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