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Technical information:(202) 691-6567 USDL 09-0841 http://www.bls.gov/cew/ For release: 10:00 A.M. EDT Media contact: (202) 691-5902 Tuesday, July 21, 2009 COUNTY EMPLOYMENT AND WAGES: FOURTH QUARTER 2008 From December 2007 to December 2008, employment declined in 285 of the 334 largest U.S. counties, according to preliminary data released today by the Bureau of Labor Statistics of the U.S. Department of Labor. Elkhart County, Ind., located about 100 miles east of Chicago, posted the largest percentage decline, with a loss of 17.8 percent over the year, compared with a national job decrease of 2.3 percent. Manufacturing sustained the largest employment losses in Elkhart. Montgomery County, Texas, which is about 20 miles north of Houston, experienced the largest over-the-year percentage increase in employment among the largest counties in the U.S., with a gain of 2.7 percent. St. Louis City, Mo., had the largest over-the-year gain in average weekly wages in the fourth quarter of 2008, with an increase of 56.8 percent coming predominantly from the professional and business services and manufacturing supersectors. The U.S. average weekly wage rose by 2.2 percent over the same time span. Of the 334 largest counties in the United States (as measured by 2007 annual average employment) 151 had over-the-year percentage change in employment below the national average (-2.3 percent) in December 2008; 174 large counties experienced changes above the national average. The percent change in average weekly wages was higher than the national average (2.2 percent) in 180 of the largest U.S. counties, but was below the national average in 137 counties. Table A. Top 10 large counties ranked by December 2008 employment, December 2007-08 employment decrease, and December 2007-08 percent decrease in employment -------------------------------------------------------------------------------------------------------- Employment in large counties -------------------------------------------------------------------------------------------------------- December 2008 employment | Decrease in employment, | Percent decrease in employment, (thousands) | December 2007-08 | December 2007-08 | (thousands) | -------------------------------------------------------------------------------------------------------- | | United States 133,870.4| United States -3,170.1| United States -2.3 -------------------------------------------------------------------------------------------------------- | | Los Angeles, Calif. 4,152.9| Los Angeles, Calif. -147.8| Elkhart, Ind. -17.8 Cook, Ill. 2,480.0| Maricopa, Ariz. -107.2| Lee, Fla. -9.2 New York, N.Y. 2,386.4| Orange, Calif. -73.8| Sarasota, Fla. -8.1 Harris, Texas 2,078.1| Cook, Ill. -71.0| Collier, Fla. -8.0 Maricopa, Ariz. 1,741.0| Clark, Nev. -60.0| Marion, Fla. -7.9 Dallas, Texas 1,484.4| Riverside, Calif. -44.7| Macomb, Mich. -7.9 Orange, Calif. 1,451.2| Miami-Dade, Fla. -43.8| Washoe, Nev. -7.9 San Diego, Calif. 1,309.1| Broward, Fla. -43.1| Seminole, Fla. -7.5 King, Wash. 1,175.3| Wayne, Mich. -42.3| Horry, S.C. -7.1 Miami-Dade, Fla. 1,003.9| San Diego, Calif. -39.9| Riverside, Calif. -7.0 | | Genesee, Mich. -7.0 | | | | -------------------------------------------------------------------------------------------------------- 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 9.2 million employer reports cover 133.9 million full- and part-time workers. Large County Employment In December 2008, national employment, as measured by the QCEW program, was 133.9 million, down by 2.3 percent from December 2007. The 334 U.S. counties with 75,000 or more employees accounted for 71.5 percent of total U.S. employment and 77.2 percent of total wages. These 334 counties had a net job decline of 2,467,500 over the year, accounting for 77.8 percent of the overall U.S. employment decrease. Employment declined in 285 counties from December 2007 to December 2008. The largest percentage decline in employment was in Elkhart, Ind. (-17.8 percent). Lee, Fla., had the next largest percentage decline (-9.2 percent), followed by the counties of Sarasota, Fla. (-8.1 percent), Collier, Fla. (-8.0 percent), and Marion, Fla., Macomb, Mich., and Washoe, Nev. (-7.9 percent each). The largest decline in employment levels occurred in Los Angeles, Calif. (-147,800), followed by the counties of Maricopa, Ariz. (-107,200), Orange, Calif. (-73,800), Cook, Ill. (-71,000), and Clark, Nev. (-60,000). (See table A.) Combined employment losses in these five counties over the year totaled 459,800 or 14.5 percent of the employment decline for the U.S. as a whole. Employment rose in 37 of the large counties from December 2007 to December 2008. More than a third of these growing counties were located in Texas (13 counties). Neighboring Louisiana had the second largest number of counties (4) that experienced employment growth. Montgomery, Texas, had the largest over-the-year percentage increase in employment (2.7 percent) among the largest counties in the U.S. Jefferson, Texas, had the next largest increase, 2.5 percent, followed by the counties of Lubbock, Texas (2.4 percent), Fort Bend, Texas (2.2 percent), and Orleans, La. (2.1 percent). The largest gains in the level of employment from December 2007 to December 2008 were recorded in the counties of Harris, Texas (20,000), Orleans, La. (3,500), Montgomery, Texas (3,400), Bronx, N.Y. (3,200), and Jefferson, Texas (3,100). Table B. Top 10 large counties ranked by fourth quarter 2008 average weekly wages, fourth quarter 2007-08 growth in average weekly wages, and fourth quarter 2007-08 percent growth in average weekly wages -------------------------------------------------------------------------------------------------------- Average weekly wage in large counties -------------------------------------------------------------------------------------------------------- Average weekly wage, | Growth in average weekly | Percent growth in average fourth quarter 2008 | wage, fourth quarter 2007-08 | weekly wage, fourth | | quarter 2007-08 -------------------------------------------------------------------------------------------------------- | | United States $918| United States $20| United States 2.2 -------------------------------------------------------------------------------------------------------- | | New York, N.Y. $1,856| St. Louis City, Mo. $546| St. Louis City, Mo. 56.8 Fairfield, Conn. 1,596| Mercer, N.J. 89| Clayton, Ga. 9.9 Washington, D.C. 1,570| Clayton, Ga. 77| Calcasieu, La. 9.0 Suffolk, Mass. 1,568| Washington, D.C. 76| East Baton Rouge, La. 8.0 Santa Clara, Calif. 1,566| Madison, Ala. 73| Jefferson, Texas 8.0 Arlington, Va. 1,509| Jefferson, Texas 70| Madison, Ala. 7.9 St. Louis City, Mo. 1,508| Calcasieu, La. 69| Mercer, N.J. 7.7 Somerset, N.J. 1,498| Alexandria City, Va. 69| Lake, Ind. 7.4 San Francisco, Calif. 1,491| East Baton Rouge, La. 65| Bristol, Mass. 7.3 San Mateo, Calif. 1,439| Providence, R.I. 62| Providence, R.I. 7.1 | | Newport News City, Va. 7.1 | | -------------------------------------------------------------------------------------------------------- Large County Average Weekly Wages The national average weekly wage in the fourth quarter of 2008 was $918. Average weekly wages were higher than the national average in 106 of the largest 334 U.S. counties. New York, N.Y., held the top position among the highest-paid large counties with an average weekly wage of $1,856. Fairfield, Conn., was second with an average weekly wage of $1,596, followed by Washington, D.C. ($1,570), Suffolk, Mass. ($1,568), and Santa Clara, Calif. ($1,566). (See table B.) Over the year, the national average weekly wage rose by 2.2 percent. Among the largest counties, St. Louis City, Mo., led the nation in growth in average weekly wages with an increase of 56.8 percent from the fourth quarter of 2007. Clayton, Ga., was second with growth of 9.9 percent, followed by the counties of Calcasieu, La. (9.0 percent), and East Baton Rouge, La. and Jefferson, Texas (8.0 percent each). Average weekly wages are affected by the number of high-paying and low-paying jobs in an industry. The 2.2 percent over-the-year gain in average weekly wages for the nation is partially due to large employment declines in several industries. The largest over-the-year December percent employment declines were in construction (-10.2 percent), manufacturing (-6.2 percent), professional and business services (-4.1 percent), and trade, transportation, and utilities (-3.5 percent). (See table 2.) Trade, transportation and utilities posted the largest number of jobs lost (-957,500) followed by manufacturing (-850,400), construction (-749,900), and professional and business services (-735,400). Among these industries, average weekly wage growth was strongest in construction (4.9 percent), and professional and business services (3.7 percent). (See Technical Note.) There were 228 counties with an average weekly wage below the national average in the fourth quarter of 2008. The lowest average weekly wage was reported in Hidalgo, Texas ($574), followed by the counties of Horry, S.C. ($581), Cameron, Texas ($584), Webb, Texas ($600), and Yakima, Wash. ($624). (See table 1.) Forty-three large counties experienced over-the-year declines in average weekly wages. Pulaski, Ark., had the largest decrease (-14.3 percent), followed by the counties of Lake, Ill. (-9.9 percent), Santa Clara, Calif. (-7.8 percent), Douglas, Colo. (-5.9 percent), and San Mateo, Calif. (-5.4 percent). Ten Largest U.S. Counties Nine of the 10 largest counties (based on 2007 annual average employment levels) experienced over-the-year percent declines in employment in December 2008. Maricopa, Ariz., experienced the largest decline in employment among the 10 largest counties with a 5.8 percent decrease. Within Maricopa, every private industry group except education and health services experienced employment declines, with construction experiencing the largest decline, -25.3 percent. (See table 2.) Orange, Calif., had the next largest decline in employment, -4.8 percent, followed by Miami-Dade, Fla. (-4.2 percent). Harris, Texas, experienced the only percentage gain in employment (1.0 percent) among the 10 largest counties. Within Harris County, the largest gains in employment were in natural resources and mining (7.1 percent) and education and health services (3.1 percent). Dallas, Texas, had the smallest decrease in employment, -1.2 percent, followed by New York, N.Y. (-1.3 percent). Nine of the 10 largest U.S. counties saw an over-the-year increase in average weekly wages. King, Wash., had the fastest growth in wages among the 10 largest counties, with a gain of 4.0 percent. Within King County, average weekly wages increased the most in the natural resources and mining industry (11.8 percent). Miami-Dade, Fla., and Harris, Texas, tied for second in wage growth with a gain of 2.6 percent each. The only wage decrease occurred in New York, N.Y. (-0.6 percent). Dallas, Texas, had the smallest increase in wages, 1.1 percent, followed by Orange, Calif. (1.4 percent). Largest County by State Table 3 shows December 2008 employment and the 2008 fourth quarter average weekly wage in the largest county in each state, which is based on 2007 annual average employment levels. The employment levels in the counties in table 3 in December 2008 ranged from approximately 4.15 million in Los Angeles County, Calif., to 43,800 in Laramie County, Wyo. The highest average weekly wage of these counties was in New York, N.Y. ($1,856), while the lowest average weekly wage was in Yellowstone, Mont. ($738). For More Information The tables included in this release contain data for the nation and for the 334 counties with annual average employment levels of 75,000 or more in 2007. December 2008 employment and 2008 fourth-quarter average weekly wages for all states are provided in table 4 of this release. For additional information about the quarterly employment and wages data, please read the Technical Note. Final data for all states, metropolitan statistical areas, counties, and the nation through the fourth quarter of 2007 are available on the BLS Web site at http://www.bls.gov/cew/. Updated data for first, second, and third quarter 2008, as well as preliminary data for fourth quarter 2008 and preliminary annual averages for 2008, will be available later online. Additional information about the QCEW data may be obtained by calling (202) 691-6567. Several BLS regional offices are issuing QCEW news releases targeted to local data users. For links to these releases, see http://www.bls.gov/cew/cewregional.htm. ____________________________________________________ The County Employment and Wages release for first quarter 2009 is scheduled to be released on Friday, October 16, 2009.
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 total 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. QCEW data in this re- lease are based on the 2007 North American Industry Classification System. Data for 2008 are preliminary and subject to revision. For purposes of this release, large counties are defined as having employment lev- els 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 335 counties presented in this re- lease were derived using 2007 preliminary annual averages of employment. For 2008 data, six counties have been added to the publication tables: Shelby, Ala., Boone, Ky., St. Tammany, La., Yellowstone, Mont., Warren, Ohio, and Potter, Texas. These counties will be included in all 2008 quarterly 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' con- tinuing 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 procedure, 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 dif- ferences and the intended uses of the program products. (See table.) Additional in- formation on each program can be obtained from the program Web sites shown in the table. 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 9.1 | ministrative records| ments | million establish- | submitted by 7.1 | | 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 fed- | establishments with | ing agriculture, pri- | eral 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, and at the | | | state private-sector| | | total level | | |--Future expansions | | | will include data | | | with greater indus- | | | try detail and data | | | at the county and | | | MSA 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 | | | --------------------------------------------------------------------------------- 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 ci- vilian workers covered by the Unemployment Compensation for Federal Employees (UCFE) program, employment and wage data are compiled from quarterly reports submitted by four major federal payroll processing centers on behalf of all federal agencies, with the exception of a few agencies which still report directly to the individual SWA. In addition to the quarterly contribution reports, employers who operate multi- ple establishments within a state complete a questionnaire, called the "Multiple Worksite Report," which provides detailed information on the location and industry of each of their establishments. QCEW employment and wage data are derived from mi- crodata summaries of 9.0 million employer reports of employment and wages submitted by states to the BLS in 2007. 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. UI and UCFE programs covered workers in 135.4 million jobs. The estimated 130.3 million workers in these jobs (after adjustment for multiple jobholders) represented 96.2 percent of civilian wage and salary employment. Covered workers received $6.018 trillion in pay, representing 94.6 percent of the wage and salary component of per- sonal income and 43.6 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 domestic 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. Cover- age changes may affect the over-the-year comparisons presented in this news release. Concepts and methodology Monthly employment is based on the number of workers who worked during or received pay for the pay period including the 12th of the month. With few exceptions, all em- ployees of covered firms are reported, including production and sales workers, cor- poration officials, executives, supervisory personnel, and clerical workers. Work- ers 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 aver- ages 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 gra- tuities, and, in some states, employer contributions to certain deferred compensa- tion plans such as 401(k) plans and stock options. Over-the-year comparisons of av- erage weekly wages may reflect fluctuations in average monthly employment and/or to- tal 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 including the 12th of the month. When comparing average weekly wage levels between industries, states, or quarters, these factors should be taken into consideration. Federal government pay levels are subject to periodic, sometimes large, fluctua- tions due to a calendar effect that consists of some quarters having more pay peri- ods than others. Most federal employees are paid on a biweekly 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 pe- riods. 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 com- parison 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 com- parisons can be pronounced in federal government due to the uniform nature of fed- eral payroll processing. This pattern may exist in private sector pay; however, be- cause there are more pay period types (weekly, biweekly, semimonthly, monthly) it is less pronounced. The effect is most visible in counties with large concentrations of federal employment. In order to ensure the highest possible quality of data, states verify with em- ployers and update, if necessary, the industry, location, and ownership classifica- tion of all establishments on a 4-year cycle. Changes in establishment classifica- tion codes resulting from this process are introduced 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 in- dividual 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 un- derlying establishment reports. This is done by modifying the prior-year levels used to calculate the over-the-year changes. Percent changes are calculated using an ad- justed version of the final 2007 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 unad- justed data maintained on the BLS Web site. Over-the-year change calculations based on data from the Web site, or from data published in prior BLS news releases, may differ substantially from the over-the-year changes presented in this news release. The adjusted data used to calculate the over-the-year change measures presented in this release account for most of the administrative changes--those occurring when employers update the industry, location, and ownership information of their estab- lishments. The most common adjustments for administrative change are the result of updated information about the county location of individual establishments. Included in these adjustments are administrative changes involving the classification of es- tablishments that were previously reported in the unknown or statewide county or un- known industry categories. Beginning with the first quarter of 2008, adjusted data will also account for administrative changes caused by multi-unit employers who start reporting for each individual establishment rather than as a single entity. 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. Com- parisons may not be valid for any time period other than the one featured in a re- lease even if the changes were calculated using adjusted data. County definitions are assigned according to Federal Information Processing Stan- dards 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 com- mon designation used in New England (and New Jersey). The regions referred to in this release are defined as census regions. Additional statistics and other information An annual bulletin, Employment and Wages, features comprehensive information by detailed industry on establishments, employment, and wages for the nation and all states. The 2007 edition of this bulletin contains selected data produced by Busi- ness Employment Dynamics (BED) on job gains and losses, as well as selected data from the first quarter 2008 version of this news release. Tables and additional con- tent from the 2007 Employment and Wages Annual Bulletin are now available online at http://www.bls.gov/cew/cewbultn07.htm. These tables present final 2007 annual aver- ages. The tables will also be included on the CD which accompanies the hardcopy version of the Annual Bulletin. Employment and Wages Annual Averages, 2007 is available for sale as a chartbook from the United States Government Printing Office, Superintendent of Documents, P.O. Box 371954, Pittsburgh, PA 15250, telephone (866) 512-1800, outside Washington, D.C. Within Washington, D.C., the telephone number is (202) 512-1800. The fax number is (202) 512-2104. News releases on quarterly measures of gross job flows also are available upon re- quest 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 335 largest counties, fourth quarter 2008(2) Employment Average weekly wage(4) Establishments, County(3) fourth quarter Percent Ranking Percent Ranking 2008 December change, by Average change, by (thousands) 2008 December percent weekly fourth percent (thousands) 2007-08(5) change wage quarter change 2007-08(5) United States(6)......... 9,177.5 133,870.4 -2.3 - $918 2.2 - Jefferson, AL............ 19.0 355.3 -3.3 234 922 2.2 181 Madison, AL.............. 9.0 182.5 -0.4 51 997 7.9 6 Mobile, AL............... 10.2 174.5 -1.6 118 806 5.1 31 Montgomery, AL........... 6.7 135.4 -3.8 261 824 5.4 27 Shelby, AL............... 5.0 75.5 -2.1 153 842 -1.2 304 Tuscaloosa, AL........... 4.5 86.0 -1.8 131 783 2.1 188 Anchorage Borough, AK.... 8.2 148.2 1.5 8 969 4.9 35 Maricopa, AZ............. 103.6 1,741.0 -5.8 305 892 2.1 188 Pima, AZ................. 21.3 366.7 -3.4 241 805 4.5 48 Benton, AR............... 5.6 94.7 -2.2 160 844 6.3 15 Pulaski, AR.............. 15.2 250.3 -1.2 92 847 -14.3 324 Washington, AR........... 5.8 90.9 -2.4 177 747 2.2 181 Alameda, CA.............. 54.4 669.9 -4.0 267 1,161 0.1 277 Butte, CA................ 8.1 74.4 -3.1 222 698 4.6 45 Contra Costa, CA......... 30.4 335.8 -3.6 252 1,135 1.7 209 Fresno, CA............... 30.9 345.9 -1.6 118 737 1.7 209 Kern, CA................. 18.5 285.6 -1.2 92 794 4.5 48 Los Angeles, CA.......... 433.9 4,152.9 -3.4 241 1,075 1.8 204 Marin, CA................ 12.1 108.6 -2.0 145 1,152 -2.0 310 Monterey, CA............. 13.0 152.3 -3.4 241 801 3.4 95 Orange, CA............... 102.7 1,451.2 -4.8 286 1,043 1.4 235 Placer, CA............... 11.0 130.5 -5.9 311 892 1.8 204 Riverside, CA............ 47.5 593.2 -7.0 317 745 2.2 181 Sacramento, CA........... 54.7 610.8 -3.6 252 1,006 3.2 107 San Bernardino, CA....... 49.8 640.3 -5.8 305 788 3.0 122 San Diego, CA............ 100.0 1,309.1 -3.0 208 981 2.0 192 San Francisco, CA........ 52.7 574.0 -0.9 76 1,491 -2.4 314 San Joaquin, CA.......... 18.1 214.5 -4.4 282 796 3.2 107 San Luis Obispo, CA...... 9.8 101.8 -2.8 196 765 1.7 209 San Mateo, CA............ 24.2 342.4 -1.6 118 1,439 -5.4 320 Santa Barbara, CA........ 14.4 180.5 -2.0 145 868 1.6 218 Santa Clara, CA.......... 61.2 901.1 -1.7 126 1,566 -7.8 322 Santa Cruz, CA........... 9.2 90.0 -4.2 273 821 -2.3 312 Solano, CA............... 10.2 124.8 -3.1 222 903 3.9 71 Sonoma, CA............... 19.0 185.8 -4.9 291 896 3.0 122 Stanislaus, CA........... 15.1 166.7 -4.3 278 759 3.8 77 Tulare, CA............... 9.7 147.6 -3.0 208 651 3.7 81 Ventura, CA.............. 23.7 310.4 -3.4 241 926 -5.1 318 Yolo, CA................. 6.0 99.1 -2.2 160 883 3.2 107 Adams, CO................ 9.2 152.4 -2.2 160 840 1.3 237 Arapahoe, CO............. 19.3 279.7 -2.2 160 1,054 -2.8 315 Boulder, CO.............. 12.9 161.1 -0.9 76 1,047 -1.5 308 Denver, CO............... 25.6 445.0 -1.5 109 1,111 -1.3 305 Douglas, CO.............. 9.5 93.8 0.5 24 933 -5.9 321 El Paso, CO.............. 17.3 241.7 -2.9 204 834 3.9 71 Jefferson, CO............ 18.5 210.9 -0.8 70 926 2.0 192 Larimer, CO.............. 10.4 129.9 -0.4 51 837 3.1 114 Weld, CO................. 6.0 82.3 -0.9 76 765 2.5 159 Fairfield, CT............ 33.1 420.2 -2.2 160 1,596 1.1 247 Hartford, CT............. 25.6 504.5 -1.5 109 1,111 1.0 253 New Haven, CT............ 22.7 366.4 -2.2 160 978 3.3 101 New London, CT........... 7.0 130.1 -0.8 70 910 -0.4 290 New Castle, DE........... 18.3 278.7 -3.7 255 1,055 2.3 169 Washington, DC........... 34.4 687.5 0.3 29 1,570 5.1 31 Alachua, FL.............. 6.8 121.7 -2.0 145 740 -0.1 282 Brevard, FL.............. 15.0 195.7 -5.8 305 856 3.9 71 Broward, FL.............. 65.6 729.6 -5.6 302 874 0.8 259 Collier, FL.............. 12.6 125.4 -8.0 324 811 (7) - Duval, FL................ 27.7 455.5 -4.3 278 874 0.8 259 Escambia, FL............. 8.1 122.9 -5.8 305 720 2.1 188 Hillsborough, FL......... 38.3 609.9 -6.1 312 872 2.7 143 Lake, FL................. 7.6 83.6 -5.5 300 665 -0.9 300 Lee, FL.................. 20.1 203.3 -9.2 326 760 -0.3 286 Leon, FL................. 8.3 141.9 -3.3 234 783 0.5 268 Manatee, FL.............. 9.4 114.1 -4.3 278 691 -0.7 297 Marion, FL............... 8.6 97.6 -7.9 321 657 3.5 88 Miami-Dade, FL........... 86.8 1,003.9 -4.2 273 924 2.6 151 Okaloosa, FL............. 6.2 77.1 -3.8 261 735 2.8 139 Orange, FL............... 36.3 678.3 -4.6 285 829 1.1 247 Palm Beach, FL........... 51.4 527.4 -6.3 314 914 1.6 218 Pasco, FL................ 10.4 100.9 -3.3 234 672 3.1 114 Pinellas, FL............. 32.1 410.9 -6.2 313 808 1.9 200 Polk, FL................. 12.8 199.3 -5.4 298 706 1.7 209 Sarasota, FL............. 15.4 144.4 -8.1 325 783 1.4 235 Seminole, FL............. 14.9 168.9 -7.5 320 789 -0.3 286 Volusia, FL.............. 14.2 158.2 -6.4 315 665 1.5 224 Bibb, GA................. 4.8 84.1 -1.0 80 716 1.8 204 Chatham, GA.............. 7.9 133.8 -3.3 234 799 4.4 52 Clayton, GA.............. 4.5 111.1 -4.0 267 856 9.9 2 Cobb, GA................. 21.2 312.7 -4.2 273 959 3.1 114 De Kalb, GA.............. 18.1 294.0 -3.1 222 936 1.6 218 Fulton, GA............... 39.9 732.2 -3.4 241 1,183 1.0 253 Gwinnett, GA............. 24.5 310.9 -5.3 297 894 -0.8 299 Muscogee, GA............. 4.9 94.7 -2.7 193 721 1.5 224 Richmond, GA............. 4.8 101.2 -1.4 104 770 5.5 23 Honolulu, HI............. 24.8 449.5 -2.4 177 850 3.8 77 Ada, ID.................. 15.0 202.9 -5.0 293 814 -1.1 301 Champaign, IL............ 4.2 92.0 -0.6 63 777 5.7 19 Cook, IL................. 141.0 2,480.0 -2.8 196 1,118 1.5 224 Du Page, IL.............. 36.2 586.1 -3.5 248 1,059 0.2 273 Kane, IL................. 12.8 203.3 -4.9 291 836 1.7 209 Lake, IL................. 21.2 328.0 -2.5 183 1,143 -9.9 323 McHenry, IL.............. 8.5 100.6 -3.1 222 784 -0.4 290 McLean, IL............... 3.7 85.9 -0.3 47 836 2.7 143 Madison, IL.............. 6.0 95.9 -0.6 63 770 5.5 23 Peoria, IL............... 4.8 105.3 0.0 38 869 3.5 88 Rock Island, IL.......... 3.5 79.4 -1.2 92 1,082 2.0 192 St. Clair, IL............ 5.5 96.8 -1.9 139 755 4.4 52 Sangamon, IL............. 5.2 128.8 -1.1 84 897 3.9 71 Will, IL................. 13.9 194.5 -2.0 145 824 3.5 88 Winnebago, IL............ 7.0 134.3 -3.0 208 775 3.1 114 Allen, IN................ 9.1 180.0 -3.0 208 748 -1.1 301 Elkhart, IN.............. 5.0 101.3 -17.8 327 686 -3.9 316 Hamilton, IN............. 7.8 111.3 -1.1 84 852 -1.3 305 Lake, IN................. 10.4 193.2 -2.6 189 826 7.4 8 Marion, IN............... 24.3 571.8 -2.8 196 913 2.8 139 St. Joseph, IN........... 6.1 121.0 -4.2 273 761 3.5 88 Tippecanoe, IN........... 3.4 76.8 -0.6 63 773 5.3 29 Vanderburgh, IN.......... 4.8 107.8 -0.4 51 767 5.5 23 Linn, IA................. 6.3 127.2 1.0 14 896 2.3 169 Polk, IA................. 14.9 273.7 -1.1 84 904 2.4 163 Scott, IA................ 5.2 88.8 -0.4 51 751 1.3 237 Johnson, KS.............. 20.7 316.0 -1.1 84 949 1.3 237 Sedgwick, KS............. 12.3 261.6 0.3 29 846 5.2 30 Shawnee, KS.............. 4.9 96.4 0.7 22 753 0.9 255 Wyandotte, KS............ 3.2 80.9 0.2 35 854 2.2 181 Boone, KY................ 3.4 74.5 -2.6 189 800 4.8 39 Fayette, KY.............. 9.0 178.1 (7) - 832 (7) - Jefferson, KY............ 22.0 423.8 -3.3 234 871 1.5 224 Caddo, LA................ 7.5 125.3 -1.9 139 762 1.7 209 Calcasieu, LA............ 5.1 87.9 0.6 23 832 9.0 3 East Baton Rouge, LA..... 14.8 265.9 0.3 29 874 8.0 4 Jefferson, LA............ 14.7 200.5 -1.5 109 876 4.0 67 Lafayette, LA............ 9.1 137.4 0.5 24 911 4.8 39 Orleans, LA.............. 11.6 173.6 2.1 5 1,002 4.2 62 St. Tammany, LA.......... 7.5 75.3 -2.2 160 749 2.6 151 Cumberland, ME........... 12.2 173.4 -2.3 175 822 3.0 122 Anne Arundel, MD......... 14.5 233.3 -1.4 104 963 3.8 77 Baltimore, MD............ 21.5 374.5 -2.7 193 963 0.7 264 Frederick, MD............ 6.0 93.4 -3.1 222 890 3.1 114 Harford, MD.............. 5.6 82.6 (7) - 846 (7) - Howard, MD............... 8.7 147.5 (7) - 1,073 3.9 71 Montgomery, MD........... 32.9 460.3 -1.3 100 1,219 1.9 200 Prince Georges, MD....... 15.7 312.5 -3.0 208 993 2.5 159 Baltimore City, MD....... 14.0 340.4 -1.6 118 1,112 1.5 224 Barnstable, MA........... 9.1 84.2 -3.2 230 813 3.2 107 Bristol, MA.............. 15.3 215.0 -3.0 208 854 7.3 9 Essex, MA................ 20.9 298.2 -1.5 109 976 3.3 101 Hampden, MA.............. 14.5 199.2 -1.4 104 867 6.4 13 Middlesex, MA............ 47.8 826.2 -0.4 51 1,296 -1.1 301 Norfolk, MA.............. 24.2 326.4 -1.1 84 1,139 2.3 169 Plymouth, MA............. 13.7 175.9 -1.9 139 894 3.5 88 Suffolk, MA.............. 21.8 593.4 -0.5 58 1,568 1.3 237 Worcester, MA............ 20.7 318.5 -2.2 160 931 2.2 181 Genesee, MI.............. 7.8 134.3 -7.0 317 804 0.2 273 Ingham, MI............... 6.8 158.3 -3.8 261 886 3.4 95 Kalamazoo, MI............ 5.6 112.1 -4.1 270 855 7.0 12 Kent, MI................. 14.3 323.8 -5.5 300 832 3.2 107 Macomb, MI............... 17.7 291.2 -7.9 321 966 5.1 31 Oakland, MI.............. 39.3 660.7 -5.4 298 1,096 4.3 58 Ottawa, MI............... 5.7 102.9 -5.2 295 794 4.3 58 Saginaw, MI.............. 4.4 81.8 -5.8 305 776 3.6 86 Washtenaw, MI............ 8.1 187.3 -3.8 261 971 1.5 224 Wayne, MI................ 32.1 709.8 -5.6 302 1,032 4.2 62 Anoka, MN................ 7.8 113.2 -3.7 255 839 1.1 247 Dakota, MN............... 10.6 172.8 -2.4 177 898 1.6 218 Hennepin, MN............. 42.5 837.8 -2.4 177 1,146 2.7 143 Olmsted, MN.............. 3.6 89.5 -1.8 131 975 6.4 13 Ramsey, MN............... 15.3 328.9 -1.3 100 980 2.3 169 St. Louis, MN............ 5.9 96.0 -1.9 139 759 4.4 52 Stearns, MN.............. 4.5 82.2 -1.2 92 700 3.6 86 Harrison, MS............. 4.6 85.0 -3.1 222 702 3.4 95 Hinds, MS................ 6.4 127.6 -1.8 131 809 3.3 101 Boone, MO................ 4.5 82.5 -0.7 69 691 3.1 114 Clay, MO................. 5.1 88.2 -3.4 241 821 -0.2 285 Greene, MO............... 8.2 155.6 -2.1 153 685 3.2 107 Jackson, MO.............. 18.8 368.6 -0.9 76 926 3.8 77 St. Charles, MO.......... 8.2 122.0 -3.0 208 733 -0.5 293 St. Louis, MO............ 32.8 600.5 -3.0 208 990 1.3 237 St. Louis City, MO....... 8.5 231.2 -1.2 92 1,508 56.8 1 Yellowstone, MT.......... 5.8 78.2 -0.2 45 738 1.2 245 Douglas, NE.............. 16.1 322.8 0.0 38 842 -2.1 311 Lancaster, NE............ 8.2 158.5 -0.1 43 726 3.7 81 Clark, NV................ 51.0 870.0 -6.5 316 856 -2.3 312 Washoe, NV............... 14.7 201.6 -7.9 321 867 0.0 280 Hillsborough, NH......... 12.4 195.9 -2.6 189 1,062 1.8 204 Rockingham, NH........... 11.0 136.1 -2.3 175 906 1.6 218 Atlantic, NJ............. 7.0 139.3 -4.0 267 818 1.7 209 Bergen, NJ............... 34.6 450.4 -2.5 183 1,188 0.4 270 Burlington, NJ........... 11.5 198.2 -3.9 266 968 3.0 122 Camden, NJ............... 13.1 205.9 -2.8 196 1,008 5.5 23 Essex, NJ................ 21.5 359.7 -2.5 183 1,170 3.3 101 Gloucester, NJ........... 6.4 104.0 -1.3 100 855 2.0 192 Hudson, NJ............... 14.1 237.1 -2.2 160 1,205 2.3 169 Mercer, NJ............... 11.3 230.4 -0.6 63 1,249 7.7 7 Middlesex, NJ............ 22.0 398.0 -3.7 255 1,148 2.2 181 Monmouth, NJ............. 20.9 254.6 -2.8 196 1,016 1.3 237 Morris, NJ............... 18.1 285.3 -2.9 204 1,351 2.5 159 Ocean, NJ................ 12.5 146.3 -2.5 183 792 2.9 131 Passaic, NJ.............. 12.6 175.4 -3.7 255 974 4.1 65 Somerset, NJ............. 10.3 173.1 -2.1 153 1,498 2.9 131 Union, NJ................ 15.1 230.8 -3.1 222 1,166 2.6 151 Bernalillo, NM........... 17.8 329.9 -2.0 145 812 3.0 122 Albany, NY............... 10.0 228.3 -1.4 104 945 4.9 35 Bronx, NY................ 16.1 230.0 1.4 10 889 (7) - Broome, NY............... 4.5 95.5 -1.1 84 727 4.3 58 Dutchess, NY............. 8.3 116.1 -2.2 160 904 3.4 95 Erie, NY................. 23.7 464.1 -0.5 58 794 3.0 122 Kings, NY................ 46.8 488.2 0.3 29 816 3.3 101 Monroe, NY............... 18.1 382.4 -0.8 70 859 1.1 247 Nassau, NY............... 52.5 611.8 -1.7 126 1,049 1.5 224 New York, NY............. 118.9 2,386.4 -1.3 100 1,856 -0.6 294 Oneida, NY............... 5.3 112.0 -0.5 58 720 5.6 20 Onondaga, NY............. 12.8 252.9 -1.6 118 849 0.7 264 Orange, NY............... 10.0 132.6 -1.5 109 778 4.4 52 Queens, NY............... 43.7 507.0 -0.3 47 926 3.7 81 Richmond, NY............. 8.7 95.5 -0.2 45 835 4.0 67 Rockland, NY............. 9.9 117.5 -1.7 126 1,002 (7) - Saratoga, NY............. 5.4 76.5 -2.2 160 762 3.4 95 Suffolk, NY.............. 50.6 626.9 -2.2 160 1,037 (7) - Westchester, NY.......... 36.5 424.3 -2.2 160 1,234 -1.4 307 Buncombe, NC............. 8.2 115.9 -2.1 153 724 1.7 209 Catawba, NC.............. 4.6 83.8 -4.8 286 695 1.5 224 Cumberland, NC........... 6.3 121.9 1.0 14 711 4.9 35 Durham, NC............... 7.2 185.0 (7) - 1,131 (7) - Forsyth, NC.............. 9.3 184.8 -2.5 183 826 2.7 143 Guilford, NC............. 14.8 275.4 -3.6 252 797 2.0 192 Mecklenburg, NC.......... 33.4 567.7 -1.7 126 1,016 1.5 224 New Hanover, NC.......... 7.5 101.0 -4.8 286 755 2.4 163 Wake, NC................. 29.2 448.8 -2.1 153 915 1.8 204 Cass, ND................. 5.9 100.7 1.5 8 778 2.1 188 Butler, OH............... 7.4 145.0 -3.8 261 788 1.3 237 Cuyahoga, OH............. 37.7 724.7 -3.0 208 926 2.0 192 Franklin, OH............. 30.0 678.4 -2.2 160 879 3.7 81 Hamilton, OH............. 24.0 514.3 -1.5 109 980 2.3 169 Lake, OH................. 6.7 99.0 -3.0 208 755 2.3 169 Lorain, OH............... 6.3 95.7 -5.0 293 742 3.2 107 Lucas, OH................ 10.7 210.6 -4.3 278 776 0.9 255 Mahoning, OH............. 6.4 100.7 -3.7 255 670 3.4 95 Montgomery, OH........... 12.9 257.6 -4.5 283 824 2.4 163 Stark, OH................ 9.0 158.3 -3.3 234 706 2.9 131 Summit, OH............... 15.0 271.3 -2.1 153 827 2.4 163 Trumbull, OH............. 4.7 75.1 -3.1 222 752 -0.1 282 Warren, OH............... 4.2 74.6 -4.1 270 763 3.0 122 Oklahoma, OK............. 23.9 427.1 0.1 36 852 5.6 20 Tulsa, OK................ 19.5 349.8 -0.1 43 838 2.3 169 Clackamas, OR............ 13.1 145.9 -4.5 283 821 0.2 273 Jackson, OR.............. 6.7 81.2 -5.7 304 665 2.3 169 Lane, OR................. 11.1 144.0 -5.8 305 711 2.6 151 Marion, OR............... 9.6 135.3 -2.9 204 711 2.3 169 Multnomah, OR............ 28.7 444.7 -2.6 189 934 2.0 192 Washington, OR........... 16.4 243.3 -4.2 273 986 -1.8 309 Allegheny, PA............ 35.3 685.4 -1.0 80 976 3.5 88 Berks, PA................ 9.2 167.8 -1.8 131 817 0.2 273 Bucks, PA................ 20.1 259.8 -3.0 208 905 2.6 151 Butler, PA............... 4.8 80.8 0.5 24 806 5.8 18 Chester, PA.............. 15.3 244.4 -0.4 51 1,181 2.2 181 Cumberland, PA........... 6.0 124.5 -1.8 131 823 3.0 122 Dauphin, PA.............. 7.4 180.8 -1.0 80 883 4.1 65 Delaware, PA............. 13.7 213.0 -0.3 47 953 1.1 247 Erie, PA................. 7.4 126.4 -1.8 131 729 4.0 67 Lackawanna, PA........... 5.9 101.2 -2.0 145 717 5.9 17 Lancaster, PA............ 12.5 226.9 -2.7 193 771 4.2 62 Lehigh, PA............... 8.8 177.6 -1.8 131 906 -0.7 297 Luzerne, PA.............. 7.9 142.5 -1.0 80 695 1.5 224 Montgomery, PA........... 27.7 488.0 -1.6 118 1,151 -0.3 286 Northampton, PA.......... 6.5 98.3 -3.3 234 805 2.7 143 Philadelphia, PA......... 31.5 637.6 -0.5 58 1,094 2.8 139 Washington, PA........... 5.4 80.5 0.9 18 814 4.5 48 Westmoreland, PA......... 9.4 135.7 -0.4 51 728 0.1 277 York, PA................. 9.2 177.6 -1.2 92 788 3.0 122 Kent, RI................. 5.7 77.6 -4.8 286 783 0.9 255 Providence, RI........... 18.1 277.8 -3.5 248 931 7.1 10 Charleston, SC........... 12.8 209.5 -1.9 139 782 -0.4 290 Greenville, SC........... 13.0 237.1 -2.8 196 795 2.7 143 Horry, SC................ 8.5 105.6 -7.1 319 581 -0.3 286 Lexington, SC............ 5.8 98.4 -1.7 126 680 1.2 245 Richland, SC............. 9.7 214.4 -2.1 153 790 3.3 101 Spartanburg, SC.......... 6.3 117.9 -5.2 295 776 4.7 42 Minnehaha, SD............ 6.4 116.8 1.2 12 741 0.8 259 Davidson, TN............. 18.6 436.1 -3.0 208 976 2.7 143 Hamilton, TN............. 8.6 189.2 -3.5 248 813 2.8 139 Knox, TN................. 11.2 228.9 -1.5 109 796 0.8 259 Rutherford, TN........... 4.3 97.5 -4.8 286 842 0.8 259 Shelby, TN............... 19.9 497.0 -3.5 248 935 0.1 277 Williamson, TN........... 6.1 87.5 -1.6 118 980 -4.9 317 Bell, TX................. 4.6 104.1 1.6 7 705 4.6 45 Bexar, TX................ 32.8 731.6 0.0 38 806 1.9 200 Brazoria, TX............. 4.7 87.8 0.0 38 871 3.9 71 Brazos, TX............... 3.9 86.9 (7) - 688 (7) - Cameron, TX.............. 6.4 124.5 -0.5 58 584 5.4 27 Collin, TX............... 17.4 297.8 0.9 18 1,040 0.7 264 Dallas, TX............... 68.6 1,484.4 -1.2 92 1,123 1.1 247 Denton, TX............... 10.7 170.5 0.0 38 798 1.9 200 El Paso, TX.............. 13.6 273.0 -0.6 63 643 2.9 131 Fort Bend, TX............ 8.5 132.3 2.2 4 967 0.5 268 Galveston, TX............ 5.2 93.8 -4.1 270 829 0.0 280 Harris, TX............... 98.1 2,078.1 1.0 14 1,187 2.6 151 Hidalgo, TX.............. 10.7 222.4 0.9 18 574 2.0 192 Jefferson, TX............ 5.9 127.9 2.5 2 941 8.0 4 Lubbock, TX.............. 6.8 126.4 2.4 3 699 2.3 169 McLennan, TX............. 4.9 103.6 (7) - 718 2.4 163 Montgomery, TX........... 8.3 129.6 2.7 1 876 3.7 81 Nueces, TX............... 8.1 156.1 0.8 21 806 4.9 35 Potter, TX............... 3.8 77.5 1.3 11 797 (7) - Smith, TX................ 5.3 95.7 1.2 12 809 6.2 16 Tarrant, TX.............. 37.6 770.8 -0.8 70 919 2.7 143 Travis, TX............... 29.3 578.8 0.1 36 1,009 -0.6 294 Webb, TX................. 4.8 89.4 0.4 28 600 1.5 224 Williamson, TX........... 7.3 121.6 -0.3 47 895 -5.1 318 Davis, UT................ 7.4 101.4 -2.2 160 737 0.7 264 Salt Lake, UT............ 38.9 588.6 -1.5 109 847 0.4 270 Utah, UT................. 13.3 172.2 -3.2 230 727 1.7 209 Weber, UT................ 5.7 93.0 -2.8 196 677 0.4 270 Chittenden, VT........... 6.0 95.3 -1.4 104 896 2.4 163 Arlington, VA............ 7.8 158.6 1.9 6 1,509 3.1 114 Chesterfield, VA......... 7.7 120.0 -2.9 204 825 2.9 131 Fairfax, VA.............. 34.3 589.2 -0.8 70 1,407 3.5 88 Henrico, VA.............. 9.7 178.0 -2.4 177 916 1.3 237 Loudoun, VA.............. 9.2 133.8 0.3 29 1,091 0.9 255 Prince William, VA....... 7.3 103.6 -1.2 92 816 -0.6 294 Alexandria City, VA...... 6.2 102.2 0.5 24 1,311 5.6 20 Chesapeake City, VA...... 5.8 98.5 -3.7 255 714 1.6 218 Newport News City, VA.... 4.0 99.2 -1.8 131 850 7.1 10 Norfolk City, VA......... 5.9 143.7 -1.1 84 906 4.3 58 Richmond City, VA........ 7.5 157.8 (7) - 1,024 (7) - Virginia Beach City, VA.. 11.7 170.8 -3.0 208 726 2.5 159 Clark, WA................ 12.3 129.9 -2.8 196 817 2.9 131 King, WA................. 77.6 1,175.3 -1.5 109 1,130 4.0 67 Kitsap, WA............... 6.6 82.7 -2.4 177 822 4.6 45 Pierce, WA............... 20.8 269.4 -3.4 241 814 4.4 52 Snohomish, WA............ 17.9 250.2 -2.5 183 928 2.9 131 Spokane, WA.............. 15.5 207.2 -2.0 145 737 4.4 52 Thurston, WA............. 7.0 100.0 -0.8 70 807 2.9 131 Whatcom, WA.............. 6.9 80.6 -3.2 230 708 2.6 151 Yakima, WA............... 8.3 93.5 1.0 14 624 5.1 31 Kanawha, WV.............. 6.1 109.0 -0.6 63 799 4.7 42 Brown, WI................ 6.8 148.1 -1.9 139 821 3.1 114 Dane, WI................. 14.2 304.1 -1.1 84 878 4.8 39 Milwaukee, WI............ 21.3 495.4 -1.6 118 923 2.6 151 Outagamie, WI............ 5.1 103.6 -2.0 145 784 4.5 48 Racine, WI............... 4.2 74.7 -3.0 208 879 -0.1 282 Waukesha, WI............. 13.3 231.0 -3.2 230 920 2.3 169 Winnebago, WI............ 3.8 91.1 0.3 29 855 4.7 42 San Juan, PR............. 13.0 291.7 -2.5 (8) 621 2.3 (8) (1) Includes workers covered by Unemployment Insurance (UI) and Unemployment Compensation for Federal Employees (UCFE) programs. These 334 U.S. counties comprise 71.5 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) Average weekly wages were calculated using unrounded data. (5) Percent changes were computed from quarterly employment and pay data adjusted for noneconomic county reclassifications. See Technical Note. (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 10 largest counties, fourth quarter 2008(2) Employment Average weekly wage(3) Establishments, fourth quarter County by NAICS supersector 2008 Percent Percent (thousands) December change, Average change, 2008 December weekly fourth (thousands) 2007-08(4) wage quarter 2007-08(4) United States(5)............................. 9,177.5 133,870.4 -2.3 $918 2.2 Private industry........................... 8,884.3 111,752.9 -2.9 919 2.0 Natural resources and mining............. 127.0 1,802.7 2.0 996 5.1 Construction............................. 881.7 6,636.1 -10.2 1,052 4.9 Manufacturing............................ 360.0 12,891.3 -6.2 1,094 1.8 Trade, transportation, and utilities..... 1,925.3 26,316.1 -3.5 766 1.1 Information.............................. 147.4 2,948.2 -3.4 1,360 0.1 Financial activities..................... 862.8 7,853.7 -3.2 1,390 -0.4 Professional and business services....... 1,537.6 17,366.1 -4.1 1,201 3.7 Education and health services............ 857.4 18,304.3 2.9 872 3.7 Leisure and hospitality.................. 742.2 12,957.7 -1.7 390 1.8 Other services........................... 1,229.1 4,445.7 -0.7 581 2.8 Government................................. 293.2 22,117.5 0.9 914 4.0 Los Angeles, CA.............................. 433.9 4,152.9 -3.4 1,075 1.8 Private industry........................... 430.0 3,552.8 -3.8 1,064 1.1 Natural resources and mining............. 0.5 10.5 -2.7 1,261 5.4 Construction............................. 14.0 136.7 -12.3 1,138 4.8 Manufacturing............................ 14.5 417.6 -5.9 1,107 3.8 Trade, transportation, and utilities..... 53.6 802.4 -5.4 833 -0.8 Information.............................. 8.8 207.5 (6) 1,889 (6) Financial activities..................... 24.1 231.8 -5.7 1,462 -3.8 Professional and business services....... 42.6 574.2 (6) 1,306 (6) Education and health services............ 28.1 500.0 1.8 979 3.8 Leisure and hospitality.................. 27.2 396.1 -1.6 927 5.9 Other services........................... 201.1 258.8 0.5 454 1.1 Government................................. 4.0 600.1 (6) 1,141 5.6 Cook, IL..................................... 141.0 2,480.0 -2.8 1,118 1.5 Private industry........................... 139.6 2,169.2 -3.3 1,126 1.3 Natural resources and mining............. 0.1 1.1 -5.6 998 -5.0 Construction............................. 12.4 82.8 -10.5 1,478 6.9 Manufacturing............................ 7.0 219.9 -6.5 1,119 3.0 Trade, transportation, and utilities..... 27.6 467.7 -4.9 840 -0.4 Information.............................. 2.6 56.1 -3.2 1,487 -4.3 Financial activities..................... 15.7 203.7 -4.3 2,007 0.7 Professional and business services....... 29.1 423.4 -4.8 1,525 3.5 Education and health services............ 14.0 386.1 3.1 930 1.3 Leisure and hospitality.................. 11.7 227.5 -2.2 440 0.0 Other services........................... 14.6 96.1 -0.1 783 3.2 Government................................. 1.4 310.8 0.8 1,058 2.9 New York, NY................................. 118.9 2,386.4 -1.3 1,856 -0.6 Private industry........................... 118.6 1,934.3 -1.6 2,041 -0.7 Natural resources and mining............. 0.0 0.2 -3.6 1,594 4.7 Construction............................. 2.4 36.3 0.6 1,939 0.6 Manufacturing............................ 3.0 33.7 -8.3 1,565 0.7 Trade, transportation, and utilities..... 22.0 255.2 -3.3 1,294 -1.5 Information.............................. 4.6 134.5 -1.5 2,055 -0.3 Financial activities..................... 19.2 369.0 -3.9 4,085 -1.3 Professional and business services....... 25.5 489.1 -2.4 2,173 0.6 Education and health services............ 8.9 297.7 1.6 1,133 6.0 Leisure and hospitality.................. 11.8 224.3 0.8 889 -0.7 Other services........................... 18.0 90.2 0.7 1,102 7.1 Government................................. 0.3 452.1 0.0 1,062 1.6 Harris, TX................................... 98.1 2,078.1 1.0 1,187 2.6 Private industry........................... 97.6 1,820.6 0.9 1,215 2.3 Natural resources and mining............. 1.6 85.8 7.1 2,872 (6) Construction............................. 6.7 156.9 (6) 1,217 (6) Manufacturing............................ 4.6 187.7 2.4 1,468 -3.4 Trade, transportation, and utilities..... 22.5 443.1 0.6 1,035 4.0 Information.............................. 1.4 32.0 -2.4 1,393 8.2 Financial activities..................... 10.6 117.9 (6) 1,517 4.7 Professional and business services....... 19.6 336.9 (6) 1,448 3.7 Education and health services............ 10.4 224.3 3.1 958 3.2 Leisure and hospitality.................. 7.6 175.2 -0.6 404 4.7 Other services........................... 11.9 59.6 0.4 673 3.2 Government................................. 0.5 257.5 1.8 988 5.2 Maricopa, AZ................................. 103.6 1,741.0 -5.8 892 2.1 Private industry........................... 102.9 1,512.8 -6.9 893 2.2 Natural resources and mining............. 0.5 9.0 -4.9 1,026 20.6 Construction............................. 11.0 115.5 -25.3 986 3.4 Manufacturing............................ 3.6 120.8 -8.0 1,217 3.6 Trade, transportation, and utilities..... 22.9 365.7 -6.8 796 0.9 Information.............................. 1.7 29.4 -4.1 1,098 3.4 Financial activities..................... 12.9 140.1 -4.8 1,066 -0.4 Professional and business services....... 23.2 289.2 -8.5 989 5.0 Education and health services............ 10.3 216.8 5.7 999 2.3 Leisure and hospitality.................. 7.4 176.8 -5.3 420 -1.4 Other services........................... 7.4 48.4 -4.9 613 2.7 Government................................. 0.7 228.2 2.0 881 0.1 Orange, CA................................... 102.7 1,451.2 -4.8 1,043 1.4 Private industry........................... 101.3 1,301.1 -5.3 1,043 1.2 Natural resources and mining............. 0.2 4.2 -9.0 665 -2.8 Construction............................. 6.9 83.3 -14.9 1,234 4.5 Manufacturing............................ 5.3 166.4 -5.7 1,226 -0.2 Trade, transportation, and utilities..... 17.2 272.3 -6.9 947 1.4 Information.............................. 1.3 29.0 -3.8 1,423 4.0 Financial activities..................... 10.7 110.0 -7.5 1,582 -2.6 Professional and business services....... 19.1 258.3 -7.6 1,259 6.0 Education and health services............ 10.0 150.8 (6) 960 (6) Leisure and hospitality.................. 7.1 171.7 -2.2 406 1.5 Other services........................... 18.0 49.0 -0.3 569 -4.2 Government................................. 1.4 150.1 -0.8 1,044 3.2 Dallas, TX................................... 68.6 1,484.4 -1.2 1,123 1.1 Private industry........................... 68.1 1,314.7 -1.6 1,141 1.1 Natural resources and mining............. 0.6 8.5 12.6 4,744 38.9 Construction............................. 4.4 80.1 -4.3 1,075 1.7 Manufacturing............................ 3.1 129.8 -5.4 1,224 1.1 Trade, transportation, and utilities..... 15.2 308.2 -2.1 990 -4.2 Information.............................. 1.7 47.3 -4.2 1,524 3.6 Financial activities..................... 8.8 142.9 -1.2 1,429 -1.7 Professional and business services....... 15.1 275.6 -2.1 1,375 2.4 Education and health services............ 6.7 153.9 3.8 1,059 3.1 Leisure and hospitality.................. 5.4 128.5 (6) 493 (6) Other services........................... 6.6 39.0 -1.2 682 3.6 Government................................. 0.5 169.7 2.3 984 2.2 San Diego, CA................................ 100.0 1,309.1 -3.0 981 2.0 Private industry........................... 98.8 1,082.3 -3.5 960 1.6 Natural resources and mining............. 0.8 9.4 -11.4 577 0.2 Construction............................. 7.0 70.4 -14.3 1,140 5.5 Manufacturing............................ 3.1 100.4 -3.3 1,306 0.9 Trade, transportation, and utilities..... 14.2 218.3 -6.3 759 0.7 Information.............................. 1.3 38.6 0.6 1,970 2.3 Financial activities..................... 9.5 74.2 -5.7 1,171 -1.0 Professional and business services....... 16.3 210.9 -4.4 1,238 2.0 Education and health services............ 8.2 138.3 4.2 953 3.1 Leisure and hospitality.................. 6.9 158.2 -2.3 425 3.9 Other services........................... 26.9 58.4 2.0 491 1.7 Government................................. 1.3 226.8 -0.4 1,079 2.8 King, WA..................................... 77.6 1,175.3 -1.5 1,130 4.0 Private industry........................... 77.0 1,018.2 -2.0 1,140 4.0 Natural resources and mining............. 0.4 2.9 7.0 1,573 11.8 Construction............................. 6.6 63.8 -11.6 1,197 6.8 Manufacturing............................ 2.4 108.8 -3.3 1,449 7.0 Trade, transportation, and utilities..... 14.9 221.8 -2.9 955 1.0 Information.............................. 1.8 81.4 6.1 1,982 3.9 Financial activities..................... 6.9 72.4 -5.0 1,418 2.6 Professional and business services....... 13.7 185.4 -3.3 1,378 4.6 Education and health services............ 6.5 129.3 4.6 894 3.8 Leisure and hospitality.................. 6.2 108.6 -2.5 450 1.6 Other services........................... 17.6 43.7 -0.8 631 3.6 Government................................. 0.5 157.1 1.9 1,069 4.2 Miami-Dade, FL............................... 86.8 1,003.9 -4.2 924 2.6 Private industry........................... 86.4 851.3 -4.7 907 2.3 Natural resources and mining............. 0.5 9.6 -10.6 457 -11.1 Construction............................. 6.4 42.0 -21.4 973 5.3 Manufacturing............................ 2.6 41.2 -11.7 818 1.0 Trade, transportation, and utilities..... 23.5 253.4 -4.0 814 1.2 Information.............................. 1.5 19.0 -8.1 1,266 5.2 Financial activities..................... 10.2 67.2 -7.6 1,387 0.1 Professional and business services....... 18.2 132.2 -5.2 1,229 6.6 Education and health services............ 9.4 145.9 2.8 901 1.7 Leisure and hospitality.................. 6.0 104.0 -1.9 514 0.6 Other services........................... 7.6 36.2 -3.3 579 6.0 Government................................. 0.4 152.6 -1.1 1,017 (6) (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) Percent changes were computed from quarterly employment and pay data adjusted for noneconomic county reclassifications. See Technical Note. (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 2008(2) Employment Average weekly wage(4) Establishments, fourth quarter County(3) 2008 Percent Percent (thousands) December change, Average change, 2008 December weekly fourth (thousands) 2007-08(5) wage quarter 2007-08(5) United States(6)......... 9,177.5 133,870.4 -2.3 $918 2.2 Jefferson, AL............ 19.0 355.3 -3.3 922 2.2 Anchorage Borough, AK.... 8.2 148.2 1.5 969 4.9 Maricopa, AZ............. 103.6 1,741.0 -5.8 892 2.1 Pulaski, AR.............. 15.2 250.3 -1.2 847 -14.3 Los Angeles, CA.......... 433.9 4,152.9 -3.4 1,075 1.8 Denver, CO............... 25.6 445.0 -1.5 1,111 -1.3 Hartford, CT............. 25.6 504.5 -1.5 1,111 1.0 New Castle, DE........... 18.3 278.7 -3.7 1,055 2.3 Washington, DC........... 34.4 687.5 0.3 1,570 5.1 Miami-Dade, FL........... 86.8 1,003.9 -4.2 924 2.6 Fulton, GA............... 39.9 732.2 -3.4 1,183 1.0 Honolulu, HI............. 24.8 449.5 -2.4 850 3.8 Ada, ID.................. 15.0 202.9 -5.0 814 -1.1 Cook, IL................. 141.0 2,480.0 -2.8 1,118 1.5 Marion, IN............... 24.3 571.8 -2.8 913 2.8 Polk, IA................. 14.9 273.7 -1.1 904 2.4 Johnson, KS.............. 20.7 316.0 -1.1 949 1.3 Jefferson, KY............ 22.0 423.8 -3.3 871 1.5 East Baton Rouge, LA..... 14.8 265.9 0.3 874 8.0 Cumberland, ME........... 12.2 173.4 -2.3 822 3.0 Montgomery, MD........... 32.9 460.3 -1.3 1,219 1.9 Middlesex, MA............ 47.8 826.2 -0.4 1,296 -1.1 Wayne, MI................ 32.1 709.8 -5.6 1,032 4.2 Hennepin, MN............. 42.5 837.8 -2.4 1,146 2.7 Hinds, MS................ 6.4 127.6 -1.8 809 3.3 St. Louis, MO............ 32.8 600.5 -3.0 990 1.3 Yellowstone, MT.......... 5.8 78.2 -0.2 738 1.2 Douglas, NE.............. 16.1 322.8 0.0 842 -2.1 Clark, NV................ 51.0 870.0 -6.5 856 -2.3 Hillsborough, NH......... 12.4 195.9 -2.6 1,062 1.8 Bergen, NJ............... 34.6 450.4 -2.5 1,188 0.4 Bernalillo, NM........... 17.8 329.9 -2.0 812 3.0 New York, NY............. 118.9 2,386.4 -1.3 1,856 -0.6 Mecklenburg, NC.......... 33.4 567.7 -1.7 1,016 1.5 Cass, ND................. 5.9 100.7 1.5 778 2.1 Cuyahoga, OH............. 37.7 724.7 -3.0 926 2.0 Oklahoma, OK............. 23.9 427.1 0.1 852 5.6 Multnomah, OR............ 28.7 444.7 -2.6 934 2.0 Allegheny, PA............ 35.3 685.4 -1.0 976 3.5 Providence, RI........... 18.1 277.8 -3.5 931 7.1 Greenville, SC........... 13.0 237.1 -2.8 795 2.7 Minnehaha, SD............ 6.4 116.8 1.2 741 0.8 Shelby, TN............... 19.9 497.0 -3.5 935 0.1 Harris, TX............... 98.1 2,078.1 1.0 1,187 2.6 Salt Lake, UT............ 38.9 588.6 -1.5 847 0.4 Chittenden, VT........... 6.0 95.3 -1.4 896 2.4 Fairfax, VA.............. 34.3 589.2 -0.8 1,407 3.5 King, WA................. 77.6 1,175.3 -1.5 1,130 4.0 Kanawha, WV.............. 6.1 109.0 -0.6 799 4.7 Milwaukee, WI............ 21.3 495.4 -1.6 923 2.6 Laramie, WY.............. 3.2 43.8 0.3 753 2.0 San Juan, PR............. 13.0 291.7 -2.5 621 2.3 St. Thomas, VI........... 1.8 23.9 -0.3 673 -4.1 (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) Average weekly wages were calculated using unrounded data. (5) Percent changes were computed from quarterly employment and pay data adjusted for noneconomic county reclassifications. See Technical Note. (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 2008(2) Employment Average weekly wage(3) Establishments, fourth quarter State 2008 Percent Percent (thousands) December change, Average change, 2008 December weekly fourth (thousands) 2007-08 wage quarter 2007-08 United States(4)......... 9,177.5 133,870.4 -2.3 $918 2.2 Alabama.................. 121.6 1,909.8 -3.1 790 3.5 Alaska................... 21.4 303.9 1.6 927 5.7 Arizona.................. 164.5 2,557.9 -5.1 848 2.7 Arkansas................. 86.5 1,168.2 -1.5 706 -1.0 California............... 1,370.0 15,288.5 -3.2 1,042 0.7 Colorado................. 177.1 2,295.8 -1.5 932 0.5 Connecticut.............. 113.5 1,688.0 -1.7 1,164 1.2 Delaware................. 29.4 416.8 -3.0 943 1.9 District of Columbia..... 34.4 687.5 0.3 1,570 5.1 Florida.................. 623.0 7,586.6 -5.3 824 1.6 Georgia.................. 276.7 3,970.3 -3.5 853 2.3 Hawaii................... 39.3 614.7 -3.5 821 3.5 Idaho.................... 57.2 634.1 -3.9 693 1.0 Illinois................. 371.5 5,795.8 -2.3 985 1.0 Indiana.................. 161.4 2,831.3 -3.4 764 2.7 Iowa..................... 94.6 1,483.7 -1.0 756 3.1 Kansas................... 87.2 1,370.2 -0.2 769 3.1 Kentucky................. 108.4 1,783.2 -2.6 754 3.0 Louisiana................ 128.5 1,907.5 0.1 829 5.9 Maine.................... 51.1 595.3 -2.1 735 4.0 Maryland................. 164.3 2,531.8 -1.9 1,010 2.4 Massachusetts............ 215.1 3,239.6 -1.1 1,154 1.8 Michigan................. 258.2 3,993.3 -4.9 903 3.6 Minnesota................ 172.0 2,658.8 -1.9 907 2.6 Mississippi.............. 71.0 1,117.2 -2.8 679 3.8 Missouri................. 175.7 2,700.9 -1.7 842 7.9 Montana.................. 43.2 433.8 -1.5 678 2.9 Nebraska................. 60.4 923.1 -0.3 730 1.0 Nevada................... 77.5 1,206.5 -6.5 862 -1.1 New Hampshire............ 49.9 626.2 -2.0 936 2.2 New Jersey............... 273.7 3,927.7 -2.4 1,123 2.8 New Mexico............... 54.9 821.2 -1.2 768 3.9 New York................. 585.9 8,677.4 -1.0 1,169 1.4 North Carolina........... 260.1 4,003.8 -3.0 793 1.9 North Dakota............. 25.8 354.4 1.9 725 5.1 Ohio..................... 293.0 5,167.5 -3.2 816 2.6 Oklahoma................. 100.8 1,559.8 0.0 755 4.9 Oregon................... 134.1 1,676.6 -3.7 808 1.3 Pennsylvania............. 344.0 5,645.8 -1.3 897 2.6 Rhode Island............. 35.9 464.3 -3.4 887 5.7 South Carolina........... 119.5 1,837.1 -3.5 731 2.1 South Dakota............. 30.8 395.2 0.4 663 2.5 Tennessee................ 143.1 2,695.7 -3.3 824 1.4 Texas.................... 566.6 10,510.8 0.4 933 2.4 Utah..................... 88.3 1,215.0 -2.1 770 1.4 Vermont.................. 25.1 304.4 -1.7 774 4.3 Virginia................. 233.5 3,656.8 -1.3 953 3.3 Washington............... 222.8 2,885.0 -1.8 918 3.7 West Virginia............ 48.9 713.8 -0.1 735 7.1 Wisconsin................ 161.1 2,753.2 -1.9 793 3.0 Wyoming.................. 25.2 284.5 1.5 850 4.3 Puerto Rico.............. 55.3 1,028.5 -2.9 528 2.3 Virgin Islands........... 3.6 45.5 -1.4 731 -0.8 (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.