For release 10:00 a.m. (EDT), Wednesday, March 19, 2014 USDL-14-0433
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 2013
From September 2012 to September 2013, employment increased in 286 of the 334 largest U.S.
counties, the U.S. Bureau of Labor Statistics reported today. Fort Bend, Texas, had the largest increase,
with a gain of 6.0 percent over the year, compared with national job growth of 1.7 percent. Within Fort
Bend, the largest employment increase occurred in leisure and hospitality, which gained 2,234 jobs over
the year (12.1 percent). Peoria, Ill., had the largest over-the-year decrease in employment among the
largest counties in the U.S. with a loss of 3.7 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 over the year by 1.9 percent to $922 in the third quarter of
2013. San Mateo, Calif., had the largest over-the-year increase in average weekly wages with a gain of
9.9 percent. Within San Mateo, an average weekly wage gain of $2,359, or 82.1 percent, in information
made the largest contribution to the increase in average weekly wages. Pinellas, Fla., experienced the
largest decrease in average weekly wages with a loss of 4.3 percent over the year.
Table A. Large counties ranked by September 2013 employment, September 2012-13 employment
increase, and September 2012-13 percent increase in employment
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Employment in large counties
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September 2013 employment | Increase in employment, | Percent increase in employment,
(thousands) | September 2012-13 | September 2012-13
| (thousands) |
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| |
United States 134,957.5| United States 2,277.6| United States 1.7
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| |
Los Angeles, Calif. 4,093.3| Los Angeles, Calif. 95.2| Fort Bend, Texas 6.0
Cook, Ill. 2,445.8| Harris, Texas 61.7| Douglas, Colo. 5.9
New York, N.Y. 2,424.5| Dallas, Texas 47.3| Brazos, Texas 5.7
Harris, Texas 2,192.3| Maricopa, Ariz. 44.6| Lee, Fla. 5.2
Maricopa, Ariz. 1,719.1| King, Wash. 42.8| Collier, Fla. 5.1
Dallas, Texas 1,509.0| Santa Clara, Calif. 37.5| Placer, Calif. 5.0
Orange, Calif. 1,441.4| New York, N.Y. 34.2| Weld, Colo. 5.0
San Diego, Calif. 1,312.2| Orange, Calif. 32.0| Elkhart, Ind. 4.9
King, Wash. 1,212.3| San Diego, Calif. 25.2| Denton, Texas 4.9
Miami-Dade, Fla. 1,016.7| Travis, Texas 24.8| Utah, Utah 4.9
| |
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Large County Employment
In September 2013, national employment was 135.0 million (as measured by the QCEW program). Over
the year, employment increased 1.7 percent, or 2.3 million. The 334 U.S. counties with 75,000 or more
jobs accounted for 71.4 percent of total U.S. employment and 76.6 percent of total wages. These 334
counties had a net job growth of 1.7 million over the year, accounting for 75.8 percent of the overall
U.S. employment increase.
Fort Bend, Texas, had the largest percentage increase in employment (6.0 percent) among the largest
U.S. counties. The five counties with the largest increases in employment level were Los Angeles,
Calif.; Harris, Texas; Dallas, Texas; Maricopa, Ariz.; and King, Wash. These counties had a combined
over-the-year employment gain of 291,600 jobs, which was 12.8 percent of the overall job increase for
the U.S. (See table A.)
Employment declined in 44 of the large counties from September 2012 to September 2013. Peoria, Ill.,
had the largest over-the-year percentage decrease in employment (-3.7 percent). Within Peoria,
professional and business services had the largest decrease in employment, with a loss of 2,088 (-11.3
percent). Caddo, La., had the second largest percentage decrease in employment, followed by St. Clair,
Ill.; Jefferson, Texas; and Lake, Ind. (See table 1.)
Table B. Large counties ranked by third quarter 2013 average weekly wages, third quarter 2012-13
increase in average weekly wages, and third quarter 2012-13 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 2013 | wage, third quarter 2012-13 | weekly wage, third
| | quarter 2012-13
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| |
United States $922| United States $17| United States 1.9
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| |
Santa Clara, Calif. $1,868| San Mateo, Calif. $153| San Mateo, Calif. 9.9
San Mateo, Calif. 1,698| Dane, Wis. 78| Dane, Wis. 9.3
New York, N.Y. 1,667| Santa Clara, Calif. 72| Collier, Fla. 8.0
Washington, D.C. 1,560| San Francisco, Calif. 71| Whatcom, Wash. 6.9
San Francisco, Calif. 1,549| Collier, Fla. 62| Utah, Utah 6.4
Arlington, Va. 1,478| Yolo, Calif. 53| Washington, Ark. 6.0
Fairfax, Va. 1,434| Whatcom, Wash. 52| Yolo, Calif. 6.0
Suffolk, Mass. 1,429| Alexandria City, Va. 50| Hamilton, Ind. 5.7
Fairfield, Conn. 1,377| Hamilton, Ind. 48| Clay, Mo. 5.1
King, Wash. 1,376| Hartford, Conn. 46| San Francisco, Calif. 4.8
| |
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Large County Average Weekly Wages
Average weekly wages for the nation increased 1.9 percent during the year ending in the third quarter of
2013. Among the 334 largest counties, 291 had over-the-year increases in average weekly wages. San
Mateo, Calif., had the largest wage increase among the largest U.S. counties (9.9 percent).
Of the 334 largest counties, 40 experienced over-the-year decreases in average weekly wages. Pinellas,
Fla., had the largest percentage decrease in average weekly wage, with a loss of 4.3 percent. Within
Pinellas, professional and business services had the largest impact on the county’s average weekly wage
decrease. Within this industry, average weekly wages declined by $214 (-18.6 percent) over the year.
Rockland, N.Y., had the second largest percentage decrease in average weekly wages, followed by
Harford, Md.; Douglas, Colo.; and Mercer, N.J. (See table 1.)
Ten Largest U.S. Counties
All of the 10 largest counties had over-the-year percentage increases in employment in September
2013. King, Wash., had the largest gain (3.7 percent). Within King, trade, transportation, and utilities
had the largest over-the-year employment level increase among all private industry groups with a gain of
10,103 jobs, or 4.7 percent. Cook, Ill., had the smallest percentage increase in employment (1.0 percent)
among the 10 largest counties. (See table 2.)
Average weekly wages increased over-the-year in 9 of the 10 largest U.S. counties. Harris, Texas,
experienced the largest percentage gain in average weekly wages (2.9 percent). Within Harris,
professional and business services had the largest impact on the county’s average weekly wage growth.
Within this industry, average weekly wages increased by $53, or 3.9 percent, over the year. Average
weekly wages in Orange, Calif., were unchanged over the year.
For More Information
The tables included in this release contain data for the nation and for the 334 U.S. counties with annual
average employment levels of 75,000 or more in 2012. September 2013 employment and 2013 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 9.3 million employer reports cover 135.0 million full- and part-
time workers. For additional information about the quarterly employment and wages data, please read
the Technical Note. Data for the third quarter of 2013 will be available later at www.bls.gov/cew/.
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 2013 is scheduled to be released
on Thursday, June 19, 2014.
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| Changes to QCEW Data Files |
| |
| BLS discontinued its ftp service on February 28, 2014. As part of this transition, the QCEW data |
| file collection was substantially reorganized and improved. For more information, see |
| www.bls.gov/cew/dataguide.htm. |
| |
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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 2013 are preliminary and subject to revision.
For purposes of this release, large counties are defined as having employment le-
vels of 75,000 or greater. In addition, data for San Juan, Puerto Rico, are pro-
vided, 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 prelimi-
nary annual average of employment for the previous year. The 335 counties presented
in this release were derived using 2012 preliminary annual averages of employment.
For 2013 data, six counties have been added to the publication tables: Boone, Ky.;
Warren, Ohio; Jackson, Ore.; York, S.C.; Midland, Texas; and Potter, Texas. These
counties will be included in all 2013 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' con-
tinuing 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 dif-
ferences and the intended uses of the program products. (See table.) Additional in-
formation 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- | 557,000 establish-
| submitted by 9.2 | ministrative records| ments
| million establish- | submitted by 7.3 |
| ments in first | million private-sec-|
| quarter of 2013 | 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 | | |
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Coverage
Employment and wage data for workers covered by state UI laws are compiled from
quarterly contribution reports submitted to the SWAs by employers. For federal ci-
vilian workers covered by the Unemployment Compensation for Federal Employees
(UCFE) program, employment and wage data are compiled from quarterly reports sub-
mitted 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 9.1 million employer reports of employment and wages
submitted by states to the BLS in 2012. These reports are based on place of employ-
ment rather than place of residence.
UI and UCFE coverage is broad and has been basically comparable from state to state
since 1978, when the 1976 amendments to the Federal Unemployment Tax Act became ef-
fective, expanding coverage to include most State and local government employees.
In 2012, UI and UCFE programs covered workers in 131.7 million jobs. The estimated
126.9 million workers in these jobs (after adjustment for multiple jobholders)
represented 95.5 percent of civilian wage and salary employment. Covered workers
received $6.491 trillion in pay, representing 93.7 percent of the wage and salary
component of personal income and 40.0 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. Cover-
age changes may affect the over-the-year comparisons presented in this news re-
lease.
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 av-
erages 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 compen-
sation 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 week-
ly 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 employ-
ers 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 indi-
vidual 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 un-
derlying 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 2012 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 un-
adjusted 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 re-
lease.
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 estab-
lishments. The most common adjustments for administrative change are the result of
updated information about the county location of individual establishments. In-
cluded 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. Beginn-
ing with the second quarter of 2011, adjusted data account for selected large admin-
istrative changes in employment and wages. These new adjustments allow QCEW to incl-
ude county employment and wage growth rates in this news release that would other-
wise 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 Stan-
dards 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 2012 edition of this publication, which was published in September 2013,
contains selected data produced by Business Employment Dynamics (BED) on job gains
and losses, as well as selected data from the first quarter 2013 version of this
news release. Tables and additional content from Employment and Wages Annual Aver-
ages 2012 are now available online at http://www.bls.gov/cew/cewbultn12.htm. The
2013 edition of Employment and Wages Annual Averages Online will be available in
September 2014.
News releases on quarterly measures of gross job flows also are available upon re-
quest 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(1) establishments, employment, and wages in the 335 largest counties,
third quarter 2013(2)
Employment Average weekly wage(4)
Establishments,
County(3) third quarter Percent Ranking Percent Ranking
2013 September change, by Third change, by
(thousands) 2013 September percent quarter third percent
(thousands) 2012-13(5) change 2013 quarter change
2012-13(5)
United States(6)......... 9,294.8 134,957.5 1.7 - $922 1.9 -
Jefferson, AL............ 17.6 337.2 0.7 239 922 1.4 192
Madison, AL.............. 8.9 181.3 1.2 194 995 -1.2 321
Mobile, AL............... 9.5 163.6 -0.3 302 808 1.0 216
Montgomery, AL........... 6.3 127.8 -0.4 307 794 3.8 25
Tuscaloosa, AL........... 4.3 86.3 0.7 239 807 1.9 138
Anchorage Borough, AK.... 8.4 156.9 0.0 287 1,036 2.8 59
Maricopa, AZ............. 93.8 1,719.1 2.7 68 898 1.2 208
Pima, AZ................. 18.8 350.5 0.9 225 795 1.0 216
Benton, AR............... 5.7 99.0 2.0 120 917 3.6 29
Pulaski, AR.............. 14.5 244.2 0.4 266 831 1.3 202
Washington, AR........... 5.7 95.6 2.1 114 774 6.0 6
Alameda, CA.............. 56.0 681.7 2.5 85 1,199 1.7 161
Contra Costa, CA......... 29.3 335.1 2.7 68 1,121 -0.2 301
Fresno, CA............... 30.2 362.8 2.9 60 723 2.0 127
Kern, CA................. 17.1 318.3 1.8 140 787 0.4 262
Los Angeles, CA.......... 434.4 4,093.3 2.4 90 1,007 1.0 216
Marin, CA................ 11.8 109.4 2.5 85 1,076 0.9 224
Monterey, CA............. 12.8 188.1 0.9 225 791 0.9 224
Orange, CA............... 105.5 1,441.4 2.3 97 1,022 0.0 292
Placer, CA............... 11.0 138.3 5.0 6 911 0.6 248
Riverside, CA............ 51.3 594.5 3.5 34 737 2.1 108
Sacramento, CA........... 51.3 602.5 1.9 130 1,029 2.1 108
San Bernardino, CA....... 50.0 632.5 3.3 41 773 -0.1 295
San Diego, CA............ 98.4 1,312.2 2.0 120 1,022 2.0 127
San Francisco, CA........ 56.0 616.0 3.4 37 1,549 4.8 10
San Joaquin, CA.......... 16.7 216.0 2.2 107 787 0.1 283
San Luis Obispo, CA...... 9.6 107.2 0.0 287 769 3.5 31
San Mateo, CA............ 25.2 357.9 3.7 25 1,698 9.9 1
Santa Barbara, CA........ 14.5 188.5 1.2 194 880 3.7 26
Santa Clara, CA.......... 64.3 947.2 4.1 20 1,868 4.0 17
Santa Cruz, CA........... 9.0 99.8 1.7 148 858 0.9 224
Solano, CA............... 10.0 125.8 1.8 140 918 1.4 192
Sonoma, CA............... 18.7 187.6 3.6 28 875 2.3 92
Stanislaus, CA........... 14.1 173.2 2.2 107 787 1.5 181
Tulare, CA............... 9.1 147.9 -0.2 295 648 2.0 127
Ventura, CA.............. 24.4 307.6 2.0 120 926 -0.5 311
Yolo, CA................. 5.9 100.3 1.4 174 934 6.0 6
Adams, CO................ 9.1 174.7 4.3 16 900 2.5 77
Arapahoe, CO............. 19.5 298.7 3.7 25 1,067 2.2 98
Boulder, CO.............. 13.5 165.8 3.1 54 1,095 2.4 87
Denver, CO............... 27.2 445.3 3.6 28 1,122 2.1 108
Douglas, CO.............. 10.1 104.1 5.9 2 1,032 -2.5 331
El Paso, CO.............. 17.2 245.1 2.3 97 841 -0.7 314
Jefferson, CO............ 18.1 217.3 2.0 120 923 0.4 262
Larimer, CO.............. 10.4 138.4 2.5 85 831 2.1 108
Weld, CO................. 6.0 92.1 5.0 6 832 4.1 15
Fairfield, CT............ 33.5 415.9 1.5 162 1,377 0.2 275
Hartford, CT............. 26.1 497.0 0.3 271 1,124 4.3 13
New Haven, CT............ 22.9 358.6 0.4 266 968 1.3 202
New London, CT........... 7.1 122.9 -0.7 316 909 0.8 236
New Castle, DE........... 17.0 271.0 2.2 107 1,055 2.1 108
Washington, DC........... 35.6 726.2 1.5 162 1,560 3.0 48
Alachua, FL.............. 6.6 118.2 1.6 156 764 2.1 108
Brevard, FL.............. 14.7 186.7 -0.3 302 845 0.5 255
Broward, FL.............. 65.4 719.4 2.6 77 846 1.1 212
Collier, FL.............. 12.3 118.6 5.1 5 837 8.0 3
Duval, FL................ 27.8 451.2 2.6 77 865 -0.1 295
Escambia, FL............. 8.1 121.9 1.1 203 709 1.9 138
Hillsborough, FL......... 39.3 603.0 3.3 41 874 1.0 216
Lake, FL................. 7.5 83.7 3.9 23 640 1.3 202
Lee, FL.................. 19.5 210.4 5.2 4 729 0.4 262
Leon, FL................. 8.3 138.4 0.7 239 757 0.4 262
Manatee, FL.............. 9.7 103.8 2.3 97 699 1.9 138
Marion, FL............... 8.0 91.3 1.0 214 639 2.9 51
Miami-Dade, FL........... 93.4 1,016.7 2.4 90 873 2.1 108
Okaloosa, FL............. 6.1 77.6 1.1 203 757 0.5 255
Orange, FL............... 37.8 707.8 3.3 41 804 1.0 216
Palm Beach, FL........... 51.2 518.4 3.3 41 884 2.6 70
Pasco, FL................ 10.1 100.8 2.4 90 635 1.8 146
Pinellas, FL............. 31.3 390.5 1.5 162 802 -4.3 334
Polk, FL................. 12.5 193.1 1.9 130 718 1.8 146
Sarasota, FL............. 14.7 142.6 4.0 22 744 0.8 236
Seminole, FL............. 14.1 162.3 2.7 68 762 1.6 172
Volusia, FL.............. 13.4 152.7 1.9 130 650 1.1 212
Bibb, GA................. 4.6 80.3 0.8 234 726 2.8 59
Chatham, GA.............. 7.9 136.7 2.3 97 781 0.5 255
Clayton, GA.............. 4.3 110.5 0.9 225 878 2.9 51
Cobb, GA................. 22.1 313.7 3.1 54 963 0.1 283
De Kalb, GA.............. 18.3 274.6 1.6 156 937 2.1 108
Fulton, GA............... 43.0 749.2 3.2 48 1,197 1.6 172
Gwinnett, GA............. 24.6 313.7 3.3 41 898 0.8 236
Muscogee, GA............. 4.7 93.5 0.5 255 729 0.3 268
Richmond, GA............. 4.7 98.3 -0.6 313 794 -0.3 308
Honolulu, HI............. 24.8 451.2 1.4 174 873 1.4 192
Ada, ID.................. 13.8 208.2 3.6 28 814 2.4 87
Champaign, IL............ 4.4 88.6 -0.1 291 838 2.9 51
Cook, IL................. 153.0 2,445.8 1.0 214 1,049 1.5 181
Du Page, IL.............. 37.9 590.1 1.1 203 1,059 0.9 224
Kane, IL................. 13.6 205.6 2.2 107 803 0.2 275
Lake, IL................. 22.5 331.3 1.4 174 1,148 0.3 268
McHenry, IL.............. 8.8 95.7 1.4 174 759 0.4 262
McLean, IL............... 3.9 85.0 -0.2 295 890 1.7 161
Madison, IL.............. 6.1 95.0 -1.4 327 765 1.6 172
Peoria, IL............... 4.7 100.9 -3.7 334 855 0.7 244
St. Clair, IL............ 5.7 92.2 -2.3 332 751 -0.4 309
Sangamon, IL............. 5.3 126.2 0.1 279 958 2.0 127
Will, IL................. 15.7 214.2 2.8 62 812 1.8 146
Winnebago, IL............ 6.9 124.4 -0.9 320 781 1.8 146
Allen, IN................ 8.9 177.1 1.1 203 758 1.7 161
Elkhart, IN.............. 4.8 117.0 4.9 8 758 3.3 40
Hamilton, IN............. 8.7 121.9 3.6 28 897 5.7 8
Lake, IN................. 10.3 187.9 -1.7 330 839 -2.2 326
Marion, IN............... 23.8 575.7 0.9 225 946 2.2 98
St. Joseph, IN........... 5.9 116.8 -0.5 310 750 0.1 283
Tippecanoe, IN........... 3.3 79.3 -0.8 318 766 0.7 244
Vanderburgh, IN.......... 4.8 103.3 -1.2 326 739 2.2 98
Johnson, IA.............. 3.9 80.6 2.5 85 873 2.2 98
Linn, IA................. 6.4 127.6 0.6 246 885 1.3 202
Polk, IA................. 15.8 282.1 3.2 48 926 2.1 108
Scott, IA................ 5.4 89.1 0.3 271 759 1.7 161
Johnson, KS.............. 21.3 322.4 3.0 57 934 2.0 127
Sedgwick, KS............. 12.2 242.2 1.4 174 814 0.9 224
Shawnee, KS.............. 4.8 96.3 2.1 114 769 1.1 212
Wyandotte, KS............ 3.3 84.9 1.9 130 879 2.3 92
Boone, KY................ 4.0 76.8 0.1 279 804 0.6 248
Fayette, KY.............. 10.2 182.6 1.6 156 839 2.7 65
Jefferson, KY............ 24.0 435.0 1.5 162 882 0.1 283
Caddo, LA................ 7.4 114.6 -3.1 333 759 2.0 127
Calcasieu, LA............ 5.0 85.9 1.3 187 799 1.8 146
East Baton Rouge, LA..... 14.8 264.3 2.1 114 886 4.5 12
Jefferson, LA............ 13.8 191.9 2.1 114 841 -0.1 295
Lafayette, LA............ 9.3 140.2 2.6 77 902 3.4 37
Orleans, LA.............. 11.4 178.1 3.3 41 909 1.8 146
St. Tammany, LA.......... 7.7 81.3 2.4 90 794 3.5 31
Cumberland, ME........... 12.8 173.5 0.6 246 812 1.6 172
Anne Arundel, MD......... 14.7 253.6 1.8 140 1,000 0.4 262
Baltimore, MD............ 21.2 362.3 1.0 214 934 0.9 224
Frederick, MD............ 6.2 95.0 -0.2 295 873 -0.2 301
Harford, MD.............. 5.6 88.3 -0.5 310 868 -2.6 332
Howard, MD............... 9.4 160.7 0.6 246 1,111 0.9 224
Montgomery, MD........... 33.3 454.3 0.4 266 1,214 -1.5 324
Prince Georges, MD....... 15.7 299.0 -0.3 302 999 1.5 181
Baltimore City, MD....... 13.9 332.1 -0.6 313 1,094 2.1 108
Barnstable, MA........... 9.1 97.6 1.2 194 765 2.5 77
Bristol, MA.............. 16.6 215.4 0.5 255 835 2.0 127
Essex, MA................ 22.4 312.1 0.8 234 969 2.4 87
Hampden, MA.............. 16.2 201.0 1.4 174 839 1.5 181
Middlesex, MA............ 50.6 838.6 1.4 174 1,362 3.3 40
Norfolk, MA.............. 23.9 332.3 1.8 140 1,051 1.4 192
Plymouth, MA............. 14.4 181.5 1.9 130 861 2.6 70
Suffolk, MA.............. 24.8 606.9 1.5 162 1,429 2.1 108
Worcester, MA............ 22.3 323.0 0.3 271 946 3.5 31
Genesee, MI.............. 7.2 131.2 0.8 234 764 2.6 70
Ingham, MI............... 6.3 151.6 1.4 174 868 1.4 192
Kalamazoo, MI............ 5.3 112.0 1.5 162 855 1.5 181
Kent, MI................. 14.1 353.3 3.5 34 811 1.2 208
Macomb, MI............... 17.4 303.3 3.3 41 921 2.1 108
Oakland, MI.............. 38.4 678.4 1.6 156 1,003 0.9 224
Ottawa, MI............... 5.6 113.5 3.4 37 759 2.7 65
Saginaw, MI.............. 4.2 83.7 0.6 246 743 0.1 283
Washtenaw, MI............ 8.3 198.0 1.7 148 996 1.5 181
Wayne, MI................ 31.5 688.8 0.5 255 999 0.9 224
Anoka, MN................ 7.2 116.4 3.1 54 906 4.0 17
Dakota, MN............... 10.0 178.9 2.4 90 892 1.9 138
Hennepin, MN............. 42.2 860.0 1.9 130 1,162 2.5 77
Olmsted, MN.............. 3.5 92.0 0.1 279 972 2.1 108
Ramsey, MN............... 13.9 325.0 1.1 203 1,028 3.7 26
St. Louis, MN............ 5.6 96.4 1.1 203 793 1.9 138
Stearns, MN.............. 4.4 82.6 1.8 140 750 2.7 65
Harrison, MS............. 4.5 83.3 0.5 255 677 2.3 92
Hinds, MS................ 6.1 119.5 -0.3 302 810 3.1 45
Boone, MO................ 4.6 89.8 2.6 77 748 1.6 172
Clay, MO................. 5.2 90.7 1.5 162 843 5.1 9
Greene, MO............... 8.1 156.1 1.3 187 712 2.9 51
Jackson, MO.............. 19.2 348.9 1.0 214 944 2.9 51
St. Charles, MO.......... 8.5 131.5 2.3 97 728 0.8 236
St. Louis, MO............ 33.0 573.9 1.2 194 958 -0.8 316
St. Louis City, MO....... 10.1 223.2 0.7 239 1,000 0.8 236
Yellowstone, MT.......... 6.2 78.3 0.6 246 774 2.1 108
Douglas, NE.............. 18.5 322.2 1.7 148 889 4.2 14
Lancaster, NE............ 9.9 162.1 2.2 107 750 1.2 208
Clark, NV................ 50.4 843.3 2.7 68 819 1.9 138
Washoe, NV............... 13.8 191.5 2.7 68 847 2.5 77
Hillsborough, NH......... 12.1 190.7 0.5 255 989 1.9 138
Rockingham, NH........... 10.6 139.5 0.7 239 866 2.6 70
Atlantic, NJ............. 6.6 136.1 -0.2 295 764 0.3 268
Bergen, NJ............... 33.0 436.2 2.3 97 1,086 0.5 255
Burlington, NJ........... 11.1 195.6 0.1 279 957 0.9 224
Camden, NJ............... 12.0 192.6 0.3 271 896 0.2 275
Essex, NJ................ 20.5 330.5 -0.5 310 1,158 3.9 23
Gloucester, NJ........... 6.1 99.2 1.9 130 809 1.3 202
Hudson, NJ............... 14.1 236.3 0.9 225 1,250 0.9 224
Mercer, NJ............... 11.0 232.4 1.5 162 1,179 -2.4 330
Middlesex, NJ............ 21.9 389.5 0.0 287 1,110 4.0 17
Monmouth, NJ............. 20.0 244.6 0.7 239 895 0.7 244
Morris, NJ............... 17.2 278.0 1.5 162 1,330 2.2 98
Ocean, NJ................ 12.5 156.8 2.7 68 738 1.5 181
Passaic, NJ.............. 12.3 169.2 0.6 246 896 0.6 248
Somerset, NJ............. 10.1 177.3 1.9 130 1,330 -0.2 301
Union, NJ................ 14.3 220.6 -0.1 291 1,130 -0.2 301
Bernalillo, NM........... 17.8 311.2 0.6 246 808 -0.2 301
Albany, NY............... 10.1 222.4 0.3 271 977 2.6 70
Bronx, NY................ 17.3 244.5 2.8 62 903 2.5 77
Broome, NY............... 4.6 88.5 -1.4 327 726 1.5 181
Dutchess, NY............. 8.4 111.8 1.0 214 923 3.0 48
Erie, NY................. 24.4 458.6 0.5 255 811 2.5 77
Kings, NY................ 55.5 535.3 2.4 90 760 1.5 181
Monroe, NY............... 18.5 374.4 0.2 277 901 3.2 42
Nassau, NY............... 53.3 598.7 1.7 148 989 0.3 268
New York, NY............. 125.1 2,424.5 1.4 174 1,667 2.6 70
Oneida, NY............... 5.3 104.1 -1.0 322 735 3.7 26
Onondaga, NY............. 13.1 242.3 -0.1 291 841 1.1 212
Orange, NY............... 10.0 133.3 0.9 225 755 -0.1 295
Queens, NY............... 48.9 536.0 1.8 140 855 0.1 283
Richmond, NY............. 9.3 96.1 4.2 19 802 1.3 202
Rockland, NY............. 10.1 115.4 0.0 287 954 -4.1 333
Saratoga, NY............. 5.7 80.4 2.3 97 815 1.4 192
Suffolk, NY.............. 51.7 634.0 1.4 174 1,000 -2.1 325
Westchester, NY.......... 36.2 408.1 0.8 234 1,163 -0.9 317
Buncombe, NC............. 8.0 117.5 1.2 194 714 2.1 108
Catawba, NC.............. 4.3 80.8 0.8 234 694 3.0 48
Cumberland, NC........... 6.1 117.1 -0.7 316 741 -1.1 320
Durham, NC............... 7.3 184.4 1.6 156 1,189 2.9 51
Forsyth, NC.............. 8.9 176.3 1.9 130 851 2.0 127
Guilford, NC............. 14.0 268.7 1.7 148 809 0.0 292
Mecklenburg, NC.......... 32.7 586.8 2.8 62 1,055 -0.1 295
New Hanover, NC.......... 7.3 101.4 2.3 97 740 2.2 98
Wake, NC................. 29.6 478.9 3.8 24 935 0.8 236
Cass, ND................. 6.4 111.3 2.7 68 861 4.0 17
Butler, OH............... 7.5 140.0 1.5 162 796 -0.1 295
Cuyahoga, OH............. 35.7 707.9 0.7 239 956 2.5 77
Delaware, OH............. 4.5 82.6 2.8 62 892 2.1 108
Franklin, OH............. 29.9 692.6 2.4 90 927 1.4 192
Hamilton, OH............. 23.2 497.6 1.0 214 1,015 -1.2 321
Lake, OH................. 6.3 93.6 -0.1 291 760 -2.3 329
Lorain, OH............... 6.0 95.4 0.9 225 755 0.5 255
Lucas, OH................ 10.1 204.5 1.0 214 794 0.6 248
Mahoning, OH............. 6.0 99.0 0.5 255 674 1.0 216
Montgomery, OH........... 11.9 242.3 -0.2 295 804 0.8 236
Stark, OH................ 8.8 155.7 0.1 279 723 2.8 59
Summit, OH............... 14.1 258.0 0.6 246 832 1.7 161
Warren, OH............... 4.3 81.7 3.2 48 789 -1.3 323
Oklahoma, OK............. 25.6 436.6 1.2 194 906 1.7 161
Tulsa, OK................ 21.0 339.2 1.7 148 865 1.8 146
Clackamas, OR............ 13.0 144.4 2.0 120 858 2.8 59
Jackson, OR.............. 6.8 80.4 2.7 68 710 2.3 92
Lane, OR................. 11.0 139.7 1.2 194 727 1.8 146
Marion, OR............... 9.5 140.5 3.4 37 731 2.7 65
Multnomah, OR............ 30.7 455.3 2.8 62 953 1.6 172
Washington, OR........... 17.0 260.2 3.6 28 1,147 3.5 31
Allegheny, PA............ 34.4 685.8 0.4 266 1,004 1.8 146
Berks, PA................ 8.8 164.8 0.5 255 828 -2.2 326
Bucks, PA................ 19.2 248.9 1.2 194 872 0.2 275
Butler, PA............... 4.8 84.8 0.5 255 864 2.2 98
Chester, PA.............. 14.9 239.5 1.1 203 1,141 1.0 216
Cumberland, PA........... 6.0 124.6 0.1 279 852 2.7 65
Dauphin, PA.............. 7.3 176.7 0.9 225 911 1.7 161
Delaware, PA............. 13.4 213.3 1.5 162 968 2.2 98
Erie, PA................. 7.0 125.0 -0.4 307 740 0.7 244
Lackawanna, PA........... 5.7 96.8 -0.8 318 709 1.7 161
Lancaster, PA............ 12.7 222.4 0.5 255 768 2.0 127
Lehigh, PA............... 8.5 179.5 1.4 174 903 3.9 23
Luzerne, PA.............. 7.5 139.3 -0.4 307 729 2.1 108
Montgomery, PA........... 26.7 468.6 0.5 255 1,107 -0.4 309
Northampton, PA.......... 6.5 105.1 1.1 203 814 2.8 59
Philadelphia, PA......... 33.6 634.2 0.3 271 1,103 1.8 146
Washington, PA........... 5.3 86.6 0.6 246 893 2.5 77
Westmoreland, PA......... 9.2 132.9 -1.0 322 745 1.4 192
York, PA................. 8.8 172.2 0.4 266 811 0.2 275
Providence, RI........... 17.4 274.0 1.0 214 920 3.1 45
Charleston, SC........... 12.5 219.0 2.1 114 812 1.9 138
Greenville, SC........... 12.7 238.9 3.4 37 811 0.1 283
Horry, SC................ 7.9 114.2 2.0 120 564 2.0 127
Lexington, SC............ 5.9 102.9 4.3 16 702 1.0 216
Richland, SC............. 9.2 207.2 1.4 174 796 1.5 181
Spartanburg, SC.......... 5.9 120.7 3.6 28 777 1.7 161
York, SC................. 4.7 78.0 3.2 48 729 2.5 77
Minnehaha, SD............ 6.7 118.6 1.7 148 798 3.5 31
Davidson, TN............. 19.1 442.2 2.0 120 947 0.2 275
Hamilton, TN............. 8.7 187.9 1.3 187 808 0.1 283
Knox, TN................. 11.1 221.9 1.1 203 796 0.6 248
Rutherford, TN........... 4.6 108.8 4.8 11 796 0.1 283
Shelby, TN............... 19.4 469.4 -0.3 302 960 0.0 292
Williamson, TN........... 6.8 103.5 4.5 14 1,013 2.9 51
Bell, TX................. 4.9 111.1 1.4 174 770 2.5 77
Bexar, TX................ 36.3 773.3 2.6 77 827 1.2 208
Brazoria, TX............. 5.1 96.2 3.2 48 908 3.4 37
Brazos, TX............... 4.1 94.9 5.7 3 711 -1.0 318
Cameron, TX.............. 6.3 131.9 1.8 140 587 2.3 92
Collin, TX............... 20.3 330.3 4.8 11 1,070 0.8 236
Dallas, TX............... 70.6 1,509.0 3.2 48 1,115 2.8 59
Denton, TX............... 12.1 195.5 4.9 8 837 1.6 172
El Paso, TX.............. 14.3 282.4 1.5 162 666 2.0 127
Fort Bend, TX............ 10.5 157.8 6.0 1 969 3.6 29
Galveston, TX............ 5.6 98.5 2.8 62 805 0.2 275
Gregg, TX................ 4.2 77.1 0.9 225 846 4.1 15
Harris, TX............... 106.1 2,192.3 2.9 60 1,187 2.9 51
Hidalgo, TX.............. 11.6 231.7 2.6 77 595 2.1 108
Jefferson, TX............ 5.8 116.9 -2.0 331 921 0.9 224
Lubbock, TX.............. 7.2 129.1 2.3 97 736 2.6 70
McLennan, TX............. 5.0 103.3 1.2 194 748 1.4 192
Midland, TX.............. 5.1 85.3 4.5 14 1,148 3.5 31
Montgomery, TX........... 9.6 151.4 4.8 11 903 3.4 37
Nueces, TX............... 8.1 159.7 1.8 140 817 2.4 87
Potter, TX............... 3.9 77.3 1.3 187 778 1.8 146
Smith, TX................ 5.8 95.2 2.5 85 784 1.6 172
Tarrant, TX.............. 39.4 812.6 3.0 57 912 0.6 248
Travis, TX............... 33.7 637.8 4.1 20 1,028 2.4 87
Webb, TX................. 5.0 92.8 1.9 130 636 -0.2 301
Williamson, TX........... 8.4 139.9 4.3 16 928 1.5 181
Davis, UT................ 7.5 111.7 2.6 77 738 -0.7 314
Salt Lake, UT............ 39.5 611.4 3.0 57 877 2.1 108
Utah, UT................. 13.5 190.1 4.9 8 749 6.4 5
Weber, UT................ 5.6 93.2 2.0 120 710 4.6 11
Chittenden, VT........... 6.3 99.3 0.2 277 898 3.2 42
Arlington, VA............ 8.8 164.9 -1.0 322 1,478 -1.0 318
Chesterfield, VA......... 8.0 122.0 2.7 68 810 -0.6 312
Fairfax, VA.............. 35.2 586.1 -0.2 295 1,434 1.8 146
Henrico, VA.............. 10.3 179.5 0.1 279 912 1.7 161
Loudoun, VA.............. 10.3 146.7 2.0 120 1,085 -0.2 301
Prince William, VA....... 8.1 116.5 2.6 77 835 0.2 275
Alexandria City, VA...... 6.3 94.6 -1.6 329 1,315 4.0 17
Chesapeake City, VA...... 5.7 95.7 2.2 107 728 0.6 248
Newport News City, VA.... 3.7 97.4 1.1 203 906 4.0 17
Norfolk City, VA......... 5.6 136.4 -0.6 313 906 0.3 268
Richmond City, VA........ 7.1 148.1 0.1 279 1,021 1.8 146
Virginia Beach City, VA.. 11.3 170.5 2.2 107 733 0.3 268
Benton, WA............... 6.0 79.9 1.1 203 916 0.3 268
Clark, WA................ 14.5 136.1 3.5 34 866 2.2 98
King, WA................. 86.3 1,212.3 3.7 25 1,376 1.6 172
Kitsap, WA............... 6.9 80.1 -0.2 295 879 -0.6 312
Pierce, WA............... 22.9 274.0 2.1 114 842 0.5 255
Snohomish, WA............ 20.5 264.6 1.6 156 1,013 1.4 192
Spokane, WA.............. 16.7 204.2 1.3 187 796 2.2 98
Thurston, WA............. 7.9 100.1 2.3 97 829 -2.2 326
Whatcom, WA.............. 7.2 82.7 2.0 120 807 6.9 4
Yakima, WA............... 9.3 114.8 1.0 214 638 3.2 42
Kanawha, WV.............. 6.0 104.0 -1.0 322 804 1.8 146
Brown, WI................ 6.6 149.7 1.0 214 805 3.1 45
Dane, WI................. 14.4 310.3 1.3 187 921 9.3 2
Milwaukee, WI............ 24.6 481.4 1.0 214 879 0.5 255
Outagamie, WI............ 5.1 102.3 1.7 148 788 2.3 92
Waukesha, WI............. 12.6 230.8 1.3 187 904 1.7 161
Winnebago, WI............ 3.6 89.9 -0.9 320 839 1.8 146
San Juan, PR............. 11.5 255.0 -2.9 (7) 598 -0.3 (7)
(1) Includes workers covered by Unemployment Insurance (UI) and Unemployment Compensation for Federal
Employees (UCFE) programs. These 334 U.S. counties comprise 71.4 percent of the total covered workers
in the U.S.
(2) Data are preliminary.
(3) Includes areas not officially designated as counties. See Technical Note.
(4) Average weekly wages were calculated using unrounded data.
(5) Percent changes were computed from quarterly employment and pay data adjusted for noneconomic
county reclassifications. See Technical Note.
(6) Totals for the United States do not include data for Puerto Rico or the Virgin Islands.
(7) This county was not included in the U.S. rankings.
Table 2. Covered(1) establishments, employment, and wages in the 10 largest counties,
third quarter 2013(2)
Employment Average weekly
wage(3)
Establishments,
third quarter
County by NAICS supersector 2013 Percent Percent
(thousands) September change, Third change,
2013 September quarter third
(thousands) 2012-13(4) 2013 quarter
2012-13(4)
United States(5) ............................ 9,294.8 134,957.5 1.7 $922 1.9
Private industry........................... 9,000.5 113,874.9 2.1 914 1.9
Natural resources and mining............. 133.4 2,130.2 0.9 1,019 3.7
Construction............................. 750.3 6,067.8 4.4 1,005 2.6
Manufacturing............................ 336.2 12,055.6 0.4 1,125 1.7
Trade, transportation, and utilities..... 1,908.2 25,615.1 1.7 781 1.6
Information.............................. 145.6 2,691.1 0.7 1,622 4.8
Financial activities..................... 820.6 7,629.0 1.6 1,346 2.4
Professional and business services....... 1,644.1 18,635.0 2.8 1,172 1.8
Education and health services............ 1,469.8 20,222.2 1.6 864 1.5
Leisure and hospitality.................. 785.1 14,478.2 3.0 388 1.8
Other services........................... 797.0 4,152.7 1.1 623 2.5
Government................................. 294.2 21,082.6 -0.2 970 1.7
Los Angeles, CA.............................. 434.4 4,093.3 2.4 1,007 1.0
Private industry........................... 428.5 3,566.9 2.7 977 0.6
Natural resources and mining............. 0.5 9.9 7.3 1,799 -20.7
Construction............................. 12.4 117.2 5.5 1,065 1.7
Manufacturing............................ 12.5 366.8 0.1 1,126 -0.2
Trade, transportation, and utilities..... 51.9 769.9 1.7 826 1.0
Information.............................. 8.4 194.5 4.3 1,760 0.3
Financial activities..................... 22.6 210.1 -0.5 1,514 3.6
Professional and business services....... 44.0 599.2 4.0 1,222 0.5
Education and health services............ 200.1 701.3 2.2 782 0.9
Leisure and hospitality.................. 28.1 441.0 4.5 552 0.5
Other services........................... 25.3 141.1 1.6 674 1.0
Government................................. 5.8 526.4 0.1 1,220 3.3
Cook, IL..................................... 153.0 2,445.8 1.0 1,049 1.5
Private industry........................... 151.7 2,150.3 1.1 1,037 1.5
Natural resources and mining............. 0.1 0.9 2.6 1,017 1.8
Construction............................. 12.6 67.3 3.1 1,333 3.4
Manufacturing............................ 6.6 187.8 -2.1 1,094 1.8
Trade, transportation, and utilities..... 30.2 446.6 0.7 830 -0.4
Information.............................. 2.7 53.5 -2.1 1,572 4.0
Financial activities..................... 15.8 184.7 0.1 1,763 3.6
Professional and business services....... 32.5 436.8 2.1 1,318 2.1
Education and health services............ 16.1 419.6 1.7 902 0.3
Leisure and hospitality.................. 13.7 253.2 2.4 477 0.8
Other services........................... 16.8 95.7 0.7 801 2.0
Government................................. 1.3 295.5 -0.2 1,146 2.4
New York, NY................................. 125.1 2,424.5 1.4 1,667 2.6
Private industry........................... 124.8 1,988.8 1.8 1,782 2.7
Natural resources and mining............. 0.0 0.2 6.7 2,087 39.4
Construction............................. 2.2 33.7 3.3 1,684 3.6
Manufacturing............................ 2.3 26.1 0.3 1,134 4.1
Trade, transportation, and utilities..... 21.0 257.8 1.8 1,229 0.2
Information.............................. 4.5 144.5 1.5 2,320 7.3
Financial activities..................... 19.1 349.4 -1.0 3,126 3.9
Professional and business services....... 26.4 502.1 2.2 2,006 3.0
Education and health services............ 9.6 313.5 2.2 1,243 2.9
Leisure and hospitality.................. 13.5 260.3 2.6 783 2.1
Other services........................... 19.6 94.9 2.3 1,014 2.5
Government................................. 0.3 435.7 -0.1 1,137 1.1
Harris, TX................................... 106.1 2,192.3 2.9 1,187 2.9
Private industry........................... 105.6 1,936.9 3.0 1,203 2.9
Natural resources and mining............. 1.8 96.0 7.9 2,898 0.3
Construction............................. 6.6 144.6 2.2 1,187 3.1
Manufacturing............................ 4.6 195.5 2.5 1,441 1.3
Trade, transportation, and utilities..... 23.9 453.6 3.3 1,060 3.9
Information.............................. 1.2 28.3 -1.8 1,320 -6.3
Financial activities..................... 10.8 117.5 3.1 1,478 2.1
Professional and business services....... 21.3 375.8 2.1 1,405 3.9
Education and health services............ 14.6 263.0 2.4 967 4.4
Leisure and hospitality.................. 8.8 200.7 3.8 405 1.0
Other services........................... 11.5 61.0 2.7 701 2.8
Government................................. 0.6 255.4 2.4 1,066 2.4
Maricopa, AZ................................. 93.8 1,719.1 2.7 898 1.2
Private industry........................... 93.1 1,510.6 3.0 892 1.4
Natural resources and mining............. 0.5 7.0 2.1 919 4.0
Construction............................. 7.5 93.1 3.7 943 1.0
Manufacturing............................ 3.2 113.1 -0.5 1,280 0.2
Trade, transportation, and utilities..... 20.7 341.3 2.4 825 0.7
Information.............................. 1.6 31.4 3.0 1,191 2.3
Financial activities..................... 10.9 149.1 4.9 1,126 0.9
Professional and business services....... 21.9 288.3 3.2 964 3.2
Education and health services............ 10.8 254.6 2.5 915 1.6
Leisure and hospitality.................. 7.4 184.3 4.7 427 0.2
Other services........................... 6.5 46.7 0.9 624 3.5
Government................................. 0.7 208.5 0.2 954 1.5
Dallas, TX................................... 70.6 1,509.0 3.2 1,115 2.8
Private industry........................... 70.1 1,343.1 3.5 1,120 2.8
Natural resources and mining............. 0.6 9.2 5.0 3,404 0.0
Construction............................. 4.0 74.3 6.8 1,031 1.8
Manufacturing............................ 2.7 107.8 -3.7 1,296 5.2
Trade, transportation, and utilities..... 15.3 306.0 4.2 1,028 2.3
Information.............................. 1.4 48.1 5.4 1,719 3.2
Financial activities..................... 8.6 149.5 4.7 1,456 2.9
Professional and business services....... 15.7 291.1 3.5 1,249 4.0
Education and health services............ 8.6 176.3 2.6 1,021 1.4
Leisure and hospitality.................. 6.0 140.5 4.8 489 -0.2
Other services........................... 6.7 39.7 4.0 718 2.9
Government................................. 0.5 165.9 1.4 1,080 2.8
Orange, CA................................... 105.5 1,441.4 2.3 1,022 0.0
Private industry........................... 104.2 1,308.3 2.4 1,008 -0.3
Natural resources and mining............. 0.2 3.3 7.9 729 1.8
Construction............................. 6.1 79.1 7.0 1,133 -0.1
Manufacturing............................ 4.8 156.7 -1.2 1,300 2.2
Trade, transportation, and utilities..... 16.3 251.1 1.5 930 -0.9
Information.............................. 1.2 24.9 3.4 1,514 -7.0
Financial activities..................... 9.8 111.9 2.8 1,568 1.0
Professional and business services....... 19.4 264.8 1.5 1,152 1.5
Education and health services............ 25.6 180.2 3.3 866 -1.4
Leisure and hospitality.................. 7.5 190.5 3.7 443 -5.7
Other services........................... 6.2 40.9 0.8 641 1.9
Government................................. 1.3 133.1 1.5 1,171 3.2
San Diego, CA................................ 98.4 1,312.2 2.0 1,022 2.0
Private industry........................... 97.0 1,092.9 2.0 987 1.6
Natural resources and mining............. 0.7 10.6 1.1 637 7.6
Construction............................. 5.9 61.6 6.5 1,053 2.0
Manufacturing............................ 2.9 94.3 -1.2 1,359 -8.9
Trade, transportation, and utilities..... 13.8 210.0 1.2 785 0.5
Information.............................. 1.1 23.8 -1.8 1,723 9.3
Financial activities..................... 8.6 70.8 1.1 1,316 9.3
Professional and business services....... 16.9 223.3 3.4 1,441 5.0
Education and health services............ 26.7 177.6 1.1 869 0.9
Leisure and hospitality.................. 7.3 170.3 2.3 438 0.5
Other services........................... 6.6 46.1 2.4 564 1.1
Government................................. 1.4 219.3 1.6 1,207 3.6
King, WA..................................... 86.3 1,212.3 3.7 1,376 1.6
Private industry........................... 85.7 1,056.4 3.9 1,402 1.5
Natural resources and mining............. 0.4 2.7 -10.9 1,232 -7.4
Construction............................. 5.5 55.5 7.7 1,164 1.5
Manufacturing............................ 2.2 106.1 2.2 1,513 2.9
Trade, transportation, and utilities..... 14.4 223.3 4.7 1,076 3.3
Information.............................. 1.8 83.3 3.3 4,670 2.4
Financial activities..................... 6.3 65.6 3.5 1,441 0.4
Professional and business services....... 14.5 201.3 4.4 1,459 -1.2
Education and health services............ 26.2 155.8 2.8 910 2.4
Leisure and hospitality.................. 6.6 123.1 4.9 499 2.7
Other services........................... 8.0 39.7 1.4 779 5.3
Government................................. 0.5 155.9 1.8 1,202 2.3
Miami-Dade, FL............................... 93.4 1,016.7 2.4 873 2.1
Private industry........................... 93.0 879.6 2.9 854 1.9
Natural resources and mining............. 0.5 7.2 -4.4 542 2.1
Construction............................. 5.3 33.9 10.8 850 3.2
Manufacturing............................ 2.7 36.4 2.1 813 0.7
Trade, transportation, and utilities..... 27.6 260.5 2.3 795 1.7
Information.............................. 1.6 17.5 2.8 1,367 2.3
Financial activities..................... 9.6 68.9 4.9 1,308 4.3
Professional and business services....... 19.8 136.0 3.7 1,016 1.0
Education and health services............ 10.2 159.4 0.8 908 3.3
Leisure and hospitality.................. 7.1 123.2 3.6 524 -1.9
Other services........................... 8.1 36.2 2.3 569 4.0
Government................................. 0.4 137.1 -1.0 995 3.0
(1) Includes workers covered by Unemployment Insurance (UI) and Unemployment Compensation for Federal
Employees (UCFE) programs.
(2) Data are preliminary. Counties selected are based on 2012 annual average employment.
(3) Average weekly wages were calculated using unrounded data.
(4) Percent changes were computed from quarterly employment and pay data adjusted for noneconomic
county reclassifications. See Technical Note.
(5) Totals for the United States do not include data for Puerto Rico or the Virgin Islands.
Table 3. Covered(1) establishments, employment, and wages by state,
third quarter 2013(2)
Employment Average weekly
wage(3)
Establishments,
third quarter
State 2013 Percent Percent
(thousands) September change, Third change,
2013 September quarter third
(thousands) 2012-13 2013 quarter
2012-13
United States(4)........... 9,294.8 134,957.5 1.7 $922 1.9
Alabama.................... 116.4 1,847.6 0.8 794 1.3
Alaska..................... 22.0 345.0 0.4 990 3.0
Arizona.................... 146.9 2,490.9 2.2 859 1.5
Arkansas................... 87.2 1,156.5 0.1 723 2.1
California................. 1,356.2 15,526.4 2.7 1,057 2.1
Colorado................... 176.1 2,355.7 3.1 952 1.7
Connecticut................ 113.4 1,650.3 0.7 1,109 1.9
Delaware................... 28.2 416.8 2.1 941 2.1
District of Columbia....... 35.6 726.2 1.5 1,560 3.0
Florida.................... 628.3 7,501.8 2.6 808 1.1
Georgia.................... 276.1 3,928.2 2.3 867 1.5
Hawaii..................... 38.8 617.7 1.7 839 1.6
Idaho...................... 53.7 644.7 2.3 703 2.3
Illinois................... 402.1 5,731.7 0.7 959 1.5
Indiana.................... 159.5 2,883.6 1.2 784 1.6
Iowa....................... 98.1 1,512.0 1.5 772 2.1
Kansas..................... 85.1 1,347.6 1.8 776 2.0
Kentucky................... 118.4 1,794.5 1.0 760 1.1
Louisiana.................. 129.2 1,893.4 1.4 827 2.9
Maine...................... 49.6 601.5 0.7 735 1.8
Maryland................... 166.8 2,546.4 0.6 1,011 0.4
Massachusetts.............. 229.0 3,318.3 1.2 1,131 2.6
Michigan................... 239.2 4,069.7 2.1 875 1.5
Minnesota.................. 171.8 2,724.2 1.7 938 2.6
Mississippi................ 70.7 1,099.1 0.8 688 2.5
Missouri................... 181.5 2,661.0 1.3 805 1.4
Montana.................... 43.4 446.7 1.2 705 2.3
Nebraska................... 70.8 937.5 1.3 766 3.4
Nevada..................... 74.7 1,169.4 2.5 836 2.0
New Hampshire.............. 49.9 624.5 0.6 895 2.4
New Jersey................. 265.3 3,851.9 1.2 1,068 1.3
New Mexico................. 55.7 793.7 0.5 766 0.7
New York................... 617.3 8,724.8 1.3 1,108 1.7
North Carolina............. 255.9 4,006.4 1.7 817 1.4
North Dakota............... 30.9 436.7 3.4 921 5.5
Ohio....................... 288.5 5,147.5 1.4 837 1.2
Oklahoma................... 105.9 1,572.6 1.4 797 2.4
Oregon..................... 135.6 1,709.8 2.4 856 2.6
Pennsylvania............... 341.6 5,622.4 0.3 913 1.6
Rhode Island............... 35.6 465.2 1.3 878 2.6
South Carolina............. 118.1 1,859.3 2.3 751 1.9
South Dakota............... 31.8 408.9 0.9 706 3.4
Tennessee.................. 144.9 2,712.8 1.5 819 0.6
Texas...................... 609.6 11,091.9 2.8 952 2.5
Utah....................... 88.2 1,265.5 2.9 791 3.1
Vermont.................... 24.6 302.5 0.0 788 3.4
Virginia................... 240.6 3,650.1 0.6 971 1.1
Washington................. 246.7 3,017.9 2.4 1,044 2.1
West Virginia.............. 49.7 710.3 -0.7 751 3.7
Wisconsin.................. 163.9 2,752.7 1.1 793 3.0
Wyoming.................... 25.7 286.1 0.2 840 1.4
Puerto Rico................ 49.4 910.9 -2.5 501 -0.6
Virgin Islands............. 3.4 37.9 -1.9 706 -0.6
(1) Includes workers covered by Unemployment Insurance (UI) and Unemployment
Compensation for Federal Employees (UCFE) programs.
(2) Data are preliminary.
(3) Average weekly wages were calculated using unrounded data.
(4) Totals for the United States do not include data for Puerto Rico or the
Virgin Islands.
United States Department of Labor
