For release 10:00 a.m. (EST), Thursday, December 17, 2015 USDL-15-2392
Technical Information: (202) 691-6567 * QCEWInfo@bls.gov * www.bls.gov/cew
Media Contact: (202) 691-5902 * PressOffice@bls.gov
COUNTY EMPLOYMENT AND WAGES
Second Quarter 2015
From June 2014 to June 2015, employment increased in 319 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 7.5 percent over the year, compared with national job
growth of 2.0 percent. Within Utah, the largest employment increase occurred in trade, transportation,
and utilities, which gained 3,540 jobs over the year (10.3 percent). Ector, Texas, had the largest over-
the-year percentage decrease in employment among the largest counties in the U.S. with a loss of 4.2
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 3.0 percent over the year, growing to $968 in the second
quarter of 2015. Ventura, Calif., had the largest over-the-year percentage increase in average weekly
wages with a gain of 15.2 percent. Within Ventura, an average weekly wage gain of $934, or 53.8
percent, in manufacturing made the largest contribution to the county’s increase in average weekly
wages. Olmsted, Minn., experienced the largest percentage decrease in average weekly wages with a
loss of 5.2 percent over the year.
Table A. Large counties ranked by June 2015 employment, June 2014-15 employment increase, and
June 2014-15 percent increase in employment
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Employment in large counties
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June 2015 employment | Increase in employment, | Percent increase in employment,
(thousands) | June 2014-15 | June 2014-15
| (thousands) |
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| |
United States 140,594.9| United States 2,820.2| United States 2.0
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| |
Los Angeles, Calif. 4,232.7| Los Angeles, Calif. 82.8| Utah, Utah 7.5
Cook, Ill. 2,548.6| Dallas, Texas 64.1| Lee, Fla. 6.4
New York, N.Y. 2,378.9| Maricopa, Ariz. 54.8| Williamson, Tenn. 6.3
Harris, Texas 2,295.1| New York, N.Y. 54.5| Hall, Ga. 5.8
Maricopa, Ariz. 1,774.4| King, Wash. 46.7| Brazoria, Texas 5.6
Dallas, Texas 1,607.2| Orange, Calif. 39.8| Denton, Texas 5.1
Orange, Calif. 1,519.8| Santa Clara, Calif. 39.2| Calcasieu, La. 5.0
San Diego, Calif. 1,374.7| Harris, Texas 38.7| Davis, Utah 5.0
King, Wash. 1,285.2| Cook, Ill. 38.4| Benton, Ark. 4.9
Miami-Dade, Fla. 1,061.4| San Diego, Calif. 36.7| Manatee, Fla. 4.9
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Large County Employment
In June 2015, national employment was 140.6 million (as measured by the QCEW program). Over the
year, employment increased 2.0 percent, or 2.8 million. In June 2015, the 342 U.S. counties with 75,000
or more jobs accounted for 72.1 percent of total U.S. employment and 77.2 percent of total wages. These
342 counties had a net job growth of 2.2 million over the year, accounting for 78.3 percent of the overall
U.S. employment increase.
Utah, Utah, had the largest percentage increase in employment (7.5 percent) among the largest U.S.
counties. The five counties with the largest increases in employment levels were Los Angeles, Calif.;
Dallas, Texas; Maricopa, Ariz.; New York, N.Y.; and King, Wash. These counties had a combined over-
the-year employment gain of 302,900 jobs, which was 10.7 percent of the overall job increase for the
U.S. (See table A.)
Employment declined in 20 of the largest counties from June 2014 to June 2015. Ector, Texas, had the
largest over-the-year percentage decrease in employment (-4.2 percent). Within Ector, natural resources
and mining had the largest decrease in employment, with a loss of 2,352 jobs (-19.0 percent). Atlantic,
N.J., had the second largest percentage decrease in employment, followed by Gregg, Texas; Midland,
Texas; and Lafayette, La. (See table 1.)
Table B. Large counties ranked by second quarter 2015 average weekly wages, second quarter 2014-15
increase in average weekly wages, and second 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
second quarter 2015 | wage, second quarter 2014-15 | weekly wage, second
| | quarter 2014-15
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| |
United States $968| United States $28| United States 3.0
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| |
Santa Clara, Calif. $2,109| Santa Clara, Calif. $214| Ventura, Calif. 15.2
San Mateo, Calif. 1,863| Ventura, Calif. 143| Santa Clara, Calif. 11.3
New York, N.Y. 1,842| San Francisco, Calif. 137| Forsyth, N.C. 10.9
San Francisco, Calif. 1,730| San Mateo, Calif. 114| Riverside, Calif. 8.7
Washington, D.C. 1,599| Middlesex, Mass. 104| San Francisco, Calif. 8.6
Arlington, Va. 1,546| Forsyth, N.C. 91| Davidson, Tenn. 8.1
Fairfax, Va. 1,517| Davidson, Tenn. 78| Santa Barbara, Calif. 7.8
Suffolk, Mass. 1,512| Marin, Calif. 77| Middlesex, Mass. 7.5
Fairfield, Conn. 1,497| Santa Barbara, Calif. 69| Marin, Calif. 6.6
Middlesex, Mass. 1,491| Riverside, Calif. 66| San Mateo, Calif. 6.5
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Large County Average Weekly Wages
Average weekly wages for the nation increased to $968, a 3.0 percent increase, during the year ending in
the second quarter of 2015. Among the 342 largest counties, 323 had over-the-year increases in average
weekly wages. Ventura, Calif., had the largest percentage wage increase among the largest U.S. counties
(15.2 percent).
Of the 342 largest counties, 16 experienced over-the-year decreases in average weekly wages. Olmsted,
Minn., had the largest percentage decrease in average weekly wages, with a loss of 5.2 percent. Within
Olmsted, education and health services had the largest impact on the county’s average weekly wage
decrease. Within this industry, average weekly wages declined by $150 (-10.5 percent) over the year.
Ector, Texas, had the second largest percentage decrease in average weekly wages, followed by
Midland, Texas; Hillsborough, N.H.; and Lorain, Ohio. (See table 1.)
Ten Largest U.S. Counties
All of the 10 largest counties had over-the-year percentage increases in employment in June 2015.
Dallas, Texas, had the largest gain (4.2 percent). Within Dallas, trade, transportation, and utilities had
the largest over-the-year employment level increase, with a gain of 17,164 jobs, or 5.6 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 (4.9 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 $87, or 7.0 percent, over the year. Harris,
Texas, was the only county with unchanged average weekly wages 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. June 2015 employment and 2015 second quarter
average weekly wages for all states are provided in table 3 of this release.
The employment and wage data by county are compiled under the QCEW program, also known as the
ES-202 program. The data are derived from reports submitted by every employer subject to
unemployment insurance (UI) laws. The 9.6 million employer reports cover 140.6 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
second 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 third quarter 2015 is scheduled to be released on
Wednesday, March 9, 2016.
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| |
| County Name Change Effective with the BLS Release of Data for the Third Quarter of 2015 |
| |
| On May 1st, 2015, Shannon, S.D., was officially renamed Oglala Lakota, S.D. This county is not part of |
| this release because it has fewer than 75,000 jobs. However, BLS does publish data for this county. The |
| name change will be implemented with the BLS release of data for the third quarter of 2015. Data prior |
| to third quarter 2015 will still be available under Shannon, S.D. |
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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
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| 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 7.6 |
| 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| -7 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 2014 edition
of this publication, which was published in September 2015, contains selected data produced by
Business Employment Dynamics (BED) on job gains and losses, as well as selected data from the
first quarter 2015 version of this news release. Tables and additional content from the 2014 edition
of Employment and Wages Annual Averages Online are now available at
http://www.bls.gov/cew/cewbultn14.htm. The 2015 edition of Employment and Wages Annual
Averages Online will be available in September 2016.
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,
second quarter 2015
Employment Average weekly wage(2)
Establishments,
County(1) second quarter Percent Ranking Percent Ranking
2015 June change, by Second change, by
(thousands) 2015 June percent quarter second percent
(thousands) 2014-15(3) change 2015 quarter change
2014-15(3)
United States(4)......... 9,575.3 140,594.9 2.0 - $968 3.0 -
Jefferson, AL............ 17.7 339.4 0.4 303 945 1.7 252
Madison, AL.............. 9.1 186.1 1.7 183 1,051 0.3 319
Mobile, AL............... 9.6 167.6 0.1 315 827 1.7 252
Montgomery, AL........... 6.3 129.7 0.5 298 821 2.5 160
Shelby, AL............... 5.4 83.8 2.4 130 901 1.8 240
Tuscaloosa, AL........... 4.3 91.2 3.3 71 811 1.4 276
Anchorage Borough, AK.... 8.4 155.8 0.4 303 1,070 2.1 207
Maricopa, AZ............. 95.3 1,774.4 3.2 76 948 1.7 252
Pima, AZ................. 19.0 347.4 0.1 315 828 1.2 289
Benton, AR............... 5.9 111.2 4.9 9 931 3.8 51
Pulaski, AR.............. 14.5 244.7 0.8 275 877 2.5 160
Washington, AR........... 5.8 100.6 3.8 42 783 3.4 79
Alameda, CA.............. 59.0 730.8 3.1 82 1,257 5.0 19
Butte, CA................ 7.9 78.5 2.2 147 728 4.1 37
Contra Costa, CA......... 30.6 348.2 1.9 166 1,163 3.0 114
Fresno, CA............... 32.0 372.9 2.4 130 746 3.9 45
Kern, CA................. 17.5 309.0 -1.0 333 814 -1.0 333
Los Angeles, CA.......... 452.5 4,232.7 2.0 160 1,058 3.6 69
Marin, CA................ 12.2 113.4 2.7 102 1,243 6.6 9
Monterey, CA............. 13.0 198.7 0.4 303 809 2.7 143
Orange, CA............... 111.2 1,519.8 2.7 102 1,086 4.9 21
Placer, CA............... 11.8 148.9 3.4 67 965 4.8 24
Riverside, CA............ 55.7 656.4 4.1 26 828 8.7 4
Sacramento, CA........... 53.8 628.0 2.8 96 1,057 3.0 114
San Bernardino, CA....... 53.3 682.8 3.8 42 823 2.9 120
San Diego, CA............ 103.6 1,374.7 2.7 102 1,073 3.1 105
San Francisco, CA........ 58.7 668.9 4.5 15 1,730 8.6 5
San Joaquin, CA.......... 17.0 233.7 4.1 26 796 3.6 69
San Luis Obispo, CA...... 10.0 116.2 2.9 93 794 3.7 65
San Mateo, CA............ 26.8 383.4 4.8 11 1,863 6.5 10
Santa Barbara, CA........ 14.8 197.4 1.5 207 957 7.8 7
Santa Clara, CA.......... 67.7 1,018.7 4.0 32 2,109 11.3 2
Santa Cruz, CA........... 9.3 105.6 2.0 160 860 3.2 96
Solano, CA............... 10.5 132.2 3.3 71 998 3.4 79
Sonoma, CA............... 19.2 197.4 2.3 138 893 4.3 35
Stanislaus, CA........... 14.6 179.7 2.2 147 808 5.2 17
Tulare, CA............... 9.4 160.4 1.3 226 667 3.7 65
Ventura, CA.............. 25.3 316.8 0.8 275 1,085 15.2 1
Yolo, CA................. 6.3 99.2 2.6 113 990 2.7 143
Adams, CO................ 9.9 194.2 4.5 15 930 1.6 264
Arapahoe, CO............. 20.6 319.5 3.3 71 1,090 1.7 252
Boulder, CO.............. 14.2 174.0 2.7 102 1,137 3.3 87
Denver, CO............... 29.3 481.5 4.6 14 1,180 4.8 24
Douglas, CO.............. 10.9 115.0 3.7 47 1,108 -0.4 328
El Paso, CO.............. 17.9 259.2 2.8 96 864 1.8 240
Jefferson, CO............ 18.8 230.6 2.5 120 981 2.5 160
Larimer, CO.............. 11.1 149.8 3.4 67 845 2.1 207
Weld, CO................. 6.6 101.4 1.1 243 862 3.1 105
Fairfield, CT............ 34.4 431.1 1.6 202 1,497 3.0 114
Hartford, CT............. 26.8 513.5 1.0 256 1,162 0.3 319
New Haven, CT............ 23.3 364.4 0.6 291 1,007 2.0 220
New London, CT........... 7.2 124.2 0.3 308 960 -0.1 326
New Castle, DE........... 18.8 285.0 2.5 120 1,110 1.0 298
Washington, DC........... 37.2 745.1 1.8 172 1,599 1.8 240
Alachua, FL.............. 6.9 120.6 1.6 202 831 1.8 240
Brevard, FL.............. 15.2 193.5 2.5 120 865 3.3 87
Broward, FL.............. 67.9 752.0 2.6 113 907 3.9 45
Collier, FL.............. 13.1 125.4 4.0 32 846 -0.6 331
Duval, FL................ 28.4 469.8 3.2 76 911 1.8 240
Escambia, FL............. 8.2 125.4 1.9 166 763 2.8 132
Hillsborough, FL......... 40.4 632.1 3.7 47 922 2.4 180
Lake, FL................. 7.9 85.7 3.9 36 665 3.1 105
Lee, FL.................. 20.7 231.9 6.4 2 775 3.3 87
Leon, FL................. 8.4 141.2 1.1 243 798 1.4 276
Manatee, FL.............. 10.3 111.5 4.9 9 750 1.8 240
Marion, FL............... 8.3 95.3 1.7 183 679 1.8 240
Miami-Dade, FL........... 96.7 1,061.4 3.5 59 931 2.1 207
Okaloosa, FL............. 6.3 79.4 0.8 275 798 3.1 105
Orange, FL............... 39.7 754.6 3.8 42 849 2.5 160
Osceola, FL.............. 6.4 82.5 4.4 17 685 3.2 96
Palm Beach, FL........... 53.9 556.3 3.6 51 937 3.1 105
Pasco, FL................ 10.6 101.9 3.5 59 718 2.9 120
Pinellas, FL............. 32.1 406.7 2.8 96 850 0.6 311
Polk, FL................. 12.9 197.9 3.2 76 735 1.4 276
Sarasota, FL............. 15.5 155.7 4.4 17 812 3.2 96
Seminole, FL............. 14.5 173.3 4.1 26 828 4.0 41
Volusia, FL.............. 13.9 156.7 3.4 67 713 2.7 143
Bibb, GA................. 4.5 83.1 1.4 220 753 2.9 120
Chatham, GA.............. 8.4 146.2 3.9 36 822 2.2 198
Clayton, GA.............. 4.4 117.4 2.8 96 909 1.7 252
Cobb, GA................. 23.1 333.9 2.6 113 1,016 2.6 154
DeKalb, GA............... 19.2 289.7 2.3 138 991 2.0 220
Fulton, GA............... 45.5 792.7 3.9 36 1,247 2.0 220
Gwinnett, GA............. 26.1 338.9 3.1 82 936 2.4 180
Hall, GA................. 4.6 80.0 5.8 4 789 3.1 105
Muscogee, GA............. 4.8 94.0 -0.4 325 758 1.9 235
Richmond, GA............. 4.7 103.7 2.2 147 805 1.6 264
Honolulu, HI............. 25.1 463.3 1.3 226 910 3.8 51
Ada, ID.................. 14.0 218.1 2.9 93 828 1.6 264
Champaign, IL............ 4.6 90.5 0.7 284 839 2.9 120
Cook, IL................. 164.0 2,548.6 1.5 207 1,116 2.5 160
DuPage, IL............... 39.9 615.5 1.5 207 1,104 2.5 160
Kane, IL................. 14.4 212.0 1.5 207 831 2.8 132
Lake, IL................. 23.6 340.1 0.0 320 1,261 5.2 17
McHenry, IL.............. 9.2 98.6 0.9 265 792 2.5 160
McLean, IL............... 4.0 85.2 0.8 275 957 0.9 305
Madison, IL.............. 6.3 97.8 -0.4 325 785 3.0 114
Peoria, IL............... 4.9 102.6 1.0 256 908 2.5 160
St. Clair, IL............ 5.8 92.4 0.5 298 764 2.4 180
Sangamon, IL............. 5.5 129.7 -0.7 331 985 2.2 198
Will, IL................. 16.9 224.9 2.3 138 858 2.5 160
Winnebago, IL............ 7.1 129.5 0.9 265 818 2.8 132
Allen, IN................ 8.7 184.2 2.3 138 765 2.3 194
Elkhart, IN.............. 4.7 126.1 2.6 113 816 2.1 207
Hamilton, IN............. 8.9 134.0 3.5 59 908 3.8 51
Lake, IN................. 10.3 187.7 -0.5 328 830 -0.1 326
Marion, IN............... 23.5 584.6 1.9 166 956 2.9 120
St. Joseph, IN........... 5.7 121.9 3.1 82 769 1.3 285
Tippecanoe, IN........... 3.3 81.2 1.8 172 815 2.1 207
Vanderburgh, IN.......... 4.7 106.9 1.1 243 789 4.1 37
Black Hawk, IA........... 3.9 74.8 -1.5 336 794 1.7 252
Johnson, IA.............. 4.0 81.9 0.6 291 898 2.6 154
Linn, IA................. 6.6 131.6 1.0 256 924 3.4 79
Polk, IA................. 16.6 293.1 1.1 243 944 2.5 160
Scott, IA................ 5.5 92.6 1.3 226 783 2.0 220
Johnson, KS.............. 22.0 338.4 2.3 138 1,021 4.6 27
Sedgwick, KS............. 12.5 248.8 1.4 220 851 1.9 235
Shawnee, KS.............. 5.0 97.4 0.6 291 794 1.1 295
Wyandotte, KS............ 3.3 90.2 2.2 147 896 2.5 160
Boone, KY................ 4.2 82.3 4.1 26 865 2.1 207
Fayette, KY.............. 10.6 189.4 2.6 113 866 3.8 51
Jefferson, KY............ 24.7 453.6 2.5 120 954 3.0 114
Caddo, LA................ 7.2 115.1 -0.1 322 787 1.5 270
Calcasieu, LA............ 4.9 92.5 5.0 7 827 0.0 324
East Baton Rouge, LA..... 14.6 264.1 0.9 265 909 1.8 240
Jefferson, LA............ 13.5 194.8 -0.5 328 862 2.5 160
Lafayette, LA............ 9.2 136.5 -2.8 337 913 -1.8 336
Orleans, LA.............. 11.9 191.4 3.7 47 908 0.6 311
St. Tammany, LA.......... 7.7 85.6 3.9 36 808 2.0 220
Cumberland, ME........... 13.1 179.9 1.0 256 870 3.4 79
Anne Arundel, MD......... 14.9 263.1 1.4 220 1,021 2.8 132
Baltimore, MD............ 21.2 374.1 1.3 226 952 1.2 289
Frederick, MD............ 6.3 100.1 2.4 130 911 1.2 289
Harford, MD.............. 5.8 91.3 0.9 265 959 1.7 252
Howard, MD............... 9.8 167.2 1.8 172 1,175 3.5 75
Montgomery, MD........... 32.7 466.6 1.0 256 1,287 3.2 96
Prince George's, MD...... 15.6 311.1 0.8 275 1,002 0.8 307
Baltimore City, MD....... 13.6 335.0 0.8 275 1,094 2.4 180
Barnstable, MA........... 9.3 105.0 0.9 265 805 2.0 220
Bristol, MA.............. 16.9 224.9 1.3 226 900 5.4 13
Essex, MA................ 23.5 326.2 1.5 207 1,025 1.6 264
Hampden, MA.............. 17.1 206.2 1.4 220 883 3.3 87
Middlesex, MA............ 52.9 883.0 2.4 130 1,491 7.5 8
Norfolk, MA.............. 24.5 349.5 1.6 202 1,132 4.6 27
Plymouth, MA............. 15.0 191.8 1.9 166 929 2.5 160
Suffolk, MA.............. 27.0 640.8 3.0 88 1,512 3.1 105
Worcester, MA............ 23.5 339.2 1.7 183 960 2.5 160
Genesee, MI.............. 6.9 134.4 0.3 308 796 4.6 27
Ingham, MI............... 6.0 146.2 0.3 308 882 -0.5 329
Kalamazoo, MI............ 5.0 116.1 1.0 256 873 2.6 154
Kent, MI................. 14.0 365.2 1.2 235 857 3.4 79
Macomb, MI............... 17.3 321.1 2.3 138 954 1.4 276
Oakland, MI.............. 38.2 717.0 1.7 183 1,067 1.7 252
Ottawa, MI............... 5.5 120.9 2.4 130 805 2.5 160
Saginaw, MI.............. 4.0 84.5 0.7 284 754 1.5 270
Washtenaw, MI............ 8.1 200.5 1.8 172 1,030 4.0 41
Wayne, MI................ 30.3 707.2 1.2 235 1,059 2.7 143
Anoka, MN................ 6.8 120.2 1.8 172 924 2.1 207
Dakota, MN............... 9.6 186.0 1.1 243 948 2.8 132
Hennepin, MN............. 38.2 894.4 2.2 147 1,196 3.8 51
Olmsted, MN.............. 3.3 94.8 1.1 243 1,007 -5.2 341
Ramsey, MN............... 13.1 329.6 1.5 207 1,079 1.2 289
St. Louis, MN............ 5.2 99.6 1.5 207 781 2.8 132
Stearns, MN.............. 4.2 85.7 0.9 265 800 3.9 45
Washington, MN........... 5.3 80.7 2.1 155 809 3.2 96
Harrison, MS............. 4.4 83.9 -0.2 323 688 0.9 305
Hinds, MS................ 5.9 120.6 2.0 160 831 0.8 307
Boone, MO................ 4.8 91.4 1.7 183 750 2.2 198
Clay, MO................. 5.4 98.7 4.8 11 875 5.0 19
Greene, MO............... 8.4 161.9 1.7 183 739 3.2 96
Jackson, MO.............. 20.7 360.7 1.5 207 975 5.3 15
St. Charles, MO.......... 8.9 141.2 3.5 59 788 1.0 298
St. Louis, MO............ 35.3 595.5 1.2 235 1,015 2.0 220
St. Louis City, MO....... 12.3 226.8 2.3 138 1,016 2.7 143
Yellowstone, MT.......... 6.4 81.7 2.5 120 839 4.4 32
Douglas, NE.............. 18.6 333.4 1.7 183 889 4.5 31
Lancaster, NE............ 10.0 166.4 1.7 183 777 2.8 132
Clark, NV................ 53.6 908.9 3.6 51 845 2.4 180
Washoe, NV............... 14.3 202.1 3.4 67 857 3.5 75
Hillsborough, NH......... 12.2 198.3 1.9 166 1,030 -2.6 338
Rockingham, NH........... 10.8 148.0 1.8 172 956 1.5 270
Atlantic, NJ............. 6.5 133.5 -3.7 340 814 2.4 180
Bergen, NJ............... 32.9 452.4 1.1 243 1,158 1.4 276
Burlington, NJ........... 11.0 201.5 0.7 284 1,014 2.7 143
Camden, NJ............... 11.9 199.4 1.1 243 940 1.8 240
Essex, NJ................ 20.3 337.6 0.2 313 1,148 2.1 207
Gloucester, NJ........... 6.2 104.3 3.1 82 837 0.8 307
Hudson, NJ............... 14.3 244.7 3.6 51 1,318 4.8 24
Mercer, NJ............... 11.1 241.1 3.7 47 1,200 1.1 295
Middlesex, NJ............ 22.1 405.9 1.3 226 1,141 2.7 143
Monmouth, NJ............. 20.0 264.2 2.5 120 954 1.5 270
Morris, NJ............... 17.0 290.1 1.5 207 1,392 2.7 143
Ocean, NJ................ 12.8 169.2 1.3 226 783 2.4 180
Passaic, NJ.............. 12.3 167.5 0.0 320 980 4.4 32
Somerset, NJ............. 10.0 187.7 1.1 243 1,432 2.9 120
Union, NJ................ 14.3 218.9 (5) - 1,282 (5) -
Bernalillo, NM........... 17.7 317.4 1.2 235 828 1.6 264
Albany, NY............... 10.4 231.1 1.1 243 1,013 2.9 120
Bronx, NY................ 18.6 299.9 2.1 155 928 2.3 194
Broome, NY............... 4.6 87.7 -1.2 335 774 2.4 180
Dutchess, NY............. 8.5 111.7 1.1 243 977 1.0 298
Erie, NY................. 24.7 468.0 0.8 275 843 2.2 198
Kings, NY................ 60.0 663.0 4.4 17 813 2.9 120
Monroe, NY............... 18.8 384.5 0.9 265 913 2.0 220
Nassau, NY............... 53.9 626.7 1.2 235 1,094 2.3 194
New York, NY............. 129.7 2,378.9 2.3 138 1,842 3.3 87
Oneida, NY............... 5.4 105.3 0.7 284 776 2.1 207
Onondaga, NY............. 13.1 244.2 0.1 315 884 2.2 198
Orange, NY............... 10.3 141.5 1.2 235 850 2.9 120
Queens, NY............... 51.3 636.5 3.8 42 905 1.0 298
Richmond, NY............. 9.7 113.4 1.8 172 853 3.0 114
Rockland, NY............. 10.5 120.6 2.2 147 979 0.2 323
Saratoga, NY............. 5.9 86.0 3.0 88 918 5.4 13
Suffolk, NY.............. 52.5 665.3 1.1 243 1,025 1.4 276
Westchester, NY.......... 36.7 429.6 2.1 155 1,274 4.1 37
Buncombe, NC............. 8.5 123.9 3.6 51 724 2.7 143
Catawba, NC.............. 4.3 82.9 2.2 147 739 2.5 160
Cumberland, NC........... 6.2 118.0 -0.3 324 760 2.0 220
Durham, NC............... 7.8 191.4 2.4 130 1,202 1.3 285
Forsyth, NC.............. 9.3 179.8 0.9 265 928 10.9 3
Guilford, NC............. 14.2 275.2 3.1 82 834 3.1 105
Mecklenburg, NC.......... 35.1 637.3 4.7 13 1,082 3.8 51
New Hanover, NC.......... 7.6 106.6 3.9 36 774 3.2 96
Wake, NC................. 31.6 515.1 3.5 59 984 4.9 21
Cass, ND................. 6.9 117.3 2.1 155 865 4.0 41
Butler, OH............... 7.5 144.5 1.7 183 855 3.4 79
Cuyahoga, OH............. 35.4 721.6 0.7 284 971 1.9 235
Delaware, OH............. 4.8 85.9 1.1 243 943 3.3 87
Franklin, OH............. 30.4 723.1 2.8 96 977 3.2 96
Hamilton, OH............. 23.3 510.8 1.7 183 1,019 2.4 180
Lake, OH................. 6.3 96.8 0.6 291 805 3.6 69
Lorain, OH............... 6.1 99.0 0.6 291 755 -2.1 337
Lucas, OH................ 10.0 209.4 1.7 183 832 1.2 289
Mahoning, OH............. 5.8 97.9 -0.7 331 679 2.4 180
Montgomery, OH........... 11.9 250.1 1.7 183 836 2.7 143
Stark, OH................ 8.6 159.9 0.4 303 720 1.3 285
Summit, OH............... 14.1 265.6 0.7 284 848 2.8 132
Warren, OH............... 4.6 90.7 3.0 88 856 5.3 15
Cleveland, OK............ 5.4 80.8 2.7 102 724 1.1 295
Oklahoma, OK............. 27.0 450.8 1.3 226 900 1.4 276
Tulsa, OK................ 21.8 349.5 1.8 172 892 0.3 319
Clackamas, OR............ 13.9 152.9 3.0 88 922 3.8 51
Jackson, OR.............. 7.0 82.7 3.1 82 723 2.7 143
Lane, OR................. 11.6 147.6 2.7 102 771 3.8 51
Marion, OR............... 10.0 147.8 3.0 88 789 3.5 75
Multnomah, OR............ 32.2 480.7 3.2 76 983 1.9 235
Washington, OR........... 18.1 276.0 3.5 59 1,204 3.8 51
Allegheny, PA............ 35.6 696.1 0.2 313 1,031 2.8 132
Berks, PA................ 8.9 170.6 1.3 226 892 2.4 180
Bucks, PA................ 19.9 261.5 1.2 235 925 2.4 180
Butler, PA............... 5.0 86.5 0.5 298 900 3.8 51
Chester, PA.............. 15.4 246.4 0.6 291 1,295 4.9 21
Cumberland, PA........... 6.3 131.4 2.0 160 908 -1.0 333
Dauphin, PA.............. 7.4 180.8 1.0 256 950 3.7 65
Delaware, PA............. 14.0 219.6 0.5 298 1,028 3.8 51
Erie, PA................. 7.2 126.6 0.1 315 755 3.3 87
Lackawanna, PA........... 5.8 97.7 0.1 315 729 2.1 207
Lancaster, PA............ 13.2 231.9 1.7 183 805 3.6 69
Lehigh, PA............... 8.6 185.7 0.9 265 950 0.6 311
Luzerne, PA.............. 7.6 142.9 0.4 303 759 2.2 198
Montgomery, PA........... 27.5 483.6 1.5 207 1,183 1.5 270
Northampton, PA.......... 6.7 109.1 1.9 166 832 2.0 220
Philadelphia, PA......... 35.1 652.7 2.1 155 1,137 2.9 120
Washington, PA........... 5.5 88.5 -0.4 325 957 2.6 154
Westmoreland, PA......... 9.3 135.4 0.5 298 779 1.7 252
York, PA................. 9.1 175.5 0.7 284 827 2.2 198
Providence, RI........... 17.5 284.3 1.7 183 959 3.3 87
Charleston, SC........... 13.4 237.1 2.7 102 837 1.9 235
Greenville, SC........... 13.5 257.8 3.2 76 835 2.0 220
Horry, SC................ 8.4 126.5 1.8 172 568 3.5 75
Lexington, SC............ 6.3 111.9 3.5 59 737 2.5 160
Richland, SC............. 9.3 211.8 1.8 172 835 1.2 289
Spartanburg, SC.......... 5.9 127.0 2.4 130 849 1.7 252
York, SC................. 5.0 85.9 4.2 22 756 -0.5 329
Minnehaha, SD............ 6.9 125.2 2.0 160 825 3.8 51
Davidson, TN............. 20.4 457.0 4.4 17 1,038 8.1 6
Hamilton, TN............. 9.1 192.5 2.6 113 870 2.8 132
Knox, TN................. 11.5 230.1 2.5 120 828 0.6 311
Rutherford, TN........... 5.0 115.8 3.6 51 879 4.6 27
Shelby, TN............... 19.8 485.0 1.7 183 956 0.6 311
Williamson, TN........... 7.6 115.5 6.3 3 1,079 2.1 207
Bell, TX................. 5.0 114.9 2.2 147 782 1.4 276
Bexar, TX................ 37.8 817.9 2.7 102 854 2.4 180
Brazoria, TX............. 5.3 104.9 5.6 5 996 4.1 37
Brazos, TX............... 4.2 94.9 3.6 51 731 1.0 298
Cameron, TX.............. 6.4 137.0 1.0 256 586 0.5 317
Collin, TX............... 22.0 365.9 4.3 21 1,145 3.8 51
Dallas, TX............... 72.4 1,607.2 4.2 22 1,154 2.8 132
Denton, TX............... 13.2 219.9 5.1 6 867 3.8 51
Ector, TX................ 3.9 73.2 -4.2 341 1,026 -5.1 340
El Paso, TX.............. 14.5 291.3 2.5 120 674 0.3 319
Fort Bend, TX............ 11.7 170.8 4.2 22 945 0.6 311
Galveston, TX............ 5.8 104.3 3.6 51 865 4.0 41
Gregg, TX................ 4.2 76.3 -3.3 339 844 -1.5 335
Harris, TX............... 110.5 2,295.1 1.7 183 1,232 0.0 324
Hidalgo, TX.............. 11.9 244.8 1.7 183 614 1.0 298
Jefferson, TX............ 5.8 124.8 1.7 183 1,001 3.1 105
Lubbock, TX.............. 7.3 133.4 1.7 183 750 3.6 69
McLennan, TX............. 5.0 107.2 1.6 202 791 3.4 79
Midland, TX.............. 5.4 89.3 -3.2 338 1,233 -3.2 339
Montgomery, TX........... 10.4 164.0 3.9 36 982 2.6 154
Nueces, TX............... 8.2 164.1 1.6 202 845 1.4 276
Potter, TX............... 3.9 79.3 0.8 275 772 4.3 35
Smith, TX................ 6.0 100.5 4.0 32 805 1.8 240
Tarrant, TX.............. 40.7 845.3 2.4 130 963 1.7 252
Travis, TX............... 36.6 690.9 4.2 22 1,090 2.9 120
Webb, TX................. 5.1 97.4 2.7 102 651 0.8 307
Williamson, TX........... 9.4 152.2 4.1 26 924 5.8 11
Davis, UT................ 7.9 120.3 5.0 7 770 3.6 69
Salt Lake, UT............ 41.8 645.2 3.3 71 920 3.7 65
Utah, UT................. 14.4 209.1 7.5 1 778 2.9 120
Weber, UT................ 5.7 98.9 3.3 71 737 2.5 160
Chittenden, VT........... 6.5 102.2 1.5 207 950 2.0 220
Arlington, VA............ 8.9 170.7 2.3 138 1,546 1.6 264
Chesterfield, VA......... 8.2 130.5 2.9 93 833 1.8 240
Fairfax, VA.............. 35.4 593.9 1.4 220 1,517 3.9 45
Henrico, VA.............. 10.6 186.0 2.5 120 921 2.2 198
Loudoun, VA.............. 10.9 155.9 2.7 102 1,108 1.7 252
Prince William, VA....... 8.6 124.4 1.8 172 837 2.1 207
Alexandria City, VA...... 6.3 97.1 1.4 220 1,324 0.5 317
Chesapeake City, VA...... 5.8 97.9 0.6 291 780 3.9 45
Newport News City, VA.... 3.7 98.0 -0.5 328 921 -0.6 331
Norfolk City, VA......... 5.6 139.7 0.3 308 948 1.5 270
Richmond City, VA........ 7.2 149.9 2.0 160 1,039 2.5 160
Virginia Beach City, VA.. 11.4 178.3 1.2 235 744 2.2 198
Benton, WA............... 5.6 89.0 3.5 59 977 3.2 96
Clark, WA................ 13.9 145.8 4.1 26 879 2.1 207
King, WA................. 84.2 1,285.2 3.8 42 1,288 3.9 45
Kitsap, WA............... 6.6 85.6 2.6 113 860 2.0 220
Pierce, WA............... 21.5 287.9 3.2 76 880 2.0 220
Snohomish, WA............ 20.1 277.6 2.7 102 1,036 2.0 220
Spokane, WA.............. 15.4 212.2 2.5 120 810 1.8 240
Thurston, WA............. 7.9 106.8 4.0 32 878 3.3 87
Whatcom, WA.............. 7.1 87.7 2.8 96 804 4.4 32
Yakima, WA............... 7.8 121.6 3.6 51 660 2.5 160
Kanawha, WV.............. 5.9 103.8 -1.0 333 848 2.4 180
Brown, WI................ 6.6 154.4 1.0 256 856 5.5 12
Dane, WI................. 14.6 323.8 1.5 207 982 3.4 79
Milwaukee, WI............ 25.7 485.0 0.9 265 921 1.3 285
Outagamie, WI............ 5.1 107.0 0.8 275 798 2.3 194
Waukesha, WI............. 12.5 239.3 1.5 207 948 2.6 154
Winnebago, WI............ 3.6 90.8 0.3 308 883 1.0 298
San Juan, PR............. 10.7 245.8 -2.6 (6) 614 2.5 (6)
(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) Data do not meet BLS or state agency disclosure standards.
(6) 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 72.1 percent of
the total covered workers in the U.S.
Table 2. Covered establishments, employment, and wages in the 10 largest counties,
second quarter 2015
Employment Average weekly
wage(1)
Establishments,
second quarter
County by NAICS supersector 2015 Percent Percent
(thousands) June change, Second change,
2015 June quarter second
(thousands) 2014-15(2) 2015 quarter
2014-15(2)
United States(3) ............................ 9,575.3 140,594.9 2.0 $968 3.0
Private industry........................... 9,276.4 119,288.6 2.3 959 3.1
Natural resources and mining............. 138.0 2,120.1 -3.3 1,053 -1.9
Construction............................. 767.1 6,569.2 4.6 1,045 3.3
Manufacturing............................ 342.2 12,372.6 1.0 1,181 2.4
Trade, transportation, and utilities..... 1,925.3 26,688.8 2.3 821 2.9
Information.............................. 152.6 2,761.1 1.0 1,671 3.9
Financial activities..................... 847.1 7,862.3 1.9 1,461 4.8
Professional and business services....... 1,727.1 19,644.7 2.6 1,257 4.2
Education and health services............ 1,522.6 20,963.7 2.3 879 2.3
Leisure and hospitality.................. 809.6 15,658.4 2.6 403 3.9
Other services........................... 827.9 4,369.9 1.5 658 3.3
Government................................. 298.8 21,306.3 0.5 1,017 2.2
Los Angeles, CA.............................. 452.5 4,232.7 2.0 1,058 3.6
Private industry........................... 446.6 3,670.0 2.0 1,025 3.6
Natural resources and mining............. 0.5 9.0 -2.3 1,259 0.6
Construction............................. 13.5 125.7 5.2 1,110 5.2
Manufacturing............................ 12.4 358.9 -1.4 1,133 1.9
Trade, transportation, and utilities..... 53.4 795.8 1.5 888 3.9
Information.............................. 9.7 199.6 1.8 1,871 2.2
Financial activities..................... 24.7 211.5 0.5 1,665 4.3
Professional and business services....... 47.6 588.2 0.9 1,312 5.5
Education and health services............ 208.1 721.4 2.4 818 2.9
Leisure and hospitality.................. 31.4 486.9 2.4 591 7.1
Other services........................... 27.8 145.6 0.2 673 5.3
Government................................. 5.9 562.7 2.0 1,277 3.5
New York, NY................................. 129.7 2,378.9 2.3 1,842 3.3
Private industry........................... 128.9 2,119.6 2.5 1,920 3.3
Natural resources and mining............. 0.0 0.2 -5.6 2,162 -6.5
Construction............................. 2.2 37.1 6.2 1,724 2.2
Manufacturing............................ 2.2 27.1 0.5 1,307 -0.5
Trade, transportation, and utilities..... 20.4 260.8 0.7 1,328 2.0
Information.............................. 4.9 152.7 1.4 2,406 -1.4
Financial activities..................... 19.2 370.2 1.4 3,599 5.4
Professional and business services....... 27.4 547.3 4.0 2,164 4.1
Education and health services............ 9.8 325.7 2.1 1,213 2.8
Leisure and hospitality.................. 13.9 289.8 2.5 815 2.9
Other services........................... 20.5 101.3 1.8 1,091 1.6
Government................................. 0.8 259.3 0.9 1,211 1.7
Cook, IL..................................... 164.0 2,548.6 1.5 1,116 2.5
Private industry........................... 162.6 2,247.6 1.6 1,099 2.4
Natural resources and mining............. 0.1 1.0 12.2 1,182 7.6
Construction............................. 13.6 74.2 6.3 1,363 4.4
Manufacturing............................ 6.8 187.8 0.1 1,133 1.0
Trade, transportation, and utilities..... 32.4 469.6 2.1 892 1.2
Information.............................. 2.8 54.5 0.1 1,699 2.6
Financial activities..................... 16.4 187.2 0.4 1,974 5.3
Professional and business services....... 35.1 464.6 1.5 1,397 1.1
Education and health services............ 17.0 432.2 1.5 926 2.8
Leisure and hospitality.................. 14.8 274.6 2.5 502 5.9
Other services........................... 18.6 96.9 -1.0 848 4.0
Government................................. 1.3 301.0 0.7 1,242 3.1
Harris, TX................................... 110.5 2,295.1 1.7 1,232 0.0
Private industry........................... 110.0 2,029.8 1.7 1,255 -0.4
Natural resources and mining............. 1.8 86.9 -6.9 3,187 -1.8
Construction............................. 7.0 163.5 5.3 1,268 0.1
Manufacturing............................ 4.8 191.1 -3.9 1,512 0.1
Trade, transportation, and utilities..... 24.8 475.3 2.6 1,121 1.7
Information.............................. 1.2 27.9 -0.5 1,453 3.8
Financial activities..................... 11.4 120.4 1.4 1,536 2.1
Professional and business services....... 22.4 396.3 0.8 1,514 -0.7
Education and health services............ 15.1 277.4 4.5 958 2.9
Leisure and hospitality.................. 9.4 224.5 4.3 429 2.4
Other services........................... 11.7 65.6 2.3 753 1.5
Government................................. 0.6 265.3 1.8 1,057 2.9
Maricopa, AZ................................. 95.3 1,774.4 3.2 948 1.7
Private industry........................... 94.5 1,595.0 3.5 932 1.6
Natural resources and mining............. 0.5 8.5 1.9 868 6.2
Construction............................. 7.2 96.1 2.0 970 2.8
Manufacturing............................ 3.2 115.5 -0.1 1,381 1.7
Trade, transportation, and utilities..... 19.9 356.3 3.1 853 2.2
Information.............................. 1.6 35.3 4.2 1,220 -2.6
Financial activities..................... 11.1 158.8 4.4 1,218 5.7
Professional and business services....... 21.9 304.5 2.8 1,024 2.0
Education and health services............ 10.8 266.6 4.2 950 -0.1
Leisure and hospitality.................. 7.6 198.0 3.7 432 -1.1
Other services........................... 6.3 49.7 4.5 671 3.1
Government................................. 0.7 179.5 0.5 1,075 2.0
Dallas, TX................................... 72.4 1,607.2 4.2 1,154 2.8
Private industry........................... 71.8 1,438.3 4.4 1,162 2.7
Natural resources and mining............. 0.6 9.5 0.9 4,023 3.2
Construction............................. 4.2 81.6 6.2 1,097 2.3
Manufacturing............................ 2.7 106.1 -0.3 1,269 -3.7
Trade, transportation, and utilities..... 15.6 326.4 5.6 1,039 3.2
Information.............................. 1.4 48.1 -0.2 1,739 3.1
Financial activities..................... 8.8 155.8 2.1 1,606 5.2
Professional and business services....... 16.2 326.1 4.9 1,362 5.0
Education and health services............ 8.9 186.4 5.0 997 1.6
Leisure and hospitality.................. 6.2 155.6 6.7 467 3.8
Other services........................... 6.8 42.1 1.8 748 1.1
Government................................. 0.5 168.8 2.5 1,085 3.4
Orange, CA................................... 111.2 1,519.8 2.7 1,086 4.9
Private industry........................... 109.9 1,369.2 2.8 1,075 5.1
Natural resources and mining............. 0.2 3.2 -8.2 800 4.3
Construction............................. 6.5 88.4 7.0 1,185 2.8
Manufacturing............................ 4.9 155.2 0.4 1,328 6.8
Trade, transportation, and utilities..... 16.7 253.6 0.9 971 3.4
Information.............................. 1.3 25.1 -0.1 1,665 2.9
Financial activities..................... 10.8 115.6 2.1 1,659 9.4
Professional and business services....... 20.4 280.1 1.5 1,337 7.0
Education and health services............ 28.4 190.4 3.6 910 2.1
Leisure and hospitality.................. 8.1 203.4 3.4 454 3.9
Other services........................... 7.0 44.6 2.3 665 3.4
Government................................. 1.4 150.6 2.1 1,178 3.2
San Diego, CA................................ 103.6 1,374.7 2.7 1,073 3.1
Private industry........................... 101.8 1,147.1 3.0 1,057 3.4
Natural resources and mining............. 0.7 9.5 -3.0 672 -3.6
Construction............................. 6.5 68.9 8.5 1,103 4.2
Manufacturing............................ 3.1 104.2 2.8 1,601 12.0
Trade, transportation, and utilities..... 14.1 214.3 0.9 823 4.0
Information.............................. 1.2 23.6 -4.1 1,608 0.0
Financial activities..................... 9.5 70.4 1.9 1,351 9.6
Professional and business services....... 18.0 227.3 2.5 1,603 -0.9
Education and health services............ 28.6 184.6 3.0 900 0.9
Leisure and hospitality.................. 7.8 186.4 2.7 462 6.9
Other services........................... 7.4 49.9 2.0 582 4.7
Government................................. 1.8 227.6 1.5 1,158 2.2
King, WA..................................... 84.2 1,285.2 3.8 1,288 3.9
Private industry........................... 83.7 1,119.3 3.9 1,296 4.1
Natural resources and mining............. 0.4 2.9 17.6 1,325 2.9
Construction............................. 6.2 63.7 12.4 1,230 2.9
Manufacturing............................ 2.4 106.4 0.1 1,544 2.0
Trade, transportation, and utilities..... 14.6 240.6 4.2 1,182 6.3
Information.............................. 2.0 89.0 3.2 2,596 5.6
Financial activities..................... 6.4 66.2 1.6 1,553 7.0
Professional and business services....... 16.3 212.5 6.0 1,533 2.8
Education and health services............ 19.7 163.3 1.8 955 2.8
Leisure and hospitality.................. 6.9 132.0 3.8 516 3.2
Other services........................... 8.8 42.8 3.4 818 2.5
Government................................. 0.5 165.9 2.7 1,235 2.5
Miami-Dade, FL............................... 96.7 1,061.4 3.5 931 2.1
Private industry........................... 96.3 939.7 4.0 896 2.4
Natural resources and mining............. 0.5 7.4 0.7 556 0.4
Construction............................. 5.7 39.0 9.3 899 3.8
Manufacturing............................ 2.8 38.9 3.2 879 3.7
Trade, transportation, and utilities..... 27.7 275.5 3.1 832 0.1
Information.............................. 1.5 17.7 -2.8 1,493 1.4
Financial activities..................... 10.1 73.3 3.9 1,454 4.7
Professional and business services....... 20.3 146.4 5.9 1,068 1.9
Education and health services............ 10.1 165.5 2.4 920 2.9
Leisure and hospitality.................. 7.3 132.9 3.3 551 7.6
Other services........................... 8.4 40.5 6.7 587 1.0
Government................................. 0.3 121.7 0.2 1,179 0.9
(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,
second quarter 2015
Employment Average weekly
wage(1)
Establishments,
second quarter
State 2015 Percent Percent
(thousands) June change, Second change,
2015 June quarter second
(thousands) 2014-15 2015 quarter
2014-15
United States(2)........... 9,575.3 140,594.9 2.0 $968 3.0
Alabama.................... 118.5 1,899.3 1.3 819 1.6
Alaska..................... 22.3 346.6 0.4 1,028 2.4
Arizona.................... 151.1 2,549.9 2.5 904 1.8
Arkansas................... 88.6 1,184.6 1.7 762 2.1
California................. 1,420.0 16,338.9 2.8 1,131 5.5
Colorado................... 185.4 2,517.1 3.2 989 3.0
Connecticut................ 115.4 1,693.1 0.9 1,177 2.0
Delaware................... 30.5 439.1 2.2 991 1.5
District of Columbia....... 37.2 745.1 1.8 1,599 1.8
Florida.................... 658.3 7,907.7 3.6 861 2.6
Georgia.................... 289.2 4,167.8 3.4 903 2.4
Hawaii..................... 39.5 635.9 1.6 876 3.8
Idaho...................... 55.4 678.5 2.9 713 2.3
Illinois................... 428.3 5,925.5 1.5 1,015 2.6
Indiana.................... 159.7 2,966.0 1.7 811 3.4
Iowa....................... 100.6 1,561.2 0.9 802 2.8
Kansas..................... 86.8 1,382.1 0.7 819 2.8
Kentucky................... 121.7 1,850.5 1.7 822 3.0
Louisiana.................. 126.5 1,930.6 0.5 850 0.8
Maine...................... 50.6 615.8 0.8 768 2.9
Maryland................... 167.3 2,631.3 1.4 1,046 2.6
Massachusetts.............. 239.5 3,488.3 2.1 1,211 4.7
Michigan................... 237.7 4,225.0 1.5 916 2.1
Minnesota.................. 164.1 2,826.3 1.5 977 3.2
Mississippi................ 71.9 1,114.7 1.1 709 0.6
Missouri................... 191.1 2,746.6 1.7 842 2.8
Montana.................... 45.4 461.5 1.8 754 2.7
Nebraska................... 71.5 968.7 1.2 787 4.1
Nevada..................... 78.4 1,248.1 3.2 855 2.6
New Hampshire.............. 50.7 647.7 1.5 967 1.3
New Jersey................. 266.9 4,000.2 1.5 1,126 2.6
New Mexico................. 56.1 808.4 0.8 805 1.4
New York................... 636.6 9,136.9 1.9 1,180 3.1
North Carolina............. 266.0 4,185.6 2.6 850 3.9
North Dakota............... 32.1 445.0 -1.8 939 0.3
Ohio....................... 290.2 5,308.1 1.4 865 2.4
Oklahoma................... 108.8 1,591.5 0.6 818 0.5
Oregon..................... 143.1 1,810.4 3.4 899 3.0
Pennsylvania............... 354.1 5,763.9 0.8 958 2.7
Rhode Island............... 36.4 480.0 1.5 925 2.9
South Carolina............. 121.2 1,963.5 2.5 782 2.1
South Dakota............... 32.4 428.6 1.3 740 3.9
Tennessee.................. 149.7 2,832.1 2.8 863 3.1
Texas...................... 635.0 11,689.4 2.4 988 1.5
Utah....................... 92.9 1,345.9 3.9 821 3.1
Vermont.................... 24.7 309.3 0.6 831 2.2
Virginia................... 247.6 3,767.2 1.7 1,000 2.5
Washington................. 235.5 3,197.6 3.3 1,026 3.1
West Virginia.............. 50.1 706.5 -0.8 803 1.4
Wisconsin.................. 166.7 2,839.8 1.0 836 2.6
Wyoming.................... 26.1 291.5 -1.5 869 -0.1
Puerto Rico................ 46.1 884.6 -1.4 513 2.0
Virgin Islands............. 3.4 37.9 0.1 748 2.2
(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
