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For release 10:00 a.m. (EST), Thursday, March 8, 2018 USDL-18-0334 Technical Information: (202) 691-6567 * QCEWInfo@bls.gov * www.bls.gov/cew Media Contact: (202) 691-5902 * PressOffice@bls.gov COUNTY EMPLOYMENT AND WAGES Third Quarter 2017 From September 2016 to September 2017, employment increased in 283 of the 346 largest U.S. counties, the U.S. Bureau of Labor Statistics reported today. Midland, Texas, had the largest percentage increase with a gain of 10.4 percent over the year, above the national job growth rate of 1.0 percent. Within Midland, the largest employment increase occurred in natural resources and mining, which gained 4,526 jobs over the year (24.4 percent). Collier, Fla., had the largest over-the-year percentage decrease in employment among the largest counties in the U.S., with a loss of 5.2 percent. Within Collier, construction had the largest decrease in employment, with a loss of 1,879 jobs (-12.8 percent). The U.S. average weekly wage decreased 0.6 percent over the year, declining to $1,021 in the third quarter of 2017. This is the third decline since first quarter 2016, and one of only nine declines in the history of the series, which dates back to 1978. Mercer, N.J., had the largest over-the-year percentage decrease in average weekly wages with a loss of 8.8 percent. Within Mercer, an average weekly wage loss of $260 (-13.1 percent) in professional and business services made the largest contribution to the county’s decrease in average weekly wages. Midland, Texas, had the largest over-the-year percentage increase in average weekly wages with a gain of 8.4 percent. Within Midland, natural resources and mining had the largest impact on the county’s average weekly wage change with an increase of $180 (9.5 percent) over the year. County employment and wage data are from the Quarterly Census of Employment and Wages (QCEW) program, which provides the only detailed quarterly and annual universe count of establishments, employment, and wages at the county, metropolitan statistical area, state, and national levels by detailed industry. These data are published within 6 months following the end of each quarter. Large County Employment In September 2017, national employment was 144.5 million (as measured by the QCEW program). Over the year, employment increased 1.0 percent, or 1.5 million. In September 2017, the 346 U.S. counties with 75,000 or more jobs accounted for 72.7 percent of total U.S. employment and 77.8 percent of total wages. These 346 counties had a net job growth of 1.1 million over the year, accounting for 77.3 percent of the overall U.S. employment increase. The 5 counties with the largest increases in employment levels had a combined over-the-year employment gain of 201,100 jobs, which was 13.8 percent of the overall job increase for the U.S. (See table A.) Employment declined in 60 of the largest counties from September 2016 to September 2017. Collier, Fla., had the largest over-the-year percentage decrease in employment (-5.2 percent), followed by Lee, Fla.; Jefferson, Texas; Sangamon, Ill.; and Brazoria, Texas. (See table 1.) Table A. Large counties ranked by September 2017 employment, September 2016-17 employment increase, and September 2016-17 percent increase in employment -------------------------------------------------------------------------------------------------------- Employment in large counties -------------------------------------------------------------------------------------------------------- September 2017 employment | Increase in employment, | Percent increase in employment, (thousands) | September 2016-17 | September 2016-17 | (thousands) | -------------------------------------------------------------------------------------------------------- | | United States 144,464.4| United States 1,459.4| United States 1.0 -------------------------------------------------------------------------------------------------------- | | Los Angeles, Calif. 4,408.1| Los Angeles, Calif. 58.1| Midland, Texas 10.4 Cook, Ill. 2,578.3| Maricopa, Ariz. 48.2| Elkhart, Ind. 5.2 New York, N.Y. 2,451.9| King, Wash. 36.7| Weld, Colo. 5.0 Harris, Texas 2,261.3| Dallas, Texas 31.1| Clark, Wash. 4.6 Maricopa, Ariz. 1,938.0| New York, N.Y. 27.0| Calcasieu, La. 4.5 Dallas, Texas 1,691.1| Kings, N.Y. 25.4| Rutherford, Tenn. 4.3 Orange, Calif. 1,598.6| Santa Clara, Calif. 23.2| Utah, Utah 4.2 San Diego, Calif. 1,439.5| Clark, Nev. 22.8| Montgomery, Texas 4.0 King, Wash. 1,367.1| San Bernardino, Calif. 22.6| Benton, Wash. 3.8 Miami-Dade, Fla. 1,092.6| Orange, Calif. 21.7| Kings, N.Y. 3.7 -------------------------------------------------------------------------------------------------------- Large County Average Weekly Wages Average weekly wages for the nation decreased to $1,021, a 0.6 percent decrease, during the year ending in the third quarter of 2017. Among the 346 largest counties, 265 had over-the-year decreases in average weekly wages. Mercer, N.J., had the largest percentage wage decrease among the largest U.S. counties (- 8.8 percent). (See table B.) Of the 346 largest counties, 71 experienced an over-the-year increase in average weekly wages. Midland, Texas, had the largest percentage increase in average weekly wages (8.4 percent), followed by Union, N.J.; Elkhart, Ind.; Forsyth, N.C.; and Maui + Kalawao, Hawaii. (See table 1.) Table B. Large counties ranked by third quarter 2017 average weekly wages, third quarter 2016-17 decrease in average weekly wages, and third quarter 2016-17 percent decrease in average weekly wages -------------------------------------------------------------------------------------------------------- Average weekly wage in large counties -------------------------------------------------------------------------------------------------------- Average weekly wage, | Decrease in average weekly | Percent decrease in average third quarter 2017 | wage, third quarter 2016-17 | weekly wage, third | | quarter 2016-17 -------------------------------------------------------------------------------------------------------- | | United States $1,021| United States -$6| United States -0.6 -------------------------------------------------------------------------------------------------------- | | Santa Clara, Calif. $2,320| Mercer, N.J. -$118| Mercer, N.J. -8.8 San Mateo, Calif. 2,123| Somerset, N.J. -74| Wyandotte, Kan. -6.0 San Francisco, Calif. 1,954| Wyandotte, Kan. -61| Clark, Nev. -5.3 New York, N.Y. 1,889| Fairfield, Conn. -58| Somerset, N.J. -5.0 Washington, D.C. 1,759| Middlesex, Mass. -57| Clay, Mo. -4.8 Suffolk, Mass. 1,691| Clark, Nev. -50| Washington, Ark. -4.7 Arlington, Va. 1,642| Clay, Mo. -43| Okaloosa, Fla. -4.3 King, Wash. 1,626| Jefferson, Ky. -42| McLean, Ill. -4.2 Fairfax, Va. 1,540| Dauphin, Pa. -42| Jefferson, Ky. -4.2 Middlesex, Mass. 1,498| Anchorage, Alaska -41| Montgomery, Ala. -4.1 | Washington, Ark. -41| Sedgwick, Kan. -4.1 | McLean, Ill. -41| | Mecklenburg, N.C. -41| | Norfolk City, Va. -41| -------------------------------------------------------------------------------------------------------- Ten Largest U.S. Counties Among the 10 largest counties, 9 had over-the-year percentage increases in employment in September 2017. King, Wash., had the largest gain (2.8 percent). Within King, trade, transportation, and utilities had the largest over-the-year employment level increase, with a gain of 16,733 jobs, or 6.6 percent. Miami-Dade, Fla., had the only percentage decrease in employment among the 10 largest counties (-1.7 percent). Within Miami-Dade, leisure and hospitality had the largest over-the-year employment level decrease, with a loss of 6,855 jobs, or -4.9 percent. (See table 2.) Average weekly wages decreased over the year in 7 of the 10 largest U.S. counties. Dallas, Texas, experienced the largest percentage loss in average weekly wages (-1.9 percent). Within Dallas, trade, transportation, and utilities had the largest impact on the county’s average weekly wage loss. Within trade, transportation, and utilities, average weekly wages decreased by $61, or -5.5 percent, over the year. King, Wash., had the largest percentage gain in average weekly wages among the 10 largest counties (2.7 percent). Within King, information had the largest impact on the county’s average weekly wage growth with an increase of $169 (3.4 percent) over the year. For More Information The tables included in this release contain data for the nation and for the 346 U.S. counties with annual average employment levels of 75,000 or more in 2016. September 2017 employment and 2017 third quarter average weekly wages for all states are provided in table 3 of this release. The data are derived from reports submitted by employers who are subject to unemployment insurance (UI) laws. The 9.9 million employer reports cover 144.5 million full- and part-time workers. Data for the third quarter of 2017 will be available later at www.bls.gov/cew. Additional information about the quarterly employment and wages data is available in the Technical Note. More information about QCEW data may be obtained by calling (202) 691-6567. The most current news release on quarterly measures of gross job flows is available from QCEW Business Employment Dynamics at www.bls.gov/news.release/pdf/cewbd.pdf. Several BLS regional offices issue QCEW news releases targeted to local data users. Links to these releases are available at www.bls.gov/cew/cewregional.htm. _____________ The County Employment and Wages release for fourth quarter 2017 is scheduled to be released on Wednesday, May 23, 2018. ---------------------------------------------------------------------------------------------------------- | | | Effects of Hurricanes Irma and Maria on the Quarterly Census of Employment and Wages | | | | Hurricanes Irma and Maria made landfall in the United States on September 7 and September 20, | | 2017, respectively, during the QCEW third quarter reference period. These events did not cause changes | | to QCEW methodology. However, they did affect data collection in Puerto Rico and the U.S. Virgin | | Islands. For more information, please visit this webpage: | | www.bls.gov/bls/hurricanes-harvey-irma-maria.htm. | | | ---------------------------------------------------------------------------------------------------------- ---------------------------------------------------------------------------------------------------------- | | | QCEW Publication Acceleration and Conversion to Two Data Releases | | | | The QCEW publication process is accelerating for a more timely release. Beginning with the fourth | | quarter 2017 release, QCEW data will be published in two parts. The current County Employment and | | Wages news release and associated data will be accelerated and published first. The full QCEW data | | release will occur two weeks later, accompanied by a data release notice. | | | ---------------------------------------------------------------------------------------------------------- ---------------------------------------------------------------------------------------------------------- | | | Alaska Area Name Changes Effective with QCEW Release for Third Quarter 2017 | | These Alaska area names have been updated for the current and future QCEW releases | | | | ------------------------------------------------------------ | | | Previous Name | Current Name | | | |------------------------------------------------------------| | | | Aleutian East Borough | Aleutians East Borough | | | | Aleutian West Census Area | Aleutians West Census Area | | | | Anchorage Borough | Anchorage Municipality | | | | Juneau Borough | Juneau City and Borough | | | | Petersburg Census Area | Petersburg Borough | | | | Sitka Borough | Sitka City and Borough | | | | Yakutat Borough | Yakutat City and Borough | | | ------------------------------------------------------------ | | | ----------------------------------------------------------------------------------------------------------
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 2017 North American Industry Classification System (NAICS). Data for 2017 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 347 counties presented in this release were derived using 2016 preliminary annual averages of employment. For 2017 data, three counties have been added to the publication tables: Sussex, Del.; Maui + Kalawao, Hawaii; and Deschutes, Ore. These counties will be included in all 2017 quarterly releases. One county, Gregg, Texas, which was published in the 2016 releases, will be excluded from this and future 2017 releases because its 2016 annual average employment level was less than 75,000. 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: QCEW, Business Employment Dynamics (BED), and Current Employment Statistics (CES). Each of these measures 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- | 651,000 establish- | submitted by 9.9 | ministrative records| ments | million establish- | submitted by 7.9 | | ments in first | million private-sec-| | quarter of 2017 | 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 | -Within 6 months | -7 months after the | -Usually the 3rd Friday | after the end of | end of each quarter| after the end of the | each quarter | | week including | | | the 12th of the month -----------|---------------------|----------------------|------------------------ 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 federal | 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 9.7 million employer reports of employment and wages submitted by states to the BLS in 2016. 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 2016, UI and UCFE programs covered workers in 141.9 million jobs. The estimated 136.6 million workers in these jobs (after adjustment for multiple jobholders) represented 96.4 percent of civilian wage and salary employment. Covered workers received $7.607 trillion in pay, representing 94.1 percent of the wage and salary component of personal income and 40.9 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 that reflect economic events or 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 2016 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 eliminate the effect of 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 2016 edition of this publication, which was published in September 2017, contains selected data produced by Business Employment Dynamics (BED) on job gains and losses, as well as selected data from the first quarter 2017 version of this news release. Tables and additional content from the 2016 edition of Employment and Wages Annual Averages Online are now available at www.bls.gov/cew/cewbultn16.htm. The 2017 edition of Employment and Wages Annual Averages Online will be available in September 2018. News releases on quarterly measures of gross job flows also are available from BED at www.bls.gov/bdm, (202) 691-6467, or data.bls.gov/cgi-bin/forms/bdm. Information in this release will be made available to sensory impaired individuals upon request. Voice phone: (202) 691-5200; TDD message referral phone number: (800) 877-8339.
Table 1. Covered establishments, employment, and wages in the 347 largest counties, third quarter 2017 Employment Average weekly wage(2) Establishments, County(1) third quarter Percent Ranking Percent Ranking 2017 September change, by Third change, by (thousands) 2017 September percent quarter third percent (thousands) 2016-17(3) change 2017 quarter change 2016-17(3) United States(4)......... 9,916.5 144,464.4 1.0 - $1,021 -0.6 - Jefferson, AL............ 18.7 344.2 1.1 145 990 -1.8 237 Madison, AL.............. 9.6 195.5 1.3 114 1,103 -1.6 217 Mobile, AL............... 10.2 170.5 0.6 209 874 -1.5 208 Montgomery, AL........... 6.4 132.7 1.0 157 825 -4.1 336 Shelby, AL............... 5.8 85.8 1.4 104 956 -1.4 195 Tuscaloosa, AL........... 4.6 94.0 1.0 157 831 1.0 35 Anchorage, AK............ 8.3 151.5 -1.2 332 1,063 -3.7 330 Maricopa, AZ............. 96.6 1,938.0 2.6 32 987 -1.1 168 Pima, AZ................. 18.7 365.6 1.1 145 869 0.6 49 Benton, AR............... 6.5 118.1 1.0 157 942 0.7 43 Pulaski, AR.............. 14.4 251.3 0.8 182 904 -2.2 266 Washington, AR........... 6.0 107.1 2.6 32 823 -4.7 341 Alameda, CA.............. 63.5 777.0 2.4 42 1,390 0.0 72 Butte, CA................ 8.5 84.2 1.2 132 789 1.3 27 Contra Costa, CA......... 32.4 368.0 0.6 209 1,240 0.0 72 Fresno, CA............... 35.5 393.4 1.6 80 804 -0.4 103 Kern, CA................. 19.0 330.3 1.7 79 844 -1.9 245 Los Angeles, CA.......... 488.1 4,408.1 1.3 114 1,147 1.1 32 Marin, CA................ 12.5 114.5 0.7 192 1,237 -0.1 82 Merced, CA............... 6.7 84.3 2.6 32 807 -0.5 111 Monterey, CA............. 13.7 204.8 0.3 244 885 -0.7 133 Napa, CA................. 5.9 79.2 1.9 67 1,020 0.7 43 Orange, CA............... 120.4 1,598.6 1.4 104 1,135 -1.1 168 Placer, CA............... 13.0 161.9 1.9 67 1,033 -1.0 156 Riverside, CA............ 63.9 711.3 2.8 28 831 -1.3 186 Sacramento, CA........... 58.1 649.7 2.1 54 1,110 -0.4 103 San Bernardino, CA....... 59.0 731.5 3.2 20 864 -1.3 186 San Diego, CA............ 110.9 1,439.5 1.2 132 1,112 -1.6 217 San Francisco, CA........ 60.7 722.3 2.4 42 1,954 3.2 8 San Joaquin, CA.......... 17.7 253.2 2.5 40 868 -0.7 133 San Luis Obispo, CA...... 10.4 118.8 3.1 22 860 -0.8 142 San Mateo, CA............ 28.3 400.2 1.3 114 2,123 1.1 32 Santa Barbara, CA........ 15.6 202.5 1.6 80 979 -2.1 263 Santa Clara, CA.......... 72.8 1,077.2 2.2 52 2,320 2.6 13 Santa Cruz, CA........... 9.6 107.6 0.6 209 924 -1.2 175 Solano, CA............... 11.5 139.7 1.8 75 1,058 -0.1 82 Sonoma, CA............... 20.1 209.0 1.5 92 993 -0.5 111 Stanislaus, CA........... 15.6 190.1 0.7 192 880 -0.8 142 Tulare, CA............... 10.4 162.9 0.5 223 737 -0.8 142 Ventura, CA.............. 27.1 321.2 1.2 132 988 -3.1 314 Yolo, CA................. 6.7 102.0 -0.5 306 1,094 -2.2 266 Adams, CO................ 11.0 207.1 3.1 22 1,015 0.0 72 Arapahoe, CO............. 22.1 329.5 1.9 67 1,187 -0.9 150 Boulder, CO.............. 15.4 181.0 2.1 54 1,237 1.5 26 Denver, CO............... 32.4 510.4 2.0 60 1,257 0.8 38 Douglas, CO.............. 12.1 121.3 2.5 40 1,114 -0.6 121 El Paso, CO.............. 19.8 272.5 2.0 60 948 1.7 17 Jefferson, CO............ 20.3 234.3 0.6 209 1,057 0.6 49 Larimer, CO.............. 12.2 160.2 2.1 54 967 3.0 10 Weld, CO................. 7.4 106.7 5.0 3 927 1.6 21 Fairfield, CT............ 35.5 420.9 -0.8 321 1,422 -3.9 333 Hartford, CT............. 28.0 509.9 0.6 209 1,185 -1.3 186 New Haven, CT............ 24.2 364.3 0.5 223 1,051 -1.2 175 New London, CT........... 7.5 124.5 0.8 182 993 -2.6 296 New Castle, DE........... 20.0 286.4 0.1 270 1,146 1.2 30 Sussex, DE............... 6.8 81.9 3.0 24 737 -1.9 245 Washington, DC........... 40.4 764.7 0.7 192 1,759 1.3 27 Alachua, FL.............. 7.1 129.1 0.6 209 881 0.0 72 Bay, FL.................. 5.6 77.5 0.3 244 729 -3.6 326 Brevard, FL.............. 15.6 201.6 -1.1 329 902 -3.0 311 Broward, FL.............. 68.8 778.9 -0.7 314 941 -1.1 168 Collier, FL.............. 13.8 128.3 -5.2 346 857 -1.6 217 Duval, FL................ 29.2 498.6 1.3 114 951 -1.7 231 Escambia, FL............. 8.0 133.7 1.2 132 802 -0.6 121 Hillsborough, FL......... 41.8 662.5 -0.5 306 976 -1.8 237 Lake, FL................. 8.1 93.8 -0.5 306 692 -3.5 323 Lee, FL.................. 21.7 239.7 -2.8 345 810 0.1 64 Leon, FL................. 8.7 146.8 -1.5 334 852 0.8 38 Manatee, FL.............. 10.7 116.1 -0.1 287 793 -3.2 316 Marion, FL............... 8.2 99.0 -0.1 287 695 -3.2 316 Miami-Dade, FL........... 97.5 1,092.6 -1.7 337 984 -0.1 82 Okaloosa, FL............. 6.3 82.9 0.7 192 819 -4.3 340 Orange, FL............... 41.9 811.7 1.3 114 895 -1.4 195 Osceola, FL.............. 6.9 90.6 2.0 60 689 -2.5 285 Palm Beach, FL........... 55.9 576.0 -1.0 328 951 -2.4 281 Pasco, FL................ 10.8 115.0 0.9 167 717 -0.1 82 Pinellas, FL............. 32.7 418.2 0.2 259 881 -1.9 245 Polk, FL................. 13.1 211.4 1.2 132 777 -0.8 142 Sarasota, FL............. 15.8 161.5 -0.8 321 841 0.5 52 Seminole, FL............. 14.9 185.3 0.4 233 866 0.1 64 Volusia, FL.............. 14.2 166.8 -0.7 314 720 -1.0 156 Bibb, GA................. 4.2 81.8 -1.5 334 799 0.8 38 Chatham, GA.............. 8.0 148.9 -0.1 287 852 -2.3 274 Clayton, GA.............. 4.0 122.9 0.9 167 1,027 4.3 6 Cobb, GA................. 21.8 356.1 1.6 80 1,066 -2.2 266 DeKalb, GA............... 17.9 298.0 1.4 104 1,031 -0.5 111 Fulton, GA............... 43.0 853.5 2.0 60 1,324 -1.5 208 Gwinnett, GA............. 24.8 351.3 1.4 104 968 -1.6 217 Hall, GA................. 4.4 85.7 2.7 31 853 -0.8 142 Muscogee, GA............. 4.5 93.2 0.7 192 842 2.8 11 Richmond, GA............. 4.4 104.5 0.6 209 866 -1.9 245 Honolulu, HI............. 26.1 472.5 0.2 259 989 -0.9 150 Maui + Kalawao, HI....... 6.2 76.4 0.2 259 891 4.6 5 Ada, ID.................. 15.9 236.5 3.2 20 902 -0.2 90 Champaign, IL............ 4.0 91.3 0.9 167 885 -1.4 195 Cook, IL................. 137.9 2,578.3 0.1 270 1,157 -0.3 98 DuPage, IL............... 34.5 621.8 0.8 182 1,161 0.5 52 Kane, IL................. 12.5 211.0 -0.4 300 921 -0.9 150 Lake, IL................. 20.1 339.7 1.6 80 1,263 -2.0 256 McHenry, IL.............. 7.8 99.1 0.2 259 833 -2.9 307 McLean, IL............... 3.4 84.0 -0.4 300 939 -4.2 338 Madison, IL.............. 5.4 99.4 1.3 114 782 -2.6 296 Peoria, IL............... 4.2 103.6 -0.4 300 1,077 1.6 21 St. Clair, IL............ 5.0 94.0 0.3 244 808 -1.9 245 Sangamon, IL............. 4.7 127.1 -2.1 343 1,012 -0.4 103 Will, IL................. 14.6 244.2 2.6 32 879 -3.5 323 Winnebago, IL............ 6.0 126.2 -0.7 314 888 1.8 16 Allen, IN................ 8.8 186.3 0.4 233 821 -1.6 217 Elkhart, IN.............. 4.7 135.7 5.2 2 924 6.5 3 Hamilton, IN............. 9.4 140.4 1.1 145 976 0.7 43 Lake, IN................. 10.4 189.3 -0.4 300 877 0.0 72 Marion, IN............... 24.0 598.2 0.1 270 1,020 -1.6 217 St. Joseph, IN........... 5.8 123.7 -1.1 329 827 -1.2 175 Tippecanoe, IN........... 3.4 83.9 -0.1 287 886 1.6 21 Vanderburgh, IN.......... 4.8 110.2 2.0 60 824 0.2 63 Johnson, IA.............. 4.2 84.5 0.7 192 965 -0.6 121 Linn, IA................. 6.9 130.6 -0.2 296 966 -3.4 321 Polk, IA................. 17.4 298.5 0.3 244 1,012 -2.7 300 Scott, IA................ 5.6 91.4 0.1 270 825 -2.3 274 Johnson, KS.............. 23.9 343.6 1.6 80 1,008 -1.9 245 Sedgwick, KS............. 12.7 247.0 -0.5 306 849 -4.1 336 Shawnee, KS.............. 5.2 96.5 -1.8 340 820 -2.3 274 Wyandotte, KS............ 3.5 92.4 1.5 92 953 -6.0 345 Boone, KY................ 4.4 87.7 3.3 16 877 -3.8 332 Fayette, KY.............. 10.9 195.8 0.5 223 893 -2.7 300 Jefferson, KY............ 25.0 468.0 0.7 192 959 -4.2 338 Caddo, LA................ 7.3 111.5 -1.7 337 812 0.0 72 Calcasieu, LA............ 5.3 99.2 4.5 5 909 -0.8 142 East Baton Rouge, LA..... 15.8 264.9 -0.1 287 940 -3.0 311 Jefferson, LA............ 14.0 189.4 -1.8 340 899 -2.5 285 Lafayette, LA............ 9.6 129.6 0.2 259 858 -3.6 326 Orleans, LA.............. 12.7 192.9 0.2 259 937 -2.5 285 St. Tammany, LA.......... 8.4 88.0 -0.7 314 844 -1.2 175 Cumberland, ME........... 14.0 184.2 1.8 75 932 -0.4 103 Anne Arundel, MD......... 15.2 271.4 0.5 223 1,071 -1.4 195 Baltimore, MD............ 21.2 373.7 -0.9 325 1,012 -1.8 237 Frederick, MD............ 6.4 101.4 1.0 157 939 -2.6 296 Harford, MD.............. 5.8 94.1 1.1 145 984 -2.5 285 Howard, MD............... 10.0 171.3 0.6 209 1,230 -2.5 285 Montgomery, MD........... 32.8 469.9 0.2 259 1,336 -1.3 186 Prince George's, MD...... 15.9 316.4 -0.3 299 1,080 -2.7 300 Baltimore City, MD....... 13.6 346.0 2.1 54 1,199 -1.2 175 Barnstable, MA........... 9.6 102.1 0.1 270 849 -0.7 133 Bristol, MA.............. 17.7 227.6 1.1 145 901 -1.3 186 Essex, MA................ 25.9 327.3 0.0 284 1,072 -0.2 90 Hampden, MA.............. 18.4 210.3 0.8 182 915 -1.8 237 Middlesex, MA............ 55.4 904.1 1.6 80 1,498 -3.7 330 Norfolk, MA.............. 25.5 352.5 0.4 233 1,142 -0.2 90 Plymouth, MA............. 16.0 194.8 1.3 114 937 -0.7 133 Suffolk, MA.............. 29.9 675.0 0.9 167 1,691 1.7 17 Worcester, MA............ 25.5 349.3 0.7 192 1,011 -0.3 98 Genesee, MI.............. 6.8 134.8 0.2 259 845 -1.2 175 Ingham, MI............... 6.0 153.2 1.1 145 935 -2.2 266 Kalamazoo, MI............ 5.0 117.7 -0.1 287 944 0.3 59 Kent, MI................. 14.5 396.4 1.5 92 891 -2.0 256 Macomb, MI............... 17.6 327.7 0.1 270 1,016 -0.2 90 Oakland, MI.............. 39.4 731.3 1.0 157 1,116 -1.3 186 Ottawa, MI............... 5.7 126.9 1.0 157 864 -0.2 90 Saginaw, MI.............. 3.9 84.3 -0.7 314 812 -2.2 266 Washtenaw, MI............ 8.2 213.1 1.5 92 1,101 0.5 52 Wayne, MI................ 30.9 722.3 0.7 192 1,092 -1.8 237 Anoka, MN................ 7.2 123.0 2.0 60 1,008 -1.9 245 Dakota, MN............... 9.9 188.7 0.7 192 959 -3.6 326 Hennepin, MN............. 41.0 927.2 1.8 75 1,236 -2.9 307 Olmsted, MN.............. 3.4 97.7 1.6 80 1,180 2.5 14 Ramsey, MN............... 13.4 334.9 0.4 233 1,124 -2.5 285 St. Louis, MN............ 5.3 99.1 0.6 209 844 -3.1 314 Stearns, MN.............. 4.3 87.2 0.9 167 877 -0.8 142 Washington, MN........... 5.5 85.4 3.3 16 851 -2.0 256 Harrison, MS............. 4.6 85.0 -1.1 329 697 -2.4 281 Hinds, MS................ 5.8 120.4 -0.7 314 855 -1.8 237 Boone, MO................ 5.1 94.6 1.2 132 819 -1.9 245 Clay, MO................. 5.7 107.4 2.8 28 856 -4.8 342 Greene, MO............... 9.1 166.2 1.3 114 781 -2.6 296 Jackson, MO.............. 22.4 369.2 1.0 157 1,019 -0.6 121 St. Charles, MO.......... 9.6 147.9 0.9 167 807 -1.7 231 St. Louis, MO............ 39.7 607.8 0.8 182 1,048 -0.7 133 St. Louis City, MO....... 14.8 228.0 0.2 259 1,066 -3.6 326 Yellowstone, MT.......... 6.8 82.0 -0.6 311 865 -1.7 231 Douglas, NE.............. 19.3 338.7 0.1 270 957 -2.5 285 Lancaster, NE............ 10.4 168.9 -0.4 300 842 -0.4 103 Clark, NV................ 55.3 970.2 2.4 42 898 -5.3 344 Washoe, NV............... 14.8 218.8 2.1 54 933 0.1 64 Hillsborough, NH......... 12.2 201.9 0.6 209 1,126 -0.8 142 Merrimack, NH............ 5.2 77.2 0.1 270 962 0.8 38 Rockingham, NH........... 11.0 151.0 0.9 167 993 0.3 59 Atlantic, NJ............. 6.5 126.2 -1.7 337 841 -0.7 133 Bergen, NJ............... 33.0 445.4 0.6 209 1,166 -0.5 111 Burlington, NJ........... 11.0 206.0 1.4 104 1,019 -3.3 319 Camden, NJ............... 12.1 206.9 1.5 92 966 -1.5 208 Essex, NJ................ 20.4 342.5 1.6 80 1,228 -2.1 263 Gloucester, NJ........... 6.3 108.3 1.1 145 848 -2.8 306 Hudson, NJ............... 15.1 262.2 1.9 67 1,363 0.1 64 Mercer, NJ............... 11.2 250.0 0.4 233 1,219 -8.8 346 Middlesex, NJ............ 22.3 425.0 1.3 114 1,152 -2.9 307 Monmouth, NJ............. 20.1 261.9 1.0 157 972 -0.5 111 Morris, NJ............... 17.1 290.8 1.5 92 1,466 -0.7 133 Ocean, NJ................ 13.2 169.6 2.3 48 797 -2.3 274 Passaic, NJ.............. 12.7 167.1 0.3 244 976 -2.3 274 Somerset, NJ............. 10.2 187.1 0.2 259 1,415 -5.0 343 Union, NJ................ 14.3 220.4 0.4 233 1,332 8.2 2 Bernalillo, NM........... 18.3 327.4 -0.2 296 876 -1.6 217 Albany, NY............... 10.4 234.4 -0.4 300 1,049 -1.0 156 Bronx, NY................ 18.9 300.9 0.1 270 1,005 1.6 21 Broome, NY............... 4.5 86.6 -0.9 325 818 1.2 30 Dutchess, NY............. 8.5 113.2 0.7 192 974 -0.6 121 Erie, NY................. 24.9 473.3 0.3 244 893 -1.7 231 Kings, NY................ 63.2 714.5 3.7 10 856 -1.2 175 Monroe, NY............... 18.9 386.8 0.7 192 947 -2.7 300 Nassau, NY............... 54.5 631.2 0.9 167 1,108 1.7 17 New York, NY............. 129.2 2,451.9 1.1 145 1,889 0.5 52 Oneida, NY............... 5.4 104.6 -0.5 306 789 -0.6 121 Onondaga, NY............. 12.9 245.6 0.0 284 924 -1.4 195 Orange, NY............... 10.5 143.8 1.4 104 850 -1.2 175 Queens, NY............... 53.4 665.8 2.3 48 970 -0.5 111 Richmond, NY............. 9.9 116.0 1.3 114 928 0.5 52 Rockland, NY............. 10.9 125.0 1.6 80 953 -3.4 321 Saratoga, NY............. 6.0 87.0 2.4 42 917 -1.1 168 Suffolk, NY.............. 53.4 665.9 0.5 223 1,098 -2.7 300 Westchester, NY.......... 36.6 428.4 1.0 157 1,235 0.1 64 Buncombe, NC............. 9.2 130.3 1.5 92 789 0.4 58 Catawba, NC.............. 4.4 87.0 1.5 92 774 -1.4 195 Cumberland, NC........... 6.2 118.3 -0.1 287 802 -1.5 208 Durham, NC............... 8.3 197.9 0.7 192 1,255 -0.3 98 Forsyth, NC.............. 9.2 184.8 0.4 233 952 5.3 4 Guilford, NC............. 14.3 279.5 0.1 270 886 0.3 59 Mecklenburg, NC.......... 37.6 685.8 2.4 42 1,132 -3.5 323 New Hanover, NC.......... 8.1 111.8 1.5 92 820 -0.1 82 Wake, NC................. 34.3 544.1 2.6 32 1,039 -1.0 156 Cass, ND................. 7.2 118.4 -0.1 287 934 -1.6 217 Butler, OH............... 7.9 155.2 2.0 60 901 -1.4 195 Cuyahoga, OH............. 36.0 721.1 0.3 244 1,028 0.1 64 Delaware, OH............. 5.3 88.1 0.5 223 974 -0.5 111 Franklin, OH............. 32.3 753.6 1.6 80 1,032 -1.1 168 Hamilton, OH............. 24.0 516.8 0.7 192 1,094 -1.9 245 Lake, OH................. 6.3 95.4 0.7 192 820 -1.4 195 Lorain, OH............... 6.2 98.3 0.5 223 787 -2.2 266 Lucas, OH................ 10.1 208.0 -0.6 311 878 -1.5 208 Mahoning, OH............. 5.9 97.8 0.3 244 730 -1.5 208 Montgomery, OH........... 11.9 255.9 1.1 145 866 -1.8 237 Stark, OH................ 8.6 159.3 0.3 244 769 0.1 64 Summit, OH............... 14.4 267.9 0.0 284 886 -2.1 263 Warren, OH............... 4.9 92.4 1.3 114 977 -1.0 156 Cleveland, OK............ 5.8 81.1 0.5 223 748 -1.7 231 Oklahoma, OK............. 28.2 451.9 0.9 167 949 -2.2 266 Tulsa, OK................ 22.6 353.3 0.8 182 908 -2.5 285 Clackamas, OR............ 14.9 163.3 2.1 54 963 -0.4 103 Deschutes, OR............ 8.5 81.4 3.5 13 858 4.1 7 Jackson, OR.............. 7.4 89.5 2.6 32 788 -1.0 156 Lane, OR................. 12.1 155.2 1.2 132 804 -0.9 150 Marion, OR............... 10.8 155.8 1.3 114 845 1.0 35 Multnomah, OR............ 35.0 504.4 1.6 80 1,070 -0.4 103 Washington, OR........... 19.3 290.9 2.4 42 1,318 -0.6 121 Allegheny, PA............ 35.6 699.0 1.1 145 1,076 -1.6 217 Berks, PA................ 9.0 172.3 0.6 209 923 -2.5 285 Bucks, PA................ 20.0 264.7 1.2 132 934 -2.4 281 Butler, PA............... 5.1 85.7 0.1 270 943 -0.6 121 Chester, PA.............. 15.5 250.8 1.2 132 1,207 -1.5 208 Cumberland, PA........... 6.5 133.6 0.6 209 917 -2.3 274 Dauphin, PA.............. 7.6 182.5 0.9 167 996 -4.0 334 Delaware, PA............. 14.1 223.3 0.9 167 1,058 -1.0 156 Erie, PA................. 7.0 123.2 -0.2 296 787 -0.6 121 Lackawanna, PA........... 5.7 98.7 0.4 233 773 -2.3 274 Lancaster, PA............ 13.5 238.4 1.1 145 855 -1.0 156 Lehigh, PA............... 8.8 191.0 0.9 167 992 -1.1 168 Luzerne, PA.............. 7.5 146.5 1.0 157 800 -3.3 319 Montgomery, PA........... 27.7 493.6 1.2 132 1,212 -1.8 237 Northampton, PA.......... 6.8 115.3 1.3 114 871 -1.5 208 Philadelphia, PA......... 35.1 676.8 1.2 132 1,212 -1.2 175 Washington, PA........... 5.5 87.8 1.5 92 985 0.0 72 Westmoreland, PA......... 9.3 134.7 0.5 223 839 1.1 32 York, PA................. 9.2 179.3 0.3 244 898 -0.1 82 Providence, RI........... 18.3 288.1 0.5 223 1,026 -2.0 256 Charleston, SC........... 15.1 244.7 0.4 233 902 -1.4 195 Greenville, SC........... 13.9 266.1 1.4 104 877 -1.6 217 Horry, SC................ 8.7 127.8 1.3 114 633 0.0 72 Lexington, SC............ 6.6 118.5 2.2 52 778 -1.6 217 Richland, SC............. 10.1 218.1 -0.6 311 893 0.8 38 Spartanburg, SC.......... 6.2 138.4 3.5 13 856 -1.0 156 York, SC................. 5.6 93.7 3.6 11 825 -0.5 111 Minnehaha, SD............ 7.3 125.8 0.8 182 902 -0.6 121 Davidson, TN............. 22.5 488.8 2.3 48 1,062 0.0 72 Hamilton, TN............. 9.7 202.0 1.5 92 903 0.7 43 Knox, TN................. 12.3 238.6 0.6 209 874 -1.6 217 Rutherford, TN........... 5.6 126.3 4.3 6 901 -1.0 156 Shelby, TN............... 20.5 493.5 0.3 244 1,028 -1.6 217 Williamson, TN........... 8.7 129.9 3.4 15 1,133 -3.2 316 Bell, TX................. 5.4 117.5 0.3 244 863 -0.3 98 Bexar, TX................ 41.0 857.8 1.3 114 905 -0.7 133 Brazoria, TX............. 5.8 107.2 -1.9 342 1,074 -0.9 150 Brazos, TX............... 4.6 102.9 1.4 104 775 1.3 27 Cameron, TX.............. 6.5 138.2 0.4 233 612 -3.0 311 Collin, TX............... 24.8 398.0 3.3 16 1,190 -0.7 133 Dallas, TX............... 76.7 1,691.1 1.9 67 1,213 -1.9 245 Denton, TX............... 14.9 239.6 3.0 24 929 -2.5 285 El Paso, TX.............. 15.1 300.9 0.8 182 717 -1.5 208 Fort Bend, TX............ 13.2 177.3 0.9 167 942 -2.0 256 Galveston, TX............ 6.2 108.5 -0.1 287 886 -1.3 186 Harris, TX............... 114.7 2,261.3 0.1 270 1,247 -1.7 231 Hidalgo, TX.............. 12.3 252.7 1.6 80 649 -0.6 121 Jefferson, TX............ 5.9 119.7 -2.3 344 1,052 -1.4 195 Lubbock, TX.............. 7.5 139.1 1.3 114 790 -2.7 300 McLennan, TX............. 5.3 112.5 0.4 233 841 0.5 52 Midland, TX.............. 5.5 91.4 10.4 1 1,283 8.4 1 Montgomery, TX........... 11.3 176.4 4.0 8 1,003 -0.5 111 Nueces, TX............... 8.3 160.5 -0.7 314 883 -0.2 90 Potter, TX............... 4.0 78.0 -0.8 321 821 -1.0 156 Smith, TX................ 6.2 102.4 0.9 167 843 0.6 49 Tarrant, TX.............. 43.2 877.8 2.3 48 1,000 -2.9 307 Travis, TX............... 40.6 728.0 2.6 32 1,188 0.9 37 Webb, TX................. 5.4 100.1 1.2 132 672 -1.0 156 Williamson, TX........... 10.7 164.6 2.9 27 1,010 -1.3 186 Davis, UT................ 8.4 128.1 3.6 11 816 -1.4 195 Salt Lake, UT............ 44.7 688.0 1.8 75 993 -0.1 82 Utah, UT................. 16.1 232.7 4.2 7 822 0.0 72 Weber, UT................ 6.0 104.4 1.9 67 781 0.1 64 Chittenden, VT........... 6.9 102.6 0.3 244 983 -1.2 175 Arlington, VA............ 9.3 176.0 0.9 167 1,642 -0.4 103 Chesterfield, VA......... 9.1 136.1 3.0 24 865 -2.0 256 Fairfax, VA.............. 37.4 603.0 0.7 192 1,540 -0.6 121 Henrico, VA.............. 11.6 194.0 1.5 92 960 -2.5 285 Loudoun, VA.............. 12.3 163.9 2.6 32 1,179 1.6 21 Prince William, VA....... 9.4 127.4 1.9 67 894 -2.2 266 Alexandria City, VA...... 6.4 92.7 -1.6 336 1,438 -0.2 90 Chesapeake City, VA...... 6.1 97.6 -0.9 325 807 -0.1 82 Newport News City, VA.... 3.9 98.0 1.3 114 993 -0.3 98 Norfolk City, VA......... 5.9 142.1 0.8 182 990 -4.0 334 Richmond City, VA........ 7.7 153.9 0.3 244 1,113 -1.2 175 Virginia Beach City, VA.. 12.2 178.7 0.3 244 775 -1.4 195 Benton, WA............... 5.7 89.6 3.8 9 1,030 -1.6 217 Clark, WA................ 14.5 158.0 4.6 4 975 0.7 43 King, WA................. 86.2 1,367.1 2.8 28 1,626 2.7 12 Kitsap, WA............... 6.7 87.5 1.4 104 947 -2.4 281 Pierce, WA............... 21.7 305.1 1.1 145 953 0.3 59 Snohomish, WA............ 20.7 283.4 -0.8 321 1,102 -0.5 111 Spokane, WA.............. 15.6 220.8 1.4 104 889 0.7 43 Thurston, WA............. 8.3 114.8 3.3 16 946 1.9 15 Whatcom, WA.............. 7.3 89.8 1.9 67 858 1.7 17 Yakima, WA............... 7.7 125.0 1.3 114 735 3.2 8 Kanawha, WV.............. 5.7 100.0 -1.4 333 880 -1.1 168 Brown, WI................ 6.9 157.1 1.2 132 884 -2.0 256 Dane, WI................. 15.7 333.1 0.7 192 1,017 -1.4 195 Milwaukee, WI............ 26.6 487.0 0.1 270 955 -1.3 186 Outagamie, WI............ 5.3 108.1 0.8 182 871 -0.2 90 Waukesha, WI............. 13.2 242.7 0.2 259 986 -1.9 245 Winnebago, WI............ 3.8 93.5 0.1 270 921 -0.9 150 San Juan, PR............. 10.8 240.6 -2.4 (5) 617 -2.2 (5) (1) Includes areas not officially designated as counties. See Technical Note. (2) Average weekly wages were calculated using unrounded data. (3) Percent changes were computed from employment and pay data adjusted for noneconomic county reclassifications. See Technical Note. (4) Totals for the United States do not include data for Puerto Rico or the Virgin Islands. (5) This county was not included in the U.S. rankings. Note: Data are preliminary. Includes workers covered by Unemployment Insurance (UI) and Unemployment Compensation for Federal Employees (UCFE) programs. These 346 U.S. counties comprise 72.7 percent of the total covered workers in the U.S.
Table 2. Covered establishments, employment, and wages in the 10 largest counties, third quarter 2017 Employment Average weekly wage(1) Establishments, third quarter County by NAICS supersector 2017 Percent Percent (thousands) September change, Third change, 2017 September quarter third (thousands) 2016-17(2) 2017 quarter 2016-17(2) United States(3) ............................ 9,916.5 144,464.4 1.0 $1,021 -0.6 Private industry........................... 9,617.8 122,881.9 1.2 1,013 -0.6 Natural resources and mining............. 137.0 1,997.3 2.5 1,016 1.2 Construction............................. 788.8 7,093.0 2.3 1,140 -0.3 Manufacturing............................ 347.3 12,443.1 0.6 1,221 -1.8 Trade, transportation, and utilities..... 1,919.2 27,119.8 0.6 861 -0.5 Information.............................. 163.5 2,788.4 -0.4 1,977 2.2 Financial activities..................... 872.5 8,101.5 1.3 1,517 -0.8 Professional and business services....... 1,789.9 20,414.8 0.9 1,310 -0.2 Education and health services............ 1,656.2 22,170.0 1.7 941 -1.6 Leisure and hospitality.................. 841.9 16,027.9 0.9 440 -0.2 Other services........................... 843.4 4,410.1 0.1 714 1.1 Government................................. 298.7 21,582.5 0.2 1,070 -0.7 Los Angeles, CA.............................. 488.1 4,408.1 1.3 1,147 1.1 Private industry........................... 481.8 3,839.8 1.4 1,113 1.4 Natural resources and mining............. 0.5 7.6 -0.3 1,094 -9.6 Construction............................. 14.4 139.7 3.3 1,202 1.6 Manufacturing............................ 12.2 344.6 -3.2 1,281 -1.0 Trade, transportation, and utilities..... 54.6 819.7 0.3 937 0.8 Information.............................. 10.3 214.9 0.2 2,194 3.6 Financial activities..................... 26.3 218.7 0.3 1,766 0.3 Professional and business services....... 48.9 615.4 2.2 1,369 1.3 Education and health services............ 230.9 780.0 2.6 874 -1.4 Leisure and hospitality.................. 33.7 521.6 1.6 626 -0.3 Other services........................... 26.6 148.3 -0.3 1,061 37.6 Government................................. 6.3 568.3 0.6 1,385 0.1 Cook, IL..................................... 137.9 2,578.3 0.1 1,157 -0.3 Private industry........................... 136.6 2,282.2 0.2 1,160 -0.5 Natural resources and mining............. 0.1 1.3 12.1 1,131 -5.0 Construction............................. 10.6 76.9 1.3 1,451 0.1 Manufacturing............................ 5.8 183.9 0.3 1,205 -3.2 Trade, transportation, and utilities..... 27.5 467.5 -0.5 955 1.0 Information.............................. 2.3 50.4 -1.6 1,805 0.2 Financial activities..................... 13.7 194.8 0.6 2,006 -0.4 Professional and business services....... 28.6 474.3 -0.5 1,480 -0.1 Education and health services............ 15.3 443.9 0.7 989 -0.4 Leisure and hospitality.................. 13.6 289.7 1.4 534 -2.6 Other services........................... 15.6 96.4 -0.4 915 -0.3 Government................................. 1.3 296.1 -0.6 1,134 2.4 New York, NY................................. 129.2 2,451.9 1.1 1,889 0.5 Private industry........................... 128.4 2,188.8 1.2 1,955 0.4 Natural resources and mining............. 0.0 0.2 12.8 1,853 -0.9 Construction............................. 2.3 41.3 -1.5 1,865 0.8 Manufacturing............................ 2.1 25.2 -5.0 1,543 12.3 Trade, transportation, and utilities..... 19.7 251.9 -0.8 1,380 2.9 Information.............................. 5.0 165.0 2.7 2,608 2.6 Financial activities..................... 19.6 373.5 0.8 3,366 -0.3 Professional and business services....... 27.3 575.1 1.6 2,185 0.2 Education and health services............ 10.1 341.1 0.7 1,336 -0.3 Leisure and hospitality.................. 14.7 300.1 1.5 903 0.6 Other services........................... 20.6 103.7 0.9 1,174 0.6 Government................................. 0.8 263.1 0.1 1,332 1.4 Harris, TX................................... 114.7 2,261.3 0.1 1,247 -1.7 Private industry........................... 114.1 1,990.5 0.1 1,257 -2.0 Natural resources and mining............. 1.6 66.4 0.3 2,994 -1.7 Construction............................. 7.4 155.9 -2.9 1,287 -4.5 Manufacturing............................ 4.8 170.0 1.4 1,598 1.8 Trade, transportation, and utilities..... 25.0 463.7 0.0 1,137 -0.6 Information.............................. 1.2 25.5 -6.4 1,530 6.4 Financial activities..................... 12.1 126.3 1.4 1,579 -0.8 Professional and business services....... 23.2 397.3 0.6 1,545 -4.7 Education and health services............ 16.1 290.1 0.4 1,024 -1.5 Leisure and hospitality.................. 10.1 227.9 -0.4 460 -0.6 Other services........................... 11.7 65.6 -0.8 781 -2.4 Government................................. 0.5 270.7 0.4 1,177 0.4 Maricopa, AZ................................. 96.6 1,938.0 2.6 987 -1.1 Private industry........................... 95.9 1,724.5 2.8 976 -1.2 Natural resources and mining............. 0.4 7.6 0.0 962 2.8 Construction............................. 6.8 112.4 7.5 1,056 0.8 Manufacturing............................ 3.1 119.7 3.2 1,347 -4.1 Trade, transportation, and utilities..... 18.1 372.4 1.8 892 -0.8 Information.............................. 1.5 34.0 0.4 1,392 -6.9 Financial activities..................... 10.7 176.3 4.0 1,253 -2.2 Professional and business services....... 20.5 327.9 0.5 1,057 0.0 Education and health services............ 10.8 297.0 2.7 1,000 -2.2 Leisure and hospitality.................. 7.8 213.0 2.9 490 4.0 Other services........................... 6.1 50.1 -2.5 730 2.1 Government................................. 0.7 213.5 0.4 1,089 0.3 Dallas, TX................................... 76.7 1,691.1 1.9 1,213 -1.9 Private industry........................... 76.1 1,518.6 2.2 1,218 -2.0 Natural resources and mining............. 0.5 8.8 7.9 3,601 0.3 Construction............................. 4.6 88.6 2.5 1,233 -1.0 Manufacturing............................ 2.8 112.7 1.3 1,438 -6.0 Trade, transportation, and utilities..... 16.0 346.1 2.9 1,052 -5.5 Information.............................. 1.4 48.2 -3.3 1,813 -0.2 Financial activities..................... 9.5 166.7 4.1 1,673 0.4 Professional and business services....... 17.2 343.4 1.5 1,408 -1.2 Education and health services............ 9.6 198.4 2.0 1,078 -1.1 Leisure and hospitality.................. 6.9 161.5 2.0 515 2.0 Other services........................... 6.9 42.8 0.5 812 -0.5 Government................................. 0.6 172.5 -0.5 1,171 -1.0 Orange, CA................................... 120.4 1,598.6 1.4 1,135 -1.1 Private industry........................... 119.0 1,454.8 1.6 1,122 -1.1 Natural resources and mining............. 0.2 2.8 -4.4 894 -4.9 Construction............................. 6.8 103.1 3.8 1,338 1.4 Manufacturing............................ 4.9 157.4 -1.0 1,385 -2.7 Trade, transportation, and utilities..... 17.1 259.1 0.8 1,029 0.5 Information.............................. 1.4 26.5 0.4 1,945 1.8 Financial activities..................... 11.3 117.9 0.3 1,813 1.2 Professional and business services....... 20.8 303.8 0.7 1,285 -2.1 Education and health services............ 33.6 211.1 3.7 953 -1.4 Leisure and hospitality.................. 8.7 218.0 1.6 506 -0.8 Other services........................... 6.8 45.3 -1.1 713 -0.6 Government................................. 1.4 143.9 -0.7 1,267 -2.6 San Diego, CA................................ 110.9 1,439.5 1.2 1,112 -1.6 Private industry........................... 108.9 1,208.2 1.4 1,073 -1.0 Natural resources and mining............. 0.6 9.4 -1.5 774 5.6 Construction............................. 7.0 81.0 4.6 1,203 0.1 Manufacturing............................ 3.2 108.4 0.3 1,533 -2.9 Trade, transportation, and utilities..... 14.3 225.3 0.9 891 -0.8 Information.............................. 1.2 24.1 -1.4 2,107 7.0 Financial activities..................... 10.1 73.5 1.0 1,419 -1.3 Professional and business services....... 18.3 232.1 0.6 1,471 -1.6 Education and health services............ 31.8 199.2 2.2 963 -0.7 Leisure and hospitality.................. 8.4 195.5 0.2 502 -0.6 Other services........................... 7.3 51.5 0.3 625 -0.8 Government................................. 1.9 231.3 0.2 1,329 -3.5 King, WA..................................... 86.2 1,367.1 2.8 1,626 2.7 Private industry........................... 85.6 1,202.3 3.2 1,659 2.5 Natural resources and mining............. 0.4 3.2 -0.9 1,355 11.9 Construction............................. 6.8 71.9 4.3 1,363 0.0 Manufacturing............................ 2.5 101.9 -2.5 1,601 -1.0 Trade, transportation, and utilities..... 14.4 269.1 6.6 1,513 8.1 Information.............................. 2.3 105.0 5.2 5,099 3.4 Financial activities..................... 6.7 68.2 1.8 1,631 -0.8 Professional and business services....... 18.0 225.9 2.2 1,669 0.4 Education and health services............ 18.0 171.1 2.7 1,033 -1.9 Leisure and hospitality.................. 7.3 141.2 2.8 596 2.2 Other services........................... 9.3 44.7 1.3 873 -4.2 Government................................. 0.5 164.8 -0.3 1,382 2.5 Miami-Dade, FL............................... 97.5 1,092.6 -1.7 984 -0.1 Private industry........................... 97.2 954.5 -1.9 951 -0.7 Natural resources and mining............. 0.5 7.1 -6.8 628 -1.9 Construction............................. 6.5 42.9 -3.8 955 -1.1 Manufacturing............................ 2.8 40.0 -1.4 881 -9.0 Trade, transportation, and utilities..... 25.3 274.4 -1.4 892 -0.7 Information.............................. 1.5 17.4 -1.6 1,602 -10.2 Financial activities..................... 10.6 74.7 0.4 1,476 -0.7 Professional and business services....... 21.9 151.7 -2.1 1,105 -1.1 Education and health services............ 10.5 175.4 -0.6 965 -1.1 Leisure and hospitality.................. 7.2 132.0 -4.9 639 8.1 Other services........................... 8.3 37.3 -5.1 625 -0.2 Government................................. 0.3 138.1 0.2 1,225 3.2 (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 2016 annual average employment. Includes workers covered by Unemployment Insurance (UI) and Unemployment Compensation for Federal Employees (UCFE) programs.
Table 3. Covered establishments, employment, and wages by state, third quarter 2017 Employment Average weekly wage(1) Establishments, third quarter State 2017 Percent Percent (thousands) September change, Third change, 2017 September quarter third (thousands) 2016-17 2017 quarter 2016-17 United States(2)........... 9,916.5 144,464.4 1.0 $1,021 -0.6 Alabama.................... 125.5 1,941.1 0.8 859 -1.3 Alaska..................... 22.2 335.4 -0.7 1,025 -2.8 Arizona.................... 158.6 2,760.1 2.4 948 -0.2 Arkansas................... 89.7 1,213.0 0.6 788 -0.6 California................. 1,534.7 17,153.4 1.7 1,215 0.5 Colorado................... 200.0 2,625.9 1.9 1,067 0.5 Connecticut................ 119.2 1,676.3 0.1 1,179 -2.2 Delaware................... 32.3 443.0 0.4 1,026 0.4 District of Columbia....... 40.4 764.7 0.7 1,759 1.3 Florida.................... 677.2 8,305.8 -0.2 896 -1.1 Georgia.................... 276.0 4,343.5 1.3 961 -0.9 Hawaii..................... 41.9 652.5 0.4 953 -0.3 Idaho...................... 61.7 722.3 2.7 778 -0.5 Illinois................... 367.3 5,969.6 0.5 1,057 -0.3 Indiana.................... 164.6 3,044.0 0.6 861 -0.6 Iowa....................... 102.2 1,546.1 -0.2 855 -2.2 Kansas..................... 90.4 1,376.4 -0.1 839 -2.1 Kentucky................... 121.9 1,890.4 0.5 837 -2.4 Louisiana.................. 131.9 1,904.3 -0.1 869 -1.7 Maine...................... 54.7 621.9 0.7 821 -0.5 Maryland................... 170.1 2,661.8 0.5 1,105 -1.7 Massachusetts.............. 255.0 3,568.0 0.9 1,265 -0.9 Michigan................... 245.2 4,334.3 0.9 964 -1.1 Minnesota.................. 171.2 2,883.0 1.1 1,030 -2.0 Mississippi................ 73.4 1,129.1 -0.1 729 -1.4 Missouri................... 209.3 2,805.8 0.9 878 -1.2 Montana.................... 49.1 468.6 0.9 793 0.1 Nebraska................... 73.5 973.3 -0.2 850 -0.8 Nevada..................... 81.3 1,337.7 2.9 914 -3.8 New Hampshire.............. 52.5 659.1 0.6 1,022 -0.4 New Jersey................. 270.6 4,043.6 1.1 1,156 -1.5 New Mexico................. 58.2 816.0 0.3 823 -0.8 New York................... 650.3 9,329.8 1.2 1,219 -0.2 North Carolina............. 274.0 4,348.0 1.3 904 -0.7 North Dakota............... 32.0 419.2 -1.0 953 -1.2 Ohio....................... 297.0 5,383.6 0.6 920 -0.8 Oklahoma................... 111.0 1,593.3 0.7 843 -1.2 Oregon..................... 152.1 1,905.3 1.8 969 -0.1 Pennsylvania............... 358.1 5,836.5 1.0 1,002 -1.1 Rhode Island............... 37.5 484.5 0.8 973 -1.8 South Carolina............. 129.5 2,027.2 0.8 828 -0.5 South Dakota............... 33.4 426.2 0.4 803 -0.7 Tennessee.................. 158.2 2,953.3 1.1 903 -1.2 Texas...................... 675.5 12,008.9 1.4 1,032 -1.0 Utah....................... 99.8 1,444.1 2.6 879 -0.2 Vermont.................... 25.6 310.3 0.1 869 -1.4 Virginia................... 272.2 3,843.6 1.0 1,053 -0.8 Washington................. 238.6 3,343.4 2.0 1,208 1.7 West Virginia.............. 50.6 694.0 0.2 826 1.1 Wisconsin.................. 173.4 2,866.9 0.5 876 -1.0 Wyoming.................... 26.3 276.2 0.3 868 0.3 Puerto Rico................ 46.3 862.8 -3.1 509 -2.7 Virgin Islands............. 3.4 36.9 -1.1 763 -1.9 (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.