 Rhode Island............. 35.9 488.9 1.0 736 4.1 South Carolina........... 119.8 1,831.2 1.6 637 5.6 South Dakota............. 29.2 381.6 1.6 567 5.4 Tennessee................ 132.8 2,724.0 2.0 689 4.6 Texas.................... 521.6 9,659.3 3.1 767 6.7 Utah..................... 82.1 1,135.1 4.7 647 6.6 Vermont.................. 24.6 303.4 0.2 663 4.7 Virginia................. 213.4 3,617.7 2.7 815 7.7 Washington............... 210.4 2,820.6 2.5 801 6.5 West Virginia............ 48.2 702.9 1.3 589 5.4 Wisconsin................ 161.7 2,783.4 1.2 688 5.4 Wyoming.................. 23.3 263.4 3.8 638 8.0 Puerto Rico.............. 57.2 1,037.4 -0.6 435 3.8 Virgin Islands........... 3.5 44.0 3.0 616 2.8 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.