For release 10:00 a.m. (EDT), Wednesday, June 8, 2016 USDL-16-1148
Technical Information: (202) 691-6567 * QCEWInfo@bls.gov * www.bls.gov/cew
Media Contact: (202) 691-5902 * PressOffice@bls.gov
COUNTY EMPLOYMENT AND WAGES
Fourth Quarter 2015
From December 2014 to December 2015, employment increased in 308 of the 342 largest U.S.
counties, the U.S. Bureau of Labor Statistics reported today. Williamson, Tenn., had the largest
percentage increase with a gain of 6.8 percent over the year, above the national job growth rate of 1.9
percent. Within Williamson, the largest employment increase occurred in professional and business
services, which gained 3,185 jobs over the year (10.9 percent). Ector, Texas, had the largest over-the-
year percentage decrease in employment among the largest counties in the U.S., with a loss of 11.8
percent. Within Ector, natural resources and mining had the largest decrease in employment, with a loss
of 4,509 jobs (-34.1 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 detailed data are published within six months following the end of each
calendar quarter.
The U.S. average weekly wage increased 4.4 percent over the year, growing to $1,082 in the fourth
quarter of 2015. Wyandotte, Kan., had the largest over-the-year percentage increase in average weekly
wages with a gain of 10.4 percent. Within Wyandotte, an average weekly wage gain of $250, or 21.2
percent, in manufacturing made the largest contribution to the county’s increase in average weekly
wages. Midland, Texas, experienced the largest percentage decrease in average weekly wages with a
loss of 11.5 percent over the year. Within Midland, natural resources and mining had the largest impact
on the county’s average weekly wage decline with a decrease of $257 (-11.6 percent) over the year.
Large County Employment
In December 2015, national employment was 141.9 million (as measured by the QCEW program). Over
the year, employment increased 1.9 percent, or 2.7 million. In December 2015, the 342 U.S. counties
with 75,000 or more jobs accounted for 72.5 percent of total U.S. employment and 77.8 percent of total
wages. These 342 counties had a net job growth of 2.2 million over the year, accounting for 81.4 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 319,200 jobs, which was 12.0 percent of the
overall job increase for the U.S. (See table A.)
Employment declined in 26 of the largest counties from December 2014 to December 2015. Ector,
Texas, had the largest over-the-year percentage decrease in employment (-11.8 percent), followed by
Midland, Texas; Lafayette, La.; Gregg, Texas; and Weld, Colo. (See table 1.)
Table A. Large counties ranked by December 2015 employment, December 2014-15 employment increase, and
December 2014-15 percent increase in employment
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Employment in large counties
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December 2015 employment | Increase in employment, | Percent increase in employment,
(thousands) | December 2014-15 | December 2014-15
| (thousands) |
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| |
United States 141,924.5| United States 2,658.0| United States 1.9
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| |
Los Angeles, Calif. 4,341.0| Los Angeles, Calif. 99.3| Williamson, Tenn. 6.8
Cook, Ill. 2,575.7| Dallas, Texas 62.4| Utah, Utah 6.6
New York, N.Y. 2,442.2| Maricopa, Ariz. 58.6| Loudoun, Va. 6.3
Harris, Texas 2,302.8| New York, N.Y. 50.7| Chesterfield, Va. 6.0
Maricopa, Ariz. 1,883.2| Cook, Ill. 48.2| Lee, Fla. 5.9
Dallas, Texas 1,651.6| Santa Clara, Calif. 38.5| Osceola, Fla. 5.8
Orange, Calif. 1,550.6| King, Wash. 36.3| Bell, Texas 5.4
San Diego, Calif. 1,399.7| San Diego, Calif. 35.4| Boone, Ky. 5.1
King, Wash. 1,297.2| Orange, Calif. 34.7| Clay, Mo. 5.1
Miami-Dade, Fla. 1,115.9| Clark, Nev. 33.0| Hall, Ga. 5.0
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Large County Average Weekly Wages
Average weekly wages for the nation increased to $1,082, a 4.4 percent increase, during the year ending
in the fourth quarter of 2015. Among the 342 largest counties, 325 had over-the-year increases in
average weekly wages. Wyandotte, Kan., had the largest percentage wage increase among the largest
U.S. counties (10.4 percent).
Of the 342 largest counties, 10 experienced over-the-year decreases in average weekly wages. Midland,
Texas, had the largest percentage decrease in average weekly wages (-11.5 percent), followed by Ector,
Texas; Lafayette, La.; Gregg, Texas; and San Mateo, Calif. (See table 1.)
Table B. Large counties ranked by fourth quarter 2015 average weekly wages, fourth quarter 2014-15
increase in average weekly wages, and fourth quarter 2014-15 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
fourth quarter 2015 | wage, fourth quarter 2014-15 | weekly wage, fourth
| | quarter 2014-15
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| |
United States $1,082| United States $46| United States 4.4
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| |
Santa Clara, Calif. $2,335| Santa Clara, Calif. $198| Wyandotte, Kan. 10.4
New York, N.Y. 2,235| Lake, Ill. 129| Sonoma, Calif. 10.0
San Mateo, Calif. 2,095| San Francisco, Calif. 118| Lake, Ill. 9.8
San Francisco, Calif. 1,961| Wyandotte, Kan. 98| Passaic, N.J. 9.4
Suffolk, Mass. 1,943| Sonoma, Calif. 95| Santa Clara, Calif. 9.3
Washington, D.C. 1,756| Passaic, N.J. 95| Anoka, Minn. 9.3
Fairfield, Conn. 1,735| Suffolk, Mass. 92| Clay, Mo. 9.2
Arlington, Va. 1,686| Wayne, Mich. 91| Collier, Fla. 9.1
Fairfax, Va. 1,618| Anoka, Minn. 88| Catawba, N.C. 8.9
Morris, N.J. 1,601| Alameda, Calif. 86| Bell, Texas 8.9
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Ten Largest U.S. Counties
Among the 10 largest counties, 9 had over-the-year percentage increases in employment in December
2015. Dallas, Texas, had the largest gain (3.9 percent). Within Dallas, trade, transportation, and utilities
had the largest over-the-year employment level increase, with a gain of 20,999 jobs, or 6.3 percent.
Harris, Texas, had the only percentage decrease in employment among the 10 largest counties (-0.5
percent). (See table 2.)
Average weekly wages increased over the year in all of the 10 largest U.S. counties. Los Angeles,
Calif., experienced the largest percentage gain in average weekly wages (5.5 percent). Within Los
Angeles, information tied with professional and business services for the largest impact on the county’s
average weekly wage growth. Within information, average weekly wages increased by $259, or 11.3
percent, over the year. Within professional and business services, average weekly wages increased by
$106, or 6.9 percent, over the year. Harris, Texas, had the smallest percentage gain in average weekly
wages among the 10 largest counties (0.4 percent).
For More Information
The tables included in this release contain data for the nation and for the 342 U.S. counties with annual
average employment levels of 75,000 or more in 2014. December 2015 employment and 2015 fourth
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 141.9 million full- and part-time workers. Data for the
fourth quarter of 2015 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 are issuing 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 first quarter 2016 is scheduled to be released on
Wednesday, September 7, 2016.
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 2015 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 343 counties
presented in this release were derived using 2014 preliminary annual averages of employment. For
2015 data, three counties have been added to the publication tables: Butte, Calif.; Hall, Ga.; and
Ector, Texas. These counties will be included in all 2015 quarterly releases. The counties in table 2
are selected and sorted each year based on the annual average employment from the preceding
year.
The preliminary QCEW data presented in this release may differ from data released by the
individual states. These potential differences result from the states' continuing receipt of UI data
over time and ongoing review and editing. The individual states determine their data release
timetables.
Differences between QCEW, BED, and CES employment measures
The Bureau publishes three different establishment-based employment measures for any given
quarter. 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 9.5 | ministrative records| ments
| million establish- | submitted by 7.6 |
| ments in first | million private-sec-|
| quarter of 2015 | 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 | -6 months after the| -7 months after the | -Usually first Friday
| end of each quar- | end of each quarter| of following month
| ter | |
-----------|---------------------|----------------------|------------------------
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 9.4
million employer reports of employment and wages submitted by states to the BLS in 2014. 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 2014, UI and UCFE programs
covered workers in 136.6 million jobs. The estimated 131.8 million workers in these jobs (after
adjustment for multiple jobholders) represented 96.3 percent of civilian wage and salary
employment. Covered workers received $7.017 trillion in pay, representing 93.8 percent of the
wage and salary component of personal income and 40.5 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 2014 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 2014 edition
of this publication, which was published in September 2015, contains selected data produced by
Business Employment Dynamics (BED) on job gains and losses, as well as selected data from the
first quarter 2015 version of this news release. Tables and additional content from the 2014 edition
of Employment and Wages Annual Averages Online are now available at
http://www.bls.gov/cew/cewbultn14.htm. The 2015 edition of Employment and Wages Annual
Averages Online will be available in September 2016.
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 343 largest counties,
fourth quarter 2015
Employment Average weekly wage(2)
Establishments,
County(1) fourth quarter Percent Ranking Percent Ranking
2015 December change, by Fourth change, by
(thousands) 2015 December percent quarter fourth percent
(thousands) 2014-15(3) change 2015 quarter change
2014-15(3)
United States(4)......... 9,685.3 141,924.5 1.9 - $1,082 4.4 -
Jefferson, AL............ 17.9 341.9 0.8 264 1,049 2.4 300
Madison, AL.............. 9.3 191.1 2.3 138 1,143 3.3 276
Mobile, AL............... 9.8 169.5 1.1 235 941 4.8 172
Montgomery, AL........... 6.4 130.5 1.0 246 935 3.4 266
Shelby, AL............... 5.6 84.6 1.3 213 1,026 3.8 246
Tuscaloosa, AL........... 4.4 92.6 0.6 281 883 1.6 313
Anchorage Borough, AK.... 8.4 152.7 -0.2 316 1,136 3.9 237
Maricopa, AZ............. 97.2 1,883.2 3.2 77 1,016 4.1 225
Pima, AZ................. 19.1 361.7 0.7 273 891 4.0 230
Benton, AR............... 6.0 112.7 3.6 51 1,042 3.5 262
Pulaski, AR.............. 14.6 250.2 1.9 171 972 3.7 254
Washington, AR........... 5.9 102.7 4.3 25 952 6.7 39
Alameda, CA.............. 60.2 741.1 3.3 69 1,407 6.5 53
Butte, CA................ 8.1 79.3 2.7 119 800 5.5 114
Contra Costa, CA......... 31.2 354.7 3.2 77 1,286 6.5 53
Fresno, CA............... 32.8 363.6 3.5 58 849 5.2 137
Kern, CA................. 17.7 305.9 -0.8 325 884 0.6 323
Los Angeles, CA.......... 463.6 4,341.0 2.3 138 1,266 5.5 114
Marin, CA................ 12.4 114.0 2.8 108 1,334 4.7 175
Monterey, CA............. 13.3 165.6 3.8 37 914 6.8 36
Orange, CA............... 113.6 1,550.6 2.3 138 1,205 4.1 225
Placer, CA............... 12.1 151.7 4.4 21 1,071 3.4 266
Riverside, CA............ 58.0 679.7 4.9 11 840 4.7 175
Sacramento, CA........... 54.7 637.1 3.7 46 1,153 5.2 137
San Bernardino, CA....... 54.3 719.8 3.6 51 888 4.7 175
San Diego, CA............ 105.7 1,399.7 2.6 125 1,184 4.3 212
San Francisco, CA........ 59.5 691.6 4.6 17 1,961 6.4 61
San Joaquin, CA.......... 17.2 233.2 4.2 27 894 7.1 28
San Luis Obispo, CA...... 10.1 113.5 3.2 77 900 8.2 15
San Mateo, CA............ 27.4 393.3 3.8 37 2,095 -2.3 332
Santa Barbara, CA........ 15.1 191.9 3.0 93 1,038 5.8 94
Santa Clara, CA.......... 69.3 1,040.8 3.8 37 2,335 9.3 5
Santa Cruz, CA........... 9.5 97.5 3.2 77 952 3.1 284
Solano, CA............... 10.7 134.0 3.4 63 1,080 5.3 127
Sonoma, CA............... 19.4 199.5 3.7 46 1,049 10.0 2
Stanislaus, CA........... 14.9 179.0 4.2 27 888 6.2 68
Tulare, CA............... 9.7 153.0 3.3 69 761 3.8 246
Ventura, CA.............. 25.8 320.6 1.1 235 1,065 3.9 237
Yolo, CA................. 6.4 96.4 3.2 77 1,151 5.4 120
Adams, CO................ 10.1 195.0 2.8 108 1,036 5.1 148
Arapahoe, CO............. 20.9 321.8 2.8 108 1,242 2.1 309
Boulder, CO.............. 14.3 175.1 2.5 130 1,265 5.0 154
Denver, CO............... 29.7 485.3 3.2 77 1,292 2.9 291
Douglas, CO.............. 11.1 115.3 3.2 77 1,291 3.0 289
El Paso, CO.............. 18.2 261.5 3.2 77 952 3.9 237
Jefferson, CO............ 19.1 232.8 3.0 93 1,082 3.9 237
Larimer, CO.............. 11.3 149.9 3.7 46 986 2.3 306
Weld, CO................. 6.7 100.2 -3.1 333 928 0.3 325
Fairfield, CT............ 34.8 429.7 0.5 286 1,735 3.5 262
Hartford, CT............. 27.2 511.0 0.3 299 1,306 4.6 189
New Haven, CT............ 23.5 366.3 0.5 286 1,128 3.7 254
New London, CT........... 7.3 122.3 0.8 264 1,053 4.2 218
New Castle, DE........... 18.9 293.2 1.9 171 1,198 2.9 291
Washington, DC........... 39.0 754.2 2.2 144 1,756 3.4 266
Alachua, FL.............. 6.8 126.3 3.0 93 911 3.1 284
Brevard, FL.............. 15.0 197.7 2.1 156 939 6.0 80
Broward, FL.............. 67.3 785.7 2.8 108 1,018 6.0 80
Collier, FL.............. 13.1 143.1 3.4 63 969 9.1 8
Duval, FL................ 27.9 485.6 3.2 77 1,008 2.0 310
Escambia, FL............. 8.0 127.8 2.0 165 857 5.2 137
Hillsborough, FL......... 39.9 671.9 4.4 21 1,029 4.7 175
Lake, FL................. 7.7 93.2 4.9 11 738 6.6 43
Lee, FL.................. 20.6 253.1 5.9 5 842 4.7 175
Leon, FL................. 8.3 145.2 1.2 221 881 4.5 196
Manatee, FL.............. 10.1 120.7 2.6 125 818 6.5 53
Marion, FL............... 8.1 98.9 2.1 156 749 5.9 88
Miami-Dade, FL........... 94.8 1,115.9 3.0 93 1,051 4.4 208
Okaloosa, FL............. 6.2 80.4 3.8 37 859 4.9 161
Orange, FL............... 39.3 786.0 4.4 21 945 5.7 100
Osceola, FL.............. 6.3 87.6 5.8 6 730 6.6 43
Palm Beach, FL........... 53.6 588.9 4.0 33 1,081 7.2 26
Pasco, FL................ 10.3 113.2 4.4 21 749 5.8 94
Pinellas, FL............. 31.9 418.7 3.6 51 979 5.6 105
Polk, FL................. 12.6 209.9 2.7 119 816 5.2 137
Sarasota, FL............. 15.3 164.4 3.8 37 914 5.9 88
Seminole, FL............. 14.4 180.3 3.9 35 897 6.0 80
Volusia, FL.............. 13.8 164.0 3.3 69 759 4.5 196
Bibb, GA................. 4.6 84.8 1.1 235 838 4.5 196
Chatham, GA.............. 8.5 147.9 3.3 69 921 5.9 88
Clayton, GA.............. 4.4 121.9 4.8 13 957 -1.8 330
Cobb, GA................. 23.5 340.6 3.0 93 1,118 3.4 266
DeKalb, GA............... 19.4 301.0 3.8 37 1,048 3.4 266
Fulton, GA............... 46.3 811.4 2.8 108 1,402 4.5 196
Gwinnett, GA............. 26.5 341.9 2.9 102 1,041 4.0 230
Hall, GA................. 4.6 82.2 5.0 10 930 7.4 20
Muscogee, GA............. 4.9 94.9 0.1 306 860 6.6 43
Richmond, GA............. 4.8 105.3 1.2 221 875 4.8 172
Honolulu, HI............. 25.5 476.5 1.9 171 997 5.6 105
Ada, ID.................. 14.3 221.3 3.8 37 938 -1.5 329
Champaign, IL............ 4.4 90.0 -0.1 312 901 4.0 230
Cook, IL................. 158.4 2,575.7 1.9 171 1,267 4.4 208
DuPage, IL............... 38.8 612.2 0.4 294 1,257 6.6 43
Kane, IL................. 14.0 209.5 0.8 264 968 6.4 61
Lake, IL................. 22.9 333.5 0.9 254 1,450 9.8 3
McHenry, IL.............. 8.9 97.0 1.2 221 904 6.5 53
McLean, IL............... 3.9 84.6 0.0 309 1,010 4.1 225
Madison, IL.............. 6.1 98.4 0.0 309 876 3.4 266
Peoria, IL............... 4.7 102.2 1.1 235 1,012 5.9 88
St. Clair, IL............ 5.6 94.1 0.4 294 838 5.1 148
Sangamon, IL............. 5.3 128.6 -1.6 331 1,063 4.3 212
Will, IL................. 16.5 225.8 2.2 144 943 5.1 148
Winnebago, IL............ 6.9 129.3 0.9 254 898 3.1 284
Allen, IN................ 8.8 185.0 2.0 165 868 7.3 23
Elkhart, IN.............. 4.7 126.2 2.9 102 886 5.6 105
Hamilton, IN............. 9.0 134.8 3.6 51 1,020 4.9 161
Lake, IN................. 10.3 187.9 0.1 306 909 1.6 313
Marion, IN............... 23.9 594.7 1.8 183 1,056 4.9 161
St. Joseph, IN........... 5.8 123.0 2.6 125 855 5.9 88
Tippecanoe, IN........... 3.4 82.8 0.9 254 904 6.0 80
Vanderburgh, IN.......... 4.8 108.0 0.5 286 886 4.1 225
Black Hawk, IA........... 3.8 74.6 -1.0 327 984 5.8 94
Johnson, IA.............. 4.1 82.0 1.2 221 951 3.7 254
Linn, IA................. 6.6 130.5 0.5 286 1,069 4.7 175
Polk, IA................. 16.7 291.7 1.6 197 1,096 6.5 53
Scott, IA................ 5.6 91.4 0.3 299 903 5.5 114
Johnson, KS.............. 22.6 339.8 1.4 208 1,097 5.3 127
Sedgwick, KS............. 12.8 250.6 0.9 254 960 4.2 218
Shawnee, KS.............. 5.0 97.9 1.3 213 856 4.4 208
Wyandotte, KS............ 3.5 90.3 1.9 171 1,037 10.4 1
Boone, KY................ 4.3 85.2 5.1 8 921 3.4 266
Fayette, KY.............. 10.6 198.1 2.8 108 935 6.9 33
Jefferson, KY............ 25.0 461.4 2.2 144 1,049 8.6 11
Caddo, LA................ 7.3 116.3 -0.6 320 877 1.7 312
Calcasieu, LA............ 5.0 92.6 1.3 213 963 5.5 114
East Baton Rouge, LA..... 15.0 272.0 0.5 286 1,014 4.2 218
Jefferson, LA............ 13.4 195.9 -0.7 323 980 5.0 154
Lafayette, LA............ 9.3 136.1 -5.6 335 990 -4.3 334
Orleans, LA.............. 12.0 195.9 3.0 93 1,021 3.2 280
St. Tammany, LA.......... 7.8 87.9 2.3 138 916 2.9 291
Cumberland, ME........... 13.3 177.1 0.9 254 1,004 5.7 100
Anne Arundel, MD......... 15.0 265.6 2.9 102 1,145 5.4 120
Baltimore, MD............ 21.2 380.9 1.2 221 1,094 5.1 148
Frederick, MD............ 6.4 100.4 2.5 130 1,005 4.3 212
Harford, MD.............. 5.8 93.3 2.3 138 1,033 5.4 120
Howard, MD............... 9.9 166.5 2.0 165 1,323 5.3 127
Montgomery, MD........... 32.8 466.0 0.7 273 1,419 5.7 100
Prince George's, MD...... 15.8 312.2 0.9 254 1,102 5.2 137
Baltimore City, MD....... 13.6 338.6 1.3 213 1,296 6.1 73
Barnstable, MA........... 9.3 90.0 1.1 235 955 7.7 17
Bristol, MA.............. 17.0 224.7 0.7 273 984 2.3 306
Essex, MA................ 23.8 323.8 1.2 221 1,149 4.9 161
Hampden, MA.............. 17.3 206.6 1.0 246 993 4.9 161
Middlesex, MA............ 53.1 889.2 1.6 197 1,563 5.3 127
Norfolk, MA.............. 24.6 349.7 1.1 235 1,338 6.2 68
Plymouth, MA............. 15.1 188.2 0.7 273 1,035 5.6 105
Suffolk, MA.............. 27.5 652.1 2.7 119 1,943 5.0 154
Worcester, MA............ 23.9 341.5 1.8 183 1,086 6.1 73
Genesee, MI.............. 6.9 134.6 -0.1 312 918 8.4 13
Ingham, MI............... 6.0 149.0 1.0 246 1,028 6.6 43
Kalamazoo, MI............ 5.0 116.2 0.6 281 1,000 7.0 30
Kent, MI................. 14.0 382.1 3.4 63 963 4.9 161
Macomb, MI............... 17.3 319.5 1.4 208 1,097 8.4 13
Oakland, MI.............. 38.5 719.3 1.8 183 1,222 4.8 172
Ottawa, MI............... 5.5 120.3 3.1 87 950 3.7 254
Saginaw, MI.............. 4.0 85.8 1.0 246 877 7.5 18
Washtenaw, MI............ 8.0 208.5 1.9 171 1,116 4.6 189
Wayne, MI................ 30.2 709.0 0.4 294 1,209 8.1 16
Anoka, MN................ 6.7 120.0 1.4 208 1,035 9.3 5
Dakota, MN............... 9.4 185.8 0.6 281 1,051 6.7 39
Hennepin, MN............. 37.0 897.3 1.7 191 1,301 3.2 280
Olmsted, MN.............. 3.3 94.8 2.5 130 1,058 3.8 246
Ramsey, MN............... 12.8 330.9 1.3 213 1,189 4.7 175
St. Louis, MN............ 5.1 96.6 -0.1 312 869 5.5 114
Stearns, MN.............. 4.1 85.7 0.8 264 884 6.6 43
Washington, MN........... 5.2 80.3 2.9 102 889 6.6 43
Harrison, MS............. 4.5 84.3 2.1 156 729 2.4 300
Hinds, MS................ 5.9 121.6 1.0 246 896 3.2 280
Boone, MO................ 4.9 93.4 1.8 183 823 4.0 230
Clay, MO................. 5.5 99.6 5.1 8 1,006 9.2 7
Greene, MO............... 8.5 164.1 1.1 235 810 4.9 161
Jackson, MO.............. 21.2 362.7 2.0 165 1,091 5.4 120
St. Charles, MO.......... 9.0 142.9 4.1 31 870 7.1 28
St. Louis, MO............ 36.2 602.5 1.8 183 1,148 2.2 308
St. Louis City, MO....... 13.0 227.3 1.2 221 1,144 7.4 20
Yellowstone, MT.......... 6.4 81.4 2.1 156 922 2.7 296
Douglas, NE.............. 18.6 338.6 2.2 144 994 6.5 53
Lancaster, NE............ 10.0 168.8 2.2 144 853 4.2 218
Clark, NV................ 54.6 928.6 3.7 46 920 3.8 246
Washoe, NV............... 14.5 208.0 4.5 18 955 3.8 246
Hillsborough, NH......... 12.3 202.1 1.6 197 1,264 4.5 196
Rockingham, NH........... 10.9 146.8 2.7 119 1,119 5.6 105
Atlantic, NJ............. 6.6 124.5 0.2 304 896 2.4 300
Bergen, NJ............... 33.3 454.1 0.7 273 1,324 2.5 298
Burlington, NJ........... 11.1 201.2 0.9 254 1,124 5.8 94
Camden, NJ............... 12.2 201.4 2.6 125 1,090 5.3 127
Essex, NJ................ 20.6 343.4 0.3 299 1,295 5.7 100
Gloucester, NJ........... 6.3 105.5 2.2 144 946 3.7 254
Hudson, NJ............... 14.8 250.3 3.1 87 1,375 3.9 237
Mercer, NJ............... 11.3 248.5 3.6 51 1,327 1.1 320
Middlesex, NJ............ 22.2 415.6 1.5 205 1,274 5.1 148
Monmouth, NJ............. 20.1 257.1 2.2 144 1,091 3.2 280
Morris, NJ............... 17.0 291.5 2.3 138 1,601 5.2 137
Ocean, NJ................ 12.9 160.5 2.8 108 890 4.3 212
Passaic, NJ.............. 12.5 169.0 -1.1 329 1,111 9.4 4
Somerset, NJ............. 10.1 186.2 1.9 171 1,576 1.0 321
Union, NJ................ 14.4 219.3 (5) - 1,373 (5) -
Bernalillo, NM........... 18.1 322.8 1.2 221 904 3.6 260
Albany, NY............... 10.4 233.5 0.8 264 1,113 5.0 154
Bronx, NY................ 18.7 304.4 1.1 235 996 3.1 284
Broome, NY............... 4.6 87.9 -0.7 323 833 6.1 73
Dutchess, NY............. 8.5 113.1 1.2 221 1,031 3.4 266
Erie, NY................. 24.8 472.4 1.0 246 958 6.7 39
Kings, NY................ 60.8 681.7 3.4 63 922 5.4 120
Monroe, NY............... 18.8 386.3 0.8 264 1,005 7.5 18
Nassau, NY............... 54.4 631.6 1.1 235 1,238 6.4 61
New York, NY............. 129.9 2,442.2 2.1 156 2,235 0.8 322
Oneida, NY............... 5.4 104.2 -0.6 320 832 4.7 175
Onondaga, NY............. 13.1 246.9 0.3 299 995 6.2 68
Orange, NY............... 10.3 142.5 0.8 264 903 6.4 61
Queens, NY............... 51.8 648.2 3.3 69 1,022 4.2 218
Richmond, NY............. 9.8 117.0 3.3 69 959 4.6 189
Rockland, NY............. 10.6 120.3 0.6 281 1,072 4.0 230
Saratoga, NY............. 5.9 84.4 1.7 191 974 6.6 43
Suffolk, NY.............. 52.6 653.0 0.9 254 1,187 5.5 114
Westchester, NY.......... 36.7 427.4 1.2 221 1,449 2.0 310
Buncombe, NC............. 8.8 127.7 3.4 63 841 5.3 127
Catawba, NC.............. 4.3 85.3 2.2 144 836 8.9 9
Cumberland, NC........... 6.3 119.7 0.7 273 814 6.1 73
Durham, NC............... 8.1 194.8 3.8 37 1,278 4.2 218
Forsyth, NC.............. 9.3 183.4 1.1 235 976 4.7 175
Guilford, NC............. 14.3 281.1 1.9 171 930 4.7 175
Mecklenburg, NC.......... 36.1 658.4 4.0 33 1,204 6.8 36
New Hanover, NC.......... 7.7 107.3 2.8 108 866 4.7 175
Wake, NC................. 32.5 525.1 3.6 51 1,071 4.2 218
Cass, ND................. 6.9 117.1 1.3 213 977 4.6 189
Butler, OH............... 7.6 150.6 2.8 108 949 7.4 20
Cuyahoga, OH............. 35.6 721.6 0.4 294 1,097 4.5 196
Delaware, OH............. 4.9 86.2 2.4 134 1,005 4.0 230
Franklin, OH............. 31.0 739.7 1.8 183 1,068 7.0 30
Hamilton, OH............. 23.5 511.5 1.5 205 1,148 4.0 230
Lake, OH................. 6.2 95.1 0.2 304 886 3.0 289
Lorain, OH............... 6.1 97.7 0.3 299 848 4.6 189
Lucas, OH................ 10.1 212.1 1.9 171 937 4.5 196
Mahoning, OH............. 5.9 99.1 -0.6 320 762 4.1 225
Montgomery, OH........... 12.0 254.7 2.1 156 924 5.0 154
Stark, OH................ 8.6 159.1 -0.1 312 818 3.8 246
Summit, OH............... 14.1 267.9 0.7 273 959 5.3 127
Warren, OH............... 4.7 87.0 3.1 87 932 6.2 68
Cleveland, OK............ 5.5 82.6 1.0 246 791 3.3 276
Oklahoma, OK............. 27.3 454.4 0.1 306 1,017 3.9 237
Tulsa, OK................ 22.0 353.6 0.6 281 978 2.8 294
Clackamas, OR............ 14.2 153.9 3.1 87 998 5.2 137
Jackson, OR.............. 7.1 84.4 2.2 144 793 6.9 33
Lane, OR................. 11.8 150.7 3.4 63 837 5.0 154
Marion, OR............... 10.2 145.5 3.5 58 853 5.2 137
Multnomah, OR............ 33.1 490.9 3.5 58 1,099 6.6 43
Washington, OR........... 18.5 280.3 3.0 93 1,285 4.9 161
Allegheny, PA............ 35.8 692.4 0.5 286 1,152 5.2 137
Berks, PA................ 9.0 171.6 0.9 254 972 6.5 53
Bucks, PA................ 19.9 258.3 0.8 264 1,037 3.4 266
Butler, PA............... 5.0 85.9 0.5 286 1,000 5.6 105
Chester, PA.............. 15.5 247.7 1.0 246 1,364 2.4 300
Cumberland, PA........... 6.4 133.6 2.9 102 951 3.3 276
Dauphin, PA.............. 7.5 178.7 0.5 286 1,078 8.6 11
Delaware, PA............. 14.0 222.2 0.7 273 1,144 5.8 94
Erie, PA................. 7.1 124.6 -0.3 317 842 4.9 161
Lackawanna, PA........... 5.8 98.1 0.0 309 810 5.7 100
Lancaster, PA............ 13.2 233.6 1.7 191 905 6.0 80
Lehigh, PA............... 8.6 189.0 2.0 165 1,076 4.9 161
Luzerne, PA.............. 7.6 147.5 1.3 213 818 4.7 175
Montgomery, PA........... 27.6 488.6 1.9 171 1,327 4.7 175
Northampton, PA.......... 6.7 110.1 1.7 191 936 6.7 39
Philadelphia, PA......... 35.1 662.5 1.6 197 1,283 5.9 88
Washington, PA........... 5.5 86.5 -2.1 332 1,064 -2.1 331
Westmoreland, PA......... 9.3 135.3 1.3 213 863 3.9 237
York, PA................. 9.0 176.4 1.2 221 923 6.0 80
Providence, RI........... 17.6 286.8 1.1 235 1,102 3.7 254
Charleston, SC........... 14.2 239.9 3.6 51 927 5.1 148
Greenville, SC........... 13.9 262.9 3.0 93 935 6.3 66
Horry, SC................ 8.7 114.4 3.5 58 653 6.9 33
Lexington, SC............ 6.5 119.5 4.2 27 794 3.9 237
Richland, SC............. 9.9 217.4 2.2 144 903 5.4 120
Spartanburg, SC.......... 6.1 130.9 3.2 77 899 4.5 196
York, SC................. 5.2 88.6 4.5 18 842 4.7 175
Minnehaha, SD............ 7.0 124.4 1.7 191 932 6.3 66
Davidson, TN............. 20.9 466.8 3.1 87 1,169 7.2 26
Hamilton, TN............. 9.2 196.8 3.3 69 1,031 6.0 80
Knox, TN................. 11.7 236.1 1.6 197 977 6.1 73
Rutherford, TN........... 5.1 120.0 4.2 27 952 4.4 208
Shelby, TN............... 19.9 497.6 1.8 183 1,096 5.2 137
Williamson, TN........... 7.9 120.3 6.8 1 1,234 0.0 326
Bell, TX................. 5.1 119.6 5.4 7 881 8.9 9
Bexar, TX................ 38.8 834.2 2.6 125 965 6.0 80
Brazoria, TX............. 5.4 104.7 2.8 108 1,106 6.1 73
Brazos, TX............... 4.3 100.5 2.4 134 785 1.4 316
Cameron, TX.............. 6.5 138.4 1.9 171 649 4.5 196
Collin, TX............... 22.7 375.2 4.8 13 1,228 3.4 266
Dallas, TX............... 73.9 1,651.6 3.9 35 1,287 4.5 196
Denton, TX............... 13.6 224.4 4.8 13 973 4.5 196
Ector, TX................ 4.0 70.6 -11.8 337 1,094 -8.0 335
El Paso, TX.............. 14.7 297.3 3.0 93 743 5.4 120
Fort Bend, TX............ 12.0 174.3 2.2 144 1,028 -1.1 328
Galveston, TX............ 5.9 105.4 4.7 16 933 1.5 315
Gregg, TX................ 4.3 76.2 -5.1 334 910 -3.2 333
Harris, TX............... 112.2 2,302.8 -0.5 319 1,382 0.4 324
Hidalgo, TX.............. 12.0 251.8 1.9 171 661 3.1 284
Jefferson, TX............ 5.9 124.3 -0.8 325 1,119 2.8 294
Lubbock, TX.............. 7.4 136.9 2.4 134 838 4.6 189
McLennan, TX............. 5.1 109.8 1.6 197 875 5.3 127
Midland, TX.............. 5.4 86.6 -9.3 336 1,263 -11.5 336
Montgomery, TX........... 10.6 168.3 1.6 197 1,043 -0.9 327
Nueces, TX............... 8.3 162.7 -1.5 330 932 1.3 319
Potter, TX............... 4.0 80.2 0.8 264 871 4.9 161
Smith, TX................ 6.0 102.8 2.5 130 900 3.6 260
Tarrant, TX.............. 41.3 858.7 2.2 144 1,093 7.3 23
Travis, TX............... 37.9 703.1 4.3 25 1,234 5.0 154
Webb, TX................. 5.2 99.1 1.2 221 706 1.4 316
Williamson, TX........... 9.6 154.9 4.5 18 1,010 5.2 137
Davis, UT................ 8.1 120.1 3.7 46 839 4.5 196
Salt Lake, UT............ 43.1 663.8 3.8 37 1,035 5.3 127
Utah, UT................. 14.9 215.7 6.6 2 869 7.3 23
Weber, UT................ 5.8 100.5 3.1 87 790 5.3 127
Chittenden, VT........... 6.6 102.5 0.9 254 1,071 3.8 246
Arlington, VA............ 9.4 173.6 3.3 69 1,686 2.4 300
Chesterfield, VA......... 8.7 140.1 6.0 4 890 2.4 300
Fairfax, VA.............. 37.5 598.9 2.8 108 1,618 2.5 298
Henrico, VA.............. 11.3 191.5 3.5 58 1,020 4.3 212
Loudoun, VA.............. 11.8 159.8 6.3 3 1,236 2.7 296
Prince William, VA....... 9.1 125.8 4.1 31 923 4.3 212
Alexandria City, VA...... 6.7 97.7 2.1 156 1,487 1.4 316
Chesapeake City, VA...... 6.0 98.9 1.2 221 825 3.8 246
Newport News City, VA.... 3.8 98.4 -0.4 318 1,017 6.2 68
Norfolk City, VA......... 5.9 142.5 1.2 221 1,066 3.5 262
Richmond City, VA........ 7.6 151.1 2.1 156 1,153 4.6 189
Virginia Beach City, VA.. 12.1 174.2 2.1 156 851 5.6 105
Benton, WA............... 5.6 80.8 1.5 205 1,063 (5) -
Clark, WA................ 14.0 147.6 (5) - 975 (5) -
King, WA................. 85.3 1,297.2 2.9 102 1,429 3.3 276
Kitsap, WA............... 6.7 86.0 2.7 119 935 7.0 30
Pierce, WA............... 21.7 291.4 (5) - 940 (5) -
Snohomish, WA............ 20.4 280.1 2.7 119 1,136 6.6 43
Spokane, WA.............. 15.6 212.4 (5) - 884 (5) -
Thurston, WA............. 8.0 107.4 2.0 165 923 5.6 105
Whatcom, WA.............. 7.1 85.8 1.4 208 848 5.6 105
Yakima, WA............... 7.8 99.7 (5) - 740 (5) -
Kanawha, WV.............. 5.9 103.8 -1.0 327 897 3.9 237
Brown, WI................ 6.8 154.1 1.7 191 986 6.4 61
Dane, WI................. 15.1 329.9 2.4 134 1,081 6.1 73
Milwaukee, WI............ 26.2 489.1 0.4 294 1,043 3.5 262
Outagamie, WI............ 5.2 106.9 1.8 183 926 6.8 36
Waukesha, WI............. 12.9 239.1 1.6 197 1,084 5.8 94
Winnebago, WI............ 3.7 91.8 1.4 208 1,033 6.5 53
San Juan, PR............. 10.6 257.3 -1.9 (6) 675 2.1 (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 quarterly 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 342 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,
fourth quarter 2015
Employment Average weekly
wage(1)
Establishments,
fourth quarter
County by NAICS supersector 2015 Percent Percent
(thousands) December change, Fourth change,
2015 December quarter fourth
(thousands) 2014-15(2) 2015 quarter
2014-15(2)
United States(3) ............................ 9,685.3 141,924.5 1.9 $1,082 4.4
Private industry........................... 9,386.7 120,234.9 2.1 1,089 4.5
Natural resources and mining............. 138.6 1,819.6 -8.9 1,157 -5.0
Construction............................. 770.9 6,521.4 4.9 1,233 5.1
Manufacturing............................ 342.8 12,291.3 0.0 1,322 4.5
Trade, transportation, and utilities..... 1,925.8 27,688.6 1.6 904 4.9
Information.............................. 155.5 2,793.9 1.6 1,907 8.0
Financial activities..................... 854.5 7,920.5 1.9 1,710 2.7
Professional and business services....... 1,754.0 19,995.4 2.2 1,438 4.3
Education and health services............ 1,557.4 21,495.3 2.5 994 5.7
Leisure and hospitality.................. 814.2 15,025.8 3.3 462 5.5
Other services........................... 832.4 4,331.1 1.7 724 5.4
Government................................. 298.5 21,689.5 0.7 1,047 4.9
Los Angeles, CA.............................. 463.6 4,341.0 2.3 1,266 5.5
Private industry........................... 457.8 3,775.7 2.4 1,260 6.1
Natural resources and mining............. 0.5 8.3 -8.3 1,573 13.6
Construction............................. 13.6 129.6 8.1 1,267 5.3
Manufacturing............................ 12.3 352.9 -2.2 1,330 8.5
Trade, transportation, and utilities..... 53.4 830.3 0.7 986 5.6
Information.............................. 9.8 213.4 4.7 2,556 11.3
Financial activities..................... 25.0 214.6 0.5 1,916 3.7
Professional and business services....... 48.0 606.0 0.7 1,652 6.9
Education and health services............ 213.6 740.6 3.1 930 3.8
Leisure and hospitality.................. 31.8 490.8 2.9 974 1.9
Other services........................... 27.8 146.7 0.3 746 8.0
Government................................. 5.8 565.3 1.9 1,309 2.3
New York, NY................................. 129.9 2,442.2 2.1 2,235 0.8
Private industry........................... 129.1 2,174.2 2.2 2,350 0.5
Natural resources and mining............. 0.0 0.2 -4.2 2,033 -3.4
Construction............................. 2.2 38.8 9.0 2,311 4.1
Manufacturing............................ 2.2 27.4 0.8 1,636 0.9
Trade, transportation, and utilities..... 20.0 268.9 -2.4 1,493 3.9
Information.............................. 4.9 155.6 1.0 2,804 2.9
Financial activities..................... 19.1 372.4 1.9 4,593 -7.8
Professional and business services....... 27.6 556.2 3.3 2,687 6.1
Education and health services............ 9.8 342.2 2.5 1,384 6.7
Leisure and hospitality.................. 13.8 297.5 2.4 1,027 4.3
Other services........................... 20.3 102.2 0.5 1,210 5.6
Government................................. 0.8 268.0 1.2 1,301 3.7
Cook, IL..................................... 158.4 2,575.7 1.9 1,267 4.4
Private industry........................... 157.1 2,276.8 2.0 1,274 4.9
Natural resources and mining............. 0.1 1.1 29.7 1,342 4.0
Construction............................. 12.8 70.8 2.3 1,635 1.8
Manufacturing............................ 6.5 186.6 -0.6 1,405 7.0
Trade, transportation, and utilities..... 31.1 486.2 1.5 971 3.2
Information.............................. 2.7 55.0 1.6 1,802 7.9
Financial activities..................... 15.7 190.0 0.7 2,298 4.1
Professional and business services....... 33.7 474.5 1.8 1,690 5.8
Education and health services............ 16.7 442.8 2.5 1,048 4.3
Leisure and hospitality.................. 14.5 267.9 5.2 543 9.7
Other services........................... 17.7 95.9 -0.1 967 6.9
Government................................. 1.3 298.9 0.9 1,221 1.7
Harris, TX................................... 112.2 2,302.8 -0.5 1,382 0.4
Private industry........................... 111.6 2,031.1 -0.8 1,414 -0.1
Natural resources and mining............. 1.8 81.2 -15.7 3,380 0.8
Construction............................. 7.1 163.5 2.3 1,530 4.7
Manufacturing............................ 4.8 179.8 -11.9 1,681 0.2
Trade, transportation, and utilities..... 25.0 486.1 0.2 1,234 1.7
Information.............................. 1.2 27.6 0.6 1,497 -2.5
Financial activities..................... 11.6 122.4 1.3 1,824 3.5
Professional and business services....... 23.0 395.7 -1.1 1,768 -0.7
Education and health services............ 15.5 286.3 3.8 1,103 4.5
Leisure and hospitality.................. 9.5 222.8 5.2 474 4.2
Other services........................... 11.7 65.0 0.5 843 3.8
Government................................. 0.6 271.7 1.9 1,147 6.5
Maricopa, AZ................................. 97.2 1,883.2 3.2 1,016 4.1
Private industry........................... 96.5 1,670.6 3.6 1,015 4.2
Natural resources and mining............. 0.4 8.3 -1.2 953 3.5
Construction............................. 7.1 98.4 4.5 1,124 4.2
Manufacturing............................ 3.2 116.6 0.6 1,451 5.8
Trade, transportation, and utilities..... 19.7 378.4 2.6 913 4.5
Information.............................. 1.6 35.1 2.1 1,358 6.2
Financial activities..................... 11.1 164.9 5.1 1,305 6.2
Professional and business services....... 21.9 322.9 2.6 1,127 2.9
Education and health services............ 10.9 279.9 3.6 1,041 3.5
Leisure and hospitality.................. 7.6 205.3 3.6 488 5.4
Other services........................... 6.2 49.8 1.4 718 5.4
Government................................. 0.7 212.6 0.0 1,024 3.9
Dallas, TX................................... 73.9 1,651.6 3.9 1,287 4.5
Private industry........................... 73.4 1,478.2 4.1 1,303 4.3
Natural resources and mining............. 0.6 9.0 -5.0 3,562 -6.1
Construction............................. 4.3 82.0 5.3 1,344 7.3
Manufacturing............................ 2.7 105.8 -1.0 1,487 3.3
Trade, transportation, and utilities..... 15.9 352.4 6.3 1,099 3.6
Information.............................. 1.4 49.1 1.7 1,813 2.3
Financial activities..................... 9.1 159.2 2.4 1,764 4.1
Professional and business services....... 16.7 333.6 4.1 1,577 5.3
Education and health services............ 9.1 191.6 4.4 1,159 8.2
Leisure and hospitality.................. 6.4 153.9 6.2 554 8.8
Other services........................... 6.9 40.9 0.4 821 2.1
Government................................. 0.5 173.5 2.1 1,152 5.7
Orange, CA................................... 113.6 1,550.6 2.3 1,205 4.1
Private industry........................... 112.3 1,406.3 2.5 1,209 3.8
Natural resources and mining............. 0.2 2.7 -11.7 919 6.1
Construction............................. 6.6 92.5 9.5 1,376 6.7
Manufacturing............................ 4.8 156.3 -0.3 1,498 4.5
Trade, transportation, and utilities..... 16.7 264.5 -0.3 1,067 4.9
Information.............................. 1.3 25.1 0.4 1,952 7.4
Financial activities..................... 10.9 117.5 2.1 1,988 1.2
Professional and business services....... 20.5 289.1 0.3 1,450 2.9
Education and health services............ 29.4 196.8 3.7 1,047 5.9
Leisure and hospitality.................. 8.1 203.5 3.3 495 6.0
Other services........................... 7.0 44.0 1.2 730 4.9
Government................................. 1.4 144.3 0.3 1,168 6.6
San Diego, CA................................ 105.7 1,399.7 2.6 1,184 4.3
Private industry........................... 103.9 1,169.6 2.7 1,172 4.3
Natural resources and mining............. 0.7 9.0 1.4 730 4.3
Construction............................. 6.5 71.7 9.0 1,260 6.6
Manufacturing............................ 3.1 104.3 0.9 1,755 11.6
Trade, transportation, and utilities..... 14.2 224.7 0.1 887 5.2
Information.............................. 1.2 23.7 -3.3 1,742 4.7
Financial activities..................... 9.6 71.8 3.3 1,533 9.0
Professional and business services....... 18.2 232.7 1.9 1,768 -1.9
Education and health services............ 29.3 190.1 3.1 1,036 6.1
Leisure and hospitality.................. 7.8 181.1 1.6 497 8.3
Other services........................... 7.5 49.3 0.6 636 5.8
Government................................. 1.8 230.1 2.1 1,247 4.6
King, WA..................................... 85.3 1,297.2 2.9 1,429 3.3
Private industry........................... 84.8 1,131.8 3.0 1,446 3.2
Natural resources and mining............. 0.4 3.0 23.3 1,336 -5.1
Construction............................. 6.3 64.2 5.4 1,383 5.6
Manufacturing............................ 2.4 105.3 -1.0 1,663 1.5
Trade, transportation, and utilities..... 14.6 250.3 3.8 1,236 5.7
Information.............................. 2.1 91.6 6.4 2,912 -2.7
Financial activities..................... 6.5 67.2 1.9 1,728 -3.2
Professional and business services....... 16.8 216.9 4.0 1,822 4.2
Education and health services............ 19.7 162.3 (4) 1,060 (4)
Leisure and hospitality.................. 7.0 128.1 4.0 568 4.4
Other services........................... 8.9 42.8 1.9 849 5.3
Government................................. 0.5 165.4 2.1 1,314 3.8
Miami-Dade, FL............................... 94.8 1,115.9 3.0 1,051 4.4
Private industry........................... 94.4 978.5 3.3 1,029 4.6
Natural resources and mining............. 0.5 9.7 5.5 661 11.7
Construction............................. 5.8 41.7 10.7 1,015 2.7
Manufacturing............................ 2.8 39.8 4.0 991 0.5
Trade, transportation, and utilities..... 26.4 288.6 1.5 927 5.6
Information.............................. 1.5 17.8 -2.4 1,648 7.2
Financial activities..................... 10.2 75.2 2.5 1,645 3.1
Professional and business services....... 20.7 154.2 4.5 1,328 1.0
Education and health services............ 10.1 171.1 2.9 1,050 7.3
Leisure and hospitality.................. 7.1 138.5 4.6 615 9.0
Other services........................... 8.1 41.3 3.4 644 4.9
Government................................. 0.3 137.3 0.8 1,206 3.7
(1) Average weekly wages were calculated using unrounded data.
(2) Percent changes were computed from quarterly employment and pay data adjusted for noneconomic
county reclassifications. See Technical Note.
(3) Totals for the United States do not include data for Puerto Rico or the Virgin Islands.
(4) Data do not meet BLS or state agency disclosure standards.
Note: Data are preliminary. Counties selected are based on 2014 annual average employment.
Includes workers covered by Unemployment Insurance (UI) and Unemployment Compensation for Federal
Employees (UCFE) programs.
Table 3. Covered establishments, employment, and wages by state,
fourth quarter 2015
Employment Average weekly
wage(1)
Establishments,
fourth quarter
State 2015 Percent Percent
(thousands) December change, Fourth change,
2015 December quarter fourth
(thousands) 2014-15 2015 quarter
2014-15
United States(2)........... 9,685.3 141,924.5 1.9 $1,082 4.4
Alabama.................... 120.6 1,916.2 1.4 912 3.4
Alaska..................... 22.4 315.9 -0.5 1,095 2.9
Arizona.................... 155.1 2,701.8 2.6 967 4.4
Arkansas................... 89.2 1,201.4 1.7 838 3.8
California................. 1,454.6 16,593.8 3.1 1,274 5.4
Colorado................... 187.6 2,537.5 2.5 1,103 3.3
Connecticut................ 116.4 1,685.1 0.3 1,334 4.3
Delaware................... 30.7 441.2 1.8 1,086 3.4
District of Columbia....... 39.0 754.2 2.2 1,756 3.4
Florida.................... 652.3 8,308.1 3.7 958 5.2
Georgia.................... 296.0 4,249.4 2.9 1,001 4.5
Hawaii..................... 40.0 653.0 2.2 957 5.4
Idaho...................... 57.0 670.1 3.4 803 2.6
Illinois................... 414.9 5,931.2 1.4 1,146 5.1
Indiana.................... 161.3 2,996.3 1.7 891 5.3
Iowa....................... 101.0 1,539.0 0.7 920 5.7
Kansas..................... 88.1 1,382.1 0.4 898 5.0
Kentucky................... 122.5 1,881.3 1.6 885 5.9
Louisiana.................. 127.4 1,937.4 -1.0 940 1.8
Maine...................... 52.1 596.9 0.7 873 5.7
Maryland................... 168.7 2,636.7 1.7 1,175 5.6
Massachusetts.............. 241.5 3,479.1 1.6 1,385 5.4
Michigan................... 240.1 4,218.9 1.5 1,043 5.9
Minnesota.................. 160.3 2,805.8 1.5 1,073 4.8
Mississippi................ 72.8 1,133.8 1.3 770 3.1
Missouri................... 195.0 2,759.6 1.8 933 4.6
Montana.................... 45.8 453.2 2.5 818 3.0
Nebraska................... 70.9 971.8 1.4 880 5.1
Nevada..................... 79.8 1,272.2 3.5 935 4.0
New Hampshire.............. 51.6 648.6 1.7 1,139 5.4
New Jersey................. 269.9 3,988.4 1.7 1,262 4.0
New Mexico................. 57.4 808.9 -0.1 865 1.8
New York................... 640.6 9,227.6 1.7 1,372 3.9
North Carolina............. 270.1 4,247.1 2.5 939 5.5
North Dakota............... 32.2 428.1 -5.9 1,021 -2.8
Ohio....................... 292.4 5,328.8 1.2 964 4.6
Oklahoma................... 109.0 1,605.0 -0.7 896 2.3
Oregon..................... 146.0 1,814.8 3.3 979 5.5
Pennsylvania............... 355.2 5,759.7 0.7 1,063 4.9
Rhode Island............... 36.7 478.1 1.5 1,043 4.0
South Carolina............. 125.7 1,987.1 2.8 860 5.3
South Dakota............... 32.8 417.7 1.2 832 5.2
Tennessee.................. 151.5 2,898.1 2.8 980 5.6
Texas...................... 645.6 11,832.1 1.4 1,099 2.7
Utah....................... 95.5 1,375.6 3.8 913 4.7
Vermont.................... 24.8 312.1 0.3 919 4.1
Virginia................... 261.3 3,806.2 3.0 1,094 3.5
Washington................. 237.7 3,137.2 2.3 1,132 4.7
West Virginia.............. 50.3 703.7 -1.3 829 1.3
Wisconsin.................. 170.0 2,820.5 1.1 944 5.6
Wyoming.................... 26.0 276.0 -2.9 937 -1.7
Puerto Rico................ 45.4 929.9 -1.6 565 1.6
Virgin Islands............. 3.3 38.4 -0.3 787 4.7
(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
