For release 10:00 a.m. (EDT), Thursday, June 19, 2014 USDL-14-1138
Technical Information: (202) 691-6567 * QCEWInfo@bls.gov * www.bls.gov/cew
Media Contact: (202) 691-5902 * PressOffice@bls.gov
COUNTY EMPLOYMENT AND WAGES
Fourth Quarter 2013
From December 2012 to December 2013, employment increased in 292 of the 334 largest U.S.
counties, the U.S. Bureau of Labor Statistics reported today. Weld, Colo., had the largest increase, with
a gain of 6.0 percent over the year, compared with national job growth of 1.8 percent. Within Weld, the
largest employment increase occurred in construction, which gained 1,864 jobs over the year (25.5
percent). St. Clair, Ill., had the largest over-the-year decrease in employment among the largest counties
in the U.S. with a loss of 3.1 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 was unchanged over the year, remaining at $1,000 in the fourth quarter
of 2013. Santa Cruz, Calif., had the largest over-the-year increase in average weekly wages with a gain
of 6.5 percent. Within Santa Cruz, an average weekly wage gain of $416, or 32.9 percent, in
manufacturing made the largest contribution to the increase in average weekly wages. Douglas, Colo.,
experienced the largest decrease in average weekly wages with a loss of 29.7 percent over the year.
Table A. Large counties ranked by December 2013 employment, December 2012-13 employment
increase, and December 2012-13 percent increase in employment
--------------------------------------------------------------------------------------------------------
Employment in large counties
--------------------------------------------------------------------------------------------------------
December 2013 employment | Increase in employment, | Percent increase in employment,
(thousands) | December 2012-13 | December 2012-13
| (thousands) |
--------------------------------------------------------------------------------------------------------
| |
United States 136,129.4| United States 2,344.4| United States 1.8
--------------------------------------------------------------------------------------------------------
| |
Los Angeles, Calif. 4,176.8| Los Angeles, Calif. 76.6| Weld, Colo. 6.0
New York, N.Y. 2,500.2| Harris, Texas 64.2| Lee, Fla. 5.5
Cook, Ill. 2,463.3| New York, N.Y. 58.4| Sonoma, Calif. 5.2
Harris, Texas 2,225.4| Maricopa, Ariz. 50.9| Douglas, Colo. 5.2
Maricopa, Ariz. 1,771.9| Dallas, Texas 48.1| Sarasota, Fla. 4.9
Dallas, Texas 1,530.1| King, Wash. 40.8| Ocean, N.J. 4.8
Orange, Calif. 1,463.1| Santa Clara, Calif. 38.2| Fort Bend, Texas 4.8
San Diego, Calif. 1,330.2| Orange, Calif. 29.0| Midland, Texas 4.8
King, Wash. 1,223.4| Cook, Ill. 27.5| Placer, Calif. 4.7
Miami-Dade, Fla. 1,047.5| Clark, Nev. 26.5| Williamson, Texas 4.7
| |
--------------------------------------------------------------------------------------------------------
Large County Employment
In December 2013, national employment was 136.1 million (as measured by the QCEW program). Over
the year, employment increased 1.8 percent, or 2.3 million. The 334 U.S. counties with 75,000 or more
jobs accounted for 71.7 percent of total U.S. employment and 77.2 percent of total wages. These 334
counties had a net job growth of 1.8 million over the year, accounting for 76.2 percent of the overall
U.S. employment increase.
Weld, Colo., had the largest percentage increase in employment (6.0 percent) among the largest U.S.
counties. The five counties with the largest increases in employment level were Los Angeles, Calif.;
Harris, Texas; New York, N.Y.; Maricopa, Ariz.; and Dallas, Texas. These counties had a combined
over-the-year employment gain of 298,200 jobs, which was 12.7 percent of the overall job increase for
the U.S. (See table A.)
Employment declined in 39 of the large counties from December 2012 to December 2013. St. Clair, Ill.,
had the largest over-the-year percentage decrease in employment (-3.1 percent). Within St. Clair,
professional and business services had the largest decrease in employment, with a loss of 798 jobs (-9.2
percent). Peoria, Ill., and Broome, N.Y., tied for the second largest percentage decrease in employment,
followed by Caddo, La., and Winnebago, Wis. (See table 1.)
Table B. Large counties ranked by fourth quarter 2013 average weekly wages, fourth quarter 2012-13
increase in average weekly wages, and fourth quarter 2012-13 percent increase in average weekly wages
--------------------------------------------------------------------------------------------------------
Average weekly wage in large counties
--------------------------------------------------------------------------------------------------------
Average weekly wage, | Increase in average weekly | Percent increase in average
fourth quarter 2013 | wage, fourth quarter 2012-13 | weekly wage, fourth
| | quarter 2012-13
--------------------------------------------------------------------------------------------------------
| |
United States $1,000| United States $0| United States 0.0
--------------------------------------------------------------------------------------------------------
| |
San Mateo, Calif. $2,724| Morris, N.J. $74| Santa Cruz, Calif. 6.5
New York, N.Y. 2,041| Santa Clara, Calif. 65| Ada, Idaho 6.4
Santa Clara, Calif. 1,972| Washington, Ore. 65| Washington, Ore. 5.9
San Francisco, Calif. 1,753| Union, N.J. 63| Union, N.J. 5.2
Suffolk, Mass. 1,741| Santa Cruz, Calif. 55| Clayton, Ga. 5.1
Fairfield, Conn. 1,653| Ada, Idaho 54| Morris, N.J. 5.0
Washington, D.C. 1,638| San Francisco, Calif. 51| Winnebago, Wis. 5.0
Arlington, Va. 1,588| Winnebago, Wis. 46| Weld, Colo. 4.8
Fairfax, Va. 1,558| Albany, N.Y. 45| Dane, Wis. 4.7
Morris, N.J. 1,553| Dane, Wis. 45| Albany, N.Y. 4.6
| |
--------------------------------------------------------------------------------------------------------
Large County Average Weekly Wages
Average weekly wages for the nation were unchanged during the year ending in the fourth quarter of
2013. Among the 334 largest counties, 185 had over-the-year increases in average weekly wages. Santa
Cruz, Calif., had the largest wage increase among the largest U.S. counties (6.5 percent).
Of the 334 largest counties, 140 experienced over-the-year decreases in average weekly wages. Douglas,
Colo., had the largest percentage decrease in average weekly wage, with a loss of 29.7 percent. Within
Douglas, professional and business services had the largest impact on the county’s average weekly wage
decrease. Within this industry, average weekly wages declined by $2,545 (-57.4 percent) over the year.
San Mateo, Calif., had the second largest percentage decrease in average weekly wages, followed by
Virginia Beach City, Va.; McHenry, Ill.; and Shawnee, Kan. (See table 1.)
Ten Largest U.S. Counties
All of the 10 largest counties had over-the-year percentage increases in employment in December 2013.
King, Wash., had the largest gain (3.5 percent). Within King, trade, transportation, and utilities had the
largest over-the-year employment level increase among all private industry groups with a gain of 10,127
jobs, or 4.6 percent. Cook, Ill., had the smallest percentage increase in employment (1.1 percent) among
the 10 largest counties. (See table 2.)
Average weekly wages increased over the year in 3 of the 10 largest U.S. counties. King, Wash.,
experienced the largest percentage gain in average weekly wages (1.9 percent). Within King,
information had the largest impact on the county’s average weekly wage growth. Within this industry,
average weekly wages increased by $68, or 2.7 percent, over the year. New York, N.Y., had the largest
decline in average weekly wages (-3.3 percent) among the 10 largest counties.
For More Information
The tables included in this release contain data for the nation and for the 334 U.S. counties with annual
average employment levels of 75,000 or more in 2012. December 2013 employment and 2013 fourth
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 9.3 million employer reports cover 136.1 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
fourth quarter of 2013 will be available 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 first quarter 2014 is scheduled to be released on
Thursday, September 18, 2014.
Technical Note
These data are the product of a federal-state cooperative program, the Quarterly
Census of Employment and Wages (QCEW) program, also known as the ES-202 program.
The data are derived from summaries of employment and total pay of workers covered
by state and federal unemployment insurance (UI) legislation and provided by State
Workforce Agencies (SWAs). The summaries are a result of the administration of
state unemployment insurance programs that require most employers to pay quarterly
taxes based on the employment and wages of workers covered by UI. QCEW data in this
release are based on the 2012 North American Industry Classification System. Data
for 2013 are preliminary and subject to revision.
For purposes of this release, large counties are defined as having employment le-
vels of 75,000 or greater. In addition, data for San Juan, Puerto Rico, are pro-
vided, but not used in calculating U.S. averages, rankings, or in the analysis in
the text. Each year, these large counties are selected on the basis of the prelimi-
nary annual average of employment for the previous year. The 335 counties presented
in this release were derived using 2012 preliminary annual averages of employment.
For 2013 data, six counties have been added to the publication tables: Boone, Ky.;
Warren, Ohio; Jackson, Ore.; York, S.C.; Midland, Texas; and Potter, Texas. These
counties will be included in all 2013 quarterly releases. The counties in table 2
are selected and sorted each year based on the annual average employment from the
preceding year.
The preliminary QCEW data presented in this release may differ from data released
by the individual states. These potential differences result from the states' con-
tinuing receipt of UI data over time and ongoing review and editing. The individual
states determine their data release timetables.
Differences between QCEW, BED, and CES employment measures
The Bureau publishes three different establishment-based employment measures for
any given quarter. Each of these measures--QCEW, Business Employment Dynamics (BED),
and Current Employment Statistics (CES)--makes use of the quarterly UI employment
reports in producing data; however, each measure has a somewhat different universe
coverage, estimation procedure, and publication product.
Differences in coverage and estimation methods can result in somewhat different
measures of employment change over time. It is important to understand program dif-
ferences and the intended uses of the program products. (See table.) Additional in-
formation on each program can be obtained from the program Web sites shown in the
table.
Summary of Major Differences between QCEW, BED, and CES Employment Measures
---------------------------------------------------------------------------------
| QCEW | BED | CES
-----------|---------------------|----------------------|------------------------
Source |--Count of UI admini-|--Count of longitudi- |--Sample survey:
| strative records | nally-linked UI ad- | 557,000 establish-
| submitted by 9.2 | ministrative records| ments
| million establish- | submitted by 7.3 |
| ments in first | million private-sec-|
| quarter of 2013 | tor employers |
-----------|---------------------|----------------------|------------------------
Coverage |--UI and UCFE cover- |--UI coverage, exclud-|Nonfarm wage and sal-
| age, including all | ing government, pri-| ary jobs:
| employers subject | vate households, and|--UI coverage, exclud-
| to state and fed- | establishments with | ing agriculture, pri-
| eral UI laws | zero employment | vate households, and
| | | self-employed workers
| | |--Other employment, in-
| | | cluding railroads,
| | | religious organiza-
| | | tions, and other non-
| | | UI-covered jobs
-----------|---------------------|----------------------|------------------------
Publication|--Quarterly |--Quarterly |--Monthly
frequency | -6 months after the| -8 months after the | -Usually first Friday
| end of each quar- | end of each quarter| of following month
| ter | |
-----------|---------------------|----------------------|------------------------
Use of UI |--Directly summarizes|--Links each new UI |--Uses UI file as a sam-
file | and publishes each | quarter to longitu- | pling frame and to an-
| new quarter of UI | dinal database and | nually realign sample-
| data | directly summarizes | based estimates to pop-
| | gross job gains and | ulation counts (bench-
| | losses | marking)
-----------|---------------------|----------------------|------------------------
Principal |--Provides a quarter-|--Provides quarterly |--Provides current month-
products | ly and annual uni- | employer dynamics | ly estimates of employ-
| verse count of es- | data on establish- | ment, hours, and earn-
| tablishments, em- | ment openings, clos-| ings at the MSA, state,
| ployment, and wages| ings, expansions, | and national level by
| at the county, MSA,| and contractions at | industry
| state, and national| the national level |
| levels by detailed | by NAICS supersec- |
| industry | tors and by size of |
| | firm, and at the |
| | state private-sector|
| | total level |
| |--Future expansions |
| | will include data |
| | with greater indus- |
| | try detail and data |
| | at the county and |
| | MSA level |
-----------|---------------------|----------------------|------------------------
Principal |--Major uses include:|--Major uses include: |--Major uses include:
uses | -Detailed locality | -Business cycle | -Principal national
| data | analysis | economic indicator
| -Periodic universe | -Analysis of employ-| -Official time series
| counts for bench- | er dynamics under- | for employment change
| marking sample | lying economic ex- | measures
| survey estimates | pansions and con- | -Input into other ma-
| -Sample frame for | tractions | jor economic indi-
| BLS establishment | -Analysis of employ-| cators
| surveys | ment expansion and |
| | contraction by size|
| | of firm |
| | |
-----------|---------------------|----------------------|------------------------
Program |--www.bls.gov/cew/ |--www.bls.gov/bdm/ |--www.bls.gov/ces/
Web sites | | |
---------------------------------------------------------------------------------
Coverage
Employment and wage data for workers covered by state UI laws are compiled from
quarterly contribution reports submitted to the SWAs by employers. For federal ci-
vilian workers covered by the Unemployment Compensation for Federal Employees
(UCFE) program, employment and wage data are compiled from quarterly reports sub-
mitted by four major federal payroll processing centers on behalf of all federal
agencies, with the exception of a few agencies which still report directly to the
individual SWA. In addition to the quarterly contribution reports, employers who
operate multiple establishments within a state complete a questionnaire, called the
"Multiple Worksite Report," which provides detailed information on the location and
industry of each of their establishments. QCEW employment and wage data are derived
from microdata summaries of 9.1 million employer reports of employment and wages
submitted by states to the BLS in 2012. These reports are based on place of employ-
ment rather than place of residence.
UI and UCFE coverage is broad and has been basically comparable from state to state
since 1978, when the 1976 amendments to the Federal Unemployment Tax Act became ef-
fective, expanding coverage to include most State and local government employees.
In 2012, UI and UCFE programs covered workers in 131.7 million jobs. The estimated
126.9 million workers in these jobs (after adjustment for multiple jobholders)
represented 95.5 percent of civilian wage and salary employment. Covered workers
received $6.491 trillion in pay, representing 93.7 percent of the wage and salary
component of personal income and 40.0 percent of the gross domestic product.
Major exclusions from UI coverage include self-employed workers, most agricultural
workers on small farms, all members of the Armed Forces, elected officials in most
states, most employees of railroads, some domestic workers, most student workers at
schools, and employees of certain small nonprofit organizations.
State and federal UI laws change periodically. These changes may have an impact on
the employment and wages reported by employers covered under the UI program. Cover-
age changes may affect the over-the-year comparisons presented in this news re-
lease.
Concepts and methodology
Monthly employment is based on the number of workers who worked during or received
pay for the pay period including the 12th of the month. With few exceptions, all
employees of covered firms are reported, including production and sales workers,
corporation officials, executives, supervisory personnel, and clerical workers.
Workers on paid vacations and part-time workers also are included.
Average weekly wage values are calculated by dividing quarterly total wages by the
average of the three monthly employment levels (all employees, as described above)
and dividing the result by 13, for the 13 weeks in the quarter. These calculations
are made using unrounded employment and wage values. The average wage values that
can be calculated using rounded data from the BLS database may differ from the av-
erages reported. Included in the quarterly wage data are non-wage cash payments
such as bonuses, the cash value of meals and lodging when supplied, tips and other
gratuities, and, in some states, employer contributions to certain deferred compen-
sation plans such as 401(k) plans and stock options. Over-the-year comparisons of
average weekly wages may reflect fluctuations in average monthly employment and/or
total quarterly wages between the current quarter and prior year levels.
Average weekly wages are affected by the ratio of full-time to part-time workers as
well as the number of individuals in high-paying and low-paying occupations and the
incidence of pay periods within a quarter. For instance, the average weekly wage of
the workforce could increase significantly when there is a large decline in the
number of employees that had been receiving below-average wages. Wages may include
payments to workers not present in the employment counts because they did not work
during the pay period including the 12th of the month. When comparing average week-
ly wage levels between industries, states, or quarters, these factors should be
taken into consideration.
Wages measured by QCEW may be subject to periodic and sometimes large fluctuations.
This variability may be due to calendar effects resulting from some quarters having
more pay dates than others. The effect is most visible in counties with a dominant
employer. In particular, this effect has been observed in counties where government
employers represent a large fraction of overall employment. Similar calendar effects
can result from private sector pay practices. However, these effects are typically
less pronounced for two reasons: employment is less concentrated in a single private
employer, and private employers use a variety of pay period types (weekly, biweekly,
semimonthly, monthly).
For example, the effect on over-the-year pay comparisons can be pronounced in federal
government due to the uniform nature of federal payroll processing. Most federal
employees are paid on a biweekly pay schedule. As a result, in some quarters federal
wages include six pay dates, while in other quarters there are seven pay dates. Over-
the-year comparisons of average weekly wages may also reflect this calendar effect.
Growth in average weekly wages may be attributed, in part, to a comparison of
quarterly wages for the current year, which include seven pay dates, with year-ago
wages that reflect only six pay dates. An opposite effect will occur when wages in
the current quarter reflecting six pay dates are compared with year-ago wages for a
quarter including seven pay dates.
In order to ensure the highest possible quality of data, states verify with employ-
ers and update, if necessary, the industry, location, and ownership classification
of all establishments on a 3-year cycle. Changes in establishment classification
codes resulting from this process are introduced with the data reported for the
first quarter of the year. Changes resulting from improved employer reporting also
are introduced in the first quarter.
QCEW data are not designed as a time series. QCEW data are simply the sums of indi-
vidual establishment records and reflect the number of establishments that exist in
a county or industry at a point in time. Establishments can move in or out of a
county or industry for a number of reasons--some reflecting economic events, others
reflecting administrative changes. For example, economic change would come from a
firm relocating into the county; administrative change would come from a company
correcting its county designation.
The over-the-year changes of employment and wages presented in this release have
been adjusted to account for most of the administrative corrections made to the un-
derlying establishment reports. This is done by modifying the prior-year levels
used to calculate the over-the-year changes. Percent changes are calculated using
an adjusted version of the final 2012 quarterly data as the base data. The adjusted
prior-year levels used to calculate the over-the-year percent change in employment
and wages are not published. These adjusted prior-year levels do not match the un-
adjusted data maintained on the BLS Web site. Over-the-year change calculations
based on data from the Web site, or from data published in prior BLS news releases,
may differ substantially from the over-the-year changes presented in this news re-
lease.
The adjusted data used to calculate the over-the-year change measures presented in
this release account for most of the administrative changes--those occurring when
employers update the industry, location, and ownership information of their estab-
lishments. The most common adjustments for administrative change are the result of
updated information about the county location of individual establishments. In-
cluded in these adjustments are administrative changes involving the classification
of establishments that were previously reported in the unknown or statewide county
or unknown industry categories. Beginning with the first quarter of 2008, adjusted
data account for administrative changes caused by multi-unit employers who start
reporting for each individual establishment rather than as a single entity. Beginn-
ing with the second quarter of 2011, adjusted data account for selected large admin-
istrative changes in employment and wages. These new adjustments allow QCEW to incl-
ude county employment and wage growth rates in this news release that would other-
wise not meet publication standards.
The adjusted data used to calculate the over-the-year change measures presented in
any County Employment and Wages news release are valid for comparisons between the
starting and ending points (a 12-month period) used in that particular release.
Comparisons may not be valid for any time period other than the one featured in a
release even if the changes were calculated using adjusted data.
County definitions are assigned according to Federal Information Processing Stan-
dards Publications (FIPS PUBS) as issued by the National Institute of Standards and
Technology, after approval by the Secretary of Commerce pursuant to Section 5131 of
the Information Technology Management Reform Act of 1996 and the Computer Security
Act of 1987, Public Law 104-106. Areas shown as counties include those designated
as independent cities in some jurisdictions and, in Alaska, those designated as
census areas where counties have not been created. County data also are presented
for the New England states for comparative purposes even though townships are the
more common designation used in New England (and New Jersey). The regions referred
to in this release are defined as census regions.
Additional statistics and other information
Employment and Wages Annual Averages Online features comprehensive information by
detailed industry on establishments, employment, and wages for the nation and all
states. The 2012 edition of this publication, which was published in September 2013,
contains selected data produced by Business Employment Dynamics (BED) on job gains
and losses, as well as selected data from the first quarter 2013 version of this
news release. Tables and additional content from Employment and Wages Annual Aver-
ages 2012 are now available online at http://www.bls.gov/cew/cewbultn12.htm. The
2013 edition of Employment and Wages Annual Averages Online will be available in
September 2014.
News releases on quarterly measures of gross job flows also are available upon re-
quest from the Division of Administrative Statistics and Labor Turnover (Business
Employment Dynamics), telephone (202) 691-6467; (http://www.bls.gov/bdm/); (e-mail:
BDMInfo@bls.gov).
Information in this release will be made available to sensory impaired individuals
upon request. Voice phone: (202) 691-5200; TDD message referral phone number: 1-800-
877-8339.
Table 1. Covered establishments, employment, and wages in the 335 largest counties,
fourth quarter 2013
Employment Average weekly wage(2)
Establishments,
County(1) fourth quarter Percent Ranking Percent Ranking
2013 December change, by Fourth change, by
(thousands) 2013 December percent quarter fourth percent
(thousands) 2012-13(3) change 2013 quarter change
2012-13(3)
United States(4)......... 9,333.7 136,129.4 1.8 - $1,000 0.0 -
Jefferson, AL............ 17.7 341.7 1.1 200 993 -1.9 302
Madison, AL.............. 9.0 183.2 0.3 271 1,078 -0.4 220
Mobile, AL............... 9.6 165.9 0.4 260 864 -1.9 302
Montgomery, AL........... 6.3 129.2 0.4 260 879 -0.6 232
Tuscaloosa, AL........... 4.3 87.5 1.2 188 847 -0.1 195
Anchorage Borough, AK.... 8.4 153.3 0.2 279 1,050 1.1 95
Maricopa, AZ............. 92.8 1,771.9 3.0 61 952 -1.3 272
Pima, AZ................. 18.6 356.3 0.5 249 840 0.2 165
Benton, AR............... 5.7 100.8 2.1 118 913 1.1 95
Pulaski, AR.............. 14.6 246.4 0.1 290 899 -3.1 323
Washington, AR........... 5.7 95.2 0.6 239 857 2.5 29
Alameda, CA.............. 56.7 687.3 2.5 90 1,267 0.8 120
Contra Costa, CA......... 29.7 339.6 2.4 98 1,191 1.9 54
Fresno, CA............... 30.7 348.0 3.7 34 771 -1.0 253
Kern, CA................. 17.2 303.9 2.6 88 849 0.4 150
Los Angeles, CA.......... 440.9 4,176.8 1.9 130 1,161 -1.9 302
Marin, CA................ 12.0 112.0 3.0 61 1,213 -0.7 238
Monterey, CA............. 13.0 155.6 1.9 130 828 2.1 43
Orange, CA............... 107.0 1,463.1 2.0 122 1,114 -1.8 299
Placer, CA............... 11.2 139.6 4.7 9 978 0.0 186
Riverside, CA............ 52.4 613.2 3.9 31 773 1.4 75
Sacramento, CA........... 52.0 610.7 2.7 81 1,069 1.2 91
San Bernardino, CA....... 50.8 653.2 3.8 33 824 -1.1 257
San Diego, CA............ 99.8 1,330.2 1.9 130 1,107 0.8 120
San Francisco, CA........ 56.9 630.5 4.1 24 1,753 3.0 19
San Joaquin, CA.......... 16.8 212.0 2.7 81 815 0.6 134
San Luis Obispo, CA...... 9.8 107.6 3.6 39 805 -1.5 280
San Mateo, CA............ 25.6 366.1 4.0 29 2,724 -15.8 333
Santa Barbara, CA........ 14.6 182.4 2.5 90 936 -3.0 321
Santa Clara, CA.......... 65.2 965.7 4.1 24 1,972 3.4 17
Santa Cruz, CA........... 9.1 92.1 2.0 122 907 6.5 1
Solano, CA............... 10.2 127.4 2.0 122 1,015 2.9 22
Sonoma, CA............... 19.0 189.5 5.2 3 913 -0.7 238
Stanislaus, CA........... 14.3 165.7 2.4 98 801 1.1 95
Tulare, CA............... 9.2 145.7 3.5 42 696 0.0 186
Ventura, CA.............. 24.8 314.0 1.2 188 978 -0.6 232
Yolo, CA................. 6.0 91.3 2.4 98 1,021 2.3 36
Adams, CO................ 9.0 177.1 4.6 11 946 2.3 36
Arapahoe, CO............. 19.2 300.5 2.8 76 1,145 -0.9 250
Boulder, CO.............. 13.3 167.6 3.0 61 1,174 3.7 13
Denver, CO............... 27.0 451.2 4.0 29 1,224 1.0 106
Douglas, CO.............. 10.0 106.3 5.2 3 1,123 -29.7 334
El Paso, CO.............. 16.9 246.4 2.0 122 887 0.2 165
Jefferson, CO............ 17.8 218.3 1.8 139 1,005 -0.2 205
Larimer, CO.............. 10.3 138.3 2.9 72 900 1.4 75
Weld, CO................. 6.0 93.2 6.0 1 871 4.8 8
Fairfield, CT............ 33.7 420.0 0.7 230 1,653 -3.3 325
Hartford, CT............. 26.2 501.8 0.2 279 1,197 -1.0 253
New Haven, CT............ 22.9 361.9 0.2 279 1,040 0.5 141
New London, CT........... 7.1 121.7 -1.5 329 971 0.1 174
New Castle, DE........... 17.3 277.4 1.8 139 1,160 -1.2 265
Washington, DC........... 36.0 727.3 0.6 239 1,638 -3.9 328
Alachua, FL.............. 6.7 119.0 1.0 207 865 3.0 19
Brevard, FL.............. 14.8 190.0 0.5 249 874 -0.2 205
Broward, FL.............. 66.2 742.7 2.8 76 920 -0.1 195
Collier, FL.............. 12.5 131.0 4.2 20 861 2.4 31
Duval, FL................ 28.0 458.8 2.2 113 946 -0.9 250
Escambia, FL............. 8.1 123.2 1.7 150 774 -1.5 280
Hillsborough, FL......... 39.7 624.7 3.4 46 960 0.4 150
Lake, FL................. 7.5 85.1 2.4 98 668 2.3 36
Lee, FL.................. 19.8 223.5 5.5 2 783 1.3 83
Leon, FL................. 8.4 141.4 1.0 207 824 1.9 54
Manatee, FL.............. 9.8 113.3 3.0 61 746 2.3 36
Marion, FL............... 8.1 93.3 0.7 230 692 0.3 160
Miami-Dade, FL........... 94.5 1,047.5 2.5 90 982 0.8 120
Okaloosa, FL............. 6.1 76.3 0.2 279 786 -0.6 232
Orange, FL............... 38.2 723.6 3.0 61 861 0.3 160
Palm Beach, FL........... 51.8 543.7 3.6 39 993 -1.3 272
Pasco, FL................ 10.2 103.2 2.1 118 695 1.6 64
Pinellas, FL............. 31.6 396.9 1.0 207 907 1.0 106
Polk, FL................. 12.6 200.6 2.4 98 748 1.1 95
Sarasota, FL............. 14.9 149.8 4.9 5 834 0.5 141
Seminole, FL............. 14.2 166.7 3.0 61 814 0.2 165
Volusia, FL.............. 13.5 155.1 2.2 113 702 -0.4 220
Bibb, GA................. 4.6 81.8 1.2 188 765 0.8 120
Chatham, GA.............. 8.1 137.1 2.3 107 839 1.3 83
Clayton, GA.............. 4.3 112.3 1.5 163 910 5.1 5
Cobb, GA................. 22.3 320.0 3.3 51 1,043 0.6 134
De Kalb, GA.............. 18.5 282.9 3.3 51 994 -1.6 289
Fulton, GA............... 43.6 761.2 2.8 76 1,290 -2.3 309
Gwinnett, GA............. 24.8 318.9 3.6 39 958 -0.9 250
Muscogee, GA............. 4.7 94.9 0.2 279 788 0.9 114
Richmond, GA............. 4.7 100.7 0.5 249 823 -0.8 245
Honolulu, HI............. 24.9 463.6 1.6 156 909 0.1 174
Ada, ID.................. 13.8 209.6 4.1 24 901 6.4 2
Champaign, IL............ 4.4 89.0 0.7 230 827 2.7 27
Cook, IL................. 153.8 2,463.3 1.1 200 1,174 -1.0 253
Du Page, IL.............. 38.1 596.0 1.2 188 1,180 1.5 69
Kane, IL................. 13.7 203.9 1.9 130 876 1.2 91
Lake, IL................. 22.7 330.7 1.2 188 1,289 0.3 160
McHenry, IL.............. 8.8 95.1 1.9 130 825 -8.8 331
McLean, IL............... 3.9 85.2 -0.7 317 956 1.1 95
Madison, IL.............. 6.1 95.4 -0.4 308 822 2.1 43
Peoria, IL............... 4.7 101.8 -2.2 332 935 0.5 141
St. Clair, IL............ 5.7 92.2 -3.1 334 779 -0.3 214
Sangamon, IL............. 5.4 126.8 0.6 239 999 1.6 64
Will, IL................. 15.8 214.6 3.1 59 860 1.3 83
Winnebago, IL............ 6.8 124.6 -0.1 296 849 2.2 41
Allen, IN................ 8.9 178.1 1.1 200 772 -0.3 214
Elkhart, IN.............. 4.8 117.4 4.3 17 787 1.0 106
Hamilton, IN............. 8.7 121.9 4.1 24 934 1.5 69
Lake, IN................. 10.3 189.5 -0.6 316 876 -2.9 320
Marion, IN............... 23.9 580.0 1.5 163 974 -1.7 293
St. Joseph, IN........... 5.9 117.7 0.5 249 786 -0.1 195
Tippecanoe, IN........... 3.3 80.3 0.3 271 816 0.9 114
Vanderburgh, IN.......... 4.8 104.8 -0.4 308 796 0.1 174
Johnson, IA.............. 3.9 80.6 1.5 163 879 3.3 18
Linn, IA................. 6.4 128.1 0.4 260 959 1.3 83
Polk, IA................. 15.9 282.5 2.9 72 991 0.8 120
Scott, IA................ 5.5 89.9 1.0 207 836 -1.1 257
Johnson, KS.............. 21.3 325.6 2.5 90 1,022 -2.1 307
Sedgwick, KS............. 12.2 245.9 1.2 188 908 -0.7 238
Shawnee, KS.............. 4.8 96.8 1.8 139 806 -5.1 330
Wyandotte, KS............ 3.3 84.8 3.4 46 899 2.5 29
Boone, KY................ 4.1 78.9 0.5 249 870 -1.1 257
Fayette, KY.............. 10.3 189.3 3.3 51 845 -1.1 257
Jefferson, KY............ 24.2 441.3 1.3 175 929 -0.5 226
Caddo, LA................ 7.5 116.0 -1.8 330 825 0.0 186
Calcasieu, LA............ 5.0 85.8 1.3 175 858 1.3 83
East Baton Rouge, LA..... 14.9 265.7 0.9 218 938 -1.3 272
Jefferson, LA............ 13.7 193.4 -0.1 296 912 -0.2 205
Lafayette, LA............ 9.3 141.8 2.0 122 987 -0.3 214
Orleans, LA.............. 11.5 186.2 4.2 20 973 -1.8 299
St. Tammany, LA.......... 7.7 82.2 1.5 163 858 1.7 62
Cumberland, ME........... 12.8 173.6 0.7 230 904 1.3 83
Anne Arundel, MD......... 14.6 254.9 1.3 175 1,066 0.5 141
Baltimore, MD............ 21.2 367.7 0.4 260 1,008 -0.5 226
Frederick, MD............ 6.2 95.4 -0.5 314 946 -1.1 257
Harford, MD.............. 5.6 89.1 -0.9 320 964 -1.7 293
Howard, MD............... 9.4 160.1 0.1 290 1,193 -1.2 265
Montgomery, MD........... 33.1 454.9 0.0 293 1,316 -2.3 309
Prince Georges, MD....... 15.6 303.1 0.0 293 1,003 -1.3 272
Baltimore City, MD....... 13.9 333.1 -0.2 300 1,163 -1.2 265
Barnstable, MA........... 8.9 86.9 1.2 188 854 1.5 69
Bristol, MA.............. 16.1 217.0 1.0 207 908 0.8 120
Essex, MA................ 21.8 314.3 1.5 163 1,047 -0.8 245
Hampden, MA.............. 15.9 201.2 1.4 170 905 0.4 150
Middlesex, MA............ 49.3 849.5 1.4 170 1,437 0.1 174
Norfolk, MA.............. 23.3 336.8 1.3 175 1,214 0.5 141
Plymouth, MA............. 14.0 180.6 1.3 175 950 2.4 31
Suffolk, MA.............. 24.1 614.3 2.3 107 1,741 0.6 134
Worcester, MA............ 21.7 326.8 1.0 207 1,000 3.5 16
Genesee, MI.............. 7.1 133.2 0.2 279 817 0.4 150
Ingham, MI............... 6.2 152.6 0.9 218 935 0.2 165
Kalamazoo, MI............ 5.2 112.3 0.9 218 908 1.2 91
Kent, MI................. 14.0 359.6 4.3 17 880 0.0 186
Macomb, MI............... 17.3 304.8 2.7 81 1,010 0.7 128
Oakland, MI.............. 38.0 690.7 1.9 130 1,115 -2.5 315
Ottawa, MI............... 5.5 111.3 3.1 59 867 4.1 11
Saginaw, MI.............. 4.1 85.3 1.3 175 804 2.0 47
Washtenaw, MI............ 8.2 200.8 1.0 207 1,030 -0.2 205
Wayne, MI................ 31.0 690.6 -0.2 300 1,085 0.0 186
Anoka, MN................ 6.9 116.6 3.0 61 902 0.6 134
Dakota, MN............... 9.5 180.3 2.5 90 941 0.2 165
Hennepin, MN............. 40.9 867.7 1.7 150 1,208 -2.4 311
Olmsted, MN.............. 3.3 91.9 -1.0 322 1,084 3.6 14
Ramsey, MN............... 13.2 323.2 1.5 163 1,095 1.9 54
St. Louis, MN............ 5.3 95.9 0.5 249 798 2.8 24
Stearns, MN.............. 4.3 83.0 1.8 139 819 1.6 64
Harrison, MS............. 4.5 83.6 1.4 170 692 0.4 150
Hinds, MS................ 6.1 120.5 -0.4 308 863 0.7 128
Boone, MO................ 4.7 89.7 2.2 113 765 0.4 150
Clay, MO................. 5.2 90.8 3.5 42 890 1.5 69
Greene, MO............... 8.1 157.4 1.2 188 737 -0.1 195
Jackson, MO.............. 19.4 350.7 0.4 260 1,003 -2.7 317
St. Charles, MO.......... 8.5 134.5 2.9 72 770 0.1 174
St. Louis, MO............ 33.3 581.9 1.7 150 1,091 0.1 174
St. Louis City, MO....... 10.3 221.1 -0.1 296 1,034 -0.6 232
Yellowstone, MT.......... 6.2 77.9 0.4 260 856 1.1 95
Douglas, NE.............. 18.2 326.7 1.9 130 890 -1.5 280
Lancaster, NE............ 9.8 163.6 2.1 118 790 -0.3 214
Clark, NV................ 50.8 854.4 3.2 57 875 0.9 114
Washoe, NV............... 13.8 193.1 3.4 46 894 1.0 106
Hillsborough, NH......... 12.1 195.3 1.6 156 1,135 -0.4 220
Rockingham, NH........... 10.6 139.6 1.3 175 986 -5.0 329
Atlantic, NJ............. 6.6 130.4 -0.9 320 813 0.0 186
Bergen, NJ............... 32.9 446.1 3.0 61 1,240 -2.7 317
Burlington, NJ........... 11.0 198.0 -0.2 300 1,029 -0.5 226
Camden, NJ............... 12.0 198.0 1.4 170 1,025 2.0 47
Essex, NJ................ 20.5 338.1 -0.4 308 1,237 1.4 75
Gloucester, NJ........... 6.1 100.4 1.8 139 901 2.6 28
Hudson, NJ............... 14.3 239.9 0.3 271 1,284 -0.6 232
Mercer, NJ............... 11.1 235.3 1.0 207 1,290 -1.6 289
Middlesex, NJ............ 21.9 397.2 0.3 271 1,186 2.4 31
Monmouth, NJ............. 20.0 246.9 1.7 150 1,034 -0.4 220
Morris, NJ............... 17.2 283.3 1.8 139 1,553 5.0 6
Ocean, NJ................ 12.6 154.5 4.8 6 826 -1.8 299
Passaic, NJ.............. 12.3 171.9 -0.8 318 990 -1.1 257
Somerset, NJ............. 10.1 179.3 1.8 139 1,484 2.8 24
Union, NJ................ 14.3 223.3 -0.5 314 1,283 5.2 4
Bernalillo, NM........... 18.0 314.8 0.9 218 836 -0.1 195
Albany, NY............... 10.1 225.8 0.7 230 1,019 4.6 10
Bronx, NY................ 17.3 247.7 2.9 72 948 1.5 69
Broome, NY............... 4.6 88.3 -2.2 332 765 0.3 160
Dutchess, NY............. 8.3 113.2 0.9 218 958 -1.7 293
Erie, NY................. 24.3 463.6 0.4 260 857 0.5 141
Kings, NY................ 55.8 557.1 4.5 14 816 -0.1 195
Monroe, NY............... 18.5 380.3 0.2 279 894 0.3 160
Nassau, NY............... 53.3 616.7 2.3 107 1,120 -1.5 280
New York, NY............. 125.1 2,500.2 2.4 98 2,041 -3.3 325
Oneida, NY............... 5.3 105.2 -0.4 308 772 -0.5 226
Onondaga, NY............. 13.0 245.8 0.5 249 914 -1.7 293
Orange, NY............... 10.0 135.7 0.6 239 815 -0.7 238
Queens, NY............... 49.0 544.5 1.5 163 955 2.1 43
Richmond, NY............. 9.3 98.9 4.4 15 849 0.2 165
Rockland, NY............. 10.1 119.5 1.8 139 1,063 -0.2 205
Saratoga, NY............. 5.7 80.6 1.9 130 887 1.1 95
Suffolk, NY.............. 51.5 640.5 1.2 188 1,079 1.9 54
Westchester, NY.......... 36.2 414.4 0.2 279 1,348 -0.3 214
Buncombe, NC............. 8.1 118.8 1.6 156 758 0.7 128
Catawba, NC.............. 4.3 81.7 1.6 156 731 0.8 120
Cumberland, NC........... 6.2 118.0 -1.0 322 766 -0.8 245
Durham, NC............... 7.4 186.9 1.3 175 1,255 3.6 14
Forsyth, NC.............. 9.0 178.2 2.7 81 895 2.2 41
Guilford, NC............. 14.1 271.6 1.1 200 858 -0.1 195
Mecklenburg, NC.......... 33.1 606.8 3.7 34 1,098 -0.5 226
New Hanover, NC.......... 7.3 101.4 2.7 81 799 1.1 95
Wake, NC................. 30.0 485.0 3.7 34 984 0.5 141
Cass, ND................. 6.4 111.7 2.7 81 894 1.4 75
Butler, OH............... 7.5 142.6 2.3 107 846 -0.1 195
Cuyahoga, OH............. 35.8 716.4 0.6 239 1,012 -0.7 238
Delaware, OH............. 4.6 82.2 1.3 175 955 -0.3 214
Franklin, OH............. 30.0 708.0 2.7 81 971 0.5 141
Hamilton, OH............. 23.3 499.0 1.2 188 1,074 -1.6 289
Lake, OH................. 6.3 94.0 -0.3 305 817 1.0 106
Lorain, OH............... 6.0 95.7 0.9 218 800 -1.7 293
Lucas, OH................ 10.1 206.7 1.9 130 852 -1.5 280
Mahoning, OH............. 6.0 99.1 0.3 271 709 -1.1 257
Montgomery, OH........... 12.0 245.6 0.4 260 861 -0.2 205
Stark, OH................ 8.8 157.2 0.2 279 758 0.7 128
Summit, OH............... 14.1 260.1 0.5 249 878 -1.3 272
Warren, OH............... 4.4 78.9 2.6 88 835 -2.8 319
Oklahoma, OK............. 25.8 440.4 0.9 218 962 0.7 128
Tulsa, OK................ 21.3 341.3 0.2 279 950 -3.6 327
Clackamas, OR............ 13.1 143.8 0.7 230 914 2.1 43
Jackson, OR.............. 6.7 79.9 2.0 122 722 2.0 47
Lane, OR................. 11.0 141.1 1.3 175 772 2.3 36
Marion, OR............... 9.6 135.8 4.3 17 778 2.4 31
Multnomah, OR............ 30.8 461.1 2.8 76 1,006 2.0 47
Washington, OR........... 17.1 262.6 3.7 34 1,163 5.9 3
Allegheny, PA............ 34.8 691.0 0.4 260 1,068 1.1 95
Berks, PA................ 8.9 166.6 0.6 239 874 0.1 174
Bucks, PA................ 19.5 250.5 0.4 260 958 0.1 174
Butler, PA............... 4.9 84.5 -0.4 308 944 3.9 12
Chester, PA.............. 15.1 242.1 0.6 239 1,293 -0.1 195
Cumberland, PA........... 6.1 126.2 0.5 249 890 2.8 24
Dauphin, PA.............. 7.3 176.4 0.8 227 970 1.8 59
Delaware, PA............. 13.6 217.9 1.3 175 1,071 0.9 114
Erie, PA................. 7.1 124.0 -0.1 296 774 -0.4 220
Lackawanna, PA........... 5.8 97.9 -0.3 305 741 1.8 59
Lancaster, PA............ 12.7 223.3 0.7 230 828 1.7 62
Lehigh, PA............... 8.6 181.5 1.7 150 962 -0.5 226
Luzerne, PA.............. 7.5 142.0 0.9 218 749 1.5 69
Montgomery, PA........... 27.1 475.1 0.3 271 1,216 -3.0 321
Northampton, PA.......... 6.5 105.7 0.8 227 851 2.0 47
Philadelphia, PA......... 34.3 641.1 0.6 239 1,181 0.0 186
Washington, PA........... 5.3 86.1 0.0 293 993 -2.4 311
Westmoreland, PA......... 9.3 132.3 -1.3 327 795 0.1 174
York, PA................. 8.9 172.5 0.1 290 839 0.2 165
Providence, RI........... 17.5 276.5 1.3 175 1,015 2.0 47
Charleston, SC........... 12.4 222.0 2.8 76 846 1.4 75
Greenville, SC........... 12.6 242.6 3.3 51 854 1.1 95
Horry, SC................ 7.8 106.9 1.6 156 587 1.4 75
Lexington, SC............ 5.8 109.7 4.1 24 723 -1.0 253
Richland, SC............. 9.3 208.0 0.8 227 846 0.6 134
Spartanburg, SC.......... 6.0 122.7 3.4 46 824 -0.4 220
York, SC................. 4.8 78.7 4.2 20 798 1.4 75
Minnehaha, SD............ 6.8 120.0 2.3 107 847 0.4 150
Davidson, TN............. 19.1 449.9 2.4 98 1,061 -1.7 293
Hamilton, TN............. 8.7 189.1 0.3 271 905 0.9 114
Knox, TN................. 11.1 223.9 0.9 218 875 -0.2 205
Rutherford, TN........... 4.6 110.7 3.5 42 880 0.1 174
Shelby, TN............... 19.4 482.4 -0.2 300 1,019 -1.1 257
Williamson, TN........... 6.9 105.6 4.6 11 1,169 -2.2 308
Bell, TX................. 4.9 111.9 1.8 139 790 0.6 134
Bexar, TX................ 36.6 784.1 2.3 107 882 0.7 128
Brazoria, TX............. 5.2 96.1 1.7 150 965 2.9 22
Brazos, TX............... 4.1 94.5 3.4 46 735 0.4 150
Cameron, TX.............. 6.4 133.7 1.2 188 598 -1.5 280
Collin, TX............... 20.5 336.7 3.9 31 1,146 -1.5 280
Dallas, TX............... 71.0 1,530.1 3.2 57 1,197 -1.2 265
Denton, TX............... 12.2 199.0 4.4 15 875 0.2 165
El Paso, TX.............. 14.3 285.7 1.0 207 686 -1.6 289
Fort Bend, TX............ 10.7 162.4 4.8 6 1,025 1.6 64
Galveston, TX............ 5.6 100.2 2.5 90 877 -2.4 311
Gregg, TX................ 4.2 77.8 1.1 200 922 3.0 19
Harris, TX............... 107.0 2,225.4 3.0 61 1,316 -1.2 265
Hidalgo, TX.............. 11.7 240.8 2.4 98 620 1.0 106
Jefferson, TX............ 5.8 119.4 -0.2 300 997 -1.2 265
Lubbock, TX.............. 7.2 131.1 2.2 113 771 -0.1 195
McLennan, TX............. 5.0 103.1 0.2 279 809 -0.7 238
Midland, TX.............. 5.2 86.5 4.8 6 1,299 2.0 47
Montgomery, TX........... 9.7 155.1 4.6 11 1,006 1.0 106
Nueces, TX............... 8.1 162.2 2.4 98 875 -1.2 265
Potter, TX............... 4.0 78.4 0.5 249 803 0.0 186
Smith, TX................ 5.8 97.3 2.5 90 854 -0.8 245
Tarrant, TX.............. 39.6 820.4 2.0 122 988 1.6 64
Travis, TX............... 34.1 644.8 3.3 51 1,108 -0.6 232
Webb, TX................. 5.0 94.4 2.0 122 670 -2.0 305
Williamson, TX........... 8.6 143.2 4.7 9 945 0.5 141
Davis, UT................ 7.7 111.8 3.0 61 767 -1.4 278
Salt Lake, UT............ 40.3 625.6 3.3 51 933 -1.5 280
Utah, UT................. 13.8 191.5 4.2 20 812 -2.5 315
Weber, UT................ 5.6 94.3 2.1 118 721 -0.8 245
Chittenden, VT........... 6.3 99.9 0.4 260 994 1.2 91
Arlington, VA............ 8.8 165.5 -1.1 324 1,588 -2.4 311
Chesterfield, VA......... 8.0 128.0 2.5 90 875 0.6 134
Fairfax, VA.............. 35.1 588.4 -1.2 326 1,558 -2.0 305
Henrico, VA.............. 10.4 180.7 -0.8 318 960 1.3 83
Loudoun, VA.............. 10.4 148.6 1.6 156 1,190 1.0 106
Prince William, VA....... 8.1 117.7 0.7 230 863 -0.7 238
Alexandria City, VA...... 6.2 95.6 -1.4 328 1,414 -3.2 324
Chesapeake City, VA...... 5.7 96.8 0.6 239 775 0.1 174
Newport News City, VA.... 3.7 99.4 1.0 207 920 0.8 120
Norfolk City, VA......... 5.6 137.9 -0.3 305 953 -1.5 280
Richmond City, VA........ 7.1 148.5 0.3 271 1,068 0.1 174
Virginia Beach City, VA.. 11.3 169.5 1.2 188 780 -10.0 332
Benton, WA............... 6.2 76.9 0.6 239 978 0.9 114
Clark, WA................ 15.0 137.2 3.7 34 896 0.4 150
King, WA................. 88.9 1,223.4 3.5 42 1,300 1.9 54
Kitsap, WA............... 7.1 81.5 1.3 175 847 -1.3 272
Pierce, WA............... 23.6 275.7 3.0 61 869 0.0 186
Snohomish, WA............ 21.2 267.3 1.8 139 1,020 1.1 95
Spokane, WA.............. 17.1 204.0 1.6 156 821 1.4 75
Thurston, WA............. 8.1 101.2 2.2 113 860 2.4 31
Whatcom, WA.............. 7.4 83.2 1.8 139 806 1.3 83
Yakima, WA............... 9.5 95.7 0.7 230 689 1.8 59
Kanawha, WV.............. 6.0 104.4 -1.1 324 844 0.4 150
Brown, WI................ 6.6 149.8 1.4 170 894 -0.2 205
Dane, WI................. 14.4 313.9 1.1 200 1,003 4.7 9
Milwaukee, WI............ 24.8 481.7 0.5 249 963 -0.2 205
Outagamie, WI............ 5.1 103.2 1.0 207 833 0.2 165
Waukesha, WI............. 12.6 231.8 1.1 200 993 -1.4 278
Winnebago, WI............ 3.6 90.0 -1.8 330 962 5.0 6
San Juan, PR............. 11.2 268.1 -2.1 (5) 659 -0.5 (5)
(1) Includes areas not officially designated as counties. See Technical Note.
(2) Average weekly wages were calculated using unrounded data.
(3) Percent changes were computed from quarterly employment and pay data adjusted for noneconomic
county reclassifications. See Technical Note.
(4) Totals for the United States do not include data for Puerto Rico or the Virgin Islands.
(5) This county was not included in the U.S. rankings.
Note: Data are preliminary. Includes workers covered by Unemployment Insurance (UI) and Unemployment
Compensation for Federal Employees (UCFE) programs. These 334 U.S. counties comprise 71.7 percent of
the total covered workers in the U.S.
Table 2. Covered establishments, employment, and wages in the 10 largest counties,
fourth quarter 2013
Employment Average weekly
wage(1)
Establishments,
fourth quarter
County by NAICS supersector 2013 Percent Percent
(thousands) December change, Fourth change,
2013 December quarter fourth
(thousands) 2012-13(2) 2013 quarter
2012-13(2)
United States(3) ............................ 9,333.7 136,129.4 1.8 $1,000 0.0
Private industry........................... 9,041.0 114,706.1 2.1 1,006 -0.2
Natural resources and mining............. 134.6 1,930.2 2.0 1,164 1.4
Construction............................. 749.4 5,840.7 3.6 1,119 1.6
Manufacturing............................ 336.5 12,051.4 0.8 1,220 1.3
Trade, transportation, and utilities..... 1,909.1 26,685.6 2.0 831 0.7
Information.............................. 146.3 2,724.5 0.5 1,754 -3.4
Financial activities..................... 825.6 7,673.9 1.2 1,587 -2.6
Professional and business services....... 1,652.0 18,834.1 2.9 1,331 -1.3
Education and health services............ 1,484.4 20,493.9 1.5 913 0.2
Leisure and hospitality.................. 787.6 14,087.6 3.0 421 1.0
Other services........................... 799.4 4,159.3 1.1 663 2.2
Government................................. 292.7 21,423.3 -0.3 965 0.5
Los Angeles, CA.............................. 440.9 4,176.8 1.9 1,161 -1.9
Private industry........................... 435.1 3,639.6 2.2 1,150 -2.3
Natural resources and mining............. 0.5 9.9 5.7 1,778 1.3
Construction............................. 12.5 117.9 5.2 1,176 1.6
Manufacturing............................ 12.5 366.0 0.2 1,183 0.3
Trade, transportation, and utilities..... 52.1 806.8 1.7 914 0.8
Information.............................. 8.7 197.8 -0.5 2,197 -3.9
Financial activities..................... 23.0 212.2 -0.7 1,765 -4.4
Professional and business services....... 45.1 615.2 3.3 1,418 -4.8
Education and health services............ 202.6 706.0 0.7 878 -0.7
Leisure and hospitality.................. 28.8 443.8 4.6 946 -5.5
Other services........................... 25.6 141.1 1.5 682 0.9
Government................................. 5.8 537.2 -0.2 1,232 1.1
Cook, IL..................................... 153.8 2,463.3 1.1 1,174 -1.0
Private industry........................... 152.5 2,167.6 1.3 1,174 -1.4
Natural resources and mining............. 0.1 0.8 0.0 1,103 2.9
Construction............................. 12.7 62.3 1.5 1,511 1.8
Manufacturing............................ 6.6 188.2 -1.1 1,265 0.9
Trade, transportation, and utilities..... 30.3 466.5 0.9 913 1.4
Information.............................. 2.8 53.5 -2.6 1,666 2.6
Financial activities..................... 15.9 183.9 -0.1 2,179 -6.5
Professional and business services....... 32.7 446.9 3.7 1,531 -3.7
Education and health services............ 16.2 420.9 1.4 960 -0.6
Leisure and hospitality.................. 13.8 245.3 1.3 488 3.4
Other services........................... 17.0 94.8 0.0 867 3.3
Government................................. 1.3 295.7 0.0 1,175 2.4
New York, NY................................. 125.1 2,500.2 2.4 2,041 -3.3
Private industry........................... 124.8 2,058.5 2.8 2,239 -4.1
Natural resources and mining............. 0.0 0.2 1.3 1,932 2.6
Construction............................. 2.2 33.4 1.0 2,099 5.1
Manufacturing............................ 2.3 26.0 0.1 1,546 0.6
Trade, transportation, and utilities..... 20.8 273.4 1.8 1,400 -8.8
Information.............................. 4.5 147.1 2.6 2,525 1.3
Financial activities..................... 19.1 356.2 0.5 4,740 -8.4
Professional and business services....... 26.3 516.7 2.8 2,446 0.4
Education and health services............ 9.6 324.6 3.1 1,261 2.8
Leisure and hospitality.................. 13.4 276.9 5.8 923 1.8
Other services........................... 19.5 97.5 2.8 1,126 3.4
Government................................. 0.3 441.7 0.4 1,126 2.4
Harris, TX................................... 107.0 2,225.4 3.0 1,316 -1.2
Private industry........................... 106.5 1,963.3 3.1 1,354 -1.4
Natural resources and mining............. 1.8 97.7 7.5 3,383 -4.2
Construction............................. 6.6 145.8 2.1 1,358 0.2
Manufacturing............................ 4.6 198.0 2.9 1,591 -6.2
Trade, transportation, and utilities..... 24.1 469.0 2.8 1,157 -2.8
Information.............................. 1.2 29.0 2.1 1,434 0.2
Financial activities..................... 11.0 118.6 2.7 1,669 -2.2
Professional and business services....... 21.5 375.2 1.8 1,667 1.6
Education and health services............ 14.7 266.6 3.0 1,016 0.3
Leisure and hospitality.................. 8.9 201.6 5.1 439 1.4
Other services........................... 11.6 61.0 2.6 763 3.0
Government................................. 0.6 262.1 2.3 1,033 0.7
Maricopa, AZ................................. 92.8 1,771.9 3.0 952 -1.3
Private industry........................... 92.1 1,562.1 3.3 951 -1.8
Natural resources and mining............. 0.5 8.2 -2.6 937 -4.2
Construction............................. 7.4 92.3 4.2 1,039 -0.8
Manufacturing............................ 3.1 113.7 0.0 1,308 1.3
Trade, transportation, and utilities..... 20.5 361.3 3.0 855 -5.2
Information.............................. 1.5 32.2 3.5 1,242 -5.0
Financial activities..................... 11.1 152.6 4.5 1,188 -1.1
Professional and business services....... 22.0 303.6 3.8 1,076 -1.2
Education and health services............ 10.8 258.0 2.1 1,009 0.6
Leisure and hospitality.................. 7.4 191.1 5.8 440 -0.9
Other services........................... 6.5 47.9 1.5 642 1.1
Government................................. 0.7 209.9 0.4 959 1.4
Dallas, TX................................... 71.0 1,530.1 3.2 1,197 -1.2
Private industry........................... 70.5 1,364.2 3.5 1,213 -1.4
Natural resources and mining............. 0.6 9.3 5.4 3,778 -12.9
Construction............................. 4.1 73.6 5.7 1,165 0.0
Manufacturing............................ 2.7 106.9 -4.0 1,373 -2.0
Trade, transportation, and utilities..... 15.4 321.3 5.6 1,058 -0.6
Information.............................. 1.4 48.7 4.1 1,779 6.5
Financial activities..................... 8.7 150.6 3.9 1,585 -4.6
Professional and business services....... 15.8 295.6 3.3 1,460 0.1
Education and health services............ 8.7 178.3 2.8 1,042 -2.0
Leisure and hospitality.................. 6.1 139.6 4.5 521 -1.3
Other services........................... 6.8 39.7 2.0 770 -0.8
Government................................. 0.5 166.0 1.3 1,064 0.5
Orange, CA................................... 107.0 1,463.1 2.0 1,114 -1.8
Private industry........................... 105.7 1,325.5 2.1 1,119 -2.0
Natural resources and mining............. 0.2 3.1 1.9 738 1.2
Construction............................. 6.1 77.8 6.0 1,240 -1.8
Manufacturing............................ 4.8 157.5 -0.6 1,341 -0.7
Trade, transportation, and utilities..... 16.4 263.5 1.3 993 -0.8
Information.............................. 1.2 24.9 1.2 1,677 -0.5
Financial activities..................... 10.0 112.5 0.6 1,915 -5.8
Professional and business services....... 19.7 265.9 0.9 1,335 -1.3
Education and health services............ 26.1 183.8 3.1 971 -2.9
Leisure and hospitality.................. 7.5 188.1 3.7 438 1.2
Other services........................... 6.2 41.0 0.7 689 6.2
Government................................. 1.3 137.6 1.2 1,067 0.4
San Diego, CA................................ 99.8 1,330.2 1.9 1,107 0.8
Private industry........................... 98.3 1,110.8 2.2 1,103 1.3
Natural resources and mining............. 0.7 9.2 1.1 662 -0.3
Construction............................. 6.0 62.1 6.4 1,121 -0.8
Manufacturing............................ 3.0 95.1 -0.1 1,499 -2.0
Trade, transportation, and utilities..... 13.8 223.2 1.1 818 -2.2
Information.............................. 1.1 23.9 -1.8 1,719 7.8
Financial activities..................... 8.8 70.8 -0.8 1,370 -1.4
Professional and business services....... 17.2 226.7 2.6 1,787 5.9
Education and health services............ 27.2 179.8 1.3 955 -0.1
Leisure and hospitality.................. 7.3 167.6 3.3 443 0.9
Other services........................... 6.7 46.1 3.1 584 -3.2
Government................................. 1.4 219.5 0.4 1,130 -1.1
King, WA..................................... 88.9 1,223.4 3.5 1,300 1.9
Private industry........................... 88.4 1,063.5 3.7 1,316 1.9
Natural resources and mining............. 0.4 2.4 -0.8 1,360 -33.2
Construction............................. 5.8 53.7 6.6 1,267 1.8
Manufacturing............................ 2.3 105.6 2.6 1,532 3.2
Trade, transportation, and utilities..... 14.8 231.3 4.6 1,110 2.4
Information.............................. 1.9 83.3 3.4 2,559 2.7
Financial activities..................... 6.4 65.5 2.2 1,589 0.5
Professional and business services....... 15.1 202.1 3.5 1,704 0.6
Education and health services............ 26.8 158.5 2.8 946 1.1
Leisure and hospitality.................. 6.8 120.7 4.5 532 9.9
Other services........................... 8.2 40.4 3.8 811 5.3
Government................................. 0.5 159.8 1.8 1,190 1.1
Miami-Dade, FL............................... 94.5 1,047.5 2.5 982 0.8
Private industry........................... 94.1 909.7 3.0 962 0.7
Natural resources and mining............. 0.5 9.5 5.0 545 -7.3
Construction............................. 5.3 33.8 10.0 945 -6.3
Manufacturing............................ 2.7 36.5 2.6 952 2.0
Trade, transportation, and utilities..... 27.8 274.6 3.0 890 4.7
Information.............................. 1.6 18.2 3.7 1,531 1.9
Financial activities..................... 9.7 69.9 3.8 1,521 0.0
Professional and business services....... 20.0 141.1 2.9 1,268 -4.1
Education and health services............ 10.2 160.5 1.0 942 1.4
Leisure and hospitality.................. 7.2 127.1 2.3 555 0.9
Other services........................... 8.2 37.2 2.8 606 2.5
Government................................. 0.3 137.9 -0.5 1,112 1.8
(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 2012 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,
fourth quarter 2013
Employment Average weekly
wage(1)
Establishments,
fourth quarter
State 2013 Percent Percent
(thousands) December change, Fourth change,
2013 December quarter fourth
(thousands) 2012-13 2013 quarter
2012-13
United States(2)........... 9,333.7 136,129.4 1.8 $1,000 0.0
Alabama.................... 117.0 1,866.5 1.0 851 -0.5
Alaska..................... 22.0 315.1 0.0 1,022 1.6
Arizona.................... 145.8 2,571.0 2.4 906 -0.5
Arkansas................... 87.5 1,154.3 -0.5 771 0.4
California................. 1,376.4 15,650.3 2.8 1,175 -0.9
Colorado................... 175.3 2,383.9 3.1 1,023 -0.9
Connecticut................ 113.7 1,661.2 0.3 1,238 -1.3
Delaware................... 28.5 419.6 1.8 1,035 -0.6
District of Columbia....... 36.0 727.3 0.6 1,638 -3.9
Florida.................... 635.5 7,739.5 2.7 883 0.2
Georgia.................... 278.6 3,986.9 2.5 924 -0.1
Hawaii..................... 38.9 632.9 1.7 871 0.3
Idaho...................... 54.1 634.5 2.6 754 3.0
Illinois................... 404.2 5,758.9 1.0 1,060 0.2
Indiana.................... 159.6 2,896.9 1.6 814 -0.2
Iowa....................... 98.4 1,510.9 1.4 834 1.6
Kansas..................... 85.1 1,359.5 1.6 832 -0.4
Kentucky................... 119.5 1,818.0 1.2 804 0.2
Louisiana.................. 129.0 1,911.6 0.9 889 0.5
Maine...................... 49.8 586.8 0.8 786 1.7
Maryland................... 166.2 2,555.1 0.4 1,076 -0.9
Massachusetts.............. 222.1 3,332.9 1.5 1,258 0.8
Michigan................... 235.7 4,072.4 2.0 952 -0.2
Minnesota.................. 164.5 2,720.6 1.7 988 0.3
Mississippi................ 71.3 1,108.1 1.1 729 1.3
Missouri................... 182.4 2,670.4 1.1 861 -0.2
Montana.................... 43.6 440.0 1.3 760 0.4
Nebraska................... 69.5 944.3 1.4 796 -0.1
Nevada..................... 75.1 1,180.5 3.0 884 0.7
New Hampshire.............. 50.1 629.3 1.4 1,017 -0.8
New Jersey................. 265.3 3,887.5 1.2 1,186 1.1
New Mexico................. 56.4 796.2 -0.1 814 1.4
New York................... 617.6 8,888.6 1.7 1,266 -1.1
North Carolina............. 258.2 4,045.5 1.9 860 0.7
North Dakota............... 31.2 435.0 3.3 980 3.8
Ohio....................... 288.9 5,175.4 1.4 887 0.0
Oklahoma................... 106.4 1,581.3 0.6 851 -0.1
Oregon..................... 136.0 1,699.6 2.5 894 2.6
Pennsylvania............... 345.9 5,650.3 0.4 976 0.4
Rhode Island............... 35.6 462.7 1.4 960 1.5
South Carolina............. 118.1 1,875.8 2.3 793 1.0
South Dakota............... 31.8 407.1 1.3 759 1.3
Tennessee.................. 144.3 2,758.3 1.8 895 -0.9
Texas...................... 613.7 11,246.3 2.6 1,027 0.0
Utah....................... 89.9 1,284.7 3.1 836 -0.9
Vermont.................... 24.6 308.5 0.6 848 2.3
Virginia................... 240.6 3,670.0 0.1 1,028 -1.3
Washington................. 253.8 2,976.0 2.5 1,034 1.7
West Virginia.............. 49.8 710.1 -0.6 792 0.5
Wisconsin.................. 164.9 2,751.8 1.0 865 1.2
Wyoming.................... 25.6 279.2 0.6 917 1.0
Puerto Rico................ 48.0 958.3 -2.3 551 0.2
Virgin Islands............. 3.3 38.5 -3.6 754 2.4
(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
