U.S. Department of Commerce

Economics and Statistics Administration

U.S. CENSUS BUREAU

U S C E N S U S B U R E A U

Helping You Make Informed Decisions

Usual Hours Worked in the Past 12 

Months for Workers 16 to 64: 2008 

and 2009

American Community Survey Briefs

By

Christin Hilgeman 

ACSBR/09-4

Issued September 2010

Usual Hours: Usual hours refer to 

the usual number of hours worked 

during a week at all jobs held. Mean 

usual hours is obtained by dividing the aggregate number of hours 

worked by the total number of workers aged 16 to 64 who have worked 

within the past 12 months. 

Occupation: Occupation describes 

the kind of work a person does on 

the job. 

Industry: Industry describes the kind 

of business conducted by a person’s 

employing organization. Individuals provide descriptions of what is 

made, what is sold, or what service is 

provided by their employer. 

Class of worker: Class of worker 

categorizes people according to the 

type of ownership of the employing 

organization. 

Universe: Usual hours are provided 

for individuals aged 16 to 64 who 

have worked within the past 12 

months. Individuals provide occupation, industry, and class of worker 

data for the person’s job during 

the reference week. For those who 

worked at two or more jobs, the data 

refer to their primary job. For people 

who are not currently employed but 

report having a job within the last 12 

months, the data refer to their last 

job. 

INTRODUCTION

Since the onset of the recession in 

December of 2007, work hours have 

trended downward because of increasing 

involuntary part-time work and a reduction in overtime hours.1 These trends 

vary by metropolitan statistical area 

(metro area) and by type of employment.2

This report will show where work-hour 

cuts have been most prevalent. It highlights changes in usual hours worked per 

week by sex, occupation, industry, and 

class of worker. 

While work hours typically vary little from 

year to year, they tend to decline during 

periods of recession. They can also be a 

leading indicator of changes in employment, because employers may increase or 

decrease work hours for current employees prior to layoff s or hiring.3 An increase 

in part-time work and a reduction in 

usual work hours are some of the indicators of underemployment, and as such, 

are important measures of the overall 

employment situation.4

1 Bureau of Labor Statistics. 2009. “The 

Employment Situation: January 2009.” Available 

online at <www.bls.gov/news.release/archives

/empsit_02062009.pdf>. 2 For more information on metropolitan statistical 

areas please see <www.whitehouse.gov/omb/assets

/omb/bulletins/fy2009/09-01.pdf>. 3 Bureau of Labor Statistics. 2008. “Involuntary 

Part-Time Work on the Rise.” Issues in Labor Statistics, 

December 2008. 4 Underemployed individuals are those who 

wanted full-time jobs but worked less than 35 hours 

per week due to slack work (a reduction in hours 

in response to unfavorable business conditions) or 

because they were only able to fi nd part-time work.
2 U.S. Census Bureau

CHANGE IN USUAL WORK 

HOURS

Work hours in the United States 

fell by about 36 minutes per week 

from 39.0 hours in 2008 to 38.4 

hours in 2009. Work hours fell 

in 46 of the 50 largest U.S. metro 

areas. None of the 50 metro areas 

reported an increase in work hours. 

Men experienced larger work-hour 

reductions than women. Men’s 

work hours declined by 48 minutes per week, from 41.7 hours 

to 40.9 hours per week. Women’s 

work hours declined by 24 minutes per week, from 36.1 hours to 

35.7 hours per week. In addition 

to experiencing a larger reduction in work hours, men’s work 

hours declined in a larger number of metro areas, falling in 43 

metro areas compared with 19 for 

women.

USUAL WORK HOURS 

BY METROPOLITAN 

STATISTICAL AREA

In 2009, residents of HoustonSugar Land-Baytown, TX, and 

Dallas-Fort Worth-Arlington, TX, 

worked the longest number of 

hours at 40.3 and 39.8 hours per 

week, respectively. Work hours 

were longest in Houston-Sugar 

Land-Baytown, TX, with men 

working 42.4 hours per week and 

women working 37.6 hours per 

week.5

Work hours were shortest in 

Buff alo-Niagara Falls, NY at 36.9 

hours per week. Men worked the 

shortest number of hours in Sacramento–Arden-Arcade–

Roseville, CA, at 39.6 hours per 

5 Work hours for men in Houston-Sugar 

Land-Baytown, TX, are not statistically different from work hours for men in Virginia 

Beach-Norfolk-Newport News, VA-NC. Work 

hours for women in Houston-Sugar LandBaytown, TX, are not statistically diff erent 

from work hours for women in WashingtonArlington-Alexandria, DC-VA-MD-WV, DallasFort Worth-Arlington, TX, and San Antonio, 

TX.

week while women worked the 

shortest number of hours in 

Buff alo-Niagara Falls, NY, at 34.1 

hours per week.6

USUAL WORK HOURS BY 

OCCUPATION7

Workers in construction, extraction, 

maintenance, and repair occupations, production, transportation, 

and material moving occupations, 

and farming, fi shing, and forestry 

occupations experienced the largest decrease in work hours. In each 

of these occupations work hours 

declined by about an hour per 

week. Workers in sales and offi ce 

occupations and managerial, professional, and related occupations 

worked 24 minutes less in 2009.

USUAL WORK HOURS BY 

INDUSTRY8

Work hours declined by about 74 

minutes per week in agriculture, 

forestry, fi shing, and hunting, and 

mining industries and by about 67 

minutes per week in construction 

industries.9 The smallest decrease 

in work hours was registered in 

the fi nance and insurance, and real 

estate and rental and leasing industries with a decrease of 5 minutes 

per week between 2008 and 2009. 

6 Work hours for men in Sacramento–

Arden-Arcade–Roseville, CA, are not statistically diff erent from work hours for men in 

Buff alo-Niagara Falls, NY, Los Angeles-Long 

Beach-Santa Ana, CA, and Riverside-San 

Bernardino-Ontario, CA. Work hours for 

women in Buff alo-Niagara Falls, NY, are not 

statistically diff erent from work hours for 

women in Providence-New Bedford-Fall River, 

RI-MA. 7 Occupational categories are based on 

the Standard Occupational Classifi cation 

2000. For more information, see 

<www.bls.gov/soc>. 8 Industry categories are based on the 

North American Industry Classifi cation 

System 2007. For more information, see 

<www.census.gov/naics>. 9 The estimate for agriculture, forestry, 

fi shing, and hunting, and mining is not 

statistically diff erent from the estimate for 

construction.

USUAL WORK HOURS BY 

CLASS OF WORKER

Self-employed workers experienced 

a greater reduction in work 

hours between 2008 and 2009 

than workers in other types 

of employment. Workers who 

were self-employed in their own 

unincorporated businesses worked 

66 minutes less in 2009 while 

those self-employed in their own 

incorporated businesses worked 49 

minutes less in 2009.10 Government 

workers were the least likely to 

work reduced hours between 2008 

and 2009 with a reduction of 17 

minutes per week.11

SOURCE AND ACCURACY

Data presented in this report are 

based on people and households 

that responded to the ACS in 2008 

and 2009. The resulting estimates 

are representative of the entire 

population. All comparisons presented in this report have taken 

sampling error into account and are 

signifi cant at the 90 percent confi -

dence level unless otherwise noted. 

Due to rounding, some details may 

not sum to totals. For information 

on sampling and estimation methods, confi dentiality protection, and 

sampling and nonsampling errors, 

please see the “2009 ACS Accuracy 

of the Data” document located at 

<www.census.gov/acs/www

/Downloads/data_documentation

/Accuracy/ACS_Accuracy_of

_Data_2009.pdf>. 

10 The estimates for self-employed in 

own not incorporated business workers and 

self-employed in own incorporated business 

workers are not statistically diff erent from the 

estimate for unpaid family workers. 11 The estimate for government workers 

is not statistically diff erent from the estimate 

for unpaid family workers.
U.S. Census Bureau 3

Table 1. 

Usual Hours Worked in the Past 12 Months for Workers Aged 16 to 64 by Metropolitan 

Statistical Area: 2008 and 20091

2008 2009 Change in 

usual hours Area Margin of Margin of worked in Margin of 

Estimate error2

 (±) Estimate error2 (±) minutes error2 (±)

 United States . . . . . . . . . . . . . . . . . . . . . . . . . . 39.0 0.1 38.4 0.1 *–36 8

Atlanta-Sandy Springs-Marietta, GA . . . . . . . . . . . . . . . . 39.9 0.2 39.1 0.2 *–48 17

Austin-Round Rock, TX . . . . . . . . . . . . . . . . . . . . . . . . . . 39.7 0.2 39.0 0.2 *–42 17

Baltimore-Towson, MD . . . . . . . . . . . . . . . . . . . . . . . . . . 39.3 0.2 38.8 0.2 *–30 17

Birmingham-Hoover, AL . . . . . . . . . . . . . . . . . . . . . . . . . 39.9 0.2 39.2 0.3 *–42 22

Boston-Cambridge-Quincy, MA-NH . . . . . . . . . . . . . . . . . 38.5 0.2 37.9 0.1 *–36 13

Buffalo-Niagara Falls, NY . . . . . . . . . . . . . . . . . . . . . . . . 37.4 0.3 36.9 0.3 *–30 25

Charlotte-Gastonia-Concord, NC-SC . . . . . . . . . . . . . . . 39.8 0.3 39.2 0.2 *–36 22

Chicago-Naperville-Joliet, IL-IN-WI . . . . . . . . . . . . . . . . . 38.9 0.1 38.3 0.1 *–36 8

Cincinnati-Middletown, OH-KY-IN . . . . . . . . . . . . . . . . . . 38.6 0.2 38.2 0.2 *–24 17

Cleveland-Elyria-Mentor, OH . . . . . . . . . . . . . . . . . . . . . . 38.5 0.2 37.6 0.2 *–54 17

Columbus, OH . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39.0 0.2 38.4 0.3 *–36 22

Dallas-Fort Worth-Arlington, TX . . . . . . . . . . . . . . . . . . . . 40.5 0.2 39.8 0.1 *–42 13

Denver-Aurora-Broomfi eld, CO . . . . . . . . . . . . . . . . . . . . 39.4 0.2 39.0 0.2 *–24 17

Detroit-Warren-Livonia, MI . . . . . . . . . . . . . . . . . . . . . . . . 38.5 0.2 37.5 0.2 *–60 17

Hartford-West Hartford-East Hartford, CT . . . . . . . . . . . . 38.3 0.3 37.6 0.2 *–42 22

Houston-Sugar Land-Baytown, TX . . . . . . . . . . . . . . . . . 41.0 0.2 40.3 0.2 *–42 17

Indianapolis-Carmel, IN . . . . . . . . . . . . . . . . . . . . . . . . . . 39.5 0.2 38.8 0.2 *–42 17

Jacksonville, FL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39.9 0.3 39.4 0.3 *–30 25

Kansas City, MO-KS . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39.7 0.2 39.0 0.2 *–42 17

Las Vegas-Paradise, NV . . . . . . . . . . . . . . . . . . . . . . . . . 39.7 0.3 38.9 0.2 *–48 22

Los Angeles-Long Beach-Santa Ana, CA . . . . . . . . . . . . 38.7 0.1 38.1 0.1 *–36 8

Louisville/Jefferson County, KY-IN . . . . . . . . . . . . . . . . . . 38.9 0.2 38.4 0.3 *–30 22

Memphis, TN-MS-AR . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39.3 0.3 38.5 0.3 *–48 25

Miami-Fort Lauderdale-Pompano Beach, FL. . . . . . . . . . 39.5 0.2 38.8 0.1 *–42 13

Milwaukee-Waukesha-West Allis, WI . . . . . . . . . . . . . . . . 38.2 0.2 37.9 0.2 *–18 17

Minneapolis-St. Paul-Bloomington, MN-WI . . . . . . . . . . . 38.2 0.1 37.8 0.3 *–24 19

Nashville-Davidson–Murfreesboro–Franklin, TN . . . . . . . 39.4 0.2 39.0 0.2 *–24 17

New Orleans-Metairie-Kenner, LA . . . . . . . . . . . . . . . . . . 39.7 0.3 39.4 0.3 –18 25

New York-Northern New Jersey-Long Island, NY-NJ-PA . . 39.2 0.2 39.1 0.1 –6 13

Oklahoma City, OK . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39.7 0.2 39.1 0.2 *–36 17

Orlando-Kissimmee, FL . . . . . . . . . . . . . . . . . . . . . . . . . . 39.3 0.2 38.6 0.3 *–42 22

Philadelphia-Camden-Wilmington, PA-NJ-DE-MD . . . . . . . 38.8 0.2 38.5 0.2 *–18 17

Phoenix-Mesa-Scottsdale, AZ . . . . . . . . . . . . . . . . . . . . . 39.5 0.1 38.8 0.2 *–42 13

Pittsburgh, PA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38.5 0.2 38.1 0.2 *–24 17

Portland-Vancouver-Beaverton, OR-WA . . . . . . . . . . . . . 38.4 0.2 37.6 0.2 *–48 17

Providence-New Bedford-Fall River, RI-MA . . . . . . . . . . . 37.5 0.2 37.6 0.3 6 22

Raleigh-Cary, NC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39.7 0.3 39.0 0.3 *–42 25

Richmond, VA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39.4 0.3 38.5 0.3 *–54 25

Riverside-San Bernardino-Ontario, CA . . . . . . . . . . . . . . 38.5 0.2 37.6 0.2 *–54 17

Sacramento–Arden-Arcade–Roseville, CA . . . . . . . . . . . 38.1 0.3 37.7 0.3 –24 25

St. Louis, MO-IL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38.3 0.2 38.0 0.2 *–18 17

Salt Lake City, UT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38.5 0.3 37.8 0.3 *–42 25

San Antonio, TX . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39.8 0.2 39.2 0.3 *–36 22

San Diego-Carlsbad-San Marcos, CA . . . . . . . . . . . . . . . 39.0 0.2 38.5 0.2 *–30 17

San Francisco-Oakland-Fremont, CA . . . . . . . . . . . . . . . 38.5 0.2 38.1 0.2 *–24 17

San Jose-Sunnyvale-Santa Clara, CA . . . . . . . . . . . . . . . 39.2 0.2 38.4 0.2 *–48 17

Seattle-Tacoma-Bellevue, WA . . . . . . . . . . . . . . . . . . . . . 39.0 0.2 38.5 0.2 *–30 17

Tampa-St. Petersburg-Clearwater, FL . . . . . . . . . . . . . . . 39.6 0.2 39.1 0.2 *–30 17

Virginia Beach-Norfolk-Newport News, VA-NC . . . . . . . . 39.8 0.3 39.2 0.3 *–36 25

Washington-Arlington-Alexandria, DC-VA-MD-WV . . . . . . . 40.0 0.2 39.5 0.2 *–30 17

* Statistically different at the 90 percent confi dence level.

1

Fifty most populous metropolitan statistical areas based on population estimates as of July 1, 2009. Metropolitan statistical area boundaries defi ned by the 

Offi ce of Management and Budget as of November 2008.

2

Data are based on a sample and are subject to sampling variability. A margin of error is a measure of an estimate’s variability. The larger the margin of error 

in relation to the size of the estimate, the less reliable the estimate. When added to and subtracted from the estimate, the margin of error forms the 90 percent 

confi dence interval.

Sources: U.S. Census Bureau, American Community Surveys, 2008 and 2009.
4 U.S. Census Bureau

Table 2.

Usual Hours Worked in the Past 12 Months for Workers Aged 16 to 64 by Sex and by 

Metropolitan Statistical Area: 2008 and 20091

Male Female

2008 2009 2008 2009

Change Change 

Area in usual in usual 

Margin Margin hours Margin Margin Margin hours Margin 

of of worked of of of worked of 

Esti- error2 Esti- error2 in min- error2 Esti- error2

 Esti- error2 in min- error2 

mate (±) mate (±) utes (±) mate (±) mate (±) utes (±)

 United States . . . . . . . . . . . . . . . . . . . . . 41.7 0.1 40.9 0.1 *–48 8 36.1 0.1 35.7 0.1 *–24 8

Atlanta-Sandy Springs-Marietta, GA . . . . . . . . . . . 42.3 0.2 41.2 0.2 *–66 17 37.3 0.2 36.8 0.2 *–30 17

Austin-Round Rock, TX . . . . . . . . . . . . . . . . . . . . . 42.0 0.4 41.0 0.3 *–60 30 36.9 0.3 36.7 0.3 –12 25

Baltimore-Towson, MD . . . . . . . . . . . . . . . . . . . . . 41.8 0.2 41.2 0.3 *–36 22 36.8 0.3 36.5 0.3 –18 25

Birmingham-Hoover, AL . . . . . . . . . . . . . . . . . . . . 42.7 0.4 41.6 0.3 *–66 30 36.9 0.4 36.5 0.4 –24 34

Boston-Cambridge-Quincy, MA-NH . . . . . . . . . . . . 41.6 0.2 40.8 0.2 *–48 17 35.3 0.2 34.9 0.2 *–24 17

Buffalo-Niagara Falls, NY . . . . . . . . . . . . . . . . . . . 40.1 0.4 39.7 0.4 –24 34 34.6 0.4 34.1 0.3 –30 30

Charlotte-Gastonia-Concord, NC-SC . . . . . . . . . . 42.6 0.3 41.5 0.3 *–66 25 36.8 0.3 36.7 0.4 –6 30

Chicago-Naperville-Joliet, IL-IN-WI . . . . . . . . . . . . 41.5 0.2 40.6 0.2 *–54 17 36.0 0.1 35.7 0.2 *–18 13

Cincinnati-Middletown, OH-KY-IN . . . . . . . . . . . . . 41.7 0.3 40.7 0.3 *–60 25 35.4 0.3 35.5 0.3 6 25

Cleveland-Elyria-Mentor, OH . . . . . . . . . . . . . . . . . 41.5 0.3 40.2 0.3 *–78 25 35.3 0.3 35.0 0.3 –18 25

Columbus, OH . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41.6 0.3 40.6 0.3 *–60 25 36.3 0.3 36.0 0.3 –18 25

Dallas-Fort Worth-Arlington, TX . . . . . . . . . . . . . . . 42.8 0.2 41.9 0.2 *–54 17 37.6 0.2 37.3 0.2 *–18 17

Denver-Aurora-Broomfi eld, CO . . . . . . . . . . . . . . . 41.8 0.2 41.2 0.2 *–36 17 36.7 0.3 36.4 0.3 –18 25

Detroit-Warren-Livonia, MI . . . . . . . . . . . . . . . . . . . 41.3 0.2 40.2 0.2 *–66 17 35.4 0.2 34.8 0.2 *–36 17

Hartford-West Hartford-East Hartford, CT . . . . . . . 40.9 0.4 40.5 0.3 –24 30 35.4 0.4 34.6 0.3 *–48 30

Houston-Sugar Land-Baytown, TX . . . . . . . . . . . . 43.5 0.2 42.4 0.2 *–66 17 37.8 0.2 37.6 0.3 –12 22

Indianapolis-Carmel, IN . . . . . . . . . . . . . . . . . . . . . 42.1 0.3 41.4 0.3 *–42 25 36.6 0.3 36.0 0.3 *–36 25

Jacksonville, FL . . . . . . . . . . . . . . . . . . . . . . . . . . . 42.0 0.4 41.6 0.3 –24 30 37.6 0.3 37.0 0.3 *–36 25

Kansas City, MO-KS . . . . . . . . . . . . . . . . . . . . . . . 42.2 0.3 41.6 0.3 *–36 25 37.0 0.3 36.3 0.3 *–42 25

Las Vegas-Paradise, NV . . . . . . . . . . . . . . . . . . . . 41.3 0.3 40.4 0.3 *–54 25 37.8 0.3 36.9 0.3 *–54 25

Los Angeles-Long Beach-Santa Ana, CA . . . . . . . 40.8 0.2 39.9 0.2 *–54 17 36.3 0.2 35.9 0.1 *–24 13

Louisville/Jefferson County, KY-IN . . . . . . . . . . . . . 41.3 0.3 40.6 0.4 *–42 30 36.4 0.3 36.1 0.4 –18 30

Memphis, TN-MS-AR . . . . . . . . . . . . . . . . . . . . . . . 41.3 0.4 40.6 0.5 *–42 38 37.2 0.4 36.5 0.4 *–42 34

Miami-Fort Lauderdale-Pompano Beach, FL. . . . . 41.5 0.2 40.5 0.3 *–60 22 37.3 0.2 36.9 0.2 *–24 17

Milwaukee-Waukesha-West Allis, WI . . . . . . . . . . . 40.9 0.3 40.4 0.3 *–30 25 35.4 0.3 35.3 0.3 –6 25

Minneapolis-St. Paul-Bloomington, MN-WI . . . . . . 40.7 0.2 40.0 0.2 *–42 17 35.5 0.2 35.3 0.2 –12 17

Nashville-Davidson–Murfreesboro–Franklin, TN . . . 41.8 0.3 41.3 0.3 *–30 25 36.7 0.3 36.5 0.3 –12 25

New Orleans-Metairie-Kenner, LA . . . . . . . . . . . . . 42.5 0.5 41.9 0.4 –36 38 36.9 0.4 36.8 0.4 –6 34

New York-Northern New Jersey-Long Island, 

NY-NJ-PA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41.8 0.2 41.6 0.2 –12 17 36.4 0.1 36.4 0.1 0 8

Oklahoma City, OK . . . . . . . . . . . . . . . . . . . . . . . . 42.2 0.4 41.5 0.3 *–42 30 36.9 0.3 36.4 0.4 –30 30

Orlando-Kissimmee, FL . . . . . . . . . . . . . . . . . . . . . 41.1 0.4 40.2 0.3 *–54 30 37.2 0.3 36.9 0.3 –18 25

Philadelphia-Camden-Wilmington, PA-NJ-DE-MD . . 41.6 0.2 41.2 0.2 *–24 17 35.8 0.2 35.6 0.2 –12 17

Phoenix-Mesa-Scottsdale, AZ . . . . . . . . . . . . . . . . 41.3 0.2 40.6 0.2 *–42 17 37.3 0.3 36.6 0.3 *–42 25

Pittsburgh, PA . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41.6 0.2 40.9 0.3 *–42 22 35.2 0.3 35.2 0.2 0 22

Portland-Vancouver-Beaverton, OR-WA . . . . . . . . 41.1 0.3 40.0 0.2 *–66 22 35.4 0.3 34.9 0.3 *–30 25

Providence-New Bedford-Fall River, RI-MA . . . . . . 40.6 0.4 40.5 0.3 –6 30 34.3 0.3 34.6 0.4 18 30

Raleigh-Cary, NC . . . . . . . . . . . . . . . . . . . . . . . . . . 42.0 0.4 41.3 0.4 *–42 34 37.1 0.5 36.4 0.4 *–42 38

Richmond, VA . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41.9 0.4 40.6 0.4 *–78 34 36.8 0.4 36.3 0.4 –30 34

Riverside-San Bernardino-Ontario, CA . . . . . . . . . 40.8 0.3 40.0 0.3 *–48 25 35.6 0.2 34.7 0.3 *–54 22

Sacramento–Arden-Arcade–Roseville, CA . . . . . . 40.3 0.3 39.6 0.3 *–42 25 35.6 0.3 35.5 0.3 –6 25

St. Louis, MO-IL . . . . . . . . . . . . . . . . . . . . . . . . . . . 40.6 0.2 40.2 0.2 *–24 17 35.8 0.2 35.6 0.3 –12 22

Salt Lake City, UT . . . . . . . . . . . . . . . . . . . . . . . . . 41.3 0.3 40.2 0.4 *–66 30 35.1 0.4 34.8 0.4 –18 34

San Antonio, TX . . . . . . . . . . . . . . . . . . . . . . . . . . . 41.9 0.3 41.2 0.3 *–42 25 37.4 0.3 37.1 0.4 –18 30

San Diego-Carlsbad-San Marcos, CA . . . . . . . . . . 41.5 0.3 40.8 0.3 *–42 25 35.9 0.2 35.6 0.3 –18 22

San Francisco-Oakland-Fremont, CA . . . . . . . . . . 40.4 0.2 40.0 0.2 *–24 17 36.3 0.2 36.1 0.3 –12 22

San Jose-Sunnyvale-Santa Clara, CA . . . . . . . . . . 41.4 0.3 40.5 0.3 *–54 25 36.4 0.4 35.7 0.3 *–42 30

Seattle-Tacoma-Bellevue, WA . . . . . . . . . . . . . . . . 41.7 0.3 41.0 0.2 *–42 22 35.9 0.2 35.6 0.3 –18 22

Tampa-St. Petersburg-Clearwater, FL . . . . . . . . . . 41.6 0.3 41.0 0.3 *–36 25 37.4 0.3 37.1 0.3 –18 25

Virginia Beach-Norfolk-Newport News, VA-NC . . . 42.4 0.4 42.1 0.4 –18 34 36.9 0.3 36.2 0.5 *–42 35

Washington-Arlington-Alexandria, DC-VA-MD-WV . . 42.1 0.2 41.4 0.2 *–42 17 37.7 0.2 37.5 0.2 –12 17

* Statistically different at the 90 percent confi dence level.

1

Fifty most populous metropolitan statistical areas based on population estimates as of July 1, 2009. Metropolitan statistical area boundaries defi ned by the Offi ce of 

Management and Budget as of November 2008.

2

Data are based on a sample and are subject to sampling variability. A margin of error is a measure of an estimate’s variability. The larger the margin of error in relation to the 

size of the estimate, the less reliable the estimate. When added to and subtracted from the estimate, the margin of error forms the 90 percent confi dence interval.

Sources: U.S. Census Bureau, American Community Surveys, 2008 and 2009.
U.S. Census Bureau 5

Usual Hours Worked in the Past 12 Months for Workers Aged 16 to 64 

by Occupation, Industry, and Class of Worker

Occupation

Industry

Class of Worker

Sources: U.S. Census Bureau, American Community Surveys, 2008 and 2009.

Change in Usual Weekly Work Hours

 in Minutes: 2008 to 2009 Usual Weekly Work Hours: 2009

0 10 20 30 40 50

Production, transportation, 

and material moving

Construction, extraction, 

maintenance, and repair

Farming, fishing, and forestry

Sales and office

Service

Managerial, professional, 

and related

-80 -60 -40 -20 0

0 10 20 30 40 50

Unpaid family workers

Self-employed in own not 

incorporated business workers

Government workers

Private not-for-profit wage 

and salary workers

Self-employed in own incorporated 

business workers

Employee of private company 

workers

-80 -60 -40 -20 0

0 10 20 30 40 50

Public administration

Other services, except 

public administration

Arts, entertainment, recreation, 

accommodation and food services

Educational services, health care 

and social assistance

Professional, scientific, management, 

and waste management

Finance and insurance, and real 

estate and rental and leasing

Information

Transportation and warehousing, 

and utilities

Retail trade

Wholesale trade

Manufacturing

Construction

Agriculture, forestry, fishing 

hunting, and mining

-80 -60 -40 -20 0

Minutes Work hours
6 U.S. Census Bureau

WHAT IS THE AMERICAN COMMUNITY SURVEY?

The American Community Survey (ACS) is a nationwide survey designed to provide communities with reliable and 

timely demographic, social, economic, and housing data for the nation, states, congressional districts, counties, 

places, and other localities every year. It has an annual sample size of about 3 million addresses across the United 

States and Puerto Rico and includes both housing units and group quarters (e.g., nursing facilities and prisons). 

The ACS is conducted in every county throughout the nation, and every municipio in Puerto Rico, where it is called 

the Puerto Rico Community Survey. Beginning in 2006, ACS data for 2005 were released for geographic areas with 

populations of 65,000 and greater. For information on the ACS sample design and other topics, visit 

<www.census.gov/acs/www>.