For release 10:00 a.m. (EST), Wednesday, February 20, 2019 USDL-19-0307
Technical Information: (202) 691-6567 * QCEWInfo@bls.gov * www.bls.gov/cew
Media Contact: (202) 691-5902 * PressOffice@bls.gov
COUNTY EMPLOYMENT AND WAGES – THIRD QUARTER 2018
From September 2017 to September 2018, employment increased in 295 of the 349 largest U.S.
counties, the U.S. Bureau of Labor Statistics reported today. In September 2018, national
employment (as measured by the QCEW program) increased to 146.8 million, a 1.6 percent
increase over the year. Midland, TX, had the largest over-the-year increase in employment with a
gain of 11.9 percent. Employment data in this release are presented for September 2018, and
average weekly wage data are presented for third quarter 2018.
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| |
| Notice Regarding South Carolina Employment and Wages Data |
| |
| South Carolina QCEW data for the first, second, and third quarters of 2018 show unusual |
| movements, which may be a result of a change in reporting. These unusual movements coincide |
| with a modernization of the South Carolina unemployment insurance system. For more |
| information please visit: www.bls.gov/cew/2018-notice-regarding-south-carolina-employment- |
| and-wages-data.htm. |
| |
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Among the 349 largest counties, 336 had over-the-year increases in average weekly wages. In
the third quarter of 2018, average weekly wages for the nation increased to $1,055, a 3.3 percent
increase over the year. Chatham, GA, had the largest third quarter over-the-year wage gain at 8.5
percent. (See table 1.)
Large County Employment in September 2018
Midland, TX, had the largest over-the-year percentage increase in employment (11.9 percent).
Within Midland, the largest employment increase occurred in natural resources and mining,
which gained 5,824 jobs over the year (23.7 percent).
New Hanover, NC, experienced the largest over-the-year percentage decrease in employment,
with a loss of 2.0 percent. Within New Hanover, leisure and hospitality had the largest
employment decrease with a loss of 1,466 jobs (-8.0 percent).
Large County Average Weekly Wage in Third Quarter 2018
Chatham, GA, had the largest over-the-year percentage increase in average weekly wages (8.5
percent). Within Chatham, an average weekly wage gain of $486 (30.7 percent) in manufacturing
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 4.2 percent. Within Elkhart, professional and business services had the largest impact,
with an average weekly wage decrease of $482 (-32.2 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 September 2018, Miami-Dade, FL, had the largest over-the-year employment
percentage gain among the 10 largest counties (3.9 percent). Within Miami-Dade, trade,
transportation, and utilities had the largest employment increase with a gain of 9,878 jobs (3.6
percent). (See table 2.)
In third quarter 2018, King, WA, experienced the largest over-the-year percentage gain in
average weekly wages among the 10 largest counties (7.9 percent). Within King, information had
the largest impact, with an average weekly wage increase of $475 (9.4 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. September 2018 employment and
third quarter 2018 average weekly wages for all states are provided in table 3 of this release.
The most current news release on quarterly measures of gross job flows is available from QCEW
Business Employment Dynamics at www.bls.gov/news.release/pdf/cewbd.pdf.
Several BLS regional offices issue QCEW news releases targeted to local data users. Links to
these releases are available at www.bls.gov/cew/cewregional.htm.
QCEW’s news release schedule is available at www.bls.gov/cew/releasecalendar.htm.
____________
The County Employment and Wages full data update for third quarter 2018 is scheduled
to be released on Wednesday, March 6, 2019, at 10:00 a.m. (EST).
The County Employment and Wages news release for fourth quarter 2018 is scheduled to
be released on Wednesday, May 22, 2019, at 10:00 a.m. (EDT).
Technical Note
These data are the product of a federal-state cooperative program, the Quarterly Census of Employment
and Wages (QCEW) program, also known as the ES-202 program. The data are derived from summaries
of employment and total pay of workers covered by state and federal unemployment insurance (UI) legis-
lation and provided by State Workforce Agencies (SWAs). The summaries are a result of the administra-
tion of state unemployment insurance programs that require most employers to pay quarterly taxes based
on the employment and wages of workers covered by UI. QCEW data in this release are based on the 2017
North American Industry Classification System (NAICS). Data for 2018 are preliminary and subject to
revision.
For purposes of this release, large counties are defined as having employment levels of 75,000 or greater.
In addition, data for San Juan, Puerto Rico, are provided, but not used in calculating U.S. averages, rank-
ings, or in the analysis in the text. Each year, these large counties are selected on the basis of the prelimi-
nary annual average of employment for the previous year. The 349 counties presented in this release were
derived using 2017 preliminary annual averages of employment. For 2018 data, three counties have been
added to the publication tables: Cabarrus, N.C.; Pitt, N.C.; and Kent, R.I. These 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 ongo-
ing 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 10.0 | ministrative records| ments
| million establish- | submitted by 8.0 |
| ments in first | million private-sec-|
| quarter of 2018 | tor employers |
-----------|---------------------|----------------------|------------------------
Coverage |--UI and UCFE cover- |--UI coverage, exclud-|Nonfarm wage and sal-
| age, including all | ing government, pri-| ary jobs:
| employers subject | vate households, and|--UI coverage, exclud-
| to state and fed- | establishments with | ing agriculture, pri-
| eral UI laws | zero employment | vate households, and
| | | self-employed workers
| | |--Other employment, in-
| | | cluding railroads,
| | | religious organiza-
| | | tions, and other non-
| | | UI-covered jobs
-----------|---------------------|----------------------|------------------------
Publication|--Quarterly |--Quarterly |--Monthly
frequency | -Within 5 months | -7 months after the | -Usually the 3rd Friday
| after the end of | end of each quarter| after the end of the
| each quarter | | week including
| | | the 12th of the month
-----------|---------------------|----------------------|------------------------
Use of UI |--Directly summarizes|--Links each new UI |--Uses UI file as a sam-
file | and publishes each | quarter to longitu- | pling frame and to an-
| new quarter of UI | dinal database and | nually realign sample-
| data | directly summarizes | based estimates to pop-
| | gross job gains and | ulation counts (bench-
| | losses | marking)
-----------|---------------------|----------------------|------------------------
Principal |--Provides a quarter-|--Provides quarterly |--Provides current month-
products | ly and annual uni- | employer dynamics | ly estimates of employ-
| verse count of es- | data on establish- | ment, hours, and earn-
| tablishments, em- | ment openings, clos-| ings at the MSA, state,
| ployment, and wages| ings, expansions, | and national level by
| at the county, MSA,| and contractions at | industry
| state, and national| the national level |
| levels by detailed | by NAICS supersec- |
| industry | tors and by size of |
| | firm, and at the |
| | state private-sector|
| | total level |
| |--Future expansions |
| | will include data |
| | with greater indus- |
| | try detail and data |
| | at the county and |
| | MSA level |
-----------|---------------------|----------------------|------------------------
Principal |--Major uses include:|--Major uses include: |--Major uses include:
uses | -Detailed locality | -Business cycle | -Principal federal
| data | analysis | economic indicator
| -Periodic universe | -Analysis of employ-| -Official time series
| counts for bench- | er dynamics under- | for employment change
| marking sample | lying economic ex- | measures
| survey estimates | pansions and con- | -Input into other ma-
| -Sample frame for | tractions | jor economic indi-
| BLS establishment | -Analysis of employ-| cators
| surveys | ment expansion and |
| | contraction by size|
| | of firm |
| | |
-----------|---------------------|----------------------|------------------------
Program |--www.bls.gov/cew |--www.bls.gov/bdm |--www.bls.gov/ces
Web sites | | |
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Coverage
Employment and wage data for workers covered by state UI laws are compiled from quarterly contribution
reports submitted to the SWAs by employers. For federal civilian workers covered by the Unemployment
Compensation for Federal Employees (UCFE) program, employment and wage data are compiled from
quarterly reports submitted by four major federal payroll processing centers on behalf of all federal agen-
cies, 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 loca-
tion and industry of each of their establishments. QCEW employment and wage data are derived from mi-
crodata summaries of 9.8 million employer reports of employment and wages submitted by states to the
BLS in 2017. These reports are based on place of employment rather than place of residence.
UI and UCFE coverage is broad and has been basically comparable from state to state since 1978, when
the 1976 amendments to the Federal 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 job-
holders) represented 96.4 percent of civilian wage and salary employment. Covered workers received
$7.968 trillion in pay, representing 94.3 percent of the wage and salary component of personal income and
40.9 percent of the gross domestic product.
Major exclusions from UI coverage include self-employed workers, most agricultural workers on small
farms, all members of the Armed Forces, elected officials in most states, most employees of railroads,
some domestic workers, most student workers at schools, and employees of certain small nonprofit organ-
izations.
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 cleri-
cal 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 av-
erage 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 peri-
od 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 coun-
ties where government employers represent a large fraction of overall employment. Similar calendar ef-
fects 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 neces-
sary, 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. Estab-
lishments 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 ac-
count 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 calcu-
lated 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, lo-
cation, and ownership information of their establishments). The most common adjustments for administra-
tive 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 estab-
lishments. 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 sin-
gle 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 Employ-
ment 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 designat-
ed as independent cities in some jurisdictions and, in Alaska, those designated as census areas where coun-
ties have not been created. County data also are presented for the New England states for comparative pur-
poses 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 publica-
tion, 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/cewbultn17.htm. The 2018 edition of Employ-
ment 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,
third quarter 2018
Employment Average weekly wage(2)
Establishments,
County(1) third quarter Percent Ranking Percent Ranking
2018 September change, by Third change, by
(thousands) 2018 September percent quarter third percent
(thousands) 2017-18(3) change 2018 quarter change
2017-18(3)
United States(4)......... 10,118.0 146,824.1 1.6 - $1,055 3.3 -
Jefferson, AL............ 19.0 350.1 1.4 139 1,022 3.3 128
Madison, AL.............. 9.8 199.4 1.5 133 1,137 3.5 115
Mobile, AL............... 10.3 170.5 0.6 219 896 2.2 259
Montgomery, AL........... 6.4 131.1 -0.7 335 839 1.3 312
Shelby, AL............... 5.9 85.0 -0.8 338 991 3.6 101
Tuscaloosa, AL........... 4.6 96.0 2.1 85 859 3.6 101
Anchorage, AK............ 8.3 150.9 -0.2 312 1,112 4.4 44
Maricopa, AZ............. 101.8 2,004.2 3.1 43 1,013 2.5 215
Pima, AZ................. 19.1 370.1 1.4 139 901 3.7 95
Benton, AR............... 6.6 120.4 1.3 148 968 2.5 215
Pulaski, AR.............. 14.5 252.4 0.1 284 923 2.1 267
Washington, AR........... 6.2 109.2 2.0 95 844 2.4 232
Alameda, CA.............. 65.2 789.0 1.8 104 1,419 2.3 241
Butte, CA................ 8.8 85.7 1.5 133 831 5.5 18
Contra Costa, CA......... 33.2 367.6 0.0 296 1,256 1.8 283
Fresno, CA............... 36.8 401.8 1.7 115 825 2.4 232
Kern, CA................. 20.1 336.6 1.8 104 875 3.3 128
Los Angeles, CA.......... 501.6 4,448.3 1.0 179 1,176 2.3 241
Marin, CA................ 12.6 115.8 1.2 161 1,287 4.4 44
Merced, CA............... 6.8 83.5 0.6 219 805 2.3 241
Monterey, CA............. 14.1 210.5 2.6 62 915 1.7 290
Napa, CA................. 5.9 81.2 1.6 123 1,036 1.8 283
Orange, CA............... 124.5 1,626.3 1.3 148 1,153 1.7 290
Placer, CA............... 13.4 169.7 3.8 26 1,048 1.6 299
Riverside, CA............ 67.0 734.8 2.7 57 846 2.2 259
Sacramento, CA........... 59.8 667.9 2.6 62 1,128 2.3 241
San Bernardino, CA....... 61.0 754.0 2.7 57 892 3.4 122
San Diego, CA............ 113.8 1,467.1 1.7 115 1,149 3.2 141
San Francisco, CA........ 61.5 745.3 3.1 43 2,097 7.6 5
San Joaquin, CA.......... 18.3 258.4 1.6 123 894 3.0 163
San Luis Obispo, CA...... 10.6 118.3 0.1 284 907 5.7 15
San Mateo, CA............ 28.8 406.1 2.1 85 2,363 7.2 9
Santa Barbara, CA........ 15.7 203.0 0.4 249 1,006 3.1 154
Santa Clara, CA.......... 74.1 1,102.4 2.2 78 2,460 7.8 3
Santa Cruz, CA........... 9.6 108.2 0.5 235 944 2.3 241
Solano, CA............... 11.8 141.9 0.8 194 1,094 3.6 101
Sonoma, CA............... 20.4 213.2 1.7 115 1,047 5.4 19
Stanislaus, CA........... 16.1 194.3 1.8 104 948 7.8 3
Tulare, CA............... 11.0 167.7 2.7 57 753 2.2 259
Ventura, CA.............. 27.9 325.0 0.8 194 1,019 2.9 169
Yolo, CA................. 6.9 105.3 1.1 168 1,097 -0.2 340
Adams, CO................ 11.3 215.9 4.0 20 1,053 3.9 73
Arapahoe, CO............. 22.5 331.9 1.5 133 1,227 3.2 141
Boulder, CO.............. 15.8 184.1 2.0 95 1,305 5.1 23
Denver, CO............... 33.7 522.0 2.3 72 1,301 3.7 95
Douglas, CO.............. 12.4 125.7 2.1 85 1,158 3.2 141
El Paso, CO.............. 20.4 277.6 1.8 104 956 0.8 327
Jefferson, CO............ 20.6 239.3 1.7 115 1,099 3.9 73
Larimer, CO.............. 12.5 163.7 2.1 85 965 0.3 333
Weld, CO................. 7.7 110.8 3.5 33 980 5.8 14
Fairfield, CT............ 36.1 420.8 -0.5 325 1,464 2.9 169
Hartford, CT............. 28.7 512.7 0.4 249 1,210 2.3 241
New Haven, CT............ 24.8 368.3 0.7 206 1,068 1.7 290
New London, CT........... 7.7 124.7 -0.1 307 1,031 4.2 52
New Castle, DE........... 20.6 289.7 0.6 219 1,164 2.0 272
Sussex, DE............... 7.2 83.7 1.7 115 759 3.4 122
Washington, DC........... 40.4 770.7 0.7 206 1,807 2.8 186
Alachua, FL.............. 7.3 132.7 2.3 72 911 3.4 122
Bay, FL.................. 5.7 79.6 2.6 62 757 3.7 95
Brevard, FL.............. 16.1 215.6 6.6 4 938 3.9 73
Broward, FL.............. 69.9 811.3 3.9 22 966 3.0 163
Collier, FL.............. 14.4 142.6 10.5 2 884 2.9 169
Duval, FL................ 29.7 515.6 3.4 38 976 2.5 215
Escambia, FL............. 8.2 136.0 2.3 72 820 2.2 259
Hillsborough, FL......... 43.5 685.5 3.5 33 1,009 3.3 128
Lake, FL................. 8.4 99.0 5.0 9 717 3.3 128
Lee, FL.................. 22.6 258.6 7.8 3 824 1.9 280
Leon, FL................. 8.8 151.6 3.5 33 863 1.3 312
Manatee, FL.............. 11.1 122.0 4.9 12 804 1.5 304
Marion, FL............... 8.5 103.1 3.6 30 711 2.3 241
Miami-Dade, FL........... 99.5 1,142.1 3.9 22 1,001 1.8 283
Okaloosa, FL............. 6.6 84.2 1.1 168 843 3.2 141
Orange, FL............... 43.4 850.5 4.6 15 931 3.9 73
Osceola, FL.............. 7.3 95.3 4.9 12 707 5.1 23
Palm Beach, FL........... 57.2 599.1 4.0 20 986 3.6 101
Pasco, FL................ 11.2 121.2 5.2 8 728 2.0 272
Pinellas, FL............. 33.6 434.0 3.5 33 902 2.5 215
Polk, FL................. 13.5 221.5 5.0 9 801 3.0 163
Sarasota, FL............. 16.2 168.7 4.3 18 866 2.7 196
Seminole, FL............. 15.2 195.5 5.0 9 916 5.9 13
Volusia, FL.............. 14.5 174.0 4.3 18 744 3.6 101
Bibb, GA................. 4.3 82.5 1.0 179 832 4.3 49
Chatham, GA.............. 8.0 154.6 3.0 50 928 8.5 1
Clayton, GA.............. 4.0 120.5 -0.5 325 1,081 5.7 15
Cobb, GA................. 21.7 367.8 2.7 57 1,090 2.7 196
DeKalb, GA............... 17.7 299.3 0.2 276 1,064 3.5 115
Fulton, GA............... 43.3 880.9 2.4 70 1,367 2.9 169
Gwinnett, GA............. 25.0 353.9 1.3 148 989 2.7 196
Hall, GA................. 4.5 89.7 3.1 43 876 3.2 141
Muscogee, GA............. 4.5 93.9 0.9 184 823 -2.3 344
Richmond, GA............. 4.4 103.7 -0.9 341 886 2.4 232
Honolulu, HI............. 26.5 472.4 -0.3 317 1,015 2.7 196
Maui + Kalawao, HI....... 6.3 77.4 0.9 184 875 -1.9 343
Ada, ID.................. 16.5 246.9 3.9 22 927 2.5 215
Champaign, IL............ 4.1 90.8 -0.9 341 916 3.6 101
Cook, IL................. 139.1 2,617.8 1.1 168 1,204 3.8 86
DuPage, IL............... 34.7 617.7 0.0 296 1,189 2.5 215
Kane, IL................. 12.6 214.8 -0.2 312 923 0.7 329
Lake, IL................. 20.3 341.9 0.0 296 1,264 1.3 312
McHenry, IL.............. 7.9 98.1 -0.5 325 852 2.2 259
McLean, IL............... 3.4 82.7 -1.6 347 983 4.7 36
Madison, IL.............. 5.4 101.6 -0.4 320 806 4.1 59
Peoria, IL............... 4.2 107.6 1.2 161 1,050 -2.5 345
St. Clair, IL............ 5.1 92.7 -0.7 335 818 0.2 336
Sangamon, IL............. 4.8 131.2 1.1 168 1,037 2.3 241
Will, IL................. 14.8 247.7 1.6 123 889 1.8 283
Winnebago, IL............ 6.0 126.5 0.3 262 906 1.8 283
Allen, IN................ 8.9 188.9 1.2 161 851 3.8 86
Elkhart, IN.............. 4.8 137.5 1.6 123 887 -4.2 349
Hamilton, IN............. 9.6 142.7 2.1 85 994 2.1 267
Lake, IN................. 10.5 188.9 0.1 284 910 3.9 73
Marion, IN............... 24.3 600.1 0.1 284 1,049 2.5 215
St. Joseph, IN........... 5.8 123.9 0.3 262 852 3.1 154
Tippecanoe, IN........... 3.5 85.0 1.1 168 921 4.2 52
Vanderburgh, IN.......... 4.8 110.2 0.1 284 838 1.3 312
Johnson, IA.............. 4.3 83.7 -0.7 335 995 3.0 163
Linn, IA................. 6.9 131.4 0.3 262 1,039 7.6 5
Polk, IA................. 17.7 301.6 0.8 194 1,045 3.6 101
Scott, IA................ 5.7 90.9 0.0 296 865 5.1 23
Johnson, KS.............. 23.8 349.5 1.1 168 1,042 3.3 128
Sedgwick, KS............. 12.7 252.2 1.9 101 880 3.8 86
Shawnee, KS.............. 5.1 96.9 0.4 249 852 3.3 128
Wyandotte, KS............ 3.5 91.6 1.0 179 992 4.9 30
Boone, KY................ 4.6 93.5 0.4 249 884 3.3 128
Fayette, KY.............. 11.2 194.5 1.3 148 907 1.5 304
Jefferson, KY............ 25.9 470.1 0.4 249 986 2.5 215
Caddo, LA................ 7.3 111.8 0.2 276 834 2.7 196
Calcasieu, LA............ 5.5 102.3 2.7 57 956 5.1 23
East Baton Rouge, LA..... 15.9 268.7 1.3 148 987 5.1 23
Jefferson, LA............ 14.1 188.2 0.2 276 910 1.3 312
Lafayette, LA............ 9.9 130.6 0.5 235 899 4.8 32
Orleans, LA.............. 13.2 195.1 0.6 219 960 2.7 196
St. Tammany, LA.......... 8.6 89.1 2.5 65 881 4.5 41
Cumberland, ME........... 13.6 185.7 0.5 235 968 3.9 73
Anne Arundel, MD......... 15.2 274.6 1.2 161 1,103 2.9 169
Baltimore, MD............ 21.2 375.8 0.0 296 1,049 3.6 101
Frederick, MD............ 6.4 103.3 1.8 104 945 0.7 329
Harford, MD.............. 5.8 94.8 1.1 168 1,026 4.8 32
Howard, MD............... 10.0 171.0 -0.1 307 1,278 3.6 101
Montgomery, MD........... 32.8 473.6 0.8 194 1,352 1.5 304
Prince George's, MD...... 16.1 321.2 0.6 219 1,095 1.7 290
Baltimore City, MD....... 13.6 346.8 0.1 284 1,203 0.6 331
Barnstable, MA........... 9.6 102.2 -0.3 317 876 3.3 128
Bristol, MA.............. 17.8 228.7 -0.2 312 930 2.8 186
Essex, MA................ 26.7 325.8 -0.6 330 1,102 3.1 154
Hampden, MA.............. 18.7 212.6 0.9 184 933 2.3 241
Middlesex, MA............ 56.1 923.5 1.4 139 1,563 4.3 49
Norfolk, MA.............. 25.5 352.7 0.1 284 1,176 3.2 141
Plymouth, MA............. 16.3 195.2 0.1 284 976 4.4 44
Suffolk, MA.............. 30.9 682.5 1.7 115 1,706 0.9 323
Worcester, MA............ 26.1 350.7 0.3 262 1,044 3.7 95
Genesee, MI.............. 6.9 135.7 0.8 194 859 1.5 304
Ingham, MI............... 6.1 152.0 -0.4 320 977 4.0 65
Kalamazoo, MI............ 5.1 120.1 1.6 123 956 1.6 299
Kent, MI................. 14.9 400.8 1.3 148 926 3.6 101
Macomb, MI............... 17.9 330.2 0.4 249 1,034 2.0 272
Oakland, MI.............. 40.2 737.3 0.5 235 1,142 2.0 272
Ottawa, MI............... 5.8 127.8 0.6 219 897 3.9 73
Saginaw, MI.............. 3.9 83.9 -1.2 345 836 3.3 128
Washtenaw, MI............ 8.4 214.4 0.3 262 1,144 4.1 59
Wayne, MI................ 31.9 727.0 0.3 262 1,115 2.3 241
Anoka, MN................ 7.6 127.0 2.5 65 1,053 4.5 41
Dakota, MN............... 10.5 190.3 0.4 249 1,018 5.4 19
Hennepin, MN............. 41.5 932.4 0.6 219 1,289 4.0 65
Olmsted, MN.............. 3.7 100.0 1.3 148 1,230 3.6 101
Ramsey, MN............... 14.1 335.2 0.4 249 1,171 4.4 44
St. Louis, MN............ 5.5 98.8 -0.4 320 887 4.8 32
Stearns, MN.............. 4.5 87.7 1.1 168 911 3.5 115
Washington, MN........... 5.9 87.8 1.1 168 871 1.5 304
Harrison, MS............. 4.7 85.9 0.5 235 719 3.5 115
Hinds, MS................ 5.9 120.0 -0.8 338 898 5.2 22
Boone, MO................ 4.9 94.7 0.2 276 837 2.2 259
Clay, MO................. 5.7 105.3 0.6 219 904 5.6 17
Greene, MO............... 9.0 168.6 1.7 115 829 6.1 12
Jackson, MO.............. 22.1 372.6 0.1 284 1,045 2.3 241
St. Charles, MO.......... 9.6 148.5 0.7 206 834 3.3 128
St. Louis, MO............ 39.6 608.0 0.4 249 1,083 3.2 141
St. Louis City, MO....... 14.7 231.5 0.2 276 1,118 4.1 59
Yellowstone, MT.......... 6.8 82.0 -0.1 307 887 2.5 215
Douglas, NE.............. 19.3 338.7 0.1 284 988 3.5 115
Lancaster, NE............ 10.5 172.0 1.4 139 858 1.9 280
Clark, NV................ 55.8 1,001.2 3.1 43 914 1.7 290
Washoe, NV............... 14.7 223.7 2.2 78 967 3.5 115
Hillsborough, NH......... 12.2 204.4 0.8 194 1,113 -1.6 342
Merrimack, NH............ 5.2 77.7 0.4 249 994 3.2 141
Rockingham, NH........... 11.1 150.8 0.0 296 1,010 1.8 283
Atlantic, NJ............. 6.5 132.2 4.5 17 850 1.1 321
Bergen, NJ............... 33.1 445.7 0.9 184 1,199 2.5 215
Burlington, NJ........... 11.0 200.3 -0.2 312 1,067 2.5 215
Camden, NJ............... 12.2 207.2 0.0 296 990 2.5 215
Essex, NJ................ 20.7 342.7 0.5 235 1,272 3.2 141
Gloucester, NJ........... 6.4 111.2 2.2 78 874 2.9 169
Hudson, NJ............... 15.1 264.5 0.6 219 1,379 1.6 299
Mercer, NJ............... 11.2 253.7 -0.1 307 1,237 2.4 232
Middlesex, NJ............ 22.4 432.0 0.9 184 1,184 2.7 196
Monmouth, NJ............. 20.2 262.6 0.5 235 1,017 4.7 36
Morris, NJ............... 17.1 292.0 0.2 276 1,469 0.0 337
Ocean, NJ................ 13.5 171.7 1.3 148 819 2.9 169
Passaic, NJ.............. 12.6 165.9 -0.4 320 991 1.3 312
Somerset, NJ............. 10.2 188.5 0.6 219 1,487 4.8 32
Union, NJ................ 14.4 227.4 0.7 206 1,263 -3.7 348
Bernalillo, NM........... 19.2 329.9 0.5 235 898 2.5 215
Albany, NY............... 10.4 233.9 0.0 296 1,073 2.6 208
Bronx, NY................ 19.1 319.6 0.7 206 1,085 3.3 128
Broome, NY............... 4.5 86.7 0.5 235 841 2.9 169
Dutchess, NY............. 8.4 113.7 0.2 276 1,001 2.9 169
Erie, NY................. 24.7 474.6 0.7 206 925 2.9 169
Kings, NY................ 64.2 766.6 3.6 30 922 2.3 241
Monroe, NY............... 18.9 391.0 0.9 184 968 2.5 215
Nassau, NY............... 54.3 629.2 -0.5 325 1,126 2.9 169
New York, NY............. 128.3 2,454.5 0.4 249 1,997 4.0 65
Oneida, NY............... 5.3 105.0 0.0 296 809 2.5 215
Onondaga, NY............. 12.9 248.3 0.8 194 963 3.3 128
Orange, NY............... 10.6 145.3 0.6 219 875 3.1 154
Queens, NY............... 53.9 708.9 2.2 78 1,047 2.8 186
Richmond, NY............. 10.0 121.8 0.1 284 997 2.8 186
Rockland, NY............. 11.0 125.7 0.7 206 975 2.3 241
Saratoga, NY............. 6.1 89.5 1.6 123 953 3.9 73
Suffolk, NY.............. 53.4 667.3 -0.3 317 1,124 2.3 241
Westchester, NY.......... 36.3 430.1 0.3 262 1,277 2.7 196
Buncombe, NC............. 9.5 133.5 2.8 55 820 3.8 86
Cabarrus, NC............. 4.8 77.6 2.1 85 758 2.8 186
Catawba, NC.............. 4.5 88.7 2.3 72 805 3.6 101
Cumberland, NC........... 6.3 118.1 -0.6 330 826 3.4 122
Durham, NC............... 8.6 204.2 3.3 40 1,303 3.4 122
Forsyth, NC.............. 9.3 186.0 0.6 219 945 -3.0 346
Guilford, NC............. 14.6 282.3 0.7 206 912 2.8 186
Mecklenburg, NC.......... 38.9 698.0 1.9 101 1,170 3.6 101
New Hanover, NC.......... 8.5 111.0 -2.0 349 874 6.2 10
Pitt, NC................. 3.9 76.3 0.2 276 868 -0.1 339
Wake, NC................. 35.5 555.2 1.4 139 1,099 4.9 30
Cass, ND................. 7.4 119.7 1.1 168 956 2.4 232
Butler, OH............... 7.9 156.0 0.6 219 919 2.5 215
Cuyahoga, OH............. 36.0 727.3 0.7 206 1,054 2.7 196
Delaware, OH............. 5.5 88.5 1.0 179 1,002 3.2 141
Franklin, OH............. 32.7 759.7 1.8 104 1,072 3.2 141
Hamilton, OH............. 23.9 520.7 0.8 194 1,116 2.3 241
Lake, OH................. 6.2 95.7 0.1 284 832 1.8 283
Lorain, OH............... 6.2 98.2 0.5 235 818 3.9 73
Lucas, OH................ 10.2 209.0 1.2 161 912 3.8 86
Mahoning, OH............. 5.9 97.2 -0.9 341 752 3.4 122
Montgomery, OH........... 11.9 255.1 0.3 262 895 3.1 154
Stark, OH................ 8.6 160.3 0.6 219 792 3.0 163
Summit, OH............... 14.3 267.5 -0.4 320 915 3.5 115
Warren, OH............... 5.1 95.0 1.8 104 1,050 7.3 8
Cleveland, OK............ 5.9 82.5 1.3 148 764 2.0 272
Oklahoma, OK............. 28.1 459.4 1.5 133 978 2.7 196
Tulsa, OK................ 22.5 356.3 0.8 194 946 4.0 65
Clackamas, OR............ 15.5 167.3 1.3 148 1,009 4.6 38
Deschutes, OR............ 9.0 84.5 3.0 50 863 0.3 333
Jackson, OR.............. 7.8 91.6 2.0 95 816 3.7 95
Lane, OR................. 12.5 157.4 0.7 206 827 3.1 154
Marion, OR............... 11.3 158.9 1.4 139 877 3.9 73
Multnomah, OR............ 36.2 513.8 1.9 101 1,125 5.0 29
Washington, OR........... 20.0 296.2 1.7 115 1,331 1.2 320
Allegheny, PA............ 35.8 703.7 0.9 184 1,109 3.1 154
Berks, PA................ 9.0 174.1 0.7 206 956 3.7 95
Bucks, PA................ 20.1 268.2 1.8 104 965 3.1 154
Butler, PA............... 5.1 86.2 -0.6 330 982 4.5 41
Chester, PA.............. 15.7 251.4 0.9 184 1,258 4.0 65
Cumberland, PA........... 6.6 134.9 0.8 194 962 4.2 52
Dauphin, PA.............. 7.6 185.7 1.8 104 1,023 2.9 169
Delaware, PA............. 14.3 225.7 1.2 161 1,080 2.2 259
Erie, PA................. 7.0 123.6 -0.2 312 791 0.8 327
Lackawanna, PA........... 5.7 98.1 -0.6 330 794 2.3 241
Lancaster, PA............ 13.7 243.6 1.8 104 877 2.6 208
Lehigh, PA............... 8.9 194.2 1.1 168 1,002 1.0 322
Luzerne, PA.............. 7.4 145.4 -0.8 338 832 3.9 73
Montgomery, PA........... 27.8 496.4 0.8 194 1,246 2.8 186
Northampton, PA.......... 6.9 116.2 0.8 194 890 2.3 241
Philadelphia, PA......... 35.2 692.4 2.1 85 1,232 1.7 290
Washington, PA........... 5.5 88.7 0.3 262 1,031 4.0 65
Westmoreland, PA......... 9.3 134.2 -0.5 325 867 4.2 52
York, PA................. 9.3 179.9 0.3 262 913 2.0 272
Kent, RI................. 5.5 76.2 0.5 235 906 2.4 232
Providence, RI........... 18.7 290.3 0.7 206 990 -3.4 347
Charleston, SC........... 16.2 251.1 2.3 72 926 2.8 186
Greenville, SC........... 14.8 273.6 2.5 65 889 1.3 312
Horry, SC................ 9.4 130.1 1.3 148 635 0.5 332
Lexington, SC............ 7.0 119.2 2.5 65 796 1.4 309
Richland, SC............. 10.7 223.6 1.6 123 890 0.0 337
Spartanburg, SC.......... 6.6 142.8 3.9 22 863 0.9 323
York, SC................. 6.2 96.0 3.6 30 842 1.4 309
Minnehaha, SD............ 7.4 127.6 1.5 133 925 2.4 232
Davidson, TN............. 23.7 503.5 3.1 43 1,131 6.2 10
Hamilton, TN............. 10.0 207.5 3.0 50 921 2.1 267
Knox, TN................. 12.7 240.3 0.7 206 915 4.6 38
Rutherford, TN........... 5.9 131.0 3.5 33 908 0.3 333
Shelby, TN............... 21.0 501.4 1.5 133 1,060 3.0 163
Williamson, TN........... 9.2 135.9 4.7 14 1,162 3.1 154
Bell, TX................. 5.6 118.0 0.6 219 882 2.2 259
Bexar, TX................ 42.1 867.5 1.2 161 930 2.9 169
Brazoria, TX............. 6.0 113.5 5.5 7 1,101 2.8 186
Brazos, TX............... 4.7 107.0 3.7 28 785 1.7 290
Cameron, TX.............. 6.5 138.3 1.4 139 632 2.6 208
Collin, TX............... 26.1 416.1 3.7 28 1,244 4.1 59
Dallas, TX............... 78.0 1,711.9 1.6 123 1,245 2.6 208
Denton, TX............... 15.5 246.5 2.2 78 946 2.3 241
El Paso, TX.............. 15.3 306.9 1.6 123 735 2.7 196
Fort Bend, TX............ 13.8 190.8 6.5 5 953 1.4 309
Galveston, TX............ 6.2 108.5 1.8 104 912 2.1 267
Harris, TX............... 115.7 2,307.6 2.1 85 1,271 2.1 267
Hidalgo, TX.............. 12.6 258.9 2.3 72 662 2.0 272
Jefferson, TX............ 5.8 123.0 3.3 40 1,060 1.6 299
Lubbock, TX.............. 7.6 139.7 1.0 179 825 4.3 49
McLennan, TX............. 5.3 113.8 1.3 148 871 3.2 141
Midland, TX.............. 5.8 105.7 11.9 1 1,401 7.4 7
Montgomery, TX........... 11.8 185.9 3.8 26 1,007 0.9 323
Nueces, TX............... 8.3 162.0 0.5 235 906 2.6 208
Potter, TX............... 4.0 77.3 0.0 296 851 3.8 86
Smith, TX................ 6.4 103.7 1.3 148 849 2.4 232
Tarrant, TX.............. 44.3 900.5 2.1 85 1,029 3.3 128
Travis, TX............... 41.8 753.0 3.3 40 1,247 4.4 44
Webb, TX................. 5.5 100.9 0.3 262 698 4.2 52
Williamson, TX........... 11.3 172.9 4.6 15 1,016 1.7 290
Davis, UT................ 8.8 131.6 2.0 95 845 2.8 186
Salt Lake, UT............ 46.9 706.9 2.9 53 1,034 4.0 65
Utah, UT................. 17.0 247.5 5.6 6 851 4.2 52
Weber, UT................ 6.3 106.0 2.4 70 810 3.6 101
Chittenden, VT........... 7.0 103.0 0.4 249 1,023 4.1 59
Arlington, VA............ 9.3 177.9 0.9 184 1,691 2.9 169
Chesterfield, VA......... 9.4 136.8 0.0 296 881 1.7 290
Fairfax, VA.............. 37.4 613.7 1.4 139 1,588 3.2 141
Henrico, VA.............. 11.9 191.6 0.3 262 987 3.8 86
Loudoun, VA.............. 12.6 168.7 2.5 65 1,220 2.5 215
Prince William, VA....... 9.5 130.3 2.2 78 929 3.8 86
Alexandria City, VA...... 6.3 91.4 -0.6 330 1,465 2.4 232
Chesapeake City, VA...... 6.2 99.4 0.6 219 826 1.6 299
Newport News City, VA.... 3.9 101.6 3.1 43 977 -1.5 341
Norfolk City, VA......... 6.1 141.4 -1.0 344 1,018 1.9 280
Richmond City, VA........ 7.9 155.2 0.9 184 1,124 0.9 323
Virginia Beach City, VA.. 12.4 176.8 -1.3 346 790 2.9 169
Benton, WA............... 5.9 91.3 2.0 95 1,063 2.9 169
Clark, WA................ 15.1 162.8 2.9 53 1,015 4.6 38
King, WA................. 89.6 1,404.0 2.8 55 1,752 7.9 2
Kitsap, WA............... 6.8 90.5 3.1 43 982 4.1 59
Pierce, WA............... 22.8 312.9 2.1 85 989 4.2 52
Snohomish, WA............ 21.6 289.2 2.2 78 1,132 3.8 86
Spokane, WA.............. 16.3 225.9 2.0 95 913 2.7 196
Thurston, WA............. 8.5 118.8 3.4 38 996 5.1 23
Whatcom, WA.............. 7.4 91.3 1.4 139 898 5.3 21
Yakima, WA............... 7.9 125.4 -0.1 307 764 3.9 73
Kanawha, WV.............. 5.7 98.0 -1.9 348 917 3.9 73
Brown, WI................ 7.2 160.5 1.6 123 917 4.0 65
Dane, WI................. 16.2 335.6 0.5 235 1,028 1.3 312
Milwaukee, WI............ 27.4 490.5 0.4 249 980 2.6 208
Outagamie, WI............ 5.5 108.0 0.3 262 895 2.6 208
Waukesha, WI............. 13.5 244.5 0.5 235 1,022 2.9 169
Winnebago, WI............ 3.9 93.6 0.3 262 936 2.0 272
San Juan, PR............. 10.7 242.0 1.3 (5) 649 6.0 (5)
(1) Includes areas not officially designated as counties. See Technical Note.
(2) Average weekly wages were calculated using unrounded data.
(3) Percent changes were computed from employment and pay data adjusted for noneconomic
county reclassifications. See Technical Note.
(4) Totals for the United States do not include data for Puerto Rico or the Virgin Islands.
(5) This county was not included in the U.S. rankings.
Note: Data are preliminary. Includes workers covered by Unemployment Insurance (UI) and Unemployment
Compensation for Federal Employees (UCFE) programs. These 349 U.S. counties comprise 73.0 percent of
the total covered workers in the U.S.
Table 2. Covered establishments, employment, and wages in the 10 largest counties,
third quarter 2018
Employment Average weekly
wage(1)
Establishments,
third quarter
County by NAICS supersector 2018 Percent Percent
(thousands) September change, Third change,
2018 September quarter third
(thousands) 2017-18(2) 2018 quarter
2017-18(2)
United States(3) ............................ 10,118.0 146,824.1 1.6 $1,055 3.3
Private industry........................... 9,818.2 125,105.2 1.7 1,047 3.5
Natural resources and mining............. 138.5 2,043.3 1.9 1,070 5.2
Construction............................. 815.3 7,427.0 4.1 1,180 3.7
Manufacturing............................ 352.0 12,705.3 1.8 1,249 2.5
Trade, transportation, and utilities..... 1,930.8 27,267.2 0.9 891 3.7
Information.............................. 172.4 2,794.2 0.1 2,161 8.0
Financial activities..................... 895.7 8,178.7 0.8 1,558 2.7
Professional and business services....... 1,849.3 20,961.4 2.0 1,358 3.5
Education and health services............ 1,713.4 22,646.3 1.9 964 2.6
Leisure and hospitality.................. 861.5 16,331.4 1.4 456 3.6
Other services........................... 856.9 4,478.9 1.2 730 4.0
Government................................. 299.8 21,718.9 0.5 1,099 2.0
Los Angeles, CA.............................. 501.6 4,448.3 1.0 1,176 2.3
Private industry........................... 495.2 3,870.6 1.0 1,143 2.3
Natural resources and mining............. 0.5 6.8 -9.8 1,101 9.4
Construction............................. 15.7 146.1 3.0 1,259 5.4
Manufacturing............................ 12.4 340.5 -1.9 1,322 3.7
Trade, transportation, and utilities..... 56.0 831.5 0.1 971 3.3
Information.............................. 11.0 191.0 -0.3 2,429 -5.4
Financial activities..................... 28.1 219.8 -0.8 1,819 3.7
Professional and business services....... 51.6 616.8 0.2 1,453 4.6
Education and health services............ 239.2 806.3 2.1 894 3.0
Leisure and hospitality.................. 35.1 529.8 0.3 659 4.9
Other services........................... 27.2 151.0 -0.7 752 4.9
Government................................. 6.4 577.8 1.6 1,413 2.0
Cook, IL..................................... 139.1 2,617.8 1.1 1,204 3.8
Private industry........................... 137.8 2,320.9 1.1 1,205 3.8
Natural resources and mining............. 0.1 1.4 6.7 1,153 3.9
Construction............................. 11.1 78.8 1.8 1,494 3.5
Manufacturing............................ 5.8 185.4 0.4 1,242 3.2
Trade, transportation, and utilities..... 28.4 471.0 0.8 985 2.9
Information.............................. 2.5 51.6 0.0 1,945 7.5
Financial activities..................... 14.0 199.8 1.2 2,127 5.9
Professional and business services....... 29.2 487.6 1.7 1,518 3.0
Education and health services............ 15.6 452.9 1.9 1,021 3.0
Leisure and hospitality.................. 13.8 294.2 0.6 561 5.1
Other services........................... 15.9 97.5 -1.4 948 4.6
Government................................. 1.3 296.9 1.2 1,192 3.2
New York, NY................................. 128.3 2,454.5 0.4 1,997 4.0
Private industry........................... 126.9 2,224.9 0.5 2,042 4.2
Natural resources and mining............. 0.0 0.2 3.6 1,874 0.9
Construction............................. 2.3 44.3 4.0 1,917 3.0
Manufacturing............................ 1.9 23.1 -5.3 1,484 -4.7
Trade, transportation, and utilities..... 18.9 251.2 -1.1 1,412 2.8
Information.............................. 5.0 174.3 -0.7 2,936 13.9
Financial activities..................... 19.2 381.0 1.7 3,368 -0.6
Professional and business services....... 27.1 589.7 0.1 2,301 5.3
Education and health services............ 10.1 347.6 2.1 1,391 4.1
Leisure and hospitality.................. 14.8 304.9 -0.8 935 4.2
Other services........................... 20.3 103.6 -0.4 1,259 8.2
Government................................. 1.4 229.7 0.3 1,556 1.8
Harris, TX................................... 115.7 2,307.6 2.1 1,271 2.1
Private industry........................... 115.1 2,033.9 2.3 1,283 2.3
Natural resources and mining............. 1.6 67.1 2.2 2,999 1.2
Construction............................. 7.6 161.0 3.4 1,351 5.2
Manufacturing............................ 4.8 176.7 3.6 1,585 -0.7
Trade, transportation, and utilities..... 24.9 469.1 2.0 1,155 1.4
Information.............................. 1.2 25.7 -1.1 1,528 1.4
Financial activities..................... 12.3 128.4 0.7 1,632 3.9
Professional and business services....... 23.2 401.2 1.1 1,614 4.4
Education and health services............ 16.3 297.6 2.6 1,028 0.7
Leisure and hospitality.................. 10.4 237.1 3.6 477 3.7
Other services........................... 11.8 67.3 2.2 810 3.8
Government................................. 0.6 273.7 1.0 1,179 0.3
Maricopa, AZ................................. 101.8 2,004.2 3.1 1,013 2.5
Private industry........................... 101.1 1,790.6 3.5 1,002 2.6
Natural resources and mining............. 0.4 7.4 -0.3 996 4.8
Construction............................. 8.0 123.1 7.3 1,102 4.4
Manufacturing............................ 3.3 124.0 3.0 1,349 0.5
Trade, transportation, and utilities..... 19.7 387.7 3.9 923 3.4
Information.............................. 1.7 36.3 0.1 1,469 5.8
Financial activities..................... 12.5 182.1 2.2 1,288 3.0
Professional and business services....... 23.4 337.3 3.0 1,071 1.9
Education and health services............ 12.3 316.4 4.1 1,015 1.5
Leisure and hospitality.................. 8.7 220.4 2.7 506 3.3
Other services........................... 6.9 53.9 3.9 747 2.8
Government................................. 0.7 213.6 -0.2 1,114 2.4
Dallas, TX................................... 78.0 1,711.9 1.6 1,245 2.6
Private industry........................... 77.5 1,537.3 1.6 1,251 2.7
Natural resources and mining............. 0.5 8.8 17.6 3,380 -14.3
Construction............................. 4.7 90.5 2.5 1,294 4.4
Manufacturing............................ 2.8 112.9 1.5 1,476 5.0
Trade, transportation, and utilities..... 15.9 350.0 2.2 1,099 4.2
Information.............................. 1.4 48.1 -3.2 1,906 4.2
Financial activities..................... 9.7 164.0 -1.7 1,697 0.8
Professional and business services....... 17.8 353.8 2.5 1,445 2.9
Education and health services............ 9.7 201.0 1.5 1,104 2.4
Leisure and hospitality.................. 7.0 162.9 1.8 510 -1.2
Other services........................... 7.0 43.5 1.8 838 5.4
Government................................. 0.5 174.7 1.3 1,191 1.8
Orange, CA................................... 124.5 1,626.3 1.3 1,153 1.7
Private industry........................... 123.1 1,479.7 1.3 1,145 2.0
Natural resources and mining............. 0.2 2.5 -7.6 891 7.7
Construction............................. 7.3 107.9 4.1 1,398 4.8
Manufacturing............................ 5.1 158.5 -0.9 1,462 5.0
Trade, transportation, and utilities..... 17.6 257.1 -0.2 1,027 1.7
Information.............................. 1.4 26.1 -0.7 2,135 9.4
Financial activities..................... 12.3 116.5 -1.6 1,797 -0.8
Professional and business services....... 22.0 313.2 0.7 1,308 0.7
Education and health services............ 35.9 218.8 2.7 969 1.8
Leisure and hospitality.................. 9.0 222.2 1.5 522 5.5
Other services........................... 7.0 46.6 0.3 726 4.0
Government................................. 1.5 146.6 1.7 1,242 -1.7
San Diego, CA................................ 113.8 1,467.1 1.7 1,149 3.2
Private industry........................... 111.8 1,234.5 1.9 1,116 4.0
Natural resources and mining............. 0.6 9.6 1.0 784 1.6
Construction............................. 7.5 85.4 4.2 1,218 1.7
Manufacturing............................ 3.4 111.9 2.0 1,577 3.3
Trade, transportation, and utilities..... 14.7 221.2 -0.1 877 3.3
Information.............................. 1.2 23.4 -3.1 2,324 10.8
Financial activities..................... 10.6 74.6 -0.7 1,465 2.8
Professional and business services....... 19.5 245.9 3.2 1,587 5.2
Education and health services............ 33.3 203.3 2.0 971 1.7
Leisure and hospitality.................. 8.6 200.1 1.1 523 4.8
Other services........................... 7.5 51.1 -2.2 662 6.1
Government................................. 2.0 232.7 0.6 1,331 0.2
King, WA..................................... 89.6 1,404.0 2.8 1,752 7.9
Private industry........................... 89.1 1,236.3 2.9 1,795 8.3
Natural resources and mining............. 0.4 3.1 -2.8 1,378 -0.9
Construction............................. 6.9 75.6 5.0 1,422 4.6
Manufacturing............................ 2.5 103.1 1.2 1,606 0.2
Trade, transportation, and utilities..... 14.1 271.9 1.4 1,703 12.8
Information.............................. 2.4 113.6 8.0 5,549 9.4
Financial activities..................... 6.8 70.3 2.6 1,698 4.2
Professional and business services....... 18.4 231.7 2.6 1,785 6.8
Education and health services............ 20.6 175.8 3.1 1,064 3.3
Leisure and hospitality.................. 7.5 145.4 2.6 619 3.9
Other services........................... 9.4 45.9 3.3 884 2.2
Government................................. 0.5 167.7 1.7 1,434 3.8
Miami-Dade, FL............................... 99.5 1,142.1 3.9 1,001 1.8
Private industry........................... 99.2 1,003.7 4.5 969 2.0
Natural resources and mining............. 0.5 7.9 11.1 686 9.4
Construction............................. 7.0 51.1 12.1 981 2.9
Manufacturing............................ 2.9 41.2 5.0 905 5.8
Trade, transportation, and utilities..... 24.8 284.7 3.6 912 2.1
Information.............................. 1.6 18.5 3.0 1,624 0.9
Financial activities..................... 10.8 75.2 1.0 1,497 1.5
Professional and business services....... 22.7 161.1 4.7 1,163 5.4
Education and health services............ 10.9 182.0 2.8 974 0.8
Leisure and hospitality.................. 7.4 141.2 6.8 608 -4.7
Other services........................... 8.5 39.4 5.0 649 4.5
Government................................. 0.3 138.4 0.2 1,241 1.4
(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 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,
third quarter 2018
Employment Average weekly
wage(1)
Establishments,
third quarter
State 2018 Percent Percent
(thousands) September change, Third change,
2018 September quarter third
(thousands) 2017-18 2018 quarter
2017-18
United States(2)........... 10,118.0 146,824.1 1.6 $1,055 3.3
Alabama.................... 127.8 1,966.0 1.2 885 3.1
Alaska..................... 22.2 334.0 -0.4 1,065 3.7
Arizona.................... 166.0 2,838.6 2.8 974 2.9
Arkansas................... 90.9 1,222.1 0.7 811 2.9
California................. 1,573.5 17,457.5 1.8 1,260 3.8
Colorado................... 206.5 2,684.0 2.1 1,104 3.5
Connecticut................ 121.3 1,681.5 0.3 1,209 2.5
Delaware................... 33.3 447.8 0.6 1,046 2.4
District of Columbia....... 40.5 770.7 0.7 1,807 2.8
Florida.................... 698.6 8,690.7 4.6 924 3.1
Georgia.................... 279.3 4,448.8 2.3 993 3.3
Hawaii..................... 43.0 654.7 0.0 975 2.4
Idaho...................... 64.0 743.5 3.0 805 3.2
Illinois................... 375.1 6,029.2 0.8 1,087 3.0
Indiana.................... 168.0 3,072.3 0.9 883 2.4
Iowa....................... 103.1 1,555.0 0.6 887 3.7
Kansas..................... 89.2 1,390.4 1.0 867 3.5
Kentucky................... 124.4 1,898.7 0.5 855 2.2
Louisiana.................. 133.7 1,915.4 0.5 901 3.7
Maine...................... 53.0 626.5 0.6 851 3.7
Maryland................... 171.3 2,683.9 0.7 1,130 2.4
Massachusetts.............. 258.8 3,598.1 0.7 1,305 3.2
Michigan................... 252.0 4,366.5 0.8 991 2.8
Minnesota.................. 178.8 2,904.3 0.8 1,074 4.2
Mississippi................ 74.9 1,133.7 0.2 754 3.4
Missouri................... 205.0 2,812.0 0.4 907 3.3
Montana.................... 50.6 473.3 1.0 815 2.8
Nebraska................... 73.6 980.3 0.6 873 2.8
Nevada..................... 82.4 1,382.9 3.4 936 2.4
New Hampshire.............. 53.3 662.3 0.5 1,040 1.7
New Jersey................. 273.3 4,072.6 0.8 1,181 2.1
New Mexico................. 60.7 826.2 1.2 855 3.9
New York................... 650.0 9,467.5 1.4 1,272 4.2
North Carolina............. 281.7 4,398.0 1.1 938 3.8
North Dakota............... 32.1 424.3 1.1 995 4.4
Ohio....................... 297.8 5,424.4 0.7 947 2.9
Oklahoma................... 110.3 1,616.8 1.2 874 3.6
Oregon..................... 157.5 1,939.8 1.5 1,005 3.8
Pennsylvania............... 360.8 5,894.8 1.0 1,031 3.0
Rhode Island............... 38.2 489.4 1.0 963 -1.3
South Carolina............. 136.7 2,088.2 2.8 834 0.8
South Dakota............... 33.8 431.5 1.3 827 3.0
Tennessee.................. 163.1 3,005.6 1.7 938 3.9
Texas...................... 693.7 12,327.0 2.6 1,064 3.1
Utah....................... 104.7 1,494.4 3.4 911 3.6
Vermont.................... 25.8 310.9 0.0 892 2.6
Virginia................... 280.5 3,889.6 1.1 1,082 2.9
Washington................. 249.0 3,425.6 2.4 1,280 6.2
West Virginia.............. 51.2 706.0 1.7 894 8.1
Wisconsin.................. 176.6 2,888.9 0.7 901 2.9
Wyoming.................... 26.3 278.2 0.6 905 4.3
Puerto Rico................ 45.4 862.5 0.2 534 5.3
Virgin Islands............. 3.5 33.4 -8.0 888 18.6
(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
