An official website of the United States government
For release 10:00 a.m. (EDT), Wednesday, May 23, 2018 USDL-18-0868 Technical Information: (202) 691-6567 * QCEWInfo@bls.gov * www.bls.gov/cew Media Contact: (202) 691-5902 * PressOffice@bls.gov COUNTY EMPLOYMENT AND WAGES Fourth Quarter 2017 From December 2016 to December 2017, employment increased in 316 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 11.5 percent over the year, above the national job growth rate of 1.5 percent. Within Midland, the largest employment increase occurred in natural resources and mining, which gained 5,247 jobs over the year (27.1 percent). Shawnee, Kan., and Caddo, La., had the largest over-the- year percentage decreases in employment among the largest counties in the U.S., with losses of 1.8 percent each. Within Shawnee, professional and business services had the largest decrease in employment, with a loss of 1,173 jobs (-8.0 percent). Within Caddo, trade, transportation, and utilities had the largest decrease in employment, with a loss of 769 jobs (-3.3 percent). The U.S. average weekly wage increased 3.9 percent over the year, growing to $1,109 in the fourth quarter of 2017. San Mateo, Calif., and Ada, Idaho, had the largest over-the-year percentage increases in average weekly wages, with gains of 11.5 percent each. Within San Mateo, an average weekly wage gain of $1,191 (23.1 percent) in information made the largest contribution to the county’s increase in average weekly wages. Within Ada, an average weekly wage gain of $1,031 (51.6 percent) in manufacturing made the largest contribution to the county’s increase in average weekly wages. Clayton, Ga., had the largest over-the-year percentage decrease in average weekly wages with a loss of 6.7 percent. Within Clayton, trade, transportation, and utilities had the largest impact on the county’s average weekly wage change with a decrease of $182 (-12.9 percent) over the year. ---------------------------------------------------------------------------------------------------------- | | | QCEW Publication Acceleration and Conversion to Two Data Releases | | | | The QCEW publication process is accelerating for a more timely release. Beginning with this release, | | QCEW data will be published in two parts. The current County Employment and Wages news release | | will be accelerated and published first. The full QCEW data release will occur on Thursday, June 7, | | 2018, accompanied by a data release notice. | | | ---------------------------------------------------------------------------------------------------------- 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 5 months following the end of each quarter. Large County Employment In December 2017, national employment was 145.9 million (as measured by the QCEW program). Over the year, employment increased 1.5 percent, or 2.1 million. In December 2017, the 346 U.S. counties with 75,000 or more jobs accounted for 73.0 percent of total U.S. employment and 78.3 percent of total wages. These 346 counties had a net job growth of 1.6 million over the year, accounting for 74.4 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 231,600 jobs, which was 10.9 percent of the overall job increase for the U.S. (See table A.) Employment declined in 25 of the largest counties from December 2016 to December 2017. Shawnee, Kan., and Caddo, La., had the largest over-the-year percentage decreases in employment (-1.8 percent each), followed by Kanawha, W. Va.; Potter, Texas; and Jefferson, La. (See table 1.) Table A. Large counties ranked by December 2017 employment, December 2016-17 employment increase, and December 2016-17 percent increase in employment -------------------------------------------------------------------------------------------------------- Employment in large counties -------------------------------------------------------------------------------------------------------- December 2017 employment | Increase in employment, | Percent increase in employment, (thousands) | December 2016-17 | December 2016-17 | (thousands) | -------------------------------------------------------------------------------------------------------- | | United States 145,921.1| United States 2,123.0| United States 1.5 -------------------------------------------------------------------------------------------------------- | | Los Angeles, Calif. 4,494.5| Los Angeles, Calif. 71.2| Midland, Texas 11.5 Cook, Ill. 2,604.2| Maricopa, Ariz. 58.5| Utah, Utah 6.0 New York, N.Y. 2,516.0| King, Wash. 38.5| Montgomery, Texas 5.9 Harris, Texas 2,293.3| New York, N.Y. 34.2| Calcasieu, La. 5.8 Maricopa, Ariz. 1,989.4| Kings, N.Y. 29.2| Elkhart, Ind. 5.4 Dallas, Texas 1,712.8| Clark, Nev. 28.7| Weld, Colo. 5.2 Orange, Calif. 1,621.4| Orange, Fla. 28.1| Rutherford, Tenn. 5.2 San Diego, Calif. 1,462.0| Dallas, Texas 28.1| Adams, Colo. 5.1 King, Wash. 1,377.5| Orange, Calif. 27.3| Clark, Wash. 4.8 Miami-Dade, Fla. 1,148.3| Santa Clara, Calif. 26.2| Yakima, Wash. 4.8 -------------------------------------------------------------------------------------------------------- Large County Average Weekly Wages Average weekly wages for the nation increased to $1,109, a 3.9 percent increase, during the year ending in the fourth quarter of 2017. Among the 346 largest counties, 339 had over-the-year increases in average weekly wages. San Mateo, Calif., and Ada, Idaho, had the largest percentage wage increases among the largest U.S. counties (11.5 percent each). (See table B.) Of the 346 largest counties, 7 experienced an over-the-year decrease in average weekly wages. Clayton, Ga., had the largest percentage decrease in average weekly wages (-6.7 percent), followed by Champaign, Ill.; Benton, Ark.; Wyandotte, Kan.; and Rockland, N.Y. (See table 1.) Table B. Large counties ranked by fourth quarter 2017 average weekly wages, fourth quarter 2016-17 increase in average weekly wages, and fourth quarter 2016-17 percent increase in average weekly wages -------------------------------------------------------------------------------------------------------- Average weekly wage in large counties -------------------------------------------------------------------------------------------------------- Average weekly wage, | Increase in average weekly | Percent increase in average fourth quarter 2017 | wage, fourth quarter 2016-17 | weekly wage, fourth | | quarter 2016-17 -------------------------------------------------------------------------------------------------------- | | United States $1,109| United States $42| United States 3.9 -------------------------------------------------------------------------------------------------------- | | Santa Clara, Calif. $2,576| San Mateo, Calif. $241| San Mateo, Calif. 11.5 New York, N.Y. 2,439| New York, N.Y. 229| Ada, Idaho 11.5 San Mateo, Calif. 2,341| Santa Clara, Calif. 211| New York, N.Y. 10.4 San Francisco, Calif. 2,232| San Francisco, Calif. 154| Douglas, Colo. 9.0 Suffolk, Mass. 1,986| Douglas, Colo. 108| Santa Clara, Calif. 8.9 Washington, D.C. 1,812| Ada, Idaho 108| Washington, Ore. 8.1 Arlington, Va. 1,727| King, Wash. 103| San Francisco, Calif. 7.4 Fairfield, Conn. 1,688| Suffolk, Mass. 101| Elkhart, Ind. 7.1 Fairfax, Va. 1,646| Washington, Ore. 98| King, Wash. 7.0 Middlesex, Mass. 1,613| Los Angeles, Calif. 81| Weld, Colo. 6.9 -------------------------------------------------------------------------------------------------------- Ten Largest U.S. Counties All of the 10 largest counties had over-the-year percentage increases in employment in December 2017. Maricopa, Ariz., had the largest gain (3.0 percent). Within Maricopa, construction had the largest over- the-year employment level increase, with a gain of 10,168 jobs, or 9.7 percent. Cook, Ill., had the smallest percentage increase in employment among the 10 largest counties (0.6 percent). Within Cook, education and health services had the largest over-the-year employment level increase, with a gain of 6,515 jobs, or 1.5 percent. (See table 2.) Average weekly wages increased over the year in all of the 10 largest U.S. counties. New York, N.Y. experienced the largest percentage gain in average weekly wages (10.4 percent). Within New York, financial activities had the largest impact on the county’s average weekly wage gain. Within financial activities, average weekly wages increased by $1,032, or 22.4 percent, over the year. Harris, Texas, had the smallest percentage gain in average weekly wages among the 10 largest counties (2.4 percent). Within Harris, trade, transportation, and utilities had the largest impact on the county’s average weekly wage growth with an increase of $49 (4.3 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. December 2017 employment and fourth quarter 2017 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 10.0 million employer reports cover 145.9 million full- and part-time workers. The full set of data for the fourth quarter of 2017 will be available on June 7, 2018, 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 full data update for fourth quarter 2017 is scheduled to be released on Thursday, June 7, 2018. The County Employment and Wages release for first quarter 2018 is scheduled to be released on Wednesday, August 22, 2018.
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 5 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, fourth quarter 2017 Employment Average weekly wage(2) Establishments, County(1) fourth quarter Percent Ranking Percent Ranking 2017 December change, by Fourth change, by (thousands) 2017 December percent quarter fourth percent (thousands) 2016-17(3) change 2017 quarter change 2016-17(3) United States(4)......... 9,969.4 145,921.1 1.5 - $1,109 3.9 - Jefferson, AL............ 18.8 350.5 1.8 102 1,083 3.8 98 Madison, AL.............. 9.7 198.9 1.9 96 1,137 3.5 122 Mobile, AL............... 10.2 171.9 1.0 196 975 4.1 78 Montgomery, AL........... 6.5 132.3 -0.1 322 939 -0.4 340 Shelby, AL............... 5.9 85.3 0.7 240 1,031 2.6 213 Tuscaloosa, AL........... 4.6 93.9 2.2 81 913 4.6 47 Anchorage, AK............ 8.3 147.3 -1.1 341 1,104 1.9 281 Maricopa, AZ............. 97.9 1,989.4 3.0 32 1,024 2.9 180 Pima, AZ................. 18.7 372.3 1.0 196 892 3.8 98 Benton, AR............... 6.5 119.7 1.3 158 1,008 -1.4 344 Pulaski, AR.............. 14.4 253.2 0.9 215 971 2.1 264 Washington, AR........... 6.0 107.4 2.6 56 1,002 5.5 18 Alameda, CA.............. 64.0 785.6 3.0 32 1,457 5.4 22 Butte, CA................ 8.6 83.1 1.3 158 826 4.8 38 Contra Costa, CA......... 32.6 369.9 0.7 240 1,344 4.0 85 Fresno, CA............... 35.7 380.2 1.8 102 888 3.6 117 Kern, CA................. 19.2 313.6 1.1 181 888 2.5 227 Los Angeles, CA.......... 494.2 4,494.5 1.6 121 1,343 6.4 11 Marin, CA................ 12.6 116.8 1.5 135 1,400 1.8 288 Merced, CA............... 6.7 79.4 3.8 16 816 1.0 317 Monterey, CA............. 13.8 175.0 0.7 240 951 4.5 52 Napa, CA................. 5.9 74.4 1.4 144 1,119 5.6 17 Orange, CA............... 121.9 1,621.4 1.7 111 1,234 2.8 188 Placer, CA............... 13.1 164.6 4.3 12 1,107 3.1 163 Riverside, CA............ 64.7 732.3 2.8 48 873 4.7 44 Sacramento, CA........... 58.6 656.4 2.4 69 1,180 4.5 52 San Bernardino, CA....... 59.4 754.0 3.3 23 906 2.0 270 San Diego, CA............ 111.9 1,462.0 1.8 102 1,221 4.3 67 San Francisco, CA........ 61.0 730.9 2.9 38 2,232 7.4 7 San Joaquin, CA.......... 17.9 251.9 2.4 69 923 3.8 98 San Luis Obispo, CA...... 10.5 115.9 1.6 121 929 4.6 47 San Mateo, CA............ 28.5 407.5 1.8 102 2,341 11.5 1 Santa Barbara, CA........ 15.6 195.4 1.6 121 1,066 3.9 92 Santa Clara, CA.......... 73.3 1,093.4 2.5 62 2,576 8.9 5 Santa Cruz, CA........... 9.6 100.4 1.1 181 970 4.1 78 Solano, CA............... 11.6 140.8 1.3 158 1,115 4.1 78 Sonoma, CA............... 20.2 208.3 2.0 90 1,070 4.8 38 Stanislaus, CA........... 15.8 187.1 3.0 32 915 3.5 122 Tulare, CA............... 10.5 159.3 0.7 240 812 4.9 35 Ventura, CA.............. 27.4 326.3 0.8 229 1,076 3.0 171 Yolo, CA................. 6.8 101.6 2.6 56 1,151 3.9 92 Adams, CO................ 11.0 211.7 5.1 8 1,075 5.4 22 Arapahoe, CO............. 22.0 331.2 1.8 102 1,268 3.4 134 Boulder, CO.............. 15.3 183.1 2.4 69 1,277 3.5 122 Denver, CO............... 32.2 514.2 2.6 56 1,334 3.7 107 Douglas, CO.............. 12.0 123.6 2.9 38 1,314 9.0 4 El Paso, CO.............. 19.8 274.4 2.5 62 967 2.8 188 Jefferson, CO............ 20.2 236.4 2.2 81 1,112 2.4 241 Larimer, CO.............. 12.2 159.0 3.0 32 1,011 3.4 134 Weld, CO................. 7.4 106.8 5.2 6 962 6.9 10 Fairfield, CT............ 35.6 424.4 -0.4 328 1,688 0.7 325 Hartford, CT............. 28.1 514.6 0.4 281 1,296 2.6 213 New Haven, CT............ 24.3 369.7 0.5 275 1,121 2.5 227 New London, CT........... 7.6 124.5 0.7 240 1,051 2.7 200 New Castle, DE........... 19.7 293.3 0.5 275 1,195 2.5 227 Sussex, DE............... 6.8 77.0 2.7 52 817 3.2 151 Washington, DC........... 41.2 769.0 0.9 215 1,812 2.7 200 Alachua, FL.............. 7.2 131.0 1.0 196 912 5.2 28 Bay, FL.................. 5.6 77.1 1.4 144 795 1.8 288 Brevard, FL.............. 15.9 211.6 2.5 62 972 3.5 122 Broward, FL.............. 69.7 814.0 1.2 167 1,041 3.7 107 Collier, FL.............. 14.1 150.4 1.3 158 966 4.4 61 Duval, FL................ 29.5 516.7 3.2 24 1,030 2.9 180 Escambia, FL............. 8.1 135.8 2.8 48 868 4.8 38 Hillsborough, FL......... 42.5 692.4 0.7 240 1,049 4.0 85 Lake, FL................. 8.2 98.9 2.3 77 740 2.5 227 Lee, FL.................. 22.1 265.5 2.9 38 864 1.9 281 Leon, FL................. 8.7 151.1 1.0 196 894 3.8 98 Manatee, FL.............. 11.0 125.7 1.7 111 819 0.9 322 Marion, FL............... 8.4 103.2 1.2 167 756 0.7 325 Miami-Dade, FL........... 98.9 1,148.3 0.8 229 1,065 3.8 98 Okaloosa, FL............. 6.4 83.0 1.4 144 875 1.3 308 Orange, FL............... 42.4 845.4 3.4 22 969 2.5 227 Osceola, FL.............. 7.0 94.4 4.4 11 740 2.5 227 Palm Beach, FL........... 56.7 611.5 1.0 196 1,103 4.5 52 Pasco, FL................ 10.9 119.4 2.1 87 760 3.0 171 Pinellas, FL............. 33.1 432.8 1.6 121 988 2.9 180 Polk, FL................. 13.3 220.7 2.9 38 821 2.6 213 Sarasota, FL............. 16.0 171.2 1.0 196 941 4.8 38 Seminole, FL............. 15.0 194.6 3.5 21 932 3.1 163 Volusia, FL.............. 14.4 173.1 1.9 96 784 3.2 151 Bibb, GA................. 4.2 83.6 -1.0 340 840 2.8 188 Chatham, GA.............. 8.0 154.2 2.2 81 906 2.3 249 Clayton, GA.............. 4.0 126.7 1.0 196 988 -6.7 346 Cobb, GA................. 21.7 363.7 1.9 96 1,125 3.5 122 DeKalb, GA............... 17.8 301.5 0.6 255 1,086 2.5 227 Fulton, GA............... 43.1 870.2 2.4 69 1,449 5.0 33 Gwinnett, GA............. 24.6 359.9 2.7 52 1,048 2.3 249 Hall, GA................. 4.4 86.8 1.8 102 979 5.8 16 Muscogee, GA............. 4.5 94.5 0.9 215 875 4.0 85 Richmond, GA............. 4.4 105.9 0.6 255 887 2.3 249 Honolulu, HI............. 26.2 480.3 0.5 275 1,030 3.4 134 Maui + Kalawao, HI....... 6.2 78.3 0.8 229 863 1.4 304 Ada, ID.................. 16.2 238.3 3.6 20 1,044 11.5 1 Champaign, IL............ 4.0 90.9 1.0 196 933 -1.6 345 Cook, IL................. 136.9 2,604.2 0.6 255 1,283 2.6 213 DuPage, IL............... 34.3 622.3 0.4 281 1,239 2.4 241 Kane, IL................. 12.4 211.2 0.1 309 1,005 4.4 61 Lake, IL................. 20.0 338.1 1.2 167 1,411 1.0 317 McHenry, IL.............. 7.7 98.6 1.4 144 917 3.9 92 McLean, IL............... 3.4 83.3 -0.9 336 944 2.3 249 Madison, IL.............. 5.4 101.7 1.8 102 857 2.0 270 Peoria, IL............... 4.2 104.2 0.9 215 1,088 1.6 300 St. Clair, IL............ 5.0 94.8 -0.4 328 856 2.4 241 Sangamon, IL............. 4.7 129.4 -0.3 326 1,065 3.3 141 Will, IL................. 14.5 245.6 1.7 111 954 2.1 264 Winnebago, IL............ 6.0 128.1 0.1 309 904 2.8 188 Allen, IN................ 8.8 187.6 1.1 181 885 4.6 47 Elkhart, IN.............. 4.7 137.8 5.4 5 983 7.1 8 Hamilton, IN............. 9.4 140.9 1.5 135 1,036 1.2 312 Lake, IN................. 10.4 189.2 0.3 295 929 2.0 270 Marion, IN............... 24.0 601.9 0.8 229 1,086 3.2 151 St. Joseph, IN........... 5.8 123.9 -0.3 326 879 1.4 304 Tippecanoe, IN........... 3.4 84.7 0.8 229 920 3.3 141 Vanderburgh, IN.......... 4.7 110.2 1.7 111 903 3.7 107 Johnson, IA.............. 4.2 85.3 1.1 181 971 2.2 260 Linn, IA................. 6.9 131.3 0.6 255 1,119 5.9 15 Polk, IA................. 17.6 300.9 1.2 167 1,117 2.7 200 Scott, IA................ 5.6 91.4 -0.2 325 899 1.7 297 Johnson, KS.............. 23.5 349.9 1.8 102 1,089 2.3 249 Sedgwick, KS............. 12.6 250.2 0.0 317 917 1.4 304 Shawnee, KS.............. 5.1 96.7 -1.8 345 860 2.0 270 Wyandotte, KS............ 3.5 93.7 3.2 24 1,027 -1.1 343 Boone, KY................ 4.4 91.3 2.4 69 914 1.8 288 Fayette, KY.............. 10.9 199.8 1.3 158 970 0.1 338 Jefferson, KY............ 25.0 472.3 0.4 281 1,048 2.3 249 Caddo, LA................ 7.3 112.8 -1.8 345 872 1.9 281 Calcasieu, LA............ 5.4 100.3 5.8 4 971 5.1 30 East Baton Rouge, LA..... 15.8 266.2 -0.1 322 1,010 0.5 331 Jefferson, LA............ 14.1 191.9 -1.2 342 976 2.3 249 Lafayette, LA............ 9.7 129.7 -0.4 328 944 3.5 122 Orleans, LA.............. 12.9 196.1 1.0 196 1,036 3.8 98 St. Tammany, LA.......... 8.4 89.7 0.1 309 916 1.0 317 Cumberland, ME........... 14.0 185.7 2.8 48 1,007 2.7 200 Anne Arundel, MD......... 15.3 273.8 0.6 255 1,177 1.3 308 Baltimore, MD............ 21.3 381.7 -0.6 332 1,118 3.1 163 Frederick, MD............ 6.5 102.7 2.0 90 989 2.0 270 Harford, MD.............. 5.8 95.9 2.0 90 1,009 2.6 213 Howard, MD............... 10.0 171.4 0.4 281 1,331 2.5 227 Montgomery, MD........... 33.0 474.5 0.0 317 1,482 4.2 71 Prince George's, MD...... 16.0 323.8 0.4 281 1,123 3.0 171 Baltimore City, MD....... 13.7 344.9 1.4 144 1,365 4.5 52 Barnstable, MA........... 9.5 91.8 0.8 229 962 2.3 249 Bristol, MA.............. 17.7 229.6 0.6 255 979 4.0 85 Essex, MA................ 25.9 329.2 1.1 181 1,161 3.3 141 Hampden, MA.............. 18.3 211.7 1.6 121 973 2.1 264 Middlesex, MA............ 55.3 915.0 1.6 121 1,613 5.1 30 Norfolk, MA.............. 25.4 356.9 0.6 255 1,358 4.1 78 Plymouth, MA............. 16.0 195.6 2.0 90 1,033 1.8 288 Suffolk, MA.............. 30.0 679.7 1.0 196 1,986 5.4 22 Worcester, MA............ 25.5 353.8 1.4 144 1,078 2.9 180 Genesee, MI.............. 6.8 136.0 0.4 281 899 0.9 322 Ingham, MI............... 6.0 153.1 0.6 255 1,041 1.1 314 Kalamazoo, MI............ 5.0 119.8 1.2 167 1,002 2.0 270 Kent, MI................. 14.5 402.5 1.2 167 956 1.9 281 Macomb, MI............... 17.6 328.8 0.6 255 1,091 2.6 213 Oakland, MI.............. 39.5 735.1 1.0 196 1,253 3.6 117 Ottawa, MI............... 5.7 124.3 1.3 158 976 2.7 200 Saginaw, MI.............. 3.9 84.6 -0.9 336 897 3.7 107 Washtenaw, MI............ 8.3 215.6 1.7 111 1,134 3.2 151 Wayne, MI................ 30.9 725.3 0.2 302 1,212 2.4 241 Anoka, MN................ 7.3 123.5 1.5 135 1,034 4.1 78 Dakota, MN............... 10.1 188.9 0.7 240 1,044 2.9 180 Hennepin, MN............. 40.4 926.8 1.1 181 1,335 3.0 171 Olmsted, MN.............. 3.5 97.8 1.6 121 1,118 4.0 85 Ramsey, MN............... 13.6 334.5 1.3 158 1,202 3.8 98 St. Louis, MN............ 5.3 98.0 0.7 240 895 3.2 151 Stearns, MN.............. 4.4 87.1 0.6 255 907 4.3 67 Washington, MN........... 5.6 86.3 4.2 13 943 5.5 18 Harrison, MS............. 4.6 85.4 0.1 309 749 2.5 227 Hinds, MS................ 5.8 122.0 -0.9 336 884 2.1 264 Boone, MO................ 5.1 94.5 1.1 181 846 0.5 331 Clay, MO................. 5.8 106.5 2.6 56 948 1.4 304 Greene, MO............... 9.1 167.2 1.2 167 841 4.2 71 Jackson, MO.............. 22.5 370.3 0.7 240 1,103 3.4 134 St. Charles, MO.......... 9.7 147.9 1.4 144 847 1.0 317 St. Louis, MO............ 39.9 613.9 0.9 215 1,168 3.6 117 St. Louis City, MO....... 14.8 226.4 0.9 215 1,145 1.7 297 Yellowstone, MT.......... 6.9 81.4 0.3 295 924 0.5 331 Douglas, NE.............. 19.5 342.7 0.7 240 1,011 3.0 171 Lancaster, NE............ 10.6 170.3 1.5 135 882 2.9 180 Clark, NV................ 54.9 981.8 3.0 32 938 3.1 163 Washoe, NV............... 14.8 221.8 3.7 17 969 2.5 227 Hillsborough, NH......... 12.2 206.0 0.4 281 1,240 3.2 151 Merrimack, NH............ 5.2 78.1 0.8 229 1,050 3.3 141 Rockingham, NH........... 11.0 150.0 1.0 196 1,116 4.9 35 Atlantic, NJ............. 6.6 123.2 0.7 240 914 2.6 213 Bergen, NJ............... 33.3 458.4 0.9 215 1,298 1.2 312 Burlington, NJ........... 11.1 209.4 0.6 255 1,105 1.7 297 Camden, NJ............... 12.2 209.4 1.9 96 1,096 2.6 213 Essex, NJ................ 20.7 349.5 1.5 135 1,318 1.8 288 Gloucester, NJ........... 6.4 112.6 2.7 52 926 1.1 314 Hudson, NJ............... 15.3 267.9 2.5 62 1,408 3.1 163 Mercer, NJ............... 11.3 253.7 1.3 158 1,355 -0.4 340 Middlesex, NJ............ 22.5 440.4 1.9 96 1,259 1.9 281 Monmouth, NJ............. 20.2 262.9 0.6 255 1,097 2.8 188 Morris, NJ............... 17.2 295.6 1.4 144 1,582 3.9 92 Ocean, NJ................ 13.4 166.9 2.4 69 883 1.3 308 Passaic, NJ.............. 12.8 170.1 0.2 302 1,066 1.9 281 Somerset, NJ............. 10.3 190.4 0.3 295 1,568 0.3 336 Union, NJ................ 14.5 224.7 0.7 240 1,385 2.4 241 Bernalillo, NM........... 18.4 328.5 0.2 302 912 1.8 288 Albany, NY............... 10.4 236.9 -0.7 335 1,135 4.2 71 Bronx, NY................ 18.8 306.8 0.9 215 1,030 2.7 200 Broome, NY............... 4.5 87.7 -0.4 328 838 4.8 38 Dutchess, NY............. 8.4 114.9 1.0 196 1,032 2.1 264 Erie, NY................. 24.9 477.5 0.6 255 965 2.7 200 Kings, NY................ 63.0 736.4 4.1 14 920 1.8 288 Monroe, NY............... 19.0 391.5 0.6 255 1,000 2.8 188 Nassau, NY............... 54.4 646.0 1.1 181 1,242 2.0 270 New York, NY............. 128.4 2,516.0 1.4 144 2,439 10.4 3 Oneida, NY............... 5.3 106.2 0.2 302 838 3.6 117 Onondaga, NY............. 12.9 248.6 0.4 281 995 2.5 227 Orange, NY............... 10.5 146.4 1.5 135 921 3.7 107 Queens, NY............... 53.3 676.3 2.0 90 1,041 2.3 249 Richmond, NY............. 9.8 118.8 1.2 167 984 4.0 85 Rockland, NY............. 10.9 126.3 1.2 167 1,030 -0.8 342 Saratoga, NY............. 6.0 87.8 3.7 17 978 3.5 122 Suffolk, NY.............. 53.2 664.9 0.1 309 1,217 6.0 13 Westchester, NY.......... 36.4 433.5 0.6 255 1,468 4.9 35 Buncombe, NC............. 9.2 132.2 1.2 167 851 1.8 288 Catawba, NC.............. 4.4 88.7 1.8 102 841 2.6 213 Cumberland, NC........... 6.2 120.6 0.4 281 829 3.5 122 Durham, NC............... 8.4 201.9 1.0 196 1,286 2.6 213 Forsyth, NC.............. 9.1 186.8 0.5 275 1,005 3.3 141 Guilford, NC............. 14.3 282.1 -0.6 332 946 5.3 26 Mecklenburg, NC.......... 37.7 693.5 2.8 48 1,232 3.2 151 New Hanover, NC.......... 8.1 111.6 1.6 121 875 1.0 317 Wake, NC................. 34.4 553.5 2.9 38 1,117 2.5 227 Cass, ND................. 7.2 118.1 0.2 302 990 3.0 171 Butler, OH............... 7.8 157.3 2.0 90 933 0.2 337 Cuyahoga, OH............. 36.0 724.9 0.2 302 1,126 3.5 122 Delaware, OH............. 5.4 88.4 1.1 181 1,021 2.0 270 Franklin, OH............. 32.3 766.4 1.2 167 1,051 2.2 260 Hamilton, OH............. 24.0 519.7 0.7 240 1,157 3.2 151 Lake, OH................. 6.3 95.3 0.4 281 893 3.4 134 Lorain, OH............... 6.2 98.4 0.6 255 830 1.1 314 Lucas, OH................ 10.2 209.9 -0.1 322 921 2.0 270 Mahoning, OH............. 5.9 98.0 0.4 281 775 3.3 141 Montgomery, OH........... 11.9 257.7 1.1 181 920 2.8 188 Stark, OH................ 8.6 161.1 1.1 181 834 5.3 26 Summit, OH............... 14.4 268.0 0.0 317 962 2.2 260 Warren, OH............... 4.9 91.7 2.5 62 954 0.7 325 Cleveland, OK............ 5.9 82.0 1.7 111 769 0.5 331 Oklahoma, OK............. 28.4 455.9 1.4 144 1,016 3.8 98 Tulsa, OK................ 22.7 357.7 0.8 229 968 2.7 200 Clackamas, OR............ 15.1 164.5 2.5 62 1,023 3.8 98 Deschutes, OR............ 8.6 80.9 3.7 17 874 4.3 67 Jackson, OR.............. 7.5 89.3 2.4 69 833 4.0 85 Lane, OR................. 12.2 156.0 1.4 144 862 2.0 270 Marion, OR............... 10.9 152.1 1.6 121 901 4.6 47 Multnomah, OR............ 35.2 510.5 1.9 96 1,146 4.2 71 Washington, OR........... 19.5 294.7 2.4 69 1,307 8.1 6 Allegheny, PA............ 35.6 702.2 1.1 181 1,172 3.1 163 Berks, PA................ 9.0 174.3 1.1 181 971 3.4 134 Bucks, PA................ 20.1 266.1 1.3 158 1,033 1.5 301 Butler, PA............... 5.1 85.9 0.6 255 1,008 0.1 338 Chester, PA.............. 15.6 253.7 1.7 111 1,339 2.5 227 Cumberland, PA........... 6.5 135.4 0.3 295 972 5.2 28 Dauphin, PA.............. 7.6 183.3 0.9 215 1,069 3.6 117 Delaware, PA............. 14.2 227.2 1.0 196 1,146 3.3 141 Erie, PA................. 7.0 121.6 0.1 309 812 0.5 331 Lackawanna, PA........... 5.7 99.5 0.6 255 831 4.5 52 Lancaster, PA............ 13.5 241.0 1.6 121 902 2.7 200 Lehigh, PA............... 8.9 192.1 1.4 144 1,084 3.1 163 Luzerne, PA.............. 7.4 147.3 1.2 167 835 2.3 249 Montgomery, PA........... 27.8 500.4 1.0 196 1,320 2.3 249 Northampton, PA.......... 6.8 116.4 0.8 229 921 2.8 188 Philadelphia, PA......... 34.8 684.6 1.4 144 1,290 4.4 61 Washington, PA........... 5.5 87.7 2.2 81 1,181 5.1 30 Westmoreland, PA......... 9.3 134.4 0.7 240 886 6.1 12 York, PA................. 9.2 180.9 0.8 229 955 5.4 22 Providence, RI........... 18.5 290.7 0.9 215 1,116 3.2 151 Charleston, SC........... 15.4 248.9 1.6 121 979 4.7 44 Greenville, SC........... 14.1 272.9 2.3 77 953 2.8 188 Horry, SC................ 8.9 122.8 2.7 52 674 2.7 200 Lexington, SC............ 6.7 122.9 3.0 32 805 1.8 288 Richland, SC............. 10.3 221.0 0.2 302 914 3.3 141 Spartanburg, SC.......... 6.3 141.2 2.3 77 906 3.2 151 York, SC................. 5.8 94.8 3.2 24 872 2.6 213 Minnehaha, SD............ 7.3 126.8 0.9 215 949 3.0 171 Davidson, TN............. 22.8 492.5 2.6 56 1,197 2.7 200 Hamilton, TN............. 9.7 204.0 2.1 87 1,041 3.7 107 Knox, TN................. 12.3 240.7 0.4 281 979 2.1 264 Rutherford, TN........... 5.7 129.3 5.2 6 946 0.6 329 Shelby, TN............... 20.6 501.9 0.5 275 1,115 2.6 213 Williamson, TN........... 8.9 132.9 2.9 38 1,258 4.1 78 Bell, TX................. 5.5 119.5 0.6 255 925 5.5 18 Bexar, TX................ 41.5 865.4 1.0 196 980 2.7 200 Brazoria, TX............. 5.8 111.0 0.6 255 1,134 3.2 151 Brazos, TX............... 4.6 105.2 2.5 62 806 4.5 52 Cameron, TX.............. 6.5 140.5 0.5 275 652 2.2 260 Collin, TX............... 25.3 409.2 4.1 14 1,249 1.9 281 Dallas, TX............... 77.5 1,712.8 1.7 111 1,318 3.1 163 Denton, TX............... 15.2 244.7 2.9 38 1,008 4.6 47 El Paso, TX.............. 15.2 305.6 1.5 135 748 2.9 180 Fort Bend, TX............ 13.5 184.7 3.2 24 1,014 3.0 171 Galveston, TX............ 6.2 110.7 0.0 317 944 3.3 141 Harris, TX............... 115.3 2,293.3 1.1 181 1,352 2.4 241 Hidalgo, TX.............. 12.4 261.8 2.2 81 664 2.8 188 Jefferson, TX............ 5.9 121.3 -0.6 332 1,136 4.4 61 Lubbock, TX.............. 7.6 140.7 0.3 295 865 3.3 141 McLennan, TX............. 5.3 113.5 0.0 317 904 5.5 18 Midland, TX.............. 5.5 94.7 11.5 1 1,341 3.7 107 Montgomery, TX........... 11.4 183.5 5.9 3 1,073 4.4 61 Nueces, TX............... 8.3 163.5 0.8 229 930 3.7 107 Potter, TX............... 4.0 78.7 -1.3 343 907 4.1 78 Smith, TX................ 6.2 104.9 1.5 135 901 4.5 52 Tarrant, TX.............. 43.6 890.8 2.2 81 1,075 4.5 52 Travis, TX............... 41.0 737.7 2.9 38 1,278 2.4 241 Webb, TX................. 5.4 101.2 1.6 121 706 3.4 134 Williamson, TX........... 10.9 169.4 3.2 24 1,041 3.0 171 Davis, UT................ 8.6 126.4 2.3 77 897 4.2 71 Salt Lake, UT............ 45.7 701.6 3.1 31 1,056 2.7 200 Utah, UT................. 16.5 238.7 6.0 2 891 3.7 107 Weber, UT................ 6.1 106.8 3.2 24 808 2.4 241 Chittenden, VT........... 6.9 103.3 1.0 196 1,045 1.3 308 Arlington, VA............ 9.3 178.6 1.7 111 1,727 2.8 188 Chesterfield, VA......... 9.2 139.9 0.4 281 917 2.9 180 Fairfax, VA.............. 37.6 611.0 1.1 181 1,646 2.0 270 Henrico, VA.............. 11.8 196.5 1.4 144 1,043 4.3 67 Loudoun, VA.............. 12.5 165.9 2.1 87 1,270 2.6 213 Prince William, VA....... 9.4 129.7 1.5 135 949 1.5 301 Alexandria City, VA...... 6.4 93.9 -0.9 336 1,531 2.5 227 Chesapeake City, VA...... 6.1 101.2 0.4 281 844 4.2 71 Newport News City, VA.... 3.9 100.5 2.9 38 1,026 0.6 329 Norfolk City, VA......... 6.0 144.2 1.6 121 1,080 0.7 325 Richmond City, VA........ 7.8 155.2 0.1 309 1,180 3.5 122 Virginia Beach City, VA.. 12.3 177.8 0.3 295 861 3.7 107 Benton, WA............... 5.7 86.6 2.6 56 1,055 4.4 61 Clark, WA................ 14.7 158.9 4.8 9 1,022 3.9 92 King, WA................. 86.6 1,377.5 2.9 38 1,583 7.0 9 Kitsap, WA............... 6.6 88.4 1.7 111 1,009 4.8 38 Pierce, WA............... 21.8 307.3 1.6 121 976 4.7 44 Snohomish, WA............ 20.8 286.1 0.1 309 1,148 3.2 151 Spokane, WA.............. 15.6 220.2 1.2 167 921 4.5 52 Thurston, WA............. 8.3 114.8 3.2 24 969 5.0 33 Whatcom, WA.............. 7.3 89.7 1.2 167 903 6.0 13 Yakima, WA............... 7.7 107.0 4.8 9 772 4.2 71 Kanawha, WV.............. 5.7 99.3 -1.5 344 904 2.8 188 Brown, WI................ 7.0 157.3 0.7 240 989 3.9 92 Dane, WI................. 15.9 336.5 0.9 215 1,070 3.5 122 Milwaukee, WI............ 27.0 492.1 1.0 196 1,056 1.5 301 Outagamie, WI............ 5.4 108.6 0.9 215 943 2.6 213 Waukesha, WI............. 13.4 244.7 0.9 215 1,082 0.8 324 Winnebago, WI............ 3.8 94.5 0.3 295 1,039 3.5 122 San Juan, PR............. 10.4 252.6 -0.9 (5) 678 0.9 (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 73.0 percent of the total covered workers in the U.S.
Table 2. Covered establishments, employment, and wages in the 10 largest counties, fourth quarter 2017 Employment Average weekly wage(1) Establishments, fourth quarter County by NAICS supersector 2017 Percent Percent (thousands) December change, Fourth change, 2017 December quarter fourth (thousands) 2016-17(2) 2017 quarter 2016-17(2) United States(3) ............................ 9,969.4 145,921.1 1.5 $1,109 3.9 Private industry........................... 9,671.1 124,015.7 1.7 1,114 4.1 Natural resources and mining............. 137.4 1,801.6 3.7 1,135 4.8 Construction............................. 792.8 6,962.2 3.7 1,282 3.9 Manufacturing............................ 347.9 12,504.4 1.2 1,331 3.6 Trade, transportation, and utilities..... 1,919.8 28,219.9 0.9 911 3.5 Information.............................. 165.0 2,825.6 0.1 2,035 8.0 Financial activities..................... 879.4 8,150.9 1.2 1,826 6.8 Professional and business services....... 1,803.3 20,625.1 1.7 1,483 4.2 Education and health services............ 1,669.5 22,468.6 1.9 995 2.4 Leisure and hospitality.................. 845.8 15,681.4 1.7 481 4.1 Other services........................... 846.4 4,441.5 1.0 746 3.8 Government................................. 298.4 21,905.4 0.2 1,076 2.6 Los Angeles, CA.............................. 494.2 4,494.5 1.6 1,343 6.4 Private industry........................... 488.0 3,914.4 1.8 1,340 7.0 Natural resources and mining............. 0.5 8.0 3.2 1,176 0.1 Construction............................. 14.5 140.3 4.5 1,332 4.6 Manufacturing............................ 12.3 343.2 -2.9 1,481 6.7 Trade, transportation, and utilities..... 54.9 859.5 0.8 994 3.9 Information.............................. 10.6 223.0 0.9 2,722 14.0 Financial activities..................... 26.7 221.9 0.4 2,186 12.7 Professional and business services....... 49.6 618.3 1.8 1,794 8.5 Education and health services............ 232.6 789.6 2.5 947 2.3 Leisure and hospitality.................. 33.9 522.3 1.3 1,074 5.9 Other services........................... 26.8 148.8 -0.2 791 7.6 Government................................. 6.2 580.2 0.4 1,363 2.8 Cook, IL..................................... 136.9 2,604.2 0.6 1,283 2.6 Private industry........................... 135.7 2,310.8 0.7 1,291 2.8 Natural resources and mining............. 0.1 1.2 17.4 1,239 -5.6 Construction............................. 10.6 72.5 0.8 1,661 2.5 Manufacturing............................ 5.8 184.8 0.5 1,369 1.0 Trade, transportation, and utilities..... 27.5 488.8 -0.3 1,009 2.5 Information.............................. 2.3 53.5 -0.8 1,838 5.7 Financial activities..................... 13.7 196.8 1.3 2,380 4.6 Professional and business services....... 28.5 482.6 0.6 1,711 2.9 Education and health services............ 15.3 450.4 1.5 1,042 1.6 Leisure and hospitality.................. 13.6 278.9 0.8 553 4.3 Other services........................... 15.4 99.0 1.7 977 2.5 Government................................. 1.3 293.4 -0.4 1,223 0.5 New York, NY................................. 128.4 2,516.0 1.4 2,439 10.4 Private industry........................... 127.5 2,246.8 1.5 2,569 10.8 Natural resources and mining............. 0.0 0.2 3.6 1,861 -9.3 Construction............................. 2.3 42.5 4.7 2,356 0.5 Manufacturing............................ 2.0 25.5 -2.6 1,681 1.7 Trade, transportation, and utilities..... 19.5 265.0 -1.2 1,546 5.6 Information.............................. 5.0 169.7 3.7 2,761 2.9 Financial activities..................... 19.5 380.3 1.9 5,637 22.4 Professional and business services....... 27.2 588.1 2.4 2,753 5.4 Education and health services............ 10.2 351.6 0.6 1,456 5.1 Leisure and hospitality.................. 14.7 309.4 0.6 1,050 2.9 Other services........................... 20.4 105.5 0.7 1,251 4.9 Government................................. 0.8 269.2 0.1 1,362 2.3 Harris, TX................................... 115.3 2,293.3 1.1 1,352 2.4 Private industry........................... 114.8 2,014.1 1.2 1,377 2.5 Natural resources and mining............. 1.6 67.2 1.7 3,255 0.1 Construction............................. 7.4 156.8 0.7 1,481 0.5 Manufacturing............................ 4.8 171.0 2.4 1,672 1.3 Trade, transportation, and utilities..... 25.0 482.2 1.1 1,178 4.3 Information.............................. 1.2 26.4 -4.6 1,584 7.7 Financial activities..................... 12.2 126.5 1.4 1,815 1.9 Professional and business services....... 23.2 394.4 1.4 1,790 2.9 Education and health services............ 16.1 292.4 0.5 1,095 1.8 Leisure and hospitality.................. 10.1 228.2 1.1 490 4.3 Other services........................... 11.6 65.9 1.4 840 1.7 Government................................. 0.5 279.3 0.5 1,173 2.5 Maricopa, AZ................................. 97.9 1,989.4 3.0 1,024 2.9 Private industry........................... 97.2 1,776.0 3.4 1,025 2.9 Natural resources and mining............. 0.4 8.7 3.5 974 4.1 Construction............................. 7.0 114.5 9.7 1,185 6.2 Manufacturing............................ 3.1 121.1 3.9 1,432 4.0 Trade, transportation, and utilities..... 18.2 393.9 1.8 919 2.8 Information.............................. 1.5 34.5 0.7 1,420 3.2 Financial activities..................... 11.1 178.9 3.1 1,344 2.1 Professional and business services....... 20.8 339.9 2.4 1,133 2.1 Education and health services............ 11.0 302.5 2.9 1,017 1.7 Leisure and hospitality.................. 8.0 218.1 3.8 495 4.0 Other services........................... 6.3 50.9 -1.0 753 4.3 Government................................. 0.7 213.5 0.0 1,012 2.7 Dallas, TX................................... 77.5 1,712.8 1.7 1,318 3.1 Private industry........................... 76.9 1,538.5 1.8 1,330 3.2 Natural resources and mining............. 0.5 9.2 14.7 3,218 -20.0 Construction............................. 4.6 88.8 2.7 1,420 3.3 Manufacturing............................ 2.8 112.6 0.8 1,584 9.9 Trade, transportation, and utilities..... 15.9 363.6 2.3 1,077 2.1 Information.............................. 1.4 48.2 -2.2 1,852 1.9 Financial activities..................... 9.6 165.4 2.2 1,802 2.3 Professional and business services....... 17.6 344.8 1.1 1,628 3.1 Education and health services............ 9.6 199.5 2.1 1,206 4.3 Leisure and hospitality.................. 6.9 160.9 1.9 560 3.3 Other services........................... 6.9 43.1 0.6 876 9.0 Government................................. 0.6 174.4 0.2 1,211 2.2 Orange, CA................................... 121.9 1,621.4 1.7 1,234 2.8 Private industry........................... 120.4 1,475.6 2.0 1,235 2.8 Natural resources and mining............. 0.2 2.5 -7.4 987 1.9 Construction............................. 6.9 103.5 4.8 1,481 6.5 Manufacturing............................ 5.0 158.2 -1.2 1,506 0.5 Trade, transportation, and utilities..... 17.2 271.1 1.2 1,055 1.0 Information.............................. 1.4 26.9 0.5 2,104 2.2 Financial activities..................... 11.6 118.5 0.0 2,187 7.6 Professional and business services....... 21.0 305.4 0.7 1,436 2.7 Education and health services............ 34.1 213.9 3.1 1,024 2.8 Leisure and hospitality.................. 8.7 217.3 2.6 552 3.4 Other services........................... 6.9 45.7 -0.4 748 2.7 Government................................. 1.5 145.8 -0.9 1,224 3.0 San Diego, CA................................ 111.9 1,462.0 1.8 1,221 4.3 Private industry........................... 110.0 1,226.5 2.0 1,198 4.5 Natural resources and mining............. 0.6 8.5 5.9 834 12.4 Construction............................. 7.0 81.5 4.0 1,329 5.7 Manufacturing............................ 3.2 109.1 0.4 1,655 4.3 Trade, transportation, and utilities..... 14.4 237.0 0.8 960 5.7 Information.............................. 1.2 24.3 -1.4 2,077 13.8 Financial activities..................... 10.3 74.6 1.3 1,581 1.5 Professional and business services....... 18.6 237.1 2.4 1,791 4.5 Education and health services............ 32.0 201.6 2.0 1,029 2.9 Leisure and hospitality.................. 8.4 192.1 0.7 528 3.1 Other services........................... 7.4 50.3 -0.5 656 1.9 Government................................. 1.9 235.5 0.7 1,335 3.0 King, WA..................................... 86.6 1,377.5 2.9 1,583 7.0 Private industry........................... 86.0 1,209.4 3.4 1,610 7.1 Natural resources and mining............. 0.4 3.0 3.8 1,321 9.8 Construction............................. 6.7 70.9 4.2 1,455 5.1 Manufacturing............................ 2.5 101.4 -1.1 1,679 1.0 Trade, transportation, and utilities..... 14.3 275.3 5.7 1,714 15.0 Information.............................. 2.3 104.5 4.9 3,127 8.9 Financial activities..................... 6.7 68.4 2.9 1,843 3.7 Professional and business services....... 18.0 227.1 2.6 1,892 3.8 Education and health services............ 18.6 174.3 2.8 1,092 3.4 Leisure and hospitality.................. 7.3 139.1 3.1 608 7.4 Other services........................... 9.2 45.4 2.3 884 3.2 Government................................. 0.5 168.2 -0.4 1,384 4.5 Miami-Dade, FL............................... 98.9 1,148.3 0.8 1,065 3.8 Private industry........................... 98.6 1,007.9 0.9 1,053 3.8 Natural resources and mining............. 0.5 9.2 1.0 643 -4.5 Construction............................. 6.6 46.0 2.0 1,031 4.8 Manufacturing............................ 2.9 41.1 1.0 985 2.8 Trade, transportation, and utilities..... 25.4 293.2 1.1 936 2.5 Information.............................. 1.5 17.8 -1.4 1,768 5.7 Financial activities..................... 10.7 76.3 0.5 1,700 3.7 Professional and business services....... 22.2 160.9 0.9 1,379 5.3 Education and health services............ 10.7 181.7 1.9 1,034 0.9 Leisure and hospitality.................. 7.4 140.7 -0.8 665 9.9 Other services........................... 8.4 39.6 -0.7 674 6.8 Government................................. 0.3 140.3 0.3 1,151 3.3 (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, fourth quarter 2017 Employment Average weekly wage(1) Establishments, fourth quarter State 2017 Percent Percent (thousands) December change, Fourth change, 2017 December quarter fourth (thousands) 2016-17 2017 quarter 2016-17 United States(2)........... 9,969.4 145,921.1 1.5 $1,109 3.9 Alabama.................... 126.5 1,955.3 1.1 928 2.9 Alaska..................... 22.1 306.7 -1.2 1,052 1.5 Arizona.................... 160.5 2,834.7 2.6 978 3.5 Arkansas................... 90.3 1,217.2 1.0 848 2.5 California................. 1,550.5 17,293.0 2.1 1,346 5.7 Colorado................... 199.3 2,653.3 2.5 1,133 4.3 Connecticut................ 119.7 1,689.7 0.3 1,317 2.2 Delaware................... 31.8 444.9 0.6 1,081 2.6 District of Columbia....... 41.2 769.0 0.9 1,812 2.7 Florida.................... 685.4 8,712.0 2.0 975 3.4 Georgia.................... 276.2 4,425.0 1.8 1,027 3.4 Hawaii..................... 42.2 664.5 0.8 984 3.1 Idaho...................... 62.6 712.4 3.0 857 7.1 Illinois................... 367.4 6,001.1 0.8 1,151 2.6 Indiana.................... 165.0 3,057.8 1.1 915 3.6 Iowa....................... 102.5 1,549.7 0.4 938 3.0 Kansas..................... 88.9 1,390.3 0.4 894 1.9 Kentucky................... 121.8 1,903.8 0.5 892 2.1 Louisiana.................. 132.4 1,918.8 0.4 933 2.1 Maine...................... 54.6 610.3 1.2 884 3.4 Maryland................... 171.5 2,683.6 0.5 1,207 3.3 Massachusetts.............. 254.7 3,582.2 1.3 1,411 4.4 Michigan................... 245.4 4,321.8 0.9 1,062 3.4 Minnesota.................. 172.6 2,875.7 1.3 1,100 3.4 Mississippi................ 74.1 1,140.6 0.5 774 2.4 Missouri................... 209.7 2,809.5 1.0 945 2.9 Montana.................... 50.1 461.4 1.0 843 2.7 Nebraska................... 74.3 980.9 0.9 901 3.0 Nevada..................... 81.1 1,351.9 3.5 955 3.2 New Hampshire.............. 52.8 661.3 0.7 1,132 3.7 New Jersey................. 273.4 4,106.9 1.6 1,262 1.8 New Mexico................. 58.5 816.7 0.6 865 2.5 New York................... 647.1 9,465.3 1.4 1,428 6.4 North Carolina............. 274.8 4,388.6 1.5 964 3.3 North Dakota............... 32.0 416.1 0.4 1,010 3.3 Ohio....................... 296.9 5,409.2 0.8 973 3.1 Oklahoma................... 111.6 1,607.8 1.2 895 3.5 Oregon..................... 153.2 1,900.4 2.0 1,014 4.5 Pennsylvania............... 357.5 5,870.4 1.2 1,075 3.5 Rhode Island............... 37.5 483.6 1.1 1,056 2.7 South Carolina............. 131.2 2,058.8 1.6 879 2.8 South Dakota............... 33.5 423.8 0.9 856 3.4 Tennessee.................. 159.2 2,984.8 1.3 1,000 3.0 Texas...................... 681.4 12,207.8 2.0 1,109 3.5 Utah....................... 102.0 1,465.5 3.6 936 2.9 Vermont.................... 25.7 314.7 0.5 919 2.5 Virginia................... 275.2 3,884.2 1.3 1,121 2.8 Washington................. 239.6 3,305.0 2.4 1,217 5.8 West Virginia.............. 50.7 693.1 0.1 847 4.7 Wisconsin.................. 175.0 2,872.6 1.0 951 3.0 Wyoming.................... 26.3 267.5 0.6 935 4.6 Puerto Rico................ 44.5 887.0 -4.4 570 2.5 Virgin Islands............. 3.4 34.3 -11.1 827 7.7 (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.