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For release 10:00 a.m. (EST), Tuesday, January 8, 2013 USDL-13-0013 Technical Information: (202) 691-6567 * QCEWInfo@bls.gov * www.bls.gov/cew Media Contact: (202) 691-5902 * PressOffice@bls.gov County Employment and Wages Second Quarter 2012 From June 2011 to June 2012, employment increased in 287 of the 328 largest U.S. counties, the U.S. Bureau of Labor Statistics reported today. Yakima, Wash., posted the largest increase, with a gain of 8.2 percent over the year, compared with national job growth of 1.8 percent. Within Yakima, the largest employment increase occurred in natural resources and mining, which gained 8,646 jobs over the year (34.6 percent). Madison, Ill., St. Clair, Ill., and Clay, Mo., had the largest over-the-year decreases in employment among the largest counties in the U.S. with losses of 2.0 percent each. County employment and wage data are compiled under the Quarterly Census of Employment and Wages (QCEW) program, which produces detailed information on local employment and wages within 7 months after the end of each quarter. The U.S. average weekly wage increased over the year by 1.3 percent to $903 in the second quarter of 2012. Washington, Ore., had the largest over-the-year increase in average weekly wages with a gain of 8.5 percent. Within Washington County, a total wage gain of $159.4 million (16.0 percent) in the manufacturing industry had the largest contribution to the increase in average weekly wages. Within this industry, large payouts, which included bonuses, significantly boosted the county’s average weekly wages. Williamson, Texas, experienced the largest decrease in average weekly wages with a loss of 17.0 percent over the year. Table A. Large counties ranked by June 2012 employment, June 2011-12 employment increase, and June 2011-12 percent increase in employment -------------------------------------------------------------------------------------------------------- Employment in large counties -------------------------------------------------------------------------------------------------------- June 2012 employment | Increase in employment, | Percent increase in employment, (thousands) | June 2011-12 | June 2011-12 | (thousands) | -------------------------------------------------------------------------------------------------------- | | United States 132,896.0| United States 2,366.8| United States 1.8 -------------------------------------------------------------------------------------------------------- | | Los Angeles, Calif. 3,961.9| Harris, Texas 78.3| Yakima, Wash. 8.2 Cook, Ill. 2,428.3| Los Angeles, Calif. 64.1| Montgomery, Texas 5.7 New York, N.Y. 2,392.0| New York, N.Y. 56.2| Elkhart, Ind. 5.6 Harris, Texas 2,121.7| Dallas, Texas 46.1| Williamson, Tenn. 5.5 Maricopa, Ariz. 1,635.4| Maricopa, Ariz. 44.3| Delaware, Ohio 5.4 Dallas, Texas 1,475.1| King, Wash. 34.7| Utah, Utah 5.0 Orange, Calif. 1,416.5| Orange, Calif. 33.4| Rutherford, Tenn. 4.9 San Diego, Calif. 1,283.3| Santa Clara, Calif. 32.8| Kern, Calif. 4.8 King, Wash. 1,174.4| Cook, Ill. 31.0| Lafayette, La. 4.8 Miami-Dade, Fla. 974.6| San Diego, Calif. 26.7| Gregg, Texas 4.8 | | -------------------------------------------------------------------------------------------------------- Large County Employment In June 2012, national employment, as measured by the QCEW program, was 132.9 million, up by 1.8 percent or 2.4 million, from June 2011. The 328 U.S. counties with 75,000 or more jobs accounted for 70.9 percent of total U.S. employment and 76.2 percent of total wages. These 328 counties had a net job growth of 1.7 million over the year, accounting for 73.3 percent of the overall U.S. employment increase. Yakima, Wash., had the largest percentage increase in employment (8.2 percent) among the largest U.S. counties. The five counties with the largest increases in employment level were Harris, Texas; Los Angeles, Calif.; New York, N.Y.; Dallas, Texas; and Maricopa, Ariz. These counties had a combined over-the-year gain of 289,000, or 12.2 percent of the overall employment increase for the U.S. (See table A.) Employment declined in 38 of the large counties from June 2011 to June 2012. Three counties, Madison, Ill., St. Clair, Ill., and Clay, Mo., had the largest over-the-year percentage decreases in employment (-2.0 percent each). Within Madison, construction was the largest contributor to the decrease in employment with a loss of 998 jobs (-17.9 percent). The largest employment decrease in St. Clair occurred within local government in the education and health services industry, which lost 463 jobs (-6.1 percent), followed by construction where 452 jobs were lost (-10.9 percent) within the private sector. Within Clay, manufacturing had the largest employment decline, with a loss of 1,584 jobs (-15.2 percent). Benton, Wash., had the second largest percentage decrease in employment, followed by New London, Conn. (See table 1.) Table B. Large counties ranked by second quarter 2012 average weekly wages, second quarter 2011-12 increase in average weekly wages, and second quarter 2011-12 percent increase in average weekly wages -------------------------------------------------------------------------------------------------------- Average weekly wage in large counties -------------------------------------------------------------------------------------------------------- Average weekly wage, | Increase in average weekly | Percent increase in average second quarter 2012 | wage, second quarter 2011-12 | weekly wage, second | | quarter 2011-12 -------------------------------------------------------------------------------------------------------- | | United States $903| United States $12| United States 1.3 -------------------------------------------------------------------------------------------------------- | | Santa Clara, Calif. $1,754| San Mateo, Calif. $100| Washington, Ore. 8.5 New York, N.Y. 1,646| Washington, Ore. 88| Washington, Pa. 7.8 Washington, D.C. 1,544| Washington, Pa. 64| McLean, Ill. 7.2 San Mateo, Calif. 1,515| McLean, Ill. 62| San Mateo, Calif. 7.1 Arlington, Va. 1,493| Jefferson, Texas 55| Weld, Colo. 6.4 San Francisco, Calif. 1,487| Davidson, Tenn. 52| Jefferson, Texas 6.3 Fairfield, Conn. 1,425| Franklin, Ohio 50| Davidson, Tenn. 5.8 Fairfax, Va. 1,422| San Francisco, Calif. 48| Franklin, Ohio 5.6 Suffolk, Mass. 1,381| Weld, Colo. 47| Lucas, Ohio 5.0 Somerset, N.J. 1,345| Harris, Texas 46| Lake, Ind. 4.8 -------------------------------------------------------------------------------------------------------- Large County Average Weekly Wages Average weekly wages for the nation increased by 1.3 percent during the year ending in the second quarter of 2012. Among the 328 largest counties, 233 had over-the-year increases in average weekly wages. Washington, Ore., had the largest wage gain among the largest U.S. counties (8.5 percent). Of the 328 largest counties, 86 experienced over-the-year declines in average weekly wages. Williamson, Texas, had the largest average weekly wage decrease with a loss of 17.0 percent. Within Williamson, total wages in trade, transportation, and utilities decreased by $212.4 million (-30.5 percent) over the year. This decline reflects a return to pay levels seen previously following a big payout in the second quarter of 2011. Williamson also received large payouts in this industry in the first quarter of 2012. Kitsap, Wash., had the second largest decline in average weekly wages, followed by Arlington, Va., Durham, N.C., and Benton, Wash. (See table 1.) Ten Largest U.S. Counties All of the 10 largest counties experienced over-the-year percentage increases in employment in June 2012. Harris, Texas, experienced the largest gain (3.8 percent). Within Harris, professional and business services had the largest over-the-year level increase among all private industry groups with a gain of 20,285 jobs (6.0 percent). Cook, Ill., had the smallest percentage increase in employment (1.3 percent) among the 10 largest counties. (See table 2.) Nine of the 10 largest U.S. counties had an over-the-year increase in average weekly wages. Harris, Texas, experienced the largest increase in average weekly wages (4.1 percent), largely due to substantial total wage gains over the year in trade, transportation, and utilities ($960.6 million or 17.3 percent). Miami-Dade, Fla., had the only average weekly wage decline (-0.5 percent) among the 10 largest counties. For More Information The tables included in this release contain data for the nation and for the 328 U.S. counties with annual average employment levels of 75,000 or more in 2011. June 2012 employment and 2012 second quarter average weekly wages for all states are provided in table 3 of this release. The employment and wage data by county are compiled under the 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 132.9 million full- and part-time workers. For additional information about the quarterly employment and wages data, please read the Technical Note. Data for the second quarter of 2012 will be available later at http://www.bls.gov/cew/. 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 third quarter 2012 is scheduled to be released on Thursday, March 28, 2013.
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 release are based on the 2012 North American Industry Classification System. Data for 2012 are preliminary and subject to revision. For purposes of this release, large counties are defined as having employment le- vels of 75,000 or greater. In addition, data for San Juan, Puerto Rico, are pro- vided, 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 prelimi- nary annual average of employment for the previous year. The 329 counties presented in this release were derived using 2011 preliminary annual averages of employment. For 2012 data, seven counties have been added to the publication tables: Okaloosa, Fla.; Tippecanoe, Ind.; Johnson, Iowa; St. Tammany, La.; Saratoga, N.Y.; Delaware, Ohio; and Gregg, Texas. These counties will be included in all 2012 quarterly re- leases. One county, Jackson, Ore., which was published in the 2011 releases, will be excluded from this and future 2012 releases because its 2011 annual average employment level was less than 75,000. 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- | 486,000 establish- | submitted by 9.2 | ministrative records| ments | million establish- | submitted by 6.8 | | ments in first | million private-sec-| | quarter of 2012 | 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 to an- | new quarter of UI | dinal database and | nually realign sample- | data | directly summarizes | based estimates to pop- | | gross job gains and | ulation estimates to | | losses | population counts (ben- | | | chmarking) -----------|---------------------|----------------------|------------------------ 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 sub- mitted 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 multiple 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 microdata summaries of 9.1 million employer reports of employment and wages submitted by states to the BLS in 2011. These reports are based on place of employ- ment rather than place of residence. UI and UCFE coverage is broad and has been basically comparable from state to state since 1978, when the 1976 amendments to the Federal Unemployment Tax Act became ef- fective, expanding coverage to include most State and local government employees. In 2011, UI and UCFE programs covered workers in 129.4 million jobs. The estimated 124.8 million workers in these jobs (after adjustment for multiple jobholders) represented 95.7 percent of civilian wage and salary employment. Covered workers received $6.217 trillion in pay, representing 93.3 percent of the wage and salary component of personal income and 41.2 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 re- lease. 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 production 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 av- erages 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 compen- sation 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 including the 12th of the month. When comparing average week- ly 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 pe- riods 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 oc- cur 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 employ- ers and update, if necessary, the industry, location, and ownership classification of all establishments on a 4-year cycle. Changes in establishment classification 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 indi- vidual 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 adjusted version of the final 2011 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 un- adjusted 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 re- lease. 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. In- cluded in these adjustments are administrative changes involving the classification of establishments that were previously reported in the unknown or statewide county or unknown industry categories. Beginning with the first quarter of 2008, adjusted data 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. 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 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 common 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 Employment and Wages Annual Averages Online features comprehensive information by detailed industry on establishments, employment, and wages for the nation and all states. The 2011 edition of this publication, which was published in October 2012, contains selected data produced by Business Employment Dynamics (BED) on job gains and losses, as well as selected data from the first quarter 2012 version of this news release. Tables and additional content from Employment and Wages Annual Aver- ages 2011 are now available online at http://www.bls.gov/cew/cewbultn11.htm. The 2012 edition of Employment and Wages Annual Averages Online will be available later in 2013. 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 329 largest counties, second quarter 2012(2) Employment Average weekly wage(4) Establishments, County(3) second quarter Percent Ranking Percent Ranking 2012 June change, by Second change, by (thousands) 2012 June percent quarter second percent (thousands) 2011-12(5) change 2012 quarter change 2011-12(5) United States(6)......... 9,224.5 132,896.0 1.8 - $903 1.3 - Jefferson, AL............ 17.6 338.2 1.5 168 913 3.4 32 Madison, AL.............. 8.9 178.5 0.1 280 1,010 0.9 182 Mobile, AL............... 9.7 164.2 -1.0 317 791 1.7 120 Montgomery, AL........... 6.3 128.3 1.3 184 783 0.0 234 Tuscaloosa, AL........... 4.2 84.7 2.4 86 792 1.8 111 Anchorage Borough, AK.... 8.3 155.5 2.1 107 998 0.9 182 Maricopa, AZ............. 95.5 1,635.4 2.8 64 905 2.6 74 Pima, AZ................. 19.1 343.5 1.9 130 795 0.4 211 Benton, AR............... 5.5 97.4 2.1 107 844 3.2 37 Pulaski, AR.............. 14.4 243.6 0.9 222 825 1.6 128 Washington, AR........... 5.5 92.7 2.2 100 728 -1.0 289 Alameda, CA.............. 58.4 660.2 2.7 72 1,181 0.2 221 Contra Costa, CA......... 31.0 326.3 2.2 100 1,091 -0.5 269 Fresno, CA............... 32.3 351.8 3.2 44 702 -0.3 254 Kern, CA................. 18.6 299.7 4.8 8 813 2.9 53 Los Angeles, CA.......... 452.9 3,961.9 1.6 158 1,006 1.3 142 Marin, CA................ 12.0 107.0 3.6 27 1,122 1.5 133 Monterey, CA............. 13.3 187.0 1.5 168 770 2.3 82 Orange, CA............... 106.6 1,416.5 2.4 86 1,014 1.3 142 Placer, CA............... 11.2 131.4 3.0 57 898 2.7 66 Riverside, CA............ 52.8 575.1 2.3 98 749 1.1 163 Sacramento, CA........... 55.6 592.7 1.7 151 1,018 1.4 140 San Bernardino, CA....... 53.7 609.8 1.4 176 791 2.3 82 San Diego, CA............ 102.8 1,283.3 2.1 107 989 0.9 182 San Francisco, CA........ 58.1 585.8 4.3 15 1,487 3.3 33 San Joaquin, CA.......... 18.2 216.3 2.0 122 758 -0.5 269 San Luis Obispo, CA...... 9.9 106.5 3.4 34 750 -1.4 302 San Mateo, CA............ 25.3 342.1 4.3 15 1,515 7.1 4 Santa Barbara, CA........ 14.9 189.4 2.0 122 863 3.1 43 Santa Clara, CA.......... 65.6 903.1 3.8 22 1,754 1.2 153 Santa Cruz, CA........... 9.4 99.3 1.7 151 834 4.0 20 Solano, CA............... 10.5 122.1 2.0 122 909 0.2 221 Sonoma, CA............... 19.6 176.5 1.5 168 842 -1.2 294 Stanislaus, CA........... 15.5 168.2 2.8 64 761 1.3 142 Tulare, CA............... 9.6 150.9 0.3 266 634 2.3 82 Ventura, CA.............. 24.7 308.1 1.1 204 926 -0.3 254 Yolo, CA................. 6.3 92.2 2.2 100 934 4.1 17 Adams, CO................ 8.9 161.9 2.4 86 834 2.6 74 Arapahoe, CO............. 18.9 288.9 3.0 57 1,041 1.8 111 Boulder, CO.............. 13.1 162.2 2.7 72 1,048 2.0 101 Denver, CO............... 26.0 435.9 3.6 27 1,088 1.0 170 Douglas, CO.............. 9.7 97.3 4.2 17 980 0.1 228 El Paso, CO.............. 16.8 240.1 0.7 235 843 2.2 87 Jefferson, CO............ 17.8 214.7 1.8 140 907 0.9 182 Larimer, CO.............. 10.1 135.9 3.3 41 782 3.7 25 Weld, CO................. 5.8 85.2 3.7 24 786 6.4 5 Fairfield, CT............ 32.8 413.5 1.8 140 1,425 -2.9 318 Hartford, CT............. 25.7 496.6 1.3 184 1,097 -0.1 243 New Haven, CT............ 22.4 358.0 0.9 222 952 0.7 196 New London, CT........... 6.9 125.5 -1.3 324 926 2.1 96 New Castle, DE........... 17.0 265.6 0.1 280 1,071 1.9 108 Washington, DC........... 35.5 717.9 0.9 222 1,544 0.3 215 Alachua, FL.............. 6.5 115.8 0.7 235 786 1.0 170 Brevard, FL.............. 14.4 188.2 -0.3 298 829 -3.2 321 Broward, FL.............. 63.2 698.7 2.0 122 830 -0.6 276 Collier, FL.............. 11.8 110.6 4.2 17 781 -1.9 311 Duval, FL................ 27.1 438.1 1.3 184 862 1.5 133 Escambia, FL............. 7.9 117.3 -0.8 313 736 1.0 170 Hillsborough, FL......... 37.9 576.6 2.4 86 868 1.0 170 Lake, FL................. 7.2 77.6 2.1 107 614 -0.5 269 Lee, FL.................. 18.6 196.2 2.0 122 730 -0.1 243 Leon, FL................. 8.2 134.8 -0.8 313 768 0.0 234 Manatee, FL.............. 9.3 100.7 2.5 83 712 -0.8 285 Marion, FL............... 7.9 89.7 1.2 196 654 -0.3 254 Miami-Dade, FL........... 88.9 974.6 2.3 98 876 -0.5 269 Okaloosa, FL............. 6.1 77.0 1.2 196 750 -0.7 282 Orange, FL............... 36.1 672.8 3.2 44 790 -0.4 262 Palm Beach, FL........... 49.6 499.9 2.8 64 873 -0.2 252 Pasco, FL................ 10.0 92.5 1.5 168 664 -0.3 254 Pinellas, FL............. 30.6 382.6 1.3 184 805 0.5 205 Polk, FL................. 12.4 184.1 0.7 235 698 2.0 101 Sarasota, FL............. 14.4 134.5 3.0 57 751 0.3 215 Seminole, FL............. 13.8 156.6 1.9 130 757 -0.3 254 Volusia, FL.............. 13.3 147.1 1.3 184 668 1.7 120 Bibb, GA................. 4.6 80.4 1.1 204 708 2.9 53 Chatham, GA.............. 7.7 134.0 2.4 86 755 0.0 234 Clayton, GA.............. 4.3 111.9 -0.1 291 869 2.2 87 Cobb, GA................. 21.4 303.1 1.9 130 959 3.0 51 De Kalb, GA.............. 17.9 276.2 -0.3 298 957 3.2 37 Fulton, GA............... 41.5 723.8 3.1 51 1,171 2.0 101 Gwinnett, GA............. 24.1 308.2 1.2 196 887 2.4 80 Muscogee, GA............. 4.6 94.2 -0.1 291 716 0.6 200 Richmond, GA............. 4.7 97.0 -1.0 317 784 2.9 53 Honolulu, HI............. 24.6 443.0 2.1 107 844 1.7 120 Ada, ID.................. 13.7 200.8 2.9 62 778 0.4 211 Champaign, IL............ 4.3 87.3 0.1 280 789 4.2 13 Cook, IL................. 148.8 2,428.3 1.3 184 1,052 1.3 142 Du Page, IL.............. 37.2 576.6 1.8 140 1,054 2.1 96 Kane, IL................. 13.3 196.8 1.6 158 795 -0.1 243 Lake, IL................. 22.1 331.3 1.5 168 1,156 2.2 87 McHenry, IL.............. 8.7 96.0 0.9 222 744 0.0 234 McLean, IL............... 3.8 86.9 1.1 204 926 7.2 3 Madison, IL.............. 6.0 94.4 -2.0 326 742 1.1 163 Peoria, IL............... 4.7 103.9 1.5 168 869 3.1 43 St. Clair, IL............ 5.6 92.5 -2.0 326 735 0.0 234 Sangamon, IL............. 5.3 130.2 -1.1 321 928 1.2 153 Will, IL................. 15.2 205.8 1.0 210 794 -0.3 254 Winnebago, IL............ 6.8 126.8 0.4 262 774 3.5 30 Allen, IN................ 8.9 176.5 0.7 235 734 -2.1 312 Elkhart, IN.............. 4.8 112.4 5.6 3 747 2.8 61 Hamilton, IN............. 8.5 114.5 0.7 235 840 2.8 61 Lake, IN................. 10.4 190.4 1.8 140 846 4.8 10 Marion, IN............... 24.0 565.7 3.6 27 905 1.3 142 St. Joseph, IN........... 6.0 116.2 0.4 262 751 2.7 66 Tippecanoe, IN........... 3.3 78.8 4.6 11 776 0.0 234 Vanderburgh, IN.......... 4.8 104.9 -1.0 317 728 -1.1 292 Johnson, IA.............. 3.6 78.0 1.7 151 826 2.9 53 Linn, IA................. 6.3 128.9 1.0 210 846 1.1 163 Polk, IA................. 15.1 275.0 2.7 72 882 1.0 170 Scott, IA................ 5.2 89.5 1.8 140 738 3.9 22 Johnson, KS.............. 21.0 313.3 3.4 34 929 2.0 101 Sedgwick, KS............. 12.3 240.6 1.2 196 818 0.2 221 Shawnee, KS.............. 4.8 94.7 0.6 246 771 -1.0 289 Wyandotte, KS............ 3.2 85.4 3.6 27 839 -1.4 302 Fayette, KY.............. 9.5 178.5 1.7 151 808 -1.6 308 Jefferson, KY............ 22.5 427.9 2.1 107 895 1.8 111 Caddo, LA................ 7.6 120.5 -0.3 298 767 0.5 205 Calcasieu, LA............ 4.9 84.9 1.6 158 764 1.3 142 East Baton Rouge, LA..... 15.0 254.7 2.7 72 855 3.1 43 Jefferson, LA............ 14.0 191.5 -1.0 317 824 0.5 205 Lafayette, LA............ 9.2 139.3 4.8 8 891 4.2 13 Orleans, LA.............. 11.4 175.7 3.1 51 902 -2.7 316 St. Tammany, LA.......... 7.6 79.2 1.2 196 740 -1.2 294 Cumberland, ME........... 12.7 174.3 1.8 140 807 0.9 182 Anne Arundel, MD......... 14.6 242.4 3.6 27 958 -0.8 285 Baltimore, MD............ 21.2 366.1 1.1 204 917 1.8 111 Frederick, MD............ 6.2 94.2 0.5 253 889 2.7 66 Harford, MD.............. 5.6 88.2 3.2 44 917 2.8 61 Howard, MD............... 9.2 162.0 2.7 72 1,106 2.7 66 Montgomery, MD........... 33.1 455.8 1.4 176 1,222 1.2 153 Prince Georges, MD....... 15.6 302.6 0.0 288 979 -0.4 262 Baltimore City, MD....... 13.9 329.9 0.1 280 1,020 -1.4 302 Barnstable, MA........... 8.9 101.1 3.3 41 758 0.4 211 Bristol, MA.............. 16.0 214.1 0.1 280 826 -1.3 299 Essex, MA................ 21.4 312.1 1.8 140 953 -3.0 319 Hampden, MA.............. 15.3 201.0 1.6 158 832 2.2 87 Middlesex, MA............ 48.8 833.8 2.1 107 1,342 -3.2 321 Norfolk, MA.............. 23.3 325.5 2.0 122 1,055 0.9 182 Plymouth, MA............. 13.9 181.4 3.1 51 867 -0.7 282 Suffolk, MA.............. 23.2 598.1 2.0 122 1,381 -0.7 282 Worcester, MA............ 21.3 320.6 0.9 222 910 0.0 234 Genesee, MI.............. 7.2 130.1 0.3 266 741 0.7 196 Ingham, MI............... 6.3 153.7 -0.4 305 839 1.7 120 Kalamazoo, MI............ 5.3 110.3 0.9 222 814 1.9 108 Kent, MI................. 14.0 337.9 4.4 14 801 1.6 128 Macomb, MI............... 17.1 294.9 1.5 168 916 3.3 33 Oakland, MI.............. 37.9 667.5 3.4 34 1,003 1.3 142 Ottawa, MI............... 5.5 110.2 2.4 86 744 2.8 61 Saginaw, MI.............. 4.2 83.4 0.1 280 727 1.0 170 Washtenaw, MI............ 8.1 192.6 3.4 34 964 3.1 43 Wayne, MI................ 31.4 690.2 1.6 158 975 1.5 133 Anoka, MN................ 7.2 112.1 1.9 130 868 0.8 192 Dakota, MN............... 9.9 176.2 1.3 184 880 -0.9 288 Hennepin, MN............. 41.4 850.1 2.1 107 1,120 0.3 215 Olmsted, MN.............. 3.5 92.1 2.8 64 1,031 1.7 120 Ramsey, MN............... 14.1 320.5 0.8 230 1,003 0.9 182 St. Louis, MN............ 5.6 95.0 -0.7 311 726 -3.2 321 Stearns, MN.............. 4.4 81.5 1.8 140 722 3.3 33 Harrison, MS............. 4.4 84.1 0.3 266 670 0.3 215 Hinds, MS................ 5.9 120.7 -1.1 321 793 1.9 108 Boone, MO................ 4.5 86.6 2.9 62 714 2.6 74 Clay, MO................. 5.1 87.4 -2.0 326 816 1.5 133 Greene, MO............... 8.0 154.0 3.6 27 695 2.2 87 Jackson, MO.............. 18.6 349.0 1.0 210 920 3.1 43 St. Charles, MO.......... 8.3 127.1 1.3 184 738 3.8 24 St. Louis, MO............ 32.1 567.5 -0.3 298 956 3.7 25 St. Louis City, MO....... 9.3 217.3 1.0 210 953 -3.1 320 Yellowstone, MT.......... 6.0 78.9 2.1 107 766 4.4 12 Douglas, NE.............. 17.3 318.6 1.7 151 810 -0.4 262 Lancaster, NE............ 9.3 157.8 2.1 107 732 1.5 133 Clark, NV................ 48.5 822.0 1.9 130 807 0.1 228 Washoe, NV............... 13.5 185.4 0.7 235 809 0.1 228 Hillsborough, NH......... 12.0 190.5 1.4 176 977 -1.2 294 Rockingham, NH........... 10.6 139.1 1.9 130 848 -0.5 269 Atlantic, NJ............. 6.7 145.6 3.4 34 765 -2.3 313 Bergen, NJ............... 33.1 433.6 1.4 176 1,127 3.6 28 Burlington, NJ........... 10.9 196.6 1.0 210 963 1.8 111 Camden, NJ............... 12.1 195.1 0.4 262 899 0.7 196 Essex, NJ................ 20.4 339.5 0.7 235 1,096 -2.6 315 Gloucester, NJ........... 6.1 99.2 0.5 253 789 -1.4 302 Hudson, NJ............... 13.8 233.8 1.4 176 1,233 0.0 234 Mercer, NJ............... 10.9 232.8 1.1 204 1,155 -2.8 317 Middlesex, NJ............ 21.7 387.6 2.4 86 1,068 -2.4 314 Monmouth, NJ............. 19.9 252.6 0.2 278 905 -1.8 310 Morris, NJ............... 17.2 275.8 0.7 235 1,266 1.3 142 Ocean, NJ................ 12.2 159.2 1.9 130 739 0.8 192 Passaic, NJ.............. 12.2 173.0 0.6 246 928 0.2 221 Somerset, NJ............. 10.0 175.6 1.0 210 1,345 2.6 74 Union, NJ................ 14.3 221.7 0.7 235 1,130 0.1 228 Bernalillo, NM........... 17.6 309.8 -0.4 305 799 2.2 87 Albany, NY............... 10.0 221.9 1.2 196 929 -0.3 254 Bronx, NY................ 17.1 237.2 0.5 253 868 -0.1 243 Broome, NY............... 4.5 91.7 -0.1 291 733 1.4 140 Dutchess, NY............. 8.2 111.6 -0.3 298 960 1.1 163 Erie, NY................. 23.8 460.0 0.6 246 793 1.8 111 Kings, NY................ 52.9 522.7 2.8 64 736 -0.4 262 Monroe, NY............... 18.2 380.1 0.6 246 862 0.9 182 Nassau, NY............... 52.6 603.4 1.4 176 1,042 0.6 200 New York, NY............. 122.7 2,392.0 2.4 86 1,646 0.2 221 Oneida, NY............... 5.3 107.2 -0.6 308 741 1.0 170 Onondaga, NY............. 12.9 243.5 -0.4 305 849 3.2 37 Orange, NY............... 9.9 133.5 0.2 278 807 -0.1 243 Queens, NY............... 47.1 521.6 2.2 100 846 -0.1 243 Richmond, NY............. 9.0 92.5 -0.1 291 770 -0.5 269 Rockland, NY............. 10.0 117.0 0.5 253 989 -0.6 276 Saratoga, NY............. 5.6 80.7 3.1 51 815 1.6 128 Suffolk, NY.............. 50.8 641.9 1.0 210 974 -0.6 276 Westchester, NY.......... 36.1 413.8 0.1 280 1,195 -0.8 285 Buncombe, NC............. 8.1 112.9 1.9 130 681 1.5 133 Catawba, NC.............. 4.4 79.2 0.8 230 686 1.3 142 Cumberland, NC........... 6.3 119.1 -0.7 311 739 -1.3 299 Durham, NC............... 7.4 185.7 2.2 100 1,180 -3.6 325 Forsyth, NC.............. 9.0 173.6 1.6 158 811 -0.5 269 Guilford, NC............. 14.2 259.5 -0.2 296 783 0.8 192 Mecklenburg, NC.......... 33.3 562.0 2.7 72 1,000 0.5 205 New Hanover, NC.......... 7.4 96.8 0.3 266 738 1.2 153 Wake, NC................. 29.8 459.5 3.1 51 890 0.9 182 Cass, ND................. 6.1 107.9 4.6 11 789 2.7 66 Butler, OH............... 7.4 139.6 1.3 184 789 0.8 192 Cuyahoga, OH............. 35.6 706.1 1.9 130 916 2.2 87 Delaware, OH............. 4.4 81.4 5.4 5 881 0.9 182 Franklin, OH............. 29.6 672.1 2.5 83 935 5.6 8 Hamilton, OH............. 23.2 494.7 1.6 158 970 1.0 170 Lake, OH................. 6.4 95.9 0.9 222 760 0.5 205 Lorain, OH............... 6.0 97.1 3.0 57 751 2.9 53 Lucas, OH................ 10.1 203.5 2.8 64 804 5.0 9 Mahoning, OH............. 5.9 97.9 2.2 100 651 0.6 200 Montgomery, OH........... 12.1 246.0 1.0 210 788 0.4 211 Stark, OH................ 8.8 155.0 1.9 130 688 0.0 234 Summit, OH............... 14.3 258.5 1.2 196 803 1.6 128 Oklahoma, OK............. 24.9 432.3 2.8 64 832 0.1 228 Tulsa, OK................ 20.5 335.7 1.6 158 837 2.7 66 Clackamas, OR............ 12.7 141.3 2.4 86 847 1.3 142 Lane, OR................. 10.8 138.8 0.4 262 712 1.1 163 Marion, OR............... 9.4 134.3 1.1 204 730 0.7 196 Multnomah, OR............ 29.8 442.3 2.1 107 920 -0.3 254 Washington, OR........... 16.5 252.2 2.6 81 1,122 8.5 1 Allegheny, PA............ 35.6 693.5 1.0 210 966 2.0 101 Berks, PA................ 9.0 164.4 0.1 280 812 0.5 205 Bucks, PA................ 19.7 253.2 0.3 266 878 2.1 96 Butler, PA............... 4.9 84.6 0.5 253 829 -1.2 294 Chester, PA.............. 15.1 239.1 0.0 288 1,158 -0.1 243 Cumberland, PA........... 6.1 125.5 1.6 158 853 2.3 82 Dauphin, PA.............. 7.5 178.3 -0.6 308 890 1.0 170 Delaware, PA............. 13.9 212.9 1.4 176 962 1.2 153 Erie, PA................. 7.7 126.7 0.3 266 722 1.7 120 Lackawanna, PA........... 5.9 96.7 -1.2 323 685 -0.4 262 Lancaster, PA............ 12.7 222.9 1.0 210 749 1.2 153 Lehigh, PA............... 8.7 178.3 0.5 253 885 2.7 66 Luzerne, PA.............. 7.8 139.8 -0.3 298 711 2.0 101 Montgomery, PA........... 27.5 471.9 1.5 168 1,111 2.4 80 Northampton, PA.......... 6.6 103.6 0.9 222 787 1.0 170 Philadelphia, PA......... 35.4 629.4 -0.1 291 1,070 4.1 17 Washington, PA........... 5.6 87.3 2.4 86 887 7.8 2 Westmoreland, PA......... 9.5 135.7 0.7 235 726 0.3 215 York, PA................. 9.1 171.3 -0.3 298 781 -1.1 292 Providence, RI........... 17.3 271.6 0.6 246 888 -1.0 289 Charleston, SC........... 11.9 219.9 3.4 34 773 -1.2 294 Greenville, SC........... 12.1 235.8 2.5 83 789 0.3 215 Horry, SC................ 7.6 118.3 0.3 266 532 1.3 142 Lexington, SC............ 5.6 96.9 2.7 72 687 3.9 22 Richland, SC............. 8.9 204.1 0.6 246 802 3.1 43 Spartanburg, SC.......... 5.8 114.8 3.2 44 801 2.2 87 Minnehaha, SD............ 6.6 118.3 2.7 72 763 3.2 37 Davidson, TN............. 18.2 429.2 2.4 86 950 5.8 7 Hamilton, TN............. 8.4 186.0 2.2 100 798 1.8 111 Knox, TN................. 10.9 218.8 0.7 235 778 1.7 120 Rutherford, TN........... 4.4 101.9 4.9 7 825 4.0 20 Shelby, TN............... 19.0 472.6 2.1 107 949 3.2 37 Williamson, TN........... 6.2 98.1 5.5 4 959 -1.4 302 Bell, TX................. 4.9 108.7 1.3 184 738 1.0 170 Bexar, TX................ 35.1 751.1 2.0 122 799 0.1 228 Brazoria, TX............. 5.0 93.3 4.1 19 899 4.2 13 Brazos, TX............... 4.0 86.3 2.6 81 689 1.5 133 Cameron, TX.............. 6.4 129.9 1.4 176 571 0.2 221 Collin, TX............... 19.2 310.6 4.1 19 1,048 -0.4 262 Dallas, TX............... 68.9 1,475.1 3.2 44 1,074 2.0 101 Denton, TX............... 11.5 186.7 3.7 24 794 1.0 170 El Paso, TX.............. 14.0 277.3 1.3 184 653 0.6 200 Fort Bend, TX............ 9.8 144.1 4.6 11 908 2.5 79 Galveston, TX............ 5.4 98.1 1.3 184 815 0.2 221 Gregg, TX................ 4.2 78.7 4.8 8 837 1.8 111 Harris, TX............... 103.2 2,121.7 3.8 22 1,165 4.1 17 Hidalgo, TX.............. 11.4 228.0 0.8 230 583 2.3 82 Jefferson, TX............ 5.8 123.2 0.5 253 929 6.3 6 Lubbock, TX.............. 7.1 125.6 1.0 210 689 0.6 200 McLennan, TX............. 4.9 101.4 0.3 266 744 3.3 33 Montgomery, TX........... 9.1 142.2 5.7 2 867 3.6 28 Nueces, TX............... 7.9 156.8 3.3 41 804 4.7 11 Smith, TX................ 5.7 93.9 0.8 230 767 1.2 153 Tarrant, TX.............. 38.6 784.7 2.4 86 895 -0.2 252 Travis, TX............... 32.1 605.7 3.4 34 1,009 3.7 25 Webb, TX................. 4.9 91.1 2.7 72 635 3.1 43 Williamson, TX........... 8.0 135.2 3.2 44 860 -17.0 328 Davis, UT................ 7.3 110.1 3.2 44 723 -0.6 276 Salt Lake, UT............ 37.6 591.7 4.0 21 855 2.6 74 Utah, UT................. 12.9 178.8 5.0 6 706 -1.3 299 Weber, UT................ 5.4 90.8 1.8 140 692 3.0 51 Chittenden, VT........... 6.1 97.9 1.8 140 918 2.9 53 Arlington, VA............ 8.5 167.3 -0.8 313 1,493 -3.8 326 Chesterfield, VA......... 7.8 118.3 2.1 107 813 1.6 128 Fairfax, VA.............. 35.1 598.1 2.1 107 1,422 -0.4 262 Henrico, VA.............. 10.2 179.4 2.8 64 896 1.2 153 Loudoun, VA.............. 10.0 144.2 3.7 24 1,076 2.9 53 Prince William, VA....... 8.0 115.2 3.5 33 812 1.1 163 Alexandria City, VA...... 6.2 95.9 1.6 158 1,293 2.8 61 Chesapeake City, VA...... 5.7 96.2 0.0 288 741 4.2 13 Newport News City, VA.... 3.8 94.7 -0.8 313 861 1.8 111 Norfolk City, VA......... 5.6 138.6 0.3 266 877 -0.1 243 Richmond City, VA........ 7.1 148.5 0.3 266 966 -1.6 308 Virginia Beach City, VA.. 11.4 169.8 1.0 210 706 -1.5 307 Benton, WA............... 5.7 83.0 -1.7 325 922 -3.4 324 Clark, WA................ 13.5 131.5 1.8 140 826 2.1 96 King, WA................. 82.2 1,174.4 3.0 57 1,167 2.9 53 Kitsap, WA............... 6.7 81.2 -0.6 308 823 -4.2 327 Pierce, WA............... 21.7 265.3 0.8 230 837 1.7 120 Snohomish, WA............ 19.2 258.2 3.1 51 974 2.2 87 Spokane, WA.............. 15.9 200.6 0.3 266 764 1.2 153 Thurston, WA............. 7.5 97.6 -0.2 296 818 -0.6 276 Whatcom, WA.............. 6.9 81.4 1.7 151 777 3.5 30 Yakima, WA............... 8.8 110.5 8.2 1 617 1.1 163 Kanawha, WV.............. 6.0 105.5 0.5 253 814 2.1 96 Brown, WI................ 6.5 149.3 0.5 253 779 3.2 37 Dane, WI................. 14.1 308.6 2.1 107 871 -0.1 243 Milwaukee, WI............ 23.1 473.3 1.2 196 877 -0.6 276 Outagamie, WI............ 5.1 104.4 1.7 151 752 1.2 153 Waukesha, WI............. 12.6 231.7 0.6 246 895 3.1 43 Winnebago, WI............ 3.6 90.1 0.3 266 839 2.7 66 San Juan, PR............. 11.4 264.2 2.8 (7) 596 -0.3 (7) (1) Includes workers covered by Unemployment Insurance (UI) and Unemployment Compensation for Federal Employees (UCFE) programs. These 328 U.S. counties comprise 70.9 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) This county was not included in the U.S. rankings.
Table 2. Covered(1) establishments, employment, and wages in the 10 largest counties, second quarter 2012(2) Employment Average weekly wage(3) Establishments, second quarter County by NAICS supersector 2012 Percent Percent (thousands) June change, Second change, 2012 June quarter second (thousands) 2011-12(4) 2012 quarter 2011-12(4) United States(5)............................. 9,224.5 132,896.0 1.8 $903 1.3 Private industry........................... 8,928.1 111,708.5 2.4 891 1.9 Natural resources and mining............. 130.2 2,120.8 7.1 996 3.8 Construction............................. 748.7 5,726.3 1.7 966 3.3 Manufacturing............................ 335.3 11,996.6 2.0 1,111 1.6 Trade, transportation, and utilities..... 1,883.2 25,240.5 1.7 768 2.1 Information.............................. 143.8 2,686.3 0.0 1,437 2.8 Financial activities..................... 809.7 7,540.1 1.3 1,320 2.6 Professional and business services....... 1,589.9 17,985.5 3.7 1,153 1.7 Education and health services............ 926.8 19,330.2 2.0 847 1.4 Leisure and hospitality.................. 769.1 14,307.6 3.5 374 2.5 Other services........................... 1,375.6 4,552.6 1.7 576 2.1 Government................................. 296.4 21,187.6 -1.2 964 -1.0 Los Angeles, CA.............................. 452.9 3,961.9 1.6 1,006 1.3 Private industry........................... 447.3 3,414.8 2.3 977 1.7 Natural resources and mining............. 0.4 9.7 0.6 1,287 5.8 Construction............................. 12.1 109.3 3.4 1,046 4.2 Manufacturing............................ 12.6 367.7 -0.2 1,067 -1.5 Trade, transportation, and utilities..... 50.8 750.7 1.6 826 2.7 Information.............................. 8.3 187.1 -1.8 1,749 3.3 Financial activities..................... 21.9 210.6 1.1 1,459 2.7 Professional and business services....... 41.9 569.4 4.2 1,222 1.2 Education and health services............ 29.6 527.2 2.2 958 2.0 Leisure and hospitality.................. 27.3 420.2 5.4 543 -0.5 Other services........................... 217.6 242.8 -1.3 457 4.6 Government................................. 5.6 547.0 -2.1 1,187 0.3 Cook, IL..................................... 148.8 2,428.3 1.3 1,052 1.3 Private industry........................... 147.4 2,128.9 1.7 1,034 1.2 Natural resources and mining............. 0.1 0.8 -5.4 955 6.9 Construction............................. 12.4 64.9 -2.8 1,241 1.5 Manufacturing............................ 6.6 194.9 0.3 1,100 1.1 Trade, transportation, and utilities..... 28.9 441.8 0.6 826 0.6 Information.............................. 2.7 54.3 0.0 1,535 2.1 Financial activities..................... 15.6 185.2 -0.5 1,812 1.0 Professional and business services....... 31.4 425.7 3.6 1,328 1.8 Education and health services............ 15.6 410.4 2.2 881 2.2 Leisure and hospitality.................. 13.2 249.5 3.2 469 1.3 Other services........................... 16.4 98.1 1.1 777 1.8 Government................................. 1.4 299.4 -1.2 1,174 1.9 New York, NY................................. 122.7 2,392.0 2.4 1,646 0.2 Private industry........................... 122.4 1,956.8 3.0 1,769 0.2 Natural resources and mining............. 0.0 0.1 3.5 1,652 -7.7 Construction............................. 2.1 31.3 2.6 1,621 0.0 Manufacturing............................ 2.4 26.3 -0.6 1,202 -2.3 Trade, transportation, and utilities..... 20.7 249.8 3.1 1,233 0.2 Information.............................. 4.3 143.8 4.1 2,046 2.3 Financial activities..................... 18.8 355.2 -0.5 3,249 1.5 Professional and business services....... 25.3 488.0 3.1 2,025 0.5 Education and health services............ 9.2 303.5 1.2 1,120 0.1 Leisure and hospitality.................. 12.9 257.2 7.0 763 0.3 Other services........................... 18.9 92.6 3.5 1,022 5.3 Government................................. 0.3 435.3 0.0 1,101 -1.2 Harris, TX................................... 103.2 2,121.7 3.8 1,165 4.1 Private industry........................... 102.6 1,869.5 4.8 1,191 4.4 Natural resources and mining............. 1.7 87.9 9.4 2,933 -4.0 Construction............................. 6.5 139.9 5.5 1,143 4.2 Manufacturing............................ 4.5 189.4 7.1 1,415 2.7 Trade, transportation, and utilities..... 23.2 441.0 3.6 1,141 13.4 Information.............................. 1.3 28.0 -0.6 1,337 4.0 Financial activities..................... 10.7 114.2 2.3 1,423 3.0 Professional and business services....... 20.6 357.6 6.0 1,374 2.4 Education and health services............ 11.7 251.5 3.5 898 0.4 Leisure and hospitality.................. 8.5 195.8 5.2 398 1.5 Other services........................... 13.7 62.9 1.3 660 3.0 Government................................. 0.6 252.2 -2.7 979 0.5 Maricopa, AZ................................. 95.5 1,635.4 2.8 905 2.6 Private industry........................... 94.8 1,456.7 3.0 890 2.7 Natural resources and mining............. 0.5 7.8 -5.6 828 10.0 Construction............................. 8.0 86.0 4.0 941 5.4 Manufacturing............................ 3.2 113.7 3.2 1,329 -0.6 Trade, transportation, and utilities..... 21.5 338.2 1.7 838 2.4 Information.............................. 1.6 28.5 2.6 1,123 2.6 Financial activities..................... 10.9 141.4 2.8 1,107 3.8 Professional and business services....... 22.2 270.6 3.2 953 3.8 Education and health services............ 10.5 243.0 3.2 927 1.8 Leisure and hospitality.................. 7.2 175.5 2.8 419 4.5 Other services........................... 6.6 47.3 -0.5 606 2.9 Government................................. 0.7 178.7 1.5 1,014 2.1 Dallas, TX................................... 68.9 1,475.1 3.2 1,074 2.0 Private industry........................... 68.4 1,313.4 3.9 1,082 2.4 Natural resources and mining............. 0.6 9.9 12.9 3,563 15.4 Construction............................. 4.0 70.2 3.3 1,003 4.3 Manufacturing............................ 2.8 112.7 0.9 1,294 6.0 Trade, transportation, and utilities..... 14.9 294.5 3.6 992 2.0 Information.............................. 1.5 46.4 1.6 1,615 0.8 Financial activities..................... 8.5 143.0 3.2 1,446 3.8 Professional and business services....... 15.2 286.8 6.9 1,188 1.1 Education and health services............ 7.5 172.7 2.8 964 -1.7 Leisure and hospitality.................. 5.8 135.7 3.9 446 0.7 Other services........................... 7.2 41.0 1.7 677 2.0 Government................................. 0.5 161.7 -1.9 1,011 -1.0 Orange, CA................................... 106.6 1,416.5 2.4 1,014 1.3 Private industry........................... 105.3 1,271.5 2.9 1,000 1.6 Natural resources and mining............. 0.2 3.5 -12.8 736 12.5 Construction............................. 6.0 70.7 0.7 1,126 4.4 Manufacturing............................ 4.8 158.6 1.4 1,236 0.3 Trade, transportation, and utilities..... 16.1 245.1 0.8 948 1.8 Information.............................. 1.2 24.0 -1.9 1,390 -1.5 Financial activities..................... 9.6 107.6 2.7 1,501 2.6 Professional and business services....... 18.7 260.2 5.4 1,140 0.2 Education and health services............ 10.6 161.5 1.8 939 3.1 Leisure and hospitality.................. 7.2 183.9 5.2 445 4.5 Other services........................... 23.1 50.1 2.4 563 5.4 Government................................. 1.4 145.0 -1.6 1,135 -0.8 San Diego, CA................................ 102.8 1,283.3 2.1 989 0.9 Private industry........................... 101.4 1,063.1 2.7 966 2.1 Natural resources and mining............. 0.7 10.9 9.0 634 6.2 Construction............................. 5.8 57.6 2.9 1,048 2.5 Manufacturing............................ 2.9 93.6 -0.8 1,355 1.7 Trade, transportation, and utilities..... 13.5 205.4 1.8 802 3.6 Information.............................. 1.1 24.5 0.3 1,479 0.2 Financial activities..................... 8.4 70.0 2.9 1,188 2.9 Professional and business services....... 16.1 216.4 3.3 1,379 1.6 Education and health services............ 8.7 155.7 2.5 939 3.0 Leisure and hospitality.................. 7.1 163.7 3.7 414 2.7 Other services........................... 30.2 59.5 2.7 503 -1.6 Government................................. 1.4 220.2 -0.6 1,099 -3.6 King, WA..................................... 82.2 1,174.4 3.0 1,167 2.9 Private industry........................... 81.7 1,015.7 3.6 1,171 3.2 Natural resources and mining............. 0.3 3.0 7.5 1,372 -7.5 Construction............................. 5.3 49.3 6.2 1,143 1.2 Manufacturing............................ 2.2 103.3 5.6 1,417 -0.3 Trade, transportation, and utilities..... 14.3 214.6 3.7 1,019 2.9 Information.............................. 1.7 81.8 1.6 2,243 9.4 Financial activities..................... 6.2 63.4 0.7 1,383 0.9 Professional and business services....... 13.7 191.2 5.5 1,434 2.0 Education and health services............ 7.2 138.5 2.7 968 4.5 Leisure and hospitality.................. 6.4 117.1 3.0 444 2.8 Other services........................... 24.3 53.6 0.4 606 4.7 Government................................. 0.5 158.7 -0.3 1,143 1.2 Miami-Dade, FL............................... 88.9 974.6 2.3 876 -0.5 Private industry........................... 88.5 851.4 3.1 832 -0.1 Natural resources and mining............. 0.5 7.5 2.1 533 1.9 Construction............................. 5.0 29.6 -3.1 808 -6.6 Manufacturing............................ 2.6 35.6 -2.1 795 -1.4 Trade, transportation, and utilities..... 25.8 255.4 3.5 780 -0.3 Information.............................. 1.5 17.1 0.3 1,365 -8.2 Financial activities..................... 9.1 67.3 5.4 1,241 -1.7 Professional and business services....... 18.6 126.0 2.3 1,047 1.7 Education and health services............ 9.9 157.6 2.1 856 0.8 Leisure and hospitality.................. 6.8 118.4 5.9 505 4.8 Other services........................... 7.9 35.7 4.3 540 1.5 Government................................. 0.4 123.2 -3.1 1,156 -0.3 (1) Includes workers covered by Unemployment Insurance (UI) and Unemployment Compensation for Federal Employees (UCFE) programs. (2) Data are preliminary. Counties selected are based on 2011 annual average employment. (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.
Table 3. Covered(1) establishments, employment, and wages by state, second quarter 2012(2) Employment Average weekly wage(3) Establishments, second quarter State 2012 Percent Percent (thousands) June change, Second change, 2012 June quarter second (thousands) 2011-12 2012 quarter 2011-12 United States(4)......... 9,224.5 132,896.0 1.8 $903 1.3 Alabama.................. 116.1 1,841.7 0.9 783 2.0 Alaska................... 21.8 342.9 2.1 955 1.5 Arizona.................. 147.3 2,393.9 2.6 862 2.1 Arkansas................. 85.4 1,157.4 1.1 717 2.1 California............... 1,434.5 15,045.8 2.4 1,034 1.8 Colorado................. 171.4 2,291.8 2.5 918 2.0 Connecticut.............. 111.3 1,650.0 1.2 1,111 -0.4 Delaware................. 27.6 409.3 0.2 948 2.4 District of Columbia..... 35.5 717.9 0.9 1,544 0.3 Florida.................. 606.9 7,233.7 2.0 805 0.4 Georgia.................. 269.5 3,854.7 1.4 848 1.9 Hawaii................... 38.4 603.7 2.1 812 1.8 Idaho.................... 53.5 626.1 1.5 673 0.9 Illinois................. 391.4 5,698.0 1.1 953 1.6 Indiana.................. 160.5 2,832.6 2.3 763 1.9 Iowa..................... 95.2 1,502.7 1.5 743 2.5 Kansas................... 84.6 1,334.4 1.7 763 1.1 Kentucky................. 109.8 1,780.7 1.6 772 1.6 Louisiana................ 129.2 1,877.2 1.6 806 1.5 Maine.................... 49.4 601.8 1.2 719 1.0 Maryland................. 166.9 2,550.2 1.5 992 0.7 Massachusetts............ 219.0 3,301.5 1.9 1,109 -1.2 Michigan................. 239.4 3,984.0 2.1 859 1.7 Minnesota................ 168.7 2,695.1 1.5 907 1.1 Mississippi.............. 68.6 1,087.4 0.6 681 2.9 Missouri................. 176.9 2,629.1 0.4 791 2.2 Montana.................. 42.4 442.0 2.0 700 2.6 Nebraska................. 66.7 930.9 2.0 719 0.7 Nevada................... 72.6 1,141.7 1.6 815 -0.1 New Hampshire............ 48.9 623.8 1.4 891 0.3 New Jersey............... 262.3 3,884.0 1.4 1,056 0.0 New Mexico............... 55.1 791.9 0.4 783 2.6 New York................. 603.3 8,701.2 1.5 1,096 0.4 North Carolina........... 259.0 3,919.1 1.5 787 0.5 North Dakota............. 29.2 420.3 9.9 854 11.1 Ohio..................... 287.4 5,104.0 1.9 817 2.8 Oklahoma................. 104.4 1,543.4 1.9 768 2.7 Oregon................... 133.1 1,663.9 1.6 837 2.3 Pennsylvania............. 352.3 5,645.9 0.7 893 2.1 Rhode Island............. 35.3 463.1 0.9 859 -0.3 South Carolina........... 111.5 1,830.7 1.5 736 1.4 South Dakota............. 31.3 412.8 1.9 677 3.2 Tennessee................ 141.0 2,669.1 2.0 816 2.8 Texas.................... 592.9 10,779.5 3.0 922 2.6 Utah..................... 84.8 1,225.8 3.6 766 1.3 Vermont.................. 24.7 300.2 1.0 792 2.6 Virginia................. 238.1 3,659.9 1.2 952 0.3 Washington............... 234.6 2,948.3 2.4 947 2.2 West Virginia............ 49.3 712.3 1.4 776 1.4 Wisconsin................ 160.0 2,749.7 1.4 778 1.4 Wyoming.................. 25.5 288.9 1.6 842 2.7 Puerto Rico.............. 49.2 933.3 1.8 499 0.6 Virgin Islands........... 3.5 40.2 -8.6 819 9.8 (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.