For release 10:00 a.m. (EDT), Wednesday, May 20, 2020 USDL-20-1012
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 2019
From December 2018 to December 2019, employment increased in 285 of the 355 largest U.S.
counties, the U.S. Bureau of Labor Statistics reported today. In December 2019, national
employment (as measured by the QCEW program) increased to 149.9 million, a 1.2 percent
increase over the year. Cleveland, OK, had the largest over-the-year increase in employment
with a gain of 5.8 percent. Employment data in this release are presented for December 2019,
and average weekly wage data are presented for fourth quarter 2019.
Among the 355 largest counties, 341 had over-the-year increases in average weekly wages. In
the fourth quarter of 2019, average weekly wages for the nation increased to $1,185, a 3.5
percent increase over the year. Santa Cruz, CA, had the largest fourth quarter over-the-year wage
gain at 20.7 percent. (See table 1.)
Large County Employment in December 2019
Cleveland, OK, had the largest over-the-year percentage increase in employment (5.8 percent).
Within Cleveland, the largest employment increase occurred in trade, transportation, and
utilities, which gained 4,579 jobs over the year (28.7 percent).
Ector, TX, experienced the largest over-the-year percentage decrease in employment, with a loss
of 4.2 percent. Within Ector, natural resources and mining had the largest employment decrease
with a loss of 2,297 jobs (-15.2 percent).
Large County Average Weekly Wage in Fourth Quarter 2019
Santa Cruz, CA, had the largest over-the-year percentage increase in average weekly wages (20.7
percent). Within Santa Cruz, an average weekly wage gain of $1,679 (109.0 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 7.1 percent. Within Linn, manufacturing had the largest impact, with an average weekly wage
decrease of $646 (-27.7 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 December 2019, Maricopa, AZ, had the largest over-the-year employment
percentage gain among the 10 largest counties (3.5 percent). Within Maricopa, education and
health services had the largest employment increase with a gain of 14,967 jobs (4.6 percent).
(See table 2.)
In fourth quarter 2019, King, WA, experienced the largest over-the-year percentage gain in
average weekly wages among the 10 largest counties (7.8 percent). Within King, information had
the largest impact, with an average weekly wage increase of $336 (9.4 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. December 2019 employment and
fourth 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 fourth quarter 2019 is scheduled
to be released on Wednesday, June 3, 2020, at 10:00 a.m. (EDT).
The County Employment and Wages news release for first quarter 2020 is scheduled to be
released on Wednesday, August 19, 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 8.2 |
| 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,
fourth quarter 2019
Employment Average weekly wage(2)
Establishments,
County(1) fourth quarter Percent Ranking Percent Ranking
2019 December change, by Fourth change, by
(thousands) 2019 December percent quarter fourth percent
(thousands) 2018-19(3) change 2019 quarter change
2018-19(3)
United States(4)......... 10,384.1 149,857.1 1.2 - $1,185 3.5 -
Jefferson, AL............ 19.4 358.0 0.7 204 1,142 0.8 336
Madison, AL.............. 10.1 210.6 3.0 20 1,238 5.0 45
Mobile, AL............... 10.4 174.3 0.0 286 1,001 1.9 293
Montgomery, AL........... 6.5 131.0 -0.6 322 996 3.4 161
Shelby, AL............... 6.0 85.6 -0.3 303 1,099 1.4 322
Tuscaloosa, AL........... 4.7 98.4 1.7 94 939 2.4 255
Anchorage, AK............ 8.3 147.0 -0.5 314 1,199 3.3 172
Maricopa, AZ............. 108.0 2,132.1 3.5 9 1,108 4.0 109
Pima, AZ................. 19.3 382.6 0.4 241 971 4.9 49
Benton, AR............... 6.9 125.5 3.0 20 1,113 4.0 109
Pulaski, AR.............. 14.7 253.3 -0.5 314 1,010 3.0 203
Washington, AR........... 6.4 111.6 1.8 86 1,009 3.0 203
Alameda, CA.............. 66.7 798.9 0.1 273 1,577 3.5 149
Butte, CA................ 8.6 82.8 0.0 286 907 4.6 60
Contra Costa, CA......... 34.3 371.6 0.1 273 1,415 1.7 304
Fresno, CA............... 38.3 401.8 2.2 59 942 4.4 71
Kern, CA................. 21.6 331.4 1.1 160 967 4.8 53
Los Angeles, CA.......... 518.6 4,589.5 1.2 144 1,437 4.1 98
Marin, CA................ 12.8 118.3 1.6 104 1,499 2.3 261
Merced, CA............... 6.9 79.0 0.1 273 888 4.1 98
Monterey, CA............. 14.4 181.3 0.4 241 1,001 4.1 98
Napa, CA................. 6.0 76.8 0.6 220 1,188 2.6 234
Orange, CA............... 129.4 1,664.7 0.8 199 1,297 4.6 60
Placer, CA............... 14.0 174.1 2.0 73 1,182 5.5 27
Riverside, CA............ 70.1 779.7 3.0 20 918 4.2 91
Sacramento, CA........... 62.5 686.8 1.5 114 1,272 4.0 109
San Bernardino, CA....... 63.9 797.7 2.7 39 973 4.4 71
San Diego, CA............ 117.7 1,512.7 1.5 114 1,311 4.1 98
San Francisco, CA........ 62.4 776.3 3.0 20 2,523 2.3 261
San Joaquin, CA.......... 18.7 262.0 2.8 34 995 3.4 161
San Luis Obispo, CA...... 10.7 118.2 0.9 184 1,051 8.1 7
San Mateo, CA............ 29.4 423.5 2.7 39 2,622 8.2 6
Santa Barbara, CA........ 15.9 211.5 1.9 81 1,120 4.1 98
Santa Clara, CA.......... 76.1 1,138.5 1.9 81 2,825 5.6 21
Santa Cruz, CA........... 9.8 102.7 1.7 94 1,241 20.7 1
Solano, CA............... 12.0 143.7 -0.4 305 1,200 4.0 109
Sonoma, CA............... 20.6 212.5 0.2 266 1,201 6.1 16
Stanislaus, CA........... 16.5 190.9 1.1 160 991 5.1 41
Tulare, CA............... 11.6 163.1 -0.4 305 850 4.9 49
Ventura, CA.............. 28.4 334.5 1.2 144 1,165 6.0 18
Yolo, CA................. 7.2 106.1 1.0 175 1,235 2.1 279
Adams, CO................ 11.7 233.0 5.1 2 1,121 2.6 234
Arapahoe, CO............. 23.0 338.2 1.5 114 1,354 4.3 79
Boulder, CO.............. 16.2 191.8 3.0 20 1,418 4.5 66
Denver, CO............... 35.2 535.3 2.3 53 1,458 3.0 203
Douglas, CO.............. 12.9 133.5 2.8 34 1,511 17.2 3
El Paso, CO.............. 21.0 286.8 2.2 59 1,043 2.8 222
Jefferson, CO............ 21.2 245.0 1.5 114 1,221 0.7 339
Larimer, CO.............. 12.9 166.8 2.0 73 1,094 3.0 203
Weld, CO................. 8.0 114.4 2.3 53 1,045 3.2 181
Fairfield, CT............ 37.0 421.6 -1.0 338 1,756 2.7 226
Hartford, CT............. 29.4 517.8 0.0 286 1,385 4.2 91
New Haven, CT............ 25.3 374.3 -0.1 295 1,191 5.6 21
New London, CT........... 7.8 122.3 -1.4 346 1,101 2.9 213
New Castle, DE........... 21.4 298.5 0.9 184 1,252 2.1 279
Sussex, DE............... 7.6 81.5 1.9 81 874 4.8 53
Washington, DC........... 41.6 782.4 0.8 199 1,992 2.5 246
Alachua, FL.............. 7.6 135.9 1.1 160 977 2.2 272
Bay, FL.................. 5.9 74.3 1.2 144 879 3.9 117
Brevard, FL.............. 16.7 225.6 1.3 138 1,054 5.3 31
Broward, FL.............. 72.7 844.2 1.7 94 1,098 3.2 181
Collier, FL.............. 15.4 158.7 1.9 81 1,027 2.3 261
Duval, FL................ 31.4 537.4 1.6 104 1,092 1.8 296
Escambia, FL............. 8.5 140.2 2.1 66 917 2.7 226
Hillsborough, FL......... 46.2 731.8 3.4 11 1,120 3.6 140
Lake, FL................. 8.9 104.2 2.0 73 793 2.1 279
Lee, FL.................. 23.9 277.7 2.6 43 935 3.9 117
Leon, FL................. 9.2 155.1 0.5 231 941 3.4 161
Manatee, FL.............. 11.8 135.3 2.3 53 890 4.1 98
Marion, FL............... 8.9 108.0 1.7 94 810 5.6 21
Miami-Dade, FL........... 103.9 1,188.8 1.6 104 1,137 2.6 234
Okaloosa, FL............. 6.8 85.4 1.6 104 956 2.1 279
Orange, FL............... 45.9 884.5 2.0 73 1,047 4.2 91
Osceola, FL.............. 7.8 101.6 2.8 34 788 4.1 98
Palm Beach, FL........... 59.6 631.6 1.5 114 1,150 2.3 261
Pasco, FL................ 11.7 124.8 0.9 184 831 4.9 49
Pinellas, FL............. 35.1 446.6 1.2 144 1,070 4.4 71
Polk, FL................. 14.5 235.0 2.6 43 867 2.0 289
St. Johns, FL............ 8.1 81.8 3.2 15 904 1.3 324
St. Lucie, FL............ 7.1 82.5 3.1 17 846 4.6 60
Sarasota, FL............. 16.9 175.4 0.9 184 971 -0.8 348
Seminole, FL............. 15.9 205.1 2.0 73 1,004 2.9 213
Volusia, FL.............. 15.4 175.3 -0.4 305 855 6.1 16
Bibb, GA................. 4.3 83.6 0.3 251 889 2.2 272
Chatham, GA.............. 8.4 159.5 1.0 175 977 5.2 33
Clayton, GA.............. 4.2 125.1 0.7 204 1,093 4.1 98
Cobb, GA................. 22.6 379.8 2.0 73 1,195 3.7 133
DeKalb, GA............... 18.4 306.9 1.5 114 1,167 3.5 149
Forsyth, GA.............. 6.1 78.4 0.9 184 1,046 4.4 71
Fulton, GA............... 45.5 914.1 1.5 114 1,517 3.1 196
Gwinnett, GA............. 26.3 367.6 2.2 59 1,088 1.6 310
Hall, GA................. 4.7 91.3 1.5 114 1,031 3.9 117
Muscogee, GA............. 4.6 95.6 0.5 231 857 1.8 296
Richmond, GA............. 4.6 105.9 0.6 220 936 3.2 181
Honolulu, HI............. 27.3 473.4 -1.1 340 1,108 3.7 133
Maui + Kalawao, HI....... 6.7 81.5 -0.7 327 930 4.3 79
Ada, ID.................. 16.9 256.3 3.2 15 1,106 1.5 315
Champaign, IL............ 4.1 92.9 1.4 132 980 3.4 161
Cook, IL................. 140.1 2,636.5 0.3 251 1,369 2.5 246
DuPage, IL............... 34.9 617.2 -0.9 335 1,301 1.5 315
Kane, IL................. 12.7 211.9 -1.1 340 1,033 2.3 261
Lake, IL................. 20.5 339.7 -0.7 327 1,458 0.8 336
McHenry, IL.............. 8.0 96.7 -0.8 331 927 2.2 272
McLean, IL............... 3.4 82.3 0.0 286 991 2.6 234
Madison, IL.............. 5.4 103.1 0.7 204 917 1.3 324
Peoria, IL............... 4.2 103.6 -2.4 352 1,157 4.9 49
St. Clair, IL............ 5.0 93.6 -0.9 335 907 4.3 79
Sangamon, IL............. 4.8 132.0 2.0 73 1,128 6.0 18
Will, IL................. 15.2 254.0 2.1 66 971 0.9 333
Winnebago, IL............ 5.9 126.3 -1.2 343 967 -0.4 345
Allen, IN................ 9.1 194.0 0.7 204 956 4.3 79
Elkhart, IN.............. 4.8 132.3 -3.2 354 964 3.0 203
Hamilton, IN............. 9.9 145.1 2.1 66 1,095 4.3 79
Lake, IN................. 10.4 190.4 0.3 251 978 -0.7 347
Marion, IN............... 25.2 613.3 1.1 160 1,150 3.4 161
St. Joseph, IN........... 5.8 125.8 0.2 266 929 3.2 181
Tippecanoe, IN........... 3.6 86.6 -0.2 298 988 -5.8 354
Vanderburgh, IN.......... 4.8 109.6 -0.6 322 916 3.7 133
Johnson, IA.............. 4.4 84.0 0.4 241 1,026 2.1 279
Linn, IA................. 7.1 132.6 0.5 231 1,079 -7.1 355
Polk, IA................. 18.3 305.3 1.0 175 1,167 3.0 203
Scott, IA................ 5.8 90.7 -1.1 340 961 3.1 196
Johnson, KS.............. 24.0 361.8 2.3 53 1,163 3.1 196
Sedgwick, KS............. 12.7 261.4 1.8 86 968 2.4 255
Shawnee, KS.............. 5.1 96.6 -0.1 295 917 2.8 222
Wyandotte, KS............ 3.5 92.4 -0.3 303 1,136 4.7 58
Boone, KY................ 4.5 100.1 3.0 20 960 3.3 172
Fayette, KY.............. 11.2 199.2 2.2 59 1,023 3.6 140
Jefferson, KY............ 25.4 480.4 1.2 144 1,152 5.4 29
Caddo, LA................ 7.4 111.3 -1.5 348 941 3.5 149
Calcasieu, LA............ 5.5 100.4 -2.9 353 1,065 2.2 272
East Baton Rouge, LA..... 16.5 265.4 -2.1 350 1,087 1.2 328
Jefferson, LA............ 14.4 192.1 0.4 241 1,047 4.1 98
Lafayette, LA............ 10.2 132.0 -0.4 305 978 0.9 333
Orleans, LA.............. 13.7 202.7 2.3 53 1,084 3.2 181
St. Tammany, LA.......... 8.9 91.3 1.2 144 964 2.1 279
Cumberland, ME........... 14.0 188.8 1.1 160 1,088 5.4 29
Anne Arundel, MD......... 15.4 278.2 0.7 204 1,219 3.5 149
Baltimore, MD............ 21.4 386.7 0.6 220 1,178 2.3 261
Frederick, MD............ 6.5 105.7 0.6 220 1,058 3.7 133
Harford, MD.............. 6.0 97.1 -0.5 314 1,074 4.3 79
Howard, MD............... 10.1 175.5 1.8 86 1,429 4.2 91
Montgomery, MD........... 33.0 477.4 0.3 251 1,532 2.4 255
Prince George's, MD...... 16.6 325.1 0.1 273 1,207 4.4 71
Baltimore City, MD....... 13.9 347.7 1.1 160 1,430 5.2 33
Barnstable, MA........... 9.7 91.5 -0.4 305 1,041 3.8 126
Bristol, MA.............. 17.8 231.1 -0.4 305 1,064 4.2 91
Essex, MA................ 27.2 328.6 0.1 273 1,225 3.4 161
Hampden, MA.............. 18.7 215.1 0.0 286 1,002 2.3 261
Middlesex, MA............ 56.8 953.7 1.5 114 1,724 4.3 79
Norfolk, MA.............. 25.6 357.8 0.1 273 1,402 -0.6 346
Plymouth, MA............. 16.5 197.6 0.3 251 1,094 2.3 261
Suffolk, MA.............. 32.0 708.1 2.5 48 2,146 3.9 117
Worcester, MA............ 26.5 357.7 0.7 204 1,141 4.5 66
Genesee, MI.............. 7.3 139.0 0.9 184 987 7.5 10
Ingham, MI............... 6.6 155.6 0.9 184 1,126 4.6 60
Kalamazoo, MI............ 5.5 121.5 -0.6 322 1,052 0.8 336
Kent, MI................. 16.2 414.0 0.4 241 1,026 3.7 133
Macomb, MI............... 19.2 334.1 0.7 204 1,147 3.2 181
Oakland, MI.............. 43.2 753.0 -0.1 295 1,311 3.6 140
Ottawa, MI............... 6.3 128.6 1.1 160 1,002 1.7 304
Saginaw, MI.............. 4.1 84.9 -0.4 305 943 2.4 255
Washtenaw, MI............ 9.2 224.0 1.4 132 1,204 2.6 234
Wayne, MI................ 35.4 746.1 0.7 204 1,264 4.1 98
Anoka, MN................ 7.9 128.3 0.3 251 1,092 4.2 91
Dakota, MN............... 10.8 192.1 -0.2 298 1,180 1.4 322
Hennepin, MN............. 41.5 943.5 0.2 266 1,425 4.0 109
Olmsted, MN.............. 3.8 101.8 1.6 104 1,319 4.4 71
Ramsey, MN............... 14.3 335.8 0.1 273 1,255 2.5 246
St. Louis, MN............ 5.4 97.8 -0.8 331 983 2.7 226
Stearns, MN.............. 4.4 87.7 0.3 251 949 1.8 296
Washington, MN........... 6.1 89.3 1.8 86 987 2.5 246
Harrison, MS............. 4.6 86.5 0.4 241 789 2.3 261
Hinds, MS................ 5.7 120.1 -0.4 305 937 3.0 203
Boone, MO................ 5.0 95.9 1.3 138 969 8.5 5
Clay, MO................. 5.9 105.7 0.6 220 1,041 8.7 4
Greene, MO............... 9.4 172.4 1.0 175 916 7.0 12
Jackson, MO.............. 22.8 377.9 0.5 231 1,184 2.8 222
St. Charles, MO.......... 10.0 156.9 4.0 5 929 5.6 21
St. Louis, MO............ 41.0 618.2 0.7 204 1,225 -0.3 342
St. Louis City, MO....... 15.3 230.5 0.2 266 1,222 3.2 181
Yellowstone, MT.......... 6.7 82.8 0.5 231 1,005 1.5 315
Douglas, NE.............. 18.8 346.3 0.8 199 1,091 5.2 33
Lancaster, NE............ 10.0 174.1 0.5 231 948 5.0 45
Clark, NV................ 57.2 1,045.3 2.9 28 1,005 1.6 310
Washoe, NV............... 15.3 230.8 2.2 59 1,060 3.2 181
Hillsborough, NH......... 12.4 208.7 0.6 220 1,293 3.6 140
Merrimack, NH............ 5.3 78.8 0.7 204 1,100 3.5 149
Rockingham, NH........... 11.2 153.1 1.1 160 1,171 1.6 310
Atlantic, NJ............. 6.7 128.1 -0.2 298 970 3.5 149
Bergen, NJ............... 33.8 457.0 0.7 204 1,352 2.4 255
Burlington, NJ........... 11.3 204.5 0.5 231 1,176 2.7 226
Camden, NJ............... 12.5 208.9 0.4 241 1,155 3.2 181
Essex, NJ................ 21.3 353.0 1.0 175 1,403 2.3 261
Gloucester, NJ........... 6.5 118.2 1.5 114 958 2.0 289
Hudson, NJ............... 15.9 276.8 1.1 160 1,482 2.7 226
Mercer, NJ............... 11.5 266.1 1.2 144 1,450 -0.8 348
Middlesex, NJ............ 22.9 440.4 0.1 273 1,312 1.3 324
Monmouth, NJ............. 20.6 267.1 1.2 144 1,153 1.9 293
Morris, NJ............... 17.3 300.1 1.2 144 1,691 4.3 79
Ocean, NJ................ 13.9 171.0 2.1 66 944 3.7 133
Passaic, NJ.............. 13.0 169.9 1.3 138 1,091 1.2 328
Somerset, NJ............. 10.4 191.6 -0.6 322 1,628 0.1 341
Union, NJ................ 14.9 232.9 0.1 273 1,462 7.9 8
Bernalillo, NM........... 20.5 338.4 1.3 138 984 3.9 117
Albany, NY............... 10.4 235.5 -1.0 338 1,203 3.1 196
Bronx, NY................ 19.4 330.3 1.0 175 1,158 4.1 98
Broome, NY............... 4.4 86.4 -1.6 349 923 5.0 45
Dutchess, NY............. 8.5 115.1 -0.9 335 1,101 4.8 53
Erie, NY................. 24.6 477.5 -0.4 305 1,041 3.6 140
Kings, NY................ 66.0 819.6 0.7 204 1,023 4.3 79
Monroe, NY............... 19.0 394.7 0.0 286 1,047 3.1 196
Nassau, NY............... 54.6 645.2 0.0 286 1,291 2.7 226
New York, NY............. 129.9 2,579.8 0.9 184 2,502 4.3 79
Oneida, NY............... 5.3 106.6 0.0 286 899 3.5 149
Onondaga, NY............. 12.8 251.1 0.3 251 1,072 1.8 296
Orange, NY............... 10.8 150.5 0.4 241 996 3.3 172
Queens, NY............... 54.5 728.1 1.4 132 1,159 2.1 279
Richmond, NY............. 10.2 132.3 3.5 9 1,094 1.0 332
Rockland, NY............. 11.2 132.4 2.2 59 1,064 1.6 310
Saratoga, NY............. 6.0 90.1 0.3 251 1,049 2.9 213
Suffolk, NY.............. 53.9 669.2 -0.5 314 1,279 3.0 203
Westchester, NY.......... 36.4 438.6 -0.5 314 1,526 4.4 71
Buncombe, NC............. 10.0 136.4 1.4 132 917 2.5 246
Cabarrus, NC............. 4.9 78.6 1.5 114 856 3.5 149
Catawba, NC.............. 4.5 89.2 -0.7 327 885 2.7 226
Cumberland, NC........... 6.3 122.3 0.7 204 869 -0.9 350
Durham, NC............... 8.7 213.4 3.0 20 1,388 2.2 272
Forsyth, NC.............. 9.5 193.6 1.9 81 1,045 3.5 149
Guilford, NC............. 14.8 288.4 1.0 175 992 4.0 109
Mecklenburg, NC.......... 39.6 727.5 2.9 28 1,316 3.7 133
New Hanover, NC.......... 8.7 119.3 2.9 28 936 2.9 213
Pitt, NC................. 3.8 78.2 -0.2 298 914 1.7 304
Wake, NC................. 36.6 582.9 2.7 39 1,213 -3.3 352
Cass, ND................. 7.4 121.6 1.3 138 1,059 3.3 172
Butler, OH............... 8.1 160.0 0.6 220 981 2.6 234
Cuyahoga, OH............. 36.4 736.9 0.2 266 1,197 4.5 66
Delaware, OH............. 5.7 90.2 0.6 220 1,108 4.0 109
Franklin, OH............. 34.1 780.2 1.5 114 1,123 3.2 181
Greene, OH............... 3.8 77.8 0.6 220 1,159 4.5 66
Hamilton, OH............. 24.5 524.7 0.5 231 1,240 1.6 310
Lake, OH................. 6.3 97.2 0.6 220 940 2.0 289
Lorain, OH............... 6.3 98.5 0.3 251 889 2.9 213
Lucas, OH................ 10.1 210.8 0.4 241 1,006 6.9 13
Mahoning, OH............. 5.9 98.2 -0.6 322 817 2.6 234
Montgomery, OH........... 12.1 258.2 0.2 266 989 3.3 172
Stark, OH................ 8.7 159.6 -0.5 314 874 2.5 246
Summit, OH............... 14.6 270.1 0.2 266 1,009 1.2 328
Warren, OH............... 5.3 98.1 3.3 13 1,038 2.2 272
Cleveland, OK............ 6.1 88.9 5.8 1 825 2.6 234
Oklahoma, OK............. 28.7 469.9 1.1 160 1,066 1.8 296
Tulsa, OK................ 22.9 368.9 0.7 204 1,014 1.3 324
Clackamas, OR............ 15.8 171.6 2.5 48 1,109 3.4 161
Deschutes, OR............ 9.4 86.4 3.0 20 967 5.6 21
Jackson, OR.............. 7.9 91.7 1.6 104 897 6.2 15
Lane, OR................. 12.9 159.5 0.7 204 918 3.8 126
Marion, OR............... 11.6 160.1 2.6 43 983 4.6 60
Multnomah, OR............ 37.0 528.7 1.7 94 1,251 3.6 140
Washington, OR........... 20.6 306.2 1.0 175 1,407 7.3 11
Allegheny, PA............ 36.0 708.1 0.3 251 1,255 4.2 91
Berks, PA................ 9.0 178.0 0.9 184 1,012 2.5 246
Bucks, PA................ 20.5 270.9 1.0 175 1,075 2.3 261
Butler, PA............... 5.1 87.6 0.1 273 1,063 2.8 222
Chester, PA.............. 16.0 255.8 0.8 199 1,420 2.9 213
Cumberland, PA........... 6.6 139.1 1.5 114 1,036 3.4 161
Dauphin, PA.............. 7.5 188.0 1.2 144 1,130 3.2 181
Delaware, PA............. 14.3 232.1 1.7 94 1,186 2.7 226
Erie, PA................. 6.9 121.6 -0.8 331 861 2.5 246
Lackawanna, PA........... 5.7 98.4 -0.7 327 866 1.5 315
Lancaster, PA............ 13.9 249.0 1.2 144 954 3.0 203
Lehigh, PA............... 8.9 198.8 1.5 114 1,148 5.1 41
Luzerne, PA.............. 7.5 147.8 1.1 160 896 1.9 293
Montgomery, PA........... 28.2 513.0 1.1 160 1,387 2.1 279
Northampton, PA.......... 6.9 121.0 1.4 132 964 1.8 296
Philadelphia, PA......... 35.4 712.6 1.8 86 1,400 6.4 14
Washington, PA........... 5.6 87.9 -1.4 346 1,135 3.1 196
Westmoreland, PA......... 9.3 134.4 0.3 251 931 1.5 315
York, PA................. 9.3 182.9 0.5 231 1,002 1.7 304
Kent, RI................. 5.6 77.0 -1.3 345 1,006 3.9 117
Providence, RI........... 19.0 294.3 0.9 184 1,129 1.8 296
Charleston, SC........... 17.2 260.6 1.1 160 1,056 5.2 33
Greenville, SC........... 15.5 282.0 0.9 184 999 3.8 126
Horry, SC................ 9.8 129.1 1.6 104 721 4.8 53
Lexington, SC............ 7.1 126.9 4.1 3 876 3.8 126
Richland, SC............. 10.8 225.9 0.9 184 969 4.6 60
Spartanburg, SC.......... 6.7 148.5 1.5 114 922 1.7 304
York, SC................. 6.5 102.7 4.1 3 939 3.4 161
Minnehaha, SD............ 7.8 129.7 0.9 184 1,018 3.9 117
Davidson, TN............. 25.0 522.1 3.3 13 1,249 1.5 315
Hamilton, TN............. 10.4 211.7 1.2 144 1,050 2.1 279
Knox, TN................. 13.2 244.6 0.9 184 1,025 3.2 181
Rutherford, TN........... 6.2 137.4 1.2 144 1,000 2.6 234
Shelby, TN............... 21.4 510.1 0.6 220 1,156 -0.3 342
Williamson, TN........... 9.8 144.0 3.4 11 1,368 -5.0 353
Bell, TX................. 5.8 123.0 1.4 132 992 4.0 109
Bexar, TX................ 43.6 891.1 1.6 104 1,055 3.2 181
Brazoria, TX............. 6.2 117.9 1.8 86 1,121 -1.2 351
Brazos, TX............... 4.8 111.3 2.9 28 848 5.0 45
Cameron, TX.............. 6.6 143.7 1.3 138 701 2.9 213
Collin, TX............... 28.1 443.0 2.9 28 1,336 3.4 161
Dallas, TX............... 80.1 1,786.4 2.8 34 1,398 3.6 140
Denton, TX............... 16.6 267.1 2.7 39 1,025 3.6 140
Ector, TX................ 4.2 80.2 -4.2 355 1,272 -0.3 342
El Paso, TX.............. 15.6 317.3 1.1 160 798 3.0 203
Fort Bend, TX............ 14.6 202.0 2.6 43 1,039 1.8 296
Galveston, TX............ 6.4 112.3 2.5 48 1,026 4.4 71
Harris, TX............... 118.6 2,375.0 1.2 144 1,426 2.5 246
Hidalgo, TX.............. 12.7 273.0 2.8 34 705 3.5 149
Jefferson, TX............ 5.9 124.2 1.2 144 1,150 2.1 279
Lubbock, TX.............. 7.9 144.1 1.8 86 916 3.3 172
McLennan, TX............. 5.5 115.4 1.1 160 953 5.2 33
Midland, TX.............. 6.1 107.1 -2.3 351 1,529 0.9 333
Montgomery, TX........... 12.4 194.9 1.7 94 1,127 1.1 331
Nueces, TX............... 8.3 165.0 0.7 204 982 2.2 272
Potter, TX............... 4.0 78.2 1.6 104 972 3.2 181
Smith, TX................ 6.5 104.9 0.3 251 962 2.9 213
Tarrant, TX.............. 45.9 944.2 2.0 73 1,141 2.9 213
Travis, TX............... 44.3 795.4 3.6 8 1,412 3.9 117
Webb, TX................. 5.6 105.6 1.2 144 750 2.0 289
Williamson, TX........... 12.0 186.3 3.8 6 1,300 19.5 2
Davis, UT................ 9.2 134.1 2.3 53 973 4.8 53
Salt Lake, UT............ 50.0 734.7 2.4 51 1,155 5.3 31
Utah, UT................. 18.6 256.8 2.9 28 993 5.8 20
Weber, UT................ 6.5 111.0 2.6 43 867 3.2 181
Chittenden, VT........... 7.2 103.4 -0.2 298 1,138 3.8 126
Arlington, VA............ 9.2 184.8 2.4 51 1,963 4.7 58
Chesterfield, VA......... 9.5 141.2 1.8 86 970 3.3 172
Fairfax, VA.............. 37.2 631.3 2.1 66 1,735 3.3 172
Henrico, VA.............. 12.0 193.7 0.1 273 1,102 2.4 255
Loudoun, VA.............. 12.9 177.2 3.1 17 1,360 0.2 340
Prince William, VA....... 9.7 133.9 0.9 184 1,028 3.5 149
Alexandria City, VA...... 6.3 91.1 -0.5 314 1,645 1.7 304
Chesapeake City, VA...... 6.3 104.1 1.7 94 903 4.3 79
Newport News City, VA.... 4.0 104.9 0.8 199 1,105 3.4 161
Norfolk City, VA......... 6.2 142.4 0.1 273 1,173 3.5 149
Richmond City, VA........ 8.1 159.6 1.5 114 1,268 5.6 21
Virginia Beach City, VA.. 12.5 179.4 0.4 241 911 3.8 126
Benton, WA............... 6.1 91.7 3.8 6 1,123 3.1 196
Clark, WA................ 15.7 166.1 1.5 114 1,128 5.2 33
King, WA................. 91.0 1,459.8 3.1 17 1,818 7.8 9
Kitsap, WA............... 7.0 93.3 2.2 59 1,094 3.6 140
Pierce, WA............... 23.6 321.3 2.1 66 1,073 4.3 79
Snohomish, WA............ 21.9 294.2 1.5 114 1,233 3.9 117
Spokane, WA.............. 16.9 230.8 1.7 94 1,003 5.1 41
Thurston, WA............. 8.8 119.2 1.7 94 1,078 5.5 27
Whatcom, WA.............. 7.5 91.9 0.3 251 984 4.5 66
Yakima, WA............... 8.2 108.7 1.6 104 851 5.2 33
Kanawha, WV.............. 5.6 96.9 -1.2 343 959 2.6 234
Brown, WI................ 7.4 160.4 0.1 273 1,060 2.6 234
Dane, WI................. 16.8 347.6 2.1 66 1,185 5.2 33
Milwaukee, WI............ 28.4 490.2 -0.5 314 1,119 3.3 172
Outagamie, WI............ 5.7 110.0 0.0 286 1,024 2.6 234
Racine, WI............... 4.8 75.4 -0.8 331 1,023 3.8 126
Waukesha, WI............. 14.1 247.9 0.5 231 1,195 5.1 41
Winnebago, WI............ 4.0 93.9 0.3 251 1,089 1.5 315
San Juan, PR............. 11.5 252.6 1.6 (5) 688 -0.3 (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 73.7 percent of
the total covered workers in the U.S.
Table 2. Covered establishments, employment, and wages in the 10 largest counties,
fourth quarter 2019
Employment Average weekly
wage(1)
Establishments,
fourth quarter
County by NAICS supersector 2019 Percent Percent
(thousands) December change, Fourth change,
2019 December quarter fourth
(thousands) 2018-19(2) 2019 quarter
2018-19(2)
United States(3) ............................ 10,384.1 149,857.1 1.2 $1,185 3.5
Private industry........................... 10,081.7 127,640.0 1.2 1,189 3.8
Natural resources and mining............. 140.7 1,804.9 -2.0 1,212 2.6
Construction............................. 841.3 7,427.5 1.8 1,367 3.7
Manufacturing............................ 357.5 12,755.4 -0.3 1,393 3.0
Trade, transportation, and utilities..... 1,951.4 28,526.8 0.9 968 3.4
Information.............................. 189.8 2,858.2 1.2 2,314 6.6
Financial activities..................... 926.6 8,402.6 1.6 1,906 4.4
Professional and business services....... 1,939.2 21,450.3 1.2 1,595 3.8
Education and health services............ 1,790.0 23,425.9 1.8 1,053 2.9
Leisure and hospitality.................. 890.2 16,249.3 1.3 523 4.0
Other services........................... 869.9 4,564.9 1.4 802 3.8
Government................................. 302.4 22,217.1 0.8 1,166 2.8
Los Angeles, CA.............................. 518.6 4,589.5 1.2 1,437 4.1
Private industry........................... 512.3 4,002.0 1.3 1,426 4.2
Natural resources and mining............. 0.5 5.8 -11.0 1,159 2.3
Construction............................. 17.2 150.0 1.1 1,475 5.6
Manufacturing............................ 12.7 338.7 -0.5 1,518 4.5
Trade, transportation, and utilities..... 59.6 875.4 0.2 1,075 3.1
Information.............................. 13.8 198.8 1.4 3,143 2.5
Financial activities..................... 30.9 226.9 0.9 2,210 6.3
Professional and business services....... 57.7 652.6 1.3 1,877 3.6
Education and health services............ 247.5 848.5 3.4 999 3.4
Leisure and hospitality.................. 40.4 550.4 1.5 1,208 5.9
Other services........................... 29.5 153.5 0.6 848 4.7
Government................................. 6.4 587.5 0.1 1,513 3.3
Cook, IL..................................... 140.1 2,636.5 0.3 1,369 2.5
Private industry........................... 138.8 2,341.8 0.3 1,379 2.8
Natural resources and mining............. 0.1 1.4 7.2 1,333 1.5
Construction............................. 11.2 75.7 2.1 1,754 3.3
Manufacturing............................ 5.7 183.8 -0.5 1,439 3.0
Trade, transportation, and utilities..... 28.6 490.6 0.1 1,082 2.4
Information.............................. 2.6 53.3 0.8 2,057 3.7
Financial activities..................... 14.2 207.8 1.5 2,562 4.0
Professional and business services....... 29.4 482.8 -0.8 1,812 3.8
Education and health services............ 15.6 457.6 0.7 1,104 -1.0
Leisure and hospitality.................. 14.0 289.8 1.5 589 3.3
Other services........................... 16.4 98.3 -2.2 1,040 4.5
Government................................. 1.3 294.7 0.2 1,290 0.6
New York, NY................................. 129.9 2,579.8 0.9 2,502 4.3
Private industry........................... 128.5 2,343.8 0.9 2,582 4.5
Natural resources and mining............. 0.0 0.2 10.9 2,130 0.7
Construction............................. 2.4 43.2 -4.9 2,443 4.3
Manufacturing............................ 1.8 21.5 -3.2 1,759 -0.1
Trade, transportation, and utilities..... 18.5 266.7 -1.1 1,558 4.0
Information.............................. 5.2 184.7 2.8 3,264 7.5
Financial activities..................... 19.4 391.5 1.4 4,909 2.7
Professional and business services....... 27.9 635.9 1.0 2,916 5.3
Education and health services............ 10.2 369.5 1.6 1,548 2.3
Leisure and hospitality.................. 14.8 315.7 0.1 1,172 5.4
Other services........................... 20.2 108.8 1.5 1,336 3.2
Government................................. 1.4 236.0 0.4 1,716 1.1
Harris, TX................................... 118.6 2,375.0 1.2 1,426 2.5
Private industry........................... 118.0 2,089.0 1.0 1,447 2.3
Natural resources and mining............. 1.6 65.5 -4.2 3,478 5.6
Construction............................. 7.8 168.7 1.6 1,588 3.9
Manufacturing............................ 4.9 180.2 0.4 1,718 1.8
Trade, transportation, and utilities..... 25.2 485.9 0.1 1,228 2.2
Information.............................. 1.3 26.6 2.4 1,594 3.4
Financial activities..................... 12.9 132.0 2.4 1,951 3.0
Professional and business services....... 24.0 413.9 1.6 1,865 0.5
Education and health services............ 16.8 304.3 1.1 1,157 3.8
Leisure and hospitality.................. 10.7 241.1 2.2 527 3.7
Other services........................... 11.9 69.1 0.9 923 4.4
Government................................. 0.6 286.0 2.3 1,276 4.2
Maricopa, AZ................................. 108.0 2,132.1 3.5 1,108 4.0
Private industry........................... 107.2 1,912.7 3.7 1,109 4.0
Natural resources and mining............. 0.5 8.1 2.0 1,037 1.0
Construction............................. 8.6 134.7 6.2 1,279 2.9
Manufacturing............................ 3.5 130.7 3.0 1,580 7.3
Trade, transportation, and utilities..... 21.0 416.0 2.9 980 3.0
Information.............................. 2.3 39.4 2.1 1,505 2.6
Financial activities..................... 14.1 197.1 4.9 1,479 7.1
Professional and business services....... 26.9 364.5 3.4 1,214 1.9
Education and health services............ 13.6 338.3 4.6 1,068 2.8
Leisure and hospitality.................. 9.3 228.6 2.4 542 4.6
Other services........................... 7.0 55.1 3.2 829 6.4
Government................................. 0.7 219.4 1.7 1,101 3.6
Dallas, TX................................... 80.1 1,786.4 2.8 1,398 3.6
Private industry........................... 79.6 1,607.6 2.9 1,407 3.5
Natural resources and mining............. 0.5 9.6 2.8 3,334 0.0
Construction............................. 4.9 93.4 2.5 1,509 4.8
Manufacturing............................ 2.9 119.3 4.0 1,525 1.1
Trade, transportation, and utilities..... 16.4 377.2 3.0 1,162 2.7
Information.............................. 1.5 45.3 -1.3 2,002 5.4
Financial activities..................... 10.1 169.7 2.8 1,947 6.0
Professional and business services....... 18.3 374.1 3.8 1,731 3.6
Education and health services............ 10.0 206.2 1.7 1,257 2.3
Leisure and hospitality.................. 7.3 167.9 3.2 596 4.2
Other services........................... 7.2 43.9 1.7 899 5.5
Government................................. 0.5 178.8 1.7 1,311 3.5
Orange, CA................................... 129.4 1,664.7 0.8 1,297 4.6
Private industry........................... 128.0 1,518.7 0.8 1,293 4.7
Natural resources and mining............. 0.2 2.2 0.8 1,020 5.6
Construction............................. 7.9 105.9 0.3 1,600 3.1
Manufacturing............................ 5.3 160.2 -0.6 1,621 3.8
Trade, transportation, and utilities..... 18.8 267.4 0.0 1,116 3.4
Information.............................. 1.6 26.1 -0.8 2,103 6.4
Financial activities..................... 13.4 118.8 1.8 2,253 9.4
Professional and business services....... 24.3 328.1 -0.3 1,519 5.4
Education and health services............ 38.6 233.4 3.9 1,043 2.7
Leisure and hospitality.................. 9.9 227.6 1.0 569 5.8
Other services........................... 7.8 48.9 2.3 796 4.6
Government................................. 1.4 146.0 0.0 1,335 3.6
San Diego, CA................................ 117.7 1,512.7 1.5 1,311 4.1
Private industry........................... 115.7 1,269.8 1.5 1,284 4.4
Natural resources and mining............. 0.7 9.6 8.2 813 -2.3
Construction............................. 8.1 84.3 1.1 1,411 5.8
Manufacturing............................ 3.5 116.9 2.6 1,738 3.1
Trade, transportation, and utilities..... 15.5 232.5 -0.3 955 4.7
Information.............................. 1.4 23.3 -1.6 2,166 8.6
Financial activities..................... 11.4 77.7 1.6 1,749 3.6
Professional and business services....... 21.5 258.5 2.7 1,983 4.6
Education and health services............ 35.5 215.3 3.4 1,069 3.2
Leisure and hospitality.................. 9.3 198.1 -0.5 568 3.1
Other services........................... 8.3 53.4 2.0 695 2.5
Government................................. 2.0 242.9 1.3 1,453 3.0
King, WA..................................... 91.0 1,459.8 3.1 1,818 7.8
Private industry........................... 90.3 1,286.1 3.4 1,859 8.1
Natural resources and mining............. 0.4 3.0 1.9 1,357 -3.3
Construction............................. 7.0 75.1 0.2 1,622 5.1
Manufacturing............................ 2.5 105.3 0.8 1,768 3.8
Trade, transportation, and utilities..... 13.6 289.2 4.7 1,920 8.9
Information.............................. 2.7 125.4 9.0 3,911 9.4
Financial activities..................... 6.9 72.0 2.8 2,084 9.9
Professional and business services....... 18.8 239.3 2.8 2,192 8.0
Education and health services............ 21.7 184.0 2.5 1,165 3.1
Leisure and hospitality.................. 7.5 144.2 0.7 672 4.2
Other services........................... 9.2 48.6 7.7 989 7.6
Government................................. 0.6 173.7 1.1 1,513 4.1
Miami-Dade, FL............................... 103.9 1,188.8 1.6 1,137 2.6
Private industry........................... 103.6 1,047.4 1.7 1,124 2.6
Natural resources and mining............. 0.5 9.4 3.1 696 2.7
Construction............................. 7.4 52.2 0.7 1,128 5.2
Manufacturing............................ 2.8 42.0 2.0 1,055 3.9
Trade, transportation, and utilities..... 24.6 303.4 1.5 1,000 3.3
Information.............................. 1.6 19.6 1.4 1,610 -2.3
Financial activities..................... 11.2 77.4 0.6 1,812 -0.4
Professional and business services....... 24.1 168.6 2.8 1,526 4.2
Education and health services............ 11.7 189.1 2.0 1,096 2.4
Leisure and hospitality.................. 7.7 144.6 0.9 670 0.3
Other services........................... 9.1 39.0 0.4 715 3.5
Government................................. 0.3 141.3 0.5 1,231 2.6
(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,
fourth quarter 2019
Employment Average weekly
wage(1)
Establishments,
fourth quarter
State 2019 Percent Percent
(thousands) December change, Fourth change,
2019 December quarter fourth
(thousands) 2018-19 2019 quarter
2018-19
United States(2)........... 10,384.1 149,857.1 1.2 $1,185 3.5
Alabama.................... 131.3 2,007.9 1.0 985 2.6
Alaska..................... 22.6 309.9 0.6 1,139 3.2
Arizona.................... 168.8 2,999.8 2.7 1,059 4.1
Arkansas................... 92.8 1,232.9 0.5 898 3.2
California................. 1,625.3 17,836.3 1.5 1,457 4.7
Colorado................... 211.7 2,772.6 2.2 1,227 4.0
Connecticut................ 123.7 1,687.4 -0.7 1,383 3.8
Delaware................... 34.6 455.3 0.8 1,136 2.6
District of Columbia....... 41.6 782.5 0.8 1,992 2.5
Florida.................... 735.6 9,085.5 2.0 1,044 3.6
Georgia.................... 296.0 4,576.1 1.7 1,090 3.6
Hawaii..................... 44.9 665.1 -0.8 1,053 3.5
Idaho...................... 65.3 756.9 3.1 918 3.1
Illinois................... 380.1 6,043.5 0.2 1,221 2.7
Indiana.................... 169.5 3,106.0 0.6 969 3.0
Iowa....................... 104.6 1,560.4 0.1 984 1.9
Kansas..................... 88.7 1,410.7 0.6 959 3.5
Kentucky................... 121.5 1,928.3 0.8 955 3.2
Louisiana.................. 136.4 1,927.7 -0.5 993 2.5
Maine...................... 54.2 620.2 0.7 955 5.3
Maryland................... 175.3 2,728.1 0.9 1,271 3.5
Massachusetts.............. 262.1 3,660.8 0.9 1,511 3.8
Michigan................... 266.4 4,385.3 0.4 1,115 3.4
Minnesota.................. 182.3 2,912.8 0.4 1,177 3.2
Mississippi................ 75.1 1,145.0 0.0 818 3.2
Missouri................... 212.1 2,846.2 0.9 1,010 3.0
Montana.................... 51.6 474.1 1.1 918 3.4
Nebraska................... 71.4 990.9 0.7 969 4.2
Nevada..................... 85.2 1,435.5 2.7 1,030 2.4
New Hampshire.............. 54.6 671.3 0.8 1,192 2.9
New Jersey................. 281.6 4,157.4 0.8 1,332 2.5
New Mexico................. 63.9 844.0 1.5 942 4.0
New York................... 650.3 9,691.0 0.8 1,499 3.7
North Carolina............. 290.1 4,546.9 1.9 1,036 2.4
North Dakota............... 32.1 424.6 0.5 1,085 2.6
Ohio....................... 302.7 5,477.2 0.5 1,037 3.1
Oklahoma................... 112.4 1,639.4 0.3 945 1.4
Oregon..................... 161.5 1,969.3 1.6 1,100 4.6
Pennsylvania............... 364.6 5,985.9 0.8 1,143 3.6
Rhode Island............... 39.0 489.8 0.6 1,099 1.1
South Carolina............. 140.8 2,144.8 1.2 931 4.0
South Dakota............... 34.5 430.7 0.6 916 3.5
Tennessee.................. 168.9 3,085.4 1.6 1,047 1.6
Texas...................... 718.5 12,793.0 2.0 1,187 3.4
Utah....................... 111.3 1,547.8 2.5 1,022 5.0
Vermont.................... 26.1 314.0 -0.4 987 3.5
Virginia................... 285.5 3,978.7 1.2 1,204 3.4
Washington................. 253.7 3,457.7 2.2 1,370 6.4
West Virginia.............. 51.2 690.3 -2.0 904 -1.4
Wisconsin.................. 183.2 2,898.0 0.2 1,022 3.3
Wyoming.................... 27.1 276.3 1.4 1,007 3.0
Puerto Rico................ 48.4 910.7 1.5 575 -0.2
Virgin Islands............. 3.3 39.2 10.8 1,065 13.5
(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
