For release 10:00 a.m. (EDT), Wednesday, March 9, 2016 USDL-16-0462
Technical Information: (202) 691-6567 * QCEWInfo@bls.gov * www.bls.gov/cew
Media Contact: (202) 691-5902 * PressOffice@bls.gov
COUNTY EMPLOYMENT AND WAGES
Third Quarter 2015
From September 2014 to September 2015, employment increased in 312 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.
Williamson, Tenn., had the largest percentage increase, with a gain of 6.5 percent over the year, above
the national job growth of 1.9 percent. Within Williamson, the largest employment increase occurred in
professional and business services, which gained 2,538 jobs over the year (8.8 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 8.3 percent. Within Ector, natural resources and mining had the largest decrease in
employment, with a loss of 3,752 jobs (-28.4 percent). County employment and wage data are compiled
under the Quarterly Census of Employment and Wages (QCEW) program, which produces detailed
information on county employment and wages within 6 months after the end of each quarter.
The U.S. average weekly wage increased 2.6 percent over the year, growing to $974 in the third quarter
of 2015. Rockland, N.Y., had the largest over-the-year percentage increase in average weekly wages
with a gain of 24.9 percent. Within Rockland, an average weekly wage gain of $3,170, or 220.4 percent,
in manufacturing made the largest contribution to the county’s increase in average weekly wages.
Midland, Texas, experienced the largest percentage decrease in average weekly wages with a loss of 6.7
percent over the year. Within Midland, natural resources and mining had the largest impact on the
county’s average weekly wage decline with a decrease of $163 (-8.1 percent) over the year.
Table A. Large counties ranked by September 2015 employment, September 2014-15 employment increase, and
September 2014-15 percent increase in employment
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Employment in large counties
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September 2015 employment | Increase in employment, | Percent increase in employment,
(thousands) | September 2014-15 | September 2014-15
| (thousands) |
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| |
United States 140,442.2| United States 2,679.6| United States 1.9
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| |
Los Angeles, Calif. 4,261.8| Los Angeles, Calif. 93.0| Williamson, Tenn. 6.5
Cook, Ill. 2,535.6| Maricopa, Ariz. 65.4| Utah, Utah 6.3
New York, N.Y. 2,370.4| Dallas, Texas 62.9| Denton, Texas 6.1
Harris, Texas 2,287.6| Orange, Calif. 49.0| Chesterfield, Va. 5.7
Maricopa, Ariz. 1,824.7| New York, N.Y. 48.3| Lee, Fla. 5.5
Dallas, Texas 1,616.8| King, Wash. 42.0| Osceola, Fla. 5.4
Orange, Calif. 1,524.0| Santa Clara, Calif. 39.8| Loudoun, Va. 5.3
San Diego, Calif. 1,384.0| San Diego, Calif. 38.7| San Francisco, Calif. 5.2
King, Wash. 1,292.1| Cook, Ill. 37.8| Clay, Mo. 5.1
Miami-Dade, Fla. 1,076.1| San Francisco, Calif. 34.0| San Mateo, Calif. 5.0
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Large County Employment
In September 2015, national employment was 140.4 million (as measured by the QCEW program). Over
the year, employment increased 1.9 percent, or 2.7 million. In September 2015, the 342 U.S. counties
with 75,000 or more jobs accounted for 72.2 percent of total U.S. employment and 77.3 percent of total
wages. These 342 counties had a net job growth of 2.1 million over the year, accounting for 79.6 percent
of the overall U.S. employment increase.
Williamson, Tenn., had the largest percentage increase in employment (6.5 percent) among the largest
U.S. counties. The five counties with the largest increases in employment levels were Los Angeles,
Calif.; Maricopa, Ariz.; Dallas, Texas; Orange, Calif.; and New York, N.Y. These counties had a
combined over-the-year employment gain of 318,600 jobs, which was 11.9 percent of the overall job
increase for the U.S. (See table A.)
Employment declined in 24 of the largest counties from September 2014 to September 2015. Ector,
Texas, had the largest over-the-year percentage decrease in employment (-8.3 percent). Midland, Texas,
had the second largest percentage decrease in employment, followed by Gregg, Texas; Lafayette, La.;
and Atlantic, N.J. (See table 1.)
Table B. Large counties ranked by third quarter 2015 average weekly wages, third quarter 2014-15
increase in average weekly wages, and third 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
third quarter 2015 | wage, third quarter 2014-15 | weekly wage, third
| | quarter 2014-15
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| |
United States $974| United States $25| United States 2.6
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| |
Santa Clara, Calif. $2,090| Rockland, N.Y. $233| Rockland, N.Y. 24.9
San Mateo, Calif. 1,894| Lake, Ill. 136| Lake, Ill. 11.7
New York, N.Y. 1,829| Washington, Ore. 78| Onondaga, N.Y. 6.5
San Francisco, Calif. 1,712| Marin, Calif. 68| Washington, Ore. 6.4
Washington, D.C. 1,667| Santa Clara, Calif. 65| Marin, Calif. 6.1
Arlington, Va. 1,587| San Mateo, Calif. 62| Santa Cruz, Calif. 6.1
Suffolk, Mass. 1,559| Somerset, N.J. 60| Genesee, Mich. 5.6
King, Wash. 1,463| Onondaga, N.Y. 56| Davidson, Tenn. 5.5
Fairfax, Va. 1,462| Davidson, Tenn. 54| Placer, Calif. 5.4
Somerset, N.J. 1,447| Williamson, Tenn. 54| Williamson, Tenn. 5.2
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Large County Average Weekly Wages
Average weekly wages for the nation increased to $974, a 2.6 percent increase, during the year ending in
the third quarter of 2015. Among the 342 largest counties, 319 had over-the-year increases in average
weekly wages. Rockland, N.Y., had the largest percentage wage increase among the largest U.S.
counties (24.9 percent).
Of the 342 largest counties, 20 experienced over-the-year decreases in average weekly wages. Midland,
Texas, had the largest percentage decrease in average weekly wages, with a loss of 6.7 percent. Ector,
Texas, had the second largest percentage decrease in average weekly wages, followed by Lafayette, La.;
Stark, Ohio; and Gregg, Texas. (See table 1.)
Ten Largest U.S. Counties
All of the 10 largest counties had over-the-year percentage increases in employment in September
2015. Dallas, Texas, had the largest gain (4.0 percent). Within Dallas, trade, transportation, and utilities
had the largest over-the-year employment level increase, with a gain of 17,638 jobs, or 5.6 percent.
Harris, Texas, had the smallest percentage increase in employment (0.8 percent) among the 10 largest
counties. (See table 2.)
Average weekly wages increased over the year in all of the 10 largest U.S. counties. San Diego, Calif.,
experienced the largest percentage gain in average weekly wages (4.2 percent). Within San Diego,
professional and business services had the largest impact on the county’s average weekly wage growth.
Within this industry, average weekly wages increased by $120, or 8.4 percent, over the year. Harris,
Texas, had the smallest percentage gain in average weekly wages (0.1 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. September 2015 employment and 2015
third 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 140.4 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
third 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 fourth quarter 2015 is scheduled to be released
on Wednesday, June 8, 2016.
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| |
| Census Area Name Change Effective with BLS Release of Data for Fourth Quarter of 2015 |
| |
| On July 1, 2015, Wade Hampton, Alaska, was officially renamed Kusilvak, Alaska. This census area is |
| not part of this release because it has fewer than 75,000 jobs. However, BLS does publish data for this |
| census area. This name change is not reflected in this quarter’s data release. The census area name |
| change will be implemented by BLS with the fourth quarter 2015 news release. The name change will |
| also be retroactively implemented for the third quarter data. Data prior to third quarter 2015 will |
| still be available under Wade Hampton, Alaska. |
| |
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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- | 623,000 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,
third quarter 2015
Employment Average weekly wage(2)
Establishments,
County(1) third quarter Percent Ranking Percent Ranking
2015 September change, by Third change, by
(thousands) 2015 September percent quarter third percent
(thousands) 2014-15(3) change 2015 quarter change
2014-15(3)
United States(4)......... 9,633.8 140,442.2 1.9 - $974 2.6 -
Jefferson, AL............ 17.8 338.3 0.7 266 962 0.8 311
Madison, AL.............. 9.2 186.5 1.7 186 1,053 1.5 265
Mobile, AL............... 9.7 166.6 0.4 292 835 1.7 253
Montgomery, AL........... 6.3 128.7 0.7 266 819 1.0 300
Shelby, AL............... 5.5 83.6 1.0 231 914 4.2 33
Tuscaloosa, AL........... 4.4 92.2 1.6 192 811 -0.4 326
Anchorage Borough, AK.... 8.4 156.9 0.5 284 1,084 1.6 259
Maricopa, AZ............. 95.7 1,824.7 3.7 45 929 1.4 274
Pima, AZ................. 19.1 354.7 0.6 276 816 0.0 320
Benton, AR............... 5.9 111.7 4.8 12 949 2.4 178
Pulaski, AR.............. 14.5 247.2 1.7 186 869 2.1 215
Washington, AR........... 5.8 101.3 4.2 22 786 2.3 193
Alameda, CA.............. 59.6 734.5 3.3 71 1,289 2.3 193
Butte, CA................ 8.0 79.1 1.8 172 731 3.8 53
Contra Costa, CA......... 30.9 349.0 2.4 128 1,168 3.2 91
Fresno, CA............... 32.5 375.9 2.2 143 772 3.3 82
Kern, CA................. 17.7 323.2 -0.5 325 828 -0.5 328
Los Angeles, CA.......... 457.6 4,261.8 2.2 143 1,074 3.7 60
Marin, CA................ 12.3 112.3 2.6 117 1,185 6.1 5
Monterey, CA............. 13.2 202.3 2.3 137 825 3.3 82
Orange, CA............... 112.3 1,524.0 3.3 71 1,077 2.1 215
Placer, CA............... 12.0 149.9 3.9 39 989 5.4 9
Riverside, CA............ 57.0 659.5 4.8 12 781 3.0 117
Sacramento, CA........... 54.3 629.7 2.8 104 1,068 1.6 259
San Bernardino, CA....... 53.8 686.9 3.2 81 815 3.0 117
San Diego, CA............ 104.5 1,384.0 2.9 101 1,071 4.2 33
San Francisco, CA........ 59.2 684.1 5.2 8 1,712 1.4 274
San Joaquin, CA.......... 17.1 238.6 4.2 22 834 4.0 44
San Luis Obispo, CA...... 10.1 114.6 2.9 101 814 4.1 37
San Mateo, CA............ 27.1 387.8 5.0 10 1,894 3.4 77
Santa Barbara, CA........ 14.9 197.6 1.6 192 934 3.9 47
Santa Clara, CA.......... 68.6 1,026.6 4.0 32 2,090 3.2 91
Santa Cruz, CA........... 9.4 103.4 2.3 137 888 6.1 5
Solano, CA............... 10.6 132.7 3.5 58 981 2.4 178
Sonoma, CA............... 19.3 200.6 2.8 104 935 4.7 19
Stanislaus, CA........... 14.8 183.5 3.2 81 840 4.3 30
Tulare, CA............... 9.5 158.9 1.7 186 685 3.6 64
Ventura, CA.............. 25.5 313.4 1.5 204 961 1.6 259
Yolo, CA................. 6.4 102.8 1.1 227 1,006 3.1 104
Adams, CO................ 10.1 194.1 4.1 25 952 3.0 117
Arapahoe, CO............. 21.0 318.6 2.9 101 1,117 1.3 279
Boulder, CO.............. 14.4 173.8 2.7 112 1,158 3.1 104
Denver, CO............... 29.9 483.7 3.6 49 1,194 2.0 228
Douglas, CO.............. 11.2 112.9 3.3 71 1,033 -1.0 333
El Paso, CO.............. 18.2 259.7 3.5 58 876 1.9 241
Jefferson, CO............ 19.2 230.2 2.8 104 992 4.5 25
Larimer, CO.............. 11.3 149.5 3.7 45 892 3.8 53
Weld, CO................. 6.7 101.2 -1.3 331 861 -1.4 335
Fairfield, CT............ 34.7 422.5 0.8 252 1,406 0.4 316
Hartford, CT............. 27.2 506.0 0.4 292 1,142 1.9 241
New Haven, CT............ 23.5 361.0 0.0 313 1,021 3.2 91
New London, CT........... 7.3 122.6 0.2 307 943 1.6 259
New Castle, DE........... 18.8 284.0 1.9 162 1,066 -0.7 332
Washington, DC........... 38.2 743.6 1.4 211 1,667 2.3 193
Alachua, FL.............. 6.8 124.4 1.9 162 805 2.0 228
Brevard, FL.............. 15.0 193.9 1.9 162 873 2.5 165
Broward, FL.............. 66.3 759.7 2.4 128 898 3.3 82
Collier, FL.............. 12.9 128.7 4.0 32 815 1.2 286
Duval, FL................ 27.7 474.0 3.6 49 909 2.0 228
Escambia, FL............. 8.0 126.6 1.5 204 760 3.5 72
Hillsborough, FL......... 39.4 641.6 3.6 49 914 2.0 228
Lake, FL................. 7.6 90.2 4.0 32 680 3.7 60
Lee, FL.................. 20.4 236.2 5.5 5 766 3.1 104
Leon, FL................. 8.3 142.4 0.2 307 795 2.4 178
Manatee, FL.............. 10.0 111.9 4.4 18 740 4.8 13
Marion, FL............... 8.1 96.4 1.3 217 658 2.0 228
Miami-Dade, FL........... 93.2 1,076.1 2.8 104 924 3.9 47
Okaloosa, FL............. 6.2 80.2 2.1 145 816 4.7 19
Orange, FL............... 38.7 765.8 4.0 32 854 4.1 37
Osceola, FL.............. 6.2 85.1 5.4 6 671 2.8 138
Palm Beach, FL........... 52.7 559.3 3.6 49 924 2.2 204
Pasco, FL................ 10.3 109.2 3.1 89 676 4.5 25
Pinellas, FL............. 31.4 407.8 2.8 104 846 2.3 193
Polk, FL................. 12.5 203.5 3.7 45 740 1.5 265
Sarasota, FL............. 15.2 158.1 3.6 49 777 3.2 91
Seminole, FL............. 14.1 174.9 3.6 49 803 3.2 91
Volusia, FL.............. 13.7 160.7 3.0 95 697 5.0 11
Bibb, GA................. 4.5 82.9 1.4 211 760 3.0 117
Chatham, GA.............. 8.4 146.7 3.2 81 821 2.5 165
Clayton, GA.............. 4.4 117.0 3.3 71 912 2.5 165
Cobb, GA................. 23.2 334.6 3.1 89 1,006 2.2 204
DeKalb, GA............... 19.2 291.1 2.5 124 977 3.1 104
Fulton, GA............... 45.8 796.3 3.2 81 1,266 2.3 193
Gwinnett, GA............. 26.2 335.6 2.8 104 962 2.2 204
Hall, GA................. 4.6 80.5 4.3 19 825 3.3 82
Muscogee, GA............. 4.9 93.3 -0.7 328 761 1.9 241
Richmond, GA............. 4.8 104.7 2.1 145 819 2.4 178
Honolulu, HI............. 25.2 462.1 1.1 227 932 3.1 104
Ada, ID.................. 14.1 219.2 4.1 25 841 1.1 294
Champaign, IL............ 4.6 90.3 -0.2 319 877 3.4 77
Cook, IL................. 165.4 2,535.6 1.5 204 1,108 3.4 77
DuPage, IL............... 40.2 603.6 0.6 276 1,121 4.8 13
Kane, IL................. 14.5 209.2 0.5 284 867 4.5 25
Lake, IL................. 23.8 334.6 0.7 266 1,298 11.7 2
McHenry, IL.............. 9.3 97.7 0.3 301 808 2.9 129
McLean, IL............... 4.0 84.7 0.5 284 895 0.3 317
Madison, IL.............. 6.4 98.8 -0.4 324 794 3.1 104
Peoria, IL............... 4.9 100.7 0.7 266 910 3.8 53
St. Clair, IL............ 5.8 93.3 1.0 231 787 2.3 193
Sangamon, IL............. 5.5 129.3 -0.5 325 1,001 1.1 294
Will, IL................. 17.1 224.7 1.9 162 858 2.4 178
Winnebago, IL............ 7.1 128.1 0.0 313 811 1.8 247
Allen, IN................ 8.7 183.8 2.4 128 796 2.7 147
Elkhart, IN.............. 4.7 125.0 3.2 81 790 1.2 286
Hamilton, IN............. 8.9 134.8 3.9 39 913 2.6 154
Lake, IN................. 10.3 187.8 0.2 307 841 -0.6 329
Marion, IN............... 23.8 586.7 1.8 172 966 1.7 253
St. Joseph, IN........... 5.8 122.4 3.1 89 795 2.3 193
Tippecanoe, IN........... 3.3 82.1 0.9 243 831 3.6 64
Vanderburgh, IN.......... 4.7 106.4 0.5 284 786 3.6 64
Black Hawk, IA........... 3.9 74.0 -2.5 335 808 0.9 305
Johnson, IA.............. 4.1 82.1 1.0 231 920 3.0 117
Linn, IA................. 6.6 129.4 0.6 276 929 1.5 265
Polk, IA................. 16.8 289.6 0.8 252 981 2.4 178
Scott, IA................ 5.5 91.6 0.8 252 799 4.7 19
Johnson, KS.............. 22.4 334.6 1.9 162 968 1.3 279
Sedgwick, KS............. 12.7 247.5 1.0 231 831 0.8 311
Shawnee, KS.............. 5.0 97.5 0.5 284 783 2.1 215
Wyandotte, KS............ 3.4 90.4 2.3 137 942 3.2 91
Boone, KY................ 4.3 81.9 4.3 19 826 2.6 154
Fayette, KY.............. 10.6 190.8 2.6 117 879 4.4 29
Jefferson, KY............ 24.8 453.3 1.6 192 933 3.9 47
Caddo, LA................ 7.3 114.9 0.4 292 799 0.6 314
Calcasieu, LA............ 5.0 92.0 3.3 71 881 0.9 305
East Baton Rouge, LA..... 14.9 270.6 0.5 284 914 3.3 82
Jefferson, LA............ 13.6 192.5 -0.2 319 876 1.9 241
Lafayette, LA............ 9.3 136.7 -3.9 337 919 -3.2 339
Orleans, LA.............. 12.1 190.0 3.2 81 922 -0.2 322
St. Tammany, LA.......... 7.8 86.2 3.8 42 833 1.8 247
Cumberland, ME........... 13.1 176.9 1.0 231 857 3.1 104
Anne Arundel, MD......... 15.0 261.8 2.4 128 1,048 2.9 129
Baltimore, MD............ 21.2 371.9 1.2 221 980 1.9 241
Frederick, MD............ 6.4 99.4 2.4 128 911 0.9 305
Harford, MD.............. 5.7 90.7 1.4 211 923 2.6 154
Howard, MD............... 9.9 166.0 1.8 172 1,181 -1.3 334
Montgomery, MD........... 32.7 461.1 0.9 243 1,277 2.7 147
Prince George's, MD...... 15.7 308.6 1.0 231 1,058 2.1 215
Baltimore City, MD....... 13.6 335.1 -0.2 319 1,152 2.3 193
Barnstable, MA........... 9.3 101.0 1.7 186 808 3.3 82
Bristol, MA.............. 17.0 221.9 0.3 301 882 5.0 11
Essex, MA................ 23.7 320.9 1.0 231 1,009 0.8 311
Hampden, MA.............. 17.2 204.1 0.8 252 884 2.9 129
Middlesex, MA............ 53.3 874.9 2.0 151 1,419 2.6 154
Norfolk, MA.............. 24.7 344.2 1.4 211 1,112 2.5 165
Plymouth, MA............. 15.1 188.3 0.8 252 909 4.0 44
Suffolk, MA.............. 27.5 639.1 2.0 151 1,559 3.1 104
Worcester, MA............ 23.8 334.7 0.8 252 969 3.2 91
Genesee, MI.............. 7.0 132.7 0.0 313 816 5.6 7
Ingham, MI............... 6.0 147.3 0.4 292 898 1.8 247
Kalamazoo, MI............ 5.1 114.7 0.8 252 898 2.4 178
Kent, MI................. 14.1 373.7 3.3 71 875 3.6 64
Macomb, MI............... 17.5 317.3 2.4 128 950 1.1 294
Oakland, MI.............. 38.8 709.0 1.6 192 1,061 3.0 117
Ottawa, MI............... 5.6 122.8 3.1 89 818 2.0 228
Saginaw, MI.............. 4.0 84.8 1.0 231 777 2.4 178
Washtenaw, MI............ 8.2 202.0 0.9 243 1,052 2.7 147
Wayne, MI................ 30.6 700.9 0.9 243 1,059 3.1 104
Anoka, MN................ 6.6 119.3 0.7 266 968 3.5 72
Dakota, MN............... 9.3 184.0 0.3 301 944 2.8 138
Hennepin, MN............. 37.1 888.5 2.0 151 1,198 2.0 228
Olmsted, MN.............. 3.2 94.2 1.7 186 1,113 3.4 77
Ramsey, MN............... 12.7 330.0 1.2 221 1,073 1.7 253
St. Louis, MN............ 5.1 97.7 -0.2 319 836 1.6 259
Stearns, MN.............. 4.1 84.9 0.2 307 825 4.8 13
Washington, MN........... 5.2 79.4 2.8 104 810 3.6 64
Harrison, MS............. 4.4 83.2 0.6 276 701 1.2 286
Hinds, MS................ 5.9 120.0 0.8 252 832 2.2 204
Boone, MO................ 4.9 92.5 1.8 172 795 3.7 60
Clay, MO................. 5.4 99.2 5.1 9 856 3.0 117
Greene, MO............... 8.5 162.0 1.1 227 753 3.9 47
Jackson, MO.............. 21.0 358.0 2.0 151 989 2.6 154
St. Charles, MO.......... 8.9 141.2 4.8 12 774 1.2 286
St. Louis, MO............ 35.8 593.3 1.6 192 1,004 0.9 305
St. Louis City, MO....... 12.6 228.3 1.9 162 1,045 1.6 259
Yellowstone, MT.......... 6.4 81.5 2.4 128 845 4.7 19
Douglas, NE.............. 18.9 333.2 2.1 145 928 4.7 19
Lancaster, NE............ 10.2 167.3 1.6 192 797 3.8 53
Clark, NV................ 53.9 913.4 3.5 58 843 2.4 178
Washoe, NV............... 14.4 205.1 4.3 19 877 2.5 165
Hillsborough, NH......... 12.3 197.5 1.3 217 1,031 1.5 265
Rockingham, NH........... 10.8 146.0 2.7 112 938 2.2 204
Atlantic, NJ............. 6.5 127.8 -2.8 336 814 2.5 165
Bergen, NJ............... 32.8 444.0 0.6 276 1,135 2.9 129
Burlington, NJ........... 10.9 197.9 1.1 227 994 3.0 117
Camden, NJ............... 11.9 198.5 2.0 151 943 4.8 13
Essex, NJ................ 20.1 333.1 0.6 276 1,177 2.0 228
Gloucester, NJ........... 6.2 103.2 1.8 172 835 2.1 215
Hudson, NJ............... 14.2 244.8 3.2 81 1,280 1.0 300
Mercer, NJ............... 11.1 239.9 2.7 112 1,234 1.3 279
Middlesex, NJ............ 21.8 404.6 1.0 231 1,142 2.1 215
Monmouth, NJ............. 19.9 255.5 2.6 117 932 1.7 253
Morris, NJ............... 16.8 286.4 1.6 192 1,380 2.4 178
Ocean, NJ................ 12.8 163.4 2.7 112 766 1.7 253
Passaic, NJ.............. 12.3 164.1 -0.6 327 944 2.6 154
Somerset, NJ............. 10.0 182.4 1.2 221 1,447 4.3 30
Union, NJ................ 14.2 217.0 (5) - 1,188 (5) -
Bernalillo, NM........... 18.1 320.1 1.2 221 842 1.3 279
Albany, NY............... 10.5 228.9 0.5 284 1,038 3.1 104
Bronx, NY................ 18.7 297.9 0.3 301 938 2.4 178
Broome, NY............... 4.6 86.6 -1.6 333 753 2.2 204
Dutchess, NY............. 8.5 111.4 1.6 192 926 -0.6 329
Erie, NY................. 24.8 466.1 0.7 266 856 2.4 178
Kings, NY................ 60.5 658.2 3.4 66 835 2.1 215
Monroe, NY............... 18.8 379.9 0.9 243 929 2.5 165
Nassau, NY............... 54.2 615.4 1.4 211 1,063 3.6 64
New York, NY............. 129.9 2,370.4 2.1 145 1,829 2.5 165
Oneida, NY............... 5.4 104.5 0.7 266 740 -0.3 323
Onondaga, NY............. 13.1 243.9 0.4 292 913 6.5 3
Orange, NY............... 10.3 139.7 0.9 243 807 3.9 47
Queens, NY............... 51.6 637.6 3.5 58 930 3.3 82
Richmond, NY............. 9.8 113.1 2.0 151 877 4.3 30
Rockland, NY............. 10.5 119.7 2.6 117 1,168 24.9 1
Saratoga, NY............. 5.9 83.4 2.4 128 864 2.0 228
Suffolk, NY.............. 52.7 651.7 1.6 192 1,053 2.1 215
Westchester, NY.......... 36.7 421.4 2.0 151 1,222 1.3 279
Buncombe, NC............. 8.6 125.2 3.4 66 760 4.0 44
Catawba, NC.............. 4.3 83.5 1.8 172 746 4.2 33
Cumberland, NC........... 6.3 116.8 0.1 311 767 2.7 147
Durham, NC............... 8.0 190.5 1.7 186 1,231 2.9 129
Forsyth, NC.............. 9.4 181.9 1.8 172 886 -0.3 323
Guilford, NC............. 14.3 277.3 2.5 124 857 1.5 265
Mecklenburg, NC.......... 35.5 638.2 3.7 45 1,119 4.2 33
New Hanover, NC.......... 7.7 107.7 3.8 42 769 2.8 138
Wake, NC................. 32.0 514.6 4.0 32 983 2.5 165
Cass, ND................. 6.9 116.9 0.8 252 910 1.3 279
Butler, OH............... 7.6 146.4 2.3 137 850 1.8 247
Cuyahoga, OH............. 35.5 713.1 0.7 266 985 1.1 294
Delaware, OH............. 4.9 84.8 0.8 252 929 1.0 300
Franklin, OH............. 30.7 722.9 2.1 145 982 3.5 72
Hamilton, OH............. 23.4 509.0 1.9 162 1,055 2.2 204
Lake, OH................. 6.2 94.7 0.3 301 795 1.5 265
Lorain, OH............... 6.1 96.4 -0.9 329 775 1.4 274
Lucas, OH................ 10.1 208.7 1.8 172 839 1.2 286
Mahoning, OH............. 5.9 98.0 -1.6 333 707 3.8 53
Montgomery, OH........... 11.9 249.6 1.4 211 837 3.0 117
Stark, OH................ 8.6 158.4 0.3 301 740 -2.1 338
Summit, OH............... 14.1 264.7 0.6 276 876 3.2 91
Warren, OH............... 4.7 88.6 3.1 89 854 3.4 77
Cleveland, OK............ 5.5 82.0 2.0 151 719 1.1 294
Oklahoma, OK............. 27.2 451.6 1.0 231 934 -1.4 335
Tulsa, OK................ 22.1 347.9 0.8 252 904 1.2 286
Clackamas, OR............ 14.1 153.0 3.0 95 926 4.8 13
Jackson, OR.............. 7.0 84.8 3.5 58 764 4.1 37
Lane, OR................. 11.7 147.7 2.6 117 775 2.9 129
Marion, OR............... 10.1 150.2 3.6 49 788 3.7 60
Multnomah, OR............ 32.7 481.4 3.4 66 1,006 2.9 129
Washington, OR........... 18.3 275.3 2.5 124 1,288 6.4 4
Allegheny, PA............ 35.5 687.5 0.4 292 1,051 2.6 154
Berks, PA................ 8.9 171.3 1.8 172 866 1.5 265
Bucks, PA................ 19.8 255.6 0.7 266 909 1.8 247
Butler, PA............... 5.0 85.6 0.4 292 920 3.5 72
Chester, PA.............. 15.3 244.7 1.2 221 1,208 4.1 37
Cumberland, PA........... 6.3 131.2 2.7 112 883 2.0 228
Dauphin, PA.............. 7.4 178.0 0.5 284 962 2.4 178
Delaware, PA............. 13.9 218.0 0.8 252 1,010 2.0 228
Erie, PA................. 7.1 126.0 0.9 243 775 2.8 138
Lackawanna, PA........... 5.8 97.3 -0.2 319 749 1.9 241
Lancaster, PA............ 13.2 230.9 2.0 151 815 3.2 91
Lehigh, PA............... 8.6 185.2 1.5 204 938 1.4 274
Luzerne, PA.............. 7.5 142.9 -0.1 317 779 2.6 154
Montgomery, PA........... 27.4 479.5 1.8 172 1,158 2.0 228
Northampton, PA.......... 6.7 108.5 1.9 162 849 3.2 91
Philadelphia, PA......... 34.9 651.7 0.9 243 1,160 3.0 117
Washington, PA........... 5.5 87.3 -1.3 331 948 0.9 305
Westmoreland, PA......... 9.3 134.1 0.6 276 785 2.1 215
York, PA................. 9.0 176.2 2.0 151 849 3.2 91
Providence, RI........... 17.5 283.8 1.0 231 961 2.6 154
Charleston, SC........... 13.8 235.9 3.4 66 873 4.1 37
Greenville, SC........... 13.8 257.7 3.5 58 859 2.4 178
Horry, SC................ 8.6 121.1 3.0 95 598 3.6 64
Lexington, SC............ 6.4 112.8 4.1 25 741 2.1 215
Richland, SC............. 9.6 214.1 2.1 145 833 2.3 193
Spartanburg, SC.......... 6.0 128.1 3.0 95 814 2.8 138
York, SC................. 5.1 84.9 4.1 25 763 0.5 315
Minnehaha, SD............ 7.0 123.5 1.5 204 850 3.3 82
Davidson, TN............. 20.7 459.2 3.3 71 1,030 5.5 8
Hamilton, TN............. 9.2 194.7 3.0 95 865 4.5 25
Knox, TN................. 11.6 233.2 1.6 192 834 2.3 193
Rutherford, TN........... 5.1 117.2 3.9 39 843 2.1 215
Shelby, TN............... 19.9 483.8 1.5 204 979 1.5 265
Williamson, TN........... 7.8 116.9 6.5 1 1,101 5.2 10
Bell, TX................. 5.1 116.2 4.2 22 823 2.6 154
Bexar, TX................ 38.2 821.4 3.3 71 874 2.2 204
Brazoria, TX............. 5.4 103.4 4.0 32 992 2.8 138
Brazos, TX............... 4.3 99.8 4.5 16 734 -0.4 326
Cameron, TX.............. 6.4 135.7 1.2 221 615 2.2 204
Collin, TX............... 22.4 366.9 4.9 11 1,126 2.5 165
Dallas, TX............... 73.2 1,616.8 4.0 32 1,157 1.4 274
Denton, TX............... 13.4 221.4 6.1 3 885 3.0 117
Ector, TX................ 4.0 72.0 -8.3 340 1,037 -4.9 340
El Paso, TX.............. 14.6 292.0 3.1 89 698 2.6 154
Fort Bend, TX............ 11.9 170.6 3.6 49 949 -0.3 323
Galveston, TX............ 5.8 102.8 3.5 58 853 3.5 72
Gregg, TX................ 4.3 76.1 -4.2 338 846 -1.5 337
Harris, TX............... 111.2 2,287.6 0.8 252 1,240 0.1 319
Hidalgo, TX.............. 11.9 243.9 2.5 124 624 1.0 300
Jefferson, TX............ 5.9 123.1 0.4 292 1,003 2.7 147
Lubbock, TX.............. 7.4 135.0 2.4 128 779 2.1 215
McLennan, TX............. 5.1 108.1 1.9 162 792 2.2 204
Midland, TX.............. 5.4 86.8 -7.3 339 1,177 -6.7 341
Montgomery, TX........... 10.5 165.3 3.2 81 957 0.0 320
Nueces, TX............... 8.2 163.0 0.8 252 861 1.2 286
Potter, TX............... 4.0 79.1 1.6 192 804 0.2 318
Smith, TX................ 6.1 100.2 4.1 25 810 -0.6 329
Tarrant, TX.............. 41.1 844.9 2.6 117 967 2.5 165
Travis, TX............... 37.3 692.4 4.6 15 1,122 3.9 47
Webb, TX................. 5.1 97.7 2.6 117 658 0.9 305
Williamson, TX........... 9.5 150.8 4.5 16 937 1.7 253
Davis, UT................ 8.0 119.8 3.5 58 785 2.7 147
Salt Lake, UT............ 42.4 649.8 3.6 49 933 4.1 37
Utah, UT................. 14.6 211.7 6.3 2 767 2.8 138
Weber, UT................ 5.8 98.7 3.3 71 744 3.2 91
Chittenden, VT........... 6.6 101.7 0.9 243 928 1.8 247
Arlington, VA............ 9.4 171.3 3.0 95 1,587 1.5 265
Chesterfield, VA......... 8.6 131.8 5.7 4 833 1.1 294
Fairfax, VA.............. 37.1 589.0 2.0 151 1,462 1.2 286
Henrico, VA.............. 11.2 188.3 4.1 25 945 2.5 165
Loudoun, VA.............. 11.6 156.0 5.3 7 1,126 2.0 228
Prince William, VA....... 9.1 123.3 4.1 25 860 2.1 215
Alexandria City, VA...... 6.7 96.4 1.6 192 1,372 2.2 204
Chesapeake City, VA...... 6.0 97.3 0.4 292 766 2.8 138
Newport News City, VA.... 3.8 97.3 -0.1 317 957 3.1 104
Norfolk City, VA......... 5.8 140.1 0.1 311 1,002 2.9 129
Richmond City, VA........ 7.5 150.9 1.8 172 1,089 4.1 37
Virginia Beach City, VA.. 11.9 174.0 1.5 204 767 2.5 165
Benton, WA............... 5.6 84.5 3.3 71 965 3.1 104
Clark, WA................ 13.9 147.9 3.8 42 915 3.0 117
King, WA................. 84.3 1,292.1 3.4 66 1,463 1.0 300
Kitsap, WA............... 6.6 85.6 2.3 137 921 2.4 178
Pierce, WA............... 21.5 288.5 1.9 162 898 3.6 64
Snohomish, WA............ 20.1 277.8 2.8 104 1,050 3.2 91
Spokane, WA.............. 15.5 211.6 1.8 172 842 2.3 193
Thurston, WA............. 7.8 107.1 2.3 137 919 4.8 13
Whatcom, WA.............. 7.1 84.9 1.8 172 801 2.7 147
Yakima, WA............... 7.7 121.3 (5) - 679 2.9 129
Kanawha, WV.............. 5.9 102.6 -1.2 330 839 1.3 279
Brown, WI................ 6.7 152.3 1.0 231 856 3.8 53
Dane, WI................. 14.7 322.8 1.8 172 938 4.6 24
Milwaukee, WI............ 25.7 484.9 0.0 313 925 2.8 138
Outagamie, WI............ 5.1 105.4 1.3 217 835 3.3 82
Waukesha, WI............. 12.6 237.0 1.3 217 953 3.8 53
Winnebago, WI............ 3.7 90.6 0.7 266 888 3.1 104
San Juan, PR............. 10.6 250.4 0.4 (6) 614 1.7 (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 72.2 percent of
the total covered workers in the U.S.
Table 2. Covered establishments, employment, and wages in the 10 largest counties,
third quarter 2015
Employment Average weekly
wage(1)
Establishments,
third quarter
County by NAICS supersector 2015 Percent Percent
(thousands) September change, Third change,
2015 September quarter third
(thousands) 2014-15(2) 2015 quarter
2014-15(2)
United States(3) ............................ 9,633.8 140,442.2 1.9 $974 2.6
Private industry........................... 9,335.3 119,146.1 2.2 965 2.7
Natural resources and mining............. 138.2 2,071.5 -6.3 1,024 -4.7
Construction............................. 770.3 6,645.3 4.0 1,084 4.2
Manufacturing............................ 343.0 12,338.5 0.7 1,170 1.8
Trade, transportation, and utilities..... 1,927.7 26,664.0 2.1 823 2.6
Information.............................. 154.0 2,743.1 1.1 1,783 2.7
Financial activities..................... 849.6 7,846.0 1.9 1,444 3.7
Professional and business services....... 1,739.8 19,704.3 2.3 1,241 3.0
Education and health services............ 1,539.9 21,123.8 2.3 905 2.7
Leisure and hospitality.................. 810.2 15,377.8 3.0 413 3.5
Other services........................... 829.3 4,303.4 1.4 666 3.7
Government................................. 298.5 21,296.1 0.5 1,029 2.5
Los Angeles, CA.............................. 457.6 4,261.8 2.2 1,074 3.7
Private industry........................... 451.6 3,707.3 2.3 1,035 3.4
Natural resources and mining............. 0.5 8.6 -2.1 1,344 -0.7
Construction............................. 13.6 127.9 6.1 1,129 6.1
Manufacturing............................ 12.4 357.2 -1.5 1,167 2.8
Trade, transportation, and utilities..... 53.7 803.0 1.8 876 2.5
Information.............................. 9.8 202.6 0.9 1,957 5.8
Financial activities..................... 24.9 212.8 1.3 1,717 4.0
Professional and business services....... 48.0 595.8 0.8 1,317 5.5
Education and health services............ 211.3 731.3 2.7 821 2.5
Leisure and hospitality.................. 31.6 488.8 2.9 591 2.6
Other services........................... 27.8 147.2 0.9 691 5.8
Government................................. 6.0 554.4 1.5 1,348 5.2
New York, NY................................. 129.9 2,370.4 2.1 1,829 2.5
Private industry........................... 129.0 2,107.5 2.2 1,892 2.4
Natural resources and mining............. 0.0 0.2 -4.3 1,928 -40.9
Construction............................. 2.2 38.0 5.4 1,789 4.7
Manufacturing............................ 2.2 27.3 2.1 1,346 7.4
Trade, transportation, and utilities..... 20.2 257.3 -0.9 1,276 1.4
Information.............................. 4.9 151.6 0.6 2,571 7.6
Financial activities..................... 19.2 367.7 1.6 3,292 0.2
Professional and business services....... 27.4 542.5 3.3 2,122 2.6
Education and health services............ 9.8 326.8 1.8 1,309 3.9
Leisure and hospitality.................. 13.7 284.4 2.3 854 4.0
Other services........................... 20.4 99.7 0.7 1,098 4.6
Government................................. 0.8 263.0 1.2 1,312 1.8
Cook, IL..................................... 165.4 2,535.6 1.5 1,108 3.4
Private industry........................... 164.1 2,240.3 1.7 1,111 3.6
Natural resources and mining............. 0.1 1.1 18.4 1,222 10.3
Construction............................. 13.6 73.3 2.4 1,430 3.4
Manufacturing............................ 6.8 186.6 0.4 1,155 1.9
Trade, transportation, and utilities..... 32.5 467.5 1.9 898 3.2
Information.............................. 2.8 55.0 2.2 1,640 0.8
Financial activities..................... 16.5 188.0 0.6 1,923 4.6
Professional and business services....... 35.2 465.6 1.3 1,425 5.4
Education and health services............ 17.1 432.6 1.5 939 1.8
Leisure and hospitality.................. 15.0 270.1 3.1 512 6.7
Other services........................... 18.8 95.0 -0.1 876 3.7
Government................................. 1.3 295.3 0.5 1,084 0.6
Harris, TX................................... 111.2 2,287.6 0.8 1,240 0.1
Private industry........................... 110.7 2,023.3 0.6 1,252 -0.2
Natural resources and mining............. 1.8 84.1 -11.5 2,990 -3.4
Construction............................. 7.1 164.9 3.2 1,302 3.7
Manufacturing............................ 4.8 185.5 -7.3 1,459 -2.1
Trade, transportation, and utilities..... 24.9 474.9 1.7 1,109 0.5
Information.............................. 1.2 27.4 0.5 1,393 1.0
Financial activities..................... 11.5 120.4 0.6 1,570 4.7
Professional and business services....... 22.5 394.5 -0.6 1,527 1.3
Education and health services............ 15.3 283.0 4.7 1,017 4.6
Leisure and hospitality.................. 9.4 222.4 5.3 440 5.0
Other services........................... 11.7 65.3 2.6 800 6.4
Government................................. 0.6 264.3 2.3 1,147 2.5
Maricopa, AZ................................. 95.7 1,824.7 3.7 929 1.4
Private industry........................... 95.0 1,614.6 4.2 922 1.4
Natural resources and mining............. 0.4 7.5 7.0 926 0.2
Construction............................. 7.1 97.1 3.6 966 2.8
Manufacturing............................ 3.2 115.8 0.5 1,307 0.0
Trade, transportation, and utilities..... 19.7 361.0 3.8 856 2.9
Information.............................. 1.5 34.2 2.8 1,226 -0.6
Financial activities..................... 11.0 160.6 4.9 1,190 4.1
Professional and business services....... 21.7 307.4 3.5 1,002 0.2
Education and health services............ 10.8 273.5 4.1 948 1.4
Leisure and hospitality.................. 7.5 199.3 4.4 435 0.5
Other services........................... 6.1 49.0 1.9 670 3.4
Government................................. 0.7 210.2 0.4 989 1.7
Dallas, TX................................... 73.2 1,616.8 4.0 1,157 1.4
Private industry........................... 72.7 1,445.7 4.2 1,161 1.5
Natural resources and mining............. 0.6 9.2 -2.8 3,478 -9.2
Construction............................. 4.2 82.0 6.2 1,141 5.0
Manufacturing............................ 2.7 105.9 -0.4 1,256 -0.6
Trade, transportation, and utilities..... 15.8 330.0 5.6 1,051 2.1
Information.............................. 1.4 48.5 1.0 1,752 2.0
Financial activities..................... 8.9 157.7 2.7 1,563 2.2
Professional and business services....... 16.4 328.5 4.5 1,338 3.4
Education and health services............ 9.0 187.8 4.2 1,048 1.4
Leisure and hospitality.................. 6.3 154.4 6.6 477 -0.6
Other services........................... 6.9 41.0 2.0 760 -0.8
Government................................. 0.5 171.1 2.4 1,126 1.1
Orange, CA................................... 112.3 1,524.0 3.3 1,077 2.1
Private industry........................... 110.9 1,384.4 3.3 1,062 2.0
Natural resources and mining............. 0.2 3.0 -8.9 831 1.7
Construction............................. 6.6 92.1 8.9 1,221 4.6
Manufacturing............................ 4.9 155.4 0.1 1,321 1.9
Trade, transportation, and utilities..... 16.8 255.4 1.2 943 1.3
Information.............................. 1.3 25.0 0.6 1,654 1.2
Financial activities..................... 10.9 117.0 3.6 1,666 5.2
Professional and business services....... 20.5 282.5 1.5 1,284 0.8
Education and health services............ 29.0 193.7 4.2 907 2.6
Leisure and hospitality.................. 8.1 205.0 4.0 468 2.9
Other services........................... 7.0 44.5 2.4 674 4.3
Government................................. 1.4 139.6 3.7 1,237 2.9
San Diego, CA................................ 104.5 1,384.0 2.9 1,071 4.2
Private industry........................... 102.7 1,157.0 3.2 1,031 4.7
Natural resources and mining............. 0.7 9.7 -7.3 630 1.3
Construction............................. 6.5 71.5 9.5 1,115 3.6
Manufacturing............................ 3.1 104.8 2.6 1,404 -0.1
Trade, transportation, and utilities..... 14.3 215.7 1.2 813 3.7
Information.............................. 1.2 23.5 -4.4 1,773 1.6
Financial activities..................... 9.6 70.9 2.9 1,343 6.3
Professional and business services....... 18.2 229.5 2.4 1,541 8.4
Education and health services............ 29.0 187.4 3.5 900 3.1
Leisure and hospitality.................. 7.8 185.3 2.6 482 5.9
Other services........................... 7.4 50.3 1.8 582 2.6
Government................................. 1.8 227.0 1.4 1,287 2.4
King, WA..................................... 84.3 1,292.1 3.4 1,463 1.0
Private industry........................... 83.8 1,129.2 3.4 1,487 0.9
Natural resources and mining............. 0.4 3.1 18.1 1,196 -5.2
Construction............................. 6.2 64.8 7.0 1,263 4.2
Manufacturing............................ 2.4 107.0 -0.1 1,568 1.9
Trade, transportation, and utilities..... 14.6 243.4 4.2 1,186 5.3
Information.............................. 2.1 91.3 3.8 4,798 -5.6
Financial activities..................... 6.5 66.7 1.8 1,556 4.1
Professional and business services....... 16.4 216.6 5.2 1,538 2.1
Education and health services............ 19.4 161.5 (4) 964 (4)
Leisure and hospitality.................. 6.9 132.1 4.4 545 6.0
Other services........................... 8.9 42.7 3.0 821 4.5
Government................................. 0.5 162.9 3.2 1,299 2.9
Miami-Dade, FL............................... 93.2 1,076.1 2.8 924 3.9
Private industry........................... 92.8 940.9 3.2 905 3.9
Natural resources and mining............. 0.5 7.0 -6.2 572 0.0
Construction............................. 5.7 40.4 9.3 937 6.4
Manufacturing............................ 2.7 39.1 3.2 860 4.2
Trade, transportation, and utilities..... 25.9 274.1 2.0 838 4.2
Information.............................. 1.4 17.6 -2.6 1,444 2.8
Financial activities..................... 9.9 73.7 3.5 1,421 3.9
Professional and business services....... 20.0 147.7 5.0 1,080 2.8
Education and health services............ 9.9 168.7 2.5 953 4.4
Leisure and hospitality.................. 6.8 131.0 2.6 548 5.0
Other services........................... 8.0 39.8 5.1 586 1.9
Government................................. 0.3 135.2 -0.1 1,058 4.1
(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.
(4) Data do not meet BLS or state agency disclosure standards.
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,
third quarter 2015
Employment Average weekly
wage(1)
Establishments,
third quarter
State 2015 Percent Percent
(thousands) September change, Third change,
2015 September quarter third
(thousands) 2014-15 2015 quarter
2014-15
United States(2)........... 9,633.8 140,442.2 1.9 $974 2.6
Alabama.................... 119.6 1,893.6 1.2 830 1.8
Alaska..................... 22.5 346.4 0.4 1,041 2.2
Arizona.................... 152.6 2,613.9 2.9 889 1.5
Arkansas................... 88.3 1,193.4 1.9 756 2.6
California................. 1,436.2 16,474.4 3.0 1,134 3.4
Colorado................... 187.9 2,513.0 2.9 1,006 2.4
Connecticut................ 116.5 1,668.3 0.2 1,147 2.0
Delaware................... 30.6 436.3 2.1 963 0.3
District of Columbia....... 38.2 743.6 1.4 1,667 2.3
Florida.................... 644.2 8,023.2 3.5 852 3.1
Georgia.................... 291.9 4,171.1 2.8 916 2.8
Hawaii..................... 39.8 635.4 1.4 896 3.1
Idaho...................... 55.9 680.3 3.3 736 2.1
Illinois................... 432.2 5,888.6 1.3 1,020 3.9
Indiana.................... 160.3 2,971.7 1.6 818 2.4
Iowa....................... 101.2 1,535.9 0.4 823 3.0
Kansas..................... 87.6 1,370.9 0.6 809 1.8
Kentucky................... 122.3 1,852.5 1.4 804 2.9
Louisiana.................. 127.4 1,926.3 -0.2 858 0.7
Maine...................... 51.3 609.7 0.7 779 3.3
Maryland................... 167.6 2,607.8 1.3 1,067 2.4
Massachusetts.............. 241.6 3,446.9 1.4 1,197 3.0
Michigan................... 242.0 4,203.0 1.6 921 2.7
Minnesota.................. 159.4 2,800.7 1.4 990 2.6
Mississippi................ 72.1 1,118.9 1.2 706 1.3
Missouri................... 193.3 2,737.9 1.9 846 2.2
Montana.................... 45.5 457.9 1.9 759 3.7
Nebraska................... 72.6 964.0 1.4 811 4.2
Nevada..................... 79.0 1,254.5 3.2 862 2.5
New Hampshire.............. 51.3 642.8 1.5 952 2.7
New Jersey................. 265.4 3,933.9 1.4 1,116 2.6
New Mexico................. 57.0 809.2 0.6 798 1.3
New York................... 639.5 9,065.4 1.8 1,180 3.1
North Carolina............. 267.8 4,194.1 2.5 863 3.0
North Dakota............... 32.3 438.0 -3.8 956 -2.3
Ohio....................... 291.4 5,282.7 1.2 878 1.9
Oklahoma................... 109.4 1,598.0 0.2 825 0.0
Oregon..................... 144.8 1,812.8 3.0 924 4.4
Pennsylvania............... 353.4 5,722.1 0.8 961 2.5
Rhode Island............... 36.6 477.4 1.2 919 2.6
South Carolina............. 123.5 1,959.7 2.9 788 2.6
South Dakota............... 32.6 419.5 0.9 756 3.1
Tennessee.................. 150.8 2,850.6 2.7 864 3.2
Texas...................... 640.7 11,681.0 2.1 999 1.1
Utah....................... 94.1 1,353.9 3.7 829 3.2
Vermont.................... 24.8 308.2 0.5 829 3.0
Virginia................... 258.8 3,759.7 2.5 1,014 2.5
Washington................. 235.4 3,187.6 2.5 1,111 2.2
West Virginia.............. 50.1 702.4 -1.1 785 0.9
Wisconsin.................. 168.5 2,815.7 0.9 834 3.5
Wyoming.................... 26.2 287.4 -1.5 866 -1.1
Puerto Rico................ 45.4 891.1 -0.7 512 1.4
Virgin Islands............. 3.4 36.8 -2.1 738 2.1
(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
