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For release 10:00 a.m. (EST), Thursday, February 20, 2020 USDL-20-0300 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 2019 From September 2018 to September 2019, employment increased in 283 of the 355 largest U.S. counties, the U.S. Bureau of Labor Statistics reported today. In September 2019, national employment (as measured by the QCEW program) increased to 148.6 million, a 1.1 percent increase over the year. New Hanover, NC, had the largest over-the-year increase in employment with a gain of 5.8 percent. Employment data in this release are presented for September 2019, and average weekly wage data are presented for third quarter 2019. Among the 355 largest counties, 350 had over-the-year increases in average weekly wages. In the third quarter of 2019, average weekly wages for the nation increased to $1,093, a 3.6 percent increase over the year. Boulder, CO, had the largest third quarter over-the-year wage gain at 18.4 percent. (See table 1.) Large County Employment in September 2019 New Hanover, NC, had the largest over-the-year percentage increase in employment (5.8 percent). Within New Hanover, the largest employment increase occurred in leisure and hospitality, which gained 1,725 jobs over the year (10.2 percent). Bay, FL, experienced the largest over-the-year percentage decrease in employment, with a loss of 5.9 percent. Within Bay, education and health services had the largest employment decrease with a loss of 2,347 jobs (-21.1 percent). Large County Average Weekly Wage in Third Quarter 2019 Boulder, CO, had the largest over-the-year percentage increase in average weekly wages (18.4 percent). Within Boulder, an average weekly wage gain of $1,016 (51.8 percent) in professional and business services made the largest contribution to the county’s increase in average weekly wages. Linn, IA, had the largest over-the-year percentage decrease in average weekly wages with a loss of 2.6 percent. Within Linn, manufacturing had the largest impact, with an average weekly wage decrease of $285 (-14.7 percent) over the year. Ten Largest Counties All of the 10 largest counties had over-the-year percentage increases in employment and average weekly wages. In September 2019, Maricopa, AZ, had the largest over-the-year employment percentage gain among the 10 largest counties (3.2 percent). Within Maricopa, education and health services had the largest employment increase with a gain of 14,264 jobs (4.5 percent). (See table 2.) In third quarter 2019, Dallas, TX, experienced the largest over-the-year percentage gain in average weekly wages among the 10 largest counties (4.9 percent). Within Dallas, professional and business services had the largest impact, with an average weekly wage increase of $93 (6.5 percent) over the year. For More Information The tables included in this release contain data for the nation and for the 355 U.S. counties with annual average employment levels of 75,000 or more in 2018. September 2019 employment and third quarter 2019 average weekly wages for all states are provided in table 3 of this release. The most current news release on quarterly measures of gross job flows is available from QCEW Business Employment Dynamics at www.bls.gov/news.release/pdf/cewbd.pdf. Several BLS regional offices issue QCEW news releases targeted to local data users. Links to these releases are available at www.bls.gov/cew/regional-resources.htm. QCEW data are available in the Census Business Builder suite of web tools assisting business owners and regional analysts in data-driven decision making at www.census.gov/data/data- tools/cbb.html. The QCEW news release schedule is available at www.bls.gov/cew/release-calendar.htm. ____________ The County Employment and Wages full data update for third quarter 2019 is scheduled to be released on Wednesday, March 4, 2020, at 10:00 a.m. (EST). The County Employment and Wages news release for fourth quarter 2019 is scheduled to be released on Wednesday, May 20, 2020, at 10:00 a.m. (EDT).
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 2017 North American Industry Classification System (NAICS). Data for 2019 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, PR, 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 356 counties presented in this release were derived using 2018 preliminary annual averages of employment. For 2019 data, six counties have been added to the publication tables: St. Johns, FL; St. Lucie, FL; Forsyth, GA; Greene, OH; Ector, TX; and Racine, WI. These counties will be included in all 2019 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: QCEW, Business Employment Dynamics (BED), and Current Employment Statistics (CES). Each of these measures 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- | 697,000 establish- | submitted by 10.2 | ministrative records| ments | million establish- | submitted by 8.0 | | ments in first | million private-sec-| | quarter of 2019 | 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 | -Within 5 months | -7 months after the | -Usually the 3rd Friday | after the end of | end of each quarter| after the end of the | each quarter | | week including | | | the 12th of the month -----------|---------------------|----------------------|------------------------ 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 federal | 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 10.0 million employer reports of employment and wages submitted by states to the BLS in 2018. 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 2018, UI and UCFE programs covered workers in 146.1 million jobs. The estimated 140.5 million workers in these jobs (after adjustment for multiple jobholders) represented 96.2 percent of civilian wage and salary employment. Covered workers received $8.368 trillion in pay, representing 94.2 percent of the wage and salary component of personal income and 40.7 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 that reflect economic events or 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 2018 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 eliminate the effect of 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 2018 edition of this publication, which was published in September 2019, contains selected data produced by Business Employment Dynamics (BED) on job gains and losses, as well as selected data from the first quarter 2019 version of this news release. Tables and additional content from the 2018 edition of Employment and Wages Annual Averages Online are now available at www.bls.gov/cew/publications/employment-and-wages-annual-averages/2018/home.htm. The 2019 edition of Employment and Wages Annual Averages Online will be available in September 2020. News releases on quarterly measures of gross job flows also are available from BED at www.bls.gov/bdm, (202) 691-6467, or data.bls.gov/cgi-bin/forms/bdm. Information in this release will be made available to sensory impaired individuals upon request. Voice phone: (202) 691-5200; TDD message referral phone number: (800) 877-8339.
Table 1. Covered establishments, employment, and wages in the 356 largest counties, third quarter 2019 Employment Average weekly wage(2) Establishments, County(1) third quarter Percent Ranking Percent Ranking 2019 September change, by Third change, by (thousands) 2019 September percent quarter third percent (thousands) 2018-19(3) change 2019 quarter change 2018-19(3) United States(4)......... 10,325.3 148,556.5 1.1 - $1,093 3.6 - Jefferson, AL............ 19.3 353.6 1.1 153 1,055 2.9 238 Madison, AL.............. 10.1 206.3 3.0 25 1,194 4.9 42 Mobile, AL............... 10.4 171.7 0.5 226 922 2.9 238 Montgomery, AL........... 6.5 129.9 -0.8 333 909 8.5 6 Shelby, AL............... 6.0 84.4 -0.8 333 1,009 1.7 314 Tuscaloosa, AL........... 4.7 98.2 2.2 60 887 3.4 182 Anchorage, AK............ 8.3 150.8 -0.1 294 1,150 3.4 182 Maricopa, AZ............. 106.6 2,068.7 3.2 17 1,060 4.5 66 Pima, AZ................. 19.3 375.8 0.8 196 934 3.8 132 Benton, AR............... 6.8 123.5 2.5 45 1,012 4.8 47 Pulaski, AR.............. 14.6 250.4 -0.6 321 961 4.2 93 Washington, AR........... 6.3 110.8 1.0 171 882 4.5 66 Alameda, CA.............. 66.2 792.6 0.1 274 1,490 4.2 93 Butte, CA................ 8.6 83.4 -2.4 351 866 4.8 47 Contra Costa, CA......... 33.9 368.7 -0.2 301 1,311 4.4 78 Fresno, CA............... 37.9 409.4 1.5 110 856 3.9 122 Kern, CA................. 21.3 347.3 2.5 45 907 3.8 132 Los Angeles, CA.......... 511.6 4,499.4 1.0 171 1,225 3.7 151 Marin, CA................ 12.7 116.5 0.9 186 1,328 3.1 212 Merced, CA............... 6.8 85.6 2.9 31 846 3.5 168 Monterey, CA............. 14.3 214.1 1.1 153 936 2.4 276 Napa, CA................. 6.0 82.6 1.5 110 1,077 4.1 103 Orange, CA............... 127.5 1,649.2 1.1 153 1,204 4.3 85 Placer, CA............... 13.9 173.1 2.1 64 1,101 4.8 47 Riverside, CA............ 69.0 756.0 2.4 51 881 4.1 103 Sacramento, CA........... 61.8 679.6 1.8 84 1,183 4.1 103 San Bernardino, CA....... 63.4 769.9 1.7 93 927 3.9 122 San Diego, CA............ 116.3 1,490.2 1.5 110 1,197 4.1 103 San Francisco, CA........ 62.0 765.2 3.5 10 2,273 7.6 7 San Joaquin, CA.......... 18.7 260.7 1.2 142 937 4.6 60 San Luis Obispo, CA...... 10.7 119.6 1.0 171 946 4.5 66 San Mateo, CA............ 29.2 419.1 3.1 21 2,366 -0.1 351 Santa Barbara, CA........ 15.8 209.3 2.6 43 1,041 3.6 157 Santa Clara, CA.......... 75.5 1,121.9 1.8 84 2,447 -0.3 352 Santa Cruz, CA........... 9.8 109.6 1.3 132 992 5.1 31 Solano, CA............... 11.9 144.4 0.7 206 1,142 4.4 78 Sonoma, CA............... 20.6 215.2 1.0 171 1,090 4.3 85 Stanislaus, CA........... 16.4 197.5 0.8 196 949 0.2 349 Tulare, CA............... 11.5 169.6 0.6 217 787 4.7 55 Ventura, CA.............. 28.1 328.8 1.0 171 1,065 4.3 85 Yolo, CA................. 7.1 108.4 1.8 84 1,199 7.2 10 Adams, CO................ 11.7 228.9 5.3 2 1,092 3.6 157 Arapahoe, CO............. 23.2 335.5 1.6 102 1,284 5.0 38 Boulder, CO.............. 16.2 189.5 2.9 31 1,547 18.4 1 Denver, CO............... 35.4 533.2 2.1 64 1,369 5.1 31 Douglas, CO.............. 12.9 130.9 2.5 45 1,236 6.1 15 El Paso, CO.............. 21.1 285.0 2.5 45 1,000 4.6 60 Jefferson, CO............ 21.2 244.3 1.9 76 1,149 4.7 55 Larimer, CO.............. 12.9 167.3 2.3 54 1,014 5.4 21 Weld, CO................. 8.0 115.7 3.8 7 1,052 7.5 8 Fairfield, CT............ 37.0 418.0 -0.6 321 1,475 0.8 342 Hartford, CT............. 29.3 513.3 0.0 284 1,254 3.6 157 New Haven, CT............ 25.4 370.0 0.2 261 1,092 2.6 261 New London, CT........... 7.8 122.9 -1.2 342 1,048 1.4 328 New Castle, DE........... 21.3 291.8 0.7 206 1,201 3.4 182 Sussex, DE............... 7.5 86.1 2.8 35 782 3.2 202 Washington, DC........... 41.6 776.3 0.6 217 1,851 2.5 267 Alachua, FL.............. 7.5 134.4 1.0 171 937 2.7 257 Bay, FL.................. 5.8 74.7 -5.9 355 815 8.7 5 Brevard, FL.............. 16.6 221.8 1.8 84 958 1.9 305 Broward, FL.............. 72.1 819.4 0.5 226 1,000 4.1 103 Collier, FL.............. 15.2 145.8 2.2 60 920 4.0 114 Duval, FL................ 31.1 525.2 1.7 93 1,008 2.9 238 Escambia, FL............. 8.4 137.9 1.4 122 856 4.5 66 Hillsborough, FL......... 45.7 708.0 2.8 35 1,046 3.8 132 Lake, FL................. 8.8 101.3 1.9 76 741 2.5 267 Lee, FL.................. 23.7 264.3 2.1 64 854 3.6 157 Leon, FL................. 9.0 152.9 0.6 217 901 4.6 60 Manatee, FL.............. 11.7 128.0 3.1 21 840 4.0 114 Marion, FL............... 8.9 105.1 1.9 76 735 3.4 182 Miami-Dade, FL........... 102.9 1,157.2 1.3 132 1,039 3.6 157 Okaloosa, FL............. 6.8 85.7 1.9 76 894 5.7 17 Orange, FL............... 45.5 868.5 1.9 76 958 3.1 212 Osceola, FL.............. 7.7 99.8 3.2 17 720 1.6 320 Palm Beach, FL........... 59.0 606.6 1.3 132 1,009 2.3 283 Pasco, FL................ 11.7 122.7 1.1 153 775 5.2 25 Pinellas, FL............. 34.8 438.4 0.8 196 931 3.3 195 Polk, FL................. 14.4 228.3 2.7 37 822 2.5 267 St. Johns, FL............ 8.0 79.0 2.3 54 851 3.0 227 St. Lucie, FL............ 7.0 79.9 3.0 25 788 1.4 328 Sarasota, FL............. 16.7 169.5 1.3 132 898 3.9 122 Seminole, FL............. 15.7 200.2 1.4 122 929 0.9 339 Volusia, FL.............. 15.2 173.5 -0.6 321 763 2.1 292 Bibb, GA................. 4.3 82.6 -0.4 308 847 1.7 314 Chatham, GA.............. 8.2 156.8 0.4 238 909 -1.6 354 Clayton, GA.............. 4.1 123.6 2.3 54 1,100 1.9 305 Cobb, GA................. 22.1 375.9 1.6 102 1,131 4.1 103 DeKalb, GA............... 18.0 301.2 0.7 206 1,099 2.2 288 Forsyth, GA.............. 6.0 77.5 2.1 64 968 5.2 25 Fulton, GA............... 44.5 900.1 1.1 153 1,422 3.6 157 Gwinnett, GA............. 25.8 362.2 2.0 69 1,007 1.6 320 Hall, GA................. 4.6 89.6 0.1 274 915 4.8 47 Muscogee, GA............. 4.5 93.6 -0.4 308 850 3.3 195 Richmond, GA............. 4.5 104.5 1.4 122 919 3.3 195 Honolulu, HI............. 27.4 465.8 -0.5 314 1,059 3.8 132 Maui + Kalawao, HI....... 6.7 80.0 -0.1 294 906 4.6 60 Ada, ID.................. 17.6 254.8 3.2 17 970 4.5 66 Champaign, IL............ 4.2 91.8 1.1 153 947 3.7 151 Cook, IL................. 139.7 2,617.8 0.1 274 1,244 3.8 132 DuPage, IL............... 34.9 615.9 -0.7 327 1,221 2.8 252 Kane, IL................. 12.7 213.4 0.5 226 948 2.6 261 Lake, IL................. 20.4 341.4 -0.7 327 1,302 3.3 195 McHenry, IL.............. 7.9 97.8 -0.9 336 846 2.3 283 McLean, IL............... 3.4 82.2 -0.4 308 986 -0.5 353 Madison, IL.............. 5.4 103.2 1.1 153 840 3.8 132 Peoria, IL............... 4.2 103.7 -2.6 352 1,066 1.9 305 St. Clair, IL............ 5.0 92.7 -0.9 336 859 5.3 22 Sangamon, IL............. 4.8 131.7 -0.7 327 1,177 12.8 2 Will, IL................. 15.1 250.5 1.7 93 916 3.2 202 Winnebago, IL............ 6.0 125.7 -2.0 350 928 3.0 227 Allen, IN................ 9.1 191.2 1.1 153 879 3.0 227 Elkhart, IN.............. 4.8 132.4 -3.8 354 890 0.3 347 Hamilton, IN............. 9.8 143.9 1.0 171 1,018 2.5 267 Lake, IN................. 10.4 189.4 0.1 274 925 1.5 325 Marion, IN............... 24.5 608.7 1.2 142 1,098 4.7 55 St. Joseph, IN........... 5.8 125.4 0.5 226 891 4.8 47 Tippecanoe, IN........... 3.5 87.0 1.2 142 924 1.5 325 Vanderburgh, IN.......... 4.8 108.7 -1.0 339 883 5.5 18 Johnson, IA.............. 4.4 83.8 0.3 246 1,014 1.9 305 Linn, IA................. 7.1 132.3 0.7 206 1,010 -2.6 355 Polk, IA................. 18.2 303.0 0.9 186 1,078 3.4 182 Scott, IA................ 5.8 91.7 0.3 246 891 3.1 212 Johnson, KS.............. 24.3 353.5 1.2 142 1,074 3.1 212 Sedgwick, KS............. 12.8 257.7 1.7 93 894 1.6 320 Shawnee, KS.............. 5.1 96.0 -0.6 321 872 2.5 267 Wyandotte, KS............ 3.5 91.7 0.2 261 1,036 4.5 66 Boone, KY................ 4.5 94.9 1.5 110 917 3.9 122 Fayette, KY.............. 11.3 196.6 1.1 153 938 3.4 182 Jefferson, KY............ 25.8 472.4 0.5 226 1,037 5.1 31 Caddo, LA................ 7.4 110.5 -1.1 341 856 2.0 295 Calcasieu, LA............ 5.5 100.8 -3.0 353 980 2.0 295 East Baton Rouge, LA..... 16.4 263.5 -1.6 348 1,010 2.6 261 Jefferson, LA............ 14.4 188.0 0.0 284 945 3.8 132 Lafayette, LA............ 10.1 130.6 0.5 226 922 3.1 212 Orleans, LA.............. 13.6 199.2 1.9 76 993 3.2 202 St. Tammany, LA.......... 8.8 90.0 1.6 102 899 3.5 168 Cumberland, ME........... 13.9 188.1 0.7 206 1,007 4.0 114 Anne Arundel, MD......... 15.3 274.9 -0.1 294 1,139 4.7 55 Baltimore, MD............ 21.3 375.9 -0.3 304 1,061 1.3 333 Frederick, MD............ 6.5 105.3 1.0 171 1,007 5.1 31 Harford, MD.............. 5.9 94.4 -1.3 344 1,053 3.6 157 Howard, MD............... 10.1 175.7 1.5 110 1,320 2.9 238 Montgomery, MD........... 32.8 473.7 0.3 246 1,404 3.9 122 Prince George's, MD...... 16.4 318.0 -0.5 314 1,156 5.5 18 Baltimore City, MD....... 13.7 343.6 -0.7 327 1,239 2.9 238 Barnstable, MA........... 9.7 102.4 -0.4 308 914 4.3 85 Bristol, MA.............. 18.1 229.2 0.6 217 973 4.2 93 Essex, MA................ 27.6 327.4 0.1 274 1,154 5.2 25 Hampden, MA.............. 19.0 214.2 0.2 261 947 1.7 314 Middlesex, MA............ 57.0 939.3 1.6 102 1,625 4.4 78 Norfolk, MA.............. 25.7 352.4 -0.4 308 1,216 3.7 151 Plymouth, MA............. 16.6 197.1 0.2 261 1,036 6.1 15 Suffolk, MA.............. 32.1 700.7 2.4 51 1,784 4.3 85 Worcester, MA............ 26.7 352.9 0.2 261 1,063 1.9 305 Genesee, MI.............. 7.4 137.9 0.6 217 877 2.3 283 Ingham, MI............... 6.7 155.4 1.0 171 994 2.2 288 Kalamazoo, MI............ 5.5 120.7 -0.5 314 993 4.2 93 Kent, MI................. 16.3 405.1 0.3 246 960 3.7 151 Macomb, MI............... 19.3 331.2 0.3 246 1,049 1.6 320 Oakland, MI.............. 43.5 745.3 0.0 284 1,165 1.9 305 Ottawa, MI............... 6.3 130.2 0.8 196 921 2.8 252 Saginaw, MI.............. 4.1 84.6 -0.1 294 861 3.2 202 Washtenaw, MI............ 9.3 221.2 2.0 69 1,179 3.2 202 Wayne, MI................ 35.7 736.3 0.4 238 1,150 3.2 202 Anoka, MN................ 7.8 128.3 0.7 206 1,094 3.8 132 Dakota, MN............... 10.7 191.5 0.0 284 1,057 4.0 114 Hennepin, MN............. 41.1 939.0 0.5 226 1,322 2.4 276 Olmsted, MN.............. 3.8 100.7 0.1 274 1,279 3.8 132 Ramsey, MN............... 14.2 337.0 0.1 274 1,196 2.3 283 St. Louis, MN............ 5.4 98.0 -1.0 339 915 3.0 227 Stearns, MN.............. 4.4 88.0 0.2 261 943 3.5 168 Washington, MN........... 6.1 88.2 0.9 186 908 3.1 212 Harrison, MS............. 4.6 86.9 0.9 186 722 1.1 338 Hinds, MS................ 5.7 119.8 0.0 284 910 4.0 114 Boone, MO................ 5.0 95.2 0.5 226 896 6.7 13 Clay, MO................. 5.8 105.3 0.0 284 920 2.0 295 Greene, MO............... 9.3 170.8 1.5 110 838 0.8 342 Jackson, MO.............. 22.6 375.6 0.4 238 1,070 2.5 267 St. Charles, MO.......... 9.8 153.8 3.0 25 862 2.9 238 St. Louis, MO............ 40.5 609.5 0.3 246 1,131 4.3 85 St. Louis City, MO....... 15.1 231.4 0.3 246 1,172 4.7 55 Yellowstone, MT.......... 6.7 82.8 0.4 238 919 3.5 168 Douglas, NE.............. 19.3 341.7 0.9 186 1,027 3.8 132 Lancaster, NE............ 10.3 172.6 0.2 261 891 4.0 114 Clark, NV................ 57.0 1,025.9 2.3 54 950 3.9 122 Washoe, NV............... 15.3 227.3 1.4 122 1,007 4.1 103 Hillsborough, NH......... 12.3 205.5 0.5 226 1,146 3.0 227 Merrimack, NH............ 5.3 78.0 0.3 246 1,018 2.9 238 Rockingham, NH........... 11.2 153.0 1.1 153 1,048 3.8 132 Atlantic, NJ............. 6.6 130.8 -1.2 342 877 2.7 257 Bergen, NJ............... 33.5 446.3 1.0 171 1,238 3.3 195 Burlington, NJ........... 11.1 201.9 0.3 246 1,096 3.1 212 Camden, NJ............... 12.2 206.5 -0.1 294 1,038 5.2 25 Essex, NJ................ 20.9 345.9 0.9 186 1,330 5.0 38 Gloucester, NJ........... 6.4 114.5 2.9 31 890 1.4 328 Hudson, NJ............... 15.4 268.8 1.0 171 1,384 0.4 345 Mercer, NJ............... 11.3 260.1 1.1 153 1,296 3.0 227 Middlesex, NJ............ 22.7 432.4 0.3 246 1,216 3.1 212 Monmouth, NJ............. 20.4 265.2 0.7 206 1,032 1.3 333 Morris, NJ............... 17.3 294.5 0.7 206 1,534 4.5 66 Ocean, NJ................ 13.8 174.8 2.0 69 850 3.5 168 Passaic, NJ.............. 12.6 166.6 1.0 171 1,005 1.7 314 Somerset, NJ............. 10.3 188.7 -0.3 304 1,518 1.6 320 Union, NJ................ 14.7 228.0 0.7 206 1,272 0.3 347 Bernalillo, NM........... 20.2 334.8 1.2 142 939 4.4 78 Albany, NY............... 10.2 233.8 -0.7 327 1,122 4.3 85 Bronx, NY................ 18.8 324.0 1.1 153 1,131 4.5 66 Broome, NY............... 4.3 85.6 -1.3 344 887 5.2 25 Dutchess, NY............. 8.3 113.8 0.0 284 1,041 3.8 132 Erie, NY................. 24.2 473.4 -0.7 327 953 2.9 238 Kings, NY................ 63.1 796.4 1.1 153 953 4.2 93 Monroe, NY............... 18.6 391.4 0.0 284 1,009 4.5 66 Nassau, NY............... 53.3 628.3 0.1 274 1,162 3.2 202 New York, NY............. 125.9 2,515.1 1.2 142 2,055 2.9 238 Oneida, NY............... 5.2 105.2 0.5 226 838 3.5 168 Onondaga, NY............. 12.6 249.2 0.3 246 997 3.9 122 Orange, NY............... 10.4 147.8 0.8 196 918 5.0 38 Queens, NY............... 52.8 718.6 1.3 132 1,076 2.9 238 Richmond, NY............. 9.8 127.8 4.7 3 1,028 2.8 252 Rockland, NY............. 10.9 130.3 3.5 10 998 2.0 295 Saratoga, NY............. 5.9 89.5 -0.4 308 978 1.9 305 Suffolk, NY.............. 52.7 669.2 0.0 284 1,158 3.1 212 Westchester, NY.......... 35.5 428.7 -0.6 321 1,308 2.5 267 Buncombe, NC............. 9.8 135.1 1.0 171 846 3.3 195 Cabarrus, NC............. 4.9 76.6 2.0 69 806 5.2 25 Catawba, NC.............. 4.5 88.5 -0.5 314 830 3.2 202 Cumberland, NC........... 6.2 119.9 1.5 110 848 2.8 252 Durham, NC............... 8.6 211.5 4.1 5 1,327 1.9 305 Forsyth, NC.............. 9.4 191.1 1.8 84 967 2.5 267 Guilford, NC............. 14.7 285.0 0.3 246 925 2.0 295 Mecklenburg, NC.......... 39.4 716.8 2.7 37 1,214 3.6 157 New Hanover, NC.......... 8.6 117.8 5.8 1 873 1.4 328 Pitt, NC................. 3.8 77.7 1.5 110 906 4.6 60 Wake, NC................. 36.3 574.6 3.3 15 1,157 5.3 22 Cass, ND................. 7.3 121.5 1.5 110 991 3.7 151 Butler, OH............... 8.1 158.3 0.6 217 945 3.5 168 Cuyahoga, OH............. 36.3 731.6 0.2 261 1,084 3.1 212 Delaware, OH............. 5.6 89.5 0.4 238 1,046 4.5 66 Franklin, OH............. 33.9 764.2 0.6 217 1,096 2.5 267 Greene, OH............... 3.7 76.5 1.6 102 1,081 3.0 227 Hamilton, OH............. 24.4 523.1 0.5 226 1,186 6.4 14 Lake, OH................. 6.3 97.4 1.7 93 913 9.9 4 Lorain, OH............... 6.3 99.0 1.1 153 823 0.9 339 Lucas, OH................ 10.2 208.8 0.2 261 933 1.5 325 Mahoning, OH............. 5.9 98.2 -0.1 294 767 2.0 295 Montgomery, OH........... 12.1 255.8 0.0 284 925 2.8 252 Stark, OH................ 8.7 159.1 -0.5 314 810 2.0 295 Summit, OH............... 14.5 267.4 -0.2 301 927 1.2 336 Warren, OH............... 5.3 97.7 2.4 51 1,144 7.2 10 Cleveland, OK............ 6.0 86.3 3.8 7 777 2.0 295 Oklahoma, OK............. 28.5 465.6 0.8 196 1,000 2.6 261 Tulsa, OK................ 22.8 364.0 1.3 132 969 2.9 238 Clackamas, OR............ 15.7 170.5 2.0 69 1,040 3.1 212 Deschutes, OR............ 9.3 86.7 2.5 45 907 5.1 31 Jackson, OR.............. 7.9 92.0 0.2 261 849 3.8 132 Lane, OR................. 12.8 158.6 0.4 238 856 3.5 168 Marion, OR............... 11.5 161.8 1.5 110 908 3.9 122 Multnomah, OR............ 36.7 519.9 1.4 122 1,159 2.9 238 Washington, OR........... 20.5 304.3 1.7 93 1,371 3.4 182 Allegheny, PA............ 35.9 704.8 0.3 246 1,147 3.4 182 Berks, PA................ 9.0 176.2 0.8 196 967 1.3 333 Bucks, PA................ 20.4 269.5 1.2 142 981 1.9 305 Butler, PA............... 5.1 87.8 0.7 206 995 0.4 345 Chester, PA.............. 15.8 254.0 1.2 142 1,271 1.4 328 Cumberland, PA........... 6.6 136.7 1.0 171 981 2.0 295 Dauphin, PA.............. 7.5 186.5 0.7 206 1,063 4.0 114 Delaware, PA............. 14.2 227.3 0.9 186 1,105 2.4 276 Erie, PA................. 7.0 122.6 -0.9 336 816 3.0 227 Lackawanna, PA........... 5.6 97.2 -0.8 333 813 2.3 283 Lancaster, PA............ 13.8 246.2 1.1 153 908 3.5 168 Lehigh, PA............... 8.9 196.5 0.9 186 1,045 3.7 151 Luzerne, PA.............. 7.5 146.1 0.2 261 848 2.0 295 Montgomery, PA........... 28.0 505.4 1.3 132 1,287 3.5 168 Northampton, PA.......... 6.9 119.0 1.8 84 905 1.7 314 Philadelphia, PA......... 35.2 704.1 1.6 102 1,290 4.5 66 Washington, PA........... 5.6 88.6 -0.2 301 1,047 1.7 314 Westmoreland, PA......... 9.3 134.6 0.3 246 889 2.7 257 York, PA................. 9.2 180.9 0.2 261 939 2.7 257 Kent, RI................. 5.6 76.4 0.1 274 939 2.6 261 Providence, RI........... 18.8 290.7 0.3 246 1,011 2.1 292 Charleston, SC........... 17.1 258.9 3.0 25 965 4.8 47 Greenville, SC........... 15.4 279.0 1.7 93 910 2.1 292 Horry, SC................ 9.8 135.2 3.2 17 660 4.3 85 Lexington, SC............ 7.2 122.0 2.7 37 841 5.1 31 Richland, SC............. 10.8 225.6 1.3 132 923 3.5 168 Spartanburg, SC.......... 6.7 147.0 2.7 37 889 2.9 238 York, SC................. 6.5 100.6 4.4 4 876 3.8 132 Minnehaha, SD............ 7.7 128.8 0.9 186 946 2.4 276 Davidson, TN............. 24.8 520.0 3.4 14 1,179 4.1 103 Hamilton, TN............. 10.4 209.4 1.3 132 963 4.2 93 Knox, TN................. 13.1 241.4 0.8 196 936 2.4 276 Rutherford, TN........... 6.1 134.4 1.4 122 939 3.8 132 Shelby, TN............... 21.2 504.1 0.8 196 1,061 0.2 349 Williamson, TN........... 9.7 141.3 3.7 9 1,251 4.1 103 Bell, TX................. 5.7 120.9 1.3 132 916 4.2 93 Bexar, TX................ 43.4 878.4 1.2 142 965 3.8 132 Brazoria, TX............. 6.1 116.6 2.0 69 1,095 0.9 339 Brazos, TX............... 4.7 109.2 2.2 60 815 3.6 157 Cameron, TX.............. 6.6 141.5 1.5 110 659 4.9 42 Collin, TX............... 27.4 432.0 3.0 25 1,278 3.1 212 Dallas, TX............... 79.3 1,750.7 2.7 37 1,303 4.9 42 Denton, TX............... 16.3 260.9 3.5 10 962 3.0 227 Ector, TX................ 4.2 81.1 -1.3 344 1,227 2.4 276 El Paso, TX.............. 15.6 312.9 1.4 122 765 4.2 93 Fort Bend, TX............ 14.5 195.8 2.2 60 977 3.5 168 Galveston, TX............ 6.3 110.1 0.9 186 959 4.1 103 Harris, TX............... 117.9 2,350.4 1.9 76 1,315 3.1 212 Hidalgo, TX.............. 12.7 265.0 2.3 54 683 3.6 157 Jefferson, TX............ 5.9 123.1 1.9 76 1,084 3.5 168 Lubbock, TX.............. 7.8 141.6 1.1 153 861 4.1 103 McLennan, TX............. 5.5 114.4 1.4 122 892 4.4 78 Midland, TX.............. 6.1 107.7 0.5 226 1,459 3.4 182 Montgomery, TX........... 12.4 192.0 2.6 43 1,061 4.2 93 Nueces, TX............... 8.3 163.4 1.1 153 937 2.9 238 Potter, TX............... 4.0 77.1 1.6 102 888 3.3 195 Smith, TX................ 6.4 103.4 0.4 238 893 5.1 31 Tarrant, TX.............. 45.5 923.3 1.6 102 1,079 4.9 42 Travis, TX............... 43.9 779.9 3.1 21 1,312 4.8 47 Webb, TX................. 5.6 103.0 0.8 196 712 2.2 288 Williamson, TX........... 11.9 181.4 3.9 6 1,143 12.3 3 Davis, UT................ 9.0 134.7 2.3 54 873 3.8 132 Salt Lake, UT............ 49.1 726.1 2.7 37 1,081 4.5 66 Utah, UT................. 18.1 255.1 3.3 15 914 7.4 9 Weber, UT................ 6.4 109.3 3.0 25 831 2.6 261 Chittenden, VT........... 7.2 103.3 0.2 261 1,065 5.0 38 Arlington, VA............ 9.1 182.9 2.5 45 1,744 3.0 227 Chesterfield, VA......... 9.4 135.4 1.1 153 926 4.4 78 Fairfax, VA.............. 36.6 622.2 1.5 110 1,651 4.0 114 Henrico, VA.............. 11.8 191.7 -0.5 314 1,023 3.9 122 Loudoun, VA.............. 12.6 174.8 2.9 31 1,228 0.5 344 Prince William, VA....... 9.6 132.8 1.0 171 960 3.4 182 Alexandria City, VA...... 6.2 89.5 -1.6 348 1,505 3.4 182 Chesapeake City, VA...... 6.2 101.1 1.4 122 860 4.2 93 Newport News City, VA.... 3.9 103.3 1.2 142 1,019 4.9 42 Norfolk City, VA......... 6.1 139.6 0.1 274 1,063 4.6 60 Richmond City, VA........ 8.0 158.7 1.8 84 1,198 6.8 12 Virginia Beach City, VA.. 12.3 177.9 0.6 217 823 3.9 122 Benton, WA............... 6.1 94.3 3.5 10 1,099 2.4 276 Clark, WA................ 15.5 165.5 1.7 93 1,060 4.8 47 King, WA................. 90.7 1,445.3 3.1 21 1,814 3.6 157 Kitsap, WA............... 7.0 92.4 2.1 64 1,014 3.4 182 Pierce, WA............... 23.5 320.3 1.7 93 1,039 5.3 22 Snohomish, WA............ 21.9 293.3 2.0 69 1,167 3.8 132 Spokane, WA.............. 16.8 231.0 1.8 84 953 4.4 78 Thurston, WA............. 8.7 119.4 1.2 142 1,051 5.5 18 Whatcom, WA.............. 7.4 91.5 0.6 217 932 3.8 132 Yakima, WA............... 8.1 127.1 1.4 122 790 3.1 212 Kanawha, WV.............. 5.7 96.3 -1.5 347 950 3.5 168 Brown, WI................ 7.4 159.1 -0.6 321 947 3.2 202 Dane, WI................. 16.6 342.3 1.8 84 1,062 3.2 202 Milwaukee, WI............ 28.3 488.2 -0.5 314 1,008 3.0 227 Outagamie, WI............ 5.7 108.7 -0.3 304 917 2.2 288 Racine, WI............... 4.8 75.5 -0.3 304 910 1.2 336 Waukesha, WI............. 14.0 246.3 0.4 238 1,054 3.1 212 Winnebago, WI............ 4.0 92.7 -0.1 294 968 3.4 182 San Juan, PR............. 11.4 242.9 1.7 (5) 637 -0.9 (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 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 355 U.S. counties comprise 73.4 percent of the total covered workers in the U.S.
Table 2. Covered establishments, employment, and wages in the 10 largest counties, third quarter 2019 Employment Average weekly wage(1) Establishments, third quarter County by NAICS supersector 2019 Percent Percent (thousands) September change, Third change, 2019 September quarter third (thousands) 2018-19(2) 2019 quarter 2018-19(2) United States(3) ............................ 10,325.3 148,556.5 1.1 $1,093 3.6 Private industry........................... 10,022.4 126,655.3 1.2 1,086 3.7 Natural resources and mining............. 140.3 2,036.2 -0.4 1,112 3.8 Construction............................. 838.1 7,646.3 2.5 1,224 3.7 Manufacturing............................ 357.2 12,774.8 0.3 1,269 1.7 Trade, transportation, and utilities..... 1,948.8 27,339.7 0.4 924 3.9 Information.............................. 186.8 2,839.0 1.5 2,221 2.7 Financial activities..................... 919.6 8,328.4 1.4 1,614 3.7 Professional and business services....... 1,921.0 21,301.3 1.3 1,419 4.3 Education and health services............ 1,775.3 23,112.6 1.8 996 3.4 Leisure and hospitality.................. 885.7 16,576.0 1.1 479 5.0 Other services........................... 865.5 4,530.8 1.1 760 4.1 Government................................. 302.9 21,901.2 0.8 1,135 3.2 Los Angeles, CA.............................. 511.6 4,499.4 1.0 1,225 3.7 Private industry........................... 505.2 3,927.0 1.3 1,184 3.6 Natural resources and mining............. 0.5 6.6 1.5 1,082 -3.0 Construction............................. 17.1 150.8 2.1 1,323 5.8 Manufacturing............................ 12.8 337.8 -0.4 1,354 1.8 Trade, transportation, and utilities..... 59.6 835.1 -0.6 1,015 4.9 Information.............................. 13.3 203.0 4.3 2,455 1.3 Financial activities..................... 30.4 221.8 -0.2 1,857 2.5 Professional and business services....... 56.8 638.9 1.7 1,481 2.3 Education and health services............ 245.2 832.8 2.9 926 3.7 Leisure and hospitality.................. 39.5 546.4 1.6 705 7.1 Other services........................... 29.5 153.5 0.9 829 10.4 Government................................. 6.5 572.4 -0.5 1,515 4.3 Cook, IL..................................... 139.7 2,617.8 0.1 1,244 3.8 Private industry........................... 138.4 2,320.0 0.1 1,251 3.8 Natural resources and mining............. 0.1 1.5 7.5 1,210 4.8 Construction............................. 11.2 81.0 2.2 1,538 3.0 Manufacturing............................ 5.7 183.6 0.1 1,276 2.7 Trade, transportation, and utilities..... 28.5 468.6 -0.5 1,036 5.1 Information.............................. 2.6 53.7 3.0 2,027 3.9 Financial activities..................... 14.1 207.4 1.6 2,161 1.4 Professional and business services....... 29.4 480.9 -0.6 1,565 3.9 Education and health services............ 15.6 451.3 0.0 1,058 3.6 Leisure and hospitality.................. 14.0 295.9 0.8 597 6.4 Other services........................... 16.2 95.8 -2.5 976 3.7 Government................................. 1.3 297.8 0.6 1,186 3.1 New York, NY................................. 125.9 2,515.1 1.2 2,055 2.9 Private industry........................... 124.4 2,282.2 1.2 2,101 3.0 Natural resources and mining............. 0.0 0.3 18.1 2,060 9.0 Construction............................. 2.4 43.8 -3.2 1,949 2.5 Manufacturing............................ 1.8 21.8 -4.2 1,513 1.5 Trade, transportation, and utilities..... 18.3 251.0 -1.0 1,460 2.4 Information.............................. 5.1 182.5 3.8 2,851 -1.7 Financial activities..................... 19.2 388.5 2.1 3,437 2.1 Professional and business services....... 27.5 618.5 1.1 2,395 4.1 Education and health services............ 10.1 358.3 1.7 1,435 3.9 Leisure and hospitality.................. 14.6 308.6 0.8 993 6.1 Other services........................... 19.5 105.4 1.0 1,252 -1.8 Government................................. 1.4 232.9 1.2 1,599 2.4 Harris, TX................................... 117.9 2,350.4 1.9 1,315 3.1 Private industry........................... 117.3 2,073.8 1.9 1,325 3.2 Natural resources and mining............. 1.6 67.0 -0.6 3,247 7.4 Construction............................. 7.8 173.8 5.7 1,379 2.8 Manufacturing............................ 4.9 180.7 1.8 1,591 0.2 Trade, transportation, and utilities..... 25.2 468.8 0.3 1,189 3.2 Information.............................. 1.2 26.1 2.4 1,564 2.8 Financial activities..................... 12.8 130.8 2.7 1,691 3.2 Professional and business services....... 23.7 411.2 1.8 1,645 2.1 Education and health services............ 16.7 303.1 1.4 1,067 4.0 Leisure and hospitality.................. 10.6 241.7 2.7 514 7.5 Other services........................... 11.8 68.9 2.1 849 4.6 Government................................. 0.6 276.6 1.8 1,241 3.3 Maricopa, AZ................................. 106.6 2,068.7 3.2 1,060 4.5 Private industry........................... 105.9 1,851.0 3.5 1,050 4.6 Natural resources and mining............. 0.5 7.5 2.5 1,035 4.2 Construction............................. 8.6 133.0 7.4 1,157 5.4 Manufacturing............................ 3.5 129.4 3.0 1,391 3.6 Trade, transportation, and utilities..... 20.8 388.9 1.5 955 3.1 Information.............................. 2.2 38.2 3.1 1,460 -0.5 Financial activities..................... 13.7 192.4 4.0 1,405 9.3 Professional and business services....... 26.4 352.4 3.9 1,124 4.7 Education and health services............ 13.4 331.1 4.5 1,050 3.3 Leisure and hospitality.................. 9.1 223.2 2.4 524 4.0 Other services........................... 7.0 54.4 1.9 794 6.4 Government................................. 0.7 217.7 1.5 1,147 3.2 Dallas, TX................................... 79.3 1,750.7 2.7 1,303 4.9 Private industry........................... 78.8 1,573.6 2.8 1,312 5.1 Natural resources and mining............. 0.5 9.4 4.2 3,581 7.0 Construction............................. 4.9 93.0 3.5 1,323 2.0 Manufacturing............................ 2.9 119.1 4.1 1,587 7.5 Trade, transportation, and utilities..... 16.4 353.9 1.8 1,146 4.5 Information.............................. 1.4 45.9 -0.3 1,983 6.0 Financial activities..................... 9.9 168.0 3.1 1,767 4.1 Professional and business services....... 18.0 366.8 3.7 1,532 6.5 Education and health services............ 9.9 204.7 1.6 1,134 3.0 Leisure and hospitality.................. 7.2 167.9 3.6 551 7.2 Other services........................... 7.1 43.7 1.4 854 2.3 Government................................. 0.5 177.1 1.4 1,220 2.4 Orange, CA................................... 127.5 1,649.2 1.1 1,204 4.3 Private industry........................... 126.1 1,499.5 1.0 1,194 4.5 Natural resources and mining............. 0.2 2.3 -6.5 912 3.1 Construction............................. 7.8 107.6 -0.7 1,469 4.9 Manufacturing............................ 5.3 159.1 -0.7 1,512 2.4 Trade, transportation, and utilities..... 18.6 255.6 -0.6 1,067 4.2 Information.............................. 1.6 25.5 -2.4 2,133 1.8 Financial activities..................... 13.1 116.7 0.2 1,997 10.7 Professional and business services....... 23.8 327.4 2.1 1,349 4.4 Education and health services............ 38.0 228.5 3.6 996 3.4 Leisure and hospitality.................. 9.8 228.5 1.4 538 3.7 Other services........................... 7.7 48.3 0.9 769 6.1 Government................................. 1.4 149.7 2.4 1,318 3.1 San Diego, CA................................ 116.3 1,490.2 1.5 1,197 4.1 Private industry........................... 114.2 1,254.7 1.6 1,160 3.8 Natural resources and mining............. 0.7 10.5 8.9 799 2.2 Construction............................. 8.0 85.7 1.0 1,302 6.0 Manufacturing............................ 3.6 115.3 2.1 1,630 4.0 Trade, transportation, and utilities..... 15.5 221.2 -0.1 907 4.9 Information.............................. 1.4 23.1 -2.1 2,492 9.6 Financial activities..................... 11.2 76.2 0.7 1,552 6.0 Professional and business services....... 21.2 255.2 2.7 1,626 1.3 Education and health services............ 35.0 211.2 3.3 996 3.0 Leisure and hospitality.................. 9.3 203.2 0.7 546 4.6 Other services........................... 8.3 52.9 1.3 681 3.3 Government................................. 2.0 235.5 1.2 1,401 5.2 King, WA..................................... 90.7 1,445.3 3.1 1,814 3.6 Private industry........................... 90.0 1,275.1 3.4 1,855 3.4 Natural resources and mining............. 0.4 3.0 -3.0 1,288 -7.9 Construction............................. 7.0 76.7 1.6 1,500 5.5 Manufacturing............................ 2.5 105.6 2.6 1,650 3.0 Trade, transportation, and utilities..... 13.7 279.1 3.3 1,750 3.2 Information.............................. 2.6 124.5 8.1 5,367 -2.7 Financial activities..................... 6.8 71.7 1.7 1,812 6.8 Professional and business services....... 18.6 238.6 3.7 1,873 5.3 Education and health services............ 21.6 181.0 2.9 1,108 4.2 Leisure and hospitality.................. 7.5 146.9 1.6 644 3.2 Other services........................... 9.3 48.1 5.4 939 7.2 Government................................. 0.6 170.1 0.9 1,507 4.9 Miami-Dade, FL............................... 102.9 1,157.2 1.3 1,039 3.6 Private industry........................... 102.6 1,017.7 1.5 1,008 3.7 Natural resources and mining............. 0.5 8.5 5.9 679 -0.7 Construction............................. 7.3 52.3 2.6 1,012 2.7 Manufacturing............................ 2.8 41.8 2.2 937 3.2 Trade, transportation, and utilities..... 24.6 287.6 0.9 941 3.6 Information.............................. 1.6 19.2 3.8 1,610 -2.4 Financial activities..................... 11.1 75.7 0.3 1,596 6.3 Professional and business services....... 23.8 165.2 2.7 1,201 2.1 Education and health services............ 11.6 185.2 1.5 1,024 4.3 Leisure and hospitality.................. 7.7 142.4 0.8 633 4.6 Other services........................... 8.8 38.2 -1.1 676 4.3 Government................................. 0.3 139.5 0.5 1,277 3.2 (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 2018 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 2019 Employment Average weekly wage(1) Establishments, third quarter State 2019 Percent Percent (thousands) September change, Third change, 2019 September quarter third (thousands) 2018-19 2019 quarter 2018-19 United States(2)........... 10,325.3 148,556.5 1.1 $1,093 3.6 Alabama.................... 130.9 1,989.5 1.1 919 3.8 Alaska..................... 22.4 338.0 1.2 1,105 3.7 Arizona.................... 168.0 2,913.4 2.6 1,018 4.5 Arkansas................... 92.3 1,222.8 0.0 841 3.8 California................. 1,608.8 17,713.1 1.4 1,309 3.8 Colorado................... 212.1 2,749.0 2.4 1,170 6.1 Connecticut................ 123.6 1,676.6 -0.3 1,236 2.3 Delaware................... 34.4 453.2 1.1 1,078 3.3 District of Columbia....... 41.6 776.4 0.6 1,851 2.5 Florida.................... 724.8 8,838.2 1.7 955 3.4 Georgia.................... 289.8 4,509.7 1.4 1,026 3.4 Hawaii..................... 44.9 654.1 -0.3 1,012 3.9 Idaho...................... 68.3 765.2 2.9 838 4.1 Illinois................... 380.6 6,023.1 0.0 1,125 3.6 Indiana.................... 168.5 3,083.5 0.3 914 3.5 Iowa....................... 104.5 1,556.9 0.1 914 3.0 Kansas..................... 89.8 1,395.9 0.4 893 2.9 Kentucky................... 122.9 1,910.8 0.7 884 3.4 Louisiana.................. 136.0 1,913.5 -0.3 923 2.6 Maine...................... 54.3 632.6 0.9 887 4.2 Maryland................... 173.6 2,696.9 0.2 1,169 3.6 Massachusetts.............. 264.1 3,642.5 0.9 1,359 4.2 Michigan................... 268.2 4,375.8 0.2 1,021 3.0 Minnesota.................. 180.6 2,917.8 0.4 1,107 3.0 Mississippi................ 74.6 1,135.8 0.1 768 2.7 Missouri................... 209.8 2,826.5 0.6 942 3.9 Montana.................... 50.6 478.9 1.2 848 3.9 Nebraska................... 73.3 984.7 0.3 908 4.0 Nevada..................... 84.6 1,412.2 2.1 973 4.1 New Hampshire.............. 54.2 667.9 0.8 1,075 3.4 New Jersey................. 278.1 4,104.0 0.9 1,217 3.0 New Mexico................. 62.8 842.1 1.7 899 5.1 New York................... 651.0 9,575.4 1.1 1,314 3.3 North Carolina............. 287.7 4,501.3 2.2 972 3.6 North Dakota............... 31.9 428.4 0.9 1,028 3.3 Ohio....................... 302.1 5,443.3 0.3 976 3.1 Oklahoma................... 111.8 1,628.8 0.5 897 2.6 Oregon..................... 160.7 1,970.7 1.4 1,037 3.2 Pennsylvania............... 363.4 5,947.9 0.8 1,064 3.2 Rhode Island............... 38.9 491.3 0.6 991 2.8 South Carolina............. 141.1 2,132.4 2.2 866 3.7 South Dakota............... 34.3 433.4 0.4 855 3.4 Tennessee.................. 167.8 3,060.8 1.9 966 2.8 Texas...................... 713.5 12,603.2 2.1 1,109 4.1 Utah....................... 109.1 1,535.2 2.8 954 4.8 Vermont.................... 26.1 311.0 0.0 927 4.3 Virginia................... 279.6 3,931.4 1.0 1,125 4.0 Washington................. 252.6 3,489.8 2.1 1,335 4.3 West Virginia.............. 51.6 694.4 -1.8 897 0.3 Wisconsin.................. 182.2 2,893.8 0.1 929 3.1 Wyoming.................... 27.0 283.1 1.5 942 4.2 Puerto Rico................ 47.9 878.9 1.9 528 -0.8 Virgin Islands............. 3.3 37.8 9.6 1,012 12.8 (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.