Monthly Labor Review, June 2012: The hard truth about telecommuting

Transcript

1 Telecommuting The hard truth about telecommuting Telecommuting has not permeated the American workplace, and where it has become commonly used, it is not helpful in reducing work-family conflicts; telecommuting appears, instead, to have become instrumental in the general expansion of work hours, facilitating workers’ needs for additional worktime beyond the standard workweek and/or the ability of employers to increase or intensify work demands among their salaried employees Mary C. Noonan - Evidence also reveals that an increasing num elecommuting, defined here as and ber of jobs in the American economy could be work tasks regularly performed Jennifer L. Glass T performed at home if employers were willing at home, has achieved enough 5 Often, employees to allow employees to do so. traction in the American workplace to can perform jobs at home without supervision merit intensive scrutiny, with 24 percent in the “high-tech” sector, in the financial sector, of employed Americans reporting in recent surveys that they work at least some hours and many in the communication sector that are 1 technology dependent. The obstacles or barriers The definitions of at home each week. - telecommuting are quite diverse. In this ar - to telecommuting seem to be more organiza - tional, stemming from the managers’ reluctance ticle, we define telecommuters as employ to give up direct supervisory control of workers ees who work regularly, but not exclusively, and from their fears of shirking among workers at home. In our definition, at-home work 6 activities do not need to be technologically who telecommute. mediated nor do telecommuters need a Where the impact of telecommuting has formal arrangement with their employer to been empirically evaluated, it seems to boost work at home. productivity, decrease absenteeism, and increase 7 But can telecommuting live up to its retention. Telecommuting is popular with policy makers and activists, with proponents promise as an effective work-family policy that - pointing out the multiple ways in which helps employees meet their nonwork responsi bilities? To do so, telecommuting needs to be telecommuting can cut commuting time 2 both (1) widely used by workers who most need and costs, reduce energy consumption it and (2) instrumental in substituting hours at and traffic congestion, and contribute to 8 home for hours onsite. Popular perceptions of worklife balance for those with caregiving 3 Mary C. Noonan is an Associate responsibilities. Changes in the structure - telecommuting conjure images of workers re Professor at the Department of Sociology, The University of Iowa; placing hours worked onsite with hours more of jobs that enable mothers to more effec - Jennifer L. Glass is the Barbara comfortably worked at home, for mothers and tively compete in the workplace, such as Bush Regents Professor of Liberal Arts at the Department of Sociol - other care workers, especially. Yet, we know little telecommuting, may be needed to finally ogy and Population Research - eliminate the gender gap in earnings and about how telecommuting in practice has be Texas at Center, University of Austin. Email: [email protected] direct more earned income to children, come institutionalized in American workplaces. uiowa.edu or j [email protected] 4 austin.utexas.edu . - Which workers telecommute? Is telecom both important public policy goals. • June 2012 38 Monthly Labor Review

2 muting an effective strategy that lowers employees’ Methods - average hours worked onsite, or is telecommuting as The NLSY is a national probability sample of 12,686 women sociated with longer average weekly work hours? To and men living in the United States and born between 1957 preview our results here, we find that telecommuting and 1964. The sample was interviewed annually from 1979 to - not has extensively permeated the American work began ask NLSY 1994 and biennially thereafter. In 1989, the - become commonly used, it is has place, and where it ing questions about the amount of time respondents worked at not unequivocally helpful in reducing work-family CPS supplements home. To most closely match the years of the conflicts. Instead, telecommuting appears to have be - (described in the next paragraph), we pool 3 years from the come instrumental in the general expansion of work for our analysis: 1998, 2002, and 2004. NLSY hours, facilitating workers’ needs for additional work - is a monthly survey of about 50,000 households The CPS time beyond the standard workweek and/or the ability representing the nation’s civilian noninstitutional population of employers to increase or intensify work demands 16 years of age and over. We use data from the special Work among their salaried employees. Schedules and Work at Home supplement to the May 1997, We use two nationally representative data sources, , which asks workers whether they worked 2001, and 2004 CPS the National Longitudinal Survey of Youth ( NLSY ) data is CPS at home as part of their job. The advantage of the ) and special NLSY 1979 panel (hereafter, noted as the that, unlike the , it covers a broader age range of workers NLSY supplements from the U.S. Census Current Population so that we can compare a cohort similar in age with the NLSY, Survey ( CPS ), to ascertain (1) trends over time in the as well as a younger cohort of workers who might be more use of telecommuting among employees in the civilian - technologically sophisticated and more amenable to telecom labor force, (2) who telecommutes across the population CPS sample to workers 22 to muting. As such, we restrict the of employees, and (3) the relationship between telecom - 40 years of age in 1997, 26 to 44 in 2001, and 29 to 47 in 2004. muting and longer work hours among employees. These We further restrict our sample to individuals who worked two data sources provide information on telecommut - at least 20 hours per week in nonagricultural jobs and who ing from the mid- to late 1990s through the mid-2000s, provided valid data on all the key variables. Workers who a period in which interest and capacity for telecom - were self-employed or worked exclusively at home are also muting dramatically increased among U.S. businesses. excluded from the sample. The final sample sizes are 16,298 (Note that we did not use more recent data because the NLSY for the CPS . and 50,452 for the supple Work Schedules and Work at Home May CPS - Our two main variables of interest are total hours worked ment was not fielded after 2004.) per week for the main job and total hours worked per week at Together, these two datasets allow us to ascertain 9 - . home for the main job We use these two measures to cre any general changes over time in the proportion of ate two dummy variables indicating respondents who worked employees who telecommute and the time intensity of overtime (i.e., more than 40, 50, and 60 hours per week) and - their telecommuting at their main job. We further disag who telecommuted (i.e., worked at least 1 hour at home per gregate telecommuting hours into those hours that are week), respectively. Finally, for those respondents who tele - encapsulated within the 40-hour workweek (such that, commuted, we disaggregate telecommuting hours into r - egu - regardless of the day or time worked, these telecommut lar telecommuting hours and overtime telecommuting hours . We ing hours do not raise total work hours per week above create these two variables by first creating a variable equal the statutory 40-hour threshold) and those hours that - hours worked per week onsite for the main job . If total on to extended the total number of hours worked per week site work hours are less than 40, we categorize telecommut - beyond 40. By dividing telecommuting hours into these ing hours that do not raise total work hours above 40 hours - two categories, we are able to determine whether tele as regular telecommuting hours. If total onsite work hours replaces commuting either hours that otherwise would equal 40 or more, we categorize all telecommuting hours as have been worked onsite during a standard 40-hour overtime telecommuting hours. We do not know the day or expands the workweek beyond the 40 or workweek or time that onsite and/or telecommuting hours were worked; more hours already worked onsite. instead, in our categorization, we assume that onsite hours In the following sections, we briefly describe our data are “worked first” and telecommuting hours come second. sources and measures, provide results from our analysis - Note that some workers reported both types of telecommut of the data, and summarize the lessons learned from ing hours. For example, a worker reporting 45 total hours of investigating the implementation of telecommuting in work per week, of which 10 are worked exclusively at home, American workplaces. • June 2012 39 Monthly Labor Review

3 Telecommuting estimates, approximately CPS and NLSY According to our would yield 5 hours of regular telecommuting and 5 hours 10 percent of workers telecommuted in the mid-1990s of overtime telecommuting by our definitions. Control variables include occupation (measured with (chart 1). The rate of telecommuting increased slightly to three categories: managerial/professional, sales, and 17 percent in the early 2000s and then remained constant 10 Our results suggest that telecommut - education (measured with four categories: less than to the mid-2000s. other), - ing rates are not significantly different between younger high school, high school diploma, some college, and col lege degree or higher), gender, race/ethnicity (measured and older cohorts of workers. Furthermore, no evidence with three categories: other [White, Asian, etc.], Black, suggests that, among telecommuters, the number of hours - marital status (measured with three catego and Hispanic), spent telecommuting has increased over time (results not shown). For the remainder of our analysis, we use a single - ries: never married, married, and separated/divorced/wid CPS sample, not differentiated by age (i.e., the younger (dummy variable indicating whether parental status owed), and older cohorts are pooled together). a child 0 to 18 years old lives in household), and age. Next, we examine how telecommuting varies by educa Finally, we create synthetic age cohorts for the CPS data - tional attainment, occupation, and parental status (chart 2). sample (32 to 40 years NLSY based on the age range of the CPS for the Here, we present data from the old in 1997). We define the only; results from the CPS as 32 to older cohort CPS are similar to the 40 years old in 1997, 36 to 44 years old in 2001, and 39 to results show that CPS NLSY results. CPS parents are no more likely than the population as a whole , by from the 47 years old in 2004. The younger cohort to telecommute, and mothers do not telecommute more contrast, incorporated workers who were 22 to 29 years old than fathers (about 17 percent for each group, results not in 1997, maturing to 26 to 33 in 2001 and 29 to 36 in 2004. shown). However, college-educated workers and those in These two cohorts effectively cover the career stages in which managerial and professional occupations are significantly most earnings growth occurs, from the mid-20s to late 40s. To begin our analysis, we present trends over time in the more likely to telecommute than the population as a whole. use of telecommuting for each sample as a whole and then - Table 1 presents descriptive statistics on our key vari for various demographic groups. Next, we present descriptive NLSY ables by telecommuting status for both datasets, CPS (1998, 2002, 2004) and - statistics on all variables by telecommuting status for the CPS (1997, 2001, 2004). Most no tably, telecommuters worked between 5 and 7 total hours NLSY sample. For each sample, we perform sample and the more per week than nontelecommuters. Telecommuters - statistical tests to determine if differences exist between tele were significantly less likely to work a regular schedule commuters and nontelecommuters. We pay special attention to the average hours of telecommuting among telecommuters (i.e., between 20 and 40 hours per week) and were more - likely to work overtime, regardless of how overtime is de and discuss how much telecommuting replaces onsite hours - fined (i.e., as working more than 40, 50, or 60 hours per within the first 40 hours worked and how much telecom week). muting extends the workweek beyond 40 hours. Finally, we Among telecommuters, the average number of hours estimate logistic regression models to predict the likelihood spent telecommuting each week is relatively modest, ap - - of working overtime based on telecommuting status, includ proximately 6 hours per week in both the CPS and NLSY ing the control variables just described. Important to note is that neither the CPS nor the NLSY provides information on samples. But fully 67 percent (i.e., 4.17/6.20) of telecom - muting hours in the NLSY and almost 50 percent (i.e., whether or not the employee has an option to telecommute. 3.21/6.75) in the CPS occur in the overtime portion of Our regression model assumes that all workers are able to telecommute and that “telecommuting status” is exogenous the weekly hours distribution (see table 1, “Hours worked by location”). This finding suggests that telecommuting is to work hours. In reality, the ability to telecommute is likely not being predominately used as a substitute for working a function of one’s occupational type and, within occupation, onsite during the first 40 hours worked per week. - one’s performance. Both occupation and employee perfor Telecommuters are significantly more likely to have mance are likely correlated with hours worked. We deal with this endogeneity problem by controlling for occupation in a college degree and to work in managerial/professional our models; data on employee performance are not available. occupations compared with those who do not work at home. Interestingly, parents are only slightly more pre - Results - dominant among telecommuters than nontelecommut ers. Telecommuters are less likely to be Black or Hispanic To begin, we examine trends over time in telecommuting and less likely to be married compared with those not for all workers and then for various demographic groups. telecommuting. 40 Monthly Labor Review • June 2012

4 Chart 1. Percentage of workers telecommuting over time, by cohort Percent Percent 50 50 40 40 NLSY , older cohort , older cohort CPS 30 30 , younger cohort CPS 20 20 10 10 0 0 1997/1998 2001/2002 2004 N : Younger cohort is 22–29 years old in 1997. Older cohort is 32–40 years old in 1997. OTES ) 1979 panel and special supplement from the U.S. Census Current Population NLSY Youth ( : National Longitudinal Survey of OURCES S ). Survey ( CPS Chart 2. Percentage of workers telecommuting over time, by education, occupation, and parental status Percent Percent 50 50 All College educated 40 40 Managerial/professional Parent 30 30 20 20 10 10 0 0 2004 1997 2001 ). CPS : Special supplement from the U.S. Census Current Population Survey ( OURCE S • June 2012 41 Monthly Labor Review

5 Telecommuting Table 1. Descriptive statistics by telecommuting status NLSY (1998, 2002, 2004) CPS (1997, 2001, 2004) Statistical Statistical Telecommuting status Telecommuting status Variable test test Ye s No Ye s No 1 1 40.79 41.11 ) 47.81 45.45 ( ( ) Total hours worked per week Hours worked per week (percent) 1 1 22 ) 72 47 ( 20–40 ) 73 ( 1 1 78 ) 27 53 ( 28 ) ( 41 or more 1 30 ( ) 9 22 7 51 or more 1 1 61 or more ( 2 ) 2 6 ( 7 ) Hours worked by location 2 1 41.61 ( ( ) 40.79 38.70 Onsite 41.11 ) At home — 6.20 — — 6.75 — — Regular — 2.03 — — 3.54 — 3.21 — Overtime — 4.17 — Occupation (percent) 1 1 71 ) 27 26 ( 70 ) Managerial/professional ( 1 1 ( Sales ) 10 14 ( 7 ) 12 1 1 18 ( ( ) 63 15 Other 67 ) Education (percent) 1 1 1 Less than high school ) 11 2 ( ( ) 8 1 1 17 ( High school diploma ) 35 11 ( 47 ) 1 1 Some college 25 20 ( ) ) 30 20 ( 1 1 ) 60 ( 68 24 ) 21 College degree or higher ( Gender (percent) 3 3 53 Male ( ) 55 54 ( ) 51 3 3 46 ( ( ) 45 47 Female 49 ) Race/ethnicity (percent) 1 1 88 ( 78 ) 73 88 ( Other (White, Asian, etc.) ) 1 1 ( 16 ) 12 6 ( Black ) 8 1 1 7 5 ( 14 ) ) 6 ( Hispanic Marital status (percent) 1 1 15 11 ) Never married ) 26 20 ( ( 1 1 ) 63 75 ( Married ) 60 69 ( 1 1 11 ) 14 ) ( 14 ( Separated/divorced/widowed 22 Parental status (percent) 1 1 ( ) ) 75 77 ( Parent (1 = yes) 65 74 3 1 34.88 40.24 ) Age 36.30 ( ( ) 40.54 Number 14,100 2,198 — 43,188 7,264 — 1 p < .001. data, CPS) ( Survey Population In Current onsite. 1 hour worked at least 2 status < .10. p the parental and 2004; question is only asked in 2001 statistics 3 < .01. p for this variable represent only these years. ( NLSY ) 1979 panel : National OURCES Longitudinal Survey of Youth N OTES : All statistics are weighted. Sample includes respondents who S were not self-employed, worked at least 20 hours per week, and CPS and special supplement from the U.S. Census . Table 2 presents the results of our logistic regression than 60 total hours per week. In each model, we control models predicting the likelihood of working overtime for occupation, education, gender, race/ethnicity, marital as a function of telecommuting status. We model three did not collect data CPS status, and age. Since the 2001 versions of overtime: working more than 40 total hours on parental status, we do not include this variable in the per week, more than 50 total hours per week, and more models. Because logistic regression coefficients do not • June 2012 42 Monthly Labor Review

6 Table 2. Logistic regression coefficients predicting working overtime CPS hours worked per week NLSY hours worked per week (1998, 2002, 2004) (1997, 2001, 2004) Variable 41 or more 51 or more 41 or more 51 or more 61 or more 61 or more 0.89 0.85 1.70 0.95 1.79 2.17 Telecommute status (1 = yes) 1 1 1 1 1 1 (.07) (.16) (.08) (.04) (.03) (.08) Occupation .18 .36 .23 .11 Managerial/professional .39 –.43 1 3 1 1 2 (.03) (.05) (.09) (.06) (.19) (.09) Sales –.46 .46 .39 .17 .42 .07 2 1 1 1 (.04) (.26) (.06) (.10) (.09) (.13) 4 4 4 4 4 4 ( ) ( ) ( ( ) ) ( ( ) Other ) Education .36 –.04 –.28 –.17 Less than high school .31 –.17 1 3 3 2 (.10) (.05) (.15) (.08) (.15) (.29) –.10 High school diploma –.06 –.02 .05 .30 .32 5 2 (.10) (.20) (.03) (.05) (.09) (.06) .17 .17 –.06 –.05 .00 Some college –.19 2 2 5 (.07) (.03) (.05) (.09) (.10) (.21) 4 4 4 4 4 4 ( ( ) ( ) ) ( ) ) ( ( College degree or higher ) Gender Female –1.40 –1.07 –1.20 –1.18 –1.32 –1.33 1 1 1 1 1 1 (.15) (.04) (.02) (.07) (.05) (.07) 4 4 4 4 4 4 ( ( ) ( ) ) ( Male ) ( ( ) ) Race/ethnicity 4 4 4 4 4 4 ) ( ( ) ( ) ) ( White ) ( ( ) Black –.26 .38 –.51 –.32 –.12 .01 1 1 1 1 (.04) (.13) (.07) (.12) (.07) (.05) –.21 –.02 –.42 –.42 –.01 –.46 Hispanic 1 1 1 1 (.08) (.15) (.06) (.13) (.04) (.07) Marital status Never married –.18 –.04 .05 –.13 –.16 –.07 1 5 1 (.17) (.10) (.07) (.05) (.08) (.03) 4 4 4 4 4 4 ( ) ) ( ( ) ( ( ) Married ) ) ( .12 Separated/divorced/widowed .29 .06 –.04 .17 .20 3 2 2 3 (.06) (.08) (.16) (.03) (.05) (.09) Age –.01 –.01 .02 .00 .00 –.01 (.01) (.02) (.01) (.01) (.01) (.01) –4.51 –1.95 –3.04 Constant –2.04 –.14 –.44 5 5 1 1 1 (.12) (.08) (.20) (.79) (.27) (.40) 16,298 16,298 50,452 50,452 50,452 Number 16,298 1 respondents Sample p who < .001. N OTES : All statistics are weighted. includes 2 were p < .10. and worked per week, 20 hours at least worked not self-employed, 3 models in regression not included status < .05. p Parental at least 1 hour onsite. 4 CPS). Omitted category. because it is not available in the 1997 Current Population Survey ( 5 ( < .01. S OURCES : National Longitudinal Survey of Youth p NLSY ) 1979 panel and special supplement from the U.S. Census CPS . • June 2012 43 Monthly Labor Review

7 Telecommuting week is relatively modest, around 6 hours per week in both show how much the probability of an event changes when NLSY and CPS the the predictors change, we translate the coefficients into samples. No evidence suggests that the predicted probabilities for four “ideal types” (cases) in table number of hours spent telecommuting is increasing over time. 3. For each case, we calculate the probability of working Our descriptive results suggest that labor demand for not a telecommuter overtime, assuming the individual is work-family accommodation does not seem to propel and again assuming the individual is a telecommuter. In - both datasets and in all models, the probability of work the distribution of telecommuting hours. None of the expected relationships under such a scenario are present ing overtime is higher for telecommuters compared with nontelecommuters. The difference in the probability of in the data—parents of dependent children, for example, working overtime between the two groups is largest when are no more likely to telecommute than the population we define overtime as 41 hours or more, and smaller, but as a whole. Meanwhile, indicators that suggest a supply- still significant, when overtime is defined as working 61 side explanation—such as occupational sector and work hours or more. hours—are more strongly related to telecommuting hours. As others have noted, the ability to work at home UR ANALYSIS OF TELECOMMUTING O - appears to be systematically related to authority and sta has yielded several surprising findings. Though more and more employ - tus in the workplace. Managerial and professional work - ers claim to be offering flexible work options, the propor - ers are more likely than others to have the type of tasks and autonomous control of their work schedule necessary tion of workers who telecommute has been essentially flat over the mid-1990s to mid-2000s and is no larger among to perform work at home. While telecommuting may in younger cohorts of workers than older cohorts. Moreover, theory be a solution to the dilemmas of combining work the average number of hours spent telecommuting each - and family, telecommuting in practice does not unequiv Table 3. Predicted probability of working overtime as a function of telecommuting status and other variables [In percent] NLSY hours worked per week CPS hours worked per week (1997, 2001, 2004) (1998, 2002, 2004) Case 51 or more 61 or more 51 or more 61 or more 41 or more 41 or more Case 1: Man, college degree, managerial/professional 49 10 1 47 16 4 No telecommuting Yes telecommuting 40 8 68 33 8 90 6 40 30 21 17 5 Difference Case 2: Man, high school diploma, other occupation No telecommuting 11 3 37 13 4 37 84 43 15 59 Yes telecommuting 8 27 Difference 47 32 12 22 15 4 Case 3: Woman, college degree, managerial/professional 1 No telecommuting 21 3 0 23 5 Yes telecommuting 70 2 42 13 3 15 49 2 2 19 7 Difference 12 Case 4: Woman, high school diploma, other occupation No telecommuting 14 3 1 17 4 1 Yes telecommuting 58 17 4 33 10 3 Difference 3 16 6 1 13 45 OTES : In all predictions, the worker is White, married, and N S OURCES : National Longitudinal Survey of Youth ( NLSY ) 1979 panel and 40 years ). special supplement from the U.S. Census Current Population Survey ( CPS old. Predictions based on estimated coefficients from table 2. 44 Monthly Labor Review • June 2012

8 - ocally meet the needs of workers with significant caregiv Future research employing longitudinal data should explore ing responsibilities. - whether employees increase their work hours after initia tion of telecommuting. The most telling problem with telecommuting as a - Since telecommuting is intrinsically linked to infor worklife solution is its strong relationship to long work 11 hours and the “work devotion schema.” mation technologies that facilitate 24/7 communication Fully 67 percent of telecommuting hours in the - NLSY and almost 50 per - between clients, coworkers, and supervisors, telecommut ing can potentially increase the penetration of work tasks CPS cent in the push respondents’ work hours above 40 per week and essentially occur as overtime work. This dynamic into home time. Bolstering this interpretation, the 2008 Pew Networked Workers survey reports that the majority suggests that telecommuting in practice expands to meet - workers’ needs for additional worktime beyond the stan of wired workers report telecommuting technology has increased their overall work hours and that workers use dard workweek. As a strategy of resistance to longer work hours at the office, telecommuting appears to be somewhat technology, especially email, to perform work tasks even 12 successful in relocating those hours but not eliminating when sick or on vacation. Careful monitoring of this them. A less sanguine interpretation is that the ability of blurred boundary between work and home time and the erosion of “normal working hours” in many professions employees to work at home may actually allow employers can help us understand the expansion of work hours over to raise expectations for work availability during evenings - all among salaried workers. and weekends and foster longer workdays and workweeks. N OTES 1 - how many hours did you actually work at your job?” To measure tele , USDL American Time Use Survey—2010 Results See -11-0919 CPS commuting, all three May questionnaires have a lead-in question (U.S. Bureau of Labor Statistics, June 22, 2011). asking, “As part of this job, do you do any of your work at home?” The 2 See Aleksandra Todorova, “Company Programs Help Employees follow-up question varies slightly depending on which year of the CPS Smart Money Save on Gas,” , May 29, 2008, http://www.smartmoney. questionnaire asks, “Last week, CPS survey is being used. The May 1997 com/spend/family-money/company-programs-help-employees- of the ___ actual hours of work you did, approximately how many of . save-on-gas-23179 - ques CPS them did you do at home for this job?” The May 2001/2004 3 For review, see Ravi S. Gajendran and David A. Harrison, “The tionnaire, on the other hand, asks, “When you work at home, how - Good, the Bad, and the Unknown about Telecommuting: Meta-Anal many hours per week do you work at home for this job?” Furthermore, ysis of Psychological Mediators and Individual Consequences,” Journal NLSY is slightly different than the the questionnaire wording in the of Applied Psychology 92, no. 6 (2007), pp. 1,524–1,541. . The NLSY question on hours worked (both at home and not at CPS 4 actual hours: “How many hours per home) measures hours, not usual Who Pays for the Kids? Gender and the Structure See Nancy Folbre, week do you usually work at this job?” and then, “How many hours per of Constraint (New York: Routledge, 1995), and see Joan Williams, Un - week do you usually work at this job at home?” Studies comparing the bending Gender: Why Family and Work Conflict and What to Do about It two measures of hours worked (actual versus usual) find that estimates (New York: Oxford University Press, 2000). of actual hours worked are generally lower than estimates of usual 5 See Gartner, Inc., “Dataquest Insight: Teleworking, The Quiet hours worked (See Richard D. Williams, “Investigating Hours Worked (May 14, 2007). Gartner Revolution (2007 Update),” Labor Market Trends 112, no. 2 (2004), pp. 71– Measurements,” 2004, 6 79. Our results suggest a similar pattern. Finally, “it varies” is a valid See Mary Blair-Loy, Competing Devotions: Career and Family response option in the May 2001/2004 question asking workers CPS among Women Executives (Cambridge, MA : Harvard University Press, for the number of hours worked at home. Approximately one-third The Time Bind 2003). See Arlie Hochschild, (New York: Metropolitan of the telecommuters in each year selected “it varies” as their response. Opting Out? Why Women Really Quit Books, 1995); See Pamela Stone, We imputed the mean telecommuting hours for those who replied “it (Berkeley, CA : University of California Press, Careers and Head Home varies” (6.40 for 2001 and 6.74 for 2004) and created a dummy variable 2007). to indicate that the respondent’s value for telecommuting hours was 7 - See Gajendran and Harrison, “The Good, the Bad, and the Un imputed. This indicator was included in the logistic regression models known about Telecommuting,” pp. 1,524–1,541. predicting overtime; the substantive results from these models are not 8 We use the term “onsite” to mean the location where workers labor sensitive to the inclusion of the indicator variable. under the direction of their employer—an office, store, or other work - 10 Our telecommuting estimates from 2004 are lower than the site. In the datasets we use for the analysis, we have measures of total ) estimates for 2010: 17 percent ver AT U S American Time Use Survey ( - hours worked and total hours worked at home. For simplicity, we refer sus 24 percent. The most likely explanation for the difference is sample to the “hours worked not at home” as hours worked “onsite.” We use composition. We exclude workers who are self-employed and/or who the terms “work at home” and “telecommuting” interchangeably. AT U S does not. work exclusively at home; the 9 Differences exist in questionnaire wording both (1) over time in 11 Outlined by Blair-Loy, Competing Devotions. and and (2) between the that limit comparability of the CPS CPS NLSY 12 Networked Workers See Mary Madden and Sydney Jones, (Pew work hour estimates across time periods and surveys. With all three http://pewinternet.org/Re - Research Center, September 24, 2008), CPS surveys (1997, 2001, and 2004), we measure total work hours with ports/2008/Networked-Workers.aspx . hours of work (pehract1). “Last week, actual a question referring to • June 2012 45 Monthly Labor Review

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