For release 10:00 a.m. (EST), Wednesday, January 13, 2010 USDL-10-0009
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 2009
From June 2008 to June 2009, employment declined in 324 of the 334
largest U.S. counties according to preliminary data, the U.S. Bureau
of Labor Statistics reported today. Elkhart County, Ind., located
about 100 miles east of Chicago, posted the largest percentage
decline, with a loss of 21.9 percent over the year, compared with a
national job decrease of 5.1 percent. Nearly 70 percent of the
employment decline in Elkhart occurred in manufacturing, which lost
18,400 jobs over the year (-32.2 percent). Yakima County, Wash.,
experienced the largest over-the-year percentage increase in
employment among the largest counties in the U.S., with a gain of 1.5
percent.
The U.S. average weekly wage fell over the year by 0.1 percent in the
second quarter of 2009. This is the second consecutive over-the-year
decline in average weekly wages and one of only four declines dating
back to 1978, when these quarterly data were first comparable. (See
Technical Note.) Large employment and wage losses in both the
financial activities and manufacturing supersectors contributed
significantly to the overall decline in the U.S. average weekly wages
this quarter. Average weekly wages fell 1.8 percent in financial
activities and 0.3 percent in manufacturing. Among the large counties
in the U.S., Weld County, Colo., had the largest over-the-year
decrease in average weekly wages in the second quarter of 2009, with
a loss of 9.0 percent. Within Weld, trade, transportation, and
utilities had the largest over-the-year decline in average weekly
wages with a loss of 32.0 percent. Olmsted, Minn., experienced the
largest growth in average weekly wages with a gain of 10.8 percent.
Table A. Top 10 large counties ranked by June 2009 employment, June 2008-09 employment
decrease, and June 2008-09 percent decrease in employment
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Employment in large counties
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June 2009 employment | Decrease in employment, | Percent decrease in employment,
(thousands) | June 2008-09 | June 2008-09
| (thousands) |
--------------------------------------------------------------------------------------------------------
| |
United States 129,674.8| United States -6,941.9| United States -5.1
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| |
Los Angeles, Calif. 3,947.3| Los Angeles, Calif. -256.7| Elkhart, Ind. -21.9
Cook, Ill. 2,395.8| Maricopa, Ariz. -149.9| Macomb, Mich. -13.2
New York, N.Y. 2,280.5| Cook, Ill. -137.7| Trumbull, Ohio -12.2
Harris, Texas 2,009.3| Orange, Calif. -119.7| Wayne, Mich. -11.6
Maricopa, Ariz. 1,588.7| New York, N.Y. -113.2| Collier, Fla. -11.3
Dallas, Texas 1,416.7| Clark, Nev. -98.5| Ottawa, Mich. -11.0
Orange, Calif. 1,380.6| Wayne, Mich. -85.5| Clark, Nev. -10.7
San Diego, Calif. 1,258.2| San Diego, Calif. -77.5| Washoe, Nev. -10.5
King, Wash. 1,138.3| Dallas, Texas -71.6| Oakland, Mich. -9.6
Miami-Dade, Fla. 932.3| Oakland, Mich. -65.6| Sarasota, Fla. -9.2
| |
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Of the 334 largest counties in the United States (as measured by 2008
annual average employment), 157 had over-the-year percentage declines
in employment greater than or equal to the national average (-5.1
percent) in June 2009; 167 large counties experienced smaller
declines than the national average, while 2 counties experienced no
change and 3 counties experienced employment gains. The percent
change in average weekly wages was equal to or lower than the
national average (-0.1 percent) in 140 of the largest U.S. counties
and was above the national average in 190 counties.
The employment and average weekly wage data by county are compiled
under the Quarterly Census of Employment and Wages (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 9.1 million employer reports cover 129.7 million full- and
part-time workers.
Large County Employment
In June 2009, national employment, as measured by the QCEW program,
was 129.7 million, down by 5.1 percent from June 2008. The 334 U.S.
counties with 75,000 or more employees accounted for 71.2 percent of
total U.S. employment and 76.6 percent of total wages. These 334
counties had a net job decline of 5,117,900 over the year, accounting
for 73.7 percent of the overall U.S. employment decrease.
Employment declined in 324 counties from June 2008 to June 2009. The
largest percentage decline in employment was in Elkhart, Ind. (-21.9
percent). Macomb, Mich., had the next largest percentage decline (-
13.2 percent), followed by the counties of Trumbull, Ohio (-12.2
percent), Wayne, Mich. (-11.6 percent), and Collier, Fla. (-11.3
percent). The largest decline in employment levels occurred in Los
Angeles, Calif. (-256,700), followed by the counties of Maricopa,
Ariz. (-149,900), Cook, Ill. (-137,700), Orange, Calif. (-119,700),
and New York, N.Y. (-113,200). (See table A.) Combined employment
losses in these five counties over the year totaled 777,200 or
11.2 percent of the employment decline for the U.S. as a whole.
Employment rose in three of the large counties from June 2008 to June
2009. None of the large counties grew by more than two percent over
the year. Yakima, Wash., had the largest over-the-year percentage
increase in employment (1.5 percent) among the largest counties in
the U.S. Arlington, Va., had the next largest increase (1.4 percent),
followed by Bronx, N.Y. (1.2 percent). The largest gains in the level
of employment from June 2008 to June 2009 were recorded in the
counties of Bronx, N.Y. (2,800), Arlington, Va. (2,300), and Yakima,
Wash. (1,600).
Table B. Top 10 large counties ranked by second quarter 2009 average weekly wages, second quarter 2008-09
decrease in average weekly wages, and second quarter 2008-09 percent decrease in average weekly wages
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Average weekly wage in large counties
--------------------------------------------------------------------------------------------------------
Average weekly wage, | Decrease in average weekly | Percent decrease in average
second quarter 2009 | wage, second quarter 2008-09 | weekly wage, second
| | quarter 2008-09
--------------------------------------------------------------------------------------------------------
| |
United States $840| United States -$1| United States -0.1
--------------------------------------------------------------------------------------------------------
| |
New York, N.Y. $1,520| Santa Clara, Calif. -$79| Weld, Colo. -9.0
Santa Clara, Calif. 1,449| Weld, Colo. -68| Trumbull, Ohio -7.6
Arlington, Va. 1,423| Douglas, Colo. -55| Douglas, Colo. -6.1
Washington, D.C. 1,421| Trumbull, Ohio -53| Brazoria, Texas -5.3
Fairfax, Va. 1,348| New York, N.Y. -49| Santa Clara, Calif. -5.2
Fairfield, Conn. 1,316| Brazoria, Texas -44| Rock Island, Ill. -4.8
San Mateo, Calif. 1,309| Middlesex, Mass. -43| Montgomery, Texas -4.1
San Francisco, Calif. 1,307| Hennepin, Minn. -42| Oakland, Mich. -3.9
Suffolk, Mass. 1,299| Rock Island, Ill. -41| Hennepin, Minn. -3.9
Somerset, N.J. 1,244| Somerset, N.J. -41| Catawba, N.C. -3.8
| |
| |
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Large County Average Weekly Wages
Average weekly wages for the nation fell 0.1 percent over the year in
the second quarter of 2009. This is the second consecutive over-the-
year decline in average weekly wages and one of only four declines
dating back to 1978. Among the 334 largest counties, 140 had over-
the-year decreases in average weekly wages in the second quarter. The
largest wage loss occurred in Weld, Colo., with a decline of 9.0
percent from the second quarter of 2008. Trumbull, Ohio, had the
second largest decline (-7.6 percent), followed by the counties of
Douglas, Colo. (-6.1 percent), Brazoria, Texas (-5.3 percent), and
Santa Clara, Calif. (-5.2 percent). (See table B.)
Of the 334 largest counties, 175 experienced growth in average weekly
wages. Olmsted, Minn., led the nation in growth in average weekly
wages with an increase of 10.8 percent from the second quarter of
2008. Large wage gains occurred in the education and health services
supersector where average weekly wages grew 19.9 percent over the
year. Saginaw, Mich., and Kitsap, Wash., were second with a gain of
5.1 percent each, followed by the counties of Madison, Ala. (5.0
percent) and Newport News City, Va. (4.9 percent).
The national average weekly wage in the second quarter of 2009 was
$840. Average weekly wages were higher than the national average in
109 of the 334 largest U.S. counties. New York, N.Y., held the top
position among the highest-paid large counties with an average weekly
wage of $1,520. Santa Clara, Calif., was second with an average
weekly wage of $1,449, followed by Arlington, Va. ($1,423),
Washington, D.C. ($1,421), and Fairfax, Va. ($1,348). There were 225
counties with an average weekly wage below the national average in
the second quarter of 2009. The lowest average weekly wage was
reported in Horry, S.C. ($520), followed by the counties of Cameron,
Texas, and Hidalgo, Texas ($544 each), Webb, Texas ($558), and
Yakima, Wash. ($589). (See table 1.)
Average weekly wages are affected not only by changes in total wages
but also by employment changes in high- and low-paying industries.
(See Technical Note.) The 0.1-percent over-the-year decrease in
average weekly wages for the nation was partially due to large
employment declines in high-paying industries such as manufacturing.
(See table 2.)
Ten Largest U.S. Counties
All of the 10 largest counties (based on 2008 annual average
employment levels) experienced over-the-year percent declines in
employment in June 2009. Maricopa, Ariz., experienced the largest
decline in employment among the 10 largest counties with an 8.6
percent decrease. Within Maricopa, every private industry group
except education and health services experienced an employment
decline, with construction experiencing the largest decline (-31.5
percent). (See table 2.) Orange, Calif., had the next largest decline
in employment, -8.0 percent, followed by Los Angeles, Calif. (-6.1
percent). Harris, Texas, experienced the smallest decline in
employment (-3.1 percent) among the 10 largest counties. New York,
N.Y. (-4.7 percent), and Dallas, Texas (-4.8 percent), had the second
and third smallest employment losses, respectively.
Seven of the 10 largest U.S. counties saw an over-the-year decrease
in average weekly wages. New York, N.Y., experienced the largest
decline in average weekly wages among the 10 largest counties with a
decrease of 3.1 percent. Within New York County, financial activities
sustained the largest total wage loss (-$1.9 billion) over the year.
Average weekly wages for this supersector fell by 5.4 percent. New
York’s average weekly wage loss was followed by Harris, Texas (-2.5
percent), and San Diego, Calif. (-1.5 percent). King, Wash., had the
only wage increase (2.0 percent). Maricopa, Ariz., and Orange,
Calif., both held the second highest position with average weekly
wages unchanged over the year.
Largest County by State
Table 3 shows June 2009 employment and the 2009 second quarter
average weekly wage in the largest county in each state, which is
based on 2008 annual average employment levels. The employment levels
in the counties in table 3 in June 2009 ranged from approximately
four million in Los Angeles County, Calif., to 43,500 in Laramie
County, Wyo. The highest average weekly wage of these counties was in
New York, N.Y. ($1,520), while the lowest average weekly wage was in
Minnehaha, S.D. ($688).
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 2008. June 2009 employment and 2009 second-
quarter average weekly wages for all states are provided in table 4
of this release.
For additional information about the quarterly employment and wages
data, please read the Technical Note. Data for the second quarter of
2009 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 2009 is
scheduled to be released on Thursday, April 1, 2010.
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 2007 North American Industry Classification System. Data
for 2009 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 2008 preliminary annual averages of employment.
For 2009 data, two counties have been added to the publication tables: Johnson,
Iowa, and Gregg, Texas. These counties will be included in all 2009 quarterly re-
leases. Two counties, Boone, Ky., and St. Tammany, La., which were published in the
2008 releases, will be excluded from this and future 2009 releases because their
2008 annual average employment levels were less than 75,000. The counties in table
2 are selected and sorted each year based on the annual average employment from the
preceding year.
The preliminary QCEW data presented in this release may differ from data released
by the individual states. These potential differences result from the states' 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- | 400,000 establish-
| submitted by 9.1 | ministrative records| ments
| million establish- | submitted by 6.8 |
| ments in the first | million private-sec-|
| quarter 2009 | 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 | -7 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 annu-
| new quarter of UI | dinal database and | ally realigns (bench-
| data | directly summarizes | marks) sample esti-
| | gross job gains and | mates to first quar-
| | losses | ter UI levels
-----------|---------------------|----------------------|------------------------
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 2008. 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 2008, UI and UCFE programs covered workers in 134.8 million jobs. The estimated
129.4 million workers in these jobs (after adjustment for multiple jobholders)
represented 95.5 percent of civilian wage and salary employment. Covered workers
received $6.142 trillion in pay, representing 93.8 percent of the wage and salary
component of personal income and 42.5 percent of the gross domestic product.
Major exclusions from UI coverage include self-employed workers, most agricultural
workers on small farms, all members of the Armed Forces, elected officials in most
states, most employees of railroads, some domestic workers, most student workers at
schools, and employees of certain small nonprofit organizations.
State and federal UI laws change periodically. These changes may have an impact on
the employment and wages reported by employers covered under the UI program. 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 work force 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.
Federal government pay levels are subject to periodic, sometimes large, fluctua-
tions due to a calendar effect that consists of some quarters having more pay pe-
riods than others. Most federal employees are paid on a biweekly pay schedule. As a
result of this schedule, in some quarters, federal wages contain payments for six
pay periods, while in other quarters their wages include payments for seven pay pe-
riods. Over-the-year comparisons of average weekly wages may reflect this calendar
effect. Higher growth in average weekly wages may be attributed, in part, to a com-
parison of quarterly wages for the current year, which include seven pay periods,
with year-ago wages that reflect only six pay periods. An opposite effect will oc-
cur when wages in the current period, which contain six pay periods, are compared
with year-ago wages that include seven pay periods. The effect on over-the-year pay
comparisons can be pronounced in federal government due to the uniform nature of
federal payroll processing. This pattern may exist in private sector pay; however,
because there are more pay period types (weekly, biweekly, semimonthly, monthly) it
is less pronounced. The effect is most visible in counties with large concentra-
tions of federal employment.
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 4-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 2008 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.
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
An annual bulletin, Employment and Wages, features comprehensive information by de-
tailed industry on establishments, employment, and wages for the nation and all
states. The 2007 edition of this bulletin contains selected data produced by Busi-
ness Employment Dynamics (BED) on job gains and losses, as well as selected data
from the first quarter 2008 version of this news release. Tables and additional
content from the 2007 Employment and Wages Annual Bulletin are now available online
at http://www.bls.gov/cew/cewbultn07.htm. These tables present final 2007 annual
averages. The tables are included on the CD which accompanies the hardcopy version
of the Annual Bulletin. Employment and Wages Annual Averages, 2007 is available
for sale as a chartbook from the United States Government Printing Office, Superin-
tendent of Documents, P.O. Box 371954, Pittsburgh, PA 15250, telephone (866) 512-
1800, outside Washington, D.C. Within Washington, D.C., the telephone number is
(202) 512-1800. The fax number is (202) 512-2104.
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 2009(2)
Employment Average weekly wage(4)
Establishments,
County(3) second quarter Percent Ranking Percent Ranking
2009 June change, by Average change, by
(thousands) 2009 June percent weekly second percent
(thousands) 2008-09(5) change wage quarter change
2008-09(5)
United States(6)......... 9,055.3 129,674.8 -5.1 - $840 -0.1 -
Jefferson, AL............ 18.3 337.9 -7.0 269 845 0.6 132
Madison, AL.............. 8.8 179.7 -2.1 24 938 5.0 4
Mobile, AL............... 9.8 165.7 -6.5 257 737 4.4 6
Montgomery, AL........... 6.4 131.4 -5.6 208 736 0.1 169
Shelby, AL............... 4.9 71.4 -6.9 264 795 2.7 22
Tuscaloosa, AL........... 4.3 80.4 -7.1 272 719 -0.6 229
Anchorage Borough, AK.... 8.1 148.4 -1.8 22 948 3.6 11
Maricopa, AZ............. 98.2 1,588.7 -8.6 309 846 0.0 176
Pima, AZ................. 20.2 341.4 -6.7 260 752 0.5 145
Benton, AR............... 5.5 91.0 -5.3 186 806 2.4 29
Pulaski, AR.............. 15.0 243.6 -3.8 104 781 2.5 27
Washington, AR........... 5.6 89.6 -4.0 111 710 1.7 55
Alameda, CA.............. 52.9 640.5 -7.2 278 1,092 -0.3 206
Butte, CA................ 7.8 71.8 -5.8 219 666 3.9 8
Contra Costa, CA......... 29.5 325.0 -5.9 223 1,072 1.6 64
Fresno, CA............... 30.0 344.1 -6.9 264 689 0.6 132
Kern, CA................. 17.7 272.7 -4.9 160 764 1.9 42
Los Angeles, CA.......... 419.7 3,947.3 -6.1 239 940 -0.6 229
Marin, CA................ 11.6 103.3 -6.7 260 1,042 -2.3 298
Monterey, CA............. 12.6 181.7 -3.0 59 748 -0.5 226
Orange, CA............... 100.1 1,380.6 -8.0 300 953 0.0 176
Placer, CA............... 10.7 127.0 -8.8 313 821 0.0 176
Riverside, CA............ 47.0 570.5 -8.8 313 721 0.4 152
Sacramento, CA........... 53.3 604.9 -5.0 168 948 0.3 164
San Bernardino, CA....... 49.0 610.6 -7.6 294 744 0.5 145
San Diego, CA............ 96.6 1,258.2 -5.8 219 912 -1.5 274
San Francisco, CA........ 51.3 545.0 -5.5 200 1,307 -2.1 294
San Joaquin, CA.......... 17.5 220.0 -5.7 215 740 0.5 145
San Luis Obispo, CA...... 9.6 100.8 -5.9 223 726 1.4 76
San Mateo, CA............ 23.7 323.3 -6.3 247 1,309 1.6 64
Santa Barbara, CA........ 14.2 184.5 -5.5 200 811 1.4 76
Santa Clara, CA.......... 60.0 853.5 -7.1 272 1,449 -5.2 326
Santa Cruz, CA........... 8.9 100.5 -3.6 89 754 -0.1 191
Solano, CA............... 9.9 123.3 -4.4 132 859 0.9 111
Sonoma, CA............... 18.4 179.3 -8.0 300 813 -1.6 279
Stanislaus, CA........... 14.7 168.2 -6.5 257 732 1.9 42
Tulare, CA............... 9.4 152.1 -7.3 283 599 1.7 55
Ventura, CA.............. 23.4 305.3 -5.5 200 885 1.3 86
Yolo, CA................. 5.9 99.8 -3.6 89 824 1.6 64
Adams, CO................ 9.1 153.4 -5.2 178 763 -0.7 237
Arapahoe, CO............. 19.3 275.7 -4.1 115 965 0.4 152
Boulder, CO.............. 12.9 153.3 -5.5 200 970 0.8 119
Denver, CO............... 25.5 424.1 -6.0 233 1,011 -1.0 256
Douglas, CO.............. 9.5 92.2 -4.7 147 850 -6.1 328
El Paso, CO.............. 17.2 236.9 -4.9 160 787 1.9 42
Jefferson, CO............ 18.3 206.3 -4.7 147 858 -2.3 298
Larimer, CO.............. 10.2 128.9 -4.1 115 723 -0.6 229
Weld, CO................. 6.0 79.5 -6.3 247 686 -9.0 330
Fairfield, CT............ 33.0 404.6 -5.3 186 1,316 -0.8 244
Hartford, CT............. 25.5 491.8 -4.6 140 1,014 0.1 169
New Haven, CT............ 22.6 352.8 -4.7 147 906 0.9 111
New London, CT........... 7.0 128.4 -4.1 115 880 -0.2 198
New Castle, DE........... 18.1 268.1 -5.7 215 959 -0.3 206
Washington, DC........... 33.7 690.9 -0.1 6 1,421 -0.9 250
Alachua, FL.............. 6.6 115.2 -4.7 147 713 2.7 22
Brevard, FL.............. 14.7 190.3 -6.3 247 820 1.9 42
Broward, FL.............. 62.9 684.6 -7.6 294 805 0.8 119
Collier, FL.............. 11.9 104.5 -11.3 325 767 -2.3 298
Duval, FL................ 26.7 434.4 -6.0 233 815 1.0 101
Escambia, FL............. 8.0 117.3 -6.2 242 688 2.1 37
Hillsborough, FL......... 37.1 562.9 -7.8 298 821 1.9 42
Lake, FL................. 7.3 76.7 -6.9 264 607 -1.6 279
Lee, FL.................. 18.9 187.8 -8.8 313 720 -1.1 261
Leon, FL................. 8.1 137.1 -3.5 85 722 1.0 101
Manatee, FL.............. 9.2 106.5 -6.0 233 665 -1.6 279
Marion, FL............... 8.1 91.5 -8.7 312 626 0.5 145
Miami-Dade, FL........... 83.9 932.3 -5.9 223 833 -0.6 229
Okaloosa, FL............. 6.0 77.1 -2.5 37 722 3.0 17
Orange, FL............... 35.2 638.2 -7.4 287 766 0.1 169
Palm Beach, FL........... 49.3 491.0 -7.8 298 837 -0.1 191
Pasco, FL................ 9.8 89.1 -6.3 247 624 -3.7 320
Pinellas, FL............. 31.0 390.8 -7.5 292 742 1.0 101
Polk, FL................. 12.6 185.9 -6.5 257 663 0.0 176
Sarasota, FL............. 14.8 130.7 -9.2 320 727 -0.1 191
Seminole, FL............. 14.2 158.6 -8.8 313 732 -1.7 284
Volusia, FL.............. 13.7 147.1 -7.4 287 635 -0.2 198
Bibb, GA................. 4.7 80.1 -7.1 272 668 3.7 10
Chatham, GA.............. 7.7 129.6 -5.5 200 725 0.7 126
Clayton, GA.............. 4.4 108.3 -4.5 139 765 0.0 176
Cobb, GA................. 20.6 298.7 -7.1 272 881 0.9 111
De Kalb, GA.............. 17.7 279.6 -6.1 239 889 0.8 119
Fulton, GA............... 39.2 696.1 -6.4 255 1,087 0.6 132
Gwinnett, GA............. 23.8 297.5 -7.4 287 819 -2.4 303
Muscogee, GA............. 4.8 92.0 -5.2 178 675 0.7 126
Richmond, GA............. 4.7 98.0 -3.6 89 715 (7) -
Honolulu, HI............. 24.9 434.7 -3.7 96 802 1.6 64
Ada, ID.................. 14.7 195.9 -8.1 303 734 -1.6 279
Champaign, IL............ 4.2 89.0 -4.1 115 739 3.2 15
Cook, IL................. 142.0 2,395.8 -5.4 195 986 -1.4 270
Du Page, IL.............. 36.2 556.9 -6.9 264 958 -2.4 303
Kane, IL................. 12.9 198.0 -7.2 278 754 0.0 176
Lake, IL................. 21.3 324.1 -6.1 239 1,042 -0.2 198
McHenry, IL.............. 8.6 98.5 -7.3 283 706 -3.4 316
McLean, IL............... 3.7 84.8 -2.5 37 825 2.4 29
Madison, IL.............. 6.0 90.7 -6.2 242 699 0.7 126
Peoria, IL............... 4.8 99.3 -7.1 272 784 -0.6 229
Rock Island, IL.......... 3.5 75.9 -5.9 223 822 -4.8 325
St. Clair, IL............ 5.5 94.6 -3.3 74 713 3.5 13
Sangamon, IL............. 5.3 128.3 -2.3 28 862 2.4 29
Will, IL................. 14.2 192.2 -5.0 168 748 -1.7 284
Winnebago, IL............ 7.0 125.8 -8.6 309 706 -0.8 244
Allen, IN................ 9.0 167.2 -8.0 300 703 -0.4 217
Elkhart, IN.............. 4.9 93.7 -21.9 329 686 -2.4 303
Hamilton, IN............. 7.9 109.5 -6.4 255 787 -1.0 256
Lake, IN................. 10.3 185.3 -5.6 208 721 -3.2 312
Marion, IN............... 24.0 545.2 -5.3 186 850 0.4 152
St. Joseph, IN........... 6.1 113.9 -8.1 303 713 0.4 152
Tippecanoe, IN........... 3.3 71.8 -5.4 195 716 -0.7 237
Vanderburgh, IN.......... 4.8 103.5 -4.0 111 706 1.1 93
Johnson, IA.............. 3.5 74.7 -1.7 20 779 2.5 27
Linn, IA................. 6.3 125.3 -1.6 17 796 0.6 132
Polk, IA................. 14.8 271.9 -2.8 49 823 0.1 169
Scott, IA................ 5.3 85.4 -6.3 247 668 -0.1 191
Johnson, KS.............. 20.7 304.6 -4.9 160 871 -1.6 279
Sedgwick, KS............. 12.3 247.4 -6.3 247 789 0.4 152
Shawnee, KS.............. 4.9 94.4 -3.0 59 735 2.7 22
Wyandotte, KS............ 3.2 79.0 -3.4 80 808 -0.4 217
Fayette, KY.............. 9.3 170.5 -4.9 160 785 1.6 64
Jefferson, KY............ 22.1 413.2 -5.1 173 823 0.4 152
Caddo, LA................ 7.4 122.1 -2.6 41 719 0.0 176
Calcasieu, LA............ 4.9 85.5 -3.5 85 722 -1.2 263
East Baton Rouge, LA..... 14.4 256.1 -1.7 20 805 1.8 50
Jefferson, LA............ 13.8 195.6 -2.8 49 781 0.8 119
Lafayette, LA............ 8.9 131.2 -3.6 89 793 -2.1 294
Orleans, LA.............. 10.4 168.9 -1.2 12 913 -0.9 250
Cumberland, ME........... 12.2 170.2 -4.0 111 756 0.0 176
Anne Arundel, MD......... 14.5 229.3 -3.4 80 912 1.9 42
Baltimore, MD............ 21.6 368.3 -3.8 104 873 1.4 76
Frederick, MD............ 6.0 93.0 -3.3 74 824 1.7 55
Harford, MD.............. 5.7 82.3 -2.8 49 782 2.9 18
Howard, MD............... 8.8 146.7 -3.3 74 1,009 2.6 26
Montgomery, MD........... 32.8 449.4 -2.4 32 1,129 1.5 69
Prince Georges, MD....... 15.9 308.3 -3.0 59 932 0.6 132
Baltimore City, MD....... 13.9 328.9 -3.1 64 1,012 1.5 69
Barnstable, MA........... 9.0 97.4 -4.1 115 727 0.6 132
Bristol, MA.............. 15.3 210.3 -5.5 200 776 0.4 152
Essex, MA................ 20.7 296.2 -3.3 74 891 -1.3 268
Hampden, MA.............. 14.5 194.8 -3.6 89 778 1.8 50
Middlesex, MA............ 47.2 801.2 -4.4 132 1,194 -3.5 318
Norfolk, MA.............. 23.3 314.7 -4.0 111 994 -1.7 284
Plymouth, MA............. 13.5 174.4 -3.7 96 842 1.8 50
Suffolk, MA.............. 21.7 576.0 -3.8 104 1,299 -1.0 256
Worcester, MA............ 20.5 311.3 -4.4 132 858 -1.3 268
Genesee, MI.............. 7.6 126.0 -9.1 319 720 -0.7 237
Ingham, MI............... 6.6 151.4 -6.9 264 828 1.1 93
Kalamazoo, MI............ 5.5 109.3 -6.3 247 767 -0.8 244
Kent, MI................. 14.1 305.9 -8.5 308 767 -0.4 217
Macomb, MI............... 17.3 268.9 -13.2 328 849 -3.6 319
Oakland, MI.............. 38.3 618.3 -9.6 321 955 -3.9 322
Ottawa, MI............... 5.6 98.5 -11.0 324 686 -2.0 293
Saginaw, MI.............. 4.3 78.5 -7.7 296 725 5.1 2
Washtenaw, MI............ 8.0 178.5 -4.8 154 898 -0.2 198
Wayne, MI................ 31.4 654.9 -11.6 326 920 -3.1 309
Anoka, MN................ 7.5 109.4 -5.5 200 838 -0.2 198
Dakota, MN............... 10.3 171.1 -4.2 123 852 0.6 132
Hennepin, MN............. 42.8 811.1 -4.9 160 1,027 -3.9 322
Olmsted, MN.............. 3.4 89.4 -2.6 41 953 10.8 1
Ramsey, MN............... 14.8 319.1 -5.2 178 931 1.3 86
St. Louis, MN............ 5.8 93.7 -5.9 223 694 -2.3 298
Stearns, MN.............. 4.4 77.9 -5.1 173 680 2.9 18
Harrison, MS............. 4.6 83.7 -4.7 147 669 2.0 40
Hinds, MS................ 6.2 125.4 -1.6 17 746 2.1 37
Boone, MO................ 4.4 81.3 -2.7 45 678 2.3 34
Clay, MO................. 5.0 88.5 -2.3 28 788 -0.9 250
Greene, MO............... 8.1 148.7 -5.3 186 664 0.5 145
Jackson, MO.............. 18.5 357.1 (7) - 862 -0.3 206
St. Charles, MO.......... 8.2 121.2 -4.4 132 704 0.0 176
St. Louis, MO............ 32.1 580.7 -5.8 219 893 -1.4 270
St. Louis City, MO....... 8.5 220.3 (7) - 899 (7) -
Yellowstone, MT.......... 5.8 77.3 -1.6 17 690 0.0 176
Douglas, NE.............. 15.8 314.2 -2.8 49 783 -0.8 244
Lancaster, NE............ 8.1 154.7 -3.1 64 676 0.7 126
Clark, NV................ 49.9 820.9 -10.7 323 793 -0.4 217
Washoe, NV............... 14.5 188.8 -10.5 322 797 1.1 93
Hillsborough, NH......... 12.1 189.0 -5.0 168 913 -1.8 288
Rockingham, NH........... 10.8 135.0 -4.6 140 810 -0.9 250
Atlantic, NJ............. 7.0 141.1 -7.5 292 754 0.0 176
Bergen, NJ............... 34.5 434.1 -4.6 140 1,032 0.1 169
Burlington, NJ........... 11.5 200.7 -3.1 64 892 -2.2 297
Camden, NJ............... 13.1 200.4 -5.3 186 863 -0.8 244
Essex, NJ................ 21.4 345.8 -4.4 132 1,066 0.4 152
Gloucester, NJ........... 6.4 101.9 -4.6 140 778 0.0 176
Hudson, NJ............... 14.1 232.0 -3.5 85 1,154 1.1 93
Mercer, NJ............... 11.2 226.8 -3.2 69 1,103 1.0 101
Middlesex, NJ............ 22.1 384.0 -5.2 178 1,040 -0.4 217
Monmouth, NJ............. 20.9 256.4 -4.6 140 893 0.1 169
Morris, NJ............... 18.1 278.1 -4.1 115 1,188 -0.7 237
Ocean, NJ................ 12.4 154.4 -3.6 89 714 0.1 169
Passaic, NJ.............. 12.6 170.0 -6.2 242 899 1.2 90
Somerset, NJ............. 10.3 170.4 -4.6 140 1,244 -3.2 312
Union, NJ................ 15.0 220.5 -6.2 242 1,054 -0.1 191
Bernalillo, NM........... 17.6 319.0 -4.8 154 763 1.7 55
Albany, NY............... 9.9 224.5 -2.6 41 907 2.7 22
Bronx, NY................ 16.3 232.5 1.2 3 828 0.5 145
Broome, NY............... 4.5 94.1 -3.2 69 692 0.6 132
Dutchess, NY............. 8.3 113.6 -3.5 85 899 1.9 42
Erie, NY................. 23.6 452.5 -3.0 59 746 -0.3 206
Kings, NY................ 47.6 480.2 -0.5 7 733 0.5 145
Monroe, NY............... 18.0 373.6 -3.7 96 835 1.7 55
Nassau, NY............... 52.3 597.8 -2.6 41 977 1.0 101
New York, NY............. 118.6 2,280.5 -4.7 147 1,520 -3.1 309
Oneida, NY............... 5.3 110.4 -2.4 32 683 0.4 152
Onondaga, NY............. 12.8 247.0 -3.9 108 797 1.3 86
Orange, NY............... 10.0 130.7 -2.7 45 773 2.8 20
Queens, NY............... 44.1 497.6 -2.8 49 826 -1.5 274
Richmond, NY............. 8.8 93.6 -1.2 12 745 -1.5 274
Rockland, NY............. 9.9 114.9 -3.3 74 911 -0.4 217
Saratoga, NY............. 5.4 77.5 -2.4 32 720 0.4 152
Suffolk, NY.............. 50.4 620.0 -3.8 104 921 -0.2 198
Westchester, NY.......... 36.2 411.0 -4.4 132 1,114 -2.3 298
Buncombe, NC............. 8.0 109.6 -5.4 195 658 -0.2 198
Catawba, NC.............. 4.6 77.6 -8.9 317 639 -3.8 321
Cumberland, NC........... 6.3 119.7 0.0 4 693 2.1 37
Durham, NC............... 7.1 180.7 -2.5 37 1,090 -1.9 290
Forsyth, NC.............. 9.2 176.7 -5.3 186 771 1.2 90
Guilford, NC............. 14.7 258.8 -7.2 278 746 -0.3 206
Mecklenburg, NC.......... 33.2 534.4 -5.9 223 937 -1.1 261
New Hanover, NC.......... 7.4 97.9 -5.6 208 697 1.5 69
Wake, NC................. 29.1 433.2 -4.2 123 833 -0.7 237
Cass, ND................. 5.8 99.8 -1.5 16 710 1.4 76
Butler, OH............... 7.4 136.9 -7.3 283 734 -0.7 237
Cuyahoga, OH............. 37.1 697.5 -6.2 242 849 -2.4 303
Franklin, OH............. 29.6 654.0 -4.3 126 818 0.2 168
Hamilton, OH............. 23.7 496.9 -4.9 160 897 0.3 164
Lake, OH................. 6.6 95.1 -7.4 287 703 1.0 101
Lorain, OH............... 6.2 94.0 -7.2 278 674 -1.9 290
Lucas, OH................ 10.6 197.2 -8.4 306 732 1.7 55
Mahoning, OH............. 6.3 97.4 -6.0 233 615 0.8 119
Montgomery, OH........... 12.7 243.8 -7.4 287 756 -0.3 206
Stark, OH................ 9.0 151.5 -6.3 247 649 -1.2 263
Summit, OH............... 14.8 256.9 -6.8 263 767 0.0 176
Trumbull, OH............. 4.7 67.3 -12.2 327 645 -7.6 329
Warren, OH............... 4.2 77.5 -2.7 45 696 0.6 132
Oklahoma, OK............. 23.8 410.4 -3.6 89 765 -1.5 274
Tulsa, OK................ 19.6 333.8 -5.0 168 763 -0.5 226
Clackamas, OR............ 12.6 141.5 -7.2 278 778 -0.3 206
Jackson, OR.............. 6.5 77.0 -7.1 272 659 1.5 69
Lane, OR................. 10.9 137.6 -9.0 318 675 0.9 111
Marion, OR............... 9.3 136.8 -5.2 178 696 2.8 20
Multnomah, OR............ 28.0 424.6 -5.9 223 868 0.6 132
Washington, OR........... 16.0 234.0 -7.0 269 941 -0.2 198
Allegheny, PA............ 35.0 678.2 -2.9 57 892 -0.6 229
Berks, PA................ 9.1 161.1 -5.5 200 784 1.7 55
Bucks, PA................ 19.8 254.3 -5.6 208 837 -0.9 250
Butler, PA............... 4.8 79.4 -2.8 49 723 -1.9 290
Chester, PA.............. 15.2 238.3 -3.7 96 1,105 -0.3 206
Cumberland, PA........... 6.0 121.6 -4.9 160 794 1.4 76
Dauphin, PA.............. 7.3 182.3 -2.3 28 824 0.7 126
Delaware, PA............. 13.6 204.5 -3.7 96 885 -0.7 237
Erie, PA................. 7.5 122.4 -5.9 223 669 -1.2 263
Lackawanna, PA........... 5.9 98.5 -3.9 108 659 1.4 76
Lancaster, PA............ 12.5 221.4 -5.4 195 706 -1.0 256
Lehigh, PA............... 8.7 172.3 -5.1 173 825 -2.1 294
Luzerne, PA.............. 7.8 139.9 -2.8 49 661 0.9 111
Montgomery, PA........... 27.5 471.9 -4.8 154 1,040 1.1 93
Northampton, PA.......... 6.5 97.7 -3.2 69 741 -0.3 206
Philadelphia, PA......... 31.4 622.8 -1.8 22 998 0.6 132
Washington, PA........... 5.4 79.2 -3.4 80 733 -0.8 244
Westmoreland, PA......... 9.4 133.8 -3.9 108 672 -3.2 312
York, PA................. 9.1 169.3 -5.2 178 746 0.7 126
Kent, RI................. 5.6 75.0 -7.0 269 743 1.0 101
Providence, RI........... 17.7 269.2 -4.9 160 833 1.0 101
Charleston, SC........... 11.9 204.6 -5.7 215 729 1.5 69
Greenville, SC........... 12.4 223.5 -7.7 296 736 -0.1 191
Horry, SC................ 8.0 115.5 -8.4 306 520 -3.3 315
Lexington, SC............ 5.6 93.3 -5.4 195 629 -0.9 250
Richland, SC............. 9.2 205.4 -5.1 173 753 2.3 34
Spartanburg, SC.......... 6.1 111.0 -8.2 305 733 -0.3 206
Minnehaha, SD............ 6.4 114.7 -2.4 32 688 1.0 101
Davidson, TN............. 18.4 412.7 -5.3 186 843 -0.6 229
Hamilton, TN............. 8.5 178.4 -8.6 309 726 0.6 132
Knox, TN................. 11.0 216.3 -5.6 208 716 0.3 164
Rutherford, TN........... 4.3 92.5 -7.3 283 748 0.4 152
Shelby, TN............... 19.7 472.9 -5.6 208 854 0.4 152
Williamson, TN........... 6.1 84.7 -5.9 223 898 0.0 176
Bell, TX................. 4.6 103.0 -0.5 7 684 4.4 6
Bexar, TX................ 32.8 718.7 -2.3 28 748 1.8 50
Brazoria, TX............. 4.7 83.7 -3.7 96 783 -5.3 327
Brazos, TX............... 3.9 84.9 (7) - 643 1.4 76
Cameron, TX.............. 6.4 123.0 -1.4 15 544 1.5 69
Collin, TX............... 17.3 282.1 (7) - 975 (7) -
Dallas, TX............... 67.7 1,416.7 -4.8 154 1,007 -0.3 206
Denton, TX............... 10.7 166.3 -2.8 49 740 1.0 101
El Paso, TX.............. 13.5 264.7 -2.1 24 608 0.8 119
Fort Bend, TX............ 8.6 130.3 (7) - 874 (7) -
Galveston, TX............ 5.2 93.2 -4.6 140 801 0.6 132
Gregg, TX................ 4.0 72.0 -4.8 154 715 -3.4 316
Harris, TX............... 97.9 2,009.3 -3.1 64 1,042 -2.5 307
Hidalgo, TX.............. 10.6 216.1 -1.1 10 544 1.3 86
Jefferson, TX............ 5.9 119.3 -5.3 186 830 1.1 93
Lubbock, TX.............. 6.8 123.0 -1.1 10 647 1.4 76
McLennan, TX............. 4.9 102.0 -2.1 24 665 0.8 119
Montgomery, TX........... 8.3 126.2 0.0 4 763 -4.1 324
Nueces, TX............... 8.0 149.6 -4.1 115 716 -1.5 274
Potter, TX............... 3.8 75.1 -0.6 9 724 0.3 164
Smith, TX................ 5.3 91.5 -3.7 96 717 -1.2 263
Tarrant, TX.............. 37.2 748.6 -3.4 80 837 -0.4 217
Travis, TX............... 29.3 561.0 -3.2 69 916 -1.2 263
Webb, TX................. 4.7 84.5 -4.8 154 558 -0.5 226
Williamson, TX........... 7.3 121.1 -2.5 37 798 -0.6 229
Davis, UT................ 7.2 101.7 -4.1 115 700 0.9 111
Salt Lake, UT............ 37.5 560.2 -5.3 186 797 2.4 29
Utah, UT................. 12.8 165.5 -6.0 233 686 -1.4 270
Weber, UT................ 5.7 90.1 -5.7 215 648 -1.4 270
Chittenden, VT........... 6.0 92.5 -3.0 59 834 0.0 176
Arlington, VA............ 7.9 159.2 1.4 2 1,423 3.6 11
Chesterfield, VA......... 7.6 116.5 -4.3 126 768 1.1 93
Fairfax, VA.............. 34.2 576.8 -2.4 32 1,348 1.8 50
Henrico, VA.............. 9.7 171.9 -5.2 178 856 -1.8 288
Loudoun, VA.............. 9.2 131.6 -2.7 45 1,020 -2.5 307
Prince William, VA....... 7.4 103.7 -3.2 69 774 1.2 90
Alexandria City, VA...... 6.2 99.1 -1.3 14 1,170 -3.1 309
Chesapeake City, VA...... 5.8 95.1 -5.1 173 681 1.9 42
Newport News City, VA.... 4.0 96.1 -4.3 126 795 4.9 5
Norfolk City, VA......... 5.9 139.9 -3.4 80 848 1.1 93
Richmond City, VA........ 7.3 150.7 -4.2 123 960 1.4 76
Virginia Beach City, VA.. 11.5 171.0 -4.7 147 677 2.4 29
Clark, WA................ 12.4 128.5 -4.3 126 777 0.9 111
King, WA................. 77.1 1,138.3 -5.2 178 1,077 2.0 40
Kitsap, WA............... 6.5 82.5 -2.9 57 817 5.1 2
Pierce, WA............... 20.7 265.6 -4.4 132 790 1.5 69
Snohomish, WA............ 18.0 243.5 -5.8 219 901 3.1 16
Spokane, WA.............. 15.4 204.1 -4.3 126 718 3.9 8
Thurston, WA............. 7.0 98.6 -3.1 64 797 3.4 14
Whatcom, WA.............. 6.8 79.9 -5.0 168 700 2.2 36
Yakima, WA............... 8.2 107.3 1.5 1 589 1.4 76
Kanawha, WV.............. 6.0 107.1 -2.2 27 765 1.7 55
Brown, WI................ 6.6 145.6 -4.3 126 724 -0.1 191
Dane, WI................. 13.7 297.1 -3.7 96 821 1.7 55
Milwaukee, WI............ 20.7 474.7 -5.6 208 848 -0.4 217
Outagamie, WI............ 5.0 102.0 -5.9 223 706 -0.4 217
Racine, WI............... 4.1 72.2 -6.7 260 764 0.9 111
Waukesha, WI............. 12.9 224.3 -6.0 233 824 -1.0 256
Winnebago, WI............ 3.7 88.7 -3.3 74 757 -1.7 284
San Juan, PR............. 12.4 270.8 -4.2 (8) 582 2.8 (8)
(1) Includes workers covered by Unemployment Insurance (UI) and Unemployment Compensation for Federal
Employees (UCFE) programs. These 334 U.S. counties comprise 71.2 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 2009(2)
Employment Average weekly
wage(3)
Establishments,
second quarter
County by NAICS supersector 2009 Percent Percent
(thousands) June change, Average change,
2009 June weekly second
(thousands) 2008-09(4) wage quarter
2008-09(4)
United States(5)............................. 9,055.3 129,674.8 -5.1 $840 -0.1
Private industry........................... 8,761.5 107,832.0 -6.1 823 -0.5
Natural resources and mining............. 126.2 1,907.4 -4.7 846 -6.2
Construction............................. 844.9 6,116.2 -17.2 906 0.4
Manufacturing............................ 353.8 11,730.7 -13.5 1,005 -0.3
Trade, transportation, and utilities..... 1,897.1 24,670.7 -5.9 710 -1.1
Information.............................. 146.6 2,827.5 -6.7 1,272 -0.9
Financial activities..................... 844.5 7,638.6 -5.0 1,185 -1.8
Professional and business services....... 1,529.4 16,479.3 -8.1 1,060 1.4
Education and health services............ 865.1 18,256.0 2.0 804 2.3
Leisure and hospitality.................. 739.2 13,540.3 -3.3 348 -0.9
Other services........................... 1,218.1 4,434.5 -2.9 543 0.0
Government................................. 293.9 21,842.9 0.4 922 1.2
Los Angeles, CA.............................. 419.7 3,947.3 -6.1 940 -0.6
Private industry........................... 415.7 3,346.7 -7.0 911 -1.1
Natural resources and mining............. 0.5 10.6 -7.1 1,018 -22.9
Construction............................. 13.8 118.2 -20.1 998 0.9
Manufacturing............................ 14.2 392.7 -11.3 1,026 1.7
Trade, transportation, and utilities..... 53.1 735.8 -7.9 757 -1.8
Information.............................. 8.8 191.7 -12.2 1,636 3.4
Financial activities..................... 23.6 220.7 -6.9 1,374 -1.9
Professional and business services....... 42.7 526.1 -10.4 1,120 -0.4
Education and health services............ 28.5 490.1 1.6 885 3.1
Leisure and hospitality.................. 27.2 390.7 -4.8 521 -0.8
Other services........................... 194.9 260.4 2.6 422 -5.6
Government................................. 4.0 600.6 -1.1 1,101 0.5
Cook, IL..................................... 142.0 2,395.8 -5.4 986 -1.4
Private industry........................... 140.6 2,082.5 -6.2 971 -1.9
Natural resources and mining............. 0.1 1.1 -3.4 884 -8.0
Construction............................. 12.3 77.3 -16.7 1,205 -2.4
Manufacturing............................ 6.9 200.9 -12.1 978 -2.3
Trade, transportation, and utilities..... 27.6 438.1 -7.1 767 -2.7
Information.............................. 2.6 52.7 (6) 1,415 (6)
Financial activities..................... 15.5 195.8 -6.4 1,629 -3.9
Professional and business services....... 29.3 396.3 -9.7 1,260 1.2
Education and health services............ 14.3 385.6 2.8 850 0.7
Leisure and hospitality.................. 12.1 234.2 -4.1 431 -2.0
Other services........................... 14.8 95.9 -3.0 728 1.1
Government................................. 1.4 313.3 0.0 1,084 1.6
New York, NY................................. 118.6 2,280.5 -4.7 1,520 -3.1
Private industry........................... 118.3 1,830.8 -5.7 1,629 -3.6
Natural resources and mining............. 0.0 0.2 -6.7 2,277 -33.5
Construction............................. 2.3 33.7 -10.4 1,498 -1.4
Manufacturing............................ 2.8 28.8 -18.9 1,236 -2.6
Trade, transportation, and utilities..... 21.2 228.7 -8.5 1,121 -3.6
Information.............................. 4.5 127.3 -7.0 1,951 -2.0
Financial activities..................... 18.9 348.3 -8.7 2,876 -5.4
Professional and business services....... 25.1 463.9 -7.3 1,794 -1.9
Education and health services............ 8.8 289.8 1.2 1,063 3.5
Leisure and hospitality.................. 11.7 215.6 -2.5 731 -1.6
Other services........................... 18.2 87.6 -2.4 949 0.3
Government................................. 0.3 449.7 -0.5 1,076 2.2
Harris, TX................................... 97.9 2,009.3 -3.1 1,042 -2.5
Private industry........................... 97.3 1,751.1 -3.9 1,056 -3.0
Natural resources and mining............. 1.5 81.1 (6) 2,663 -13.2
Construction............................. 6.7 143.9 -10.1 1,060 0.7
Manufacturing............................ 4.6 174.4 -8.1 1,254 -3.5
Trade, transportation, and utilities..... 22.3 415.3 -3.4 924 -0.6
Information.............................. 1.4 30.8 -4.8 1,194 -3.6
Financial activities..................... 10.4 115.8 -4.5 1,205 -6.9
Professional and business services....... 19.5 315.7 -7.5 1,239 1.4
Education and health services............ 10.5 228.1 4.3 880 1.5
Leisure and hospitality.................. 7.7 184.5 0.6 379 -0.3
Other services........................... 12.0 59.9 -1.9 616 -2.2
Government................................. 0.5 258.2 2.8 947 1.5
Maricopa, AZ................................. 98.2 1,588.7 -8.6 846 0.0
Private industry........................... 97.5 1,409.2 -9.4 826 0.0
Natural resources and mining............. 0.5 8.6 -5.6 671 -12.1
Construction............................. 10.0 95.4 -31.5 871 -0.3
Manufacturing............................ 3.4 108.3 -13.4 1,157 0.8
Trade, transportation, and utilities..... 22.1 338.1 -8.3 781 -0.3
Information.............................. 1.5 28.8 -6.2 1,028 -0.4
Financial activities..................... 12.0 135.7 -5.6 1,014 -2.4
Professional and business services....... 21.7 261.6 -12.2 885 2.5
Education and health services............ 10.1 214.0 2.3 903 1.3
Leisure and hospitality.................. 7.1 169.2 -6.1 397 0.0
Other services........................... 7.0 48.1 -6.5 569 -2.1
Government................................. 0.7 179.5 -1.7 979 -0.9
Dallas, TX................................... 67.7 1,416.7 -4.8 1,007 -0.3
Private industry........................... 67.2 1,251.5 -5.4 1,012 -0.4
Natural resources and mining............. 0.6 8.4 1.8 2,809 -10.4
Construction............................. 4.3 75.0 -13.3 904 -2.0
Manufacturing............................ 3.0 120.8 -10.9 1,158 0.3
Trade, transportation, and utilities..... 14.9 284.6 (6) 930 -1.2
Information.............................. 1.6 46.1 -6.8 1,431 2.2
Financial activities..................... 8.7 139.4 (6) 1,287 (6)
Professional and business services....... 14.8 251.1 -9.5 1,136 1.0
Education and health services............ 6.7 156.8 (6) 978 1.8
Leisure and hospitality.................. 5.4 130.0 (6) 469 1.7
Other services........................... 6.8 38.5 -3.7 641 3.6
Government................................. 0.5 165.2 -0.3 970 0.7
Orange, CA................................... 100.1 1,380.6 -8.0 953 0.0
Private industry........................... 98.7 1,225.7 -8.6 933 -0.3
Natural resources and mining............. 0.2 4.3 -19.5 593 1.9
Construction............................. 6.8 75.0 -19.0 1,082 0.6
Manufacturing............................ 5.3 154.6 -11.8 1,132 1.1
Trade, transportation, and utilities..... 16.9 247.5 -9.4 896 0.4
Information.............................. 1.3 27.5 -7.6 1,292 -5.3
Financial activities..................... 10.4 105.5 (6) 1,326 -1.6
Professional and business services....... 19.1 239.8 -11.2 1,083 1.4
Education and health services............ 10.2 149.6 0.1 871 1.9
Leisure and hospitality.................. 7.1 170.9 -5.4 408 -1.2
Other services........................... 18.7 47.8 -4.7 523 -2.2
Government................................. 1.4 154.9 -2.6 1,107 0.7
San Diego, CA................................ 96.6 1,258.2 -5.8 912 -1.5
Private industry........................... 95.3 1,029.9 -6.9 877 -2.3
Natural resources and mining............. 0.7 11.0 -5.5 535 -4.5
Construction............................. 6.8 61.9 -20.6 990 2.0
Manufacturing............................ 3.1 95.5 -9.0 1,248 (6)
Trade, transportation, and utilities..... 14.1 198.0 -8.0 722 (6)
Information.............................. 1.2 37.3 -4.3 1,627 -29.3
Financial activities..................... 9.1 70.6 -6.5 1,064 -2.2
Professional and business services....... 16.3 196.7 -8.9 1,144 2.3
Education and health services............ 8.3 141.5 3.3 859 1.8
Leisure and hospitality.................. 6.9 156.2 -7.2 389 -4.0
Other services........................... 26.2 58.2 -1.4 476 0.8
Government................................. 1.3 228.3 -0.3 1,071 0.9
King, WA..................................... 77.1 1,138.3 -5.2 1,077 2.0
Private industry........................... 76.6 977.8 -6.3 1,080 2.0
Natural resources and mining............. 0.4 3.0 -4.8 1,156 -12.6
Construction............................. 6.4 56.1 -21.6 1,101 3.6
Manufacturing............................ 2.4 102.2 -8.9 1,386 4.1
Trade, transportation, and utilities..... 14.8 205.8 -6.3 926 1.6
Information.............................. 1.8 80.1 0.9 1,923 1.1
Financial activities..................... 6.8 69.5 -6.9 1,313 1.4
Professional and business services....... 13.8 173.4 -10.8 1,273 (6)
Education and health services............ 6.7 131.2 3.9 880 4.0
Leisure and hospitality.................. 6.3 109.9 -5.0 427 (6)
Other services........................... 17.3 46.7 0.1 610 -1.1
Government................................. 0.5 160.5 1.8 1,056 2.1
Miami-Dade, FL............................... 83.9 932.3 -5.9 833 -0.6
Private industry........................... 83.6 799.9 -6.6 802 -0.2
Natural resources and mining............. 0.5 7.5 -9.9 480 0.6
Construction............................. 5.8 35.9 -24.0 870 3.2
Manufacturing............................ 2.6 37.1 -17.5 746 -0.1
Trade, transportation, and utilities..... 22.9 234.2 -6.8 756 0.1
Information.............................. 1.5 18.1 -8.0 1,216 -12.2
Financial activities..................... 9.6 62.8 -8.5 1,148 -1.2
Professional and business services....... 17.5 121.9 -9.3 978 -0.6
Education and health services............ 9.4 145.5 2.6 834 2.8
Leisure and hospitality.................. 6.0 101.7 -1.8 475 1.3
Other services........................... 7.5 34.9 -5.3 539 1.3
Government................................. 0.3 132.3 -1.1 1,009 -2.6
(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) 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.
(6) Data do not meet BLS or State agency disclosure standards.
Table 3. Covered(1) establishments, employment, and wages in the largest county by
state, second quarter 2009(2)
Employment Average weekly
wage(4)
Establishments,
second quarter
County(3) 2009 Percent Percent
(thousands) June change, Average change,
2009 June weekly second
(thousands) 2008-09(5) wage quarter
2008-09(5)
United States(6)......... 9,055.3 129,674.8 -5.1 $840 -0.1
Jefferson, AL............ 18.3 337.9 -7.0 845 0.6
Anchorage Borough, AK.... 8.1 148.4 -1.8 948 3.6
Maricopa, AZ............. 98.2 1,588.7 -8.6 846 0.0
Pulaski, AR.............. 15.0 243.6 -3.8 781 2.5
Los Angeles, CA.......... 419.7 3,947.3 -6.1 940 -0.6
Denver, CO............... 25.5 424.1 -6.0 1,011 -1.0
Hartford, CT............. 25.5 491.8 -4.6 1,014 0.1
New Castle, DE........... 18.1 268.1 -5.7 959 -0.3
Washington, DC........... 33.7 690.9 -0.1 1,421 -0.9
Miami-Dade, FL........... 83.9 932.3 -5.9 833 -0.6
Fulton, GA............... 39.2 696.1 -6.4 1,087 0.6
Honolulu, HI............. 24.9 434.7 -3.7 802 1.6
Ada, ID.................. 14.7 195.9 -8.1 734 -1.6
Cook, IL................. 142.0 2,395.8 -5.4 986 -1.4
Marion, IN............... 24.0 545.2 -5.3 850 0.4
Polk, IA................. 14.8 271.9 -2.8 823 0.1
Johnson, KS.............. 20.7 304.6 -4.9 871 -1.6
Jefferson, KY............ 22.1 413.2 -5.1 823 0.4
East Baton Rouge, LA..... 14.4 256.1 -1.7 805 1.8
Cumberland, ME........... 12.2 170.2 -4.0 756 0.0
Montgomery, MD........... 32.8 449.4 -2.4 1,129 1.5
Middlesex, MA............ 47.2 801.2 -4.4 1,194 -3.5
Wayne, MI................ 31.4 654.9 -11.6 920 -3.1
Hennepin, MN............. 42.8 811.1 -4.9 1,027 -3.9
Hinds, MS................ 6.2 125.4 -1.6 746 2.1
St. Louis, MO............ 32.1 580.7 -5.8 893 -1.4
Yellowstone, MT.......... 5.8 77.3 -1.6 690 0.0
Douglas, NE.............. 15.8 314.2 -2.8 783 -0.8
Clark, NV................ 49.9 820.9 -10.7 793 -0.4
Hillsborough, NH......... 12.1 189.0 -5.0 913 -1.8
Bergen, NJ............... 34.5 434.1 -4.6 1,032 0.1
Bernalillo, NM........... 17.6 319.0 -4.8 763 1.7
New York, NY............. 118.6 2,280.5 -4.7 1,520 -3.1
Mecklenburg, NC.......... 33.2 534.4 -5.9 937 -1.1
Cass, ND................. 5.8 99.8 -1.5 710 1.4
Cuyahoga, OH............. 37.1 697.5 -6.2 849 -2.4
Oklahoma, OK............. 23.8 410.4 -3.6 765 -1.5
Multnomah, OR............ 28.0 424.6 -5.9 868 0.6
Allegheny, PA............ 35.0 678.2 -2.9 892 -0.6
Providence, RI........... 17.7 269.2 -4.9 833 1.0
Greenville, SC........... 12.4 223.5 -7.7 736 -0.1
Minnehaha, SD............ 6.4 114.7 -2.4 688 1.0
Shelby, TN............... 19.7 472.9 -5.6 854 0.4
Harris, TX............... 97.9 2,009.3 -3.1 1,042 -2.5
Salt Lake, UT............ 37.5 560.2 -5.3 797 2.4
Chittenden, VT........... 6.0 92.5 -3.0 834 0.0
Fairfax, VA.............. 34.2 576.8 -2.4 1,348 1.8
King, WA................. 77.1 1,138.3 -5.2 1,077 2.0
Kanawha, WV.............. 6.0 107.1 -2.2 765 1.7
Milwaukee, WI............ 20.7 474.7 -5.6 848 -0.4
Laramie, WY.............. 3.2 43.5 -2.9 723 2.4
San Juan, PR............. 12.4 270.8 -4.2 582 2.8
St. Thomas, VI........... 1.9 22.8 -4.1 668 1.2
(1) Includes workers covered by Unemployment Insurance (UI) and Unemployment
Compensation for Federal Employees (UCFE) programs.
(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.
Table 4. Covered(1) establishments, employment, and wages by state,
second quarter 2009(2)
Employment Average weekly
wage(3)
Establishments,
second quarter
State 2009 Percent Percent
(thousands) June change, Average change,
2009 June weekly second
(thousands) 2008-09 wage quarter
2008-09
United States(4)......... 9,055.3 129,674.8 -5.1 $840 -0.1
Alabama.................. 117.8 1,836.9 -6.1 733 1.8
Alaska................... 21.3 326.3 -1.4 892 3.7
Arizona.................. 155.0 2,335.1 -8.2 807 0.1
Arkansas................. 86.0 1,136.5 -4.1 668 1.1
California............... 1,338.0 14,794.5 -6.1 949 -0.6
Colorado................. 176.1 2,222.2 -5.3 851 -0.8
Connecticut.............. 112.6 1,636.4 -4.8 1,034 -0.3
Delaware................. 29.1 408.4 -5.2 858 -0.3
District of Columbia..... 33.7 690.9 -0.1 1,421 -0.9
Florida.................. 599.7 7,085.9 -6.8 766 0.4
Georgia.................. 271.6 3,806.5 -6.2 791 0.6
Hawaii................... 39.3 594.0 -5.0 775 1.6
Idaho.................... 56.4 624.8 -6.9 633 -0.5
Illinois................. 374.3 5,610.6 -5.4 883 -1.1
Indiana.................. 159.8 2,701.2 -7.0 710 -0.7
Iowa..................... 94.4 1,470.4 -3.5 686 0.4
Kansas................... 87.7 1,331.4 -4.1 718 -0.3
Kentucky................. 109.1 1,723.7 -5.2 722 0.6
Louisiana................ 123.8 1,853.6 -2.4 753 0.3
Maine.................... 50.2 595.8 -4.0 681 0.7
Maryland................. 165.0 2,500.8 -3.0 935 1.6
Massachusetts............ 213.0 3,182.7 -4.1 1,028 -1.5
Michigan................. 255.7 3,804.8 -8.7 809 -1.8
Minnesota................ 170.2 2,608.6 -4.7 842 -0.8
Mississippi.............. 70.5 1,083.4 -4.9 639 0.6
Missouri................. 173.7 2,645.0 -4.2 747 -0.8
Montana.................. 42.8 434.1 -3.6 637 1.1
Nebraska................. 59.9 911.4 -2.6 674 -0.3
Nevada................... 76.0 1,141.7 -10.2 799 0.4
New Hampshire............ 48.8 615.8 -4.1 829 -0.7
New Jersey............... 273.5 3,869.8 -4.4 1,002 -0.2
New Mexico............... 54.4 798.9 -4.5 724 1.0
New York................. 587.1 8,475.8 -3.3 1,026 -1.3
North Carolina........... 257.6 3,842.8 -5.6 734 -0.3
North Dakota............. 25.8 356.2 -0.1 666 1.7
Ohio..................... 290.4 4,980.6 -6.3 754 -0.3
Oklahoma................. 101.1 1,498.5 -3.8 695 -1.0
Oregon................... 130.7 1,635.4 -6.3 767 0.4
Pennsylvania............. 342.5 5,519.9 -3.9 829 0.2
Rhode Island............. 35.4 458.0 -4.9 806 1.3
South Carolina........... 113.6 1,782.7 -6.7 685 0.6
South Dakota............. 30.8 400.8 -2.0 614 1.3
Tennessee................ 141.8 2,569.3 -6.6 749 0.5
Texas.................... 564.5 10,168.5 -3.3 839 -1.2
Utah..................... 85.6 1,165.7 -5.5 723 1.0
Vermont.................. 24.9 294.0 -4.0 725 1.0
Virginia................. 231.3 3,588.9 -3.5 899 1.6
Washington............... 222.1 2,884.3 -4.0 881 2.2
West Virginia............ 48.6 697.0 -2.6 710 2.2
Wisconsin................ 156.8 2,690.4 -5.3 729 0.0
Wyoming.................. 25.2 283.8 -4.5 768 -1.5
Puerto Rico.............. 53.0 955.5 -4.5 485 2.5
Virgin Islands........... 3.6 43.4 -5.6 720 2.4
(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
