For release 10:00 a.m. (EDT), Thursday, September 17, 2015 USDL-15-1788
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 2015
From March 2014 to March 2015, employment increased in 323 of the 342 largest U.S. counties
(counties with 75,000 or more jobs in 2014), the U.S. Bureau of Labor Statistics reported today. Utah,
Utah, had the largest percentage increase, with a gain of 6.7 percent over the year, compared with
national job growth of 2.1 percent. Within Utah, the largest employment increase occurred in trade,
transportation, and utilities, which gained 2,962 jobs over the year (8.9 percent). Atlantic, N.J., had the
largest over-the-year percentage decrease in employment among the largest counties in the U.S. with a
loss of 4.3 percent. County employment and wage data are compiled under the Quarterly Census of
Employment and Wages (QCEW) program, which produces detailed information on county employment
and wages within 6 months after the end of each quarter.
The U.S. average weekly wage increased 2.1 percent over the year, growing to $1,048 in the first
quarter of 2015. Olmsted, Minn., had the largest over-the-year percentage increase in average weekly
wages with a gain of 11.7 percent. Within Olmsted, an average weekly wage gain of $243, or 18.6
percent, in education and health services made the largest contribution to the county’s increase in
average weekly wages. Snohomish, Wash., experienced the largest percentage decrease in average
weekly wages with a loss of 4.8 percent over the year.
Table A. Large counties ranked by March 2015 employment, March 2014-15 employment increase, and
March 2014-15 percent increase in employment
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Employment in large counties
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March 2015 employment | Increase in employment, | Percent increase in employment,
(thousands) | March 2014-15 | March 2014-15
| (thousands) |
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| |
United States 137,412.4| United States 2,872.7| United States 2.1
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| |
Los Angeles, Calif. 4,204.3| Los Angeles, Calif. 86.4| Utah, Utah 6.7
Cook, Ill. 2,470.3| Harris, Texas 66.6| Adams, Colo. 5.8
New York, N.Y. 2,346.5| New York, N.Y. 60.5| Denton, Texas 5.8
Harris, Texas 2,288.8| Dallas, Texas 56.6| Montgomery, Texas 5.8
Maricopa, Ariz. 1,803.5| Maricopa, Ariz. 52.0| Lee, Fla. 5.7
Dallas, Texas 1,570.9| King, Wash. 42.5| Chatham, Ga. 5.3
Orange, Calif. 1,501.2| Santa Clara, Calif. 38.5| Calcasieu, La. 5.3
San Diego, Calif. 1,352.1| Clark, Nev. 37.0| Clay, Mo. 5.3
King, Wash. 1,254.9| Orange, Calif. 36.4| Weld, Colo. 5.2
Miami-Dade, Fla. 1,074.6| Cook, Ill. 36.1| Collier, Fla. 5.2
| | Williamson, Tenn. 5.2
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Large County Employment
In March 2015, national employment was 137.4 million (as measured by the QCEW program). Over the
year, employment increased 2.1 percent, or 2.9 million. In March 2015, the 342 U.S. counties with
75,000 or more jobs accounted for 72.3 percent of total U.S. employment and 78.6 percent of total
wages. These 342 counties had a net job growth of 2.2 million over the year, accounting for 75.9 percent
of the overall U.S. employment increase.
Utah, Utah, had the largest percentage increase in employment (6.7 percent) among the largest U.S.
counties. The five counties with the largest increases in employment levels were Los Angeles, Calif.;
Harris, Texas; New York, N.Y.; Dallas, Texas; and Maricopa, Ariz. These counties had a combined
over-the-year employment gain of 322,100 jobs, which was 11.2 percent of the overall job increase for
the U.S. (See table A.)
Employment declined in 17 of the largest counties from March 2014 to March 2015. Atlantic, N.J., had
the largest over-the-year percentage decrease in employment (-4.3 percent). Within Atlantic, leisure and
hospitality had the largest decrease in employment, with a loss of 6,587 jobs (-15.9 percent). New
London, Conn., had the second largest percentage decrease in employment, followed by Cumberland,
N.C.; Broome, N.Y.; and Lafayette, La. (See table 1.)
Table B. Large counties ranked by first quarter 2015 average weekly wages, first quarter 2014-15
increase in average weekly wages, and first quarter 2014-15 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 2015 | wage, first quarter 2014-15 | weekly wage, first
| | quarter 2014-15
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| |
United States $1,048| United States $22| United States 2.1
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| |
New York, N.Y. $2,847| Lake, Ill. $134| Olmsted, Minn. 11.7
Santa Clara, Calif. 2,203| Olmsted, Minn. 120| Washington, Pa. 10.7
Somerset, N.J. 2,080| Washington, Pa. 118| Riverside, Calif. 10.1
San Francisco, Calif. 2,070| San Francisco, Calif. 115| Lake, Ill. 9.2
San Mateo, Calif. 2,066| Morris, N.J. 112| Orange, Calif. 9.1
Fairfield, Conn. 1,938| Santa Clara, Calif. 111| Weld, Colo. 7.5
Suffolk, Mass. 1,909| Orange, Calif. 102| Bristol, Mass. 7.4
Washington, D.C. 1,764| Williamson, Tenn. 82| Jefferson, Ala. 7.1
Morris, N.J. 1,755| Riverside, Calif. 79| Williamson, Tenn. 6.9
Arlington, Va. 1,732| Middlesex, Mass. 77| Ottawa, Mich. 6.8
| | Morris, N.J. 6.8
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Large County Average Weekly Wages
Average weekly wages for the nation increased to $1,048, a 2.1 percent increase, during the year ending
in the first quarter of 2015. Among the 342 largest counties, 297 had over-the-year increases in average
weekly wages. Olmsted, Minn., had the largest percentage wage increase among the largest U.S.
counties (11.7 percent).
Of the 342 largest counties, 39 experienced over-the-year decreases in average weekly wages.
Snohomish, Wash., had the largest percentage decrease in average weekly wages, with a loss of 4.8
percent. Within Snohomish, manufacturing had the largest impact on the county’s average weekly wage
decrease. Within this industry, average weekly wages declined by $290 (-13.4 percent) over the year.
Chester, Pa., had the second largest percentage decrease in average weekly wages, followed by
Williamson, Texas; Saginaw, Mich.; and Palm Beach, Fla. (See table 1.)
Ten Largest U.S. Counties
All of the 10 largest counties had over-the-year percentage increases in employment in March 2015.
Dallas, Texas, had the largest gain (3.7 percent). Within Dallas, professional and business services had
the largest over-the-year employment level increase among all private industry groups with a gain of
16,702 jobs, or 5.5 percent. Cook, Ill., had the smallest percentage increase in employment (1.5 percent)
among the 10 largest counties. (See table 2.)
Average weekly wages increased over the year in 9 of the 10 largest U.S. counties. Orange, Calif.,
experienced the largest percentage gain in average weekly wages (9.1 percent). Within Orange,
professional and business services had the largest impact on the county’s average weekly wage growth.
Within this industry, average weekly wages increased by $373, or 27.6 percent, over the year. New
York, N.Y., had the only decrease in average weekly wages (-1.3 percent) among the 10 largest
counties.
For More Information
The tables included in this release contain data for the nation and for the 342 U.S. counties with annual
average employment levels of 75,000 or more in 2014. March 2015 employment and 2015 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 9.5 million employer reports cover 137.4 million full- and part-
time workers. The QCEW program provides a quarterly and annual universe count of establishments,
employment, and wages at the county, MSA, state, and national levels by detailed industry. Data for the
first quarter of 2015 will be available electronically later at www.bls.gov/cew/. For additional
information about the quarterly employment and wages data, please read the Technical Note. Additional
information about the QCEW data may be obtained by calling (202) 691-6567.
Several BLS regional offices are issuing QCEW news releases targeted to local data users. For links to
these releases, see www.bls.gov/cew/cewregional.htm.
_____________
The County Employment and Wages release for second quarter 2015 is scheduled to be released
on Thursday, December 17, 2015.
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| |
| County Changes for the 2015 County Employment and Wages News Releases |
| |
| Counties with annual average employment of 75,000 or more in 2014 are included in this release and |
| will be included in future 2015 releases. Three counties have been added to the publication tables: |
| Butte, Calif.; Hall, Ga.; and Ector, Texas. |
| |
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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 2015 are preliminary and subject to revision.
For purposes of this release, large counties are defined as having employment levels of 75,000 or
greater. In addition, data for San Juan, Puerto Rico, are provided, but not used in calculating U.S.
averages, rankings, or in the analysis in the text. Each year, these large counties are selected on the
basis of the preliminary annual average of employment for the previous year. The 343 counties
presented in this release were derived using 2014 preliminary annual averages of employment. For
2015 data, three counties have been added to the publication tables: Butte, Calif.; Hall, Ga.; and
Ector, Texas. These counties will be included in all 2015 quarterly releases. The counties in table 2
are selected and sorted each year based on the annual average employment from the preceding
year.
The preliminary QCEW data presented in this release may differ from data released by the
individual states. These potential differences result from the states' continuing receipt of UI data
over time and ongoing review and editing. The individual states determine their data release
timetables.
Differences between QCEW, BED, and CES employment measures
The Bureau publishes three different establishment-based employment measures for any given
quarter. Each of these measures--QCEW, Business Employment Dynamics (BED), and Current
Employment Statistics (CES)--makes use of the quarterly UI employment reports in producing
data; however, each measure has a somewhat different universe coverage, estimation procedure,
and publication product.
Differences in coverage and estimation methods can result in somewhat different measures of
employment change over time. It is important to understand program differences and the intended
uses of the program products. (See table.) Additional information on each program can be obtained
from the program Web sites shown in the table.
Summary of Major Differences between QCEW, BED, and CES Employment Measures
---------------------------------------------------------------------------------
| QCEW | BED | CES
-----------|---------------------|----------------------|------------------------
Source |--Count of UI admini-|--Count of longitudi- |--Sample survey:
| strative records | nally-linked UI ad- | 588,000 establish-
| submitted by 9.5 | ministrative records| ments
| million establish- | submitted by 7.5 |
| ments in first | million private-sec-|
| quarter of 2015 | 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 civilian workers covered by
the Unemployment Compensation for Federal Employees (UCFE) program, employment and
wage data are compiled from quarterly reports submitted by four major federal payroll processing
centers on behalf of all federal agencies, with the exception of a few agencies which still report
directly to the individual SWA. In addition to the quarterly contribution reports, employers who
operate multiple establishments within a state complete a questionnaire, called the "Multiple
Worksite Report," which provides detailed information on the location and industry of each of their
establishments. QCEW employment and wage data are derived from microdata summaries of 9.4
million employer reports of employment and wages submitted by states to the BLS in 2014. These
reports are based on place of employment rather than place of residence.
UI and UCFE coverage is broad and has been basically comparable from state to state since 1978,
when the 1976 amendments to the Federal Unemployment Tax Act became effective, expanding
coverage to include most state and local government employees. In 2014, UI and UCFE programs
covered workers in 136.6 million jobs. The estimated 131.8 million workers in these jobs (after
adjustment for multiple jobholders) represented 96.3 percent of civilian wage and salary
employment. Covered workers received $7.017 trillion in pay, representing 93.8 percent of the
wage and salary component of personal income and 40.5 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. Coverage changes
may affect the over-the-year comparisons presented in this news release.
Concepts and methodology
Monthly employment is based on the number of workers who worked during or received pay for
the pay period including the 12th of the month. With few exceptions, all employees of covered
firms are reported, including production and sales workers, corporation officials, executives,
supervisory personnel, and clerical workers. Workers on paid vacations and part-time workers also
are included.
Average weekly wage values are calculated by dividing quarterly total wages by the average of the
three monthly employment levels (all employees, as described above) and dividing the result by
13, for the 13 weeks in the quarter. These calculations are made using unrounded employment and
wage values. The average wage values that can be calculated using rounded data from the BLS
database may differ from the averages reported. Included in the quarterly wage data are non-wage
cash payments such as bonuses, the cash value of meals and lodging when supplied, tips and other
gratuities, and, in some states, employer contributions to certain deferred compensation plans such
as 401(k) plans and stock options. Over-the-year comparisons of average weekly wages may
reflect fluctuations in average monthly employment and/or total quarterly wages between the
current quarter and prior year levels.
Average weekly wages are affected by the ratio of full-time to part-time workers as well as the
number of individuals in high-paying and low-paying occupations and the incidence of pay periods
within a quarter. For instance, the average weekly wage of the workforce could increase
significantly when there is a large decline in the number of employees that had been receiving
below-average wages. Wages may include payments to workers not present in the employment
counts because they did not work during the pay period including the 12th of the month. When
comparing average weekly wage levels between industries, states, or quarters, these factors should
be taken into consideration.
Wages measured by QCEW may be subject to periodic and sometimes large fluctuations. This
variability may be due to calendar effects resulting from some quarters having more pay dates than
others. The effect is most visible in counties with a dominant employer. In particular, this effect
has been observed in counties where government employers represent a large fraction of overall
employment. Similar calendar effects can result from private sector pay practices. However, these
effects are typically less pronounced for two reasons: employment is less concentrated in a single
private employer, and private employers use a variety of pay period types (weekly, biweekly,
semimonthly, monthly).
For example, the effect on over-the-year pay comparisons can be pronounced in federal
government due to the uniform nature of federal payroll processing. Most federal employees are
paid on a biweekly pay schedule. As a result, in some quarters federal wages include six pay dates,
while in other quarters there are seven pay dates. Over-the-year comparisons of average weekly
wages may also reflect this calendar effect. Growth in average weekly wages may be attributed, in
part, to a comparison of quarterly wages for the current year, which include seven pay dates, with
year-ago wages that reflect only six pay dates. An opposite effect will occur when wages in the
current quarter reflecting six pay dates are compared with year-ago wages for a quarter including
seven pay dates.
In order to ensure the highest possible quality of data, states verify with employers and update, if
necessary, the industry, location, and ownership classification of all establishments on a 3-year
cycle. Changes in establishment classification codes resulting from this process are introduced with
the data reported for the first quarter of the year. Changes resulting from improved employer
reporting also are introduced in the first quarter.
QCEW data are not designed as a time series. QCEW data are simply the sums of individual
establishment records and reflect the number of establishments that exist in a county or industry at
a point in time. Establishments can move in or out of a county or industry for a number of reasons-
-some reflecting economic events, others reflecting administrative changes. For example,
economic change would come from a firm relocating into the county; administrative change would
come from a company correcting its county designation.
The over-the-year changes of employment and wages presented in this release have been adjusted
to account for most of the administrative corrections made to the underlying establishment reports.
This is done by modifying the prior-year levels used to calculate the over-the-year changes.
Percent changes are calculated using an adjusted version of the final 2014 quarterly data as the
base data. The adjusted prior-year levels used to calculate the over-the-year percent change in
employment and wages are not published. These adjusted prior-year levels do not match the
unadjusted data maintained on the BLS Web site. Over-the-year change calculations based on data
from the Web site, or from data published in prior BLS news releases, may differ substantially
from the over-the-year changes presented in this news release.
The adjusted data used to calculate the over-the-year change measures presented in this release
account for most of the administrative changes--those occurring when employers update the
industry, location, and ownership information of their establishments. The most common
adjustments for administrative change are the result of updated information about the county
location of individual establishments. Included in these adjustments are administrative changes
involving the classification of establishments that were previously reported in the unknown or
statewide county or unknown industry categories. Adjusted data account for improvements in
reporting employment and wages for individual and multi-unit establishments. To accomplish this,
adjustments were implemented to account for: administrative changes caused by multi-unit
employers who start reporting for each individual establishment rather than as a single entity (first
quarter of 2008); selected large administrative changes in employment and wages (second quarter
of 2011); and state verified improvements in reporting of employment and wages (third quarter of
2014). These adjustments allow QCEW to include county employment and wage growth rates in
this news release that would otherwise not meet publication standards.
The adjusted data used to calculate the over-the-year change measures presented in any County
Employment and Wages news release are valid for comparisons between the starting and ending
points (a 12-month period) used in that particular release. Comparisons may not be valid for any
time period other than the one featured in a release even if the changes were calculated using
adjusted data.
County definitions are assigned according to Federal Information Processing Standards
Publications (FIPS PUBS) as issued by the National Institute of Standards and Technology, after
approval by the Secretary of Commerce pursuant to Section 5131 of the Information Technology
Management Reform Act of 1996 and the Computer Security Act of 1987, Public Law 104-106.
Areas shown as counties include those designated as independent cities in some jurisdictions and,
in Alaska, those designated as census areas where counties have not been created. County data also
are presented for the New England states for comparative purposes even though townships are the
more common designation used in New England (and New Jersey). The regions referred to in this
release are defined as census regions.
Additional statistics and other information
Employment and Wages Annual Averages Online features comprehensive information by detailed
industry on establishments, employment, and wages for the nation and all states. The 2013 edition
of this publication, which was published in September 2014, contains selected data produced by
Business Employment Dynamics (BED) on job gains and losses, as well as selected data from the
first quarter 2014 version of this news release. Tables and additional content from Employment
and Wages Annual Averages 2013 are now available online at
http://www.bls.gov/cew/cewbultn13.htm. The 2014 edition of Employment and Wages Annual
Averages Online will be available in September 2015.
News releases on quarterly measures of gross job flows also are available upon request from the
Division of Administrative Statistics and Labor Turnover (Business Employment Dynamics),
telephone (202) 691-6467; (http://www.bls.gov/bdm/); (e-mail: BDMInfo@bls.gov).
Information in this release will be made available to sensory impaired individuals upon request.
Voice phone: (202) 691-5200; TDD message referral phone number: 1-800-877-8339.
Table 1. Covered establishments, employment, and wages in the 343 largest counties,
first quarter 2015
Employment Average weekly wage(2)
Establishments,
County(1) first quarter Percent Ranking Percent Ranking
2015 March change, by First change, by
(thousands) 2015 March percent quarter first percent
(thousands) 2014-15(3) change 2015 quarter change
2014-15(3)
United States(4)......... 9,531.3 137,412.4 2.1 - $1,048 2.1 -
Jefferson, AL............ 17.6 335.5 0.3 309 1,066 7.1 8
Madison, AL.............. 9.1 183.8 1.9 165 1,052 0.2 292
Mobile, AL............... 9.6 166.9 1.1 243 834 1.8 162
Montgomery, AL........... 6.3 128.0 0.6 290 801 1.5 195
Shelby, AL............... 5.4 82.3 2.4 135 991 2.5 108
Tuscaloosa, AL........... 4.3 90.8 3.7 45 795 -0.5 314
Anchorage Borough, AK.... 8.3 151.6 0.7 282 1,105 3.0 67
Maricopa, AZ............. 94.7 1,803.5 3.0 90 986 0.9 256
Pima, AZ................. 18.9 355.6 0.8 267 820 0.0 298
Benton, AR............... 5.9 107.0 3.7 45 1,302 0.0 298
Pulaski, AR.............. 14.4 242.3 0.2 314 890 0.9 256
Washington, AR........... 5.7 98.6 3.8 40 775 -0.4 310
Alameda, CA.............. 58.3 719.4 3.4 64 1,336 2.8 81
Butte, CA................ 7.9 76.9 2.8 103 726 5.4 17
Contra Costa, CA......... 30.3 341.1 1.7 186 1,296 3.5 48
Fresno, CA............... 31.6 355.1 2.9 97 771 2.1 133
Kern, CA................. 17.4 295.9 1.1 243 871 0.8 266
Los Angeles, CA.......... 448.7 4,204.3 2.1 155 1,120 2.6 96
Marin, CA................ 12.1 110.5 2.1 155 1,241 4.5 29
Monterey, CA............. 13.0 168.3 3.0 90 850 1.8 162
Orange, CA............... 110.2 1,501.2 2.5 127 1,221 9.1 5
Placer, CA............... 11.7 145.6 3.6 52 990 4.0 38
Riverside, CA............ 55.0 648.7 3.8 40 861 10.1 3
Sacramento, CA........... 53.5 618.6 3.4 64 1,106 2.6 96
San Bernardino, CA....... 52.8 675.7 4.0 31 811 2.0 142
San Diego, CA............ 102.6 1,352.1 2.5 127 1,130 0.2 292
San Francisco, CA........ 58.3 660.1 4.6 15 2,070 5.9 15
San Joaquin, CA.......... 16.9 224.7 4.1 29 818 2.4 114
San Luis Obispo, CA...... 9.9 113.4 3.2 76 806 2.9 76
San Mateo, CA............ 26.6 375.8 4.6 15 2,066 -0.2 307
Santa Barbara, CA........ 14.7 192.4 1.3 223 936 3.0 67
Santa Clara, CA.......... 67.2 1,001.6 4.0 31 2,203 5.3 20
Santa Cruz, CA........... 9.3 95.9 3.4 64 857 2.1 133
Solano, CA............... 10.4 128.6 2.8 103 1,051 1.6 185
Sonoma, CA............... 19.1 193.5 2.6 119 925 6.2 13
Stanislaus, CA........... 14.6 174.3 3.1 83 830 3.1 62
Tulare, CA............... 9.3 149.2 1.9 165 687 5.0 23
Ventura, CA.............. 25.2 317.8 0.7 282 1,039 -1.8 337
Yolo, CA................. 6.3 94.2 3.5 58 1,015 0.2 292
Adams, CO................ 9.8 188.3 5.8 2 930 1.8 162
Arapahoe, CO............. 20.4 310.2 3.2 76 1,256 0.6 279
Boulder, CO.............. 14.0 170.9 2.9 97 1,196 3.0 67
Denver, CO............... 29.0 470.6 4.7 14 1,350 1.7 175
Douglas, CO.............. 10.8 109.1 3.9 37 1,205 5.0 23
El Paso, CO.............. 17.7 250.5 2.6 119 892 1.8 162
Jefferson, CO............ 18.7 223.5 3.2 76 1,020 2.6 96
Larimer, CO.............. 11.0 143.0 4.0 31 906 5.3 20
Weld, CO................. 6.5 101.8 5.2 9 932 7.5 6
Fairfield, CT............ 34.3 415.4 1.4 214 1,938 0.8 266
Hartford, CT............. 26.7 499.6 1.1 243 1,407 1.8 162
New Haven, CT............ 23.2 356.4 0.6 290 1,032 0.4 286
New London, CT........... 7.1 118.1 -1.4 341 1,037 1.4 210
New Castle, DE........... 18.7 280.6 3.0 90 1,272 -1.2 330
Washington, DC........... 37.0 732.6 1.4 214 1,764 3.2 58
Alachua, FL.............. 6.9 122.5 2.3 142 804 2.6 96
Brevard, FL.............. 15.2 194.7 2.5 127 856 0.9 256
Broward, FL.............. 68.1 756.7 2.8 103 922 1.3 223
Collier, FL.............. 13.1 140.2 5.2 9 828 0.4 286
Duval, FL................ 28.3 466.2 3.0 90 992 1.1 239
Escambia, FL............. 8.2 124.8 1.9 165 771 4.3 34
Hillsborough, FL......... 40.4 639.6 3.1 83 973 1.9 150
Lake, FL................. 7.9 89.7 4.4 19 649 1.4 210
Lee, FL.................. 20.6 241.2 5.7 5 762 1.9 150
Leon, FL................. 8.5 142.8 1.5 210 774 0.7 274
Manatee, FL.............. 10.2 116.4 4.4 19 725 2.1 133
Marion, FL............... 8.3 96.9 3.7 45 664 1.2 235
Miami-Dade, FL........... 96.4 1,074.6 3.3 73 982 3.4 54
Okaloosa, FL............. 6.3 79.0 0.7 282 814 3.3 55
Orange, FL............... 39.5 754.1 3.4 64 891 2.1 133
Osceola, FL.............. 6.3 84.0 3.8 40 671 -1.5 334
Palm Beach, FL........... 53.8 566.5 4.0 31 991 -2.0 338
Pasco, FL................ 10.6 108.6 4.6 15 658 1.4 210
Pinellas, FL............. 32.1 407.1 3.1 83 865 2.5 108
Polk, FL................. 12.9 204.6 2.3 142 735 1.0 245
Sarasota, FL............. 15.5 160.4 4.5 18 796 0.8 266
Seminole, FL............. 14.5 170.7 3.3 73 826 2.5 108
Volusia, FL.............. 13.9 161.6 2.7 112 691 1.0 245
Bibb, GA................. 4.5 82.6 1.4 214 775 0.8 266
Chatham, GA.............. 8.3 143.5 5.3 6 852 1.5 195
Clayton, GA.............. 4.4 115.9 4.0 31 976 1.7 175
Cobb, GA................. 22.9 330.1 2.4 135 1,137 3.2 58
DeKalb, GA............... 19.1 286.9 1.9 165 1,066 0.4 286
Fulton, GA............... 45.3 781.8 4.2 26 1,506 0.7 274
Gwinnett, GA............. 25.8 332.9 3.7 45 996 0.5 282
Hall, GA................. 4.5 78.0 4.0 31 824 2.7 88
Muscogee, GA............. 4.8 94.0 -0.1 326 823 2.6 96
Richmond, GA............. 4.7 105.0 3.0 90 825 3.1 62
Honolulu, HI............. 24.9 461.9 1.1 243 918 2.8 81
Ada, ID.................. 13.9 212.7 2.1 155 873 2.0 142
Champaign, IL............ 4.5 88.5 0.6 290 852 2.3 119
Cook, IL................. 161.8 2,470.3 1.5 210 1,280 2.2 127
DuPage, IL............... 39.6 592.4 0.8 267 1,202 1.3 223
Kane, IL................. 14.2 201.2 0.3 309 856 1.3 223
Lake, IL................. 23.5 320.7 0.4 306 1,585 9.2 4
McHenry, IL.............. 9.1 93.0 -0.2 328 808 -0.4 310
McLean, IL............... 4.0 84.1 0.6 290 1,033 -1.1 327
Madison, IL.............. 6.3 96.0 0.8 267 803 1.0 245
Peoria, IL............... 4.9 99.2 -0.4 332 988 2.6 96
St. Clair, IL............ 5.8 92.1 1.1 243 766 0.7 274
Sangamon, IL............. 5.5 126.8 0.4 306 1,001 0.5 282
Will, IL................. 16.7 214.7 1.0 254 859 -0.7 318
Winnebago, IL............ 7.0 124.3 0.5 303 848 2.3 119
Allen, IN................ 8.8 177.9 2.2 150 841 1.1 239
Elkhart, IN.............. 4.7 122.3 3.6 52 834 3.0 67
Hamilton, IN............. 8.9 128.0 3.9 37 1,027 0.9 256
Lake, IN................. 10.3 183.9 0.2 314 890 3.6 45
Marion, IN............... 23.5 575.0 1.9 165 1,071 0.8 266
St. Joseph, IN........... 5.8 117.6 1.7 186 790 2.1 133
Tippecanoe, IN........... 3.3 81.4 2.7 112 867 4.8 26
Vanderburgh, IN.......... 4.7 105.2 1.1 243 822 2.2 127
Black Hawk, IA........... 3.8 74.1 -0.6 335 815 -1.5 334
Johnson, IA.............. 4.0 81.0 0.8 267 897 2.7 88
Linn, IA................. 6.6 127.8 1.4 214 1,003 4.8 26
Polk, IA................. 16.5 281.9 0.8 267 1,073 2.7 88
Scott, IA................ 5.5 89.0 1.7 186 793 1.5 195
Johnson, KS.............. 21.9 329.2 2.8 103 1,087 1.6 185
Sedgwick, KS............. 12.5 246.3 1.4 214 910 0.7 274
Shawnee, KS.............. 4.8 96.0 0.0 324 817 0.0 298
Wyandotte, KS............ 3.3 87.6 2.6 119 969 3.2 58
Boone, KY................ 4.2 79.1 3.0 90 833 1.3 223
Fayette, KY.............. 10.5 184.9 2.7 112 883 1.6 185
Jefferson, KY............ 24.6 442.3 2.5 127 1,016 2.3 119
Caddo, LA................ 7.2 115.3 0.7 282 794 1.5 195
Calcasieu, LA............ 4.9 91.6 5.3 6 858 0.2 292
East Baton Rouge, LA..... 14.5 268.2 2.2 150 942 3.1 62
Jefferson, LA............ 13.5 194.5 0.6 290 887 1.3 223
Lafayette, LA............ 9.2 139.7 -0.9 338 952 -0.5 314
Orleans, LA.............. 11.8 189.4 2.7 112 1,004 3.0 67
St. Tammany, LA.......... 7.6 84.1 3.5 58 871 2.6 96
Cumberland, ME........... 12.9 169.6 1.1 243 924 1.3 223
Anne Arundel, MD......... 14.5 253.2 1.1 243 1,077 1.8 162
Baltimore, MD............ 21.2 367.1 0.9 260 999 1.5 195
Frederick, MD............ 6.2 96.2 1.6 199 954 -0.7 318
Harford, MD.............. 5.6 87.9 0.8 267 936 2.0 142
Howard, MD............... 9.4 158.2 0.6 290 1,248 1.1 239
Montgomery, MD........... 32.6 454.0 0.9 260 1,407 2.9 76
Prince George's, MD...... 15.8 302.5 1.0 254 1,040 3.3 55
Baltimore City, MD....... 13.7 329.1 1.6 199 1,240 3.7 41
Barnstable, MA........... 9.2 82.8 0.3 309 840 1.1 239
Bristol, MA.............. 16.8 214.2 0.1 320 939 7.4 7
Essex, MA................ 23.2 311.5 1.5 210 1,057 1.0 245
Hampden, MA.............. 16.9 199.2 1.2 234 921 -0.4 310
Middlesex, MA............ 52.4 857.2 2.4 135 1,627 5.0 23
Norfolk, MA.............. 24.4 332.6 0.9 260 1,170 1.5 195
Plymouth, MA............. 14.9 180.2 0.9 260 904 1.5 195
Suffolk, MA.............. 26.6 626.0 2.2 150 1,909 2.8 81
Worcester, MA............ 23.3 329.4 1.9 165 986 0.8 266
Genesee, MI.............. 6.9 131.1 -0.2 328 822 4.2 37
Ingham, MI............... 6.0 144.7 -0.2 328 943 1.8 162
Kalamazoo, MI............ 5.1 113.2 1.2 234 954 2.9 76
Kent, MI................. 13.9 364.3 3.1 83 879 1.3 223
Macomb, MI............... 17.2 307.9 1.6 199 1,001 1.5 195
Oakland, MI.............. 38.2 688.9 1.4 214 1,147 3.5 48
Ottawa, MI............... 5.5 115.2 2.8 103 835 6.8 10
Saginaw, MI.............. 4.0 82.1 0.6 290 787 -2.4 339
Washtenaw, MI............ 8.1 201.7 1.8 177 1,033 3.7 41
Wayne, MI................ 30.3 692.5 1.2 234 1,141 1.2 235
Anoka, MN................ 6.9 116.4 1.7 186 914 3.5 48
Dakota, MN............... 9.7 180.1 2.3 142 1,013 1.9 150
Hennepin, MN............. 40.6 868.0 2.2 150 1,385 4.4 31
Olmsted, MN.............. 3.4 91.4 0.5 303 1,148 11.7 1
Ramsey, MN............... 13.3 321.1 1.3 223 1,259 5.4 17
St. Louis, MN............ 5.3 95.6 1.6 199 830 2.3 119
Stearns, MN.............. 4.3 82.7 1.9 165 795 5.4 17
Washington, MN........... 5.4 75.8 2.0 163 870 3.8 39
Harrison, MS............. 4.4 82.1 0.3 309 723 2.3 119
Hinds, MS................ 5.9 119.8 1.0 254 844 1.0 245
Boone, MO................ 4.8 90.1 1.6 199 771 3.1 62
Clay, MO................. 5.4 95.0 5.3 6 886 1.7 175
Greene, MO............... 8.4 159.3 1.8 177 750 1.8 162
Jackson, MO.............. 20.4 354.4 1.7 186 1,007 1.3 223
St. Charles, MO.......... 8.8 136.5 3.7 45 848 2.8 81
St. Louis, MO............ 35.0 580.5 1.3 223 1,099 2.6 96
St. Louis City, MO....... 11.9 221.2 1.8 177 1,175 0.8 266
Yellowstone, MT.......... 6.4 79.3 3.2 76 836 1.6 185
Douglas, NE.............. 18.4 326.6 1.3 223 960 2.7 88
Lancaster, NE............ 9.9 163.4 1.6 199 797 2.6 96
Clark, NV................ 53.1 898.1 4.3 22 853 -0.4 310
Washoe, NV............... 14.2 196.7 3.4 64 850 -0.7 318
Hillsborough, NH......... 12.1 194.0 1.7 186 1,070 -1.4 332
Rockingham, NH........... 10.6 138.3 1.9 165 983 3.7 41
Atlantic, NJ............. 6.6 120.9 -4.3 342 831 2.6 96
Bergen, NJ............... 32.8 435.1 1.0 254 1,226 -0.2 307
Burlington, NJ........... 11.1 192.8 1.3 223 1,056 1.6 185
Camden, NJ............... 11.9 197.5 2.5 127 946 1.0 245
Essex, NJ................ 20.4 334.2 0.6 290 1,359 1.4 210
Gloucester, NJ........... 6.2 100.7 2.3 142 849 1.0 245
Hudson, NJ............... 14.3 242.2 3.5 58 1,548 -0.9 323
Mercer, NJ............... 11.0 234.9 2.6 119 1,521 3.5 48
Middlesex, NJ............ 22.0 393.6 0.4 306 1,334 2.5 108
Monmouth, NJ............. 20.0 244.0 1.3 223 992 -0.9 323
Morris, NJ............... 17.0 278.7 0.8 267 1,755 6.8 10
Ocean, NJ................ 12.8 152.5 1.7 186 790 1.2 235
Passaic, NJ.............. 12.3 163.8 0.0 324 971 1.8 162
Somerset, NJ............. 10.1 178.9 1.8 177 2,080 0.6 279
Union, NJ................ 14.3 218.1 1.0 254 1,327 4.4 31
Bernalillo, NM........... 18.0 313.2 0.9 260 844 1.1 239
Albany, NY............... 10.3 227.6 2.1 155 1,007 0.0 298
Bronx, NY................ 18.4 297.6 3.2 76 901 0.9 256
Broome, NY............... 4.6 85.8 -1.0 339 759 1.7 175
Dutchess, NY............. 8.4 108.5 0.9 260 961 1.7 175
Erie, NY................. 24.5 453.3 0.6 290 885 1.3 223
Kings, NY................ 59.3 649.7 4.3 22 818 3.3 55
Monroe, NY............... 18.6 373.6 0.1 320 935 1.0 245
Nassau, NY............... 53.5 605.0 1.2 234 1,103 1.4 210
New York, NY............. 129.0 2,346.5 2.6 119 2,847 -1.3 331
Oneida, NY............... 5.4 101.8 0.6 290 763 1.6 185
Onondaga, NY............. 13.0 238.2 0.1 320 895 -1.5 334
Orange, NY............... 10.2 137.0 1.9 165 805 1.0 245
Queens, NY............... 50.9 621.3 3.5 58 936 1.4 210
Richmond, NY............. 9.7 111.3 2.1 155 825 0.9 256
Rockland, NY............. 10.4 116.0 2.7 112 1,012 -1.4 332
Saratoga, NY............. 5.9 80.6 2.5 127 882 1.5 195
Suffolk, NY.............. 52.0 624.0 0.7 282 1,048 2.4 114
Westchester, NY.......... 36.5 411.8 1.6 199 1,419 -1.1 327
Buncombe, NC............. 8.5 121.5 3.5 58 728 -0.1 304
Catawba, NC.............. 4.3 82.1 1.7 186 761 4.8 26
Cumberland, NC........... 6.2 116.0 -1.3 340 741 1.4 210
Durham, NC............... 7.8 189.3 2.8 103 1,369 2.2 127
Forsyth, NC.............. 9.3 179.0 1.8 177 1,017 -0.8 322
Guilford, NC............. 14.2 273.0 2.4 135 901 1.6 185
Mecklenburg, NC.......... 34.7 627.7 4.3 22 1,397 1.2 235
New Hanover, NC.......... 7.6 103.4 3.6 52 782 1.0 245
Wake, NC................. 31.4 500.0 3.7 45 1,039 1.5 195
Cass, ND................. 6.8 113.6 2.8 103 914 5.3 20
Butler, OH............... 7.5 141.6 1.4 214 909 3.6 45
Cuyahoga, OH............. 35.5 699.7 0.3 309 1,071 1.8 162
Delaware, OH............. 4.8 81.2 0.2 314 1,107 -0.5 314
Franklin, OH............. 30.3 703.4 2.5 127 1,045 1.9 150
Hamilton, OH............. 23.1 495.3 1.3 223 1,122 0.6 279
Lake, OH................. 6.3 92.9 0.7 282 829 1.0 245
Lorain, OH............... 6.0 94.1 0.7 282 809 0.7 274
Lucas, OH................ 10.0 202.8 0.8 267 887 2.1 133
Mahoning, OH............. 5.8 96.4 0.7 282 698 2.2 127
Montgomery, OH........... 11.9 244.7 1.6 199 858 0.4 286
Stark, OH................ 8.6 155.4 0.8 267 759 1.3 223
Summit, OH............... 14.1 259.4 1.2 234 938 1.5 195
Warren, OH............... 4.6 83.9 2.0 163 873 1.7 175
Cleveland, OK............ 5.4 80.4 1.7 186 702 1.4 210
Oklahoma, OK............. 26.9 446.7 1.8 177 1,005 3.6 45
Tulsa, OK................ 21.7 347.5 2.3 142 980 0.9 256
Clackamas, OR............ 13.8 147.8 2.7 112 914 3.5 48
Jackson, OR.............. 6.9 80.3 3.4 64 744 1.8 162
Lane, OR................. 11.5 144.0 2.5 127 760 2.6 96
Marion, OR............... 9.9 140.8 4.2 26 770 2.1 133
Multnomah, OR............ 31.8 472.3 3.4 64 1,026 1.5 195
Washington, OR........... 17.9 270.5 3.6 52 1,278 6.0 14
Allegheny, PA............ 35.5 675.6 0.1 320 1,200 6.3 12
Berks, PA................ 8.9 167.1 1.4 214 881 1.5 195
Bucks, PA................ 19.7 250.0 1.2 234 930 1.1 239
Butler, PA............... 5.0 83.3 -0.6 335 919 1.9 150
Chester, PA.............. 15.2 240.1 0.6 290 1,363 -4.0 341
Cumberland, PA........... 6.2 127.7 2.4 135 908 -0.5 314
Dauphin, PA.............. 7.4 173.7 0.6 290 1,036 0.0 298
Delaware, PA............. 13.9 215.4 0.8 267 1,143 1.9 150
Erie, PA................. 7.2 122.6 1.0 254 770 1.4 210
Lackawanna, PA........... 5.8 95.8 -0.1 326 752 1.3 223
Lancaster, PA............ 13.1 224.3 1.9 165 818 1.9 150
Lehigh, PA............... 8.5 178.7 1.1 243 1,006 2.9 76
Luzerne, PA.............. 7.5 139.9 0.8 267 783 1.4 210
Montgomery, PA........... 27.3 468.0 0.8 267 1,387 3.0 67
Northampton, PA.......... 6.6 105.8 1.3 223 882 1.4 210
Philadelphia, PA......... 35.0 646.1 1.6 199 1,214 2.4 114
Washington, PA........... 5.4 86.3 1.5 210 1,219 10.7 2
Westmoreland, PA......... 9.3 129.5 0.2 314 785 1.3 223
York, PA................. 9.0 171.5 0.8 267 854 1.4 210
Providence, RI........... 17.4 276.1 1.3 223 1,077 1.9 150
Charleston, SC........... 13.2 230.2 3.1 83 880 1.5 195
Greenville, SC........... 13.2 252.2 2.8 103 858 0.5 282
Horry, SC................ 8.2 113.6 3.3 73 583 2.1 133
Lexington, SC............ 6.2 110.4 3.4 64 748 3.2 58
Richland, SC............. 9.6 210.8 1.1 243 862 1.4 210
Spartanburg, SC.......... 6.0 125.9 2.1 155 834 2.3 119
York, SC................. 5.2 83.6 3.1 83 800 3.0 67
Minnehaha, SD............ 6.9 121.0 2.1 155 872 2.1 133
Davidson, TN............. 20.5 444.7 2.9 97 1,085 2.6 96
Hamilton, TN............. 9.1 189.2 2.3 142 880 2.0 142
Knox, TN................. 11.6 228.0 2.6 119 858 2.5 108
Rutherford, TN........... 5.0 113.6 2.6 119 861 3.0 67
Shelby, TN............... 20.0 477.2 1.3 223 1,009 -0.7 318
Williamson, TN........... 7.6 111.2 5.2 9 1,262 6.9 9
Bell, TX................. 5.0 113.1 1.8 177 813 -0.9 323
Bexar, TX................ 37.7 810.1 3.2 76 937 2.4 114
Brazoria, TX............. 5.3 102.3 4.3 22 1,073 4.4 31
Brazos, TX............... 4.2 99.1 4.2 26 736 3.1 62
Cameron, TX.............. 6.4 135.3 1.4 214 593 2.4 114
Collin, TX............... 21.8 357.2 4.1 29 1,244 1.9 150
Dallas, TX............... 72.0 1,570.9 3.7 45 1,303 1.7 175
Denton, TX............... 13.0 214.3 5.8 2 905 2.3 119
Ector, TX................ 3.9 76.1 2.3 142 1,067 -1.1 327
El Paso, TX.............. 14.4 287.5 1.7 186 689 0.0 298
Fort Bend, TX............ 11.5 167.4 5.0 12 1,028 0.1 297
Galveston, TX............ 5.8 100.6 1.8 177 891 -0.9 323
Gregg, TX................ 4.2 78.0 1.1 243 875 0.3 290
Harris, TX............... 110.1 2,288.8 3.0 90 1,455 3.8 39
Hidalgo, TX.............. 11.9 245.2 2.8 103 607 1.7 175
Jefferson, TX............ 5.8 123.6 2.9 97 1,077 5.9 15
Lubbock, TX.............. 7.3 132.7 2.2 150 760 1.6 185
McLennan, TX............. 5.0 104.6 0.2 314 797 3.0 67
Midland, TX.............. 5.4 90.4 1.7 186 1,326 -0.2 307
Montgomery, TX........... 10.3 164.5 5.8 2 1,049 1.9 150
Nueces, TX............... 8.2 164.5 1.9 165 888 2.7 88
Potter, TX............... 3.9 78.4 1.2 234 794 2.8 81
Smith, TX................ 6.0 98.4 3.6 52 802 0.9 256
Tarrant, TX.............. 40.4 828.0 2.1 155 1,028 2.7 88
Travis, TX............... 36.3 676.0 3.8 40 1,153 3.7 41
Webb, TX................. 5.1 96.8 3.1 83 663 2.2 127
Williamson, TX........... 9.3 147.9 2.9 97 1,091 -3.1 340
Davis, UT................ 7.7 115.1 3.9 37 785 0.8 266
Salt Lake, UT............ 41.0 635.8 3.4 64 966 2.0 142
Utah, UT................. 14.1 202.0 6.7 1 786 1.9 150
Weber, UT................ 5.6 97.9 3.8 40 721 -0.1 304
Chittenden, VT........... 6.5 99.3 1.6 199 942 0.5 282
Arlington, VA............ 8.9 165.9 1.3 223 1,732 2.7 88
Chesterfield, VA......... 8.1 125.4 1.8 177 863 1.8 162
Fairfax, VA.............. 35.3 574.5 0.5 303 1,635 2.7 88
Henrico, VA.............. 10.6 181.7 2.9 97 1,061 0.3 290
Loudoun, VA.............. 10.9 146.9 0.9 260 1,246 0.2 292
Prince William, VA....... 8.5 121.2 1.7 186 862 -0.1 304
Alexandria City, VA...... 6.3 94.5 1.2 234 1,395 1.5 195
Chesapeake City, VA...... 5.7 95.5 -0.8 337 765 0.9 256
Newport News City, VA.... 3.7 97.0 -0.4 332 1,032 4.3 34
Norfolk City, VA......... 5.6 133.1 -0.4 332 979 1.6 185
Richmond City, VA........ 7.1 147.4 0.6 290 1,206 4.5 29
Virginia Beach City, VA.. 11.3 169.4 1.7 186 780 1.7 175
Benton, WA............... 5.7 80.4 4.4 19 970 1.7 175
Clark, WA................ 14.0 142.5 4.8 13 898 1.8 162
King, WA................. 84.6 1,254.9 3.5 58 1,391 2.8 81
Kitsap, WA............... 6.7 82.6 2.3 142 907 2.3 119
Pierce, WA............... 21.8 280.8 2.4 135 881 2.0 142
Snohomish, WA............ 20.2 271.7 2.4 135 1,102 -4.8 342
Spokane, WA.............. 15.6 207.4 2.7 112 848 2.9 76
Thurston, WA............. 7.9 105.1 3.2 76 881 2.0 142
Whatcom, WA.............. 7.1 84.4 2.6 119 811 1.6 185
Yakima, WA............... 7.9 102.9 3.6 52 658 0.9 256
Kanawha, WV.............. 6.0 102.5 -0.3 331 860 2.0 142
Brown, WI................ 6.5 148.0 0.8 267 900 2.5 108
Dane, WI................. 14.2 314.4 1.6 199 1,003 3.5 48
Milwaukee, WI............ 25.3 475.8 0.8 267 1,015 2.2 127
Outagamie, WI............ 5.0 102.8 1.9 165 845 1.9 150
Waukesha, WI............. 12.2 229.5 1.2 234 1,034 4.3 34
Winnebago, WI............ 3.5 88.7 0.2 314 954 2.8 81
San Juan, PR............. 10.8 250.4 -2.1 (5) 631 1.6 (5)
(1) Includes areas not officially designated as counties. See Technical Note.
(2) Average weekly wages were calculated using unrounded data.
(3) Percent changes were computed from quarterly employment and pay data adjusted for noneconomic
county reclassifications. See Technical Note.
(4) Totals for the United States do not include data for Puerto Rico or the Virgin Islands.
(5) This county was not included in the U.S. rankings.
Note: Data are preliminary. Includes workers covered by Unemployment Insurance (UI) and Unemployment
Compensation for Federal Employees (UCFE) programs. These 342 U.S. counties comprise 72.3 percent of
the total covered workers in the U.S.
Table 2. Covered establishments, employment, and wages in the 10 largest counties,
first quarter 2015
Employment Average weekly
wage(1)
Establishments,
first quarter
County by NAICS supersector 2015 Percent Percent
(thousands) March change, First change,
2015 March quarter first
(thousands) 2014-15(2) 2015 quarter
2014-15(2)
United States(3) ............................ 9,531.3 137,412.4 2.1 $1,048 2.1
Private industry........................... 9,232.7 115,901.4 2.5 1,056 2.0
Natural resources and mining............. 137.8 1,933.8 0.2 1,278 2.8
Construction............................. 760.9 6,016.1 4.9 1,016 1.7
Manufacturing............................ 341.4 12,219.9 1.4 1,275 1.3
Trade, transportation, and utilities..... 1,921.4 26,120.2 2.3 860 2.3
Information.............................. 150.6 2,717.9 0.7 1,959 2.5
Financial activities..................... 843.0 7,723.3 1.7 2,161 2.0
Professional and business services....... 1,710.4 19,178.9 2.9 1,391 2.9
Education and health services............ 1,512.6 20,903.3 2.0 865 1.6
Leisure and hospitality.................. 802.9 14,546.2 2.7 400 3.4
Other services........................... 823.3 4,237.2 1.6 657 2.5
Government................................. 298.6 21,511.0 0.4 1,006 2.3
Los Angeles, CA.............................. 448.7 4,204.3 2.1 1,120 2.6
Private industry........................... 442.8 3,647.3 2.1 1,097 2.7
Natural resources and mining............. 0.5 8.9 -7.4 1,717 26.3
Construction............................. 13.2 121.6 3.7 1,080 1.8
Manufacturing............................ 12.3 358.9 -1.5 1,258 3.5
Trade, transportation, and utilities..... 52.8 791.1 2.2 896 2.3
Information.............................. 9.4 202.4 -0.2 2,186 4.3
Financial activities..................... 24.4 209.1 -0.4 2,227 4.0
Professional and business services....... 46.8 590.4 1.9 1,381 2.8
Education and health services............ 206.1 718.8 1.4 805 2.7
Leisure and hospitality.................. 30.7 470.7 2.9 575 5.7
Other services........................... 27.4 144.2 1.1 671 4.4
Government................................. 5.9 557.1 1.8 1,278 2.5
New York, NY................................. 129.0 2,346.5 2.6 2,847 -1.3
Private industry........................... 128.1 2,084.4 2.8 3,049 -1.5
Natural resources and mining............. 0.0 0.1 -8.7 3,085 -22.0
Construction............................. 2.2 35.0 4.9 1,795 5.5
Manufacturing............................ 2.2 26.9 0.9 1,615 -10.0
Trade, transportation, and utilities..... 20.5 256.8 1.1 1,352 1.3
Information.............................. 4.9 152.0 1.7 3,177 -0.8
Financial activities..................... 19.2 361.9 1.6 8,932 -4.0
Professional and business services....... 27.1 532.3 3.7 2,667 4.1
Education and health services............ 9.8 332.7 3.0 1,215 1.2
Leisure and hospitality.................. 13.8 280.0 3.2 834 3.1
Other services........................... 20.4 99.0 2.0 1,153 6.5
Government................................. 0.8 262.1 1.6 1,232 0.9
Cook, IL..................................... 161.8 2,470.3 1.5 1,280 2.2
Private industry........................... 160.5 2,175.2 1.6 1,292 2.3
Natural resources and mining............. 0.1 0.9 18.2 1,104 29.6
Construction............................. 13.4 64.3 9.7 1,356 2.0
Manufacturing............................ 6.7 186.3 -0.1 1,231 0.3
Trade, transportation, and utilities..... 32.0 457.1 1.8 971 3.5
Information.............................. 2.8 54.0 2.5 2,076 2.6
Financial activities..................... 16.3 183.9 0.1 3,492 6.7
Professional and business services....... 34.7 453.5 2.8 1,543 -0.4
Education and health services............ 16.9 427.3 0.4 891 1.0
Leisure and hospitality.................. 14.6 248.5 1.1 477 5.1
Other services........................... 18.4 95.5 1.2 938 -3.9
Government................................. 1.3 295.2 0.7 1,192 1.0
Harris, TX................................... 110.1 2,288.8 3.0 1,455 3.8
Private industry........................... 109.6 2,020.0 3.1 1,505 3.9
Natural resources and mining............. 1.9 90.4 -1.1 4,460 7.1
Construction............................. 7.0 161.2 6.9 1,333 1.4
Manufacturing............................ 4.8 197.4 0.5 1,833 10.7
Trade, transportation, and utilities..... 24.8 471.8 3.5 1,364 5.2
Information.............................. 1.1 27.3 -2.4 1,527 1.3
Financial activities..................... 11.3 119.9 2.2 2,230 3.1
Professional and business services....... 22.2 392.5 2.2 1,741 1.8
Education and health services............ 15.0 276.1 4.6 966 0.9
Leisure and hospitality.................. 9.3 217.4 4.8 428 3.9
Other services........................... 11.8 65.0 3.5 795 3.9
Government................................. 0.6 268.8 2.0 1,078 3.1
Maricopa, AZ................................. 94.7 1,803.5 3.0 986 0.9
Private industry........................... 94.0 1,591.6 3.2 995 0.7
Natural resources and mining............. 0.5 8.6 3.1 1,179 1.1
Construction............................. 7.3 94.4 2.6 965 -1.5
Manufacturing............................ 3.2 114.0 -0.8 1,532 2.3
Trade, transportation, and utilities..... 20.0 354.4 2.8 905 1.2
Information.............................. 1.6 34.1 4.0 1,405 -4.4
Financial activities..................... 11.2 157.5 4.1 1,479 -2.2
Professional and business services....... 22.1 301.2 2.7 1,092 3.2
Education and health services............ 10.8 268.5 4.2 922 1.9
Leisure and hospitality.................. 7.5 204.3 2.9 451 2.5
Other services........................... 6.4 49.7 4.9 658 -1.5
Government................................. 0.7 211.9 0.9 917 1.9
Dallas, TX................................... 72.0 1,570.9 3.7 1,303 1.7
Private industry........................... 71.5 1,401.7 4.0 1,328 1.6
Natural resources and mining............. 0.6 9.6 2.4 4,845 7.2
Construction............................. 4.1 78.0 4.7 1,099 0.1
Manufacturing............................ 2.7 105.1 -0.2 1,687 5.4
Trade, transportation, and utilities..... 15.6 318.4 5.1 1,094 0.6
Information.............................. 1.4 47.9 1.6 2,383 0.4
Financial activities..................... 8.7 153.8 2.7 2,155 2.1
Professional and business services....... 16.2 319.3 5.5 1,448 3.4
Education and health services............ 8.9 182.8 3.5 1,032 -3.4
Leisure and hospitality.................. 6.2 145.9 4.8 500 4.4
Other services........................... 6.8 40.4 1.9 787 1.7
Government................................. 0.5 169.2 1.4 1,094 2.3
Orange, CA................................... 110.2 1,501.2 2.5 1,221 9.1
Private industry........................... 108.8 1,352.3 2.5 1,209 10.1
Natural resources and mining............. 0.2 3.3 -12.1 961 11.9
Construction............................. 6.4 84.7 4.4 1,190 2.8
Manufacturing............................ 4.8 154.7 0.1 1,423 3.0
Trade, transportation, and utilities..... 16.5 250.7 1.5 1,043 7.2
Information.............................. 1.2 25.4 -0.8 1,921 6.5
Financial activities..................... 10.7 114.4 2.8 1,953 5.9
Professional and business services....... 20.1 279.1 1.2 1,726 27.6
Education and health services............ 27.9 189.8 2.0 882 2.3
Leisure and hospitality.................. 8.0 196.1 3.0 460 5.5
Other services........................... 6.8 43.2 2.1 657 3.8
Government................................. 1.4 148.9 2.2 1,336 1.8
San Diego, CA................................ 102.6 1,352.1 2.5 1,130 0.2
Private industry........................... 100.9 1,126.3 2.7 1,110 -0.2
Natural resources and mining............. 0.7 9.3 1.4 624 1.1
Construction............................. 6.3 65.8 5.8 1,092 2.5
Manufacturing............................ 3.0 103.4 2.7 1,681 -6.1
Trade, transportation, and utilities..... 14.0 212.5 1.2 864 -6.0
Information.............................. 1.1 23.1 -4.5 1,703 -4.8
Financial activities..................... 9.4 69.1 0.8 1,633 4.4
Professional and business services....... 17.7 224.4 1.7 1,716 3.7
Education and health services............ 28.4 184.6 2.6 881 1.7
Leisure and hospitality.................. 7.7 177.0 2.9 449 5.2
Other services........................... 7.3 48.2 1.3 572 4.4
Government................................. 1.7 225.9 1.0 1,230 1.7
King, WA..................................... 84.6 1,254.9 3.5 1,391 2.8
Private industry........................... 84.1 1,091.4 3.8 1,412 2.8
Natural resources and mining............. 0.4 2.5 9.3 1,440 -6.7
Construction............................. 6.1 61.2 14.2 1,206 3.1
Manufacturing............................ 2.3 106.6 0.6 1,792 -6.8
Trade, transportation, and utilities..... 14.8 235.5 5.1 1,233 5.9
Information.............................. 2.0 85.3 2.2 3,025 7.8
Financial activities..................... 6.5 65.1 1.1 2,017 5.1
Professional and business services....... 16.2 208.5 5.4 1,664 2.4
Education and health services............ 20.1 162.1 0.9 932 3.8
Leisure and hospitality.................. 6.9 122.8 3.0 491 4.0
Other services........................... 8.7 41.8 2.8 817 2.6
Government................................. 0.5 163.5 1.9 1,248 1.8
Miami-Dade, FL............................... 96.4 1,074.6 3.3 982 3.4
Private industry........................... 96.1 938.7 4.0 967 3.4
Natural resources and mining............. 0.5 9.8 -1.4 512 6.4
Construction............................. 5.6 38.3 9.9 913 1.7
Manufacturing............................ 2.8 38.2 2.1 907 0.7
Trade, transportation, and utilities..... 27.7 274.5 3.7 905 4.1
Information.............................. 1.5 17.8 -0.8 1,575 0.0
Financial activities..................... 10.1 72.8 4.3 1,886 5.1
Professional and business services....... 20.2 145.6 5.4 1,148 3.0
Education and health services............ 10.1 165.6 2.3 934 3.3
Leisure and hospitality.................. 7.2 133.6 2.5 544 3.6
Other services........................... 8.3 39.8 5.4 576 0.9
Government................................. 0.3 135.9 -0.9 1,084 3.2
(1) Average weekly wages were calculated using unrounded data.
(2) Percent changes were computed from quarterly employment and pay data adjusted for noneconomic
county reclassifications. See Technical Note.
(3) Totals for the United States do not include data for Puerto Rico or the Virgin Islands.
Note: Data are preliminary. Counties selected are based on 2014 annual average employment.
Includes workers covered by Unemployment Insurance (UI) and Unemployment Compensation for Federal
Employees (UCFE) programs.
Table 3. Covered establishments, employment, and wages by state,
first quarter 2015
Employment Average weekly
wage(1)
Establishments,
first quarter
State 2015 Percent Percent
(thousands) March change, First change,
2015 March quarter first
(thousands) 2014-15 2015 quarter
2014-15
United States(2)........... 9,531.3 137,412.4 2.1 $1,048 2.1
Alabama.................... 118.4 1,873.5 1.3 844 2.2
Alaska..................... 22.2 322.2 1.0 1,051 2.6
Arizona.................... 150.1 2,605.6 2.5 926 1.0
Arkansas................... 88.2 1,166.6 1.3 790 0.8
California................. 1,408.9 16,029.5 3.0 1,207 3.7
Colorado................... 183.9 2,458.0 3.7 1,071 2.4
Connecticut................ 114.9 1,640.5 0.8 1,382 1.5
Delaware................... 30.4 422.8 2.5 1,105 -0.5
District of Columbia....... 37.0 732.6 1.4 1,764 3.2
Florida.................... 657.1 8,018.0 3.6 885 1.8
Georgia.................... 288.1 4,107.0 3.5 989 1.7
Hawaii..................... 39.4 633.7 1.3 881 2.8
Idaho...................... 55.0 650.3 3.1 736 2.2
Illinois................... 422.9 5,724.6 1.2 1,130 2.4
Indiana.................... 159.4 2,894.8 1.8 857 1.4
Iowa....................... 100.4 1,504.3 1.3 848 2.9
Kansas..................... 86.4 1,357.1 1.0 851 1.4
Kentucky................... 121.5 1,810.3 1.5 823 1.5
Louisiana.................. 126.2 1,927.1 1.0 885 2.0
Maine...................... 50.1 571.4 0.9 793 0.9
Maryland................... 165.9 2,540.8 1.2 1,113 2.5
Massachusetts.............. 237.3 3,338.6 1.7 1,341 3.2
Michigan................... 237.2 4,079.5 1.8 969 1.9
Minnesota.................. 166.9 2,709.2 1.8 1,079 4.3
Mississippi................ 72.0 1,102.3 0.6 711 0.7
Missouri................... 189.9 2,678.0 1.7 882 1.8
Montana.................... 45.3 441.0 2.7 750 2.6
Nebraska................... 70.8 943.1 1.4 818 2.5
Nevada..................... 77.8 1,227.7 3.7 865 -0.2
New Hampshire.............. 50.1 623.5 1.5 982 1.2
New Jersey................. 266.6 3,834.6 1.4 1,288 1.9
New Mexico................. 56.6 798.7 1.4 805 1.5
New York................... 631.3 8,865.0 1.9 1,463 0.2
North Carolina............. 264.8 4,099.4 2.5 930 1.9
North Dakota............... 31.9 436.0 1.6 984 4.2
Ohio....................... 290.4 5,144.5 1.4 922 1.4
Oklahoma................... 108.5 1,592.7 1.3 869 2.0
Oregon..................... 141.5 1,748.7 3.5 919 2.9
Pennsylvania............... 351.8 5,606.9 0.9 1,031 2.4
Rhode Island............... 36.1 456.1 1.4 1,008 1.2
South Carolina............. 122.2 1,919.1 2.5 801 1.8
South Dakota............... 32.1 406.5 1.5 763 3.0
Tennessee.................. 150.6 2,772.7 2.1 886 1.4
Texas...................... 632.2 11,557.0 2.9 1,089 2.5
Utah....................... 91.3 1,318.8 3.7 845 1.7
Vermont.................... 24.6 303.9 0.9 824 2.0
Virginia................... 246.1 3,649.3 1.1 1,068 1.7
Washington................. 237.6 3,064.4 3.2 1,087 1.8
West Virginia.............. 49.9 692.4 -0.3 792 1.4
Wisconsin.................. 165.7 2,734.3 1.5 877 2.5
Wyoming.................... 25.9 277.8 0.8 892 1.7
Puerto Rico................ 46.2 904.9 -1.1 524 1.0
Virgin Islands............. 3.4 38.5 0.0 738 -0.7
(1) Average weekly wages were calculated using unrounded data.
(2) Totals for the United States do not include data for Puerto Rico or the Virgin Islands.
Note: Data are preliminary. Includes workers covered by Unemployment Insurance (UI) and
Unemployment Compensation for Federal Employees (UCFE) programs.
United States Department of Labor
