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For release 10:00 a.m. (EDT), Thursday, September 26, 2013 USDL-13-1942 Technical Information: (202) 691-6567 * [email protected] * www.bls.gov/cew Media Contact: (202) 691-5902 * [email protected] COUNTY EMPLOYMENT AND WAGES First Quarter 2013 From March 2012 to March 2013, employment increased in 282 of the 334 largest U.S. counties, the U.S. Bureau of Labor Statistics reported today. Fort Bend, Texas, posted the largest increase, with a gain of 7.0 percent over the year, compared with national job growth of 1.6 percent. Within Fort Bend, the largest employment increase occurred in leisure and hospitality, which gained 2,204 jobs over the year (12.5 percent). Sangamon, Ill., had the largest over-the-year decrease in employment among the largest counties in the U.S. with a loss of 2.4 percent. County employment and wage data are compiled under the Quarterly Census of Employment and Wages (QCEW) program, which produces detailed information on county employment and wages within 6 months after the end of each quarter. The U.S. average weekly wage increased over the year by 0.6 percent to $989 in the first quarter of 2013. San Mateo, Calif., had the largest over-the-year increase in average weekly wages with a gain of 14.8 percent. Within San Mateo, an average weekly wage gain of $2,996 or 104.1 percent in information had the largest contribution to the increase in average weekly wages. Williamson, Texas, experienced the largest decrease in average weekly wages with a loss of 13.4 percent over the year. Table A. Large counties ranked by March 2013 employment, March 2012-13 employment increase, and March 2012-13 percent increase in employment -------------------------------------------------------------------------------------------------------- Employment in large counties -------------------------------------------------------------------------------------------------------- March 2013 employment | Increase in employment, | Percent increase in employment, (thousands) | March 2012-13 | March 2012-13 | (thousands) | -------------------------------------------------------------------------------------------------------- | | United States 132,338.9| United States 2,082.4| United States 1.6 -------------------------------------------------------------------------------------------------------- | | Los Angeles, Calif. 4,041.3| Los Angeles, Calif. 85.7| Fort Bend, Texas 7.0 New York, N.Y. 2,403.9| Harris, Texas 77.7| Midland, Texas 6.9 Cook, Ill. 2,394.0| Maricopa, Ariz. 43.0| Elkhart, Ind. 6.0 Harris, Texas 2,163.6| Dallas, Texas 41.3| Douglas, Colo. 5.6 Maricopa, Ariz. 1,710.2| New York, N.Y. 39.2| Utah, Utah 5.5 Dallas, Texas 1,473.4| Orange, Calif. 38.9| Rutherford, Tenn. 5.3 Orange, Calif. 1,433.5| King, Wash. 35.4| Placer, Calif. 5.2 San Diego, Calif. 1,297.9| Santa Clara, Calif. 33.8| Montgomery, Texas 5.0 King, Wash. 1,175.0| San Diego, Calif. 29.1| Brazos, Texas 4.7 Miami-Dade, Fla. 1,016.2| Cook, Ill. 27.4| Weld, Colo. 4.4 | | -------------------------------------------------------------------------------------------------------- Large County Employment In March 2013, national employment was 132.3 million (as measured by the QCEW program). Over the year employment was up by 1.6 percent or 2.1 million. The 334 U.S. counties with 75,000 or more jobs accounted for 71.6 percent of total U.S. employment and 77.7 percent of total wages. These 334 counties had a net job growth of 1.6 million over the year, accounting for 78.6 percent of the overall U.S. employment increase. Fort Bend, Texas, had the largest percentage increase in employment (7.0 percent) among the largest U.S. counties. The five counties with the largest increases in employment level were Los Angeles, Calif.; Harris, Texas; Maricopa, Ariz.; Dallas, Texas; and New York, N.Y. These counties had a combined over- the-year employment gain of 286,800 jobs, which was 13.8 percent of the overall job increase for the U.S. (See table A.) Employment declined in 46 of the large counties from March 2012 to March 2013. Sangamon, Ill., had the largest over-the-year percentage decrease in employment (-2.4 percent). Within Sangamon, professional and business services had the largest decrease in employment with a loss of 1,630 jobs (-14.5 percent). Vanderburgh, Ind., had the second largest percentage decrease in employment, followed by Broome, N.Y., and Jefferson, Texas, which tied for the third largest percentage decrease. Two counties, Peoria, Ill., and Oneida, N.Y., tied for the fifth largest percentage decrease. (See table 1.) Table B. Large counties ranked by first quarter 2013 average weekly wages, first quarter 2012-13 increase in average weekly wages, and first quarter 2012-13 percent increase in average weekly wages -------------------------------------------------------------------------------------------------------- Average weekly wage in large counties -------------------------------------------------------------------------------------------------------- Average weekly wage, | Increase in average weekly | Percent increase in average first quarter 2013 | wage, first quarter 2012-13 | weekly wage, first | | quarter 2012-13 -------------------------------------------------------------------------------------------------------- | | United States $989| United States $6| United States 0.6 -------------------------------------------------------------------------------------------------------- | | New York, N.Y. $2,448| San Mateo, Calif. $239| San Mateo, Calif. 14.8 Somerset, N.J. 2,009| Benton, Ark. 168| Benton, Ark. 14.3 Santa Clara, Calif. 1,937| Somerset, N.J. 126| McLean, Ill. 11.8 Fairfield, Conn. 1,878| McLean, Ill. 112| Clayton, Ga. 6.7 San Mateo, Calif. 1,859| Mercer, N.J. 89| Somerset, N.J. 6.7 San Francisco, Calif. 1,778| Clayton, Ga. 58| Mercer, N.J. 6.4 Suffolk, Mass. 1,698| Williamson, Tenn. 53| Hampden, Mass. 4.8 Arlington, Va. 1,621| Ramsey, Minn. 49| Williamson, Tenn. 4.6 Washington, D.C. 1,613| Lake, Ill. 41| Winnebago, Wis. 4.6 Morris, N.J. 1,582| Hampden, Mass. 41| Ramsey, Minn. 4.4 | | -------------------------------------------------------------------------------------------------------- Large County Average Weekly Wages Average weekly wages for the nation increased 0.6 percent during the year ending in the first quarter of 2013. Among the 334 largest counties, 232 had over-the-year increases in average weekly wages. San Mateo, Calif., had the largest wage increase among the largest U.S. counties (14.8 percent). Of the 334 largest counties, 92 experienced over-the-year decreases in average weekly wages. Williamson, Texas, had the largest average weekly wage decrease with a loss of 13.4 percent. Within Williamson, trade, transportation, and utilities had the largest impact on the county�s average weekly wage decrease. Within this industry, average weekly wages declined by $436 (-24.2 percent) over the year. Middlesex, N.J., had the second largest decrease in average weekly wages, followed by Peoria, Ill.; Washington, Ore.; and Santa Cruz, Calif. (See table 1.) Ten Largest U.S. Counties All of the 10 largest counties had over-the-year percentage increases in employment in March 2013. Harris, Texas, had the largest gain (3.7 percent). Within Harris, professional and business services had the largest over-the-year employment level increase among all private industry groups with a gain of 16,474 jobs or 4.7 percent. Cook, Ill., had the smallest percentage increase in employment (1.2 percent) among the 10 largest counties. (See table 2.) Three of the 10 largest U.S. counties had over-the-year increases in average weekly wages. King, Wash., experienced the largest gain in average weekly wages (1.6 percent). Within King, professional and business services had the largest impact on the county�s average weekly wage growth. Within this industry, average weekly wages increased by $42 or 2.7 percent over the year. Los Angeles, Calif., had the largest average weekly wage decrease (-1.8 percent) among the 10 largest counties. For More Information The tables included in this release contain data for the nation and for the 334 U.S. counties with annual average employment levels of 75,000 or more in 2012. March 2013 employment and 2013 first quarter average weekly wages for all states are provided in table 3 of this release. The employment and wage data by county are compiled under the QCEW program, also known as the ES-202 program. The data are derived from reports submitted by every employer subject to unemployment insurance (UI) laws. The 9.2 million employer reports cover 132.3 million full- and part- time workers. For additional information about the quarterly employment and wages data, please read the Technical Note. Data for the first quarter of 2013 will be available later at http://www.bls.gov/cew/. Additional information about the QCEW data may be obtained by calling (202) 691-6567. Several BLS regional offices are issuing QCEW news releases targeted to local data users. For links to these releases, see http://www.bls.gov/cew/cewregional.htm. _____________ The County Employment and Wages release for second quarter 2013 is scheduled to be released on Wednesday, December 18, 2013. --------------------------------------------------------------------------------------------------- | | | County Changes for the 2013 County Employment and Wages News Releases | | | | Counties with annual average employment of 75,000 or more in 2012 are included in this release | | and will be included in future 2013 releases. Six counties have been added to the publication | | tables: Boone, Ky.; Warren, Ohio; Jackson, Ore.; York, S.C.; Midland, Texas; and Potter, Texas. | | | --------------------------------------------------------------------------------------------------- --------------------------------------------------------------------------------------------------- | | | Updated MSA Definitions | | | | New Metropolitan Statistical Area (MSA) definitions, and those for other types of Core Based | | Statistical Areas (CBSA), were announced in March 2013. The QCEW program will be using those | | definitions for tabulating data referencing 2013 and future years and will begin releasing that | | data effective with today�s release. Prior year data will not be re-tabulated to the new | | definitions. | | | | For more information regarding the new area definitions, see | | http://www.whitehouse.gov/omb/inforeg_statpolicy#ms. | | | --------------------------------------------------------------------------------------------------- --------------------------------------------------------------------------------------------------- | | | Notable Industry Changes | | | | Each first quarter, QCEW incorporates improved industry assignments. Usually this activity is | | distributed across industries. In 2013, the improvements also include substantial changes to two | | specific industries--funds, trusts, and other financial vehicles (NAICS 525) as well as private | | households (NAICS 814110). | | | | Establishments in funds, trusts, and other financial vehicles are legal entities with little to | | no employment. Establishments with employees who manage such funds are typically coded in other | | financial investment activities (NAICS 5239), although they may also be classified as other | | industries within finance. The QCEW program examined establishments with employment classified | | within funds, trusts, and other financial vehicles and reclassified them into other industries | | based on each establishment�s primary economic activity. | | | | The QCEW program also reviewed establishments that provide non-medical, home-based services for | | the elderly and persons with disabilities and classified these establishments into services for | | the elderly and persons with disabilities (NAICS 624120). Many of these establishments were | | previously classified in the private households industry. | | | | These changes apply not only to the data published by QCEW, but also data based on QCEW such as | | BLS Current Employment Statistics (CES) and Bureau of Economic Analysis (BEA) Personal Income. | | For more information about the industry changes affecting the data in these programs, contact the | | QCEW program at (202) 691-6567, the CES program at (202) 691-6555, or BEA at (202) 606-9272. | | | ---------------------------------------------------------------------------------------------------
Technical Note These data are the product of a federal-state cooperative program, the Quarterly Census of Employment and Wages (QCEW) program, also known as the ES-202 program. The data are derived from summaries of employment and total pay of workers covered by state and federal unemployment insurance (UI) legislation and provided by State Workforce Agencies (SWAs). The summaries are a result of the administration of state unemployment insurance programs that require most employers to pay quarterly taxes based on the employment and wages of workers covered by UI. QCEW data in this release are based on the 2012 North American Industry Classification System. Data for 2013 are preliminary and subject to revision. For purposes of this release, large counties are defined as having employment le- vels of 75,000 or greater. In addition, data for San Juan, Puerto Rico, are pro- vided, but not used in calculating U.S. averages, rankings, or in the analysis in the text. Each year, these large counties are selected on the basis of the prelimi- nary annual average of employment for the previous year. The 335 counties presented in this release were derived using 2012 preliminary annual averages of employment. For 2013 data, six counties have been added to the publication tables: Boone, Ky.; Warren, Ohio; Jackson, Ore.; York, S.C.; Midland, Texas; and Potter, Texas. These counties will be included in all 2013 quarterly releases. The counties in table 2 are selected and sorted each year based on the annual average employment from the preceding year. The preliminary QCEW data presented in this release may differ from data released by the individual states. These potential differences result from the states' con- tinuing receipt of UI data over time and ongoing review and editing. The individual states determine their data release timetables. Differences between QCEW, BED, and CES employment measures The Bureau publishes three different establishment-based employment measures for any given quarter. Each of these measures--QCEW, Business Employment Dynamics (BED), and Current Employment Statistics (CES)--makes use of the quarterly UI employment reports in producing data; however, each measure has a somewhat different universe coverage, estimation procedure, and publication product. Differences in coverage and estimation methods can result in somewhat different measures of employment change over time. It is important to understand program dif- ferences and the intended uses of the program products. (See table.) Additional in- formation on each program can be obtained from the program Web sites shown in the table. Summary of Major Differences between QCEW, BED, and CES Employment Measures --------------------------------------------------------------------------------- | QCEW | BED | CES -----------|---------------------|----------------------|------------------------ Source |--Count of UI admini-|--Count of longitudi- |--Sample survey: | strative records | nally-linked UI ad- | 557,000 establish- | submitted by 9.2 | ministrative records| ments | million establish- | submitted by 6.8 | | ments in first | million private-sec-| | quarter of 2013 | tor employers | -----------|---------------------|----------------------|------------------------ Coverage |--UI and UCFE cover- |--UI coverage, exclud-|Nonfarm wage and sal- | age, including all | ing government, pri-| ary jobs: | employers subject | vate households, and|--UI coverage, exclud- | to state and fed- | establishments with | ing agriculture, pri- | eral UI laws | zero employment | vate households, and | | | self-employed workers | | |--Other employment, in- | | | cluding railroads, | | | religious organiza- | | | tions, and other non- | | | UI-covered jobs -----------|---------------------|----------------------|------------------------ Publication|--Quarterly |--Quarterly |--Monthly frequency | -6 months after the| -8 months after the | -Usually first Friday | end of each quar- | end of each quarter| of following month | ter | | -----------|---------------------|----------------------|------------------------ Use of UI |--Directly summarizes|--Links each new UI |--Uses UI file as a sam- file | and publishes each | quarter to longitu- | pling frame and to an- | new quarter of UI | dinal database and | nually realign sample- | data | directly summarizes | based estimates to pop- | | gross job gains and | ulation counts (bench- | | losses | marking) -----------|---------------------|----------------------|------------------------ Principal |--Provides a quarter-|--Provides quarterly |--Provides current month- products | ly and annual uni- | employer dynamics | ly estimates of employ- | verse count of es- | data on establish- | ment, hours, and earn- | tablishments, em- | ment openings, clos-| ings at the MSA, state, | ployment, and wages| ings, expansions, | and national level by | at the county, MSA,| and contractions at | industry | state, and national| the national level | | levels by detailed | by NAICS supersec- | | industry | tors and by size of | | | firm, and at the | | | state private-sector| | | total level | | |--Future expansions | | | will include data | | | with greater indus- | | | try detail and data | | | at the county and | | | MSA level | -----------|---------------------|----------------------|------------------------ Principal |--Major uses include:|--Major uses include: |--Major uses include: uses | -Detailed locality | -Business cycle | -Principal national | data | analysis | economic indicator | -Periodic universe | -Analysis of employ-| -Official time series | counts for bench- | er dynamics under- | for employment change | marking sample | lying economic ex- | measures | survey estimates | pansions and con- | -Input into other ma- | -Sample frame for | tractions | jor economic indi- | BLS establishment | -Analysis of employ-| cators | surveys | ment expansion and | | | contraction by size| | | of firm | | | | -----------|---------------------|----------------------|------------------------ Program |--www.bls.gov/cew/ |--www.bls.gov/bdm/ |--www.bls.gov/ces/ Web sites | | | --------------------------------------------------------------------------------- Coverage Employment and wage data for workers covered by state UI laws are compiled from quarterly contribution reports submitted to the SWAs by employers. For federal ci- vilian workers covered by the Unemployment Compensation for Federal Employees (UCFE) program, employment and wage data are compiled from quarterly reports sub- mitted by four major federal payroll processing centers on behalf of all federal agencies, with the exception of a few agencies which still report directly to the individual SWA. In addition to the quarterly contribution reports, employers who operate multiple establishments within a state complete a questionnaire, called the "Multiple Worksite Report," which provides detailed information on the location and industry of each of their establishments. QCEW employment and wage data are derived from microdata summaries of 9.1 million employer reports of employment and wages submitted by states to the BLS in 2012. These reports are based on place of employ- ment rather than place of residence. UI and UCFE coverage is broad and has been basically comparable from state to state since 1978, when the 1976 amendments to the Federal Unemployment Tax Act became ef- fective, expanding coverage to include most State and local government employees. In 2012, UI and UCFE programs covered workers in 131.7 million jobs. The estimated 126.9 million workers in these jobs (after adjustment for multiple jobholders) represented 95.5 percent of civilian wage and salary employment. Covered workers received $6.491 trillion in pay, representing 93.7 percent of the wage and salary component of personal income and 40.0 percent of the gross domestic product. Major exclusions from UI coverage include self-employed workers, most agricultural workers on small farms, all members of the Armed Forces, elected officials in most states, most employees of railroads, some domestic workers, most student workers at schools, and employees of certain small nonprofit organizations. State and federal UI laws change periodically. These changes may have an impact on the employment and wages reported by employers covered under the UI program. Cover- age changes may affect the over-the-year comparisons presented in this news re- lease. Concepts and methodology Monthly employment is based on the number of workers who worked during or received pay for the pay period including the 12th of the month. With few exceptions, all employees of covered firms are reported, including production and sales workers, corporation officials, executives, supervisory personnel, and clerical workers. Workers on paid vacations and part-time workers also are included. Average weekly wage values are calculated by dividing quarterly total wages by the average of the three monthly employment levels (all employees, as described above) and dividing the result by 13, for the 13 weeks in the quarter. These calculations are made using unrounded employment and wage values. The average wage values that can be calculated using rounded data from the BLS database may differ from the av- erages reported. Included in the quarterly wage data are non-wage cash payments such as bonuses, the cash value of meals and lodging when supplied, tips and other gratuities, and, in some states, employer contributions to certain deferred compen- sation plans such as 401(k) plans and stock options. Over-the-year comparisons of average weekly wages may reflect fluctuations in average monthly employment and/or total quarterly wages between the current quarter and prior year levels. Average weekly wages are affected by the ratio of full-time to part-time workers as well as the number of individuals in high-paying and low-paying occupations and the incidence of pay periods within a quarter. For instance, the average weekly wage of the workforce could increase significantly when there is a large decline in the number of employees that had been receiving below-average wages. Wages may include payments to workers not present in the employment counts because they did not work during the pay period including the 12th of the month. When comparing average week- ly wage levels between industries, states, or quarters, these factors should be taken into consideration. Wages measured by QCEW may be subject to periodic and sometimes large fluctuations. This variability may be due to calendar effects resulting from some quarters having more pay dates than others. The effect is most visible in counties with a dominant employer. In particular, this effect has been observed in counties where government employers represent a large fraction of overall employment. Similar calendar effects can result from private sector pay practices. However, these effects are typically less pronounced for two reasons: employment is less concentrated in a single private employer, and private employers use a variety of pay period types (weekly, biweekly, semimonthly, monthly). For example, the effect on over-the-year pay comparisons can be pronounced in federal government due to the uniform nature of federal payroll processing. Most federal employees are paid on a biweekly pay schedule. As a result, in some quarters federal wages include six pay dates, while in other quarters there are seven pay dates. Over- the-year comparisons of average weekly wages may also reflect this calendar effect. Growth in average weekly wages may be attributed, in part, to a comparison of quarterly wages for the current year, which include seven pay dates, with year-ago wages that reflect only six pay dates. An opposite effect will occur when wages in the current quarter reflecting six pay dates are compared with year-ago wages for a quarter including seven pay dates. In order to ensure the highest possible quality of data, states verify with employ- ers and update, if necessary, the industry, location, and ownership classification of all establishments on a 3-year cycle. Changes in establishment classification codes resulting from this process are introduced with the data reported for the first quarter of the year. Changes resulting from improved employer reporting also are introduced in the first quarter. QCEW data are not designed as a time series. QCEW data are simply the sums of indi- vidual establishment records and reflect the number of establishments that exist in a county or industry at a point in time. Establishments can move in or out of a county or industry for a number of reasons--some reflecting economic events, others reflecting administrative changes. For example, economic change would come from a firm relocating into the county; administrative change would come from a company correcting its county designation. The over-the-year changes of employment and wages presented in this release have been adjusted to account for most of the administrative corrections made to the un- derlying establishment reports. This is done by modifying the prior-year levels used to calculate the over-the-year changes. Percent changes are calculated using an adjusted version of the final 2012 quarterly data as the base data. The adjusted prior-year levels used to calculate the over-the-year percent change in employment and wages are not published. These adjusted prior-year levels do not match the un- adjusted data maintained on the BLS Web site. Over-the-year change calculations based on data from the Web site, or from data published in prior BLS news releases, may differ substantially from the over-the-year changes presented in this news re- lease. The adjusted data used to calculate the over-the-year change measures presented in this release account for most of the administrative changes--those occurring when employers update the industry, location, and ownership information of their estab- lishments. The most common adjustments for administrative change are the result of updated information about the county location of individual establishments. In- cluded in these adjustments are administrative changes involving the classification of establishments that were previously reported in the unknown or statewide county or unknown industry categories. Beginning with the first quarter of 2008, adjusted data account for administrative changes caused by multi-unit employers who start reporting for each individual establishment rather than as a single entity. Beginn- ing with the second quarter of 2011, adjusted data account for selected large admin- istrative changes in employment and wages. These new adjustments allow QCEW to incl- ude county employment and wage growth rates in this news release that would other- wise not meet publication standards. The adjusted data used to calculate the over-the-year change measures presented in any County Employment and Wages news release are valid for comparisons between the starting and ending points (a 12-month period) used in that particular release. Comparisons may not be valid for any time period other than the one featured in a release even if the changes were calculated using adjusted data. County definitions are assigned according to Federal Information Processing Stan- dards Publications (FIPS PUBS) as issued by the National Institute of Standards and Technology, after approval by the Secretary of Commerce pursuant to Section 5131 of the Information Technology Management Reform Act of 1996 and the Computer Security Act of 1987, Public Law 104-106. Areas shown as counties include those designated as independent cities in some jurisdictions and, in Alaska, those designated as census areas where counties have not been created. County data also are presented for the New England states for comparative purposes even though townships are the more common designation used in New England (and New Jersey). The regions referred to in this release are defined as census regions. Additional statistics and other information Employment and Wages Annual Averages Online features comprehensive information by detailed industry on establishments, employment, and wages for the nation and all states. The 2012 edition of this publication, which will be available shortly after this release, contains selected data produced by Business Employment Dynamics (BED) on job gains and losses, as well as selected data from this news release. Tables and additional content from Employment and Wages Annual Averages 2012 will be available online at http://www.bls.gov/cew/cewbultn12.htm. News releases on quarterly measures of gross job flows also are available upon re- quest from the Division of Administrative Statistics and Labor Turnover (Business Employment Dynamics), telephone (202) 691-6467; (http://www.bls.gov/bdm/); (e-mail: [email protected]). Information in this release will be made available to sensory impaired individuals upon request. Voice phone: (202) 691-5200; TDD message referral phone number: 1-800- 877-8339.
Table 1. Covered(1) establishments, employment, and wages in the 335 largest counties, first quarter 2013(2) Employment Average weekly wage(4) Establishments, County(3) first quarter Percent Ranking Percent Ranking 2013 March change, by First change, by (thousands) 2013 March percent quarter first percent (thousands) 2012-13(5) change 2013 quarter change 2012-13(5) United States(6)......... 9,193.5 132,338.9 1.6 - $989 0.6 - Jefferson, AL............ 17.8 336.6 0.9 199 983 0.5 179 Madison, AL.............. 9.0 180.1 1.8 133 1,030 0.3 200 Mobile, AL............... 9.7 163.4 0.0 283 812 2.8 24 Montgomery, AL........... 6.4 128.7 1.7 142 781 -3.1 328 Tuscaloosa, AL........... 4.3 85.7 2.0 117 797 -1.0 299 Anchorage Borough, AK.... 8.3 150.7 0.1 273 1,038 1.5 90 Maricopa, AZ............. 94.3 1,710.2 2.6 74 945 0.0 233 Pima, AZ................. 18.9 352.1 0.9 199 809 1.0 133 Benton, AR............... 5.6 98.4 2.7 63 1,339 14.3 2 Pulaski, AR.............. 14.5 243.8 0.6 232 853 -0.9 296 Washington, AR........... 5.7 93.7 3.1 41 759 1.6 78 Alameda, CA.............. 54.6 672.8 3.2 36 1,249 -1.6 311 Contra Costa, CA......... 28.8 328.6 3.0 45 1,251 0.1 216 Fresno, CA............... 29.1 336.5 3.0 45 736 0.3 200 Kern, CA................. 16.8 284.6 3.7 19 848 0.0 233 Los Angeles, CA.......... 412.4 4,041.3 2.2 101 1,061 -1.8 317 Marin, CA................ 11.6 106.9 2.9 51 1,138 1.2 115 Monterey, CA............. 12.2 156.7 1.8 133 834 0.5 179 Orange, CA............... 102.8 1,433.5 2.8 57 1,086 -0.5 269 Placer, CA............... 10.8 135.9 5.2 7 933 1.4 99 Riverside, CA............ 48.4 592.6 3.4 29 770 0.1 216 Sacramento, CA........... 49.0 598.0 1.8 133 1,054 -1.7 314 San Bernardino, CA....... 47.4 622.4 2.3 91 791 0.4 192 San Diego, CA............ 100.4 1,297.9 2.3 91 1,056 -1.7 314 San Francisco, CA........ 54.6 603.8 4.3 11 1,778 -0.7 283 San Joaquin, CA.......... 15.9 206.7 2.5 84 785 0.5 179 San Luis Obispo, CA...... 9.4 107.1 3.5 25 785 1.9 57 San Mateo, CA............ 24.5 349.0 3.4 29 1,859 14.8 1 Santa Barbara, CA........ 14.1 183.4 3.3 32 900 -1.9 318 Santa Clara, CA.......... 62.0 923.6 3.8 15 1,937 -0.2 249 Santa Cruz, CA........... 8.9 91.1 4.2 12 865 -3.4 330 Solano, CA............... 9.6 123.4 3.1 41 1,019 3.6 12 Sonoma, CA............... 18.0 179.5 3.4 29 863 0.7 168 Stanislaus, CA........... 13.7 165.0 3.8 15 791 -1.0 299 Tulare, CA............... 8.7 140.7 1.9 125 649 1.1 122 Ventura, CA.............. 23.7 312.6 2.1 107 1,027 0.3 200 Yolo, CA................. 6.1 89.3 1.7 142 977 -2.8 325 Adams, CO................ 9.0 169.0 3.7 19 893 1.4 99 Arapahoe, CO............. 19.3 290.4 3.7 19 1,193 1.0 133 Boulder, CO.............. 13.3 161.8 2.7 63 1,120 -0.1 243 Denver, CO............... 26.8 432.5 4.0 14 1,265 0.0 233 Douglas, CO.............. 10.0 99.6 5.6 4 1,109 -1.9 318 El Paso, CO.............. 17.0 239.8 2.3 91 858 -0.5 269 Jefferson, CO............ 17.9 212.3 2.8 57 946 -2.3 323 Larimer, CO.............. 10.3 133.5 3.8 15 824 0.1 216 Weld, CO................. 5.9 88.2 4.4 10 825 0.5 179 Fairfield, CT............ 33.1 406.5 0.7 225 1,878 -1.9 318 Hartford, CT............. 25.9 489.0 0.4 250 1,315 -0.5 269 New Haven, CT............ 22.7 352.4 -0.2 290 1,013 1.4 99 New London, CT........... 7.0 121.0 -0.7 311 975 -1.1 302 New Castle, DE........... 16.7 266.3 1.2 178 1,235 -0.6 279 Washington, DC........... 35.5 717.6 1.0 193 1,613 0.5 179 Alachua, FL.............. 6.7 118.3 0.6 232 769 1.7 71 Brevard, FL.............. 14.7 189.3 0.4 250 848 -0.5 269 Broward, FL.............. 64.7 716.3 2.4 86 878 0.1 216 Collier, FL.............. 12.2 127.3 2.3 91 823 1.4 99 Duval, FL................ 27.5 446.5 2.4 86 963 1.4 99 Escambia, FL............. 8.0 121.4 1.4 164 722 -0.1 243 Hillsborough, FL......... 38.8 603.7 2.1 107 925 0.4 192 Lake, FL................. 7.4 83.9 2.7 63 624 1.1 122 Lee, FL.................. 19.3 213.9 2.7 63 738 0.3 200 Leon, FL................. 8.3 136.8 -0.3 295 748 -0.5 269 Manatee, FL.............. 9.5 108.2 2.1 107 705 0.1 216 Marion, FL............... 8.0 91.7 0.9 199 649 1.1 122 Miami-Dade, FL........... 92.2 1,016.2 2.6 74 912 0.9 141 Okaloosa, FL............. 6.1 77.3 0.2 269 780 0.8 154 Orange, FL............... 37.1 703.5 3.2 36 846 0.1 216 Palm Beach, FL........... 50.7 525.7 2.8 57 936 0.5 179 Pasco, FL................ 10.1 100.8 1.4 164 638 1.9 57 Pinellas, FL............. 31.1 390.4 1.9 125 823 -0.8 290 Polk, FL................. 12.5 196.9 1.6 150 706 1.1 122 Sarasota, FL............. 14.6 144.3 3.2 36 763 0.5 179 Seminole, FL............. 14.0 159.3 1.5 156 792 2.1 47 Volusia, FL.............. 13.4 154.0 0.9 199 659 0.2 210 Bibb, GA................. 4.5 79.2 -0.2 290 745 2.1 47 Chatham, GA.............. 7.9 135.6 2.6 74 810 0.2 210 Clayton, GA.............. 4.3 110.1 0.0 283 920 6.7 4 Cobb, GA................. 21.9 309.1 2.0 117 1,092 1.8 65 De Kalb, GA.............. 18.1 272.1 0.4 250 1,011 -0.5 269 Fulton, GA............... 42.4 736.5 3.3 32 1,419 -0.7 283 Gwinnett, GA............. 24.4 307.0 1.6 150 953 1.6 78 Muscogee, GA............. 4.7 93.6 -0.5 305 784 0.9 141 Richmond, GA............. 4.7 99.6 0.8 212 794 0.0 233 Honolulu, HI............. 24.7 451.1 2.1 107 878 1.0 133 Ada, ID.................. 13.6 202.2 3.5 25 805 -0.4 259 Champaign, IL............ 4.3 87.7 0.8 212 826 4.0 11 Cook, IL................. 151.0 2,394.0 1.2 178 1,185 -1.0 299 Du Page, IL.............. 37.7 581.2 1.7 142 1,160 -0.1 243 Kane, IL................. 13.5 195.9 1.0 193 823 2.1 47 Lake, IL................. 22.4 316.8 0.9 199 1,379 3.1 19 McHenry, IL.............. 8.7 90.8 -0.4 299 781 2.0 54 McLean, IL............... 3.8 84.7 0.9 199 1,059 11.8 3 Madison, IL.............. 6.0 94.4 0.0 283 787 1.3 108 Peoria, IL............... 4.7 101.5 -1.8 329 973 -5.5 332 St. Clair, IL............ 5.6 92.2 -1.4 324 752 -0.4 259 Sangamon, IL............. 5.3 123.1 -2.4 334 962 2.2 46 Will, IL................. 15.5 202.5 0.4 250 827 0.7 168 Winnebago, IL............ 6.9 122.4 -1.5 326 814 0.0 233 Allen, IN................ 9.0 172.3 0.5 243 809 -0.4 259 Elkhart, IN.............. 4.8 113.3 6.0 3 756 0.9 141 Hamilton, IN............. 8.7 116.1 2.6 74 983 2.9 23 Lake, IN................. 10.4 186.9 0.7 225 869 2.4 39 Marion, IN............... 24.0 563.3 1.2 178 1,052 2.6 31 St. Joseph, IN........... 6.0 114.3 -0.5 305 768 1.2 115 Tippecanoe, IN........... 3.3 78.3 -0.4 299 818 -0.8 290 Vanderburgh, IN.......... 4.8 103.7 -2.0 333 776 1.3 108 Johnson, IA.............. 3.8 78.7 2.2 101 845 1.1 122 Linn, IA................. 6.4 124.9 -0.4 299 926 2.4 39 Polk, IA................. 15.4 271.3 1.8 133 1,014 1.5 90 Scott, IA................ 5.4 87.0 0.8 212 770 0.8 154 Johnson, KS.............. 20.7 314.6 2.7 63 1,018 -0.1 243 Sedgwick, KS............. 12.1 240.1 0.7 225 867 -1.6 311 Shawnee, KS.............. 4.7 94.5 -0.2 290 807 1.6 78 Wyandotte, KS............ 3.2 81.2 0.7 225 891 -0.8 290 Boone, KY................ 3.9 76.1 1.3 172 817 0.4 192 Fayette, KY.............. 9.9 178.3 2.0 117 844 -0.6 279 Jefferson, KY............ 23.3 427.0 2.3 91 962 0.7 168 Caddo, LA................ 7.4 117.1 -1.6 327 760 -0.7 283 Calcasieu, LA............ 4.9 85.5 2.2 101 831 0.5 179 East Baton Rouge, LA..... 14.5 262.3 1.4 164 907 2.8 24 Jefferson, LA............ 13.6 191.2 0.0 283 856 -0.3 254 Lafayette, LA............ 9.1 139.4 2.7 63 913 0.0 233 Orleans, LA.............. 11.1 178.7 2.3 91 965 -1.5 310 St. Tammany, LA.......... 7.5 79.9 2.1 107 843 2.8 24 Cumberland, ME........... 12.6 165.8 0.6 232 896 3.5 15 Anne Arundel, MD......... 14.9 247.2 1.8 133 1,065 -0.2 249 Baltimore, MD............ 21.5 359.3 0.8 212 976 0.6 176 Frederick, MD............ 6.3 94.1 0.8 212 946 -0.9 296 Harford, MD.............. 5.7 87.6 0.3 260 917 2.5 36 Howard, MD............... 9.5 157.0 0.6 232 1,188 0.1 216 Montgomery, MD........... 33.8 448.2 0.3 260 1,317 -3.1 328 Prince Georges, MD....... 15.9 299.0 -0.2 290 988 0.2 210 Baltimore City, MD....... 14.1 331.4 0.8 212 1,161 -1.2 304 Barnstable, MA........... 8.9 81.4 1.4 164 816 0.9 141 Bristol, MA.............. 16.2 208.9 0.0 283 850 0.8 154 Essex, MA................ 21.9 300.0 0.3 260 1,021 1.7 71 Hampden, MA.............. 15.6 194.1 -0.4 299 899 4.8 7 Middlesex, MA............ 49.5 823.8 1.5 156 1,465 0.4 192 Norfolk, MA.............. 23.5 321.6 1.5 156 1,137 0.4 192 Plymouth, MA............. 14.1 173.6 1.6 150 878 2.3 42 Suffolk, MA.............. 23.9 598.8 1.7 142 1,698 -0.7 283 Worcester, MA............ 21.6 316.7 0.1 273 952 0.8 154 Genesee, MI.............. 7.3 131.0 1.3 172 776 -2.3 323 Ingham, MI............... 6.4 149.8 0.8 212 949 2.5 36 Kalamazoo, MI............ 5.4 109.9 0.2 269 907 3.3 17 Kent, MI................. 14.2 341.8 3.0 45 839 -1.4 308 Macomb, MI............... 17.5 297.2 3.1 41 974 -0.1 243 Oakland, MI.............. 38.7 668.9 2.7 63 1,072 -0.8 290 Ottawa, MI............... 5.7 107.8 2.7 63 755 1.2 115 Saginaw, MI.............. 4.2 82.0 0.1 273 780 2.8 24 Washtenaw, MI............ 8.3 196.7 2.3 91 989 0.8 154 Wayne, MI................ 31.7 681.6 0.9 199 1,053 -1.3 307 Anoka, MN................ 7.2 111.7 3.0 45 873 0.1 216 Dakota, MN............... 10.0 172.9 2.1 107 965 1.6 78 Hennepin, MN............. 42.3 846.0 2.6 74 1,274 -0.2 249 Olmsted, MN.............. 3.5 91.3 2.4 86 1,005 0.2 210 Ramsey, MN............... 13.9 315.3 1.9 125 1,169 4.4 10 St. Louis, MN............ 5.6 93.7 2.5 84 788 0.9 141 Stearns, MN.............. 4.4 79.7 1.1 186 748 1.8 65 Harrison, MS............. 4.4 82.2 0.3 260 703 0.7 168 Hinds, MS................ 6.0 120.3 -0.7 311 814 1.2 115 Boone, MO................ 4.6 87.3 2.0 117 739 2.1 47 Clay, MO................. 5.2 87.2 -0.6 309 858 1.5 90 Greene, MO............... 8.1 153.1 0.8 212 711 0.9 141 Jackson, MO.............. 19.0 346.9 0.8 212 983 1.3 108 St. Charles, MO.......... 8.4 127.7 2.0 117 793 -0.4 259 St. Louis, MO............ 32.7 563.9 0.8 212 1,031 -0.5 269 St. Louis City, MO....... 9.7 221.2 0.2 269 1,120 0.2 210 Yellowstone, MT.......... 6.2 76.7 2.1 107 783 1.4 99 Douglas, NE.............. 18.0 315.3 1.2 178 914 1.9 57 Lancaster, NE............ 9.6 157.4 1.0 193 760 1.6 78 Clark, NV................ 49.9 828.7 2.6 74 831 -0.6 279 Washoe, NV............... 13.7 183.9 1.8 133 832 0.5 179 Hillsborough, NH......... 12.0 187.9 0.6 232 1,042 0.8 154 Rockingham, NH........... 10.5 132.9 0.9 199 917 2.6 31 Atlantic, NJ............. 6.6 128.5 -1.4 324 796 -0.7 283 Bergen, NJ............... 32.9 425.5 1.0 193 1,187 -1.7 314 Burlington, NJ........... 11.0 196.4 2.6 74 1,013 1.0 133 Camden, NJ............... 12.0 193.8 0.5 243 962 1.4 99 Essex, NJ................ 20.4 335.1 0.3 260 1,326 0.8 154 Gloucester, NJ........... 6.1 96.4 0.1 273 823 1.6 78 Hudson, NJ............... 14.0 235.0 1.4 164 1,521 0.3 200 Mercer, NJ............... 10.9 229.5 0.3 260 1,477 6.4 6 Middlesex, NJ............ 21.7 386.6 1.0 193 1,257 -5.8 333 Monmouth, NJ............. 19.9 237.8 0.5 243 985 1.8 65 Morris, NJ............... 17.1 272.5 0.6 232 1,582 0.0 233 Ocean, NJ................ 12.3 147.0 1.9 125 786 1.3 108 Passaic, NJ.............. 12.2 167.8 -0.7 311 964 1.2 115 Somerset, NJ............. 10.1 173.0 1.1 186 2,009 6.7 4 Union, NJ................ 14.3 221.2 1.3 172 1,249 -0.8 290 Bernalillo, NM........... 17.8 307.4 0.5 243 829 0.1 216 Albany, NY............... 10.1 221.0 1.2 178 998 2.6 31 Bronx, NY................ 17.3 240.9 2.2 101 864 1.2 115 Broome, NY............... 4.6 87.9 -1.9 331 734 0.8 154 Dutchess, NY............. 8.3 109.5 -0.6 309 958 0.1 216 Erie, NY................. 24.1 450.6 0.3 260 853 1.1 122 Kings, NY................ 54.8 527.5 1.7 142 750 0.8 154 Monroe, NY............... 18.4 370.8 -0.2 290 903 1.5 90 Nassau, NY............... 53.0 587.1 0.8 212 1,078 0.9 141 New York, NY............. 124.7 2,403.9 1.7 142 2,448 -0.5 269 Oneida, NY............... 5.3 102.1 -1.8 329 761 3.0 21 Onondaga, NY............. 13.0 238.5 0.1 273 882 0.9 141 Orange, NY............... 9.9 130.7 0.5 243 789 0.0 233 Queens, NY............... 48.3 525.3 2.2 101 894 1.7 71 Richmond, NY............. 9.2 93.4 2.8 57 784 0.9 141 Rockland, NY............. 10.0 114.1 -0.3 295 1,042 -0.6 279 Saratoga, NY............. 5.6 77.2 1.8 133 861 3.1 19 Suffolk, NY.............. 51.2 614.8 0.6 232 1,033 -1.1 302 Westchester, NY.......... 36.2 399.8 -0.4 299 1,370 -2.8 325 Buncombe, NC............. 7.9 115.4 3.0 45 717 0.8 154 Catawba, NC.............. 4.3 79.9 1.1 186 709 1.6 78 Cumberland, NC........... 6.2 118.6 -0.8 316 748 1.8 65 Durham, NC............... 7.3 182.4 2.0 117 1,319 -2.2 322 Forsyth, NC.............. 8.9 175.9 1.7 142 927 -1.2 304 Guilford, NC............. 14.0 266.7 1.5 156 867 1.8 65 Mecklenburg, NC.......... 32.5 579.2 3.0 45 1,315 3.0 21 New Hanover, NC.......... 7.3 98.0 1.2 178 762 2.3 42 Wake, NC................. 29.4 465.9 3.2 36 989 1.4 99 Cass, ND................. 6.2 107.3 3.5 25 837 1.1 122 Butler, OH............... 7.5 136.4 0.4 250 848 1.8 65 Cuyahoga, OH............. 35.6 696.5 0.9 199 1,012 0.6 176 Delaware, OH............. 4.4 78.5 2.3 91 1,084 0.9 141 Franklin, OH............. 29.7 674.8 1.9 125 985 1.2 115 Hamilton, OH............. 23.1 485.2 0.1 273 1,109 1.6 78 Lake, OH................. 6.3 91.6 -0.3 295 825 3.4 16 Lorain, OH............... 6.1 92.4 -1.0 317 794 -0.4 259 Lucas, OH................ 10.1 198.3 -0.3 295 852 1.1 122 Mahoning, OH............. 5.9 96.1 0.4 250 671 0.1 216 Montgomery, OH........... 12.0 239.7 -0.5 305 836 0.8 154 Stark, OH................ 8.8 153.2 0.9 199 737 -0.9 296 Summit, OH............... 14.2 251.4 -0.1 289 895 0.3 200 Warren, OH............... 4.3 76.3 0.8 212 835 1.7 71 Oklahoma, OK............. 25.3 431.9 1.3 172 935 1.7 71 Tulsa, OK................ 20.9 334.7 1.4 164 932 2.5 36 Clackamas, OR............ 13.1 141.0 2.9 51 849 0.6 176 Jackson, OR.............. 6.7 76.5 3.3 32 696 1.5 90 Lane, OR................. 11.0 137.1 1.3 172 717 1.1 122 Marion, OR............... 9.6 130.8 2.3 91 739 1.5 90 Multnomah, OR............ 30.7 445.6 2.0 117 986 0.8 154 Washington, OR........... 17.1 250.5 1.8 133 1,161 -3.5 331 Allegheny, PA............ 35.0 678.0 0.5 243 1,080 1.9 57 Berks, PA................ 8.9 163.5 0.6 232 835 0.2 210 Bucks, PA................ 19.5 245.2 0.0 283 906 1.3 108 Butler, PA............... 4.9 83.2 -0.7 311 894 2.3 42 Chester, PA.............. 15.2 236.6 0.5 243 1,240 -1.4 308 Cumberland, PA........... 6.0 123.3 1.1 186 894 2.1 47 Dauphin, PA.............. 7.3 175.0 0.2 269 989 2.6 31 Delaware, PA............. 13.8 209.0 0.7 225 1,057 -1.2 304 Erie, PA................. 7.1 122.1 -1.3 323 758 1.6 78 Lackawanna, PA........... 5.8 96.3 0.1 273 721 0.3 200 Lancaster, PA............ 12.8 217.5 0.4 250 787 1.7 71 Lehigh, PA............... 8.6 175.3 0.7 225 944 -0.4 259 Luzerne, PA.............. 7.6 137.4 -0.4 299 746 0.5 179 Montgomery, PA........... 27.1 464.6 0.4 250 1,290 -0.5 269 Northampton, PA.......... 6.6 103.1 0.9 199 844 1.6 78 Philadelphia, PA......... 34.9 634.0 0.7 225 1,158 0.8 154 Washington, PA........... 5.4 84.3 0.1 273 991 0.3 200 Westmoreland, PA......... 9.3 130.4 -1.0 317 760 0.1 216 York, PA................. 8.9 168.9 -0.7 311 838 1.3 108 Providence, RI........... 17.4 266.9 0.3 260 999 2.7 28 Charleston, SC........... 12.2 215.3 1.9 125 839 1.0 133 Greenville, SC........... 12.4 234.7 2.1 107 834 0.5 179 Horry, SC................ 7.7 107.1 2.1 107 564 0.9 141 Lexington, SC............ 5.8 100.6 2.7 63 715 2.6 31 Richland, SC............. 9.0 205.5 0.8 212 835 1.5 90 Spartanburg, SC.......... 5.8 118.5 2.1 107 794 -0.3 254 York, SC................. 4.7 75.9 1.3 172 762 0.1 216 Minnehaha, SD............ 6.6 116.0 2.3 91 809 1.3 108 Davidson, TN............. 18.7 435.7 2.4 86 1,008 -0.3 254 Hamilton, TN............. 8.6 185.3 1.0 193 838 -0.8 290 Knox, TN................. 11.0 218.4 0.3 260 831 3.6 12 Rutherford, TN........... 4.6 106.7 5.3 6 815 -0.2 249 Shelby, TN............... 19.3 471.0 1.1 186 979 0.1 216 Williamson, TN........... 6.6 100.7 3.8 15 1,201 4.6 8 Bell, TX................. 4.9 109.2 1.2 178 788 1.9 57 Bexar, TX................ 35.8 764.9 2.9 51 891 0.7 168 Brazoria, TX............. 5.1 94.7 2.8 57 966 3.2 18 Brazos, TX............... 4.0 91.3 4.7 9 696 -0.1 243 Cameron, TX.............. 6.3 131.0 0.6 232 573 1.1 122 Collin, TX............... 19.9 320.4 3.7 19 1,173 -2.9 327 Dallas, TX............... 69.7 1,473.4 2.9 51 1,215 0.4 192 Denton, TX............... 11.9 190.8 3.5 25 868 2.7 28 El Paso, TX.............. 14.2 280.1 1.5 156 664 -0.4 259 Fort Bend, TX............ 10.2 153.7 7.0 1 1,029 0.1 216 Galveston, TX............ 5.6 98.0 2.7 63 882 2.1 47 Gregg, TX................ 4.2 78.0 1.6 150 845 -1.9 318 Harris, TX............... 104.9 2,163.6 3.7 19 1,333 -0.4 259 Hidalgo, TX.............. 11.6 234.6 1.9 125 580 0.0 233 Jefferson, TX............ 5.8 119.3 -1.9 331 979 -0.5 269 Lubbock, TX.............. 7.1 127.4 2.6 74 714 2.1 47 McLennan, TX............. 4.9 101.6 1.2 178 767 0.3 200 Midland, TX.............. 5.1 82.9 6.9 2 1,207 0.5 179 Montgomery, TX........... 9.5 146.8 5.0 8 998 1.0 133 Nueces, TX............... 8.0 160.2 3.2 36 835 2.0 54 Potter, TX............... 3.9 76.9 1.5 156 755 0.4 192 Smith, TX................ 5.8 94.3 1.6 150 763 0.1 216 Tarrant, TX.............. 39.1 795.6 2.4 86 960 0.7 168 Travis, TX............... 33.1 625.3 4.1 13 1,058 -0.4 259 Webb, TX................. 5.0 91.9 1.5 156 632 1.0 133 Williamson, TX........... 8.2 136.7 3.3 32 1,053 -13.4 334 Davis, UT................ 7.4 107.2 2.2 101 769 0.7 168 Salt Lake, UT............ 38.1 598.8 3.6 24 916 0.5 179 Utah, UT................. 13.1 181.3 5.5 5 729 0.8 154 Weber, UT................ 5.4 92.8 2.6 74 687 0.1 216 Chittenden, VT........... 6.2 96.3 0.1 273 930 1.6 78 Arlington, VA............ 8.8 164.7 -1.6 327 1,621 -0.3 254 Chesterfield, VA......... 8.0 119.1 2.6 74 860 1.4 99 Fairfax, VA.............. 35.5 586.2 0.9 199 1,565 0.4 192 Henrico, VA.............. 10.4 178.9 1.6 150 1,039 1.0 133 Loudoun, VA.............. 10.3 143.7 2.9 51 1,198 2.4 39 Prince William, VA....... 8.2 115.1 2.9 51 831 -0.4 259 Alexandria City, VA...... 6.4 95.4 1.4 164 1,296 0.7 168 Chesapeake City, VA...... 5.8 94.7 0.4 250 764 1.9 57 Newport News City, VA.... 3.7 96.6 0.9 199 962 3.6 12 Norfolk City, VA......... 5.7 135.8 -1.1 320 936 1.5 90 Richmond City, VA........ 7.1 146.8 -0.5 305 1,111 -0.7 283 Virginia Beach City, VA.. 11.4 165.8 1.7 142 755 0.3 200 Benton, WA............... 5.9 77.4 -1.2 322 948 -0.7 283 Clark, WA................ 14.1 131.0 2.7 63 867 1.9 57 King, WA................. 84.1 1,175.0 3.1 41 1,288 1.6 78 Kitsap, WA............... 6.8 79.3 0.1 273 876 0.9 141 Pierce, WA............... 22.3 264.9 1.4 164 864 2.7 28 Snohomish, WA............ 19.9 260.2 2.8 57 1,085 1.7 71 Spokane, WA.............. 16.3 199.6 2.0 117 814 0.9 141 Thurston, WA............. 7.8 98.6 1.8 133 846 2.3 42 Whatcom, WA.............. 7.1 81.0 0.4 250 802 1.1 122 Yakima, WA............... 9.1 96.0 1.9 125 641 1.9 57 Kanawha, WV.............. 6.0 103.9 -1.1 320 821 -1.6 311 Brown, WI................ 6.5 144.7 0.6 232 838 -0.2 249 Dane, WI................. 14.2 303.7 1.1 186 938 -0.3 254 Milwaukee, WI............ 23.7 469.8 0.6 232 975 0.0 233 Outagamie, WI............ 5.0 100.8 1.5 156 805 2.0 54 Waukesha, WI............. 12.5 224.5 1.1 186 971 1.5 90 Winnebago, WI............ 3.6 88.3 -1.0 317 909 4.6 8 San Juan, PR............. 11.2 260.7 -1.0 (7) 617 -0.3 (7) (1) Includes workers covered by Unemployment Insurance (UI) and Unemployment Compensation for Federal Employees (UCFE) programs. These 334 U.S. counties comprise 71.6 percent of the total covered workers in the U.S. (2) Data are preliminary. (3) Includes areas not officially designated as counties. See Technical Note. (4) Average weekly wages were calculated using unrounded data. (5) Percent changes were computed from quarterly employment and pay data adjusted for noneconomic county reclassifications. See Technical Note. (6) Totals for the United States do not include data for Puerto Rico or the Virgin Islands. (7) This county was not included in the U.S. rankings.
Table 2. Covered(1) establishments, employment, and wages in the 10 largest counties, first quarter 2013(2) Employment Average weekly wage(3) Establishments, first quarter County by NAICS supersector 2013 Percent Percent (thousands) March change, First change, 2013 March quarter first (thousands) 2012-13(4) 2013 quarter 2012-13(4) United States(5) ............................ 9,193.5 132,338.9 1.6 $989 0.6 Private industry........................... 8,899.6 110,877.4 2.0 995 0.5 Natural resources and mining............. 132.2 1,880.6 1.8 1,190 -0.3 Construction............................. 745.2 5,476.8 3.4 979 0.9 Manufacturing............................ 335.3 11,908.5 0.9 1,227 0.1 Trade, transportation, and utilities..... 1,897.2 25,080.6 1.3 819 0.9 Information.............................. 143.8 2,682.6 0.1 1,772 3.6 Financial activities..................... 815.6 7,539.8 1.8 1,924 1.1 Professional and business services....... 1,622.7 18,132.5 2.9 1,288 -0.6 Education and health services............ 1,415.9 20,157.2 1.9 837 1.2 Leisure and hospitality.................. 777.2 13,703.0 2.8 382 -0.3 Other services........................... 794.2 4,100.7 0.6 620 1.5 Government................................. 293.9 21,461.5 -0.4 958 0.9 Los Angeles, CA.............................. 412.4 4,041.3 2.2 1,061 -1.8 Private industry........................... 406.7 3,503.8 2.9 1,037 -2.1 Natural resources and mining............. 0.4 10.3 7.5 1,615 -7.1 Construction............................. 12.2 112.7 5.6 1,039 0.7 Manufacturing............................ 12.5 365.4 -0.3 1,219 0.3 Trade, transportation, and utilities..... 51.7 761.6 1.8 851 0.2 Information.............................. 8.4 193.9 3.7 1,922 -7.0 Financial activities..................... 22.3 210.4 0.7 1,931 -3.0 Professional and business services....... 43.1 581.0 2.6 1,297 -2.2 Education and health services............ 179.3 690.5 5.2 782 -2.1 Leisure and hospitality.................. 27.9 424.0 4.7 541 -3.4 Other services........................... 25.2 138.8 -2.6 635 3.3 Government................................. 5.7 537.5 -2.3 1,216 0.4 Cook, IL..................................... 151.0 2,394.0 1.2 1,185 -1.0 Private industry........................... 149.6 2,095.7 1.4 1,191 -1.2 Natural resources and mining............. 0.1 0.7 -6.4 823 -5.0 Construction............................. 12.4 56.2 -0.3 1,297 -0.6 Manufacturing............................ 6.6 187.1 -1.4 1,171 -0.5 Trade, transportation, and utilities..... 29.7 443.3 1.9 900 -0.2 Information.............................. 2.7 53.7 -1.4 1,958 3.5 Financial activities..................... 15.6 181.8 -0.7 2,788 -5.0 Professional and business services....... 32.0 420.6 2.8 1,524 -0.2 Education and health services............ 15.9 415.6 1.4 872 1.0 Leisure and hospitality.................. 13.4 238.9 3.1 447 -2.2 Other services........................... 16.5 94.1 -0.9 819 2.9 Government................................. 1.4 298.3 -0.2 1,140 0.5 New York, NY................................. 124.7 2,403.9 1.7 2,448 -0.5 Private industry........................... 124.4 1,965.8 2.0 2,744 -0.8 Natural resources and mining............. 0.0 0.1 8.8 2,201 -20.4 Construction............................. 2.1 32.7 7.3 1,674 1.9 Manufacturing............................ 2.3 25.5 -0.7 1,488 -8.3 Trade, transportation, and utilities..... 20.9 251.2 1.8 1,318 -1.3 Information.............................. 4.4 139.5 1.1 2,939 3.3 Financial activities..................... 18.9 348.1 -1.1 7,659 2.3 Professional and business services....... 26.0 494.3 3.0 2,444 -4.4 Education and health services............ 9.4 318.0 2.4 1,175 4.4 Leisure and hospitality.................. 13.1 254.6 1.8 791 0.1 Other services........................... 19.5 93.8 3.3 1,040 -0.2 Government................................. 0.3 438.1 0.2 1,119 0.5 Harris, TX................................... 104.9 2,163.6 3.7 1,333 -0.4 Private industry........................... 104.4 1,906.2 4.1 1,376 -0.8 Natural resources and mining............. 1.7 92.2 8.0 3,984 -5.8 Construction............................. 6.5 145.9 6.3 1,258 3.7 Manufacturing............................ 4.6 193.0 4.6 1,641 -1.2 Trade, transportation, and utilities..... 23.6 444.5 3.2 1,248 -0.9 Information.............................. 1.2 28.0 -4.0 1,455 2.5 Financial activities..................... 10.8 114.6 2.5 1,967 2.1 Professional and business services....... 21.0 370.4 4.7 1,526 -1.9 Education and health services............ 14.4 258.8 3.2 936 1.2 Leisure and hospitality.................. 8.7 197.6 4.7 410 -2.6 Other services........................... 11.3 60.1 3.9 712 0.1 Government................................. 0.5 257.4 0.7 1,016 2.0 Maricopa, AZ................................. 94.3 1,710.2 2.6 945 0.0 Private industry........................... 93.6 1,501.1 2.9 953 0.0 Natural resources and mining............. 0.5 8.3 3.5 1,180 -7.3 Construction............................. 7.4 88.2 6.2 940 0.6 Manufacturing............................ 3.1 113.2 1.4 1,473 -3.0 Trade, transportation, and utilities..... 20.7 335.9 0.3 871 -2.0 Information.............................. 1.6 31.5 2.6 1,289 2.2 Financial activities..................... 10.7 148.7 5.5 1,439 4.9 Professional and business services....... 21.7 283.9 4.2 1,004 0.3 Education and health services............ 10.6 251.4 2.0 892 -0.7 Leisure and hospitality.................. 7.3 188.6 3.1 433 0.7 Other services........................... 6.4 47.2 0.3 620 1.8 Government................................. 0.7 209.1 0.4 889 0.6 Dallas, TX................................... 69.7 1,473.4 2.9 1,215 0.4 Private industry........................... 69.2 1,310.3 3.3 1,237 0.2 Natural resources and mining............. 0.6 9.0 7.1 4,291 -17.9 Construction............................. 3.9 70.0 5.9 1,041 3.8 Manufacturing............................ 2.7 110.0 -1.4 1,547 2.1 Trade, transportation, and utilities..... 15.1 294.6 2.5 1,038 -1.3 Information.............................. 1.5 46.6 3.4 2,323 4.8 Financial activities..................... 8.5 145.8 4.0 1,921 2.0 Professional and business services....... 15.4 286.2 4.4 1,314 -0.5 Education and health services............ 8.5 174.0 3.4 1,006 1.0 Leisure and hospitality.................. 6.0 134.9 4.2 472 -3.3 Other services........................... 6.6 38.6 1.8 714 3.0 Government................................. 0.5 163.1 -0.3 1,039 1.6 Orange, CA................................... 102.8 1,433.5 2.8 1,086 -0.5 Private industry........................... 101.5 1,291.0 3.2 1,064 -0.2 Natural resources and mining............. 0.2 3.5 5.2 664 -11.5 Construction............................. 6.0 73.7 7.2 1,120 0.4 Manufacturing............................ 4.8 157.1 0.2 1,357 -0.3 Trade, transportation, and utilities..... 16.3 248.3 1.9 979 -0.1 Information.............................. 1.2 24.9 1.5 1,730 3.6 Financial activities..................... 9.7 111.7 4.8 1,789 0.2 Professional and business services....... 19.1 262.0 3.2 1,235 0.1 Education and health services............ 23.3 179.4 3.6 861 -1.1 Leisure and hospitality.................. 7.5 185.2 4.9 432 -2.0 Other services........................... 6.1 40.1 -0.8 622 2.1 Government................................. 1.3 142.5 -0.8 1,288 -1.7 San Diego, CA................................ 100.4 1,297.9 2.3 1,056 -1.7 Private industry........................... 99.0 1,079.3 2.8 1,032 -1.7 Natural resources and mining............. 0.7 10.0 -2.5 577 0.7 Construction............................. 5.9 58.7 5.4 1,030 -3.3 Manufacturing............................ 2.9 94.6 0.3 1,533 -1.0 Trade, transportation, and utilities..... 13.8 207.4 2.0 867 3.0 Information.............................. 1.1 24.0 -1.3 1,615 -3.2 Financial activities..................... 8.5 71.0 3.2 1,542 -1.5 Professional and business services....... 16.5 225.3 3.5 1,433 -4.8 Education and health services............ 29.2 177.6 2.4 859 0.1 Leisure and hospitality.................. 7.3 161.5 3.7 425 -1.2 Other services........................... 6.5 44.5 1.5 556 1.1 Government................................. 1.4 218.6 -0.1 1,179 -0.8 King, WA..................................... 84.1 1,175.0 3.1 1,288 1.6 Private industry........................... 83.6 1,017.5 3.6 1,308 1.6 Natural resources and mining............. 0.4 2.5 -5.5 1,708 20.5 Construction............................. 5.3 49.9 11.2 1,164 0.6 Manufacturing............................ 2.2 104.1 3.3 1,738 1.1 Trade, transportation, and utilities..... 14.3 214.6 4.4 1,109 2.5 Information.............................. 1.8 80.6 0.9 2,507 -1.5 Financial activities..................... 6.3 64.0 2.7 1,852 3.6 Professional and business services....... 14.1 193.6 4.6 1,586 2.7 Education and health services............ 25.1 154.8 0.9 890 1.9 Leisure and hospitality.................. 6.4 114.3 4.2 453 1.8 Other services........................... 7.8 38.8 1.8 792 3.3 Government................................. 0.5 157.5 0.2 1,162 2.1 Miami-Dade, FL............................... 92.2 1,016.2 2.6 912 0.9 Private industry........................... 91.9 878.0 3.0 897 0.8 Natural resources and mining............. 0.5 9.4 -7.2 525 10.1 Construction............................. 5.2 31.7 7.8 814 -4.9 Manufacturing............................ 2.6 35.4 -1.3 872 2.2 Trade, transportation, and utilities..... 27.2 258.4 2.2 841 0.8 Information.............................. 1.5 17.3 2.2 1,471 1.4 Financial activities..................... 9.4 67.8 2.9 1,632 5.4 Professional and business services....... 19.3 136.5 5.1 1,066 -2.4 Education and health services............ 10.2 159.4 1.0 887 1.4 Leisure and hospitality.................. 7.0 124.8 5.3 514 -0.2 Other services........................... 8.1 36.4 3.4 552 2.0 Government................................. 0.3 138.2 -0.1 1,012 2.3 (1) Includes workers covered by Unemployment Insurance (UI) and Unemployment Compensation for Federal Employees (UCFE) programs. (2) Data are preliminary. Counties selected are based on 2012 annual average employment. (3) Average weekly wages were calculated using unrounded data. (4) Percent changes were computed from quarterly employment and pay data adjusted for noneconomic county reclassifications. See Technical Note. (5) Totals for the United States do not include data for Puerto Rico or the Virgin Islands.
Table 3. Covered(1) establishments, employment, and wages by state, first quarter 2013(2) Employment Average weekly wage(3) Establishments, first quarter State 2013 Percent Percent (thousands) March change, First change, 2013 March quarter first (thousands) 2012-13 2013 quarter 2012-13 United States(4)........... 9,193.5 132,338.9 1.6 $989 0.6 Alabama.................... 117.4 1,840.4 1.0 812 0.5 Alaska..................... 21.8 317.9 0.5 988 1.5 Arizona.................... 146.8 2,494.6 2.2 891 0.6 Arkansas................... 86.6 1,151.1 0.0 765 2.4 California................. 1,315.6 15,168.9 3.0 1,116 -0.2 Colorado................... 173.7 2,298.0 3.0 1,004 0.1 Connecticut................ 112.3 1,618.4 0.4 1,319 -0.5 Delaware................... 27.8 403.7 1.4 1,070 -0.2 District of Columbia....... 35.5 717.6 1.0 1,613 0.5 Florida.................... 621.6 7,540.7 2.2 843 0.7 Georgia.................... 273.7 3,878.7 1.8 940 1.0 Hawaii..................... 38.6 616.3 2.4 842 1.2 Idaho...................... 53.2 613.4 3.0 695 0.6 Illinois................... 398.1 5,601.4 0.7 1,058 -0.2 Indiana.................... 160.6 2,808.1 1.1 832 1.2 Iowa....................... 96.9 1,463.2 1.0 799 1.8 Kansas..................... 83.9 1,322.0 0.7 807 0.4 Kentucky................... 115.1 1,765.2 0.9 791 0.8 Louisiana.................. 127.1 1,885.8 1.0 847 1.3 Maine...................... 49.4 561.6 0.0 771 1.8 Maryland................... 170.0 2,509.0 0.8 1,066 -0.6 Massachusetts.............. 222.9 3,218.5 1.0 1,236 0.7 Michigan................... 239.1 3,950.7 2.1 922 0.3 Minnesota.................. 170.2 2,632.9 1.9 1,002 1.2 Mississippi................ 70.0 1,088.9 0.4 696 1.2 Missouri................... 179.8 2,610.3 0.7 842 0.6 Montana.................... 42.9 427.4 1.9 707 0.1 Nebraska................... 68.7 914.9 1.0 777 1.7 Nevada..................... 74.2 1,144.1 2.3 844 -0.2 New Hampshire.............. 48.9 606.0 0.7 938 1.6 New Jersey................. 262.7 3,780.4 1.1 1,234 0.6 New Mexico................. 55.8 784.7 0.6 778 -0.6 New York................... 612.6 8,565.7 1.0 1,362 0.4 North Carolina............. 255.1 3,934.4 1.6 884 1.7 North Dakota............... 30.2 415.0 4.4 885 3.1 Ohio....................... 287.7 5,004.8 0.7 884 1.1 Oklahoma................... 105.3 1,551.3 1.2 823 2.4 Oregon..................... 136.1 1,644.4 1.9 864 0.0 Pennsylvania............... 348.2 5,543.3 0.1 968 0.9 Rhode Island............... 35.4 445.3 0.8 954 2.4 South Carolina............. 114.9 1,823.7 1.4 773 1.2 South Dakota............... 31.5 394.3 1.0 709 0.9 Tennessee.................. 143.6 2,675.0 1.5 854 0.8 Texas...................... 602.5 10,928.5 3.0 1,015 0.3 Utah....................... 85.7 1,233.4 3.3 804 0.6 Vermont.................... 24.4 299.3 0.7 791 2.3 Virginia................... 243.0 3,616.8 0.9 1,027 0.8 Washington................. 240.7 2,890.8 2.3 1,028 1.8 West Virginia.............. 49.6 701.0 -0.7 767 -0.1 Wisconsin.................. 160.7 2,664.9 0.9 833 0.8 Wyoming.................... 25.5 272.2 0.1 859 0.8 Puerto Rico................ 48.3 931.3 0.0 515 -1.2 Virgin Islands............. 3.4 39.8 -6.7 726 0.4 (1) Includes workers covered by Unemployment Insurance (UI) and Unemployment Compensation for Federal Employees (UCFE) programs. (2) Data are preliminary. (3) Average weekly wages were calculated using unrounded data. (4) Totals for the United States do not include data for Puerto Rico or the Virgin Islands.