For release 10:00 a.m. (EDT), Wednesday, May 22, 2019 USDL-19-0857
Technical Information: (202) 691-6567 * QCEWInfo@bls.gov * www.bls.gov/cew
Media Contact: (202) 691-5902 * PressOffice@bls.gov
COUNTY EMPLOYMENT AND WAGES – FOURTH QUARTER 2018
From December 2017 to December 2018, employment increased in 296 of the 349 largest U.S.
counties, the U.S. Bureau of Labor Statistics reported today. In December 2018, national
employment (as measured by the QCEW program) increased to 148.1 million, a 1.5 percent
increase over the year. Midland, TX, had the largest over-the-year increase in employment with a
gain of 10.0 percent. Employment data in this release are presented for December 2018, and
average weekly wage data are presented for fourth quarter 2018.
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| |
| Notice Regarding South Carolina Employment and Wages Data |
| |
| South Carolina QCEW data for the first, second, third, and fourth quarters of 2018 show unusual |
| movements, which may be a result of a change in reporting. These unusual movements coincide |
| with a modernization of the South Carolina unemployment insurance system. For more |
| information please visit: www.bls.gov/cew/2018-notice-regarding-south-carolina-employment- |
| and-wages-data.htm. |
| |
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Among the 349 largest counties, 332 had over-the-year increases in average weekly wages. In
the fourth quarter of 2018, average weekly wages for the nation increased to $1,144, a 3.2
percent increase over the year. Tippecanoe, IN, had the largest fourth quarter over-the-year wage
gain at 15.1 percent. (See table 1.)
Large County Employment in December 2018
Midland, TX, had the largest over-the-year percentage increase in employment (10.0 percent).
Within Midland, the largest employment increase occurred in natural resources and mining,
which gained 5,305 jobs over the year (20.3 percent).
Bay, FL, experienced the largest over-the-year percentage decrease in employment, with a loss
of 5.6 percent. Within Bay, education and health services had the largest employment decrease
with a loss of 1,878 jobs (-16.4 percent).
Large County Average Weekly Wage in Fourth Quarter 2018
Tippecanoe, IN, had the largest over-the-year percentage increase in average weekly wages (15.1
percent). Within Tippecanoe, an average weekly wage gain of $1,046 (128.5 percent) in
professional and business services made the largest contribution to the county’s increase in
average weekly wages.
Washington, PA, had the largest over-the-year percentage decrease in average weekly wages
with a loss of 6.6 percent. Within Washington, natural resources and mining had the largest
impact, with an average weekly wage decrease of $2,287 (-56.1 percent) over the year.
Ten Largest Counties
All of the 10 largest counties had over-the-year percentage increases in employment, while 9 had
over-the-year percentage increases in average weekly wages. In December 2018, Maricopa, AZ,
had the largest over-the-year employment percentage gain among the 10 largest counties (3.2
percent). Within Maricopa, trade, transportation, and utilities had the largest employment
increase with a gain of 15,496 jobs (3.9 percent). (See table 2.)
In fourth quarter 2018, King, WA, experienced the largest over-the-year percentage gain in
average weekly wages among the 10 largest counties (7.0 percent). Within King, information had
the largest impact, with an average weekly wage increase of $464 (14.9 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. December 2018 employment and
fourth 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.
QCEW’s news release schedule is available at www.bls.gov/cew/releasecalendar.htm.
____________
The County Employment and Wages full data update for fourth quarter 2018 is scheduled
to be released on Wednesday, June 5, 2019, at 10:00 a.m. (EDT).
The County Employment and Wages news release for first quarter 2019 is scheduled to be
released on Wednesday, August 21, 2019, at 10:00 a.m. (EDT).
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| |
| BLS Local Data App Now Available for Android Devices |
| |
| The BLS Local Data app, first released for iPhones last fall, is now available for Android |
| devices. Search using your current location, a zip code, or a location name to find employment |
| and wage data for detailed industries and occupations. BLS continues to partner with the U.S. |
| Department of Labor’s Office of the Chief Information Officer to expand the features and data in |
| the app. For more information please visit: https://beta.bls.gov/labs/blogs/2019/04/17/bls- |
| local-data-app-now-available-for-android-devices/. |
| |
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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 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 addition, data for San Juan, Puerto Rico, are
provided, but not used in calculating U.S. averages, rankings, or in the analysis in the text. Each year, these large counties are selected on the basis of the
preliminary annual average of employment for the previous year. The 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: Cabarrus, N.C.; Pitt, N.C.; and Kent, R.I. These coun-
ties 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 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 under-
stand 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- | 689,000 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 | | |
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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 establish-
ments within a state complete a questionnaire, called the "Multiple Worksite Report," which provides detailed information on the location and industry of
each of their establishments. QCEW employment and wage data are derived from microdata summaries of 9.8 million employer reports of employment
and wages submitted by states to the BLS in 2017. 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 Unem-
ployment Tax Act became effective, expanding coverage to include most state and local government employees. In 2017, UI and UCFE programs cov-
ered 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 percent 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 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 per-
sonnel, 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 val-
ues. 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 ob-
served 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 pro-
cessing. 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 quar-
ter 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 classifi-
cation of all establishments on a 3-year cycle. Changes in establishment classification codes resulting from this process are introduced with the data re-
ported 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 establish-
ments 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 chang-
es 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 sub-
stantially 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 in-
dustry categories. Adjusted data account for improvements in reporting employment and wages for individual and multi-unit establishments. To accom-
plish 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 com-
parisons 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 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,
fourth quarter 2018
Employment Average weekly wage(2)
Establishments,
County(1) fourth quarter Percent Ranking Percent Ranking
2018 December change, by Fourth change, by
(thousands) 2018 December percent quarter fourth percent
(thousands) 2017-18(3) change 2018 quarter change
2017-18(3)
United States(4)......... 10,169.1 148,061.8 1.5 - $1,144 3.2 -
Jefferson, AL............ 19.1 355.8 1.5 134 1,110 2.4 227
Madison, AL.............. 9.9 203.5 2.1 69 1,182 4.0 89
Mobile, AL............... 10.3 174.1 1.7 114 982 0.5 322
Montgomery, AL........... 6.5 131.5 -0.6 335 964 2.6 210
Shelby, AL............... 5.9 85.7 0.6 227 1,084 4.9 58
Tuscaloosa, AL........... 4.6 96.5 2.6 41 918 0.5 322
Anchorage, AK............ 8.3 147.6 0.3 258 1,160 5.1 51
Maricopa, AZ............. 102.3 2,060.6 3.2 22 1,064 3.9 97
Pima, AZ................. 19.1 377.9 1.8 100 926 3.6 113
Benton, AR............... 6.7 121.5 1.3 149 1,067 5.5 34
Pulaski, AR.............. 14.6 254.7 0.3 258 982 1.1 310
Washington, AR........... 6.3 109.2 1.4 139 984 -1.8 343
Alameda, CA.............. 65.6 793.0 1.5 134 1,511 4.2 80
Butte, CA................ 8.8 82.8 -0.9 340 869 5.5 34
Contra Costa, CA......... 33.4 370.1 0.1 282 1,387 3.4 137
Fresno, CA............... 37.0 392.0 3.1 24 904 1.7 279
Kern, CA................. 20.4 326.1 3.6 14 923 3.6 113
Los Angeles, CA.......... 507.9 4,515.9 1.2 161 1,380 2.1 249
Marin, CA................ 12.7 116.7 0.6 227 1,466 4.2 80
Merced, CA............... 6.8 79.6 0.4 251 837 2.6 210
Monterey, CA............. 14.2 178.2 1.7 114 966 1.7 279
Napa, CA................. 5.9 76.3 2.0 81 1,158 3.5 128
Orange, CA............... 126.1 1,647.4 0.8 198 1,251 0.6 320
Placer, CA............... 13.6 170.1 2.6 41 1,120 1.4 296
Riverside, CA............ 67.7 750.5 2.1 69 883 1.1 310
Sacramento, CA........... 60.3 672.6 2.2 60 1,208 2.9 187
San Bernardino, CA....... 61.7 773.0 2.2 60 934 3.0 176
San Diego, CA............ 115.0 1,485.8 1.6 126 1,260 3.2 155
San Francisco, CA........ 61.9 759.6 3.8 11 2,452 10.4 7
San Joaquin, CA.......... 18.4 255.0 1.3 149 961 4.0 89
San Luis Obispo, CA...... 10.6 117.3 1.8 100 976 4.6 65
San Mateo, CA............ 29.1 412.5 2.0 81 2,410 -1.1 341
Santa Barbara, CA........ 15.8 197.8 1.3 149 1,111 4.5 69
Santa Clara, CA.......... 74.7 1,118.3 2.3 54 2,670 4.7 62
Santa Cruz, CA........... 9.7 101.1 0.6 227 1,023 5.7 31
Solano, CA............... 11.9 143.4 1.2 161 1,153 3.3 146
Sonoma, CA............... 20.5 211.6 1.4 139 1,122 5.1 51
Stanislaus, CA........... 16.2 188.4 0.8 198 943 3.1 164
Tulare, CA............... 11.2 162.8 1.9 86 813 0.1 332
Ventura, CA.............. 28.2 330.2 0.5 242 1,102 2.4 227
Yolo, CA................. 6.9 104.0 2.0 81 1,204 3.6 113
Adams, CO................ 11.3 220.0 3.7 12 1,094 2.0 257
Arapahoe, CO............. 22.2 334.5 1.6 126 1,306 3.0 176
Boulder, CO.............. 15.6 185.8 1.9 86 1,354 6.0 25
Denver, CO............... 33.5 522.9 1.8 100 1,414 6.0 25
Douglas, CO.............. 12.3 127.7 1.7 114 1,272 -3.6 346
El Paso, CO.............. 20.2 280.0 2.2 60 1,013 4.6 65
Jefferson, CO............ 20.3 241.3 1.6 126 1,211 8.7 8
Larimer, CO.............. 12.4 163.5 2.7 36 1,064 6.0 25
Weld, CO................. 7.6 111.4 3.9 9 1,014 5.3 43
Fairfield, CT............ 36.2 426.5 0.0 297 1,705 0.4 325
Hartford, CT............. 28.8 517.5 0.4 251 1,331 2.9 187
New Haven, CT............ 24.8 373.4 0.7 215 1,131 0.9 316
New London, CT........... 7.7 124.2 -0.1 303 1,064 1.9 265
New Castle, DE........... 20.7 296.1 0.7 215 1,228 2.7 203
Sussex, DE............... 7.3 79.9 3.1 24 833 2.5 220
Washington, DC........... 40.3 775.1 0.6 227 1,943 7.3 12
Alachua, FL.............. 7.4 133.9 1.8 100 956 4.6 65
Bay, FL.................. 5.8 73.5 -5.6 349 852 7.4 11
Brevard, FL.............. 16.2 220.9 3.9 9 995 1.9 265
Broward, FL.............. 71.0 828.2 1.3 149 1,064 2.4 227
Collier, FL.............. 14.7 155.8 2.7 36 1,002 4.0 89
Duval, FL................ 30.1 528.1 2.4 50 1,060 2.5 220
Escambia, FL............. 8.3 137.3 1.7 114 897 3.1 164
Hillsborough, FL......... 44.3 705.2 1.6 126 1,079 2.8 194
Lake, FL................. 8.5 101.6 2.1 69 778 4.7 62
Lee, FL.................. 23.1 270.5 2.0 81 897 2.6 210
Leon, FL................. 8.8 153.9 2.1 69 911 2.0 257
Manatee, FL.............. 11.3 128.8 2.3 54 850 3.7 104
Marion, FL............... 8.6 106.3 2.2 60 770 2.1 249
Miami-Dade, FL........... 101.0 1,169.8 1.4 139 1,104 2.8 194
Okaloosa, FL............. 6.6 84.1 1.2 161 931 6.4 18
Orange, FL............... 44.1 865.6 2.2 60 1,006 3.6 113
Osceola, FL.............. 7.4 97.7 3.4 17 753 1.6 287
Palm Beach, FL........... 58.1 622.1 1.8 100 1,122 2.8 194
Pasco, FL................ 11.4 123.0 3.1 24 782 3.0 176
Pinellas, FL............. 34.1 440.6 1.4 139 1,026 4.1 84
Polk, FL................. 13.8 228.2 3.1 24 833 1.2 306
Sarasota, FL............. 16.5 174.8 1.9 86 978 3.9 97
Seminole, FL............. 15.4 199.2 2.1 69 968 4.0 89
Volusia, FL.............. 14.8 176.0 1.9 86 805 2.0 257
Bibb, GA................. 4.3 82.9 -0.6 335 867 3.7 104
Chatham, GA.............. 8.2 157.2 1.3 149 933 2.5 220
Clayton, GA.............. 4.0 124.5 1.5 134 1,024 3.3 146
Cobb, GA................. 22.1 369.2 1.3 149 1,156 2.8 194
DeKalb, GA............... 17.8 303.1 0.1 282 1,127 4.1 84
Fulton, GA............... 43.4 889.3 1.9 86 1,480 1.7 279
Gwinnett, GA............. 25.0 358.2 0.3 258 1,068 2.0 257
Hall, GA................. 4.4 90.0 2.3 54 997 2.6 210
Muscogee, GA............. 4.5 95.0 0.9 185 844 -3.7 347
Richmond, GA............. 4.5 105.8 -0.3 318 902 1.7 279
Honolulu, HI............. 26.4 483.6 0.6 227 1,059 2.9 187
Maui + Kalawao, HI....... 6.3 78.8 0.2 273 908 5.2 47
Ada, ID.................. 16.9 248.4 3.7 12 1,087 3.4 137
Champaign, IL............ 4.1 91.4 -0.1 303 952 2.1 249
Cook, IL................. 138.1 2,625.3 0.6 227 1,335 3.7 104
DuPage, IL............... 34.5 620.1 -0.3 318 1,280 3.6 113
Kane, IL................. 12.5 214.3 -0.3 318 1,012 1.2 306
Lake, IL................. 20.2 339.5 0.5 242 1,449 3.6 113
McHenry, IL.............. 7.8 96.8 -1.2 346 909 1.9 265
McLean, IL............... 3.4 82.7 -1.1 345 959 1.5 292
Madison, IL.............. 5.4 101.5 -1.0 342 902 5.7 31
Peoria, IL............... 4.2 106.2 -0.1 303 1,107 -0.1 335
St. Clair, IL............ 5.1 93.9 -0.1 303 867 0.3 329
Sangamon, IL............. 4.8 129.3 -0.5 332 1,065 0.2 331
Will, IL................. 14.8 249.1 -0.3 318 961 1.3 301
Winnebago, IL............ 5.9 127.1 -0.9 340 975 7.6 10
Allen, IN................ 9.0 191.7 1.9 86 917 3.6 113
Elkhart, IN.............. 4.8 136.7 -1.0 342 936 -5.0 348
Hamilton, IN............. 9.6 142.9 1.7 114 1,049 1.7 279
Lake, IN................. 10.5 189.9 0.6 227 984 5.9 30
Marion, IN............... 24.4 604.9 0.3 258 1,114 2.6 210
St. Joseph, IN........... 5.8 125.3 1.5 134 901 2.2 239
Tippecanoe, IN........... 3.5 86.4 1.7 114 1,057 15.1 1
Vanderburgh, IN.......... 4.8 110.9 0.8 198 882 -2.6 344
Johnson, IA.............. 4.3 83.9 -1.5 347 1,001 3.1 164
Linn, IA................. 7.0 131.9 0.3 258 1,162 3.8 101
Polk, IA................. 17.8 303.5 0.7 215 1,140 2.2 239
Scott, IA................ 5.7 92.1 0.7 215 931 3.6 113
Johnson, KS.............. 24.1 354.2 0.8 198 1,128 3.3 146
Sedgwick, KS............. 12.7 255.7 1.8 100 946 3.4 137
Shawnee, KS.............. 5.1 97.2 0.1 282 892 3.2 155
Wyandotte, KS............ 3.5 92.6 1.3 149 1,083 6.4 18
Boone, KY................ 4.4 97.0 0.2 273 932 3.4 137
Fayette, KY.............. 11.1 194.6 -0.3 318 986 1.8 274
Jefferson, KY............ 25.2 474.7 0.2 273 1,093 3.5 128
Caddo, LA................ 7.4 112.9 -0.2 312 903 3.3 146
Calcasieu, LA............ 5.4 102.3 1.5 134 1,030 6.1 22
East Baton Rouge, LA..... 16.1 270.8 1.4 139 1,077 6.5 17
Jefferson, LA............ 14.2 191.0 0.0 297 1,008 3.5 128
Lafayette, LA............ 10.0 133.1 2.3 54 976 3.1 164
Orleans, LA.............. 13.3 198.6 0.4 251 1,043 1.3 301
St. Tammany, LA.......... 8.7 90.5 1.9 86 956 4.9 58
Cumberland, ME........... 13.7 186.6 0.3 258 1,030 2.2 239
Anne Arundel, MD......... 15.3 276.2 1.1 167 1,190 1.0 314
Baltimore, MD............ 21.3 384.7 0.2 273 1,149 2.7 203
Frederick, MD............ 6.5 103.8 1.0 175 1,008 1.9 265
Harford, MD.............. 5.9 96.8 1.0 175 1,022 2.2 239
Howard, MD............... 10.1 171.6 0.4 251 1,364 2.2 239
Montgomery, MD........... 32.9 475.9 0.5 242 1,498 1.0 314
Prince George's, MD...... 16.2 324.8 0.9 185 1,153 2.5 220
Baltimore City, MD....... 13.7 345.0 -0.1 303 1,358 -0.3 337
Barnstable, MA........... 9.6 91.8 -0.4 329 1,001 4.1 84
Bristol, MA.............. 18.0 233.2 0.9 185 1,021 3.8 101
Essex, MA................ 27.1 327.1 -0.6 335 1,192 2.8 194
Hampden, MA.............. 19.0 214.3 1.1 167 980 1.6 287
Middlesex, MA............ 56.7 937.8 1.8 100 1,660 3.3 146
Norfolk, MA.............. 25.7 356.9 0.1 282 1,413 2.2 239
Plymouth, MA............. 16.4 195.5 -0.1 303 1,069 3.6 113
Suffolk, MA.............. 31.4 689.4 2.1 69 2,055 3.4 137
Worcester, MA............ 26.5 354.2 0.2 273 1,090 1.6 287
Genesee, MI.............. 6.8 136.4 0.4 251 923 2.4 227
Ingham, MI............... 6.0 152.6 0.1 282 1,077 3.0 176
Kalamazoo, MI............ 5.0 120.5 0.6 227 1,032 3.2 155
Kent, MI................. 14.8 409.3 1.8 100 988 2.8 194
Macomb, MI............... 17.7 332.1 0.7 215 1,112 2.0 257
Oakland, MI.............. 39.9 744.6 0.9 185 1,262 0.5 322
Ottawa, MI............... 5.8 125.9 1.2 161 984 0.8 319
Saginaw, MI.............. 3.9 84.9 -0.2 312 925 3.1 164
Washtenaw, MI............ 8.3 218.4 0.9 185 1,172 3.4 137
Wayne, MI................ 31.6 734.9 0.9 185 1,218 0.9 316
Anoka, MN................ 7.6 127.0 1.9 86 1,048 1.7 279
Dakota, MN............... 10.4 190.8 0.3 258 1,163 10.8 5
Hennepin, MN............. 42.0 942.9 1.4 139 1,367 2.2 239
Olmsted, MN.............. 3.7 99.8 1.1 167 1,261 13.0 3
Ramsey, MN............... 14.1 334.5 0.3 258 1,224 1.9 265
St. Louis, MN............ 5.4 98.6 0.7 215 953 6.2 21
Stearns, MN.............. 4.4 87.5 0.7 215 929 2.1 249
Washington, MN........... 5.9 87.9 0.2 273 962 1.4 296
Harrison, MS............. 4.7 85.9 0.1 282 774 3.2 155
Hinds, MS................ 5.8 121.0 -0.7 338 911 3.2 155
Boone, MO................ 4.9 94.5 -0.1 303 890 5.1 51
Clay, MO................. 5.8 105.1 0.0 297 960 5.4 37
Greene, MO............... 9.1 170.7 2.4 50 854 1.3 301
Jackson, MO.............. 22.4 374.4 0.5 242 1,153 3.5 128
St. Charles, MO.......... 9.7 150.0 1.3 149 878 3.7 104
St. Louis, MO............ 39.9 613.3 0.2 273 1,228 5.0 56
St. Louis City, MO....... 14.9 231.0 0.8 198 1,182 2.2 239
Yellowstone, MT.......... 6.9 81.2 -0.3 318 974 5.4 37
Douglas, NE.............. 18.8 342.8 0.3 258 1,039 3.0 176
Lancaster, NE............ 10.2 172.4 0.5 242 904 2.6 210
Clark, NV................ 56.2 1,015.8 3.3 20 988 5.3 43
Washoe, NV............... 15.0 225.5 1.8 100 1,026 5.2 47
Hillsborough, NH......... 12.3 207.6 0.8 198 1,247 0.4 325
Merrimack, NH............ 5.2 78.3 0.1 282 1,067 1.5 292
Rockingham, NH........... 11.1 151.1 0.3 258 1,151 3.0 176
Atlantic, NJ............. 6.6 128.1 4.6 3 933 1.9 265
Bergen, NJ............... 33.3 457.0 0.5 242 1,321 1.4 296
Burlington, NJ........... 11.1 202.7 0.0 297 1,148 1.2 306
Camden, NJ............... 12.3 209.1 0.2 273 1,118 1.9 265
Essex, NJ................ 20.8 348.5 0.3 258 1,374 4.2 80
Gloucester, NJ........... 6.4 114.0 1.1 167 942 1.4 296
Hudson, NJ............... 15.3 271.5 1.7 114 1,445 3.1 164
Mercer, NJ............... 11.2 259.2 0.8 198 1,439 7.1 13
Middlesex, NJ............ 22.6 446.0 0.9 185 1,291 2.4 227
Monmouth, NJ............. 20.3 262.8 0.2 273 1,130 2.7 203
Morris, NJ............... 17.2 295.4 -0.2 312 1,620 2.1 249
Ocean, NJ................ 13.7 168.6 1.6 126 907 2.6 210
Passaic, NJ.............. 12.6 169.0 -0.5 332 1,082 1.3 301
Somerset, NJ............. 10.3 191.5 0.7 215 1,625 3.2 155
Union, NJ................ 14.6 230.5 0.3 258 1,359 -0.4 339
Bernalillo, NM........... 19.3 332.5 0.8 198 944 3.5 128
Albany, NY............... 10.3 236.1 -0.2 312 1,165 2.6 210
Bronx, NY................ 19.1 326.6 1.0 175 1,111 1.6 287
Broome, NY............... 4.5 87.5 0.6 227 876 3.2 155
Dutchess, NY............. 8.4 115.9 0.7 215 1,048 1.5 292
Erie, NY................. 24.6 476.9 0.1 282 1,004 3.1 164
Kings, NY................ 64.4 793.1 1.4 139 992 1.8 274
Monroe, NY............... 18.9 394.0 0.4 251 1,018 2.1 249
Nassau, NY............... 54.4 646.4 0.3 258 1,259 1.2 306
New York, NY............. 128.3 2,521.0 0.7 215 2,400 -3.3 345
Oneida, NY............... 5.3 106.6 0.0 297 867 3.3 146
Onondaga, NY............. 12.8 250.3 0.6 227 1,054 3.7 104
Orange, NY............... 10.6 148.5 1.0 175 966 5.0 56
Queens, NY............... 53.9 716.6 1.7 114 1,132 2.4 227
Richmond, NY............. 10.0 127.6 2.6 41 1,081 3.4 137
Rockland, NY............. 11.0 129.2 1.7 114 1,044 1.6 287
Saratoga, NY............. 6.0 89.2 0.6 227 1,009 2.9 187
Suffolk, NY.............. 53.4 669.0 0.1 282 1,244 1.7 279
Westchester, NY.......... 36.4 437.9 0.8 198 1,464 -0.7 340
Buncombe, NC............. 9.4 134.3 1.9 86 896 5.2 47
Cabarrus, NC............. 4.7 80.4 2.7 36 813 3.2 155
Catawba, NC.............. 4.4 89.5 1.3 149 862 2.3 236
Cumberland, NC........... 6.1 121.2 -0.1 303 879 6.8 15
Durham, NC............... 8.5 207.9 3.0 28 1,357 5.4 37
Forsyth, NC.............. 9.2 188.8 0.9 185 1,010 0.4 325
Guilford, NC............. 14.4 284.5 0.6 227 961 1.7 279
Mecklenburg, NC.......... 38.0 706.6 2.1 69 1,271 3.5 128
New Hanover, NC.......... 8.3 115.6 2.2 60 912 4.1 84
Pitt, NC................. 3.8 78.7 2.2 60 900 4.5 69
Wake, NC................. 34.9 566.6 1.6 126 1,252 11.9 4
Cass, ND................. 7.4 120.0 1.7 114 1,024 3.4 137
Butler, OH............... 7.9 157.4 -0.2 312 962 3.6 113
Cuyahoga, OH............. 36.1 730.2 0.6 227 1,145 1.9 265
Delaware, OH............. 5.5 89.0 1.1 167 1,064 4.0 89
Franklin, OH............. 32.9 768.3 1.4 139 1,089 3.1 164
Hamilton, OH............. 24.0 522.5 0.6 227 1,219 5.4 37
Lake, OH................. 6.2 96.6 1.1 167 921 3.4 137
Lorain, OH............... 6.2 98.4 0.7 215 865 4.0 89
Lucas, OH................ 10.1 211.3 -0.3 318 940 2.4 227
Mahoning, OH............. 5.9 97.7 -0.5 332 798 3.0 176
Montgomery, OH........... 11.9 256.3 0.0 297 954 3.6 113
Stark, OH................ 8.6 160.6 -0.3 318 851 2.3 236
Summit, OH............... 14.3 268.8 0.1 282 997 3.7 104
Warren, OH............... 5.3 94.6 2.3 54 1,020 6.6 16
Cleveland, OK............ 5.9 83.9 2.6 41 802 4.4 74
Oklahoma, OK............. 28.3 463.3 1.4 139 1,048 2.8 194
Tulsa, OK................ 22.8 365.8 2.1 69 1,002 3.6 113
Clackamas, OR............ 15.6 167.3 1.1 167 1,073 4.7 62
Deschutes, OR............ 9.1 83.7 2.8 32 916 4.4 74
Jackson, OR.............. 7.8 90.2 0.9 185 843 1.3 301
Lane, OR................. 12.6 157.7 0.7 215 884 2.6 210
Marion, OR............... 11.4 156.9 2.6 41 940 4.4 74
Multnomah, OR............ 36.3 519.7 1.8 100 1,208 5.4 37
Washington, OR........... 20.1 300.3 1.8 100 1,312 0.4 325
Allegheny, PA............ 35.7 708.2 1.0 175 1,205 2.9 187
Berks, PA................ 8.9 175.9 0.5 242 987 1.8 274
Bucks, PA................ 20.2 269.8 1.9 86 1,053 2.2 239
Butler, PA............... 5.1 86.5 -0.4 329 1,031 2.4 227
Chester, PA.............. 15.8 254.7 1.2 161 1,381 2.5 220
Cumberland, PA........... 6.6 136.7 1.0 175 1,001 2.1 249
Dauphin, PA.............. 7.5 186.9 2.1 69 1,095 2.7 203
Delaware, PA............. 14.2 228.5 0.8 198 1,159 0.9 316
Erie, PA................. 6.9 122.5 0.3 258 840 3.6 113
Lackawanna, PA........... 5.6 99.1 -0.3 318 852 2.4 227
Lancaster, PA............ 13.7 246.0 1.7 114 927 2.9 187
Lehigh, PA............... 8.8 194.9 0.8 198 1,084 -0.1 335
Luzerne, PA.............. 7.4 146.4 -0.3 318 877 4.9 58
Montgomery, PA........... 27.9 505.1 1.0 175 1,363 3.0 176
Northampton, PA.......... 6.8 118.5 2.1 69 948 3.0 176
Philadelphia, PA......... 34.6 700.1 2.0 81 1,314 2.1 249
Washington, PA........... 5.5 88.6 0.5 242 1,103 -6.6 349
Westmoreland, PA......... 9.3 134.0 -0.3 318 913 3.0 176
York, PA................. 9.2 181.4 0.1 282 985 3.2 155
Kent, RI................. 5.5 77.9 0.8 198 950 2.0 257
Providence, RI........... 18.7 291.3 0.3 258 1,114 -0.3 337
Charleston, SC........... 16.7 257.8 3.4 17 1,002 3.0 176
Greenville, SC........... 15.1 279.6 2.7 36 961 0.6 320
Horry, SC................ 9.6 126.6 3.2 22 685 1.8 274
Lexington, SC............ 7.1 122.0 0.4 251 837 3.7 104
Richland, SC............. 10.8 224.8 0.9 185 925 1.5 292
Spartanburg, SC.......... 6.7 146.0 3.5 15 905 0.3 329
York, SC................. 6.4 98.5 5.1 2 904 2.7 203
Minnehaha, SD............ 7.5 128.6 1.4 139 979 3.1 164
Davidson, TN............. 23.9 507.5 3.3 20 1,229 2.5 220
Hamilton, TN............. 10.0 209.9 2.4 50 1,026 -1.3 342
Knox, TN................. 12.8 243.2 1.0 175 999 2.0 257
Rutherford, TN........... 5.9 133.2 1.6 126 981 3.7 104
Shelby, TN............... 21.0 508.4 0.8 198 1,157 4.1 84
Williamson, TN........... 9.3 138.6 4.3 7 1,423 13.1 2
Bell, TX................. 5.6 120.3 0.8 198 958 3.3 146
Bexar, TX................ 42.6 879.4 1.6 126 1,022 4.5 69
Brazoria, TX............. 6.1 115.0 4.4 5 1,149 0.0 333
Brazos, TX............... 4.7 108.1 2.6 41 807 0.0 333
Cameron, TX.............. 6.6 140.7 1.3 149 685 4.3 78
Collin, TX............... 26.6 424.3 2.8 32 1,291 3.1 164
Dallas, TX............... 78.6 1,741.5 1.9 86 1,353 2.7 203
Denton, TX............... 15.7 252.4 3.4 17 1,019 2.0 257
El Paso, TX.............. 15.5 311.9 1.7 114 775 3.6 113
Fort Bend, TX............ 14.0 195.5 4.3 7 1,033 2.5 220
Galveston, TX............ 6.3 110.0 0.9 185 972 1.9 265
Harris, TX............... 116.8 2,346.8 2.1 69 1,390 3.1 164
Hidalgo, TX.............. 12.6 267.0 1.8 100 680 2.9 187
Jefferson, TX............ 5.9 123.9 1.0 175 1,136 1.1 310
Lubbock, TX.............. 7.7 141.5 1.0 175 885 2.7 203
McLennan, TX............. 5.4 115.2 1.8 100 916 1.1 310
Midland, TX.............. 5.9 107.6 10.0 1 1,499 10.7 6
Montgomery, TX........... 11.9 191.7 2.8 32 1,116 3.3 146
Nueces, TX............... 8.3 165.8 1.1 167 959 3.3 146
Potter, TX............... 4.0 78.4 0.5 242 934 3.5 128
Smith, TX................ 6.4 105.8 0.8 198 930 5.4 37
Tarrant, TX.............. 44.7 915.7 2.2 60 1,105 3.1 164
Travis, TX............... 42.4 764.4 3.5 15 1,352 5.1 51
Webb, TX................. 5.5 103.0 1.3 149 740 5.1 51
Williamson, TX........... 11.4 177.8 4.4 5 1,083 3.5 128
Davis, UT................ 9.0 130.9 2.6 41 931 2.8 194
Salt Lake, UT............ 48.0 718.2 2.8 32 1,094 3.5 128
Utah, UT................. 17.7 250.3 4.5 4 943 6.1 22
Weber, UT................ 6.4 108.6 2.2 60 838 3.8 101
Chittenden, VT........... 7.1 103.1 0.1 282 1,099 4.0 89
Arlington, VA............ 9.3 179.9 0.8 198 1,870 7.9 9
Chesterfield, VA......... 9.5 143.5 1.9 86 938 2.2 239
Fairfax, VA.............. 37.7 619.5 1.2 161 1,684 2.3 236
Henrico, VA.............. 12.1 193.3 0.1 282 1,075 3.9 97
Loudoun, VA.............. 12.8 171.1 2.7 36 1,356 6.3 20
Prince William, VA....... 9.6 131.4 1.3 149 995 4.6 65
Alexandria City, VA...... 6.4 92.0 -1.0 342 1,627 6.1 22
Chesapeake City, VA...... 6.3 102.2 0.1 282 866 1.4 296
Newport News City, VA.... 4.0 103.7 2.6 41 1,076 5.3 43
Norfolk City, VA......... 6.2 144.0 -0.8 339 1,136 4.5 69
Richmond City, VA........ 8.1 156.4 0.9 185 1,203 1.8 274
Virginia Beach City, VA.. 12.5 177.9 -0.2 312 879 2.8 194
Benton, WA............... 5.9 88.7 2.4 50 1,092 3.6 113
Clark, WA................ 15.2 163.9 2.9 30 1,077 5.6 33
King, WA................. 89.8 1,415.9 2.9 30 1,694 7.0 14
Kitsap, WA............... 6.9 91.3 3.0 28 1,060 6.0 25
Pierce, WA............... 22.9 313.9 1.8 100 1,032 6.0 25
Snohomish, WA............ 21.7 290.7 1.9 86 1,192 4.2 80
Spokane, WA.............. 16.4 225.9 2.1 69 958 3.9 97
Thurston, WA............. 8.4 117.8 2.6 41 1,028 5.5 34
Whatcom, WA.............. 7.4 91.9 2.3 54 944 4.8 61
Yakima, WA............... 7.8 107.0 -0.1 303 809 4.5 69
Kanawha, WV.............. 5.7 98.3 -1.5 347 938 4.0 89
Brown, WI................ 7.2 161.1 1.9 86 1,030 4.4 74
Dane, WI................. 16.3 340.0 0.8 198 1,126 5.2 47
Milwaukee, WI............ 27.6 493.5 0.1 282 1,083 2.6 210
Outagamie, WI............ 5.5 108.8 0.9 185 995 5.3 43
Waukesha, WI............. 13.6 246.7 0.6 227 1,136 4.3 78
Winnebago, WI............ 3.9 94.5 -0.4 329 1,074 3.7 104
San Juan, PR............. 10.6 251.1 -0.8 (5) 693 1.9 (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 73.2 percent of
the total covered workers in the U.S.
Table 2. Covered establishments, employment, and wages in the 10 largest counties,
fourth quarter 2018
Employment Average weekly
wage(1)
Establishments,
fourth quarter
County by NAICS supersector 2018 Percent Percent
(thousands) December change, Fourth change,
2018 December quarter fourth
(thousands) 2017-18(2) 2018 quarter
2017-18(2)
United States(3) ............................ 10,169.1 148,061.8 1.5 $1,144 3.2
Private industry........................... 9,869.6 126,030.9 1.6 1,146 2.8
Natural resources and mining............. 139.2 1,839.3 2.0 1,181 4.2
Construction............................. 820.9 7,267.0 3.8 1,319 3.0
Manufacturing............................ 353.6 12,767.4 1.8 1,354 1.9
Trade, transportation, and utilities..... 1,938.5 28,301.1 0.6 938 3.3
Information.............................. 175.9 2,817.1 0.1 2,169 5.0
Financial activities..................... 903.0 8,232.3 0.8 1,825 -0.1
Professional and business services....... 1,872.3 21,144.1 1.9 1,531 3.0
Education and health services............ 1,725.2 22,960.7 1.8 1,023 2.9
Leisure and hospitality.................. 868.7 16,005.0 1.6 501 4.2
Other services........................... 860.8 4,500.2 1.1 772 3.6
Government................................. 299.5 22,030.9 0.5 1,134 4.4
Los Angeles, CA.............................. 507.9 4,515.9 1.2 1,380 2.1
Private industry........................... 501.6 3,933.1 1.3 1,367 1.2
Natural resources and mining............. 0.5 6.9 8.4 1,146 3.0
Construction............................. 16.3 147.5 3.9 1,399 5.3
Manufacturing............................ 12.7 338.5 -1.4 1,452 3.6
Trade, transportation, and utilities..... 57.8 870.2 0.3 1,041 4.2
Information.............................. 12.0 192.1 1.0 3,090 -3.6
Financial activities..................... 29.1 222.5 -0.8 2,077 -5.0
Professional and business services....... 54.2 634.3 1.7 1,810 -0.6
Education and health services............ 240.9 817.9 2.5 968 2.5
Leisure and hospitality.................. 37.0 539.1 1.7 1,115 1.2
Other services........................... 27.9 152.4 -0.1 806 5.6
Government................................. 6.3 582.8 0.4 1,466 7.6
Cook, IL..................................... 138.1 2,625.3 0.6 1,335 3.7
Private industry........................... 136.8 2,331.5 0.7 1,341 3.6
Natural resources and mining............. 0.1 1.3 7.8 1,313 6.1
Construction............................. 11.0 73.8 1.0 1,698 2.5
Manufacturing............................ 5.7 185.1 0.8 1,400 1.9
Trade, transportation, and utilities..... 28.2 488.8 0.1 1,056 4.5
Information.............................. 2.5 52.0 1.2 1,982 3.2
Financial activities..................... 14.0 199.5 0.2 2,485 4.5
Professional and business services....... 29.0 488.8 0.8 1,752 2.2
Education and health services............ 15.5 456.5 1.3 1,108 6.1
Leisure and hospitality.................. 13.8 285.6 1.6 569 2.5
Other services........................... 15.9 99.5 -1.8 1,006 4.1
Government................................. 1.3 293.8 0.1 1,282 4.8
New York, NY................................. 128.3 2,521.0 0.7 2,400 -3.3
Private industry........................... 126.9 2,286.9 0.8 2,472 -3.8
Natural resources and mining............. 0.0 0.2 16.1 2,098 11.0
Construction............................. 2.3 44.5 2.6 2,361 0.9
Manufacturing............................ 1.9 23.4 -3.7 1,777 2.6
Trade, transportation, and utilities..... 19.0 264.8 -0.6 1,486 -3.6
Information.............................. 5.0 177.7 0.1 3,077 10.5
Financial activities..................... 19.3 386.6 1.5 4,764 -15.3
Professional and business services....... 27.3 606.6 0.9 2,769 0.7
Education and health services............ 10.1 358.8 2.3 1,513 3.8
Leisure and hospitality.................. 14.8 313.7 -0.5 1,102 5.2
Other services........................... 20.2 106.1 0.1 1,287 4.2
Government................................. 1.4 234.1 -0.3 1,703 2.7
Harris, TX................................... 116.8 2,346.8 2.1 1,390 3.1
Private industry........................... 116.3 2,065.9 2.3 1,413 3.0
Natural resources and mining............. 1.6 67.7 1.8 3,278 0.7
Construction............................. 7.6 163.3 4.4 1,534 3.8
Manufacturing............................ 4.8 178.0 4.2 1,690 1.6
Trade, transportation, and utilities..... 25.2 487.5 1.1 1,201 2.6
Information.............................. 1.2 26.1 -2.9 1,527 -1.9
Financial activities..................... 12.3 129.4 0.7 1,896 4.9
Professional and business services....... 23.4 406.1 2.6 1,860 4.0
Education and health services............ 16.4 299.3 2.1 1,119 2.6
Leisure and hospitality.................. 10.5 237.3 3.0 510 4.1
Other services........................... 11.8 68.0 2.2 875 4.0
Government................................. 0.5 280.9 0.6 1,223 4.2
Maricopa, AZ................................. 102.3 2,060.6 3.2 1,064 3.9
Private industry........................... 101.6 1,844.9 3.5 1,064 3.7
Natural resources and mining............. 0.4 8.1 -3.3 1,040 8.3
Construction............................. 8.1 125.8 7.9 1,247 5.1
Manufacturing............................ 3.3 125.3 2.7 1,477 3.7
Trade, transportation, and utilities..... 19.9 409.7 3.9 950 2.9
Information.............................. 1.8 37.8 2.1 1,456 3.6
Financial activities..................... 12.9 185.9 2.6 1,375 2.2
Professional and business services....... 24.2 349.8 3.0 1,189 4.8
Education and health services............ 12.5 322.4 3.9 1,037 2.5
Leisure and hospitality.................. 8.8 224.5 2.3 521 5.3
Other services........................... 6.9 54.0 2.6 781 3.9
Government................................. 0.7 215.6 0.7 1,063 5.5
Dallas, TX................................... 78.6 1,741.5 1.9 1,353 2.7
Private industry........................... 78.0 1,565.5 2.0 1,362 2.4
Natural resources and mining............. 0.5 9.2 14.9 3,335 2.1
Construction............................. 4.8 89.4 1.5 1,439 1.2
Manufacturing............................ 2.8 113.7 1.5 1,523 -4.0
Trade, transportation, and utilities..... 16.0 368.3 2.0 1,133 4.9
Information.............................. 1.4 48.5 -3.0 1,912 3.4
Financial activities..................... 9.7 167.1 0.0 1,846 2.7
Professional and business services....... 17.9 358.6 3.3 1,670 3.1
Education and health services............ 9.7 202.6 2.1 1,235 2.2
Leisure and hospitality.................. 7.1 162.1 1.8 569 1.4
Other services........................... 7.0 43.6 1.0 856 -0.8
Government................................. 0.5 176.0 0.9 1,267 4.6
Orange, CA................................... 126.1 1,647.4 0.8 1,251 0.6
Private industry........................... 124.7 1,501.6 0.9 1,247 0.1
Natural resources and mining............. 0.2 2.1 -4.7 920 -0.5
Construction............................. 7.5 105.3 0.9 1,554 5.6
Manufacturing............................ 5.3 163.1 -0.6 1,652 -1.3
Trade, transportation, and utilities..... 18.1 268.0 -0.6 1,080 3.8
Information.............................. 1.5 26.3 -2.0 1,994 -4.1
Financial activities..................... 12.8 117.2 -2.9 2,051 -6.1
Professional and business services....... 22.9 321.9 1.9 1,444 1.1
Education and health services............ 36.6 223.1 3.3 1,021 -0.7
Leisure and hospitality.................. 9.3 224.0 2.1 538 4.7
Other services........................... 7.2 47.0 0.5 757 2.6
Government................................. 1.4 145.9 -0.3 1,288 5.1
San Diego, CA................................ 115.0 1,485.8 1.6 1,260 3.2
Private industry........................... 113.0 1,248.4 1.7 1,228 2.5
Natural resources and mining............. 0.7 8.9 1.7 833 -0.2
Construction............................. 7.7 83.6 1.6 1,327 0.4
Manufacturing............................ 3.5 112.9 1.8 1,686 1.8
Trade, transportation, and utilities..... 15.1 232.8 0.0 919 3.8
Information.............................. 1.3 23.6 -2.1 2,041 0.7
Financial activities..................... 10.9 75.6 -0.8 1,682 5.5
Professional and business services....... 20.4 250.7 3.1 1,881 1.5
Education and health services............ 33.8 207.2 2.6 1,037 1.6
Leisure and hospitality.................. 8.9 198.8 2.0 551 4.8
Other services........................... 7.7 51.7 1.2 678 3.4
Government................................. 2.0 237.5 1.0 1,423 6.4
King, WA..................................... 89.8 1,415.9 2.9 1,694 7.0
Private industry........................... 89.2 1,245.0 3.1 1,727 7.2
Natural resources and mining............. 0.4 2.9 -3.3 1,434 7.1
Construction............................. 6.9 75.3 6.1 1,546 6.4
Manufacturing............................ 2.5 104.2 2.8 1,711 1.8
Trade, transportation, and utilities..... 14.0 277.4 1.2 1,780 4.0
Information.............................. 2.5 113.7 8.5 3,574 14.9
Financial activities..................... 6.8 69.9 1.7 1,911 4.0
Professional and business services....... 18.5 233.4 2.8 2,054 7.9
Education and health services............ 20.8 179.2 3.0 1,134 4.1
Leisure and hospitality.................. 7.5 143.6 3.1 638 5.1
Other services........................... 9.3 45.3 1.3 931 5.8
Government................................. 0.6 170.9 1.6 1,454 5.1
Miami-Dade, FL............................... 101.0 1,169.8 1.4 1,104 2.8
Private industry........................... 100.7 1,029.1 1.5 1,091 2.6
Natural resources and mining............. 0.5 9.0 -0.2 678 5.8
Construction............................. 7.1 51.3 5.5 1,075 3.7
Manufacturing............................ 2.9 41.1 2.6 1,007 2.7
Trade, transportation, and utilities..... 24.9 297.9 1.5 970 3.0
Information.............................. 1.6 19.4 5.4 1,640 -7.0
Financial activities..................... 10.8 76.6 0.3 1,835 7.8
Professional and business services....... 23.0 165.1 1.2 1,442 4.5
Education and health services............ 11.0 184.5 1.1 1,061 -0.9
Leisure and hospitality.................. 7.5 142.5 0.8 669 0.1
Other services........................... 8.6 39.5 -0.7 689 2.2
Government................................. 0.3 140.7 0.4 1,199 3.9
(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,
fourth quarter 2018
Employment Average weekly
wage(1)
Establishments,
fourth quarter
State 2018 Percent Percent
(thousands) December change, Fourth change,
2018 December quarter fourth
(thousands) 2017-18 2018 quarter
2017-18
United States(2)........... 10,169.1 148,061.8 1.5 $1,144 3.2
Alabama.................... 128.6 1,986.6 1.6 957 3.1
Alaska..................... 22.2 308.3 0.4 1,103 4.9
Arizona.................... 164.4 2,921.1 3.0 1,017 4.1
Arkansas................... 91.6 1,227.0 0.8 869 2.4
California................. 1,591.7 17,556.7 1.7 1,392 3.3
Colorado................... 205.5 2,713.7 2.2 1,180 4.1
Connecticut................ 121.5 1,697.9 0.5 1,334 1.3
Delaware................... 33.5 451.2 1.1 1,107 2.4
District of Columbia....... 40.3 775.1 0.6 1,943 7.3
Florida.................... 707.3 8,902.7 2.1 1,006 3.1
Georgia.................... 285.3 4,499.8 1.8 1,053 2.4
Hawaii..................... 43.3 669.3 0.6 1,016 3.3
Idaho...................... 65.2 734.4 3.2 890 3.6
Illinois................... 372.6 6,026.0 0.3 1,189 3.3
Indiana.................... 168.4 3,086.2 0.9 941 2.8
Iowa....................... 103.5 1,558.4 0.5 966 3.0
Kansas..................... 89.8 1,402.2 0.8 927 3.7
Kentucky................... 121.3 1,914.0 0.3 924 3.2
Louisiana.................. 134.2 1,934.1 0.7 968 3.8
Maine...................... 53.3 618.4 1.3 906 2.5
Maryland................... 172.9 2,702.5 0.8 1,228 1.7
Massachusetts.............. 262.1 3,620.3 1.0 1,457 3.3
Michigan................... 250.4 4,366.5 1.0 1,077 1.3
Minnesota.................. 179.2 2,902.3 0.9 1,140 3.6
Mississippi................ 75.1 1,144.3 0.2 793 2.5
Missouri................... 207.4 2,821.3 0.5 980 3.6
Montana.................... 51.6 468.8 1.6 888 5.2
Nebraska................... 71.6 983.0 0.2 930 3.2
Nevada..................... 82.8 1,397.4 3.3 1,006 5.3
New Hampshire.............. 53.6 666.0 0.7 1,158 2.3
New Jersey................. 275.3 4,125.6 0.8 1,298 2.7
New Mexico................. 60.6 830.2 1.5 905 4.6
New York................... 648.8 9,613.2 1.5 1,445 1.0
North Carolina............. 277.0 4,458.9 1.6 1,013 5.1
North Dakota............... 32.0 422.3 1.5 1,057 4.7
Ohio....................... 298.4 5,442.9 0.5 1,006 3.4
Oklahoma................... 111.1 1,632.3 1.5 932 4.1
Oregon..................... 158.5 1,935.8 1.7 1,052 3.7
Pennsylvania............... 359.3 5,932.5 1.0 1,103 2.6
Rhode Island............... 38.3 487.2 0.8 1,085 2.6
South Carolina............. 140.0 2,119.6 2.8 893 1.9
South Dakota............... 34.0 428.4 1.2 885 3.4
Tennessee.................. 164.2 3,039.8 1.8 1,030 3.0
Texas...................... 700.3 12,531.7 2.5 1,148 3.5
Utah....................... 107.2 1,511.5 3.2 972 3.8
Vermont.................... 25.9 314.2 -0.4 954 3.2
Virginia................... 283.3 3,927.2 1.1 1,164 3.8
Washington................. 249.3 3,384.2 2.4 1,292 6.3
West Virginia.............. 51.3 704.2 1.5 917 8.3
Wisconsin.................. 177.8 2,892.3 0.6 989 4.0
Wyoming.................... 26.4 272.1 1.8 978 4.4
Puerto Rico................ 44.7 896.4 0.8 576 0.9
Virgin Islands............. 3.4 34.5 0.5 925 2.3
(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.
United States Department of Labor
