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For release 10:00 a.m. (EDT), Thursday, June 27, 2013 USDL-13-1244 Technical Information: (202) 691-6567 * QCEWInfo@bls.gov * www.bls.gov/cew Media Contact: (202) 691-5902 * PressOffice@bls.gov County Employment and Wages Fourth Quarter 2012 From December 2011 to December 2012, employment increased in 287 of the 328 largest U.S. counties, the U.S. Bureau of Labor Statistics reported today. Elkhart, Ind., posted the largest increase, with a gain of 7.4 percent over the year, compared with national job growth of 1.9 percent. Within Elkhart, the largest employment increase occurred in manufacturing, which gained 5,479 jobs over the year (11.6 percent). Sangamon, Ill., had the largest over-the-year decrease in employment among the largest counties in the U.S. with a loss of 2.5 percent. County employment and wage data are compiled under the Quarterly Census of Employment and Wages (QCEW) program, which produces detailed information on county employment and wages within 7 months after the end of each quarter. The U.S. average weekly wage increased over the year by 4.7 percent to $1,000 in the fourth quarter of 2012. San Mateo, Calif., had the largest over-the-year increase in average weekly wages with a gain of 107.3 percent. Within San Mateo, a total wage gain of $6.9 billion (379.6 percent) in professional and business services had the largest contribution to the increase in average weekly wages. Lake, Ohio, experienced the largest decrease in average weekly wages with a loss of 3.2 percent over the year. Table A. Large counties ranked by December 2012 employment, December 2011-12 employment increase, and December 2011-12 percent increase in employment -------------------------------------------------------------------------------------------------------- Employment in large counties -------------------------------------------------------------------------------------------------------- December 2012 employment | Increase in employment, | Percent increase in employment, (thousands) | December 2011-12 | December 2011-12 | (thousands) | -------------------------------------------------------------------------------------------------------- | | United States 133,726.8| United States 2,440.6| United States 1.9 -------------------------------------------------------------------------------------------------------- | | Los Angeles, Calif. 4,082.2| Harris, Texas 82.2| Elkhart, Ind. 7.4 Cook, Ill. 2,441.2| Los Angeles, Calif. 74.2| Lexington, S.C. 6.9 New York, N.Y. 2,437.9| New York, N.Y. 50.2| Rutherford, Tenn. 6.4 Harris, Texas 2,160.8| Dallas, Texas 49.6| Utah, Utah 6.0 Maricopa, Ariz. 1,721.1| Maricopa, Ariz. 46.0| Montgomery, Texas 5.7 Dallas, Texas 1,499.2| Orange, Calif. 37.9| Fort Bend, Texas 5.3 Orange, Calif. 1,436.6| King, Wash. 34.5| Douglas, Colo. 5.1 San Diego, Calif. 1,302.0| Santa Clara, Calif. 33.0| Collin, Texas 4.8 King, Wash. 1,185.3| San Diego, Calif. 29.2| Brazos, Texas 4.4 Miami-Dade, Fla. 1,020.6| Cook, Ill. 28.9| Travis, Texas 4.3 | | Salt Lake, Utah 4.3 | | -------------------------------------------------------------------------------------------------------- Large County Employment In December 2012, national employment, as measured by the QCEW program, was 133.7 million, up by 1.9 percent or 2.4 million from December 2011. The 328 U.S. counties with 75,000 or more jobs accounted for 71.3 percent of total U.S. employment and 77.0 percent of total wages. These 328 counties had a net job growth of 1.8 million over the year, accounting for 73.3 percent of the overall U.S. employment increase. Elkhart, Ind., had the largest percentage increase in employment (7.4 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 employment gain of 302,200, which was 12.4 percent of the overall job increase for the U.S. (See table A.) Employment declined in 38 of the large counties from December 2011 to December 2012. Sangamon, Ill., had the largest over-the-year percentage decrease in employment (-2.5 percent). Within Sangamon, public administration within state government had the largest decrease in employment with a loss of 1,067 jobs (-2.9 percent). Caddo, La., had the second largest percentage decrease in employment, followed by Jefferson, Texas. Two counties, Vanderburgh, Ind., and Benton, Wash., tied for the fourth largest percentage decrease. (See table 1.) Table B. Large counties ranked by fourth quarter 2012 average weekly wages, fourth quarter 2011-12 increase in average weekly wages, and fourth 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 fourth quarter 2012 | wage, fourth quarter 2011-12 | weekly wage, fourth | | quarter 2011-12 -------------------------------------------------------------------------------------------------------- | | United States $1,000| United States $45| United States 4.7 -------------------------------------------------------------------------------------------------------- | | San Mateo, Calif. $3,240| San Mateo, Calif. $1,677| San Mateo, Calif. 107.3 New York, N.Y. 2,107| Douglas, Colo. 516| Douglas, Colo. 48.0 Santa Clara, Calif. 1,906| New York, N.Y. 217| Virginia Beach City, Va. 13.3 Suffolk, Mass. 1,724| Suffolk, Mass. 127| Rockingham, N.H. 12.0 Fairfield, Conn. 1,704| San Francisco, Calif. 119| New York, N.Y. 11.5 Washington, D.C. 1,703| Rockingham, N.H. 111| Washington, Pa. 11.5 San Francisco, Calif. 1,694| Fairfield, Conn. 109| McHenry, Ill. 11.2 Arlington, Va. 1,625| Washington, Pa. 105| Utah, Utah 9.4 Douglas, Colo. 1,591| Virginia Beach City, Va. 101| Elkhart, Ind. 8.9 Fairfax, Va. 1,588| Santa Clara, Calif. 91| Yolo, Calif. 8.6 | McHenry, Ill. 91| | Harris, Texas 91| | | -------------------------------------------------------------------------------------------------------- Large County Average Weekly Wages Average weekly wages for the nation increased by 4.7 percent during the year ending in the fourth quarter of 2012. Among the 328 largest counties, 316 had over-the-year increases in average weekly wages. San Mateo, Calif., had the largest wage increase among the largest U.S. counties (107.3 percent). Of the 328 largest counties, 10 experienced over-the-year decreases in average weekly wages. Lake, Ohio, had the largest average weekly wage decrease with a loss of 3.2 percent. Within Lake, total wages in manufacturing declined by $45.3 million (-12.3 percent) over the year. Passaic, N.J., had the second largest decrease in average weekly wages, followed by Genesee, Mich.; Atlantic, N.J.; and Benton, Wash. (See table 1.) Ten Largest U.S. Counties All of the 10 largest counties had over-the-year percentage increases in employment in December 2012. Harris, Texas, had the largest gain (4.0 percent). Within Harris, professional and business services had the largest over-the-year employment level increase among all private industry groups with a gain of 20,112 jobs (5.9 percent). Cook, Ill., had the smallest percentage increase in employment (1.2 percent) among the 10 largest counties. (See table 2.) All of the 10 largest U.S. counties had over-the-year increases in average weekly wages. New York, N.Y., experienced the largest gain in average weekly wages (11.5 percent). Within New York, financial activities had the largest impact on the county’s average weekly wage growth. Within this industry, employment declined by 2,288 (-0.6 percent) while total wages increased by $4.8 billion (25.6 percent). Maricopa, Ariz., had the smallest average weekly wage increase (3.4 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. December 2012 employment and 2012 fourth 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 133.7 million full- and part-time workers. For additional information about the quarterly employment and wages data, please read the Technical Note. Data for the fourth 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 first quarter 2013 is scheduled to be released on Thursday, September 26, 2013. -------------------------------------------------------------------- | | | Hurricane Sandy | | | | Hurricane Sandy made landfall in the United States on October 29, | | 2012, during the QCEW fourth quarter reference period. This event | | did not warrant changes to QCEW methodology. | | | --------------------------------------------------------------------
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 levels were 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- | 557,000 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. Wages measured by QCEW may be subject to periodic and sometimes large fluctuations. This variability may be due to calendar effects resulting from some quarters having more pay dates than others. The effect is most visible in counties with a dominant employer. In particular, this effect has been observed in counties where government employers represent a large fraction of overall employment. Similar calendar effects can result from private sector pay practices. However, these effects are typically less pronounced for two reasons: employment is less concentrated in a single private employer, and private employers use a variety of pay period types (weekly, biweekly, semimonthly, monthly). For example, the effect on over-the-year pay comparisons can be pronounced in federal government due to the uniform nature of federal payroll processing. Most federal employees are paid on a biweekly pay schedule. As a result, in some quarters federal wages include six pay dates, while in other quarters there are seven pay dates. Over- the-year comparisons of average weekly wages may also reflect this calendar effect. Growth in average weekly wages may be attributed, in part, to a comparison of quarterly wages for the current year, which include seven pay dates, with year-ago wages that reflect only six pay dates. An opposite effect will occur when wages in the current quarter reflecting six pay dates are compared with year-ago wages for a quarter including seven pay dates. 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 3-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, fourth quarter 2012(2) Employment Average weekly wage(4) Establishments, County(3) fourth quarter Percent Ranking Percent Ranking 2012 December change, by Fourth change, by (thousands) 2012 December percent quarter fourth percent (thousands) 2011-12(5) change 2012 quarter change 2011-12(5) United States(6)......... 9,205.6 133,726.8 1.9 - $1,000 4.7 - Jefferson, AL............ 17.8 339.3 0.9 223 1,011 5.0 71 Madison, AL.............. 9.0 181.4 1.4 181 1,077 1.5 265 Mobile, AL............... 9.7 165.3 0.1 280 881 0.6 301 Montgomery, AL........... 6.4 128.4 1.0 213 883 0.3 306 Tuscaloosa, AL........... 4.3 86.4 2.3 94 848 2.3 219 Anchorage Borough, AK.... 8.3 153.1 0.6 244 1,046 3.9 119 Maricopa, AZ............. 95.2 1,721.1 2.7 69 964 3.4 150 Pima, AZ................. 19.0 353.5 1.5 172 839 2.1 234 Benton, AR............... 5.6 99.2 1.9 134 900 3.9 119 Pulaski, AR.............. 14.5 246.3 1.0 213 927 6.9 24 Washington, AR........... 5.6 94.6 3.7 23 837 0.8 294 Alameda, CA.............. 54.7 670.7 4.0 17 1,265 3.9 119 Contra Costa, CA......... 29.0 331.8 2.9 59 1,168 2.9 183 Fresno, CA............... 29.2 335.2 1.8 143 777 2.9 183 Kern, CA................. 17.0 295.3 3.0 52 842 2.1 234 Los Angeles, CA.......... 421.5 4,082.2 1.9 134 1,185 6.6 29 Marin, CA................ 11.7 109.0 3.8 20 1,225 3.4 150 Monterey, CA............. 12.4 152.4 3.2 41 809 1.4 271 Orange, CA............... 104.2 1,436.6 2.7 69 1,131 4.4 91 Placer, CA............... 10.9 132.5 2.8 65 979 4.5 85 Riverside, CA............ 49.2 585.6 3.4 33 765 1.5 265 Sacramento, CA........... 50.1 595.1 2.7 69 1,056 1.3 276 San Bernardino, CA....... 48.6 629.4 2.2 106 830 2.6 202 San Diego, CA............ 100.5 1,302.0 2.3 94 1,099 5.5 45 San Francisco, CA........ 54.7 603.3 4.2 12 1,694 7.6 15 San Joaquin, CA.......... 16.3 205.2 1.5 172 810 1.6 261 San Luis Obispo, CA...... 9.5 103.9 4.1 15 809 1.3 276 San Mateo, CA............ 24.8 349.2 3.6 25 3,240 107.3 1 Santa Barbara, CA........ 14.3 180.5 3.6 25 961 7.4 17 Santa Clara, CA.......... 63.0 928.0 3.7 23 1,906 5.0 71 Santa Cruz, CA........... 8.9 90.4 3.5 29 849 0.1 313 Solano, CA............... 9.7 123.9 3.3 39 998 7.4 17 Sonoma, CA............... 18.3 179.8 3.2 41 918 2.6 202 Stanislaus, CA........... 13.8 162.3 2.5 80 793 2.3 219 Tulare, CA............... 8.9 139.8 0.4 265 697 3.6 139 Ventura, CA.............. 23.9 311.0 3.1 48 984 3.3 157 Yolo, CA................. 6.0 89.4 1.2 194 997 8.6 10 Adams, CO................ 9.0 162.3 3.3 39 886 3.1 166 Arapahoe, CO............. 19.2 292.3 3.5 29 1,159 4.5 85 Boulder, CO.............. 13.3 163.5 2.5 80 1,134 2.0 246 Denver, CO............... 26.5 443.1 4.2 12 1,222 4.6 81 Douglas, CO.............. 9.9 98.5 5.1 7 1,591 48.0 2 El Paso, CO.............. 17.0 241.2 1.9 134 884 0.8 294 Jefferson, CO............ 18.0 215.8 2.6 76 1,010 5.1 64 Larimer, CO.............. 10.3 134.0 2.7 69 887 4.1 104 Weld, CO................. 5.9 86.9 4.2 12 831 2.8 189 Fairfield, CT............ 33.1 416.4 1.0 213 1,704 6.8 26 Hartford, CT............. 25.8 499.9 1.2 194 1,210 5.1 64 New Haven, CT............ 22.6 361.7 1.2 194 1,034 2.9 183 New London, CT........... 7.0 123.3 -0.6 308 971 1.5 265 New Castle, DE........... 17.1 272.7 1.0 213 1,178 7.0 21 Washington, DC........... 36.8 721.5 1.7 154 1,703 2.2 227 Alachua, FL.............. 6.6 117.9 1.0 213 843 2.1 234 Brevard, FL.............. 14.5 188.7 -1.1 316 874 1.0 290 Broward, FL.............. 64.5 720.5 2.3 94 920 3.4 150 Collier, FL.............. 12.1 125.6 2.0 123 839 4.1 104 Duval, FL................ 27.5 449.6 2.0 123 953 5.0 71 Escambia, FL............. 8.0 120.6 0.6 244 787 2.7 193 Hillsborough, FL......... 38.7 604.4 2.5 80 953 3.5 146 Lake, FL................. 7.4 83.9 3.5 29 653 1.1 287 Lee, FL.................. 19.1 210.6 2.7 69 774 2.2 227 Leon, FL................. 8.3 140.2 0.8 236 810 0.2 308 Manatee, FL.............. 9.5 110.2 2.8 65 733 -0.1 319 Marion, FL............... 8.0 92.5 2.6 76 688 2.1 234 Miami-Dade, FL........... 91.3 1,020.6 2.3 94 976 4.1 104 Okaloosa, FL............. 6.1 75.4 -0.1 291 779 2.6 202 Orange, FL............... 36.9 698.7 3.2 41 860 3.9 119 Palm Beach, FL........... 50.6 522.9 2.1 114 1,003 7.6 15 Pasco, FL................ 10.1 102.2 2.1 114 681 2.6 202 Pinellas, FL............. 31.1 389.9 1.6 162 901 1.7 256 Polk, FL................. 12.4 194.6 1.4 181 740 3.1 166 Sarasota, FL............. 14.7 142.5 3.2 41 824 3.1 166 Seminole, FL............. 14.0 162.0 1.8 143 818 5.1 64 Volusia, FL.............. 13.4 150.8 0.8 236 709 4.9 76 Bibb, GA................. 4.6 81.4 1.2 194 760 2.3 219 Chatham, GA.............. 7.8 134.2 2.0 123 828 2.3 219 Clayton, GA.............. 4.3 112.0 -0.1 291 914 1.4 271 Cobb, GA................. 21.8 306.0 1.1 207 1,033 4.3 94 De Kalb, GA.............. 18.2 278.8 -0.1 291 1,026 4.4 91 Fulton, GA............... 42.4 738.0 3.1 48 1,317 7.2 20 Gwinnett, GA............. 24.5 312.0 1.6 162 968 4.8 78 Muscogee, GA............. 4.7 94.6 0.1 280 783 3.2 161 Richmond, GA............. 4.7 99.3 0.2 274 826 3.0 173 Honolulu, HI............. 24.8 455.0 1.8 143 908 3.1 166 Ada, ID.................. 13.6 202.4 2.7 69 843 1.0 290 Champaign, IL............ 4.3 88.1 0.6 244 806 2.5 209 Cook, IL................. 150.3 2,441.2 1.2 194 1,184 5.3 60 Du Page, IL.............. 37.4 578.3 2.1 114 1,168 4.5 85 Kane, IL................. 13.4 195.7 1.2 194 874 2.0 246 Lake, IL................. 22.3 326.3 2.1 114 1,272 6.7 28 McHenry, IL.............. 8.7 93.6 0.8 236 907 11.2 7 McLean, IL............... 3.8 87.3 1.5 172 948 1.2 281 Madison, IL.............. 6.0 94.7 -0.4 305 804 1.5 265 Peoria, IL............... 4.7 103.7 0.6 244 936 1.1 287 St. Clair, IL............ 5.6 94.0 -1.2 318 781 0.4 303 Sangamon, IL............. 5.3 126.8 -2.5 328 986 3.0 173 Will, IL................. 15.4 204.8 0.7 242 847 2.8 189 Winnebago, IL............ 6.8 124.6 -0.9 315 824 1.2 281 Allen, IN................ 9.0 177.8 1.8 143 774 -0.3 322 Elkhart, IN.............. 4.8 112.6 7.4 1 782 8.9 9 Hamilton, IN............. 8.6 115.3 1.7 154 921 5.3 60 Lake, IN................. 10.4 191.3 1.3 186 902 3.9 119 Marion, IN............... 24.1 570.6 2.3 94 992 4.2 100 St. Joseph, IN........... 6.0 117.2 -0.6 308 786 4.4 91 Tippecanoe, IN........... 3.3 79.7 1.9 134 809 0.1 313 Vanderburgh, IN.......... 4.8 105.3 -1.5 324 792 0.9 293 Johnson, IA.............. 3.7 79.1 2.3 94 854 3.5 146 Linn, IA................. 6.3 127.3 0.1 280 948 0.7 298 Polk, IA................. 15.3 274.1 1.6 162 981 4.1 104 Scott, IA................ 5.3 89.0 1.2 194 845 5.5 45 Johnson, KS.............. 21.3 316.2 2.7 69 1,046 6.1 38 Sedgwick, KS............. 12.4 243.5 1.4 181 915 4.1 104 Shawnee, KS.............. 4.8 95.4 0.4 265 856 8.5 11 Wyandotte, KS............ 3.2 84.2 1.6 162 874 0.3 306 Fayette, KY.............. 9.8 183.7 2.2 106 852 2.0 246 Jefferson, KY............ 23.2 436.8 3.8 20 936 2.3 219 Caddo, LA................ 7.5 119.2 -2.4 327 818 0.2 308 Calcasieu, LA............ 4.9 84.6 2.9 59 846 3.4 150 East Baton Rouge, LA..... 14.7 262.3 2.1 114 948 6.9 24 Jefferson, LA............ 13.7 195.5 -0.3 301 916 2.6 202 Lafayette, LA............ 9.1 139.1 1.9 134 989 4.0 115 Orleans, LA.............. 11.2 180.7 2.2 106 992 1.2 281 St. Tammany, LA.......... 7.5 80.9 2.0 123 843 4.5 85 Cumberland, ME........... 12.7 172.4 0.9 223 890 2.9 183 Anne Arundel, MD......... 14.8 244.7 2.6 76 1,050 1.9 251 Baltimore, MD............ 21.4 371.0 1.2 194 1,014 2.7 193 Frederick, MD............ 6.3 94.5 1.9 134 963 2.3 219 Harford, MD.............. 5.7 89.1 2.2 106 974 4.8 78 Howard, MD............... 9.4 161.8 2.3 94 1,212 3.5 146 Montgomery, MD........... 33.8 456.9 0.6 244 1,345 1.9 251 Prince Georges, MD....... 15.9 304.4 -0.2 295 1,019 0.2 308 Baltimore City, MD....... 14.2 333.0 0.2 274 1,180 6.1 38 Barnstable, MA........... 8.9 85.3 2.5 80 842 2.1 234 Bristol, MA.............. 16.0 213.4 0.4 265 901 5.5 45 Essex, MA................ 21.7 309.3 1.5 172 1,056 2.2 227 Hampden, MA.............. 15.5 197.4 0.2 274 898 3.9 119 Middlesex, MA............ 49.4 841.1 1.9 134 1,434 4.5 85 Norfolk, MA.............. 23.4 328.3 1.5 172 1,212 4.6 81 Plymouth, MA............. 14.0 179.0 2.5 80 924 2.3 219 Suffolk, MA.............. 23.6 602.9 1.6 162 1,724 8.0 12 Worcester, MA............ 21.4 319.2 -0.3 301 964 -0.1 319 Genesee, MI.............. 7.3 131.9 0.9 223 816 -1.7 326 Ingham, MI............... 6.4 155.8 0.1 280 924 1.7 256 Kalamazoo, MI............ 5.4 110.9 0.9 223 892 4.0 115 Kent, MI................. 14.2 341.8 3.0 52 879 2.9 183 Macomb, MI............... 17.4 294.7 1.7 154 1,012 1.2 281 Oakland, MI.............. 38.8 675.6 3.4 33 1,141 3.4 150 Ottawa, MI............... 5.6 110.1 3.8 20 831 0.1 313 Saginaw, MI.............. 4.2 84.6 0.5 257 789 0.8 294 Washtenaw, MI............ 8.2 198.6 2.3 94 1,030 3.6 139 Wayne, MI................ 31.9 696.3 1.1 207 1,083 0.6 301 Anoka, MN................ 7.2 112.7 2.1 114 900 3.0 173 Dakota, MN............... 10.0 174.8 1.8 143 943 4.9 76 Hennepin, MN............. 42.4 857.2 1.9 134 1,236 6.5 31 Olmsted, MN.............. 3.4 92.4 3.2 41 1,047 1.7 256 Ramsey, MN............... 14.0 321.0 0.6 244 1,071 4.1 104 St. Louis, MN............ 5.6 94.9 1.7 154 776 0.1 313 Stearns, MN.............. 4.4 81.7 1.2 194 799 5.5 45 Harrison, MS............. 4.4 82.5 -0.2 295 693 1.0 290 Hinds, MS................ 6.0 120.9 -1.3 319 856 3.8 128 Boone, MO................ 4.6 87.7 2.4 91 762 4.0 115 Clay, MO................. 5.2 86.3 -1.4 320 879 1.9 251 Greene, MO............... 8.1 156.0 2.9 59 737 3.8 128 Jackson, MO.............. 19.0 351.3 1.2 194 1,026 6.2 36 St. Charles, MO.......... 8.4 129.8 3.4 33 763 2.6 202 St. Louis, MO............ 32.6 574.9 0.6 244 1,088 7.0 21 St. Louis City, MO....... 9.5 218.1 -0.5 307 1,059 2.7 193 Yellowstone, MT.......... 6.1 78.7 1.7 154 846 5.4 55 Douglas, NE.............. 17.8 321.6 2.0 123 905 5.5 45 Lancaster, NE............ 9.5 160.1 2.5 80 792 3.8 128 Clark, NV................ 49.3 827.6 2.3 94 867 3.1 166 Washoe, NV............... 13.7 186.7 0.9 223 886 3.0 173 Hillsborough, NH......... 12.1 192.2 0.5 257 1,137 3.7 136 Rockingham, NH........... 10.6 137.1 1.0 213 1,034 12.0 4 Atlantic, NJ............. 6.7 131.7 -0.2 295 816 -1.4 325 Bergen, NJ............... 33.1 435.0 0.3 272 1,272 6.2 36 Burlington, NJ........... 11.0 198.1 2.8 65 1,035 1.8 255 Camden, NJ............... 12.1 195.2 -0.2 295 1,002 1.4 271 Essex, NJ................ 20.6 343.5 -0.3 301 1,221 3.6 139 Gloucester, NJ........... 6.1 98.6 0.5 257 873 2.3 219 Hudson, NJ............... 14.1 238.6 2.0 123 1,285 1.3 276 Mercer, NJ............... 11.0 233.0 1.5 172 1,312 3.6 139 Middlesex, NJ............ 21.8 393.4 1.9 134 1,162 1.6 261 Monmouth, NJ............. 20.0 243.6 0.2 274 1,031 2.6 202 Morris, NJ............... 17.3 276.1 0.9 223 1,476 5.4 55 Ocean, NJ................ 12.3 147.3 1.2 194 835 4.6 81 Passaic, NJ.............. 12.2 175.1 0.3 272 998 -2.1 327 Somerset, NJ............. 10.1 174.1 0.9 223 1,429 2.2 227 Union, NJ................ 14.4 222.2 0.5 257 1,228 0.2 308 Bernalillo, NM........... 17.9 313.9 1.3 186 836 0.7 298 Albany, NY............... 10.0 222.6 0.9 223 976 2.0 246 Bronx, NY................ 17.2 239.7 1.8 143 932 2.5 209 Broome, NY............... 4.5 90.2 -0.8 313 764 2.1 234 Dutchess, NY............. 8.2 112.3 -0.8 313 975 2.2 227 Erie, NY................. 24.0 461.8 0.5 257 853 3.0 173 Kings, NY................ 54.0 529.5 2.0 123 821 2.8 189 Monroe, NY............... 18.3 380.0 0.1 280 890 0.2 308 Nassau, NY............... 53.0 609.0 1.8 143 1,134 2.0 246 New York, NY............. 123.7 2,437.9 2.1 114 2,107 11.5 5 Oneida, NY............... 5.3 105.4 -1.4 320 777 3.7 136 Onondaga, NY............. 13.0 244.7 0.5 257 929 5.9 41 Orange, NY............... 9.9 134.2 0.4 265 820 1.9 251 Queens, NY............... 47.9 533.4 2.9 59 938 2.2 227 Richmond, NY............. 9.1 95.3 1.6 162 843 3.8 128 Rockland, NY............. 9.9 116.6 -0.2 295 1,054 6.3 35 Saratoga, NY............. 5.6 78.8 2.0 123 876 3.8 128 Suffolk, NY.............. 50.9 630.9 1.1 207 1,056 0.0 317 Westchester, NY.......... 36.1 413.1 0.6 244 1,346 5.4 55 Buncombe, NC............. 8.1 116.2 2.5 80 752 2.7 193 Catawba, NC.............. 4.4 79.3 0.1 280 733 0.4 303 Cumberland, NC........... 6.3 118.5 -1.4 320 770 -0.1 319 Durham, NC............... 7.5 188.2 2.6 76 1,225 1.6 261 Forsyth, NC.............. 9.0 177.3 1.7 154 883 3.3 157 Guilford, NC............. 14.2 266.9 1.1 207 863 5.5 45 Mecklenburg, NC.......... 33.5 584.2 3.0 52 1,103 5.1 64 New Hanover, NC.......... 7.5 97.1 1.6 162 797 2.4 214 Wake, NC................. 30.1 464.2 3.4 33 967 2.4 214 Cass, ND................. 6.3 108.7 3.6 25 883 6.6 29 Butler, OH............... 7.4 140.6 -0.2 295 844 2.8 189 Cuyahoga, OH............. 35.7 711.1 2.0 123 1,020 5.4 55 Delaware, OH............. 4.4 81.4 4.1 15 950 3.9 119 Franklin, OH............. 29.8 687.2 2.5 80 970 1.6 261 Hamilton, OH............. 23.2 491.7 0.4 265 1,092 6.0 40 Lake, OH................. 6.4 94.7 0.0 288 813 -3.2 328 Lorain, OH............... 6.0 94.5 -0.7 310 816 2.5 209 Lucas, OH................ 10.1 203.3 0.8 236 866 2.1 234 Mahoning, OH............. 5.9 98.8 1.3 186 718 3.8 128 Montgomery, OH........... 12.1 245.6 0.6 244 864 2.7 193 Stark, OH................ 8.8 155.6 0.8 236 752 3.0 173 Summit, OH............... 14.2 258.8 1.1 207 893 4.3 94 Oklahoma, OK............. 25.2 434.9 1.6 162 947 5.3 60 Tulsa, OK................ 20.7 341.5 1.8 143 962 0.0 317 Clackamas, OR............ 12.9 142.2 2.2 106 893 4.2 100 Lane, OR................. 10.9 138.1 1.0 213 758 2.7 193 Marion, OR............... 9.5 129.9 0.5 257 760 3.4 150 Multnomah, OR............ 30.4 447.5 2.0 123 988 2.1 234 Washington, OR........... 16.8 252.7 1.2 194 1,101 1.4 271 Allegheny, PA............ 35.8 689.7 0.7 242 1,058 5.0 71 Berks, PA................ 9.0 166.3 1.3 186 869 2.1 234 Bucks, PA................ 19.8 250.5 0.4 265 957 2.9 183 Butler, PA............... 4.9 82.9 -0.3 301 895 3.6 139 Chester, PA.............. 15.1 238.7 0.1 280 1,283 0.7 298 Cumberland, PA........... 6.2 125.7 0.6 244 866 3.0 173 Dauphin, PA.............. 7.5 174.5 0.5 257 955 4.3 94 Delaware, PA............. 14.0 215.7 1.3 186 1,076 6.4 33 Erie, PA................. 7.6 124.6 -0.7 310 775 1.7 256 Lackawanna, PA........... 5.9 98.1 -0.4 305 726 1.4 271 Lancaster, PA............ 12.8 221.7 0.9 223 816 3.7 136 Lehigh, PA............... 8.7 177.8 -0.1 291 964 3.0 173 Luzerne, PA.............. 7.7 139.5 -1.4 320 746 3.2 161 Montgomery, PA........... 27.5 473.9 1.0 213 1,250 5.9 41 Northampton, PA.......... 6.6 104.9 2.0 123 842 1.3 276 Philadelphia, PA......... 36.5 638.0 1.1 207 1,180 4.1 104 Washington, PA........... 5.6 86.3 0.8 236 1,016 11.5 5 Westmoreland, PA......... 9.5 133.8 0.9 223 795 -0.7 323 York, PA................. 9.1 172.6 0.4 265 837 3.6 139 Providence, RI........... 17.5 272.7 1.4 181 992 3.0 173 Charleston, SC........... 12.1 219.0 2.5 80 837 1.5 265 Greenville, SC........... 12.3 238.5 2.3 94 838 2.7 193 Horry, SC................ 7.7 104.7 2.5 80 576 1.2 281 Lexington, SC............ 5.7 105.1 6.9 2 732 2.4 214 Richland, SC............. 9.0 206.5 1.4 181 843 2.4 214 Spartanburg, SC.......... 5.8 117.4 2.4 91 832 2.1 234 Minnehaha, SD............ 6.7 118.1 2.5 80 850 4.3 94 Davidson, TN............. 18.5 441.6 3.0 52 1,090 6.5 31 Hamilton, TN............. 8.5 188.0 2.2 106 897 3.8 128 Knox, TN................. 10.9 222.5 0.6 244 875 3.9 119 Rutherford, TN........... 4.4 107.4 6.4 3 877 4.2 100 Shelby, TN............... 19.1 481.9 1.7 154 1,023 5.5 45 Williamson, TN........... 6.4 100.6 4.0 17 1,121 6.4 33 Bell, TX................. 4.9 109.4 1.7 154 783 1.7 256 Bexar, TX................ 35.7 765.3 2.9 59 877 1.5 265 Brazoria, TX............. 5.1 94.2 3.6 25 934 4.1 104 Brazos, TX............... 4.0 90.3 4.4 9 735 3.5 146 Cameron, TX.............. 6.4 131.7 2.1 114 609 2.7 193 Collin, TX............... 19.7 318.7 4.8 8 1,158 5.5 45 Dallas, TX............... 70.1 1,499.2 3.4 33 1,209 5.5 45 Denton, TX............... 11.7 189.8 3.1 48 877 5.0 71 El Paso, TX.............. 14.2 282.0 2.2 106 697 3.4 150 Fort Bend, TX............ 10.0 149.5 5.3 6 1,007 5.1 64 Galveston, TX............ 5.5 97.3 1.3 186 903 4.0 115 Gregg, TX................ 4.2 78.1 0.6 244 913 3.8 128 Harris, TX............... 104.3 2,160.8 4.0 17 1,331 7.3 19 Hidalgo, TX.............. 11.5 235.2 2.3 94 612 2.2 227 Jefferson, TX............ 5.8 121.3 -2.3 326 1,006 4.1 104 Lubbock, TX.............. 7.1 128.2 1.8 143 772 8.0 12 McLennan, TX............. 4.9 103.2 2.1 114 813 5.4 55 Montgomery, TX........... 9.3 146.4 5.7 5 985 7.7 14 Nueces, TX............... 7.9 157.6 3.2 41 885 5.1 64 Smith, TX................ 5.7 94.6 0.1 280 867 6.8 26 Tarrant, TX.............. 39.0 800.8 3.0 52 974 4.5 85 Travis, TX............... 32.7 619.4 4.3 10 1,114 3.1 166 Webb, TX................. 4.9 92.6 1.3 186 683 5.1 64 Williamson, TX........... 8.1 136.2 3.5 29 934 2.5 209 Davis, UT................ 7.5 108.7 2.4 91 778 0.8 294 Salt Lake, UT............ 38.8 606.5 4.3 10 947 5.5 45 Utah, UT................. 13.3 183.9 6.0 4 834 9.4 8 Weber, UT................ 5.5 91.8 2.2 106 721 2.4 214 Chittenden, VT........... 6.1 99.0 0.2 274 981 4.1 104 Arlington, VA............ 8.7 165.9 -1.1 316 1,625 2.1 234 Chesterfield, VA......... 8.0 121.2 3.1 48 881 3.2 161 Fairfax, VA.............. 35.3 597.8 0.9 223 1,588 4.3 94 Henrico, VA.............. 10.3 181.9 2.9 59 949 1.3 276 Loudoun, VA.............. 10.2 144.2 3.2 41 1,171 2.7 193 Prince William, VA....... 8.1 115.9 3.4 33 863 2.1 234 Alexandria City, VA...... 6.3 97.2 1.6 162 1,460 2.5 209 Chesapeake City, VA...... 5.8 96.8 0.0 288 775 3.3 157 Newport News City, VA.... 3.7 98.3 1.8 143 912 3.1 166 Norfolk City, VA......... 5.7 138.7 0.0 288 972 4.2 100 Richmond City, VA........ 7.2 149.1 0.6 244 1,066 4.1 104 Virginia Beach City, VA.. 11.5 165.4 1.8 143 862 13.3 3 Benton, WA............... 5.9 76.4 -1.5 324 969 -1.0 324 Clark, WA................ 14.0 132.3 3.0 52 894 5.8 44 King, WA................. 84.1 1,185.3 3.0 52 1,276 4.7 80 Kitsap, WA............... 6.8 80.7 0.2 274 860 3.2 161 Pierce, WA............... 22.1 266.8 1.2 194 869 3.2 161 Snohomish, WA............ 19.6 261.7 2.8 65 1,005 0.4 303 Spokane, WA.............. 16.2 200.1 1.0 213 809 3.3 157 Thurston, WA............. 7.7 97.8 1.5 172 839 1.1 287 Whatcom, WA.............. 7.0 81.0 2.3 94 801 3.6 139 Yakima, WA............... 9.0 95.1 1.5 172 679 4.6 81 Kanawha, WV.............. 6.0 105.5 -0.7 310 843 1.2 281 Brown, WI................ 6.6 148.3 1.0 213 892 5.2 63 Dane, WI................. 14.3 310.5 1.5 172 957 5.9 41 Milwaukee, WI............ 23.8 476.8 0.9 223 969 3.0 173 Outagamie, WI............ 5.1 104.1 1.6 162 830 4.3 94 Waukesha, WI............. 12.7 229.5 0.9 223 1,004 7.0 21 Winnebago, WI............ 3.6 90.2 1.3 186 924 3.9 119 San Juan, PR............. 11.0 275.6 1.5 (7) 661 0.8 (7) (1) Includes workers covered by Unemployment Insurance (UI) and Unemployment Compensation for Federal Employees (UCFE) programs. These 328 U.S. counties comprise 71.3 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, fourth quarter 2012(2) Employment Average weekly wage(3) Establishments, fourth quarter County by NAICS supersector 2012 Percent Percent (thousands) December change, Fourth change, 2012 December quarter fourth (thousands) 2011-12(4) 2012 quarter 2011-12(4) United States(5)............................. 9,205.6 133,726.8 1.9 $1,000 4.7 Private industry........................... 8,911.3 112,271.7 2.3 1,008 5.3 Natural resources and mining............. 131.9 1,888.3 2.0 1,148 6.1 Construction............................. 750.2 5,627.0 2.8 1,102 5.0 Manufacturing............................ 335.7 11,950.0 1.4 1,207 3.2 Trade, transportation, and utilities..... 1,894.7 26,179.3 1.5 827 3.9 Information.............................. 143.9 2,696.7 0.4 1,620 8.0 Financial activities..................... 815.1 7,595.9 1.7 1,629 11.4 Professional and business services....... 1,617.5 18,205.1 3.3 1,370 8.2 Education and health services............ 942.0 19,708.0 2.0 928 2.7 Leisure and hospitality.................. 776.6 13,631.9 3.7 416 3.0 Other services........................... 1,280.2 4,575.7 3.4 604 1.0 Government................................. 294.2 21,455.1 -0.2 960 1.6 Los Angeles, CA.............................. 421.5 4,082.2 1.9 1,185 6.6 Private industry........................... 415.8 3,546.4 2.5 1,179 6.9 Natural resources and mining............. 0.5 10.0 1.4 1,731 19.5 Construction............................. 12.2 110.9 3.6 1,160 4.2 Manufacturing............................ 12.5 364.8 0.2 1,182 3.6 Trade, transportation, and utilities..... 51.3 792.1 2.0 907 4.3 Information.............................. 8.5 203.9 5.3 2,224 6.3 Financial activities..................... 22.2 213.7 1.4 1,841 19.4 Professional and business services....... 43.1 584.3 3.7 1,483 5.1 Education and health services............ 30.0 541.1 2.4 1,096 4.3 Leisure and hospitality.................. 27.9 421.3 4.7 985 6.5 Other services........................... 182.1 284.5 -2.4 418 9.4 Government................................. 5.7 535.8 -2.5 1,221 4.3 Cook, IL..................................... 150.3 2,441.2 1.2 1,184 5.3 Private industry........................... 148.9 2,144.5 1.4 1,189 5.6 Natural resources and mining............. 0.1 0.7 -6.1 1,088 -1.0 Construction............................. 12.4 61.7 0.1 1,482 5.8 Manufacturing............................ 6.6 194.1 0.4 1,255 4.6 Trade, transportation, and utilities..... 29.2 462.5 1.2 899 4.2 Information.............................. 2.7 54.1 0.1 1,627 3.4 Financial activities..................... 15.6 184.1 -0.2 2,350 16.6 Professional and business services....... 31.8 432.2 2.4 1,565 5.0 Education and health services............ 15.8 414.1 1.1 968 1.0 Leisure and hospitality.................. 13.3 241.5 3.6 473 2.8 Other services........................... 16.7 95.8 -0.3 843 4.1 Government................................. 1.4 296.7 -0.4 1,149 3.6 New York, NY................................. 123.7 2,437.9 2.1 2,107 11.5 Private industry........................... 123.5 1,999.2 2.5 2,331 12.5 Natural resources and mining............. 0.0 0.1 5.0 1,862 18.1 Construction............................. 2.1 32.5 8.5 2,003 2.1 Manufacturing............................ 2.4 26.6 1.0 1,552 -7.1 Trade, transportation, and utilities..... 20.9 267.0 2.6 1,529 13.0 Information.............................. 4.4 142.9 1.7 2,447 5.5 Financial activities..................... 18.8 353.5 -0.5 5,186 26.4 Professional and business services....... 25.7 500.5 3.8 2,430 6.3 Education and health services............ 9.4 314.8 1.5 1,226 3.2 Leisure and hospitality.................. 13.1 259.9 3.2 907 2.4 Other services........................... 19.2 94.5 3.7 1,094 2.1 Government................................. 0.3 438.7 0.4 1,100 1.3 Harris, TX................................... 104.3 2,160.8 4.0 1,331 7.3 Private industry........................... 103.8 1,904.6 4.4 1,372 7.6 Natural resources and mining............. 1.7 91.3 6.9 3,544 9.5 Construction............................. 6.5 142.3 5.3 1,335 7.9 Manufacturing............................ 4.6 192.7 5.0 1,704 9.8 Trade, transportation, and utilities..... 23.4 459.4 3.0 1,196 8.4 Information.............................. 1.2 27.2 -2.9 1,463 5.3 Financial activities..................... 10.7 116.0 2.9 1,708 10.7 Professional and business services....... 20.8 362.7 5.9 1,639 4.5 Education and health services............ 11.9 256.5 3.4 1,021 6.4 Leisure and hospitality.................. 8.6 192.7 5.9 430 3.6 Other services........................... 13.7 62.6 3.1 718 5.4 Government................................. 0.5 256.2 0.8 1,026 3.0 Maricopa, AZ................................. 95.2 1,721.1 2.7 964 3.4 Private industry........................... 94.5 1,512.0 3.0 967 3.8 Natural resources and mining............. 0.5 8.5 3.7 918 0.7 Construction............................. 7.6 88.3 7.2 1,049 7.7 Manufacturing............................ 3.2 113.7 1.8 1,290 0.8 Trade, transportation, and utilities..... 21.2 357.3 1.7 917 2.5 Information.............................. 1.6 28.5 2.3 1,253 5.4 Financial activities..................... 10.9 146.2 3.0 1,194 5.7 Professional and business services....... 22.0 285.1 3.5 1,086 5.6 Education and health services............ 10.6 252.8 2.4 1,001 1.5 Leisure and hospitality.................. 7.2 180.6 2.7 444 3.3 Other services........................... 6.5 46.9 1.1 634 4.1 Government................................. 0.7 209.1 1.1 946 1.4 Dallas, TX................................... 70.1 1,499.2 3.4 1,209 5.5 Private industry........................... 69.6 1,335.4 3.8 1,228 5.8 Natural resources and mining............. 0.6 10.1 8.5 3,980 -9.0 Construction............................. 4.0 71.0 6.4 1,171 6.1 Manufacturing............................ 2.8 111.8 -0.1 1,398 7.0 Trade, transportation, and utilities..... 15.2 305.9 3.3 1,065 5.7 Information.............................. 1.5 47.6 3.6 1,640 1.9 Financial activities..................... 8.6 145.1 2.6 1,663 12.1 Professional and business services....... 15.5 292.6 5.6 1,451 4.8 Education and health services............ 7.9 176.8 3.9 1,068 3.1 Leisure and hospitality.................. 5.9 133.2 4.6 528 6.5 Other services........................... 7.3 40.4 1.4 756 8.2 Government................................. 0.5 163.8 0.2 1,059 3.6 Orange, CA................................... 104.2 1,436.6 2.7 1,131 4.4 Private industry........................... 102.8 1,300.9 3.1 1,138 4.5 Natural resources and mining............. 0.2 3.0 -5.3 746 7.8 Construction............................. 6.0 73.2 5.3 1,269 7.2 Manufacturing............................ 4.8 158.6 0.5 1,352 3.8 Trade, transportation, and utilities..... 16.2 258.9 1.8 1,002 2.3 Information.............................. 1.2 24.2 -0.4 1,692 11.8 Financial activities..................... 9.6 111.6 4.3 2,030 6.8 Professional and business services....... 19.1 264.5 4.3 1,329 5.1 Education and health services............ 10.7 165.4 2.1 1,064 2.7 Leisure and hospitality.................. 7.4 182.5 4.2 431 4.6 Other services........................... 19.6 52.9 4.2 534 -1.1 Government................................. 1.4 135.7 -1.3 1,064 3.5 San Diego, CA................................ 100.5 1,302.0 2.3 1,099 5.5 Private industry........................... 99.1 1,084.0 2.6 1,090 5.7 Natural resources and mining............. 0.7 9.0 1.1 665 4.2 Construction............................. 5.8 57.5 3.6 1,133 0.7 Manufacturing............................ 2.9 93.7 -0.6 1,534 5.6 Trade, transportation, and utilities..... 13.6 219.0 2.1 843 6.7 Information.............................. 1.1 24.9 1.7 1,580 -1.8 Financial activities..................... 8.5 71.5 3.4 1,381 12.3 Professional and business services....... 16.6 221.5 2.7 1,670 8.8 Education and health services............ 8.8 157.7 1.4 1,044 3.0 Leisure and hospitality.................. 7.2 160.2 3.3 439 0.5 Other services........................... 26.6 63.5 5.2 503 2.2 Government................................. 1.4 218.1 0.7 1,143 4.4 King, WA..................................... 84.1 1,185.3 3.0 1,276 4.7 Private industry........................... 83.5 1,028.2 3.4 1,291 5.1 Natural resources and mining............. 0.4 2.5 -8.0 2,021 35.5 Construction............................. 5.3 50.4 9.5 1,248 -1.3 Manufacturing............................ 2.2 103.6 3.5 1,482 -2.8 Trade, transportation, and utilities..... 14.4 223.0 3.6 1,086 6.2 Information.............................. 1.8 80.9 0.7 2,489 11.8 Financial activities..................... 6.2 64.2 2.5 1,587 8.3 Professional and business services....... 14.1 195.1 4.8 1,689 6.7 Education and health services............ 7.3 140.3 2.3 1,007 2.2 Leisure and hospitality.................. 6.5 115.6 4.0 485 1.7 Other services........................... 25.2 52.7 -0.7 627 6.8 Government................................. 0.5 157.1 0.5 1,177 1.2 Miami-Dade, FL............................... 91.3 1,020.6 2.3 976 4.1 Private industry........................... 90.9 882.1 2.8 957 5.4 Natural resources and mining............. 0.5 8.9 -2.7 609 2.5 Construction............................. 5.0 30.8 2.8 1,017 10.8 Manufacturing............................ 2.7 35.7 -1.2 930 3.8 Trade, transportation, and utilities..... 26.4 266.5 1.9 852 4.8 Information.............................. 1.5 17.7 1.3 1,489 10.9 Financial activities..................... 9.3 69.2 3.8 1,483 8.4 Professional and business services....... 19.1 134.1 4.2 1,342 8.8 Education and health services............ 10.1 159.0 1.0 929 0.7 Leisure and hospitality.................. 7.0 122.8 6.0 554 3.2 Other services........................... 8.0 35.7 2.8 586 3.9 Government................................. 0.4 138.5 -1.2 1,092 -2.3 (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, fourth quarter 2012(2) Employment Average weekly wage(3) Establishments, fourth quarter State 2012 Percent Percent (thousands) December change, Fourth change, 2012 December quarter fourth (thousands) 2011-12 2012 quarter 2011-12 United States(4)......... 9,205.6 133,726.8 1.9 $1,000 4.7 Alabama.................. 117.0 1,847.3 1.1 854 2.6 Alaska................... 21.9 314.8 1.1 1,007 2.7 Arizona.................. 147.5 2,509.2 2.4 912 3.3 Arkansas................. 85.1 1,160.3 0.2 767 4.2 California............... 1,337.1 15,216.3 3.3 1,186 7.8 Colorado................. 173.6 2,311.4 2.7 1,032 5.8 Connecticut.............. 111.9 1,657.6 1.0 1,253 5.3 Delaware................. 27.8 411.0 1.2 1,044 6.1 District of Columbia..... 36.8 721.5 1.7 1,703 2.2 Florida.................. 618.3 7,535.5 2.3 880 3.9 Georgia.................. 273.7 3,889.9 1.7 927 4.7 Hawaii................... 38.6 620.7 2.1 868 2.7 Idaho.................... 53.4 618.4 2.0 732 2.1 Illinois................. 396.4 5,697.9 1.1 1,058 4.4 Indiana.................. 160.4 2,850.5 1.8 816 3.4 Iowa..................... 96.0 1,486.6 1.3 821 3.7 Kansas................... 84.9 1,339.2 1.5 835 4.4 Kentucky................. 113.2 1,796.0 1.4 801 1.8 Louisiana................ 127.1 1,891.9 1.0 884 4.1 Maine.................... 49.7 582.2 0.2 773 2.4 Maryland................. 169.1 2,544.1 1.2 1,086 2.5 Massachusetts............ 221.0 3,279.3 1.3 1,248 4.8 Michigan................. 238.9 3,988.9 1.9 954 2.3 Minnesota................ 170.1 2,677.2 1.6 985 5.1 Mississippi.............. 69.4 1,096.5 1.1 720 3.2 Missouri................. 179.3 2,641.9 0.9 863 4.6 Montana.................. 42.8 434.6 1.9 757 4.1 Nebraska................. 68.0 931.3 2.2 797 4.6 Nevada................... 73.5 1,145.8 1.9 877 2.9 New Hampshire............ 49.5 620.8 0.8 1,023 5.5 New Jersey............... 263.8 3,846.4 1.1 1,172 2.9 New Mexico............... 55.7 796.8 1.5 802 0.4 New York................. 608.4 8,741.9 1.4 1,280 6.9 North Carolina........... 259.9 3,963.9 1.9 854 3.6 North Dakota............. 30.1 421.0 6.1 944 8.4 Ohio..................... 287.1 5,098.0 1.3 887 3.6 Oklahoma................. 104.9 1,565.3 1.9 847 3.9 Oregon................... 134.8 1,654.1 1.4 871 2.5 Pennsylvania............. 354.4 5,629.8 0.5 972 3.8 Rhode Island............. 35.4 456.4 1.0 945 2.7 South Carolina........... 113.9 1,832.2 2.0 784 2.8 South Dakota............. 31.6 401.7 1.2 749 3.5 Tennessee................ 142.1 2,710.4 2.1 903 5.2 Texas.................... 599.6 10,956.4 3.2 1,027 5.5 Utah..................... 87.2 1,246.6 3.7 844 4.5 Vermont.................. 24.5 306.1 0.7 829 2.5 Virginia................. 242.5 3,663.7 1.1 1,042 3.7 Washington............... 239.6 2,902.0 2.1 1,017 4.0 West Virginia............ 49.6 714.3 0.0 788 1.5 Wisconsin................ 162.9 2,723.6 1.2 855 4.8 Wyoming.................. 25.6 277.6 0.2 908 3.7 Puerto Rico.............. 47.3 978.6 1.6 550 -0.4 Virgin Islands........... 3.4 39.8 -7.9 738 -3.9 (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.