BLS WORKING PAPERS

U.S. Department of Labor

U.S. Bureau of Labor Statistics

Office of Employment Research and Program Development

Who Telecommutes? Where is the Time Saved Spent?

By Harley Frazis

Working Paper 523

April 2020

All views expressed in this paper are those of the authors and do not necessarily reflect the views or policies of the 

U.S. Bureau of Labor Statistics.

JEL codes: J32, J22, D12.

Keywords: Telecommuting, Telework, Fringe Benefits, Time Use.

I thank Mark Loewenstein, Anne Polivka, Jay Stewart, Rachel Krantz-Kent and seminar participants at the 

University of Maryland Time-Use Group for helpful comments and suggestions.
Introduction

Over the past two decades, data sources indicate an increase in the number of employees 

working at home. The American Community Survey shows a 115 percent increase over the period 2005-

2017 in the number of workers who work from home at least half the time (Global Workplace Analytics, 

2017), to about 3 percent of the workforce. Gallup estimates an increase in the proportion of workers 

who have ever telecommuted from 9 percent in 1995 to 37 percent in 2015 (Jones, 2015). The 

American Time Use Survey (ATUS) estimates that the percentage of workers on a given day who work at 

home rose from 18.6 percent in 2003 to 23.7 percent in 2018, and that average hours per day worked at 

home for those who worked at home rose from 2.56 hours in 2003 to 2.94 hours in 2018.

Work at home done for pay as part of an arrangement with the employer--hereafter 

“telecommuting”—has been of increased interest to policy makers and analysts in recent years. It has 

been argued that telecommuting is family-friendly, allowing the flexibility to attend to the needs of 

children or elderly parents (Chartrand 1997, Trinko 2013). Moreover, commuting typically scores low on 

measures of subjective well-being (Kahneman and Krueger 2006, Krueger et al. 2009), so telecommuting 

allows for commuting time to be reallocated to higher value activities.

This paper uses the ATUS to examine telecommuting. With time diaries for a single day from 

approximately 10,000 respondents or more for every year from 2003 through 2018, and information on 

the location of work, the ATUS would appear ideal to analyze trends in telecommuting. However, when 

analyzing work from home it is necessary to distinguish between telecommuting and unpaid overtime. 

While the motivations for performing work at home and the implications for worker welfare may be 

quite different between telecommuting and unpaid overtime1, the basic ATUS has no means of 

distinguishing between them.

1 Song (2009) discusses a variety of motives for supplying unpaid overtime and attempts to distinguish between 

them using the 2001 Work Schedules and Work at Home Supplement to the Current Population Survey.
2

In 2017 and 2018 the ATUS administered the Leave and Job Flexibilities Module. Among other 

questions, the module includes questions allowing us to distinguish (to a certain extent) between paid 

work (hereafter telework or telecommuting) and unpaid work at home. The module contains questions 

on whether the respondent works at home, whether they are paid for the work at home, whether there 

are days they work only at home, and the frequency of such work. In this paper I use the module to 

describe what types of workdays are most likely to be telework as opposed to unpaid work at home, 

examine whether telework is associated with increased hours of work, characterize teleworkers, and 

estimate how time is reallocated between activities as a result of telecommuting.

Data and Characterization of Telework Days

The ATUS is a single-day time-diary survey that is administered to a sample of individuals in 

households that have recently completed their participation in the Current Population Survey, the main 

labor force survey for the United States. In the time diary portion of the interview, ATUS respondents 

are asked to sequentially report their activities on the previous day, along with information on the start 

and stop time and where the respondent was.2For episodes of work, we thus have information on 

whether the respondent was at a workplace, home, or somewhere else.3

The 2017-18 Leave and Job Flexibilities Module was administered to every respondent who was 

a wage and salary worker, resulting in a sample size of 10,071. I classify workers as telecommuters who 

in response to questions about working at home replied that they were able to and did work at home, 

that they worked entirely at home on some days, and that they were paid for at least some of the hours 

they work at home. 4 Workers were classed as persons who worked unpaid overtime if they were able

2 For further description of the ATUS, see Hamermesh, Frazis and Stewart (2005) and Frazis and Stewart (2007).

3 I do not distinguish between “workplace” and “somewhere else” in this paper.

4 The specific question is “Are you paid for the hours that you work at home, or do you just take work home from 

the job?” Of those responding either “paid” or “both”, 13 percent (weighted) answered “both”.
3

to and did work at home and were not telecommuters. There are 1,454 telecommuters and 1,384 

unpaid overtime workers in the sample.

From the diary data, I examine all workdays where work at the main job is done entirely at 

home. I use the telecommuting status of the worker as an indicator of whether the day is 

telecommuting or unpaid overtime. (Note that workers who state that they never work at home in 

response to supplement questions may nevertheless have workdays at home in the diary.) Of course, 

some days worked at home by telecommuters may be unpaid overtime. For example, it is unlikely that 

a workday consisting of a half-hour of work at home is telecommuting even if performed by a 

telecommuter.

In 2017-18, work was done entirely at home on 9.3 percent of days where any work was done 

on the main job. Of these, only 52 percent were performed by telecommuters, so a substantial 

proportion of home workdays were unpaid work at home. The proportion of days worked at home 

performed by telecommuters varies substantially by the length of the workday and by day of week.

Hereafter, I refer to workdays of 4 hours or less as short workdays and workdays of greater than 4 hours 

as long. Days that accord with the conventional full-time workday are more likely to be performed by 

telecommuters. As shown in Table 1, long home workdays are worked by telecommuters 82 percent of 

the time, and restricting to weekday long home workdays are worked by telecommuters 87 percent of 

the time. The Job Flexibilities supplement includes questions on which days of the week are usual 

workdays, so we can be somewhat more specific about whether the diary day is usually a workday. The 

results are similar to using weekdays—restricting to usual workdays, 86 percent of long home workdays 

are worked by telecommuters. In contrast, short home workdays are worked by telecommuters only 31 

percent of the time.
4

Trends in Telework

The ATUS cannot directly track trends in paid telework. However, it can detect trends in work at 

home and so give an indirect indication of the growth of telework.5 Chart 1 shows that the proportion 

of workdays spent entirely at home increased between 2003 and 2018. (The denominator here is total 

person-days with any work.) Chart 2 shows a similar increase in the proportion of workdays at home for 

long weekdays, the type of day most likely to be worked by telecommuters in 2017-18. The proportion 

of long weekday workdays spent at home was only 1.1 percent in 2003. In 2017-18, 3.8 percent of long 

weekday workdays were spent at home by teleworkers (along with 0.6 percent of long weekday 

workdays spent at home by workers without telework arrangements), indicating that days of this type 

spent teleworking must have substantially increased in this period. As shown in Charts 2 and 3, the 

percentage of workdays spent entirely at home increased in all categories of days over the 2003-18 

period.

How has the trend toward increased telecommuting affected total hours of work? The previous 

literature has investigated the association between working at home and total hours of work, with 

mixed results.6I regressed diary minutes of work on calendar year and demographic variables to 

estimate the 2003-18 trend. (The demographic variables were indicators for married, any children 17 

and younger, number of children, female, interactions of female with the first three variables, education 

categories (high school, some college, BA+), age groups (25-54, 55+), Black, Hispanic, and full-time 

status.) Total daily work irrespective of workplace showed a small and not statistically significant trend.7

Work at home increased at the rate of .57 (standard error .06) minutes per day per year, while work not

5 For a complementary analysis of work at home in 2013-17 and trends in work at home over the period 2003-07 

to 2013-17, see Krantz-Kent (2019).

6 Golden (2008) and Natti et al. (2011) show a positive association between work at home and hours of work, while

Powell and Craig (2015) show a negative one.

7 All standard errors are computed using the replicate weights supplied on the ATUS data file (Bureau of Labor 

Statistics, 2019).
5

at home decreased by an estimated .30 (.19) minutes per day per year. To get an idea on the extent 

telework as opposed to unpaid work at home contributed to these trends, I estimated the probability of 

being a telecommuter and the probability of being an unpaid overtime worker in 2017-18 with a 

multinomial logit model, using the demographic covariates already included and indicators for industry 

and occupation at the one-digit level. Identification of the estimated probability terms accordingly 

comes from variation in industry and occupation. I used the coefficients from this model and generated 

predicted values for the entire 2003-18 sample.

Adding terms for the probability of telecommuting and the probability of being an unpaid home 

worker has essentially no effect on the trend coefficients. As shown in Table 2, both the probability of 

being a telecommuter in 2017-18 and the probability of being an unpaid home worker in the same 

period are associated with longer working hours over the 2003-18 period. Higher probabilities of 

telecommuting are associated with more time working at home and less time working outside the 

home.

Column 3 of Table 2 adds interactions of time with both the probability of telecommuting and 

the probability of being an unpaid home worker. While most of the trend coefficients are small and not 

significant, the interactions with telecommuting probability show a decrease in working hours at 

workplaces and other locations and an increase in work at home for workers with higher probabilities of

telecommuting in 2017-18. This provides more indirect evidence that paid telework is largely behind 

the growth in work at home found in the ATUS; workers in occupations and industries characterized by 

high rates of telecommuting in 2017-18 were not working at home to the same extent in earlier years. 

The interaction of probability of telecommuting with calendar year has an insignificant negative 

coefficient for total hours irrespective of location, so trends in hours provide no evidence that 

telecommuting is associated with an increase in overall hours worked.
6

In results not shown, I estimated the above regressions dividing the sample into weekdays and 

weekends. The results provide further backing for the interpretation of the above results as reflecting 

an increase in telecommuting. The trend showing a shift in hours worked from workplaces and other 

locations to home is confined to weekdays, when telecommuters are a higher percentage of workers 

spending workdays at home.

Who teleworks?

Tables 3a and 3b shows descriptive statistics for the main sample from the Leave and Job 

Flexibilities Module. Table 4 shows the percentage of telecommuters by characteristic. Overall, 12.9 

percent of the sample were telecommuters, but telework varied greatly depending on the nature of the 

job. Not surprisingly, there is wide variation in telework by occupation and industry. Managers and 

professionals have the highest rate of telework, over 20 percent in both categories. Sales and office 

workers have telework rates in neighborhood of 10 percent, while all other occupations have rates in 

the low single digits. Turning to industry, Information, Finance, and Professional and Business Services 

all have telework rates over 25 percent, while most other industries have rates under 10 percent, with 

rates for Public Administration and Manufacturing in the mid-teens.

There is also wide variation in telework rates over some personal characteristics, with college 

graduates having much higher rates of telework (approximately 25 percent) than those with less 

education. Other personal characteristics show much less variation, with men and women having 

virtually identical rates, as do persons in households with and without children.

We would expect that telework rates would be higher in large cities, where the infrastructure 

would support telework and longer commutes would generate a greater demand. This is borne out in
7

the data. Non-metropolitan areas have an estimated telework rate of less than 5 percent8, while the 

two largest categories (2.5 million to 5 million, and 5 million and over) show rates over 16 percent.

I ran a binary logit regression9to examine whether these characteristics are associated with 

telework using the demographic, industry, and occupation variables used previously as well as the 

metropolitan area indicators in Table 4.10 Table 5 shows the results of the logit. It displays the change 

in the probability of having a paid telework arrangement as the specified dummy variable goes from 0 to 

1, with all other variables equal to their weighted sample mean. Most of the variation across categories 

shown in Table 4 is reduced but still present in Table 5. The differences across industries remain very 

large, with Information, Finance, and Professional and Business services having probabilities of telework 

14-18 percentage points greater than Education and Health Services.

Personal characteristics that would be expected to increase the desirability of telework turn out 

to have little effect. Neither being female nor having household children has any discernable association 

with telework, and while the effect of being married is positive and weakly significant (p=.06), it is small 

(1.5 percentage points).

How is the time released from commuting used?

By reducing commuting, telework opens up time for other activities. How is this time used?

As a first step in answering this question, I briefly discuss the measurement of commuting. The 

ATUS coding lexicon subdivides the travel category into subcategories for travel associated with 

different activities. It codes travel in accordance with the first activity performed at the destination, 

with the exception that trips home are coded in accordance with the last activity performed at the

8 Approximately 6 percent of the “non-metropolitan” sample are observations for which metropolitan status is not 

identified.

9 A multinomial logit including unpaid overtime as another outcome category, as in the previous section, yielded 

very similar results.

10Metropolitan area size indicators were not included in the predictors of telework status in the previous section 

due to lack of continuity of the underlying CPS variables.
8

origin. For commuting trips without intervening stops, this is straightforward—direct trips from home to 

work or from work to home would both be coded as travel associated with work.11 However, the coding 

rules may lead to anomalous results for commutes with intervening stops. For example, consider a 

person driving a half-hour to a coffee shop before work, and then driving 5 more minutes to work. The 

first leg of the trip would be coded as travel associated with a consumer purchase, and only the second 

leg would be coded as travel associated with work.

To mitigate this problem, Kimbrough (2019) (building on the work of McGuckin and Nakamoto 

2004) suggested defining commuting trips as trips either beginning at home and ending at work, or 

beginning at work and ending at home, and with stops of no more than 30 minutes. I use this definition 

(implemented using the Stata code supplied by Kimbrough) in the current paper. Commuting is treated 

as missing for diaries that do not begin or end at home, and I limit the sample to observations with nonmissing measures of commuting.12

I use two methods to attempt to account for commuting time foregone by telecommuting. The 

first is simply to compare commuting with non-commuting workdays for telecommuters. I refer to this 

method as the within-telecommuters specification. I restrict this comparison to long workdays that are 

one of the respondent’s usual workdays, as the previous section demonstrated that these are the most 

likely to be worked by employees with a telecommuting arrangement, and thus are less likely to be 

unpaid work at home. Note that by restricting the comparison to telecommuters, this method does not 

suffer from bias due to choice of telecommuting status. To the extent that the activities of 

telecommuting and regular commuting days are independent of each other, the comparison will reflect 

the effect of telecommuting on activity duration for telecommuters (i.e., the effect of treatment on the 

treated). This method may give misleading results if activities are shifted between days due to the

11 There is no explicit “commuting” category in ATUS.

12 This excludes 620 observations.
9

availability of telecommuting. For example, household tasks might be shifted to telecommuting days 

from other days but the overall time in such tasks not increased.

The second method is to compare the time use of telecommuters to non-telecommuters after 

correcting for covariates. I refer to this as the regression specification. This method allows us to 

compare total time use over all days rather than just comparing commuting with telecommuting days, 

and thus corrects for shifting of activities between days. However, it does not eliminate bias due to 

endogeneity of the choice of telecommuting and telecommuting frequency.13

I categorize time use into the following mutually exclusive categories: Personal Care (mostly 

sleeping and grooming), Child and Household Care, Household Production, Leisure, Non-commuting 

travel, Work (on main job)14, Commuting, and Other. For each of these activities minutes performing 

that activity as a primary activity is used as a dependent variable; the sum of these variables adds up to 

1440 minutes. I also include TV Watching and Exercise, subsets of Leisure, as dependent variables, as 

well as Sleeping, Grooming, and Eating and Drinking, subsets of Personal Care. A list of the activity 

codes included in each category is found in Table 6. This categorization is similar to that used in Aguiar, 

Hurst and Karabarbounis (2013), with Personal Care distinguished from Leisure and Non-commuting 

Travel included to examine whether it is a complement or substitute for commuting travel. The ATUS 

also asks during which primary activities children are in the respondent’s care; the total duration of 

these activities is used as a measure of childcare as a secondary activity.15

13 Another approach would be to compare time use trends from 2003-18 for occupations/industries with varying 

proportions of telecommuters in 2017-18 (differences-in-differences). Attempts along this line yielded imprecise 

estimates.

14 Telecommuting status is collected only for the main job. Classifying work on other jobs as work increases the 

point estimate of the effect of telecommuting on work in the within-telecommuter specification, though not to the 

extent that the estimate is statistically significant. Reclassifying work on other jobs has little effect on the 

regression specification.

15 Allard et al. (2007) contains a thorough discussion of the secondary childcare measure in the ATUS and its 

relation to measures in other surveys.
10

The within-telecommuters specification is implemented as follows. Telecommuters interviewed 

about telecommuting days are drawn from the same population as telecommuters interviewed about 

commuting days, as the assignment of which day they are interviewed about is random. However, 

frequent telecommuters are more likely to be interviewed on telecommuting days than infrequent 

telecommuters, so a simple comparison of activities on telecommuting vs. non-telecommuting days 

would to some extent compare the time-use of frequent vs. infrequent telecommuters. This may lead 

to bias if frequent and infrequent telecommuters spend their time differently independently of 

telecommuting. I estimate the difference in time spent in an activity between telecommuting and 

commuting days within each category of response to the frequency of telecommuting question,16 and 

average the differences weighting by the frequency in each category (weighted by the sample weights). 

The cell sizes within each category range from 53 to 116, so I do not use covariates to further correct the 

differences.

Table 7 shows the difference in minutes between commuting days and telework days in the 

listed activities for respondents classified as telecommuters. The table indicates that telecommuting 

saves over an hour per day of commuting time. In addition, about a quarter of an hour less time is spent 

on Grooming. Increased Leisure and Sleeping account for essentially all of the time released from 

these categories. TV Watching in turn accounts for the majority of the increase in Leisure. Changes in 

the other categories of primary activities examined are small and not statistically significant.

The evidence that telecommuting increases time spent caring for others in the household is 

mixed. Child and Household Care (as a primary activity) is estimated to decrease by a statistically 

insignificant 3 minutes, but the sample includes non-parents as well as parents, who arguably are most 

likely to supply care. The results of restricting the sample to parents is shown in the second column.

16 The frequency categories are 1) 5 or more days a week, 2) 3 to 4 days a week, 3) 1 to 2 days a week, 4) At least 

once a week, 5) once every 2 weeks, 6) once a month, and 7) less than once a month.
11

Contrary to expectation, the coefficient on Child and Household Care is more strongly negative though 

still not significant. For the sample as a whole, Secondary Childcare increases 36 minutes on 

telecommuting days, significant at the 5 percent level. Restricting to parents in this case increases the 

estimate to a highly significant 144 minutes per day. Thus, while there is no evidence of any effect on 

Child and Household Care as a primary activity17, there is strong evidence that telecommuting allows 

parents to supervise their children as a secondary activity. Finally, for parents Eating and Drinking 

increases about a quarter of an hour on telecommuting days (and consequently so does the broader 

category Personal Care).

I now turn to the regression specification. In order to incorporate the information on frequency 

of telecommuting into the analysis in an economical manner, I represent telecommuting status by 

predicted days telecommuting, generated as follows. As above, I interpret as telecommuting days 

workdays that are over 4 hours, are worked entirely at home, and that are on one of the respondent’s 

usual workdays. I regress indicators for such workdays on indicators for each category of frequency of 

telecommuting as well as the regressors in the previous section and calculate predicted telecommuting 

status using the coefficients from this regression. I then regress time in an activity on predicted 

telecommuting status and the regressors in the previous section. As additional controls for scheduling 

constraints from market work, I add variables for usual hours worked on the main job and number of 

days usually worked per week.

Results are shown in Table 8. The first column shows the coefficient on predicted (long, usual) 

workdays at home for each dependent variable listed (in minutes per day). As with the withintelecommuter analysis, Leisure shows the strongest effect of telecommuting among the primary 

activities, with an increase of 66 minutes per predicted day of telecommuting. The difference between

17 Restricting to Child Care rather than Child and Household Care yields similar results.
12

the two methods in the implied effect of a day of telecommuting on Leisure is small and not statistically 

significant, as shown in the third column. In other words, the effect of telecommuting days on total 

time during the week spent in Leisure implied by the regression is accounted for by the difference 

between telecommuting and non-telecommuting days, lending support to the within-telecommuters 

estimate representing the total effect.

The estimated effect of telecommuting on Grooming is more negative in the regression 

specification than in the within-telecommuting specification. Given the nature of the activities included 

in Grooming, it seems more probable that this reflects telecommuters having a lesser taste for grooming 

more than any substitution of activities across days. Activities such as dressing or showering would be 

expected to have their main benefit on the day they occur.

The estimated effect on TV Watching is smaller than in the within-telecommuters specification 

and no longer significant, but the estimated difference in the effects between specifications is also not 

significant. Exercise does show a significant effect of telecommuting that is not apparent in the withintelecommuters specification, and the difference between the specifications is significant at the 1 

percent level. Here it is not clear whether this reflects substitution across days or telecommuters having 

a greater propensity to exercise. Other primary activities show smaller and non-significant effects. The 

point estimate for Secondary Childcare is large, implying a greater than one-for-one increase in time 

from reallocated commuting, and statistically significant both overall and in the parents’sample.

Focusing on the parents’ sample, the difference in the estimated effects on Secondary Childcare 

between the two specifications is large—over 75 minutes—but not statistically significant. It is 

interesting to note that for parents on telecommuting days, secondary childcare when work is the 

primary activity averages 117 minutes, compared to 21 minutes on ordinary commuting days.

It is of interest to compare my results to papers examining shifts in time use from exogenous 

shocks. Burda and Hamermesh (2009) examine changes in market work due to changes in
13

unemployment and find most or all of the decline in market work is offset by household production. 

Aguiar, Hurst, and Karabarbounis (2013) similarly examine changes in time use from business cycle 

movements in market work and find that recession-driven reductions in market work are reallocated 

mostly to household production (approximately 30 percent), leisure (30 percent), and personal care 

(mostly sleeping, 20 percent).18 Lee, Kawaguchi, and Hamermesh (2012) and Kawaguchi, Lee and 

Hamermesh (2012) examined the reallocation of reduced work hours due to changes in overtime 

regulations in Japan and Korea. They found that most reallocation was toward leisure in Japan and 

toward personal care in Korea, with negative effects on household production. Kawaguchi, Lee, and 

Hamermesh (2012) find that controlling for consumption expenditure has little effect, while the business 

cycle effects found in the other papers referenced imply that household production substitutes to some 

extent for lost market consumption over the business cycle. In any event, one would expect a minimal 

effect of telecommuting on consumption expenditures compared to the effect of changes in market 

work on consumption, so the absence of evidence of a substantial effect of telecommuting on 

household production is not surprising.

Conclusion

The ATUS has shown an increase in market work done at home since its inception in 2003.

Other data sources have shown an increase in formal telecommuting arrangements. In this paper I use 

the 2017-18 Leave and Job Flexibilities Module to the ATUS to provide indirect evidence confirming the 

growth of telecommuting, describe the characteristics of telecommuters, and estimate how time is 

reallocated between activities in response to telecommuting.

I find that weekdays where the worker works more than 4 hours are the type of day most likely 

to be telecommuting rather than unpaid overtime, and the proportion of these workdays worked at

18 The text of Aguiar, Hurst, and Karabarbounis (2013) shows a higher percentage reallocated to leisure as they 

classify sleeping as leisure.
14

home grew markedly over the period. In addition, workdays by workers with the characteristics most 

likely to be telecommuters in 2017-18 also had substantial growth in the proportion worked at home.

Professionals and managers, workers in the Information, Finance, and Professional and Business 

Services industries, and college graduates were most likely to be telecommuters. Somewhat surprisingly 

in view of the fact that telecommuting is often classed as a “family-friendly” benefit, women and 

parents were not more likely to be telecommuters.

I used two methods to estimate how time is reallocated due to telecommuting. One method is 

based on comparing telecommuting and regular commuting days for telecommuters, while the other 

regresses time in an activity on predicted days telecommuting. Both methods show that a high 

proportion of reallocated time is spent on leisure, and that time spent commuting and grooming is 

reduced. There is no evidence that time saved by telecommuting is allocated to Child and Household 

Care as a primary activity, or that telecommuting increases household production. However, 

telecommuting is associated with a large increase in secondary childcare. This constitutes the main 

piece of evidence supporting classifying telecommuting as a family-friendly benefit.
15

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17

Chart 1

% Workdays entirely at home

12%

10%

8%

6%

4%

2%

0%

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
18

Weekday Weekend/holiday Total

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

9%

8%

7%

6%

5%

4%

3%

2%

1%

0%

Chart 2

% workdays > 4 hours entirely at home
19

Weekday Weekend/holiday Total

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

80%

70%

60%

50%

40%

30%

20%

10%

0%

Chart 3

% workdays <= 4 hours 

entirely at home
20

Table 1

Days Worked at Home by Telecommuters as Percentage of Total Days Worked at Home

Percentage Telework, Workdays at Home

Weekdays

Weekends/ 

Holidays

Usual 

workday

Not usual 

workday Total

Days<=4 hours 28.4% 32.6% 30.1% 31.5% 30.9%

Days>4 hours 86.8% 44.4% 85.9% 39.3% 82.0%

Total 64.2% 34.1% 63.7% 32.3% 52.4%
21

Table 2

Trends in Work by Location, 2003-18

Dependent variable: Minutes of work

Year1 0.30 0.30 0.21

(0.19) (0.19) (0.30)

Prob. Unpaid Overtime 49.07 *** 4.37

(18.41) (36.88)

Prob. Telecommute 24.76 ** 57.81 **

(10.46) (24.00)

Prob. Unpaid Overtime x 4.02

Year (2.94)

Prob. Telecommute x -2.97

Year (2.12)

Dependent variable: Minutes of work workplace/other

Year1-0.27 -0.27 0.29

(0.18) (0.18) (0.29)

Prob. Unpaid Overtime -17.46 -42.80

(19.93) (36.71)

Prob. Telecommute -39.48 *** 34.74

(11.26) (23.59)

Prob. Unpaid Overtime x 2.17

Year (3.02)

Prob. Telecommute x -6.69 ***

Year (2.13)

Dependent variable: Minutes of work at home

Year1 0.57 *** 0.56 *** -0.08

(0.06) (0.06) (0.07)

Prob. Unpaid Overtime 66.53 *** 47.16 ***

(8.30) (13.43)

Prob. Telecommute 64.23 *** 23.07 **

(5.79) (10.04)

Prob. Unpaid Overtime x 1.85

Year (1.24)

Prob. Telecommute x 3.72 ***

Year (0.95)

n 109,221 . 109,221 109,221

1 Year = Calendar year - 2000.

* p < .10

** p < .05

*** p < .01
22

Table 3a

Descriptive Statistics (weighted)

Variable Mean Std. Dev.

Teleworker 0.130 0.336

Full-time 0.811 0.391

Female 0.478 0.500

Married 0.586 0.493

Any children 0.401 0.490

< HS 0.077 0.266

HS Grad 0.263 0.440

Some College 0.257 0.437

BA+ 0.403 0.490

White, Nonhisp. 0.642 0.479

Black, Nonhisp. 0.189 0.391

Hispanic 0.169 0.375

Age <= 24 0.142 0.349

Age 25-54 0.653 0.476

Age >= 55 0.205 0.404

Management 0.159 0.366

Professional 0.281 0.449

Service 0.159 0.366

Sales 0.078 0.268

Office 0.132 0.339

Agricultural Occ. 0.008 0.087

Construct. Occ. 0.041 0.198

Installation/Repair 0.029 0.167

Production 0.060 0.238

Transp. Occ. 0.054 0.225

Agriculture/Mining 0.016 0.124

Construction 0.048 0.213

Manufacturing 0.113 0.317

Whole./Ret. Trade 0.125 0.331

Transportation 0.051 0.220

Information 0.019 0.136

Finance 0.072 0.259

Prof./Bus. Services 0.117 0.321

Education/Health 0.258 0.437

Leisure 0.091 0.287

Other services 0.038 0.191

Public administration 0.053 0.224
23

Table 3a, continued

Variable Mean Std. Dev.

Non-metro. or not

identified 0.136 0.342

Metro, not identified 0.039 0.194

Metro 100K – 250K 0.070 0.255

Metro 250K –500K 0.069 0.254

Metro 500K –1M 0.114 0.318

Metro 1M – 2.5M 0.185 0.388

Metro 2.5M –5M 0.140 0.347

Metro 5M+ 0.248 0.432

N = 9,920

Table 3b

Reported frequency of telecommuting (paid telecommuters)

Reported frequency Weighted % N

5 or more days a week 16.0 242

3 to 4 days a week 12.3 153

1 to 2 days a week 17.2 240

At least once a week 10.0 149

Once every 2 weeks 12.9 185

Once a month 13.5 199

Less than once a month 18.1 247

N=1,415
24

Table 4

Percentage Telecommuters by Selected Characteristics

Total 12.9 Prod., transport, etc. occupation 1.3

Part-time 6.9 Ag, forestry, and fishing/Mining 11.1

Full-time 14.5 Construction 4.5

Men 13.1 Manufacturing 14.0

Women 12.6 Wholesale/retail 6.8

< HS 1.4 Transp. & Utilities 6.8

HS Grad 3.8 Information 26.9

Some College 7.6 Finance 26.4

BA+ 24.6 Prof/business services 31.2

Non-Hispanic White 15.4 Education and health 9.4

Non-Hispanic Nonwhite 11.4 Leisure & Hospitality 2.3

Hispanic 5.1 Other Services 9.7

No Household Children 12.5 Public Administration 13.4

Any Household Children 13.4 Non-metro. or not ident. 4.4

Spouse or partner present 15.8 Metro, not identified 9.2

Other Mar. Status 8.7 Metro area 100K - 250K 11.9

Age 16-24 2.6 Metro area 250K - 500K 7.9

Age 25-54 15.5 Metro area 500K - 1M 11.8

Age 55+ 11.8 Metro area 1M - 2.5M 13.8

Manager/Professional 23.1 Metro area 2.5M - 5M 18.3

Service occupation 2.4 Metro area 5M+ 16.4

Sales & Office occupation 10.1

Farm/fish occupation 0.6

Construction/maintenance

occupation 2.2
25

Table 5

Estimated Effects of Selected Characteristics on Telecommuting from Logit

Full-time - Part-time 0.004 Construct. - Ed./Health 0.040 *

(0.007) (0.022)

Female - Male -0.001 Mfg. - Ed./Health 0.114 ***

(0.005) (0.020)

Married - Other 0.014 *** Whole./Ret. - Ed./Health 0.043 ***

(0.005) (0.014)

Any child - No child -0.003 Transp./Util. - Ed./Health 0.069 ***

(0.005) (0.023)

Clg. Grad - Some Clg. 0.050 *** Information - Ed./Health 0.148 ***

(0.008) (0.031)

Clg. Grad - HS Grad 0.068 *** Finance - Ed./Health 0.135 ***

(0.009) (0.022)

Clg Grad. - HS Dropout 0.077 *** Prof./Bus. - Ed./Health 0.168 ***

(0.016) (0.021)

Black - Non Hisp. White -0.020 *** Leisure - Ed./Health 0.016

(0.006) (0.015)

Hisp. - Non Hisp. White -0.035 *** Other Serv. - Ed./Health 0.092 ***

(0.006) (0.026)

Age < 25 - Age 25-54 -0.044 *** Pub. Admin. - Ed./Health 0.056 ***

(0.007) (0.016)

Age > 54 - Age 25-54 -0.015 ** Nonmetro - Metro 5M+ -0.052 ***

(0.006) (0.007)

Professional - Manager -0.035 *** Unid. Metro - Metro 5M+ -0.031 ***

(0.010) (0.012)

Service - Manager -0.070 *** Met 100-250K - Met 5M+ -0.024 **

(0.010) (0.011)

Sales - Manager -0.046 *** Met 250-500K - Met 5M+ -0.043 ***

(0.011) (0.009)

Office - Manager -0.059 *** Met 500K-1M - Met 5M+ -0.030 ***

(0.009) (0.008)

Farm/fish - Manager -0.086 *** Met 1-2.5M - Met 5M+ -0.025 ***

(0.010) (0.009)

Construction - Manager -0.072 *** Met 2.5 -5M - Met 5M+ -0.011

(0.010) (0.009)

Maintenance - Manager -0.085 *** Construct. - Ed./Health 0.040 *

(0.009) (0.022)

Production - Manager -0.084 ***

(0.008)

Transport - Manager -0.084 ***

(0.008)
26

Table 6

ATUS Codes for Activity Variables

Category ATUS Codes

Personal Care 01 (Personal Care), 11 (Eating and Drinking)

Sleep 0101 (Sleeping)

Grooming 0102 (Grooming)

Eating and Drinking 11 (Eating and Drinking)

Child and Household 

Care 03 (Caring For & Helping Household Members)

Household production 02 (Household Activities), 04 (Caring For & Helping NonHH Members), 07 (Consumer 

Purchases), 08 (Professional & Personal Care Services), 09 (Household Services)

Leisure 12 (Socializing, Relaxing, and Leisure), 13 (Sports, Exercise, and Recreation)

TV 120303 (Television and movies (not religious))

Exercise 1301 (Participating in Sports, Exercise, or Recreation)

Non-commuting travel 18 (Travel), excluding commuting

Work 0501 (Work) excluding 050102 (Work on other jobs), 0502 (Work-related activities)

Other 050102 (Work on other jobs), 0503 (Income Generating Activities), 0504 (Job Search 

and Interviewing), 0599 (Work and Work-Related Activities, not elsewhere 

classified), 06 (Education), 10 (Government Services & Civic Obligations), 14

(Religious and Spiritual Activities), 15 (Volunteer Activities), 16 (Telephone Calls)
27

Table 7

Difference between telecommuting and ordinary commuting days, minutes per day, long usual 

workdays, telecommuters.

Activity Whole Sample Parents

Personal Care 6.8 32.7 ***

(9.3) (11.1)

Sleep 24.7 *** 31.9 **

(9.4) (13.7)

Grooming -15.9 ** -13.2 ***

(3.7) (3.4)

Eating and Drinking -1.8 14.3 ***

(4.6) (5.2)

Child & Household Care -2.5 -12.1

(6.2) (9.9)

Household production 1.5 -4.6

(12.7) (24.2)

Leisure 57.3 *** 52.9 ***

(14.5) (20.1)

TV 43.0 *** 40.6 **

(10.4) (18.9)

Exercise -3.5 -0.5

(2.8) (3.7)

Non-commuting travel -1.5 4.9

(4.9) (6.4)

Other 5.9 12.7

(17.6) (10.0)

Work -0.1 -30.6

(13.0) (20.7)

Commuting -67.3 *** -55.9 ***

(5.4) (5.5)

Secondary Childcare 36.1 ** 144.2 ***

(17.8) (32.9)

n 631 341
28

Table 8

Regression Results and Comparison to Within-Telecommuter Results

Coef. on Predicted Long Usual Days

at Home

Difference from WithinTelecommuter Comparison

Entire Sample Parents

Entire

Sample Parents

Personal Care -33.7 * -40.1 ** 40.5 * 72.9 ***

(17.5) (19.6) (21.6) (22.6)

Sleep 0.2 -2.4 24.5 34.3

(15.4) (18.6) (19.0) (23.0)

Grooming -35.9 *** -35.7 *** 19.9 *** 22.5 ***

(4.4) (6.1) (5.2) (6.6)

Eating and Drinking 1.0 0.1 -2.8 14.2

(6.5) (8.5) (7.8) (9.0)

Child & Household

Care 12.0 19.7 -14.6 -31.8

(8.1) (18.4) (10.1) (21.3)

Household production 8.1 39.2 -6.6 -43.9

(17.0) (27.2) (22.1) (41.9)

Leisure 61.0 *** 83.8 *** -3.7 -30.9

(22.9) (27.6) (25.9) (33.1)

TV 21.6 30.3 21.4 10.2

(20.9) (21.4) (22.7) (26.9)

Exercise 20.3 ** 19.2 ** -23.8 *** -19.6 **

(8.2) (8.9) (8.6) (9.3)

Non-commuting travel 13.0 21.2 -14.6 -16.4

(8.8) (13.2) (10.3) (14.1)

Other 14.5 9.5 -8.6 3.1

(15.9) (24.1) (26.0) (25.3)

Work -25.0 -63.0 24.9 32.4

(34.7) (42.0) (38.9) (48.3)

Commuting -50.0 *** -70.4 *** -17.3 14.6

(8.3) (8.2) (12.4) (11.4)

Secondary Childcare 85.9 *** 219.1 *** -49.9 * -74.9

(23.7) (55.6) (29.0) (62.9)

n 9,448 4,534