For release 10:00 a.m. (EST), Thursday, February 20, 2020 USDL-20-0300
Technical Information: (202) 691-6567 * QCEWInfo@bls.gov * www.bls.gov/cew
Media Contact: (202) 691-5902 * PressOffice@bls.gov
COUNTY EMPLOYMENT AND WAGES – THIRD QUARTER 2019
From September 2018 to September 2019, employment increased in 283 of the 355 largest U.S.
counties, the U.S. Bureau of Labor Statistics reported today. In September 2019, national
employment (as measured by the QCEW program) increased to 148.6 million, a 1.1 percent
increase over the year. New Hanover, NC, had the largest over-the-year increase in employment
with a gain of 5.8 percent. Employment data in this release are presented for September 2019,
and average weekly wage data are presented for third quarter 2019.
Among the 355 largest counties, 350 had over-the-year increases in average weekly wages. In
the third quarter of 2019, average weekly wages for the nation increased to $1,093, a 3.6 percent
increase over the year. Boulder, CO, had the largest third quarter over-the-year wage gain at 18.4
percent. (See table 1.)
Large County Employment in September 2019
New Hanover, NC, had the largest over-the-year percentage increase in employment (5.8
percent). Within New Hanover, the largest employment increase occurred in leisure and
hospitality, which gained 1,725 jobs over the year (10.2 percent).
Bay, FL, experienced the largest over-the-year percentage decrease in employment, with a loss
of 5.9 percent. Within Bay, education and health services had the largest employment decrease
with a loss of 2,347 jobs (-21.1 percent).
Large County Average Weekly Wage in Third Quarter 2019
Boulder, CO, had the largest over-the-year percentage increase in average weekly wages (18.4
percent). Within Boulder, an average weekly wage gain of $1,016 (51.8 percent) in professional
and business services made the largest contribution to the county’s increase in average weekly
wages.
Linn, IA, had the largest over-the-year percentage decrease in average weekly wages with a loss
of 2.6 percent. Within Linn, manufacturing had the largest impact, with an average weekly wage
decrease of $285 (-14.7 percent) over the year.
Ten Largest Counties
All of the 10 largest counties had over-the-year percentage increases in employment and average
weekly wages. In September 2019, Maricopa, AZ, had the largest over-the-year employment
percentage gain among the 10 largest counties (3.2 percent). Within Maricopa, education and
health services had the largest employment increase with a gain of 14,264 jobs (4.5 percent).
(See table 2.)
In third quarter 2019, Dallas, TX, experienced the largest over-the-year percentage gain in
average weekly wages among the 10 largest counties (4.9 percent). Within Dallas, professional
and business services had the largest impact, with an average weekly wage increase of $93 (6.5
percent) over the year.
For More Information
The tables included in this release contain data for the nation and for the 355 U.S. counties with
annual average employment levels of 75,000 or more in 2018. September 2019 employment and
third quarter 2019 average weekly wages for all states are provided in table 3 of this release.
The most current news release on quarterly measures of gross job flows is available from QCEW
Business Employment Dynamics at www.bls.gov/news.release/pdf/cewbd.pdf.
Several BLS regional offices issue QCEW news releases targeted to local data users. Links to
these releases are available at www.bls.gov/cew/regional-resources.htm.
QCEW data are available in the Census Business Builder suite of web tools assisting business
owners and regional analysts in data-driven decision making at www.census.gov/data/data-
tools/cbb.html.
The QCEW news release schedule is available at www.bls.gov/cew/release-calendar.htm.
____________
The County Employment and Wages full data update for third quarter 2019 is scheduled
to be released on Wednesday, March 4, 2020, at 10:00 a.m. (EST).
The County Employment and Wages news release for fourth quarter 2019 is scheduled to
be released on Wednesday, May 20, 2020, at 10:00 a.m. (EDT).
Technical Note
These data are the product of a federal-state cooperative program, the Quarterly Census of Employment and
Wages (QCEW) program, also known as the ES-202 program. The data are derived from summaries of
employment and total pay of workers covered by state and federal unemployment insurance (UI) legislation and
provided by State Workforce Agencies (SWAs). The summaries are a result of the administration of state
unemployment insurance programs that require most employers to pay quarterly taxes based on the employment
and wages of workers covered by UI. QCEW data in this release are based on the 2017 North American Industry
Classification System (NAICS). Data for 2019 are preliminary and subject to revision.
For purposes of this release, large counties are defined as having employment levels of 75,000 or greater. In
addition, data for San Juan, PR, are provided, but not used in calculating U.S. averages, rankings, or in the
analysis in the text. Each year, these large counties are selected on the basis of the preliminary annual average of
employment for the previous year. The 356 counties presented in this release were derived using 2018 preliminary
annual averages of employment. For 2019 data, six counties have been added to the publication tables: St. Johns,
FL; St. Lucie, FL; Forsyth, GA; Greene, OH; Ector, TX; and Racine, WI. These counties will be included in all
2019 quarterly releases. The counties in table 2 are selected and sorted each year based on the annual average
employment from the preceding year.
The preliminary QCEW data presented in this release may differ from data released by the individual states. These
potential differences result from the states' continuing receipt of UI data over time and ongoing review and
editing. The individual states determine their data release timetables.
Differences between QCEW, BED, and CES employment measures
The Bureau publishes three different establishment-based employment measures for any given quarter: QCEW,
Business Employment Dynamics (BED), and Current Employment Statistics (CES). Each of these measures
makes use of the quarterly UI employment reports in producing data; however, each measure has a somewhat
different universe coverage, estimation procedure, and publication product.
Differences in coverage and estimation methods can result in somewhat different measures of employment change
over time. It is important to understand program differences and the intended uses of the program products. (See
table.) Additional information on each program can be obtained from the program Web sites shown in the table.
Summary of Major Differences between QCEW, BED, and CES Employment Measures
----------------------------------------------------------------------------------
| QCEW | BED | CES
-----------|---------------------|----------------------|------------------------
Source |--Count of UI admini-|--Count of longitudi- |--Sample survey:
| strative records | nally-linked UI ad- | 697,000 establish-
| submitted by 10.2 | ministrative records| ments
| million establish- | submitted by 8.0 |
| ments in first | million private-sec-|
| quarter of 2019 | tor employers |
-----------|---------------------|----------------------|------------------------
Coverage |--UI and UCFE cover- |--UI coverage, exclud-|Nonfarm wage and sal-
| age, including all | ing government, pri-| ary jobs:
| employers subject | vate households, and|--UI coverage, exclud-
| to state and fed- | establishments with | ing agriculture, pri-
| eral UI laws | zero employment | vate households, and
| | | self-employed workers
| | |--Other employment, in-
| | | cluding railroads,
| | | religious organiza-
| | | tions, and other non-
| | | UI-covered jobs
-----------|---------------------|----------------------|------------------------
Publication|--Quarterly |--Quarterly |--Monthly
frequency | -Within 5 months | -7 months after the | -Usually the 3rd Friday
| after the end of | end of each quarter| after the end of the
| each quarter | | week including
| | | the 12th of the month
-----------|---------------------|----------------------|------------------------
Use of UI |--Directly summarizes|--Links each new UI |--Uses UI file as a sam-
file | and publishes each | quarter to longitu- | pling frame and to an-
| new quarter of UI | dinal database and | nually realign sample-
| data | directly summarizes | based estimates to pop-
| | gross job gains and | ulation counts (bench-
| | losses | marking)
-----------|---------------------|----------------------|------------------------
Principal |--Provides a quarter-|--Provides quarterly |--Provides current month-
products | ly and annual uni- | employer dynamics | ly estimates of employ-
| verse count of es- | data on establish- | ment, hours, and earn-
| tablishments, em- | ment openings, clos-| ings at the MSA, state,
| ployment, and wages| ings, expansions, | and national level by
| at the county, MSA,| and contractions at | industry
| state, and national| the national level |
| levels by detailed | by NAICS supersec- |
| industry | tors and by size of |
| | firm, and at the |
| | state private-sector|
| | total level |
| |--Future expansions |
| | will include data |
| | with greater indus- |
| | try detail and data |
| | at the county and |
| | MSA level |
-----------|---------------------|----------------------|------------------------
Principal |--Major uses include:|--Major uses include: |--Major uses include:
uses | -Detailed locality | -Business cycle | -Principal federal
| data | analysis | economic indicator
| -Periodic universe | -Analysis of employ-| -Official time series
| counts for bench- | er dynamics under- | for employment change
| marking sample | lying economic ex- | measures
| survey estimates | pansions and con- | -Input into other ma-
| -Sample frame for | tractions | jor economic indi-
| BLS establishment | -Analysis of employ-| cators
| surveys | ment expansion and |
| | contraction by size|
| | of firm |
| | |
-----------|---------------------|----------------------|------------------------
Program |--www.bls.gov/cew |--www.bls.gov/bdm |--www.bls.gov/ces
Web sites | | |
---------------------------------------------------------------------------------
Coverage
Employment and wage data for workers covered by state UI laws are compiled from quarterly contribution reports
submitted to the SWAs by employers. For federal civilian workers covered by the Unemployment Compensation
for Federal Employees (UCFE) program, employment and wage data are compiled from quarterly reports
submitted by four major federal payroll processing centers on behalf of all federal agencies, with the exception of
a few agencies which still report directly to the individual SWA. In addition to the quarterly contribution reports,
employers who operate multiple establishments within a state complete a questionnaire, called the "Multiple
Worksite Report," which provides detailed information on the location and industry of each of their
establishments. QCEW employment and wage data are derived from microdata summaries of 10.0 million
employer reports of employment and wages submitted by states to the BLS in 2018. These reports are based on
place of employment rather than place of residence.
UI and UCFE coverage is broad and has been basically comparable from state to state since 1978, when the 1976
amendments to the Federal Unemployment Tax Act became effective, expanding coverage to include most state
and local government employees. In 2018, UI and UCFE programs covered workers in 146.1 million jobs. The
estimated 140.5 million workers in these jobs (after adjustment for multiple jobholders) represented 96.2 percent
of civilian wage and salary employment. Covered workers received $8.368 trillion in pay, representing 94.2
percent of the wage and salary component of personal income and 40.7 percent of the gross domestic product.
Major exclusions from UI coverage include self-employed workers, most agricultural workers on small farms, all
members of the Armed Forces, elected officials in most states, most employees of railroads, some domestic
workers, most student workers at schools, and employees of certain small nonprofit organizations.
State and federal UI laws change periodically. These changes may have an impact on the employment and wages
reported by employers covered under the UI program. Coverage changes may affect the over-the-year
comparisons presented in this news release.
Concepts and methodology
Monthly employment is based on the number of workers who worked during or received pay for the pay period
including the 12th of the month. With few exceptions, all employees of covered firms are reported, including
production and sales workers, corporation officials, executives, supervisory personnel, and clerical workers.
Workers on paid vacations and part-time workers also are included.
Average weekly wage values are calculated by dividing quarterly total wages by the average of the three monthly
employment levels (all employees, as described above) and dividing the result by 13, for the 13 weeks in the
quarter. These calculations are made using unrounded employment and wage values. The average wage values
that can be calculated using rounded data from the BLS database may differ from the averages reported. Included
in the quarterly wage data are non-wage cash payments such as bonuses, the cash value of meals and lodging
when supplied, tips and other gratuities, and, in some states, employer contributions to certain deferred
compensation plans such as 401(k) plans and stock options. Over-the-year comparisons of average weekly wages
may reflect fluctuations in average monthly employment and/or total quarterly wages between the current quarter
and prior year levels.
Average weekly wages are affected by the ratio of full-time to part-time workers as well as the number of
individuals in high-paying and low-paying occupations and the incidence of pay periods within a quarter. For
instance, the average weekly wage of the workforce could increase significantly when there is a large decline in
the number of employees that had been receiving below-average wages. Wages may include payments to workers
not present in the employment counts because they did not work during the pay period including the 12th of the
month. When comparing average weekly wage levels between industries, states, or quarters, these factors should
be taken into consideration.
Wages measured by QCEW may be subject to periodic and sometimes large fluctuations. This variability may be
due to calendar effects resulting from some quarters having more pay dates than others. The effect is most visible
in counties with a dominant employer. In particular, this effect has been observed in counties where government
employers represent a large fraction of overall employment. Similar calendar effects can result from private sector
pay practices. However, these effects are typically less pronounced for two reasons: employment is less
concentrated in a single private employer, and private employers use a variety of pay period types (weekly,
biweekly, semimonthly, monthly).
For example, the effect on over-the-year pay comparisons can be pronounced in federal government due to the
uniform nature of federal payroll processing. Most federal employees are paid on a biweekly pay schedule. As a
result, in some quarters federal wages include six pay dates, while in other quarters there are seven pay dates.
Over-the-year comparisons of average weekly wages may also reflect this calendar effect. Growth in average
weekly wages may be attributed, in part, to a comparison of quarterly wages for the current year, which include
seven pay dates, with year-ago wages that reflect only six pay dates. An opposite effect will occur when wages in
the current quarter reflecting six pay dates are compared with year-ago wages for a quarter including seven pay
dates.
In order to ensure the highest possible quality of data, states verify with employers and update, if necessary, the
industry, location, and ownership classification of all establishments on a 3-year cycle. Changes in establishment
classification codes resulting from this process are introduced with the data reported for the first quarter of the
year. Changes resulting from improved employer reporting also are introduced in the first quarter.
QCEW data are not designed as a time series. QCEW data are simply the sums of individual establishment records
and reflect the number of establishments that exist in a county or industry at a point in time. Establishments can
move in or out of a county or industry for a number of reasons that reflect economic events or administrative
changes. For example, economic change would come from a firm relocating into the county; administrative
change would come from a company correcting its county designation.
The over-the-year changes of employment and wages presented in this release have been adjusted to account for
most of the administrative corrections made to the underlying establishment reports. This is done by modifying
the prior-year levels used to calculate the over-the-year changes. Percent changes are calculated using an adjusted
version of the final 2018 quarterly data as the base data. The adjusted prior-year levels used to calculate the over-
the-year percent change in employment and wages are not published. These adjusted prior-year levels do not
match the unadjusted data maintained on the BLS Web site. Over-the-year change calculations based on data from
the Web site, or from data published in prior BLS news releases, may differ substantially from the over-the-year
changes presented in this news release.
The adjusted data used to calculate the over-the-year change measures presented in this release eliminate the effect
of most of the administrative changes (those occurring when employers update the industry, location, and
ownership information of their establishments). The most common adjustments for administrative change are the
result of updated information about the county location of individual establishments. Included in these
adjustments are administrative changes involving the classification of establishments that were previously
reported in the unknown or statewide county or unknown industry categories. Adjusted data account for
improvements in reporting employment and wages for individual and multi-unit establishments. To accomplish
this, adjustments were implemented to account for: administrative changes caused by multi-unit employers who
start reporting for each individual establishment rather than as a single entity (first quarter of 2008); selected large
administrative changes in employment and wages (second quarter of 2011); and state verified improvements in
reporting of employment and wages (third quarter of 2014). These adjustments allow QCEW to include county
employment and wage growth rates in this news release that would otherwise not meet publication standards.
The adjusted data used to calculate the over-the-year change measures presented in any County Employment and
Wages news release are valid for comparisons between the starting and ending points (a 12-month period) used in
that particular release. Comparisons may not be valid for any time period other than the one featured in a release
even if the changes were calculated using adjusted data.
County definitions are assigned according to Federal Information Processing Standards Publications (FIPS PUBS)
as issued by the National Institute of Standards and Technology, after approval by the Secretary of Commerce
pursuant to Section 5131 of the Information Technology Management Reform Act of 1996 and the Computer
Security Act of 1987, Public Law 104-106. Areas shown as counties include those designated as independent
cities in some jurisdictions and, in Alaska, those designated as census areas where counties have not been created.
County data also are presented for the New England states for comparative purposes even though townships are
the more common designation used in New England (and New Jersey). The regions referred to in this release are
defined as census regions.
Additional statistics and other information
Employment and Wages Annual Averages Online features comprehensive information by detailed industry on
establishments, employment, and wages for the nation and all states. The 2018 edition of this publication, which
was published in September 2019, contains selected data produced by Business Employment Dynamics (BED) on
job gains and losses, as well as selected data from the first quarter 2019 version of this news release. Tables and
additional content from the 2018 edition of Employment and Wages Annual Averages Online are now available at
www.bls.gov/cew/publications/employment-and-wages-annual-averages/2018/home.htm. The 2019 edition of
Employment and Wages Annual Averages Online will be available in September 2020.
News releases on quarterly measures of gross job flows also are available from BED at www.bls.gov/bdm, (202)
691-6467, or data.bls.gov/cgi-bin/forms/bdm.
Information in this release will be made available to sensory impaired individuals upon request. Voice phone:
(202) 691-5200; TDD message referral phone number: (800) 877-8339.
Table 1. Covered establishments, employment, and wages in the 356 largest counties,
third quarter 2019
Employment Average weekly wage(2)
Establishments,
County(1) third quarter Percent Ranking Percent Ranking
2019 September change, by Third change, by
(thousands) 2019 September percent quarter third percent
(thousands) 2018-19(3) change 2019 quarter change
2018-19(3)
United States(4)......... 10,325.3 148,556.5 1.1 - $1,093 3.6 -
Jefferson, AL............ 19.3 353.6 1.1 153 1,055 2.9 238
Madison, AL.............. 10.1 206.3 3.0 25 1,194 4.9 42
Mobile, AL............... 10.4 171.7 0.5 226 922 2.9 238
Montgomery, AL........... 6.5 129.9 -0.8 333 909 8.5 6
Shelby, AL............... 6.0 84.4 -0.8 333 1,009 1.7 314
Tuscaloosa, AL........... 4.7 98.2 2.2 60 887 3.4 182
Anchorage, AK............ 8.3 150.8 -0.1 294 1,150 3.4 182
Maricopa, AZ............. 106.6 2,068.7 3.2 17 1,060 4.5 66
Pima, AZ................. 19.3 375.8 0.8 196 934 3.8 132
Benton, AR............... 6.8 123.5 2.5 45 1,012 4.8 47
Pulaski, AR.............. 14.6 250.4 -0.6 321 961 4.2 93
Washington, AR........... 6.3 110.8 1.0 171 882 4.5 66
Alameda, CA.............. 66.2 792.6 0.1 274 1,490 4.2 93
Butte, CA................ 8.6 83.4 -2.4 351 866 4.8 47
Contra Costa, CA......... 33.9 368.7 -0.2 301 1,311 4.4 78
Fresno, CA............... 37.9 409.4 1.5 110 856 3.9 122
Kern, CA................. 21.3 347.3 2.5 45 907 3.8 132
Los Angeles, CA.......... 511.6 4,499.4 1.0 171 1,225 3.7 151
Marin, CA................ 12.7 116.5 0.9 186 1,328 3.1 212
Merced, CA............... 6.8 85.6 2.9 31 846 3.5 168
Monterey, CA............. 14.3 214.1 1.1 153 936 2.4 276
Napa, CA................. 6.0 82.6 1.5 110 1,077 4.1 103
Orange, CA............... 127.5 1,649.2 1.1 153 1,204 4.3 85
Placer, CA............... 13.9 173.1 2.1 64 1,101 4.8 47
Riverside, CA............ 69.0 756.0 2.4 51 881 4.1 103
Sacramento, CA........... 61.8 679.6 1.8 84 1,183 4.1 103
San Bernardino, CA....... 63.4 769.9 1.7 93 927 3.9 122
San Diego, CA............ 116.3 1,490.2 1.5 110 1,197 4.1 103
San Francisco, CA........ 62.0 765.2 3.5 10 2,273 7.6 7
San Joaquin, CA.......... 18.7 260.7 1.2 142 937 4.6 60
San Luis Obispo, CA...... 10.7 119.6 1.0 171 946 4.5 66
San Mateo, CA............ 29.2 419.1 3.1 21 2,366 -0.1 351
Santa Barbara, CA........ 15.8 209.3 2.6 43 1,041 3.6 157
Santa Clara, CA.......... 75.5 1,121.9 1.8 84 2,447 -0.3 352
Santa Cruz, CA........... 9.8 109.6 1.3 132 992 5.1 31
Solano, CA............... 11.9 144.4 0.7 206 1,142 4.4 78
Sonoma, CA............... 20.6 215.2 1.0 171 1,090 4.3 85
Stanislaus, CA........... 16.4 197.5 0.8 196 949 0.2 349
Tulare, CA............... 11.5 169.6 0.6 217 787 4.7 55
Ventura, CA.............. 28.1 328.8 1.0 171 1,065 4.3 85
Yolo, CA................. 7.1 108.4 1.8 84 1,199 7.2 10
Adams, CO................ 11.7 228.9 5.3 2 1,092 3.6 157
Arapahoe, CO............. 23.2 335.5 1.6 102 1,284 5.0 38
Boulder, CO.............. 16.2 189.5 2.9 31 1,547 18.4 1
Denver, CO............... 35.4 533.2 2.1 64 1,369 5.1 31
Douglas, CO.............. 12.9 130.9 2.5 45 1,236 6.1 15
El Paso, CO.............. 21.1 285.0 2.5 45 1,000 4.6 60
Jefferson, CO............ 21.2 244.3 1.9 76 1,149 4.7 55
Larimer, CO.............. 12.9 167.3 2.3 54 1,014 5.4 21
Weld, CO................. 8.0 115.7 3.8 7 1,052 7.5 8
Fairfield, CT............ 37.0 418.0 -0.6 321 1,475 0.8 342
Hartford, CT............. 29.3 513.3 0.0 284 1,254 3.6 157
New Haven, CT............ 25.4 370.0 0.2 261 1,092 2.6 261
New London, CT........... 7.8 122.9 -1.2 342 1,048 1.4 328
New Castle, DE........... 21.3 291.8 0.7 206 1,201 3.4 182
Sussex, DE............... 7.5 86.1 2.8 35 782 3.2 202
Washington, DC........... 41.6 776.3 0.6 217 1,851 2.5 267
Alachua, FL.............. 7.5 134.4 1.0 171 937 2.7 257
Bay, FL.................. 5.8 74.7 -5.9 355 815 8.7 5
Brevard, FL.............. 16.6 221.8 1.8 84 958 1.9 305
Broward, FL.............. 72.1 819.4 0.5 226 1,000 4.1 103
Collier, FL.............. 15.2 145.8 2.2 60 920 4.0 114
Duval, FL................ 31.1 525.2 1.7 93 1,008 2.9 238
Escambia, FL............. 8.4 137.9 1.4 122 856 4.5 66
Hillsborough, FL......... 45.7 708.0 2.8 35 1,046 3.8 132
Lake, FL................. 8.8 101.3 1.9 76 741 2.5 267
Lee, FL.................. 23.7 264.3 2.1 64 854 3.6 157
Leon, FL................. 9.0 152.9 0.6 217 901 4.6 60
Manatee, FL.............. 11.7 128.0 3.1 21 840 4.0 114
Marion, FL............... 8.9 105.1 1.9 76 735 3.4 182
Miami-Dade, FL........... 102.9 1,157.2 1.3 132 1,039 3.6 157
Okaloosa, FL............. 6.8 85.7 1.9 76 894 5.7 17
Orange, FL............... 45.5 868.5 1.9 76 958 3.1 212
Osceola, FL.............. 7.7 99.8 3.2 17 720 1.6 320
Palm Beach, FL........... 59.0 606.6 1.3 132 1,009 2.3 283
Pasco, FL................ 11.7 122.7 1.1 153 775 5.2 25
Pinellas, FL............. 34.8 438.4 0.8 196 931 3.3 195
Polk, FL................. 14.4 228.3 2.7 37 822 2.5 267
St. Johns, FL............ 8.0 79.0 2.3 54 851 3.0 227
St. Lucie, FL............ 7.0 79.9 3.0 25 788 1.4 328
Sarasota, FL............. 16.7 169.5 1.3 132 898 3.9 122
Seminole, FL............. 15.7 200.2 1.4 122 929 0.9 339
Volusia, FL.............. 15.2 173.5 -0.6 321 763 2.1 292
Bibb, GA................. 4.3 82.6 -0.4 308 847 1.7 314
Chatham, GA.............. 8.2 156.8 0.4 238 909 -1.6 354
Clayton, GA.............. 4.1 123.6 2.3 54 1,100 1.9 305
Cobb, GA................. 22.1 375.9 1.6 102 1,131 4.1 103
DeKalb, GA............... 18.0 301.2 0.7 206 1,099 2.2 288
Forsyth, GA.............. 6.0 77.5 2.1 64 968 5.2 25
Fulton, GA............... 44.5 900.1 1.1 153 1,422 3.6 157
Gwinnett, GA............. 25.8 362.2 2.0 69 1,007 1.6 320
Hall, GA................. 4.6 89.6 0.1 274 915 4.8 47
Muscogee, GA............. 4.5 93.6 -0.4 308 850 3.3 195
Richmond, GA............. 4.5 104.5 1.4 122 919 3.3 195
Honolulu, HI............. 27.4 465.8 -0.5 314 1,059 3.8 132
Maui + Kalawao, HI....... 6.7 80.0 -0.1 294 906 4.6 60
Ada, ID.................. 17.6 254.8 3.2 17 970 4.5 66
Champaign, IL............ 4.2 91.8 1.1 153 947 3.7 151
Cook, IL................. 139.7 2,617.8 0.1 274 1,244 3.8 132
DuPage, IL............... 34.9 615.9 -0.7 327 1,221 2.8 252
Kane, IL................. 12.7 213.4 0.5 226 948 2.6 261
Lake, IL................. 20.4 341.4 -0.7 327 1,302 3.3 195
McHenry, IL.............. 7.9 97.8 -0.9 336 846 2.3 283
McLean, IL............... 3.4 82.2 -0.4 308 986 -0.5 353
Madison, IL.............. 5.4 103.2 1.1 153 840 3.8 132
Peoria, IL............... 4.2 103.7 -2.6 352 1,066 1.9 305
St. Clair, IL............ 5.0 92.7 -0.9 336 859 5.3 22
Sangamon, IL............. 4.8 131.7 -0.7 327 1,177 12.8 2
Will, IL................. 15.1 250.5 1.7 93 916 3.2 202
Winnebago, IL............ 6.0 125.7 -2.0 350 928 3.0 227
Allen, IN................ 9.1 191.2 1.1 153 879 3.0 227
Elkhart, IN.............. 4.8 132.4 -3.8 354 890 0.3 347
Hamilton, IN............. 9.8 143.9 1.0 171 1,018 2.5 267
Lake, IN................. 10.4 189.4 0.1 274 925 1.5 325
Marion, IN............... 24.5 608.7 1.2 142 1,098 4.7 55
St. Joseph, IN........... 5.8 125.4 0.5 226 891 4.8 47
Tippecanoe, IN........... 3.5 87.0 1.2 142 924 1.5 325
Vanderburgh, IN.......... 4.8 108.7 -1.0 339 883 5.5 18
Johnson, IA.............. 4.4 83.8 0.3 246 1,014 1.9 305
Linn, IA................. 7.1 132.3 0.7 206 1,010 -2.6 355
Polk, IA................. 18.2 303.0 0.9 186 1,078 3.4 182
Scott, IA................ 5.8 91.7 0.3 246 891 3.1 212
Johnson, KS.............. 24.3 353.5 1.2 142 1,074 3.1 212
Sedgwick, KS............. 12.8 257.7 1.7 93 894 1.6 320
Shawnee, KS.............. 5.1 96.0 -0.6 321 872 2.5 267
Wyandotte, KS............ 3.5 91.7 0.2 261 1,036 4.5 66
Boone, KY................ 4.5 94.9 1.5 110 917 3.9 122
Fayette, KY.............. 11.3 196.6 1.1 153 938 3.4 182
Jefferson, KY............ 25.8 472.4 0.5 226 1,037 5.1 31
Caddo, LA................ 7.4 110.5 -1.1 341 856 2.0 295
Calcasieu, LA............ 5.5 100.8 -3.0 353 980 2.0 295
East Baton Rouge, LA..... 16.4 263.5 -1.6 348 1,010 2.6 261
Jefferson, LA............ 14.4 188.0 0.0 284 945 3.8 132
Lafayette, LA............ 10.1 130.6 0.5 226 922 3.1 212
Orleans, LA.............. 13.6 199.2 1.9 76 993 3.2 202
St. Tammany, LA.......... 8.8 90.0 1.6 102 899 3.5 168
Cumberland, ME........... 13.9 188.1 0.7 206 1,007 4.0 114
Anne Arundel, MD......... 15.3 274.9 -0.1 294 1,139 4.7 55
Baltimore, MD............ 21.3 375.9 -0.3 304 1,061 1.3 333
Frederick, MD............ 6.5 105.3 1.0 171 1,007 5.1 31
Harford, MD.............. 5.9 94.4 -1.3 344 1,053 3.6 157
Howard, MD............... 10.1 175.7 1.5 110 1,320 2.9 238
Montgomery, MD........... 32.8 473.7 0.3 246 1,404 3.9 122
Prince George's, MD...... 16.4 318.0 -0.5 314 1,156 5.5 18
Baltimore City, MD....... 13.7 343.6 -0.7 327 1,239 2.9 238
Barnstable, MA........... 9.7 102.4 -0.4 308 914 4.3 85
Bristol, MA.............. 18.1 229.2 0.6 217 973 4.2 93
Essex, MA................ 27.6 327.4 0.1 274 1,154 5.2 25
Hampden, MA.............. 19.0 214.2 0.2 261 947 1.7 314
Middlesex, MA............ 57.0 939.3 1.6 102 1,625 4.4 78
Norfolk, MA.............. 25.7 352.4 -0.4 308 1,216 3.7 151
Plymouth, MA............. 16.6 197.1 0.2 261 1,036 6.1 15
Suffolk, MA.............. 32.1 700.7 2.4 51 1,784 4.3 85
Worcester, MA............ 26.7 352.9 0.2 261 1,063 1.9 305
Genesee, MI.............. 7.4 137.9 0.6 217 877 2.3 283
Ingham, MI............... 6.7 155.4 1.0 171 994 2.2 288
Kalamazoo, MI............ 5.5 120.7 -0.5 314 993 4.2 93
Kent, MI................. 16.3 405.1 0.3 246 960 3.7 151
Macomb, MI............... 19.3 331.2 0.3 246 1,049 1.6 320
Oakland, MI.............. 43.5 745.3 0.0 284 1,165 1.9 305
Ottawa, MI............... 6.3 130.2 0.8 196 921 2.8 252
Saginaw, MI.............. 4.1 84.6 -0.1 294 861 3.2 202
Washtenaw, MI............ 9.3 221.2 2.0 69 1,179 3.2 202
Wayne, MI................ 35.7 736.3 0.4 238 1,150 3.2 202
Anoka, MN................ 7.8 128.3 0.7 206 1,094 3.8 132
Dakota, MN............... 10.7 191.5 0.0 284 1,057 4.0 114
Hennepin, MN............. 41.1 939.0 0.5 226 1,322 2.4 276
Olmsted, MN.............. 3.8 100.7 0.1 274 1,279 3.8 132
Ramsey, MN............... 14.2 337.0 0.1 274 1,196 2.3 283
St. Louis, MN............ 5.4 98.0 -1.0 339 915 3.0 227
Stearns, MN.............. 4.4 88.0 0.2 261 943 3.5 168
Washington, MN........... 6.1 88.2 0.9 186 908 3.1 212
Harrison, MS............. 4.6 86.9 0.9 186 722 1.1 338
Hinds, MS................ 5.7 119.8 0.0 284 910 4.0 114
Boone, MO................ 5.0 95.2 0.5 226 896 6.7 13
Clay, MO................. 5.8 105.3 0.0 284 920 2.0 295
Greene, MO............... 9.3 170.8 1.5 110 838 0.8 342
Jackson, MO.............. 22.6 375.6 0.4 238 1,070 2.5 267
St. Charles, MO.......... 9.8 153.8 3.0 25 862 2.9 238
St. Louis, MO............ 40.5 609.5 0.3 246 1,131 4.3 85
St. Louis City, MO....... 15.1 231.4 0.3 246 1,172 4.7 55
Yellowstone, MT.......... 6.7 82.8 0.4 238 919 3.5 168
Douglas, NE.............. 19.3 341.7 0.9 186 1,027 3.8 132
Lancaster, NE............ 10.3 172.6 0.2 261 891 4.0 114
Clark, NV................ 57.0 1,025.9 2.3 54 950 3.9 122
Washoe, NV............... 15.3 227.3 1.4 122 1,007 4.1 103
Hillsborough, NH......... 12.3 205.5 0.5 226 1,146 3.0 227
Merrimack, NH............ 5.3 78.0 0.3 246 1,018 2.9 238
Rockingham, NH........... 11.2 153.0 1.1 153 1,048 3.8 132
Atlantic, NJ............. 6.6 130.8 -1.2 342 877 2.7 257
Bergen, NJ............... 33.5 446.3 1.0 171 1,238 3.3 195
Burlington, NJ........... 11.1 201.9 0.3 246 1,096 3.1 212
Camden, NJ............... 12.2 206.5 -0.1 294 1,038 5.2 25
Essex, NJ................ 20.9 345.9 0.9 186 1,330 5.0 38
Gloucester, NJ........... 6.4 114.5 2.9 31 890 1.4 328
Hudson, NJ............... 15.4 268.8 1.0 171 1,384 0.4 345
Mercer, NJ............... 11.3 260.1 1.1 153 1,296 3.0 227
Middlesex, NJ............ 22.7 432.4 0.3 246 1,216 3.1 212
Monmouth, NJ............. 20.4 265.2 0.7 206 1,032 1.3 333
Morris, NJ............... 17.3 294.5 0.7 206 1,534 4.5 66
Ocean, NJ................ 13.8 174.8 2.0 69 850 3.5 168
Passaic, NJ.............. 12.6 166.6 1.0 171 1,005 1.7 314
Somerset, NJ............. 10.3 188.7 -0.3 304 1,518 1.6 320
Union, NJ................ 14.7 228.0 0.7 206 1,272 0.3 347
Bernalillo, NM........... 20.2 334.8 1.2 142 939 4.4 78
Albany, NY............... 10.2 233.8 -0.7 327 1,122 4.3 85
Bronx, NY................ 18.8 324.0 1.1 153 1,131 4.5 66
Broome, NY............... 4.3 85.6 -1.3 344 887 5.2 25
Dutchess, NY............. 8.3 113.8 0.0 284 1,041 3.8 132
Erie, NY................. 24.2 473.4 -0.7 327 953 2.9 238
Kings, NY................ 63.1 796.4 1.1 153 953 4.2 93
Monroe, NY............... 18.6 391.4 0.0 284 1,009 4.5 66
Nassau, NY............... 53.3 628.3 0.1 274 1,162 3.2 202
New York, NY............. 125.9 2,515.1 1.2 142 2,055 2.9 238
Oneida, NY............... 5.2 105.2 0.5 226 838 3.5 168
Onondaga, NY............. 12.6 249.2 0.3 246 997 3.9 122
Orange, NY............... 10.4 147.8 0.8 196 918 5.0 38
Queens, NY............... 52.8 718.6 1.3 132 1,076 2.9 238
Richmond, NY............. 9.8 127.8 4.7 3 1,028 2.8 252
Rockland, NY............. 10.9 130.3 3.5 10 998 2.0 295
Saratoga, NY............. 5.9 89.5 -0.4 308 978 1.9 305
Suffolk, NY.............. 52.7 669.2 0.0 284 1,158 3.1 212
Westchester, NY.......... 35.5 428.7 -0.6 321 1,308 2.5 267
Buncombe, NC............. 9.8 135.1 1.0 171 846 3.3 195
Cabarrus, NC............. 4.9 76.6 2.0 69 806 5.2 25
Catawba, NC.............. 4.5 88.5 -0.5 314 830 3.2 202
Cumberland, NC........... 6.2 119.9 1.5 110 848 2.8 252
Durham, NC............... 8.6 211.5 4.1 5 1,327 1.9 305
Forsyth, NC.............. 9.4 191.1 1.8 84 967 2.5 267
Guilford, NC............. 14.7 285.0 0.3 246 925 2.0 295
Mecklenburg, NC.......... 39.4 716.8 2.7 37 1,214 3.6 157
New Hanover, NC.......... 8.6 117.8 5.8 1 873 1.4 328
Pitt, NC................. 3.8 77.7 1.5 110 906 4.6 60
Wake, NC................. 36.3 574.6 3.3 15 1,157 5.3 22
Cass, ND................. 7.3 121.5 1.5 110 991 3.7 151
Butler, OH............... 8.1 158.3 0.6 217 945 3.5 168
Cuyahoga, OH............. 36.3 731.6 0.2 261 1,084 3.1 212
Delaware, OH............. 5.6 89.5 0.4 238 1,046 4.5 66
Franklin, OH............. 33.9 764.2 0.6 217 1,096 2.5 267
Greene, OH............... 3.7 76.5 1.6 102 1,081 3.0 227
Hamilton, OH............. 24.4 523.1 0.5 226 1,186 6.4 14
Lake, OH................. 6.3 97.4 1.7 93 913 9.9 4
Lorain, OH............... 6.3 99.0 1.1 153 823 0.9 339
Lucas, OH................ 10.2 208.8 0.2 261 933 1.5 325
Mahoning, OH............. 5.9 98.2 -0.1 294 767 2.0 295
Montgomery, OH........... 12.1 255.8 0.0 284 925 2.8 252
Stark, OH................ 8.7 159.1 -0.5 314 810 2.0 295
Summit, OH............... 14.5 267.4 -0.2 301 927 1.2 336
Warren, OH............... 5.3 97.7 2.4 51 1,144 7.2 10
Cleveland, OK............ 6.0 86.3 3.8 7 777 2.0 295
Oklahoma, OK............. 28.5 465.6 0.8 196 1,000 2.6 261
Tulsa, OK................ 22.8 364.0 1.3 132 969 2.9 238
Clackamas, OR............ 15.7 170.5 2.0 69 1,040 3.1 212
Deschutes, OR............ 9.3 86.7 2.5 45 907 5.1 31
Jackson, OR.............. 7.9 92.0 0.2 261 849 3.8 132
Lane, OR................. 12.8 158.6 0.4 238 856 3.5 168
Marion, OR............... 11.5 161.8 1.5 110 908 3.9 122
Multnomah, OR............ 36.7 519.9 1.4 122 1,159 2.9 238
Washington, OR........... 20.5 304.3 1.7 93 1,371 3.4 182
Allegheny, PA............ 35.9 704.8 0.3 246 1,147 3.4 182
Berks, PA................ 9.0 176.2 0.8 196 967 1.3 333
Bucks, PA................ 20.4 269.5 1.2 142 981 1.9 305
Butler, PA............... 5.1 87.8 0.7 206 995 0.4 345
Chester, PA.............. 15.8 254.0 1.2 142 1,271 1.4 328
Cumberland, PA........... 6.6 136.7 1.0 171 981 2.0 295
Dauphin, PA.............. 7.5 186.5 0.7 206 1,063 4.0 114
Delaware, PA............. 14.2 227.3 0.9 186 1,105 2.4 276
Erie, PA................. 7.0 122.6 -0.9 336 816 3.0 227
Lackawanna, PA........... 5.6 97.2 -0.8 333 813 2.3 283
Lancaster, PA............ 13.8 246.2 1.1 153 908 3.5 168
Lehigh, PA............... 8.9 196.5 0.9 186 1,045 3.7 151
Luzerne, PA.............. 7.5 146.1 0.2 261 848 2.0 295
Montgomery, PA........... 28.0 505.4 1.3 132 1,287 3.5 168
Northampton, PA.......... 6.9 119.0 1.8 84 905 1.7 314
Philadelphia, PA......... 35.2 704.1 1.6 102 1,290 4.5 66
Washington, PA........... 5.6 88.6 -0.2 301 1,047 1.7 314
Westmoreland, PA......... 9.3 134.6 0.3 246 889 2.7 257
York, PA................. 9.2 180.9 0.2 261 939 2.7 257
Kent, RI................. 5.6 76.4 0.1 274 939 2.6 261
Providence, RI........... 18.8 290.7 0.3 246 1,011 2.1 292
Charleston, SC........... 17.1 258.9 3.0 25 965 4.8 47
Greenville, SC........... 15.4 279.0 1.7 93 910 2.1 292
Horry, SC................ 9.8 135.2 3.2 17 660 4.3 85
Lexington, SC............ 7.2 122.0 2.7 37 841 5.1 31
Richland, SC............. 10.8 225.6 1.3 132 923 3.5 168
Spartanburg, SC.......... 6.7 147.0 2.7 37 889 2.9 238
York, SC................. 6.5 100.6 4.4 4 876 3.8 132
Minnehaha, SD............ 7.7 128.8 0.9 186 946 2.4 276
Davidson, TN............. 24.8 520.0 3.4 14 1,179 4.1 103
Hamilton, TN............. 10.4 209.4 1.3 132 963 4.2 93
Knox, TN................. 13.1 241.4 0.8 196 936 2.4 276
Rutherford, TN........... 6.1 134.4 1.4 122 939 3.8 132
Shelby, TN............... 21.2 504.1 0.8 196 1,061 0.2 349
Williamson, TN........... 9.7 141.3 3.7 9 1,251 4.1 103
Bell, TX................. 5.7 120.9 1.3 132 916 4.2 93
Bexar, TX................ 43.4 878.4 1.2 142 965 3.8 132
Brazoria, TX............. 6.1 116.6 2.0 69 1,095 0.9 339
Brazos, TX............... 4.7 109.2 2.2 60 815 3.6 157
Cameron, TX.............. 6.6 141.5 1.5 110 659 4.9 42
Collin, TX............... 27.4 432.0 3.0 25 1,278 3.1 212
Dallas, TX............... 79.3 1,750.7 2.7 37 1,303 4.9 42
Denton, TX............... 16.3 260.9 3.5 10 962 3.0 227
Ector, TX................ 4.2 81.1 -1.3 344 1,227 2.4 276
El Paso, TX.............. 15.6 312.9 1.4 122 765 4.2 93
Fort Bend, TX............ 14.5 195.8 2.2 60 977 3.5 168
Galveston, TX............ 6.3 110.1 0.9 186 959 4.1 103
Harris, TX............... 117.9 2,350.4 1.9 76 1,315 3.1 212
Hidalgo, TX.............. 12.7 265.0 2.3 54 683 3.6 157
Jefferson, TX............ 5.9 123.1 1.9 76 1,084 3.5 168
Lubbock, TX.............. 7.8 141.6 1.1 153 861 4.1 103
McLennan, TX............. 5.5 114.4 1.4 122 892 4.4 78
Midland, TX.............. 6.1 107.7 0.5 226 1,459 3.4 182
Montgomery, TX........... 12.4 192.0 2.6 43 1,061 4.2 93
Nueces, TX............... 8.3 163.4 1.1 153 937 2.9 238
Potter, TX............... 4.0 77.1 1.6 102 888 3.3 195
Smith, TX................ 6.4 103.4 0.4 238 893 5.1 31
Tarrant, TX.............. 45.5 923.3 1.6 102 1,079 4.9 42
Travis, TX............... 43.9 779.9 3.1 21 1,312 4.8 47
Webb, TX................. 5.6 103.0 0.8 196 712 2.2 288
Williamson, TX........... 11.9 181.4 3.9 6 1,143 12.3 3
Davis, UT................ 9.0 134.7 2.3 54 873 3.8 132
Salt Lake, UT............ 49.1 726.1 2.7 37 1,081 4.5 66
Utah, UT................. 18.1 255.1 3.3 15 914 7.4 9
Weber, UT................ 6.4 109.3 3.0 25 831 2.6 261
Chittenden, VT........... 7.2 103.3 0.2 261 1,065 5.0 38
Arlington, VA............ 9.1 182.9 2.5 45 1,744 3.0 227
Chesterfield, VA......... 9.4 135.4 1.1 153 926 4.4 78
Fairfax, VA.............. 36.6 622.2 1.5 110 1,651 4.0 114
Henrico, VA.............. 11.8 191.7 -0.5 314 1,023 3.9 122
Loudoun, VA.............. 12.6 174.8 2.9 31 1,228 0.5 344
Prince William, VA....... 9.6 132.8 1.0 171 960 3.4 182
Alexandria City, VA...... 6.2 89.5 -1.6 348 1,505 3.4 182
Chesapeake City, VA...... 6.2 101.1 1.4 122 860 4.2 93
Newport News City, VA.... 3.9 103.3 1.2 142 1,019 4.9 42
Norfolk City, VA......... 6.1 139.6 0.1 274 1,063 4.6 60
Richmond City, VA........ 8.0 158.7 1.8 84 1,198 6.8 12
Virginia Beach City, VA.. 12.3 177.9 0.6 217 823 3.9 122
Benton, WA............... 6.1 94.3 3.5 10 1,099 2.4 276
Clark, WA................ 15.5 165.5 1.7 93 1,060 4.8 47
King, WA................. 90.7 1,445.3 3.1 21 1,814 3.6 157
Kitsap, WA............... 7.0 92.4 2.1 64 1,014 3.4 182
Pierce, WA............... 23.5 320.3 1.7 93 1,039 5.3 22
Snohomish, WA............ 21.9 293.3 2.0 69 1,167 3.8 132
Spokane, WA.............. 16.8 231.0 1.8 84 953 4.4 78
Thurston, WA............. 8.7 119.4 1.2 142 1,051 5.5 18
Whatcom, WA.............. 7.4 91.5 0.6 217 932 3.8 132
Yakima, WA............... 8.1 127.1 1.4 122 790 3.1 212
Kanawha, WV.............. 5.7 96.3 -1.5 347 950 3.5 168
Brown, WI................ 7.4 159.1 -0.6 321 947 3.2 202
Dane, WI................. 16.6 342.3 1.8 84 1,062 3.2 202
Milwaukee, WI............ 28.3 488.2 -0.5 314 1,008 3.0 227
Outagamie, WI............ 5.7 108.7 -0.3 304 917 2.2 288
Racine, WI............... 4.8 75.5 -0.3 304 910 1.2 336
Waukesha, WI............. 14.0 246.3 0.4 238 1,054 3.1 212
Winnebago, WI............ 4.0 92.7 -0.1 294 968 3.4 182
San Juan, PR............. 11.4 242.9 1.7 (5) 637 -0.9 (5)
(1) Includes areas not officially designated as counties. See Technical Note.
(2) Average weekly wages were calculated using unrounded data.
(3) Percent changes were computed from employment and pay data adjusted for noneconomic
county reclassifications. See Technical Note.
(4) Totals for the United States do not include data for Puerto Rico or the Virgin Islands.
(5) This county was not included in the U.S. rankings.
Note: Data are preliminary. Includes workers covered by Unemployment Insurance (UI) and Unemployment
Compensation for Federal Employees (UCFE) programs. These 355 U.S. counties comprise 73.4 percent of
the total covered workers in the U.S.
Table 2. Covered establishments, employment, and wages in the 10 largest counties,
third quarter 2019
Employment Average weekly
wage(1)
Establishments,
third quarter
County by NAICS supersector 2019 Percent Percent
(thousands) September change, Third change,
2019 September quarter third
(thousands) 2018-19(2) 2019 quarter
2018-19(2)
United States(3) ............................ 10,325.3 148,556.5 1.1 $1,093 3.6
Private industry........................... 10,022.4 126,655.3 1.2 1,086 3.7
Natural resources and mining............. 140.3 2,036.2 -0.4 1,112 3.8
Construction............................. 838.1 7,646.3 2.5 1,224 3.7
Manufacturing............................ 357.2 12,774.8 0.3 1,269 1.7
Trade, transportation, and utilities..... 1,948.8 27,339.7 0.4 924 3.9
Information.............................. 186.8 2,839.0 1.5 2,221 2.7
Financial activities..................... 919.6 8,328.4 1.4 1,614 3.7
Professional and business services....... 1,921.0 21,301.3 1.3 1,419 4.3
Education and health services............ 1,775.3 23,112.6 1.8 996 3.4
Leisure and hospitality.................. 885.7 16,576.0 1.1 479 5.0
Other services........................... 865.5 4,530.8 1.1 760 4.1
Government................................. 302.9 21,901.2 0.8 1,135 3.2
Los Angeles, CA.............................. 511.6 4,499.4 1.0 1,225 3.7
Private industry........................... 505.2 3,927.0 1.3 1,184 3.6
Natural resources and mining............. 0.5 6.6 1.5 1,082 -3.0
Construction............................. 17.1 150.8 2.1 1,323 5.8
Manufacturing............................ 12.8 337.8 -0.4 1,354 1.8
Trade, transportation, and utilities..... 59.6 835.1 -0.6 1,015 4.9
Information.............................. 13.3 203.0 4.3 2,455 1.3
Financial activities..................... 30.4 221.8 -0.2 1,857 2.5
Professional and business services....... 56.8 638.9 1.7 1,481 2.3
Education and health services............ 245.2 832.8 2.9 926 3.7
Leisure and hospitality.................. 39.5 546.4 1.6 705 7.1
Other services........................... 29.5 153.5 0.9 829 10.4
Government................................. 6.5 572.4 -0.5 1,515 4.3
Cook, IL..................................... 139.7 2,617.8 0.1 1,244 3.8
Private industry........................... 138.4 2,320.0 0.1 1,251 3.8
Natural resources and mining............. 0.1 1.5 7.5 1,210 4.8
Construction............................. 11.2 81.0 2.2 1,538 3.0
Manufacturing............................ 5.7 183.6 0.1 1,276 2.7
Trade, transportation, and utilities..... 28.5 468.6 -0.5 1,036 5.1
Information.............................. 2.6 53.7 3.0 2,027 3.9
Financial activities..................... 14.1 207.4 1.6 2,161 1.4
Professional and business services....... 29.4 480.9 -0.6 1,565 3.9
Education and health services............ 15.6 451.3 0.0 1,058 3.6
Leisure and hospitality.................. 14.0 295.9 0.8 597 6.4
Other services........................... 16.2 95.8 -2.5 976 3.7
Government................................. 1.3 297.8 0.6 1,186 3.1
New York, NY................................. 125.9 2,515.1 1.2 2,055 2.9
Private industry........................... 124.4 2,282.2 1.2 2,101 3.0
Natural resources and mining............. 0.0 0.3 18.1 2,060 9.0
Construction............................. 2.4 43.8 -3.2 1,949 2.5
Manufacturing............................ 1.8 21.8 -4.2 1,513 1.5
Trade, transportation, and utilities..... 18.3 251.0 -1.0 1,460 2.4
Information.............................. 5.1 182.5 3.8 2,851 -1.7
Financial activities..................... 19.2 388.5 2.1 3,437 2.1
Professional and business services....... 27.5 618.5 1.1 2,395 4.1
Education and health services............ 10.1 358.3 1.7 1,435 3.9
Leisure and hospitality.................. 14.6 308.6 0.8 993 6.1
Other services........................... 19.5 105.4 1.0 1,252 -1.8
Government................................. 1.4 232.9 1.2 1,599 2.4
Harris, TX................................... 117.9 2,350.4 1.9 1,315 3.1
Private industry........................... 117.3 2,073.8 1.9 1,325 3.2
Natural resources and mining............. 1.6 67.0 -0.6 3,247 7.4
Construction............................. 7.8 173.8 5.7 1,379 2.8
Manufacturing............................ 4.9 180.7 1.8 1,591 0.2
Trade, transportation, and utilities..... 25.2 468.8 0.3 1,189 3.2
Information.............................. 1.2 26.1 2.4 1,564 2.8
Financial activities..................... 12.8 130.8 2.7 1,691 3.2
Professional and business services....... 23.7 411.2 1.8 1,645 2.1
Education and health services............ 16.7 303.1 1.4 1,067 4.0
Leisure and hospitality.................. 10.6 241.7 2.7 514 7.5
Other services........................... 11.8 68.9 2.1 849 4.6
Government................................. 0.6 276.6 1.8 1,241 3.3
Maricopa, AZ................................. 106.6 2,068.7 3.2 1,060 4.5
Private industry........................... 105.9 1,851.0 3.5 1,050 4.6
Natural resources and mining............. 0.5 7.5 2.5 1,035 4.2
Construction............................. 8.6 133.0 7.4 1,157 5.4
Manufacturing............................ 3.5 129.4 3.0 1,391 3.6
Trade, transportation, and utilities..... 20.8 388.9 1.5 955 3.1
Information.............................. 2.2 38.2 3.1 1,460 -0.5
Financial activities..................... 13.7 192.4 4.0 1,405 9.3
Professional and business services....... 26.4 352.4 3.9 1,124 4.7
Education and health services............ 13.4 331.1 4.5 1,050 3.3
Leisure and hospitality.................. 9.1 223.2 2.4 524 4.0
Other services........................... 7.0 54.4 1.9 794 6.4
Government................................. 0.7 217.7 1.5 1,147 3.2
Dallas, TX................................... 79.3 1,750.7 2.7 1,303 4.9
Private industry........................... 78.8 1,573.6 2.8 1,312 5.1
Natural resources and mining............. 0.5 9.4 4.2 3,581 7.0
Construction............................. 4.9 93.0 3.5 1,323 2.0
Manufacturing............................ 2.9 119.1 4.1 1,587 7.5
Trade, transportation, and utilities..... 16.4 353.9 1.8 1,146 4.5
Information.............................. 1.4 45.9 -0.3 1,983 6.0
Financial activities..................... 9.9 168.0 3.1 1,767 4.1
Professional and business services....... 18.0 366.8 3.7 1,532 6.5
Education and health services............ 9.9 204.7 1.6 1,134 3.0
Leisure and hospitality.................. 7.2 167.9 3.6 551 7.2
Other services........................... 7.1 43.7 1.4 854 2.3
Government................................. 0.5 177.1 1.4 1,220 2.4
Orange, CA................................... 127.5 1,649.2 1.1 1,204 4.3
Private industry........................... 126.1 1,499.5 1.0 1,194 4.5
Natural resources and mining............. 0.2 2.3 -6.5 912 3.1
Construction............................. 7.8 107.6 -0.7 1,469 4.9
Manufacturing............................ 5.3 159.1 -0.7 1,512 2.4
Trade, transportation, and utilities..... 18.6 255.6 -0.6 1,067 4.2
Information.............................. 1.6 25.5 -2.4 2,133 1.8
Financial activities..................... 13.1 116.7 0.2 1,997 10.7
Professional and business services....... 23.8 327.4 2.1 1,349 4.4
Education and health services............ 38.0 228.5 3.6 996 3.4
Leisure and hospitality.................. 9.8 228.5 1.4 538 3.7
Other services........................... 7.7 48.3 0.9 769 6.1
Government................................. 1.4 149.7 2.4 1,318 3.1
San Diego, CA................................ 116.3 1,490.2 1.5 1,197 4.1
Private industry........................... 114.2 1,254.7 1.6 1,160 3.8
Natural resources and mining............. 0.7 10.5 8.9 799 2.2
Construction............................. 8.0 85.7 1.0 1,302 6.0
Manufacturing............................ 3.6 115.3 2.1 1,630 4.0
Trade, transportation, and utilities..... 15.5 221.2 -0.1 907 4.9
Information.............................. 1.4 23.1 -2.1 2,492 9.6
Financial activities..................... 11.2 76.2 0.7 1,552 6.0
Professional and business services....... 21.2 255.2 2.7 1,626 1.3
Education and health services............ 35.0 211.2 3.3 996 3.0
Leisure and hospitality.................. 9.3 203.2 0.7 546 4.6
Other services........................... 8.3 52.9 1.3 681 3.3
Government................................. 2.0 235.5 1.2 1,401 5.2
King, WA..................................... 90.7 1,445.3 3.1 1,814 3.6
Private industry........................... 90.0 1,275.1 3.4 1,855 3.4
Natural resources and mining............. 0.4 3.0 -3.0 1,288 -7.9
Construction............................. 7.0 76.7 1.6 1,500 5.5
Manufacturing............................ 2.5 105.6 2.6 1,650 3.0
Trade, transportation, and utilities..... 13.7 279.1 3.3 1,750 3.2
Information.............................. 2.6 124.5 8.1 5,367 -2.7
Financial activities..................... 6.8 71.7 1.7 1,812 6.8
Professional and business services....... 18.6 238.6 3.7 1,873 5.3
Education and health services............ 21.6 181.0 2.9 1,108 4.2
Leisure and hospitality.................. 7.5 146.9 1.6 644 3.2
Other services........................... 9.3 48.1 5.4 939 7.2
Government................................. 0.6 170.1 0.9 1,507 4.9
Miami-Dade, FL............................... 102.9 1,157.2 1.3 1,039 3.6
Private industry........................... 102.6 1,017.7 1.5 1,008 3.7
Natural resources and mining............. 0.5 8.5 5.9 679 -0.7
Construction............................. 7.3 52.3 2.6 1,012 2.7
Manufacturing............................ 2.8 41.8 2.2 937 3.2
Trade, transportation, and utilities..... 24.6 287.6 0.9 941 3.6
Information.............................. 1.6 19.2 3.8 1,610 -2.4
Financial activities..................... 11.1 75.7 0.3 1,596 6.3
Professional and business services....... 23.8 165.2 2.7 1,201 2.1
Education and health services............ 11.6 185.2 1.5 1,024 4.3
Leisure and hospitality.................. 7.7 142.4 0.8 633 4.6
Other services........................... 8.8 38.2 -1.1 676 4.3
Government................................. 0.3 139.5 0.5 1,277 3.2
(1) Average weekly wages were calculated using unrounded data.
(2) Percent changes were computed from quarterly employment and pay data adjusted for noneconomic
county reclassifications. See Technical Note.
(3) Totals for the United States do not include data for Puerto Rico or the Virgin Islands.
Note: Data are preliminary. Counties selected are based on 2018 annual average employment.
Includes workers covered by Unemployment Insurance (UI) and Unemployment Compensation for Federal
Employees (UCFE) programs.
Table 3. Covered establishments, employment, and wages by state,
third quarter 2019
Employment Average weekly
wage(1)
Establishments,
third quarter
State 2019 Percent Percent
(thousands) September change, Third change,
2019 September quarter third
(thousands) 2018-19 2019 quarter
2018-19
United States(2)........... 10,325.3 148,556.5 1.1 $1,093 3.6
Alabama.................... 130.9 1,989.5 1.1 919 3.8
Alaska..................... 22.4 338.0 1.2 1,105 3.7
Arizona.................... 168.0 2,913.4 2.6 1,018 4.5
Arkansas................... 92.3 1,222.8 0.0 841 3.8
California................. 1,608.8 17,713.1 1.4 1,309 3.8
Colorado................... 212.1 2,749.0 2.4 1,170 6.1
Connecticut................ 123.6 1,676.6 -0.3 1,236 2.3
Delaware................... 34.4 453.2 1.1 1,078 3.3
District of Columbia....... 41.6 776.4 0.6 1,851 2.5
Florida.................... 724.8 8,838.2 1.7 955 3.4
Georgia.................... 289.8 4,509.7 1.4 1,026 3.4
Hawaii..................... 44.9 654.1 -0.3 1,012 3.9
Idaho...................... 68.3 765.2 2.9 838 4.1
Illinois................... 380.6 6,023.1 0.0 1,125 3.6
Indiana.................... 168.5 3,083.5 0.3 914 3.5
Iowa....................... 104.5 1,556.9 0.1 914 3.0
Kansas..................... 89.8 1,395.9 0.4 893 2.9
Kentucky................... 122.9 1,910.8 0.7 884 3.4
Louisiana.................. 136.0 1,913.5 -0.3 923 2.6
Maine...................... 54.3 632.6 0.9 887 4.2
Maryland................... 173.6 2,696.9 0.2 1,169 3.6
Massachusetts.............. 264.1 3,642.5 0.9 1,359 4.2
Michigan................... 268.2 4,375.8 0.2 1,021 3.0
Minnesota.................. 180.6 2,917.8 0.4 1,107 3.0
Mississippi................ 74.6 1,135.8 0.1 768 2.7
Missouri................... 209.8 2,826.5 0.6 942 3.9
Montana.................... 50.6 478.9 1.2 848 3.9
Nebraska................... 73.3 984.7 0.3 908 4.0
Nevada..................... 84.6 1,412.2 2.1 973 4.1
New Hampshire.............. 54.2 667.9 0.8 1,075 3.4
New Jersey................. 278.1 4,104.0 0.9 1,217 3.0
New Mexico................. 62.8 842.1 1.7 899 5.1
New York................... 651.0 9,575.4 1.1 1,314 3.3
North Carolina............. 287.7 4,501.3 2.2 972 3.6
North Dakota............... 31.9 428.4 0.9 1,028 3.3
Ohio....................... 302.1 5,443.3 0.3 976 3.1
Oklahoma................... 111.8 1,628.8 0.5 897 2.6
Oregon..................... 160.7 1,970.7 1.4 1,037 3.2
Pennsylvania............... 363.4 5,947.9 0.8 1,064 3.2
Rhode Island............... 38.9 491.3 0.6 991 2.8
South Carolina............. 141.1 2,132.4 2.2 866 3.7
South Dakota............... 34.3 433.4 0.4 855 3.4
Tennessee.................. 167.8 3,060.8 1.9 966 2.8
Texas...................... 713.5 12,603.2 2.1 1,109 4.1
Utah....................... 109.1 1,535.2 2.8 954 4.8
Vermont.................... 26.1 311.0 0.0 927 4.3
Virginia................... 279.6 3,931.4 1.0 1,125 4.0
Washington................. 252.6 3,489.8 2.1 1,335 4.3
West Virginia.............. 51.6 694.4 -1.8 897 0.3
Wisconsin.................. 182.2 2,893.8 0.1 929 3.1
Wyoming.................... 27.0 283.1 1.5 942 4.2
Puerto Rico................ 47.9 878.9 1.9 528 -0.8
Virgin Islands............. 3.3 37.8 9.6 1,012 12.8
(1) Average weekly wages were calculated using unrounded data.
(2) Totals for the United States do not include data for Puerto Rico or the Virgin Islands.
Note: Data are preliminary. Includes workers covered by Unemployment Insurance (UI) and
Unemployment Compensation for Federal Employees (UCFE) programs.
United States Department of Labor
