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For release 10:00 a.m. (EST), Thursday, December 17, 2015 USDL-15-2392 Technical Information: (202) 691-6567 * QCEWInfo@bls.gov * www.bls.gov/cew Media Contact: (202) 691-5902 * PressOffice@bls.gov COUNTY EMPLOYMENT AND WAGES Second Quarter 2015 From June 2014 to June 2015, employment increased in 319 of the 342 largest U.S. counties (counties with 75,000 or more jobs in 2014), the U.S. Bureau of Labor Statistics reported today. Utah, Utah, had the largest percentage increase, with a gain of 7.5 percent over the year, compared with national job growth of 2.0 percent. Within Utah, the largest employment increase occurred in trade, transportation, and utilities, which gained 3,540 jobs over the year (10.3 percent). Ector, Texas, had the largest over- the-year percentage decrease in employment among the largest counties in the U.S. with a loss of 4.2 percent. County employment and wage data are compiled under the Quarterly Census of Employment and Wages (QCEW) program, which produces detailed information on county employment and wages within 6 months after the end of each quarter. The U.S. average weekly wage increased 3.0 percent over the year, growing to $968 in the second quarter of 2015. Ventura, Calif., had the largest over-the-year percentage increase in average weekly wages with a gain of 15.2 percent. Within Ventura, an average weekly wage gain of $934, or 53.8 percent, in manufacturing made the largest contribution to the county’s increase in average weekly wages. Olmsted, Minn., experienced the largest percentage decrease in average weekly wages with a loss of 5.2 percent over the year. Table A. Large counties ranked by June 2015 employment, June 2014-15 employment increase, and June 2014-15 percent increase in employment -------------------------------------------------------------------------------------------------------- Employment in large counties -------------------------------------------------------------------------------------------------------- June 2015 employment | Increase in employment, | Percent increase in employment, (thousands) | June 2014-15 | June 2014-15 | (thousands) | -------------------------------------------------------------------------------------------------------- | | United States 140,594.9| United States 2,820.2| United States 2.0 -------------------------------------------------------------------------------------------------------- | | Los Angeles, Calif. 4,232.7| Los Angeles, Calif. 82.8| Utah, Utah 7.5 Cook, Ill. 2,548.6| Dallas, Texas 64.1| Lee, Fla. 6.4 New York, N.Y. 2,378.9| Maricopa, Ariz. 54.8| Williamson, Tenn. 6.3 Harris, Texas 2,295.1| New York, N.Y. 54.5| Hall, Ga. 5.8 Maricopa, Ariz. 1,774.4| King, Wash. 46.7| Brazoria, Texas 5.6 Dallas, Texas 1,607.2| Orange, Calif. 39.8| Denton, Texas 5.1 Orange, Calif. 1,519.8| Santa Clara, Calif. 39.2| Calcasieu, La. 5.0 San Diego, Calif. 1,374.7| Harris, Texas 38.7| Davis, Utah 5.0 King, Wash. 1,285.2| Cook, Ill. 38.4| Benton, Ark. 4.9 Miami-Dade, Fla. 1,061.4| San Diego, Calif. 36.7| Manatee, Fla. 4.9 -------------------------------------------------------------------------------------------------------- Large County Employment In June 2015, national employment was 140.6 million (as measured by the QCEW program). Over the year, employment increased 2.0 percent, or 2.8 million. In June 2015, the 342 U.S. counties with 75,000 or more jobs accounted for 72.1 percent of total U.S. employment and 77.2 percent of total wages. These 342 counties had a net job growth of 2.2 million over the year, accounting for 78.3 percent of the overall U.S. employment increase. Utah, Utah, had the largest percentage increase in employment (7.5 percent) among the largest U.S. counties. The five counties with the largest increases in employment levels were Los Angeles, Calif.; Dallas, Texas; Maricopa, Ariz.; New York, N.Y.; and King, Wash. These counties had a combined over- the-year employment gain of 302,900 jobs, which was 10.7 percent of the overall job increase for the U.S. (See table A.) Employment declined in 20 of the largest counties from June 2014 to June 2015. Ector, Texas, had the largest over-the-year percentage decrease in employment (-4.2 percent). Within Ector, natural resources and mining had the largest decrease in employment, with a loss of 2,352 jobs (-19.0 percent). Atlantic, N.J., had the second largest percentage decrease in employment, followed by Gregg, Texas; Midland, Texas; and Lafayette, La. (See table 1.) Table B. Large counties ranked by second quarter 2015 average weekly wages, second quarter 2014-15 increase in average weekly wages, and second quarter 2014-15 percent increase in average weekly wages -------------------------------------------------------------------------------------------------------- Average weekly wage in large counties -------------------------------------------------------------------------------------------------------- Average weekly wage, | Increase in average weekly | Percent increase in average second quarter 2015 | wage, second quarter 2014-15 | weekly wage, second | | quarter 2014-15 -------------------------------------------------------------------------------------------------------- | | United States $968| United States $28| United States 3.0 -------------------------------------------------------------------------------------------------------- | | Santa Clara, Calif. $2,109| Santa Clara, Calif. $214| Ventura, Calif. 15.2 San Mateo, Calif. 1,863| Ventura, Calif. 143| Santa Clara, Calif. 11.3 New York, N.Y. 1,842| San Francisco, Calif. 137| Forsyth, N.C. 10.9 San Francisco, Calif. 1,730| San Mateo, Calif. 114| Riverside, Calif. 8.7 Washington, D.C. 1,599| Middlesex, Mass. 104| San Francisco, Calif. 8.6 Arlington, Va. 1,546| Forsyth, N.C. 91| Davidson, Tenn. 8.1 Fairfax, Va. 1,517| Davidson, Tenn. 78| Santa Barbara, Calif. 7.8 Suffolk, Mass. 1,512| Marin, Calif. 77| Middlesex, Mass. 7.5 Fairfield, Conn. 1,497| Santa Barbara, Calif. 69| Marin, Calif. 6.6 Middlesex, Mass. 1,491| Riverside, Calif. 66| San Mateo, Calif. 6.5 -------------------------------------------------------------------------------------------------------- Large County Average Weekly Wages Average weekly wages for the nation increased to $968, a 3.0 percent increase, during the year ending in the second quarter of 2015. Among the 342 largest counties, 323 had over-the-year increases in average weekly wages. Ventura, Calif., had the largest percentage wage increase among the largest U.S. counties (15.2 percent). Of the 342 largest counties, 16 experienced over-the-year decreases in average weekly wages. Olmsted, Minn., had the largest percentage decrease in average weekly wages, with a loss of 5.2 percent. Within Olmsted, education and health services had the largest impact on the county’s average weekly wage decrease. Within this industry, average weekly wages declined by $150 (-10.5 percent) over the year. Ector, Texas, had the second largest percentage decrease in average weekly wages, followed by Midland, Texas; Hillsborough, N.H.; and Lorain, Ohio. (See table 1.) Ten Largest U.S. Counties All of the 10 largest counties had over-the-year percentage increases in employment in June 2015. Dallas, Texas, had the largest gain (4.2 percent). Within Dallas, trade, transportation, and utilities had the largest over-the-year employment level increase, with a gain of 17,164 jobs, or 5.6 percent. Cook, Ill., had the smallest percentage increase in employment (1.5 percent) among the 10 largest counties. (See table 2.) Average weekly wages increased over the year in 9 of the 10 largest U.S. counties. Orange, Calif., experienced the largest percentage gain in average weekly wages (4.9 percent). Within Orange, professional and business services had the largest impact on the county’s average weekly wage growth. Within this industry, average weekly wages increased by $87, or 7.0 percent, over the year. Harris, Texas, was the only county with unchanged average weekly wages among the 10 largest counties. For More Information The tables included in this release contain data for the nation and for the 342 U.S. counties with annual average employment levels of 75,000 or more in 2014. June 2015 employment and 2015 second quarter average weekly wages for all states are provided in table 3 of this release. The employment and wage data by county are compiled under the QCEW program, also known as the ES-202 program. The data are derived from reports submitted by every employer subject to unemployment insurance (UI) laws. The 9.6 million employer reports cover 140.6 million full- and part- time workers. The QCEW program provides a quarterly and annual universe count of establishments, employment, and wages at the county, MSA, state, and national levels by detailed industry. Data for the second quarter of 2015 will be available electronically later at www.bls.gov/cew/. For additional information about the quarterly employment and wages data, please read the Technical Note. Additional information about the QCEW data may be obtained by calling (202) 691-6567. Several BLS regional offices are issuing QCEW news releases targeted to local data users. For links to these releases, see www.bls.gov/cew/cewregional.htm. _____________ The County Employment and Wages release for third quarter 2015 is scheduled to be released on Wednesday, March 9, 2016. ---------------------------------------------------------------------------------------------------------- | | | County Name Change Effective with the BLS Release of Data for the Third Quarter of 2015 | | | | On May 1st, 2015, Shannon, S.D., was officially renamed Oglala Lakota, S.D. This county is not part of | | this release because it has fewer than 75,000 jobs. However, BLS does publish data for this county. The | | name change will be implemented with the BLS release of data for the third quarter of 2015. Data prior | | to third quarter 2015 will still be available under Shannon, S.D. | ----------------------------------------------------------------------------------------------------------
Technical Note These data are the product of a federal-state cooperative program, the Quarterly Census of Employment and Wages (QCEW) program, also known as the ES-202 program. The data are derived from summaries of employment and total pay of workers covered by state and federal unemployment insurance (UI) legislation and provided by State Workforce Agencies (SWAs). The summaries are a result of the administration of state unemployment insurance programs that require most employers to pay quarterly taxes based on the employment and wages of workers covered by UI. QCEW data in this release are based on the 2012 North American Industry Classification System. Data for 2015 are preliminary and subject to revision. For purposes of this release, large counties are defined as having employment levels of 75,000 or greater. In addition, data for San Juan, Puerto Rico, are provided, but not used in calculating U.S. averages, rankings, or in the analysis in the text. Each year, these large counties are selected on the basis of the preliminary annual average of employment for the previous year. The 343 counties presented in this release were derived using 2014 preliminary annual averages of employment. For 2015 data, three counties have been added to the publication tables: Butte, Calif.; Hall, Ga.; and Ector, Texas. These counties will be included in all 2015 quarterly releases. The counties in table 2 are selected and sorted each year based on the annual average employment from the preceding year. The preliminary QCEW data presented in this release may differ from data released by the individual states. These potential differences result from the states' continuing receipt of UI data over time and ongoing review and editing. The individual states determine their data release timetables. Differences between QCEW, BED, and CES employment measures The Bureau publishes three different establishment-based employment measures for any given quarter. Each of these measures--QCEW, Business Employment Dynamics (BED), and Current Employment Statistics (CES)--makes use of the quarterly UI employment reports in producing data; however, each measure has a somewhat different universe coverage, estimation procedure, and publication product. Differences in coverage and estimation methods can result in somewhat different measures of employment change over time. It is important to understand program differences and the intended uses of the program products. (See table.) Additional information on each program can be obtained from the program Web sites shown in the table. Summary of Major Differences between QCEW, BED, and CES Employment Measures --------------------------------------------------------------------------------- | QCEW | BED | CES -----------|---------------------|----------------------|------------------------ Source |--Count of UI admini-|--Count of longitudi- |--Sample survey: | strative records | nally-linked UI ad- | 588,000 establish- | submitted by 9.5 | ministrative records| ments | million establish- | submitted by 7.6 | | ments in first | million private-sec-| | quarter of 2015 | tor employers | -----------|---------------------|----------------------|------------------------ Coverage |--UI and UCFE cover- |--UI coverage, exclud-|Nonfarm wage and sal- | age, including all | ing government, pri-| ary jobs: | employers subject | vate households, and|--UI coverage, exclud- | to state and fed- | establishments with | ing agriculture, pri- | eral UI laws | zero employment | vate households, and | | | self-employed workers | | |--Other employment, in- | | | cluding railroads, | | | religious organiza- | | | tions, and other non- | | | UI-covered jobs -----------|---------------------|----------------------|------------------------ Publication|--Quarterly |--Quarterly |--Monthly frequency | -6 months after the| -7 months after the | -Usually first Friday | end of each quar- | end of each quarter| of following month | ter | | -----------|---------------------|----------------------|------------------------ Use of UI |--Directly summarizes|--Links each new UI |--Uses UI file as a sam- file | and publishes each | quarter to longitu- | pling frame and to an- | new quarter of UI | dinal database and | nually realign sample- | data | directly summarizes | based estimates to pop- | | gross job gains and | ulation counts (bench- | | losses | marking) -----------|---------------------|----------------------|------------------------ Principal |--Provides a quarter-|--Provides quarterly |--Provides current month- products | ly and annual uni- | employer dynamics | ly estimates of employ- | verse count of es- | data on establish- | ment, hours, and earn- | tablishments, em- | ment openings, clos-| ings at the MSA, state, | ployment, and wages| ings, expansions, | and national level by | at the county, MSA,| and contractions at | industry | state, and national| the national level | | levels by detailed | by NAICS supersec- | | industry | tors and by size of | | | firm, and at the | | | state private-sector| | | total level | | |--Future expansions | | | will include data | | | with greater indus- | | | try detail and data | | | at the county and | | | MSA level | -----------|---------------------|----------------------|------------------------ Principal |--Major uses include:|--Major uses include: |--Major uses include: uses | -Detailed locality | -Business cycle | -Principal national | data | analysis | economic indicator | -Periodic universe | -Analysis of employ-| -Official time series | counts for bench- | er dynamics under- | for employment change | marking sample | lying economic ex- | measures | survey estimates | pansions and con- | -Input into other ma- | -Sample frame for | tractions | jor economic indi- | BLS establishment | -Analysis of employ-| cators | surveys | ment expansion and | | | contraction by size| | | of firm | | | | -----------|---------------------|----------------------|------------------------ Program |--www.bls.gov/cew/ |--www.bls.gov/bdm/ |--www.bls.gov/ces/ Web sites | | | --------------------------------------------------------------------------------- Coverage Employment and wage data for workers covered by state UI laws are compiled from quarterly contribution reports submitted to the SWAs by employers. For federal civilian workers covered by the Unemployment Compensation for Federal Employees (UCFE) program, employment and wage data are compiled from quarterly reports submitted by four major federal payroll processing centers on behalf of all federal agencies, with the exception of a few agencies which still report directly to the individual SWA. In addition to the quarterly contribution reports, employers who operate multiple establishments within a state complete a questionnaire, called the "Multiple Worksite Report," which provides detailed information on the location and industry of each of their establishments. QCEW employment and wage data are derived from microdata summaries of 9.4 million employer reports of employment and wages submitted by states to the BLS in 2014. These reports are based on place of employment rather than place of residence. UI and UCFE coverage is broad and has been basically comparable from state to state since 1978, when the 1976 amendments to the Federal Unemployment Tax Act became effective, expanding coverage to include most state and local government employees. In 2014, UI and UCFE programs covered workers in 136.6 million jobs. The estimated 131.8 million workers in these jobs (after adjustment for multiple jobholders) represented 96.3 percent of civilian wage and salary employment. Covered workers received $7.017 trillion in pay, representing 93.8 percent of the wage and salary component of personal income and 40.5 percent of the gross domestic product. Major exclusions from UI coverage include self-employed workers, most agricultural workers on small farms, all members of the Armed Forces, elected officials in most states, most employees of railroads, some domestic workers, most student workers at schools, and employees of certain small nonprofit organizations. State and federal UI laws change periodically. These changes may have an impact on the employment and wages reported by employers covered under the UI program. Coverage changes may affect the over-the-year comparisons presented in this news release. Concepts and methodology Monthly employment is based on the number of workers who worked during or received pay for the pay period including the 12th of the month. With few exceptions, all employees of covered firms are reported, including production and sales workers, corporation officials, executives, supervisory personnel, and clerical workers. Workers on paid vacations and part-time workers also are included. Average weekly wage values are calculated by dividing quarterly total wages by the average of the three monthly employment levels (all employees, as described above) and dividing the result by 13, for the 13 weeks in the quarter. These calculations are made using unrounded employment and wage values. The average wage values that can be calculated using rounded data from the BLS database may differ from the averages reported. Included in the quarterly wage data are non-wage cash payments such as bonuses, the cash value of meals and lodging when supplied, tips and other gratuities, and, in some states, employer contributions to certain deferred compensation plans such as 401(k) plans and stock options. Over-the-year comparisons of average weekly wages may reflect fluctuations in average monthly employment and/or total quarterly wages between the current quarter and prior year levels. Average weekly wages are affected by the ratio of full-time to part-time workers as well as the number of individuals in high-paying and low-paying occupations and the incidence of pay periods within a quarter. For instance, the average weekly wage of the workforce could increase significantly when there is a large decline in the number of employees that had been receiving below-average wages. Wages may include payments to workers not present in the employment counts because they did not work during the pay period including the 12th of the month. When comparing average weekly wage levels between industries, states, or quarters, these factors should be taken into consideration. Wages measured by QCEW may be subject to periodic and sometimes large fluctuations. This variability may be due to calendar effects resulting from some quarters having more pay dates than others. The effect is most visible in counties with a dominant employer. In particular, this effect has been observed in counties where government employers represent a large fraction of overall employment. Similar calendar effects can result from private sector pay practices. However, these effects are typically less pronounced for two reasons: employment is less concentrated in a single private employer, and private employers use a variety of pay period types (weekly, biweekly, semimonthly, monthly). For example, the effect on over-the-year pay comparisons can be pronounced in federal government due to the uniform nature of federal payroll processing. Most federal employees are paid on a biweekly pay schedule. As a result, in some quarters federal wages include six pay dates, while in other quarters there are seven pay dates. Over-the-year comparisons of average weekly wages may also reflect this calendar effect. Growth in average weekly wages may be attributed, in part, to a comparison of quarterly wages for the current year, which include seven pay dates, with year-ago wages that reflect only six pay dates. An opposite effect will occur when wages in the current quarter reflecting six pay dates are compared with year-ago wages for a quarter including seven pay dates. In order to ensure the highest possible quality of data, states verify with employers and update, if necessary, the industry, location, and ownership classification of all establishments on a 3-year cycle. Changes in establishment classification codes resulting from this process are introduced with the data reported for the first quarter of the year. Changes resulting from improved employer reporting also are introduced in the first quarter. QCEW data are not designed as a time series. QCEW data are simply the sums of individual establishment records and reflect the number of establishments that exist in a county or industry at a point in time. Establishments can move in or out of a county or industry for a number of reasons- -some reflecting economic events, others reflecting administrative changes. For example, economic change would come from a firm relocating into the county; administrative change would come from a company correcting its county designation. The over-the-year changes of employment and wages presented in this release have been adjusted to account for most of the administrative corrections made to the underlying establishment reports. This is done by modifying the prior-year levels used to calculate the over-the-year changes. Percent changes are calculated using an adjusted version of the final 2014 quarterly data as the base data. The adjusted prior-year levels used to calculate the over-the-year percent change in employment and wages are not published. These adjusted prior-year levels do not match the unadjusted data maintained on the BLS Web site. Over-the-year change calculations based on data from the Web site, or from data published in prior BLS news releases, may differ substantially from the over-the-year changes presented in this news release. The adjusted data used to calculate the over-the-year change measures presented in this release account for most of the administrative changes--those occurring when employers update the industry, location, and ownership information of their establishments. The most common adjustments for administrative change are the result of updated information about the county location of individual establishments. Included in these adjustments are administrative changes involving the classification of establishments that were previously reported in the unknown or statewide county or unknown industry categories. Adjusted data account for improvements in reporting employment and wages for individual and multi-unit establishments. To accomplish this, adjustments were implemented to account for: administrative changes caused by multi-unit employers who start reporting for each individual establishment rather than as a single entity (first quarter of 2008); selected large administrative changes in employment and wages (second quarter of 2011); and state verified improvements in reporting of employment and wages (third quarter of 2014). These adjustments allow QCEW to include county employment and wage growth rates in this news release that would otherwise not meet publication standards. The adjusted data used to calculate the over-the-year change measures presented in any County Employment and Wages news release are valid for comparisons between the starting and ending points (a 12-month period) used in that particular release. Comparisons may not be valid for any time period other than the one featured in a release even if the changes were calculated using adjusted data. County definitions are assigned according to Federal Information Processing Standards Publications (FIPS PUBS) as issued by the National Institute of Standards and Technology, after approval by the Secretary of Commerce pursuant to Section 5131 of the Information Technology Management Reform Act of 1996 and the Computer Security Act of 1987, Public Law 104-106. Areas shown as counties include those designated as independent cities in some jurisdictions and, in Alaska, those designated as census areas where counties have not been created. County data also are presented for the New England states for comparative purposes even though townships are the more common designation used in New England (and New Jersey). The regions referred to in this release are defined as census regions. Additional statistics and other information Employment and Wages Annual Averages Online features comprehensive information by detailed industry on establishments, employment, and wages for the nation and all states. The 2014 edition of this publication, which was published in September 2015, contains selected data produced by Business Employment Dynamics (BED) on job gains and losses, as well as selected data from the first quarter 2015 version of this news release. Tables and additional content from the 2014 edition of Employment and Wages Annual Averages Online are now available at http://www.bls.gov/cew/cewbultn14.htm. The 2015 edition of Employment and Wages Annual Averages Online will be available in September 2016. News releases on quarterly measures of gross job flows also are available upon request from the Division of Administrative Statistics and Labor Turnover (Business Employment Dynamics), telephone (202) 691-6467; (http://www.bls.gov/bdm/); (e-mail: BDMInfo@bls.gov). Information in this release will be made available to sensory impaired individuals upon request. Voice phone: (202) 691-5200; TDD message referral phone number: 1-800-877-8339.
Table 1. Covered establishments, employment, and wages in the 343 largest counties, second quarter 2015 Employment Average weekly wage(2) Establishments, County(1) second quarter Percent Ranking Percent Ranking 2015 June change, by Second change, by (thousands) 2015 June percent quarter second percent (thousands) 2014-15(3) change 2015 quarter change 2014-15(3) United States(4)......... 9,575.3 140,594.9 2.0 - $968 3.0 - Jefferson, AL............ 17.7 339.4 0.4 303 945 1.7 252 Madison, AL.............. 9.1 186.1 1.7 183 1,051 0.3 319 Mobile, AL............... 9.6 167.6 0.1 315 827 1.7 252 Montgomery, AL........... 6.3 129.7 0.5 298 821 2.5 160 Shelby, AL............... 5.4 83.8 2.4 130 901 1.8 240 Tuscaloosa, AL........... 4.3 91.2 3.3 71 811 1.4 276 Anchorage Borough, AK.... 8.4 155.8 0.4 303 1,070 2.1 207 Maricopa, AZ............. 95.3 1,774.4 3.2 76 948 1.7 252 Pima, AZ................. 19.0 347.4 0.1 315 828 1.2 289 Benton, AR............... 5.9 111.2 4.9 9 931 3.8 51 Pulaski, AR.............. 14.5 244.7 0.8 275 877 2.5 160 Washington, AR........... 5.8 100.6 3.8 42 783 3.4 79 Alameda, CA.............. 59.0 730.8 3.1 82 1,257 5.0 19 Butte, CA................ 7.9 78.5 2.2 147 728 4.1 37 Contra Costa, CA......... 30.6 348.2 1.9 166 1,163 3.0 114 Fresno, CA............... 32.0 372.9 2.4 130 746 3.9 45 Kern, CA................. 17.5 309.0 -1.0 333 814 -1.0 333 Los Angeles, CA.......... 452.5 4,232.7 2.0 160 1,058 3.6 69 Marin, CA................ 12.2 113.4 2.7 102 1,243 6.6 9 Monterey, CA............. 13.0 198.7 0.4 303 809 2.7 143 Orange, CA............... 111.2 1,519.8 2.7 102 1,086 4.9 21 Placer, CA............... 11.8 148.9 3.4 67 965 4.8 24 Riverside, CA............ 55.7 656.4 4.1 26 828 8.7 4 Sacramento, CA........... 53.8 628.0 2.8 96 1,057 3.0 114 San Bernardino, CA....... 53.3 682.8 3.8 42 823 2.9 120 San Diego, CA............ 103.6 1,374.7 2.7 102 1,073 3.1 105 San Francisco, CA........ 58.7 668.9 4.5 15 1,730 8.6 5 San Joaquin, CA.......... 17.0 233.7 4.1 26 796 3.6 69 San Luis Obispo, CA...... 10.0 116.2 2.9 93 794 3.7 65 San Mateo, CA............ 26.8 383.4 4.8 11 1,863 6.5 10 Santa Barbara, CA........ 14.8 197.4 1.5 207 957 7.8 7 Santa Clara, CA.......... 67.7 1,018.7 4.0 32 2,109 11.3 2 Santa Cruz, CA........... 9.3 105.6 2.0 160 860 3.2 96 Solano, CA............... 10.5 132.2 3.3 71 998 3.4 79 Sonoma, CA............... 19.2 197.4 2.3 138 893 4.3 35 Stanislaus, CA........... 14.6 179.7 2.2 147 808 5.2 17 Tulare, CA............... 9.4 160.4 1.3 226 667 3.7 65 Ventura, CA.............. 25.3 316.8 0.8 275 1,085 15.2 1 Yolo, CA................. 6.3 99.2 2.6 113 990 2.7 143 Adams, CO................ 9.9 194.2 4.5 15 930 1.6 264 Arapahoe, CO............. 20.6 319.5 3.3 71 1,090 1.7 252 Boulder, CO.............. 14.2 174.0 2.7 102 1,137 3.3 87 Denver, CO............... 29.3 481.5 4.6 14 1,180 4.8 24 Douglas, CO.............. 10.9 115.0 3.7 47 1,108 -0.4 328 El Paso, CO.............. 17.9 259.2 2.8 96 864 1.8 240 Jefferson, CO............ 18.8 230.6 2.5 120 981 2.5 160 Larimer, CO.............. 11.1 149.8 3.4 67 845 2.1 207 Weld, CO................. 6.6 101.4 1.1 243 862 3.1 105 Fairfield, CT............ 34.4 431.1 1.6 202 1,497 3.0 114 Hartford, CT............. 26.8 513.5 1.0 256 1,162 0.3 319 New Haven, CT............ 23.3 364.4 0.6 291 1,007 2.0 220 New London, CT........... 7.2 124.2 0.3 308 960 -0.1 326 New Castle, DE........... 18.8 285.0 2.5 120 1,110 1.0 298 Washington, DC........... 37.2 745.1 1.8 172 1,599 1.8 240 Alachua, FL.............. 6.9 120.6 1.6 202 831 1.8 240 Brevard, FL.............. 15.2 193.5 2.5 120 865 3.3 87 Broward, FL.............. 67.9 752.0 2.6 113 907 3.9 45 Collier, FL.............. 13.1 125.4 4.0 32 846 -0.6 331 Duval, FL................ 28.4 469.8 3.2 76 911 1.8 240 Escambia, FL............. 8.2 125.4 1.9 166 763 2.8 132 Hillsborough, FL......... 40.4 632.1 3.7 47 922 2.4 180 Lake, FL................. 7.9 85.7 3.9 36 665 3.1 105 Lee, FL.................. 20.7 231.9 6.4 2 775 3.3 87 Leon, FL................. 8.4 141.2 1.1 243 798 1.4 276 Manatee, FL.............. 10.3 111.5 4.9 9 750 1.8 240 Marion, FL............... 8.3 95.3 1.7 183 679 1.8 240 Miami-Dade, FL........... 96.7 1,061.4 3.5 59 931 2.1 207 Okaloosa, FL............. 6.3 79.4 0.8 275 798 3.1 105 Orange, FL............... 39.7 754.6 3.8 42 849 2.5 160 Osceola, FL.............. 6.4 82.5 4.4 17 685 3.2 96 Palm Beach, FL........... 53.9 556.3 3.6 51 937 3.1 105 Pasco, FL................ 10.6 101.9 3.5 59 718 2.9 120 Pinellas, FL............. 32.1 406.7 2.8 96 850 0.6 311 Polk, FL................. 12.9 197.9 3.2 76 735 1.4 276 Sarasota, FL............. 15.5 155.7 4.4 17 812 3.2 96 Seminole, FL............. 14.5 173.3 4.1 26 828 4.0 41 Volusia, FL.............. 13.9 156.7 3.4 67 713 2.7 143 Bibb, GA................. 4.5 83.1 1.4 220 753 2.9 120 Chatham, GA.............. 8.4 146.2 3.9 36 822 2.2 198 Clayton, GA.............. 4.4 117.4 2.8 96 909 1.7 252 Cobb, GA................. 23.1 333.9 2.6 113 1,016 2.6 154 DeKalb, GA............... 19.2 289.7 2.3 138 991 2.0 220 Fulton, GA............... 45.5 792.7 3.9 36 1,247 2.0 220 Gwinnett, GA............. 26.1 338.9 3.1 82 936 2.4 180 Hall, GA................. 4.6 80.0 5.8 4 789 3.1 105 Muscogee, GA............. 4.8 94.0 -0.4 325 758 1.9 235 Richmond, GA............. 4.7 103.7 2.2 147 805 1.6 264 Honolulu, HI............. 25.1 463.3 1.3 226 910 3.8 51 Ada, ID.................. 14.0 218.1 2.9 93 828 1.6 264 Champaign, IL............ 4.6 90.5 0.7 284 839 2.9 120 Cook, IL................. 164.0 2,548.6 1.5 207 1,116 2.5 160 DuPage, IL............... 39.9 615.5 1.5 207 1,104 2.5 160 Kane, IL................. 14.4 212.0 1.5 207 831 2.8 132 Lake, IL................. 23.6 340.1 0.0 320 1,261 5.2 17 McHenry, IL.............. 9.2 98.6 0.9 265 792 2.5 160 McLean, IL............... 4.0 85.2 0.8 275 957 0.9 305 Madison, IL.............. 6.3 97.8 -0.4 325 785 3.0 114 Peoria, IL............... 4.9 102.6 1.0 256 908 2.5 160 St. Clair, IL............ 5.8 92.4 0.5 298 764 2.4 180 Sangamon, IL............. 5.5 129.7 -0.7 331 985 2.2 198 Will, IL................. 16.9 224.9 2.3 138 858 2.5 160 Winnebago, IL............ 7.1 129.5 0.9 265 818 2.8 132 Allen, IN................ 8.7 184.2 2.3 138 765 2.3 194 Elkhart, IN.............. 4.7 126.1 2.6 113 816 2.1 207 Hamilton, IN............. 8.9 134.0 3.5 59 908 3.8 51 Lake, IN................. 10.3 187.7 -0.5 328 830 -0.1 326 Marion, IN............... 23.5 584.6 1.9 166 956 2.9 120 St. Joseph, IN........... 5.7 121.9 3.1 82 769 1.3 285 Tippecanoe, IN........... 3.3 81.2 1.8 172 815 2.1 207 Vanderburgh, IN.......... 4.7 106.9 1.1 243 789 4.1 37 Black Hawk, IA........... 3.9 74.8 -1.5 336 794 1.7 252 Johnson, IA.............. 4.0 81.9 0.6 291 898 2.6 154 Linn, IA................. 6.6 131.6 1.0 256 924 3.4 79 Polk, IA................. 16.6 293.1 1.1 243 944 2.5 160 Scott, IA................ 5.5 92.6 1.3 226 783 2.0 220 Johnson, KS.............. 22.0 338.4 2.3 138 1,021 4.6 27 Sedgwick, KS............. 12.5 248.8 1.4 220 851 1.9 235 Shawnee, KS.............. 5.0 97.4 0.6 291 794 1.1 295 Wyandotte, KS............ 3.3 90.2 2.2 147 896 2.5 160 Boone, KY................ 4.2 82.3 4.1 26 865 2.1 207 Fayette, KY.............. 10.6 189.4 2.6 113 866 3.8 51 Jefferson, KY............ 24.7 453.6 2.5 120 954 3.0 114 Caddo, LA................ 7.2 115.1 -0.1 322 787 1.5 270 Calcasieu, LA............ 4.9 92.5 5.0 7 827 0.0 324 East Baton Rouge, LA..... 14.6 264.1 0.9 265 909 1.8 240 Jefferson, LA............ 13.5 194.8 -0.5 328 862 2.5 160 Lafayette, LA............ 9.2 136.5 -2.8 337 913 -1.8 336 Orleans, LA.............. 11.9 191.4 3.7 47 908 0.6 311 St. Tammany, LA.......... 7.7 85.6 3.9 36 808 2.0 220 Cumberland, ME........... 13.1 179.9 1.0 256 870 3.4 79 Anne Arundel, MD......... 14.9 263.1 1.4 220 1,021 2.8 132 Baltimore, MD............ 21.2 374.1 1.3 226 952 1.2 289 Frederick, MD............ 6.3 100.1 2.4 130 911 1.2 289 Harford, MD.............. 5.8 91.3 0.9 265 959 1.7 252 Howard, MD............... 9.8 167.2 1.8 172 1,175 3.5 75 Montgomery, MD........... 32.7 466.6 1.0 256 1,287 3.2 96 Prince George's, MD...... 15.6 311.1 0.8 275 1,002 0.8 307 Baltimore City, MD....... 13.6 335.0 0.8 275 1,094 2.4 180 Barnstable, MA........... 9.3 105.0 0.9 265 805 2.0 220 Bristol, MA.............. 16.9 224.9 1.3 226 900 5.4 13 Essex, MA................ 23.5 326.2 1.5 207 1,025 1.6 264 Hampden, MA.............. 17.1 206.2 1.4 220 883 3.3 87 Middlesex, MA............ 52.9 883.0 2.4 130 1,491 7.5 8 Norfolk, MA.............. 24.5 349.5 1.6 202 1,132 4.6 27 Plymouth, MA............. 15.0 191.8 1.9 166 929 2.5 160 Suffolk, MA.............. 27.0 640.8 3.0 88 1,512 3.1 105 Worcester, MA............ 23.5 339.2 1.7 183 960 2.5 160 Genesee, MI.............. 6.9 134.4 0.3 308 796 4.6 27 Ingham, MI............... 6.0 146.2 0.3 308 882 -0.5 329 Kalamazoo, MI............ 5.0 116.1 1.0 256 873 2.6 154 Kent, MI................. 14.0 365.2 1.2 235 857 3.4 79 Macomb, MI............... 17.3 321.1 2.3 138 954 1.4 276 Oakland, MI.............. 38.2 717.0 1.7 183 1,067 1.7 252 Ottawa, MI............... 5.5 120.9 2.4 130 805 2.5 160 Saginaw, MI.............. 4.0 84.5 0.7 284 754 1.5 270 Washtenaw, MI............ 8.1 200.5 1.8 172 1,030 4.0 41 Wayne, MI................ 30.3 707.2 1.2 235 1,059 2.7 143 Anoka, MN................ 6.8 120.2 1.8 172 924 2.1 207 Dakota, MN............... 9.6 186.0 1.1 243 948 2.8 132 Hennepin, MN............. 38.2 894.4 2.2 147 1,196 3.8 51 Olmsted, MN.............. 3.3 94.8 1.1 243 1,007 -5.2 341 Ramsey, MN............... 13.1 329.6 1.5 207 1,079 1.2 289 St. Louis, MN............ 5.2 99.6 1.5 207 781 2.8 132 Stearns, MN.............. 4.2 85.7 0.9 265 800 3.9 45 Washington, MN........... 5.3 80.7 2.1 155 809 3.2 96 Harrison, MS............. 4.4 83.9 -0.2 323 688 0.9 305 Hinds, MS................ 5.9 120.6 2.0 160 831 0.8 307 Boone, MO................ 4.8 91.4 1.7 183 750 2.2 198 Clay, MO................. 5.4 98.7 4.8 11 875 5.0 19 Greene, MO............... 8.4 161.9 1.7 183 739 3.2 96 Jackson, MO.............. 20.7 360.7 1.5 207 975 5.3 15 St. Charles, MO.......... 8.9 141.2 3.5 59 788 1.0 298 St. Louis, MO............ 35.3 595.5 1.2 235 1,015 2.0 220 St. Louis City, MO....... 12.3 226.8 2.3 138 1,016 2.7 143 Yellowstone, MT.......... 6.4 81.7 2.5 120 839 4.4 32 Douglas, NE.............. 18.6 333.4 1.7 183 889 4.5 31 Lancaster, NE............ 10.0 166.4 1.7 183 777 2.8 132 Clark, NV................ 53.6 908.9 3.6 51 845 2.4 180 Washoe, NV............... 14.3 202.1 3.4 67 857 3.5 75 Hillsborough, NH......... 12.2 198.3 1.9 166 1,030 -2.6 338 Rockingham, NH........... 10.8 148.0 1.8 172 956 1.5 270 Atlantic, NJ............. 6.5 133.5 -3.7 340 814 2.4 180 Bergen, NJ............... 32.9 452.4 1.1 243 1,158 1.4 276 Burlington, NJ........... 11.0 201.5 0.7 284 1,014 2.7 143 Camden, NJ............... 11.9 199.4 1.1 243 940 1.8 240 Essex, NJ................ 20.3 337.6 0.2 313 1,148 2.1 207 Gloucester, NJ........... 6.2 104.3 3.1 82 837 0.8 307 Hudson, NJ............... 14.3 244.7 3.6 51 1,318 4.8 24 Mercer, NJ............... 11.1 241.1 3.7 47 1,200 1.1 295 Middlesex, NJ............ 22.1 405.9 1.3 226 1,141 2.7 143 Monmouth, NJ............. 20.0 264.2 2.5 120 954 1.5 270 Morris, NJ............... 17.0 290.1 1.5 207 1,392 2.7 143 Ocean, NJ................ 12.8 169.2 1.3 226 783 2.4 180 Passaic, NJ.............. 12.3 167.5 0.0 320 980 4.4 32 Somerset, NJ............. 10.0 187.7 1.1 243 1,432 2.9 120 Union, NJ................ 14.3 218.9 (5) - 1,282 (5) - Bernalillo, NM........... 17.7 317.4 1.2 235 828 1.6 264 Albany, NY............... 10.4 231.1 1.1 243 1,013 2.9 120 Bronx, NY................ 18.6 299.9 2.1 155 928 2.3 194 Broome, NY............... 4.6 87.7 -1.2 335 774 2.4 180 Dutchess, NY............. 8.5 111.7 1.1 243 977 1.0 298 Erie, NY................. 24.7 468.0 0.8 275 843 2.2 198 Kings, NY................ 60.0 663.0 4.4 17 813 2.9 120 Monroe, NY............... 18.8 384.5 0.9 265 913 2.0 220 Nassau, NY............... 53.9 626.7 1.2 235 1,094 2.3 194 New York, NY............. 129.7 2,378.9 2.3 138 1,842 3.3 87 Oneida, NY............... 5.4 105.3 0.7 284 776 2.1 207 Onondaga, NY............. 13.1 244.2 0.1 315 884 2.2 198 Orange, NY............... 10.3 141.5 1.2 235 850 2.9 120 Queens, NY............... 51.3 636.5 3.8 42 905 1.0 298 Richmond, NY............. 9.7 113.4 1.8 172 853 3.0 114 Rockland, NY............. 10.5 120.6 2.2 147 979 0.2 323 Saratoga, NY............. 5.9 86.0 3.0 88 918 5.4 13 Suffolk, NY.............. 52.5 665.3 1.1 243 1,025 1.4 276 Westchester, NY.......... 36.7 429.6 2.1 155 1,274 4.1 37 Buncombe, NC............. 8.5 123.9 3.6 51 724 2.7 143 Catawba, NC.............. 4.3 82.9 2.2 147 739 2.5 160 Cumberland, NC........... 6.2 118.0 -0.3 324 760 2.0 220 Durham, NC............... 7.8 191.4 2.4 130 1,202 1.3 285 Forsyth, NC.............. 9.3 179.8 0.9 265 928 10.9 3 Guilford, NC............. 14.2 275.2 3.1 82 834 3.1 105 Mecklenburg, NC.......... 35.1 637.3 4.7 13 1,082 3.8 51 New Hanover, NC.......... 7.6 106.6 3.9 36 774 3.2 96 Wake, NC................. 31.6 515.1 3.5 59 984 4.9 21 Cass, ND................. 6.9 117.3 2.1 155 865 4.0 41 Butler, OH............... 7.5 144.5 1.7 183 855 3.4 79 Cuyahoga, OH............. 35.4 721.6 0.7 284 971 1.9 235 Delaware, OH............. 4.8 85.9 1.1 243 943 3.3 87 Franklin, OH............. 30.4 723.1 2.8 96 977 3.2 96 Hamilton, OH............. 23.3 510.8 1.7 183 1,019 2.4 180 Lake, OH................. 6.3 96.8 0.6 291 805 3.6 69 Lorain, OH............... 6.1 99.0 0.6 291 755 -2.1 337 Lucas, OH................ 10.0 209.4 1.7 183 832 1.2 289 Mahoning, OH............. 5.8 97.9 -0.7 331 679 2.4 180 Montgomery, OH........... 11.9 250.1 1.7 183 836 2.7 143 Stark, OH................ 8.6 159.9 0.4 303 720 1.3 285 Summit, OH............... 14.1 265.6 0.7 284 848 2.8 132 Warren, OH............... 4.6 90.7 3.0 88 856 5.3 15 Cleveland, OK............ 5.4 80.8 2.7 102 724 1.1 295 Oklahoma, OK............. 27.0 450.8 1.3 226 900 1.4 276 Tulsa, OK................ 21.8 349.5 1.8 172 892 0.3 319 Clackamas, OR............ 13.9 152.9 3.0 88 922 3.8 51 Jackson, OR.............. 7.0 82.7 3.1 82 723 2.7 143 Lane, OR................. 11.6 147.6 2.7 102 771 3.8 51 Marion, OR............... 10.0 147.8 3.0 88 789 3.5 75 Multnomah, OR............ 32.2 480.7 3.2 76 983 1.9 235 Washington, OR........... 18.1 276.0 3.5 59 1,204 3.8 51 Allegheny, PA............ 35.6 696.1 0.2 313 1,031 2.8 132 Berks, PA................ 8.9 170.6 1.3 226 892 2.4 180 Bucks, PA................ 19.9 261.5 1.2 235 925 2.4 180 Butler, PA............... 5.0 86.5 0.5 298 900 3.8 51 Chester, PA.............. 15.4 246.4 0.6 291 1,295 4.9 21 Cumberland, PA........... 6.3 131.4 2.0 160 908 -1.0 333 Dauphin, PA.............. 7.4 180.8 1.0 256 950 3.7 65 Delaware, PA............. 14.0 219.6 0.5 298 1,028 3.8 51 Erie, PA................. 7.2 126.6 0.1 315 755 3.3 87 Lackawanna, PA........... 5.8 97.7 0.1 315 729 2.1 207 Lancaster, PA............ 13.2 231.9 1.7 183 805 3.6 69 Lehigh, PA............... 8.6 185.7 0.9 265 950 0.6 311 Luzerne, PA.............. 7.6 142.9 0.4 303 759 2.2 198 Montgomery, PA........... 27.5 483.6 1.5 207 1,183 1.5 270 Northampton, PA.......... 6.7 109.1 1.9 166 832 2.0 220 Philadelphia, PA......... 35.1 652.7 2.1 155 1,137 2.9 120 Washington, PA........... 5.5 88.5 -0.4 325 957 2.6 154 Westmoreland, PA......... 9.3 135.4 0.5 298 779 1.7 252 York, PA................. 9.1 175.5 0.7 284 827 2.2 198 Providence, RI........... 17.5 284.3 1.7 183 959 3.3 87 Charleston, SC........... 13.4 237.1 2.7 102 837 1.9 235 Greenville, SC........... 13.5 257.8 3.2 76 835 2.0 220 Horry, SC................ 8.4 126.5 1.8 172 568 3.5 75 Lexington, SC............ 6.3 111.9 3.5 59 737 2.5 160 Richland, SC............. 9.3 211.8 1.8 172 835 1.2 289 Spartanburg, SC.......... 5.9 127.0 2.4 130 849 1.7 252 York, SC................. 5.0 85.9 4.2 22 756 -0.5 329 Minnehaha, SD............ 6.9 125.2 2.0 160 825 3.8 51 Davidson, TN............. 20.4 457.0 4.4 17 1,038 8.1 6 Hamilton, TN............. 9.1 192.5 2.6 113 870 2.8 132 Knox, TN................. 11.5 230.1 2.5 120 828 0.6 311 Rutherford, TN........... 5.0 115.8 3.6 51 879 4.6 27 Shelby, TN............... 19.8 485.0 1.7 183 956 0.6 311 Williamson, TN........... 7.6 115.5 6.3 3 1,079 2.1 207 Bell, TX................. 5.0 114.9 2.2 147 782 1.4 276 Bexar, TX................ 37.8 817.9 2.7 102 854 2.4 180 Brazoria, TX............. 5.3 104.9 5.6 5 996 4.1 37 Brazos, TX............... 4.2 94.9 3.6 51 731 1.0 298 Cameron, TX.............. 6.4 137.0 1.0 256 586 0.5 317 Collin, TX............... 22.0 365.9 4.3 21 1,145 3.8 51 Dallas, TX............... 72.4 1,607.2 4.2 22 1,154 2.8 132 Denton, TX............... 13.2 219.9 5.1 6 867 3.8 51 Ector, TX................ 3.9 73.2 -4.2 341 1,026 -5.1 340 El Paso, TX.............. 14.5 291.3 2.5 120 674 0.3 319 Fort Bend, TX............ 11.7 170.8 4.2 22 945 0.6 311 Galveston, TX............ 5.8 104.3 3.6 51 865 4.0 41 Gregg, TX................ 4.2 76.3 -3.3 339 844 -1.5 335 Harris, TX............... 110.5 2,295.1 1.7 183 1,232 0.0 324 Hidalgo, TX.............. 11.9 244.8 1.7 183 614 1.0 298 Jefferson, TX............ 5.8 124.8 1.7 183 1,001 3.1 105 Lubbock, TX.............. 7.3 133.4 1.7 183 750 3.6 69 McLennan, TX............. 5.0 107.2 1.6 202 791 3.4 79 Midland, TX.............. 5.4 89.3 -3.2 338 1,233 -3.2 339 Montgomery, TX........... 10.4 164.0 3.9 36 982 2.6 154 Nueces, TX............... 8.2 164.1 1.6 202 845 1.4 276 Potter, TX............... 3.9 79.3 0.8 275 772 4.3 35 Smith, TX................ 6.0 100.5 4.0 32 805 1.8 240 Tarrant, TX.............. 40.7 845.3 2.4 130 963 1.7 252 Travis, TX............... 36.6 690.9 4.2 22 1,090 2.9 120 Webb, TX................. 5.1 97.4 2.7 102 651 0.8 307 Williamson, TX........... 9.4 152.2 4.1 26 924 5.8 11 Davis, UT................ 7.9 120.3 5.0 7 770 3.6 69 Salt Lake, UT............ 41.8 645.2 3.3 71 920 3.7 65 Utah, UT................. 14.4 209.1 7.5 1 778 2.9 120 Weber, UT................ 5.7 98.9 3.3 71 737 2.5 160 Chittenden, VT........... 6.5 102.2 1.5 207 950 2.0 220 Arlington, VA............ 8.9 170.7 2.3 138 1,546 1.6 264 Chesterfield, VA......... 8.2 130.5 2.9 93 833 1.8 240 Fairfax, VA.............. 35.4 593.9 1.4 220 1,517 3.9 45 Henrico, VA.............. 10.6 186.0 2.5 120 921 2.2 198 Loudoun, VA.............. 10.9 155.9 2.7 102 1,108 1.7 252 Prince William, VA....... 8.6 124.4 1.8 172 837 2.1 207 Alexandria City, VA...... 6.3 97.1 1.4 220 1,324 0.5 317 Chesapeake City, VA...... 5.8 97.9 0.6 291 780 3.9 45 Newport News City, VA.... 3.7 98.0 -0.5 328 921 -0.6 331 Norfolk City, VA......... 5.6 139.7 0.3 308 948 1.5 270 Richmond City, VA........ 7.2 149.9 2.0 160 1,039 2.5 160 Virginia Beach City, VA.. 11.4 178.3 1.2 235 744 2.2 198 Benton, WA............... 5.6 89.0 3.5 59 977 3.2 96 Clark, WA................ 13.9 145.8 4.1 26 879 2.1 207 King, WA................. 84.2 1,285.2 3.8 42 1,288 3.9 45 Kitsap, WA............... 6.6 85.6 2.6 113 860 2.0 220 Pierce, WA............... 21.5 287.9 3.2 76 880 2.0 220 Snohomish, WA............ 20.1 277.6 2.7 102 1,036 2.0 220 Spokane, WA.............. 15.4 212.2 2.5 120 810 1.8 240 Thurston, WA............. 7.9 106.8 4.0 32 878 3.3 87 Whatcom, WA.............. 7.1 87.7 2.8 96 804 4.4 32 Yakima, WA............... 7.8 121.6 3.6 51 660 2.5 160 Kanawha, WV.............. 5.9 103.8 -1.0 333 848 2.4 180 Brown, WI................ 6.6 154.4 1.0 256 856 5.5 12 Dane, WI................. 14.6 323.8 1.5 207 982 3.4 79 Milwaukee, WI............ 25.7 485.0 0.9 265 921 1.3 285 Outagamie, WI............ 5.1 107.0 0.8 275 798 2.3 194 Waukesha, WI............. 12.5 239.3 1.5 207 948 2.6 154 Winnebago, WI............ 3.6 90.8 0.3 308 883 1.0 298 San Juan, PR............. 10.7 245.8 -2.6 (6) 614 2.5 (6) (1) Includes areas not officially designated as counties. See Technical Note. (2) Average weekly wages were calculated using unrounded data. (3) Percent changes were computed from quarterly employment and pay data adjusted for noneconomic county reclassifications. See Technical Note. (4) Totals for the United States do not include data for Puerto Rico or the Virgin Islands. (5) Data do not meet BLS or state agency disclosure standards. (6) This county was not included in the U.S. rankings. Note: Data are preliminary. Includes workers covered by Unemployment Insurance (UI) and Unemployment Compensation for Federal Employees (UCFE) programs. These 342 U.S. counties comprise 72.1 percent of the total covered workers in the U.S.
Table 2. Covered establishments, employment, and wages in the 10 largest counties, second quarter 2015 Employment Average weekly wage(1) Establishments, second quarter County by NAICS supersector 2015 Percent Percent (thousands) June change, Second change, 2015 June quarter second (thousands) 2014-15(2) 2015 quarter 2014-15(2) United States(3) ............................ 9,575.3 140,594.9 2.0 $968 3.0 Private industry........................... 9,276.4 119,288.6 2.3 959 3.1 Natural resources and mining............. 138.0 2,120.1 -3.3 1,053 -1.9 Construction............................. 767.1 6,569.2 4.6 1,045 3.3 Manufacturing............................ 342.2 12,372.6 1.0 1,181 2.4 Trade, transportation, and utilities..... 1,925.3 26,688.8 2.3 821 2.9 Information.............................. 152.6 2,761.1 1.0 1,671 3.9 Financial activities..................... 847.1 7,862.3 1.9 1,461 4.8 Professional and business services....... 1,727.1 19,644.7 2.6 1,257 4.2 Education and health services............ 1,522.6 20,963.7 2.3 879 2.3 Leisure and hospitality.................. 809.6 15,658.4 2.6 403 3.9 Other services........................... 827.9 4,369.9 1.5 658 3.3 Government................................. 298.8 21,306.3 0.5 1,017 2.2 Los Angeles, CA.............................. 452.5 4,232.7 2.0 1,058 3.6 Private industry........................... 446.6 3,670.0 2.0 1,025 3.6 Natural resources and mining............. 0.5 9.0 -2.3 1,259 0.6 Construction............................. 13.5 125.7 5.2 1,110 5.2 Manufacturing............................ 12.4 358.9 -1.4 1,133 1.9 Trade, transportation, and utilities..... 53.4 795.8 1.5 888 3.9 Information.............................. 9.7 199.6 1.8 1,871 2.2 Financial activities..................... 24.7 211.5 0.5 1,665 4.3 Professional and business services....... 47.6 588.2 0.9 1,312 5.5 Education and health services............ 208.1 721.4 2.4 818 2.9 Leisure and hospitality.................. 31.4 486.9 2.4 591 7.1 Other services........................... 27.8 145.6 0.2 673 5.3 Government................................. 5.9 562.7 2.0 1,277 3.5 New York, NY................................. 129.7 2,378.9 2.3 1,842 3.3 Private industry........................... 128.9 2,119.6 2.5 1,920 3.3 Natural resources and mining............. 0.0 0.2 -5.6 2,162 -6.5 Construction............................. 2.2 37.1 6.2 1,724 2.2 Manufacturing............................ 2.2 27.1 0.5 1,307 -0.5 Trade, transportation, and utilities..... 20.4 260.8 0.7 1,328 2.0 Information.............................. 4.9 152.7 1.4 2,406 -1.4 Financial activities..................... 19.2 370.2 1.4 3,599 5.4 Professional and business services....... 27.4 547.3 4.0 2,164 4.1 Education and health services............ 9.8 325.7 2.1 1,213 2.8 Leisure and hospitality.................. 13.9 289.8 2.5 815 2.9 Other services........................... 20.5 101.3 1.8 1,091 1.6 Government................................. 0.8 259.3 0.9 1,211 1.7 Cook, IL..................................... 164.0 2,548.6 1.5 1,116 2.5 Private industry........................... 162.6 2,247.6 1.6 1,099 2.4 Natural resources and mining............. 0.1 1.0 12.2 1,182 7.6 Construction............................. 13.6 74.2 6.3 1,363 4.4 Manufacturing............................ 6.8 187.8 0.1 1,133 1.0 Trade, transportation, and utilities..... 32.4 469.6 2.1 892 1.2 Information.............................. 2.8 54.5 0.1 1,699 2.6 Financial activities..................... 16.4 187.2 0.4 1,974 5.3 Professional and business services....... 35.1 464.6 1.5 1,397 1.1 Education and health services............ 17.0 432.2 1.5 926 2.8 Leisure and hospitality.................. 14.8 274.6 2.5 502 5.9 Other services........................... 18.6 96.9 -1.0 848 4.0 Government................................. 1.3 301.0 0.7 1,242 3.1 Harris, TX................................... 110.5 2,295.1 1.7 1,232 0.0 Private industry........................... 110.0 2,029.8 1.7 1,255 -0.4 Natural resources and mining............. 1.8 86.9 -6.9 3,187 -1.8 Construction............................. 7.0 163.5 5.3 1,268 0.1 Manufacturing............................ 4.8 191.1 -3.9 1,512 0.1 Trade, transportation, and utilities..... 24.8 475.3 2.6 1,121 1.7 Information.............................. 1.2 27.9 -0.5 1,453 3.8 Financial activities..................... 11.4 120.4 1.4 1,536 2.1 Professional and business services....... 22.4 396.3 0.8 1,514 -0.7 Education and health services............ 15.1 277.4 4.5 958 2.9 Leisure and hospitality.................. 9.4 224.5 4.3 429 2.4 Other services........................... 11.7 65.6 2.3 753 1.5 Government................................. 0.6 265.3 1.8 1,057 2.9 Maricopa, AZ................................. 95.3 1,774.4 3.2 948 1.7 Private industry........................... 94.5 1,595.0 3.5 932 1.6 Natural resources and mining............. 0.5 8.5 1.9 868 6.2 Construction............................. 7.2 96.1 2.0 970 2.8 Manufacturing............................ 3.2 115.5 -0.1 1,381 1.7 Trade, transportation, and utilities..... 19.9 356.3 3.1 853 2.2 Information.............................. 1.6 35.3 4.2 1,220 -2.6 Financial activities..................... 11.1 158.8 4.4 1,218 5.7 Professional and business services....... 21.9 304.5 2.8 1,024 2.0 Education and health services............ 10.8 266.6 4.2 950 -0.1 Leisure and hospitality.................. 7.6 198.0 3.7 432 -1.1 Other services........................... 6.3 49.7 4.5 671 3.1 Government................................. 0.7 179.5 0.5 1,075 2.0 Dallas, TX................................... 72.4 1,607.2 4.2 1,154 2.8 Private industry........................... 71.8 1,438.3 4.4 1,162 2.7 Natural resources and mining............. 0.6 9.5 0.9 4,023 3.2 Construction............................. 4.2 81.6 6.2 1,097 2.3 Manufacturing............................ 2.7 106.1 -0.3 1,269 -3.7 Trade, transportation, and utilities..... 15.6 326.4 5.6 1,039 3.2 Information.............................. 1.4 48.1 -0.2 1,739 3.1 Financial activities..................... 8.8 155.8 2.1 1,606 5.2 Professional and business services....... 16.2 326.1 4.9 1,362 5.0 Education and health services............ 8.9 186.4 5.0 997 1.6 Leisure and hospitality.................. 6.2 155.6 6.7 467 3.8 Other services........................... 6.8 42.1 1.8 748 1.1 Government................................. 0.5 168.8 2.5 1,085 3.4 Orange, CA................................... 111.2 1,519.8 2.7 1,086 4.9 Private industry........................... 109.9 1,369.2 2.8 1,075 5.1 Natural resources and mining............. 0.2 3.2 -8.2 800 4.3 Construction............................. 6.5 88.4 7.0 1,185 2.8 Manufacturing............................ 4.9 155.2 0.4 1,328 6.8 Trade, transportation, and utilities..... 16.7 253.6 0.9 971 3.4 Information.............................. 1.3 25.1 -0.1 1,665 2.9 Financial activities..................... 10.8 115.6 2.1 1,659 9.4 Professional and business services....... 20.4 280.1 1.5 1,337 7.0 Education and health services............ 28.4 190.4 3.6 910 2.1 Leisure and hospitality.................. 8.1 203.4 3.4 454 3.9 Other services........................... 7.0 44.6 2.3 665 3.4 Government................................. 1.4 150.6 2.1 1,178 3.2 San Diego, CA................................ 103.6 1,374.7 2.7 1,073 3.1 Private industry........................... 101.8 1,147.1 3.0 1,057 3.4 Natural resources and mining............. 0.7 9.5 -3.0 672 -3.6 Construction............................. 6.5 68.9 8.5 1,103 4.2 Manufacturing............................ 3.1 104.2 2.8 1,601 12.0 Trade, transportation, and utilities..... 14.1 214.3 0.9 823 4.0 Information.............................. 1.2 23.6 -4.1 1,608 0.0 Financial activities..................... 9.5 70.4 1.9 1,351 9.6 Professional and business services....... 18.0 227.3 2.5 1,603 -0.9 Education and health services............ 28.6 184.6 3.0 900 0.9 Leisure and hospitality.................. 7.8 186.4 2.7 462 6.9 Other services........................... 7.4 49.9 2.0 582 4.7 Government................................. 1.8 227.6 1.5 1,158 2.2 King, WA..................................... 84.2 1,285.2 3.8 1,288 3.9 Private industry........................... 83.7 1,119.3 3.9 1,296 4.1 Natural resources and mining............. 0.4 2.9 17.6 1,325 2.9 Construction............................. 6.2 63.7 12.4 1,230 2.9 Manufacturing............................ 2.4 106.4 0.1 1,544 2.0 Trade, transportation, and utilities..... 14.6 240.6 4.2 1,182 6.3 Information.............................. 2.0 89.0 3.2 2,596 5.6 Financial activities..................... 6.4 66.2 1.6 1,553 7.0 Professional and business services....... 16.3 212.5 6.0 1,533 2.8 Education and health services............ 19.7 163.3 1.8 955 2.8 Leisure and hospitality.................. 6.9 132.0 3.8 516 3.2 Other services........................... 8.8 42.8 3.4 818 2.5 Government................................. 0.5 165.9 2.7 1,235 2.5 Miami-Dade, FL............................... 96.7 1,061.4 3.5 931 2.1 Private industry........................... 96.3 939.7 4.0 896 2.4 Natural resources and mining............. 0.5 7.4 0.7 556 0.4 Construction............................. 5.7 39.0 9.3 899 3.8 Manufacturing............................ 2.8 38.9 3.2 879 3.7 Trade, transportation, and utilities..... 27.7 275.5 3.1 832 0.1 Information.............................. 1.5 17.7 -2.8 1,493 1.4 Financial activities..................... 10.1 73.3 3.9 1,454 4.7 Professional and business services....... 20.3 146.4 5.9 1,068 1.9 Education and health services............ 10.1 165.5 2.4 920 2.9 Leisure and hospitality.................. 7.3 132.9 3.3 551 7.6 Other services........................... 8.4 40.5 6.7 587 1.0 Government................................. 0.3 121.7 0.2 1,179 0.9 (1) Average weekly wages were calculated using unrounded data. (2) Percent changes were computed from quarterly employment and pay data adjusted for noneconomic county reclassifications. See Technical Note. (3) Totals for the United States do not include data for Puerto Rico or the Virgin Islands. Note: Data are preliminary. Counties selected are based on 2014 annual average employment. Includes workers covered by Unemployment Insurance (UI) and Unemployment Compensation for Federal Employees (UCFE) programs.
Table 3. Covered establishments, employment, and wages by state, second quarter 2015 Employment Average weekly wage(1) Establishments, second quarter State 2015 Percent Percent (thousands) June change, Second change, 2015 June quarter second (thousands) 2014-15 2015 quarter 2014-15 United States(2)........... 9,575.3 140,594.9 2.0 $968 3.0 Alabama.................... 118.5 1,899.3 1.3 819 1.6 Alaska..................... 22.3 346.6 0.4 1,028 2.4 Arizona.................... 151.1 2,549.9 2.5 904 1.8 Arkansas................... 88.6 1,184.6 1.7 762 2.1 California................. 1,420.0 16,338.9 2.8 1,131 5.5 Colorado................... 185.4 2,517.1 3.2 989 3.0 Connecticut................ 115.4 1,693.1 0.9 1,177 2.0 Delaware................... 30.5 439.1 2.2 991 1.5 District of Columbia....... 37.2 745.1 1.8 1,599 1.8 Florida.................... 658.3 7,907.7 3.6 861 2.6 Georgia.................... 289.2 4,167.8 3.4 903 2.4 Hawaii..................... 39.5 635.9 1.6 876 3.8 Idaho...................... 55.4 678.5 2.9 713 2.3 Illinois................... 428.3 5,925.5 1.5 1,015 2.6 Indiana.................... 159.7 2,966.0 1.7 811 3.4 Iowa....................... 100.6 1,561.2 0.9 802 2.8 Kansas..................... 86.8 1,382.1 0.7 819 2.8 Kentucky................... 121.7 1,850.5 1.7 822 3.0 Louisiana.................. 126.5 1,930.6 0.5 850 0.8 Maine...................... 50.6 615.8 0.8 768 2.9 Maryland................... 167.3 2,631.3 1.4 1,046 2.6 Massachusetts.............. 239.5 3,488.3 2.1 1,211 4.7 Michigan................... 237.7 4,225.0 1.5 916 2.1 Minnesota.................. 164.1 2,826.3 1.5 977 3.2 Mississippi................ 71.9 1,114.7 1.1 709 0.6 Missouri................... 191.1 2,746.6 1.7 842 2.8 Montana.................... 45.4 461.5 1.8 754 2.7 Nebraska................... 71.5 968.7 1.2 787 4.1 Nevada..................... 78.4 1,248.1 3.2 855 2.6 New Hampshire.............. 50.7 647.7 1.5 967 1.3 New Jersey................. 266.9 4,000.2 1.5 1,126 2.6 New Mexico................. 56.1 808.4 0.8 805 1.4 New York................... 636.6 9,136.9 1.9 1,180 3.1 North Carolina............. 266.0 4,185.6 2.6 850 3.9 North Dakota............... 32.1 445.0 -1.8 939 0.3 Ohio....................... 290.2 5,308.1 1.4 865 2.4 Oklahoma................... 108.8 1,591.5 0.6 818 0.5 Oregon..................... 143.1 1,810.4 3.4 899 3.0 Pennsylvania............... 354.1 5,763.9 0.8 958 2.7 Rhode Island............... 36.4 480.0 1.5 925 2.9 South Carolina............. 121.2 1,963.5 2.5 782 2.1 South Dakota............... 32.4 428.6 1.3 740 3.9 Tennessee.................. 149.7 2,832.1 2.8 863 3.1 Texas...................... 635.0 11,689.4 2.4 988 1.5 Utah....................... 92.9 1,345.9 3.9 821 3.1 Vermont.................... 24.7 309.3 0.6 831 2.2 Virginia................... 247.6 3,767.2 1.7 1,000 2.5 Washington................. 235.5 3,197.6 3.3 1,026 3.1 West Virginia.............. 50.1 706.5 -0.8 803 1.4 Wisconsin.................. 166.7 2,839.8 1.0 836 2.6 Wyoming.................... 26.1 291.5 -1.5 869 -0.1 Puerto Rico................ 46.1 884.6 -1.4 513 2.0 Virgin Islands............. 3.4 37.9 0.1 748 2.2 (1) Average weekly wages were calculated using unrounded data. (2) Totals for the United States do not include data for Puerto Rico or the Virgin Islands. Note: Data are preliminary. Includes workers covered by Unemployment Insurance (UI) and Unemployment Compensation for Federal Employees (UCFE) programs.