For release 10:00 a.m. (EST), Wednesday, December 18, 2013 USDL-13-2392
Technical Information: (202)691-6567 * QCEWInfo@bls.gov * www.bls.gov/cew
Media Contact: (202)691-5902 * PressOffice@bls.gov
COUNTY EMPLOYMENT AND WAGES
Second Quarter 2013
From June 2012 to June 2013, employment increased in 288 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 7.0
percent over the year, compared with national job growth of 1.6 percent. Within Fort Bend, the largest
employment increase occurred in construction, which gained 2,285 jobs over the year (21.0 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 4.5 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 2.1 percent to $921 in the second quarter of
2013. Union, N.J., had the largest over-the-year increase in average weekly wages with a gain of 8.1
percent. Within Union, an average weekly wage gain of $377, or 28.5 percent, in professional and
business services made the largest contribution to the increase in average weekly wages. Davidson,
Tenn., experienced the largest decrease in average weekly wages with a loss of 2.2 percent over the year.
Table A. Large counties ranked by June 2013 employment, June 2012-13 employment
increase, and June 2012-13 percent increase in employment
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Employment in large counties
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June 2013 employment | Increase in employment, | Percent increase in employment,
(thousands) | June 2012-13 | June 2012-13
| (thousands) |
--------------------------------------------------------------------------------------------------------
| |
United States 135,094.0| United States 2,088.2| United States 1.6
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| |
Los Angeles, Calif. 4,070.9| Los Angeles, Calif. 80.6| Fort Bend, Texas 7.0
Cook, Ill. 2,452.3| Harris, Texas 67.4| Midland, Texas 6.0
New York, N.Y. 2,434.0| Maricopa, Ariz. 42.3| Douglas, Colo. 5.8
Harris, Texas 2,189.9| Dallas, Texas 39.1| Elkhart, Ind. 5.1
Maricopa, Ariz. 1,678.7| Orange, Calif. 37.5| Placer, Calif. 4.9
Dallas, Texas 1,495.5| New York, N.Y. 35.9| Weld, Colo. 4.8
Orange, Calif. 1,448.0| Santa Clara, Calif. 33.7| Travis, Texas 4.8
San Diego, Calif. 1,310.5| King, Wash. 33.2| Utah, Utah 4.7
King, Wash. 1,205.5| Travis, Texas 29.1| Hamilton, Ind. 4.6
Miami-Dade, Fla. 999.8| Cook, Ill. 28.0| Williamson, Tenn. 4.2
| |
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Large County Employment
In June 2013, national employment was 135.1 million (as measured by the QCEW program). Over the
year, employment increased 1.6 percent, or 2.1 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 1.6 million over the year, accounting for 78.3 percent of the overall
U.S. employment increase.
Fort Bend, Texas, had the largest percentage increase in employment (7.0 percent) among the largest
U.S. counties. The five counties with the largest increases in employment level were Los Angeles, Calif.;
Harris, Texas; Maricopa, Ariz.; Dallas, Texas; and Orange, Calif. These counties had a combined over-
the-year employment gain of 266,900 jobs, which was 12.8 percent of the overall job increase for the
U.S. (See table A.)
Employment declined in 36 of the large counties from June 2012 to June 2013. Atlantic, N.J., had the
largest over-the-year percentage decrease in employment (-4.5 percent). Within Atlantic, natural
resources and mining had the largest decrease in employment with a loss of 4,199 (-53.9 percent).
Caddo, La., had the second largest percentage decrease in employment, followed by Oneida, N.Y., and
Peoria, Ill. Three counties, Winnebago, Ill., Broome, N.Y., and Jefferson, Texas, tied for the fifth largest
percentage decrease. (See table 1.)
Table B. Large counties ranked by second quarter 2013 average weekly wages, second quarter 2012-13
increase in average weekly wages, and second 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
second quarter 2013 | wage, second quarter 2012-13 | weekly wage, second
| | quarter 2012-13
--------------------------------------------------------------------------------------------------------
| |
United States $921| United States $19| United States 2.1
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| |
Santa Clara, Calif. $1,810| San Mateo, Calif. $121| Union, N.J. 8.1
New York, N.Y. 1,675| Union, N.J. 91| San Mateo, Calif. 8.0
San Mateo, Calif. 1,632| Williamson, Tenn. 76| Williamson, Tenn. 7.8
Washington, D.C. 1,575| Santa Clara, Calif. 73| Rockingham, N.H. 6.9
Arlington, Va. 1,525| Rockingham, N.H. 59| Dane, Wis. 6.0
San Francisco, Calif. 1,512| Lake, Ill. 56| Clayton, Ga. 5.6
Fairfax, Va. 1,459| Midland, Texas 56| Saratoga, N.Y. 5.5
Fairfield, Conn. 1,435| Chester, Pa. 53| Fort Bend, Texas 5.1
Suffolk, Mass. 1,410| Morris, N.J. 52| Midland, Texas 5.1
Middlesex, Mass. 1,371| Dane, Wis. 52| Lake, Ill. 4.9
| | Montgomery, Texas 4.9
| |
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Large County Average Weekly Wages
Average weekly wages for the nation increased 2.1 percent during the year ending in the second quarter
of 2013. Among the 334 largest counties, 304 had over-the-year increases in average weekly wages.
Union, N.J., had the largest wage increase among the largest U.S. counties (8.1 percent).
Of the 334 largest counties, 18 experienced over-the-year decreases in average weekly wages. Davidson,
Tenn., had the largest average weekly wage decrease with a loss of 2.2 percent. Within Davidson,
financial activities had the largest impact on the county’s average weekly wage decrease. Within this
industry, average weekly wages declined by $254 (-16.2 percent) over the year. Whatcom, Wash., had
the second largest decrease in average weekly wages, followed by Washington, Ore., and Shelby, Tenn.,
which tied for the third largest percentage decrease. Two counties, El Paso, Colo., and Wyandotte, Kan.,
tied for the fifth largest percentage decrease. (See table 1.)
Ten Largest U.S. Counties
All of the 10 largest counties had over-the-year percentage increases in employment in June 2013.
Harris, Texas, had the largest gain (3.2 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 13,618,
or 3.1 percent. Cook, Ill., had the smallest percentage increase in employment (1.2 percent) among the 10
largest counties. (See table 2.)
All of the 10 largest U.S. counties had over-the-year increases in average weekly wages. San Diego,
Calif., experienced the largest gain in average weekly wages (4.0 percent). Within San Diego,
professional and business services had the largest impact on the county’s average weekly wage growth.
Within this industry, average weekly wages increased by $130, or 9.2 percent, over the year. Los
Angeles and Orange, Calif., tied for the smallest average weekly wage increase (0.4 percent each) among
the 10 largest counties.
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. June 2013 employment and 2013 second 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 9.2 million employer reports cover 135.1 million full- and part-
time workers. For additional information about the quarterly employment and wages data, please read
the Technical Note. Data for the second quarter of 2013 will be available later at
http://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 http://www.bls.gov/cew/cewregional.htm.
_____________
The County Employment and Wages release for third quarter 2013 is scheduled to be released on
Wednesday, March 19, 2014.
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 | | |
---------------------------------------------------------------------------------
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,
second quarter 2013(2)
Employment Average weekly wage(4)
Establishments,
County(3) second quarter Percent Ranking Percent Ranking
2013 June change, by Second change, by
(thousands) 2013 June percent quarter second percent
(thousands) 2012-13(5) change 2013 quarter change
2012-13(5)
United States(6)......... 9,248.7 135,094.0 1.6 - $921 2.1 -
Jefferson, AL............ 17.5 340.1 1.0 203 917 0.3 297
Madison, AL.............. 8.9 182.9 2.2 99 1,030 1.7 170
Mobile, AL............... 9.5 164.8 0.3 266 804 1.8 159
Montgomery, AL........... 6.3 129.7 1.1 191 784 0.0 305
Tuscaloosa, AL........... 4.2 85.5 0.9 216 797 0.9 254
Anchorage Borough, AK.... 8.4 155.4 0.0 289 1,009 1.3 218
Maricopa, AZ............. 93.4 1,678.7 2.6 69 919 1.5 197
Pima, AZ................. 18.7 343.6 -0.1 298 812 2.3 98
Benton, AR............... 5.7 98.7 2.8 50 900 3.0 59
Pulaski, AR.............. 14.6 242.7 -0.6 314 844 2.4 95
Washington, AR........... 5.7 95.3 2.7 62 751 3.3 43
Alameda, CA.............. 55.3 682.8 2.8 50 1,175 0.3 297
Contra Costa, CA......... 29.1 334.4 2.1 106 1,123 3.3 43
Fresno, CA............... 29.5 361.3 2.2 99 706 1.0 248
Kern, CA................. 17.0 309.3 2.3 91 803 -0.6 320
Los Angeles, CA.......... 425.8 4,070.9 2.0 114 1,002 0.4 290
Marin, CA................ 11.8 110.2 3.0 42 1,136 2.1 123
Monterey, CA............. 12.6 192.2 2.1 106 779 1.6 183
Orange, CA............... 104.9 1,448.0 2.7 62 1,019 0.4 290
Placer, CA............... 11.0 138.7 4.9 5 895 1.5 197
Riverside, CA............ 50.5 597.9 2.8 50 761 2.4 95
Sacramento, CA........... 50.5 603.2 1.3 172 1,016 0.3 297
San Bernardino, CA....... 49.3 628.5 2.0 114 791 0.5 286
San Diego, CA............ 98.6 1,310.5 1.6 150 1,031 4.0 20
San Francisco, CA........ 55.3 611.2 3.5 22 1,512 2.2 111
San Joaquin, CA.......... 16.4 215.2 -1.8 326 757 0.3 297
San Luis Obispo, CA...... 9.6 109.2 1.6 150 760 1.7 170
San Mateo, CA............ 24.9 355.5 3.4 23 1,632 8.0 2
Santa Barbara, CA........ 14.4 191.3 2.1 106 885 2.5 85
Santa Clara, CA.......... 63.4 939.4 3.7 18 1,810 4.2 16
Santa Cruz, CA........... 9.0 102.0 1.8 131 830 0.5 286
Solano, CA............... 9.8 126.0 2.1 106 933 3.9 21
Sonoma, CA............... 18.5 184.0 3.2 28 842 1.0 248
Stanislaus, CA........... 13.9 170.7 1.6 150 754 -0.3 316
Tulare, CA............... 9.0 154.0 2.4 84 639 0.9 254
Ventura, CA.............. 24.2 311.5 1.3 172 951 3.5 34
Yolo, CA................. 5.9 92.7 0.3 266 944 1.4 209
Adams, CO................ 9.0 175.8 4.0 12 886 2.3 98
Arapahoe, CO............. 19.3 298.6 3.3 24 1,061 2.8 69
Boulder, CO.............. 13.3 165.7 2.8 50 1,074 2.7 76
Denver, CO............... 27.0 441.4 3.3 24 1,093 1.1 237
Douglas, CO.............. 10.0 105.2 5.8 3 1,014 1.0 248
El Paso, CO.............. 17.0 245.7 2.2 99 835 -1.1 326
Jefferson, CO............ 17.9 219.5 2.8 50 937 3.4 36
Larimer, CO.............. 10.3 140.1 3.0 42 786 0.4 290
Weld, CO................. 5.9 90.3 4.8 6 791 0.6 281
Fairfield, CT............ 33.3 419.7 1.3 172 1,435 0.7 267
Hartford, CT............. 26.0 502.2 1.1 191 1,120 2.2 111
New Haven, CT............ 22.8 361.9 0.8 226 968 1.8 159
New London, CT........... 7.0 124.3 -1.1 320 939 1.4 209
New Castle, DE........... 16.9 269.9 1.6 150 1,093 2.3 98
Washington, DC........... 34.9 725.0 0.9 216 1,575 2.1 123
Alachua, FL.............. 6.6 116.0 0.2 276 799 1.9 143
Brevard, FL.............. 14.6 186.7 -0.5 311 839 0.6 281
Broward, FL.............. 65.0 710.2 2.6 69 861 3.4 36
Collier, FL.............. 12.3 114.9 3.8 16 798 2.2 111
Duval, FL................ 27.6 447.0 1.6 150 878 2.0 133
Escambia, FL............. 8.0 120.9 2.3 91 728 0.0 305
Hillsborough, FL......... 39.0 594.9 2.6 69 884 1.7 170
Lake, FL................. 7.4 79.2 3.2 28 633 2.8 69
Lee, FL.................. 19.4 204.2 3.6 20 739 1.2 227
Leon, FL................. 8.3 135.4 0.4 254 768 0.0 305
Manatee, FL.............. 9.6 103.9 3.0 42 721 2.0 133
Marion, FL............... 8.0 90.7 0.7 233 668 2.0 133
Miami-Dade, FL........... 92.6 999.8 2.5 78 885 1.1 237
Okaloosa, FL............. 6.1 77.4 0.8 226 766 0.7 267
Orange, FL............... 37.4 699.4 3.6 20 806 2.0 133
Palm Beach, FL........... 50.9 517.0 2.8 50 892 2.3 98
Pasco, FL................ 10.1 94.4 3.0 42 687 3.3 43
Pinellas, FL............. 31.2 390.2 2.0 114 809 0.5 286
Polk, FL................. 12.5 188.2 1.9 124 712 1.9 143
Sarasota, FL............. 14.7 139.8 3.7 18 777 2.9 62
Seminole, FL............. 14.0 159.9 2.3 91 784 3.7 29
Volusia, FL.............. 13.5 148.6 0.8 226 675 1.4 209
Bibb, GA................. 4.5 79.9 0.2 276 743 (7) -
Chatham, GA.............. 7.9 137.0 2.2 99 763 0.8 262
Clayton, GA.............. 4.3 111.0 0.3 266 871 5.6 6
Cobb, GA................. 22.1 312.8 1.9 124 985 2.5 85
De Kalb, GA.............. 18.2 274.6 0.4 254 957 2.1 123
Fulton, GA............... 42.7 743.4 2.6 69 1,204 1.9 143
Gwinnett, GA............. 24.5 311.2 2.5 78 900 1.9 143
Muscogee, GA............. 4.7 94.3 0.0 289 730 2.1 123
Richmond, GA............. 4.7 98.7 1.2 183 782 -0.1 314
Honolulu, HI............. 24.8 451.5 1.7 143 856 1.5 197
Ada, ID.................. 13.6 206.3 3.2 28 793 1.4 209
Champaign, IL............ 4.4 87.8 0.4 254 795 0.8 262
Cook, IL................. 152.6 2,452.3 1.2 183 1,067 1.3 218
Du Page, IL.............. 38.0 597.6 1.7 143 1,065 1.4 209
Kane, IL................. 13.7 203.5 1.4 164 801 1.6 183
Lake, IL................. 22.6 335.2 1.3 172 1,206 4.9 10
McHenry, IL.............. 8.8 95.9 0.1 282 766 3.1 53
McLean, IL............... 3.9 85.4 0.2 276 955 3.1 53
Madison, IL.............. 6.1 95.0 -0.6 314 753 1.1 237
Peoria, IL............... 4.7 102.9 -2.0 330 871 1.0 248
St. Clair, IL............ 5.7 91.8 -1.3 321 737 0.0 305
Sangamon, IL............. 5.3 126.5 -1.4 323 941 2.1 123
Will, IL................. 15.7 213.2 2.7 62 810 1.4 209
Winnebago, IL............ 6.9 124.3 -1.9 327 793 2.5 85
Allen, IN................ 8.9 176.0 0.6 241 745 1.5 197
Elkhart, IN.............. 4.8 117.7 5.1 4 767 3.0 59
Hamilton, IN............. 8.7 122.0 4.6 9 860 2.0 133
Lake, IN................. 10.4 189.2 -0.1 298 847 0.4 290
Marion, IN............... 24.0 572.6 1.1 191 923 2.1 123
St. Joseph, IN........... 5.9 114.1 0.0 289 752 -0.5 319
Tippecanoe, IN........... 3.3 78.4 -0.7 316 786 1.2 227
Vanderburgh, IN.......... 4.8 103.8 -1.5 325 753 3.6 30
Johnson, IA.............. 3.8 79.7 2.0 114 848 2.5 85
Linn, IA................. 6.4 129.7 0.5 244 876 3.5 34
Polk, IA................. 15.6 281.8 2.7 62 897 1.5 197
Scott, IA................ 5.4 90.2 0.5 244 750 1.8 159
Johnson, KS.............. 21.1 323.6 2.6 69 950 2.7 76
Sedgwick, KS............. 12.1 242.3 0.9 216 843 3.1 53
Shawnee, KS.............. 4.7 95.6 1.1 191 784 1.7 170
Wyandotte, KS............ 3.2 83.9 1.1 191 832 -1.1 326
Boone, KY................ 4.0 77.4 0.5 244 835 1.6 183
Fayette, KY.............. 10.1 180.3 1.0 203 821 1.6 183
Jefferson, KY............ 23.8 432.1 1.2 183 905 1.2 227
Caddo, LA................ 7.4 115.2 -3.1 332 751 0.7 267
Calcasieu, LA............ 4.9 86.1 1.4 164 778 1.8 159
East Baton Rouge, LA..... 14.7 259.4 1.8 131 882 3.3 43
Jefferson, LA............ 13.6 194.6 1.5 158 828 1.3 218
Lafayette, LA............ 9.2 140.9 1.3 172 900 1.8 159
Orleans, LA.............. 11.3 177.1 2.3 91 910 0.8 262
St. Tammany, LA.......... 7.6 80.7 2.6 69 770 3.9 21
Cumberland, ME........... 12.7 175.5 0.8 226 825 2.2 111
Anne Arundel, MD......... 14.9 255.8 2.1 106 981 0.6 281
Baltimore, MD............ 21.5 364.5 1.0 203 920 1.0 248
Frederick, MD............ 6.3 96.5 0.9 216 880 -0.9 324
Harford, MD.............. 5.7 90.1 1.1 191 900 (7) -
Howard, MD............... 9.5 162.7 0.3 266 1,114 1.9 143
Montgomery, MD........... 33.7 458.2 0.5 244 1,246 2.0 133
Prince Georges, MD....... 15.9 303.3 0.5 244 979 0.0 305
Baltimore City, MD....... 14.1 332.2 0.3 266 1,049 2.5 85
Barnstable, MA........... 9.0 102.3 0.8 226 768 1.2 227
Bristol, MA.............. 16.3 217.5 0.7 233 842 2.1 123
Essex, MA................ 22.1 315.0 0.3 266 979 2.8 69
Hampden, MA.............. 15.9 201.1 -0.3 306 832 0.0 305
Middlesex, MA............ 49.8 847.7 1.9 124 1,371 2.2 111
Norfolk, MA.............. 23.6 335.1 1.8 131 1,066 1.1 237
Plymouth, MA............. 14.2 184.1 1.5 158 889 2.5 85
Suffolk, MA.............. 24.3 608.1 1.7 143 1,410 1.8 159
Worcester, MA............ 21.9 328.3 1.2 183 926 1.3 218
Genesee, MI.............. 7.2 132.8 1.4 164 751 1.1 237
Ingham, MI............... 6.3 150.5 0.9 216 855 1.1 237
Kalamazoo, MI............ 5.3 112.3 1.3 172 842 3.2 49
Kent, MI................. 14.1 349.5 2.8 50 809 0.7 267
Macomb, MI............... 17.4 305.9 3.2 28 928 1.9 143
Oakland, MI.............. 38.4 686.8 2.4 84 1,015 1.4 209
Ottawa, MI............... 5.6 111.9 2.9 48 762 2.3 98
Saginaw, MI.............. 4.2 83.6 0.5 244 733 0.7 267
Washtenaw, MI............ 8.3 194.9 1.1 191 979 1.3 218
Wayne, MI................ 31.5 691.1 0.9 216 998 2.3 98
Anoka, MN................ 7.2 116.8 3.8 16 881 1.4 209
Dakota, MN............... 10.1 180.3 1.7 143 900 2.6 82
Hennepin, MN............. 41.0 866.7 2.4 84 1,141 1.7 170
Olmsted, MN.............. 3.5 93.8 1.1 191 1,053 2.3 98
Ramsey, MN............... 14.0 322.3 1.2 183 1,029 2.2 111
St. Louis, MN............ 5.6 97.3 1.7 143 750 3.2 49
Stearns, MN.............. 4.4 82.5 1.4 164 750 3.3 43
Harrison, MS............. 4.5 83.7 -0.4 310 677 1.7 170
Hinds, MS................ 6.0 120.3 -0.1 298 811 1.8 159
Boone, MO................ 4.6 89.1 2.8 50 719 0.8 262
Clay, MO................. 5.2 91.2 2.4 84 839 3.2 49
Greene, MO............... 8.1 155.1 1.0 203 708 1.9 143
Jackson, MO.............. 19.1 351.5 1.0 203 920 0.0 305
St. Charles, MO.......... 8.4 132.8 3.3 24 756 1.6 183
St. Louis, MO............ 32.7 575.9 1.5 158 971 1.6 183
St. Louis City, MO....... 9.8 221.4 0.1 282 972 3.1 53
Yellowstone, MT.......... 6.2 78.5 1.0 203 806 4.8 12
Douglas, NE.............. 18.3 321.0 0.7 233 831 2.6 82
Lancaster, NE............ 9.8 160.2 1.3 172 743 1.6 183
Clark, NV................ 49.9 842.7 2.5 78 822 1.9 143
Washoe, NV............... 13.7 190.0 2.1 106 814 0.7 267
Hillsborough, NH......... 12.1 192.0 0.4 254 987 0.9 254
Rockingham, NH........... 10.5 141.2 1.3 172 908 6.9 4
Atlantic, NJ............. 6.6 138.8 -4.5 333 785 2.5 85
Bergen, NJ............... 32.9 440.1 1.8 131 1,124 -0.4 317
Burlington, NJ........... 11.0 201.4 1.4 164 975 1.5 197
Camden, NJ............... 12.0 197.4 0.0 289 904 1.2 227
Essex, NJ................ 20.4 336.5 0.2 276 1,129 3.4 36
Gloucester, NJ........... 6.1 99.7 0.2 276 809 2.5 85
Hudson, NJ............... 14.0 236.3 0.9 216 1,248 1.1 237
Mercer, NJ............... 11.0 235.9 1.1 191 1,179 2.3 98
Middlesex, NJ............ 21.8 392.5 0.5 244 1,095 2.7 76
Monmouth, NJ............. 20.0 253.9 1.0 203 932 2.3 98
Morris, NJ............... 17.1 282.3 1.7 143 1,323 4.1 19
Ocean, NJ................ 12.4 161.9 1.4 164 761 2.4 95
Passaic, NJ.............. 12.2 171.1 -0.2 304 934 0.4 290
Somerset, NJ............. 10.1 181.2 1.8 131 1,370 1.5 197
Union, NJ................ 14.3 225.2 0.8 226 1,217 8.1 1
Bernalillo, NM........... 17.7 310.4 0.4 254 802 0.0 305
Albany, NY............... 10.1 224.5 0.5 244 965 3.9 21
Bronx, NY................ 17.4 244.4 2.4 84 888 1.8 159
Broome, NY............... 4.6 90.0 -1.9 327 745 1.5 197
Dutchess, NY............. 8.4 112.4 0.7 233 961 -0.1 314
Erie, NY................. 24.1 459.3 -0.2 304 807 1.6 183
Kings, NY................ 55.3 537.5 2.4 84 744 1.1 237
Monroe, NY............... 18.4 380.2 0.0 289 869 0.9 254
Nassau, NY............... 53.3 609.5 1.8 131 1,046 0.1 302
New York, NY............. 125.0 2,434.0 1.5 158 1,675 1.8 159
Oneida, NY............... 5.3 105.1 -2.3 331 761 2.8 69
Onondaga, NY............. 13.0 243.6 -0.1 298 856 0.7 267
Orange, NY............... 9.9 134.6 0.3 266 820 1.7 170
Queens, NY............... 48.6 537.1 2.6 69 852 0.7 267
Richmond, NY............. 9.2 95.0 3.1 37 787 2.2 111
Rockland, NY............. 10.1 118.6 0.7 233 995 0.7 267
Saratoga, NY............. 5.7 82.5 1.7 143 859 5.5 7
Suffolk, NY.............. 51.6 652.8 1.3 172 996 2.2 111
Westchester, NY.......... 36.2 416.2 0.4 254 1,244 4.2 16
Buncombe, NC............. 8.0 116.4 2.6 69 690 1.3 218
Catawba, NC.............. 4.3 80.5 0.7 233 694 1.9 143
Cumberland, NC........... 6.1 119.4 -0.1 298 748 0.5 286
Durham, NC............... 7.3 185.0 2.0 114 1,202 3.4 36
Forsyth, NC.............. 9.0 175.0 1.8 131 834 3.6 30
Guilford, NC............. 14.0 265.7 1.9 124 809 3.6 30
Mecklenburg, NC.......... 32.8 578.7 3.1 37 1,026 2.2 111
New Hanover, NC.......... 7.3 99.5 1.6 150 738 0.4 290
Wake, NC................. 29.6 475.3 2.5 78 929 3.3 43
Cass, ND................. 6.3 110.2 2.3 91 810 2.9 62
Butler, OH............... 7.4 139.8 1.4 164 805 2.2 111
Cuyahoga, OH............. 35.7 715.5 1.2 183 931 1.7 170
Delaware, OH............. 4.5 83.0 2.2 99 908 2.7 76
Franklin, OH............. 29.7 689.6 2.2 99 935 0.2 301
Hamilton, OH............. 23.1 498.6 0.6 241 999 3.0 59
Lake, OH................. 6.3 95.3 0.0 289 754 -0.7 323
Lorain, OH............... 6.0 97.0 -0.1 298 764 1.9 143
Lucas, OH................ 10.1 203.1 0.1 282 800 -0.6 320
Mahoning, OH............. 6.0 97.5 0.1 282 656 1.2 227
Montgomery, OH........... 11.9 243.8 -0.5 311 801 1.6 183
Stark, OH................ 8.8 157.0 0.9 216 706 2.8 69
Summit, OH............... 14.1 258.9 0.5 244 816 1.6 183
Warren, OH............... 4.3 84.5 2.8 50 800 4.7 13
Oklahoma, OK............. 25.5 436.7 1.0 203 875 4.2 16
Tulsa, OK................ 21.0 336.7 0.7 233 862 3.4 36
Clackamas, OR............ 12.9 145.7 2.7 62 861 1.3 218
Jackson, OR.............. 6.7 78.8 3.1 37 708 3.8 27
Lane, OR................. 10.9 140.8 1.3 172 735 3.4 36
Marion, OR............... 9.5 139.0 3.2 28 745 2.1 123
Multnomah, OR............ 30.3 454.7 2.6 69 943 2.5 85
Washington, OR........... 16.8 258.6 2.5 78 1,105 -1.3 328
Allegheny, PA............ 34.8 695.4 0.3 266 1,001 3.9 21
Berks, PA................ 8.8 164.8 0.4 254 846 3.9 21
Bucks, PA................ 19.5 254.1 0.7 233 891 1.4 209
Butler, PA............... 4.9 85.6 -0.3 306 865 3.2 49
Chester, PA.............. 15.0 240.7 0.3 266 1,213 4.6 14
Cumberland, PA........... 6.1 126.4 0.8 226 877 2.7 76
Dauphin, PA.............. 7.3 179.6 0.4 254 903 1.7 170
Delaware, PA............. 13.7 215.1 1.3 172 973 1.6 183
Erie, PA................. 7.1 125.5 -0.8 317 731 1.1 237
Lackawanna, PA........... 5.8 96.9 0.2 276 696 1.2 227
Lancaster, PA............ 12.8 224.5 0.4 254 758 1.3 218
Lehigh, PA............... 8.6 181.2 1.6 150 912 3.1 53
Luzerne, PA.............. 7.6 139.8 0.1 282 723 1.7 170
Montgomery, PA........... 27.0 475.1 0.6 241 1,145 2.9 62
Northampton, PA.......... 6.5 105.2 1.1 191 802 3.1 53
Philadelphia, PA......... 34.7 633.7 0.5 244 1,100 2.9 62
Washington, PA........... 5.3 87.1 0.1 282 895 1.9 143
Westmoreland, PA......... 9.3 134.4 -1.3 321 740 1.9 143
York, PA................. 8.9 172.5 1.1 191 805 2.8 69
Providence, RI........... 17.4 273.2 1.0 203 908 2.0 133
Charleston, SC........... 12.3 218.7 1.0 203 799 3.6 30
Greenville, SC........... 12.6 239.1 3.2 28 796 0.1 302
Horry, SC................ 7.9 121.0 1.9 124 537 0.9 254
Lexington, SC............ 5.9 101.7 2.3 91 707 2.6 82
Richland, SC............. 9.1 206.4 1.8 131 804 0.6 281
Spartanburg, SC.......... 5.8 120.0 3.3 24 811 1.5 197
York, SC................. 4.7 78.5 3.1 37 722 -0.6 320
Minnehaha, SD............ 6.7 120.1 1.8 131 772 1.2 227
Davidson, TN............. 18.8 441.2 2.8 50 928 -2.2 331
Hamilton, TN............. 8.6 187.3 1.2 183 819 1.9 143
Knox, TN................. 11.0 219.0 0.0 289 795 2.3 98
Rutherford, TN........... 4.6 109.0 (7) - 799 (7) -
Shelby, TN............... 19.2 473.7 0.0 289 945 -1.3 328
Williamson, TN........... 6.7 103.2 4.2 10 1,055 7.8 3
Bell, TX................. 4.9 110.1 1.0 203 755 2.0 133
Bexar, TX................ 36.0 773.2 3.0 42 812 1.6 183
Brazoria, TX............. 5.1 95.2 1.5 158 916 1.7 170
Brazos, TX............... 4.1 88.9 1.9 124 701 2.2 111
Cameron, TX.............. 6.3 132.7 1.6 150 572 0.7 267
Collin, TX............... 20.0 328.0 3.9 14 1,076 1.5 197
Dallas, TX............... 70.1 1,495.5 2.7 62 1,106 2.9 62
Denton, TX............... 12.0 196.2 4.1 11 822 3.9 21
El Paso, TX.............. 14.2 281.4 0.9 216 658 0.9 254
Fort Bend, TX............ 10.3 158.1 7.0 1 951 5.1 8
Galveston, TX............ 5.6 100.4 2.2 99 808 -0.9 324
Gregg, TX................ 4.2 77.7 1.4 164 838 2.9 62
Harris, TX............... 105.6 2,189.9 3.2 28 1,190 2.1 123
Hidalgo, TX.............. 11.6 234.4 2.8 50 592 1.2 227
Jefferson, TX............ 5.8 119.6 -1.9 327 925 0.1 302
Lubbock, TX.............. 7.2 128.5 2.3 91 702 1.9 143
McLennan, TX............. 4.9 103.3 1.8 131 751 1.1 237
Midland, TX.............. 5.1 85.4 6.0 2 1,150 5.1 8
Montgomery, TX........... 9.5 149.3 3.9 14 917 4.9 10
Nueces, TX............... 8.0 161.1 2.4 84 809 0.6 281
Potter, TX............... 3.9 77.7 1.9 124 736 0.8 262
Smith, TX................ 5.8 96.0 1.8 131 769 0.9 254
Tarrant, TX.............. 39.3 809.4 2.7 62 908 1.8 159
Travis, TX............... 33.3 639.7 4.8 6 1,008 0.0 305
Webb, TX................. 5.0 92.8 2.1 106 647 1.7 170
Williamson, TX........... 8.3 140.3 3.2 28 896 3.8 27
Davis, UT................ 7.5 111.9 2.0 114 737 1.7 170
Salt Lake, UT............ 38.8 609.5 3.2 28 875 2.3 98
Utah, UT................. 13.3 187.1 4.7 8 735 4.3 15
Weber, UT................ 5.5 93.3 2.0 114 700 0.7 267
Chittenden, VT........... 6.2 98.8 0.4 254 945 3.4 36
Arlington, VA............ 8.8 166.0 -1.0 319 1,525 1.5 197
Chesterfield, VA......... 7.9 123.9 3.0 42 821 1.9 143
Fairfax, VA.............. 35.2 595.9 0.4 254 1,459 2.7 76
Henrico, VA.............. 10.2 180.4 0.3 266 918 2.5 85
Loudoun, VA.............. 10.2 149.0 2.0 114 1,090 0.7 267
Prince William, VA....... 8.1 119.5 2.9 48 819 0.4 290
Alexandria City, VA...... 6.3 95.5 -0.3 306 1,323 2.3 98
Chesapeake City, VA...... 5.7 96.3 1.0 203 740 -0.4 317
Newport News City, VA.... 3.7 97.7 4.0 12 873 0.9 254
Norfolk City, VA......... 5.6 136.8 -0.9 318 888 1.3 218
Richmond City, VA........ 7.1 147.8 0.4 254 987 2.0 133
Virginia Beach City, VA.. 11.3 175.2 2.0 114 725 2.0 133
Benton, WA............... 5.9 83.3 0.1 282 932 1.1 237
Clark, WA................ 14.3 134.8 2.3 91 842 1.9 143
King, WA................. 85.2 1,205.5 2.8 50 1,202 2.9 62
Kitsap, WA............... 6.9 80.9 -0.3 306 829 0.7 267
Pierce, WA............... 22.6 271.6 2.0 114 850 1.6 183
Snohomish, WA............ 20.2 265.3 2.5 78 992 1.6 183
Spokane, WA.............. 16.5 204.3 1.5 158 779 2.1 123
Thurston, WA............. 7.9 100.4 1.8 131 834 2.2 111
Whatcom, WA.............. 7.2 83.5 2.1 106 763 -1.5 330
Yakima, WA............... 9.3 114.0 3.1 37 629 2.3 98
Kanawha, WV.............. 6.0 105.1 -0.5 311 819 0.7 267
Brown, WI................ 6.6 150.4 1.0 203 805 2.8 69
Dane, WI................. 14.4 311.3 1.1 191 925 6.0 5
Milwaukee, WI............ 24.1 474.5 0.0 289 892 1.8 159
Outagamie, WI............ 5.0 104.1 1.2 183 761 1.5 197
Waukesha, WI............. 12.6 233.7 0.9 216 905 1.2 227
Winnebago, WI............ 3.6 90.4 -1.4 323 842 1.0 248
San Juan, PR............. 11.3 258.3 -2.0 (8) 601 0.8 (8)
(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) Data do not meet BLS or state agency disclosure standards.
(8) This county was not included in the U.S. rankings.
Table 2. Covered(1) establishments, employment, and wages in the 10 largest counties,
second quarter 2013(2)
Employment Average weekly
wage(3)
Establishments,
second quarter
County by NAICS supersector 2013 Percent Percent
(thousands) June change, Second change,
2013 June quarter second
(thousands) 2012-13(4) 2013 quarter
2012-13(4)
United States(5) ............................ 9,248.7 135,094.0 1.6 $921 2.1
Private industry........................... 8,954.6 113,985.0 1.9 910 2.2
Natural resources and mining............. 132.9 2,151.6 1.3 1,033 3.4
Construction............................. 747.6 5,967.8 3.9 986 2.3
Manufacturing............................ 335.9 12,061.7 0.4 1,130 1.9
Trade, transportation, and utilities..... 1,905.5 25,608.9 1.5 781 2.1
Information.............................. 145.0 2,713.2 0.6 1,527 5.1
Financial activities..................... 819.5 7,661.7 1.8 1,360 3.1
Professional and business services....... 1,635.6 18,540.3 2.6 1,183 2.4
Education and health services............ 1,444.8 20,098.1 1.6 844 1.4
Leisure and hospitality.................. 781.8 14,776.7 2.9 379 1.3
Other services........................... 795.3 4,217.3 0.7 621 2.8
Government................................. 294.1 21,108.9 -0.4 979 1.7
Los Angeles, CA.............................. 425.8 4,070.9 2.0 1,002 0.4
Private industry........................... 420.0 3,534.9 2.7 971 0.3
Natural resources and mining............. 0.5 10.2 10.2 1,457 10.0
Construction............................. 12.3 115.9 5.4 1,053 0.7
Manufacturing............................ 12.5 367.2 -0.4 1,087 2.1
Trade, transportation, and utilities..... 52.1 766.3 1.7 833 1.3
Information.............................. 8.4 192.5 5.6 1,727 -3.0
Financial activities..................... 22.6 211.9 0.7 1,497 2.6
Professional and business services....... 43.7 586.8 3.0 1,217 -1.1
Education and health services............ 187.1 682.3 2.4 801 0.9
Leisure and hospitality.................. 28.0 443.0 5.1 542 -1.8
Other services........................... 25.3 141.2 -0.9 637 3.6
Government................................. 5.8 536.0 -2.2 1,203 1.3
Cook, IL..................................... 152.6 2,452.3 1.2 1,067 1.3
Private industry........................... 151.2 2,150.6 1.2 1,048 1.2
Natural resources and mining............. 0.1 0.9 -2.7 998 6.2
Construction............................. 12.6 65.7 2.1 1,287 3.8
Manufacturing............................ 6.6 188.8 -1.9 1,083 -1.5
Trade, transportation, and utilities..... 30.2 447.9 1.2 849 2.9
Information.............................. 2.8 54.5 -1.2 1,582 3.0
Financial activities..................... 15.8 185.6 0.2 1,819 0.3
Professional and business services....... 32.4 433.5 2.1 1,351 1.0
Education and health services............ 16.1 416.9 1.4 891 1.4
Leisure and hospitality.................. 13.6 258.2 3.2 480 2.3
Other services........................... 16.9 95.6 -1.6 798 2.6
Government................................. 1.3 301.8 1.1 1,199 2.2
New York, NY................................. 125.0 2,434.0 1.5 1,675 1.8
Private industry........................... 124.7 1,998.2 1.8 1,802 2.0
Natural resources and mining............. 0.0 0.2 4.0 2,366 49.7
Construction............................. 2.2 33.4 4.0 1,668 3.2
Manufacturing............................ 2.3 25.9 0.5 1,194 0.7
Trade, transportation, and utilities..... 21.0 256.9 1.5 1,289 5.0
Information.............................. 4.5 143.9 0.8 2,230 8.5
Financial activities..................... 19.1 351.9 -1.2 3,321 2.5
Professional and business services....... 26.3 505.1 2.8 2,040 0.9
Education and health services............ 9.5 313.5 2.6 1,145 2.5
Leisure and hospitality.................. 13.4 265.8 2.7 760 -0.3
Other services........................... 19.5 95.3 2.2 1,061 4.3
Government................................. 0.3 435.9 0.0 1,100 -0.2
Harris, TX................................... 105.6 2,189.9 3.2 1,190 2.1
Private industry........................... 105.0 1,935.5 3.5 1,214 1.9
Natural resources and mining............. 1.7 95.1 7.4 3,103 1.3
Construction............................. 6.5 146.6 5.7 1,208 4.7
Manufacturing............................ 4.6 195.1 3.1 1,450 3.4
Trade, transportation, and utilities..... 23.8 451.5 3.1 1,057 -4.9
Information.............................. 1.2 28.6 -1.2 1,371 5.0
Financial activities..................... 10.8 116.5 2.3 1,428 0.5
Professional and business services....... 21.2 375.6 3.1 1,459 6.4
Education and health services............ 14.5 260.3 2.6 921 3.6
Leisure and hospitality.................. 8.7 203.2 4.1 398 -0.3
Other services........................... 11.4 61.8 3.6 697 2.2
Government................................. 0.6 254.5 0.9 1,006 2.8
Maricopa, AZ................................. 93.4 1,678.7 2.6 919 1.5
Private industry........................... 92.7 1,502.8 3.1 903 1.5
Natural resources and mining............. 0.5 8.3 7.5 846 3.0
Construction............................. 7.4 92.4 6.2 947 1.3
Manufacturing............................ 3.1 113.6 0.0 1,329 0.1
Trade, transportation, and utilities..... 20.6 337.6 1.6 824 -0.2
Information.............................. 1.6 31.6 2.1 1,160 1.8
Financial activities..................... 10.8 148.6 5.5 1,163 4.2
Professional and business services....... 21.8 290.1 4.4 978 2.3
Education and health services............ 10.7 248.2 2.0 941 1.4
Leisure and hospitality.................. 7.3 182.4 3.8 424 1.2
Other services........................... 6.5 47.4 -0.4 631 4.3
Government................................. 0.7 175.9 -1.6 1,038 2.4
Dallas, TX................................... 70.1 1,495.5 2.7 1,106 2.9
Private industry........................... 69.6 1,332.6 2.9 1,113 2.8
Natural resources and mining............. 0.6 9.3 7.3 4,333 12.1
Construction............................. 4.0 72.2 4.5 1,027 2.8
Manufacturing............................ 2.7 109.1 -3.0 1,314 1.3
Trade, transportation, and utilities..... 15.2 300.1 2.9 1,012 2.2
Information.............................. 1.5 47.4 4.5 1,772 7.7
Financial activities..................... 8.6 148.3 4.4 1,476 2.5
Professional and business services....... 15.6 288.3 2.8 1,234 3.4
Education and health services............ 8.5 174.9 3.1 967 1.3
Leisure and hospitality.................. 6.0 142.3 5.1 451 0.9
Other services........................... 6.7 40.1 1.5 714 1.9
Government................................. 0.5 162.9 0.7 1,045 3.4
Orange, CA................................... 104.9 1,448.0 2.7 1,019 0.4
Private industry........................... 103.5 1,303.3 3.0 1,006 0.5
Natural resources and mining............. 0.2 3.4 -2.8 694 -5.4
Construction............................. 6.1 77.4 9.7 1,129 1.3
Manufacturing............................ 4.8 157.2 -0.8 1,246 1.0
Trade, transportation, and utilities..... 16.4 251.5 2.2 932 -1.1
Information.............................. 1.2 25.1 3.3 1,446 2.7
Financial activities..................... 9.8 113.3 4.8 1,566 4.1
Professional and business services....... 19.3 260.8 2.7 1,173 0.7
Education and health services............ 24.7 178.4 2.9 883 -0.3
Leisure and hospitality.................. 7.5 190.4 3.7 438 -1.8
Other services........................... 6.2 41.1 0.7 632 -1.1
Government................................. 1.4 144.7 -0.2 1,136 0.1
San Diego, CA................................ 98.6 1,310.5 1.6 1,031 4.0
Private industry........................... 97.2 1,090.4 1.9 1,014 4.8
Natural resources and mining............. 0.7 10.9 -0.5 658 4.9
Construction............................. 5.9 61.2 5.3 1,048 0.4
Manufacturing............................ 2.9 94.0 -1.1 1,448 6.8
Trade, transportation, and utilities..... 13.9 209.6 1.4 798 0.3
Information.............................. 1.1 24.2 -1.8 1,515 2.3
Financial activities..................... 8.6 71.3 2.2 1,306 9.7
Professional and business services....... 16.7 221.5 2.1 1,549 9.2
Education and health services............ 26.9 175.8 1.2 876 1.2
Leisure and hospitality.................. 7.3 171.5 3.5 423 1.9
Other services........................... 6.6 46.3 1.6 559 2.8
Government................................. 1.4 220.1 0.0 1,114 1.0
King, WA..................................... 85.2 1,205.5 2.8 1,202 2.9
Private industry........................... 84.6 1,045.7 3.2 1,208 3.1
Natural resources and mining............. 0.4 3.0 1.4 1,355 -1.4
Construction............................. 5.3 52.5 6.7 1,153 1.3
Manufacturing............................ 2.2 105.5 2.5 1,484 4.6
Trade, transportation, and utilities..... 14.4 220.5 3.7 1,064 4.3
Information.............................. 1.8 82.5 1.0 2,328 3.7
Financial activities..................... 6.3 65.1 3.0 1,445 4.5
Professional and business services....... 14.3 198.0 3.2 1,471 2.4
Education and health services............ 25.6 155.1 1.4 906 1.0
Leisure and hospitality.................. 6.5 123.4 5.4 456 2.9
Other services........................... 7.9 40.1 2.3 789 5.1
Government................................. 0.5 159.8 0.7 1,164 1.8
Miami-Dade, FL............................... 92.6 999.8 2.5 885 1.1
Private industry........................... 92.3 877.5 2.9 844 1.6
Natural resources and mining............. 0.5 7.5 -0.9 542 3.6
Construction............................. 5.2 32.3 8.8 831 3.2
Manufacturing............................ 2.6 36.2 1.8 824 3.6
Trade, transportation, and utilities..... 27.5 261.2 2.6 796 2.2
Information.............................. 1.6 17.4 3.2 1,444 5.2
Financial activities..................... 9.5 67.8 3.8 1,316 3.9
Professional and business services....... 19.5 135.8 4.2 1,026 -0.6
Education and health services............ 10.2 158.3 0.5 869 1.5
Leisure and hospitality.................. 7.0 123.9 4.0 489 -2.6
Other services........................... 8.1 36.6 1.7 565 3.9
Government................................. 0.3 122.3 -0.7 1,154 -0.2
(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,
second quarter 2013(2)
Employment Average weekly
wage(3)
Establishments,
second quarter
State 2013 Percent Percent
(thousands) June change, Second change,
2013 June quarter second
(thousands) 2012-13 2013 quarter
2012-13
United States(4)........... 9,248.7 135,094.0 1.6 $921 2.1
Alabama.................... 115.8 1,859.5 0.9 794 1.4
Alaska..................... 22.1 342.6 -0.1 970 1.6
Arizona.................... 145.8 2,438.1 1.8 877 1.7
Arkansas................... 87.2 1,150.4 -0.6 734 2.4
California................. 1,347.4 15,485.8 2.4 1,048 2.0
Colorado................... 174.3 2,359.4 2.9 933 1.6
Connecticut................ 112.8 1,666.3 1.0 1,128 1.5
Delaware................... 28.0 417.8 1.8 966 2.0
District of Columbia....... 34.9 725.0 0.9 1,575 2.1
Florida.................... 623.7 7,402.0 2.4 822 2.0
Georgia.................... 274.6 3,917.2 1.7 867 2.2
Hawaii..................... 38.7 617.0 1.9 823 1.6
Idaho...................... 53.5 642.7 2.7 683 1.9
Illinois................... 401.9 5,750.0 0.8 971 1.9
Indiana.................... 160.1 2,863.4 1.1 776 1.7
Iowa....................... 97.4 1,523.9 1.3 757 2.0
Kansas..................... 84.6 1,350.0 1.2 779 2.1
Kentucky................... 117.1 1,790.6 0.6 782 1.3
Louisiana.................. 128.1 1,894.7 0.9 824 2.4
Maine...................... 49.4 604.4 0.4 732 1.8
Maryland................... 169.6 2,570.3 0.9 1,005 1.4
Massachusetts.............. 225.0 3,352.7 1.3 1,131 2.0
Michigan................... 238.9 4,073.7 2.2 875 2.0
Minnesota.................. 171.0 2,745.2 1.9 929 2.4
Mississippi................ 70.3 1,094.9 0.7 691 1.5
Missouri................... 180.0 2,668.2 1.2 803 1.6
Montana.................... 43.2 448.4 1.5 717 2.4
Nebraska................... 69.8 941.0 0.9 737 2.6
Nevada..................... 74.2 1,168.3 2.3 829 1.7
New Hampshire.............. 49.3 629.1 0.8 916 2.9
New Jersey................. 263.6 3,917.5 1.0 1,084 2.6
New Mexico................. 55.1 795.0 0.4 781 -0.3
New York................... 615.1 8,804.9 1.1 1,118 2.0
North Carolina............. 256.4 3,985.1 1.7 808 2.5
North Dakota............... 30.6 433.7 3.2 887 3.7
Ohio....................... 287.7 5,162.3 1.1 830 1.7
Oklahoma................... 105.6 1,560.7 0.9 794 3.5
Oregon..................... 134.6 1,708.0 2.5 848 1.3
Pennsylvania............... 346.0 5,665.9 0.3 918 2.8
Rhode Island............... 35.5 465.5 1.0 880 2.3
South Carolina............. 116.5 1,864.9 1.8 747 1.5
South Dakota............... 31.7 417.0 1.0 689 1.8
Tennessee.................. 143.4 2,709.3 1.5 820 0.5
Texas...................... 606.1 11,078.8 2.7 944 2.4
Utah....................... 87.0 1,259.7 2.8 783 2.2
Vermont.................... 24.5 303.1 0.3 808 2.7
Virginia................... 239.6 3,685.4 0.7 968 1.7
Washington................. 243.6 3,013.3 2.2 969 2.4
West Virginia.............. 49.8 713.1 -0.1 781 0.6
Wisconsin.................. 162.1 2,768.2 0.6 801 3.0
Wyoming.................... 25.5 290.4 0.4 845 0.5
Puerto Rico................ 48.9 926.1 -1.1 503 1.0
Virgin Islands............. 3.4 38.9 -3.0 706 -13.8
(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
