P70-132

Issued October 2012

Current Population Reports

Household Economic Studies

Home-Based Workers in the

United States: 2010 

By Peter J. Mateyka, Melanie A. Rapino,

and Liana Christin Landivar 

U.S. Department of Commerce

Economics and Statistics Administration

U.S. CENSUS BUREAU

census.gov

INTRODUCTION

As communication and information technologies advance, 

workers are increasingly able to perform work at home. 

Since work is often more than simply a means of making a 

living, but tends to dictate how people organize their lives, 

changes in work patterns have both economic and social 

implications. Accordingly, timely data on the characteristics 

of home-based workers is of interest to researchers and

policy makers, including those in the fields of transportation, employment, planning, and housing. This report

provides information about the characteristics of

home-based workers in 2010. 

The data used in this report primarily come from two 

different surveys administered by the U.S. Census Bureau. 

The Survey of Income and Program Participation (SIPP) is 

a nationally representative longitudinal survey of about 

50,000 households. Since the mid-1990s, the Work Schedule Topical Module of SIPP has included questions specifically designed to estimate the number of people who work 

at home at least one full day during a typical workweek.1 

One of the goals of this module is to capture work done at 

home instead of another location for employed respondents 

who are at least 15 years old. These data are the source of 

a large, detailed set of tabulations, some of which are available in this report.2

1 The Work Schedule Topical Module of the 2008 Panel of the SIPP asked 

respondents which days they worked “during a typical week last month.” 

Respondents were then asked, “As part of the work schedule for that week, 

were there any days when [he/she] worked only at home...?” A copy of

the topical module instrument can be found on the SIPP Web site at

<www.census.gov/sipp/top_mod/top_mods_chart.html>. For more

information on data collection, see the section “Sources of Data” at the end

of this report. 2 Many additional tables are available at the Census Bureau’s Commuting 

(Journey to Work) Web site at <www.census.gov/hhes/commuting/data

/workathome.html>. 

The American Community Survey (ACS), fully implemented 

in 2005, is a nationally representative survey with an initial 

sample size of about 3 million addresses. This survey 

produces annual estimates of population and housing 

characteristics for the nation and at subnational levels. 

The ACS includes a question on means of transportation to 

work asked of all respondents aged 16 and over who were 

employed during the week prior to the ACS interview week. 

This report focuses on respondents who selected “worked 

at home” to the means of transportation to work question. 

Figure 1 is a reproduction of this questionnaire item.

Source: U.S. Census Bureau, 2010 American Community Survey.

Figure 1.

Reproduction of the Question on the Means 

of Transportation to Work From the 2010 

American Community Survey
2 U.S. Census Bureau

Prior to the ACS, means of transportation to work was asked in 

the “long form” portion of the 

once-a-decade decennial census.3

The “long form” questionnaire was 

administered to approximately 1 

out of every 6 housing units in 

the United States. For comparison 

purposes, this report uses data on 

home-based workers collected in 

the Census 2000 to examine time 

trends in home-based work from 

2000 to 2010. However, the primary focus of this report is on the 

more recent data on home-based 

workers collected in the SIPP and 

the ACS.

The SIPP and ACS provide both 

timely and comprehensive data 

on home-based workers in the 

United States. The first section of 

this report looks at recent historical trends in the number of people 

who work from home using multiple years of the SIPP and ACS. 

In the second section, data from 

both surveys, as well as previous 

decennial censuses, are used to disaggregate home-based workers by 

socio-demographic and economic 

characteristics. The third section 

of the report uses SIPP data to 

describe the characteristics of the 

work schedules of those that work 

from home. The final section uses 

ACS data to examine metropolitan 

variation in home-based work.

HIGHLIGHTS

• The percentage of all workers 

who worked at least 1 day at 

home increased from 7.0 percent 

in 1997 to 9.5 percent in 2010, 

according to SIPP. During this 

same time period, the population working exclusively from 

home in SIPP increased from 

4.8 percent of all workers to 

6.6 percent. The population 

working both at home and at 

3 The “long form” portion of the decennial 

census was discontinued after 2000, replaced 

with the annual ACS.

another location increased from 

2.2 percent to 2.8 percent of 

all workers.4

• The percentage of workers who 

worked the majority of the workweek at home increased from 

3.6 percent to 4.3 percent of the 

population between 2005 and 

2010, according to the ACS.

• About 1 in 10 workers who 

worked exclusively from home 

were over the age of 65 in 2010. 

• About one-fourth of homebased workers were in management, business, and 

financial occupations.

• Home-based work in computer, 

engineering, and science occupations increased by 69 percent 

between 2000 and 2010.

• Nearly half of home-based 

workers were self-employed.

• The most popular days worked 

at home for those who work 

both at home and onsite were 

Monday and Friday. 

• Those who usually worked both 

onsite and at home during a typical week worked similar hours 

per week when compared to 

those who worked only onsite.

• Many metropolitan areas in 

the Southeast, Southwest, and 

West had the largest percentage 

of workers who worked from 

home. 

• In 2010, the Boulder, Colorado,

metropolitan area had among 

the highest percentage of 

workers who worked from home 

most of the workweek with 

10.9 percent. 

4 Because of rounding, the sum of the 

percentage of those that worked exclusively 

at home and those that worked both at home 

and at another location in SIPP do not add 

up to the total percentage of workers who 

worked at least 1 day at home. 

SIPP and ACS Home-Based 

Worker Estimates

The estimates of home-based 

workers from the SIPP and ACS are 

not directly comparable because 

each survey queries workers about 

home-based activities differently. 

The SIPP asks workers aged 15 and 

over to indicate which days of the 

workweek they work entirely from 

home. Thus, to be regarded as an 

at-home worker by this survey, a 

respondent must report having 

worked only at home on a given 

workday. Individuals who check 

e-mail or carry out other work 

activities at home, but outside of 

normal work hours are not counted 

as home-based workers in SIPP. 

In this report, those who indicate 

working from home in SIPP are classified into two groups (see Definitions box). Those who worked 

exclusively at home (every day 

they worked, they reported working at home) are considered “home 

workers.” The second group, called 

“mixed workers,” includes those 

who reported working at home at 

least 1 full day in a typical week, 

but also reported working other 

days in a location outside of their 

home. The latter group includes 

private wage and government 

employees who formally telework, 

or perform some work activities at 

home as part of their normal work 

schedule, as well as self-employed 

individuals who work both in and 

outside of the home.5

Workers aged 16 and over in the 

ACS are asked to report how they 

“usually” got to work last week 

(Figure 1). Those who used several 

methods of getting to work, either 

in the same week or in the same 

day, are asked to list the mode 

used most often. If two or more 

5 For a more detailed discussion of

“telework” and other types of home-based 

work, see Pratt, Joanne H., Counting the New 

Mobile Workforce, U.S. Bureau of Transportation Statistics, 1997.
U.S. Census Bureau 3

modes are used with the same frequency, the respondent selects the 

mode used for the longest distance. 

Respondents who select work 

at home, presumably, work the 

majority of the week from home. 

This measure of home-based work 

is more conservative than the SIPP 

measure, and excludes respondents 

who work at home during off hours 

or those who sometimes telework 

from home but for less than the 

majority of a workweek. 

Home-based worker estimates 

between the two surveys may also 

differ because of differences in 

labor force definitions and survey 

design. In SIPP, the labor force estimates in the Work Schedule Topical 

Module refer to a typical week in 

the month prior to the interview 

month, but the ACS estimates 

are based on work activities that 

occur during the week prior to 

the interview week. The SIPP also 

includes more extensive labor force 

questions aimed at measuring contingent and unpaid family work.6 

Lastly, the SIPP uses two interview 

modes (personal visit and telephone), while the ACS uses three 

(mail, phone, and personal visit). 

Taken together, these differences 

may increase the likelihood that 

SIPP identifies respondents who 

work irregular schedules. Later

sections of this report will show 

that home-based workers were 

more likely to work irregular

schedules than other workers.

Some additional differences, the 

effects of which are more difficult 

to speculate, included the survey 

6 Prior to 2008, the ACS employment 

question specifically noted that respondents 

who helped in a family-owned farm or business without pay for at least 15 hours a week 

should answer “yes” to the question about 

working for pay or profit in the previous 

week. This note was dropped from the ACS 

starting in 2008. See <www.census.gov

/hhes/www/laborfor/researchnote092209

.html> for a discussion of changes to the 

labor force questions in the ACS from 

2007 to 2008. 

collection period—1 year for the 

ACS and 4 months for SIPP—editing 

and imputation procedures, and the 

calculation of survey weights. 

Estimates of Home-Based Work 

Table 1 shows the number of

people who work from home for 

their primary job for different

survey years of the SIPP and ACS.7

The SIPP data reveal that during

a typical week in 2010, 13.4 million people worked at least 1 full 

workday at home.8 This number 

represents about 9.5 percent of all 

workers in the United States.

Comparing the SIPP estimates for 

home-based workers from 1997 to 

those of 2010 reveals the increase 

in home-based work in recent 

years. From 1997 to 2010, the 

number of people who worked 

at least 1 day a week at home 

increased by about 4.2 million, or 

from 7.0 percent of all employed 

people to 9.5 percent. Much of 

the increase in home-based work 

occurred between 2005 and 

2010, when the overall number of 

employed people declined. During 

this time, the number of homebased workers went from 7.8 percent of all workers to 9.5 percent, 

an increase of about 2 million. 

7 In general, the word “job” implies an 

arrangement for work or pay where payment 

is in cash wages or salaries, at piece rate, in 

tips, by commission, or in-kind (e.g., meals, 

living quarters, supplies received). In this 

report, “job” also includes self-employment 

at a business, professional practice, or farm. 

A business is defined as an activity that 

involves the use of machinery or equipment 

in which money has been invested or an 

activity requiring an office or “place of business,” or an activity that requires advertising. 

Payment may be in the form of profits or fees. 

SIPP collects information on multiple jobs but 

this report focus on the “primary,” or main, 

job held by individuals during the fourth 

month of the reference period. In situations 

where a respondent held more than one job 

during the reference month, the primary job 

is the job the respondent worked the most 

hours at during the month. 8 Data from the SIPP Work Schedule Topical 

Module, which includes questions on homebased work, were collected from January to 

April of 2010. 

DEFINITIONS

Onsite worker: Onsite workers reported they did not work any full days 

at home as part of their work schedule. 

Mixed worker: Mixed workers reported working at home at least 1 full 

day in a typical week, but also reported working onsite.

Home worker: Home workers reported working at home every day they 

worked and did not report working onsite.

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.

Occupation: Occupation describes the kind of work a person does on 

the job.

Class of worker: Class of worker categorizes people according to the 

type of ownership of the employing organization.

Rotating shift: A rotating shift changes regularly from days to evenings 

to nights. 

Split shift: A split shift consists of a working shift divided into two or 

more periods, such as morning and evening, with a break of several 

hours between them.

Irregular schedule: An irregular schedule changes from day to day.
4 U.S. Census Bureau

According to the 2010 ACS estimates, 5.8 million (4.3 percent

of all workers) usually worked

at home during the week before

the interview (Table 1). While the 

ACS reports fewer home-based 

workers overall compared with SIPP, 

the number of home-based workers in the ACS increased from 4.8 

million (3.6 percent of all workers) 

in 2005 to 5.8 million (4.3 percent 

of all workers) in 2010. These 

estimates suggest that, regardless 

of differences in how each survey 

counts home-based workers, there 

were substantial increases between 

1997 and 2010. 

Table 2 contains the SIPP homebased workers estimates from 

1997 to 2010 organized by those 

who worked only at home (home 

Table 1.

Home-Based Worker Estimates: 1997–2010 

(Numbers in thousands)

Year

American Community Survey1 Survey of Income and Program Participation2

Total employed Work at home Total employed Work at home

Number

Margin 

of error3

(±) Number

Margin 

of error3

(±) Percent

Margin 

of error3

(±) Number

Margin 

of error3

(±) Number

Margin 

of error3

(±) Percent

Margin 

of error3

(±)

1997. . . . . . . . . . . . – – – – – – 132,229 727 9,241 314 7.0 0.2

1999. . . . . . . . . . . . – – – – – – 135,955 806 9,477 352 7.0 0.3

20004. . . . . . . . . . . 127,156 34 4,160 5 3.3 0.1 – – – – – –

2002. . . . . . . . . . . . – – – – – – 137,930 907 10,393 398 7.5 0.3

2005. . . . . . . . . . . . 132,383 116 4,793 33 3.6 0.1 144,557 782 11,313 350 7.8 0.2

2006. . . . . . . . . . . . 137,295 128 5,301 36 3.9 0.1 – – – – – –

2007. . . . . . . . . . . . 138,282 107 5,567 43 4.0 0.1 – – – – – –

2008. . . . . . . . . . . . 142,544 139 5,794 38 4.1 0.1 – – – – – –

2009. . . . . . . . . . . . 137,312 147 5,812 37 4.2 0.1 – – – – – –

2010. . . . . . . . . . . . 135,906 147 5,815 41 4.3 0.1 141,646 839 13,401 389 9.5 0.3

– Represents a year where no survey data was available.

1

 This report defines workers in the American Community Survey as those aged 16 and over who were employed and at work in the previous week. Individuals 

working at home are those who reported ‘‘work at home’’ on a question about how they ‘‘usually’’ commute to work (see Figure 1).

2

 This report defines workers in the Survey of Income and Program Participation as those aged 15 and over and were employed during the fourth month of the 

reference period. Individuals working at home worked at least 1 full day at home during a ‘‘typical’’ week of that month.

3

 This number, when added to or subtracted from the estimate, represents the 90 percent confidence interval around the estimate.

4

 The estimate from 2000 is from the Decennial Census. This report defines workers in the Decennial Census as those aged 16 and over who were employed at 

work during the previous week. Individuals working at home are those who reported ‘‘work at home’’ on a question about how they ‘‘usually’’ commute to work.

Note: Estimates from this table exclude those in the Armed Forces. 

Source: U.S. Census Bureau, Census 2000, 2005 through 2010 1-year American Community Surveys, and Survey of Income and Program Participation,

2008 Panel, wave 5; 2004 Panel, wave 4; 2001 Panel, wave 4; 1996 Panel, wave 8; and 1996 Panel, wave 4.

Table 2.

Work-at-Home Status of Employed Civilians, Primary Job Only:

Survey of Income and Program Participation, 1997–2010

(Civilian employed aged 15 years and older. Numbers in thousands)

Year

Work-at-home status

Onsite workers1 Mixed workers2 Home workers3

Number

Margin 

of error4

(±) Percent

Margin 

of error4

(±) Number

Margin 

of error4

(±) Percent

Margin 

of error4

(±) Number

Margin 

of error4

(±) Percent

Margin 

of error4

(±)

1997. . . . . . 122,988 744 93.0 0.2 2,862 177 2.2 0.1 6,379 263 4.8 0.2

1999. . . . . . 126,823 825 93.0 0.3 2,735 191 2.0 0.1 6,742 297 4.9 0.2

2002. . . . . . 127,537 926 92.5 0.3 3,117 222 2.3 0.2 7,276 335 5.3 0.2

2005. . . . . . 133,244 799 92.2 0.2 3,186 189 2.2 0.1 8,127 299 5.6 0.2

2010. . . . . . 128,244 850 90.5 0.3 4,028 218 2.8 0.2 9,374 328 6.6 0.2

1

 Onsite workers are defined as those who did not work a full workday at home as part of their work schedule.

2

 Mixed workers are defined as those who worked at home at least 1 full day a week but also worked other days in a location outside of their home.

3

 Home workers are defined as those who worked exclusively at home (i.e., every day they worked, they worked at home).

4

 This number, when added to or subtracted from the estimate, represents the 90 percent confidence interval around the estimate.

Note: Estimates from this table exclude those in the Armed Forces. 

Source: U.S. Census Bureau, Survey of Income and Program Participation, 1996 Panel, wave 4; 1996 Panel, wave 10; 2001 Panel, wave 4; 2004 Panel, wave 4; 

2008 Panel, wave 5.
U.S. Census Bureau 5

Table 3.

Selected Demographic Characteristics of Workers by Work-at-Home Status:

Survey of Income and Program Participation, 2010—Con.

(Civilian employed aged 15 years and older. Numbers in thousands)

Characteristic Total employed

Work-at-home status

Onsite workers1 Mixed workers2 Home workers3

Number Percent Number Percent Number Percent Number Percent

 Total . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141,646 100.0 128,244 100.0 4,028 100.0 9,374 100.0

Age

15 to 24 years. . . . . . . . . . . . . . . . . . . . . . . . . . 17,852 12.6 17,282 13.5 124 3.1 447 4.8

25 to 34 years. . . . . . . . . . . . . . . . . . . . . . . . . . 30,036 21.2 28,013 21.8 692 17.2 1,330 14.2

35 to 44 years. . . . . . . . . . . . . . . . . . . . . . . . . . 31,194 22.0 28,023 21.9 1,064 26.4 2,108 22.5

45 to 54 years. . . . . . . . . . . . . . . . . . . . . . . . . . 33,990 24.0 30,259 23.6 1,193 29.6 2,539 27.1

55 to 64 years. . . . . . . . . . . . . . . . . . . . . . . . . . 22,045 15.6 19,322 15.1 748 18.6 1,976 21.1

65 years and over. . . . . . . . . . . . . . . . . . . . . . . 6,529 4.6 5,346 4.2 207 5.1 976 10.4

Sex

Male. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74,338 52.5 67,331 52.5 2,201 54.7 4,806 51.3

Female. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67,308 47.5 60,914 47.5 1,826 45.3 4,568 48.7

Race and Hispanic origin

White alone. . . . . . . . . . . . . . . . . . . . . . . . . . . . 116,453 82.2 104,791 81.7 3,511 87.2 8,151 87.0

 White, non-Hispanic alone. . . . . . . . . . . . . . . . 98,375 69.5 87,443 68.2 3,322 82.5 7,610 81.2

Black alone. . . . . . . . . . . . . . . . . . . . . . . . . . . . 15,365 10.8 14,473 11.3 234 5.8 659 7.0

Asian alone. . . . . . . . . . . . . . . . . . . . . . . . . . . . 5,650 4.0 5,121 4.0 176 4.4 353 3.8

All other races, alone or in combination. . . . . . . 4,178 2.9 3,860 3.0 107 2.7 211 2.3

Hispanic, of any race, alone or

in combination. . . . . . . . . . . . . . . . . . . . . . . . . 19,648 13.9 18,856 14.7 204 5.1 588 6.3

Nativity and citizenship

Native-born. . . . . . . . . . . . . . . . . . . . . . . . . . . . 120,731 85.2 108,725 84.8 3,628 90.1 8,378 89.4

Foreign-born. . . . . . . . . . . . . . . . . . . . . . . . . . . 20,914 14.8 19,520 15.2 399 9.9 996 10.6

 Citizen. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9,708 6.9 8,908 6.9 218 5.4 582 6.2

 Noncitizen. . . . . . . . . . . . . . . . . . . . . . . . . . . . 11,207 7.9 10,612 8.3 182 4.5 413 4.4

Marital status

Married. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80,200 56.6 71,070 55.4 2,756 68.4 6,374 68.0

 Spouse present. . . . . . . . . . . . . . . . . . . . . . . . 78,486 55.4 69,480 54.2 2,726 67.7 6,279 67.0

 Spouse absent. . . . . . . . . . . . . . . . . . . . . . . . . 1,715 1.2 1,590 1.2 30 0.7 95 1.0

Widowed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2,764 2.0 2,519 2.0 57 1.4 188 2.0

Separated. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3,025 2.1 2,814 2.2 84 2.1 127 1.4

Divorced. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15,686 11.1 14,212 11.1 402 10.0 1,072 11.4

Never married. . . . . . . . . . . . . . . . . . . . . . . . . . 39,971 28.2 37,628 29.3 729 18.1 1,613 17.2

Own children under 18 years

Not present. . . . . . . . . . . . . . . . . . . . . . . . . . . . 91,893 64.9 83,414 65.0 2,434 60.4 6,045 64.5

Present. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49,753 35.1 44,830 35.0 1,593 39.6 3,329 35.5

Annual personal earnings (in dollars)

 Under $25,000. . . . . . . . . . . . . . . . . . . . . . . . . 61,014 43.1 55,189 43.0 1,144 28.4 4,681 49.9

 $25,000 to $49,999. . . . . . . . . . . . . . . . . . . . . 41,654 29.4 39,376 30.7 744 18.5 1,533 16.4

 $50,000 to $74,999. . . . . . . . . . . . . . . . . . . . . 20,532 14.5 18,473 14.4 822 20.4 1,237 13.2

 $75,000 to $99,999. . . . . . . . . . . . . . . . . . . . . 9,710 6.9 8,202 6.4 660 16.4 848 9.0

 $100,000 or more. . . . . . . . . . . . . . . . . . . . . . . 8,736 6.2 7,003 5.5 658 16.3 1,075 11.5

Median annual personal earnings . . . . . . . . . . . $30,300 – $30,000 – $52,800 – $25,500 –

Annual household income (in dollars)

 Under $25,000. . . . . . . . . . . . . . . . . . . . . . . . . 17,367 12.3 15,407 12.0 363 9.0 1,597 17.0

 $25,000 to $49,999. . . . . . . . . . . . . . . . . . . . . 32,143 22.7 30,219 23.6 475 11.8 1,449 15.5

 $50,000 to $74,999. . . . . . . . . . . . . . . . . . . . . 30,329 21.4 28,021 21.8 612 15.2 1,696 18.1

 $75,000 to $99,999. . . . . . . . . . . . . . . . . . . . . 22,236 15.7 20,279 15.8 661 16.4 1,296 13.8

 $100,000 or more. . . . . . . . . . . . . . . . . . . . . . . 39,571 27.9 34,319 26.8 1,916 47.6 3,336 35.6

Median annual household earnings. . . . . . . . . . . $67,000 – $65,600 – $96,300 – $74,000 –

Educational attainment

Less than high school diploma. . . . . . . . . . . . . 13,008 9.2 12,447 9.7 66 1.6 495 5.3

High school graduate. . . . . . . . . . . . . . . . . . . . . 33,166 23.4 31,515 24.6 325 8.1 1,327 14.2

Some college, associate’s degree. . . . . . . . . . . 50,099 35.4 46,196 36.0 1,088 27.0 2,815 30.0

Bachelor’s degree or more. . . . . . . . . . . . . . . . . 45,373 32.0 38,087 29.7 2,549 63.3 4,738 50.5

See footnotes at end of table.
6 U.S. Census Bureau

workers) and those who worked 

both at home and at another

location (mixed workers). By

dividing the data into these two 

groups, it is possible to see if the 

increase in home-based work during these years extended to both 

types of workers. Focusing on 

home workers, who compose the

majority of home-based workers, 

this group increased from about 

6.4 million workers (4.8 percent of 

all workers) in 1997 to 9.4 million 

in 2010 (6.6 percent of all workers). Moving to mixed workers, this 

group also increased from about 

2.9 million workers in 1997 to 

4.0 million in 2010. However, this 

increase occurred predominately 

between 2005 and 2010, when 

about 800,000 more primary jobs 

included regularly working both 

at home and at another location. 

Overall, jobs that involve working 

exclusively from home have been 

increasing since at least the mid1990s, while jobs that include work 

at home and onsite have increased 

largely since 2005. 

Demographic Characteristics 

This section focuses on select 

demographic characteristics of 

SIPP home-based workers in 2010, 

including median household 

income and personal earnings, 

race and Hispanic origin, and 

age.9 Table 3 displays the distribution of all workers, those who 

worked only outside of the home 

(onsite), those who worked both 

in the home and onsite (mixed 

workers), and those who worked 

only in the home (home workers) 

by the aforementioned demographic characteristics and many 

additional characteristics. 

Median annual household income 

and personal earnings differed 

by work-at-home status. Median 

personal earnings for mixed 

workers were significantly higher 

($52,800) compared with onsite 

($30,000) and home ($25,500) 

workers.10 While home workers 

had lower personal earnings than 

onsite workers did, respondents 

9 Data on the demographic characteristics 

of the 2010 ACS home-based workers are 

available at <http://factfinder2.census.gov

/faces/nav/jsf/pages/index.xhtml>. 10 Personal incomes for home workers 

are heavily skewed toward the lower end 

of the income distribution. The bottom 

25 percent of home workers have personal 

earnings of less than $250 a month, while the 

upper 25 percent have personal earnings of 

over $5,000 a month. See Table 4, “Median 

Monthly Personal Earnings by Age, Sex, Race, 

Hispanic Origin, and Work-at-Home Status: 

2010,” available at <www.census.gov/hhes

/commuting/data/workathome.html.>

that reported working at least 

1 day at home had significantly 

higher household incomes than 

respondents that reported working only onsite. Median household 

income for mixed workers was 

$96,300, compared with $74,000 

for home workers and $65,600 for 

onsite workers. 

Work-at-home status varies by

race and Hispanic origin. Blacks 

and Hispanics are less likely to 

work in home-based jobs compared 

to onsite jobs. For example,

Hispanics composed about 6 

percent of all home workers, but 

almost 15 percent of all onsite 

workers. Mixed and home workers 

were more likely to be non-Hispanic 

White (82.5 percent and 81.2 

percent, respectively) than onsite 

workers (68.2 percent).

Figure 2 displays workers by workat-home status and age group. 

Mixed workers were more likely 

to fall into the middle age groups 

(35 to 44 years and 45 to 54 years) 

than younger age groups (15 to 24 

years and 25 to 34 years) compared with onsite workers. Home 

workers were more likely to fall 

into the older age groups (55 to 

64 years and 65 years and over) 

Table 3.

Selected Demographic Characteristics of Workers by Work-at-Home Status:

Survey of Income and Program Participation, 2010—Con.

(Civilian employed aged 15 years and older; numbers in thousands)

Characteristic Total employed

Work-at-home status

Onsite workers1 Mixed workers2 Home workers3

Number Percent Number Percent Number Percent Number Percent

Metropolitan status

Nonmetropolitan. . . . . . . . . . . . . . . . . . . . . . . . 22,849 16.1 20,815 16.2 426 10.6 1,608 17.2

Metropolitan. . . . . . . . . . . . . . . . . . . . . . . . . . . . 118,797 83.9 107,429 83.8 3,602 89.4 7,766 82.8

Region of residence

Northeast. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25,995 18.4 23,574 18.4 867 21.5 1,553 16.6

Midwest. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32,398 22.9 29,501 23.0 850 21.1 2,048 21.8

South. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50,972 36.0 46,569 36.3 1,202 29.8 3,202 34.2

West. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32,281 22.8 28,601 22.3 1,109 27.5 2,571 27.4

1

 Onsite workers are defined as those who did not work a full workday at home as part of their work schedule.

2

 Mixed workers are defined as those who worked at home at least 1 full day a week but also worked other days in a location outside of their home. 

3

 Home workers are defined as those who worked exclusively at home (i.e., every day they worked, they worked at home). 

Note: Estimates from this table exclude those in the Armed Forces. 

Source: U.S. Census Bureau, Survey of Income and Program Participation, 2008 Panel, wave 5.
U.S. Census Bureau 7

compared with onsite workers.

In particular, working exclusively 

from home was more prevalent 

among workers aged 65 and over 

(10.4 percent), than onsite work 

(4.2 percent) and mixed work

(5.1 percent).

Employment Characteristics—

SIPP

In this section, SIPP data from 2005 

and 2010 are used to examine 

recent trends in home-based work 

by class of worker for both homebased workers and mixed workers. 

Trends in home-based workers’ 

employment were analyzed in 

several previous Census Bureau 

reports, the results of which are 

briefly summarized here.11 Since 

the Census Bureau began collecting 

data on home-based work in the 

1960 Census, the characteristics of 

home-based workers have changed 

significantly. However, selfemployed workers have remained 

consistently overrepresented in 

home-based work. In the 1960s, 

home-based workers were primarily 

self-employed family farmers and 

professionals, including doctors 

and lawyers. Home-based work 

in the United States declined from 

1960 to 1980, driven by changes in 

market conditions and the agriculture industry that began decades 

prior and favored large specialized 

11 See Salopek, Phillip, “Increase in

At-Home Workers Reverses Earlier Trend,”

U.S. Census Bureau, 1998 and Kuenzi, Jeffrey 

J. and Clara A. Reschovsky, “Home-Based 

Workers in the United States: 1997,” Current 

Population Reports, U.S. Census Bureau, 

2001. 

firms over family farms.12 In 1980, 

the multiple-decade decline in 

home-based work reversed, led 

partly by self-employed homebased workers in professional and 

service industries.13

Table 4 presents SIPP estimates 

for home-based work in 2010 by 

class of worker. Because the SIPP 

data are divided into mixed and 

home workers, they provide a 

more nuanced picture of the type 

of employment of home-based 

workers than historical census 

estimates. Self-employed workers 

continued to be overrepresented 

12 See Dimitri, Carolyn, Anne Effland, and 

Neilson Conklin, “The 20th Century Transformation of U.S. Agriculture and Farm Policy,” 

United States Department of Agriculture, 

2005. 13 This includes professional, scientific, 

and management services, business and 

repair services, personal services, entertainment and recreation services, and other 

professional and related services.

Figure 2.

Age of Worker by Work-At-Home Status: Survey of Income and Program 

Participation, 2010

Source: U.S. Census Bureau, Survey of Income and Program Participation, 2008 Panel, wave 5.

Percent

Total workers Onsite workers Mixed workers Home workers

0

5

10

15

20

25

30

65 years 

and over

55 to 

64 years

45 to 

54 years

35 to 

44 years

25 to 

34 years

15 to 

24 years
8 U.S. Census Bureau

Table 4.

Class of Worker by Work-at-Home Status: Survey of Income and Program Participation, 

2010

(Civilian employed aged 15 years and older. Numbers in thousands)

Characteristic Total employed

Work-at-home status

Onsite workers1 Mixed workers2 Home workers3

Number Percent Number Percent Number Percent Number Percent

 Total . . . . . . . . . . . . . . . . . . . . . . . . . . 141,646 100.0 128,244 100.0 4,028 100.0 9,374 100.0

Class of worker

Private wage and salary. . . . . . . . . . . . . . . . 101,938 72.0 95,734 74.6 2,392 59.4 3,812 40.7

Government. . . . . . . . . . . . . . . . . . . . . . . . . . 22,378 15.8 21,112 16.5 500 12.4 765 8.2

Self-employed4. . . . . . . . . . . . . . . . . . . . . . . . 15,355 10.8 10,083 7.9 1,056 26.2 4,216 45.0

Unpaid family .. . . . . . . . . . . . . . . . . . . . . . . . 1,132 0.8 587 0.5 54 1.3 491 5.2

Not classified. . . . . . . . . . . . . . . . . . . . . . . . . 843 0.6 728 0.6 25 0.6 90 1.0

1

 Onsite workers are defined as those who did not work a full workday at home as part of their work schedule.

2

 Mixed workers are defined as those who worked at home at least 1 full day a week but also worked other days in a location outside of their home. 

3

 Home workers are defined as those who worked exclusively at home (i.e., every day they worked, they worked at home). 

4

 This estimate includes both incorporated and unincorporated self-employed. 

Note: Estimates from this table exclude those in the Armed Forces. 

Source: U.S. Census Bureau, Survey of Income and Program Participation, 2008 Panel, wave 5.

Table 5.

Work-at-Home Status by Class of Worker: Survey of Income and Program Participation, 

2005–2010

(Civilian employed aged 15 years and older. Numbers in thousands)

Characteristic

2005 2010 2005–2010

Number Percent Number Percent Percent change

TOTAL

Total employed. . . . . . . . . . . . . . . . . . . . . . . . . . 144,592 – 141,646 – –

Mixed workers1. . . . . . . . . . . . . . . . . . . . . . . . . . 3,186 2.2 4,028 2.8 *29.0

Home workers2. . . . . . . . . . . . . . . . . . . . . . . . . 8,127 5.6 9,374 6.6 *17.7

CLASS OF WORKER

Private wage and salary

 Total employed. . . . . . . . . . . . . . . . . . . . . . . . . 105,811 – 101,938 – –

 Mixed workers. . . . . . . . . . . . . . . . . . . . . . . . . 1,644 1.6 2,392 2.3 *51.1

 Home workers. . . . . . . . . . . . . . . . . . . . . . . . . 2,772 2.6 3,812 3.7 *42.8

Government

 Total employed. . . . . . . . . . . . . . . . . . . . . . . . . 21,180 – 22,378 – –

 Mixed workers. . . . . . . . . . . . . . . . . . . . . . . . . 378 1.8 500 2.2 25.3

Home workers. . . . . . . . . . . . . . . . . . . . . . . . . 618 2.9 765 3.4 17.2

Self-employed3

 Total employed. . . . . . . . . . . . . . . . . . . . . . . . . 15,781 – 15,355 – –

 Mixed workers. . . . . . . . . . . . . . . . . . . . . . . . . 1,087 6.9 1,056 6.9 –0.2

 Home workers. . . . . . . . . . . . . . . . . . . . . . . . . 4,155 26.3 4,216 27.5 4.3

Unpaid family 

 Total employed. . . . . . . . . . . . . . . . . . . . . . . . . 984 – 1,132 – –

 Mixed workers. . . . . . . . . . . . . . . . . . . . . . . . . 53 5.4 54 4.7 –12.1

 Home workers. . . . . . . . . . . . . . . . . . . . . . . . . 490 49.8 491 43.4 –12.8

– Represents omitted estimates.

*Statistically different from zero at the 90 percent confidence level.

1

 Mixed workers are defined as those who worked at home at least 1 full day a week but also worked other days in a location outside of their home. 

2

 Home workers are defined as those who worked exclusively at home (i.e., every day they worked, they worked at home). 

3

 This estimate includes both incorporated and unincorporated self-employed. 

Note: Estimates from this table exclude those in the Armed Forces. 

Source: U.S. Census Bureau, Survey of Income and Program Participation, 2008 Panel, wave 5.
U.S. Census Bureau 9

Table 6.

Class of Worker, Industry, and Occupation for Work-at-Home Workers:

American Community Survey, 2010

(Civilian employed aged 16 years and older. Numbers in thousands)

Characteristic

2010

Total Work at home1

Number Percent Number Percent

 Total . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135,906 100.0 5,815 4.3

Class of worker

Employee of private company. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90,813 66.8 2,284 39.3

Private not-for-profit wage and salary. . . . . . . . . . . . . . . . . . . . . . . 10,970 8.1 307 5.3

Local government. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10,454 7.7 114 2.0

State government. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6,270 4.6 151 2.6

Federal government. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3,904 2.9 57 1.0

Self-employed in own not incorporated business. . . . . . . . . . . . . . 8,467 6.2 2,030 34.9

Self-employed in own incorporated business. . . . . . . . . . . . . . . . . 4,860 3.6 834 14.3

Unpaid family. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 168 0.1 37 0.6

Industry2

Agriculture, forestry, fishing, hunting, and mining. . . . . . . . . . . . . . 2,574 1.9 311 5.4

Construction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8,446 6.2 361 6.2

Manufacturing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14,187 10.4 390 6.7

Wholesale trade. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3,880 2.9 225 3.9

Retail trade. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15,863 11.7 422 7.3

Transportation and warehousing and utilities. . . . . . . . . . . . . . . . . . 6,669 4.9 173 3.0

Information. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2,961 2.2 198 3.4

Finance and insurance, and real estate, and rental and leasing. . . 9,129 6.7 565 9.7

Professional, scientific, and management, and administrative

and waste management. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14,439 10.6 1,342 23.1

Educational services, and health care and social assistance. . . . . 31,326 23.0 999 17.2

Arts, entertainment, and recreation, and accommodation

and food services. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12,574 9.3 311 5.3

Other services, except public administration. . . . . . . . . . . . . . . . . . 6,786 5.0 400 6.9

Public administration. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7,073 5.2 116 2.0

Occupation3

Management, business, and financial. . . . . . . . . . . . . . . . . . . . . . . 19,690 14.5 1,450 24.9

Computer, engineering, and science. . . . . . . . . . . . . . . . . . . . . . . . 7,066 5.2 432 7.4

Education, legal, community service, arts, and media. . . . . . . . . . 14,647 10.8 770 13.2

Healthcare practitioners and technicians. . . . . . . . . . . . . . . . . . . . . 7,465 5.5 100 1.7

Service. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24,454 18.0 956 16.4

Sales and related. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15,094 11.1 849 14.6

Office and administrative support. . . . . . . . . . . . . . . . . . . . . . . . . . 18,952 13.9 595 10.2

Farming, fishing, and forestry. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,007 0.7 55 0.9

Construction and extraction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6,861 5.0 197 3.4

Installation, maintenance, and repair. . . . . . . . . . . . . . . . . . . . . . . . 4,496 3.3 116 2.0

Production, transportation, and material moving. . . . . . . . . . . . . . . 16,174 11.9 296 5.1

1

 ACS work at home workers are defined as those who reported working at home on a question about how they usually commuted to work (see Figure 1).

2

 Industry codes are based on the 2007 North American Industry Classification System. 

3

 Occupation codes are based on the 2010 Standard Occupational Classification (SOC). The 2010 Census occupation codes were updated in accordance with 

the 2010 revision of the SOC. Occupation data from 2010 are not strictly comparable to data from prior years. 

Note: Estimates from this table exclude those in the Armed Forces. 

Source: U.S. Census Bureau, 2010 1-year American Community Survey.
10 U.S. Census Bureau

among those that worked exclusively from home in 2010.14 Among 

home workers, 45 percent were 

self-employed. Among mixed 

workers, 59 percent were private 

wage and salary workers. The data 

14 Census reports and table packages have 

estimated self-employed workers several different ways. Some publications include both 

incorporated and unincorporated business 

owners under “self-employed,” while others 

estimate both of these groups separately. The 

official estimates from the Bureau of Labor 

Statistics include self-employed workers in 

Incorporated businesses as private wage and 

salary workers. This is done because, legally, 

these individuals are employees of their own 

companies. This report uses all three strategies mentioned above. See Hipple, Steven 

F., “Self-employment in the United States,” 

Monthly Labor Review, September 2010 for a 

more detailed discussion of estimating selfemployment rates. 

indicate that being self-employed 

may allow workers to perform all 

of their work from home, while 

working for an employer may be 

more conducive to partial homebased work. According to the SIPP 

estimates, there was significant 

growth in home-based work in the 

private sector during the decade. 

Table 5 shows that between 2005 

and 2010, there was a 51 percent 

increase in mixed home work, and 

a 43 percent increase in home work 

in the private sector. 

Employment Characteristics—

ACS

A major advantage of the ACS is its 

sample size and timeliness, with 

data collected from about 3 million households annually. Because 

home-based work is performed 

by a relatively small percentage of 

individuals, the ACS can provide 

more reliable industrial and occupational information on this subgroup 

of workers due to its large sample 

size. The ACS data are collected 

every month and published on an 

annual basis. This allows us to 

track changes in home-based work 

over time.

Table 6 presents home-based 

worker estimates by class of 

worker, industry, and occupation 

for the 2010 ACS. According to the 

ACS, 4.3 percent of workers were 

0

10

20

30

40

50

60

70

Unpaid 

family workers

Self-employed, 

not incorporated

State and

local government

Federal 

government2

Private wage 

and salary1

Figure 3.

Distribution of Home-Based Workers by Class of Worker: 1980 to 2010

1 The estimates combine self-employed workers in incorporated businesses with private wage and salary workers. See Table 7 for 

the 2000–2010 changes using the expanded class of worker definition that separates the two aforementioned categories.

2 Armed Forces are included in the estimates for 1980, 1990, and 2000. Armed Forces personnel who were on ships at sea during 

the census were enumerated as working at home in 1990, but not in 1980 or 2000.

Source: U.S. Census Bureau, 2010 1-year American Community Survey and “Class of Worker for Workers Who Worked 

at Home for the United States: 1980 to 2000,” available at <www.census.gov/population/www/cen2000/briefs/phc-t35/tables

/tab01-5.pdf>.

Percent

1980 1990 2000 2010
U.S. Census Bureau 11

Table 7. 

Percentage of Workers Who Work From Home by Class of Worker, Industry, and 

Occupation: Census 2000, and 2005 and 2010 American Community Surveys—Con.

(Civilian employed aged 16 years and older. Numbers in thousands)

Characteristic

Decennial census American Community Survey

2000 2005 2010

2000–

2010

2005–

2010

Total 

employed

Work at

home1

Total 

employed

Work at

home1

Total 

employed

Work at

home1

Number Number Percent Number Number Percent Number Number Percent

Percent 

change

Percent 

change

 Total . . . . . . . . . . . 127,156 4,160 3.3 132,383 4,793 3.6 135,906 5,815 4.3 *30.3 *19.4

Class of worker

Employee of private

company. . . . . . . . . . . . 86,595 1,304 1.5 89,398 1,468 1.6 90,813 2,284 2.5 *67.0 *53.2

Private not-for-profit wage

and salary. . . . . . . . . . . 9,135 170 1.9 9,536 173 1.8 10,970 307 2.8 *50.3 *53.8

Local government. . . . . . 9,045 63 0.7 10,217 74 0.7 10,454 114 1.1 *56.5 *51.4

State government. . . . . . 6,045 63 1.0 5,400 74 1.4 6,270 151 2.4 *132.5 *76.0

Federal government. . . . . 3,491 27 0.8 3,440 27 0.8 3,904 57 1.5 *88.1 *88.8

Self-employed in own

not incorporated

 business. . . . . . . . . . . . 8,416 1,911 22.7 9,114 2,164 23.7 8,467 2,030 24.0 *5.6 1.0

Self-employed in own

incorporated business. . 4,031 510 12.7 4,976 729 14.6 4,860 834 17.1 *35.5 *17.1

Unpaid family. . . . . . . . . . 398 112 28.2 302 84 27.7 168 37 22.2 *–21.1 *–19.8

Industry2

Agriculture, forestry,

fishing, hunting,

 and mining. . . . . . . . . . 2,376 379 15.9 2,320 339 14.6 2,574 311 12.1 *–24.1 *–17.3

Construction. . . . . . . . . . . 8,594 210 2.4 10,221 336 3.3 8,446 361 4.3 *75.0 *30.1

Manufacturing. . . . . . . . . 17,932 279 1.6 15,856 299 1.9 14,187 390 2.7 *76.4 *45.7

Wholesale trade. . . . . . . . 4,593 165 3.6 4,725 211 4.5 3,880 225 5.8 *61.4 *29.7

Retail trade. . . . . . . . . . . 14,899 339 2.3 15,440 364 2.4 15,863 422 2.7 *17.0 *13.0

Transportation and

warehousing and

 utilities. . . . . . . . . . . . . 6,574 89 1.4 6,647 122 1.8 6,669 173 2.6 *92.3 *44.4

Information. . . . . . . . . . . . 3,934 151 3.8 3,309 160 4.8 2,961 198 6.7 *73.8 *38.2

Finance and insurance,

and real estate, and

 rental and leasing. . . . 8,801 341 3.9 9,693 484 5.0 9,129 565 6.2 *60.1 *24.1

Professional, scientific,

and management, and

 administrative and

 waste management. . 11,845 843 7.1 13,125 1,060 8.1 14,439 1,342 9.3 *30.6 *15.1

Educational services,

and health care and

 social assistance. . . . . 25,323 752 3.0 27,008 738 2.7 31,326 999 3.2 *7.4 *16.8

Arts, entertainment,

and recreation, and

 accommodation and

 food services. . . . . . . 9,955 232 2.3 11,200 263 2.3 12,574 311 2.5 *5.9 *5.3

Other services, except

public administration. . . 6,206 327 5.3 6,428 353 5.5 6,786 400 5.9 *11.8 *7.3

Public administration. . . . 6,124 53 0.9 6,411 64 1.0 7,073 116 1.6 *91.6 *64.0

See footnotes at end of table.
12 U.S. Census Bureau

home-based workers. Of these, 

the majority were self-employed. 

In 2010, about 35 percent of homebased workers were self-employed 

in their own unincorporated businesses, while 14 percent were 

self-employed in their own incorporated businesses.

Using decennial census data from 

1980, 1990, and 2000 and ACS 

2010 data, Figure 3 shows that 

the self-employed make up a large 

share of the home-based workforce. However, the share of homebased work performed by private 

wage and salary workers has been 

increasing. Table 7 presents the 

change in home-based worker 

estimates by class of worker, 

industry, and occupation during 

the 2000s using data from Census 

2000, the 2005 ACS, and the 2010 

ACS. Between 2000 and 2010, 

there was a 67 percent increase in 

home-based work for employees of 

private companies. Although still 

underrepresented among homebased workers, the largest increase 

in home-based work during this 

decade was among government 

workers, increasing 133 percent 

among state government workers and 88 percent among federal 

government workers.15 Government 

15 Because the margin of error for unpaid 

workers is large, these estimates are not 

statistically different from the estimate for 

unpaid family workers.

Table 7. 

Percentage of Workers Who Work From Home by Class of Worker, Industry, and 

Occupation: Census 2000, and 2005 and 2010 American Community Surveys—Con.

(Civilian employed aged 16 years and older. Numbers in thousands)

Characteristic

Decennial census American Community Survey

2000 2005 2010

2000–

2010

2005–

2010

Total 

employed

Work at

home1

Total 

employed

Work at

home1

Total 

employed

Work at

home1

Number Number

Percent Number NumberPercent Number NumberPercent

Percent 

change

Percent 

change

Occupation3

Management,

business, and

 financial. . . . . . . . . . . . 17,249 1,022 5.9 18,245 1,216 6.7 19,690 1,450 7.4 *24.3 *10.5

Computer, engineering,

and science. . . . . . . . . . 6,951 252 3.6 6,972 327 4.7 7,066 432 6.1 *68.6 *30.2

Education, legal,

community service,

 arts, and media. . . . . . 12,966 538 4.2 13,348 610 4.6 14,647 770 5.3 *26.6 *15.0

Healthcare practitioners

and technicians. . . . . . . 5,858 55 0.9 6,523 61 0.9 7,465 100 1.3 *40.9 *41.7

Service. . . . . . . . . . . . . . . 18,782 769 4.1 21,489 798 3.7 24,454 956 3.9 *–4.5 *5.3

Sales and related. . . . . . . 14,308 582 4.1 15,259 746 4.9 15,094 849 5.6 *38.4 *15.1

Office and administrative

support. . . . . . . . . . . . . 19,654 457 2.3 19,174 484 2.5 18,952 595 3.1 *35.1 *24.3

Farming, fishing, and

forestry. . . . . . . . . . . . . . (

4

) (

4

) (4) (4) (4) (4) (4) (4) (4) (

4

) (

4

)

Construction and

extraction. . . . . . . . . . . . 6,964 87 1.3 8,429 167 2.0 6,861 197 2.9 *129.0 *44.9

Installation, maintenance,

and repair. . . . . . . . . . . 5,014 93 1.8 4,805 100 2.1 4,496 116 2.6 *39.7 *23.6

Production,

transportation, and

 material moving. . . . . . 18,485 243 1.3 17,238 229 1.3 16,174 296 1.8 *38.9 *37.8

* Statistically different from zero at the 90 percent confidence level.

1

 ACS work at home workers are defined as those who reported working at home on a question about how they usually commuted to work (see Figure 1).

2

 Industry codes are based on the 2002 and 2007 North American Industry Classification System. 

3

 Occupation codes are based on the 2000 and 2010 Standard Occupational Classification (SOC). Occupation data from 2010 are not strictly comparable

to data from prior years.

4

 Due to changes in the 2010 SOC, some cases previously coded to farming, fishing, and forestry are now coded to management, business, and financial

occupations making direct comparisons of farming, fishing, and forestry across years inappropriate in this report. Comparisons are allowed for management,

business, and financial occupations because the cases previously coded to farming, fishing, and forestry make up a small proportion of the category and

we determined that the changes had no meaningful impact on the resulting estimates. For more information on the 2010 occupation changes, please visit

<www.census.gov/hhes/www/ioindex/crosswalks.html>. 

Note: Estimates from this table exclude those in the Armed Forces. 

Source: U.S. Census Bureau, 2010 1-year American Community Survey.
U.S. Census Bureau 13

Figure 4.

Occupation Distribution by Total Workforce and Home-Based Worker Workforce:

American Community Survey, 2010

Source: U.S. Census Bureau, 2010 1-year American Community Survey.

Share of workforce

Percent

Share of home-based workers

0 5 10 15 20 25

Farming, fishing,

and forestry 

Healthcare practitioners

and technical 

Installation, maintenance,

and repair 

Construction and extraction

Production, transportation,

and material moving

Computer, engineering,

and science

Office and

administrative support

Education, legal, community

service, arts, and media

Sales and related

Service

Management, business,

science, and arts
14 U.S. Census Bureau

workers are disproportionately 

likely to be in management, business, computer, and science occupations, which have higher rates 

of home-based work, due, in part, 

to work portability. Recent government initiatives and industrial 

changes have contributed to the 

growth in home-based work. With 

the aging of the population, there 

is a growing number of homebased care providers. Communication technologies have also made 

work from home more accessible. 

In 2000, the government enacted 

legislation (Public Law No. 106-346 

§ 359) instructing federal agencies 

to review the expansion of telework (home-based and mixed) to 

save taxpayer dollars by reducing 

real estate costs, to reduce traffic 

congestion and emissions, and to 

ensure continuity of operations during emergency or weather-related 

events, among other reasons. The 

Office of Personnel Management’s 

annual reports to Congress show 

a modest increase in home-based 

work across federal agencies.16

Professional and scientific industries, followed by educational 

services and health care industries 

made up the largest share of homebased work in 2010 (Table 6). 

16 United States Office of Personnel 

Management. 2010. “Status of Telework in 

the Federal Government,” available at

 <www.telework.gov>.

Of home-based workers, 23 percent 

were in professional, scientific, and 

management, and administrative 

and waste management industries. 

An additional 17 percent were in 

educational services, and health 

care and social assistance. However, compared with their share of 

the industrial distribution, educational services and health care 

industries were underrepresented 

in home-based work, while professional and scientific industries 

were overrepresented among 

home-based workers. Professional 

and scientific workers made up 11 

percent of the workforce, but were 

23 percent of home-based workers. 

Source: U.S. Census Bureau, 2010 1-year American Community Survey.

0

10

20

30

40

50

60

70

80

90

100

Home-based

women

Home-based

men

Total employed

women

Total employed

men

Figure 5.

Class of Worker Distribution by Sex for Total Workforce and Home-Based Worker

Workforce: American Community Survey, 2010

Percent

Unpaid family workers

Self-employed,

incorporated workers

Self-employed, 

not incorporated workers

Federal government workers

State government workers

Local government workers

Private not-for-profit wage

and salary workers

Employee of private 

company workers
U.S. Census Bureau 15

In contrast, educational services 

and healthcare workers made up 23 

percent of the workforce, but only 

17 percent of home-based workers. 

Table 7 presents workforce shares 

of home-based workers by different categories of class of worker, 

industry, and occupation for

Census 2000, the 2005 ACS, and 

the 2010 ACS. Workers in management, business, and financial 

occupations make up the largest 

share of home-based workers. 

Between 2000 and 2010, homebased work increased 24 percent 

in this category. Home-based work 

in computer, engineering, and 

science occupations increased 69 

percent between 2000 and 2010 

(Table 7). Workers in these occupations are more likely to be able to 

benefit from computer technology, 

voice and electronic communications, and remote connectivity to 

complete their work. In contrast, 

occupations in which work must 

be performed onsite are less likely 

to benefit from this technology. 

Figure 4 shows the occupational 

distribution of home-based workers compared to their share of the 

workforce. Healthcare practitioners, construction, installation, and 

production workers are much less 

likely to be home-based workers, 

and they have disproportionately 

low rates of home-based work 

compared with their share of the 

workforce. 

Some studies have examined 

whether women work from home 

to increase compatibility between 

work and family responsibilities.17 

Appendix Table 1 presents estimates for class of worker, industry, 

and occupation by sex and work 

location. This table reflects that 

men and women work in different sectors of the economy and 

have different jobs. However, there 

were few large differences between 

men and women in the percentage that were home-based workers 

after accounting for their class of 

worker, industrial, and occupational 

distributions. Figure 5 shows that 

men and women were both more 

likely to work from home when 

they were self-employed, but Figure 

6 shows that a larger proportion of 

17 See Edwards, Linda N. and Elizabeth 

Field-Hendrey. 2002. “Home-Based Work and 

Women’s Labor Force Decisions,” Journal of 

Labor Economics 20(1):170–200.

Figure 6.

Percentage of Workers Who Work From Home by Class of Worker and Sex:

American Community Survey, 2010

Source: U.S. Census Bureau, 2010 1-year American Community Survey.

Percent Men Women

0

5

10

15

20

25

30

35

Unpaid

family

workers

Self-employed,

incorporated

workers

Selfemployed,

not

incorporated

workers

Federal

government

workers

State

government

workers

Local

government

workers

Private

not-for-profit

wage and

salary workers

Employee of

private

company

workers
16 U.S. Census Bureau

self-employed women worked from 

home. Among self-employed workers in unincorporated businesses, 

20 percent of men worked from 

home compared with 30 percent 

of women. Among self-employed 

workers in incorporated businesses, 15 percent of men worked 

from home while 24 percent of 

women worked from home. There 

was a 10 percentage-point gap 

among unpaid family workers, as 

17 percent of men worked from 

home and 27 percent of women 

worked from home. No other category approaches a 10 percentagepoint differential.

Work Schedule Characteristics 

The Work Schedule Topical Module 

in SIPP includes questions about 

days worked, hours worked, type 

of work schedule, and reason for 

work schedule. Table 8 shows days 

worked and days worked only at 

home by work-at-home status. 

While the overall days of the week 

worked for mixed workers tended 

to parallel the days for home workers, mixed workers were as likely, 

or more likely to work each day 

of the week compared with home 

workers.18 Looking at the days of 

the week worked only at home for 

both of these groups, home workers were more likely to work each 

day of the week at home than were 

mixed workers. About 90 percent 

of home workers reported working 

Monday through Friday at home, 

compared with less than 40 percent of mixed workers. The most 

popular days worked at home for 

18 Mixed workers were more likely than 

home workers to work Tuesday, Wednesday, 

Thursday, Friday, and Sunday. The difference 

between the percentage of mixed workers 

that worked on Monday and Saturday compared to home workers was not statistically 

significant. 

mixed workers were Monday (37.6 

percent) and Friday (37.8 percent), 

indicating this group spent the 

majority of the workweek at locations other than home.19

Table 9 displays estimates of work 

schedule type (see Definitions 

box) and the reason for working 

that schedule by work-at-home 

status. Across all groups, working 

a regular daytime schedule was by 

far the most common schedule, 

although onsite workers reported 

working this type of schedule more 

often than both types of homebased workers. About 73 percent 

19 The percentage of mixed workers that 

worked at home on Monday was not statistically significantly different from the percentage that worked at home on Friday. Over half 

of mixed workers (57.5 percent) reported 

working only 1 day at home per week during a typical workweek. See “Table 8: Days 

Worked at Home for Home Workers and Mixed 

Workers: 2010,” available at <www.census

.gov/hhes/commuting/data/workathome

.html>.

Table 8.

Days Worked and Days Worked Only at Home by Work-at-Home Status:

Survey of Income and Program Participation, 2010

(Civilian employed aged 15 years and older. Numbers in thousands)

Characteristic Total employed

Work-at-home status

Onsite workers1 Mixed workers2 Home workers3

Number Percent Number Percent Number Percent Number Percent

 Total . . . . . . . . . . . . . . . . . . . . 141,646 100.0 128,244 100.0 4,028 100.0 9,374 100.0

Days worked

Sunday. . . . . . . . . . . . . . . . . . . . . . . . 21,032 14.8 18,475 14.4 778 19.3 1,779 19.0

Monday. . . . . . . . . . . . . . . . . . . . . . . 126,288 89.2 113,906 88.8 3,774 93.7 8,607 91.8

Tuesday. . . . . . . . . . . . . . . . . . . . . . . 127,633 90.1 115,243 89.9 3,799 94.3 8,592 91.7

Wednesday. . . . . . . . . . . . . . . . . . . . 127,737 90.2 115,339 89.9 3,815 94.7 8,583 91.6

Thursday. . . . . . . . . . . . . . . . . . . . . . 126,789 89.5 114,549 89.3 3,764 93.4 8,477 90.4

Friday. . . . . . . . . . . . . . . . . . . . . . . . . 123,988 87.5 112,143 87.4 3,562 88.5 8,283 88.4

Saturday. . . . . . . . . . . . . . . . . . . . . . 31,242 22.1 27,362 21.3 1,270 31.5 2,610 27.8

Days worked only at home

Sunday. . . . . . . . . . . . . . . . . . . . . . . . 2,060 1.5 – – 281 7.0 1,779 19.0

Monday. . . . . . . . . . . . . . . . . . . . . . . 10,123 7.1 – – 1,516 37.6 8,607 91.8

Tuesday. . . . . . . . . . . . . . . . . . . . . . . 9,922 7.0 – – 1,330 33.0 8,592 91.7

Wednesday. . . . . . . . . . . . . . . . . . . . 9,922 7.0 – – 1,339 33.3 8,583 91.6

Thursday. . . . . . . . . . . . . . . . . . . . . . 9,624 6.8 – – 1,147 28.5 8,477 90.4

Friday. . . . . . . . . . . . . . . . . . . . . . . . . 9,806 6.9 – – 1,522 37.8 8,283 88.4

Saturday. . . . . . . . . . . . . . . . . . . . . . 3,102 2.2 – – 492 12.2 2,610 27.8

– Represents or rounds to zero.

1

 Onsite workers are defined as those who did not work a full workday at home as part of their work schedule.

2

 Mixed workers are defined as those who worked at home at least 1 full day a week but also worked other days in a location outside of their home. 

3

 Home workers are defined as those who worked exclusively at home (i.e., every day they worked, they worked at home). 

Note: Estimates from this table exclude those in the Armed Forces. 

Source: U.S. Census Bureau, Survey of Income and Program Participation, 2008 Panel, wave 5.
U.S. Census Bureau 17

of onsite workers, 66 percent of 

mixed workers, and 59 percent 

of home workers worked a regular daytime schedule. Both mixed 

(25.3 percent) and home workers 

(30.5 percent) were more likely to 

report working irregular schedules 

compared with onsite workers 

(12.1 percent). 

When asked the reason for working the reported schedule, workers 

were allowed to choose voluntary 

or involuntary reasons. The reasons 

selected by mixed workers tended 

to be similar to those selected 

by onsite workers, while home 

workers chose voluntary reasons 

more frequently than the other 

two groups. For example, about 

80 percent of onsite workers and 

mixed workers reported involuntary reasons for working their 

work schedules, while less than 

75 percent of home workers did 

so. The most common voluntary 

reason selected by home workers 

was “other voluntary reason” (16.3 

percent), a response selected by 

only 7.4 percent of onsite workers and 11.7 percent of mixed 

workers.20 The tendency for home 

workers, but not mixed workers, 

to select voluntary reasons more 

frequently than onsite workers may be explained by greater 

20 The difference between onsite and 

mixed workers, and mixed and home 

workers, is not statistically significant. 

levels of self-employment among 

home workers. 

Figure 7 also shows hours worked 

per typical workweek by work-athome status. Home workers were 

less likely to fall in the 35 hours 

or more category (52.6 percent) 

than mixed workers (67.6 percent) 

and onsite workers (66.8 percent), 

and more likely to report that their 

hours varied from week to week 

(23.9 percent) compared with the 

other two groups. The latter finding 

for home workers is not surprising given that 30 percent of this 

group worked irregular schedules. 

However, one quarter of mixed 

workers also reported working 

irregular schedules, but there were 

Table 9.

Work Schedule and Reason for Work Schedule by Work-at-Home Status:

Survey of Income and Program Participation, 2010

(Civilian employed aged 15 years and older. Numbers in thousands)

Characteristic

Total employed

Work-at-home status

Onsite1 Mixed workers2 Home workers3

Number Percent Number Percent Number Percent Number Percent

 Total . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141,646 100.0 128,244 100.0 4,028 100.0 9,374 100.0

Type of schedule

Regular daytime schedule. . . . . . . . . . . . . . . . . . . . . . . . . . . . 101,977 72.0 93,766 73.1 2,675 66.4 5,536 59.1

Regular evening shift. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7,239 5.1 7,018 5.5 51 1.3 169 1.8

Regular night shift. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3,729 2.6 3,652 2.8 12 0.3 66 0.7

Rotating shift. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4,729 3.3 4,478 3.5 117 2.9 134 1.4

Split shift. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,139 0.8 1,009 0.8 39 1.0 90 1.0

Irregular schedule. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19,383 13.7 15,506 12.1 1,020 25.3 2,857 30.5

Other. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3,451 2.4 2,815 2.2 113 2.8 522 5.6

Reason for working schedule

Voluntary reasons

 Total . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25,364 18.0 22,059 17.2 801 19.9 2,504 26.7

 Better child care arrangements. . . . . . . . . . . . . . . . . . . . . . 4,376 3.1 3,768 2.9 166 4.1 442 4.7

 Better pay. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2,545 1.8 2,315 1.8 70 1.7 159 1.7

 Better arrangements for care of other family members. . . . 2,373 1.7 2,043 1.6 60 1.5 271 2.9

 Allows time for school. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4,618 3.3 4,483 3.5 36 0.9 100 1.1

 Other voluntary reasons. . . . . . . . . . . . . . . . . . . . . . . . . . . . 11,452 8.1 9,450 7.4 470 11.7 1,532 16.3

Involuntary reasons

 Total . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116,282 82.0 106,185 82.8 3,227 80.2 6,870 73.3

 Could not get any other job. . . . . . . . . . . . . . . . . . . . . . . . . 2,576 1.8 2,370 1.8 75 1.9 132 1.4

 Requirement of the job. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111,256 78.5 101,785 79.4 3,068 76.2 6,403 68.3

 Other involuntary reasons. . . . . . . . . . . . . . . . . . . . . . . . . . 2,449 1.7 2,030 1.6 84 2.1 335 3.6

1

 Onsite workers are defined as those who did not work a full workday at home as part of their work schedule.

2

 Mixed workers are defined as those who worked at home at least 1 full day a week but also worked other days in a location outside of their home. 

3

 Home workers are defined as those who worked exclusively at home (i.e., every day they worked, they worked at home). 

Note: Estimates from this table exclude those in the Armed Forces. 

Source: U.S. Census Bureau, Survey of Income and Program Participation, 2008 Panel, wave 5.
18 U.S. Census Bureau

Figure 7.

Hours Worked Per Typical Work Week by Work-at-Home Status: Survey of Income

and Program Participation, 2010

Source: U.S. Census Bureau, Survey of Income and Program Participation, 2008 Panel, wave 5.

Percent

Total workers Onsite workers Mixed workers Home workers

0

10

20

30

40

50

60

70

80

0-7 hours 8-15 hours 16-23 hours 24-34 hours 35 or more hours Hours vary

Table 10.

Top 10 U.S. Metropolitan Statistical Areas Ranked by 

Percentage of Workers 16 Years and Over Who Worked 

From Home: American Community Survey, 2010

Rank Metropolitan statistical area Percent1

Margin of

error2 (±)

1 Boulder, CO*. . . . . . . . . . . . . . . . . . . . . . . . 10.9 1.3

2 Medford, OR. . . . . . . . . . . . . . . . . . . . . . . . 8.4 1.9

3 Santa Fe, NM. . . . . . . . . . . . . . . . . . . . . . . . 8.3 1.9

4 Kingston, NY. . . . . . . . . . . . . . . . . . . . . . . . 8.1 2.1

5 Santa Rosa-Petaluma, CA. . . . . . . . . . . . . . 7.9 1.1

6 Mankato-North Mankato, MN. . . . . . . . . . . 7.7 1.9

7 Prescott, AZ. . . . . . . . . . . . . . . . . . . . . . . . . 7.6 1.8

8 St. Cloud, MN. . . . . . . . . . . . . . . . . . . . . . . 7.6 1.1

9 Athens-Clarke County, GA. . . . . . . . . . . . . . 7.5 2.0

10 Austin-Round Rock-San Marcos, TX. . . . . . 7.3 0.5

*Statistically different from the other metropolitan statistical areas at the 90 percent confidence level.

1

 ACS home workers are defined as those who reported “working at home” on a question about how 

they “usually” commuted to work (see Figure 1).

2

 This number, when added to or subtracted from the estimate, represents the 90 percent confidence 

interval around the estimate. 

Note: Because of sampling error, the estimates in this table may not be significantly different from one 

another. Estimates from this table exclude those in the Armed Forces.

Source: U.S. Census Bureau, 2010 1-year American Community Survey.
U.S. Census Bureau 19 Source: U.S. Census Bureau, 2010 1-year American Community Survey. 0 500Miles 0 100 Miles 0 100 Miles Percent5.00 - 7.49 2.50 - 4.99 Less than 2.50 Note: Metropolitan Statistical Areas defined by the Office of Management and Budget as of December 2009. 7.50 and over Percentage of Workers Who Work From Home Fi by Metropolitan Statistical Area: 2010 gure 8.
20 U.S. Census Bureau

no significant differences in hours 

worked between mixed workers 

and onsite workers.

Mixed workers appear to work the 

same number of hours as onsite 

workers, but they substitute some 

days in the office with days working at home. 

Metropolitan Variation

Based on data from the ACS,

Table 10 shows ten of the highestranked metropolitan statistical 

areas (metro areas) by percentage 

of workers who worked from home 

in 2010. The list includes metro 

areas in the West and the South 

with the exceptions of Kingston, 

NY (located about 100 miles north 

of New York City and about 20 

miles north of Poughkeepsie, NY) 

and Mankato-North Mankato, MN 

(located about 85 miles southwest 

of Minneapolis, MN). Boulder, CO, 

had among the highest percentage 

of workers who worked from home 

with 10.9 percent.21 

Appendix Table 2 shows the percentage of workers who worked 

at home for all metro areas (based 

on 2010 ACS population estimates) 

for 2005 and 2010 and the percentage difference between the 

2 data years. Some metro areas 

had declines in respondents who 

reported that they worked from 

home between 2005 and 2010, 

but most metro areas saw a small 

increase. The percentage point

difference from 2005 to 2010 

ranged from –2.8 percent to 

4.6 percent. Wenatchee-East 

Wenatchee, WA, went from

21 The estimate for Boulder, CO, was 

significantly different from the estimate for 

the other nine metropolitan areas on the top 

ten list at a 90 percent confidence interval, but not from that of Muskegon-Norton 

Shores, MI, the 11th ranked metro area. The 

estimates for the other nine metropolitan 

areas on the top ten list were not statistically 

different from one another.

6.3 percent to 3.5 percent and 

Muskegon-Norton Shores, MI, 

and Athens-Clarke County, GA, 

each had increases of 4.6 percent (2.6 percent to 7.2 percent 

and 2.9 percent to 7.5 percent, 

respectively).22 In 2005, the percentage who worked from home 

in all metro areas ranged from 

0.5 percent (Anniston-Oxford, AL) 

to 9.6 percent (Boulder, CO). In 

2010, Boulder, CO, had among the 

highest percentage of home-based 

workers, with 10.9 percent, while 

Anniston-Oxford, AL, was among 

the smallest, with 0.5 percent. 

Figure 8 is a map of the 366 metro 

areas by the percentage of workers 

who worked at home, according to 

the 2010 ACS. Following the pattern of the top ten metro areas with 

percentage of workers who worked 

at home, many of the metro areas 

in the Southeast, Southwest, and 

West fall into the two highest categories, 5 percent to 7.49 percent 

and 7.5 percent and over. Many of 

the metro areas that fall into the 

highest category (7.5 percent and 

over) also tend to be among the 

smaller metro areas. Generally, the 

metro areas that are in the middle 

of the country (from Minnesota to 

Texas) and in the Northeast tend to 

have less than 5 percent of workers 

that worked at home. 

CONCLUSION

This report shows that the percentage of the workforce that worked 

at least 1 day at home during a 

typical workweek increased substantially between 1997 and 2010. 

This increase extended to those 

that worked exclusively at home 

and to those who worked both at 

home and onsite. 

22 The percentage point difference 

between the home-based worker estimates in 

2005 and 2010 for Muskegon-Norton Shores, 

MI, is not statistically significant. 

There was some geographic 

variation in the expansion of 

home-based work across metropolitan areas, but in general, the 

majority of metropolitan areas saw 

increases in home-based workers. 

Many of the most notable changes 

in home-based work between 2000 

and 2010 were in the characteristics of home-based workers. While 

the self-employed have been more 

likely to work from home, this is 

changing. Between 2000 and 2010, 

the increase in home-based workers was primarily among private 

wage and salary workers and 

government workers. Differences 

by occupation in the expansion of 

home-based work were also apparent. Between 2000 and 2010, there 

was substantial growth of homebased work in computer, engineering, and science occupations. 

Although both jobs that included 

working exclusively from home 

and those that required working 

both at home and onsite increased 

during the previous decade, these 

two groups differed on a number 

of characteristics. Private wage 

and salary workers predominately 

engaged in mixed work while home 

workers were more likely to be 

self-employed. Mixed workers also 

tended to work the same number 

of hours per week and give similar reasons for working the work 

schedule as onsite workers, who 

were also predominately private 

wage and salary workers. Homeworkers worked shorter hours and 

were more likely to report their 

hours varied from week to week 

compared with mixed and onsite 

workers. One of the most striking 

differences between these groups 

was in personal earnings. Mixed 

workers had median personal earnings ($52,800), more than twice 

that of home-workers ($25,500).
U.S. Census Bureau 21

SOURCES OF THE DATA

The data in this report are from 

the 2008 Survey of Income and 

Program Participation (SIPP) and 

the 2010 American Community 

Survey (ACS). Some estimates are 

also derived from Census 2000, as 

well as previous years of the SIPP 

and ACS. 

The population represented (the 

population universe) in the 2008 

SIPP is the civilian noninstitutionalized population living in 

the United States. The SIPP is a 

longitudinal survey conducted at 

4-month intervals. The data in this 

report were collected from January 

through April of 2010 in the fifth 

wave (interview) of the 2008 SIPP. 

The data highlighted in this report 

come primarily from the main survey and the work schedule topical 

module that included questions 

for all people 15 years and older, 

employed during the fourth month 

of the reference period on employment history and job details, 

including whether respondents 

worked at home during a typical 

week in the previous month and 

whether there were any days when 

the respondent worked entirely at 

home. Although the main focus 

of the SIPP is information on labor 

force participation, jobs, income, 

and participation in federal assistance programs, information on 

other topics, such as home-based 

workers, is also collected in topical 

modules on a rotating basis. The 

institutionalized population, which 

is excluded from the population 

universe, is composed primarily 

of the population in correctional 

institutions and nursing homes 

(91 percent of the 4.1 million 

institutionalized population in 

Census 2000).

The population represented (the 

population universe) in the ACS 

and the Census 2000 is the population living in both households and 

group quarters (that is, the resident 

population). The group quarters 

population consists of the institutionalized population (such as 

people in correctional intuitions or 

nursing homes) and noninstitutionalized population (most of whom 

are in college dormitories). This 

report excludes the institutionalized population.

ACCURACY OF 

THE ESTIMATES

Statistics from surveys are subject 

to sampling and nonsampling error. 

All comparisons presented in this 

report have taken sampling error 

into account and are significant 

at the 90 percent confidence level 

unless otherwise noted. This means 

the 90 percent confidence interval for the difference between the 

estimates being compared does not 

include zero. Nonsampling errors 

in surveys may be attributed to a 

variety of sources, such as how the 

survey was designed, how people 

interpret questions, how able and 

willing people are to provide correct answers, and how accurately 

answers are coded and classified. 

To minimize these errors, the Census Bureau employs quality control 

procedures during all stages of the 

production process, including the 

design of surveys, the wording of 

questions, review of the work of 

interviewers and coders, and statistical review of reports. The SIPP 

weighting procedure uses ratio estimation, whereby sample estimates 

are adjusted to independent estimates of the national population by 

age, race, sex, and Hispanic origin. 

This weighting partially corrects 

for bias due to undercoverage, but 

biases may still be present when 

people who are missed by the survey differ from those interviewed in 

ways other than age, race, sex, and 

Hispanic origin. All of these considerations affect comparisons across 

different surveys or data sources.

Further information on the source 

of the data and accuracy of the estimates, including standard errors 

and confidence intervals, is available at <www.census.gov/sipp

/sourceac/S&A08_W1toW6(S&A-13)

.pdf>, or contact Tracy Mattingly of 

the Census Bureau’s Demographic 

Statistical Methods Division via 

e-mail at <tracy.l.mattingly

@census.gov>.

The final ACS population estimates 

are adjusted in the weighting procedure for coverage error by controlling specific survey estimates to 

independent population controls by 

sex, age, race, and Hispanic origin. 

The final ACS estimates of housing 

units are controlled to independent 

estimates of total housing. This 

weighting partially corrects for bias 

due to over- or undercoverage, 

but biases may still be present, for 

example, when people who are 

missed by the survey differ from 

those interviewed in ways other 

than sex, age, race, and Hispanic 

origin. How this weighting procedure affects other variables in the 

survey is not precisely known. 

All these considerations affect

comparisons across different

surveys or data sources. 
22 U.S. Census Bureau

For further information on the ACS 

sample, weighting procedures, 

sampling error, nonsampling error, 

and quality measures from the ACS, 

see <www.census.gov/acs

/www/Downloads/data

_documentation/Accuracy/ACS

_Accuracy_of_Data_2010.pdf>.

Census 2000 data contained in this 

report are based on the sample of 

households who responded to the 

Census 2000 long form. Nationally, approximately 1 out of every 

6 housing units was included in 

this sample. As a result, the sample 

estimates may differ somewhat 

from the 100-percent figures that 

would have been obtained if all 

housing units, people within those 

housing units, and people living 

in group quarters had been enumerated using the same questionnaires, instructions, enumerators, 

and so forth. The sample estimates 

also differ from the values that 

would have been obtained from 

different samples of housing units, 

people within those housing units, 

and people living in group quarters. 

While it is impossible to completely 

eliminate error from an operation 

as large and complex as the decennial census, the Census Bureau 

attempts to control the sources of 

such error during the data collection and processing operations. The 

primary sources of error and the 

programs instituted to control error 

in Census 2000 are described in 

detail in Summary File 3 Technical 

Documentation under Chapter 8, 

“Accuracy of the Data,” located at 

<www.census.gov/prod/cen2000

/doc/sf3.pdf>.

FOR MORE INFORMATION

Additional information on the SIPP 

can be found at the following 

Web sites: <www.census.gov

/sipp/index.html> (the main SIPP 

Web site), <www.census.gov/sipp

/workpapr/wp230.pdf> (SIPP Quality Profile), and <www.census.gov

/sipp/usrguide/sipp2001.pdf> 

(SIPP User’s Guide).

To access additional information

on working from home, visit the 

Census Bureau’s Commuting

(Journey to Work) Web site at

<www.census.gov/hhes

/commuting/data

/workathome.html>. 

To access ACS tables about working from home, visit the American 

FactFinder on the Census Bureau’s 

Web site at <http://factfinder2

.census.gov>.

Information on other population 

and housing topics is presented in 

the Census 2000 Brief and Special 

Reports Series, located on the 

Census Bureau’s Web site at

<www.census.gov/population

/www/cen2000/briefs/index

.html>. These series present information about race, Hispanic origin, 

age, sex, household type, housing 

tenure, and other social, economic, 

and housing characteristics. 

Census 2000 information and 

data can also be accessed via the 

Census 2000 Gateway Web site at 

<www.census.gov/main/www

/cen2000.html>. 

For more information about 

Census 2000, including data products, call our Customer Services 

Center at 301-763-INFO (4636) or 

e-mail <webmaster@census.gov>.

CONTACTS

Contact U.S. Census Bureau

Customer Services Center at 

800-923-8282 (toll free) or visit 

<ask.census.gov> for further 

information.

For additional information on these 

topics, contact the authors of this 

report—

Journey to Work and Migration 

Statistics Branch:

Peter J. Mateyka

301-763-2356

peter.mateyka@census.gov

Melanie A. Rapino

301-763-5877

melanie.rapino@census.gov

Industry and Occupation 

Statistics Branch:

Liana Christin Landivar

301-763-5878

liana.christin.landivar@census.gov

SUGGESTED CITATION

Mateyka, Peter J., Melanie A. 

Rapino, and Liana Christin Landivar, 

P70-132, “Home-Based Workers in 

the United States: 2010,” Current 

Population Reports, U.S. Census 

Bureau, Washington, DC, 2012.
U.S. Census Bureau 23

Appendix Table 1. 

Percentage of Workers Who Work From Home by Class of Worker, Industry, Occupation, 

and Sex: American Community Survey, 2010

(Civilian employed aged 16 years and older. Numbers in thousands)

Characteristic

Men Women

Malefemale 

difference in 

percent 

homebased2

Total

employed

Home-based

workers1

Total

employed

Home-based

workers1

NumberPercentNumberPercent

Percentage of 

workers 

homebasedNumberPercentNumberPercent

Percentage of 

workers 

homebased

Class of worker

Employee of private company. . . . . . . . . . . . . . 49,500 69.7 1,166 39.2 2.4 41,313 63.7 1,118 39.4 2.7 *–0.4

Private not-for-profit wage and salary. . . . . . . . 3,743 5.3 126 4.2 3.4 7,227 11.1 181 6.4 2.5 *0.9

Local government. . . . . . . . . . . . . . . . . . . . . . . 4,240 6.0 40 1.3 0.9 6,213 9.6 74 2.6 1.2 *–0.3

State government. . . . . . . . . . . . . . . . . . . . . . . . 2,611 3.7 57 1.9 2.2 3,659 5.6 94 3.3 2.6 *–0.4

Federal government. . . . . . . . . . . . . . . . . . . . . . 2,223 3.1 29 1.0 1.3 1,681 2.6 29 1.0 1.7 *–0.4

Self-employed in own not incorporated

 business. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5,195 7.3 1,036 34.8 19.9 3,272 5.0 994 35.0 30.4 *–10.5

Self-employed in own incorporated

 business. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3,474 4.9 507 17.1 14.6 1,387 2.1 326 11.5 23.5 *–8.9

Unpaid family. . . . . . . . . . . . . . . . . . . . . . . . . . . 78 0.1 13 0.4 17.0 90 0.1 24 0.8 26.8 *–9.9

Industry3

Agriculture, forestry, fishing, and hunting,

 and mining. . . . . . . . . . . . . . . . . . . . . . . . . . . . 2,117 3.0 239 8.0 11.3 457 0.7 72 2.5 15.8 *–4.5

Construction. . . . . . . . . . . . . . . . . . . . . . . . . . . . 7,669 10.8 284 9.6 3.7 776 1.2 77 2.7 9.9 *–6.2

Manufacturing. . . . . . . . . . . . . . . . . . . . . . . . . . 10,055 14.1 256 8.6 2.5 4,132 6.4 134 4.7 3.3 *–0.7

Wholesale trade. . . . . . . . . . . . . . . . . . . . . . . . . 2,729 3.8 149 5.0 5.5 1,151 1.8 76 2.7 6.6 *–1.1

Retail trade. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8,011 11.3 203 6.8 2.5 7,852 12.1 220 7.7 2.8 *–0.3

Transportation and warehousing and

 utilities. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5,065 7.1 124 4.2 2.4 1,605 2.5 49 1.7 3.1 *–0.6

Information. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,679 2.4 112 3.8 6.6 1,281 2.0 86 3.0 6.7 –0.1

Finance and insurance, and real estate, and

 rental and leasing. . . . . . . . . . . . . . . . . . . . . . . 4,088 5.8 292 9.8 7.1 5,041 7.8 273 9.6 5.4 *1.7

Professional, scientific, and management,

 and administrative and waste management. . . 8,387 11.8 721 24.3 8.6 6,052 9.3 621 21.9 10.3 *–1.7

Educational services, and health care and

 social assistance. . . . . . . . . . . . . . . . . . . . . . . 7,998 11.3 225 7.6 2.8 23,328 36.0 774 27.3 3.3 *–0.5

Arts, entertainment, and recreation, and

 accommodation and food services. . . . . . . . . 6,152 8.7 154 5.2 2.5 6,422 9.9 157 5.5 2.4 0.1

Other services, except public administration. . . 3,205 4.5 166 5.6 5.2 3,582 5.5 234 8.2 6.5 *–1.4

Public administration. . . . . . . . . . . . . . . . . . . . . 3,909 5.5 49 1.7 1.3 3,164 4.9 67 2.4 2.1 *–0.9

Occupation4

Management, business, and financial. . . . . . . . 11,012 15.5 877 29.5 8.0 8,678 13.4 573 20.2 6.6 *1.4

Computer, engineering, and science. . . . . . . . . 5,252 7.4 309 10.4 5.9 1,814 2.8 123 4.3 6.8 *–0.9

Education, legal, community service, arts,

 and media. . . . . . . . . . . . . . . . . . . . . . . . . . . . 5,226 7.4 330 11.1 6.3 9,421 14.5 439 15.5 4.7 *1.7

Healthcare practitioners and technicians. . . . . . 1,895 2.7 27 0.9 1.4 5,569 8.6 72 2.5 1.3 *0.2

Service. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10,643 15.0 264 8.9 2.5 13,811 21.3 692 24.3 5.0 *–2.5

Sales and related. . . . . . . . . . . . . . . . . . . . . . . . 7,628 10.7 501 16.8 6.6 7,466 11.5 349 12.3 4.7 *1.9

Office and administrative support. . . . . . . . . . . 5,081 7.2 118 4.0 2.3 13,871 21.4 477 16.8 3.4 *–1.1

Farming, fishing, and forestry. . . . . . . . . . . . . . 815 1.1 44 1.5 5.4 193 0.3 11 0.4 5.7 –0.4

Construction and extraction. . . . . . . . . . . . . . . . 6,676 9.4 187 6.3 2.8 185 0.3 10 0.3 5.3 *–2.5

Installation, maintenance, and repair. . . . . . . . . 4,315 6.1 109 3.7 2.5 181 0.3 7 0.3 4.1 –1.5

Production, transportation, and material

 moving. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12,521 17.6 208 7.0 1.7 3,654 5.6 88 3.1 2.4 *–0.7

*Statistically different from zero at the 90 percent confidence level.

1

 ACS home workers are defined as those who reported working at home on a question about how they usually commuted to work.

2 Calculated using unrounded percents. 

3 Industry codes are based on the 2007 North American Industry Classification System. 

4 Occupation codes are based on 2010 Standard Occupational Classification. 

Note: Estimates from this table exclude those in the Armed Forces.

Source: U.S. Census Bureau, 2010 1-year American Community Survey.
24 U.S. Census Bureau

Appendix Table 2. 

Percentage of Workers Who Work From Home for All Metropolitan Statistical Areas: 

American Community Survey, 2005 and 2010—Con.

(Civilian employed aged 16 years and older)

Metropolitan area

2005 Worked at home 2010 Worked at home

Change in worked at home–

(2010 less 2005) 

Estimate

Margin 

of error

Percent

Margin of 

error Estimate

Margin 

of error

Percent

Margin of 

error

EstimateMargin 

of error

PercentMargin 

of error

Abilene, TX. . . . . . . . . . . . . . . . . . . . . . . 1,615 569 2.6 0.9 2,278 1,081 3.3 1.6 663 1,225 0.7 1.8

Akron, OH. . . . . . . . . . . . . . . . . . . . . . . . 10,163 1,524 3.1 0.5 9,130 1,400 2.9 0.4 –1,033 2,073 –0.2 0.6

Albany, GA. . . . . . . . . . . . . . . . . . . . . . . . 1,944 796 3.0 1.2 858 623 1.5 1.1 –1,086 1,012 –1.5 1.6

Albany-Schenectady-Troy, NY. . . . . . . . . 10,653 1,617 2.7 0.4 16,227 2,280 3.8 0.5 5,574 2,801 *1.1 0.7

Albuquerque, NM. . . . . . . . . . . . . . . . . . . 15,070 2,165 4.1 0.6 17,610 2,394 4.5 0.6 2,540 3,233 0.3 0.8

Alexandria, LA. . . . . . . . . . . . . . . . . . . . . 965 433 1.6 0.7 1,086 569 1.9 1.0 121 717 0.3 1.2

Allentown-Bethlehem-Easton, PA-NJ. . . 11,387 1,744 3.1 0.5 17,019 2,839 4.6 0.7 5,632 3,339 *1.5 0.9

Altoona, PA. . . . . . . . . . . . . . . . . . . . . . . 1,367 490 2.5 0.9 1,894 705 3.3 1.2 527 861 0.8 1.5

Amarillo, TX. . . . . . . . . . . . . . . . . . . . . . . 1,892 674 1.7 0.6 4,018 1,270 3.3 1.0 2,126 1,441 *1.6 1.2

Ames, IA. . . . . . . . . . . . . . . . . . . . . . . . . 1,212 433 3.0 1.1 1,346 558 2.9 1.2 134 707 –0.1 1.6

Anchorage, AK. . . . . . . . . . . . . . . . . . . . 6,696 1,461 4.3 0.9 7,503 1,514 4.2 0.8 807 2,107 –0.1 1.3

Anderson, IN. . . . . . . . . . . . . . . . . . . . . . 1,321 476 2.5 0.9 1,062 460 2.0 0.8 –259 663 –0.5 1.2

Anderson, SC. . . . . . . . . . . . . . . . . . . . . 1,330 528 1.8 0.7 2,433 800 3.2 1.1 1,103 961 *1.4 1.3

Ann Arbor, MI. . . . . . . . . . . . . . . . . . . . . 7,138 1,259 4.5 0.8 8,374 1,547 5.1 0.9 1,236 1,998 0.6 1.2

Anniston-Oxford, AL. . . . . . . . . . . . . . . . 327 204 0.7 0.4 200 206 0.5 0.5 –127 291 –0.2 0.7

Appleton, WI. . . . . . . . . . . . . . . . . . . . . . 3,790 1,005 3.4 0.9 5,551 1,412 4.8 1.2 1,761 1,737 1.4 1.5

Asheville, NC. . . . . . . . . . . . . . . . . . . . . . 7,953 1,245 4.4 0.7 11,424 1,832 6.3 1.0 3,471 2,220 *1.8 1.2

Athens-Clarke County, GA. . . . . . . . . . . 2,273 732 2.9 0.9 5,906 1,548 7.5 2.0 3,633 1,716 *4.6 2.2

Atlanta-Sandy Springs-Marietta, GA. . . . 105,809 6,535 4.5 0.3 136,442 6,004 5.8 0.3 30,633 8,887 *1.3 0.4

Atlantic City-Hammonton, NJ. . . . . . . . . 2,089 693 1.7 0.6 2,918 757 2.4 0.6 829 1,028 0.7 0.8

Auburn-Opelika, AL. . . . . . . . . . . . . . . . . 950 440 1.7 0.8 1,544 671 2.5 1.1 594 804 0.8 1.3

Augusta-Richmond County, GA-SC. . . . . 5,363 1,256 2.5 0.6 6,671 1,508 3.0 0.7 1,308 1,966 0.6 0.9

Austin-Round Rock-San Marcos, TX. . . . 35,981 3,469 5.0 0.5 61,911 4,050 7.3 0.5 25,930 5,342 *2.3 0.7

Bakersfield-Delano, CA. . . . . . . . . . . . . . 7,503 1,167 2.6 0.4 10,412 1,803 3.5 0.6 2,909 2,153 *0.8 0.7

Baltimore-Towson, MD. . . . . . . . . . . . . . 46,327 4,449 3.7 0.3 51,303 3,915 4.0 0.3 4,976 5,934 0.2 0.5

Bangor, ME. . . . . . . . . . . . . . . . . . . . . . . 2,560 742 3.8 1.1 3,047 906 4.2 1.2 487 1,173 0.5 1.6

Barnstable Town, MA. . . . . . . . . . . . . . . . 4,372 1,181 4.3 1.2 4,180 1,080 4.4 1.1 –192 1,602 0.2 1.6

Baton Rouge, LA. . . . . . . . . . . . . . . . . . . 5,554 1,216 1.7 0.4 8,891 1,711 2.5 0.5 3,337 2,103 *0.7 0.6

Battle Creek, MI. . . . . . . . . . . . . . . . . . . . 864 388 1.4 0.6 1,762 549 3.2 1.0 898 674 *1.7 1.2

Bay City, MI. . . . . . . . . . . . . . . . . . . . . . . 1,192 592 2.4 1.2 1,853 827 4.0 1.8 661 1,019 1.5 2.1

Beaumont-Port Arthur, TX. . . . . . . . . . . . 2,683 800 1.8 0.6 4,161 1,244 2.7 0.8 1,478 1,482 0.9 1.0

Bellingham, WA. . . . . . . . . . . . . . . . . . . . 4,544 1,210 5.1 1.4 4,961 1,287 5.4 1.4 417 1,769 0.3 2.0

Bend, OR. . . . . . . . . . . . . . . . . . . . . . . . . 3,012 722 4.4 1.0 4,284 1,080 6.6 1.7 1,272 1,302 *2.2 2.0

Billings, MT. . . . . . . . . . . . . . . . . . . . . . . 3,016 780 4.0 1.0 3,485 902 4.4 1.1 469 1,194 0.4 1.5

Binghamton, NY. . . . . . . . . . . . . . . . . . . 3,354 1,059 3.1 0.9 3,376 797 3.1 0.7 22 1,326 0.0 1.2

Birmingham-Hoover, AL. . . . . . . . . . . . . 12,741 2,154 2.6 0.4 15,026 2,198 3.1 0.5 2,285 3,083 0.5 0.6

Bismarck, ND. . . . . . . . . . . . . . . . . . . . . . 2,193 704 4.0 1.3 2,182 571 3.7 1.0 –11 907 –0.3 1.6

Blacksburg-ChristiansburgRadford, VA. . . . . . . . . . . . . . . . . . . . . . 2,268 758 3.5 1.1 2,793 734 3.9 1.1 525 1,057 0.4 1.6

Bloomington, IN. . . . . . . . . . . . . . . . . . . . 2,817 888 3.8 1.2 2,496 656 3.0 0.8 –321 1,105 –0.8 1.4

Bloomington-Normal, IL. . . . . . . . . . . . . . 2,064 652 2.7 0.9 2,200 759 2.5 0.9 136 1,002 –0.2 1.2

Boise City-Nampa, ID. . . . . . . . . . . . . . . 13,365 2,520 5.2 1.0 18,682 2,869 6.9 1.0 5,317 3,825 *1.7 1.4

Boston-Cambridge-Quincy, MA-NH. . . . . 76,469 4,029 3.6 0.2 99,802 4,913 4.4 0.2 23,333 6,366 *0.8 0.3

Boulder, CO. . . . . . . . . . . . . . . . . . . . . . . 13,481 1,834 9.6 1.3 16,437 2,053 10.9 1.3 2,956 2,758 1.3 1.9

Bowling Green, KY. . . . . . . . . . . . . . . . . 906 431 1.8 0.8 1,742 724 3.1 1.3 836 845 1.4 1.5

Bremerton-Silverdale, WA. . . . . . . . . . . . 3,433 854 3.5 0.9 6,022 1,182 5.8 1.1 2,589 1,461 *2.4 1.4

Bridgeport-Stamford-Norwalk, CT. . . . . . 17,788 2,146 4.2 0.5 20,847 2,240 4.9 0.5 3,059 3,107 0.7 0.7

Brownsville-Harlingen, TX. . . . . . . . . . . . 1,808 685 1.5 0.6 4,303 1,116 3.1 0.8 2,495 1,312 *1.6 1.0

Brunswick, GA. . . . . . . . . . . . . . . . . . . . . 698 363 1.6 0.9 2,092 1,085 4.7 2.4 1,394 1,147 *3.1 2.5

Buffalo-Niagara Falls, NY. . . . . . . . . . . . 13,194 2,381 2.6 0.5 11,761 1,690 2.3 0.3 –1,433 2,923 –0.3 0.6

Burlington, NC. . . . . . . . . . . . . . . . . . . . . 917 370 1.4 0.6 2,661 987 4.1 1.5 1,744 1,057 *2.6 1.6

Burlington-South Burlington, VT. . . . . . . 4,868 926 4.7 0.9 7,936 1,463 7.1 1.3 3,068 1,735 *2.4 1.6

Canton-Massillon, OH. . . . . . . . . . . . . . . 4,568 916 2.4 0.5 5,985 1,267 3.4 0.7 1,417 1,567 *1.0 0.9

See footnotes at end of table.
U.S. Census Bureau 25

Appendix Table 2. 

Percentage of Workers Who Work From Home for All Metropolitan Statistical Areas: 

American Community Survey, 2005 and 2010—Con.

(Civilian employed aged 16 years and older)

Metropolitan area

2005 Worked at home 2010 Worked at home

Change in worked at home–

(2010 less 2005) 

Estimate

Margin 

of error

Percent

Margin of 

error Estimate

Margin 

of error

Percent

Margin of 

error

EstimateMargin 

of error

PercentMargin 

of error

Cape Coral-Fort Myers, FL. . . . . . . . . . . 8,419 1,303 3.6 0.6 14,671 2,458 6.4 1.0 6,252 2,788 *2.8 1.2

Cape Girardeau-Jackson, MO-IL1. . . . . . 1,670 660 3.9 1.5 558 296 1.3 0.7 –1,112 723 *–2.6 1.7

Carson City, NV. . . . . . . . . . . . . . . . . . . . 684 459 3.0 2.1 320 271 1.4 1.2 –364 533 –1.6 2.4

Casper, WY. . . . . . . . . . . . . . . . . . . . . . . 1,382 513 3.9 1.4 938 508 2.4 1.3 –444 723 –1.4 1.9

Cedar Rapids, IA. . . . . . . . . . . . . . . . . . . 3,748 693 3.0 0.6 6,148 1,121 4.6 0.9 2,400 1,321 *1.6 1.0

Champaign-Urbana, IL. . . . . . . . . . . . . . 2,641 632 2.6 0.6 5,150 1,035 4.7 0.9 2,509 1,215 *2.1 1.1

Charleston, WV. . . . . . . . . . . . . . . . . . . . 4,172 1,350 3.3 1.1 2,081 719 1.7 0.6 –2,091 1,530 *–1.7 1.2

Charleston-North CharlestonSummerville, SC. . . . . . . . . . . . . . . . . . 6,730 1,490 2.5 0.6 11,619 2,061 3.9 0.7 4,889 2,548 *1.4 0.9

Charlotte-Gastonia-Rock Hill, NC-SC. . . 26,188 2,806 3.6 0.4 40,876 3,566 5.1 0.4 14,688 4,546 *1.5 0.6

Charlottesville, VA. . . . . . . . . . . . . . . . . . 4,367 1,064 5.2 1.3 6,505 1,666 7.1 1.8 2,138 1,982 1.9 2.2

Chattanooga, TN-GA. . . . . . . . . . . . . . . . 4,734 1,105 2.2 0.5 8,551 1,466 3.7 0.6 3,817 1,839 *1.5 0.8

Cheyenne, WY. . . . . . . . . . . . . . . . . . . . . 1,890 651 5.0 1.7 2,241 983 5.1 2.3 351 1,181 0.1 2.8

Chicago-Joliet-Naperville, IL-IN-WI. . . . . 141,614 6,621 3.3 0.2 193,266 6,953 4.5 0.2 51,652 9,617 *1.2 0.2

Chico, CA. . . . . . . . . . . . . . . . . . . . . . . . 5,661 1,317 6.4 1.5 4,374 884 5.2 1.1 –1,287 1,587 –1.2 1.8

Cincinnati-Middletown, OH-KY-IN. . . . . . 34,010 3,580 3.5 0.4 33,031 3,236 3.4 0.3 –979 4,832 –0.1 0.5

Clarksville, TN-KY. . . . . . . . . . . . . . . . . . 2,232 663 2.5 0.7 2,104 747 2.1 0.8 –128 1,001 –0.4 1.1

Cleveland, TN. . . . . . . . . . . . . . . . . . . . . 1,299 800 2.9 1.8 2,082 976 4.4 2.0 783 1,265 1.6 2.7

Cleveland-Elyria-Mentor, OH. . . . . . . . . . 26,564 2,156 2.8 0.2 34,978 3,853 3.7 0.4 8,414 4,425 *1.0 0.5

Coeur d’Alene, ID. . . . . . . . . . . . . . . . . . 3,302 1,018 5.4 1.7 4,068 1,264 7.0 2.1 766 1,626 1.6 2.7

College Station-Bryan, TX. . . . . . . . . . . . 2,849 1,009 3.2 1.2 2,561 745 2.6 0.7 –288 1,256 –0.7 1.4

Colorado Springs, CO. . . . . . . . . . . . . . . 15,493 2,559 5.9 1.0 18,121 2,324 6.4 0.8 2,628 3,462 0.6 1.3

Columbia, MO. . . . . . . . . . . . . . . . . . . . . 2,203 586 3.0 0.8 2,903 870 3.4 1.0 700 1,051 0.5 1.3

Columbia, SC. . . . . . . . . . . . . . . . . . . . . 6,857 1,228 2.2 0.4 11,380 2,146 3.4 0.6 4,523 2,478 *1.2 0.7

Columbus, GA-AL. . . . . . . . . . . . . . . . . . 3,317 1,115 3.2 1.1 2,612 661 2.4 0.6 –705 1,297 –0.8 1.2

Columbus, IN. . . . . . . . . . . . . . . . . . . . . . 977 482 2.8 1.4 1,258 446 3.5 1.2 281 657 0.7 1.8

Columbus, OH. . . . . . . . . . . . . . . . . . . . . 31,323 2,434 3.9 0.3 39,835 3,092 4.6 0.4 8,512 3,942 *0.7 0.5

Corpus Christi, TX. . . . . . . . . . . . . . . . . . 3,992 1,118 2.4 0.7 4,802 1,409 2.7 0.8 810 1,802 0.3 1.0

Corvallis, OR. . . . . . . . . . . . . . . . . . . . . . 2,041 703 5.5 1.9 2,620 805 6.8 2.0 579 1,070 1.3 2.8

Crestview-Fort Walton BeachDestin, FL2

. . . . . . . . . . . . . . . . . . . . . . . 1,832 613 2.5 0.8 2,911 1,121 3.7 1.4 1,079 1,281 1.2 1.6

Cumberland, MD-WV. . . . . . . . . . . . . . . 379 234 0.9 0.6 1,676 791 4.4 2.0 1,297 828 *3.4 2.1

Dallas-Fort Worth-Arlington, TX. . . . . . . . 106,198 7,222 3.9 0.3 136,711 6,026 4.6 0.2 30,513 9,418 *0.7 0.3

Dalton, GA. . . . . . . . . . . . . . . . . . . . . . . . 523 293 0.9 0.5 1,166 797 2.0 1.4 643 852 1.1 1.5

Danville, IL. . . . . . . . . . . . . . . . . . . . . . . . 578 271 1.8 0.8 641 308 2.0 1.0 63 411 0.3 1.3

Danville, VA. . . . . . . . . . . . . . . . . . . . . . . 838 415 1.9 1.0 844 382 2.0 0.9 6 565 0.1 1.3

Davenport-Moline-Rock Island, IA-IL. . . . 6,101 1,104 3.4 0.6 6,352 1,151 3.6 0.6 251 1,597 0.1 0.9

Dayton, OH. . . . . . . . . . . . . . . . . . . . . . . 7,687 1,042 2.1 0.3 10,966 1,767 3.1 0.5 3,279 2,056 *1.0 0.6

Decatur, AL. . . . . . . . . . . . . . . . . . . . . . . 1,150 492 1.7 0.7 1,190 695 1.9 1.1 40 853 0.2 1.3

Decatur, IL. . . . . . . . . . . . . . . . . . . . . . . . 939 451 2.0 0.9 1,047 455 2.3 1.0 108 642 0.3 1.4

Deltona-Daytona BeachOrmond Beach, FL. . . . . . . . . . . . . . . . 9,093 2,282 4.5 1.1 8,830 2,243 4.7 1.2 –263 3,204 0.2 1.6

Denver-Aurora-Broomfield, CO. . . . . . . . 62,522 3,845 5.3 0.3 77,163 5,930 6.2 0.5 14,641 7,083 *0.8 0.6

Des Moines-West Des Moines, IA. . . . . . 8,294 1,108 3.0 0.4 13,850 2,178 4.7 0.7 5,556 2,450 *1.7 0.8

Detroit-Warren-Livonia, MI. . . . . . . . . . . . 52,143 3,385 2.7 0.2 51,963 3,735 3.0 0.2 –180 5,049 *0.3 0.3

Dothan, AL. . . . . . . . . . . . . . . . . . . . . . . . 1,066 319 1.8 0.5 1,444 435 2.3 0.7 378 540 0.5 0.9

Dover, DE. . . . . . . . . . . . . . . . . . . . . . . . 2,411 732 3.7 1.1 2,374 795 3.5 1.2 –37 1,083 –0.2 1.6

Dubuque, IA. . . . . . . . . . . . . . . . . . . . . . . 1,938 747 4.3 1.6 1,992 683 4.1 1.4 54 1,014 –0.2 2.1

Duluth, MN-WI. . . . . . . . . . . . . . . . . . . . . 4,251 649 3.5 0.5 4,695 804 3.7 0.6 444 1,035 0.2 0.8

Durham-Chapel Hill, NC. . . . . . . . . . . . . 8,057 1,325 3.7 0.6 10,720 1,756 4.7 0.7 2,663 2,204 *1.0 1.0

Eau Claire, WI. . . . . . . . . . . . . . . . . . . . . 3,444 724 4.5 1.0 3,157 650 3.9 0.8 –287 974 –0.6 1.3

El Centro, CA. . . . . . . . . . . . . . . . . . . . . . 1,384 697 2.7 1.4 2,166 770 4.0 1.4 782 1,040 1.3 2.0

Elizabethtown, KY. . . . . . . . . . . . . . . . . . 1,257 527 2.8 1.2 1,348 552 3.1 1.2 91 764 0.2 1.7

See footnotes at end of table.
26 U.S. Census Bureau

Appendix Table 2. 

Percentage of Workers Who Work From Home for All Metropolitan Statistical Areas: 

American Community Survey, 2005 and 2010—Con.

(Civilian employed aged 16 years and older)

Metropolitan area

2005 Worked at home 2010 Worked at home

Change in worked at home–

(2010 less 2005) 

Estimate

Margin 

of error

Percent

Margin of 

error Estimate

Margin 

of error

Percent

Margin of 

error

EstimateMargin 

of error

PercentMargin 

of error

Elkhart-Goshen, IN. . . . . . . . . . . . . . . . . 3,334 994 3.6 1.1 1,618 692 1.9 0.8 –1,716 1,213 *–1.7 1.4

Elmira, NY. . . . . . . . . . . . . . . . . . . . . . . . 1,096 561 3.2 1.6 1,078 537 2.7 1.4 –18 778 –0.5 2.1

El Paso, TX. . . . . . . . . . . . . . . . . . . . . . . 5,875 1,305 2.3 0.5 7,558 1,560 2.5 0.5 1,683 2,037 0.2 0.7

Erie, PA. . . . . . . . . . . . . . . . . . . . . . . . . . 2,377 593 2.0 0.5 2,786 833 2.3 0.7 409 1,025 0.3 0.8

Eugene-Springfield, OR. . . . . . . . . . . . . 7,056 1,569 4.7 1.1 9,116 1,328 6.0 0.9 2,060 2,059 1.2 1.4

Evansville, IN-KY. . . . . . . . . . . . . . . . . . . 4,535 1,003 2.8 0.6 4,423 1,326 2.7 0.8 –112 1,666 –0.1 1.0

Fairbanks, AK. . . . . . . . . . . . . . . . . . . . . 814 447 2.2 1.2 2,395 1,010 5.4 2.3 1,581 1,107 *3.3 2.6

Fargo, ND-MN. . . . . . . . . . . . . . . . . . . . . 3,425 734 3.4 0.7 3,931 854 3.3 0.7 506 1,128 –0.1 1.0

Farmington, NM. . . . . . . . . . . . . . . . . . . . 2,385 1,045 4.5 1.9 2,008 747 4.0 1.5 –377 1,286 –0.5 2.4

Fayetteville, NC. . . . . . . . . . . . . . . . . . . . 2,501 927 2.2 0.8 2,426 1,091 1.8 0.8 –75 1,434 –0.3 1.2

Fayetteville-SpringdaleRogers, AR-MO. . . . . . . . . . . . . . . . . . . 7,409 1,956 3.8 1.0 7,160 1,765 3.4 0.8 –249 2,638 –0.4 1.3

Flagstaff, AZ. . . . . . . . . . . . . . . . . . . . . . 2,178 782 3.8 1.3 3,692 1,032 6.4 1.8 1,514 1,298 *2.6 2.2

Flint, MI. . . . . . . . . . . . . . . . . . . . . . . . . . 5,076 1,297 2.8 0.7 4,487 1,090 3.0 0.7 –589 1,696 0.3 1.0

Florence, SC. . . . . . . . . . . . . . . . . . . . . . 2,437 803 2.9 1.0 2,581 697 3.3 0.9 144 1,065 0.3 1.3

Florence-Muscle Shoals, AL. . . . . . . . . . 831 509 1.5 0.9 1,093 419 1.8 0.7 262 660 0.3 1.2

Fond du Lac, WI. . . . . . . . . . . . . . . . . . . 1,576 428 3.1 0.9 2,360 577 4.6 1.1 784 720 *1.4 1.4

Fort Collins-Loveland, CO. . . . . . . . . . . . 8,962 2,189 6.4 1.5 7,509 1,368 5.1 0.9 –1,453 2,583 –1.3 1.8

Fort Smith, AR-OK. . . . . . . . . . . . . . . . . 2,366 736 2.1 0.6 2,729 843 2.2 0.7 363 1,121 0.1 0.9

Fort Wayne, IN. . . . . . . . . . . . . . . . . . . . . 4,855 965 2.6 0.5 7,453 1,688 4.1 0.9 2,598 1,949 *1.5 1.1

Fresno, CA. . . . . . . . . . . . . . . . . . . . . . . . 13,696 2,803 4.0 0.8 14,223 2,478 4.1 0.7 527 3,746 0.1 1.1

Gadsden, AL. . . . . . . . . . . . . . . . . . . . . . 568 323 1.4 0.8 801 440 2.1 1.2 233 547 0.8 1.4

Gainesville, FL. . . . . . . . . . . . . . . . . . . . . 4,257 1,290 3.8 1.1 5,043 1,557 4.4 1.3 786 2,026 0.5 1.7

Gainesville, GA. . . . . . . . . . . . . . . . . . . . 2,264 739 2.9 1.0 3,346 1,344 4.5 1.8 1,082 1,537 1.6 2.0

Glens Falls, NY. . . . . . . . . . . . . . . . . . . . 1,829 525 3.1 0.9 3,092 963 5.3 1.6 1,263 1,099 *2.2 1.9

Goldsboro, NC. . . . . . . . . . . . . . . . . . . . . 1,045 374 2.2 0.8 923 492 1.9 1.0 –122 619 –0.4 1.3

Grand Forks, ND-MN. . . . . . . . . . . . . . . . 1,929 539 4.0 1.1 1,757 462 3.5 0.9 –172 710 –0.6 1.5

Grand Junction, CO. . . . . . . . . . . . . . . . . 4,071 1,398 6.7 2.3 3,373 1,165 5.3 1.8 –698 1,822 –1.4 2.9

Grand Rapids-Wyoming, MI. . . . . . . . . . 11,177 1,559 3.1 0.4 14,420 1,698 4.3 0.5 3,243 2,309 *1.1 0.7

Great Falls, MT. . . . . . . . . . . . . . . . . . . . 901 439 2.6 1.3 1,615 623 4.5 1.7 714 763 1.8 2.1

Greeley, CO. . . . . . . . . . . . . . . . . . . . . . . 7,399 1,459 6.7 1.3 5,898 1,232 5.1 1.0 –1,501 1,912 –1.6 1.7

Green Bay, WI. . . . . . . . . . . . . . . . . . . . . 5,452 1,117 3.7 0.8 7,214 1,155 4.9 0.8 1,762 1,609 *1.2 1.1

Greensboro-High Point, NC. . . . . . . . . . . 10,324 1,763 3.3 0.6 9,402 1,450 3.0 0.4 –922 2,285 –0.3 0.7

Greenville, NC. . . . . . . . . . . . . . . . . . . . . 912 481 1.3 0.7 1,811 707 2.1 0.8 899 857 0.8 1.1

Greenville-Mauldin-Easley, SC. . . . . . . . 6,312 1,772 2.4 0.7 8,510 1,408 3.1 0.5 2,198 2,266 0.7 0.8

Gulfport-Biloxi, MS. . . . . . . . . . . . . . . . . 2,335 1,002 2.5 1.0 2,107 815 2.0 0.8 –228 1,293 –0.4 1.3

Hagerstown-Martinsburg, MD-WV. . . . . . 3,668 1,247 3.2 1.1 3,129 795 2.7 0.7 –539 1,480 –0.5 1.3

Hanford-Corcoran, CA. . . . . . . . . . . . . . . 1,171 434 2.8 1.0 1,538 1,026 3.0 2.0 367 1,117 0.2 2.2

Harrisburg-Carlisle, PA. . . . . . . . . . . . . . 7,133 1,390 2.9 0.6 8,524 1,217 3.2 0.5 1,391 1,850 0.3 0.7

Harrisonburg, VA. . . . . . . . . . . . . . . . . . . 1,542 690 3.1 1.4 2,757 782 5.0 1.4 1,215 1,045 1.9 2.0

Hartford-West HartfordEast Hartford, CT. . . . . . . . . . . . . . . . . 16,715 1,886 3.0 0.3 18,718 2,095 3.2 0.4 2,003 2,824 0.2 0.5

Hattiesburg, MS. . . . . . . . . . . . . . . . . . . . 1,385 611 2.4 1.1 1,860 740 3.1 1.2 475 962 0.7 1.6

Hickory-Lenoir-Morganton, NC. . . . . . . . 4,147 976 2.6 0.6 4,591 1,290 3.0 0.8 444 1,621 0.4 1.0

Hinesville-Fort Stewart, GA. . . . . . . . . . . 126 120 0.7 0.6 325 273 1.3 1.1 199 299 0.6 1.3

Holland-Grand Haven, MI. . . . . . . . . . . . 3,499 971 2.9 0.8 3,973 954 3.3 0.8 474 1,364 0.3 1.1

Honolulu, HI. . . . . . . . . . . . . . . . . . . . . . . 14,798 2,243 3.8 0.6 14,322 2,066 3.2 0.5 –476 3,054 –0.6 0.7

Hot Springs, AR. . . . . . . . . . . . . . . . . . . . 401 297 1.0 0.8 671 374 1.8 1.0 270 479 0.8 1.3

Houma-Bayou Cane-Thibodaux, LA. . . . 2,359 789 3.0 1.0 1,605 732 1.8 0.8 –754 1,078 –1.2 1.3

Houston-Sugar Land-Baytown, TX. . . . . 68,987 5,042 2.9 0.2 92,170 5,951 3.4 0.2 23,183 7,814 *0.5 0.3

Huntington-Ashland, WV-KY-OH. . . . . . . 2,702 1,013 2.5 0.9 2,173 764 2.0 0.7 –529 1,270 –0.5 1.2

Huntsville, AL. . . . . . . . . . . . . . . . . . . . . . 3,644 959 2.2 0.6 6,825 1,589 3.6 0.8 3,181 1,860 *1.5 1.0

See footnotes at end of table.
U.S. Census Bureau 27

Appendix Table 2. 

Percentage of Workers Who Work From Home for All Metropolitan Statistical Areas: 

American Community Survey, 2005 and 2010—Con.

(Civilian employed aged 16 years and older)

Metropolitan area

2005 Worked at home 2010 Worked at home

Change in worked at home–

(2010 less 2005) 

Estimate

Margin 

of error

Percent

Margin of 

error Estimate

Margin 

of error

Percent

Margin of 

error

EstimateMargin 

of error

PercentMargin 

of error

Idaho Falls, ID. . . . . . . . . . . . . . . . . . . . . 3,010 776 5.8 1.5 1,938 592 3.4 1.0 –1,072 977 *–2.4 1.8

Indianapolis-Carmel, IN. . . . . . . . . . . . . . 28,551 3,309 3.7 0.4 34,671 3,116 4.3 0.4 6,120 4,552 *0.6 0.6

Iowa City, IA. . . . . . . . . . . . . . . . . . . . . . . 3,196 781 4.3 1.1 3,417 862 4.2 1.0 221 1,165 –0.1 1.5

Ithaca, NY. . . . . . . . . . . . . . . . . . . . . . . . 2,190 781 5.0 1.8 3,048 922 6.5 2.0 858 1,211 1.5 2.7

Jackson, MI. . . . . . . . . . . . . . . . . . . . . . . 1,978 616 2.9 0.9 2,077 747 3.4 1.2 99 970 0.5 1.5

Jackson, MS. . . . . . . . . . . . . . . . . . . . . . 5,203 1,388 2.3 0.6 7,366 1,794 3.2 0.8 2,163 2,272 0.8 1.0

Jackson, TN. . . . . . . . . . . . . . . . . . . . . . . 1,206 467 2.5 1.0 2,186 638 4.7 1.4 980 792 *2.2 1.7

Jacksonville, FL. . . . . . . . . . . . . . . . . . . . 15,840 2,134 2.8 0.4 23,716 2,926 4.0 0.5 7,876 3,629 *1.2 0.6

Jacksonville, NC. . . . . . . . . . . . . . . . . . . 2,004 709 4.6 1.6 1,326 708 2.5 1.3 –678 1,003 *–2.2 2.1

Janesville, WI. . . . . . . . . . . . . . . . . . . . . . 1,714 614 2.3 0.8 3,240 893 4.3 1.2 1,526 1,086 *2.0 1.5

Jefferson City, MO. . . . . . . . . . . . . . . . . . 2,606 581 3.7 0.8 2,285 549 3.2 0.8 –321 800 –0.5 1.1

Johnson City, TN. . . . . . . . . . . . . . . . . . . 1,742 602 2.0 0.7 3,082 1,023 3.7 1.2 1,340 1,190 *1.7 1.4

Johnstown, PA. . . . . . . . . . . . . . . . . . . . . 1,186 417 2.0 0.7 1,521 483 2.5 0.8 335 639 0.5 1.1

Jonesboro, AR. . . . . . . . . . . . . . . . . . . . . 1,491 594 2.9 1.1 1,916 893 3.8 1.7 425 1,075 0.9 2.1

Joplin, MO. . . . . . . . . . . . . . . . . . . . . . . . 3,395 881 4.6 1.2 2,958 1,013 3.7 1.3 –437 1,345 –0.9 1.8

Kalamazoo-Portage, MI. . . . . . . . . . . . . . 4,124 797 2.8 0.6 4,695 901 3.3 0.6 571 1,205 0.4 0.8

Kankakee-Bradley, IL. . . . . . . . . . . . . . . . 1,079 415 2.4 0.9 1,611 588 3.4 1.2 532 721 0.9 1.6

Kansas City, MO-KS. . . . . . . . . . . . . . . . 36,715 3,176 3.9 0.3 39,757 3,550 4.1 0.4 3,042 4,771 0.2 0.5

Kennewick-Pasco-Richland, WA. . . . . . . 2,667 686 2.7 0.7 5,019 1,224 4.5 1.1 2,352 1,406 *1.8 1.3

Killeen-Temple-Fort Hood, TX. . . . . . . . . 2,597 802 2.3 0.7 3,861 1,259 2.6 0.8 1,264 1,496 0.3 1.1

Kingsport-Bristol-Bristol, TN-VA. . . . . . . 3,669 1,193 2.8 0.9 2,754 827 2.3 0.7 –915 1,453 –0.6 1.1

Kingston, NY. . . . . . . . . . . . . . . . . . . . . . 4,525 1,164 5.3 1.3 6,603 1,713 8.1 2.1 2,078 2,075 *2.8 2.5

Knoxville, TN. . . . . . . . . . . . . . . . . . . . . . 9,197 1,533 3.1 0.5 10,896 1,876 3.5 0.6 1,699 2,427 0.4 0.8

Kokomo, IN. . . . . . . . . . . . . . . . . . . . . . . 1,202 560 2.8 1.3 1,036 507 2.5 1.3 –166 757 –0.2 1.8

La Crosse, WI-MN. . . . . . . . . . . . . . . . . . 2,156 608 3.3 0.9 3,374 850 4.9 1.2 1,218 1,047 *1.6 1.5

Lafayette, IN. . . . . . . . . . . . . . . . . . . . . . 2,409 673 3.0 0.8 2,258 604 2.4 0.6 –151 905 –0.6 1.1

Lafayette, LA. . . . . . . . . . . . . . . . . . . . . . 2,902 940 2.6 0.8 2,462 697 2.0 0.6 –440 1,171 –0.6 1.0

Lake Charles, LA. . . . . . . . . . . . . . . . . . . 1,487 668 1.9 0.8 1,635 750 2.0 0.9 148 1,006 0.1 1.2

Lake Havasu City-Kingman, AZ1. . . . . . . 1,339 516 1.9 0.7 2,389 867 3.3 1.2 1,050 1,011 *1.4 1.4

Lakeland-Winter Haven, FL. . . . . . . . . . . 6,208 1,327 2.7 0.6 7,765 1,811 3.3 0.8 1,557 2,250 0.6 1.0

Lancaster, PA. . . . . . . . . . . . . . . . . . . . . . 10,800 1,828 4.7 0.8 11,843 2,044 4.9 0.8 1,043 2,747 0.2 1.1

Lansing-East Lansing, MI. . . . . . . . . . . . 6,064 999 2.9 0.5 8,061 1,233 3.9 0.6 1,997 1,590 *0.9 0.8

Laredo, TX. . . . . . . . . . . . . . . . . . . . . . . . 1,754 724 2.2 0.9 4,065 1,301 4.2 1.3 2,311 1,493 *2.0 1.6

Las Cruces, NM. . . . . . . . . . . . . . . . . . . . 2,977 910 4.0 1.2 3,739 1,200 4.5 1.5 762 1,509 0.5 1.9

Las Vegas-Paradise, NV. . . . . . . . . . . . . 19,981 2,910 2.5 0.4 28,538 3,483 3.3 0.4 8,557 4,547 *0.9 0.5

Lawrence, KS. . . . . . . . . . . . . . . . . . . . . 2,593 736 4.6 1.3 2,340 840 4.0 1.5 –253 1,119 –0.5 2.0

Lawton, OK. . . . . . . . . . . . . . . . . . . . . . . 476 231 1.2 0.6 675 277 1.4 0.6 199 361 0.3 0.8

Lebanon, PA. . . . . . . . . . . . . . . . . . . . . . 2,082 688 3.4 1.1 1,714 524 2.8 0.9 –368 866 –0.6 1.4

Lewiston, ID-WA. . . . . . . . . . . . . . . . . . . 751 287 3.0 1.1 804 397 3.0 1.5 53 491 0.0 1.9

Lewiston-Auburn, ME. . . . . . . . . . . . . . . 962 435 1.9 0.8 872 437 1.8 0.9 –90 617 –0.1 1.2

Lexington-Fayette, KY. . . . . . . . . . . . . . . 8,327 1,489 4.1 0.7 7,480 1,232 3.3 0.6 –847 1,935 –0.8 0.9

Lima, OH. . . . . . . . . . . . . . . . . . . . . . . . . 975 413 2.2 0.9 1,278 906 2.9 2.1 303 998 0.8 2.3

Lincoln, NE. . . . . . . . . . . . . . . . . . . . . . . 4,330 831 3.0 0.6 5,721 1,292 3.7 0.8 1,391 1,540 0.7 1.0

Little Rock-North Little RockConway, AR. . . . . . . . . . . . . . . . . . . . . . 7,124 1,294 2.4 0.4 7,892 1,596 2.5 0.5 768 2,058 0.1 0.7

Logan, UT-ID. . . . . . . . . . . . . . . . . . . . . . 1,973 611 3.9 1.2 2,634 744 4.6 1.3 661 965 0.7 1.8

Longview, TX. . . . . . . . . . . . . . . . . . . . . . 2,710 916 3.1 1.0 2,545 856 2.9 0.9 –165 1,255 –0.2 1.4

Longview, WA. . . . . . . . . . . . . . . . . . . . . 1,270 461 3.2 1.1 1,526 821 4.1 2.1 256 944 0.9 2.4

Los Angeles-Long BeachSanta Ana, CA. . . . . . . . . . . . . . . . . . . 231,796 9,571 4.1 0.2 281,778 9,696 5.0 0.2 49,982 13,645 *0.8 0.2

Louisville/Jefferson County, KY-IN. . . . . . 13,733 1,792 2.5 0.3 17,887 1,936 3.1 0.3 4,154 2,643 *0.6 0.5

Lubbock, TX. . . . . . . . . . . . . . . . . . . . . . . 4,461 1,159 3.9 1.0 5,743 1,336 4.3 1.0 1,282 1,772 0.4 1.4

Lynchburg, VA. . . . . . . . . . . . . . . . . . . . . 3,788 1,137 3.5 1.0 3,830 1,068 3.5 1.0 42 1,562 –0.1 1.4

Macon, GA. . . . . . . . . . . . . . . . . . . . . . . . 1,525 727 1.7 0.8 1,586 724 1.9 0.9 61 1,027 0.2 1.2

See footnotes at end of table.
28 U.S. Census Bureau

Appendix Table 2. 

Percentage of Workers Who Work From Home for All Metropolitan Statistical Areas: 

American Community Survey, 2005 and 2010—Con.

(Civilian employed aged 16 years and older)

Metropolitan area

2005 Worked at home 2010 Worked at home

Change in worked at home–

(2010 less 2005) 

Estimate

Margin 

of error

Percent

Margin of 

error Estimate

Margin 

of error

Percent

Margin of 

error

EstimateMargin 

of error

PercentMargin 

of error

Madera-Chowchilla, CA. . . . . . . . . . . . . . 2,979 1,052 5.6 1.9 2,715 890 6.5 2.0 –264 1,379 0.9 2.8

Madison, WI. . . . . . . . . . . . . . . . . . . . . . . 12,278 1,358 4.2 0.5 12,945 1,679 4.2 0.5 667 2,163 0.0 0.7

Manchester-Nashua, NH. . . . . . . . . . . . . 8,747 1,519 4.2 0.7 13,754 2,611 6.7 1.3 5,007 3,027 *2.5 1.5

Manhattan, KS1. . . . . . . . . . . . . . . . . . . . 1,829 708 3.9 1.5 3,813 1,216 6.7 2.1 1,984 1,410 *2.7 2.6

Mankato-North Mankato, MN1. . . . . . . . . 2,058 485 4.5 1.1 4,053 996 7.7 1.9 1,995 1,110 *3.2 2.2

Mansfield, OH. . . . . . . . . . . . . . . . . . . . . 1,963 1,030 3.5 1.8 1,643 691 3.3 1.4 –320 1,242 –0.2 2.3

McAllen-Edinburg-Mission, TX. . . . . . . . 5,909 1,587 2.8 0.7 10,573 2,233 3.9 0.8 4,664 2,745 *1.1 1.1

Medford, OR. . . . . . . . . . . . . . . . . . . . . . 6,063 1,586 6.7 1.7 6,966 1,622 8.4 1.9 903 2,272 1.7 2.6

Memphis, TN-MS-AR. . . . . . . . . . . . . . . . 11,156 1,529 2.0 0.3 15,324 2,278 2.7 0.4 4,168 2,749 *0.7 0.5

Merced, CA. . . . . . . . . . . . . . . . . . . . . . . 3,604 1,104 4.0 1.2 3,091 933 3.4 1.0 –513 1,448 –0.6 1.6

Miami-Fort LauderdalePompano Beach, FL. . . . . . . . . . . . . . . 77,913 5,360 3.3 0.2 105,740 6,434 4.4 0.3 27,827 8,389 *1.1 0.4

Michigan City-La Porte, IN. . . . . . . . . . . . 1,718 601 3.5 1.2 1,180 439 2.6 0.9 –538 745 –0.9 1.5

Midland, TX. . . . . . . . . . . . . . . . . . . . . . . 1,177 425 2.1 0.8 1,822 874 2.8 1.3 645 974 0.7 1.5

Milwaukee-Waukesha-West Allis, WI. . . . 21,563 2,169 3.1 0.3 24,533 2,358 3.3 0.3 2,970 3,209 0.2 0.5

Minneapolis-St. PaulBloomington, MN-WI. . . . . . . . . . . . . . . 68,733 3,590 4.2 0.2 82,556 4,277 5.0 0.3 13,823 5,594 *0.7 0.3

Missoula, MT. . . . . . . . . . . . . . . . . . . . . . 3,062 955 5.9 1.8 3,384 948 6.0 1.7 322 1,347 0.2 2.5

Mobile, AL. . . . . . . . . . . . . . . . . . . . . . . . 4,712 1,113 2.8 0.7 5,487 1,745 3.3 1.0 775 2,074 0.5 1.2

Modesto, CA. . . . . . . . . . . . . . . . . . . . . . 7,021 1,457 3.6 0.7 9,479 1,477 4.9 0.7 2,458 2,077 *1.3 1.0

Monroe, LA. . . . . . . . . . . . . . . . . . . . . . . 1,589 553 2.2 0.8 1,007 473 1.3 0.6 –582 729 –0.9 1.0

Monroe, MI. . . . . . . . . . . . . . . . . . . . . . . 2,483 971 3.4 1.3 1,923 786 3.0 1.2 –560 1,251 –0.4 1.8

Montgomery, AL. . . . . . . . . . . . . . . . . . . 3,860 1,186 2.6 0.8 4,287 1,067 2.7 0.7 427 1,598 0.1 1.0

Morgantown, WV. . . . . . . . . . . . . . . . . . . 1,339 697 2.7 1.5 1,604 530 2.7 0.9 265 876 –0.1 1.7

Morristown, TN. . . . . . . . . . . . . . . . . . . . 1,734 728 2.8 1.2 912 431 1.7 0.8 –822 847 –1.1 1.4

Mount Vernon-Anacortes, WA. . . . . . . . . 1,933 595 3.7 1.1 1,733 560 3.6 1.2 –200 818 –0.2 1.6

Muncie, IN. . . . . . . . . . . . . . . . . . . . . . . . 2,279 1,053 4.9 2.2 1,294 529 2.7 1.1 –985 1,179 –2.3 2.4

Muskegon-Norton Shores, MI. . . . . . . . . 1,994 588 2.6 0.8 4,776 3,272 7.2 4.9 2,782 3,334 4.6 5.0

Myrtle Beach-North Myrtle BeachConway, SC. . . . . . . . . . . . . . . . . . . . . . 3,490 1,105 3.2 1.0 3,232 836 2.8 0.7 –258 1,388 –0.4 1.2

Napa, CA. . . . . . . . . . . . . . . . . . . . . . . . . 2,138 698 3.4 1.1 3,166 878 5.2 1.4 1,028 1,124 *1.8 1.8

Naples-Marco Island, FL. . . . . . . . . . . . . 5,232 1,463 4.1 1.2 7,836 1,368 6.4 1.2 2,604 2,005 *2.3 1.6

Nashville-Davidson—Murfreesboro—

Franklin, TN. . . . . . . . . . . . . . . . . . . . . . 24,240 2,982 3.5 0.4 33,237 3,247 4.6 0.4 8,997 4,416 *1.1 0.6

New Haven-Milford, CT. . . . . . . . . . . . . . 10,189 1,520 2.6 0.4 15,898 2,295 3.9 0.5 5,709 2,758 *1.3 0.7

New Orleans-Metairie-Kenner, LA. . . . . . 15,132 1,858 2.8 0.3 13,073 1,944 2.5 0.4 –2,059 2,693 –0.2 0.5

New York-Northern New Jersey-Long 

Island, NY-NJ-PA. . . . . . . . . . . . . . . . . . 285,670 11,148 3.5 0.1 337,860 9,416 3.9 0.1 52,190 14,611 *0.5 0.2

Niles-Benton Harbor, MI. . . . . . . . . . . . . 2,143 679 3.1 1.0 2,042 612 3.1 0.9 –101 915 0.0 1.3

North Port-Bradenton-Sarasota, FL2. . . . 15,008 2,590 5.4 0.9 14,109 2,125 5.3 0.8 –899 3,354 –0.2 1.2

Norwich-New London, CT. . . . . . . . . . . . 3,818 786 2.9 0.6 3,545 768 2.7 0.6 –273 1,100 –0.2 0.8

Ocala, FL. . . . . . . . . . . . . . . . . . . . . . . . . 6,620 2,299 5.7 2.0 6,236 1,812 5.6 1.6 –384 2,931 –0.1 2.6

Ocean City, NJ. . . . . . . . . . . . . . . . . . . . . 979 419 2.3 1.0 1,617 594 3.8 1.4 638 729 1.5 1.7

Odessa, TX. . . . . . . . . . . . . . . . . . . . . . . 695 388 1.3 0.7 1,627 885 2.7 1.4 932 969 1.4 1.6

Ogden-Clearfield, UT. . . . . . . . . . . . . . . . 10,640 1,952 4.7 0.8 8,528 1,341 3.5 0.6 –2,112 2,371 *–1.1 1.0

Oklahoma City, OK. . . . . . . . . . . . . . . . . 15,311 2,065 2.9 0.4 19,564 2,031 3.4 0.4 4,253 2,901 0.5 0.5

Olympia, WA. . . . . . . . . . . . . . . . . . . . . . 3,959 1,043 3.7 1.0 3,831 1,042 3.5 1.0 –128 1,476 –0.1 1.4

Omaha-Council Bluffs, NE-IA. . . . . . . . . 13,813 1,587 3.4 0.4 15,257 2,021 3.5 0.5 1,444 2,574 0.0 0.6

Orlando-Kissimmee-Sanford, FL. . . . . . . 33,828 3,551 3.8 0.4 42,222 3,307 4.4 0.3 8,394 4,860 *0.6 0.5

Oshkosh-Neenah, WI. . . . . . . . . . . . . . . 2,120 974 2.7 1.2 2,438 670 2.9 0.8 318 1,184 0.2 1.5

Owensboro, KY. . . . . . . . . . . . . . . . . . . . 885 475 1.8 1.0 921 431 1.9 0.9 36 642 0.0 1.3

Oxnard-Thousand Oaks-Ventura, CA. . . 17,149 2,234 4.7 0.6 22,551 4,516 6.0 1.2 5,402 5,050 1.3 1.3

Palm Bay-Melbourne-Titusville, FL. . . . . 5,760 1,078 2.5 0.5 10,743 1,773 4.8 0.8 4,983 2,080 *2.3 0.9

Palm Coast, FL1. . . . . . . . . . . . . . . . . . . . 1,110 490 3.8 1.7 1,288 650 4.1 2.1 178 815 0.3 2.7

See footnotes at end of table.
U.S. Census Bureau 29

Appendix Table 2. 

Percentage of Workers Who Work From Home for All Metropolitan Statistical Areas: 

American Community Survey, 2005 and 2010—Con.

(Civilian employed aged 16 years and older)

Metropolitan area

2005 Worked at home 2010 Worked at home

Change in worked at home–

(2010 less 2005) 

Estimate

Margin 

of error

Percent

Margin of 

error Estimate

Margin 

of error

Percent

Margin of 

error

EstimateMargin 

of error

PercentMargin 

of error

Panama City-Lynn Haven-Panama City 

Beach, FL. . . . . . . . . . . . . . . . . . . . . . . 1,983 894 2.9 1.3 1,808 940 2.5 1.3 –175 1,300 –0.4 1.8

Parkersburg-Marietta-Vienna, WV-OH. . . 1,878 591 2.7 0.9 2,155 664 3.3 1.0 277 891 0.6 1.3

Pascagoula, MS. . . . . . . . . . . . . . . . . . . 1,421 645 2.3 1.0 979 536 1.5 0.8 –442 840 –0.7 1.3

Pensacola-Ferry Pass-Brent, FL. . . . . . . 6,344 1,788 3.6 1.0 7,876 1,773 4.5 1.0 1,532 2,522 1.0 1.4

Peoria, IL. . . . . . . . . . . . . . . . . . . . . . . . . 5,357 1,245 3.2 0.7 5,007 842 3.0 0.5 –350 1,505 –0.2 0.9

Philadelphia-Camden-Wilmington,

PA-NJ-DE-MD. . . . . . . . . . . . . . . . . . . . 85,966 5,505 3.3 0.2 102,251 5,326 3.8 0.2 16,285 7,671 *0.5 0.3

Phoenix-Mesa-Glendale, AZ. . . . . . . . . . 72,648 5,389 4.2 0.3 106,373 5,359 6.0 0.3 33,725 7,611 *1.8 0.4

Pine Bluff, AR. . . . . . . . . . . . . . . . . . . . . 828 388 2.1 1.0 1,004 695 2.7 1.9 176 798 0.6 2.1

Pittsburgh, PA. . . . . . . . . . . . . . . . . . . . . 31,691 2,589 3.0 0.2 37,655 3,027 3.5 0.3 5,964 3,990 *0.5 0.4

Pittsfield, MA. . . . . . . . . . . . . . . . . . . . . . 2,779 951 4.8 1.6 2,077 666 3.4 1.1 –702 1,162 –1.3 1.9

Pocatello, ID. . . . . . . . . . . . . . . . . . . . . . 723 415 1.9 1.1 1,023 485 2.7 1.3 300 639 0.7 1.7

Portland-South PortlandBiddeford, ME. . . . . . . . . . . . . . . . . . . . 15,116 2,108 5.8 0.8 13,153 1,743 5.1 0.7 –1,963 2,739 –0.7 1.0

Portland-Vancouver-Hillsboro,

OR-WA. . . . . . . . . . . . . . . . . . . . . . . . . 51,646 3,293 5.2 0.3 66,384 4,189 6.5 0.4 14,738 5,339 *1.4 0.5

Port St. Lucie, FL. . . . . . . . . . . . . . . . . . . 7,585 1,550 5.0 1.0 7,588 1,728 4.7 1.1 3 2,325 –0.3 1.5

Poughkeepsie-NewburghMiddletown, NY. . . . . . . . . . . . . . . . . . . 9,688 1,815 3.2 0.6 14,074 1,931 4.7 0.6 4,386 2,655 *1.5 0.9

Prescott, AZ. . . . . . . . . . . . . . . . . . . . . . . 5,835 1,418 7.2 1.7 5,795 1,444 7.6 1.8 –40 2,027 0.4 2.5

Providence-New BedfordFall River, RI-MA. . . . . . . . . . . . . . . . . . 19,409 2,383 2.6 0.3 22,946 2,568 3.2 0.4 3,537 3,509 *0.6 0.5

Provo-Orem, UT. . . . . . . . . . . . . . . . . . . 11,673 2,532 5.9 1.3 13,321 1,766 6.2 0.8 1,648 3,090 0.3 1.5

Pueblo, CO. . . . . . . . . . . . . . . . . . . . . . . 2,273 741 3.6 1.2 1,981 741 3.1 1.1 –292 1,049 –0.5 1.6

Punta Gorda, FL. . . . . . . . . . . . . . . . . . . 2,626 1,040 4.6 1.8 3,248 1,202 6.4 2.3 622 1,592 1.7 2.9

Racine, WI. . . . . . . . . . . . . . . . . . . . . . . . 2,117 594 2.2 0.6 2,489 784 2.8 0.9 372 986 0.6 1.1

Raleigh-Cary, NC. . . . . . . . . . . . . . . . . . . 22,173 2,344 4.7 0.5 31,850 3,117 5.9 0.6 9,677 3,908 *1.2 0.7

Rapid City, SD. . . . . . . . . . . . . . . . . . . . . 2,301 700 4.2 1.2 2,118 665 3.6 1.1 –183 967 –0.6 1.7

Reading, PA. . . . . . . . . . . . . . . . . . . . . . . 5,122 1,176 2.7 0.6 6,468 1,398 3.4 0.7 1,346 1,830 0.7 1.0

Redding, CA. . . . . . . . . . . . . . . . . . . . . . 2,908 803 4.2 1.1 4,352 1,227 7.0 1.9 1,444 1,470 *2.8 2.2

Reno-Sparks, NV. . . . . . . . . . . . . . . . . . . 9,028 1,925 4.7 1.0 11,018 1,895 5.8 1.0 1,990 2,705 1.1 1.4

Richmond, VA. . . . . . . . . . . . . . . . . . . . . 17,179 2,199 3.1 0.4 26,130 3,212 4.5 0.5 8,951 3,901 *1.4 0.7

Riverside-San BernardinoOntario, CA. . . . . . . . . . . . . . . . . . . . . . 58,759 5,466 3.7 0.4 59,902 4,287 3.8 0.3 1,143 6,955 0.0 0.4

Roanoke, VA. . . . . . . . . . . . . . . . . . . . . . 4,006 998 3.1 0.8 5,214 1,212 3.7 0.8 1,208 1,573 0.6 1.1

Rochester, MN. . . . . . . . . . . . . . . . . . . . . 3,539 729 3.7 0.8 5,575 1,202 5.7 1.2 2,036 1,409 *1.9 1.4

Rochester, NY. . . . . . . . . . . . . . . . . . . . . 13,318 1,740 2.9 0.4 17,216 1,972 3.6 0.4 3,898 2,635 *0.7 0.6

Rockford, IL. . . . . . . . . . . . . . . . . . . . . . . 5,627 1,446 3.6 0.9 5,100 1,095 3.4 0.7 –527 1,816 –0.2 1.2

Rocky Mount, NC. . . . . . . . . . . . . . . . . . 1,498 616 2.3 1.0 1,730 698 2.8 1.1 232 932 0.4 1.5

Rome, GA. . . . . . . . . . . . . . . . . . . . . . . . 617 438 1.7 1.2 642 449 1.7 1.2 25 628 0.1 1.7

Sacramento—Arden-Arcade—

Roseville, CA. . . . . . . . . . . . . . . . . . . . . 42,028 3,633 4.7 0.4 48,880 4,051 5.5 0.4 6,852 5,451 *0.8 0.6

Saginaw-Saginaw Township

North, MI. . . . . . . . . . . . . . . . . . . . . . . . 2,865 678 3.6 0.8 1,775 523 2.3 0.7 –1,090 858 *–1.3 1.1

St. Cloud, MN. . . . . . . . . . . . . . . . . . . . . 5,104 912 5.3 0.9 7,349 1,153 7.6 1.1 2,245 1,473 *2.3 1.5

St. George, UT. . . . . . . . . . . . . . . . . . . . . 2,863 1,004 5.9 2.0 1,923 618 3.8 1.2 –940 1,179 –2.1 2.4

St. Joseph, MO-KS. . . . . . . . . . . . . . . . . 1,175 458 2.2 0.9 2,037 748 3.6 1.3 862 879 1.4 1.6

St. Louis, MO-IL. . . . . . . . . . . . . . . . . . . . 41,361 3,439 3.2 0.3 48,007 4,233 3.7 0.3 6,646 5,464 *0.5 0.4

Salem, OR. . . . . . . . . . . . . . . . . . . . . . . . 6,303 1,264 3.9 0.8 8,344 1,831 5.2 1.1 2,041 2,229 1.4 1.4

Salinas, CA. . . . . . . . . . . . . . . . . . . . . . . 4,965 1,197 3.0 0.7 10,089 3,812 5.9 2.2 5,124 4,006 *2.9 2.3

Salisbury, MD. . . . . . . . . . . . . . . . . . . . . 1,362 617 2.6 1.1 2,223 949 4.0 1.7 861 1,134 1.4 2.0

Salt Lake City, UT. . . . . . . . . . . . . . . . . . 21,160 2,081 4.2 0.4 21,059 2,225 4.0 0.4 –101 3,052 –0.2 0.6

San Angelo, TX. . . . . . . . . . . . . . . . . . . . 1,331 620 2.9 1.3 2,310 761 5.1 1.7 979 984 *2.2 2.1

See footnotes at end of table.
30 U.S. Census Bureau

Appendix Table 2. 

Percentage of Workers Who Work From Home for All Metropolitan Statistical Areas: 

American Community Survey, 2005 and 2010—Con.

(Civilian employed aged 16 years and older)

Metropolitan area

2005 Worked at home 2010 Worked at home

Change in worked at home–

(2010 less 2005) 

Estimate

Margin 

of error

Percent

Margin of 

error Estimate

Margin 

of error

Percent

Margin of 

error

EstimateMargin 

of error

PercentMargin 

of error

San Antonio-New Braunfels, TX. . . . . . . 28,060 3,635 3.5 0.4 27,520 2,878 2.9 0.3 –540 4,642 *–0.6 0.5

San Diego-Carlsbad-San Marcos, CA. . . 57,848 4,272 4.6 0.3 70,097 5,303 5.3 0.4 12,249 6,823 *0.8 0.5

Sandusky, OH. . . . . . . . . . . . . . . . . . . . . 896 451 2.6 1.3 1,148 760 3.3 2.1 252 886 0.7 2.5

San Francisco-Oakland-Fremont, CA. . . 94,762 6,243 4.9 0.3 126,057 6,772 6.2 0.3 31,295 9,226 *1.2 0.5

San Jose-Sunnyvale-Santa Clara, CA. . . 32,389 3,018 4.0 0.4 42,548 3,928 5.1 0.5 10,159 4,963 *1.1 0.6

San Luis Obispo-Paso Robles, CA. . . . . 6,455 1,310 5.9 1.2 6,669 1,405 5.8 1.2 214 1,925 –0.1 1.7

Santa Barbara-Santa MariaGoleta, CA. . . . . . . . . . . . . . . . . . . . . . . 7,479 1,310 4.4 0.8 10,224 1,579 5.7 0.9 2,745 2,056 *1.3 1.2

Santa Cruz-Watsonville, CA. . . . . . . . . . 10,335 1,953 8.4 1.5 6,783 1,241 5.9 1.0 –3,552 2,316 *–2.5 1.8

Santa Fe, NM. . . . . . . . . . . . . . . . . . . . . 4,704 989 6.5 1.4 5,434 1,211 8.3 1.9 730 1,566 1.8 2.3

Santa Rosa-Petaluma, CA. . . . . . . . . . . 13,140 2,690 6.1 1.3 17,264 2,421 7.9 1.1 4,124 3,624 *1.8 1.7

Savannah, GA. . . . . . . . . . . . . . . . . . . . . 3,071 1,057 2.2 0.8 6,717 1,696 4.4 1.1 3,646 2,003 *2.2 1.3

Scranton--Wilkes-Barre, PA. . . . . . . . . . . 6,292 1,105 2.7 0.5 6,083 1,192 2.4 0.5 –209 1,628 –0.3 0.7

Seattle-Tacoma-Bellevue, WA. . . . . . . . . 69,535 4,458 4.5 0.3 90,098 4,892 5.5 0.3 20,563 6,630 *1.0 0.4

Sebastian-Vero Beach, FL2. . . . . . . . . . . 1,729 609 3.4 1.2 1,728 557 3.4 1.1 –1 826 0.0 1.6

Sheboygan, WI. . . . . . . . . . . . . . . . . . . . 2,058 742 3.5 1.3 1,799 516 3.2 0.9 –259 905 –0.4 1.5

Sherman-Denison, TX. . . . . . . . . . . . . . . 2,041 978 4.0 1.9 1,925 738 3.8 1.5 –116 1,227 –0.2 2.4

Shreveport-Bossier City, LA. . . . . . . . . . . 2,434 889 1.5 0.6 3,486 1,125 2.0 0.6 1,052 1,437 0.5 0.9

Sioux City, IA-NE-SD. . . . . . . . . . . . . . . . 1,999 469 3.0 0.7 2,947 782 4.2 1.1 948 914 1.1 1.3

Sioux Falls, SD. . . . . . . . . . . . . . . . . . . . 4,610 1,137 4.1 1.0 4,355 844 3.6 0.7 –255 1,418 –0.6 1.2

South Bend-Mishawaka, IN-MI. . . . . . . . 3,839 1,024 2.7 0.7 3,349 758 2.5 0.6 –490 1,275 –0.3 0.9

Spartanburg, SC. . . . . . . . . . . . . . . . . . . 2,507 818 2.1 0.7 3,016 959 2.5 0.8 509 1,263 0.4 1.1

Spokane, WA. . . . . . . . . . . . . . . . . . . . . . 10,429 2,712 5.3 1.4 8,700 1,597 4.2 0.8 –1,729 3,150 –1.1 1.6

Springfield, IL. . . . . . . . . . . . . . . . . . . . . 3,723 1,054 3.6 1.0 3,327 1,183 3.3 1.2 –396 1,587 –0.4 1.5

Springfield, MA. . . . . . . . . . . . . . . . . . . . 9,634 1,662 3.1 0.5 16,952 2,318 5.5 0.7 7,318 2,858 *2.4 0.9

Springfield, MO. . . . . . . . . . . . . . . . . . . . 7,080 1,073 3.7 0.6 7,726 1,577 4.0 0.8 646 1,912 0.2 1.0

Springfield, OH. . . . . . . . . . . . . . . . . . . . 1,084 468 1.8 0.8 1,225 464 2.2 0.8 141 660 0.3 1.1

State College, PA. . . . . . . . . . . . . . . . . . . 2,118 602 3.5 1.0 3,006 712 4.2 1.0 888 934 0.7 1.4

Steubenville-Weirton, OH-WV2. . . . . . . . 1,381 717 2.7 1.4 1,573 651 3.1 1.3 192 970 0.4 1.9

Stockton, CA. . . . . . . . . . . . . . . . . . . . . . 6,995 1,326 2.7 0.5 13,401 2,352 5.2 0.9 6,406 2,706 *2.6 1.0

Sumter, SC. . . . . . . . . . . . . . . . . . . . . . . 392 342 1.0 0.9 811 478 2.1 1.2 419 589 1.1 1.5

Syracuse, NY. . . . . . . . . . . . . . . . . . . . . . 8,340 1,763 2.9 0.6 9,122 1,274 3.1 0.4 782 2,177 0.2 0.7

Tallahassee, FL. . . . . . . . . . . . . . . . . . . . 3,014 982 1.9 0.6 4,778 1,202 3.0 0.8 1,764 1,555 *1.2 1.0

Tampa-St. Petersburg-Clearwater, FL. . . 51,259 4,263 4.4 0.4 58,938 4,850 5.0 0.4 7,679 6,468 *0.5 0.6

Terre Haute, IN. . . . . . . . . . . . . . . . . . . . 1,287 460 1.8 0.7 1,639 586 2.3 0.8 352 746 0.5 1.0

Texarkana, TX-Texarkana, AR. . . . . . . . . 1,826 664 3.5 1.3 856 517 1.5 0.9 –970 843 *–1.9 1.6

Toledo, OH. . . . . . . . . . . . . . . . . . . . . . . . 6,436 1,291 2.2 0.4 7,972 1,861 2.8 0.7 1,536 2,269 0.6 0.8

Topeka, KS. . . . . . . . . . . . . . . . . . . . . . . 3,745 866 3.5 0.8 3,786 947 3.5 0.9 41 1,285 0.1 1.2

Trenton-Ewing, NJ. . . . . . . . . . . . . . . . . . 5,316 1,053 3.2 0.6 8,280 1,617 4.9 1.0 2,964 1,933 *1.7 1.1

Tucson, AZ. . . . . . . . . . . . . . . . . . . . . . . . 15,542 1,796 3.9 0.5 22,769 3,508 5.6 0.8 7,227 3,950 *1.7 1.0

Tulsa, OK. . . . . . . . . . . . . . . . . . . . . . . . . 12,708 2,004 3.1 0.5 18,506 2,070 4.4 0.5 5,798 2,886 *1.3 0.7

Tuscaloosa, AL. . . . . . . . . . . . . . . . . . . . 530 310 0.7 0.4 2,027 838 2.2 0.9 1,497 896 *1.5 1.0

Tyler, TX. . . . . . . . . . . . . . . . . . . . . . . . . . 2,368 780 3.0 1.0 2,555 912 2.7 0.9 187 1,202 –0.3 1.3

Utica-Rome, NY. . . . . . . . . . . . . . . . . . . . 3,798 1,043 3.0 0.8 3,556 803 2.8 0.7 –242 1,318 –0.2 1.0

Valdosta, GA. . . . . . . . . . . . . . . . . . . . . . 893 574 1.7 1.1 1,374 559 2.5 1.0 481 802 0.9 1.5

Vallejo-Fairfield, CA. . . . . . . . . . . . . . . . . 6,117 1,790 3.6 1.1 6,479 1,432 3.7 0.8 362 2,295 0.1 1.3

Victoria, TX. . . . . . . . . . . . . . . . . . . . . . . 1,081 503 2.1 1.0 1,071 527 2.2 1.1 –10 729 0.1 1.5

Vineland-Millville-Bridgeton, NJ. . . . . . . . 1,359 591 2.3 1.0 757 350 1.3 0.6 –602 687 –1.0 1.1

Virginia Beach-NorfolkNewport News, VA-NC. . . . . . . . . . . . . 18,055 2,106 2.5 0.3 22,508 2,658 3.0 0.4 4,453 3,398 0.5 0.5

Visalia-Porterville, CA. . . . . . . . . . . . . . . 4,426 914 3.0 0.6 5,799 1,294 3.5 0.8 1,373 1,587 0.5 1.0

Waco, TX. . . . . . . . . . . . . . . . . . . . . . . . . 2,228 760 2.3 0.8 1,950 783 1.9 0.8 –278 1,093 –0.4 1.1

Warner Robins, GA. . . . . . . . . . . . . . . . . 1,071 737 1.9 1.3 714 367 1.2 0.6 –357 824 –0.7 1.5

See footnotes at end of table.
Appendix Table 2. 

Percentage of Workers Who Work From Home for All Metropolitan Statistical Areas: 

American Community Survey, 2005 and 2010—Con.

(Civilian employed aged 16 years and older)

Metropolitan area

2005 Worked at home 2010 Worked at home

Change in worked at home–

(2010 less 2005) 

Estimate

Margin 

of error

Percent

Margin of 

error Estimate

Margin 

of error

Percent

Margin of 

error

EstimateMargin 

of error

PercentMargin 

of error

Washington-Arlington-Alexandria,

DC-VA-MD-WV. . . . . . . . . . . . . . . . . . . 109,072 5,985 4.2 0.2 141,634 6,082 4.9 0.2 32,562 8,546 *0.7 0.3

Waterloo-Cedar Falls, IA. . . . . . . . . . . . . 1,916 583 2.5 0.8 2,902 736 3.6 0.9 986 940 1.1 1.2

Wausau, WI. . . . . . . . . . . . . . . . . . . . . . . 3,064 653 4.4 1.0 3,214 666 4.7 1.0 150 934 0.2 1.4

Wenatchee-East Wenatchee, WA. . . . . . 2,938 1,009 6.3 2.1 1,676 442 3.5 0.9 –1,262 1,102 *–2.8 2.3

Wheeling, WV-OH. . . . . . . . . . . . . . . . . . 1,070 368 1.9 0.6 923 401 1.5 0.7 –147 545 –0.3 0.9

Wichita, KS. . . . . . . . . . . . . . . . . . . . . . . 10,216 1,954 3.7 0.7 9,972 1,735 3.5 0.6 –244 2,616 –0.2 0.9

Wichita Falls, TX. . . . . . . . . . . . . . . . . . . 1,496 640 2.5 1.0 1,036 442 1.7 0.7 –460 778 –0.8 1.3

Williamsport, PA. . . . . . . . . . . . . . . . . . . 1,589 539 3.0 1.0 1,521 647 2.9 1.2 –68 844 –0.1 1.6

Wilmington, NC. . . . . . . . . . . . . . . . . . . . 4,950 1,107 3.4 0.8 6,046 1,711 3.9 1.1 1,096 2,042 0.5 1.3

Winchester, VA-WV. . . . . . . . . . . . . . . . . 1,654 657 2.9 1.1 900 380 1.6 0.7 –754 759 –1.3 1.3

Winston-Salem, NC. . . . . . . . . . . . . . . . . 6,886 1,409 3.4 0.7 7,062 1,174 3.4 0.6 176 1,837 0.0 0.9

Worcester, MA. . . . . . . . . . . . . . . . . . . . . 12,014 2,289 3.2 0.6 14,407 2,188 3.9 0.6 2,393 3,171 0.6 0.8

Yakima, WA. . . . . . . . . . . . . . . . . . . . . . . 2,580 871 2.7 0.9 3,988 1,039 4.1 1.0 1,408 1,358 *1.4 1.4

York-Hanover, PA. . . . . . . . . . . . . . . . . . . 4,370 846 2.2 0.4 7,011 1,508 3.3 0.7 2,641 1,733 *1.2 0.8

Youngstown-WarrenBoardman, OH-PA. . . . . . . . . . . . . . . . . 5,618 912 2.2 0.4 8,698 1,389 3.8 0.6 3,080 1,666 *1.6 0.7

Yuba City, CA. . . . . . . . . . . . . . . . . . . . . . 1,513 544 2.6 0.9 2,038 785 3.5 1.4 525 957 0.9 1.6

Yuma, AZ. . . . . . . . . . . . . . . . . . . . . . . . . 1,147 532 1.9 0.9 2,469 946 3.7 1.4 1,322 1,088 *1.8 1.7

*Statistically different from zero at the 90 percent confidence level.

1

 These metro areas were defined as micropolitan statistical areas in 2005 and by 2010 were defined as metropolitan statistical areas. The Office of 

Management and Budget (OMB) Bulletins announcing updates to metropolitan and micropolitan statistical areas can be found at <www.census.gov

/population/metro/data/omb.html>.

2

 These metro areas had name changes due to the population of the principal cities changing. The Office of Management and Budget (OMB) Bulletins

announcing updates to metropolitan and micropolitan statistical areas can be found at <www.census.gov/population/metro/data/omb.html>. 

Note: Estimates from this table exclude those in the Armed Forces.

Source: U.S. Census Bureau, 2010 1-year American Community Survey.
U.S. Department of Commerce

Economics and Statistics Administration

U.S. CENSUS BUREAU

Washington, DC 20233

OFFICIAL BUSINESS

Penalty for Private Use $300

FIRST-CLASS MAIL

POSTAGE & FEES PAID

U.S. Census Bureau

Permit No. G-58