An official website of the United States government
For release 10:00 a.m. (EDT), Wednesday, June 8, 2016 USDL-16-1148 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 2015 From December 2014 to December 2015, employment increased in 308 of the 342 largest U.S. counties, the U.S. Bureau of Labor Statistics reported today. Williamson, Tenn., had the largest percentage increase with a gain of 6.8 percent over the year, above the national job growth rate of 1.9 percent. Within Williamson, the largest employment increase occurred in professional and business services, which gained 3,185 jobs over the year (10.9 percent). Ector, Texas, had the largest over-the- year percentage decrease in employment among the largest counties in the U.S., with a loss of 11.8 percent. Within Ector, natural resources and mining had the largest decrease in employment, with a loss of 4,509 jobs (-34.1 percent). County employment and wage data are from the Quarterly Census of Employment and Wages (QCEW) program, which provides the only detailed quarterly and annual universe count of establishments, employment, and wages at the county, MSA, state, and national levels by detailed industry. These detailed data are published within six months following the end of each calendar quarter. The U.S. average weekly wage increased 4.4 percent over the year, growing to $1,082 in the fourth quarter of 2015. Wyandotte, Kan., had the largest over-the-year percentage increase in average weekly wages with a gain of 10.4 percent. Within Wyandotte, an average weekly wage gain of $250, or 21.2 percent, in manufacturing made the largest contribution to the county’s increase in average weekly wages. Midland, Texas, experienced the largest percentage decrease in average weekly wages with a loss of 11.5 percent over the year. Within Midland, natural resources and mining had the largest impact on the county’s average weekly wage decline with a decrease of $257 (-11.6 percent) over the year. Large County Employment In December 2015, national employment was 141.9 million (as measured by the QCEW program). Over the year, employment increased 1.9 percent, or 2.7 million. In December 2015, the 342 U.S. counties with 75,000 or more jobs accounted for 72.5 percent of total U.S. employment and 77.8 percent of total wages. These 342 counties had a net job growth of 2.2 million over the year, accounting for 81.4 percent of the overall U.S. employment increase. The five counties with the largest increases in employment levels had a combined over-the-year employment gain of 319,200 jobs, which was 12.0 percent of the overall job increase for the U.S. (See table A.) Employment declined in 26 of the largest counties from December 2014 to December 2015. Ector, Texas, had the largest over-the-year percentage decrease in employment (-11.8 percent), followed by Midland, Texas; Lafayette, La.; Gregg, Texas; and Weld, Colo. (See table 1.) Table A. Large counties ranked by December 2015 employment, December 2014-15 employment increase, and December 2014-15 percent increase in employment -------------------------------------------------------------------------------------------------------- Employment in large counties -------------------------------------------------------------------------------------------------------- December 2015 employment | Increase in employment, | Percent increase in employment, (thousands) | December 2014-15 | December 2014-15 | (thousands) | -------------------------------------------------------------------------------------------------------- | | United States 141,924.5| United States 2,658.0| United States 1.9 -------------------------------------------------------------------------------------------------------- | | Los Angeles, Calif. 4,341.0| Los Angeles, Calif. 99.3| Williamson, Tenn. 6.8 Cook, Ill. 2,575.7| Dallas, Texas 62.4| Utah, Utah 6.6 New York, N.Y. 2,442.2| Maricopa, Ariz. 58.6| Loudoun, Va. 6.3 Harris, Texas 2,302.8| New York, N.Y. 50.7| Chesterfield, Va. 6.0 Maricopa, Ariz. 1,883.2| Cook, Ill. 48.2| Lee, Fla. 5.9 Dallas, Texas 1,651.6| Santa Clara, Calif. 38.5| Osceola, Fla. 5.8 Orange, Calif. 1,550.6| King, Wash. 36.3| Bell, Texas 5.4 San Diego, Calif. 1,399.7| San Diego, Calif. 35.4| Boone, Ky. 5.1 King, Wash. 1,297.2| Orange, Calif. 34.7| Clay, Mo. 5.1 Miami-Dade, Fla. 1,115.9| Clark, Nev. 33.0| Hall, Ga. 5.0 -------------------------------------------------------------------------------------------------------- Large County Average Weekly Wages Average weekly wages for the nation increased to $1,082, a 4.4 percent increase, during the year ending in the fourth quarter of 2015. Among the 342 largest counties, 325 had over-the-year increases in average weekly wages. Wyandotte, Kan., had the largest percentage wage increase among the largest U.S. counties (10.4 percent). Of the 342 largest counties, 10 experienced over-the-year decreases in average weekly wages. Midland, Texas, had the largest percentage decrease in average weekly wages (-11.5 percent), followed by Ector, Texas; Lafayette, La.; Gregg, Texas; and San Mateo, Calif. (See table 1.) Table B. Large counties ranked by fourth quarter 2015 average weekly wages, fourth quarter 2014-15 increase in average weekly wages, and fourth quarter 2014-15 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 2015 | wage, fourth quarter 2014-15 | weekly wage, fourth | | quarter 2014-15 -------------------------------------------------------------------------------------------------------- | | United States $1,082| United States $46| United States 4.4 -------------------------------------------------------------------------------------------------------- | | Santa Clara, Calif. $2,335| Santa Clara, Calif. $198| Wyandotte, Kan. 10.4 New York, N.Y. 2,235| Lake, Ill. 129| Sonoma, Calif. 10.0 San Mateo, Calif. 2,095| San Francisco, Calif. 118| Lake, Ill. 9.8 San Francisco, Calif. 1,961| Wyandotte, Kan. 98| Passaic, N.J. 9.4 Suffolk, Mass. 1,943| Sonoma, Calif. 95| Santa Clara, Calif. 9.3 Washington, D.C. 1,756| Passaic, N.J. 95| Anoka, Minn. 9.3 Fairfield, Conn. 1,735| Suffolk, Mass. 92| Clay, Mo. 9.2 Arlington, Va. 1,686| Wayne, Mich. 91| Collier, Fla. 9.1 Fairfax, Va. 1,618| Anoka, Minn. 88| Catawba, N.C. 8.9 Morris, N.J. 1,601| Alameda, Calif. 86| Bell, Texas 8.9 -------------------------------------------------------------------------------------------------------- Ten Largest U.S. Counties Among the 10 largest counties, 9 had over-the-year percentage increases in employment in December 2015. Dallas, Texas, had the largest gain (3.9 percent). Within Dallas, trade, transportation, and utilities had the largest over-the-year employment level increase, with a gain of 20,999 jobs, or 6.3 percent. Harris, Texas, had the only percentage decrease in employment among the 10 largest counties (-0.5 percent). (See table 2.) Average weekly wages increased over the year in all of the 10 largest U.S. counties. Los Angeles, Calif., experienced the largest percentage gain in average weekly wages (5.5 percent). Within Los Angeles, information tied with professional and business services for the largest impact on the county’s average weekly wage growth. Within information, average weekly wages increased by $259, or 11.3 percent, over the year. Within professional and business services, average weekly wages increased by $106, or 6.9 percent, over the year. Harris, Texas, had the smallest percentage gain in average weekly wages among the 10 largest counties (0.4 percent). For More Information The tables included in this release contain data for the nation and for the 342 U.S. counties with annual average employment levels of 75,000 or more in 2014. December 2015 employment and 2015 fourth quarter average weekly wages for all states are provided in table 3 of this release. The data are derived from reports submitted by every employer subject to unemployment insurance (UI) laws. The 9.7 million employer reports cover 141.9 million full- and part-time workers. Data for the fourth quarter of 2015 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 first quarter 2016 is scheduled to be released on Wednesday, September 7, 2016.
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 2015 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 343 counties presented in this release were derived using 2014 preliminary annual averages of employment. For 2015 data, three counties have been added to the publication tables: Butte, Calif.; Hall, Ga.; and Ector, Texas. These counties will be included in all 2015 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- | 623,000 establish- | submitted by 9.5 | ministrative records| ments | million establish- | submitted by 7.6 | | ments in first | million private-sec-| | quarter of 2015 | 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| -7 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 9.4 million employer reports of employment and wages submitted by states to the BLS in 2014. 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 2014, UI and UCFE programs covered workers in 136.6 million jobs. The estimated 131.8 million workers in these jobs (after adjustment for multiple jobholders) represented 96.3 percent of civilian wage and salary employment. Covered workers received $7.017 trillion in pay, representing 93.8 percent of the wage and salary component of personal income and 40.5 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 2014 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. Adjusted data account for improvements in reporting employment and wages for individual and multi-unit establishments. To accomplish this, adjustments were implemented to account for: administrative changes caused by multi-unit employers who start reporting for each individual establishment rather than as a single entity (first quarter of 2008); selected large administrative changes in employment and wages (second quarter of 2011); and state verified improvements in reporting of employment and wages (third quarter of 2014). 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 2014 edition of this publication, which was published in September 2015, contains selected data produced by Business Employment Dynamics (BED) on job gains and losses, as well as selected data from the first quarter 2015 version of this news release. Tables and additional content from the 2014 edition of Employment and Wages Annual Averages Online are now available at http://www.bls.gov/cew/cewbultn14.htm. The 2015 edition of Employment and Wages Annual Averages Online will be available in September 2016. 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 343 largest counties, fourth quarter 2015 Employment Average weekly wage(2) Establishments, County(1) fourth quarter Percent Ranking Percent Ranking 2015 December change, by Fourth change, by (thousands) 2015 December percent quarter fourth percent (thousands) 2014-15(3) change 2015 quarter change 2014-15(3) United States(4)......... 9,685.3 141,924.5 1.9 - $1,082 4.4 - Jefferson, AL............ 17.9 341.9 0.8 264 1,049 2.4 300 Madison, AL.............. 9.3 191.1 2.3 138 1,143 3.3 276 Mobile, AL............... 9.8 169.5 1.1 235 941 4.8 172 Montgomery, AL........... 6.4 130.5 1.0 246 935 3.4 266 Shelby, AL............... 5.6 84.6 1.3 213 1,026 3.8 246 Tuscaloosa, AL........... 4.4 92.6 0.6 281 883 1.6 313 Anchorage Borough, AK.... 8.4 152.7 -0.2 316 1,136 3.9 237 Maricopa, AZ............. 97.2 1,883.2 3.2 77 1,016 4.1 225 Pima, AZ................. 19.1 361.7 0.7 273 891 4.0 230 Benton, AR............... 6.0 112.7 3.6 51 1,042 3.5 262 Pulaski, AR.............. 14.6 250.2 1.9 171 972 3.7 254 Washington, AR........... 5.9 102.7 4.3 25 952 6.7 39 Alameda, CA.............. 60.2 741.1 3.3 69 1,407 6.5 53 Butte, CA................ 8.1 79.3 2.7 119 800 5.5 114 Contra Costa, CA......... 31.2 354.7 3.2 77 1,286 6.5 53 Fresno, CA............... 32.8 363.6 3.5 58 849 5.2 137 Kern, CA................. 17.7 305.9 -0.8 325 884 0.6 323 Los Angeles, CA.......... 463.6 4,341.0 2.3 138 1,266 5.5 114 Marin, CA................ 12.4 114.0 2.8 108 1,334 4.7 175 Monterey, CA............. 13.3 165.6 3.8 37 914 6.8 36 Orange, CA............... 113.6 1,550.6 2.3 138 1,205 4.1 225 Placer, CA............... 12.1 151.7 4.4 21 1,071 3.4 266 Riverside, CA............ 58.0 679.7 4.9 11 840 4.7 175 Sacramento, CA........... 54.7 637.1 3.7 46 1,153 5.2 137 San Bernardino, CA....... 54.3 719.8 3.6 51 888 4.7 175 San Diego, CA............ 105.7 1,399.7 2.6 125 1,184 4.3 212 San Francisco, CA........ 59.5 691.6 4.6 17 1,961 6.4 61 San Joaquin, CA.......... 17.2 233.2 4.2 27 894 7.1 28 San Luis Obispo, CA...... 10.1 113.5 3.2 77 900 8.2 15 San Mateo, CA............ 27.4 393.3 3.8 37 2,095 -2.3 332 Santa Barbara, CA........ 15.1 191.9 3.0 93 1,038 5.8 94 Santa Clara, CA.......... 69.3 1,040.8 3.8 37 2,335 9.3 5 Santa Cruz, CA........... 9.5 97.5 3.2 77 952 3.1 284 Solano, CA............... 10.7 134.0 3.4 63 1,080 5.3 127 Sonoma, CA............... 19.4 199.5 3.7 46 1,049 10.0 2 Stanislaus, CA........... 14.9 179.0 4.2 27 888 6.2 68 Tulare, CA............... 9.7 153.0 3.3 69 761 3.8 246 Ventura, CA.............. 25.8 320.6 1.1 235 1,065 3.9 237 Yolo, CA................. 6.4 96.4 3.2 77 1,151 5.4 120 Adams, CO................ 10.1 195.0 2.8 108 1,036 5.1 148 Arapahoe, CO............. 20.9 321.8 2.8 108 1,242 2.1 309 Boulder, CO.............. 14.3 175.1 2.5 130 1,265 5.0 154 Denver, CO............... 29.7 485.3 3.2 77 1,292 2.9 291 Douglas, CO.............. 11.1 115.3 3.2 77 1,291 3.0 289 El Paso, CO.............. 18.2 261.5 3.2 77 952 3.9 237 Jefferson, CO............ 19.1 232.8 3.0 93 1,082 3.9 237 Larimer, CO.............. 11.3 149.9 3.7 46 986 2.3 306 Weld, CO................. 6.7 100.2 -3.1 333 928 0.3 325 Fairfield, CT............ 34.8 429.7 0.5 286 1,735 3.5 262 Hartford, CT............. 27.2 511.0 0.3 299 1,306 4.6 189 New Haven, CT............ 23.5 366.3 0.5 286 1,128 3.7 254 New London, CT........... 7.3 122.3 0.8 264 1,053 4.2 218 New Castle, DE........... 18.9 293.2 1.9 171 1,198 2.9 291 Washington, DC........... 39.0 754.2 2.2 144 1,756 3.4 266 Alachua, FL.............. 6.8 126.3 3.0 93 911 3.1 284 Brevard, FL.............. 15.0 197.7 2.1 156 939 6.0 80 Broward, FL.............. 67.3 785.7 2.8 108 1,018 6.0 80 Collier, FL.............. 13.1 143.1 3.4 63 969 9.1 8 Duval, FL................ 27.9 485.6 3.2 77 1,008 2.0 310 Escambia, FL............. 8.0 127.8 2.0 165 857 5.2 137 Hillsborough, FL......... 39.9 671.9 4.4 21 1,029 4.7 175 Lake, FL................. 7.7 93.2 4.9 11 738 6.6 43 Lee, FL.................. 20.6 253.1 5.9 5 842 4.7 175 Leon, FL................. 8.3 145.2 1.2 221 881 4.5 196 Manatee, FL.............. 10.1 120.7 2.6 125 818 6.5 53 Marion, FL............... 8.1 98.9 2.1 156 749 5.9 88 Miami-Dade, FL........... 94.8 1,115.9 3.0 93 1,051 4.4 208 Okaloosa, FL............. 6.2 80.4 3.8 37 859 4.9 161 Orange, FL............... 39.3 786.0 4.4 21 945 5.7 100 Osceola, FL.............. 6.3 87.6 5.8 6 730 6.6 43 Palm Beach, FL........... 53.6 588.9 4.0 33 1,081 7.2 26 Pasco, FL................ 10.3 113.2 4.4 21 749 5.8 94 Pinellas, FL............. 31.9 418.7 3.6 51 979 5.6 105 Polk, FL................. 12.6 209.9 2.7 119 816 5.2 137 Sarasota, FL............. 15.3 164.4 3.8 37 914 5.9 88 Seminole, FL............. 14.4 180.3 3.9 35 897 6.0 80 Volusia, FL.............. 13.8 164.0 3.3 69 759 4.5 196 Bibb, GA................. 4.6 84.8 1.1 235 838 4.5 196 Chatham, GA.............. 8.5 147.9 3.3 69 921 5.9 88 Clayton, GA.............. 4.4 121.9 4.8 13 957 -1.8 330 Cobb, GA................. 23.5 340.6 3.0 93 1,118 3.4 266 DeKalb, GA............... 19.4 301.0 3.8 37 1,048 3.4 266 Fulton, GA............... 46.3 811.4 2.8 108 1,402 4.5 196 Gwinnett, GA............. 26.5 341.9 2.9 102 1,041 4.0 230 Hall, GA................. 4.6 82.2 5.0 10 930 7.4 20 Muscogee, GA............. 4.9 94.9 0.1 306 860 6.6 43 Richmond, GA............. 4.8 105.3 1.2 221 875 4.8 172 Honolulu, HI............. 25.5 476.5 1.9 171 997 5.6 105 Ada, ID.................. 14.3 221.3 3.8 37 938 -1.5 329 Champaign, IL............ 4.4 90.0 -0.1 312 901 4.0 230 Cook, IL................. 158.4 2,575.7 1.9 171 1,267 4.4 208 DuPage, IL............... 38.8 612.2 0.4 294 1,257 6.6 43 Kane, IL................. 14.0 209.5 0.8 264 968 6.4 61 Lake, IL................. 22.9 333.5 0.9 254 1,450 9.8 3 McHenry, IL.............. 8.9 97.0 1.2 221 904 6.5 53 McLean, IL............... 3.9 84.6 0.0 309 1,010 4.1 225 Madison, IL.............. 6.1 98.4 0.0 309 876 3.4 266 Peoria, IL............... 4.7 102.2 1.1 235 1,012 5.9 88 St. Clair, IL............ 5.6 94.1 0.4 294 838 5.1 148 Sangamon, IL............. 5.3 128.6 -1.6 331 1,063 4.3 212 Will, IL................. 16.5 225.8 2.2 144 943 5.1 148 Winnebago, IL............ 6.9 129.3 0.9 254 898 3.1 284 Allen, IN................ 8.8 185.0 2.0 165 868 7.3 23 Elkhart, IN.............. 4.7 126.2 2.9 102 886 5.6 105 Hamilton, IN............. 9.0 134.8 3.6 51 1,020 4.9 161 Lake, IN................. 10.3 187.9 0.1 306 909 1.6 313 Marion, IN............... 23.9 594.7 1.8 183 1,056 4.9 161 St. Joseph, IN........... 5.8 123.0 2.6 125 855 5.9 88 Tippecanoe, IN........... 3.4 82.8 0.9 254 904 6.0 80 Vanderburgh, IN.......... 4.8 108.0 0.5 286 886 4.1 225 Black Hawk, IA........... 3.8 74.6 -1.0 327 984 5.8 94 Johnson, IA.............. 4.1 82.0 1.2 221 951 3.7 254 Linn, IA................. 6.6 130.5 0.5 286 1,069 4.7 175 Polk, IA................. 16.7 291.7 1.6 197 1,096 6.5 53 Scott, IA................ 5.6 91.4 0.3 299 903 5.5 114 Johnson, KS.............. 22.6 339.8 1.4 208 1,097 5.3 127 Sedgwick, KS............. 12.8 250.6 0.9 254 960 4.2 218 Shawnee, KS.............. 5.0 97.9 1.3 213 856 4.4 208 Wyandotte, KS............ 3.5 90.3 1.9 171 1,037 10.4 1 Boone, KY................ 4.3 85.2 5.1 8 921 3.4 266 Fayette, KY.............. 10.6 198.1 2.8 108 935 6.9 33 Jefferson, KY............ 25.0 461.4 2.2 144 1,049 8.6 11 Caddo, LA................ 7.3 116.3 -0.6 320 877 1.7 312 Calcasieu, LA............ 5.0 92.6 1.3 213 963 5.5 114 East Baton Rouge, LA..... 15.0 272.0 0.5 286 1,014 4.2 218 Jefferson, LA............ 13.4 195.9 -0.7 323 980 5.0 154 Lafayette, LA............ 9.3 136.1 -5.6 335 990 -4.3 334 Orleans, LA.............. 12.0 195.9 3.0 93 1,021 3.2 280 St. Tammany, LA.......... 7.8 87.9 2.3 138 916 2.9 291 Cumberland, ME........... 13.3 177.1 0.9 254 1,004 5.7 100 Anne Arundel, MD......... 15.0 265.6 2.9 102 1,145 5.4 120 Baltimore, MD............ 21.2 380.9 1.2 221 1,094 5.1 148 Frederick, MD............ 6.4 100.4 2.5 130 1,005 4.3 212 Harford, MD.............. 5.8 93.3 2.3 138 1,033 5.4 120 Howard, MD............... 9.9 166.5 2.0 165 1,323 5.3 127 Montgomery, MD........... 32.8 466.0 0.7 273 1,419 5.7 100 Prince George's, MD...... 15.8 312.2 0.9 254 1,102 5.2 137 Baltimore City, MD....... 13.6 338.6 1.3 213 1,296 6.1 73 Barnstable, MA........... 9.3 90.0 1.1 235 955 7.7 17 Bristol, MA.............. 17.0 224.7 0.7 273 984 2.3 306 Essex, MA................ 23.8 323.8 1.2 221 1,149 4.9 161 Hampden, MA.............. 17.3 206.6 1.0 246 993 4.9 161 Middlesex, MA............ 53.1 889.2 1.6 197 1,563 5.3 127 Norfolk, MA.............. 24.6 349.7 1.1 235 1,338 6.2 68 Plymouth, MA............. 15.1 188.2 0.7 273 1,035 5.6 105 Suffolk, MA.............. 27.5 652.1 2.7 119 1,943 5.0 154 Worcester, MA............ 23.9 341.5 1.8 183 1,086 6.1 73 Genesee, MI.............. 6.9 134.6 -0.1 312 918 8.4 13 Ingham, MI............... 6.0 149.0 1.0 246 1,028 6.6 43 Kalamazoo, MI............ 5.0 116.2 0.6 281 1,000 7.0 30 Kent, MI................. 14.0 382.1 3.4 63 963 4.9 161 Macomb, MI............... 17.3 319.5 1.4 208 1,097 8.4 13 Oakland, MI.............. 38.5 719.3 1.8 183 1,222 4.8 172 Ottawa, MI............... 5.5 120.3 3.1 87 950 3.7 254 Saginaw, MI.............. 4.0 85.8 1.0 246 877 7.5 18 Washtenaw, MI............ 8.0 208.5 1.9 171 1,116 4.6 189 Wayne, MI................ 30.2 709.0 0.4 294 1,209 8.1 16 Anoka, MN................ 6.7 120.0 1.4 208 1,035 9.3 5 Dakota, MN............... 9.4 185.8 0.6 281 1,051 6.7 39 Hennepin, MN............. 37.0 897.3 1.7 191 1,301 3.2 280 Olmsted, MN.............. 3.3 94.8 2.5 130 1,058 3.8 246 Ramsey, MN............... 12.8 330.9 1.3 213 1,189 4.7 175 St. Louis, MN............ 5.1 96.6 -0.1 312 869 5.5 114 Stearns, MN.............. 4.1 85.7 0.8 264 884 6.6 43 Washington, MN........... 5.2 80.3 2.9 102 889 6.6 43 Harrison, MS............. 4.5 84.3 2.1 156 729 2.4 300 Hinds, MS................ 5.9 121.6 1.0 246 896 3.2 280 Boone, MO................ 4.9 93.4 1.8 183 823 4.0 230 Clay, MO................. 5.5 99.6 5.1 8 1,006 9.2 7 Greene, MO............... 8.5 164.1 1.1 235 810 4.9 161 Jackson, MO.............. 21.2 362.7 2.0 165 1,091 5.4 120 St. Charles, MO.......... 9.0 142.9 4.1 31 870 7.1 28 St. Louis, MO............ 36.2 602.5 1.8 183 1,148 2.2 308 St. Louis City, MO....... 13.0 227.3 1.2 221 1,144 7.4 20 Yellowstone, MT.......... 6.4 81.4 2.1 156 922 2.7 296 Douglas, NE.............. 18.6 338.6 2.2 144 994 6.5 53 Lancaster, NE............ 10.0 168.8 2.2 144 853 4.2 218 Clark, NV................ 54.6 928.6 3.7 46 920 3.8 246 Washoe, NV............... 14.5 208.0 4.5 18 955 3.8 246 Hillsborough, NH......... 12.3 202.1 1.6 197 1,264 4.5 196 Rockingham, NH........... 10.9 146.8 2.7 119 1,119 5.6 105 Atlantic, NJ............. 6.6 124.5 0.2 304 896 2.4 300 Bergen, NJ............... 33.3 454.1 0.7 273 1,324 2.5 298 Burlington, NJ........... 11.1 201.2 0.9 254 1,124 5.8 94 Camden, NJ............... 12.2 201.4 2.6 125 1,090 5.3 127 Essex, NJ................ 20.6 343.4 0.3 299 1,295 5.7 100 Gloucester, NJ........... 6.3 105.5 2.2 144 946 3.7 254 Hudson, NJ............... 14.8 250.3 3.1 87 1,375 3.9 237 Mercer, NJ............... 11.3 248.5 3.6 51 1,327 1.1 320 Middlesex, NJ............ 22.2 415.6 1.5 205 1,274 5.1 148 Monmouth, NJ............. 20.1 257.1 2.2 144 1,091 3.2 280 Morris, NJ............... 17.0 291.5 2.3 138 1,601 5.2 137 Ocean, NJ................ 12.9 160.5 2.8 108 890 4.3 212 Passaic, NJ.............. 12.5 169.0 -1.1 329 1,111 9.4 4 Somerset, NJ............. 10.1 186.2 1.9 171 1,576 1.0 321 Union, NJ................ 14.4 219.3 (5) - 1,373 (5) - Bernalillo, NM........... 18.1 322.8 1.2 221 904 3.6 260 Albany, NY............... 10.4 233.5 0.8 264 1,113 5.0 154 Bronx, NY................ 18.7 304.4 1.1 235 996 3.1 284 Broome, NY............... 4.6 87.9 -0.7 323 833 6.1 73 Dutchess, NY............. 8.5 113.1 1.2 221 1,031 3.4 266 Erie, NY................. 24.8 472.4 1.0 246 958 6.7 39 Kings, NY................ 60.8 681.7 3.4 63 922 5.4 120 Monroe, NY............... 18.8 386.3 0.8 264 1,005 7.5 18 Nassau, NY............... 54.4 631.6 1.1 235 1,238 6.4 61 New York, NY............. 129.9 2,442.2 2.1 156 2,235 0.8 322 Oneida, NY............... 5.4 104.2 -0.6 320 832 4.7 175 Onondaga, NY............. 13.1 246.9 0.3 299 995 6.2 68 Orange, NY............... 10.3 142.5 0.8 264 903 6.4 61 Queens, NY............... 51.8 648.2 3.3 69 1,022 4.2 218 Richmond, NY............. 9.8 117.0 3.3 69 959 4.6 189 Rockland, NY............. 10.6 120.3 0.6 281 1,072 4.0 230 Saratoga, NY............. 5.9 84.4 1.7 191 974 6.6 43 Suffolk, NY.............. 52.6 653.0 0.9 254 1,187 5.5 114 Westchester, NY.......... 36.7 427.4 1.2 221 1,449 2.0 310 Buncombe, NC............. 8.8 127.7 3.4 63 841 5.3 127 Catawba, NC.............. 4.3 85.3 2.2 144 836 8.9 9 Cumberland, NC........... 6.3 119.7 0.7 273 814 6.1 73 Durham, NC............... 8.1 194.8 3.8 37 1,278 4.2 218 Forsyth, NC.............. 9.3 183.4 1.1 235 976 4.7 175 Guilford, NC............. 14.3 281.1 1.9 171 930 4.7 175 Mecklenburg, NC.......... 36.1 658.4 4.0 33 1,204 6.8 36 New Hanover, NC.......... 7.7 107.3 2.8 108 866 4.7 175 Wake, NC................. 32.5 525.1 3.6 51 1,071 4.2 218 Cass, ND................. 6.9 117.1 1.3 213 977 4.6 189 Butler, OH............... 7.6 150.6 2.8 108 949 7.4 20 Cuyahoga, OH............. 35.6 721.6 0.4 294 1,097 4.5 196 Delaware, OH............. 4.9 86.2 2.4 134 1,005 4.0 230 Franklin, OH............. 31.0 739.7 1.8 183 1,068 7.0 30 Hamilton, OH............. 23.5 511.5 1.5 205 1,148 4.0 230 Lake, OH................. 6.2 95.1 0.2 304 886 3.0 289 Lorain, OH............... 6.1 97.7 0.3 299 848 4.6 189 Lucas, OH................ 10.1 212.1 1.9 171 937 4.5 196 Mahoning, OH............. 5.9 99.1 -0.6 320 762 4.1 225 Montgomery, OH........... 12.0 254.7 2.1 156 924 5.0 154 Stark, OH................ 8.6 159.1 -0.1 312 818 3.8 246 Summit, OH............... 14.1 267.9 0.7 273 959 5.3 127 Warren, OH............... 4.7 87.0 3.1 87 932 6.2 68 Cleveland, OK............ 5.5 82.6 1.0 246 791 3.3 276 Oklahoma, OK............. 27.3 454.4 0.1 306 1,017 3.9 237 Tulsa, OK................ 22.0 353.6 0.6 281 978 2.8 294 Clackamas, OR............ 14.2 153.9 3.1 87 998 5.2 137 Jackson, OR.............. 7.1 84.4 2.2 144 793 6.9 33 Lane, OR................. 11.8 150.7 3.4 63 837 5.0 154 Marion, OR............... 10.2 145.5 3.5 58 853 5.2 137 Multnomah, OR............ 33.1 490.9 3.5 58 1,099 6.6 43 Washington, OR........... 18.5 280.3 3.0 93 1,285 4.9 161 Allegheny, PA............ 35.8 692.4 0.5 286 1,152 5.2 137 Berks, PA................ 9.0 171.6 0.9 254 972 6.5 53 Bucks, PA................ 19.9 258.3 0.8 264 1,037 3.4 266 Butler, PA............... 5.0 85.9 0.5 286 1,000 5.6 105 Chester, PA.............. 15.5 247.7 1.0 246 1,364 2.4 300 Cumberland, PA........... 6.4 133.6 2.9 102 951 3.3 276 Dauphin, PA.............. 7.5 178.7 0.5 286 1,078 8.6 11 Delaware, PA............. 14.0 222.2 0.7 273 1,144 5.8 94 Erie, PA................. 7.1 124.6 -0.3 317 842 4.9 161 Lackawanna, PA........... 5.8 98.1 0.0 309 810 5.7 100 Lancaster, PA............ 13.2 233.6 1.7 191 905 6.0 80 Lehigh, PA............... 8.6 189.0 2.0 165 1,076 4.9 161 Luzerne, PA.............. 7.6 147.5 1.3 213 818 4.7 175 Montgomery, PA........... 27.6 488.6 1.9 171 1,327 4.7 175 Northampton, PA.......... 6.7 110.1 1.7 191 936 6.7 39 Philadelphia, PA......... 35.1 662.5 1.6 197 1,283 5.9 88 Washington, PA........... 5.5 86.5 -2.1 332 1,064 -2.1 331 Westmoreland, PA......... 9.3 135.3 1.3 213 863 3.9 237 York, PA................. 9.0 176.4 1.2 221 923 6.0 80 Providence, RI........... 17.6 286.8 1.1 235 1,102 3.7 254 Charleston, SC........... 14.2 239.9 3.6 51 927 5.1 148 Greenville, SC........... 13.9 262.9 3.0 93 935 6.3 66 Horry, SC................ 8.7 114.4 3.5 58 653 6.9 33 Lexington, SC............ 6.5 119.5 4.2 27 794 3.9 237 Richland, SC............. 9.9 217.4 2.2 144 903 5.4 120 Spartanburg, SC.......... 6.1 130.9 3.2 77 899 4.5 196 York, SC................. 5.2 88.6 4.5 18 842 4.7 175 Minnehaha, SD............ 7.0 124.4 1.7 191 932 6.3 66 Davidson, TN............. 20.9 466.8 3.1 87 1,169 7.2 26 Hamilton, TN............. 9.2 196.8 3.3 69 1,031 6.0 80 Knox, TN................. 11.7 236.1 1.6 197 977 6.1 73 Rutherford, TN........... 5.1 120.0 4.2 27 952 4.4 208 Shelby, TN............... 19.9 497.6 1.8 183 1,096 5.2 137 Williamson, TN........... 7.9 120.3 6.8 1 1,234 0.0 326 Bell, TX................. 5.1 119.6 5.4 7 881 8.9 9 Bexar, TX................ 38.8 834.2 2.6 125 965 6.0 80 Brazoria, TX............. 5.4 104.7 2.8 108 1,106 6.1 73 Brazos, TX............... 4.3 100.5 2.4 134 785 1.4 316 Cameron, TX.............. 6.5 138.4 1.9 171 649 4.5 196 Collin, TX............... 22.7 375.2 4.8 13 1,228 3.4 266 Dallas, TX............... 73.9 1,651.6 3.9 35 1,287 4.5 196 Denton, TX............... 13.6 224.4 4.8 13 973 4.5 196 Ector, TX................ 4.0 70.6 -11.8 337 1,094 -8.0 335 El Paso, TX.............. 14.7 297.3 3.0 93 743 5.4 120 Fort Bend, TX............ 12.0 174.3 2.2 144 1,028 -1.1 328 Galveston, TX............ 5.9 105.4 4.7 16 933 1.5 315 Gregg, TX................ 4.3 76.2 -5.1 334 910 -3.2 333 Harris, TX............... 112.2 2,302.8 -0.5 319 1,382 0.4 324 Hidalgo, TX.............. 12.0 251.8 1.9 171 661 3.1 284 Jefferson, TX............ 5.9 124.3 -0.8 325 1,119 2.8 294 Lubbock, TX.............. 7.4 136.9 2.4 134 838 4.6 189 McLennan, TX............. 5.1 109.8 1.6 197 875 5.3 127 Midland, TX.............. 5.4 86.6 -9.3 336 1,263 -11.5 336 Montgomery, TX........... 10.6 168.3 1.6 197 1,043 -0.9 327 Nueces, TX............... 8.3 162.7 -1.5 330 932 1.3 319 Potter, TX............... 4.0 80.2 0.8 264 871 4.9 161 Smith, TX................ 6.0 102.8 2.5 130 900 3.6 260 Tarrant, TX.............. 41.3 858.7 2.2 144 1,093 7.3 23 Travis, TX............... 37.9 703.1 4.3 25 1,234 5.0 154 Webb, TX................. 5.2 99.1 1.2 221 706 1.4 316 Williamson, TX........... 9.6 154.9 4.5 18 1,010 5.2 137 Davis, UT................ 8.1 120.1 3.7 46 839 4.5 196 Salt Lake, UT............ 43.1 663.8 3.8 37 1,035 5.3 127 Utah, UT................. 14.9 215.7 6.6 2 869 7.3 23 Weber, UT................ 5.8 100.5 3.1 87 790 5.3 127 Chittenden, VT........... 6.6 102.5 0.9 254 1,071 3.8 246 Arlington, VA............ 9.4 173.6 3.3 69 1,686 2.4 300 Chesterfield, VA......... 8.7 140.1 6.0 4 890 2.4 300 Fairfax, VA.............. 37.5 598.9 2.8 108 1,618 2.5 298 Henrico, VA.............. 11.3 191.5 3.5 58 1,020 4.3 212 Loudoun, VA.............. 11.8 159.8 6.3 3 1,236 2.7 296 Prince William, VA....... 9.1 125.8 4.1 31 923 4.3 212 Alexandria City, VA...... 6.7 97.7 2.1 156 1,487 1.4 316 Chesapeake City, VA...... 6.0 98.9 1.2 221 825 3.8 246 Newport News City, VA.... 3.8 98.4 -0.4 318 1,017 6.2 68 Norfolk City, VA......... 5.9 142.5 1.2 221 1,066 3.5 262 Richmond City, VA........ 7.6 151.1 2.1 156 1,153 4.6 189 Virginia Beach City, VA.. 12.1 174.2 2.1 156 851 5.6 105 Benton, WA............... 5.6 80.8 1.5 205 1,063 (5) - Clark, WA................ 14.0 147.6 (5) - 975 (5) - King, WA................. 85.3 1,297.2 2.9 102 1,429 3.3 276 Kitsap, WA............... 6.7 86.0 2.7 119 935 7.0 30 Pierce, WA............... 21.7 291.4 (5) - 940 (5) - Snohomish, WA............ 20.4 280.1 2.7 119 1,136 6.6 43 Spokane, WA.............. 15.6 212.4 (5) - 884 (5) - Thurston, WA............. 8.0 107.4 2.0 165 923 5.6 105 Whatcom, WA.............. 7.1 85.8 1.4 208 848 5.6 105 Yakima, WA............... 7.8 99.7 (5) - 740 (5) - Kanawha, WV.............. 5.9 103.8 -1.0 327 897 3.9 237 Brown, WI................ 6.8 154.1 1.7 191 986 6.4 61 Dane, WI................. 15.1 329.9 2.4 134 1,081 6.1 73 Milwaukee, WI............ 26.2 489.1 0.4 294 1,043 3.5 262 Outagamie, WI............ 5.2 106.9 1.8 183 926 6.8 36 Waukesha, WI............. 12.9 239.1 1.6 197 1,084 5.8 94 Winnebago, WI............ 3.7 91.8 1.4 208 1,033 6.5 53 San Juan, PR............. 10.6 257.3 -1.9 (6) 675 2.1 (6) (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) Data do not meet BLS or state agency disclosure standards. (6) 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 342 U.S. counties comprise 72.5 percent of the total covered workers in the U.S.
Table 2. Covered establishments, employment, and wages in the 10 largest counties, fourth quarter 2015 Employment Average weekly wage(1) Establishments, fourth quarter County by NAICS supersector 2015 Percent Percent (thousands) December change, Fourth change, 2015 December quarter fourth (thousands) 2014-15(2) 2015 quarter 2014-15(2) United States(3) ............................ 9,685.3 141,924.5 1.9 $1,082 4.4 Private industry........................... 9,386.7 120,234.9 2.1 1,089 4.5 Natural resources and mining............. 138.6 1,819.6 -8.9 1,157 -5.0 Construction............................. 770.9 6,521.4 4.9 1,233 5.1 Manufacturing............................ 342.8 12,291.3 0.0 1,322 4.5 Trade, transportation, and utilities..... 1,925.8 27,688.6 1.6 904 4.9 Information.............................. 155.5 2,793.9 1.6 1,907 8.0 Financial activities..................... 854.5 7,920.5 1.9 1,710 2.7 Professional and business services....... 1,754.0 19,995.4 2.2 1,438 4.3 Education and health services............ 1,557.4 21,495.3 2.5 994 5.7 Leisure and hospitality.................. 814.2 15,025.8 3.3 462 5.5 Other services........................... 832.4 4,331.1 1.7 724 5.4 Government................................. 298.5 21,689.5 0.7 1,047 4.9 Los Angeles, CA.............................. 463.6 4,341.0 2.3 1,266 5.5 Private industry........................... 457.8 3,775.7 2.4 1,260 6.1 Natural resources and mining............. 0.5 8.3 -8.3 1,573 13.6 Construction............................. 13.6 129.6 8.1 1,267 5.3 Manufacturing............................ 12.3 352.9 -2.2 1,330 8.5 Trade, transportation, and utilities..... 53.4 830.3 0.7 986 5.6 Information.............................. 9.8 213.4 4.7 2,556 11.3 Financial activities..................... 25.0 214.6 0.5 1,916 3.7 Professional and business services....... 48.0 606.0 0.7 1,652 6.9 Education and health services............ 213.6 740.6 3.1 930 3.8 Leisure and hospitality.................. 31.8 490.8 2.9 974 1.9 Other services........................... 27.8 146.7 0.3 746 8.0 Government................................. 5.8 565.3 1.9 1,309 2.3 New York, NY................................. 129.9 2,442.2 2.1 2,235 0.8 Private industry........................... 129.1 2,174.2 2.2 2,350 0.5 Natural resources and mining............. 0.0 0.2 -4.2 2,033 -3.4 Construction............................. 2.2 38.8 9.0 2,311 4.1 Manufacturing............................ 2.2 27.4 0.8 1,636 0.9 Trade, transportation, and utilities..... 20.0 268.9 -2.4 1,493 3.9 Information.............................. 4.9 155.6 1.0 2,804 2.9 Financial activities..................... 19.1 372.4 1.9 4,593 -7.8 Professional and business services....... 27.6 556.2 3.3 2,687 6.1 Education and health services............ 9.8 342.2 2.5 1,384 6.7 Leisure and hospitality.................. 13.8 297.5 2.4 1,027 4.3 Other services........................... 20.3 102.2 0.5 1,210 5.6 Government................................. 0.8 268.0 1.2 1,301 3.7 Cook, IL..................................... 158.4 2,575.7 1.9 1,267 4.4 Private industry........................... 157.1 2,276.8 2.0 1,274 4.9 Natural resources and mining............. 0.1 1.1 29.7 1,342 4.0 Construction............................. 12.8 70.8 2.3 1,635 1.8 Manufacturing............................ 6.5 186.6 -0.6 1,405 7.0 Trade, transportation, and utilities..... 31.1 486.2 1.5 971 3.2 Information.............................. 2.7 55.0 1.6 1,802 7.9 Financial activities..................... 15.7 190.0 0.7 2,298 4.1 Professional and business services....... 33.7 474.5 1.8 1,690 5.8 Education and health services............ 16.7 442.8 2.5 1,048 4.3 Leisure and hospitality.................. 14.5 267.9 5.2 543 9.7 Other services........................... 17.7 95.9 -0.1 967 6.9 Government................................. 1.3 298.9 0.9 1,221 1.7 Harris, TX................................... 112.2 2,302.8 -0.5 1,382 0.4 Private industry........................... 111.6 2,031.1 -0.8 1,414 -0.1 Natural resources and mining............. 1.8 81.2 -15.7 3,380 0.8 Construction............................. 7.1 163.5 2.3 1,530 4.7 Manufacturing............................ 4.8 179.8 -11.9 1,681 0.2 Trade, transportation, and utilities..... 25.0 486.1 0.2 1,234 1.7 Information.............................. 1.2 27.6 0.6 1,497 -2.5 Financial activities..................... 11.6 122.4 1.3 1,824 3.5 Professional and business services....... 23.0 395.7 -1.1 1,768 -0.7 Education and health services............ 15.5 286.3 3.8 1,103 4.5 Leisure and hospitality.................. 9.5 222.8 5.2 474 4.2 Other services........................... 11.7 65.0 0.5 843 3.8 Government................................. 0.6 271.7 1.9 1,147 6.5 Maricopa, AZ................................. 97.2 1,883.2 3.2 1,016 4.1 Private industry........................... 96.5 1,670.6 3.6 1,015 4.2 Natural resources and mining............. 0.4 8.3 -1.2 953 3.5 Construction............................. 7.1 98.4 4.5 1,124 4.2 Manufacturing............................ 3.2 116.6 0.6 1,451 5.8 Trade, transportation, and utilities..... 19.7 378.4 2.6 913 4.5 Information.............................. 1.6 35.1 2.1 1,358 6.2 Financial activities..................... 11.1 164.9 5.1 1,305 6.2 Professional and business services....... 21.9 322.9 2.6 1,127 2.9 Education and health services............ 10.9 279.9 3.6 1,041 3.5 Leisure and hospitality.................. 7.6 205.3 3.6 488 5.4 Other services........................... 6.2 49.8 1.4 718 5.4 Government................................. 0.7 212.6 0.0 1,024 3.9 Dallas, TX................................... 73.9 1,651.6 3.9 1,287 4.5 Private industry........................... 73.4 1,478.2 4.1 1,303 4.3 Natural resources and mining............. 0.6 9.0 -5.0 3,562 -6.1 Construction............................. 4.3 82.0 5.3 1,344 7.3 Manufacturing............................ 2.7 105.8 -1.0 1,487 3.3 Trade, transportation, and utilities..... 15.9 352.4 6.3 1,099 3.6 Information.............................. 1.4 49.1 1.7 1,813 2.3 Financial activities..................... 9.1 159.2 2.4 1,764 4.1 Professional and business services....... 16.7 333.6 4.1 1,577 5.3 Education and health services............ 9.1 191.6 4.4 1,159 8.2 Leisure and hospitality.................. 6.4 153.9 6.2 554 8.8 Other services........................... 6.9 40.9 0.4 821 2.1 Government................................. 0.5 173.5 2.1 1,152 5.7 Orange, CA................................... 113.6 1,550.6 2.3 1,205 4.1 Private industry........................... 112.3 1,406.3 2.5 1,209 3.8 Natural resources and mining............. 0.2 2.7 -11.7 919 6.1 Construction............................. 6.6 92.5 9.5 1,376 6.7 Manufacturing............................ 4.8 156.3 -0.3 1,498 4.5 Trade, transportation, and utilities..... 16.7 264.5 -0.3 1,067 4.9 Information.............................. 1.3 25.1 0.4 1,952 7.4 Financial activities..................... 10.9 117.5 2.1 1,988 1.2 Professional and business services....... 20.5 289.1 0.3 1,450 2.9 Education and health services............ 29.4 196.8 3.7 1,047 5.9 Leisure and hospitality.................. 8.1 203.5 3.3 495 6.0 Other services........................... 7.0 44.0 1.2 730 4.9 Government................................. 1.4 144.3 0.3 1,168 6.6 San Diego, CA................................ 105.7 1,399.7 2.6 1,184 4.3 Private industry........................... 103.9 1,169.6 2.7 1,172 4.3 Natural resources and mining............. 0.7 9.0 1.4 730 4.3 Construction............................. 6.5 71.7 9.0 1,260 6.6 Manufacturing............................ 3.1 104.3 0.9 1,755 11.6 Trade, transportation, and utilities..... 14.2 224.7 0.1 887 5.2 Information.............................. 1.2 23.7 -3.3 1,742 4.7 Financial activities..................... 9.6 71.8 3.3 1,533 9.0 Professional and business services....... 18.2 232.7 1.9 1,768 -1.9 Education and health services............ 29.3 190.1 3.1 1,036 6.1 Leisure and hospitality.................. 7.8 181.1 1.6 497 8.3 Other services........................... 7.5 49.3 0.6 636 5.8 Government................................. 1.8 230.1 2.1 1,247 4.6 King, WA..................................... 85.3 1,297.2 2.9 1,429 3.3 Private industry........................... 84.8 1,131.8 3.0 1,446 3.2 Natural resources and mining............. 0.4 3.0 23.3 1,336 -5.1 Construction............................. 6.3 64.2 5.4 1,383 5.6 Manufacturing............................ 2.4 105.3 -1.0 1,663 1.5 Trade, transportation, and utilities..... 14.6 250.3 3.8 1,236 5.7 Information.............................. 2.1 91.6 6.4 2,912 -2.7 Financial activities..................... 6.5 67.2 1.9 1,728 -3.2 Professional and business services....... 16.8 216.9 4.0 1,822 4.2 Education and health services............ 19.7 162.3 (4) 1,060 (4) Leisure and hospitality.................. 7.0 128.1 4.0 568 4.4 Other services........................... 8.9 42.8 1.9 849 5.3 Government................................. 0.5 165.4 2.1 1,314 3.8 Miami-Dade, FL............................... 94.8 1,115.9 3.0 1,051 4.4 Private industry........................... 94.4 978.5 3.3 1,029 4.6 Natural resources and mining............. 0.5 9.7 5.5 661 11.7 Construction............................. 5.8 41.7 10.7 1,015 2.7 Manufacturing............................ 2.8 39.8 4.0 991 0.5 Trade, transportation, and utilities..... 26.4 288.6 1.5 927 5.6 Information.............................. 1.5 17.8 -2.4 1,648 7.2 Financial activities..................... 10.2 75.2 2.5 1,645 3.1 Professional and business services....... 20.7 154.2 4.5 1,328 1.0 Education and health services............ 10.1 171.1 2.9 1,050 7.3 Leisure and hospitality.................. 7.1 138.5 4.6 615 9.0 Other services........................... 8.1 41.3 3.4 644 4.9 Government................................. 0.3 137.3 0.8 1,206 3.7 (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. (4) Data do not meet BLS or state agency disclosure standards. Note: Data are preliminary. Counties selected are based on 2014 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, fourth quarter 2015 Employment Average weekly wage(1) Establishments, fourth quarter State 2015 Percent Percent (thousands) December change, Fourth change, 2015 December quarter fourth (thousands) 2014-15 2015 quarter 2014-15 United States(2)........... 9,685.3 141,924.5 1.9 $1,082 4.4 Alabama.................... 120.6 1,916.2 1.4 912 3.4 Alaska..................... 22.4 315.9 -0.5 1,095 2.9 Arizona.................... 155.1 2,701.8 2.6 967 4.4 Arkansas................... 89.2 1,201.4 1.7 838 3.8 California................. 1,454.6 16,593.8 3.1 1,274 5.4 Colorado................... 187.6 2,537.5 2.5 1,103 3.3 Connecticut................ 116.4 1,685.1 0.3 1,334 4.3 Delaware................... 30.7 441.2 1.8 1,086 3.4 District of Columbia....... 39.0 754.2 2.2 1,756 3.4 Florida.................... 652.3 8,308.1 3.7 958 5.2 Georgia.................... 296.0 4,249.4 2.9 1,001 4.5 Hawaii..................... 40.0 653.0 2.2 957 5.4 Idaho...................... 57.0 670.1 3.4 803 2.6 Illinois................... 414.9 5,931.2 1.4 1,146 5.1 Indiana.................... 161.3 2,996.3 1.7 891 5.3 Iowa....................... 101.0 1,539.0 0.7 920 5.7 Kansas..................... 88.1 1,382.1 0.4 898 5.0 Kentucky................... 122.5 1,881.3 1.6 885 5.9 Louisiana.................. 127.4 1,937.4 -1.0 940 1.8 Maine...................... 52.1 596.9 0.7 873 5.7 Maryland................... 168.7 2,636.7 1.7 1,175 5.6 Massachusetts.............. 241.5 3,479.1 1.6 1,385 5.4 Michigan................... 240.1 4,218.9 1.5 1,043 5.9 Minnesota.................. 160.3 2,805.8 1.5 1,073 4.8 Mississippi................ 72.8 1,133.8 1.3 770 3.1 Missouri................... 195.0 2,759.6 1.8 933 4.6 Montana.................... 45.8 453.2 2.5 818 3.0 Nebraska................... 70.9 971.8 1.4 880 5.1 Nevada..................... 79.8 1,272.2 3.5 935 4.0 New Hampshire.............. 51.6 648.6 1.7 1,139 5.4 New Jersey................. 269.9 3,988.4 1.7 1,262 4.0 New Mexico................. 57.4 808.9 -0.1 865 1.8 New York................... 640.6 9,227.6 1.7 1,372 3.9 North Carolina............. 270.1 4,247.1 2.5 939 5.5 North Dakota............... 32.2 428.1 -5.9 1,021 -2.8 Ohio....................... 292.4 5,328.8 1.2 964 4.6 Oklahoma................... 109.0 1,605.0 -0.7 896 2.3 Oregon..................... 146.0 1,814.8 3.3 979 5.5 Pennsylvania............... 355.2 5,759.7 0.7 1,063 4.9 Rhode Island............... 36.7 478.1 1.5 1,043 4.0 South Carolina............. 125.7 1,987.1 2.8 860 5.3 South Dakota............... 32.8 417.7 1.2 832 5.2 Tennessee.................. 151.5 2,898.1 2.8 980 5.6 Texas...................... 645.6 11,832.1 1.4 1,099 2.7 Utah....................... 95.5 1,375.6 3.8 913 4.7 Vermont.................... 24.8 312.1 0.3 919 4.1 Virginia................... 261.3 3,806.2 3.0 1,094 3.5 Washington................. 237.7 3,137.2 2.3 1,132 4.7 West Virginia.............. 50.3 703.7 -1.3 829 1.3 Wisconsin.................. 170.0 2,820.5 1.1 944 5.6 Wyoming.................... 26.0 276.0 -2.9 937 -1.7 Puerto Rico................ 45.4 929.9 -1.6 565 1.6 Virgin Islands............. 3.3 38.4 -0.3 787 4.7 (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.