For release 10:00 a.m. (EDT), Wednesday, August 21, 2019 USDL-19-1519
Technical Information: (202) 691-6567 * QCEWInfo@bls.gov * www.bls.gov/cew
Media Contact: (202) 691-5902 * PressOffice@bls.gov
COUNTY EMPLOYMENT AND WAGES – FIRST QUARTER 2019
From March 2018 to March 2019, employment increased in 298 of the 355 largest U.S.
counties, the U.S. Bureau of Labor Statistics reported today. In March 2019, national
employment (as measured by the QCEW program) increased to 146.5 million, a 1.4 percent
increase over the year. Midland, TX, had the largest over-the-year increase in employment with a
gain of 5.8 percent. Employment data in this release are presented for March 2019, and average
weekly wage data are presented for first quarter 2019.
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| |
| Notice Regarding South Carolina Employment and Wages Data |
| |
| South Carolina QCEW data for 2018 and first quarter 2019 show unusual movements, which |
| may be a result of a change in reporting. These unusual movements coincide with a |
| modernization of the South Carolina unemployment insurance system. For more information |
| please visit: www.bls.gov/cew/notices/2018/notice-regarding-south-carolina-employment-and- |
| wages-data.htm. |
| |
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Among the 355 largest counties, 325 had over-the-year increases in average weekly wages. In
the first quarter of 2019, average weekly wages for the nation increased to $1,184, a 2.8 percent
increase over the year. San Francisco, CA, had the largest first quarter over-the-year wage gain at
10.2 percent. (See table 1.)
Large County Employment in March 2019
Midland, TX, had the largest over-the-year percentage increase in employment (5.8 percent).
Within Midland, the largest employment increase occurred in natural resources and mining,
which gained 2,745 jobs over the year (9.6 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 2,449 jobs (-21.8 percent).
Large County Average Weekly Wage in First Quarter 2019
San Francisco, CA, had the largest over-the-year percentage increase in average weekly wages
(10.2 percent). Within San Francisco, an average weekly wage gain of $1,391 (77.5 percent) in
trade, transportation, and utilities made the largest contribution to the county’s increase in
average weekly wages.
Elkhart, IN, had the largest over-the-year percentage decrease in average weekly wages with a
loss of 7.6 percent. Within Elkhart, manufacturing had the largest impact, with an average
weekly wage decrease of $137 (-12.7 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 March 2019, Maricopa, AZ, had the largest over-the-year employment
percentage gain among the 10 largest counties (2.9 percent). Within Maricopa, professional and
business services had the largest employment increase with a gain of 11,317 jobs (3.4 percent).
(See table 2.)
In first quarter 2019, King, WA, experienced the largest over-the-year percentage gain in average
weekly wages among the 10 largest counties (5.4 percent). Within King, information had the
largest impact, with an average weekly wage increase of $337 (7.5 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. March 2019 employment and first
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’s news release schedule is available at www.bls.gov/cew/release-calendar.htm.
____________
The County Employment and Wages full data update for first quarter 2019 is scheduled to
be released on Wednesday, September 4, 2019, at 10:00 a.m. (EDT).
The County Employment and Wages news release for second quarter 2019 is scheduled to
be released on Wednesday, November 20, 2019, at 10:00 a.m. (EST).
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| |
| County Changes for the 2019 County Employment and Wages News Releases |
| |
| Counties with annual average employment of 75,000 or more in 2018 are included in this release |
| and will be included in future 2019 releases. Six counties have been added to the publication |
| tables: St. Johns, FL; St. Lucie, FL; Forsyth, GA; Greene, OH; Ector, TX; and Racine, WI. |
| |
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| |
| QCEW Data Now Available in Census Business Builder Version 2.6 |
| |
| The Quarterly Census of Employment and Wages data is now available in Census Business |
| Builder Version 2.6, a suite of U.S. Census Bureau web tools that assists business owners and |
| regional analysts in data-driven decision making. As the first collaboration of this type between |
| the Bureau of Labor Statistics and the U.S. Census Bureau, this data-sharing project makes data |
| more accessible for local users and enhances the efficiency of digital service delivery. The |
| Census Business Builder is available at: www.census.gov/data/data-tools/cbb.html. |
| |
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Technical Note
These data are the product of a federal-state cooperative program, the Quarterly Census of
Employment and Wages (QCEW) program, also known as the ES-202 program. The data are
derived from summaries of employment and total pay of workers covered by state and federal
unemployment insurance (UI) legislation and provided by State Workforce Agencies (SWAs). The
summaries are a result of the administration of state unemployment insurance programs that
require most employers to pay quarterly taxes based on the employment and wages of workers
covered by UI. QCEW data in this release are based on the 2017 North American Industry
Classification System (NAICS). Data for 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, Puerto Rico, are provided, but not used in calculating U.S.
averages, rankings, or in the analysis in the text. Each year, these large counties are selected on the
basis of the preliminary annual average of employment for the previous year. The 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- | 689,000 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 | | |
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Coverage
Employment and wage data for workers covered by state UI laws are compiled from quarterly
contribution reports submitted to the SWAs by employers. For federal civilian workers covered by
the Unemployment Compensation for Federal Employees (UCFE) program, employment and
wage data are compiled from quarterly reports submitted by four major federal payroll processing
centers on behalf of all federal agencies, with the exception of a few agencies which still report
directly to the individual SWA. In addition to the quarterly contribution reports, employers who
operate multiple 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 2017 edition
of this publication, which was published in September 2018, contains selected data produced by
Business Employment Dynamics (BED) on job gains and losses, as well as selected data from the
first quarter 2018 version of this news release. Tables and additional content from the 2017 edition
of Employment and Wages Annual Averages Online are now available at
www.bls.gov/cew/publications/employment-and-wages-annual-averages/2017/home.htm. The
2018 edition of Employment and Wages Annual Averages Online will be available in September
2019.
News releases on quarterly measures of gross job flows also are available from BED at
www.bls.gov/bdm, (202) 691-6467, or data.bls.gov/cgi-bin/forms/bdm.
Information in this release will be made available to sensory impaired individuals upon request.
Voice phone: (202) 691-5200; TDD message referral phone number: (800) 877-8339.
Table 1. Covered establishments, employment, and wages in the 356 largest counties,
first quarter 2019
Employment Average weekly wage(2)
Establishments,
County(1) first quarter Percent Ranking Percent Ranking
2019 March change, by First change, by
(thousands) 2019 March percent quarter first percent
(thousands) 2018-19(3) change 2019 quarter change
2018-19(3)
United States(4)......... 10,203.0 146,497.6 1.4 - $1,184 2.8 -
Jefferson, AL............ 19.2 352.1 1.4 138 1,150 0.9 298
Madison, AL.............. 9.9 203.1 2.4 48 1,206 4.8 40
Mobile, AL............... 10.3 171.2 1.0 184 916 1.6 258
Montgomery, AL........... 6.4 129.9 -0.2 308 882 1.0 292
Shelby, AL............... 5.9 84.8 0.1 285 1,147 4.2 60
Tuscaloosa, AL........... 4.6 96.0 2.3 60 900 2.5 176
Anchorage, AK............ 8.3 145.7 -0.4 325 1,159 3.5 92
Maricopa, AZ............. 103.8 2,042.9 2.9 30 1,118 3.1 130
Pima, AZ................. 19.1 376.9 1.7 110 945 2.5 176
Benton, AR............... 6.8 121.4 1.2 159 1,496 -0.2 331
Pulaski, AR.............. 14.6 251.7 1.0 184 998 2.4 191
Washington, AR........... 6.3 109.2 1.6 118 865 1.4 269
Alameda, CA.............. 65.4 788.4 0.9 201 1,551 2.8 153
Butte, CA................ 8.6 81.3 -2.2 353 849 6.5 8
Contra Costa, CA......... 33.5 367.3 -0.3 316 1,412 1.7 248
Fresno, CA............... 37.2 390.7 3.5 15 840 0.8 303
Kern, CA................. 20.5 313.7 3.3 22 932 2.0 227
Los Angeles, CA.......... 505.4 4,484.6 1.4 138 1,282 3.5 92
Marin, CA................ 12.6 115.0 0.6 232 1,475 4.6 46
Merced, CA............... 6.8 79.5 2.5 43 810 1.5 262
Monterey, CA............. 14.2 178.8 0.4 254 946 3.4 102
Napa, CA................. 5.9 78.0 1.4 138 1,078 2.4 191
Orange, CA............... 125.3 1,640.1 1.2 159 1,287 1.8 241
Placer, CA............... 13.6 171.2 2.8 34 1,101 1.9 234
Riverside, CA............ 67.9 752.3 2.4 48 927 4.2 60
Sacramento, CA........... 60.6 673.6 2.3 60 1,210 3.0 136
San Bernardino, CA....... 62.1 760.7 1.7 110 931 3.4 102
San Diego, CA............ 114.7 1,469.9 1.1 173 1,253 2.8 153
San Francisco, CA........ 61.6 753.1 3.9 9 2,759 10.2 1
San Joaquin, CA.......... 18.5 251.8 2.1 78 909 3.4 102
San Luis Obispo, CA...... 10.6 119.7 2.0 85 966 5.9 10
San Mateo, CA............ 28.8 408.3 2.3 60 2,645 1.0 292
Santa Barbara, CA........ 15.7 198.0 1.1 173 1,055 3.5 92
Santa Clara, CA.......... 74.4 1,110.2 2.4 48 2,758 3.3 110
Santa Cruz, CA........... 9.7 101.6 1.7 110 1,021 2.7 161
Solano, CA............... 11.9 141.4 0.5 241 1,252 5.3 23
Sonoma, CA............... 20.4 208.7 1.0 184 1,076 4.1 67
Stanislaus, CA........... 16.2 189.5 1.4 138 944 4.1 67
Tulare, CA............... 11.3 157.3 1.2 159 793 3.1 130
Ventura, CA.............. 28.0 329.4 1.1 173 1,157 5.3 23
Yolo, CA................. 6.9 104.2 1.6 118 1,171 -1.2 346
Adams, CO................ 11.4 218.7 4.0 6 1,079 3.3 110
Arapahoe, CO............. 22.3 328.1 0.9 201 1,442 5.5 16
Boulder, CO.............. 15.7 185.2 2.4 48 1,409 7.5 4
Denver, CO............... 33.7 518.5 1.4 138 1,533 4.9 36
Douglas, CO.............. 12.4 126.4 1.0 184 1,366 -0.4 334
El Paso, CO.............. 20.3 277.3 1.9 88 1,028 5.1 31
Jefferson, CO............ 20.4 238.4 1.0 184 1,229 6.7 7
Larimer, CO.............. 12.5 161.3 2.0 85 1,062 4.3 55
Weld, CO................. 7.6 112.1 3.6 13 1,076 3.9 72
Fairfield, CT............ 36.5 412.3 -0.4 325 2,070 4.3 55
Hartford, CT............. 29.0 506.4 -0.1 304 1,483 1.0 292
New Haven, CT............ 25.0 363.1 0.2 275 1,125 2.4 191
New London, CT........... 7.7 121.3 -0.6 333 1,192 2.2 212
New Castle, DE........... 20.6 289.8 1.2 159 1,368 -1.2 346
Sussex, DE............... 7.2 79.1 2.3 60 801 3.1 130
Washington, DC........... 40.4 773.5 0.5 241 1,921 0.2 319
Alachua, FL.............. 7.6 134.7 1.9 88 941 0.5 312
Bay, FL.................. 5.8 73.5 -5.9 355 822 9.3 2
Brevard, FL.............. 16.5 220.8 2.5 43 976 3.5 92
Broward, FL.............. 71.6 819.2 0.8 215 1,093 4.9 36
Collier, FL.............. 14.9 156.2 3.1 25 954 2.1 218
Duval, FL................ 30.3 519.8 1.7 110 1,125 2.6 169
Escambia, FL............. 8.4 138.1 2.4 48 906 3.3 110
Hillsborough, FL......... 45.0 710.3 2.2 69 1,130 3.0 136
Lake, FL................. 8.7 100.9 2.2 69 740 3.8 75
Lee, FL.................. 23.3 272.5 2.1 78 877 1.7 248
Leon, FL................. 8.9 154.2 1.8 94 875 1.7 248
Manatee, FL.............. 11.6 132.2 3.4 19 836 1.5 262
Marion, FL............... 8.7 105.3 2.1 78 739 2.5 176
Miami-Dade, FL........... 101.9 1,164.7 1.8 94 1,129 1.7 248
Okaloosa, FL............. 6.7 85.3 0.7 224 884 3.5 92
Orange, FL............... 44.5 868.9 2.4 48 1,006 2.4 191
Osceola, FL.............. 7.6 99.6 3.8 10 726 2.1 218
Palm Beach, FL........... 58.7 620.7 1.5 128 1,121 3.1 130
Pasco, FL................ 11.6 122.4 1.8 94 759 3.7 81
Pinellas, FL............. 34.5 440.1 0.9 201 962 2.1 218
Polk, FL................. 14.0 227.6 3.4 19 839 1.8 241
St. Johns, FL............ 7.8 79.0 2.9 30 908 0.4 315
St. Lucie, FL............ 6.8 80.3 3.0 28 777 0.3 318
Sarasota, FL............. 16.6 175.0 1.0 184 914 -0.9 342
Seminole, FL............. 15.6 199.9 2.4 48 975 3.0 136
Volusia, FL.............. 14.8 176.7 0.9 201 789 3.5 92
Bibb, GA................. 4.3 82.8 -0.6 333 874 2.6 169
Chatham, GA.............. 8.1 158.4 1.3 150 935 1.7 248
Clayton, GA.............. 4.0 121.4 0.9 201 1,388 4.8 40
Cobb, GA................. 21.8 365.3 1.7 110 1,249 2.5 176
DeKalb, GA............... 17.7 300.8 1.5 128 1,185 1.5 262
Forsyth, GA.............. 5.9 76.7 2.6 39 958 1.7 248
Fulton, GA............... 43.6 890.0 2.3 60 1,711 2.6 169
Gwinnett, GA............. 25.4 357.8 1.2 159 1,078 2.3 200
Hall, GA................. 4.6 89.3 2.4 48 889 1.4 269
Muscogee, GA............. 4.5 95.1 0.8 215 955 -0.4 334
Richmond, GA............. 4.4 105.1 -0.3 316 906 4.4 52
Honolulu, HI............. 27.8 471.8 -0.2 308 1,054 3.3 110
Maui + Kalawao, HI....... 6.5 79.3 -0.2 308 900 3.3 110
Ada, ID.................. 17.1 248.7 2.9 30 967 2.5 176
Champaign, IL............ 4.1 90.2 0.5 241 926 2.5 176
Cook, IL................. 138.4 2,568.4 0.1 285 1,468 3.4 102
DuPage, IL............... 34.6 612.3 -0.3 316 1,340 2.3 200
Kane, IL................. 12.6 209.2 -0.5 331 947 1.4 269
Lake, IL................. 20.2 332.1 0.2 275 1,729 1.9 234
McHenry, IL.............. 7.8 94.7 -1.2 346 846 1.7 248
McLean, IL............... 3.4 80.9 -2.0 352 1,107 -0.9 342
Madison, IL.............. 5.4 100.4 -0.6 333 876 4.7 44
Peoria, IL............... 4.2 103.8 -1.3 347 1,448 -2.4 352
St. Clair, IL............ 5.0 91.4 -0.8 342 833 2.0 227
Sangamon, IL............. 4.8 127.8 -0.8 342 1,057 -0.9 342
Will, IL................. 14.9 241.2 0.5 241 944 4.2 60
Winnebago, IL............ 5.9 124.6 -1.6 348 983 4.6 46
Allen, IN................ 9.0 188.7 1.6 118 941 1.1 288
Elkhart, IN.............. 4.8 135.0 -1.8 350 930 -7.6 355
Hamilton, IN............. 9.8 142.2 2.0 85 1,132 0.1 324
Lake, IN................. 10.5 186.6 0.3 263 946 2.7 161
Marion, IN............... 24.5 600.2 1.0 184 1,232 1.5 262
St. Joseph, IN........... 5.8 124.0 1.4 138 868 0.8 303
Tippecanoe, IN........... 3.5 86.8 2.2 69 960 0.4 315
Vanderburgh, IN.......... 4.8 109.7 0.9 201 962 8.2 3
Johnson, IA.............. 4.3 83.2 -0.8 342 995 1.9 234
Linn, IA................. 7.0 129.9 0.3 263 1,082 4.3 55
Polk, IA................. 17.9 296.5 0.4 254 1,179 1.6 258
Scott, IA................ 5.7 89.1 -0.7 339 899 2.5 176
Johnson, KS.............. 23.8 345.4 0.4 254 1,168 3.4 102
Sedgwick, KS............. 12.6 253.5 2.1 78 1,001 3.7 81
Shawnee, KS.............. 5.1 95.8 -0.3 316 910 0.8 303
Wyandotte, KS............ 3.5 89.8 1.4 138 1,042 1.8 241
Boone, KY................ 4.5 93.7 2.5 43 922 2.0 227
Fayette, KY.............. 11.2 191.5 0.0 299 952 2.8 153
Jefferson, KY............ 25.6 466.4 0.5 241 1,140 2.1 218
Caddo, LA................ 7.4 111.5 -0.3 316 859 2.3 200
Calcasieu, LA............ 5.5 102.5 -1.6 348 1,007 5.1 31
East Baton Rouge, LA..... 16.1 267.6 0.3 263 1,050 2.2 212
Jefferson, LA............ 14.2 188.2 -0.2 308 960 3.3 110
Lafayette, LA............ 10.0 130.4 1.0 184 914 2.8 153
Orleans, LA.............. 13.3 197.8 0.9 201 1,064 0.8 303
St. Tammany, LA.......... 8.7 89.3 2.2 69 921 2.8 153
Cumberland, ME........... 13.7 181.8 0.9 201 1,083 3.2 125
Anne Arundel, MD......... 15.4 270.5 0.3 263 1,194 3.3 110
Baltimore, MD............ 21.4 378.7 0.3 263 1,122 1.4 269
Frederick, MD............ 6.5 104.2 1.8 94 1,022 2.9 144
Harford, MD.............. 5.9 94.2 0.9 201 1,052 5.7 13
Howard, MD............... 10.1 171.0 0.5 241 1,389 3.3 110
Montgomery, MD........... 32.9 468.7 0.3 263 1,580 -0.4 334
Prince George's, MD...... 16.3 319.3 1.4 138 1,115 0.0 326
Baltimore City, MD....... 13.7 341.5 0.3 263 1,316 2.3 200
Barnstable, MA........... 9.6 86.8 0.1 285 969 4.5 50
Bristol, MA.............. 18.2 224.8 0.6 232 999 4.2 60
Essex, MA................ 27.2 321.5 0.2 275 1,229 3.5 92
Hampden, MA.............. 19.1 210.0 1.6 118 1,015 3.8 75
Middlesex, MA............ 56.8 926.1 1.6 118 1,886 5.4 18
Norfolk, MA.............. 25.7 348.6 -0.3 316 1,324 3.4 102
Plymouth, MA............. 16.5 191.6 0.7 224 1,058 5.4 18
Suffolk, MA.............. 31.7 686.4 2.4 48 2,270 0.2 319
Worcester, MA............ 26.7 348.5 0.7 224 1,121 3.0 136
Genesee, MI.............. 6.8 132.0 -0.7 339 898 0.9 298
Ingham, MI............... 6.0 151.3 0.0 299 1,044 0.8 303
Kalamazoo, MI............ 5.0 120.2 0.5 241 1,100 4.7 44
Kent, MI................. 14.8 407.4 0.8 215 975 2.2 212
Macomb, MI............... 17.6 324.7 -0.1 304 1,117 -1.3 348
Oakland, MI.............. 39.5 732.3 0.3 263 1,251 -2.0 351
Ottawa, MI............... 5.8 125.4 0.2 275 924 -1.0 345
Saginaw, MI.............. 3.8 82.5 -0.5 331 897 0.1 324
Washtenaw, MI............ 8.3 218.1 1.3 150 1,173 3.7 81
Wayne, MI................ 31.4 724.8 0.9 201 1,254 -0.6 340
Anoka, MN................ 7.8 125.0 1.1 173 998 1.3 279
Dakota, MN............... 10.6 186.4 -0.4 325 1,166 3.3 110
Hennepin, MN............. 41.6 924.6 0.8 215 1,539 2.9 144
Olmsted, MN.............. 3.8 99.0 0.2 275 1,210 -5.1 354
Ramsey, MN............... 14.3 330.4 0.4 254 1,361 1.0 292
St. Louis, MN............ 5.4 96.6 -0.3 316 914 4.6 46
Stearns, MN.............. 4.4 86.0 0.5 241 926 -0.3 332
Washington, MN........... 6.0 85.6 1.0 184 953 -0.1 328
Harrison, MS............. 4.7 85.5 0.1 285 752 0.0 326
Hinds, MS................ 5.8 120.1 -0.1 304 928 2.8 153
Boone, MO................ 4.9 94.2 0.2 275 855 2.8 153
Clay, MO................. 5.8 102.7 -0.4 325 960 1.2 283
Greene, MO............... 9.2 169.2 2.2 69 847 3.7 81
Jackson, MO.............. 22.2 370.5 0.7 224 1,113 2.4 191
St. Charles, MO.......... 9.7 149.0 1.4 138 994 3.5 92
St. Louis, MO............ 40.0 604.1 0.2 275 1,240 2.9 144
St. Louis City, MO....... 14.9 227.8 0.2 275 1,283 2.3 200
Yellowstone, MT.......... 6.5 80.5 0.4 254 951 4.3 55
Douglas, NE.............. 19.0 336.9 0.4 254 1,057 2.3 200
Lancaster, NE............ 10.2 169.7 -0.2 308 894 2.1 218
Clark, NV................ 56.8 1,014.9 3.1 25 977 0.7 309
Washoe, NV............... 15.1 222.2 1.8 94 986 3.0 136
Hillsborough, NH......... 12.2 204.7 1.5 128 1,269 2.3 200
Merrimack, NH............ 5.2 77.7 0.2 275 1,039 4.0 69
Rockingham, NH........... 11.1 147.9 0.8 215 1,142 4.2 60
Atlantic, NJ............. 6.6 126.0 4.9 2 908 -0.1 328
Bergen, NJ............... 33.3 440.6 0.8 215 1,333 1.5 262
Burlington, NJ........... 11.1 199.6 0.1 285 1,172 1.4 269
Camden, NJ............... 12.2 204.3 0.7 224 1,077 2.6 169
Essex, NJ................ 20.8 344.9 1.0 184 1,535 1.9 234
Gloucester, NJ........... 6.4 113.1 2.7 36 903 0.9 298
Hudson, NJ............... 15.4 269.8 1.9 88 1,745 -0.1 328
Mercer, NJ............... 11.3 256.6 1.1 173 1,636 0.8 303
Middlesex, NJ............ 22.6 428.4 1.4 138 1,340 1.7 248
Monmouth, NJ............. 20.5 258.1 1.3 150 1,137 1.2 283
Morris, NJ............... 17.3 290.1 0.3 263 1,892 5.1 31
Ocean, NJ................ 13.7 166.3 2.2 69 878 1.5 262
Passaic, NJ.............. 12.7 165.3 0.1 285 1,051 1.5 262
Somerset, NJ............. 10.3 187.3 1.0 184 2,139 2.5 176
Union, NJ................ 14.6 227.5 0.6 232 1,409 0.9 298
Bernalillo, NM........... 19.5 329.0 0.6 232 941 2.6 169
Albany, NY............... 10.2 232.1 0.0 299 1,142 3.0 136
Bronx, NY................ 18.8 323.7 1.5 128 1,086 4.6 46
Broome, NY............... 4.4 85.6 0.0 299 900 5.1 31
Dutchess, NY............. 8.3 113.6 1.3 150 1,060 2.3 200
Erie, NY................. 24.3 468.5 0.5 241 1,017 2.4 191
Kings, NY................ 63.2 780.2 0.5 241 954 4.0 69
Monroe, NY............... 18.6 387.4 0.3 263 1,015 2.2 212
Nassau, NY............... 53.5 626.6 0.1 285 1,215 1.8 241
New York, NY............. 126.7 2,500.7 1.4 138 3,153 2.1 218
Oneida, NY............... 5.2 105.4 0.5 241 866 3.1 130
Onondaga, NY............. 12.6 245.3 1.1 173 1,030 1.7 248
Orange, NY............... 10.4 145.8 1.1 173 931 3.8 75
Queens, NY............... 52.8 709.9 1.8 94 1,099 2.7 161
Richmond, NY............. 9.9 125.9 3.7 11 1,006 2.9 144
Rockland, NY............. 10.8 126.9 2.3 60 1,077 0.5 312
Saratoga, NY............. 5.9 86.7 -0.4 325 1,029 3.7 81
Suffolk, NY.............. 52.7 652.3 0.9 201 1,171 2.9 144
Westchester, NY.......... 35.9 428.2 1.0 184 1,587 4.5 50
Buncombe, NC............. 9.6 134.4 2.6 39 848 4.3 55
Cabarrus, NC............. 4.8 76.2 2.5 43 822 2.5 176
Catawba, NC.............. 4.5 88.5 0.5 241 857 1.9 234
Cumberland, NC........... 6.1 120.3 0.1 285 840 5.4 18
Durham, NC............... 8.6 208.8 3.5 15 1,482 4.2 60
Forsyth, NC.............. 9.3 189.8 1.6 118 1,060 0.7 309
Guilford, NC............. 14.6 284.8 1.1 173 959 0.9 298
Mecklenburg, NC.......... 38.8 706.6 2.5 43 1,533 1.6 258
New Hanover, NC.......... 8.5 115.9 2.1 78 908 4.4 52
Pitt, NC................. 3.8 77.6 0.8 215 897 5.3 23
Wake, NC................. 35.7 566.0 2.6 39 1,213 5.3 23
Cass, ND................. 7.4 118.1 1.7 110 985 1.4 269
Butler, OH............... 8.0 155.6 1.0 184 1,016 1.4 269
Cuyahoga, OH............. 36.2 719.8 0.6 232 1,176 2.3 200
Delaware, OH............. 5.6 87.9 1.7 110 1,256 3.5 92
Franklin, OH............. 33.3 752.0 1.2 159 1,187 3.8 75
Greene, OH............... 3.7 75.5 1.8 94 1,058 3.1 130
Hamilton, OH............. 24.2 514.7 1.0 184 1,284 5.9 10
Lake, OH................. 6.3 95.4 1.6 118 926 4.4 52
Lorain, OH............... 6.2 97.0 0.7 224 860 1.8 241
Lucas, OH................ 10.2 206.2 -0.3 316 1,003 -0.3 332
Mahoning, OH............. 5.9 96.4 -0.9 345 772 3.3 110
Montgomery, OH........... 12.0 253.5 -0.2 308 950 2.9 144
Stark, OH................ 8.6 157.5 -0.2 308 842 3.2 125
Summit, OH............... 14.4 263.7 -0.1 304 1,003 2.5 176
Warren, OH............... 5.2 94.0 3.3 22 1,099 5.9 10
Cleveland, OK............ 6.0 82.8 2.4 48 781 2.8 153
Oklahoma, OK............. 28.3 459.4 1.3 150 1,095 3.2 125
Tulsa, OK................ 22.8 359.5 1.4 138 1,072 6.1 9
Clackamas, OR............ 15.7 167.6 2.2 69 1,033 3.0 136
Deschutes, OR............ 9.3 82.5 1.5 128 887 2.5 176
Jackson, OR.............. 7.9 88.7 0.3 263 819 3.0 136
Lane, OR................. 12.8 156.3 0.4 254 845 2.4 191
Marion, OR............... 11.5 155.3 1.5 128 901 4.0 69
Multnomah, OR............ 36.8 514.9 1.8 94 1,200 2.5 176
Washington, OR........... 20.5 299.4 1.2 159 1,497 5.6 15
Allegheny, PA............ 35.6 694.9 0.6 232 1,251 1.4 269
Berks, PA................ 8.9 174.4 1.2 159 988 1.1 288
Bucks, PA................ 20.2 264.4 1.6 118 1,026 2.6 169
Butler, PA............... 5.1 86.3 0.2 275 1,033 5.7 13
Chester, PA.............. 15.7 249.7 1.1 173 1,501 1.4 269
Cumberland, PA........... 6.6 134.1 0.8 215 1,023 2.5 176
Dauphin, PA.............. 7.5 183.5 2.3 60 1,107 2.1 218
Delaware, PA............. 14.1 224.0 1.2 159 1,265 -0.4 334
Erie, PA................. 6.9 120.4 0.1 285 834 1.6 258
Lackawanna, PA........... 5.6 96.7 -0.4 325 814 1.0 292
Lancaster, PA............ 13.7 241.3 1.2 159 911 1.2 283
Lehigh, PA............... 8.8 192.6 1.3 150 1,121 3.9 72
Luzerne, PA.............. 7.4 143.2 -0.2 308 867 3.7 81
Montgomery, PA........... 27.8 498.1 1.5 128 1,547 3.3 110
Northampton, PA.......... 6.8 117.1 2.8 34 955 2.7 161
Philadelphia, PA......... 34.8 691.8 2.2 69 1,374 3.6 89
Washington, PA........... 5.6 86.7 0.9 201 1,262 2.4 191
Westmoreland, PA......... 9.2 132.1 0.7 224 901 2.5 176
York, PA................. 9.2 178.4 0.1 285 945 1.3 279
Kent, RI................. 5.6 75.1 0.7 224 1,021 3.8 75
Providence, RI........... 18.8 285.9 0.3 263 1,146 0.5 312
Charleston, SC........... 16.8 256.9 2.6 39 1,013 3.8 75
Greenville, SC........... 15.1 277.3 2.3 60 942 1.0 292
Horry, SC................ 9.6 129.6 1.8 94 652 3.3 110
Lexington, SC............ 7.0 119.7 1.2 159 851 5.5 16
Richland, SC............. 10.7 224.0 0.4 254 966 2.2 212
Spartanburg, SC.......... 6.6 145.3 2.7 36 928 0.2 319
York, SC................. 6.3 98.5 4.7 3 972 3.4 102
Minnehaha, SD............ 7.5 126.3 0.9 201 967 2.0 227
Davidson, TN............. 24.3 503.7 3.4 19 1,222 -0.5 339
Hamilton, TN............. 10.2 206.4 1.8 94 996 3.3 110
Knox, TN................. 13.0 239.7 1.3 150 952 -2.7 353
Rutherford, TN........... 6.0 132.4 1.6 118 948 5.1 31
Shelby, TN............... 21.2 498.3 1.2 159 1,104 2.9 144
Williamson, TN........... 9.5 137.8 4.0 6 1,389 7.4 5
Bell, TX................. 5.7 119.7 0.6 232 917 4.9 36
Bexar, TX................ 42.8 868.1 1.3 150 1,025 2.3 200
Brazoria, TX............. 6.1 115.6 3.5 15 1,211 0.2 319
Brazos, TX............... 4.7 108.8 3.0 28 803 -0.4 334
Cameron, TX.............. 6.6 140.2 0.1 285 648 3.2 125
Collin, TX............... 26.9 423.1 2.4 48 1,390 1.2 283
Dallas, TX............... 78.6 1,708.8 1.9 88 1,464 2.9 144
Denton, TX............... 16.0 251.4 3.2 24 1,006 2.1 218
Ector, TX................ 4.2 81.4 4.4 4 1,242 5.4 18
El Paso, TX.............. 15.6 310.1 1.3 150 759 1.2 283
Fort Bend, TX............ 14.3 194.1 3.6 13 1,046 0.7 309
Galveston, TX............ 6.3 110.2 1.2 159 995 -1.4 349
Harris, TX............... 117.4 2,333.5 1.8 94 1,551 3.9 72
Hidalgo, TX.............. 12.7 266.0 1.5 128 662 1.7 248
Jefferson, TX............ 5.9 123.5 1.8 94 1,180 2.7 161
Lubbock, TX.............. 7.7 140.2 1.1 173 839 1.3 279
McLennan, TX............. 5.4 113.2 1.8 94 889 1.8 241
Midland, TX.............. 6.0 107.2 5.8 1 1,599 4.8 40
Montgomery, TX........... 12.2 191.7 4.0 6 1,226 5.2 29
Nueces, TX............... 8.3 162.4 -0.3 316 942 2.3 200
Potter, TX............... 4.0 76.5 0.4 254 875 2.7 161
Smith, TX................ 6.4 104.0 1.0 184 874 2.5 176
Tarrant, TX.............. 45.2 910.6 1.6 118 1,148 3.2 125
Travis, TX............... 43.0 767.0 3.5 15 1,365 3.6 89
Webb, TX................. 5.5 103.1 1.8 94 705 2.2 212
Williamson, TX........... 11.6 178.3 4.3 5 1,249 6.9 6
Davis, UT................ 8.9 129.9 2.1 78 887 2.7 161
Salt Lake, UT............ 47.4 710.0 2.9 30 1,130 3.3 110
Utah, UT................. 17.5 248.6 3.7 11 938 4.2 60
Weber, UT................ 6.3 108.3 1.8 94 823 2.1 218
Chittenden, VT........... 7.1 101.2 0.9 201 1,104 4.8 40
Arlington, VA............ 9.3 180.0 1.9 88 1,966 2.0 227
Chesterfield, VA......... 9.6 135.0 1.0 184 937 -1.6 350
Fairfax, VA.............. 37.6 613.3 1.7 110 1,837 2.0 227
Henrico, VA.............. 12.0 191.5 0.6 232 1,133 1.9 234
Loudoun, VA.............. 12.9 171.7 3.1 25 1,331 3.4 102
Prince William, VA....... 9.6 130.9 1.2 159 938 0.4 315
Alexandria City, VA...... 6.4 90.6 -0.6 333 1,508 1.3 279
Chesapeake City, VA...... 6.3 101.3 0.1 285 864 1.9 234
Newport News City, VA.... 4.0 102.9 1.5 128 1,073 3.6 89
Norfolk City, VA......... 6.2 140.2 -0.6 333 1,074 1.1 288
Richmond City, VA........ 8.1 158.7 2.3 60 1,298 -0.6 340
Virginia Beach City, VA.. 12.5 176.4 0.1 285 834 2.3 200
Benton, WA............... 5.9 88.4 1.0 184 1,096 3.3 110
Clark, WA................ 15.2 162.2 1.5 128 1,047 3.7 81
King, WA................. 89.7 1,412.3 2.7 36 1,853 5.4 18
Kitsap, WA............... 6.9 90.8 2.4 48 988 3.5 92
Pierce, WA............... 22.8 312.6 1.9 88 1,032 5.3 23
Snohomish, WA............ 21.7 289.7 2.2 69 1,309 2.7 161
Spokane, WA.............. 16.4 223.9 1.1 173 962 2.9 144
Thurston, WA............. 8.4 117.8 1.8 94 1,034 5.3 23
Whatcom, WA.............. 7.3 91.8 2.1 78 958 3.7 81
Yakima, WA............... 7.8 108.2 -3.0 354 773 2.0 227
Kanawha, WV.............. 5.7 96.2 -1.8 350 957 2.6 169
Brown, WI................ 7.2 157.5 0.6 232 1,012 1.8 241
Dane, WI................. 16.3 335.5 0.8 215 1,203 5.2 29
Milwaukee, WI............ 27.5 484.3 -0.7 339 1,095 0.2 319
Outagamie, WI............ 5.5 107.2 0.1 285 946 1.4 269
Racine, WI............... 4.6 74.4 0.0 299 944 1.1 288
Waukesha, WI............. 13.7 241.7 0.5 241 1,167 2.4 191
Winnebago, WI............ 3.9 92.0 -0.6 333 1,053 4.9 36
San Juan, PR............. 10.9 241.0 0.6 (5) 674 -3.3 (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 73.5 percent of
the total covered workers in the U.S.
Table 2. Covered establishments, employment, and wages in the 10 largest counties,
first quarter 2019
Employment Average weekly
wage(1)
Establishments,
first quarter
County by NAICS supersector 2019 Percent Percent
(thousands) March change, First change,
2019 March quarter first
(thousands) 2018-19(2) 2019 quarter
2018-19(2)
United States(3) ............................ 10,203.0 146,497.6 1.4 $1,184 2.8
Private industry........................... 9,902.4 124,457.1 1.5 1,198 3.0
Natural resources and mining............. 139.1 1,814.0 1.2 1,334 4.8
Construction............................. 824.0 7,138.6 3.2 1,194 2.7
Manufacturing............................ 354.6 12,751.4 1.6 1,419 1.2
Trade, transportation, and utilities..... 1,940.1 27,104.9 0.5 975 3.8
Information.............................. 180.4 2,828.7 0.9 2,509 5.9
Financial activities..................... 909.3 8,218.5 1.0 2,431 1.6
Professional and business services....... 1,887.1 20,858.3 1.6 1,589 3.5
Education and health services............ 1,749.3 23,017.0 1.9 965 2.7
Leisure and hospitality.................. 873.5 16,053.6 1.5 461 3.6
Other services........................... 857.4 4,492.7 1.3 759 3.5
Government................................. 300.5 22,040.5 0.5 1,108 1.8
Los Angeles, CA.............................. 505.4 4,484.6 1.4 1,282 3.5
Private industry........................... 499.1 3,902.1 1.4 1,258 3.8
Natural resources and mining............. 0.5 6.2 4.7 1,134 -2.2
Construction............................. 16.5 146.3 3.1 1,296 3.3
Manufacturing............................ 12.8 340.3 0.0 1,454 5.0
Trade, transportation, and utilities..... 58.9 832.5 -0.1 1,046 3.5
Information.............................. 12.6 210.4 -0.2 2,709 6.4
Financial activities..................... 29.7 221.7 -0.5 2,427 4.0
Professional and business services....... 55.1 632.7 3.1 1,570 2.3
Education and health services............ 242.4 821.3 2.8 913 2.5
Leisure and hospitality.................. 38.1 538.2 2.0 682 7.4
Other services........................... 28.9 151.3 0.4 768 5.5
Government................................. 6.3 582.5 0.9 1,447 1.8
Cook, IL..................................... 138.4 2,568.4 0.1 1,468 3.4
Private industry........................... 137.1 2,275.4 0.1 1,490 3.4
Natural resources and mining............. 0.1 1.3 10.3 1,110 5.2
Construction............................. 11.1 71.1 -0.1 1,520 0.5
Manufacturing............................ 5.7 183.7 0.3 1,391 1.1
Trade, transportation, and utilities..... 28.3 464.8 -0.1 1,156 4.8
Information.............................. 2.5 53.0 1.4 2,434 3.5
Financial activities..................... 14.0 203.2 0.6 4,054 4.0
Professional and business services....... 29.1 468.6 0.1 1,772 4.1
Education and health services............ 15.5 451.9 -0.1 1,010 1.8
Leisure and hospitality.................. 13.8 278.3 0.0 538 3.5
Other services........................... 16.0 98.8 -0.6 986 1.1
Government................................. 1.3 293.0 0.1 1,294 2.8
New York, NY................................. 126.7 2,500.7 1.4 3,153 2.1
Private industry........................... 125.3 2,269.9 1.5 3,314 2.1
Natural resources and mining............. 0.0 0.2 4.1 2,302 -5.5
Construction............................. 2.4 43.5 0.4 2,023 3.5
Manufacturing............................ 1.9 22.2 -5.8 1,797 -2.1
Trade, transportation, and utilities..... 18.7 249.7 -0.3 1,570 2.5
Information.............................. 5.1 178.5 3.5 3,660 3.3
Financial activities..................... 19.3 384.7 2.2 9,566 0.4
Professional and business services....... 27.3 607.4 1.1 2,825 3.0
Education and health services............ 10.2 368.8 2.6 1,355 1.4
Leisure and hospitality.................. 14.7 304.6 0.1 963 5.6
Other services........................... 19.9 105.4 1.2 1,335 2.9
Government................................. 1.4 230.8 0.3 1,567 3.2
Harris, TX................................... 117.4 2,333.5 1.8 1,551 3.9
Private industry........................... 116.8 2,053.5 2.1 1,605 4.0
Natural resources and mining............. 1.6 68.0 3.1 5,232 7.5
Construction............................. 7.8 168.6 4.3 1,505 3.2
Manufacturing............................ 4.9 180.2 5.3 1,971 3.2
Trade, transportation, and utilities..... 25.1 468.1 0.6 1,445 4.0
Information.............................. 1.2 26.5 1.1 1,739 4.9
Financial activities..................... 12.5 128.3 1.1 2,472 2.1
Professional and business services....... 23.6 404.4 1.7 1,980 4.0
Education and health services............ 16.5 299.0 1.6 1,049 3.0
Leisure and hospitality.................. 10.5 239.4 2.6 468 2.4
Other services........................... 11.8 68.2 1.6 856 2.1
Government................................. 0.6 279.9 0.0 1,154 2.2
Maricopa, AZ................................. 103.8 2,042.9 2.9 1,118 3.1
Private industry........................... 103.1 1,827.0 3.1 1,125 3.2
Natural resources and mining............. 0.4 7.9 -4.2 1,365 2.7
Construction............................. 8.3 128.3 8.2 1,171 4.8
Manufacturing............................ 3.4 126.6 2.2 1,659 2.6
Trade, transportation, and utilities..... 20.3 384.4 2.3 1,018 2.2
Information.............................. 2.0 38.1 1.8 1,687 -1.5
Financial activities..................... 13.3 187.8 2.9 1,670 2.6
Professional and business services....... 25.3 345.9 3.4 1,217 6.0
Education and health services............ 12.8 322.3 3.5 1,026 1.7
Leisure and hospitality.................. 9.0 230.5 1.9 514 4.5
Other services........................... 7.0 54.1 2.8 776 4.3
Government................................. 0.7 215.9 1.4 1,059 2.0
Dallas, TX................................... 78.6 1,708.8 1.9 1,464 2.9
Private industry........................... 78.0 1,533.7 2.1 1,496 3.0
Natural resources and mining............. 0.5 9.2 8.2 4,787 -3.9
Construction............................. 4.8 89.7 2.8 1,374 4.0
Manufacturing............................ 2.8 116.2 2.9 1,925 -0.6
Trade, transportation, and utilities..... 16.0 347.3 2.1 1,222 5.2
Information.............................. 1.4 46.1 -3.3 2,764 3.7
Financial activities..................... 9.7 163.7 2.0 2,442 2.3
Professional and business services....... 17.8 352.1 2.3 1,681 3.4
Education and health services............ 9.7 202.3 1.8 1,141 3.0
Leisure and hospitality.................. 7.1 162.2 2.2 528 3.3
Other services........................... 7.1 43.0 2.1 955 6.0
Government................................. 0.5 175.1 0.1 1,181 1.5
Orange, CA................................... 125.3 1,640.1 1.2 1,287 1.8
Private industry........................... 123.8 1,482.1 1.3 1,271 1.9
Natural resources and mining............. 0.2 2.4 -7.6 944 13.3
Construction............................. 7.6 105.4 1.5 1,438 1.3
Manufacturing............................ 5.3 159.6 -0.5 1,790 0.5
Trade, transportation, and utilities..... 18.4 255.6 -0.3 1,112 3.6
Information.............................. 1.5 25.7 -1.0 2,603 10.3
Financial activities..................... 12.8 115.2 -2.2 2,200 3.6
Professional and business services....... 23.1 319.2 2.2 1,452 1.1
Education and health services............ 36.9 226.2 3.9 982 1.1
Leisure and hospitality.................. 9.6 225.4 2.5 515 4.7
Other services........................... 7.5 47.2 1.8 760 6.4
Government................................. 1.4 158.0 0.6 1,428 0.1
San Diego, CA................................ 114.7 1,469.9 1.1 1,253 2.8
Private industry........................... 112.7 1,232.7 1.2 1,239 3.5
Natural resources and mining............. 0.7 9.5 -0.4 779 3.3
Construction............................. 7.8 82.5 -0.3 1,282 3.1
Manufacturing............................ 3.5 113.7 1.7 1,975 5.8
Trade, transportation, and utilities..... 15.3 220.1 -0.1 960 2.7
Information.............................. 1.4 23.3 -2.1 2,038 6.6
Financial activities..................... 11.0 74.6 -0.9 1,772 2.0
Professional and business services....... 20.7 248.9 1.3 1,816 4.3
Education and health services............ 34.3 208.2 3.2 991 1.5
Leisure and hospitality.................. 9.1 199.5 2.3 524 4.0
Other services........................... 8.1 52.0 1.4 662 4.4
Government................................. 2.0 237.2 0.7 1,324 -0.4
King, WA..................................... 89.7 1,412.3 2.7 1,853 5.4
Private industry........................... 89.1 1,240.3 3.0 1,906 5.2
Natural resources and mining............. 0.4 2.8 -1.6 1,206 4.1
Construction............................. 6.9 73.7 3.2 1,478 3.6
Manufacturing............................ 2.5 105.2 3.8 2,190 4.1
Trade, transportation, and utilities..... 13.8 270.4 1.3 1,838 8.1
Information.............................. 2.6 116.7 9.3 4,810 7.5
Financial activities..................... 6.8 70.1 1.4 2,286 2.4
Professional and business services....... 18.6 231.7 2.4 2,058 -1.6
Education and health services............ 20.8 180.3 2.2 1,088 4.4
Leisure and hospitality.................. 7.5 142.0 2.6 588 3.9
Other services........................... 9.3 47.4 6.7 946 4.1
Government................................. 0.6 172.0 0.7 1,473 6.3
Miami-Dade, FL............................... 101.9 1,164.7 1.8 1,129 1.7
Private industry........................... 101.6 1,023.8 1.9 1,109 1.7
Natural resources and mining............. 0.5 10.5 3.5 632 2.9
Construction............................. 7.2 52.5 6.6 1,039 -2.3
Manufacturing............................ 2.8 41.2 2.2 1,114 19.0
Trade, transportation, and utilities..... 24.8 289.7 1.0 1,024 2.4
Information.............................. 1.6 19.0 1.2 1,980 -2.0
Financial activities..................... 11.0 76.6 0.0 2,166 1.5
Professional and business services....... 23.3 161.5 2.7 1,344 -2.3
Education and health services............ 11.3 186.2 2.5 1,003 2.3
Leisure and hospitality.................. 7.5 145.8 1.1 641 2.9
Other services........................... 8.6 39.1 1.2 734 11.9
Government................................. 0.3 140.9 1.0 1,279 2.4
(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,
first quarter 2019
Employment Average weekly
wage(1)
Establishments,
first quarter
State 2019 Percent Percent
(thousands) March change, First change,
2019 March quarter first
(thousands) 2018-19 2019 quarter
2018-19
United States(2)........... 10,203.0 146,497.6 1.4 $1,184 2.8
Alabama.................... 129.4 1,978.0 1.6 944 2.5
Alaska..................... 22.1 312.4 0.3 1,108 3.3
Arizona.................... 164.5 2,895.1 2.5 1,056 3.0
Arkansas................... 92.0 1,218.5 0.7 896 2.2
California................. 1,586.4 17,436.4 1.8 1,401 3.8
Colorado................... 207.6 2,690.3 1.9 1,231 4.8
Connecticut................ 122.4 1,650.6 0.0 1,487 2.3
Delaware................... 33.2 444.1 1.3 1,199 -0.1
District of Columbia....... 40.5 773.5 0.5 1,921 0.2
Florida.................... 717.6 8,894.3 2.1 1,015 2.7
Georgia.................... 283.8 4,488.6 2.1 1,121 2.6
Hawaii..................... 44.5 658.1 -0.4 1,006 3.4
Idaho...................... 66.2 732.3 2.7 828 2.3
Illinois................... 375.1 5,912.0 0.1 1,275 2.7
Indiana.................... 169.7 3,059.1 1.2 963 0.9
Iowa....................... 103.6 1,527.1 0.1 942 2.3
Kansas..................... 88.8 1,379.3 0.6 940 3.2
Kentucky................... 122.8 1,882.6 0.6 920 2.2
Louisiana.................. 134.8 1,916.8 -0.1 954 2.5
Maine...................... 54.1 599.8 1.2 919 3.1
Maryland................... 174.1 2,670.3 0.9 1,228 1.7
Massachusetts.............. 263.0 3,558.1 1.1 1,561 3.5
Michigan................... 249.7 4,307.4 0.6 1,078 0.1
Minnesota.................. 180.5 2,840.8 0.5 1,203 2.3
Mississippi................ 74.4 1,129.8 0.4 779 1.8
Missouri................... 206.7 2,788.4 0.5 986 2.6
Montana.................... 48.7 458.8 0.9 844 3.1
Nebraska................... 72.2 965.6 0.1 917 2.2
Nevada..................... 83.2 1,392.2 3.0 992 1.5
New Hampshire.............. 53.0 656.2 1.2 1,156 3.1
New Jersey................. 276.7 4,040.2 1.3 1,399 1.7
New Mexico................. 61.3 825.4 1.3 890 3.2
New York................... 648.9 9,453.5 1.5 1,639 2.6
North Carolina............. 282.4 4,458.5 2.0 1,054 3.2
North Dakota............... 31.7 414.3 1.5 1,021 3.3
Ohio....................... 300.2 5,363.2 0.7 1,035 3.0
Oklahoma................... 111.3 1,617.0 1.1 953 4.3
Oregon..................... 160.6 1,921.9 1.3 1,060 3.3
Pennsylvania............... 359.9 5,850.3 1.1 1,146 2.8
Rhode Island............... 38.7 474.7 0.8 1,104 1.8
South Carolina............. 138.2 2,110.0 2.0 901 3.0
South Dakota............... 34.0 419.0 0.4 865 2.7
Tennessee.................. 166.2 3,004.2 2.0 996 1.9
Texas...................... 705.3 12,455.6 2.2 1,204 3.1
Utah....................... 106.0 1,501.4 3.0 978 3.1
Vermont.................... 25.9 309.1 0.4 950 3.7
Virginia................... 284.6 3,896.9 1.2 1,186 2.1
Washington................. 250.4 3,371.1 1.8 1,368 4.9
West Virginia.............. 51.3 687.1 0.3 896 3.2
Wisconsin.................. 178.1 2,838.9 0.1 992 2.6
Wyoming.................... 26.7 269.0 1.9 948 3.7
Puerto Rico................ 45.9 875.8 2.2 553 -2.1
Virgin Islands............. 3.4 36.6 9.6 966 -1.0
(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
