For release 10:00 a.m. (EST), Wednesday, November 21, 2018 USDL-18-1859
Technical Information: (202) 691-6567 * QCEWInfo@bls.gov * www.bls.gov/cew
Media Contact: (202) 691-5902 * PressOffice@bls.gov
COUNTY EMPLOYMENT AND WAGES – SECOND QUARTER 2018
From June 2017 to June 2018, employment increased in 309 of the 349 largest U.S. counties, the
U.S. Bureau of Labor Statistics reported today. In June 2018, national employment (as measured
by the Quarterly Census of Employment and Wages program) increased to 147.4 million, a 1.5
percent increase over the year. Midland, TX, had the largest over-the-year increase in
employment with a gain of 11.6 percent. Employment data in this release are presented for June
2018, and average weekly wage data are presented for second quarter 2018.
Among the 349 largest counties, 340 had over-the-year increases in average weekly wages. In
the second quarter of 2018, average weekly wages for the nation increased to $1,055, a 3.4
percent increase over the year. Marin, CA, had the largest second quarter over-the-year wage
gain at 11.7 percent. (See table 1.)
Large County Employment in June 2018
Midland, TX, had the largest over-the-year percentage increase in employment (11.6 percent).
Within Midland, the largest employment increase occurred in natural resources and mining,
which gained 6,009 jobs over the year (25.7 percent).
McLean, IL, experienced the largest over-the-year percentage decrease in employment, with a
loss of 2.0 percent. Within McLean, financial activities had the largest decrease in employment
with a loss of 892 jobs (-4.5 percent) over the year.
Large County Average Weekly Wage in Second Quarter 2018
Marin, CA, had the largest over-the-year percentage increase in average weekly wages (11.7
percent). Within Marin, an average weekly wage gain of $439 (26.5 percent) over the year in
professional and business services made the largest contribution to the county’s increase in
average weekly wages.
New Hanover, NC, had the largest over-the-year percentage decrease in average weekly wages
with a loss of 6.4 percent. Within New Hanover, professional and business services had the
largest impact on the county’s change, with an average weekly wage decrease of $511 (-33.2
percent) over the year.
Ten Largest Counties
All of the 10 largest counties had over-the-year percentage increases in employment and average
weekly wages. In June 2018, Maricopa, AZ, had the largest over-the-year employment
percentage gain among the 10 largest counties (2.8 percent). Within Maricopa, trade,
transportation, and utilities had the largest over-the-year employment increase with a gain of
10,775 jobs (2.9 percent). (See table 2.)
In second quarter 2018, King, WA, experienced the largest over-the-year average weekly wage
percentage gain among the 10 largest counties (9.3 percent). Within King, trade, transportation,
and utilities had the largest impact on the county’s change, with an average weekly wage
increase of $270 (16.7 percent) over the year.
For More Information
The tables included in this release contain data for the nation and for the 349 U.S. counties with
annual average employment levels of 75,000 or more in 2017. June 2018 employment and
second quarter 2018 average weekly wages for all states are provided in table 3 of this release.
The most current news release on quarterly measures of gross job flows is available from QCEW
Business Employment Dynamics at www.bls.gov/news.release/pdf/cewbd.pdf.
Several BLS regional offices issue QCEW news releases targeted to local data users. Links to
these releases are available at www.bls.gov/cew/cewregional.htm.
The QCEW news release schedule is available at www.bls.gov/cew/releasecalendar.htm.
____________
The County Employment and Wages full data update for second quarter 2018 is scheduled
to be released on Thursday, December 6, 2018, at 10:00 a.m. (EST).
The County Employment and Wages news release for third quarter 2018 is scheduled to be
released on Wednesday, February 20, 2019, at 10:00 a.m. (EST).
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| |
| New BLS Local Data iPhone App Includes QCEW Data |
| |
| BLS has partnered with the U.S. Department of Labor’s Office of the Chief Information Officer |
| to develop a new mobile app for iPhones. The BLS Local Data app is ideal for customers, such |
| as jobseekers and economic and workforce development professionals, who want to know more |
| about local labor markets. For more information, please go to: |
| https://blogs.bls.gov/blog/2018/10/18/new-bls-local-data-app-now-available/ |
| |
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Technical Note
These data are the product of a federal-state cooperative program, the Quarterly Census of Employment and Wag-
es (QCEW) program, also known as the ES-202 program. The data are derived from summaries of employment
and total pay of workers covered by state and federal unemployment insurance (UI) legislation and provided by
State Workforce Agencies (SWAs). The summaries are a result of the administration of state unemployment in-
surance programs that require most employers to pay quarterly taxes based on the employment and wages of
workers covered by UI. QCEW data in this release are based on the 2017 North American Industry Classification
System (NAICS). Data for 2018 are preliminary and subject to revision.
For purposes of this release, large counties are defined as having employment levels of 75,000 or greater. In addi-
tion, data for San Juan, Puerto Rico, are provided, but not used in calculating U.S. averages, rankings, or in the
analysis in the text. Each year, these large counties are selected on the basis of the preliminary annual average of
employment for the previous year. The 349 counties presented in this release were derived using 2017 preliminary
annual averages of employment. For 2018 data, three counties have been added to the publication tables: Cabar-
rus, N.C.; Pitt, N.C.; and Kent, R.I. These counties will be included in all 2018 quarterly releases. The counties in
table 2 are selected and sorted each year based on the annual average employment from the preceding year.
The preliminary QCEW data presented in this release may differ from data released by the individual states. These
potential differences result from the states' continuing receipt of UI data over time and ongoing review and edit-
ing. The individual states determine their data release timetables.
Differences between QCEW, BED, and CES employment measures
The Bureau publishes three different establishment-based employment measures for any given quarter: QCEW,
Business Employment Dynamics (BED), and Current Employment Statistics (CES). Each of these measures
makes use of the quarterly UI employment reports in producing data; however, each measure has a somewhat dif-
ferent universe coverage, estimation procedure, and publication product.
Differences in coverage and estimation methods can result in somewhat different measures of employment change
over time. It is important to understand program differences and the intended uses of the program products. (See
table.) Additional information on each program can be obtained from the program Web sites shown in the table.
Summary of Major Differences between QCEW, BED, and CES Employment Measures
----------------------------------------------------------------------------------
| QCEW | BED | CES
-----------|---------------------|----------------------|------------------------
Source |--Count of UI admini-|--Count of longitudi- |--Sample survey:
| strative records | nally-linked UI ad- | 651,000 establish-
| submitted by 10.0 | ministrative records| ments
| million establish- | submitted by 8.0 |
| ments in first | million private-sec-|
| quarter of 2018 | tor employers |
-----------|---------------------|----------------------|------------------------
Coverage |--UI and UCFE cover- |--UI coverage, exclud-|Nonfarm wage and sal-
| age, including all | ing government, pri-| ary jobs:
| employers subject | vate households, and|--UI coverage, exclud-
| to state and fed- | establishments with | ing agriculture, pri-
| eral UI laws | zero employment | vate households, and
| | | self-employed workers
| | |--Other employment, in-
| | | cluding railroads,
| | | religious organiza-
| | | tions, and other non-
| | | UI-covered jobs
-----------|---------------------|----------------------|------------------------
Publication|--Quarterly |--Quarterly |--Monthly
frequency | -Within 5 months | -7 months after the | -Usually the 3rd Friday
| after the end of | end of each quarter| after the end of the
| each quarter | | week including
| | | the 12th of the month
-----------|---------------------|----------------------|------------------------
Use of UI |--Directly summarizes|--Links each new UI |--Uses UI file as a sam-
file | and publishes each | quarter to longitu- | pling frame and to an-
| new quarter of UI | dinal database and | nually realign sample-
| data | directly summarizes | based estimates to pop-
| | gross job gains and | ulation counts (bench-
| | losses | marking)
-----------|---------------------|----------------------|------------------------
Principal |--Provides a quarter-|--Provides quarterly |--Provides current month-
products | ly and annual uni- | employer dynamics | ly estimates of employ-
| verse count of es- | data on establish- | ment, hours, and earn-
| tablishments, em- | ment openings, clos-| ings at the MSA, state,
| ployment, and wages| ings, expansions, | and national level by
| at the county, MSA,| and contractions at | industry
| state, and national| the national level |
| levels by detailed | by NAICS supersec- |
| industry | tors and by size of |
| | firm, and at the |
| | state private-sector|
| | total level |
| |--Future expansions |
| | will include data |
| | with greater indus- |
| | try detail and data |
| | at the county and |
| | MSA level |
-----------|---------------------|----------------------|------------------------
Principal |--Major uses include:|--Major uses include: |--Major uses include:
uses | -Detailed locality | -Business cycle | -Principal federal
| data | analysis | economic indicator
| -Periodic universe | -Analysis of employ-| -Official time series
| counts for bench- | er dynamics under- | for employment change
| marking sample | lying economic ex- | measures
| survey estimates | pansions and con- | -Input into other ma-
| -Sample frame for | tractions | jor economic indi-
| BLS establishment | -Analysis of employ-| cators
| surveys | ment expansion and |
| | contraction by size|
| | of firm |
| | |
-----------|---------------------|----------------------|------------------------
Program |--www.bls.gov/cew |--www.bls.gov/bdm |--www.bls.gov/ces
Web sites | | |
---------------------------------------------------------------------------------
Coverage
Employment and wage data for workers covered by state UI laws are compiled from quarterly contribution reports
submitted to the SWAs by employers. For federal civilian workers covered by the Unemployment Compensation
for Federal Employees (UCFE) program, employment and wage data are compiled from quarterly reports submit-
ted by four major federal payroll processing centers on behalf of all federal agencies, with the exception of a few
agencies which still report directly to the individual SWA. In addition to the quarterly contribution reports, em-
ployers who operate multiple establishments within a state complete a questionnaire, called the "Multiple
Worksite Report," which provides detailed information on the location and industry of each of their establish-
ments. QCEW employment and wage data are derived from microdata summaries of 9.8 million employer reports
of employment and wages submitted by states to the BLS in 2017. These reports are based on place of employ-
ment rather than place of residence.
UI and UCFE coverage is broad and has been basically comparable from state to state since 1978, when the 1976
amendments to the Federal Unemployment Tax Act became effective, expanding coverage to include most state
and local government employees. In 2017, UI and UCFE programs covered workers in 143.9 million jobs. The
estimated 138.6 million workers in these jobs (after adjustment for multiple jobholders) represented 96.4 percent
of civilian wage and salary employment. Covered workers received $7.968 trillion in pay, representing 94.3 per-
cent of the wage and salary component of personal income and 40.9 percent of the gross domestic product.
Major exclusions from UI coverage include self-employed workers, most agricultural workers on small farms, all
members of the Armed Forces, elected officials in most states, most employees of railroads, some domestic work-
ers, most student workers at schools, and employees of certain small nonprofit organizations.
State and federal UI laws change periodically. These changes may have an impact on the employment and wages
reported by employers covered under the UI program. Coverage changes may affect the over-the-year compari-
sons presented in this news release.
Concepts and methodology
Monthly employment is based on the number of workers who worked during or received pay for the pay period
including the 12th of the month. With few exceptions, all employees of covered firms are reported, including pro-
duction and sales workers, corporation officials, executives, supervisory personnel, and clerical workers. Workers
on paid vacations and part-time workers also are included.
Average weekly wage values are calculated by dividing quarterly total wages by the average of the three monthly
employment levels (all employees, as described above) and dividing the result by 13, for the 13 weeks in the quar-
ter. These calculations are made using unrounded employment and wage values. The average wage values that can
be calculated using rounded data from the BLS database may differ from the averages reported. Included in the
quarterly wage data are non-wage cash payments such as bonuses, the cash value of meals and lodging when sup-
plied, tips and other gratuities, and, in some states, employer contributions to certain deferred compensation plans
such as 401(k) plans and stock options. Over-the-year comparisons of average weekly wages may reflect fluctua-
tions in average monthly employment and/or total quarterly wages between the current quarter and prior year lev-
els.
Average weekly wages are affected by the ratio of full-time to part-time workers as well as the number of individ-
uals in high-paying and low-paying occupations and the incidence of pay periods within a quarter. For instance,
the average weekly wage of the workforce could increase significantly when there is a large decline in the number
of employees that had been receiving below-average wages. Wages may include payments to workers not present
in the employment counts because they did not work during the pay period including the 12th of the month. When
comparing average weekly wage levels between industries, states, or quarters, these factors should be taken into
consideration.
Wages measured by QCEW may be subject to periodic and sometimes large fluctuations. This variability may be
due to calendar effects resulting from some quarters having more pay dates than others. The effect is most visible
in counties with a dominant employer. In particular, this effect has been observed in counties where government
employers represent a large fraction of overall employment. Similar calendar effects can result from private sector
pay practices. However, these effects are typically less pronounced for two reasons: employment is less concen-
trated in a single private employer, and private employers use a variety of pay period types (weekly, biweekly,
semimonthly, monthly).
For example, the effect on over-the-year pay comparisons can be pronounced in federal government due to the
uniform nature of federal payroll processing. Most federal employees are paid on a biweekly pay schedule. As a
result, in some quarters federal wages include six pay dates, while in other quarters there are seven pay dates.
Over-the-year comparisons of average weekly wages may also reflect this calendar effect. Growth in average
weekly wages may be attributed, in part, to a comparison of quarterly wages for the current year, which include
seven pay dates, with year-ago wages that reflect only six pay dates. An opposite effect will occur when wages in
the current quarter reflecting six pay dates are compared with year-ago wages for a quarter including seven pay
dates.
In order to ensure the highest possible quality of data, states verify with employers and update, if necessary, the
industry, location, and ownership classification of all establishments on a 3-year cycle. Changes in establishment
classification codes resulting from this process are introduced with the data reported for the first quarter of the
year. Changes resulting from improved employer reporting also are introduced in the first quarter.
QCEW data are not designed as a time series. QCEW data are simply the sums of individual establishment records
and reflect the number of establishments that exist in a county or industry at a point in time. Establishments can
move in or out of a county or industry for a number of reasons that reflect economic events or administrative
changes. For example, economic change would come from a firm relocating into the county; administrative
change would come from a company correcting its county designation.
The over-the-year changes of employment and wages presented in this release have been adjusted to account for
most of the administrative corrections made to the underlying establishment reports. This is done by modifying
the prior-year levels used to calculate the over-the-year changes. Percent changes are calculated using an adjusted
version of the final 2017 quarterly data as the base data. The adjusted prior-year levels used to calculate the over-
the-year percent change in employment and wages are not published. These adjusted prior-year levels do not
match the unadjusted data maintained on the BLS Web site. Over-the-year change calculations based on data from
the Web site, or from data published in prior BLS news releases, may differ substantially from the over-the-year
changes presented in this news release.
The adjusted data used to calculate the over-the-year change measures presented in this release eliminate the effect
of most of the administrative changes (those occurring when employers update the industry, location, and owner-
ship information of their establishments). The most common adjustments for administrative change are the result
of updated information about the county location of individual establishments. Included in these adjustments are
administrative changes involving the classification of establishments that were previously reported in the un-
known or statewide county or unknown industry categories. Adjusted data account for improvements in reporting
employment and wages for individual and multi-unit establishments. To accomplish this, adjustments were im-
plemented to account for: administrative changes caused by multi-unit employers who start reporting for each in-
dividual establishment rather than as a single entity (first quarter of 2008); selected large administrative changes in
employment and wages (second quarter of 2011); and state verified improvements in reporting of employment and
wages (third quarter of 2014). These adjustments allow QCEW to include county employment and wage growth
rates in this news release that would otherwise not meet publication standards.
The adjusted data used to calculate the over-the-year change measures presented in any County Employment and
Wages news release are valid for comparisons between the starting and ending points (a 12-month period) used in
that particular release. Comparisons may not be valid for any time period other than the one featured in a release
even if the changes were calculated using adjusted data.
County definitions are assigned according to Federal Information Processing Standards Publications (FIPS PUBS)
as issued by the National Institute of Standards and Technology, after approval by the Secretary of Commerce
pursuant to Section 5131 of the Information Technology Management Reform Act of 1996 and the Computer Se-
curity Act of 1987, Public Law 104-106. Areas shown as counties include those designated as independent cities
in some jurisdictions and, in Alaska, those designated as census areas where counties have not been created.
County data also are presented for the New England states for comparative purposes even though townships are
the more common designation used in New England (and New Jersey). The regions referred to in this release are
defined as census regions.
Additional statistics and other information
Employment and Wages Annual Averages Online features comprehensive information by detailed industry on es-
tablishments, employment, and wages for the nation and all states. The 2017 edition of this publication, which was
published in September 2018, contains selected data produced by Business Employment Dynamics (BED) on job
gains and losses, as well as selected data from the first quarter 2018 version of this news release. Tables and addi-
tional content from the 2017 edition of Employment and Wages Annual Averages Online are now available at
www.bls.gov/cew/cewbultn17.htm. The 2018 edition of Employment and Wages Annual Averages Online will be
available in September 2019.
News releases on quarterly measures of gross job flows also are available from BED at www.bls.gov/bdm, (202)
691-6467, or data.bls.gov/cgi-bin/forms/bdm.
Information in this release will be made available to sensory impaired individuals upon request. Voice phone:
(202) 691-5200; TDD message referral phone number: (800) 877-8339.
Table 1. Covered establishments, employment, and wages in the 350 largest counties,
second quarter 2018
Employment Average weekly wage(2)
Establishments,
County(1) second quarter Percent Ranking Percent Ranking
2018 June change, by Second change, by
(thousands) 2018 June percent quarter second percent
(thousands) 2017-18(3) change 2018 quarter change
2017-18(3)
United States(4)......... 10,048.0 147,431.2 1.5 - $1,055 3.4 -
Jefferson, AL............ 18.9 350.6 1.4 144 1,034 2.7 204
Madison, AL.............. 9.7 200.7 1.7 118 1,102 2.9 185
Mobile, AL............... 10.3 171.5 0.9 206 874 1.9 278
Montgomery, AL........... 6.4 132.3 -0.8 343 860 2.4 233
Shelby, AL............... 5.9 85.5 0.3 281 985 3.8 86
Tuscaloosa, AL........... 4.6 93.0 0.9 206 861 1.3 313
Anchorage, AK............ 8.3 150.7 -0.8 343 1,105 3.9 77
Maricopa, AZ............. 100.0 1,950.6 2.8 44 1,016 3.0 172
Pima, AZ................. 19.0 364.3 1.6 129 884 3.8 86
Benton, AR............... 6.6 120.2 0.9 206 1,029 1.0 323
Pulaski, AR.............. 14.4 251.8 0.3 281 922 1.5 301
Washington, AR........... 6.2 108.3 2.0 94 869 0.1 339
Alameda, CA.............. 64.7 793.7 2.1 82 1,421 3.3 136
Butte, CA................ 8.6 84.0 1.6 129 798 3.8 86
Contra Costa, CA......... 32.9 371.2 0.4 271 1,278 3.0 172
Fresno, CA............... 36.4 398.7 1.3 159 832 3.5 112
Kern, CA................. 19.7 327.5 1.0 193 869 3.1 160
Los Angeles, CA.......... 497.6 4,442.1 1.3 159 1,177 4.0 69
Marin, CA................ 12.5 117.7 0.8 219 1,422 11.7 1
Merced, CA............... 6.7 81.9 1.0 193 790 0.5 331
Monterey, CA............. 14.0 214.4 3.0 39 894 2.2 253
Napa, CA................. 5.9 81.4 1.7 118 1,036 2.7 204
Orange, CA............... 123.2 1,628.9 1.7 118 1,157 2.7 204
Placer, CA............... 13.3 169.6 3.5 19 1,042 3.1 160
Riverside, CA............ 66.1 740.7 3.0 39 852 3.3 136
Sacramento, CA........... 59.2 667.5 2.6 55 1,136 3.0 172
San Bernardino, CA....... 60.4 749.4 2.8 44 883 2.3 244
San Diego, CA............ 112.9 1,473.5 2.0 94 1,137 3.4 124
San Francisco, CA........ 61.2 741.6 3.2 28 2,083 7.6 8
San Joaquin, CA.......... 18.1 254.9 2.1 82 887 2.8 197
San Luis Obispo, CA...... 10.5 120.1 0.5 257 910 4.7 32
San Mateo, CA............ 28.5 405.3 1.7 118 2,357 9.0 4
Santa Barbara, CA........ 15.5 205.2 1.6 129 1,028 4.7 32
Santa Clara, CA.......... 73.6 1,106.1 2.3 72 2,573 8.2 6
Santa Cruz, CA........... 9.6 110.2 -0.2 323 983 4.0 69
Solano, CA............... 11.6 142.7 1.4 144 1,075 1.5 301
Sonoma, CA............... 20.3 212.7 1.8 110 1,015 4.2 59
Stanislaus, CA........... 16.0 192.1 1.9 103 884 2.9 185
Tulare, CA............... 10.8 170.5 -0.1 316 739 4.4 44
Ventura, CA.............. 27.7 331.3 0.9 206 1,036 2.2 253
Yolo, CA................. 6.8 105.8 0.9 206 1,144 3.8 86
Adams, CO................ 11.3 214.1 3.5 19 1,019 4.7 32
Arapahoe, CO............. 22.4 335.9 1.8 110 1,201 2.8 197
Boulder, CO.............. 15.7 185.6 2.6 55 1,235 3.5 112
Denver, CO............... 33.4 524.6 3.1 34 1,269 4.7 32
Douglas, CO.............. 12.4 128.2 1.9 103 1,170 3.0 172
El Paso, CO.............. 20.3 279.0 2.0 94 936 4.1 66
Jefferson, CO............ 20.6 242.1 2.0 94 1,082 3.3 136
Larimer, CO.............. 12.5 165.6 3.1 34 931 4.3 50
Weld, CO................. 7.6 110.7 4.2 9 954 6.8 9
Fairfield, CT............ 35.9 429.1 -0.3 331 1,488 -1.1 345
Hartford, CT............. 28.5 518.7 0.7 235 1,219 0.5 331
New Haven, CT............ 24.7 371.7 0.4 271 1,071 0.5 331
New London, CT........... 7.7 127.9 0.5 257 1,007 0.9 325
New Castle, DE........... 20.2 291.6 1.2 176 1,143 1.0 323
Sussex, DE............... 7.0 86.8 2.1 82 748 2.6 216
Washington, DC........... 40.0 777.2 1.3 159 1,713 2.6 216
Alachua, FL.............. 7.2 130.0 1.7 118 878 3.9 77
Bay, FL.................. 5.6 80.8 2.2 76 772 2.0 268
Brevard, FL.............. 16.0 214.1 3.3 27 946 1.5 301
Broward, FL.............. 69.5 803.2 1.2 176 998 4.5 40
Collier, FL.............. 14.3 139.4 2.5 63 927 5.7 17
Duval, FL................ 29.7 513.7 2.6 55 980 2.0 268
Escambia, FL............. 8.1 134.9 1.4 144 810 3.3 136
Hillsborough, FL......... 43.1 674.6 1.6 129 1,002 3.6 104
Lake, FL................. 8.4 94.5 2.1 82 730 4.0 69
Lee, FL.................. 22.3 253.4 2.6 55 864 4.2 59
Leon, FL................. 8.7 149.4 2.0 94 841 3.3 136
Manatee, FL.............. 11.0 119.4 2.5 63 827 4.8 31
Marion, FL............... 8.4 102.3 1.4 144 740 3.5 112
Miami-Dade, FL........... 99.0 1,125.0 0.9 206 1,000 3.0 172
Okaloosa, FL............. 6.6 84.4 1.3 159 885 2.4 233
Orange, FL............... 43.0 841.3 3.5 19 919 1.9 278
Osceola, FL.............. 7.2 92.3 3.5 19 731 2.1 261
Palm Beach, FL........... 56.8 597.9 0.8 219 1,015 1.2 317
Pasco, FL................ 11.1 112.7 3.0 39 760 1.6 295
Pinellas, FL............. 33.4 434.5 1.9 103 913 2.8 197
Polk, FL................. 13.4 214.0 1.9 103 800 3.5 112
Sarasota, FL............. 16.1 168.3 2.7 48 871 3.4 124
Seminole, FL............. 15.1 193.1 3.2 28 917 2.9 185
Volusia, FL.............. 14.5 169.2 1.5 138 770 2.4 233
Bibb, GA................. 4.3 82.7 0.5 257 806 4.1 66
Chatham, GA.............. 8.0 156.0 1.6 129 887 3.1 160
Clayton, GA.............. 4.0 122.9 2.8 44 1,022 1.3 313
Cobb, GA................. 21.8 365.1 1.8 110 1,067 0.4 335
DeKalb, GA............... 17.7 302.2 0.8 219 1,053 2.5 225
Fulton, GA............... 43.4 874.4 2.1 82 1,353 1.2 317
Gwinnett, GA............. 25.0 355.9 1.8 110 971 0.5 331
Hall, GA................. 4.5 87.9 1.3 159 906 5.5 21
Muscogee, GA............. 4.5 94.6 1.2 176 797 2.4 233
Richmond, GA............. 4.4 104.7 0.2 293 855 1.4 307
Honolulu, HI............. 26.0 474.7 0.2 293 994 1.9 278
Maui + Kalawao, HI....... 6.3 78.1 0.5 257 869 3.7 93
Ada, ID.................. 16.2 246.5 3.9 16 921 3.7 93
Champaign, IL............ 4.1 90.4 0.3 281 913 3.3 136
Cook, IL................. 138.7 2,626.3 0.9 206 1,220 3.2 150
DuPage, IL............... 34.7 628.3 0.1 303 1,160 1.6 295
Kane, IL................. 12.6 218.6 -0.5 335 930 2.5 225
Lake, IL................. 20.3 348.1 -0.1 316 1,411 9.3 2
McHenry, IL.............. 7.8 100.7 0.2 293 856 3.5 112
McLean, IL............... 3.4 82.2 -2.0 349 1,002 9.0 4
Madison, IL.............. 5.4 101.6 3.0 39 817 3.7 93
Peoria, IL............... 4.2 107.8 1.3 159 1,054 3.3 136
St. Clair, IL............ 5.1 92.3 -0.5 335 818 -0.1 342
Sangamon, IL............. 4.8 131.7 -0.2 323 1,001 1.3 313
Will, IL................. 14.7 249.6 1.3 159 898 1.8 285
Winnebago, IL............ 6.0 128.4 -0.1 316 869 3.2 150
Allen, IN................ 8.9 189.7 1.7 118 858 3.5 112
Elkhart, IN.............. 4.8 139.8 3.2 28 940 2.6 216
Hamilton, IN............. 9.5 144.7 2.4 69 978 2.5 225
Lake, IN................. 10.4 188.9 0.7 235 879 2.7 204
Marion, IN............... 24.2 599.7 0.1 303 1,048 2.0 268
St. Joseph, IN........... 5.8 124.2 0.2 293 852 3.1 160
Tippecanoe, IN........... 3.5 84.6 2.3 72 899 2.9 185
Vanderburgh, IN.......... 4.8 109.4 1.3 159 826 -0.1 342
Johnson, IA.............. 4.3 84.4 0.6 250 980 3.7 93
Linn, IA................. 6.9 133.7 0.7 235 1,008 3.9 77
Polk, IA................. 17.6 306.6 0.9 206 1,050 3.7 93
Scott, IA................ 5.7 92.7 -0.1 316 842 3.8 86
Johnson, KS.............. 23.6 352.2 2.0 94 1,068 2.9 185
Sedgwick, KS............. 12.6 250.8 1.2 176 882 2.7 204
Shawnee, KS.............. 5.1 96.4 -0.1 316 900 6.3 13
Wyandotte, KS............ 3.5 90.8 2.2 76 1,009 3.2 150
Boone, KY................ 4.5 94.0 4.0 10 907 3.0 172
Fayette, KY.............. 11.1 193.8 1.0 193 934 2.0 268
Jefferson, KY............ 25.5 471.6 0.7 235 1,032 1.6 295
Caddo, LA................ 7.3 112.1 -0.3 331 836 3.5 112
Calcasieu, LA............ 5.4 102.6 4.0 10 926 5.0 28
East Baton Rouge, LA..... 15.9 263.6 0.8 219 989 3.6 104
Jefferson, LA............ 14.1 189.8 -0.9 346 941 4.3 50
Lafayette, LA............ 9.8 129.5 0.3 281 883 2.7 204
Orleans, LA.............. 13.0 192.9 -0.1 316 967 4.2 59
St. Tammany, LA.......... 8.5 89.0 1.6 129 878 3.7 93
Cumberland, ME........... 13.7 189.9 1.4 144 944 3.6 104
Anne Arundel, MD......... 15.2 276.8 0.9 206 1,118 2.8 197
Baltimore, MD............ 21.3 382.5 0.2 293 1,039 3.7 93
Frederick, MD............ 6.5 103.6 1.5 138 946 1.6 295
Harford, MD.............. 5.8 95.9 1.4 144 989 4.4 44
Howard, MD............... 10.0 174.2 0.3 281 1,268 3.5 112
Montgomery, MD........... 32.9 478.4 0.3 281 1,392 4.0 69
Prince George's, MD...... 16.1 320.3 0.0 310 1,112 4.3 50
Baltimore City, MD....... 13.6 345.5 1.1 186 1,222 3.4 124
Barnstable, MA........... 9.6 108.3 -0.6 340 893 2.9 185
Bristol, MA.............. 18.0 232.8 0.2 293 975 2.3 244
Essex, MA................ 26.6 334.2 0.8 219 1,163 6.6 10
Hampden, MA.............. 18.8 210.5 0.5 257 916 2.1 261
Middlesex, MA............ 56.0 934.8 1.7 118 1,571 3.4 124
Norfolk, MA.............. 25.6 359.5 0.1 303 1,230 3.3 136
Plymouth, MA............. 16.3 200.4 0.7 235 999 0.1 339
Suffolk, MA.............. 30.8 684.7 1.9 103 1,711 3.7 93
Worcester, MA............ 26.1 354.0 0.7 235 1,039 2.9 185
Genesee, MI.............. 6.8 136.5 0.3 281 861 3.1 160
Ingham, MI............... 6.0 150.6 -0.8 343 1,005 3.2 150
Kalamazoo, MI............ 5.0 121.2 1.3 159 963 2.9 185
Kent, MI................. 14.6 411.6 3.4 25 900 1.7 289
Macomb, MI............... 17.6 339.5 1.5 138 1,042 3.4 124
Oakland, MI.............. 39.4 750.3 0.8 219 1,168 3.3 136
Ottawa, MI............... 5.7 130.2 1.7 118 883 3.4 124
Saginaw, MI.............. 3.9 84.0 -1.3 348 841 2.9 185
Washtenaw, MI............ 8.2 210.6 1.5 138 1,126 3.0 172
Wayne, MI................ 31.1 731.5 0.8 219 1,125 1.4 307
Anoka, MN................ 7.5 128.0 2.6 55 1,018 3.2 150
Dakota, MN............... 10.4 191.4 0.0 310 1,041 3.8 86
Hennepin, MN............. 40.8 931.1 0.8 219 1,318 3.5 112
Olmsted, MN.............. 3.6 100.8 0.8 219 1,122 4.3 50
Ramsey, MN............... 14.0 333.9 0.3 281 1,142 0.9 325
St. Louis, MN............ 5.4 100.5 0.3 281 885 3.3 136
Stearns, MN.............. 4.4 88.0 0.2 293 871 4.3 50
Washington, MN........... 5.9 89.8 2.5 63 910 2.5 225
Harrison, MS............. 4.6 86.4 -0.5 335 734 2.2 253
Hinds, MS................ 5.8 120.5 -0.5 335 865 2.1 261
Boone, MO................ 4.8 93.2 -0.2 323 835 1.7 289
Clay, MO................. 5.6 106.1 1.4 144 916 3.5 112
Greene, MO............... 8.9 167.2 0.8 219 822 4.3 50
Jackson, MO.............. 21.9 373.6 -0.2 323 1,061 3.2 150
St. Charles, MO.......... 9.5 149.4 0.0 310 847 3.5 112
St. Louis, MO............ 39.0 612.0 0.7 235 1,137 7.8 7
St. Louis City, MO....... 14.6 230.2 0.3 281 1,108 1.3 313
Yellowstone, MT.......... 6.7 82.5 -0.2 323 901 3.0 172
Douglas, NE.............. 19.1 342.1 0.4 271 960 2.7 204
Lancaster, NE............ 10.4 172.1 1.6 129 847 3.0 172
Clark, NV................ 55.4 992.6 2.7 48 916 3.3 136
Washoe, NV............... 14.7 222.5 2.3 72 944 4.1 66
Hillsborough, NH......... 12.2 206.7 0.8 219 1,127 4.2 59
Merrimack, NH............ 5.2 78.3 0.2 293 987 4.7 32
Rockingham, NH........... 11.0 153.4 0.5 257 1,030 2.0 268
Atlantic, NJ............. 6.6 135.3 2.2 76 903 5.6 20
Bergen, NJ............... 33.3 452.3 0.8 219 1,197 1.2 317
Burlington, NJ........... 11.1 205.1 0.2 293 1,070 1.4 307
Camden, NJ............... 12.2 209.2 -0.1 316 1,013 1.8 285
Essex, NJ................ 20.7 347.6 0.4 271 1,263 2.5 225
Gloucester, NJ........... 6.4 112.6 2.6 55 890 2.1 261
Hudson, NJ............... 15.2 265.4 0.3 281 1,408 4.7 32
Mercer, NJ............... 11.2 257.8 0.5 257 1,287 1.8 285
Middlesex, NJ............ 22.5 432.6 1.0 193 1,199 1.4 307
Monmouth, NJ............. 20.3 274.4 0.6 250 1,019 3.1 160
Morris, NJ............... 17.1 300.8 1.0 193 1,496 -2.4 347
Ocean, NJ................ 13.5 179.6 1.7 118 826 2.6 216
Passaic, NJ.............. 12.7 168.7 0.3 281 1,018 2.0 268
Somerset, NJ............. 10.3 193.1 0.8 219 1,549 6.2 14
Union, NJ................ 14.5 230.5 1.3 159 1,271 3.9 77
Bernalillo, NM........... 18.9 329.4 0.5 257 886 2.3 244
Albany, NY............... 10.4 235.5 0.4 271 1,138 4.2 59
Bronx, NY................ 19.2 322.2 1.2 176 1,058 2.3 244
Broome, NY............... 4.5 87.9 0.7 235 866 3.7 93
Dutchess, NY............. 8.4 114.5 0.7 235 1,038 1.4 307
Erie, NY................. 24.7 475.0 0.4 271 949 3.2 150
Kings, NY................ 64.2 772.5 2.5 63 918 2.2 253
Monroe, NY............... 19.0 391.6 0.0 310 996 3.1 160
Nassau, NY............... 54.3 647.2 0.5 257 1,175 2.5 225
New York, NY............. 128.9 2,474.7 0.7 235 2,025 4.4 44
Oneida, NY............... 5.3 107.4 0.1 303 833 2.6 216
Onondaga, NY............. 12.9 249.4 0.5 257 984 3.7 93
Orange, NY............... 10.5 148.5 1.8 110 941 4.0 69
Queens, NY............... 54.0 708.1 2.1 82 1,062 3.9 77
Richmond, NY............. 10.0 124.0 1.4 144 997 3.4 124
Rockland, NY............. 11.0 129.3 2.0 94 1,016 2.6 216
Saratoga, NY............. 6.0 92.7 2.7 48 995 4.3 50
Suffolk, NY.............. 53.4 688.3 0.1 303 1,134 3.4 124
Westchester, NY.......... 36.4 441.9 0.9 206 1,353 1.4 307
Buncombe, NC............. 9.3 132.8 3.2 28 805 2.7 204
Cabarrus, NC............. 4.8 77.3 2.0 94 760 1.7 289
Catawba, NC.............. 4.4 88.7 1.0 193 812 2.4 233
Cumberland, NC........... 6.2 120.9 1.4 144 820 3.4 124
Durham, NC............... 8.5 204.4 2.7 48 1,256 1.8 285
Forsyth, NC.............. 9.2 187.1 2.4 69 928 0.9 325
Guilford, NC............. 14.4 281.1 0.8 219 906 1.7 289
Mecklenburg, NC.......... 38.5 698.8 2.5 63 1,201 4.4 44
New Hanover, NC.......... 8.4 116.0 2.1 82 829 -6.4 349
Pitt, NC................. 3.8 77.5 3.1 34 824 2.0 268
Wake, NC................. 35.2 568.9 3.2 28 1,100 5.1 25
Cass, ND................. 7.3 118.7 -0.2 323 951 3.7 93
Butler, OH............... 7.8 155.4 1.2 176 903 0.4 335
Cuyahoga, OH............. 35.8 732.7 0.5 257 1,059 2.9 185
Delaware, OH............. 5.4 90.7 1.1 186 988 2.4 233
Franklin, OH............. 32.3 758.5 1.5 138 1,029 1.6 295
Hamilton, OH............. 23.8 524.3 0.5 257 1,105 3.0 172
Lake, OH................. 6.3 97.7 0.5 257 858 2.8 197
Lorain, OH............... 6.2 100.5 1.1 186 809 2.3 244
Lucas, OH................ 10.1 210.1 1.3 159 869 2.7 204
Mahoning, OH............. 5.9 99.1 1.7 118 735 2.2 253
Montgomery, OH........... 11.8 255.7 0.2 293 897 3.6 104
Stark, OH................ 8.6 162.2 1.1 186 778 2.0 268
Summit, OH............... 14.3 268.9 -0.3 331 918 3.4 124
Warren, OH............... 5.1 97.5 2.1 82 914 1.9 278
Cleveland, OK............ 5.9 80.3 0.9 206 777 3.9 77
Oklahoma, OK............. 28.2 457.2 1.4 144 979 3.1 160
Tulsa, OK................ 22.6 358.3 1.3 159 942 3.0 172
Clackamas, OR............ 15.4 168.1 1.1 186 1,007 -2.0 346
Deschutes, OR............ 8.9 85.1 3.1 34 860 1.5 301
Jackson, OR.............. 7.7 90.6 2.6 55 800 1.1 320
Lane, OR................. 12.4 158.0 0.4 271 836 2.7 204
Marion, OR............... 11.2 159.6 2.0 94 888 3.9 77
Multnomah, OR............ 35.7 513.5 1.4 144 1,109 3.4 124
Washington, OR........... 19.7 297.7 1.3 159 1,344 6.6 10
Allegheny, PA............ 35.7 709.8 1.0 193 1,127 4.3 50
Berks, PA................ 9.0 174.9 1.2 176 954 2.7 204
Bucks, PA................ 20.1 272.0 1.4 144 975 2.6 216
Butler, PA............... 5.1 87.0 -0.2 323 968 2.3 244
Chester, PA.............. 15.7 254.2 1.2 176 1,350 1.7 289
Cumberland, PA........... 6.6 135.2 0.7 235 968 3.5 112
Dauphin, PA.............. 7.6 188.3 1.9 103 1,013 1.6 295
Delaware, PA............. 14.3 227.2 1.4 144 1,094 2.8 197
Erie, PA................. 7.0 123.7 0.0 310 793 3.0 172
Lackawanna, PA........... 5.7 98.9 0.5 257 807 2.9 185
Lancaster, PA............ 13.7 245.3 2.1 82 860 2.4 233
Lehigh, PA............... 8.9 196.0 1.8 110 989 1.1 320
Luzerne, PA.............. 7.4 146.6 0.1 303 833 4.5 40
Montgomery, PA........... 27.8 502.6 1.0 193 1,246 3.3 136
Northampton, PA.......... 6.8 115.6 0.7 235 897 2.5 225
Philadelphia, PA......... 35.0 687.3 2.2 76 1,197 2.4 233
Washington, PA........... 5.5 89.9 1.0 193 1,011 1.9 278
Westmoreland, PA......... 9.3 136.0 0.4 271 845 3.6 104
York, PA................. 9.3 180.4 1.3 159 921 3.1 160
Kent, RI................. 5.5 77.2 0.4 271 906 0.9 325
Providence, RI........... 18.5 289.3 0.7 235 1,033 1.7 289
Charleston, SC........... 16.1 258.9 4.0 10 918 0.4 335
Greenville, SC........... 14.7 278.0 3.7 17 910 0.8 329
Horry, SC................ 9.3 139.4 2.1 82 625 0.3 338
Lexington, SC............ 6.9 121.0 4.0 10 778 0.0 341
Richland, SC............. 10.6 224.0 1.0 193 870 2.0 268
Spartanburg, SC.......... 6.5 142.7 4.0 10 862 -2.9 348
York, SC................. 6.1 98.3 5.2 2 834 0.8 329
Minnehaha, SD............ 7.3 128.8 1.0 193 896 2.3 244
Davidson, TN............. 23.3 498.9 2.7 48 1,081 2.4 233
Hamilton, TN............. 9.9 206.4 1.6 129 923 3.6 104
Knox, TN................. 12.6 239.2 0.9 206 923 5.1 25
Rutherford, TN........... 5.8 129.4 2.7 48 937 1.1 320
Shelby, TN............... 20.8 501.1 1.1 186 1,036 2.7 204
Williamson, TN........... 9.0 135.9 4.3 8 1,191 6.1 15
Bell, TX................. 5.5 118.5 -0.6 340 900 3.2 150
Bexar, TX................ 41.7 866.2 1.5 138 942 3.3 136
Brazoria, TX............. 5.9 113.2 3.2 28 1,094 1.5 301
Brazos, TX............... 4.6 101.5 3.6 18 794 4.3 50
Cameron, TX.............. 6.5 139.3 0.8 219 642 4.4 44
Collin, TX............... 25.6 417.5 3.5 19 1,236 5.7 17
Dallas, TX............... 77.5 1,710.0 1.8 110 1,246 2.5 225
Denton, TX............... 15.3 247.5 2.2 76 955 3.1 160
El Paso, TX.............. 15.2 303.7 1.1 186 733 2.4 233
Fort Bend, TX............ 13.6 190.5 5.0 3 958 2.6 216
Galveston, TX............ 6.2 110.9 1.4 144 905 -0.4 344
Harris, TX............... 115.0 2,309.3 1.3 159 1,269 3.1 160
Hidalgo, TX.............. 12.5 260.9 2.1 82 645 2.4 233
Jefferson, TX............ 5.8 124.0 0.4 271 1,063 4.0 69
Lubbock, TX.............. 7.6 139.6 0.9 206 842 5.3 23
McLennan, TX............. 5.3 113.4 0.7 235 886 6.6 10
Midland, TX.............. 5.7 103.7 11.6 1 1,377 4.2 59
Montgomery, TX........... 11.6 186.7 4.8 6 1,050 4.5 40
Nueces, TX............... 8.3 164.8 -0.2 323 892 3.6 104
Potter, TX............... 4.0 77.6 0.0 310 860 3.9 77
Smith, TX................ 6.4 103.6 1.3 159 858 4.9 30
Tarrant, TX.............. 43.9 900.6 1.9 103 1,038 3.0 172
Travis, TX............... 41.5 751.7 3.0 39 1,226 3.3 136
Webb, TX................. 5.5 101.2 1.0 193 687 3.2 150
Williamson, TX........... 11.2 174.6 4.0 10 1,012 2.0 268
Davis, UT................ 8.7 132.0 2.2 76 871 3.1 160
Salt Lake, UT............ 46.0 704.9 3.1 34 1,010 4.4 44
Utah, UT................. 16.7 242.4 4.8 6 859 5.7 17
Weber, UT................ 6.2 105.9 2.6 55 791 3.8 86
Chittenden, VT........... 6.9 103.0 -0.5 335 1,023 4.6 39
Arlington, VA............ 9.2 180.0 0.6 250 1,653 2.9 185
Chesterfield, VA......... 9.3 139.0 0.6 250 881 2.1 261
Fairfax, VA.............. 37.3 619.8 1.4 144 1,577 2.2 253
Henrico, VA.............. 11.8 194.3 1.0 193 982 2.3 244
Loudoun, VA.............. 12.6 171.8 1.7 118 1,191 1.9 278
Prince William, VA....... 9.4 133.6 2.1 82 925 4.5 40
Alexandria City, VA...... 6.3 93.5 -0.4 334 1,416 2.2 253
Chesapeake City, VA...... 6.1 102.4 1.3 159 829 2.1 261
Newport News City, VA.... 3.9 102.9 5.0 3 994 2.1 261
Norfolk City, VA......... 6.0 143.9 0.6 250 1,064 2.3 244
Richmond City, VA........ 7.8 155.0 0.8 219 1,115 2.6 216
Virginia Beach City, VA.. 12.3 183.0 -0.7 342 808 3.9 77
Benton, WA............... 5.8 95.4 2.3 72 1,022 1.5 301
Clark, WA................ 14.9 163.4 3.4 25 1,003 5.1 25
King, WA................. 89.2 1,405.6 2.5 63 1,605 9.3 2
Kitsap, WA............... 6.7 91.0 2.4 69 1,016 4.0 69
Pierce, WA............... 22.6 313.3 2.7 48 978 5.2 24
Snohomish, WA............ 21.5 290.2 1.6 129 1,149 4.2 59
Spokane, WA.............. 16.2 226.7 1.8 110 909 4.7 32
Thurston, WA............. 8.4 117.7 3.5 19 989 5.8 16
Whatcom, WA.............. 7.3 92.9 2.8 44 908 5.5 21
Yakima, WA............... 7.8 128.5 5.0 3 737 3.2 150
Kanawha, WV.............. 5.7 99.5 -1.2 347 896 2.2 253
Brown, WI................ 7.1 161.6 0.7 235 900 4.0 69
Dane, WI................. 16.0 339.3 1.2 176 1,040 3.6 104
Milwaukee, WI............ 27.1 493.3 0.6 250 987 1.9 278
Outagamie, WI............ 5.4 111.1 1.2 176 892 3.4 124
Waukesha, WI............. 13.3 249.2 0.6 250 1,029 2.8 197
Winnebago, WI............ 3.9 94.8 0.1 303 969 5.0 28
San Juan, PR............. 10.4 241.4 0.2 (5) 668 6.9 (5)
(1) Includes areas not officially designated as counties. See Technical Note.
(2) Average weekly wages were calculated using unrounded data.
(3) Percent changes were computed from employment and pay data adjusted for noneconomic
county reclassifications. See Technical Note.
(4) Totals for the United States do not include data for Puerto Rico or the Virgin Islands.
(5) This county was not included in the U.S. rankings.
Note: Data are preliminary. Includes workers covered by Unemployment Insurance (UI) and Unemployment
Compensation for Federal Employees (UCFE) programs. These 349 U.S. counties comprise 72.9 percent of
the total covered workers in the U.S.
Table 2. Covered establishments, employment, and wages in the 10 largest counties,
second quarter 2018
Employment Average weekly
wage(1)
Establishments,
second quarter
County by NAICS supersector 2018 Percent Percent
(thousands) June change, Second change,
2018 June quarter second
(thousands) 2017-18(2) 2018 quarter
2017-18(2)
United States(3) ............................ 10,048.0 147,431.2 1.5 $1,055 3.4
Private industry........................... 9,748.2 125,712.2 1.7 1,045 3.5
Natural resources and mining............. 137.9 2,065.1 2.9 1,075 5.7
Construction............................. 807.3 7,407.6 3.7 1,159 3.7
Manufacturing............................ 350.4 12,717.5 1.6 1,264 2.2
Trade, transportation, and utilities..... 1,923.2 27,365.7 1.0 891 3.6
Information.............................. 169.4 2,823.9 0.4 2,055 9.1
Financial activities..................... 889.8 8,230.6 1.0 1,589 3.4
Professional and business services....... 1,830.5 20,939.2 1.8 1,365 3.3
Education and health services............ 1,697.7 22,519.3 1.7 951 2.6
Leisure and hospitality.................. 856.3 16,797.5 1.2 449 4.2
Other services........................... 851.1 4,574.8 1.3 725 3.4
Government................................. 299.7 21,718.9 0.4 1,113 2.7
Los Angeles, CA.............................. 497.6 4,442.1 1.3 1,177 4.0
Private industry........................... 491.3 3,859.4 1.4 1,149 4.4
Natural resources and mining............. 0.5 6.8 -18.7 1,064 8.0
Construction............................. 15.2 144.3 3.3 1,243 5.3
Manufacturing............................ 12.3 341.7 -2.4 1,358 4.5
Trade, transportation, and utilities..... 55.4 826.6 0.1 958 3.1
Information.............................. 10.6 188.0 2.3 2,427 9.1
Financial activities..................... 27.6 221.4 -0.1 1,876 6.0
Professional and business services....... 50.8 610.3 1.2 1,482 3.8
Education and health services............ 237.2 801.7 2.4 881 3.0
Leisure and hospitality.................. 34.5 535.1 1.1 681 8.4
Other services........................... 27.1 151.9 0.0 775 3.5
Government................................. 6.3 582.7 0.2 1,367 2.5
Cook, IL..................................... 138.7 2,626.3 0.9 1,220 3.2
Private industry........................... 137.5 2,326.8 1.0 1,208 3.2
Natural resources and mining............. 0.1 1.3 0.8 1,168 -0.4
Construction............................. 10.9 79.6 3.7 1,457 2.1
Manufacturing............................ 5.8 185.3 0.6 1,249 0.9
Trade, transportation, and utilities..... 28.2 472.0 0.5 1,003 3.1
Information.............................. 2.4 51.5 -2.0 1,936 8.3
Financial activities..................... 14.0 200.3 1.6 2,122 2.5
Professional and business services....... 29.1 480.3 0.9 1,567 4.7
Education and health services............ 15.5 450.2 1.5 987 2.6
Leisure and hospitality.................. 13.8 303.2 0.8 558 3.9
Other services........................... 15.8 102.2 2.4 937 -0.1
Government................................. 1.3 299.5 0.2 1,319 3.3
New York, NY................................. 128.9 2,474.7 0.7 2,025 4.4
Private industry........................... 127.5 2,245.0 0.8 2,066 4.5
Natural resources and mining............. 0.0 0.2 14.1 1,993 3.2
Construction............................. 2.4 43.6 4.6 1,924 3.8
Manufacturing............................ 2.0 24.0 -3.5 1,502 5.1
Trade, transportation, and utilities..... 19.3 252.6 -1.3 1,495 10.4
Information.............................. 5.0 174.1 1.5 2,766 9.9
Financial activities..................... 19.5 385.7 1.9 3,665 2.1
Professional and business services....... 27.4 597.2 0.9 2,277 3.5
Education and health services............ 10.2 345.0 0.7 1,396 4.6
Leisure and hospitality.................. 14.8 312.9 0.2 906 4.3
Other services........................... 20.4 105.6 0.3 1,216 -2.3
Government................................. 1.4 229.7 -0.2 1,633 3.1
Harris, TX................................... 115.0 2,309.3 1.3 1,269 3.1
Private industry........................... 114.4 2,035.0 1.4 1,286 3.2
Natural resources and mining............. 1.6 66.1 -0.5 3,065 4.6
Construction............................. 7.6 160.9 1.3 1,361 3.0
Manufacturing............................ 4.8 175.1 3.0 1,613 3.7
Trade, transportation, and utilities..... 24.8 469.3 1.4 1,154 3.0
Information.............................. 1.2 26.3 -3.5 1,447 4.4
Financial activities..................... 12.2 128.6 0.7 1,634 0.4
Professional and business services....... 23.2 403.3 1.4 1,594 4.3
Education and health services............ 16.1 294.7 1.1 1,044 1.4
Leisure and hospitality.................. 10.2 240.3 1.6 477 5.8
Other services........................... 11.7 67.9 1.3 820 2.0
Government................................. 0.6 274.3 0.9 1,149 2.3
Maricopa, AZ................................. 100.0 1,950.6 2.8 1,016 3.0
Private industry........................... 99.2 1,764.6 3.0 1,004 3.0
Natural resources and mining............. 0.4 8.5 1.3 944 5.2
Construction............................. 7.7 120.8 7.1 1,087 4.7
Manufacturing............................ 3.3 123.4 3.6 1,486 4.4
Trade, transportation, and utilities..... 19.0 381.3 2.9 927 2.8
Information.............................. 1.6 37.1 -0.4 1,359 0.4
Financial activities..................... 11.9 180.3 2.5 1,319 5.0
Professional and business services....... 22.5 332.2 2.1 1,078 1.6
Education and health services............ 11.7 303.0 3.1 982 0.6
Leisure and hospitality.................. 8.4 219.5 2.6 503 5.9
Other services........................... 6.7 54.0 2.8 752 5.3
Government................................. 0.7 186.1 0.5 1,114 3.5
Dallas, TX................................... 77.5 1,710.0 1.8 1,246 2.5
Private industry........................... 77.0 1,537.0 2.0 1,251 2.5
Natural resources and mining............. 0.5 8.5 15.2 3,488 3.2
Construction............................. 4.7 90.9 2.5 1,262 2.9
Manufacturing............................ 2.8 113.3 2.0 1,437 1.3
Trade, transportation, and utilities..... 15.8 348.2 2.7 1,085 3.4
Information.............................. 1.4 49.6 -1.7 1,836 -0.8
Financial activities..................... 9.7 164.1 -0.9 1,715 -0.1
Professional and business services....... 17.7 351.3 3.0 1,463 3.6
Education and health services............ 9.6 199.1 1.4 1,129 1.6
Leisure and hospitality.................. 6.9 165.6 2.2 517 5.7
Other services........................... 7.0 44.4 0.7 895 12.6
Government................................. 0.6 173.0 0.4 1,200 2.6
Orange, CA................................... 123.2 1,628.9 1.7 1,157 2.7
Private industry........................... 121.7 1,472.0 1.8 1,142 2.7
Natural resources and mining............. 0.2 2.5 -6.5 909 1.9
Construction............................. 7.1 105.2 3.9 1,367 4.2
Manufacturing............................ 5.1 158.4 -1.4 1,486 6.3
Trade, transportation, and utilities..... 17.4 256.8 0.2 1,023 3.2
Information.............................. 1.4 26.3 -1.2 2,027 6.2
Financial activities..................... 12.0 118.0 -0.5 1,764 3.0
Professional and business services....... 21.6 308.6 2.5 1,344 -0.2
Education and health services............ 35.3 215.9 3.1 941 3.3
Leisure and hospitality.................. 8.9 222.8 1.1 512 3.9
Other services........................... 6.9 47.0 0.6 724 1.4
Government................................. 1.5 156.9 1.0 1,302 2.7
San Diego, CA................................ 112.9 1,473.5 2.0 1,137 3.4
Private industry........................... 110.9 1,234.6 2.2 1,096 3.7
Natural resources and mining............. 0.7 10.0 6.4 764 6.6
Construction............................. 7.4 84.3 5.7 1,204 2.8
Manufacturing............................ 3.3 112.0 2.5 1,508 1.1
Trade, transportation, and utilities..... 14.6 221.3 0.6 857 3.0
Information.............................. 1.2 23.8 -2.7 2,087 12.7
Financial activities..................... 10.5 75.1 0.2 1,486 3.3
Professional and business services....... 19.2 244.2 3.2 1,574 4.0
Education and health services............ 32.9 201.1 1.4 952 2.8
Leisure and hospitality.................. 8.5 202.6 1.2 522 4.4
Other services........................... 7.4 51.7 -1.6 639 2.2
Government................................. 2.0 238.9 0.6 1,350 2.8
King, WA..................................... 89.2 1,405.6 2.5 1,605 9.3
Private industry........................... 88.6 1,233.6 2.7 1,638 9.9
Natural resources and mining............. 0.4 3.1 -3.2 1,412 13.0
Construction............................. 6.8 74.1 4.2 1,406 5.5
Manufacturing............................ 2.5 102.2 -0.3 1,660 2.2
Trade, transportation, and utilities..... 14.1 271.4 2.4 1,886 16.7
Information.............................. 2.4 111.4 7.6 3,384 13.4
Financial activities..................... 6.8 70.7 3.5 1,705 4.0
Professional and business services....... 18.3 230.6 2.3 1,801 8.5
Education and health services............ 20.4 176.1 2.2 1,076 4.6
Leisure and hospitality.................. 7.4 147.6 2.6 597 3.1
Other services........................... 9.3 46.4 2.1 904 3.2
Government................................. 0.5 171.9 0.6 1,370 3.9
Miami-Dade, FL............................... 99.0 1,125.0 0.9 1,000 3.0
Private industry........................... 98.7 999.0 0.9 977 2.8
Natural resources and mining............. 0.5 8.3 4.6 671 7.4
Construction............................. 6.9 50.5 3.4 963 3.9
Manufacturing............................ 2.8 40.3 0.0 888 3.4
Trade, transportation, and utilities..... 24.9 284.1 1.2 925 2.9
Information.............................. 1.6 18.5 0.2 1,678 -1.2
Financial activities..................... 10.7 75.5 0.1 1,532 2.3
Professional and business services....... 22.4 162.3 2.2 1,170 3.5
Education and health services............ 10.8 178.7 0.6 985 1.3
Leisure and hospitality.................. 7.4 140.1 -1.4 608 4.6
Other services........................... 8.4 39.3 -0.8 651 5.3
Government................................. 0.3 126.0 0.7 1,168 3.7
(1) Average weekly wages were calculated using unrounded data.
(2) Percent changes were computed from quarterly employment and pay data adjusted for noneconomic
county reclassifications. See Technical Note.
(3) Totals for the United States do not include data for Puerto Rico or the Virgin Islands.
Note: Data are preliminary. Counties selected are based on 2017 annual average employment.
Includes workers covered by Unemployment Insurance (UI) and Unemployment Compensation for Federal
Employees (UCFE) programs.
Table 3. Covered establishments, employment, and wages by state,
second quarter 2018
Employment Average weekly
wage(1)
Establishments,
second quarter
State 2018 Percent Percent
(thousands) June change, Second change,
2018 June quarter second
(thousands) 2017-18 2018 quarter
2017-18
United States(2)........... 10,048.0 147,431.2 1.5 $1,055 3.4
Alabama.................... 127.2 1,969.9 1.2 882 2.8
Alaska..................... 22.1 335.8 -0.9 1,043 3.7
Arizona.................... 163.5 2,770.8 2.6 973 3.3
Arkansas................... 90.5 1,214.6 0.7 824 1.7
California................. 1,559.5 17,473.1 1.9 1,265 4.6
Colorado................... 204.9 2,704.4 2.4 1,075 3.2
Connecticut................ 120.8 1,704.5 0.3 1,218 0.1
Delaware................... 32.6 454.3 1.3 1,023 1.4
District of Columbia....... 40.0 777.3 1.3 1,713 2.6
Florida.................... 688.9 8,568.9 2.1 931 2.9
Georgia.................... 278.7 4,440.5 2.0 979 2.3
Hawaii..................... 42.7 658.3 0.5 956 2.5
Idaho...................... 62.5 745.3 3.1 794 3.8
Illinois................... 375.1 6,061.1 0.8 1,097 3.4
Indiana.................... 167.6 3,075.8 1.1 883 2.8
Iowa....................... 102.8 1,583.7 0.8 880 3.3
Kansas..................... 89.0 1,393.3 1.0 879 3.4
Kentucky................... 123.2 1,905.9 0.9 882 2.3
Louisiana.................. 133.1 1,918.6 0.4 901 3.7
Maine...................... 53.3 636.8 1.0 843 3.6
Maryland................... 172.4 2,712.0 0.7 1,141 3.4
Massachusetts.............. 259.0 3,650.1 1.0 1,322 3.5
Michigan................... 246.8 4,424.7 1.3 997 2.9
Minnesota.................. 177.1 2,925.6 0.8 1,072 3.3
Mississippi................ 74.2 1,130.7 0.2 752 2.7
Missouri................... 203.4 2,829.0 0.5 924 3.9
Montana.................... 49.6 478.7 1.1 817 2.5
Nebraska................... 72.7 990.8 0.6 859 3.1
Nevada..................... 81.9 1,372.4 3.1 931 3.3
New Hampshire.............. 52.7 670.8 0.8 1,049 3.3
New Jersey................. 274.2 4,157.0 0.9 1,201 2.3
New Mexico................. 59.7 823.6 1.0 852 3.5
New York................... 650.3 9,579.2 1.7 1,297 4.5
North Carolina............. 278.9 4,450.2 2.2 933 3.3
North Dakota............... 31.9 426.1 0.8 986 3.4
Ohio....................... 296.8 5,461.3 0.7 933 2.3
Oklahoma................... 110.9 1,606.4 1.2 875 3.2
Oregon..................... 155.8 1,947.3 1.5 999 3.3
Pennsylvania............... 359.9 5,924.9 1.1 1,031 3.1
Rhode Island............... 37.9 491.0 0.7 998 1.7
South Carolina............. 135.9 2,126.5 3.4 833 0.0
South Dakota............... 33.6 439.7 0.9 807 2.8
Tennessee.................. 161.7 2,994.1 1.6 932 2.9
Texas...................... 687.2 12,326.3 2.2 1,062 3.4
Utah....................... 103.1 1,483.9 3.4 899 4.3
Vermont.................... 25.6 312.4 -0.8 907 4.3
Virginia................... 277.4 3,941.0 1.3 1,073 2.6
Washington................. 247.5 3,444.1 2.7 1,218 6.9
West Virginia.............. 50.9 702.9 1.6 868 4.8
Wisconsin.................. 174.9 2,933.5 0.9 904 3.3
Wyoming.................... 26.3 282.2 0.5 901 3.0
Puerto Rico................ 44.3 853.5 -2.3 543 5.2
Virgin Islands............. 3.4 33.4 -14.4 838 12.8
(1) Average weekly wages were calculated using unrounded data.
(2) Totals for the United States do not include data for Puerto Rico or the Virgin Islands.
Note: Data are preliminary. Includes workers covered by Unemployment Insurance (UI) and
Unemployment Compensation for Federal Employees (UCFE) programs.
United States Department of Labor
