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For release 10:00 a.m. (EDT), Thursday, March 19, 2015 USDL-15-0427 Technical Information: (202) 691-6567 * QCEWInfo@bls.gov * www.bls.gov/cew Media Contact: (202) 691-5902 * PressOffice@bls.gov COUNTY EMPLOYMENT AND WAGES Third Quarter 2014 From September 2013 to September 2014, employment increased in 306 of the 339 largest U.S. counties, the U.S. Bureau of Labor Statistics reported today. Weld, Colo., had the largest increase, with a gain of 8.8 percent over the year, compared with national job growth of 2.0 percent. Within Weld, the largest employment increase occurred in natural resources and mining, which gained 2,299 jobs over the year (22.1 percent). Atlantic, N.J., had the largest over-the-year decrease in employment among the largest counties in the U.S. with a loss of 4.0 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 6 months after the end of each quarter. The U.S. average weekly wage increased 2.9 percent over the year, growing to $949 in the third quarter of 2014. Olmsted, Minn., had the largest over-the-year increase in average weekly wages with a gain of 11.1 percent. Within Olmsted, an average weekly wage gain of $238, or 19.7 percent, in education and health services made the largest contribution to the county’s increase in average weekly wages. Collier, Fla., experienced the largest decrease in average weekly wages with a loss of 3.9 percent over the year. Table A. Large counties ranked by September 2014 employment, September 2013-14 employment increase, and September 2013-14 percent increase in employment -------------------------------------------------------------------------------------------------------- Employment in large counties -------------------------------------------------------------------------------------------------------- September 2014 employment | Increase in employment, | Percent increase in employment, (thousands) | September 2013-14 | September 2013-14 | (thousands) | -------------------------------------------------------------------------------------------------------- | | United States 137,724.1| United States 2,708.5| United States 2.0 -------------------------------------------------------------------------------------------------------- | | Los Angeles, Calif. 4,184.4| Los Angeles, Calif. 87.6| Weld, Colo. 8.8 New York, N.Y. 2,494.4| Harris, Texas 79.2| Benton, Ark. 7.4 Cook, Ill. 2,481.9| New York, N.Y. 65.7| Midland, Texas 7.4 Harris, Texas 2,269.5| Dallas, Texas 53.1| Lee, Fla. 6.1 Maricopa, Ariz. 1,756.8| King, Wash. 41.5| Sarasota, Fla. 6.1 Dallas, Texas 1,558.5| Santa Clara, Calif. 41.4| Adams, Colo. 5.7 Orange, Calif. 1,475.0| Clark, Nev. 39.8| Kings, N.Y. 5.4 San Diego, Calif. 1,344.5| Maricopa, Ariz. 34.1| Williamson, Tenn. 5.4 King, Wash. 1,252.8| Orange, Calif. 32.6| San Francisco, Calif. 5.1 Miami-Dade, Fla. 1,047.0| San Francisco, Calif. 31.4| Fort Bend, Texas 5.1 | | Montgomery, Texas 5.1 -------------------------------------------------------------------------------------------------------- Large County Employment In September 2014, national employment was 137.7 million (as measured by the QCEW program). Over the year, employment increased 2.0 percent, or 2.7 million. The 339 U.S. counties with 75,000 or more jobs accounted for 71.8 percent of total U.S. employment and 76.9 percent of total wages. These 339 counties had a net job growth of 2.0 million over the year, accounting for 74.1 percent of the overall U.S. employment increase. Weld, Colo., had the largest percentage increase in employment (8.8 percent) among the largest U.S. counties. The five counties with the largest increases in employment level were Los Angeles, Calif.; Harris, Texas; New York, N.Y.; Dallas, Texas; and King, Wash. These counties had a combined over- the-year employment gain of 327,100 jobs, which was 12.1 percent of the overall job increase for the U.S. (See table A.) Employment declined in 25 of the largest counties from September 2013 to September 2014. Atlantic, N.J., had the largest over-the-year percentage decrease in employment (-4.0 percent). Within Atlantic, leisure and hospitality had the largest decrease in employment, with a loss of 5,853 jobs (-12.0 percent). Passaic, N.J., had the second largest percentage decrease in employment, followed by McLean, Ill.; Peoria, Ill.; and Burlington, N.J. (See table 1.) Table B. Large counties ranked by third quarter 2014 average weekly wages, third quarter 2013-14 increase in average weekly wages, and third quarter 2013-14 percent increase in average weekly wages -------------------------------------------------------------------------------------------------------- Average weekly wage in large counties -------------------------------------------------------------------------------------------------------- Average weekly wage, | Increase in average weekly | Percent increase in average third quarter 2014 | wage, third quarter 2013-14 | weekly wage, third | | quarter 2013-14 -------------------------------------------------------------------------------------------------------- | | United States $949| United States $27| United States 2.9 -------------------------------------------------------------------------------------------------------- | | Santa Clara, Calif. $2,012| Santa Clara, Calif. $138| Olmsted, Minn. 11.1 San Mateo, Calif. 1,824| San Francisco, Calif. 134| San Francisco, Calif. 8.6 New York, N.Y. 1,733| San Mateo, Calif. 121| Santa Clara, Calif. 7.4 San Francisco, Calif. 1,685| Olmsted, Minn. 108| San Mateo, Calif. 7.1 Washington, D.C. 1,631| Suffolk, Mass. 84| Brazoria, Texas 7.1 Arlington, Va. 1,545| Midland, Texas 80| Midland, Texas 6.8 Suffolk, Mass. 1,515| Washington, Ore. 71| Washington, Ore. 6.2 King, Wash. 1,452| Arlington, Va. 71| Howard, Md. 6.0 Fairfax, Va. 1,447| King, Wash. 71| Hamilton, Ohio 6.0 Fairfield, Conn. 1,400| Howard, Md. 67| Suffolk, Mass. 5.9 | | -------------------------------------------------------------------------------------------------------- Large County Average Weekly Wages Average weekly wages for the nation increased to $949, a 2.9 percent increase, during the year ending in the third quarter of 2014. Among the 339 largest counties, 328 had over-the-year increases in average weekly wages. Olmsted, Minn., had the largest wage increase among the largest U.S. counties (11.1 percent). Of the 339 largest counties, 10 experienced over-the-year decreases in average weekly wages. Collier, Fla., had the largest percentage decrease in average weekly wages, with a loss of 3.9 percent. Within Collier, professional and business services had the largest impact on the county’s average weekly wage decrease. Within this industry, average weekly wages declined by $498 (-33.2 percent) over the year. Dane, Wis., had the second largest percentage decrease in average weekly wages, followed by Williamson, Texas; Hamilton, Ind.; and Shawnee, Kan. (See table 1.) Ten Largest U.S. Counties All of the 10 largest counties had over-the-year percentage increases in employment in September 2014. Harris, Texas, had the largest gain (3.6 percent). Within Harris, trade, transportation, and utilities had the largest over-the-year employment level increase among all private industry groups with a gain of 15,547 jobs, or 3.4 percent. Cook, Ill., had the smallest percentage increase in employment (1.2 percent) among the 10 largest counties. (See table 2.) Average weekly wages increased over the year in all of the 10 largest U.S. counties. King, Wash., experienced the largest percentage gain in average weekly wages (5.1 percent). Within King, information had the largest impact on the county’s average weekly wage growth. Within this industry, average weekly wages increased by $437, or 9.3 percent, over the year. San Diego, Calif., had the smallest increase in average weekly wages (0.8 percent) among the 10 largest counties. For More Information The tables included in this release contain data for the nation and for the 339 U.S. counties with annual average employment levels of 75,000 or more in 2013. September 2014 employment and 2014 third 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 9.4 million employer reports cover 137.7 million full- and part- time workers. The QCEW program provides a quarterly and annual universe count of establishments, employment, and wages at the county, MSA, state, and national levels by detailed industry. Data for the third quarter of 2014 will be available electronically later at www.bls.gov/cew/. For additional information about the quarterly employment and wages data, please read the Technical Note. 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 www.bls.gov/cew/cewregional.htm. _____________ The County Employment and Wages release for fourth quarter 2014 is scheduled to be released on Wednesday, June 17, 2015.
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 2014 are preliminary and subject to revision. For purposes of this release, large counties are defined as having employment levels of 75,000 or greater. In addition, data for San Juan, Puerto Rico, are provided, but not used in calculating U.S. averages, rankings, or in the analysis in the text. Each year, these large counties are selected on the basis of the preliminary annual average of employment for the previous year. The 340 counties presented in this release were derived using 2013 preliminary annual averages of employment. For 2014 data, five counties have been added to the publication tables: Shelby, Ala.; Osceola, Fla.; Black Hawk, Iowa; Washington, Minn.; and Cleveland, Okla. These counties will be included in all 2014 quarterly releases. The counties in table 2 are selected and sorted each year based on the annual average employment from the preceding year. The preliminary QCEW data presented in this release may differ from data released by the individual states. These potential differences result from the states' continuing receipt of UI data over time and ongoing review and editing. The individual states determine their data release timetables. Differences between QCEW, BED, and CES employment measures The Bureau publishes three different establishment-based employment measures for any given quarter. Each of these measures-QCEW, Business Employment Dynamics (BED), and Current Employment Statistics (CES)-makes use of the quarterly UI employment reports in producing data; however, each measure has a somewhat different universe coverage, estimation 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 differences and the intended uses of the program products. (See table.) Additional information 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- | 588,000 establish- | submitted by 9.4 | ministrative records| ments | million establish- | submitted by 7.5 | | ments in first | million private-sec-| | quarter of 2014 | 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 | -6 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 counts (bench- | | losses | marking) -----------|---------------------|----------------------|------------------------ 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 civilian workers covered by the Unemployment Compensation for Federal Employees (UCFE) program, employment and wage data are compiled from quarterly reports submitted by four major federal payroll processing centers on behalf of all federal agencies, with the exception of a few agencies which still report directly to the individual SWA. In addition to the quarterly contribution reports, employers who operate 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 9.2 million employer reports of employment and wages submitted by states to the BLS in 2013. These reports are based on place of employment 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 effective, expanding coverage to include most State and local government employees. In 2013, UI and UCFE programs covered workers in 134.0 million jobs. The estimated 128.7 million workers in these jobs (after adjustment for multiple jobholders) represented 95.8 percent of civilian wage and salary employment. Covered workers received $6.673 trillion in pay, representing 93.7 percent of the wage and salary component of personal income and 39.8 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. Coverage changes may affect the over-the-year comparisons presented in this news release. Concepts and methodology Monthly employment is based on the number of workers who worked during or received pay for the pay period including the 12th of the month. With few exceptions, all 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 averages reported. Included in the quarterly wage data are non-wage cash payments such as bonuses, the cash value of meals and lodging when supplied, tips and other gratuities, and, in some states, employer contributions to certain deferred compensation plans such as 401(k) plans and stock options. Over-the-year comparisons of average weekly wages may reflect fluctuations in average monthly employment and/or total quarterly wages between the current quarter and prior year levels. Average weekly wages are affected by the ratio of full-time to part-time workers as well as the number of individuals in high-paying and low-paying occupations and the incidence of pay periods within a quarter. For instance, the average weekly wage of the workforce could increase significantly when there is a large decline in the number of employees that had been receiving below-average wages. Wages may include payments to workers not present in the employment counts because they did not work during the pay period including the 12th of the month. When comparing average weekly wage levels between industries, states, or quarters, these factors should be taken into consideration. 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 employers 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 individual establishment records and reflect the number of establishments that exist in a county or industry at a point in time. Establishments can move in or out of a county or industry for a number of reasons-some reflecting economic events, others reflecting administrative changes. For example, economic change would come from a firm relocating into the county; administrative change would come from a company correcting its county designation. The over-the-year changes of employment and wages presented in this release have been adjusted to account for most of the administrative corrections made to the underlying establishment reports. This is done by modifying the prior-year levels used to calculate the over-the-year changes. Percent changes are calculated using an adjusted version of the final 2013 quarterly data as the base data. The adjusted prior-year levels used to calculate the over-the-year percent change in employment and wages are not published. These adjusted prior-year levels do not match the unadjusted data maintained on the BLS Web site. Over-the-year change calculations based on data from the Web site, or from data published in prior BLS news releases, may differ substantially from the over-the-year changes presented in this news release. The adjusted data used to calculate the over-the-year change measures presented in this release account for most of the administrative changes-those occurring when employers update the industry, location, and ownership information of their establishments. The most common adjustments for administrative change are the result of updated information about the county location of individual establishments. Included in these 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. Beginning with the second quarter of 2011, adjusted data account for selected large administrative changes in employment and wages. Beginning with the third quarter of 2014, adjusted data account for state verified improvements in reporting of employment and wages. These adjustments allow QCEW to include county employment and wage growth rates in this news release that would otherwise not meet publication standards. The adjusted data used to calculate the over-the-year change measures presented in any County Employment and Wages news release are valid for comparisons between the starting and ending points (a 12-month period) used in that particular release. Comparisons may not be valid for any time period other than the one featured in a release even if the changes were calculated using adjusted data. County definitions are assigned according to Federal Information Processing Standards Publications (FIPS PUBS) as issued by the National Institute of Standards and Technology, after approval by the Secretary of Commerce pursuant to Section 5131 of the Information Technology Management Reform Act of 1996 and the Computer Security Act of 1987, Public Law 104-106. Areas shown as counties include those designated as independent cities in some jurisdictions and, in Alaska, those designated as census areas where counties have not been created. County data also are presented for the New England states for comparative purposes even though townships are the more common designation used in New England (and New Jersey). The regions 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 2013 edition of this publication, which was published in September 2014, contains selected data produced by Business Employment Dynamics (BED) on job gains and losses, as well as selected data from the first quarter 2014 version of this news release. Tables and additional content from Employment and Wages Annual Averages 2013 are now available online at http://www.bls.gov/cew/cewbultn13.htm. The 2014 edition of Employment and Wages Annual Averages Online will be available in September 2015. News releases on quarterly measures of gross job flows also are available upon request from the Division of Administrative Statistics and Labor Turnover (Business Employment Dynamics), telephone (202) 691-6467; (http://www.bls.gov/bdm/); (e-mail: BDMInfo@bls.gov). Information in this release will be made available to sensory impaired individuals upon request. Voice phone: (202) 691-5200; TDD message referral phone number: 1-800-877-8339.
Table 1. Covered establishments, employment, and wages in the 340 largest counties, third quarter 2014 Employment Average weekly wage(2) Establishments, County(1) third quarter Percent Ranking Percent Ranking 2014 September change, by Third change, by (thousands) 2014 September percent quarter third percent (thousands) 2013-14(3) change 2014 quarter change 2013-14(3) United States(4)......... 9,419.7 137,724.1 2.0 - $949 2.9 - Jefferson, AL............ 17.8 339.0 0.4 282 956 3.7 55 Madison, AL.............. 9.1 183.2 1.0 228 1,036 4.0 40 Mobile, AL............... 9.6 165.7 1.0 228 820 1.6 274 Montgomery, AL........... 6.3 128.1 0.5 276 810 1.8 257 Shelby, AL............... 5.1 80.1 2.7 96 868 2.0 231 Tuscaloosa, AL........... 4.3 90.6 4.5 17 814 1.1 293 Anchorage Borough, AK.... 8.4 156.2 -0.2 318 1,066 3.2 91 Maricopa, AZ............. 93.4 1,756.8 2.0 145 914 1.8 257 Pima, AZ................. 18.7 352.4 0.1 299 818 2.9 125 Benton, AR............... 5.7 106.5 7.4 2 926 0.5 317 Pulaski, AR.............. 14.3 242.9 0.2 293 849 2.3 194 Washington, AR........... 5.7 96.8 1.5 171 774 0.3 325 Alameda, CA.............. 57.5 706.5 3.2 65 1,247 4.2 31 Contra Costa, CA......... 29.9 342.7 2.2 128 1,142 2.1 223 Fresno, CA............... 31.1 365.4 1.0 228 747 3.2 91 Kern, CA................. 17.3 326.4 2.2 128 819 3.8 50 Los Angeles, CA.......... 442.4 4,184.4 2.1 137 1,036 3.1 103 Marin, CA................ 12.1 111.3 3.0 77 1,120 3.9 44 Monterey, CA............. 12.9 197.1 4.0 34 797 1.0 300 Orange, CA............... 108.0 1,475.0 2.3 119 1,050 2.6 154 Placer, CA............... 11.4 143.7 3.8 42 937 2.9 125 Riverside, CA............ 53.6 626.4 4.4 19 756 2.6 154 Sacramento, CA........... 52.7 618.0 3.3 61 1,050 2.0 231 San Bernardino, CA....... 51.4 658.1 4.1 29 793 2.7 140 San Diego, CA............ 100.8 1,344.5 2.3 119 1,030 0.8 306 San Francisco, CA........ 57.6 648.6 5.1 9 1,685 8.6 2 San Joaquin, CA.......... 16.7 224.0 3.6 51 799 1.4 286 San Luis Obispo, CA...... 9.8 110.8 3.8 42 785 2.2 206 San Mateo, CA............ 26.1 375.4 4.4 19 1,824 7.1 4 Santa Barbara, CA........ 14.6 194.6 3.3 61 901 2.3 194 Santa Clara, CA.......... 66.2 986.6 4.4 19 2,012 7.4 3 Santa Cruz, CA........... 9.2 100.8 2.1 137 837 2.6 154 Solano, CA............... 10.3 128.3 2.1 137 958 4.1 38 Sonoma, CA............... 18.9 195.1 3.5 54 896 1.9 244 Stanislaus, CA........... 14.4 177.6 2.5 107 801 1.9 244 Tulare, CA............... 9.2 151.6 2.8 86 667 3.6 61 Ventura, CA.............. 24.9 310.7 1.2 199 945 2.1 223 Yolo, CA................. 6.1 101.2 0.8 248 972 4.5 23 Adams, CO................ 9.4 185.4 5.7 6 924 2.8 129 Arapahoe, CO............. 19.7 307.9 2.8 86 1,096 2.7 140 Boulder, CO.............. 13.7 170.1 2.8 86 1,129 3.0 117 Denver, CO............... 28.0 467.4 4.9 13 1,175 4.6 22 Douglas, CO.............. 10.3 107.9 3.7 48 1,037 0.4 323 El Paso, CO.............. 17.2 250.4 1.8 153 859 2.3 194 Jefferson, CO............ 18.2 223.8 3.2 65 951 3.0 117 Larimer, CO.............. 10.6 143.7 3.8 42 859 3.4 74 Weld, CO................. 6.3 100.7 8.8 1 869 4.3 28 Fairfield, CT............ 34.1 420.4 0.8 248 1,400 1.7 264 Hartford, CT............. 26.4 503.0 1.0 228 1,123 0.0 329 New Haven, CT............ 23.1 361.2 0.7 263 987 2.0 231 New London, CT........... 7.1 122.3 -0.6 330 927 2.0 231 New Castle, DE........... 18.3 278.7 2.3 119 1,074 1.9 244 Washington, DC........... 36.3 732.9 1.5 171 1,631 3.8 50 Alachua, FL.............. 6.7 121.5 2.5 107 790 3.4 74 Brevard, FL.............. 14.9 190.0 1.7 162 851 1.2 291 Broward, FL.............. 66.5 739.9 2.8 86 869 2.2 206 Collier, FL.............. 12.7 123.9 4.3 24 806 -3.9 339 Duval, FL................ 27.7 456.5 1.3 192 890 2.8 129 Escambia, FL............. 8.2 124.9 2.1 137 733 3.2 91 Hillsborough, FL......... 39.7 620.0 2.9 83 891 2.6 154 Lake, FL................. 7.7 86.2 2.8 86 656 2.5 165 Lee, FL.................. 20.1 223.2 6.1 4 743 1.6 274 Leon, FL................. 8.4 142.2 2.8 86 771 1.7 264 Manatee, FL.............. 10.0 106.5 3.1 73 706 1.0 300 Marion, FL............... 8.1 94.9 3.2 65 644 1.1 293 Miami-Dade, FL........... 94.3 1,047.0 3.0 77 891 2.2 206 Okaloosa, FL............. 6.2 78.2 0.1 299 779 2.8 129 Orange, FL............... 38.7 735.7 3.6 51 821 2.1 223 Osceola, FL.............. 6.1 80.9 3.0 77 656 2.2 206 Palm Beach, FL........... 52.6 538.4 3.9 36 903 1.9 244 Pasco, FL................ 10.3 105.7 4.2 26 650 2.7 140 Pinellas, FL............. 31.7 397.8 2.1 137 826 2.5 165 Polk, FL................. 12.7 196.2 1.9 147 730 1.5 282 Sarasota, FL............. 15.1 152.5 6.1 4 754 1.3 290 Seminole, FL............. 14.2 169.0 4.1 29 777 1.8 257 Volusia, FL.............. 13.7 156.2 2.6 102 664 2.3 194 Bibb, GA................. 4.5 81.8 1.8 153 737 1.5 282 Chatham, GA.............. 8.2 141.8 4.4 19 800 1.7 264 Clayton, GA.............. 4.3 113.0 2.4 113 892 1.9 244 Cobb, GA................. 22.6 325.4 3.8 42 988 2.7 140 De Kalb, GA.............. 18.8 283.6 3.4 57 947 0.9 304 Fulton, GA............... 44.4 772.1 3.5 54 1,236 3.2 91 Gwinnett, GA............. 25.3 327.9 3.8 42 932 3.4 74 Muscogee, GA............. 4.8 94.4 0.5 276 744 2.1 223 Richmond, GA............. 4.7 102.5 3.0 77 800 1.5 282 Honolulu, HI............. 24.9 456.1 0.8 248 906 4.0 40 Ada, ID.................. 14.1 210.3 1.1 212 831 2.1 223 Champaign, IL............ 4.5 90.0 1.3 192 850 1.7 264 Cook, IL................. 158.3 2,481.9 1.2 199 1,071 2.0 231 Du Page, IL.............. 39.2 601.5 0.9 241 1,067 1.7 264 Kane, IL................. 14.1 206.8 0.7 263 831 3.4 74 Lake, IL................. 23.3 333.9 -0.4 323 1,180 2.4 183 McHenry, IL.............. 9.1 97.3 1.7 162 782 3.0 117 McLean, IL............... 3.9 84.0 -1.2 336 892 0.3 325 Madison, IL.............. 6.2 98.1 2.4 113 771 1.6 274 Peoria, IL............... 4.8 100.0 -1.2 336 870 1.6 274 St. Clair, IL............ 5.7 92.1 -0.7 332 769 2.5 165 Sangamon, IL............. 5.5 128.8 1.2 199 983 3.1 103 Will, IL................. 16.4 218.0 1.2 199 835 2.6 154 Winnebago, IL............ 7.0 128.0 1.5 171 798 2.8 129 Allen, IN................ 8.8 179.2 1.3 192 775 2.0 231 Elkhart, IN.............. 4.7 121.3 3.7 48 778 2.6 154 Hamilton, IN............. 8.8 127.7 4.1 29 891 -0.7 336 Lake, IN................. 10.2 187.3 -0.4 323 848 1.1 293 Marion, IN............... 23.4 580.5 0.9 241 947 0.2 327 St. Joseph, IN........... 5.8 118.8 1.4 184 772 3.1 103 Tippecanoe, IN........... 3.3 81.9 2.6 102 800 4.4 26 Vanderburgh, IN.......... 4.7 106.0 2.0 145 759 2.6 154 Black Hawk, IA........... 3.8 75.9 0.1 299 802 3.1 103 Johnson, IA.............. 4.0 81.1 0.4 282 891 2.2 206 Linn, IA................. 6.5 128.6 0.5 276 915 3.5 66 Polk, IA................. 16.5 287.2 1.7 162 958 3.5 66 Scott, IA................ 5.5 90.4 1.1 212 764 1.1 293 Johnson, KS.............. 21.6 328.7 2.4 113 955 2.5 165 Sedgwick, KS............. 12.4 245.5 1.2 199 825 1.4 286 Shawnee, KS.............. 4.8 97.7 1.5 171 769 -0.4 335 Wyandotte, KS............ 3.3 88.3 4.1 29 914 3.9 44 Boone, KY................ 4.2 78.7 3.3 61 803 0.5 317 Fayette, KY.............. 10.4 184.8 0.8 248 844 0.7 310 Jefferson, KY............ 24.4 446.1 2.5 107 897 2.3 194 Caddo, LA................ 7.3 114.1 -0.4 323 788 3.7 55 Calcasieu, LA............ 4.9 88.6 4.0 34 849 5.5 13 East Baton Rouge, LA..... 14.6 271.7 2.3 119 889 1.0 300 Jefferson, LA............ 13.5 191.4 0.5 276 855 2.2 206 Lafayette, LA............ 9.2 142.1 1.1 212 949 4.4 26 Orleans, LA.............. 11.6 186.4 3.2 65 931 2.4 183 St. Tammany, LA.......... 7.6 83.1 2.3 119 816 3.3 81 Cumberland, ME........... 12.7 174.8 0.8 248 832 2.3 194 Anne Arundel, MD......... 14.5 255.5 0.9 241 1,021 2.4 183 Baltimore, MD............ 21.0 365.5 0.2 293 959 2.6 154 Frederick, MD............ 6.3 96.1 0.8 248 905 4.0 40 Harford, MD.............. 5.6 88.8 0.1 299 904 3.1 103 Howard, MD............... 9.4 161.2 0.8 248 1,183 6.0 8 Montgomery, MD........... 32.5 455.9 0.4 282 1,243 2.2 206 Prince Georges, MD....... 15.6 306.2 1.6 167 1,033 2.9 125 Baltimore City, MD....... 13.7 337.3 2.2 128 1,123 2.7 140 Barnstable, MA........... 9.2 98.9 1.4 184 782 2.2 206 Bristol, MA.............. 16.5 220.5 1.7 162 839 0.8 306 Essex, MA................ 22.8 316.3 1.4 184 1,000 3.4 74 Hampden, MA.............. 16.6 202.2 0.7 263 860 2.4 183 Middlesex, MA............ 51.4 857.9 1.8 153 1,382 1.7 264 Norfolk, MA.............. 24.0 337.6 1.4 184 1,079 2.5 165 Plymouth, MA............. 14.5 185.4 1.8 153 880 2.3 194 Suffolk, MA.............. 25.6 621.9 2.1 137 1,515 5.9 10 Worcester, MA............ 22.6 331.4 2.3 119 949 0.7 310 Genesee, MI.............. 7.0 133.7 1.2 199 777 2.5 165 Ingham, MI............... 6.1 150.5 -0.4 323 899 3.2 91 Kalamazoo, MI............ 5.1 113.2 0.2 293 875 3.2 91 Kent, MI................. 13.9 363.8 2.7 96 837 3.3 81 Macomb, MI............... 17.2 307.6 1.0 228 942 2.4 183 Oakland, MI.............. 38.1 693.8 1.4 184 1,028 2.2 206 Ottawa, MI............... 5.5 119.1 4.2 26 801 4.8 19 Saginaw, MI.............. 4.0 83.9 -0.1 315 763 2.7 140 Washtenaw, MI............ 8.0 198.9 0.9 241 1,028 3.6 61 Wayne, MI................ 30.4 694.5 0.8 248 1,027 2.9 125 Anoka, MN................ 6.9 118.2 1.9 147 937 3.8 50 Dakota, MN............... 9.6 181.5 1.3 192 919 3.6 61 Hennepin, MN............. 40.3 872.8 1.5 171 1,175 1.1 293 Olmsted, MN.............. 3.4 92.5 -0.3 322 1,077 11.1 1 Ramsey, MN............... 13.3 326.1 0.3 290 1,057 2.7 140 St. Louis, MN............ 5.3 97.6 0.5 276 827 4.2 31 Stearns, MN.............. 4.2 83.6 0.7 263 793 5.7 12 Washington, MN........... 5.3 76.7 0.4 282 783 2.6 154 Harrison, MS............. 4.5 82.9 -0.2 318 694 2.5 165 Hinds, MS................ 6.0 119.5 0.2 293 817 1.0 300 Boone, MO................ 4.7 91.0 1.5 171 764 1.9 244 Clay, MO................. 5.2 95.0 4.1 29 838 0.4 323 Greene, MO............... 8.2 159.6 2.3 119 725 1.8 257 Jackson, MO.............. 19.9 349.2 0.0 307 961 1.7 264 St. Charles, MO.......... 8.6 133.6 1.4 184 763 4.8 19 St. Louis, MO............ 34.1 582.5 1.1 212 993 3.7 55 St. Louis City, MO....... 11.1 224.7 1.1 212 1,031 3.1 103 Yellowstone, MT.......... 6.3 79.5 1.2 199 807 3.9 44 Douglas, NE.............. 18.8 326.7 1.2 199 885 -0.1 330 Lancaster, NE............ 10.2 164.4 1.2 199 769 2.5 165 Clark, NV................ 51.8 883.2 4.7 15 823 0.5 317 Washoe, NV............... 13.9 196.6 2.7 96 854 0.6 315 Hillsborough, NH......... 12.2 195.0 1.8 153 1,014 2.7 140 Rockingham, NH........... 10.6 142.1 1.2 199 918 5.8 11 Atlantic, NJ............. 6.6 131.2 -4.0 339 790 3.3 81 Bergen, NJ............... 32.7 439.0 0.8 248 1,106 2.0 231 Burlington, NJ........... 11.0 195.0 -1.1 335 969 0.5 317 Camden, NJ............... 11.8 198.1 2.4 113 893 -0.1 330 Essex, NJ................ 20.3 327.9 -0.4 323 1,159 0.7 310 Gloucester, NJ........... 6.1 101.9 2.5 107 812 -0.1 330 Hudson, NJ............... 14.1 238.0 0.4 282 1,275 1.9 244 Mercer, NJ............... 11.0 235.6 1.5 171 1,229 3.2 91 Middlesex, NJ............ 21.8 393.8 0.9 241 1,120 0.5 317 Monmouth, NJ............. 20.0 248.8 1.5 171 917 2.6 154 Morris, NJ............... 16.9 281.1 0.6 271 1,341 0.7 310 Ocean, NJ................ 12.6 158.9 1.5 171 749 1.6 274 Passaic, NJ.............. 12.2 164.5 -2.2 338 926 3.5 66 Somerset, NJ............. 10.0 180.8 2.2 128 1,372 2.2 206 Union, NJ................ 14.2 220.6 0.3 290 1,146 1.8 257 Bernalillo, NM........... 18.2 315.5 1.1 212 826 2.2 206 Albany, NY............... 10.3 226.7 1.6 167 1,008 3.5 66 Bronx, NY................ 17.7 254.0 3.1 73 901 0.9 304 Broome, NY............... 4.6 88.2 0.0 307 737 1.9 244 Dutchess, NY............. 8.5 109.4 0.2 293 943 2.3 194 Erie, NY................. 24.6 461.2 0.6 271 836 2.8 129 Kings, NY................ 57.5 569.3 5.4 7 789 4.1 38 Monroe, NY............... 18.6 376.9 0.7 263 905 0.1 328 Nassau, NY............... 53.5 606.5 1.3 192 1,022 3.2 91 New York, NY............. 127.7 2,494.4 2.7 96 1,733 3.8 50 Oneida, NY............... 5.4 103.5 0.0 307 746 3.2 91 Onondaga, NY............. 13.1 242.9 0.0 307 856 1.9 244 Orange, NY............... 10.2 137.6 1.8 153 777 2.5 165 Queens, NY............... 50.0 558.8 3.9 36 884 2.8 129 Richmond, NY............. 9.6 98.9 1.0 228 805 0.6 315 Rockland, NY............. 10.3 116.6 2.5 107 955 -0.1 330 Saratoga, NY............. 5.8 81.2 0.4 282 844 3.6 61 Suffolk, NY.............. 52.1 640.3 0.8 248 1,031 3.1 103 Westchester, NY.......... 36.6 413.6 1.2 199 1,196 3.1 103 Buncombe, NC............. 8.2 120.2 1.9 147 731 2.5 165 Catawba, NC.............. 4.3 81.5 1.3 192 715 2.7 140 Cumberland, NC........... 6.2 116.5 -0.8 334 748 1.1 293 Durham, NC............... 7.6 188.7 1.5 171 1,219 2.4 183 Forsyth, NC.............. 9.1 179.0 1.1 212 889 5.0 17 Guilford, NC............. 14.2 270.9 0.8 248 843 4.2 31 Mecklenburg, NC.......... 33.8 612.5 4.2 26 1,071 1.7 264 New Hanover, NC.......... 7.4 104.1 3.2 65 750 1.1 293 Wake, NC................. 30.6 491.8 3.1 73 953 1.9 244 Cass, ND................. 6.7 116.3 4.4 19 897 4.3 28 Butler, OH............... 7.5 143.5 2.2 128 827 3.5 66 Cuyahoga, OH............. 35.5 707.9 0.1 299 974 1.8 257 Delaware, OH............. 4.7 82.5 -0.7 332 921 2.7 140 Franklin, OH............. 30.3 709.8 2.5 107 948 2.4 183 Hamilton, OH............. 23.3 502.1 1.2 199 1,073 6.0 8 Lake, OH................. 6.3 94.8 0.8 248 786 3.8 50 Lorain, OH............... 6.1 97.0 1.3 192 767 1.7 264 Lucas, OH................ 10.0 205.2 0.0 307 827 4.2 31 Mahoning, OH............. 5.9 99.5 0.6 271 683 1.6 274 Montgomery, OH........... 12.0 247.4 1.9 147 814 1.4 286 Stark, OH................ 8.7 159.1 1.6 167 755 4.3 28 Summit, OH............... 14.1 261.3 1.0 228 851 2.5 165 Warren, OH............... 4.4 83.9 1.9 147 824 3.5 66 Cleveland, OK............ 5.3 80.2 1.1 212 709 2.2 206 Oklahoma, OK............. 26.5 445.2 1.4 184 949 4.5 23 Tulsa, OK................ 21.4 344.4 1.4 184 893 3.4 74 Clackamas, OR............ 13.3 147.9 2.2 128 874 1.9 244 Jackson, OR.............. 6.8 81.7 1.6 167 740 4.2 31 Lane, OR................. 11.2 143.1 2.3 119 754 3.7 55 Marion, OR............... 9.7 144.2 2.9 83 764 4.2 31 Multnomah, OR............ 31.2 466.7 2.7 96 979 2.8 129 Washington, OR........... 17.3 267.1 2.3 119 1,216 6.2 7 Allegheny, PA............ 35.1 686.2 0.1 299 1,024 2.0 231 Berks, PA................ 8.9 167.6 1.5 171 852 3.0 117 Bucks, PA................ 19.6 252.1 1.2 199 892 2.5 165 Butler, PA............... 5.0 85.2 0.0 307 889 2.7 140 Chester, PA.............. 15.1 240.9 0.9 241 1,160 1.8 257 Cumberland, PA........... 6.1 127.3 1.5 171 867 1.9 244 Dauphin, PA.............. 7.3 176.8 0.6 271 939 2.7 140 Delaware, PA............. 13.7 216.0 1.0 228 994 2.7 140 Erie, PA................. 7.2 125.2 0.1 299 755 2.0 231 Lackawanna, PA........... 5.8 97.9 0.7 263 735 3.2 91 Lancaster, PA............ 12.9 227.4 2.2 128 790 3.0 117 Lehigh, PA............... 8.5 182.0 1.1 212 926 2.3 194 Luzerne, PA.............. 7.5 141.7 1.1 212 751 2.3 194 Montgomery, PA........... 27.2 473.0 0.8 248 1,133 2.4 183 Northampton, PA.......... 6.6 106.1 0.7 263 824 1.4 286 Philadelphia, PA......... 34.7 645.3 1.8 153 1,125 2.0 231 Washington, PA........... 5.3 88.1 1.5 171 939 4.9 18 Westmoreland, PA......... 9.3 133.9 0.8 248 767 3.1 103 York, PA................. 8.9 173.0 0.2 293 825 2.1 223 Providence, RI........... 17.3 280.5 1.8 153 937 1.6 274 Charleston, SC........... 12.7 228.9 4.3 24 837 2.8 129 Greenville, SC........... 12.9 248.3 3.9 36 841 2.6 154 Horry, SC................ 8.0 118.3 3.3 61 580 2.7 140 Lexington, SC............ 5.9 107.9 3.2 65 728 2.8 129 Richland, SC............. 9.3 209.9 2.1 137 815 2.5 165 Spartanburg, SC.......... 5.9 124.1 2.9 83 795 3.9 44 York, SC................. 5.0 81.6 3.9 36 752 3.3 81 Minnehaha, SD............ 6.8 121.9 2.8 86 824 3.3 81 Davidson, TN............. 19.8 459.7 3.8 42 967 2.0 231 Hamilton, TN............. 8.9 188.1 0.6 271 831 3.1 103 Knox, TN................. 11.3 227.6 2.7 96 815 2.3 194 Rutherford, TN........... 4.8 113.0 3.2 65 825 3.3 81 Shelby, TN............... 19.6 476.1 1.0 228 965 0.8 306 Williamson, TN........... 7.2 109.5 5.4 7 1,047 2.8 129 Bell, TX................. 4.9 111.2 -0.1 315 798 3.5 66 Bexar, TX................ 37.1 796.4 2.6 102 854 3.3 81 Brazoria, TX............. 5.2 99.4 2.6 102 966 7.1 4 Brazos, TX............... 4.2 96.2 1.1 212 734 3.2 91 Cameron, TX.............. 6.3 133.5 1.0 228 603 3.1 103 Collin, TX............... 21.3 346.4 3.2 65 1,097 2.0 231 Dallas, TX............... 71.4 1,558.5 3.5 54 1,141 2.5 165 Denton, TX............... 12.6 205.8 4.5 17 871 3.6 61 El Paso, TX.............. 14.4 283.4 0.4 282 682 2.4 183 Fort Bend, TX............ 11.2 164.4 5.1 9 956 0.7 310 Galveston, TX............ 5.7 101.0 2.8 86 824 2.1 223 Gregg, TX................ 4.2 79.0 3.0 77 864 2.5 165 Harris, TX............... 108.7 2,269.5 3.6 51 1,238 4.0 40 Hidalgo, TX.............. 11.8 237.9 2.6 102 616 3.5 66 Jefferson, TX............ 5.8 124.0 4.6 16 969 4.5 23 Lubbock, TX.............. 7.3 131.5 2.2 128 764 3.7 55 McLennan, TX............. 5.0 105.0 0.7 263 775 4.2 31 Midland, TX.............. 5.4 93.1 7.4 2 1,256 6.8 6 Montgomery, TX........... 10.0 159.5 5.1 9 954 5.5 13 Nueces, TX............... 8.1 164.1 3.4 57 860 5.5 13 Potter, TX............... 4.0 77.3 0.5 276 802 3.4 74 Smith, TX................ 5.9 96.9 1.7 162 818 3.9 44 Tarrant, TX.............. 39.9 825.6 1.9 147 944 3.9 44 Travis, TX............... 35.3 658.1 3.9 36 1,074 3.7 55 Webb, TX................. 5.0 95.0 2.4 113 653 3.3 81 Williamson, TX........... 9.0 144.5 2.4 113 923 -0.8 337 Davis, UT................ 7.8 115.9 3.9 36 762 3.0 117 Salt Lake, UT............ 40.8 627.0 2.8 86 897 2.4 183 Utah, UT................. 13.9 198.8 4.8 14 747 -0.1 330 Weber, UT................ 5.6 95.3 1.8 153 721 1.7 264 Chittenden, VT........... 6.4 100.7 1.1 212 916 2.1 223 Arlington, VA............ 8.8 164.7 0.0 307 1,545 4.8 19 Chesterfield, VA......... 8.1 123.2 0.9 241 825 2.2 206 Fairfax, VA.............. 35.1 579.3 -0.4 323 1,447 1.2 291 Henrico, VA.............. 10.5 178.7 1.1 212 922 2.2 206 Loudoun, VA.............. 10.6 147.9 1.0 228 1,105 1.9 244 Prince William, VA....... 8.4 118.5 1.0 228 845 0.8 306 Alexandria City, VA...... 6.2 94.9 -0.1 315 1,345 2.3 194 Chesapeake City, VA...... 5.7 96.0 0.0 307 743 2.2 206 Newport News City, VA.... 3.7 97.7 0.1 299 928 2.4 183 Norfolk City, VA......... 5.5 134.6 -0.4 323 931 3.3 81 Richmond City, VA........ 7.1 149.5 1.0 228 1,041 2.2 206 Virginia Beach City, VA.. 11.3 172.3 1.1 212 751 2.0 231 Benton, WA............... 5.7 82.7 3.4 57 930 1.5 282 Clark, WA................ 14.0 143.0 5.0 12 890 2.8 129 King, WA................. 84.3 1,252.8 3.4 57 1,452 5.1 16 Kitsap, WA............... 6.7 83.1 3.0 77 904 3.3 81 Pierce, WA............... 21.7 282.3 2.8 86 870 3.0 117 Snohomish, WA............ 20.2 269.9 2.2 128 1,019 0.5 317 Spokane, WA.............. 15.7 208.5 2.1 137 823 3.1 103 Thurston, WA............. 7.9 104.3 3.7 48 877 1.6 274 Whatcom, WA.............. 7.1 83.5 1.1 212 782 2.2 206 Yakima, WA............... 8.1 119.2 3.1 73 658 3.1 103 Kanawha, WV.............. 5.9 103.9 -0.2 318 828 3.0 117 Brown, WI................ 6.4 149.6 -0.2 318 829 3.1 103 Dane, WI................. 14.1 314.7 1.1 212 900 -2.2 338 Milwaukee, WI............ 25.0 482.4 0.4 282 902 2.5 165 Outagamie, WI............ 5.0 103.4 0.8 248 808 2.5 165 Waukesha, WI............. 12.3 232.1 0.3 290 929 2.5 165 Winnebago, WI............ 3.5 89.7 -0.6 330 865 3.2 91 San Juan, PR............. 11.4 249.3 -1.8 (5) 603 1.3 (5) (1) Includes areas not officially designated as counties. See Technical Note. (2) Average weekly wages were calculated using unrounded data. (3) Percent changes were computed from quarterly employment and pay data adjusted for noneconomic county reclassifications. See Technical Note. (4) Totals for the United States do not include data for Puerto Rico or the Virgin Islands. (5) This county was not included in the U.S. rankings. Note: Data are preliminary. Includes workers covered by Unemployment Insurance (UI) and Unemployment Compensation for Federal Employees (UCFE) programs. These 339 U.S. counties comprise 71.8 percent of the total covered workers in the U.S.
Table 2. Covered establishments, employment, and wages in the 10 largest counties, third quarter 2014 Employment Average weekly wage(1) Establishments, third quarter County by NAICS supersector 2014 Percent Percent (thousands) September change, Third change, 2014 September quarter third (thousands) 2013-14(2) 2014 quarter 2013-14(2) United States(3) ............................ 9,419.7 137,724.1 2.0 $949 2.9 Private industry........................... 9,125.2 116,563.5 2.3 940 2.8 Natural resources and mining............. 136.7 2,200.5 3.5 1,072 6.1 Construction............................. 758.2 6,371.0 4.7 1,040 3.6 Manufacturing............................ 339.4 12,226.3 1.3 1,153 2.7 Trade, transportation, and utilities..... 1,918.4 26,099.3 2.0 802 2.8 Information.............................. 152.3 2,723.0 0.5 1,726 6.3 Financial activities..................... 837.0 7,692.6 0.9 1,392 3.4 Professional and business services....... 1,685.8 19,249.6 2.9 1,202 2.3 Education and health services............ 1,489.1 20,622.5 1.8 882 2.1 Leisure and hospitality.................. 798.5 14,882.7 2.5 399 3.1 Other services........................... 816.1 4,240.0 1.8 644 3.4 Government................................. 294.5 21,160.6 0.4 1,004 3.4 Los Angeles, CA.............................. 442.4 4,184.4 2.1 1,036 3.1 Private industry........................... 436.6 3,647.8 2.2 1,000 2.7 Natural resources and mining............. 0.5 9.5 -1.4 1,618 -8.9 Construction............................. 13.5 121.9 2.5 1,062 0.0 Manufacturing............................ 12.6 361.0 -2.0 1,136 1.6 Trade, transportation, and utilities..... 54.0 788.2 2.3 857 3.4 Information.............................. 9.7 198.6 0.0 1,840 6.6 Financial activities..................... 24.6 207.8 -0.4 1,624 7.7 Professional and business services....... 47.8 604.0 0.9 1,242 0.9 Education and health services............ 203.5 719.8 2.3 802 2.8 Leisure and hospitality.................. 30.8 468.3 4.5 578 5.1 Other services........................... 28.1 148.2 3.8 688 2.7 Government................................. 5.8 536.7 1.9 1,288 5.5 New York, NY................................. 127.7 2,494.4 2.7 1,733 3.8 Private industry........................... 127.3 2,060.9 3.3 1,845 3.4 Natural resources and mining............. 0.0 0.2 -3.2 3,140 48.3 Construction............................. 2.2 35.6 4.4 1,716 2.6 Manufacturing............................ 2.2 25.4 -0.7 1,196 6.4 Trade, transportation, and utilities..... 20.7 261.4 1.4 1,265 4.7 Information.............................. 4.8 150.4 1.6 2,360 1.0 Financial activities..................... 19.3 360.5 2.7 3,285 5.1 Professional and business services....... 26.9 523.6 3.6 2,074 3.2 Education and health services............ 9.7 320.6 3.7 1,265 0.9 Leisure and hospitality.................. 13.7 277.2 4.5 818 5.5 Other services........................... 20.0 98.5 2.9 1,049 3.8 Government................................. 0.4 433.5 0.0 1,195 4.6 Cook, IL..................................... 158.3 2,481.9 1.2 1,071 2.0 Private industry........................... 157.0 2,187.8 1.5 1,071 3.3 Natural resources and mining............. 0.1 1.0 3.4 1,112 7.0 Construction............................. 13.1 71.8 5.9 1,387 3.7 Manufacturing............................ 6.7 185.4 -0.3 1,137 4.8 Trade, transportation, and utilities..... 31.4 454.8 2.1 869 3.7 Information.............................. 2.9 54.4 1.2 1,627 3.0 Financial activities..................... 16.2 184.2 -0.1 1,848 5.1 Professional and business services....... 33.7 455.4 2.0 1,345 2.9 Education and health services............ 16.6 421.7 0.8 919 1.5 Leisure and hospitality.................. 14.3 258.7 2.0 481 1.3 Other services........................... 17.9 96.8 0.8 838 4.9 Government................................. 1.3 294.1 -0.6 1,075 -6.0 Harris, TX................................... 108.7 2,269.5 3.6 1,238 4.0 Private industry........................... 108.1 2,009.9 3.9 1,254 4.0 Natural resources and mining............. 1.8 95.1 6.5 3,079 7.5 Construction............................. 6.8 158.6 9.9 1,262 6.4 Manufacturing............................ 4.7 198.0 3.6 1,491 4.9 Trade, transportation, and utilities..... 24.6 468.6 3.4 1,103 3.7 Information.............................. 1.2 27.9 -2.4 1,373 4.4 Financial activities..................... 11.1 119.1 1.8 1,490 0.7 Professional and business services....... 21.9 396.3 2.7 1,513 3.6 Education and health services............ 14.9 270.6 2.7 972 0.8 Leisure and hospitality.................. 9.1 211.1 5.5 419 3.7 Other services........................... 11.7 63.7 3.7 755 5.4 Government................................. 0.6 259.6 1.6 1,112 4.3 Maricopa, AZ................................. 93.4 1,756.8 2.0 914 1.8 Private industry........................... 92.7 1,547.6 2.2 907 1.7 Natural resources and mining............. 0.5 7.0 0.2 920 0.2 Construction............................. 7.3 92.3 -1.4 935 -0.5 Manufacturing............................ 3.2 114.0 0.7 1,313 3.0 Trade, transportation, and utilities..... 20.0 347.8 2.1 832 0.8 Information.............................. 1.5 33.2 5.1 1,213 2.4 Financial activities..................... 11.0 153.0 2.8 1,145 1.2 Professional and business services....... 21.8 295.4 1.5 996 3.8 Education and health services............ 10.7 261.8 2.6 935 2.2 Leisure and hospitality.................. 7.4 191.3 3.0 433 1.6 Other services........................... 6.3 48.3 2.6 651 -0.9 Government................................. 0.7 209.2 0.3 972 1.9 Dallas, TX................................... 71.4 1,558.5 3.5 1,141 2.5 Private industry........................... 70.9 1,390.9 3.8 1,144 2.4 Natural resources and mining............. 0.6 10.0 4.9 3,840 18.3 Construction............................. 4.1 77.9 5.7 1,084 5.3 Manufacturing............................ 2.7 107.1 -0.4 1,278 -1.4 Trade, transportation, and utilities..... 15.4 312.5 4.2 1,030 2.3 Information.............................. 1.4 49.2 0.4 1,722 0.2 Financial activities..................... 8.6 152.3 2.5 1,532 5.1 Professional and business services....... 16.0 313.4 5.7 1,290 1.7 Education and health services............ 8.8 182.2 3.5 1,027 0.8 Leisure and hospitality.................. 6.1 145.9 4.9 480 4.8 Other services........................... 6.8 39.9 0.6 751 4.5 Government................................. 0.5 167.6 1.1 1,112 2.9 Orange, CA................................... 108.0 1,475.0 2.3 1,050 2.6 Private industry........................... 106.7 1,341.6 2.4 1,037 2.9 Natural resources and mining............. 0.2 3.3 -0.8 822 16.8 Construction............................. 6.5 83.4 4.8 1,174 4.4 Manufacturing............................ 4.9 158.1 0.3 1,387 6.3 Trade, transportation, and utilities..... 16.8 254.2 2.4 939 1.4 Information.............................. 1.3 23.5 -4.9 1,600 5.2 Financial activities..................... 10.7 113.2 1.2 1,568 0.3 Professional and business services....... 20.7 276.1 2.2 1,205 4.4 Education and health services............ 27.2 185.7 2.5 879 1.2 Leisure and hospitality.................. 7.9 194.3 2.1 457 4.1 Other services........................... 6.9 43.5 4.0 647 1.6 Government................................. 1.3 133.4 0.6 1,196 0.9 San Diego, CA................................ 100.8 1,344.5 2.3 1,030 0.8 Private industry........................... 99.4 1,124.1 2.6 987 0.1 Natural resources and mining............. 0.7 11.2 4.8 610 -3.6 Construction............................. 6.4 64.7 4.6 1,069 1.9 Manufacturing............................ 3.1 96.9 1.4 1,414 3.9 Trade, transportation, and utilities..... 14.2 212.0 1.4 773 -0.4 Information.............................. 1.2 24.3 -1.2 1,751 0.7 Financial activities..................... 9.4 69.8 -1.3 1,340 2.1 Professional and business services....... 18.2 227.7 1.7 1,417 -2.1 Education and health services............ 28.0 184.1 3.1 874 0.8 Leisure and hospitality.................. 7.7 178.4 3.3 457 3.9 Other services........................... 7.3 49.6 5.8 570 1.4 Government................................. 1.4 220.5 0.5 1,262 4.5 King, WA..................................... 84.3 1,252.8 3.4 1,452 5.1 Private industry........................... 83.7 1,094.8 3.7 1,479 5.3 Natural resources and mining............. 0.4 2.8 2.5 1,221 1.6 Construction............................. 6.0 60.5 9.5 1,213 4.3 Manufacturing............................ 2.3 106.6 0.3 1,542 1.7 Trade, transportation, and utilities..... 14.8 234.4 4.6 1,125 4.5 Information.............................. 2.0 87.1 4.4 5,134 9.3 Financial activities..................... 6.5 65.9 1.2 1,490 3.3 Professional and business services....... 15.8 209.1 4.3 1,528 4.9 Education and health services............ 20.8 160.4 3.3 949 4.3 Leisure and hospitality.................. 6.8 127.1 3.3 510 2.2 Other services........................... 8.4 41.0 3.2 794 2.5 Government................................. 0.5 158.0 1.3 1,262 3.0 Miami-Dade, FL............................... 94.3 1,047.0 3.0 891 2.2 Private industry........................... 94.0 911.7 3.6 873 2.2 Natural resources and mining............. 0.5 7.4 6.0 573 4.8 Construction............................. 5.4 37.1 11.4 887 4.5 Manufacturing............................ 2.7 37.3 1.9 850 4.7 Trade, transportation, and utilities..... 27.4 269.0 3.1 806 1.6 Information.............................. 1.6 18.4 4.5 1,402 2.0 Financial activities..................... 9.9 71.8 4.2 1,362 4.0 Professional and business services....... 19.8 141.3 4.2 1,055 3.5 Education and health services............ 10.1 163.4 2.2 913 0.6 Leisure and hospitality.................. 7.2 127.3 2.9 521 -0.4 Other services........................... 8.2 37.3 3.2 576 2.7 Government................................. 0.3 135.3 -1.3 1,017 2.2 (1) Average weekly wages were calculated using unrounded data. (2) Percent changes were computed from quarterly employment and pay data adjusted for noneconomic county reclassifications. See Technical Note. (3) Totals for the United States do not include data for Puerto Rico or the Virgin Islands. Note: Data are preliminary. Counties selected are based on 2013 annual average employment. Includes workers covered by Unemployment Insurance (UI) and Unemployment Compensation for Federal Employees (UCFE) programs.
Table 3. Covered establishments, employment, and wages by state, third quarter 2014 Employment Average weekly wage(1) Establishments, third quarter State 2014 Percent Percent (thousands) September change, Third change, 2014 September quarter third (thousands) 2013-14 2014 quarter 2013-14 United States(2)........... 9,419.7 137,724.1 2.0 $949 2.9 Alabama.................... 118.3 1,871.2 1.3 815 2.5 Alaska..................... 22.4 344.7 -0.1 1,019 3.0 Arizona.................... 147.4 2,539.6 1.8 876 2.0 Arkansas................... 87.3 1,170.9 1.3 737 1.8 California................. 1,386.5 16,013.4 3.1 1,095 3.7 Colorado................... 180.9 2,443.0 3.7 982 3.0 Connecticut................ 114.4 1,663.2 0.8 1,124 1.4 Delaware................... 30.1 426.1 1.9 961 2.2 District of Columbia....... 36.3 732.9 0.8 1,631 4.5 Florida.................... 642.5 7,748.4 3.3 826 2.1 Georgia.................... 283.0 4,059.0 3.4 891 2.8 Hawaii..................... 39.0 625.1 0.9 870 3.9 Idaho...................... 55.4 658.4 2.1 721 2.6 Illinois................... 416.0 5,807.4 1.2 982 2.5 Indiana.................... 158.6 2,924.7 1.4 799 1.9 Iowa....................... 100.2 1,528.8 1.1 800 3.6 Kansas..................... 86.0 1,363.1 1.2 794 2.3 Kentucky................... 121.0 1,827.8 1.8 781 2.5 Louisiana.................. 127.2 1,928.3 1.7 852 3.1 Maine...................... 49.4 604.5 0.3 754 2.6 Maryland................... 164.9 2,574.5 1.1 1,042 3.1 Massachusetts.............. 232.1 3,386.7 1.8 1,164 3.0 Michigan................... 236.5 4,141.0 1.7 896 2.4 Minnesota.................. 165.9 2,757.9 1.1 965 2.9 Mississippi................ 71.5 1,105.0 0.5 697 1.3 Missouri................... 185.7 2,686.4 1.0 828 2.7 Montana.................... 44.3 449.5 0.7 732 3.7 Nebraska................... 72.1 950.0 1.1 779 1.8 Nevada..................... 76.2 1,215.8 4.0 840 0.5 New Hampshire.............. 50.3 633.5 1.4 927 3.6 New Jersey................. 264.4 3,880.4 0.8 1,087 1.7 New Mexico................. 57.2 804.0 1.1 786 2.6 New York................... 627.7 8,902.1 2.0 1,145 3.2 North Carolina............. 260.3 4,085.5 1.9 839 2.8 North Dakota............... 31.7 455.9 4.3 977 6.1 Ohio....................... 290.0 5,219.1 1.4 863 3.1 Oklahoma................... 107.4 1,592.3 1.0 826 3.6 Oregon..................... 137.5 1,752.8 2.4 887 3.6 Pennsylvania............... 349.5 5,676.2 1.0 937 2.6 Rhode Island............... 35.9 471.8 1.4 895 1.8 South Carolina............. 118.7 1,902.7 2.4 768 2.4 South Dakota............... 32.1 415.8 1.7 733 3.7 Tennessee.................. 146.2 2,775.5 2.4 837 2.1 Texas...................... 623.1 11,433.6 3.1 988 3.8 Utah....................... 90.8 1,304.7 3.1 803 1.5 Vermont.................... 24.5 306.5 1.2 805 2.3 Virginia................... 242.4 3,667.9 0.6 989 2.0 Washington................. 236.9 3,112.8 3.2 1,087 3.9 West Virginia.............. 49.8 709.3 -0.2 778 3.5 Wisconsin.................. 166.2 2,783.1 1.1 808 1.9 Wyoming.................... 25.6 291.3 1.7 877 4.4 Puerto Rico................ 49.0 896.7 -1.5 505 0.8 Virgin Islands............. 3.4 37.5 -1.0 720 2.0 (1) Average weekly wages were calculated using unrounded data. (2) Totals for the United States do not include data for Puerto Rico or the Virgin Islands. Note: Data are preliminary. Includes workers covered by Unemployment Insurance (UI) and Unemployment Compensation for Federal Employees (UCFE) programs.