For release 10:00 a.m. (EST), Wednesday, December 7, 2016 USDL-16-2253
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 2016
From June 2015 to June 2016, employment increased in 291 of the 344 largest U.S. counties, the U.S.
Bureau of Labor Statistics reported today. Williamson, Tenn., had the largest percentage increase with a
gain of 6.7 percent over the year, above the national job growth rate of 1.5 percent. Within Williamson,
the largest employment increase occurred in professional and business services, which gained 3,033 jobs
over the year (9.6 percent). Midland, Texas, had the largest over-the-year percentage decrease in
employment among the largest counties in the U.S., with a loss of 8.3 percent. Within Midland, natural
resources and mining had the largest decrease in employment, with a loss of 2,767 jobs (-13.1 percent).
County employment and wage data are from the Quarterly Census of Employment and Wages (QCEW)
program, which provides the only detailed quarterly and annual universe count of establishments,
employment, and wages at the county, MSA, state, and national levels by detailed industry. These data
are published within 6 months following the end of each quarter.
The U.S. average weekly wage increased 2.2 percent over the year, growing to $989 in the second
quarter of 2016. McLean, Ill., had the largest over-the-year percentage increase in average weekly wages
with a gain of 21.0 percent. Within McLean, an average weekly wage gain of $739 (42.2 percent) in
financial activities made the largest contribution to the county’s increase in average weekly wages.
Ventura, Calif., experienced the largest percentage decrease in average weekly wages with a loss of 8.4
percent over the year. Within Ventura, manufacturing had the largest impact on the county’s average
weekly wage decline with a decrease of $912 (-34.4 percent) over the year.
Large County Employment
In June 2016, national employment was 142.7 million (as measured by the QCEW program). Over the
year, employment increased 1.5 percent, or 2.1 million. In June 2016, the 344 U.S. counties with 75,000
or more jobs accounted for 72.5 percent of total U.S. employment and 77.6 percent of total wages. These
344 counties had a net job growth of 1.7 million over the year, accounting for 82.0 percent of the overall
U.S. employment increase. The five counties with the largest increases in employment levels had a
combined over-the-year employment gain of 254,900 jobs, which was 12.1 percent of the overall job
increase for the U.S. (See table A.)
Employment declined in 46 of the largest counties from June 2015 to June 2016. Midland, Texas, had
the largest over-the-year percentage decrease in employment (-8.3 percent), followed by Lafayette, La.;
Gregg, Texas; Peoria, Ill.; McLean, Ill.; and Washington, Pa. (See table 1.)
Table A. Large counties ranked by June 2016 employment, June 2015-16 employment increase, and
June 2015-16 percent increase in employment
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Employment in large counties
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June 2016 employment | Increase in employment, | Percent increase in employment,
(thousands) | June 2015-16 | June 2015-16
| (thousands) |
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| |
United States 142,717.2| United States 2,100.9| United States 1.5
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| |
Los Angeles, Calif. 4,337.3| Los Angeles, Calif. 76.7| Williamson, Tenn. 6.7
Cook, Ill. 2,584.0| Maricopa, Ariz. 51.5| Utah, Utah 6.5
New York, N.Y. 2,415.6| Dallas, Texas 46.2| Loudoun, Va. 5.2
Harris, Texas 2,272.1| King, Wash. 43.8| Williamson, Texas 4.7
Maricopa, Ariz. 1,827.4| New York, N.Y. 36.7| Rutherford, Tenn. 4.6
Dallas, Texas 1,649.4| Fulton, Ga. 31.2| Denton, Texas 4.6
Orange, Calif. 1,557.3| Clark, Nev. 30.7| Lee, Fla. 4.5
San Diego, Calif. 1,405.5| Santa Clara, Calif. 30.0| Seminole, Fla. 4.5
King, Wash. 1,326.1| Orange, Calif. 28.4| Clay, Mo. 4.5
Miami-Dade, Fla. 1,088.1| San Diego, Calif. 27.6| York, S.C. 4.5
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Large County Average Weekly Wages
Average weekly wages for the nation increased to $989, a 2.2 percent increase, during the year ending in
the second quarter of 2016. Among the 344 largest counties, 304 had over-the-year increases in average
weekly wages. McLean, Ill., had the largest percentage wage increase among the largest U.S. counties
(21.0 percent). (See table B.)
Of the 344 largest counties, 36 experienced over-the-year decreases in average weekly wages. Ventura,
Calif., had the largest percentage decrease in average weekly wages (-8.4 percent), followed by Forsyth,
N.C.; Lafayette, La.; Gregg, Texas; and Midland, Texas. (See table 1.)
Table B. Large counties ranked by second quarter 2016 average weekly wages, second quarter 2015-16
increase in average weekly wages, and second quarter 2015-16 percent increase in average weekly wages
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Average weekly wage in large counties
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Average weekly wage, | Increase in average weekly | Percent increase in average
second quarter 2016 | wage, second quarter 2015-16 | weekly wage, second
| | quarter 2015-16
--------------------------------------------------------------------------------------------------------
| |
United States $989| United States $21| United States 2.2
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| |
Santa Clara, Calif. $2,252| McLean, Ill. $201| McLean, Ill. 21.0
San Mateo, Calif. 1,871| Santa Clara, Calif. 112| Elkhart, Ind. 8.5
New York, N.Y. 1,866| King, Wash. 104| King, Wash. 8.1
San Francisco, Calif. 1,806| Washington, Ore. 89| Washington, Ore. 7.4
Washington, D.C. 1,623| Somerset, N.J. 74| Albany, N.Y. 7.0
Suffolk, Mass. 1,571| San Francisco, Calif. 72| Benton, Ark. 6.5
Arlington, Va. 1,559| Albany, N.Y. 71| Nassau, N.Y. 6.4
Fairfield, Conn. 1,535| Nassau, N.Y. 70| Ingham, Mich. 6.0
Somerset, N.J. 1,508| Elkhart, Ind. 69| Tulare, Calif. 5.8
Fairfax, Va. 1,492| Benton, Ark. 61| Napa, Calif. 5.6
| | Kane, Ill. 5.6
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Ten Largest U.S. Counties
Among the 10 largest counties, 9 had over-the-year percentage increases in employment in June 2016.
King, Wash., had the largest gain (3.4 percent). Within King, trade, transportation, and utilities had the
largest over-the-year employment level increase, with a gain of 10,557 jobs, or 4.4 percent. Harris,
Texas, had the only percentage decrease in employment among the 10 largest counties (-0.8 percent).
(See table 2.)
Average weekly wages increased over the year in 8 of the 10 largest U.S. counties. King, Wash., also
experienced the largest percentage gain in average weekly wages (8.1 percent). Within King, trade,
transportation, and utilities had the largest impact on the county’s average weekly wage growth. Within
trade, transportation, and utilities, average weekly wages increased by $257, or 21.9 percent, over the
year. Harris, Texas, had the only percentage loss in average weekly wages among the 10 largest counties
(-0.1 percent).
For More Information
The tables included in this release contain data for the nation and for the 344 U.S. counties with annual
average employment levels of 75,000 or more in 2015. June 2016 employment and 2016 second quarter
average weekly wages for all states are provided in table 3 of this release.
The data are derived from reports submitted by every employer subject to unemployment insurance (UI)
laws. The 9.7 million employer reports cover 142.7 million full- and part-time workers. Data for the
second quarter of 2016 will be available electronically later at www.bls.gov/cew/. For additional
information about the quarterly employment and wages data, please read the Technical Note. Additional
information about the QCEW data may be obtained by calling (202) 691-6567.
Several BLS regional offices issue QCEW news releases targeted to local data users. For links to these
releases, see www.bls.gov/cew/cewregional.htm.
_____________
The County Employment and Wages release for third quarter 2016 is scheduled to be released on
Tuesday, March 7, 2017.
Technical Note
These data are the product of a federal-state cooperative program, the Quarterly Census of
Employment and Wages (QCEW) program, also known as the ES-202 program. The data are
derived from summaries of employment and total pay of workers covered by state and federal
unemployment insurance (UI) legislation and provided by State Workforce Agencies (SWAs). The
summaries are a result of the administration of state unemployment insurance programs that
require most employers to pay quarterly taxes based on the employment and wages of workers
covered by UI. QCEW data in this release are based on the 2012 North American Industry
Classification System. Data for 2016 are preliminary and subject to revision.
For purposes of this release, large counties are defined as having employment levels of 75,000 or
greater. In addition, data for San Juan, Puerto Rico, are provided, but not used in calculating U.S.
averages, rankings, or in the analysis in the text. Each year, these large counties are selected on the
basis of the preliminary annual average of employment for the previous year. The 345 counties
presented in this release were derived using 2015 preliminary annual averages of employment. For
2016 data, four counties have been added to the publication tables: Merced, Calif.; Napa, Calif.;
Bay, Fla.; and Merrimack, N.H. These counties will be included in all 2016 quarterly releases. Two
counties, Black Hawk, Iowa, and Ector, Texas, which were published in the 2015 releases, will be
excluded from this and future 2016 releases because their 2015 annual average employment levels
were less than 75,000. The counties in table 2 are selected and sorted each year based on the annual
average employment from the preceding year.
The preliminary QCEW data presented in this release may differ from data released by the
individual states. These potential differences result from the states' continuing receipt of UI data
over time and ongoing review and editing. The individual states determine their data release
timetables.
Differences between QCEW, BED, and CES employment measures
The Bureau publishes three different establishment-based employment measures for any given
quarter. Each of these measures--QCEW, Business Employment Dynamics (BED), and Current
Employment Statistics (CES)--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- | 623,000 establish-
| submitted by 9.7 | ministrative records| ments
| million establish- | submitted by 7.7 |
| ments in first | million private-sec-|
| quarter of 2016 | 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 6 months | -7 months after the | -Usually first Friday
| after the end of | end of each quarter| of following month
| each quarter | |
-----------|---------------------|----------------------|------------------------
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 national
| data | analysis | economic indicator
| -Periodic universe | -Analysis of employ-| -Official time series
| counts for bench- | er dynamics under- | for employment change
| marking sample | lying economic ex- | measures
| survey estimates | pansions and con- | -Input into other ma-
| -Sample frame for | tractions | jor economic indi-
| BLS establishment | -Analysis of employ-| cators
| surveys | ment expansion and |
| | contraction by size|
| | of firm |
| | |
-----------|---------------------|----------------------|------------------------
Program |--www.bls.gov/cew/ |--www.bls.gov/bdm/ |--www.bls.gov/ces/
Web sites | | |
---------------------------------------------------------------------------------
Coverage
Employment and wage data for workers covered by state UI laws are compiled from quarterly
contribution reports submitted to the SWAs by employers. For federal civilian workers covered by
the Unemployment Compensation for Federal Employees (UCFE) program, employment and
wage data are compiled from quarterly reports submitted by four major federal payroll processing
centers on behalf of all federal agencies, with the exception of a few agencies which still report
directly to the individual SWA. In addition to the quarterly contribution reports, employers who
operate multiple establishments within a state complete a questionnaire, called the "Multiple
Worksite Report," which provides detailed information on the location and industry of each of their
establishments. QCEW employment and wage data are derived from microdata summaries of 9.5
million employer reports of employment and wages submitted by states to the BLS in 2015. 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 2015, UI and UCFE programs
covered workers in 139.5 million jobs. The estimated 134.4 million workers in these jobs (after
adjustment for multiple jobholders) represented 96.5 percent of civilian wage and salary
employment. Covered workers received $7.385 trillion in pay, representing 94.0 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 organizations.
State and federal UI laws change periodically. These changes may have an impact on the
employment and wages reported by employers covered under the UI program. Coverage changes
may affect the over-the-year comparisons presented in this news release.
Concepts and methodology
Monthly employment is based on the number of workers who worked during or received pay for
the pay period including the 12th of the month. With few exceptions, all employees of covered
firms are reported, including production and sales workers, corporation officials, executives,
supervisory personnel, and clerical workers. Workers on paid vacations and part-time workers also
are included.
Average weekly wage values are calculated by dividing quarterly total wages by the average of the
three monthly employment levels (all employees, as described above) and dividing the result by
13, for the 13 weeks in the quarter. These calculations are made using unrounded employment and
wage values. The average wage values that can be calculated using rounded data from the BLS
database may differ from the averages reported. Included in the quarterly wage data are non-wage
cash payments such as bonuses, the cash value of meals and lodging when supplied, tips and other
gratuities, and, in some states, employer contributions to certain deferred compensation plans such
as 401(k) plans and stock options. Over-the-year comparisons of average weekly wages may
reflect fluctuations in average monthly employment and/or total quarterly wages between the
current quarter and prior year levels.
Average weekly wages are affected by the ratio of full-time to part-time workers as well as the
number of individuals in high-paying and low-paying occupations and the incidence of pay periods
within a quarter. For instance, the average weekly wage of the workforce could increase
significantly when there is a large decline in the number of employees that had been receiving
below-average wages. Wages may include payments to workers not present in the employment
counts because they did not work during the pay period including the 12th of the month. When
comparing average weekly wage levels between industries, states, or quarters, these factors should
be taken into consideration.
Wages measured by QCEW may be subject to periodic and sometimes large fluctuations. This
variability may be due to calendar effects resulting from some quarters having more pay dates than
others. The effect is most visible in counties with a dominant employer. In particular, this effect
has been observed in counties where government employers represent a large fraction of overall
employment. Similar calendar effects can result from private sector pay practices. However, these
effects are typically less pronounced for two reasons: employment is less concentrated in a single
private employer, and private employers use a variety of pay period types (weekly, biweekly,
semimonthly, monthly).
For example, the effect on over-the-year pay comparisons can be pronounced in federal
government due to the uniform nature of federal payroll processing. Most federal employees are
paid on a biweekly pay schedule. As a result, in some quarters federal wages include six pay dates,
while in other quarters there are seven pay dates. Over-the-year comparisons of average weekly
wages may also reflect this calendar effect. Growth in average weekly wages may be attributed, in
part, to a comparison of quarterly wages for the current year, which include seven pay dates, with
year-ago wages that reflect only six pay dates. An opposite effect will occur when wages in the
current quarter reflecting six pay dates are compared with year-ago wages for a quarter including
seven pay dates.
In order to ensure the highest possible quality of data, states verify with employers and update, if
necessary, the industry, location, and ownership classification of all establishments on a 3 year
cycle. Changes in establishment classification codes resulting from this process are introduced with
the data reported for the first quarter of the year. Changes resulting from improved employer
reporting also are introduced in the first quarter.
QCEW data are not designed as a time series. QCEW data are simply the sums of individual
establishment records and reflect the number of establishments that exist in a county or industry at
a point in time. Establishments can move in or out of a county or industry for a number of reasons-
-some reflecting economic events, others reflecting 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 2015 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
account for most of the administrative changes--those occurring when employers update the
industry, location, and ownership information of their establishments. The most common
adjustments for administrative change are the result of updated information about the county
location of individual establishments. Included in these adjustments are administrative changes
involving the classification of establishments that were previously reported in the unknown or
statewide county or unknown industry categories. Adjusted data account for improvements in
reporting employment and wages for individual and multi-unit establishments. To accomplish this,
adjustments were implemented to account for: administrative changes caused by multi-unit
employers who start reporting for each individual establishment rather than as a single entity (first
quarter of 2008); selected large administrative changes in employment and wages (second quarter
of 2011); and state verified improvements in reporting of employment and wages (third quarter of
2014). These adjustments allow QCEW to include county employment and wage growth rates in
this news release that would otherwise not meet publication standards.
The adjusted data used to calculate the over-the-year change measures presented in any County
Employment and Wages news release are valid for comparisons between the starting and ending
points (a 12-month period) used in that particular release. Comparisons may not be valid for any
time period other than the one featured in a release even if the changes were calculated using
adjusted data.
County definitions are assigned according to Federal Information Processing Standards
Publications (FIPS PUBS) as issued by the National Institute of Standards and Technology, after
approval by the Secretary of Commerce pursuant to Section 5131 of the Information Technology
Management Reform Act of 1996 and the Computer Security Act of 1987, Public Law 104-106.
Areas shown as counties include those designated as independent cities in some jurisdictions and,
in Alaska, those designated as census areas where counties have not been created. County data also
are presented for the New England states for comparative purposes even though townships are the
more common designation used in New England (and New Jersey). The regions referred to in this
release are defined as census regions.
Additional statistics and other information
Employment and Wages Annual Averages Online features comprehensive information by detailed
industry on establishments, employment, and wages for the nation and all states. The 2015 edition
of this publication, which was published in September 2016, contains selected data produced by
Business Employment Dynamics (BED) on job gains and losses, as well as selected data from the
first quarter 2016 version of this news release. Tables and additional content from the 2015 edition
of Employment and Wages Annual Averages Online are now available at
http://www.bls.gov/cew/cewbultn15.htm. The 2016 edition of Employment and Wages Annual
Averages Online will be available in September 2017.
News releases on quarterly measures of gross job flows also are available upon request from the
Division of Administrative Statistics and Labor Turnover (Business Employment Dynamics),
telephone (202) 691-6467; (http://www.bls.gov/bdm/); (e-mail: BDMInfo@bls.gov).
Information in this release will be made available to sensory impaired individuals upon request.
Voice phone: (202) 691-5200; TDD message referral phone number: 1-800-877-8339.
Table 1. Covered establishments, employment, and wages in the 345 largest counties,
second quarter 2016
Employment Average weekly wage(2)
Establishments,
County(1) second quarter Percent Ranking Percent Ranking
2016 June change, by Second change, by
(thousands) 2016 June percent quarter second percent
(thousands) 2015-16(3) change 2016 quarter change
2015-16(3)
United States(4)......... 9,741.4 142,717.2 1.5 - $989 2.2 -
Jefferson, AL............ 18.1 341.2 0.6 257 967 2.3 172
Madison, AL.............. 9.4 191.7 2.7 76 1,050 -0.2 311
Mobile, AL............... 9.9 170.3 1.4 179 844 2.2 181
Montgomery, AL........... 6.4 132.0 1.7 148 834 1.5 241
Shelby, AL............... 5.6 84.5 0.6 257 922 2.8 107
Tuscaloosa, AL........... 4.4 91.2 -0.2 303 811 0.1 304
Anchorage Borough, AK.... 8.3 152.3 -2.1 335 1,050 -1.8 333
Maricopa, AZ............. 94.8 1,827.4 2.9 66 970 2.2 181
Pima, AZ................. 18.7 351.9 1.1 204 827 0.0 305
Benton, AR............... 6.1 115.3 2.8 69 994 6.5 6
Pulaski, AR.............. 14.4 247.5 1.3 188 896 1.8 224
Washington, AR........... 5.9 104.2 3.4 36 809 3.5 66
Alameda, CA.............. 61.1 753.8 2.4 95 1,301 3.4 73
Butte, CA................ 8.2 80.6 2.2 112 749 3.0 100
Contra Costa, CA......... 31.5 361.2 3.2 48 1,203 3.5 66
Fresno, CA............... 33.5 383.4 2.6 84 775 4.0 52
Kern, CA................. 18.0 315.3 1.2 200 824 2.0 201
Los Angeles, CA.......... 467.7 4,337.3 1.8 142 1,079 2.8 107
Marin, CA................ 12.4 115.3 1.4 179 1,268 2.8 107
Merced, CA............... 6.3 78.0 1.6 160 761 5.5 12
Monterey, CA............. 13.5 204.4 2.2 112 839 4.1 47
Napa, CA................. 5.8 77.5 0.6 257 977 5.6 10
Orange, CA............... 114.8 1,557.3 1.9 134 1,103 1.8 224
Placer, CA............... 12.4 157.2 4.3 12 997 4.2 41
Riverside, CA............ 59.5 688.0 3.8 23 811 -1.6 330
Sacramento, CA........... 55.3 639.5 2.9 66 1,069 2.7 122
San Bernardino, CA....... 55.6 703.7 2.5 87 843 2.7 122
San Diego, CA............ 106.7 1,405.5 2.0 128 1,073 0.0 305
San Francisco, CA........ 59.7 700.3 4.0 18 1,806 4.2 41
San Joaquin, CA.......... 17.4 238.4 1.6 160 829 4.3 35
San Luis Obispo, CA...... 10.2 116.0 2.1 125 836 4.6 28
San Mateo, CA............ 27.5 390.7 2.8 69 1,871 -0.8 321
Santa Barbara, CA........ 15.2 197.7 0.1 287 947 -0.7 320
Santa Clara, CA.......... 70.2 1,047.1 3.0 59 2,252 5.2 17
Santa Cruz, CA........... 9.5 107.8 1.4 179 902 4.6 28
Solano, CA............... 10.9 136.8 2.5 87 1,014 1.4 246
Sonoma, CA............... 19.6 202.6 2.3 105 936 4.9 21
Stanislaus, CA........... 14.9 185.4 3.0 59 819 1.9 216
Tulare, CA............... 9.9 165.1 1.4 179 706 5.8 9
Ventura, CA.............. 26.1 321.1 0.5 270 986 -8.4 344
Yolo, CA................. 6.5 100.7 1.5 169 1,058 5.5 12
Adams, CO................ 10.4 200.6 3.0 59 956 2.7 122
Arapahoe, CO............. 21.3 323.5 1.9 134 1,118 2.4 156
Boulder, CO.............. 14.7 178.2 2.3 105 1,140 0.2 302
Denver, CO............... 30.6 495.0 2.4 95 1,175 -0.3 313
Douglas, CO.............. 11.4 119.2 2.2 112 1,084 -3.0 338
El Paso, CO.............. 18.6 266.7 3.1 53 877 1.6 235
Jefferson, CO............ 19.4 235.7 1.9 134 1,004 2.4 156
Larimer, CO.............. 11.6 155.6 3.6 32 866 2.6 133
Weld, CO................. 6.9 100.2 -1.3 329 849 -1.8 333
Fairfield, CT............ 35.0 431.6 0.1 287 1,535 2.5 146
Hartford, CT............. 27.4 511.2 -0.4 308 1,194 2.6 133
New Haven, CT............ 23.7 365.2 0.3 276 1,045 3.7 59
New London, CT........... 7.4 124.2 -0.1 298 1,004 4.4 31
New Castle, DE........... 19.4 287.5 0.6 257 1,099 -1.7 331
Washington, DC........... 38.1 756.0 1.7 148 1,623 1.1 265
Alachua, FL.............. 7.0 124.1 2.4 95 855 3.4 73
Bay, FL.................. 5.5 78.6 1.7 148 730 3.4 73
Brevard, FL.............. 15.2 199.4 3.2 48 875 1.9 216
Broward, FL.............. 67.6 770.4 2.2 112 926 2.1 189
Collier, FL.............. 13.3 131.4 4.0 18 868 2.7 122
Duval, FL................ 28.5 484.2 3.0 59 933 2.1 189
Escambia, FL............. 8.0 128.5 2.4 95 784 2.6 133
Hillsborough, FL......... 40.5 656.9 3.4 36 950 3.1 93
Lake, FL................. 7.8 88.7 3.5 35 681 2.4 156
Lee, FL.................. 21.0 242.0 4.5 7 803 3.6 61
Leon, FL................. 8.5 143.7 1.4 179 816 2.1 189
Manatee, FL.............. 10.3 114.5 2.4 95 776 3.5 66
Marion, FL............... 8.1 98.3 3.4 36 718 5.4 14
Miami-Dade, FL........... 95.7 1,088.1 2.5 87 958 2.6 133
Okaloosa, FL............. 6.3 82.0 3.1 53 828 3.1 93
Orange, FL............... 40.3 782.5 2.8 69 867 2.4 156
Osceola, FL.............. 6.5 86.4 4.1 17 692 1.3 254
Palm Beach, FL........... 54.2 580.6 4.2 15 963 2.0 201
Pasco, FL................ 10.5 106.4 3.9 20 734 2.5 146
Pinellas, FL............. 32.1 415.8 2.2 112 876 3.1 93
Polk, FL................. 12.8 203.1 2.6 84 768 4.3 35
Sarasota, FL............. 15.4 159.2 2.4 95 816 0.5 296
Seminole, FL............. 14.5 181.5 4.5 7 847 2.7 122
Volusia, FL.............. 13.9 163.7 3.8 23 730 2.4 156
Bibb, GA................. 4.5 81.5 1.7 148 772 2.3 172
Chatham, GA.............. 8.7 149.6 2.5 87 831 1.1 265
Clayton, GA.............. 4.5 120.3 2.8 69 934 2.8 107
Cobb, GA................. 23.9 346.4 3.0 59 1,036 2.0 201
DeKalb, GA............... 19.7 294.3 1.6 160 1,017 2.7 122
Fulton, GA............... 47.1 823.3 3.9 20 1,287 2.6 133
Gwinnett, GA............. 27.0 344.6 3.2 48 964 2.7 122
Hall, GA................. 4.7 81.6 2.1 125 810 2.5 146
Muscogee, GA............. 4.9 92.4 -0.4 308 775 2.0 201
Richmond, GA............. 4.8 104.0 1.0 218 820 2.1 189
Honolulu, HI............. 25.5 468.3 0.8 238 942 3.4 73
Ada, ID.................. 14.7 227.6 4.3 12 858 3.2 87
Champaign, IL............ 4.3 89.8 -1.9 333 857 2.6 133
Cook, IL................. 151.8 2,584.0 0.9 229 1,146 2.6 133
DuPage, IL............... 37.8 619.7 0.4 275 1,118 1.2 259
Kane, IL................. 13.6 210.1 -1.0 324 880 5.6 10
Lake, IL................. 22.2 339.6 -0.5 312 1,263 1.1 265
McHenry, IL.............. 8.7 99.5 1.0 218 824 4.2 41
McLean, IL............... 3.8 83.1 -2.5 338 1,159 21.0 1
Madison, IL.............. 5.9 97.3 -0.9 322 779 -0.6 317
Peoria, IL............... 4.6 100.0 -3.2 340 928 2.7 122
St. Clair, IL............ 5.4 92.5 0.0 292 767 0.9 277
Sangamon, IL............. 5.2 129.7 -1.5 330 996 1.4 246
Will, IL................. 16.0 233.4 1.6 160 878 2.6 133
Winnebago, IL............ 6.6 128.4 -1.6 332 831 2.3 172
Allen, IN................ 8.8 183.5 0.0 292 804 5.0 20
Elkhart, IN.............. 4.7 128.6 2.3 105 885 8.5 2
Hamilton, IN............. 9.1 138.4 3.1 53 927 2.0 201
Lake, IN................. 10.3 186.9 -0.7 317 838 1.1 265
Marion, IN............... 23.7 590.2 0.8 238 981 2.4 156
St. Joseph, IN........... 5.7 123.2 1.5 169 798 3.2 87
Tippecanoe, IN........... 3.4 82.6 1.5 169 852 4.5 30
Vanderburgh, IN.......... 4.8 107.4 0.8 238 790 0.3 300
Johnson, IA.............. 4.1 82.9 1.3 188 916 2.0 201
Linn, IA................. 6.6 131.8 -0.3 306 946 2.5 146
Polk, IA................. 17.0 297.2 1.6 160 974 3.2 87
Scott, IA................ 5.6 91.6 -0.6 314 794 1.4 246
Johnson, KS.............. 23.1 338.7 0.3 276 1,020 0.0 305
Sedgwick, KS............. 12.7 248.7 0.2 283 858 0.7 289
Shawnee, KS.............. 5.2 97.6 0.8 238 802 0.9 277
Wyandotte, KS............ 3.6 91.1 1.6 160 928 3.5 66
Boone, KY................ 4.3 83.6 1.4 179 903 4.3 35
Fayette, KY.............. 10.8 192.1 1.7 148 882 2.0 201
Jefferson, KY............ 25.2 463.3 2.4 95 971 1.9 216
Caddo, LA................ 7.2 114.7 -0.6 314 797 1.7 229
Calcasieu, LA............ 5.1 93.9 1.1 204 860 3.7 59
East Baton Rouge, LA..... 15.1 265.2 0.6 257 933 2.8 107
Jefferson, LA............ 13.5 194.2 -0.7 317 868 1.2 259
Lafayette, LA............ 9.3 128.8 -5.8 342 859 -6.2 342
Orleans, LA.............. 12.1 192.1 0.0 292 925 2.0 201
St. Tammany, LA.......... 7.9 87.7 0.9 229 819 0.6 293
Cumberland, ME........... 13.6 183.4 1.5 169 902 3.9 54
Anne Arundel, MD......... 15.1 268.8 1.5 169 1,046 3.0 100
Baltimore, MD............ 21.3 377.4 0.8 238 973 2.4 156
Frederick, MD............ 6.4 100.0 -0.1 298 913 0.6 293
Harford, MD.............. 5.8 92.3 1.0 218 939 -2.4 335
Howard, MD............... 10.0 169.7 0.9 229 1,197 1.7 229
Montgomery, MD........... 32.8 471.0 0.7 253 1,319 2.3 172
Prince George's, MD...... 15.9 311.5 0.0 292 1,020 1.6 235
Baltimore City, MD....... 13.6 337.6 0.5 270 1,137 4.3 35
Barnstable, MA........... 9.4 106.2 0.5 270 833 3.5 66
Bristol, MA.............. 17.4 227.4 1.1 204 938 4.3 35
Essex, MA................ 24.5 328.4 0.6 257 1,054 2.9 105
Hampden, MA.............. 17.8 208.0 -0.1 298 885 0.8 284
Middlesex, MA............ 54.2 893.1 1.1 204 1,470 -1.3 326
Norfolk, MA.............. 25.0 355.3 1.3 188 1,162 2.2 181
Plymouth, MA............. 15.5 193.6 0.8 238 954 3.0 100
Suffolk, MA.............. 28.4 658.6 2.5 87 1,571 4.0 52
Worcester, MA............ 24.4 343.9 1.1 204 992 3.4 73
Genesee, MI.............. 6.8 134.8 0.5 270 827 3.5 66
Ingham, MI............... 6.0 149.0 2.7 76 948 6.0 8
Kalamazoo, MI............ 5.0 117.6 1.2 200 914 4.7 24
Kent, MI................. 14.2 391.0 3.3 43 850 2.4 156
Macomb, MI............... 17.6 326.1 1.9 134 980 2.3 172
Oakland, MI.............. 39.0 731.8 1.8 142 1,090 2.1 189
Ottawa, MI............... 5.6 125.5 3.8 23 841 4.3 35
Saginaw, MI.............. 3.9 85.4 0.8 238 787 4.7 24
Washtenaw, MI............ 8.1 202.5 1.1 204 1,076 4.4 31
Wayne, MI................ 30.5 715.7 1.2 200 1,087 2.4 156
Anoka, MN................ 6.7 121.7 1.0 218 959 3.8 58
Dakota, MN............... 9.4 186.4 0.0 292 965 1.8 224
Hennepin, MN............. 39.1 906.6 1.4 179 1,211 0.9 277
Olmsted, MN.............. 3.2 97.1 2.0 128 1,033 2.8 107
Ramsey, MN............... 12.6 326.4 -0.9 322 1,118 3.9 54
St. Louis, MN............ 5.1 98.2 -1.1 327 784 0.5 296
Stearns, MN.............. 4.2 86.2 0.8 238 828 3.4 73
Washington, MN........... 5.2 83.6 2.5 87 834 2.8 107
Harrison, MS............. 4.5 85.2 1.0 218 698 1.9 216
Hinds, MS................ 5.9 121.1 0.1 287 843 1.9 216
Boone, MO................ 4.9 92.4 1.3 188 791 5.2 17
Clay, MO................. 5.5 103.9 4.5 7 881 0.9 277
Greene, MO............... 8.5 163.8 1.1 204 767 3.4 73
Jackson, MO.............. 21.0 365.7 1.5 169 986 0.9 277
St. Charles, MO.......... 9.0 146.3 2.7 76 827 4.8 23
St. Louis, MO............ 36.3 603.2 1.1 204 1,043 2.7 122
St. Louis City, MO....... 13.3 226.6 0.6 257 1,027 1.1 265
Yellowstone, MT.......... 6.5 82.6 1.1 204 846 0.7 289
Douglas, NE.............. 19.0 337.8 1.3 188 913 2.6 133
Lancaster, NE............ 10.2 168.6 1.0 218 787 1.4 246
Clark, NV................ 55.9 939.5 3.4 36 866 2.5 146
Washoe, NV............... 14.9 210.6 4.3 12 874 2.0 201
Hillsborough, NH......... 12.2 201.5 1.7 148 1,050 1.8 224
Merrimack, NH............ 5.1 77.0 1.0 218 908 0.4 298
Rockingham, NH........... 10.9 149.2 0.9 229 997 4.4 31
Atlantic, NJ............. 6.6 131.9 -0.7 317 835 2.3 172
Bergen, NJ............... 33.0 453.4 0.6 257 1,173 0.9 277
Burlington, NJ........... 11.0 205.1 1.3 188 1,012 0.7 289
Camden, NJ............... 12.0 204.6 2.7 76 954 1.5 241
Essex, NJ................ 20.5 341.0 1.5 169 1,179 2.6 133
Gloucester, NJ........... 6.3 106.3 2.6 84 867 3.3 85
Hudson, NJ............... 14.8 252.6 3.3 43 1,300 -1.7 331
Mercer, NJ............... 11.2 248.1 2.2 112 1,224 1.1 265
Middlesex, NJ............ 22.0 415.6 2.4 95 1,161 1.8 224
Monmouth, NJ............. 20.1 267.5 1.3 188 976 2.1 189
Morris, NJ............... 17.0 291.5 0.7 253 1,426 2.1 189
Ocean, NJ................ 13.0 172.4 2.5 87 795 1.4 246
Passaic, NJ.............. 12.4 168.6 0.9 229 964 -1.5 328
Somerset, NJ............. 10.1 188.5 2.3 105 1,508 5.2 17
Union, NJ................ 14.3 220.4 0.9 229 1,288 0.8 284
Bernalillo, NM........... 18.3 323.2 1.1 204 853 3.0 100
Albany, NY............... 10.4 233.3 0.6 257 1,082 7.0 5
Bronx, NY................ 18.7 300.6 0.7 253 943 1.5 241
Broome, NY............... 4.6 87.2 -0.1 298 801 3.6 61
Dutchess, NY............. 8.5 112.1 0.2 283 992 1.2 259
Erie, NY................. 24.8 471.3 0.6 257 879 3.9 54
Kings, NY................ 61.5 690.4 3.8 23 823 1.6 235
Monroe, NY............... 19.0 388.7 0.6 257 933 1.7 229
Nassau, NY............... 54.2 635.3 1.9 134 1,168 6.4 7
New York, NY............. 130.2 2,415.6 1.5 169 1,866 1.2 259
Oneida, NY............... 5.4 105.7 0.8 238 788 0.9 277
Onondaga, NY............. 13.1 246.6 0.8 238 921 3.4 73
Orange, NY............... 10.4 143.3 1.7 148 881 3.2 87
Queens, NY............... 52.4 648.7 1.6 160 941 3.5 66
Richmond, NY............. 9.8 115.6 2.4 95 887 3.6 61
Rockland, NY............. 10.7 123.3 1.3 188 998 1.3 254
Saratoga, NY............. 6.0 86.9 0.9 229 938 2.4 156
Suffolk, NY.............. 52.9 672.2 0.7 253 1,080 4.7 24
Westchester, NY.......... 36.7 431.1 1.0 218 1,294 1.2 259
Buncombe, NC............. 9.0 127.2 3.7 29 760 4.7 24
Catawba, NC.............. 4.4 85.8 4.4 11 759 3.4 73
Cumberland, NC........... 6.2 120.2 1.1 204 750 -0.9 322
Durham, NC............... 8.1 197.1 2.4 95 1,197 -0.1 309
Forsyth, NC.............. 9.2 182.7 1.7 148 868 -6.5 343
Guilford, NC............. 14.3 275.2 0.6 257 856 2.6 133
Mecklenburg, NC.......... 37.1 662.2 3.7 29 1,108 2.8 107
New Hanover, NC.......... 7.9 110.2 3.0 59 790 1.9 216
Wake, NC................. 33.3 534.6 3.9 20 989 2.2 181
Cass, ND................. 7.0 118.0 0.6 257 883 2.0 201
Butler, OH............... 7.6 149.2 2.3 105 866 0.8 284
Cuyahoga, OH............. 35.6 723.3 0.1 287 995 2.5 146
Delaware, OH............. 5.0 87.2 1.8 142 954 1.1 265
Franklin, OH............. 31.3 735.5 1.9 134 987 1.2 259
Hamilton, OH............. 23.6 513.9 1.3 188 1,032 2.1 189
Lake, OH................. 6.3 96.3 -0.2 303 797 -1.5 328
Lorain, OH............... 6.2 98.3 -0.5 312 772 2.8 107
Lucas, OH................ 10.1 213.7 2.2 112 867 4.1 47
Mahoning, OH............. 5.9 98.1 0.2 283 684 0.7 289
Montgomery, OH........... 11.9 251.7 0.5 270 850 1.9 216
Stark, OH................ 8.6 159.7 -0.4 308 731 0.8 284
Summit, OH............... 14.2 266.1 0.3 276 871 2.5 146
Warren, OH............... 4.8 93.7 1.5 169 914 4.2 41
Cleveland, OK............ 5.6 79.4 -0.2 303 743 3.2 87
Oklahoma, OK............. 27.5 447.3 -1.0 324 917 2.0 201
Tulsa, OK................ 22.1 348.8 -1.0 324 892 0.3 300
Clackamas, OR............ 14.3 159.7 2.3 105 936 2.1 189
Jackson, OR.............. 7.2 85.7 3.3 43 749 3.9 54
Lane, OR................. 11.8 152.6 2.7 76 783 2.1 189
Marion, OR............... 10.3 153.2 2.8 69 821 4.2 41
Multnomah, OR............ 33.4 492.9 2.5 87 1,012 3.1 93
Washington, OR........... 18.6 284.9 3.2 48 1,291 7.4 4
Allegheny, PA............ 36.0 698.6 0.3 276 1,045 1.5 241
Berks, PA................ 9.0 171.2 0.3 276 901 1.0 274
Bucks, PA................ 20.0 264.9 1.1 204 939 1.3 254
Butler, PA............... 5.1 85.9 -0.4 308 910 1.1 265
Chester, PA.............. 15.6 250.6 1.2 200 1,263 -3.1 339
Cumberland, PA........... 6.4 132.1 0.0 292 893 -1.0 324
Dauphin, PA.............. 7.6 183.7 1.0 218 946 -0.5 316
Delaware, PA............. 14.1 222.2 1.6 160 1,064 4.1 47
Erie, PA................. 7.1 124.3 -2.0 334 772 2.1 189
Lackawanna, PA........... 5.8 97.2 -0.6 314 759 4.4 31
Lancaster, PA............ 13.4 236.7 2.0 128 820 2.0 201
Lehigh, PA............... 8.8 188.7 1.6 160 978 2.9 105
Luzerne, PA.............. 7.5 145.3 1.7 148 768 0.8 284
Montgomery, PA........... 27.7 487.5 0.8 238 1,203 1.9 216
Northampton, PA.......... 6.8 112.1 2.7 76 845 1.4 246
Philadelphia, PA......... 35.3 661.6 1.7 148 1,150 1.0 274
Washington, PA........... 5.6 86.7 -2.5 338 934 -1.4 327
Westmoreland, PA......... 9.4 134.8 -0.8 320 781 -0.3 313
York, PA................. 9.1 177.2 0.9 229 849 2.5 146
Providence, RI........... 17.7 285.1 0.1 287 993 3.4 73
Charleston, SC........... 14.3 245.2 3.3 43 880 4.9 21
Greenville, SC........... 13.4 263.3 1.8 142 863 3.2 87
Horry, SC................ 8.4 130.2 2.8 69 598 5.3 15
Lexington, SC............ 6.6 116.1 3.1 53 756 2.7 122
Richland, SC............. 9.8 215.9 1.7 148 849 1.7 229
Spartanburg, SC.......... 6.0 131.9 3.6 32 864 2.2 181
York, SC................. 5.2 89.6 4.5 7 784 3.0 100
Minnehaha, SD............ 7.1 125.9 0.8 238 847 2.8 107
Davidson, TN............. 21.3 470.0 3.1 53 1,013 -2.6 337
Hamilton, TN............. 9.2 198.1 2.2 112 875 0.6 293
Knox, TN................. 11.8 234.9 2.1 125 850 2.8 107
Rutherford, TN........... 5.2 119.0 4.6 5 912 3.1 93
Shelby, TN............... 20.0 491.5 0.8 238 974 2.2 181
Williamson, TN........... 8.2 125.8 6.7 1 1,088 1.6 235
Bell, TX................. 5.2 119.3 3.8 23 814 4.1 47
Bexar, TX................ 39.5 837.1 2.0 128 876 2.3 172
Brazoria, TX............. 5.5 104.4 -1.1 327 992 -0.2 311
Brazos, TX............... 4.4 97.5 3.1 53 725 -0.4 315
Cameron, TX.............. 6.5 139.4 2.2 112 602 2.4 156
Collin, TX............... 23.2 380.9 3.6 32 1,150 0.4 298
Dallas, TX............... 74.5 1,649.4 2.9 66 1,184 2.2 181
Denton, TX............... 13.9 230.4 4.6 5 894 2.6 133
El Paso, TX.............. 14.7 295.3 1.5 169 694 2.8 107
Fort Bend, TX............ 12.3 175.4 2.2 112 920 -2.4 335
Galveston, TX............ 6.1 108.6 3.2 48 874 1.3 254
Gregg, TX................ 4.2 74.0 -3.5 341 814 -3.7 341
Harris, TX............... 112.5 2,272.1 -0.8 320 1,233 -0.1 309
Hidalgo, TX.............. 12.1 248.4 1.3 188 626 2.0 201
Jefferson, TX............ 5.9 122.6 (5) - 1,015 1.5 241
Lubbock, TX.............. 7.4 137.0 2.7 76 762 1.6 235
McLennan, TX............. 5.1 110.4 2.7 76 821 4.1 47
Midland, TX.............. 5.4 82.8 -8.3 343 1,192 -3.2 340
Montgomery, TX........... 10.8 168.2 2.0 128 978 -0.6 317
Nueces, TX............... 8.3 159.6 -2.1 335 844 0.2 302
Potter, TX............... 4.0 79.2 0.2 283 789 2.3 172
Smith, TX................ 6.1 103.2 2.2 112 821 2.2 181
Tarrant, TX.............. 41.7 856.6 1.9 134 972 1.7 229
Travis, TX............... 38.5 707.6 2.8 69 1,120 3.3 85
Webb, TX................. 5.2 98.0 1.3 188 659 1.1 265
Williamson, TX........... 9.9 160.5 4.7 4 933 0.0 305
Davis, UT................ 8.1 122.1 3.4 36 797 2.4 156
Salt Lake, UT............ 43.0 669.4 3.8 23 942 2.4 156
Utah, UT................. 15.0 222.3 6.5 2 802 2.8 107
Weber, UT................ 5.9 102.3 1.7 148 747 2.3 172
Chittenden, VT........... 6.6 102.5 -0.3 306 975 2.8 107
Arlington, VA............ 9.5 174.0 1.8 142 1,559 1.4 246
Chesterfield, VA......... 8.9 135.2 2.2 112 840 1.4 246
Fairfax, VA.............. 37.8 603.7 1.3 188 1,492 -0.9 322
Henrico, VA.............. 11.6 191.1 1.8 142 965 4.2 41
Loudoun, VA.............. 12.1 163.9 5.2 3 1,132 3.1 93
Prince William, VA....... 9.4 129.1 3.4 36 859 2.6 133
Alexandria City, VA...... 6.7 96.2 0.8 238 1,357 1.6 235
Chesapeake City, VA...... 6.1 98.6 0.3 276 787 1.0 274
Newport News City, VA.... 3.9 96.3 -2.4 337 911 -1.1 325
Norfolk City, VA......... 5.9 139.8 -0.1 298 970 2.4 156
Richmond City, VA........ 7.9 149.5 1.4 179 1,061 1.3 254
Virginia Beach City, VA.. 12.2 182.0 2.0 128 761 2.1 189
Benton, WA............... 5.7 90.3 1.0 218 997 2.0 201
Clark, WA................ 14.2 151.0 4.2 15 903 2.8 107
King, WA................. 85.5 1,326.1 3.4 36 1,393 8.1 3
Kitsap, WA............... 6.6 86.5 1.1 204 889 3.4 73
Pierce, WA............... 21.6 297.5 3.7 29 904 2.4 156
Snohomish, WA............ 20.5 284.9 2.2 112 1,071 3.1 93
Spokane, WA.............. 15.5 217.4 3.0 59 833 2.5 146
Thurston, WA............. 8.1 110.5 3.3 43 897 3.6 61
Whatcom, WA.............. 7.2 88.8 0.9 229 803 -0.6 317
Yakima, WA............... 7.7 122.5 1.0 218 687 3.6 61
Kanawha, WV.............. 5.8 102.5 -1.5 330 865 2.5 146
Brown, WI................ 6.7 155.4 0.8 238 860 2.4 156
Dane, WI................. 14.9 330.8 2.3 105 1,005 2.7 122
Milwaukee, WI............ 25.4 486.7 0.3 276 947 2.0 201
Outagamie, WI............ 5.2 108.7 1.4 179 837 5.3 15
Waukesha, WI............. 12.8 243.1 1.1 204 984 3.4 73
Winnebago, WI............ 3.7 93.4 1.9 134 903 1.7 229
San Juan, PR............. 11.0 242.6 -1.4 (6) 611 -0.8 (6)
(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) Data do not meet BLS or state agency disclosure standards.
(6) 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 344 U.S. counties comprise 72.5 percent of
the total covered workers in the U.S.
Table 2. Covered establishments, employment, and wages in the 10 largest counties,
second quarter 2016
Employment Average weekly
wage(1)
Establishments,
second quarter
County by NAICS supersector 2016 Percent Percent
(thousands) June change, Second change,
2016 June quarter second
(thousands) 2015-16(2) 2016 quarter
2015-16(2)
United States(3) ............................ 9,741.4 142,717.2 1.5 $989 2.2
Private industry........................... 9,442.6 121,256.3 1.6 979 2.1
Natural resources and mining............. 137.6 1,976.1 -7.4 1,010 -4.5
Construction............................. 774.4 6,823.9 3.5 1,077 3.1
Manufacturing............................ 344.6 12,357.8 -0.5 1,203 1.8
Trade, transportation, and utilities..... 1,921.0 26,932.2 1.0 839 2.7
Information.............................. 156.8 2,809.4 0.9 1,755 5.5
Financial activities..................... 856.4 7,979.1 1.5 1,492 2.0
Professional and business services....... 1,756.4 20,019.1 1.7 1,280 1.7
Education and health services............ 1,589.5 21,487.5 2.4 903 2.8
Leisure and hospitality.................. 819.7 16,119.6 2.6 415 3.2
Other services........................... 832.8 4,438.0 1.4 676 2.7
Government................................. 298.8 21,460.8 0.7 1,040 2.2
Los Angeles, CA.............................. 467.7 4,337.3 1.8 1,079 2.8
Private industry........................... 461.5 3,763.0 1.8 1,047 3.2
Natural resources and mining............. 0.5 9.2 0.9 1,230 -2.2
Construction............................. 13.7 131.2 3.5 1,133 3.1
Manufacturing............................ 12.5 357.9 -2.8 1,227 3.9
Trade, transportation, and utilities..... 53.6 805.2 0.7 896 3.1
Information.............................. 9.4 228.8 3.6 1,752 4.4
Financial activities..................... 25.0 217.5 0.9 1,727 3.7
Professional and business services....... 47.2 590.2 1.0 1,331 3.2
Education and health services............ 218.2 745.2 2.8 846 3.9
Leisure and hospitality.................. 32.1 508.3 3.5 597 1.0
Other services........................... 26.9 146.2 0.6 685 2.9
Government................................. 6.1 574.3 1.7 1,295 1.3
Cook, IL..................................... 151.8 2,584.0 0.9 1,146 2.6
Private industry........................... 150.5 2,283.5 1.0 1,133 2.9
Natural resources and mining............. 0.1 1.2 19.0 1,137 -4.5
Construction............................. 12.2 74.9 0.7 1,395 2.8
Manufacturing............................ 6.3 187.4 -0.5 1,187 4.4
Trade, transportation, and utilities..... 29.7 474.1 0.6 925 2.9
Information.............................. 2.6 52.9 0.3 1,740 1.9
Financial activities..................... 15.1 193.5 0.5 2,022 3.2
Professional and business services....... 32.0 471.8 0.2 1,443 3.6
Education and health services............ 16.2 438.7 2.0 947 1.7
Leisure and hospitality.................. 14.0 286.5 2.7 521 4.4
Other services........................... 17.1 96.7 -0.9 893 4.8
Government................................. 1.3 300.4 -0.3 1,245 0.3
New York, NY................................. 130.2 2,415.6 1.5 1,866 1.2
Private industry........................... 129.4 2,154.8 1.7 1,938 0.9
Natural resources and mining............. 0.0 0.2 4.3 2,100 1.4
Construction............................. 2.2 40.7 5.7 1,816 4.1
Manufacturing............................ 2.2 26.7 -1.3 1,345 3.1
Trade, transportation, and utilities..... 19.6 254.0 -2.9 1,379 3.9
Information.............................. 4.9 154.9 0.8 2,526 5.0
Financial activities..................... 19.3 375.7 1.6 3,517 -2.3
Professional and business services....... 27.5 557.6 2.0 2,173 0.4
Education and health services............ 9.7 333.0 2.4 1,251 3.1
Leisure and hospitality.................. 13.6 294.8 1.2 845 3.6
Other services........................... 20.3 101.8 0.2 1,173 6.8
Government................................. 0.8 260.8 0.6 1,278 4.8
Harris, TX................................... 112.5 2,272.1 -0.8 1,233 -0.1
Private industry........................... 112.0 1,999.0 -1.3 1,251 -0.5
Natural resources and mining............. 1.8 76.3 -16.4 3,256 0.8
Construction............................. 7.2 163.2 0.7 1,308 3.4
Manufacturing............................ 4.8 170.8 -11.0 1,534 0.3
Trade, transportation, and utilities..... 24.9 465.3 -0.6 1,099 0.5
Information.............................. 1.2 27.8 1.2 1,438 -2.1
Financial activities..................... 11.7 123.0 1.6 1,588 3.7
Professional and business services....... 23.0 386.8 -2.4 1,524 0.1
Education and health services............ 15.5 285.8 3.8 1,004 5.5
Leisure and hospitality.................. 9.7 233.7 3.8 431 0.9
Other services........................... 11.7 65.2 -0.7 773 3.3
Government................................. 0.6 273.1 2.9 1,099 4.0
Maricopa, AZ................................. 94.8 1,827.4 2.9 970 2.2
Private industry........................... 94.1 1,645.2 3.0 957 2.6
Natural resources and mining............. 0.4 8.5 -0.1 850 -2.0
Construction............................. 6.9 102.1 5.0 997 2.9
Manufacturing............................ 3.1 115.7 -0.6 1,450 5.2
Trade, transportation, and utilities..... 18.7 362.7 1.9 879 2.9
Information.............................. 1.5 35.1 0.0 1,383 13.5
Financial activities..................... 10.8 165.5 4.9 1,255 2.5
Professional and business services....... 20.8 315.6 2.0 1,044 2.2
Education and health services............ 10.6 274.8 2.8 954 0.4
Leisure and hospitality.................. 7.5 203.3 3.1 452 4.6
Other services........................... 6.0 50.3 0.0 685 2.7
Government................................. 0.7 182.3 2.0 1,077 0.0
Dallas, TX................................... 74.5 1,649.4 2.9 1,184 2.2
Private industry........................... 73.9 1,477.5 3.0 1,192 2.1
Natural resources and mining............. 0.6 8.6 -10.0 3,604 -10.4
Construction............................. 4.4 85.2 3.6 1,129 3.1
Manufacturing............................ 2.7 109.1 0.1 1,441 10.4
Trade, transportation, and utilities..... 15.8 333.5 2.8 1,058 1.4
Information.............................. 1.3 48.7 2.6 1,848 5.5
Financial activities..................... 9.1 157.7 3.6 1,653 2.4
Professional and business services....... 16.7 333.6 3.2 1,371 0.6
Education and health services............ 9.2 194.0 3.5 1,041 3.9
Leisure and hospitality.................. 6.6 162.4 4.5 483 3.6
Other services........................... 7.0 43.7 1.9 756 1.6
Government................................. 0.6 171.9 1.8 1,115 2.8
Orange, CA................................... 114.8 1,557.3 1.9 1,103 1.8
Private industry........................... 113.3 1,403.3 1.9 1,088 1.4
Natural resources and mining............. 0.2 3.4 4.9 785 3.3
Construction............................. 6.6 95.6 5.1 1,235 4.0
Manufacturing............................ 4.9 154.9 -0.7 1,344 2.1
Trade, transportation, and utilities..... 16.8 254.4 -0.7 991 4.1
Information.............................. 1.3 25.7 2.1 1,780 4.6
Financial activities..................... 10.9 115.6 0.6 1,700 2.0
Professional and business services....... 20.3 289.1 1.4 1,319 -1.9
Education and health services............ 30.3 197.7 3.0 921 2.2
Leisure and hospitality.................. 8.4 214.0 4.0 473 4.6
Other services........................... 6.9 45.5 2.0 694 4.4
Government................................. 1.5 154.0 1.6 1,240 4.6
San Diego, CA................................ 106.7 1,405.5 2.0 1,073 0.0
Private industry........................... 104.8 1,173.6 2.0 1,045 -0.9
Natural resources and mining............. 0.6 9.7 2.0 708 5.8
Construction............................. 6.6 74.4 5.9 1,146 3.9
Manufacturing............................ 3.2 107.3 0.5 1,480 -8.2
Trade, transportation, and utilities..... 14.2 216.2 0.1 825 2.2
Information.............................. 1.2 23.2 -1.4 1,621 1.6
Financial activities..................... 9.6 71.8 2.2 1,391 3.2
Professional and business services....... 17.9 230.4 1.1 1,533 -4.2
Education and health services............ 30.1 191.3 2.9 930 3.4
Leisure and hospitality.................. 8.0 192.1 2.6 476 2.6
Other services........................... 7.4 50.7 0.7 600 3.3
Government................................. 1.9 232.0 2.1 1,217 4.7
King, WA..................................... 85.5 1,326.1 3.4 1,393 8.1
Private industry........................... 84.9 1,158.0 3.6 1,408 8.6
Natural resources and mining............. 0.4 3.1 2.2 1,225 -5.9
Construction............................. 6.4 66.8 5.4 1,293 5.5
Manufacturing............................ 2.4 105.0 -2.2 1,648 7.0
Trade, transportation, and utilities..... 14.6 250.9 4.4 1,431 21.9
Information.............................. 2.1 96.4 8.9 2,781 6.4
Financial activities..................... 6.5 67.2 2.3 1,610 3.6
Professional and business services....... 17.1 219.3 3.3 1,593 3.9
Education and health services............ 19.3 166.7 3.6 993 2.4
Leisure and hospitality.................. 7.0 137.8 3.5 575 11.7
Other services........................... 9.0 44.9 5.0 833 2.3
Government................................. 0.5 168.1 1.9 1,296 4.9
Miami-Dade, FL............................... 95.7 1,088.1 2.5 958 2.6
Private industry........................... 95.3 964.4 2.6 922 2.6
Natural resources and mining............. 0.5 7.7 9.3 616 9.4
Construction............................. 6.1 43.3 10.2 912 2.4
Manufacturing............................ 2.8 40.5 3.1 869 -0.7
Trade, transportation, and utilities..... 26.3 277.4 0.2 865 3.3
Information.............................. 1.5 18.1 1.8 1,574 4.0
Financial activities..................... 10.4 74.1 1.4 1,433 -0.6
Professional and business services....... 21.0 153.0 4.3 1,100 2.0
Education and health services............ 10.2 171.0 2.7 966 5.0
Leisure and hospitality.................. 7.2 138.4 3.2 561 1.3
Other services........................... 8.2 40.2 2.6 607 2.4
Government................................. 0.3 123.7 1.6 1,216 3.1
(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 2015 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 2016
Employment Average weekly
wage(1)
Establishments,
second quarter
State 2016 Percent Percent
(thousands) June change, Second change,
2016 June quarter second
(thousands) 2015-16 2016 quarter
2015-16
United States(2)........... 9,741.4 142,717.2 1.5 $989 2.2
Alabama.................... 121.8 1,923.5 1.2 835 2.0
Alaska..................... 22.4 338.7 -2.4 1,011 -1.7
Arizona.................... 153.5 2,619.6 2.6 921 1.9
Arkansas................... 88.7 1,197.5 1.1 785 3.0
California................. 1,473.1 16,754.1 2.5 1,157 2.4
Colorado................... 191.1 2,574.5 2.3 999 1.0
Connecticut................ 117.2 1,689.9 -0.1 1,213 3.0
Delaware................... 31.4 444.0 0.9 990 -0.6
District of Columbia....... 38.1 756.0 1.7 1,623 1.1
Florida.................... 658.1 8,161.8 3.2 883 2.6
Georgia.................... 299.7 4,269.5 2.7 929 2.7
Hawaii..................... 40.2 643.4 1.0 906 3.5
Idaho...................... 57.9 699.7 3.3 740 3.8
Illinois................... 401.9 5,945.0 0.2 1,038 2.4
Indiana.................... 161.4 2,995.4 1.0 828 2.1
Iowa....................... 101.4 1,566.0 0.3 825 2.9
Kansas..................... 90.0 1,378.4 -0.2 829 1.2
Kentucky................... 122.8 1,877.2 1.5 838 1.9
Louisiana.................. 128.0 1,905.2 -1.4 852 0.2
Maine...................... 52.8 622.8 1.0 795 3.5
Maryland................... 170.0 2,656.0 0.9 1,070 2.5
Massachusetts.............. 247.1 3,538.2 1.2 1,233 2.0
Michigan................... 240.3 4,300.9 1.9 942 2.7
Minnesota.................. 161.2 2,846.8 0.7 997 2.0
Mississippi................ 73.3 1,120.1 0.5 727 2.5
Missouri................... 194.2 2,785.6 1.4 863 2.4
Montana.................... 46.5 468.6 2.2 767 1.7
Nebraska................... 72.5 978.3 0.9 805 2.4
Nevada..................... 81.9 1,289.4 3.3 874 2.2
New Hampshire.............. 51.5 655.1 1.1 1,003 3.7
New Jersey................. 268.9 4,051.2 1.7 1,147 1.7
New Mexico................. 58.3 808.1 -0.3 812 0.9
New York................... 643.4 9,264.0 1.5 1,210 2.5
North Carolina............. 270.5 4,285.3 2.5 865 2.1
North Dakota............... 32.1 423.3 -4.9 908 -3.3
Ohio....................... 293.1 5,353.1 0.8 882 2.0
Oklahoma................... 109.2 1,570.5 -1.4 823 0.6
Oregon..................... 146.4 1,867.8 2.7 933 4.1
Pennsylvania............... 357.9 5,786.8 0.4 971 1.4
Rhode Island............... 36.9 482.9 0.6 949 2.5
South Carolina............. 124.4 2,013.7 2.4 804 2.8
South Dakota............... 33.0 432.7 1.0 760 2.7
Tennessee.................. 153.0 2,900.4 2.4 874 1.3
Texas...................... 652.6 11,810.7 1.0 1,000 1.2
Utah....................... 95.6 1,395.9 3.8 840 2.3
Vermont.................... 24.9 310.6 -0.1 850 2.4
Virginia................... 267.5 3,833.4 1.6 1,011 1.2
Washington................. 238.0 3,281.6 2.8 1,083 5.4
West Virginia.............. 50.3 693.2 -1.9 800 -0.4
Wisconsin.................. 169.2 2,869.1 0.9 856 2.4
Wyoming.................... 26.2 281.7 -3.7 849 -2.2
Puerto Rico................ 46.8 879.5 -0.7 512 0.2
Virgin Islands............. 3.4 38.4 0.9 743 -0.4
(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
