Critics of the public sector have long suspected that government employees work less than private-sector employees, but adequately testing that theory has been impossible without good data.
In a recent paper for the Heritage Foundation, I used the American Time Use Survey (ATUS) to compare work time in the public and private sectors. During a typical workweek, government employees work about three fewer hours than private employees. Over the course of a full calendar year, government employees work around one month less. (See graphics below.)
In a recent paper for the Heritage Foundation, I used the American Time Use Survey (ATUS) to compare work time in the public and private sectors. During a typical workweek, government employees work about three fewer hours than private employees. Over the course of a full calendar year, government employees work around one month less. (See graphics below.)
To understand why the ATUS yields a much more reliable answer to the work time question, it's worth reviewing the two previous methods for comparing work time.
If we simply compare "contract hours"--meaning the amount of time that employees are officially supposed to be at work--we'll find that government employees receive more paid leave, on average, than private-sector employees. Alternatively, if we compare how public and private employees respond to broad survey questions such as, "In the weeks that you worked, how many hours per week did you work?" we'll again find that private employees work more.
But neither of these comparisons is dispositive.
Contract hours exclude time worked off-the-clock, which is especially problematic for school teachers and other employees who work at home as well as at the office. Meanwhile, broad survey questions are vague, requiring respondents both to define for themselves what constitutes work and to accurately measure how much time they spend on it.
Enter the ATUS. Sponsored by the Bureau of Labor Statistics, the ATUS is a "time diary" dataset that catalogues in detail each respondent's activities, including work time, during a full 24-hour period. A major strength of the ATUS is that it does not undercount working at home versus working at an office, or working evenings rather than regular business hours.
Another advantage of the ATUS interview process is that respondents are never asked, "How many hours did you work yesterday?" as part of the time diary. Instead, respondents describe all of their specific activities--such as, "I wrote an e-mail"--and then state whether that activity was performed for their job. No other large-scale, nationally representative dataset used to estimate work time has this kind of objectivity and precision, and the results speak for themselves.
If we simply compare "contract hours"--meaning the amount of time that employees are officially supposed to be at work--we'll find that government employees receive more paid leave, on average, than private-sector employees. Alternatively, if we compare how public and private employees respond to broad survey questions such as, "In the weeks that you worked, how many hours per week did you work?" we'll again find that private employees work more.
But neither of these comparisons is dispositive.
Contract hours exclude time worked off-the-clock, which is especially problematic for school teachers and other employees who work at home as well as at the office. Meanwhile, broad survey questions are vague, requiring respondents both to define for themselves what constitutes work and to accurately measure how much time they spend on it.
Enter the ATUS. Sponsored by the Bureau of Labor Statistics, the ATUS is a "time diary" dataset that catalogues in detail each respondent's activities, including work time, during a full 24-hour period. A major strength of the ATUS is that it does not undercount working at home versus working at an office, or working evenings rather than regular business hours.
Another advantage of the ATUS interview process is that respondents are never asked, "How many hours did you work yesterday?" as part of the time diary. Instead, respondents describe all of their specific activities--such as, "I wrote an e-mail"--and then state whether that activity was performed for their job. No other large-scale, nationally representative dataset used to estimate work time has this kind of objectivity and precision, and the results speak for themselves.
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