For release 10:00 a.m. (EDT), Thursday, March 19, 2015 USDL-15-0427
Technical Information: (202) 691-6567 * QCEWInfo@bls.gov * www.bls.gov/cew
Media Contact: (202) 691-5902 * PressOffice@bls.gov
COUNTY EMPLOYMENT AND WAGES
Third Quarter 2014
From September 2013 to September 2014, employment increased in 306 of the 339 largest U.S.
counties, the U.S. Bureau of Labor Statistics reported today. Weld, Colo., had the largest increase, with
a gain of 8.8 percent over the year, compared with national job growth of 2.0 percent. Within Weld, the
largest employment increase occurred in natural resources and mining, which gained 2,299 jobs over the
year (22.1 percent). Atlantic, N.J., had the largest over-the-year decrease in employment among the
largest counties in the U.S. with a loss of 4.0 percent. County employment and wage data are compiled
under the Quarterly Census of Employment and Wages (QCEW) program, which produces detailed
information on county employment and wages within 6 months after the end of each quarter.
The U.S. average weekly wage increased 2.9 percent over the year, growing to $949 in the third quarter
of 2014. Olmsted, Minn., had the largest over-the-year increase in average weekly wages with a gain of
11.1 percent. Within Olmsted, an average weekly wage gain of $238, or 19.7 percent, in education and
health services made the largest contribution to the county’s increase in average weekly wages. Collier,
Fla., experienced the largest decrease in average weekly wages with a loss of 3.9 percent over the year.
Table A. Large counties ranked by September 2014 employment, September 2013-14 employment
increase, and September 2013-14 percent increase in employment
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Employment in large counties
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September 2014 employment | Increase in employment, | Percent increase in employment,
(thousands) | September 2013-14 | September 2013-14
| (thousands) |
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| |
United States 137,724.1| United States 2,708.5| United States 2.0
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| |
Los Angeles, Calif. 4,184.4| Los Angeles, Calif. 87.6| Weld, Colo. 8.8
New York, N.Y. 2,494.4| Harris, Texas 79.2| Benton, Ark. 7.4
Cook, Ill. 2,481.9| New York, N.Y. 65.7| Midland, Texas 7.4
Harris, Texas 2,269.5| Dallas, Texas 53.1| Lee, Fla. 6.1
Maricopa, Ariz. 1,756.8| King, Wash. 41.5| Sarasota, Fla. 6.1
Dallas, Texas 1,558.5| Santa Clara, Calif. 41.4| Adams, Colo. 5.7
Orange, Calif. 1,475.0| Clark, Nev. 39.8| Kings, N.Y. 5.4
San Diego, Calif. 1,344.5| Maricopa, Ariz. 34.1| Williamson, Tenn. 5.4
King, Wash. 1,252.8| Orange, Calif. 32.6| San Francisco, Calif. 5.1
Miami-Dade, Fla. 1,047.0| San Francisco, Calif. 31.4| Fort Bend, Texas 5.1
| | Montgomery, Texas 5.1
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Large County Employment
In September 2014, national employment was 137.7 million (as measured by the QCEW program). Over
the year, employment increased 2.0 percent, or 2.7 million. The 339 U.S. counties with 75,000 or more
jobs accounted for 71.8 percent of total U.S. employment and 76.9 percent of total wages. These 339
counties had a net job growth of 2.0 million over the year, accounting for 74.1 percent of the overall
U.S. employment increase.
Weld, Colo., had the largest percentage increase in employment (8.8 percent) among the largest U.S.
counties. The five counties with the largest increases in employment level were Los Angeles, Calif.;
Harris, Texas; New York, N.Y.; Dallas, Texas; and King, Wash. These counties had a combined over-
the-year employment gain of 327,100 jobs, which was 12.1 percent of the overall job increase for the
U.S. (See table A.)
Employment declined in 25 of the largest counties from September 2013 to September 2014. Atlantic,
N.J., had the largest over-the-year percentage decrease in employment (-4.0 percent). Within Atlantic,
leisure and hospitality had the largest decrease in employment, with a loss of 5,853 jobs (-12.0 percent).
Passaic, N.J., had the second largest percentage decrease in employment, followed by McLean, Ill.;
Peoria, Ill.; and Burlington, N.J. (See table 1.)
Table B. Large counties ranked by third quarter 2014 average weekly wages, third quarter 2013-14
increase in average weekly wages, and third quarter 2013-14 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
third quarter 2014 | wage, third quarter 2013-14 | weekly wage, third
| | quarter 2013-14
--------------------------------------------------------------------------------------------------------
| |
United States $949| United States $27| United States 2.9
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| |
Santa Clara, Calif. $2,012| Santa Clara, Calif. $138| Olmsted, Minn. 11.1
San Mateo, Calif. 1,824| San Francisco, Calif. 134| San Francisco, Calif. 8.6
New York, N.Y. 1,733| San Mateo, Calif. 121| Santa Clara, Calif. 7.4
San Francisco, Calif. 1,685| Olmsted, Minn. 108| San Mateo, Calif. 7.1
Washington, D.C. 1,631| Suffolk, Mass. 84| Brazoria, Texas 7.1
Arlington, Va. 1,545| Midland, Texas 80| Midland, Texas 6.8
Suffolk, Mass. 1,515| Washington, Ore. 71| Washington, Ore. 6.2
King, Wash. 1,452| Arlington, Va. 71| Howard, Md. 6.0
Fairfax, Va. 1,447| King, Wash. 71| Hamilton, Ohio 6.0
Fairfield, Conn. 1,400| Howard, Md. 67| Suffolk, Mass. 5.9
| |
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Large County Average Weekly Wages
Average weekly wages for the nation increased to $949, a 2.9 percent increase, during the year ending in
the third quarter of 2014. Among the 339 largest counties, 328 had over-the-year increases in average
weekly wages. Olmsted, Minn., had the largest wage increase among the largest U.S. counties (11.1
percent).
Of the 339 largest counties, 10 experienced over-the-year decreases in average weekly wages. Collier,
Fla., had the largest percentage decrease in average weekly wages, with a loss of 3.9 percent. Within
Collier, professional and business services had the largest impact on the county’s average weekly wage
decrease. Within this industry, average weekly wages declined by $498 (-33.2 percent) over the year.
Dane, Wis., had the second largest percentage decrease in average weekly wages, followed by
Williamson, Texas; Hamilton, Ind.; and Shawnee, Kan. (See table 1.)
Ten Largest U.S. Counties
All of the 10 largest counties had over-the-year percentage increases in employment in September
2014. Harris, Texas, had the largest gain (3.6 percent). Within Harris, trade, transportation, and utilities
had the largest over-the-year employment level increase among all private industry groups with a gain of
15,547 jobs, or 3.4 percent. Cook, Ill., had the smallest percentage increase in employment (1.2 percent)
among the 10 largest counties. (See table 2.)
Average weekly wages increased over the year in all of the 10 largest U.S. counties. King, Wash.,
experienced the largest percentage gain in average weekly wages (5.1 percent). Within King,
information had the largest impact on the county’s average weekly wage growth. Within this industry,
average weekly wages increased by $437, or 9.3 percent, over the year. San Diego, Calif., had the
smallest increase in average weekly wages (0.8 percent) among the 10 largest counties.
For More Information
The tables included in this release contain data for the nation and for the 339 U.S. counties with annual
average employment levels of 75,000 or more in 2013. September 2014 employment and 2014 third
quarter average weekly wages for all states are provided in table 3 of this release.
The employment and wage data by county are compiled under the QCEW program, also known as the
ES-202 program. The data are derived from reports submitted by every employer subject to
unemployment insurance (UI) laws. The 9.4 million employer reports cover 137.7 million full- and part-
time workers. The QCEW program provides a quarterly and annual universe count of establishments,
employment, and wages at the county, MSA, state, and national levels by detailed industry. Data for the
third quarter of 2014 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 fourth quarter 2014 is scheduled to be released
on Wednesday, June 17, 2015.
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 2014 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 340 counties
presented in this release were derived using 2013 preliminary annual averages of employment. For
2014 data, five counties have been added to the publication tables: Shelby, Ala.; Osceola, Fla.;
Black Hawk, Iowa; Washington, Minn.; and Cleveland, Okla. These counties will be included in
all 2014 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- | 588,000 establish-
| submitted by 9.4 | ministrative records| ments
| million establish- | submitted by 7.5 |
| ments in first | million private-sec-|
| quarter of 2014 | 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| -8 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 9.2
million employer reports of employment and wages submitted by states to the BLS in 2013. 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 2013, UI and UCFE programs
covered workers in 134.0 million jobs. The estimated 128.7 million workers in these jobs (after
adjustment for multiple jobholders) represented 95.8 percent of civilian wage and salary
employment. Covered workers received $6.673 trillion in pay, representing 93.7 percent of the
wage and salary component of personal income and 39.8 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 2013 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. Beginning with the first quarter of 2008,
adjusted data account for administrative changes caused by multi-unit employers who start
reporting for each individual establishment rather than as a single entity. Beginning with the
second quarter of 2011, adjusted data account for selected large administrative changes in
employment and wages. Beginning with the third quarter of 2014, adjusted data account for state
verified improvements in reporting of employment and wages. 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 2013 edition
of this publication, which was published in September 2014, contains selected data produced by
Business Employment Dynamics (BED) on job gains and losses, as well as selected data from the
first quarter 2014 version of this news release. Tables and additional content from Employment and
Wages Annual Averages 2013 are now available online at
http://www.bls.gov/cew/cewbultn13.htm. The 2014 edition of Employment and Wages Annual
Averages Online will be available in September 2015.
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 340 largest counties,
third quarter 2014
Employment Average weekly wage(2)
Establishments,
County(1) third quarter Percent Ranking Percent Ranking
2014 September change, by Third change, by
(thousands) 2014 September percent quarter third percent
(thousands) 2013-14(3) change 2014 quarter change
2013-14(3)
United States(4)......... 9,419.7 137,724.1 2.0 - $949 2.9 -
Jefferson, AL............ 17.8 339.0 0.4 282 956 3.7 55
Madison, AL.............. 9.1 183.2 1.0 228 1,036 4.0 40
Mobile, AL............... 9.6 165.7 1.0 228 820 1.6 274
Montgomery, AL........... 6.3 128.1 0.5 276 810 1.8 257
Shelby, AL............... 5.1 80.1 2.7 96 868 2.0 231
Tuscaloosa, AL........... 4.3 90.6 4.5 17 814 1.1 293
Anchorage Borough, AK.... 8.4 156.2 -0.2 318 1,066 3.2 91
Maricopa, AZ............. 93.4 1,756.8 2.0 145 914 1.8 257
Pima, AZ................. 18.7 352.4 0.1 299 818 2.9 125
Benton, AR............... 5.7 106.5 7.4 2 926 0.5 317
Pulaski, AR.............. 14.3 242.9 0.2 293 849 2.3 194
Washington, AR........... 5.7 96.8 1.5 171 774 0.3 325
Alameda, CA.............. 57.5 706.5 3.2 65 1,247 4.2 31
Contra Costa, CA......... 29.9 342.7 2.2 128 1,142 2.1 223
Fresno, CA............... 31.1 365.4 1.0 228 747 3.2 91
Kern, CA................. 17.3 326.4 2.2 128 819 3.8 50
Los Angeles, CA.......... 442.4 4,184.4 2.1 137 1,036 3.1 103
Marin, CA................ 12.1 111.3 3.0 77 1,120 3.9 44
Monterey, CA............. 12.9 197.1 4.0 34 797 1.0 300
Orange, CA............... 108.0 1,475.0 2.3 119 1,050 2.6 154
Placer, CA............... 11.4 143.7 3.8 42 937 2.9 125
Riverside, CA............ 53.6 626.4 4.4 19 756 2.6 154
Sacramento, CA........... 52.7 618.0 3.3 61 1,050 2.0 231
San Bernardino, CA....... 51.4 658.1 4.1 29 793 2.7 140
San Diego, CA............ 100.8 1,344.5 2.3 119 1,030 0.8 306
San Francisco, CA........ 57.6 648.6 5.1 9 1,685 8.6 2
San Joaquin, CA.......... 16.7 224.0 3.6 51 799 1.4 286
San Luis Obispo, CA...... 9.8 110.8 3.8 42 785 2.2 206
San Mateo, CA............ 26.1 375.4 4.4 19 1,824 7.1 4
Santa Barbara, CA........ 14.6 194.6 3.3 61 901 2.3 194
Santa Clara, CA.......... 66.2 986.6 4.4 19 2,012 7.4 3
Santa Cruz, CA........... 9.2 100.8 2.1 137 837 2.6 154
Solano, CA............... 10.3 128.3 2.1 137 958 4.1 38
Sonoma, CA............... 18.9 195.1 3.5 54 896 1.9 244
Stanislaus, CA........... 14.4 177.6 2.5 107 801 1.9 244
Tulare, CA............... 9.2 151.6 2.8 86 667 3.6 61
Ventura, CA.............. 24.9 310.7 1.2 199 945 2.1 223
Yolo, CA................. 6.1 101.2 0.8 248 972 4.5 23
Adams, CO................ 9.4 185.4 5.7 6 924 2.8 129
Arapahoe, CO............. 19.7 307.9 2.8 86 1,096 2.7 140
Boulder, CO.............. 13.7 170.1 2.8 86 1,129 3.0 117
Denver, CO............... 28.0 467.4 4.9 13 1,175 4.6 22
Douglas, CO.............. 10.3 107.9 3.7 48 1,037 0.4 323
El Paso, CO.............. 17.2 250.4 1.8 153 859 2.3 194
Jefferson, CO............ 18.2 223.8 3.2 65 951 3.0 117
Larimer, CO.............. 10.6 143.7 3.8 42 859 3.4 74
Weld, CO................. 6.3 100.7 8.8 1 869 4.3 28
Fairfield, CT............ 34.1 420.4 0.8 248 1,400 1.7 264
Hartford, CT............. 26.4 503.0 1.0 228 1,123 0.0 329
New Haven, CT............ 23.1 361.2 0.7 263 987 2.0 231
New London, CT........... 7.1 122.3 -0.6 330 927 2.0 231
New Castle, DE........... 18.3 278.7 2.3 119 1,074 1.9 244
Washington, DC........... 36.3 732.9 1.5 171 1,631 3.8 50
Alachua, FL.............. 6.7 121.5 2.5 107 790 3.4 74
Brevard, FL.............. 14.9 190.0 1.7 162 851 1.2 291
Broward, FL.............. 66.5 739.9 2.8 86 869 2.2 206
Collier, FL.............. 12.7 123.9 4.3 24 806 -3.9 339
Duval, FL................ 27.7 456.5 1.3 192 890 2.8 129
Escambia, FL............. 8.2 124.9 2.1 137 733 3.2 91
Hillsborough, FL......... 39.7 620.0 2.9 83 891 2.6 154
Lake, FL................. 7.7 86.2 2.8 86 656 2.5 165
Lee, FL.................. 20.1 223.2 6.1 4 743 1.6 274
Leon, FL................. 8.4 142.2 2.8 86 771 1.7 264
Manatee, FL.............. 10.0 106.5 3.1 73 706 1.0 300
Marion, FL............... 8.1 94.9 3.2 65 644 1.1 293
Miami-Dade, FL........... 94.3 1,047.0 3.0 77 891 2.2 206
Okaloosa, FL............. 6.2 78.2 0.1 299 779 2.8 129
Orange, FL............... 38.7 735.7 3.6 51 821 2.1 223
Osceola, FL.............. 6.1 80.9 3.0 77 656 2.2 206
Palm Beach, FL........... 52.6 538.4 3.9 36 903 1.9 244
Pasco, FL................ 10.3 105.7 4.2 26 650 2.7 140
Pinellas, FL............. 31.7 397.8 2.1 137 826 2.5 165
Polk, FL................. 12.7 196.2 1.9 147 730 1.5 282
Sarasota, FL............. 15.1 152.5 6.1 4 754 1.3 290
Seminole, FL............. 14.2 169.0 4.1 29 777 1.8 257
Volusia, FL.............. 13.7 156.2 2.6 102 664 2.3 194
Bibb, GA................. 4.5 81.8 1.8 153 737 1.5 282
Chatham, GA.............. 8.2 141.8 4.4 19 800 1.7 264
Clayton, GA.............. 4.3 113.0 2.4 113 892 1.9 244
Cobb, GA................. 22.6 325.4 3.8 42 988 2.7 140
De Kalb, GA.............. 18.8 283.6 3.4 57 947 0.9 304
Fulton, GA............... 44.4 772.1 3.5 54 1,236 3.2 91
Gwinnett, GA............. 25.3 327.9 3.8 42 932 3.4 74
Muscogee, GA............. 4.8 94.4 0.5 276 744 2.1 223
Richmond, GA............. 4.7 102.5 3.0 77 800 1.5 282
Honolulu, HI............. 24.9 456.1 0.8 248 906 4.0 40
Ada, ID.................. 14.1 210.3 1.1 212 831 2.1 223
Champaign, IL............ 4.5 90.0 1.3 192 850 1.7 264
Cook, IL................. 158.3 2,481.9 1.2 199 1,071 2.0 231
Du Page, IL.............. 39.2 601.5 0.9 241 1,067 1.7 264
Kane, IL................. 14.1 206.8 0.7 263 831 3.4 74
Lake, IL................. 23.3 333.9 -0.4 323 1,180 2.4 183
McHenry, IL.............. 9.1 97.3 1.7 162 782 3.0 117
McLean, IL............... 3.9 84.0 -1.2 336 892 0.3 325
Madison, IL.............. 6.2 98.1 2.4 113 771 1.6 274
Peoria, IL............... 4.8 100.0 -1.2 336 870 1.6 274
St. Clair, IL............ 5.7 92.1 -0.7 332 769 2.5 165
Sangamon, IL............. 5.5 128.8 1.2 199 983 3.1 103
Will, IL................. 16.4 218.0 1.2 199 835 2.6 154
Winnebago, IL............ 7.0 128.0 1.5 171 798 2.8 129
Allen, IN................ 8.8 179.2 1.3 192 775 2.0 231
Elkhart, IN.............. 4.7 121.3 3.7 48 778 2.6 154
Hamilton, IN............. 8.8 127.7 4.1 29 891 -0.7 336
Lake, IN................. 10.2 187.3 -0.4 323 848 1.1 293
Marion, IN............... 23.4 580.5 0.9 241 947 0.2 327
St. Joseph, IN........... 5.8 118.8 1.4 184 772 3.1 103
Tippecanoe, IN........... 3.3 81.9 2.6 102 800 4.4 26
Vanderburgh, IN.......... 4.7 106.0 2.0 145 759 2.6 154
Black Hawk, IA........... 3.8 75.9 0.1 299 802 3.1 103
Johnson, IA.............. 4.0 81.1 0.4 282 891 2.2 206
Linn, IA................. 6.5 128.6 0.5 276 915 3.5 66
Polk, IA................. 16.5 287.2 1.7 162 958 3.5 66
Scott, IA................ 5.5 90.4 1.1 212 764 1.1 293
Johnson, KS.............. 21.6 328.7 2.4 113 955 2.5 165
Sedgwick, KS............. 12.4 245.5 1.2 199 825 1.4 286
Shawnee, KS.............. 4.8 97.7 1.5 171 769 -0.4 335
Wyandotte, KS............ 3.3 88.3 4.1 29 914 3.9 44
Boone, KY................ 4.2 78.7 3.3 61 803 0.5 317
Fayette, KY.............. 10.4 184.8 0.8 248 844 0.7 310
Jefferson, KY............ 24.4 446.1 2.5 107 897 2.3 194
Caddo, LA................ 7.3 114.1 -0.4 323 788 3.7 55
Calcasieu, LA............ 4.9 88.6 4.0 34 849 5.5 13
East Baton Rouge, LA..... 14.6 271.7 2.3 119 889 1.0 300
Jefferson, LA............ 13.5 191.4 0.5 276 855 2.2 206
Lafayette, LA............ 9.2 142.1 1.1 212 949 4.4 26
Orleans, LA.............. 11.6 186.4 3.2 65 931 2.4 183
St. Tammany, LA.......... 7.6 83.1 2.3 119 816 3.3 81
Cumberland, ME........... 12.7 174.8 0.8 248 832 2.3 194
Anne Arundel, MD......... 14.5 255.5 0.9 241 1,021 2.4 183
Baltimore, MD............ 21.0 365.5 0.2 293 959 2.6 154
Frederick, MD............ 6.3 96.1 0.8 248 905 4.0 40
Harford, MD.............. 5.6 88.8 0.1 299 904 3.1 103
Howard, MD............... 9.4 161.2 0.8 248 1,183 6.0 8
Montgomery, MD........... 32.5 455.9 0.4 282 1,243 2.2 206
Prince Georges, MD....... 15.6 306.2 1.6 167 1,033 2.9 125
Baltimore City, MD....... 13.7 337.3 2.2 128 1,123 2.7 140
Barnstable, MA........... 9.2 98.9 1.4 184 782 2.2 206
Bristol, MA.............. 16.5 220.5 1.7 162 839 0.8 306
Essex, MA................ 22.8 316.3 1.4 184 1,000 3.4 74
Hampden, MA.............. 16.6 202.2 0.7 263 860 2.4 183
Middlesex, MA............ 51.4 857.9 1.8 153 1,382 1.7 264
Norfolk, MA.............. 24.0 337.6 1.4 184 1,079 2.5 165
Plymouth, MA............. 14.5 185.4 1.8 153 880 2.3 194
Suffolk, MA.............. 25.6 621.9 2.1 137 1,515 5.9 10
Worcester, MA............ 22.6 331.4 2.3 119 949 0.7 310
Genesee, MI.............. 7.0 133.7 1.2 199 777 2.5 165
Ingham, MI............... 6.1 150.5 -0.4 323 899 3.2 91
Kalamazoo, MI............ 5.1 113.2 0.2 293 875 3.2 91
Kent, MI................. 13.9 363.8 2.7 96 837 3.3 81
Macomb, MI............... 17.2 307.6 1.0 228 942 2.4 183
Oakland, MI.............. 38.1 693.8 1.4 184 1,028 2.2 206
Ottawa, MI............... 5.5 119.1 4.2 26 801 4.8 19
Saginaw, MI.............. 4.0 83.9 -0.1 315 763 2.7 140
Washtenaw, MI............ 8.0 198.9 0.9 241 1,028 3.6 61
Wayne, MI................ 30.4 694.5 0.8 248 1,027 2.9 125
Anoka, MN................ 6.9 118.2 1.9 147 937 3.8 50
Dakota, MN............... 9.6 181.5 1.3 192 919 3.6 61
Hennepin, MN............. 40.3 872.8 1.5 171 1,175 1.1 293
Olmsted, MN.............. 3.4 92.5 -0.3 322 1,077 11.1 1
Ramsey, MN............... 13.3 326.1 0.3 290 1,057 2.7 140
St. Louis, MN............ 5.3 97.6 0.5 276 827 4.2 31
Stearns, MN.............. 4.2 83.6 0.7 263 793 5.7 12
Washington, MN........... 5.3 76.7 0.4 282 783 2.6 154
Harrison, MS............. 4.5 82.9 -0.2 318 694 2.5 165
Hinds, MS................ 6.0 119.5 0.2 293 817 1.0 300
Boone, MO................ 4.7 91.0 1.5 171 764 1.9 244
Clay, MO................. 5.2 95.0 4.1 29 838 0.4 323
Greene, MO............... 8.2 159.6 2.3 119 725 1.8 257
Jackson, MO.............. 19.9 349.2 0.0 307 961 1.7 264
St. Charles, MO.......... 8.6 133.6 1.4 184 763 4.8 19
St. Louis, MO............ 34.1 582.5 1.1 212 993 3.7 55
St. Louis City, MO....... 11.1 224.7 1.1 212 1,031 3.1 103
Yellowstone, MT.......... 6.3 79.5 1.2 199 807 3.9 44
Douglas, NE.............. 18.8 326.7 1.2 199 885 -0.1 330
Lancaster, NE............ 10.2 164.4 1.2 199 769 2.5 165
Clark, NV................ 51.8 883.2 4.7 15 823 0.5 317
Washoe, NV............... 13.9 196.6 2.7 96 854 0.6 315
Hillsborough, NH......... 12.2 195.0 1.8 153 1,014 2.7 140
Rockingham, NH........... 10.6 142.1 1.2 199 918 5.8 11
Atlantic, NJ............. 6.6 131.2 -4.0 339 790 3.3 81
Bergen, NJ............... 32.7 439.0 0.8 248 1,106 2.0 231
Burlington, NJ........... 11.0 195.0 -1.1 335 969 0.5 317
Camden, NJ............... 11.8 198.1 2.4 113 893 -0.1 330
Essex, NJ................ 20.3 327.9 -0.4 323 1,159 0.7 310
Gloucester, NJ........... 6.1 101.9 2.5 107 812 -0.1 330
Hudson, NJ............... 14.1 238.0 0.4 282 1,275 1.9 244
Mercer, NJ............... 11.0 235.6 1.5 171 1,229 3.2 91
Middlesex, NJ............ 21.8 393.8 0.9 241 1,120 0.5 317
Monmouth, NJ............. 20.0 248.8 1.5 171 917 2.6 154
Morris, NJ............... 16.9 281.1 0.6 271 1,341 0.7 310
Ocean, NJ................ 12.6 158.9 1.5 171 749 1.6 274
Passaic, NJ.............. 12.2 164.5 -2.2 338 926 3.5 66
Somerset, NJ............. 10.0 180.8 2.2 128 1,372 2.2 206
Union, NJ................ 14.2 220.6 0.3 290 1,146 1.8 257
Bernalillo, NM........... 18.2 315.5 1.1 212 826 2.2 206
Albany, NY............... 10.3 226.7 1.6 167 1,008 3.5 66
Bronx, NY................ 17.7 254.0 3.1 73 901 0.9 304
Broome, NY............... 4.6 88.2 0.0 307 737 1.9 244
Dutchess, NY............. 8.5 109.4 0.2 293 943 2.3 194
Erie, NY................. 24.6 461.2 0.6 271 836 2.8 129
Kings, NY................ 57.5 569.3 5.4 7 789 4.1 38
Monroe, NY............... 18.6 376.9 0.7 263 905 0.1 328
Nassau, NY............... 53.5 606.5 1.3 192 1,022 3.2 91
New York, NY............. 127.7 2,494.4 2.7 96 1,733 3.8 50
Oneida, NY............... 5.4 103.5 0.0 307 746 3.2 91
Onondaga, NY............. 13.1 242.9 0.0 307 856 1.9 244
Orange, NY............... 10.2 137.6 1.8 153 777 2.5 165
Queens, NY............... 50.0 558.8 3.9 36 884 2.8 129
Richmond, NY............. 9.6 98.9 1.0 228 805 0.6 315
Rockland, NY............. 10.3 116.6 2.5 107 955 -0.1 330
Saratoga, NY............. 5.8 81.2 0.4 282 844 3.6 61
Suffolk, NY.............. 52.1 640.3 0.8 248 1,031 3.1 103
Westchester, NY.......... 36.6 413.6 1.2 199 1,196 3.1 103
Buncombe, NC............. 8.2 120.2 1.9 147 731 2.5 165
Catawba, NC.............. 4.3 81.5 1.3 192 715 2.7 140
Cumberland, NC........... 6.2 116.5 -0.8 334 748 1.1 293
Durham, NC............... 7.6 188.7 1.5 171 1,219 2.4 183
Forsyth, NC.............. 9.1 179.0 1.1 212 889 5.0 17
Guilford, NC............. 14.2 270.9 0.8 248 843 4.2 31
Mecklenburg, NC.......... 33.8 612.5 4.2 26 1,071 1.7 264
New Hanover, NC.......... 7.4 104.1 3.2 65 750 1.1 293
Wake, NC................. 30.6 491.8 3.1 73 953 1.9 244
Cass, ND................. 6.7 116.3 4.4 19 897 4.3 28
Butler, OH............... 7.5 143.5 2.2 128 827 3.5 66
Cuyahoga, OH............. 35.5 707.9 0.1 299 974 1.8 257
Delaware, OH............. 4.7 82.5 -0.7 332 921 2.7 140
Franklin, OH............. 30.3 709.8 2.5 107 948 2.4 183
Hamilton, OH............. 23.3 502.1 1.2 199 1,073 6.0 8
Lake, OH................. 6.3 94.8 0.8 248 786 3.8 50
Lorain, OH............... 6.1 97.0 1.3 192 767 1.7 264
Lucas, OH................ 10.0 205.2 0.0 307 827 4.2 31
Mahoning, OH............. 5.9 99.5 0.6 271 683 1.6 274
Montgomery, OH........... 12.0 247.4 1.9 147 814 1.4 286
Stark, OH................ 8.7 159.1 1.6 167 755 4.3 28
Summit, OH............... 14.1 261.3 1.0 228 851 2.5 165
Warren, OH............... 4.4 83.9 1.9 147 824 3.5 66
Cleveland, OK............ 5.3 80.2 1.1 212 709 2.2 206
Oklahoma, OK............. 26.5 445.2 1.4 184 949 4.5 23
Tulsa, OK................ 21.4 344.4 1.4 184 893 3.4 74
Clackamas, OR............ 13.3 147.9 2.2 128 874 1.9 244
Jackson, OR.............. 6.8 81.7 1.6 167 740 4.2 31
Lane, OR................. 11.2 143.1 2.3 119 754 3.7 55
Marion, OR............... 9.7 144.2 2.9 83 764 4.2 31
Multnomah, OR............ 31.2 466.7 2.7 96 979 2.8 129
Washington, OR........... 17.3 267.1 2.3 119 1,216 6.2 7
Allegheny, PA............ 35.1 686.2 0.1 299 1,024 2.0 231
Berks, PA................ 8.9 167.6 1.5 171 852 3.0 117
Bucks, PA................ 19.6 252.1 1.2 199 892 2.5 165
Butler, PA............... 5.0 85.2 0.0 307 889 2.7 140
Chester, PA.............. 15.1 240.9 0.9 241 1,160 1.8 257
Cumberland, PA........... 6.1 127.3 1.5 171 867 1.9 244
Dauphin, PA.............. 7.3 176.8 0.6 271 939 2.7 140
Delaware, PA............. 13.7 216.0 1.0 228 994 2.7 140
Erie, PA................. 7.2 125.2 0.1 299 755 2.0 231
Lackawanna, PA........... 5.8 97.9 0.7 263 735 3.2 91
Lancaster, PA............ 12.9 227.4 2.2 128 790 3.0 117
Lehigh, PA............... 8.5 182.0 1.1 212 926 2.3 194
Luzerne, PA.............. 7.5 141.7 1.1 212 751 2.3 194
Montgomery, PA........... 27.2 473.0 0.8 248 1,133 2.4 183
Northampton, PA.......... 6.6 106.1 0.7 263 824 1.4 286
Philadelphia, PA......... 34.7 645.3 1.8 153 1,125 2.0 231
Washington, PA........... 5.3 88.1 1.5 171 939 4.9 18
Westmoreland, PA......... 9.3 133.9 0.8 248 767 3.1 103
York, PA................. 8.9 173.0 0.2 293 825 2.1 223
Providence, RI........... 17.3 280.5 1.8 153 937 1.6 274
Charleston, SC........... 12.7 228.9 4.3 24 837 2.8 129
Greenville, SC........... 12.9 248.3 3.9 36 841 2.6 154
Horry, SC................ 8.0 118.3 3.3 61 580 2.7 140
Lexington, SC............ 5.9 107.9 3.2 65 728 2.8 129
Richland, SC............. 9.3 209.9 2.1 137 815 2.5 165
Spartanburg, SC.......... 5.9 124.1 2.9 83 795 3.9 44
York, SC................. 5.0 81.6 3.9 36 752 3.3 81
Minnehaha, SD............ 6.8 121.9 2.8 86 824 3.3 81
Davidson, TN............. 19.8 459.7 3.8 42 967 2.0 231
Hamilton, TN............. 8.9 188.1 0.6 271 831 3.1 103
Knox, TN................. 11.3 227.6 2.7 96 815 2.3 194
Rutherford, TN........... 4.8 113.0 3.2 65 825 3.3 81
Shelby, TN............... 19.6 476.1 1.0 228 965 0.8 306
Williamson, TN........... 7.2 109.5 5.4 7 1,047 2.8 129
Bell, TX................. 4.9 111.2 -0.1 315 798 3.5 66
Bexar, TX................ 37.1 796.4 2.6 102 854 3.3 81
Brazoria, TX............. 5.2 99.4 2.6 102 966 7.1 4
Brazos, TX............... 4.2 96.2 1.1 212 734 3.2 91
Cameron, TX.............. 6.3 133.5 1.0 228 603 3.1 103
Collin, TX............... 21.3 346.4 3.2 65 1,097 2.0 231
Dallas, TX............... 71.4 1,558.5 3.5 54 1,141 2.5 165
Denton, TX............... 12.6 205.8 4.5 17 871 3.6 61
El Paso, TX.............. 14.4 283.4 0.4 282 682 2.4 183
Fort Bend, TX............ 11.2 164.4 5.1 9 956 0.7 310
Galveston, TX............ 5.7 101.0 2.8 86 824 2.1 223
Gregg, TX................ 4.2 79.0 3.0 77 864 2.5 165
Harris, TX............... 108.7 2,269.5 3.6 51 1,238 4.0 40
Hidalgo, TX.............. 11.8 237.9 2.6 102 616 3.5 66
Jefferson, TX............ 5.8 124.0 4.6 16 969 4.5 23
Lubbock, TX.............. 7.3 131.5 2.2 128 764 3.7 55
McLennan, TX............. 5.0 105.0 0.7 263 775 4.2 31
Midland, TX.............. 5.4 93.1 7.4 2 1,256 6.8 6
Montgomery, TX........... 10.0 159.5 5.1 9 954 5.5 13
Nueces, TX............... 8.1 164.1 3.4 57 860 5.5 13
Potter, TX............... 4.0 77.3 0.5 276 802 3.4 74
Smith, TX................ 5.9 96.9 1.7 162 818 3.9 44
Tarrant, TX.............. 39.9 825.6 1.9 147 944 3.9 44
Travis, TX............... 35.3 658.1 3.9 36 1,074 3.7 55
Webb, TX................. 5.0 95.0 2.4 113 653 3.3 81
Williamson, TX........... 9.0 144.5 2.4 113 923 -0.8 337
Davis, UT................ 7.8 115.9 3.9 36 762 3.0 117
Salt Lake, UT............ 40.8 627.0 2.8 86 897 2.4 183
Utah, UT................. 13.9 198.8 4.8 14 747 -0.1 330
Weber, UT................ 5.6 95.3 1.8 153 721 1.7 264
Chittenden, VT........... 6.4 100.7 1.1 212 916 2.1 223
Arlington, VA............ 8.8 164.7 0.0 307 1,545 4.8 19
Chesterfield, VA......... 8.1 123.2 0.9 241 825 2.2 206
Fairfax, VA.............. 35.1 579.3 -0.4 323 1,447 1.2 291
Henrico, VA.............. 10.5 178.7 1.1 212 922 2.2 206
Loudoun, VA.............. 10.6 147.9 1.0 228 1,105 1.9 244
Prince William, VA....... 8.4 118.5 1.0 228 845 0.8 306
Alexandria City, VA...... 6.2 94.9 -0.1 315 1,345 2.3 194
Chesapeake City, VA...... 5.7 96.0 0.0 307 743 2.2 206
Newport News City, VA.... 3.7 97.7 0.1 299 928 2.4 183
Norfolk City, VA......... 5.5 134.6 -0.4 323 931 3.3 81
Richmond City, VA........ 7.1 149.5 1.0 228 1,041 2.2 206
Virginia Beach City, VA.. 11.3 172.3 1.1 212 751 2.0 231
Benton, WA............... 5.7 82.7 3.4 57 930 1.5 282
Clark, WA................ 14.0 143.0 5.0 12 890 2.8 129
King, WA................. 84.3 1,252.8 3.4 57 1,452 5.1 16
Kitsap, WA............... 6.7 83.1 3.0 77 904 3.3 81
Pierce, WA............... 21.7 282.3 2.8 86 870 3.0 117
Snohomish, WA............ 20.2 269.9 2.2 128 1,019 0.5 317
Spokane, WA.............. 15.7 208.5 2.1 137 823 3.1 103
Thurston, WA............. 7.9 104.3 3.7 48 877 1.6 274
Whatcom, WA.............. 7.1 83.5 1.1 212 782 2.2 206
Yakima, WA............... 8.1 119.2 3.1 73 658 3.1 103
Kanawha, WV.............. 5.9 103.9 -0.2 318 828 3.0 117
Brown, WI................ 6.4 149.6 -0.2 318 829 3.1 103
Dane, WI................. 14.1 314.7 1.1 212 900 -2.2 338
Milwaukee, WI............ 25.0 482.4 0.4 282 902 2.5 165
Outagamie, WI............ 5.0 103.4 0.8 248 808 2.5 165
Waukesha, WI............. 12.3 232.1 0.3 290 929 2.5 165
Winnebago, WI............ 3.5 89.7 -0.6 330 865 3.2 91
San Juan, PR............. 11.4 249.3 -1.8 (5) 603 1.3 (5)
(1) Includes areas not officially designated as counties. See Technical Note.
(2) Average weekly wages were calculated using unrounded data.
(3) Percent changes were computed from 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) 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 339 U.S. counties comprise 71.8 percent of
the total covered workers in the U.S.
Table 2. Covered establishments, employment, and wages in the 10 largest counties,
third quarter 2014
Employment Average weekly
wage(1)
Establishments,
third quarter
County by NAICS supersector 2014 Percent Percent
(thousands) September change, Third change,
2014 September quarter third
(thousands) 2013-14(2) 2014 quarter
2013-14(2)
United States(3) ............................ 9,419.7 137,724.1 2.0 $949 2.9
Private industry........................... 9,125.2 116,563.5 2.3 940 2.8
Natural resources and mining............. 136.7 2,200.5 3.5 1,072 6.1
Construction............................. 758.2 6,371.0 4.7 1,040 3.6
Manufacturing............................ 339.4 12,226.3 1.3 1,153 2.7
Trade, transportation, and utilities..... 1,918.4 26,099.3 2.0 802 2.8
Information.............................. 152.3 2,723.0 0.5 1,726 6.3
Financial activities..................... 837.0 7,692.6 0.9 1,392 3.4
Professional and business services....... 1,685.8 19,249.6 2.9 1,202 2.3
Education and health services............ 1,489.1 20,622.5 1.8 882 2.1
Leisure and hospitality.................. 798.5 14,882.7 2.5 399 3.1
Other services........................... 816.1 4,240.0 1.8 644 3.4
Government................................. 294.5 21,160.6 0.4 1,004 3.4
Los Angeles, CA.............................. 442.4 4,184.4 2.1 1,036 3.1
Private industry........................... 436.6 3,647.8 2.2 1,000 2.7
Natural resources and mining............. 0.5 9.5 -1.4 1,618 -8.9
Construction............................. 13.5 121.9 2.5 1,062 0.0
Manufacturing............................ 12.6 361.0 -2.0 1,136 1.6
Trade, transportation, and utilities..... 54.0 788.2 2.3 857 3.4
Information.............................. 9.7 198.6 0.0 1,840 6.6
Financial activities..................... 24.6 207.8 -0.4 1,624 7.7
Professional and business services....... 47.8 604.0 0.9 1,242 0.9
Education and health services............ 203.5 719.8 2.3 802 2.8
Leisure and hospitality.................. 30.8 468.3 4.5 578 5.1
Other services........................... 28.1 148.2 3.8 688 2.7
Government................................. 5.8 536.7 1.9 1,288 5.5
New York, NY................................. 127.7 2,494.4 2.7 1,733 3.8
Private industry........................... 127.3 2,060.9 3.3 1,845 3.4
Natural resources and mining............. 0.0 0.2 -3.2 3,140 48.3
Construction............................. 2.2 35.6 4.4 1,716 2.6
Manufacturing............................ 2.2 25.4 -0.7 1,196 6.4
Trade, transportation, and utilities..... 20.7 261.4 1.4 1,265 4.7
Information.............................. 4.8 150.4 1.6 2,360 1.0
Financial activities..................... 19.3 360.5 2.7 3,285 5.1
Professional and business services....... 26.9 523.6 3.6 2,074 3.2
Education and health services............ 9.7 320.6 3.7 1,265 0.9
Leisure and hospitality.................. 13.7 277.2 4.5 818 5.5
Other services........................... 20.0 98.5 2.9 1,049 3.8
Government................................. 0.4 433.5 0.0 1,195 4.6
Cook, IL..................................... 158.3 2,481.9 1.2 1,071 2.0
Private industry........................... 157.0 2,187.8 1.5 1,071 3.3
Natural resources and mining............. 0.1 1.0 3.4 1,112 7.0
Construction............................. 13.1 71.8 5.9 1,387 3.7
Manufacturing............................ 6.7 185.4 -0.3 1,137 4.8
Trade, transportation, and utilities..... 31.4 454.8 2.1 869 3.7
Information.............................. 2.9 54.4 1.2 1,627 3.0
Financial activities..................... 16.2 184.2 -0.1 1,848 5.1
Professional and business services....... 33.7 455.4 2.0 1,345 2.9
Education and health services............ 16.6 421.7 0.8 919 1.5
Leisure and hospitality.................. 14.3 258.7 2.0 481 1.3
Other services........................... 17.9 96.8 0.8 838 4.9
Government................................. 1.3 294.1 -0.6 1,075 -6.0
Harris, TX................................... 108.7 2,269.5 3.6 1,238 4.0
Private industry........................... 108.1 2,009.9 3.9 1,254 4.0
Natural resources and mining............. 1.8 95.1 6.5 3,079 7.5
Construction............................. 6.8 158.6 9.9 1,262 6.4
Manufacturing............................ 4.7 198.0 3.6 1,491 4.9
Trade, transportation, and utilities..... 24.6 468.6 3.4 1,103 3.7
Information.............................. 1.2 27.9 -2.4 1,373 4.4
Financial activities..................... 11.1 119.1 1.8 1,490 0.7
Professional and business services....... 21.9 396.3 2.7 1,513 3.6
Education and health services............ 14.9 270.6 2.7 972 0.8
Leisure and hospitality.................. 9.1 211.1 5.5 419 3.7
Other services........................... 11.7 63.7 3.7 755 5.4
Government................................. 0.6 259.6 1.6 1,112 4.3
Maricopa, AZ................................. 93.4 1,756.8 2.0 914 1.8
Private industry........................... 92.7 1,547.6 2.2 907 1.7
Natural resources and mining............. 0.5 7.0 0.2 920 0.2
Construction............................. 7.3 92.3 -1.4 935 -0.5
Manufacturing............................ 3.2 114.0 0.7 1,313 3.0
Trade, transportation, and utilities..... 20.0 347.8 2.1 832 0.8
Information.............................. 1.5 33.2 5.1 1,213 2.4
Financial activities..................... 11.0 153.0 2.8 1,145 1.2
Professional and business services....... 21.8 295.4 1.5 996 3.8
Education and health services............ 10.7 261.8 2.6 935 2.2
Leisure and hospitality.................. 7.4 191.3 3.0 433 1.6
Other services........................... 6.3 48.3 2.6 651 -0.9
Government................................. 0.7 209.2 0.3 972 1.9
Dallas, TX................................... 71.4 1,558.5 3.5 1,141 2.5
Private industry........................... 70.9 1,390.9 3.8 1,144 2.4
Natural resources and mining............. 0.6 10.0 4.9 3,840 18.3
Construction............................. 4.1 77.9 5.7 1,084 5.3
Manufacturing............................ 2.7 107.1 -0.4 1,278 -1.4
Trade, transportation, and utilities..... 15.4 312.5 4.2 1,030 2.3
Information.............................. 1.4 49.2 0.4 1,722 0.2
Financial activities..................... 8.6 152.3 2.5 1,532 5.1
Professional and business services....... 16.0 313.4 5.7 1,290 1.7
Education and health services............ 8.8 182.2 3.5 1,027 0.8
Leisure and hospitality.................. 6.1 145.9 4.9 480 4.8
Other services........................... 6.8 39.9 0.6 751 4.5
Government................................. 0.5 167.6 1.1 1,112 2.9
Orange, CA................................... 108.0 1,475.0 2.3 1,050 2.6
Private industry........................... 106.7 1,341.6 2.4 1,037 2.9
Natural resources and mining............. 0.2 3.3 -0.8 822 16.8
Construction............................. 6.5 83.4 4.8 1,174 4.4
Manufacturing............................ 4.9 158.1 0.3 1,387 6.3
Trade, transportation, and utilities..... 16.8 254.2 2.4 939 1.4
Information.............................. 1.3 23.5 -4.9 1,600 5.2
Financial activities..................... 10.7 113.2 1.2 1,568 0.3
Professional and business services....... 20.7 276.1 2.2 1,205 4.4
Education and health services............ 27.2 185.7 2.5 879 1.2
Leisure and hospitality.................. 7.9 194.3 2.1 457 4.1
Other services........................... 6.9 43.5 4.0 647 1.6
Government................................. 1.3 133.4 0.6 1,196 0.9
San Diego, CA................................ 100.8 1,344.5 2.3 1,030 0.8
Private industry........................... 99.4 1,124.1 2.6 987 0.1
Natural resources and mining............. 0.7 11.2 4.8 610 -3.6
Construction............................. 6.4 64.7 4.6 1,069 1.9
Manufacturing............................ 3.1 96.9 1.4 1,414 3.9
Trade, transportation, and utilities..... 14.2 212.0 1.4 773 -0.4
Information.............................. 1.2 24.3 -1.2 1,751 0.7
Financial activities..................... 9.4 69.8 -1.3 1,340 2.1
Professional and business services....... 18.2 227.7 1.7 1,417 -2.1
Education and health services............ 28.0 184.1 3.1 874 0.8
Leisure and hospitality.................. 7.7 178.4 3.3 457 3.9
Other services........................... 7.3 49.6 5.8 570 1.4
Government................................. 1.4 220.5 0.5 1,262 4.5
King, WA..................................... 84.3 1,252.8 3.4 1,452 5.1
Private industry........................... 83.7 1,094.8 3.7 1,479 5.3
Natural resources and mining............. 0.4 2.8 2.5 1,221 1.6
Construction............................. 6.0 60.5 9.5 1,213 4.3
Manufacturing............................ 2.3 106.6 0.3 1,542 1.7
Trade, transportation, and utilities..... 14.8 234.4 4.6 1,125 4.5
Information.............................. 2.0 87.1 4.4 5,134 9.3
Financial activities..................... 6.5 65.9 1.2 1,490 3.3
Professional and business services....... 15.8 209.1 4.3 1,528 4.9
Education and health services............ 20.8 160.4 3.3 949 4.3
Leisure and hospitality.................. 6.8 127.1 3.3 510 2.2
Other services........................... 8.4 41.0 3.2 794 2.5
Government................................. 0.5 158.0 1.3 1,262 3.0
Miami-Dade, FL............................... 94.3 1,047.0 3.0 891 2.2
Private industry........................... 94.0 911.7 3.6 873 2.2
Natural resources and mining............. 0.5 7.4 6.0 573 4.8
Construction............................. 5.4 37.1 11.4 887 4.5
Manufacturing............................ 2.7 37.3 1.9 850 4.7
Trade, transportation, and utilities..... 27.4 269.0 3.1 806 1.6
Information.............................. 1.6 18.4 4.5 1,402 2.0
Financial activities..................... 9.9 71.8 4.2 1,362 4.0
Professional and business services....... 19.8 141.3 4.2 1,055 3.5
Education and health services............ 10.1 163.4 2.2 913 0.6
Leisure and hospitality.................. 7.2 127.3 2.9 521 -0.4
Other services........................... 8.2 37.3 3.2 576 2.7
Government................................. 0.3 135.3 -1.3 1,017 2.2
(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 2013 annual average employment.
Includes workers covered by Unemployment Insurance (UI) and Unemployment Compensation for Federal
Employees (UCFE) programs.
Table 3. Covered establishments, employment, and wages by state,
third quarter 2014
Employment Average weekly
wage(1)
Establishments,
third quarter
State 2014 Percent Percent
(thousands) September change, Third change,
2014 September quarter third
(thousands) 2013-14 2014 quarter
2013-14
United States(2)........... 9,419.7 137,724.1 2.0 $949 2.9
Alabama.................... 118.3 1,871.2 1.3 815 2.5
Alaska..................... 22.4 344.7 -0.1 1,019 3.0
Arizona.................... 147.4 2,539.6 1.8 876 2.0
Arkansas................... 87.3 1,170.9 1.3 737 1.8
California................. 1,386.5 16,013.4 3.1 1,095 3.7
Colorado................... 180.9 2,443.0 3.7 982 3.0
Connecticut................ 114.4 1,663.2 0.8 1,124 1.4
Delaware................... 30.1 426.1 1.9 961 2.2
District of Columbia....... 36.3 732.9 0.8 1,631 4.5
Florida.................... 642.5 7,748.4 3.3 826 2.1
Georgia.................... 283.0 4,059.0 3.4 891 2.8
Hawaii..................... 39.0 625.1 0.9 870 3.9
Idaho...................... 55.4 658.4 2.1 721 2.6
Illinois................... 416.0 5,807.4 1.2 982 2.5
Indiana.................... 158.6 2,924.7 1.4 799 1.9
Iowa....................... 100.2 1,528.8 1.1 800 3.6
Kansas..................... 86.0 1,363.1 1.2 794 2.3
Kentucky................... 121.0 1,827.8 1.8 781 2.5
Louisiana.................. 127.2 1,928.3 1.7 852 3.1
Maine...................... 49.4 604.5 0.3 754 2.6
Maryland................... 164.9 2,574.5 1.1 1,042 3.1
Massachusetts.............. 232.1 3,386.7 1.8 1,164 3.0
Michigan................... 236.5 4,141.0 1.7 896 2.4
Minnesota.................. 165.9 2,757.9 1.1 965 2.9
Mississippi................ 71.5 1,105.0 0.5 697 1.3
Missouri................... 185.7 2,686.4 1.0 828 2.7
Montana.................... 44.3 449.5 0.7 732 3.7
Nebraska................... 72.1 950.0 1.1 779 1.8
Nevada..................... 76.2 1,215.8 4.0 840 0.5
New Hampshire.............. 50.3 633.5 1.4 927 3.6
New Jersey................. 264.4 3,880.4 0.8 1,087 1.7
New Mexico................. 57.2 804.0 1.1 786 2.6
New York................... 627.7 8,902.1 2.0 1,145 3.2
North Carolina............. 260.3 4,085.5 1.9 839 2.8
North Dakota............... 31.7 455.9 4.3 977 6.1
Ohio....................... 290.0 5,219.1 1.4 863 3.1
Oklahoma................... 107.4 1,592.3 1.0 826 3.6
Oregon..................... 137.5 1,752.8 2.4 887 3.6
Pennsylvania............... 349.5 5,676.2 1.0 937 2.6
Rhode Island............... 35.9 471.8 1.4 895 1.8
South Carolina............. 118.7 1,902.7 2.4 768 2.4
South Dakota............... 32.1 415.8 1.7 733 3.7
Tennessee.................. 146.2 2,775.5 2.4 837 2.1
Texas...................... 623.1 11,433.6 3.1 988 3.8
Utah....................... 90.8 1,304.7 3.1 803 1.5
Vermont.................... 24.5 306.5 1.2 805 2.3
Virginia................... 242.4 3,667.9 0.6 989 2.0
Washington................. 236.9 3,112.8 3.2 1,087 3.9
West Virginia.............. 49.8 709.3 -0.2 778 3.5
Wisconsin.................. 166.2 2,783.1 1.1 808 1.9
Wyoming.................... 25.6 291.3 1.7 877 4.4
Puerto Rico................ 49.0 896.7 -1.5 505 0.8
Virgin Islands............. 3.4 37.5 -1.0 720 2.0
(1) Average weekly wages were calculated using unrounded data.
(2) Totals for the United States do not include data for Puerto Rico or the Virgin Islands.
Note: Data are preliminary. Includes workers covered by Unemployment Insurance (UI) and
Unemployment Compensation for Federal Employees (UCFE) programs.
United States Department of Labor
