For release 10:00 a.m. (EDT), Thursday, September 26, 2013 USDL-13-1942
Technical Information: (202) 691-6567 * QCEWInfo@bls.gov * www.bls.gov/cew
Media Contact: (202) 691-5902 * PressOffice@bls.gov
COUNTY EMPLOYMENT AND WAGES
First Quarter 2013
From March 2012 to March 2013, employment increased in 282 of the 334 largest U.S. counties, the
U.S. Bureau of Labor Statistics reported today. Fort Bend, Texas, posted 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 leisure and hospitality, which gained 2,204 jobs over the
year (12.5 percent). Sangamon, Ill., had the largest over-the-year decrease in employment among the
largest counties in the U.S. with a loss of 2.4 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 0.6 percent to $989 in the first quarter of
2013. San Mateo, Calif., had the largest over-the-year increase in average weekly wages with a gain of
14.8 percent. Within San Mateo, an average weekly wage gain of $2,996 or 104.1 percent in information
had the largest contribution to the increase in average weekly wages. Williamson, Texas, experienced the
largest decrease in average weekly wages with a loss of 13.4 percent over the year.
Table A. Large counties ranked by March 2013 employment, March 2012-13 employment
increase, and March 2012-13 percent increase in employment
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Employment in large counties
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March 2013 employment | Increase in employment, | Percent increase in employment,
(thousands) | March 2012-13 | March 2012-13
| (thousands) |
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| |
United States 132,338.9| United States 2,082.4| United States 1.6
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| |
Los Angeles, Calif. 4,041.3| Los Angeles, Calif. 85.7| Fort Bend, Texas 7.0
New York, N.Y. 2,403.9| Harris, Texas 77.7| Midland, Texas 6.9
Cook, Ill. 2,394.0| Maricopa, Ariz. 43.0| Elkhart, Ind. 6.0
Harris, Texas 2,163.6| Dallas, Texas 41.3| Douglas, Colo. 5.6
Maricopa, Ariz. 1,710.2| New York, N.Y. 39.2| Utah, Utah 5.5
Dallas, Texas 1,473.4| Orange, Calif. 38.9| Rutherford, Tenn. 5.3
Orange, Calif. 1,433.5| King, Wash. 35.4| Placer, Calif. 5.2
San Diego, Calif. 1,297.9| Santa Clara, Calif. 33.8| Montgomery, Texas 5.0
King, Wash. 1,175.0| San Diego, Calif. 29.1| Brazos, Texas 4.7
Miami-Dade, Fla. 1,016.2| Cook, Ill. 27.4| Weld, Colo. 4.4
| |
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Large County Employment
In March 2013, national employment was 132.3 million (as measured by the QCEW program). Over the
year employment was up by 1.6 percent or 2.1 million. The 334 U.S. counties with 75,000 or more jobs
accounted for 71.6 percent of total U.S. employment and 77.7 percent of total wages. These 334
counties had a net job growth of 1.6 million over the year, accounting for 78.6 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 New York, N.Y. These counties had a combined over-
the-year employment gain of 286,800 jobs, which was 13.8 percent of the overall job increase for the
U.S. (See table A.)
Employment declined in 46 of the large counties from March 2012 to March 2013. Sangamon, Ill., had
the largest over-the-year percentage decrease in employment (-2.4 percent). Within Sangamon,
professional and business services had the largest decrease in employment with a loss of 1,630 jobs
(-14.5 percent). Vanderburgh, Ind., had the second largest percentage decrease in employment, followed
by Broome, N.Y., and Jefferson, Texas, which tied for the third largest percentage decrease. Two
counties, Peoria, Ill., and Oneida, N.Y., tied for the fifth largest percentage decrease. (See table 1.)
Table B. Large counties ranked by first quarter 2013 average weekly wages, first quarter 2012-13
increase in average weekly wages, and first 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
first quarter 2013 | wage, first quarter 2012-13 | weekly wage, first
| | quarter 2012-13
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| |
United States $989| United States $6| United States 0.6
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| |
New York, N.Y. $2,448| San Mateo, Calif. $239| San Mateo, Calif. 14.8
Somerset, N.J. 2,009| Benton, Ark. 168| Benton, Ark. 14.3
Santa Clara, Calif. 1,937| Somerset, N.J. 126| McLean, Ill. 11.8
Fairfield, Conn. 1,878| McLean, Ill. 112| Clayton, Ga. 6.7
San Mateo, Calif. 1,859| Mercer, N.J. 89| Somerset, N.J. 6.7
San Francisco, Calif. 1,778| Clayton, Ga. 58| Mercer, N.J. 6.4
Suffolk, Mass. 1,698| Williamson, Tenn. 53| Hampden, Mass. 4.8
Arlington, Va. 1,621| Ramsey, Minn. 49| Williamson, Tenn. 4.6
Washington, D.C. 1,613| Lake, Ill. 41| Winnebago, Wis. 4.6
Morris, N.J. 1,582| Hampden, Mass. 41| Ramsey, Minn. 4.4
| |
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Large County Average Weekly Wages
Average weekly wages for the nation increased 0.6 percent during the year ending in the first quarter of
2013. Among the 334 largest counties, 232 had over-the-year increases in average weekly wages. San
Mateo, Calif., had the largest wage increase among the largest U.S. counties (14.8 percent).
Of the 334 largest counties, 92 experienced over-the-year decreases in average weekly wages.
Williamson, Texas, had the largest average weekly wage decrease with a loss of 13.4 percent. Within
Williamson, trade, transportation, and utilities had the largest impact on the county’s average weekly
wage decrease. Within this industry, average weekly wages declined by $436 (-24.2 percent) over the
year. Middlesex, N.J., had the second largest decrease in average weekly wages, followed by Peoria, Ill.;
Washington, Ore.; and Santa Cruz, Calif. (See table 1.)
Ten Largest U.S. Counties
All of the 10 largest counties had over-the-year percentage increases in employment in March 2013.
Harris, Texas, had the largest gain (3.7 percent). Within Harris, professional and business services had
the largest over-the-year employment level increase among all private industry groups with a gain of
16,474 jobs or 4.7 percent. Cook, Ill., had the smallest percentage increase in employment (1.2 percent)
among the 10 largest counties. (See table 2.)
Three of the 10 largest U.S. counties had over-the-year increases in average weekly wages. King,
Wash., experienced the largest gain in average weekly wages (1.6 percent). Within King, professional
and business services had the largest impact on the county’s average weekly wage growth. Within this
industry, average weekly wages increased by $42 or 2.7 percent over the year. Los Angeles, Calif., had
the largest average weekly wage decrease (-1.8 percent) 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. March 2013 employment and 2013 first 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 132.3 million full- and part-
time workers. For additional information about the quarterly employment and wages data, please read
the Technical Note. Data for the first 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 second quarter 2013 is scheduled to be released on
Wednesday, December 18, 2013.
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| |
| County Changes for the 2013 County Employment and Wages News Releases |
| |
| Counties with annual average employment of 75,000 or more in 2012 are included in this release |
| and will be included in future 2013 releases. Six counties have been added to the publication |
| tables: Boone, Ky.; Warren, Ohio; Jackson, Ore.; York, S.C.; Midland, Texas; and Potter, Texas. |
| |
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| |
| Updated MSA Definitions |
| |
| New Metropolitan Statistical Area (MSA) definitions, and those for other types of Core Based |
| Statistical Areas (CBSA), were announced in March 2013. The QCEW program will be using those |
| definitions for tabulating data referencing 2013 and future years and will begin releasing that |
| data effective with today’s release. Prior year data will not be re-tabulated to the new |
| definitions. |
| |
| For more information regarding the new area definitions, see |
| http://www.whitehouse.gov/omb/inforeg_statpolicy#ms. |
| |
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| |
| Notable Industry Changes |
| |
| Each first quarter, QCEW incorporates improved industry assignments. Usually this activity is |
| distributed across industries. In 2013, the improvements also include substantial changes to two |
| specific industries--funds, trusts, and other financial vehicles (NAICS 525) as well as private |
| households (NAICS 814110). |
| |
| Establishments in funds, trusts, and other financial vehicles are legal entities with little to |
| no employment. Establishments with employees who manage such funds are typically coded in other |
| financial investment activities (NAICS 5239), although they may also be classified as other |
| industries within finance. The QCEW program examined establishments with employment classified |
| within funds, trusts, and other financial vehicles and reclassified them into other industries |
| based on each establishment’s primary economic activity. |
| |
| The QCEW program also reviewed establishments that provide non-medical, home-based services for |
| the elderly and persons with disabilities and classified these establishments into services for |
| the elderly and persons with disabilities (NAICS 624120). Many of these establishments were |
| previously classified in the private households industry. |
| |
| These changes apply not only to the data published by QCEW, but also data based on QCEW such as |
| BLS Current Employment Statistics (CES) and Bureau of Economic Analysis (BEA) Personal Income. |
| For more information about the industry changes affecting the data in these programs, contact the |
| QCEW program at (202) 691-6567, the CES program at (202) 691-6555, or BEA at (202) 606-9272. |
| |
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Technical Note
These data are the product of a federal-state cooperative program, the Quarterly
Census of Employment and Wages (QCEW) program, also known as the ES-202 program.
The data are derived from summaries of employment and total pay of workers covered
by state and federal unemployment insurance (UI) legislation and provided by State
Workforce Agencies (SWAs). The summaries are a result of the administration of
state unemployment insurance programs that require most employers to pay quarterly
taxes based on the employment and wages of workers covered by UI. QCEW data in this
release are based on the 2012 North American Industry Classification System. Data
for 2013 are preliminary and subject to revision.
For purposes of this release, large counties are defined as having employment le-
vels of 75,000 or greater. In addition, data for San Juan, Puerto Rico, are pro-
vided, but not used in calculating U.S. averages, rankings, or in the analysis in
the text. Each year, these large counties are selected on the basis of the prelimi-
nary annual average of employment for the previous year. The 335 counties presented
in this release were derived using 2012 preliminary annual averages of employment.
For 2013 data, six counties have been added to the publication tables: Boone, Ky.;
Warren, Ohio; Jackson, Ore.; York, S.C.; Midland, Texas; and Potter, Texas. These
counties will be included in all 2013 quarterly releases. The counties in table 2
are selected and sorted each year based on the annual average employment from the
preceding year.
The preliminary QCEW data presented in this release may differ from data released
by the individual states. These potential differences result from the states' con-
tinuing receipt of UI data over time and ongoing review and editing. The individual
states determine their data release timetables.
Differences between QCEW, BED, and CES employment measures
The Bureau publishes three different establishment-based employment measures for
any given quarter. Each of these measures--QCEW, Business Employment Dynamics (BED),
and Current Employment Statistics (CES)--makes use of the quarterly UI employment
reports in producing data; however, each measure has a somewhat different universe
coverage, estimation procedure, and publication product.
Differences in coverage and estimation methods can result in somewhat different
measures of employment change over time. It is important to understand program dif-
ferences and the intended uses of the program products. (See table.) Additional in-
formation on each program can be obtained from the program Web sites shown in the
table.
Summary of Major Differences between QCEW, BED, and CES Employment Measures
---------------------------------------------------------------------------------
| QCEW | BED | CES
-----------|---------------------|----------------------|------------------------
Source |--Count of UI admini-|--Count of longitudi- |--Sample survey:
| strative records | nally-linked UI ad- | 557,000 establish-
| submitted by 9.2 | ministrative records| ments
| million establish- | submitted by 6.8 |
| ments in first | million private-sec-|
| quarter of 2013 | tor employers |
-----------|---------------------|----------------------|------------------------
Coverage |--UI and UCFE cover- |--UI coverage, exclud-|Nonfarm wage and sal-
| age, including all | ing government, pri-| ary jobs:
| employers subject | vate households, and|--UI coverage, exclud-
| to state and fed- | establishments with | ing agriculture, pri-
| eral UI laws | zero employment | vate households, and
| | | self-employed workers
| | |--Other employment, in-
| | | cluding railroads,
| | | religious organiza-
| | | tions, and other non-
| | | UI-covered jobs
-----------|---------------------|----------------------|------------------------
Publication|--Quarterly |--Quarterly |--Monthly
frequency | -6 months after the| -8 months after the | -Usually first Friday
| end of each quar- | end of each quarter| of following month
| ter | |
-----------|---------------------|----------------------|------------------------
Use of UI |--Directly summarizes|--Links each new UI |--Uses UI file as a sam-
file | and publishes each | quarter to longitu- | pling frame and to an-
| new quarter of UI | dinal database and | nually realign sample-
| data | directly summarizes | based estimates to pop-
| | gross job gains and | ulation counts (bench-
| | losses | marking)
-----------|---------------------|----------------------|------------------------
Principal |--Provides a quarter-|--Provides quarterly |--Provides current month-
products | ly and annual uni- | employer dynamics | ly estimates of employ-
| verse count of es- | data on establish- | ment, hours, and earn-
| tablishments, em- | ment openings, clos-| ings at the MSA, state,
| ployment, and wages| ings, expansions, | and national level by
| at the county, MSA,| and contractions at | industry
| state, and national| the national level |
| levels by detailed | by NAICS supersec- |
| industry | tors and by size of |
| | firm, and at the |
| | state private-sector|
| | total level |
| |--Future expansions |
| | will include data |
| | with greater indus- |
| | try detail and data |
| | at the county and |
| | MSA level |
-----------|---------------------|----------------------|------------------------
Principal |--Major uses include:|--Major uses include: |--Major uses include:
uses | -Detailed locality | -Business cycle | -Principal national
| data | analysis | economic indicator
| -Periodic universe | -Analysis of employ-| -Official time series
| counts for bench- | er dynamics under- | for employment change
| marking sample | lying economic ex- | measures
| survey estimates | pansions and con- | -Input into other ma-
| -Sample frame for | tractions | jor economic indi-
| BLS establishment | -Analysis of employ-| cators
| surveys | ment expansion and |
| | contraction by size|
| | of firm |
| | |
-----------|---------------------|----------------------|------------------------
Program |--www.bls.gov/cew/ |--www.bls.gov/bdm/ |--www.bls.gov/ces/
Web sites | | |
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Coverage
Employment and wage data for workers covered by state UI laws are compiled from
quarterly contribution reports submitted to the SWAs by employers. For federal ci-
vilian workers covered by the Unemployment Compensation for Federal Employees
(UCFE) program, employment and wage data are compiled from quarterly reports sub-
mitted by four major federal payroll processing centers on behalf of all federal
agencies, with the exception of a few agencies which still report directly to the
individual SWA. In addition to the quarterly contribution reports, employers who
operate multiple establishments within a state complete a questionnaire, called the
"Multiple Worksite Report," which provides detailed information on the location and
industry of each of their establishments. QCEW employment and wage data are derived
from microdata summaries of 9.1 million employer reports of employment and wages
submitted by states to the BLS in 2012. These reports are based on place of employ-
ment rather than place of residence.
UI and UCFE coverage is broad and has been basically comparable from state to state
since 1978, when the 1976 amendments to the Federal Unemployment Tax Act became ef-
fective, expanding coverage to include most State and local government employees.
In 2012, UI and UCFE programs covered workers in 131.7 million jobs. The estimated
126.9 million workers in these jobs (after adjustment for multiple jobholders)
represented 95.5 percent of civilian wage and salary employment. Covered workers
received $6.491 trillion in pay, representing 93.7 percent of the wage and salary
component of personal income and 40.0 percent of the gross domestic product.
Major exclusions from UI coverage include self-employed workers, most agricultural
workers on small farms, all members of the Armed Forces, elected officials in most
states, most employees of railroads, some domestic workers, most student workers at
schools, and employees of certain small nonprofit organizations.
State and federal UI laws change periodically. These changes may have an impact on
the employment and wages reported by employers covered under the UI program. Cover-
age changes may affect the over-the-year comparisons presented in this news re-
lease.
Concepts and methodology
Monthly employment is based on the number of workers who worked during or received
pay for the pay period including the 12th of the month. With few exceptions, all
employees of covered firms are reported, including production and sales workers,
corporation officials, executives, supervisory personnel, and clerical workers.
Workers on paid vacations and part-time workers also are included.
Average weekly wage values are calculated by dividing quarterly total wages by the
average of the three monthly employment levels (all employees, as described above)
and dividing the result by 13, for the 13 weeks in the quarter. These calculations
are made using unrounded employment and wage values. The average wage values that
can be calculated using rounded data from the BLS database may differ from the av-
erages reported. Included in the quarterly wage data are non-wage cash payments
such as bonuses, the cash value of meals and lodging when supplied, tips and other
gratuities, and, in some states, employer contributions to certain deferred compen-
sation plans such as 401(k) plans and stock options. Over-the-year comparisons of
average weekly wages may reflect fluctuations in average monthly employment and/or
total quarterly wages between the current quarter and prior year levels.
Average weekly wages are affected by the ratio of full-time to part-time workers as
well as the number of individuals in high-paying and low-paying occupations and the
incidence of pay periods within a quarter. For instance, the average weekly wage of
the workforce could increase significantly when there is a large decline in the
number of employees that had been receiving below-average wages. Wages may include
payments to workers not present in the employment counts because they did not work
during the pay period including the 12th of the month. When comparing average week-
ly wage levels between industries, states, or quarters, these factors should be
taken into consideration.
Wages measured by QCEW may be subject to periodic and sometimes large fluctuations.
This variability may be due to calendar effects resulting from some quarters having
more pay dates than others. The effect is most visible in counties with a dominant
employer. In particular, this effect has been observed in counties where government
employers represent a large fraction of overall employment. Similar calendar effects
can result from private sector pay practices. However, these effects are typically
less pronounced for two reasons: employment is less concentrated in a single private
employer, and private employers use a variety of pay period types (weekly, biweekly,
semimonthly, monthly).
For example, the effect on over-the-year pay comparisons can be pronounced in federal
government due to the uniform nature of federal payroll processing. Most federal
employees are paid on a biweekly pay schedule. As a result, in some quarters federal
wages include six pay dates, while in other quarters there are seven pay dates. Over-
the-year comparisons of average weekly wages may also reflect this calendar effect.
Growth in average weekly wages may be attributed, in part, to a comparison of
quarterly wages for the current year, which include seven pay dates, with year-ago
wages that reflect only six pay dates. An opposite effect will occur when wages in
the current quarter reflecting six pay dates are compared with year-ago wages for a
quarter including seven pay dates.
In order to ensure the highest possible quality of data, states verify with employ-
ers and update, if necessary, the industry, location, and ownership classification
of all establishments on a 3-year cycle. Changes in establishment classification
codes resulting from this process are introduced with the data reported for the
first quarter of the year. Changes resulting from improved employer reporting also
are introduced in the first quarter.
QCEW data are not designed as a time series. QCEW data are simply the sums of indi-
vidual establishment records and reflect the number of establishments that exist in
a county or industry at a point in time. Establishments can move in or out of a
county or industry for a number of reasons--some reflecting economic events, others
reflecting administrative changes. For example, economic change would come from a
firm relocating into the county; administrative change would come from a company
correcting its county designation.
The over-the-year changes of employment and wages presented in this release have
been adjusted to account for most of the administrative corrections made to the un-
derlying establishment reports. This is done by modifying the prior-year levels
used to calculate the over-the-year changes. Percent changes are calculated using
an adjusted version of the final 2012 quarterly data as the base data. The adjusted
prior-year levels used to calculate the over-the-year percent change in employment
and wages are not published. These adjusted prior-year levels do not match the un-
adjusted data maintained on the BLS Web site. Over-the-year change calculations
based on data from the Web site, or from data published in prior BLS news releases,
may differ substantially from the over-the-year changes presented in this news re-
lease.
The adjusted data used to calculate the over-the-year change measures presented in
this release account for most of the administrative changes--those occurring when
employers update the industry, location, and ownership information of their estab-
lishments. The most common adjustments for administrative change are the result of
updated information about the county location of individual establishments. In-
cluded in these adjustments are administrative changes involving the classification
of establishments that were previously reported in the unknown or statewide county
or unknown industry categories. Beginning with the first quarter of 2008, adjusted
data account for administrative changes caused by multi-unit employers who start
reporting for each individual establishment rather than as a single entity. Beginn-
ing with the second quarter of 2011, adjusted data account for selected large admin-
istrative changes in employment and wages. These new adjustments allow QCEW to incl-
ude county employment and wage growth rates in this news release that would other-
wise not meet publication standards.
The adjusted data used to calculate the over-the-year change measures presented in
any County Employment and Wages news release are valid for comparisons between the
starting and ending points (a 12-month period) used in that particular release.
Comparisons may not be valid for any time period other than the one featured in a
release even if the changes were calculated using adjusted data.
County definitions are assigned according to Federal Information Processing Stan-
dards Publications (FIPS PUBS) as issued by the National Institute of Standards and
Technology, after approval by the Secretary of Commerce pursuant to Section 5131 of
the Information Technology Management Reform Act of 1996 and the Computer Security
Act of 1987, Public Law 104-106. Areas shown as counties include those designated
as independent cities in some jurisdictions and, in Alaska, those designated as
census areas where counties have not been created. County data also are presented
for the New England states for comparative purposes even though townships are the
more common designation used in New England (and New Jersey). The regions referred
to in this release are defined as census regions.
Additional statistics and other information
Employment and Wages Annual Averages Online features comprehensive information by
detailed industry on establishments, employment, and wages for the nation and all
states. The 2012 edition of this publication, which will be available shortly after
this release, contains selected data produced by Business Employment Dynamics (BED)
on job gains and losses, as well as selected data from this news release. Tables and
additional content from Employment and Wages Annual Averages 2012 will be available
online at http://www.bls.gov/cew/cewbultn12.htm.
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,
first quarter 2013(2)
Employment Average weekly wage(4)
Establishments,
County(3) first quarter Percent Ranking Percent Ranking
2013 March change, by First change, by
(thousands) 2013 March percent quarter first percent
(thousands) 2012-13(5) change 2013 quarter change
2012-13(5)
United States(6)......... 9,193.5 132,338.9 1.6 - $989 0.6 -
Jefferson, AL............ 17.8 336.6 0.9 199 983 0.5 179
Madison, AL.............. 9.0 180.1 1.8 133 1,030 0.3 200
Mobile, AL............... 9.7 163.4 0.0 283 812 2.8 24
Montgomery, AL........... 6.4 128.7 1.7 142 781 -3.1 328
Tuscaloosa, AL........... 4.3 85.7 2.0 117 797 -1.0 299
Anchorage Borough, AK.... 8.3 150.7 0.1 273 1,038 1.5 90
Maricopa, AZ............. 94.3 1,710.2 2.6 74 945 0.0 233
Pima, AZ................. 18.9 352.1 0.9 199 809 1.0 133
Benton, AR............... 5.6 98.4 2.7 63 1,339 14.3 2
Pulaski, AR.............. 14.5 243.8 0.6 232 853 -0.9 296
Washington, AR........... 5.7 93.7 3.1 41 759 1.6 78
Alameda, CA.............. 54.6 672.8 3.2 36 1,249 -1.6 311
Contra Costa, CA......... 28.8 328.6 3.0 45 1,251 0.1 216
Fresno, CA............... 29.1 336.5 3.0 45 736 0.3 200
Kern, CA................. 16.8 284.6 3.7 19 848 0.0 233
Los Angeles, CA.......... 412.4 4,041.3 2.2 101 1,061 -1.8 317
Marin, CA................ 11.6 106.9 2.9 51 1,138 1.2 115
Monterey, CA............. 12.2 156.7 1.8 133 834 0.5 179
Orange, CA............... 102.8 1,433.5 2.8 57 1,086 -0.5 269
Placer, CA............... 10.8 135.9 5.2 7 933 1.4 99
Riverside, CA............ 48.4 592.6 3.4 29 770 0.1 216
Sacramento, CA........... 49.0 598.0 1.8 133 1,054 -1.7 314
San Bernardino, CA....... 47.4 622.4 2.3 91 791 0.4 192
San Diego, CA............ 100.4 1,297.9 2.3 91 1,056 -1.7 314
San Francisco, CA........ 54.6 603.8 4.3 11 1,778 -0.7 283
San Joaquin, CA.......... 15.9 206.7 2.5 84 785 0.5 179
San Luis Obispo, CA...... 9.4 107.1 3.5 25 785 1.9 57
San Mateo, CA............ 24.5 349.0 3.4 29 1,859 14.8 1
Santa Barbara, CA........ 14.1 183.4 3.3 32 900 -1.9 318
Santa Clara, CA.......... 62.0 923.6 3.8 15 1,937 -0.2 249
Santa Cruz, CA........... 8.9 91.1 4.2 12 865 -3.4 330
Solano, CA............... 9.6 123.4 3.1 41 1,019 3.6 12
Sonoma, CA............... 18.0 179.5 3.4 29 863 0.7 168
Stanislaus, CA........... 13.7 165.0 3.8 15 791 -1.0 299
Tulare, CA............... 8.7 140.7 1.9 125 649 1.1 122
Ventura, CA.............. 23.7 312.6 2.1 107 1,027 0.3 200
Yolo, CA................. 6.1 89.3 1.7 142 977 -2.8 325
Adams, CO................ 9.0 169.0 3.7 19 893 1.4 99
Arapahoe, CO............. 19.3 290.4 3.7 19 1,193 1.0 133
Boulder, CO.............. 13.3 161.8 2.7 63 1,120 -0.1 243
Denver, CO............... 26.8 432.5 4.0 14 1,265 0.0 233
Douglas, CO.............. 10.0 99.6 5.6 4 1,109 -1.9 318
El Paso, CO.............. 17.0 239.8 2.3 91 858 -0.5 269
Jefferson, CO............ 17.9 212.3 2.8 57 946 -2.3 323
Larimer, CO.............. 10.3 133.5 3.8 15 824 0.1 216
Weld, CO................. 5.9 88.2 4.4 10 825 0.5 179
Fairfield, CT............ 33.1 406.5 0.7 225 1,878 -1.9 318
Hartford, CT............. 25.9 489.0 0.4 250 1,315 -0.5 269
New Haven, CT............ 22.7 352.4 -0.2 290 1,013 1.4 99
New London, CT........... 7.0 121.0 -0.7 311 975 -1.1 302
New Castle, DE........... 16.7 266.3 1.2 178 1,235 -0.6 279
Washington, DC........... 35.5 717.6 1.0 193 1,613 0.5 179
Alachua, FL.............. 6.7 118.3 0.6 232 769 1.7 71
Brevard, FL.............. 14.7 189.3 0.4 250 848 -0.5 269
Broward, FL.............. 64.7 716.3 2.4 86 878 0.1 216
Collier, FL.............. 12.2 127.3 2.3 91 823 1.4 99
Duval, FL................ 27.5 446.5 2.4 86 963 1.4 99
Escambia, FL............. 8.0 121.4 1.4 164 722 -0.1 243
Hillsborough, FL......... 38.8 603.7 2.1 107 925 0.4 192
Lake, FL................. 7.4 83.9 2.7 63 624 1.1 122
Lee, FL.................. 19.3 213.9 2.7 63 738 0.3 200
Leon, FL................. 8.3 136.8 -0.3 295 748 -0.5 269
Manatee, FL.............. 9.5 108.2 2.1 107 705 0.1 216
Marion, FL............... 8.0 91.7 0.9 199 649 1.1 122
Miami-Dade, FL........... 92.2 1,016.2 2.6 74 912 0.9 141
Okaloosa, FL............. 6.1 77.3 0.2 269 780 0.8 154
Orange, FL............... 37.1 703.5 3.2 36 846 0.1 216
Palm Beach, FL........... 50.7 525.7 2.8 57 936 0.5 179
Pasco, FL................ 10.1 100.8 1.4 164 638 1.9 57
Pinellas, FL............. 31.1 390.4 1.9 125 823 -0.8 290
Polk, FL................. 12.5 196.9 1.6 150 706 1.1 122
Sarasota, FL............. 14.6 144.3 3.2 36 763 0.5 179
Seminole, FL............. 14.0 159.3 1.5 156 792 2.1 47
Volusia, FL.............. 13.4 154.0 0.9 199 659 0.2 210
Bibb, GA................. 4.5 79.2 -0.2 290 745 2.1 47
Chatham, GA.............. 7.9 135.6 2.6 74 810 0.2 210
Clayton, GA.............. 4.3 110.1 0.0 283 920 6.7 4
Cobb, GA................. 21.9 309.1 2.0 117 1,092 1.8 65
De Kalb, GA.............. 18.1 272.1 0.4 250 1,011 -0.5 269
Fulton, GA............... 42.4 736.5 3.3 32 1,419 -0.7 283
Gwinnett, GA............. 24.4 307.0 1.6 150 953 1.6 78
Muscogee, GA............. 4.7 93.6 -0.5 305 784 0.9 141
Richmond, GA............. 4.7 99.6 0.8 212 794 0.0 233
Honolulu, HI............. 24.7 451.1 2.1 107 878 1.0 133
Ada, ID.................. 13.6 202.2 3.5 25 805 -0.4 259
Champaign, IL............ 4.3 87.7 0.8 212 826 4.0 11
Cook, IL................. 151.0 2,394.0 1.2 178 1,185 -1.0 299
Du Page, IL.............. 37.7 581.2 1.7 142 1,160 -0.1 243
Kane, IL................. 13.5 195.9 1.0 193 823 2.1 47
Lake, IL................. 22.4 316.8 0.9 199 1,379 3.1 19
McHenry, IL.............. 8.7 90.8 -0.4 299 781 2.0 54
McLean, IL............... 3.8 84.7 0.9 199 1,059 11.8 3
Madison, IL.............. 6.0 94.4 0.0 283 787 1.3 108
Peoria, IL............... 4.7 101.5 -1.8 329 973 -5.5 332
St. Clair, IL............ 5.6 92.2 -1.4 324 752 -0.4 259
Sangamon, IL............. 5.3 123.1 -2.4 334 962 2.2 46
Will, IL................. 15.5 202.5 0.4 250 827 0.7 168
Winnebago, IL............ 6.9 122.4 -1.5 326 814 0.0 233
Allen, IN................ 9.0 172.3 0.5 243 809 -0.4 259
Elkhart, IN.............. 4.8 113.3 6.0 3 756 0.9 141
Hamilton, IN............. 8.7 116.1 2.6 74 983 2.9 23
Lake, IN................. 10.4 186.9 0.7 225 869 2.4 39
Marion, IN............... 24.0 563.3 1.2 178 1,052 2.6 31
St. Joseph, IN........... 6.0 114.3 -0.5 305 768 1.2 115
Tippecanoe, IN........... 3.3 78.3 -0.4 299 818 -0.8 290
Vanderburgh, IN.......... 4.8 103.7 -2.0 333 776 1.3 108
Johnson, IA.............. 3.8 78.7 2.2 101 845 1.1 122
Linn, IA................. 6.4 124.9 -0.4 299 926 2.4 39
Polk, IA................. 15.4 271.3 1.8 133 1,014 1.5 90
Scott, IA................ 5.4 87.0 0.8 212 770 0.8 154
Johnson, KS.............. 20.7 314.6 2.7 63 1,018 -0.1 243
Sedgwick, KS............. 12.1 240.1 0.7 225 867 -1.6 311
Shawnee, KS.............. 4.7 94.5 -0.2 290 807 1.6 78
Wyandotte, KS............ 3.2 81.2 0.7 225 891 -0.8 290
Boone, KY................ 3.9 76.1 1.3 172 817 0.4 192
Fayette, KY.............. 9.9 178.3 2.0 117 844 -0.6 279
Jefferson, KY............ 23.3 427.0 2.3 91 962 0.7 168
Caddo, LA................ 7.4 117.1 -1.6 327 760 -0.7 283
Calcasieu, LA............ 4.9 85.5 2.2 101 831 0.5 179
East Baton Rouge, LA..... 14.5 262.3 1.4 164 907 2.8 24
Jefferson, LA............ 13.6 191.2 0.0 283 856 -0.3 254
Lafayette, LA............ 9.1 139.4 2.7 63 913 0.0 233
Orleans, LA.............. 11.1 178.7 2.3 91 965 -1.5 310
St. Tammany, LA.......... 7.5 79.9 2.1 107 843 2.8 24
Cumberland, ME........... 12.6 165.8 0.6 232 896 3.5 15
Anne Arundel, MD......... 14.9 247.2 1.8 133 1,065 -0.2 249
Baltimore, MD............ 21.5 359.3 0.8 212 976 0.6 176
Frederick, MD............ 6.3 94.1 0.8 212 946 -0.9 296
Harford, MD.............. 5.7 87.6 0.3 260 917 2.5 36
Howard, MD............... 9.5 157.0 0.6 232 1,188 0.1 216
Montgomery, MD........... 33.8 448.2 0.3 260 1,317 -3.1 328
Prince Georges, MD....... 15.9 299.0 -0.2 290 988 0.2 210
Baltimore City, MD....... 14.1 331.4 0.8 212 1,161 -1.2 304
Barnstable, MA........... 8.9 81.4 1.4 164 816 0.9 141
Bristol, MA.............. 16.2 208.9 0.0 283 850 0.8 154
Essex, MA................ 21.9 300.0 0.3 260 1,021 1.7 71
Hampden, MA.............. 15.6 194.1 -0.4 299 899 4.8 7
Middlesex, MA............ 49.5 823.8 1.5 156 1,465 0.4 192
Norfolk, MA.............. 23.5 321.6 1.5 156 1,137 0.4 192
Plymouth, MA............. 14.1 173.6 1.6 150 878 2.3 42
Suffolk, MA.............. 23.9 598.8 1.7 142 1,698 -0.7 283
Worcester, MA............ 21.6 316.7 0.1 273 952 0.8 154
Genesee, MI.............. 7.3 131.0 1.3 172 776 -2.3 323
Ingham, MI............... 6.4 149.8 0.8 212 949 2.5 36
Kalamazoo, MI............ 5.4 109.9 0.2 269 907 3.3 17
Kent, MI................. 14.2 341.8 3.0 45 839 -1.4 308
Macomb, MI............... 17.5 297.2 3.1 41 974 -0.1 243
Oakland, MI.............. 38.7 668.9 2.7 63 1,072 -0.8 290
Ottawa, MI............... 5.7 107.8 2.7 63 755 1.2 115
Saginaw, MI.............. 4.2 82.0 0.1 273 780 2.8 24
Washtenaw, MI............ 8.3 196.7 2.3 91 989 0.8 154
Wayne, MI................ 31.7 681.6 0.9 199 1,053 -1.3 307
Anoka, MN................ 7.2 111.7 3.0 45 873 0.1 216
Dakota, MN............... 10.0 172.9 2.1 107 965 1.6 78
Hennepin, MN............. 42.3 846.0 2.6 74 1,274 -0.2 249
Olmsted, MN.............. 3.5 91.3 2.4 86 1,005 0.2 210
Ramsey, MN............... 13.9 315.3 1.9 125 1,169 4.4 10
St. Louis, MN............ 5.6 93.7 2.5 84 788 0.9 141
Stearns, MN.............. 4.4 79.7 1.1 186 748 1.8 65
Harrison, MS............. 4.4 82.2 0.3 260 703 0.7 168
Hinds, MS................ 6.0 120.3 -0.7 311 814 1.2 115
Boone, MO................ 4.6 87.3 2.0 117 739 2.1 47
Clay, MO................. 5.2 87.2 -0.6 309 858 1.5 90
Greene, MO............... 8.1 153.1 0.8 212 711 0.9 141
Jackson, MO.............. 19.0 346.9 0.8 212 983 1.3 108
St. Charles, MO.......... 8.4 127.7 2.0 117 793 -0.4 259
St. Louis, MO............ 32.7 563.9 0.8 212 1,031 -0.5 269
St. Louis City, MO....... 9.7 221.2 0.2 269 1,120 0.2 210
Yellowstone, MT.......... 6.2 76.7 2.1 107 783 1.4 99
Douglas, NE.............. 18.0 315.3 1.2 178 914 1.9 57
Lancaster, NE............ 9.6 157.4 1.0 193 760 1.6 78
Clark, NV................ 49.9 828.7 2.6 74 831 -0.6 279
Washoe, NV............... 13.7 183.9 1.8 133 832 0.5 179
Hillsborough, NH......... 12.0 187.9 0.6 232 1,042 0.8 154
Rockingham, NH........... 10.5 132.9 0.9 199 917 2.6 31
Atlantic, NJ............. 6.6 128.5 -1.4 324 796 -0.7 283
Bergen, NJ............... 32.9 425.5 1.0 193 1,187 -1.7 314
Burlington, NJ........... 11.0 196.4 2.6 74 1,013 1.0 133
Camden, NJ............... 12.0 193.8 0.5 243 962 1.4 99
Essex, NJ................ 20.4 335.1 0.3 260 1,326 0.8 154
Gloucester, NJ........... 6.1 96.4 0.1 273 823 1.6 78
Hudson, NJ............... 14.0 235.0 1.4 164 1,521 0.3 200
Mercer, NJ............... 10.9 229.5 0.3 260 1,477 6.4 6
Middlesex, NJ............ 21.7 386.6 1.0 193 1,257 -5.8 333
Monmouth, NJ............. 19.9 237.8 0.5 243 985 1.8 65
Morris, NJ............... 17.1 272.5 0.6 232 1,582 0.0 233
Ocean, NJ................ 12.3 147.0 1.9 125 786 1.3 108
Passaic, NJ.............. 12.2 167.8 -0.7 311 964 1.2 115
Somerset, NJ............. 10.1 173.0 1.1 186 2,009 6.7 4
Union, NJ................ 14.3 221.2 1.3 172 1,249 -0.8 290
Bernalillo, NM........... 17.8 307.4 0.5 243 829 0.1 216
Albany, NY............... 10.1 221.0 1.2 178 998 2.6 31
Bronx, NY................ 17.3 240.9 2.2 101 864 1.2 115
Broome, NY............... 4.6 87.9 -1.9 331 734 0.8 154
Dutchess, NY............. 8.3 109.5 -0.6 309 958 0.1 216
Erie, NY................. 24.1 450.6 0.3 260 853 1.1 122
Kings, NY................ 54.8 527.5 1.7 142 750 0.8 154
Monroe, NY............... 18.4 370.8 -0.2 290 903 1.5 90
Nassau, NY............... 53.0 587.1 0.8 212 1,078 0.9 141
New York, NY............. 124.7 2,403.9 1.7 142 2,448 -0.5 269
Oneida, NY............... 5.3 102.1 -1.8 329 761 3.0 21
Onondaga, NY............. 13.0 238.5 0.1 273 882 0.9 141
Orange, NY............... 9.9 130.7 0.5 243 789 0.0 233
Queens, NY............... 48.3 525.3 2.2 101 894 1.7 71
Richmond, NY............. 9.2 93.4 2.8 57 784 0.9 141
Rockland, NY............. 10.0 114.1 -0.3 295 1,042 -0.6 279
Saratoga, NY............. 5.6 77.2 1.8 133 861 3.1 19
Suffolk, NY.............. 51.2 614.8 0.6 232 1,033 -1.1 302
Westchester, NY.......... 36.2 399.8 -0.4 299 1,370 -2.8 325
Buncombe, NC............. 7.9 115.4 3.0 45 717 0.8 154
Catawba, NC.............. 4.3 79.9 1.1 186 709 1.6 78
Cumberland, NC........... 6.2 118.6 -0.8 316 748 1.8 65
Durham, NC............... 7.3 182.4 2.0 117 1,319 -2.2 322
Forsyth, NC.............. 8.9 175.9 1.7 142 927 -1.2 304
Guilford, NC............. 14.0 266.7 1.5 156 867 1.8 65
Mecklenburg, NC.......... 32.5 579.2 3.0 45 1,315 3.0 21
New Hanover, NC.......... 7.3 98.0 1.2 178 762 2.3 42
Wake, NC................. 29.4 465.9 3.2 36 989 1.4 99
Cass, ND................. 6.2 107.3 3.5 25 837 1.1 122
Butler, OH............... 7.5 136.4 0.4 250 848 1.8 65
Cuyahoga, OH............. 35.6 696.5 0.9 199 1,012 0.6 176
Delaware, OH............. 4.4 78.5 2.3 91 1,084 0.9 141
Franklin, OH............. 29.7 674.8 1.9 125 985 1.2 115
Hamilton, OH............. 23.1 485.2 0.1 273 1,109 1.6 78
Lake, OH................. 6.3 91.6 -0.3 295 825 3.4 16
Lorain, OH............... 6.1 92.4 -1.0 317 794 -0.4 259
Lucas, OH................ 10.1 198.3 -0.3 295 852 1.1 122
Mahoning, OH............. 5.9 96.1 0.4 250 671 0.1 216
Montgomery, OH........... 12.0 239.7 -0.5 305 836 0.8 154
Stark, OH................ 8.8 153.2 0.9 199 737 -0.9 296
Summit, OH............... 14.2 251.4 -0.1 289 895 0.3 200
Warren, OH............... 4.3 76.3 0.8 212 835 1.7 71
Oklahoma, OK............. 25.3 431.9 1.3 172 935 1.7 71
Tulsa, OK................ 20.9 334.7 1.4 164 932 2.5 36
Clackamas, OR............ 13.1 141.0 2.9 51 849 0.6 176
Jackson, OR.............. 6.7 76.5 3.3 32 696 1.5 90
Lane, OR................. 11.0 137.1 1.3 172 717 1.1 122
Marion, OR............... 9.6 130.8 2.3 91 739 1.5 90
Multnomah, OR............ 30.7 445.6 2.0 117 986 0.8 154
Washington, OR........... 17.1 250.5 1.8 133 1,161 -3.5 331
Allegheny, PA............ 35.0 678.0 0.5 243 1,080 1.9 57
Berks, PA................ 8.9 163.5 0.6 232 835 0.2 210
Bucks, PA................ 19.5 245.2 0.0 283 906 1.3 108
Butler, PA............... 4.9 83.2 -0.7 311 894 2.3 42
Chester, PA.............. 15.2 236.6 0.5 243 1,240 -1.4 308
Cumberland, PA........... 6.0 123.3 1.1 186 894 2.1 47
Dauphin, PA.............. 7.3 175.0 0.2 269 989 2.6 31
Delaware, PA............. 13.8 209.0 0.7 225 1,057 -1.2 304
Erie, PA................. 7.1 122.1 -1.3 323 758 1.6 78
Lackawanna, PA........... 5.8 96.3 0.1 273 721 0.3 200
Lancaster, PA............ 12.8 217.5 0.4 250 787 1.7 71
Lehigh, PA............... 8.6 175.3 0.7 225 944 -0.4 259
Luzerne, PA.............. 7.6 137.4 -0.4 299 746 0.5 179
Montgomery, PA........... 27.1 464.6 0.4 250 1,290 -0.5 269
Northampton, PA.......... 6.6 103.1 0.9 199 844 1.6 78
Philadelphia, PA......... 34.9 634.0 0.7 225 1,158 0.8 154
Washington, PA........... 5.4 84.3 0.1 273 991 0.3 200
Westmoreland, PA......... 9.3 130.4 -1.0 317 760 0.1 216
York, PA................. 8.9 168.9 -0.7 311 838 1.3 108
Providence, RI........... 17.4 266.9 0.3 260 999 2.7 28
Charleston, SC........... 12.2 215.3 1.9 125 839 1.0 133
Greenville, SC........... 12.4 234.7 2.1 107 834 0.5 179
Horry, SC................ 7.7 107.1 2.1 107 564 0.9 141
Lexington, SC............ 5.8 100.6 2.7 63 715 2.6 31
Richland, SC............. 9.0 205.5 0.8 212 835 1.5 90
Spartanburg, SC.......... 5.8 118.5 2.1 107 794 -0.3 254
York, SC................. 4.7 75.9 1.3 172 762 0.1 216
Minnehaha, SD............ 6.6 116.0 2.3 91 809 1.3 108
Davidson, TN............. 18.7 435.7 2.4 86 1,008 -0.3 254
Hamilton, TN............. 8.6 185.3 1.0 193 838 -0.8 290
Knox, TN................. 11.0 218.4 0.3 260 831 3.6 12
Rutherford, TN........... 4.6 106.7 5.3 6 815 -0.2 249
Shelby, TN............... 19.3 471.0 1.1 186 979 0.1 216
Williamson, TN........... 6.6 100.7 3.8 15 1,201 4.6 8
Bell, TX................. 4.9 109.2 1.2 178 788 1.9 57
Bexar, TX................ 35.8 764.9 2.9 51 891 0.7 168
Brazoria, TX............. 5.1 94.7 2.8 57 966 3.2 18
Brazos, TX............... 4.0 91.3 4.7 9 696 -0.1 243
Cameron, TX.............. 6.3 131.0 0.6 232 573 1.1 122
Collin, TX............... 19.9 320.4 3.7 19 1,173 -2.9 327
Dallas, TX............... 69.7 1,473.4 2.9 51 1,215 0.4 192
Denton, TX............... 11.9 190.8 3.5 25 868 2.7 28
El Paso, TX.............. 14.2 280.1 1.5 156 664 -0.4 259
Fort Bend, TX............ 10.2 153.7 7.0 1 1,029 0.1 216
Galveston, TX............ 5.6 98.0 2.7 63 882 2.1 47
Gregg, TX................ 4.2 78.0 1.6 150 845 -1.9 318
Harris, TX............... 104.9 2,163.6 3.7 19 1,333 -0.4 259
Hidalgo, TX.............. 11.6 234.6 1.9 125 580 0.0 233
Jefferson, TX............ 5.8 119.3 -1.9 331 979 -0.5 269
Lubbock, TX.............. 7.1 127.4 2.6 74 714 2.1 47
McLennan, TX............. 4.9 101.6 1.2 178 767 0.3 200
Midland, TX.............. 5.1 82.9 6.9 2 1,207 0.5 179
Montgomery, TX........... 9.5 146.8 5.0 8 998 1.0 133
Nueces, TX............... 8.0 160.2 3.2 36 835 2.0 54
Potter, TX............... 3.9 76.9 1.5 156 755 0.4 192
Smith, TX................ 5.8 94.3 1.6 150 763 0.1 216
Tarrant, TX.............. 39.1 795.6 2.4 86 960 0.7 168
Travis, TX............... 33.1 625.3 4.1 13 1,058 -0.4 259
Webb, TX................. 5.0 91.9 1.5 156 632 1.0 133
Williamson, TX........... 8.2 136.7 3.3 32 1,053 -13.4 334
Davis, UT................ 7.4 107.2 2.2 101 769 0.7 168
Salt Lake, UT............ 38.1 598.8 3.6 24 916 0.5 179
Utah, UT................. 13.1 181.3 5.5 5 729 0.8 154
Weber, UT................ 5.4 92.8 2.6 74 687 0.1 216
Chittenden, VT........... 6.2 96.3 0.1 273 930 1.6 78
Arlington, VA............ 8.8 164.7 -1.6 327 1,621 -0.3 254
Chesterfield, VA......... 8.0 119.1 2.6 74 860 1.4 99
Fairfax, VA.............. 35.5 586.2 0.9 199 1,565 0.4 192
Henrico, VA.............. 10.4 178.9 1.6 150 1,039 1.0 133
Loudoun, VA.............. 10.3 143.7 2.9 51 1,198 2.4 39
Prince William, VA....... 8.2 115.1 2.9 51 831 -0.4 259
Alexandria City, VA...... 6.4 95.4 1.4 164 1,296 0.7 168
Chesapeake City, VA...... 5.8 94.7 0.4 250 764 1.9 57
Newport News City, VA.... 3.7 96.6 0.9 199 962 3.6 12
Norfolk City, VA......... 5.7 135.8 -1.1 320 936 1.5 90
Richmond City, VA........ 7.1 146.8 -0.5 305 1,111 -0.7 283
Virginia Beach City, VA.. 11.4 165.8 1.7 142 755 0.3 200
Benton, WA............... 5.9 77.4 -1.2 322 948 -0.7 283
Clark, WA................ 14.1 131.0 2.7 63 867 1.9 57
King, WA................. 84.1 1,175.0 3.1 41 1,288 1.6 78
Kitsap, WA............... 6.8 79.3 0.1 273 876 0.9 141
Pierce, WA............... 22.3 264.9 1.4 164 864 2.7 28
Snohomish, WA............ 19.9 260.2 2.8 57 1,085 1.7 71
Spokane, WA.............. 16.3 199.6 2.0 117 814 0.9 141
Thurston, WA............. 7.8 98.6 1.8 133 846 2.3 42
Whatcom, WA.............. 7.1 81.0 0.4 250 802 1.1 122
Yakima, WA............... 9.1 96.0 1.9 125 641 1.9 57
Kanawha, WV.............. 6.0 103.9 -1.1 320 821 -1.6 311
Brown, WI................ 6.5 144.7 0.6 232 838 -0.2 249
Dane, WI................. 14.2 303.7 1.1 186 938 -0.3 254
Milwaukee, WI............ 23.7 469.8 0.6 232 975 0.0 233
Outagamie, WI............ 5.0 100.8 1.5 156 805 2.0 54
Waukesha, WI............. 12.5 224.5 1.1 186 971 1.5 90
Winnebago, WI............ 3.6 88.3 -1.0 317 909 4.6 8
San Juan, PR............. 11.2 260.7 -1.0 (7) 617 -0.3 (7)
(1) Includes workers covered by Unemployment Insurance (UI) and Unemployment Compensation for Federal
Employees (UCFE) programs. These 334 U.S. counties comprise 71.6 percent of the total covered workers
in the U.S.
(2) Data are preliminary.
(3) Includes areas not officially designated as counties. See Technical Note.
(4) Average weekly wages were calculated using unrounded data.
(5) Percent changes were computed from quarterly employment and pay data adjusted for noneconomic
county reclassifications. See Technical Note.
(6) Totals for the United States do not include data for Puerto Rico or the Virgin Islands.
(7) This county was not included in the U.S. rankings.
Table 2. Covered(1) establishments, employment, and wages in the 10 largest counties,
first quarter 2013(2)
Employment Average weekly
wage(3)
Establishments,
first quarter
County by NAICS supersector 2013 Percent Percent
(thousands) March change, First change,
2013 March quarter first
(thousands) 2012-13(4) 2013 quarter
2012-13(4)
United States(5) ............................ 9,193.5 132,338.9 1.6 $989 0.6
Private industry........................... 8,899.6 110,877.4 2.0 995 0.5
Natural resources and mining............. 132.2 1,880.6 1.8 1,190 -0.3
Construction............................. 745.2 5,476.8 3.4 979 0.9
Manufacturing............................ 335.3 11,908.5 0.9 1,227 0.1
Trade, transportation, and utilities..... 1,897.2 25,080.6 1.3 819 0.9
Information.............................. 143.8 2,682.6 0.1 1,772 3.6
Financial activities..................... 815.6 7,539.8 1.8 1,924 1.1
Professional and business services....... 1,622.7 18,132.5 2.9 1,288 -0.6
Education and health services............ 1,415.9 20,157.2 1.9 837 1.2
Leisure and hospitality.................. 777.2 13,703.0 2.8 382 -0.3
Other services........................... 794.2 4,100.7 0.6 620 1.5
Government................................. 293.9 21,461.5 -0.4 958 0.9
Los Angeles, CA.............................. 412.4 4,041.3 2.2 1,061 -1.8
Private industry........................... 406.7 3,503.8 2.9 1,037 -2.1
Natural resources and mining............. 0.4 10.3 7.5 1,615 -7.1
Construction............................. 12.2 112.7 5.6 1,039 0.7
Manufacturing............................ 12.5 365.4 -0.3 1,219 0.3
Trade, transportation, and utilities..... 51.7 761.6 1.8 851 0.2
Information.............................. 8.4 193.9 3.7 1,922 -7.0
Financial activities..................... 22.3 210.4 0.7 1,931 -3.0
Professional and business services....... 43.1 581.0 2.6 1,297 -2.2
Education and health services............ 179.3 690.5 5.2 782 -2.1
Leisure and hospitality.................. 27.9 424.0 4.7 541 -3.4
Other services........................... 25.2 138.8 -2.6 635 3.3
Government................................. 5.7 537.5 -2.3 1,216 0.4
Cook, IL..................................... 151.0 2,394.0 1.2 1,185 -1.0
Private industry........................... 149.6 2,095.7 1.4 1,191 -1.2
Natural resources and mining............. 0.1 0.7 -6.4 823 -5.0
Construction............................. 12.4 56.2 -0.3 1,297 -0.6
Manufacturing............................ 6.6 187.1 -1.4 1,171 -0.5
Trade, transportation, and utilities..... 29.7 443.3 1.9 900 -0.2
Information.............................. 2.7 53.7 -1.4 1,958 3.5
Financial activities..................... 15.6 181.8 -0.7 2,788 -5.0
Professional and business services....... 32.0 420.6 2.8 1,524 -0.2
Education and health services............ 15.9 415.6 1.4 872 1.0
Leisure and hospitality.................. 13.4 238.9 3.1 447 -2.2
Other services........................... 16.5 94.1 -0.9 819 2.9
Government................................. 1.4 298.3 -0.2 1,140 0.5
New York, NY................................. 124.7 2,403.9 1.7 2,448 -0.5
Private industry........................... 124.4 1,965.8 2.0 2,744 -0.8
Natural resources and mining............. 0.0 0.1 8.8 2,201 -20.4
Construction............................. 2.1 32.7 7.3 1,674 1.9
Manufacturing............................ 2.3 25.5 -0.7 1,488 -8.3
Trade, transportation, and utilities..... 20.9 251.2 1.8 1,318 -1.3
Information.............................. 4.4 139.5 1.1 2,939 3.3
Financial activities..................... 18.9 348.1 -1.1 7,659 2.3
Professional and business services....... 26.0 494.3 3.0 2,444 -4.4
Education and health services............ 9.4 318.0 2.4 1,175 4.4
Leisure and hospitality.................. 13.1 254.6 1.8 791 0.1
Other services........................... 19.5 93.8 3.3 1,040 -0.2
Government................................. 0.3 438.1 0.2 1,119 0.5
Harris, TX................................... 104.9 2,163.6 3.7 1,333 -0.4
Private industry........................... 104.4 1,906.2 4.1 1,376 -0.8
Natural resources and mining............. 1.7 92.2 8.0 3,984 -5.8
Construction............................. 6.5 145.9 6.3 1,258 3.7
Manufacturing............................ 4.6 193.0 4.6 1,641 -1.2
Trade, transportation, and utilities..... 23.6 444.5 3.2 1,248 -0.9
Information.............................. 1.2 28.0 -4.0 1,455 2.5
Financial activities..................... 10.8 114.6 2.5 1,967 2.1
Professional and business services....... 21.0 370.4 4.7 1,526 -1.9
Education and health services............ 14.4 258.8 3.2 936 1.2
Leisure and hospitality.................. 8.7 197.6 4.7 410 -2.6
Other services........................... 11.3 60.1 3.9 712 0.1
Government................................. 0.5 257.4 0.7 1,016 2.0
Maricopa, AZ................................. 94.3 1,710.2 2.6 945 0.0
Private industry........................... 93.6 1,501.1 2.9 953 0.0
Natural resources and mining............. 0.5 8.3 3.5 1,180 -7.3
Construction............................. 7.4 88.2 6.2 940 0.6
Manufacturing............................ 3.1 113.2 1.4 1,473 -3.0
Trade, transportation, and utilities..... 20.7 335.9 0.3 871 -2.0
Information.............................. 1.6 31.5 2.6 1,289 2.2
Financial activities..................... 10.7 148.7 5.5 1,439 4.9
Professional and business services....... 21.7 283.9 4.2 1,004 0.3
Education and health services............ 10.6 251.4 2.0 892 -0.7
Leisure and hospitality.................. 7.3 188.6 3.1 433 0.7
Other services........................... 6.4 47.2 0.3 620 1.8
Government................................. 0.7 209.1 0.4 889 0.6
Dallas, TX................................... 69.7 1,473.4 2.9 1,215 0.4
Private industry........................... 69.2 1,310.3 3.3 1,237 0.2
Natural resources and mining............. 0.6 9.0 7.1 4,291 -17.9
Construction............................. 3.9 70.0 5.9 1,041 3.8
Manufacturing............................ 2.7 110.0 -1.4 1,547 2.1
Trade, transportation, and utilities..... 15.1 294.6 2.5 1,038 -1.3
Information.............................. 1.5 46.6 3.4 2,323 4.8
Financial activities..................... 8.5 145.8 4.0 1,921 2.0
Professional and business services....... 15.4 286.2 4.4 1,314 -0.5
Education and health services............ 8.5 174.0 3.4 1,006 1.0
Leisure and hospitality.................. 6.0 134.9 4.2 472 -3.3
Other services........................... 6.6 38.6 1.8 714 3.0
Government................................. 0.5 163.1 -0.3 1,039 1.6
Orange, CA................................... 102.8 1,433.5 2.8 1,086 -0.5
Private industry........................... 101.5 1,291.0 3.2 1,064 -0.2
Natural resources and mining............. 0.2 3.5 5.2 664 -11.5
Construction............................. 6.0 73.7 7.2 1,120 0.4
Manufacturing............................ 4.8 157.1 0.2 1,357 -0.3
Trade, transportation, and utilities..... 16.3 248.3 1.9 979 -0.1
Information.............................. 1.2 24.9 1.5 1,730 3.6
Financial activities..................... 9.7 111.7 4.8 1,789 0.2
Professional and business services....... 19.1 262.0 3.2 1,235 0.1
Education and health services............ 23.3 179.4 3.6 861 -1.1
Leisure and hospitality.................. 7.5 185.2 4.9 432 -2.0
Other services........................... 6.1 40.1 -0.8 622 2.1
Government................................. 1.3 142.5 -0.8 1,288 -1.7
San Diego, CA................................ 100.4 1,297.9 2.3 1,056 -1.7
Private industry........................... 99.0 1,079.3 2.8 1,032 -1.7
Natural resources and mining............. 0.7 10.0 -2.5 577 0.7
Construction............................. 5.9 58.7 5.4 1,030 -3.3
Manufacturing............................ 2.9 94.6 0.3 1,533 -1.0
Trade, transportation, and utilities..... 13.8 207.4 2.0 867 3.0
Information.............................. 1.1 24.0 -1.3 1,615 -3.2
Financial activities..................... 8.5 71.0 3.2 1,542 -1.5
Professional and business services....... 16.5 225.3 3.5 1,433 -4.8
Education and health services............ 29.2 177.6 2.4 859 0.1
Leisure and hospitality.................. 7.3 161.5 3.7 425 -1.2
Other services........................... 6.5 44.5 1.5 556 1.1
Government................................. 1.4 218.6 -0.1 1,179 -0.8
King, WA..................................... 84.1 1,175.0 3.1 1,288 1.6
Private industry........................... 83.6 1,017.5 3.6 1,308 1.6
Natural resources and mining............. 0.4 2.5 -5.5 1,708 20.5
Construction............................. 5.3 49.9 11.2 1,164 0.6
Manufacturing............................ 2.2 104.1 3.3 1,738 1.1
Trade, transportation, and utilities..... 14.3 214.6 4.4 1,109 2.5
Information.............................. 1.8 80.6 0.9 2,507 -1.5
Financial activities..................... 6.3 64.0 2.7 1,852 3.6
Professional and business services....... 14.1 193.6 4.6 1,586 2.7
Education and health services............ 25.1 154.8 0.9 890 1.9
Leisure and hospitality.................. 6.4 114.3 4.2 453 1.8
Other services........................... 7.8 38.8 1.8 792 3.3
Government................................. 0.5 157.5 0.2 1,162 2.1
Miami-Dade, FL............................... 92.2 1,016.2 2.6 912 0.9
Private industry........................... 91.9 878.0 3.0 897 0.8
Natural resources and mining............. 0.5 9.4 -7.2 525 10.1
Construction............................. 5.2 31.7 7.8 814 -4.9
Manufacturing............................ 2.6 35.4 -1.3 872 2.2
Trade, transportation, and utilities..... 27.2 258.4 2.2 841 0.8
Information.............................. 1.5 17.3 2.2 1,471 1.4
Financial activities..................... 9.4 67.8 2.9 1,632 5.4
Professional and business services....... 19.3 136.5 5.1 1,066 -2.4
Education and health services............ 10.2 159.4 1.0 887 1.4
Leisure and hospitality.................. 7.0 124.8 5.3 514 -0.2
Other services........................... 8.1 36.4 3.4 552 2.0
Government................................. 0.3 138.2 -0.1 1,012 2.3
(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,
first quarter 2013(2)
Employment Average weekly
wage(3)
Establishments,
first quarter
State 2013 Percent Percent
(thousands) March change, First change,
2013 March quarter first
(thousands) 2012-13 2013 quarter
2012-13
United States(4)........... 9,193.5 132,338.9 1.6 $989 0.6
Alabama.................... 117.4 1,840.4 1.0 812 0.5
Alaska..................... 21.8 317.9 0.5 988 1.5
Arizona.................... 146.8 2,494.6 2.2 891 0.6
Arkansas................... 86.6 1,151.1 0.0 765 2.4
California................. 1,315.6 15,168.9 3.0 1,116 -0.2
Colorado................... 173.7 2,298.0 3.0 1,004 0.1
Connecticut................ 112.3 1,618.4 0.4 1,319 -0.5
Delaware................... 27.8 403.7 1.4 1,070 -0.2
District of Columbia....... 35.5 717.6 1.0 1,613 0.5
Florida.................... 621.6 7,540.7 2.2 843 0.7
Georgia.................... 273.7 3,878.7 1.8 940 1.0
Hawaii..................... 38.6 616.3 2.4 842 1.2
Idaho...................... 53.2 613.4 3.0 695 0.6
Illinois................... 398.1 5,601.4 0.7 1,058 -0.2
Indiana.................... 160.6 2,808.1 1.1 832 1.2
Iowa....................... 96.9 1,463.2 1.0 799 1.8
Kansas..................... 83.9 1,322.0 0.7 807 0.4
Kentucky................... 115.1 1,765.2 0.9 791 0.8
Louisiana.................. 127.1 1,885.8 1.0 847 1.3
Maine...................... 49.4 561.6 0.0 771 1.8
Maryland................... 170.0 2,509.0 0.8 1,066 -0.6
Massachusetts.............. 222.9 3,218.5 1.0 1,236 0.7
Michigan................... 239.1 3,950.7 2.1 922 0.3
Minnesota.................. 170.2 2,632.9 1.9 1,002 1.2
Mississippi................ 70.0 1,088.9 0.4 696 1.2
Missouri................... 179.8 2,610.3 0.7 842 0.6
Montana.................... 42.9 427.4 1.9 707 0.1
Nebraska................... 68.7 914.9 1.0 777 1.7
Nevada..................... 74.2 1,144.1 2.3 844 -0.2
New Hampshire.............. 48.9 606.0 0.7 938 1.6
New Jersey................. 262.7 3,780.4 1.1 1,234 0.6
New Mexico................. 55.8 784.7 0.6 778 -0.6
New York................... 612.6 8,565.7 1.0 1,362 0.4
North Carolina............. 255.1 3,934.4 1.6 884 1.7
North Dakota............... 30.2 415.0 4.4 885 3.1
Ohio....................... 287.7 5,004.8 0.7 884 1.1
Oklahoma................... 105.3 1,551.3 1.2 823 2.4
Oregon..................... 136.1 1,644.4 1.9 864 0.0
Pennsylvania............... 348.2 5,543.3 0.1 968 0.9
Rhode Island............... 35.4 445.3 0.8 954 2.4
South Carolina............. 114.9 1,823.7 1.4 773 1.2
South Dakota............... 31.5 394.3 1.0 709 0.9
Tennessee.................. 143.6 2,675.0 1.5 854 0.8
Texas...................... 602.5 10,928.5 3.0 1,015 0.3
Utah....................... 85.7 1,233.4 3.3 804 0.6
Vermont.................... 24.4 299.3 0.7 791 2.3
Virginia................... 243.0 3,616.8 0.9 1,027 0.8
Washington................. 240.7 2,890.8 2.3 1,028 1.8
West Virginia.............. 49.6 701.0 -0.7 767 -0.1
Wisconsin.................. 160.7 2,664.9 0.9 833 0.8
Wyoming.................... 25.5 272.2 0.1 859 0.8
Puerto Rico................ 48.3 931.3 0.0 515 -1.2
Virgin Islands............. 3.4 39.8 -6.7 726 0.4
(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
