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For release 10:00 a.m. (EST), Wednesday, November 21, 2018 USDL-18-1859 Technical Information: (202) 691-6567 * QCEWInfo@bls.gov * www.bls.gov/cew Media Contact: (202) 691-5902 * PressOffice@bls.gov COUNTY EMPLOYMENT AND WAGES – SECOND QUARTER 2018 From June 2017 to June 2018, employment increased in 309 of the 349 largest U.S. counties, the U.S. Bureau of Labor Statistics reported today. In June 2018, national employment (as measured by the Quarterly Census of Employment and Wages program) increased to 147.4 million, a 1.5 percent increase over the year. Midland, TX, had the largest over-the-year increase in employment with a gain of 11.6 percent. Employment data in this release are presented for June 2018, and average weekly wage data are presented for second quarter 2018. Among the 349 largest counties, 340 had over-the-year increases in average weekly wages. In the second quarter of 2018, average weekly wages for the nation increased to $1,055, a 3.4 percent increase over the year. Marin, CA, had the largest second quarter over-the-year wage gain at 11.7 percent. (See table 1.) Large County Employment in June 2018 Midland, TX, had the largest over-the-year percentage increase in employment (11.6 percent). Within Midland, the largest employment increase occurred in natural resources and mining, which gained 6,009 jobs over the year (25.7 percent). McLean, IL, experienced the largest over-the-year percentage decrease in employment, with a loss of 2.0 percent. Within McLean, financial activities had the largest decrease in employment with a loss of 892 jobs (-4.5 percent) over the year. Large County Average Weekly Wage in Second Quarter 2018 Marin, CA, had the largest over-the-year percentage increase in average weekly wages (11.7 percent). Within Marin, an average weekly wage gain of $439 (26.5 percent) over the year in professional and business services made the largest contribution to the county’s increase in average weekly wages. New Hanover, NC, had the largest over-the-year percentage decrease in average weekly wages with a loss of 6.4 percent. Within New Hanover, professional and business services had the largest impact on the county’s change, with an average weekly wage decrease of $511 (-33.2 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 June 2018, Maricopa, AZ, had the largest over-the-year employment percentage gain among the 10 largest counties (2.8 percent). Within Maricopa, trade, transportation, and utilities had the largest over-the-year employment increase with a gain of 10,775 jobs (2.9 percent). (See table 2.) In second quarter 2018, King, WA, experienced the largest over-the-year average weekly wage percentage gain among the 10 largest counties (9.3 percent). Within King, trade, transportation, and utilities had the largest impact on the county’s change, with an average weekly wage increase of $270 (16.7 percent) over the year. For More Information The tables included in this release contain data for the nation and for the 349 U.S. counties with annual average employment levels of 75,000 or more in 2017. June 2018 employment and second quarter 2018 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/cewregional.htm. The QCEW news release schedule is available at www.bls.gov/cew/releasecalendar.htm. ____________ The County Employment and Wages full data update for second quarter 2018 is scheduled to be released on Thursday, December 6, 2018, at 10:00 a.m. (EST). The County Employment and Wages news release for third quarter 2018 is scheduled to be released on Wednesday, February 20, 2019, at 10:00 a.m. (EST). ----------------------------------------------------------------------------------------------------- | | | New BLS Local Data iPhone App Includes QCEW Data | | | | BLS has partnered with the U.S. Department of Labor’s Office of the Chief Information Officer | | to develop a new mobile app for iPhones. The BLS Local Data app is ideal for customers, such | | as jobseekers and economic and workforce development professionals, who want to know more | | about local labor markets. For more information, please go to: | | https://blogs.bls.gov/blog/2018/10/18/new-bls-local-data-app-now-available/ | | | -----------------------------------------------------------------------------------------------------
Technical Note These data are the product of a federal-state cooperative program, the Quarterly Census of Employment and Wag- es (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 in- surance 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 2018 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 addi- tion, 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 349 counties presented in this release were derived using 2017 preliminary annual averages of employment. For 2018 data, three counties have been added to the publication tables: Cabar- rus, N.C.; Pitt, N.C.; and Kent, R.I. These counties will be included in all 2018 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 edit- ing. 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 dif- ferent 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- | 651,000 establish- | submitted by 10.0 | ministrative records| ments | million establish- | submitted by 8.0 | | ments in first | million private-sec-| | quarter of 2018 | 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 submit- ted 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, em- ployers 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 establish- ments. QCEW employment and wage data are derived from microdata summaries of 9.8 million employer reports of employment and wages submitted by states to the BLS in 2017. These reports are based on place of employ- ment rather than place of residence. UI and UCFE coverage is broad and has been basically comparable from state to state since 1978, when the 1976 amendments to the Federal Unemployment Tax Act became effective, expanding coverage to include most state and local government employees. In 2017, UI and UCFE programs covered workers in 143.9 million jobs. The estimated 138.6 million workers in these jobs (after adjustment for multiple jobholders) represented 96.4 percent of civilian wage and salary employment. Covered workers received $7.968 trillion in pay, representing 94.3 per- cent of the wage and salary component of personal income and 40.9 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 work- ers, 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 compari- sons 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 pro- duction 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 quar- ter. 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 sup- plied, 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 fluctua- tions in average monthly employment and/or total quarterly wages between the current quarter and prior year lev- els. Average weekly wages are affected by the ratio of full-time to part-time workers as well as the number of individ- uals 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 concen- trated 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 2017 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 owner- ship 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 un- known 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 im- plemented to account for: administrative changes caused by multi-unit employers who start reporting for each in- dividual 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 Se- curity 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 es- tablishments, employment, and wages for the nation and all states. The 2017 edition of this publication, which was published in September 2018, contains selected data produced by Business Employment Dynamics (BED) on job gains and losses, as well as selected data from the first quarter 2018 version of this news release. Tables and addi- tional content from the 2017 edition of Employment and Wages Annual Averages Online are now available at www.bls.gov/cew/cewbultn17.htm. The 2018 edition of Employment and Wages Annual Averages Online will be available in September 2019. 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 350 largest counties, second quarter 2018 Employment Average weekly wage(2) Establishments, County(1) second quarter Percent Ranking Percent Ranking 2018 June change, by Second change, by (thousands) 2018 June percent quarter second percent (thousands) 2017-18(3) change 2018 quarter change 2017-18(3) United States(4)......... 10,048.0 147,431.2 1.5 - $1,055 3.4 - Jefferson, AL............ 18.9 350.6 1.4 144 1,034 2.7 204 Madison, AL.............. 9.7 200.7 1.7 118 1,102 2.9 185 Mobile, AL............... 10.3 171.5 0.9 206 874 1.9 278 Montgomery, AL........... 6.4 132.3 -0.8 343 860 2.4 233 Shelby, AL............... 5.9 85.5 0.3 281 985 3.8 86 Tuscaloosa, AL........... 4.6 93.0 0.9 206 861 1.3 313 Anchorage, AK............ 8.3 150.7 -0.8 343 1,105 3.9 77 Maricopa, AZ............. 100.0 1,950.6 2.8 44 1,016 3.0 172 Pima, AZ................. 19.0 364.3 1.6 129 884 3.8 86 Benton, AR............... 6.6 120.2 0.9 206 1,029 1.0 323 Pulaski, AR.............. 14.4 251.8 0.3 281 922 1.5 301 Washington, AR........... 6.2 108.3 2.0 94 869 0.1 339 Alameda, CA.............. 64.7 793.7 2.1 82 1,421 3.3 136 Butte, CA................ 8.6 84.0 1.6 129 798 3.8 86 Contra Costa, CA......... 32.9 371.2 0.4 271 1,278 3.0 172 Fresno, CA............... 36.4 398.7 1.3 159 832 3.5 112 Kern, CA................. 19.7 327.5 1.0 193 869 3.1 160 Los Angeles, CA.......... 497.6 4,442.1 1.3 159 1,177 4.0 69 Marin, CA................ 12.5 117.7 0.8 219 1,422 11.7 1 Merced, CA............... 6.7 81.9 1.0 193 790 0.5 331 Monterey, CA............. 14.0 214.4 3.0 39 894 2.2 253 Napa, CA................. 5.9 81.4 1.7 118 1,036 2.7 204 Orange, CA............... 123.2 1,628.9 1.7 118 1,157 2.7 204 Placer, CA............... 13.3 169.6 3.5 19 1,042 3.1 160 Riverside, CA............ 66.1 740.7 3.0 39 852 3.3 136 Sacramento, CA........... 59.2 667.5 2.6 55 1,136 3.0 172 San Bernardino, CA....... 60.4 749.4 2.8 44 883 2.3 244 San Diego, CA............ 112.9 1,473.5 2.0 94 1,137 3.4 124 San Francisco, CA........ 61.2 741.6 3.2 28 2,083 7.6 8 San Joaquin, CA.......... 18.1 254.9 2.1 82 887 2.8 197 San Luis Obispo, CA...... 10.5 120.1 0.5 257 910 4.7 32 San Mateo, CA............ 28.5 405.3 1.7 118 2,357 9.0 4 Santa Barbara, CA........ 15.5 205.2 1.6 129 1,028 4.7 32 Santa Clara, CA.......... 73.6 1,106.1 2.3 72 2,573 8.2 6 Santa Cruz, CA........... 9.6 110.2 -0.2 323 983 4.0 69 Solano, CA............... 11.6 142.7 1.4 144 1,075 1.5 301 Sonoma, CA............... 20.3 212.7 1.8 110 1,015 4.2 59 Stanislaus, CA........... 16.0 192.1 1.9 103 884 2.9 185 Tulare, CA............... 10.8 170.5 -0.1 316 739 4.4 44 Ventura, CA.............. 27.7 331.3 0.9 206 1,036 2.2 253 Yolo, CA................. 6.8 105.8 0.9 206 1,144 3.8 86 Adams, CO................ 11.3 214.1 3.5 19 1,019 4.7 32 Arapahoe, CO............. 22.4 335.9 1.8 110 1,201 2.8 197 Boulder, CO.............. 15.7 185.6 2.6 55 1,235 3.5 112 Denver, CO............... 33.4 524.6 3.1 34 1,269 4.7 32 Douglas, CO.............. 12.4 128.2 1.9 103 1,170 3.0 172 El Paso, CO.............. 20.3 279.0 2.0 94 936 4.1 66 Jefferson, CO............ 20.6 242.1 2.0 94 1,082 3.3 136 Larimer, CO.............. 12.5 165.6 3.1 34 931 4.3 50 Weld, CO................. 7.6 110.7 4.2 9 954 6.8 9 Fairfield, CT............ 35.9 429.1 -0.3 331 1,488 -1.1 345 Hartford, CT............. 28.5 518.7 0.7 235 1,219 0.5 331 New Haven, CT............ 24.7 371.7 0.4 271 1,071 0.5 331 New London, CT........... 7.7 127.9 0.5 257 1,007 0.9 325 New Castle, DE........... 20.2 291.6 1.2 176 1,143 1.0 323 Sussex, DE............... 7.0 86.8 2.1 82 748 2.6 216 Washington, DC........... 40.0 777.2 1.3 159 1,713 2.6 216 Alachua, FL.............. 7.2 130.0 1.7 118 878 3.9 77 Bay, FL.................. 5.6 80.8 2.2 76 772 2.0 268 Brevard, FL.............. 16.0 214.1 3.3 27 946 1.5 301 Broward, FL.............. 69.5 803.2 1.2 176 998 4.5 40 Collier, FL.............. 14.3 139.4 2.5 63 927 5.7 17 Duval, FL................ 29.7 513.7 2.6 55 980 2.0 268 Escambia, FL............. 8.1 134.9 1.4 144 810 3.3 136 Hillsborough, FL......... 43.1 674.6 1.6 129 1,002 3.6 104 Lake, FL................. 8.4 94.5 2.1 82 730 4.0 69 Lee, FL.................. 22.3 253.4 2.6 55 864 4.2 59 Leon, FL................. 8.7 149.4 2.0 94 841 3.3 136 Manatee, FL.............. 11.0 119.4 2.5 63 827 4.8 31 Marion, FL............... 8.4 102.3 1.4 144 740 3.5 112 Miami-Dade, FL........... 99.0 1,125.0 0.9 206 1,000 3.0 172 Okaloosa, FL............. 6.6 84.4 1.3 159 885 2.4 233 Orange, FL............... 43.0 841.3 3.5 19 919 1.9 278 Osceola, FL.............. 7.2 92.3 3.5 19 731 2.1 261 Palm Beach, FL........... 56.8 597.9 0.8 219 1,015 1.2 317 Pasco, FL................ 11.1 112.7 3.0 39 760 1.6 295 Pinellas, FL............. 33.4 434.5 1.9 103 913 2.8 197 Polk, FL................. 13.4 214.0 1.9 103 800 3.5 112 Sarasota, FL............. 16.1 168.3 2.7 48 871 3.4 124 Seminole, FL............. 15.1 193.1 3.2 28 917 2.9 185 Volusia, FL.............. 14.5 169.2 1.5 138 770 2.4 233 Bibb, GA................. 4.3 82.7 0.5 257 806 4.1 66 Chatham, GA.............. 8.0 156.0 1.6 129 887 3.1 160 Clayton, GA.............. 4.0 122.9 2.8 44 1,022 1.3 313 Cobb, GA................. 21.8 365.1 1.8 110 1,067 0.4 335 DeKalb, GA............... 17.7 302.2 0.8 219 1,053 2.5 225 Fulton, GA............... 43.4 874.4 2.1 82 1,353 1.2 317 Gwinnett, GA............. 25.0 355.9 1.8 110 971 0.5 331 Hall, GA................. 4.5 87.9 1.3 159 906 5.5 21 Muscogee, GA............. 4.5 94.6 1.2 176 797 2.4 233 Richmond, GA............. 4.4 104.7 0.2 293 855 1.4 307 Honolulu, HI............. 26.0 474.7 0.2 293 994 1.9 278 Maui + Kalawao, HI....... 6.3 78.1 0.5 257 869 3.7 93 Ada, ID.................. 16.2 246.5 3.9 16 921 3.7 93 Champaign, IL............ 4.1 90.4 0.3 281 913 3.3 136 Cook, IL................. 138.7 2,626.3 0.9 206 1,220 3.2 150 DuPage, IL............... 34.7 628.3 0.1 303 1,160 1.6 295 Kane, IL................. 12.6 218.6 -0.5 335 930 2.5 225 Lake, IL................. 20.3 348.1 -0.1 316 1,411 9.3 2 McHenry, IL.............. 7.8 100.7 0.2 293 856 3.5 112 McLean, IL............... 3.4 82.2 -2.0 349 1,002 9.0 4 Madison, IL.............. 5.4 101.6 3.0 39 817 3.7 93 Peoria, IL............... 4.2 107.8 1.3 159 1,054 3.3 136 St. Clair, IL............ 5.1 92.3 -0.5 335 818 -0.1 342 Sangamon, IL............. 4.8 131.7 -0.2 323 1,001 1.3 313 Will, IL................. 14.7 249.6 1.3 159 898 1.8 285 Winnebago, IL............ 6.0 128.4 -0.1 316 869 3.2 150 Allen, IN................ 8.9 189.7 1.7 118 858 3.5 112 Elkhart, IN.............. 4.8 139.8 3.2 28 940 2.6 216 Hamilton, IN............. 9.5 144.7 2.4 69 978 2.5 225 Lake, IN................. 10.4 188.9 0.7 235 879 2.7 204 Marion, IN............... 24.2 599.7 0.1 303 1,048 2.0 268 St. Joseph, IN........... 5.8 124.2 0.2 293 852 3.1 160 Tippecanoe, IN........... 3.5 84.6 2.3 72 899 2.9 185 Vanderburgh, IN.......... 4.8 109.4 1.3 159 826 -0.1 342 Johnson, IA.............. 4.3 84.4 0.6 250 980 3.7 93 Linn, IA................. 6.9 133.7 0.7 235 1,008 3.9 77 Polk, IA................. 17.6 306.6 0.9 206 1,050 3.7 93 Scott, IA................ 5.7 92.7 -0.1 316 842 3.8 86 Johnson, KS.............. 23.6 352.2 2.0 94 1,068 2.9 185 Sedgwick, KS............. 12.6 250.8 1.2 176 882 2.7 204 Shawnee, KS.............. 5.1 96.4 -0.1 316 900 6.3 13 Wyandotte, KS............ 3.5 90.8 2.2 76 1,009 3.2 150 Boone, KY................ 4.5 94.0 4.0 10 907 3.0 172 Fayette, KY.............. 11.1 193.8 1.0 193 934 2.0 268 Jefferson, KY............ 25.5 471.6 0.7 235 1,032 1.6 295 Caddo, LA................ 7.3 112.1 -0.3 331 836 3.5 112 Calcasieu, LA............ 5.4 102.6 4.0 10 926 5.0 28 East Baton Rouge, LA..... 15.9 263.6 0.8 219 989 3.6 104 Jefferson, LA............ 14.1 189.8 -0.9 346 941 4.3 50 Lafayette, LA............ 9.8 129.5 0.3 281 883 2.7 204 Orleans, LA.............. 13.0 192.9 -0.1 316 967 4.2 59 St. Tammany, LA.......... 8.5 89.0 1.6 129 878 3.7 93 Cumberland, ME........... 13.7 189.9 1.4 144 944 3.6 104 Anne Arundel, MD......... 15.2 276.8 0.9 206 1,118 2.8 197 Baltimore, MD............ 21.3 382.5 0.2 293 1,039 3.7 93 Frederick, MD............ 6.5 103.6 1.5 138 946 1.6 295 Harford, MD.............. 5.8 95.9 1.4 144 989 4.4 44 Howard, MD............... 10.0 174.2 0.3 281 1,268 3.5 112 Montgomery, MD........... 32.9 478.4 0.3 281 1,392 4.0 69 Prince George's, MD...... 16.1 320.3 0.0 310 1,112 4.3 50 Baltimore City, MD....... 13.6 345.5 1.1 186 1,222 3.4 124 Barnstable, MA........... 9.6 108.3 -0.6 340 893 2.9 185 Bristol, MA.............. 18.0 232.8 0.2 293 975 2.3 244 Essex, MA................ 26.6 334.2 0.8 219 1,163 6.6 10 Hampden, MA.............. 18.8 210.5 0.5 257 916 2.1 261 Middlesex, MA............ 56.0 934.8 1.7 118 1,571 3.4 124 Norfolk, MA.............. 25.6 359.5 0.1 303 1,230 3.3 136 Plymouth, MA............. 16.3 200.4 0.7 235 999 0.1 339 Suffolk, MA.............. 30.8 684.7 1.9 103 1,711 3.7 93 Worcester, MA............ 26.1 354.0 0.7 235 1,039 2.9 185 Genesee, MI.............. 6.8 136.5 0.3 281 861 3.1 160 Ingham, MI............... 6.0 150.6 -0.8 343 1,005 3.2 150 Kalamazoo, MI............ 5.0 121.2 1.3 159 963 2.9 185 Kent, MI................. 14.6 411.6 3.4 25 900 1.7 289 Macomb, MI............... 17.6 339.5 1.5 138 1,042 3.4 124 Oakland, MI.............. 39.4 750.3 0.8 219 1,168 3.3 136 Ottawa, MI............... 5.7 130.2 1.7 118 883 3.4 124 Saginaw, MI.............. 3.9 84.0 -1.3 348 841 2.9 185 Washtenaw, MI............ 8.2 210.6 1.5 138 1,126 3.0 172 Wayne, MI................ 31.1 731.5 0.8 219 1,125 1.4 307 Anoka, MN................ 7.5 128.0 2.6 55 1,018 3.2 150 Dakota, MN............... 10.4 191.4 0.0 310 1,041 3.8 86 Hennepin, MN............. 40.8 931.1 0.8 219 1,318 3.5 112 Olmsted, MN.............. 3.6 100.8 0.8 219 1,122 4.3 50 Ramsey, MN............... 14.0 333.9 0.3 281 1,142 0.9 325 St. Louis, MN............ 5.4 100.5 0.3 281 885 3.3 136 Stearns, MN.............. 4.4 88.0 0.2 293 871 4.3 50 Washington, MN........... 5.9 89.8 2.5 63 910 2.5 225 Harrison, MS............. 4.6 86.4 -0.5 335 734 2.2 253 Hinds, MS................ 5.8 120.5 -0.5 335 865 2.1 261 Boone, MO................ 4.8 93.2 -0.2 323 835 1.7 289 Clay, MO................. 5.6 106.1 1.4 144 916 3.5 112 Greene, MO............... 8.9 167.2 0.8 219 822 4.3 50 Jackson, MO.............. 21.9 373.6 -0.2 323 1,061 3.2 150 St. Charles, MO.......... 9.5 149.4 0.0 310 847 3.5 112 St. Louis, MO............ 39.0 612.0 0.7 235 1,137 7.8 7 St. Louis City, MO....... 14.6 230.2 0.3 281 1,108 1.3 313 Yellowstone, MT.......... 6.7 82.5 -0.2 323 901 3.0 172 Douglas, NE.............. 19.1 342.1 0.4 271 960 2.7 204 Lancaster, NE............ 10.4 172.1 1.6 129 847 3.0 172 Clark, NV................ 55.4 992.6 2.7 48 916 3.3 136 Washoe, NV............... 14.7 222.5 2.3 72 944 4.1 66 Hillsborough, NH......... 12.2 206.7 0.8 219 1,127 4.2 59 Merrimack, NH............ 5.2 78.3 0.2 293 987 4.7 32 Rockingham, NH........... 11.0 153.4 0.5 257 1,030 2.0 268 Atlantic, NJ............. 6.6 135.3 2.2 76 903 5.6 20 Bergen, NJ............... 33.3 452.3 0.8 219 1,197 1.2 317 Burlington, NJ........... 11.1 205.1 0.2 293 1,070 1.4 307 Camden, NJ............... 12.2 209.2 -0.1 316 1,013 1.8 285 Essex, NJ................ 20.7 347.6 0.4 271 1,263 2.5 225 Gloucester, NJ........... 6.4 112.6 2.6 55 890 2.1 261 Hudson, NJ............... 15.2 265.4 0.3 281 1,408 4.7 32 Mercer, NJ............... 11.2 257.8 0.5 257 1,287 1.8 285 Middlesex, NJ............ 22.5 432.6 1.0 193 1,199 1.4 307 Monmouth, NJ............. 20.3 274.4 0.6 250 1,019 3.1 160 Morris, NJ............... 17.1 300.8 1.0 193 1,496 -2.4 347 Ocean, NJ................ 13.5 179.6 1.7 118 826 2.6 216 Passaic, NJ.............. 12.7 168.7 0.3 281 1,018 2.0 268 Somerset, NJ............. 10.3 193.1 0.8 219 1,549 6.2 14 Union, NJ................ 14.5 230.5 1.3 159 1,271 3.9 77 Bernalillo, NM........... 18.9 329.4 0.5 257 886 2.3 244 Albany, NY............... 10.4 235.5 0.4 271 1,138 4.2 59 Bronx, NY................ 19.2 322.2 1.2 176 1,058 2.3 244 Broome, NY............... 4.5 87.9 0.7 235 866 3.7 93 Dutchess, NY............. 8.4 114.5 0.7 235 1,038 1.4 307 Erie, NY................. 24.7 475.0 0.4 271 949 3.2 150 Kings, NY................ 64.2 772.5 2.5 63 918 2.2 253 Monroe, NY............... 19.0 391.6 0.0 310 996 3.1 160 Nassau, NY............... 54.3 647.2 0.5 257 1,175 2.5 225 New York, NY............. 128.9 2,474.7 0.7 235 2,025 4.4 44 Oneida, NY............... 5.3 107.4 0.1 303 833 2.6 216 Onondaga, NY............. 12.9 249.4 0.5 257 984 3.7 93 Orange, NY............... 10.5 148.5 1.8 110 941 4.0 69 Queens, NY............... 54.0 708.1 2.1 82 1,062 3.9 77 Richmond, NY............. 10.0 124.0 1.4 144 997 3.4 124 Rockland, NY............. 11.0 129.3 2.0 94 1,016 2.6 216 Saratoga, NY............. 6.0 92.7 2.7 48 995 4.3 50 Suffolk, NY.............. 53.4 688.3 0.1 303 1,134 3.4 124 Westchester, NY.......... 36.4 441.9 0.9 206 1,353 1.4 307 Buncombe, NC............. 9.3 132.8 3.2 28 805 2.7 204 Cabarrus, NC............. 4.8 77.3 2.0 94 760 1.7 289 Catawba, NC.............. 4.4 88.7 1.0 193 812 2.4 233 Cumberland, NC........... 6.2 120.9 1.4 144 820 3.4 124 Durham, NC............... 8.5 204.4 2.7 48 1,256 1.8 285 Forsyth, NC.............. 9.2 187.1 2.4 69 928 0.9 325 Guilford, NC............. 14.4 281.1 0.8 219 906 1.7 289 Mecklenburg, NC.......... 38.5 698.8 2.5 63 1,201 4.4 44 New Hanover, NC.......... 8.4 116.0 2.1 82 829 -6.4 349 Pitt, NC................. 3.8 77.5 3.1 34 824 2.0 268 Wake, NC................. 35.2 568.9 3.2 28 1,100 5.1 25 Cass, ND................. 7.3 118.7 -0.2 323 951 3.7 93 Butler, OH............... 7.8 155.4 1.2 176 903 0.4 335 Cuyahoga, OH............. 35.8 732.7 0.5 257 1,059 2.9 185 Delaware, OH............. 5.4 90.7 1.1 186 988 2.4 233 Franklin, OH............. 32.3 758.5 1.5 138 1,029 1.6 295 Hamilton, OH............. 23.8 524.3 0.5 257 1,105 3.0 172 Lake, OH................. 6.3 97.7 0.5 257 858 2.8 197 Lorain, OH............... 6.2 100.5 1.1 186 809 2.3 244 Lucas, OH................ 10.1 210.1 1.3 159 869 2.7 204 Mahoning, OH............. 5.9 99.1 1.7 118 735 2.2 253 Montgomery, OH........... 11.8 255.7 0.2 293 897 3.6 104 Stark, OH................ 8.6 162.2 1.1 186 778 2.0 268 Summit, OH............... 14.3 268.9 -0.3 331 918 3.4 124 Warren, OH............... 5.1 97.5 2.1 82 914 1.9 278 Cleveland, OK............ 5.9 80.3 0.9 206 777 3.9 77 Oklahoma, OK............. 28.2 457.2 1.4 144 979 3.1 160 Tulsa, OK................ 22.6 358.3 1.3 159 942 3.0 172 Clackamas, OR............ 15.4 168.1 1.1 186 1,007 -2.0 346 Deschutes, OR............ 8.9 85.1 3.1 34 860 1.5 301 Jackson, OR.............. 7.7 90.6 2.6 55 800 1.1 320 Lane, OR................. 12.4 158.0 0.4 271 836 2.7 204 Marion, OR............... 11.2 159.6 2.0 94 888 3.9 77 Multnomah, OR............ 35.7 513.5 1.4 144 1,109 3.4 124 Washington, OR........... 19.7 297.7 1.3 159 1,344 6.6 10 Allegheny, PA............ 35.7 709.8 1.0 193 1,127 4.3 50 Berks, PA................ 9.0 174.9 1.2 176 954 2.7 204 Bucks, PA................ 20.1 272.0 1.4 144 975 2.6 216 Butler, PA............... 5.1 87.0 -0.2 323 968 2.3 244 Chester, PA.............. 15.7 254.2 1.2 176 1,350 1.7 289 Cumberland, PA........... 6.6 135.2 0.7 235 968 3.5 112 Dauphin, PA.............. 7.6 188.3 1.9 103 1,013 1.6 295 Delaware, PA............. 14.3 227.2 1.4 144 1,094 2.8 197 Erie, PA................. 7.0 123.7 0.0 310 793 3.0 172 Lackawanna, PA........... 5.7 98.9 0.5 257 807 2.9 185 Lancaster, PA............ 13.7 245.3 2.1 82 860 2.4 233 Lehigh, PA............... 8.9 196.0 1.8 110 989 1.1 320 Luzerne, PA.............. 7.4 146.6 0.1 303 833 4.5 40 Montgomery, PA........... 27.8 502.6 1.0 193 1,246 3.3 136 Northampton, PA.......... 6.8 115.6 0.7 235 897 2.5 225 Philadelphia, PA......... 35.0 687.3 2.2 76 1,197 2.4 233 Washington, PA........... 5.5 89.9 1.0 193 1,011 1.9 278 Westmoreland, PA......... 9.3 136.0 0.4 271 845 3.6 104 York, PA................. 9.3 180.4 1.3 159 921 3.1 160 Kent, RI................. 5.5 77.2 0.4 271 906 0.9 325 Providence, RI........... 18.5 289.3 0.7 235 1,033 1.7 289 Charleston, SC........... 16.1 258.9 4.0 10 918 0.4 335 Greenville, SC........... 14.7 278.0 3.7 17 910 0.8 329 Horry, SC................ 9.3 139.4 2.1 82 625 0.3 338 Lexington, SC............ 6.9 121.0 4.0 10 778 0.0 341 Richland, SC............. 10.6 224.0 1.0 193 870 2.0 268 Spartanburg, SC.......... 6.5 142.7 4.0 10 862 -2.9 348 York, SC................. 6.1 98.3 5.2 2 834 0.8 329 Minnehaha, SD............ 7.3 128.8 1.0 193 896 2.3 244 Davidson, TN............. 23.3 498.9 2.7 48 1,081 2.4 233 Hamilton, TN............. 9.9 206.4 1.6 129 923 3.6 104 Knox, TN................. 12.6 239.2 0.9 206 923 5.1 25 Rutherford, TN........... 5.8 129.4 2.7 48 937 1.1 320 Shelby, TN............... 20.8 501.1 1.1 186 1,036 2.7 204 Williamson, TN........... 9.0 135.9 4.3 8 1,191 6.1 15 Bell, TX................. 5.5 118.5 -0.6 340 900 3.2 150 Bexar, TX................ 41.7 866.2 1.5 138 942 3.3 136 Brazoria, TX............. 5.9 113.2 3.2 28 1,094 1.5 301 Brazos, TX............... 4.6 101.5 3.6 18 794 4.3 50 Cameron, TX.............. 6.5 139.3 0.8 219 642 4.4 44 Collin, TX............... 25.6 417.5 3.5 19 1,236 5.7 17 Dallas, TX............... 77.5 1,710.0 1.8 110 1,246 2.5 225 Denton, TX............... 15.3 247.5 2.2 76 955 3.1 160 El Paso, TX.............. 15.2 303.7 1.1 186 733 2.4 233 Fort Bend, TX............ 13.6 190.5 5.0 3 958 2.6 216 Galveston, TX............ 6.2 110.9 1.4 144 905 -0.4 344 Harris, TX............... 115.0 2,309.3 1.3 159 1,269 3.1 160 Hidalgo, TX.............. 12.5 260.9 2.1 82 645 2.4 233 Jefferson, TX............ 5.8 124.0 0.4 271 1,063 4.0 69 Lubbock, TX.............. 7.6 139.6 0.9 206 842 5.3 23 McLennan, TX............. 5.3 113.4 0.7 235 886 6.6 10 Midland, TX.............. 5.7 103.7 11.6 1 1,377 4.2 59 Montgomery, TX........... 11.6 186.7 4.8 6 1,050 4.5 40 Nueces, TX............... 8.3 164.8 -0.2 323 892 3.6 104 Potter, TX............... 4.0 77.6 0.0 310 860 3.9 77 Smith, TX................ 6.4 103.6 1.3 159 858 4.9 30 Tarrant, TX.............. 43.9 900.6 1.9 103 1,038 3.0 172 Travis, TX............... 41.5 751.7 3.0 39 1,226 3.3 136 Webb, TX................. 5.5 101.2 1.0 193 687 3.2 150 Williamson, TX........... 11.2 174.6 4.0 10 1,012 2.0 268 Davis, UT................ 8.7 132.0 2.2 76 871 3.1 160 Salt Lake, UT............ 46.0 704.9 3.1 34 1,010 4.4 44 Utah, UT................. 16.7 242.4 4.8 6 859 5.7 17 Weber, UT................ 6.2 105.9 2.6 55 791 3.8 86 Chittenden, VT........... 6.9 103.0 -0.5 335 1,023 4.6 39 Arlington, VA............ 9.2 180.0 0.6 250 1,653 2.9 185 Chesterfield, VA......... 9.3 139.0 0.6 250 881 2.1 261 Fairfax, VA.............. 37.3 619.8 1.4 144 1,577 2.2 253 Henrico, VA.............. 11.8 194.3 1.0 193 982 2.3 244 Loudoun, VA.............. 12.6 171.8 1.7 118 1,191 1.9 278 Prince William, VA....... 9.4 133.6 2.1 82 925 4.5 40 Alexandria City, VA...... 6.3 93.5 -0.4 334 1,416 2.2 253 Chesapeake City, VA...... 6.1 102.4 1.3 159 829 2.1 261 Newport News City, VA.... 3.9 102.9 5.0 3 994 2.1 261 Norfolk City, VA......... 6.0 143.9 0.6 250 1,064 2.3 244 Richmond City, VA........ 7.8 155.0 0.8 219 1,115 2.6 216 Virginia Beach City, VA.. 12.3 183.0 -0.7 342 808 3.9 77 Benton, WA............... 5.8 95.4 2.3 72 1,022 1.5 301 Clark, WA................ 14.9 163.4 3.4 25 1,003 5.1 25 King, WA................. 89.2 1,405.6 2.5 63 1,605 9.3 2 Kitsap, WA............... 6.7 91.0 2.4 69 1,016 4.0 69 Pierce, WA............... 22.6 313.3 2.7 48 978 5.2 24 Snohomish, WA............ 21.5 290.2 1.6 129 1,149 4.2 59 Spokane, WA.............. 16.2 226.7 1.8 110 909 4.7 32 Thurston, WA............. 8.4 117.7 3.5 19 989 5.8 16 Whatcom, WA.............. 7.3 92.9 2.8 44 908 5.5 21 Yakima, WA............... 7.8 128.5 5.0 3 737 3.2 150 Kanawha, WV.............. 5.7 99.5 -1.2 347 896 2.2 253 Brown, WI................ 7.1 161.6 0.7 235 900 4.0 69 Dane, WI................. 16.0 339.3 1.2 176 1,040 3.6 104 Milwaukee, WI............ 27.1 493.3 0.6 250 987 1.9 278 Outagamie, WI............ 5.4 111.1 1.2 176 892 3.4 124 Waukesha, WI............. 13.3 249.2 0.6 250 1,029 2.8 197 Winnebago, WI............ 3.9 94.8 0.1 303 969 5.0 28 San Juan, PR............. 10.4 241.4 0.2 (5) 668 6.9 (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 349 U.S. counties comprise 72.9 percent of the total covered workers in the U.S.
Table 2. Covered establishments, employment, and wages in the 10 largest counties, second quarter 2018 Employment Average weekly wage(1) Establishments, second quarter County by NAICS supersector 2018 Percent Percent (thousands) June change, Second change, 2018 June quarter second (thousands) 2017-18(2) 2018 quarter 2017-18(2) United States(3) ............................ 10,048.0 147,431.2 1.5 $1,055 3.4 Private industry........................... 9,748.2 125,712.2 1.7 1,045 3.5 Natural resources and mining............. 137.9 2,065.1 2.9 1,075 5.7 Construction............................. 807.3 7,407.6 3.7 1,159 3.7 Manufacturing............................ 350.4 12,717.5 1.6 1,264 2.2 Trade, transportation, and utilities..... 1,923.2 27,365.7 1.0 891 3.6 Information.............................. 169.4 2,823.9 0.4 2,055 9.1 Financial activities..................... 889.8 8,230.6 1.0 1,589 3.4 Professional and business services....... 1,830.5 20,939.2 1.8 1,365 3.3 Education and health services............ 1,697.7 22,519.3 1.7 951 2.6 Leisure and hospitality.................. 856.3 16,797.5 1.2 449 4.2 Other services........................... 851.1 4,574.8 1.3 725 3.4 Government................................. 299.7 21,718.9 0.4 1,113 2.7 Los Angeles, CA.............................. 497.6 4,442.1 1.3 1,177 4.0 Private industry........................... 491.3 3,859.4 1.4 1,149 4.4 Natural resources and mining............. 0.5 6.8 -18.7 1,064 8.0 Construction............................. 15.2 144.3 3.3 1,243 5.3 Manufacturing............................ 12.3 341.7 -2.4 1,358 4.5 Trade, transportation, and utilities..... 55.4 826.6 0.1 958 3.1 Information.............................. 10.6 188.0 2.3 2,427 9.1 Financial activities..................... 27.6 221.4 -0.1 1,876 6.0 Professional and business services....... 50.8 610.3 1.2 1,482 3.8 Education and health services............ 237.2 801.7 2.4 881 3.0 Leisure and hospitality.................. 34.5 535.1 1.1 681 8.4 Other services........................... 27.1 151.9 0.0 775 3.5 Government................................. 6.3 582.7 0.2 1,367 2.5 Cook, IL..................................... 138.7 2,626.3 0.9 1,220 3.2 Private industry........................... 137.5 2,326.8 1.0 1,208 3.2 Natural resources and mining............. 0.1 1.3 0.8 1,168 -0.4 Construction............................. 10.9 79.6 3.7 1,457 2.1 Manufacturing............................ 5.8 185.3 0.6 1,249 0.9 Trade, transportation, and utilities..... 28.2 472.0 0.5 1,003 3.1 Information.............................. 2.4 51.5 -2.0 1,936 8.3 Financial activities..................... 14.0 200.3 1.6 2,122 2.5 Professional and business services....... 29.1 480.3 0.9 1,567 4.7 Education and health services............ 15.5 450.2 1.5 987 2.6 Leisure and hospitality.................. 13.8 303.2 0.8 558 3.9 Other services........................... 15.8 102.2 2.4 937 -0.1 Government................................. 1.3 299.5 0.2 1,319 3.3 New York, NY................................. 128.9 2,474.7 0.7 2,025 4.4 Private industry........................... 127.5 2,245.0 0.8 2,066 4.5 Natural resources and mining............. 0.0 0.2 14.1 1,993 3.2 Construction............................. 2.4 43.6 4.6 1,924 3.8 Manufacturing............................ 2.0 24.0 -3.5 1,502 5.1 Trade, transportation, and utilities..... 19.3 252.6 -1.3 1,495 10.4 Information.............................. 5.0 174.1 1.5 2,766 9.9 Financial activities..................... 19.5 385.7 1.9 3,665 2.1 Professional and business services....... 27.4 597.2 0.9 2,277 3.5 Education and health services............ 10.2 345.0 0.7 1,396 4.6 Leisure and hospitality.................. 14.8 312.9 0.2 906 4.3 Other services........................... 20.4 105.6 0.3 1,216 -2.3 Government................................. 1.4 229.7 -0.2 1,633 3.1 Harris, TX................................... 115.0 2,309.3 1.3 1,269 3.1 Private industry........................... 114.4 2,035.0 1.4 1,286 3.2 Natural resources and mining............. 1.6 66.1 -0.5 3,065 4.6 Construction............................. 7.6 160.9 1.3 1,361 3.0 Manufacturing............................ 4.8 175.1 3.0 1,613 3.7 Trade, transportation, and utilities..... 24.8 469.3 1.4 1,154 3.0 Information.............................. 1.2 26.3 -3.5 1,447 4.4 Financial activities..................... 12.2 128.6 0.7 1,634 0.4 Professional and business services....... 23.2 403.3 1.4 1,594 4.3 Education and health services............ 16.1 294.7 1.1 1,044 1.4 Leisure and hospitality.................. 10.2 240.3 1.6 477 5.8 Other services........................... 11.7 67.9 1.3 820 2.0 Government................................. 0.6 274.3 0.9 1,149 2.3 Maricopa, AZ................................. 100.0 1,950.6 2.8 1,016 3.0 Private industry........................... 99.2 1,764.6 3.0 1,004 3.0 Natural resources and mining............. 0.4 8.5 1.3 944 5.2 Construction............................. 7.7 120.8 7.1 1,087 4.7 Manufacturing............................ 3.3 123.4 3.6 1,486 4.4 Trade, transportation, and utilities..... 19.0 381.3 2.9 927 2.8 Information.............................. 1.6 37.1 -0.4 1,359 0.4 Financial activities..................... 11.9 180.3 2.5 1,319 5.0 Professional and business services....... 22.5 332.2 2.1 1,078 1.6 Education and health services............ 11.7 303.0 3.1 982 0.6 Leisure and hospitality.................. 8.4 219.5 2.6 503 5.9 Other services........................... 6.7 54.0 2.8 752 5.3 Government................................. 0.7 186.1 0.5 1,114 3.5 Dallas, TX................................... 77.5 1,710.0 1.8 1,246 2.5 Private industry........................... 77.0 1,537.0 2.0 1,251 2.5 Natural resources and mining............. 0.5 8.5 15.2 3,488 3.2 Construction............................. 4.7 90.9 2.5 1,262 2.9 Manufacturing............................ 2.8 113.3 2.0 1,437 1.3 Trade, transportation, and utilities..... 15.8 348.2 2.7 1,085 3.4 Information.............................. 1.4 49.6 -1.7 1,836 -0.8 Financial activities..................... 9.7 164.1 -0.9 1,715 -0.1 Professional and business services....... 17.7 351.3 3.0 1,463 3.6 Education and health services............ 9.6 199.1 1.4 1,129 1.6 Leisure and hospitality.................. 6.9 165.6 2.2 517 5.7 Other services........................... 7.0 44.4 0.7 895 12.6 Government................................. 0.6 173.0 0.4 1,200 2.6 Orange, CA................................... 123.2 1,628.9 1.7 1,157 2.7 Private industry........................... 121.7 1,472.0 1.8 1,142 2.7 Natural resources and mining............. 0.2 2.5 -6.5 909 1.9 Construction............................. 7.1 105.2 3.9 1,367 4.2 Manufacturing............................ 5.1 158.4 -1.4 1,486 6.3 Trade, transportation, and utilities..... 17.4 256.8 0.2 1,023 3.2 Information.............................. 1.4 26.3 -1.2 2,027 6.2 Financial activities..................... 12.0 118.0 -0.5 1,764 3.0 Professional and business services....... 21.6 308.6 2.5 1,344 -0.2 Education and health services............ 35.3 215.9 3.1 941 3.3 Leisure and hospitality.................. 8.9 222.8 1.1 512 3.9 Other services........................... 6.9 47.0 0.6 724 1.4 Government................................. 1.5 156.9 1.0 1,302 2.7 San Diego, CA................................ 112.9 1,473.5 2.0 1,137 3.4 Private industry........................... 110.9 1,234.6 2.2 1,096 3.7 Natural resources and mining............. 0.7 10.0 6.4 764 6.6 Construction............................. 7.4 84.3 5.7 1,204 2.8 Manufacturing............................ 3.3 112.0 2.5 1,508 1.1 Trade, transportation, and utilities..... 14.6 221.3 0.6 857 3.0 Information.............................. 1.2 23.8 -2.7 2,087 12.7 Financial activities..................... 10.5 75.1 0.2 1,486 3.3 Professional and business services....... 19.2 244.2 3.2 1,574 4.0 Education and health services............ 32.9 201.1 1.4 952 2.8 Leisure and hospitality.................. 8.5 202.6 1.2 522 4.4 Other services........................... 7.4 51.7 -1.6 639 2.2 Government................................. 2.0 238.9 0.6 1,350 2.8 King, WA..................................... 89.2 1,405.6 2.5 1,605 9.3 Private industry........................... 88.6 1,233.6 2.7 1,638 9.9 Natural resources and mining............. 0.4 3.1 -3.2 1,412 13.0 Construction............................. 6.8 74.1 4.2 1,406 5.5 Manufacturing............................ 2.5 102.2 -0.3 1,660 2.2 Trade, transportation, and utilities..... 14.1 271.4 2.4 1,886 16.7 Information.............................. 2.4 111.4 7.6 3,384 13.4 Financial activities..................... 6.8 70.7 3.5 1,705 4.0 Professional and business services....... 18.3 230.6 2.3 1,801 8.5 Education and health services............ 20.4 176.1 2.2 1,076 4.6 Leisure and hospitality.................. 7.4 147.6 2.6 597 3.1 Other services........................... 9.3 46.4 2.1 904 3.2 Government................................. 0.5 171.9 0.6 1,370 3.9 Miami-Dade, FL............................... 99.0 1,125.0 0.9 1,000 3.0 Private industry........................... 98.7 999.0 0.9 977 2.8 Natural resources and mining............. 0.5 8.3 4.6 671 7.4 Construction............................. 6.9 50.5 3.4 963 3.9 Manufacturing............................ 2.8 40.3 0.0 888 3.4 Trade, transportation, and utilities..... 24.9 284.1 1.2 925 2.9 Information.............................. 1.6 18.5 0.2 1,678 -1.2 Financial activities..................... 10.7 75.5 0.1 1,532 2.3 Professional and business services....... 22.4 162.3 2.2 1,170 3.5 Education and health services............ 10.8 178.7 0.6 985 1.3 Leisure and hospitality.................. 7.4 140.1 -1.4 608 4.6 Other services........................... 8.4 39.3 -0.8 651 5.3 Government................................. 0.3 126.0 0.7 1,168 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. Note: Data are preliminary. Counties selected are based on 2017 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, second quarter 2018 Employment Average weekly wage(1) Establishments, second quarter State 2018 Percent Percent (thousands) June change, Second change, 2018 June quarter second (thousands) 2017-18 2018 quarter 2017-18 United States(2)........... 10,048.0 147,431.2 1.5 $1,055 3.4 Alabama.................... 127.2 1,969.9 1.2 882 2.8 Alaska..................... 22.1 335.8 -0.9 1,043 3.7 Arizona.................... 163.5 2,770.8 2.6 973 3.3 Arkansas................... 90.5 1,214.6 0.7 824 1.7 California................. 1,559.5 17,473.1 1.9 1,265 4.6 Colorado................... 204.9 2,704.4 2.4 1,075 3.2 Connecticut................ 120.8 1,704.5 0.3 1,218 0.1 Delaware................... 32.6 454.3 1.3 1,023 1.4 District of Columbia....... 40.0 777.3 1.3 1,713 2.6 Florida.................... 688.9 8,568.9 2.1 931 2.9 Georgia.................... 278.7 4,440.5 2.0 979 2.3 Hawaii..................... 42.7 658.3 0.5 956 2.5 Idaho...................... 62.5 745.3 3.1 794 3.8 Illinois................... 375.1 6,061.1 0.8 1,097 3.4 Indiana.................... 167.6 3,075.8 1.1 883 2.8 Iowa....................... 102.8 1,583.7 0.8 880 3.3 Kansas..................... 89.0 1,393.3 1.0 879 3.4 Kentucky................... 123.2 1,905.9 0.9 882 2.3 Louisiana.................. 133.1 1,918.6 0.4 901 3.7 Maine...................... 53.3 636.8 1.0 843 3.6 Maryland................... 172.4 2,712.0 0.7 1,141 3.4 Massachusetts.............. 259.0 3,650.1 1.0 1,322 3.5 Michigan................... 246.8 4,424.7 1.3 997 2.9 Minnesota.................. 177.1 2,925.6 0.8 1,072 3.3 Mississippi................ 74.2 1,130.7 0.2 752 2.7 Missouri................... 203.4 2,829.0 0.5 924 3.9 Montana.................... 49.6 478.7 1.1 817 2.5 Nebraska................... 72.7 990.8 0.6 859 3.1 Nevada..................... 81.9 1,372.4 3.1 931 3.3 New Hampshire.............. 52.7 670.8 0.8 1,049 3.3 New Jersey................. 274.2 4,157.0 0.9 1,201 2.3 New Mexico................. 59.7 823.6 1.0 852 3.5 New York................... 650.3 9,579.2 1.7 1,297 4.5 North Carolina............. 278.9 4,450.2 2.2 933 3.3 North Dakota............... 31.9 426.1 0.8 986 3.4 Ohio....................... 296.8 5,461.3 0.7 933 2.3 Oklahoma................... 110.9 1,606.4 1.2 875 3.2 Oregon..................... 155.8 1,947.3 1.5 999 3.3 Pennsylvania............... 359.9 5,924.9 1.1 1,031 3.1 Rhode Island............... 37.9 491.0 0.7 998 1.7 South Carolina............. 135.9 2,126.5 3.4 833 0.0 South Dakota............... 33.6 439.7 0.9 807 2.8 Tennessee.................. 161.7 2,994.1 1.6 932 2.9 Texas...................... 687.2 12,326.3 2.2 1,062 3.4 Utah....................... 103.1 1,483.9 3.4 899 4.3 Vermont.................... 25.6 312.4 -0.8 907 4.3 Virginia................... 277.4 3,941.0 1.3 1,073 2.6 Washington................. 247.5 3,444.1 2.7 1,218 6.9 West Virginia.............. 50.9 702.9 1.6 868 4.8 Wisconsin.................. 174.9 2,933.5 0.9 904 3.3 Wyoming.................... 26.3 282.2 0.5 901 3.0 Puerto Rico................ 44.3 853.5 -2.3 543 5.2 Virgin Islands............. 3.4 33.4 -14.4 838 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.