Kristine L. West - Recherche en IA spécialisée au MIT

Kristine L. West

Department of Economics

St. Catherine University
St. Paul, MN 55105 Etats-Unis

klwest@stkate.edu

NEW MEASURES OF TEACHERS’

WORK HOURS AND IMPLICATIONS

FOR WAGE COMPARISONS

Abstrait
Researchers have good data on teachers’ annual salaries
but a hazy understanding of teachers’ hours of work.
This makes it difﬁcult to calculate an accurate hourly
wage and leads policy makers to default to anecdote
rather than fact when debating teacher pay. Using data
from the American Time Use Survey, I ﬁnd that teach-
ers work an average of 34.5 hours per week on an annual
basis (38.0 hours per week during the school year and
21.5 hours per week during the summer months). I ﬁnd
that when hours per week are accurately accounted for
high school teachers earn in the range of 7–14 percent
less than demographically similar workers in other oc-
cupations. Cependant, elementary, middle, and special
education teachers earn higher wages than demograph-
ically similar workers in other occupations.

est ce que je:10.1162/EDFP_a_00133
© 2014 Association pour le financement et la politique de l'éducation

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TEACHERS’ WORK HOURS AND WAGES

1. INTRODUCTION
The efﬁcacy of policies aimed at improving teacher quality depends on a num-
ber of important teacher labor supply decisions. Who becomes a teacher, comment
much and how hard they work, and whether they stay in the profession are all
issues requiring an understanding of teachers as labor-market participants. De-
spite signiﬁcant amounts of research and money devoted to improving teacher
qualité, there remain some basic deﬁcits in our knowledge about teacher labor
marchés. One notable deﬁcit is our lack of clarity on teachers’ wages and how
they compare with wages in other occupations, which is frustrating to policy
makers and voters. Consider the following excerpt from a letter to the editor
in the Minneapolis Star Tribune (Cavanagh 2011):

Recent articles about a proposed pay freeze for teachers have contained
predictable posturing from the usual suspects—politicians, union lead-
ers, teachers and school administrators—but little information about
current teacher pay. [. . .] How many hours per year do teachers work
for this pay? How many unemployed teachers are seeking positions at
current compensation levels? And how does pay in the public sector
compare to pay in the private sector? [. . .] Let’s put the relevant facts
on the table and let the people decide whether a freeze is unfair or
overdue.

Malheureusement, it is not only the popular press that presents unclear and con-
ﬂicting information about teacher pay—the academic literature on this topic
is not much better. At the core of the problem is the fact that the research
community has good data on teachers’ annual salaries but only limited infor-
mation about teachers’ hours of work. As this frustrated letter writer alludes
à, this leads to conﬂicting estimates of teachers’ hourly wages and, hence,
very different policy prescriptions.

Basic supply and demand analysis predicts that if wages are too low, là
will be a shortage of high-quality teachers. This motivates many to call for
increased wages for teachers (Temin 2003; Allegretto, Corcoran, and Mishel
2004). Others counter that teachers’ wages are already high in comparison to
similarly educated workers and argue that raising wages will only produce a
glut of low-quality candidates (Ballou and Podgursky 1995; Podgurksy 2003;
Richwine and Biggs 2011). Traditionnellement, teachers’ wages are set by district- ou
state-mandated salary schedules that decide pay solely on years of service and
highest degree. Many have argued this is inefﬁcient and wages should vary by
assignment and/or be attached to measures of productivity (Hanushek 2007).1

1. A full discussion of the determinants of teachers’ wages and the various debates within the literature
is beyond the scope of this paper. Those interested might see reviews (Podgursky 2010) et autre

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Kristine L. West

Within this debate, there are a number of arguments about whether and
how to adjust teachers’ wages to account for the length of the school day
and year. These arguments often hinge on different assumptions about how
much time teachers spend on work-related activities outside of school hours
(Nelson and Podgursky 2003; Podgursky and Mishel 2005). At one extreme,
economists use administrative data on contract hours such as the National
Compensation Survey and assume that teachers do not work at all beyond what
is minimally required (Podgursky and Tongrut 2006).2 More commonly, ils
use self-reported data from surveys such as the Current Population Survey
(CPS), which provides data on weekly wages, and assume that teachers’ hours
of work are similar to other professionals (Allegretto, Corcoran, and Mishel
2008).3 I show that neither of these assumptions is correct.

Dans cette étude, I offer new measures of hours of work from the American
Time Use Survey (ATUS). The time diaries collected by the ATUS are a more
reliable way to estimate hours of work than either contract data or surveys.4 The
ATUS provides a unique opportunity to investigate the time teachers spend
working and whether teachers are more likely to overestimate their hours in
the CPS than other workers. My analysis of the time diary data from the ATUS
has the potential to close the debate over whether teachers work more or less
than the average worker and allow for a more accurate comparison of teachers’
wages to wages in other sectors.

I ﬁnd that teachers work an average of 34.5 hours per week annually. During
the school year, they work an average of 38.0 hours per week and during the
summer they work an average of 21.5 hours per week. Teachers work more
than they are required to work by contract, but less than self reported hours of
travail. I ﬁnd that teachers are more likely to overestimate their hours of work
in the CPS than workers in other occupations, and conclude that this is likely
because of an uneven work year. Enfin, I use time diary data from the ATUS
to compare teachers’ wages with wages in other sectors. I ﬁnd that when hours
per week are accurately accounted for, high school teachers earn in the range

recent work on topics such as unions (West and Mykerezi 2011) and spacial determinants of wages
(Winters 2011).

2. Contract hours are the hours that a teacher is formally required to work as opposed to informal

expectations about hours of work.

3. Correcting for different hours of work using the number of weeks worked and hours of work per
week, such as in Taylor (2005), assumes the self-reported data are accurate. I argue in this paper
that they are not.
Juster and Stafford (1991, p. 473) write: “The methodology for collecting time allocation data has
been well developed at this point, and the main characteristics of optimum methodology are not
in dispute. The only way in which reliable data on time allocation have been obtained is by use of
time diaries, administered to a sample of individuals in a population and organized in such a way
as to provide a probability sample of all types of days and of the different seasons of the year.” The
ATUS ﬁts this optimal methodology. The CPS and other surveys, such as the Schools and Stafﬁng
Survey (SASS), which also asks about hours of work, do not.

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233

TEACHERS’ WORK HOURS AND WAGES

of 7–14 percent less than demographically similar full-time workers in other
occupations. Elementary, middle, and special education teachers, cependant,
are paid higher wages than demographically similar full-time workers in other
occupations.

The paper proceeds as follows. In the next section I brieﬂy discuss similar
work by others. Section 3 describes the data. Section 4 outlines my methodology
for constructing time diary measures of hours of work per week by occupation,
and presents results that compare teachers’ hours of work and propensity to
overestimate hours in the CPS to other occupations. Section 5 shows the
impact of using time diary measures of hours of work in wage calculations.
Section 6 shows the impact of time diary measures of hours of work on the
wage gap between teachers and other occupations and further discusses results
by assignment, sector, genre, and degree. Section 7 concludes with a brief
discussion of the policy implications.

2. PREVIOUS LITERATURE
I am aware of only one other paper that uses ATUS data to examine teachers
and their work patterns. Krantz-Kent (2008) provides a short “visual essay” that
summarizes teacher work patterns using ATUS data. She ﬁnds that teachers’
hours of work vary throughout the year. Not surprisingly, teachers are less
likely to work during the summer months than during the school year. Almost
half of all teachers, cependant, report some work during the prior week for
interviews conducted in July, et plus 70 percent report some work during
the prior week for interviews conducted in June and August. Krantz-Kent
ﬁnds that teachers are more likely than others to work at home and to work on
Sundays, but teachers work fewer hours during the week and on Saturdays.
The net result is that teachers spend, on average, for all days of the week, 18
fewer minutes per day working than other professionals.5 The analysis stops
at summary statistics and does not consider the implications for wages nor
compare the diary hours in the ATUS to the self reports in the CPS.

Other than the ATUS, the only other time diary data on teachers was
collected and analyzed by Drago et al. (1999). They ﬁnd the average elementary
school teacher works 9.7 hours per day, almost two hours more than what is
required by contract. Drago et al. use data from a survey much smaller than the
ATUS and do not include nonteachers, therefore it is not possible to compare
teachers with other workers. They are also unable to compare diary measures
with self-reported hours of work.

5. The deﬁnition of “other professionals” in Krantz-Kent (2008) includes health care professionals,
business and ﬁnance operations professionals, architects and engineers, community and social
service professionals, managers, and unspeciﬁed “others.”

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Kristine L. West

I am not the ﬁrst researcher to use the ATUS to estimate over-reporting in
the CPS. Robinson et al. (2011) ﬁnd that all workers over-report their hours by
5 à 10 pour cent. The study most similar to my analysis of over-reporting and
its impact on estimating hourly wages is Frazis and Stewart (2004).6 Ils
compare the “usual hours of work” and “hours of work last week” variables in
the CPS to diary data in the ATUS and ﬁnd that all workers over-report usual
hours of work by an average of three hours per week. Frazis and Stewart ﬁnd
that the “hours of work last week” variable in the CPS is much closer to diary
data in the ATUS. Here the over-reporting is closer to one hour per week.7 They
also ﬁnd that women and more educated respondents are more likely to over-
report hours in the CPS relative to the diary data in the ATUS. They conclude
that accounting for over-reporting increases the college–high school earnings
ratio by 4.1 percent and the female–male hourly earnings ratio by 5.4 percent.8
Juster and Stafford (1991) note that surveys like the CPS that ask about
usual weeks of work are likely to have valid responses only when daily work
patterns have regular schedules. Teachers do not have regular work patterns
for a number of reasons, most notably because teachers’ contracts generally
require nine or ten months of work rather than twelve. When asked to rec-
ollect a usual day, it is unlikely that teachers will average in their time off
during the summer months. This makes teachers more likely than others to
over-report their hours of work. Allegretto, Corcoran, and Mishel (2004) cite
personal correspondence with the Bureau of Labor Studies urging caution
when attempting to interpret usual hours of work data for teachers; they men-
tion ﬂight crews, sales representatives, and truck drivers as other occupations
likely suffering from the same estimation challenge. Robinson and Gershuny
(2011) examine over-reporting by occupation and ﬁnd workers in legal, edu-
cation, and protective services have self-reported hours that are at least four
hours per week higher than their average diary hours.

3. DATA
The data for this paper come from the Current Population Survey (CPS) et
the American Time Use Survey (ATUS) extracted via the ATUS-X (Abraham

6. Frazis and Stewart (2009) also compare hours in the CPS and the ATUS. Other discussions
of over-reporting that rely on different data sources include Baum-Snow and Neal (2009), OMS
compare hours of work for part-time workers in the CPS and the American Community Survey,
and Robinson and Bostrom (1994), who compare hours of work in the CPS with earlier time
diary studies conducted by the University of Michigan and the University of Maryland. Podgursky
and Tongrut (2006) focus on over-reporting by teachers by comparing the CPS and the National
Compensation Survey.

7. Even less when the sample is restricted to respondents interviewed during a CPS reference week.
8. Although it is more common to talk of the male–female ratio, Frazis and Stewart (2004) state
their ﬁnding in terms of the female–male ratio. Presumably by “increase” they mean the wage gap
widens—that is, the female–male ratio becomes more negative.

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TEACHERS’ WORK HOURS AND WAGES

et autres. 2011). The CPS is a probability sample of 60,000 households conducted
monthly by the Census Bureau for the Bureau of Labor Studies. The ATUS is
a diary survey that has been collected for a subsample of individuals included
in the CPS since 2003. Speciﬁcally, one-eighth of the households selected
by the CPS retire permanently from the CPS sample each month and these
households become eligible for the ATUS two months later. Respondents
are offered $40 for participating and the response rate is approximately 52 percent.9 Only one person in each household is surveyed and he or she is only asked about his or her activities over a 24-hour period beginning at 4 a.m. on the day prior to the interview. Respondents are asked what they did and when, along with who they were with and where they were at the time. They can only indicate one activity at a time. Although time-use information is collected only for the survey respondent, CPS data are available for all members of the household. The ATUS provides survey weights and replicate weights that I use exten- sively in my analysis. The weights compensate for three important aspects of the data collection process: (1) the ATUS is a stratiﬁed random sample that oversamples some demographic groups, (2) the ATUS sample is not uniformly distributed across days of the week (speciﬁcally, weekends are oversampled), et (3) the response rates differ across demographic groups and days of the week (ATUS 2012). I pool ATUS data from 2003 through 2010. I limit the sample to include only respondents who report being employed at the time of their ATUS inter- view because the variable of interest—whether the respondent is a teacher—is available only for this group.10 A respondent is designated as a teacher if his or her primary job is coded as either an elementary, middle, high school, or special education teacher.11 I further limit the sample to include only full-time workers because time use patterns for full- and part-time workers are very different, and comparisons across occupations would be problematic if more (or fewer) teachers work part time than do workers in other occupations. The CPS and the ATUS do not report weekly earnings for persons who are self- employed so these observations (along with any observation missing weekly l D o w n o a d e d f r o m h t t p : / / direct . m je t . F / / e du e d p a r t i c e – pdlf / / / / / 9 3 2 3 1 1 6 9 1 3 6 8 e d p _ a _ 0 0 1 3 3 pd . f f par invité 0 7 Septembre 2 0 2 3 9. Research into nonresponse bias does not suggest any particular problems for this study (Abraham, Maitland, and Bianchi 2006). En plus, I check nonresponse by occupation and ﬁnd teachers have a relatively high response rate of 66 pour cent. 10. To be exact, I do not limit the sample in the sense of dropping observations because this would impact the standard error calculations using the replicate weights provided. Plutôt, I use the svy subpop option in STATA to specify the sample I describe in this paragraph. 11. Preschool, kindergarten, postsecondary, and “other” teachers and instructors are not included as teachers. The decision to group middle school teachers with elementary school teachers is entirely based on the occupation categories provided by the CPS and ATUS. This is also why kindergarten teachers are excluded from the category of elementary school teachers. 236 Kristine L. West Table 1. Teachers by Sector and Assignment Assignment Sector Elementary/Middle Secondary Special Education Public Private Total 1,168 170 1,338 503 97 600 170 21 191 Total 1,841 288 2,129 Remarques: The sample includes all ATUS respondents with at least a bachelor’s degree who are full-time workers and have reported weekly earnings. earnings for any other reason) are also dropped from the ﬁnal sample. Dernièrement, I include only workers with at least a bachelor’s degree.12 I do this because teach- ing requires a four-year degree and limiting the sample to college-educated workers provides a better comparison group when estimating the wage gap between teachers and other occupations. This decision assumes that people do not decide between not attending college and attending college to pursue teaching but rather that they decide to attend college and subsequently choose an occupation.13 There are 2,129 teachers in the ﬁnal sample and 16,646 nonteachers (teach- ers constitute 11.3 percent of the observations). Tableau 1 shows the breakdown of teachers by sector and assignment. De la 2,129 teachers in the ﬁnal sam- ple, 1,841 (86.4 pour cent) work in the public sector. There are roughly twice as many elementary/middle school teachers as there are high school teachers. Special education teachers constitute a small share of the sample. Tableau 2 shows summary statistics for teachers and nonteachers. Teachers are slightly more likely to be white than nonteachers. More notably, teachers are much more likely to be female than nonteachers and they are twice as likely to have a master’s degree than nonteachers, although they are less likely to have a PhD or professional degree. En moyenne, teachers earn $330 (dans 2010 USD) par
week less than other full-time workers with at least a bachelor’s degree.14 This
yields a naive estimate of the “teacher wage gap” of 23 percent—that is, teach-
ers earn 23 percent less than other college-educated workers ($330/$1,406).

12. As a robustness check, I also produced results that exclude workers with PhDs. This did not impact

the ﬁndings and these results are available upon request.

13. From a policy perspective, this is in keeping with discussions about how to best recruit high-aptitude
college students to teaching. Programs like Teach For America or The New Teacher Project aim
to take top college graduates and entice them to join the teaching ranks. It is generally assumed
that quality teachers are currently not in the teaching pool because they are engaged in other
occupations that require a bachelor’s degree, not because they lack a bachelor’s degree.

14. All wages are adjusted for inﬂation using the Consumer Price Index, base year 2010.

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TEACHERS’ WORK HOURS AND WAGES

Tableau 2. Summary Statistics

Variable

Age, Years

Femelle

Blanc

Master’s Degree

PhD Degree

Professional Degree

Teachers
N = 2,129

Mean (SD)

42.2 (10.7)

0.77

0.89

0.48

0.01

0.02

Nonteachers
N = 16,646

Mean (SD)

42.3 (10.6)

0.45

0.82

0.24

0.05

Weekly Earnings

$1,076 (483) $1,406 (706)

Différence

−0.1

0.32∗∗∗

0.07∗∗∗

0.24∗∗∗

−0.04∗∗∗

−0.03∗∗∗

−330∗∗∗

Remarques: Sample includes ATUS respondents with at least a bachelor’s degree who are full-time
workers with reported weekly earnings. Teachers include elementary, middle, high school, et
special education teachers. Weekly earnings are in 2010 dollars.
∗∗∗Signiﬁcant at the 1% level.

Bien sûr, this does not control for demographic characteristics and, as just
noted, teachers are different from other college-educated workers in a number
of ways. In the Results section I present wage regressions that control for the
demographic variables listed in table 2.

When studying teachers, one must be particularly concerned about the
summer months. Data for people who are employed but currently absent
from work for reasons such as vacation days, illness, and maternity leave are
included in the ﬁnal sample. Teachers who are employed but not at work
during summer months should be counted as employed and on vacation. Il
is possible that during the summer some teachers report being unemployed
(and thus are not in the ﬁnal sample) or they are categorized into another
occupation during the summer months (and thus are in the ﬁnal sample but
categorized as nonteachers). Par exemple, a teacher who is a waitress during
the summer will be categorized as a waitress rather than a teacher.

Chiffre 1 shows the weighted share of teachers by month. Le pondéré
share approximates the share of teachers in the total population. The fact
that the share of teachers falls during the summer (particularly in July) pourrait
be evidence that teachers are missing or miscategorized during the summer.
Alternativement, it may be there are fewer teachers during the summer because all
job transitions in education happen in the summer. Unlike other occupations
where retirements, layoffs, and hiring take place across the calendar year,
teachers generally retire, are laid off, or are hired only when classes are not
in session. If retirements and layoffs happen early in the summer and hiring
takes place later in the summer, this would create a drop in the share of

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Kristine L. West

Chiffre 1. Share of ATUS Respondents Who Are Teachers, by Month. Remarques: Each bar shows the
weighted share of respondents in the ATUS who are teachers by the month of the respondent’s ATUS
interview. The weights approximate the share of the population who are teachers.

teachers in July. I assume that each of these explanations contributes to the
drop in the share of teachers in the summer to some extent.15

It seems safe to assume that teachers who are working a different job
during the summer or are laid off and hoping to be rehired will not devote
any more hours to teaching than teachers who are continuously categorized as
tel. Ainsi, my calculations of the average hours devoted to teaching during
the summer months will be an upper bound of the true number of hours
devoted to teaching during the summer months.

Enfin, it is important to note that teachers’ work during the summer
months of June, Juillet, and August is an average of at least three different ac-
tivities. D'abord, some teachers are working full time either because they teach in
year-round districts or because these months include some full weeks of work
because most districts’ school year stretches into early June and starts again
in late August. Deuxième, teachers may be lesson-planning or attending profes-
sional development classes. Troisième, they may be teaching summer school.16

15.

I attempt to estimate the relative importance of these explanations using teacher turnover data
from other sources. The average number of teachers in the ATUS drops 23 percent in the summer.
Keigher (2010) uses data from the SASS and ﬁnds that 9 percent of teachers exited the profession in
2008. Harris and Adams (2007) ﬁnd a similar turnover rate using CPS data. En plus, Keigher
(2010) reports that 7 percent of teachers switched jobs within teaching; donc, environ
16 percent of teachers are potentially not counted as teachers during the summer due to normal
turnover and job transition rates. This suggests that normal turnover rates account for as much as
two-thirds of the dip observed in ﬁgure 1.

16. For the main analysis, hours of work are limited to work on the respondent’s main job, so if a
teacher considers summer school a second job, this would not be included. It is not common,
cependant, for teachers to list a second job that is also a teaching job. En plus, the way the survey

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TEACHERS’ WORK HOURS AND WAGES

4. RÉSULTATS
Average Hours of Work by Occupation

Because the ATUS collects diary data for a single day, it is impossible to
calculate the hours worked over an entire week for any one individual. Plutôt,
I calculate a weighted average of market work across all days of the week for all
respondents within an occupational category to create synthetic average work
weeks by occupation.17

I call this measure “diary hours of work” to distinguish it from the “usual
hours of work” or “hours of work last week” variables reported in the CPS. je
deﬁne weekly diary hours of work, DHrso, for occupation o as:

D Hr so =

(cid:2)

je [(D Hr sio ∗ 7)(wi )]
N

(1)

where DHrsio is hours of work on the respondent’s main job reported in the
time diary for the N respondents in occupation o.18 When comparisons between
teachers and nonteachers are made, the occupation “teachers” includes ele-
mentary, middle, secondaire, and special education teachers and “nonteachers”
includes all other respondents. In all other instances, I use detailed occupa-
tion codes provided by the ATUS. I use weights for each respondent provided
by the ATUS, wi, so that DHrso is adjusted for the sampling scheme. La plupart
importantly, these weights adjust for the fact that weekends are oversampled.
I calculate standard errors and conﬁdence intervals using replicate weights
provided by the ATUS.

The ATUS data include a variety of activities that relate to respondents’
work life. I primarily deﬁne hours of work using only time coded as “work,
main job” (ATUS time use code 050101). The ﬁrst column of table 3 reports this
result. Tableau 3 also reports results for a more inclusive deﬁnition of hours of
work that includes “working” (050100–050199) and “work-related activities”
(050200–050299).19 Using the more restrictive deﬁnition of work, teachers
travail 34.5 (SE = 0.7) hours per week annually and nonteachers work 39.8
(SE = 0.3) hours per week annually. Using the more inclusive deﬁnition of
travail, teachers work an average of 36.3 (SE = 0.8) hours per week annually
compared to 41.0 (SE = 0.3) hours per week annually for nonteachers.

Not surprisingly, teachers’ diary hours of work per week vary across the
calendar year. The second panel of table 3 shows hours of work for the school
année (all months with the exception of June, Juillet, and August). Teachers work

is worded focuses on what the respondent is currently doing so it is unlikely that summer school
would be categorized as secondary to a respondent’s school-year job unless they were to overlap.

17. This methodology is similar to Robinson and Bostrom (1994) and others who use time diaries to

compare time use by demographic characteristics.

18. To be exact, the ATUS reports hours of work in minutes but I divide by 60 and present results in

hours.

19. This does not include travel related to work.

240

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Tableau 3. Weekly Hours of Work, Various Deﬁnitions and Measures

Diary Hours Diary Hours Usual Hours Usual Hours Hours Last Week

Restrictive

Inclusive

ATUS

CPS

39.8 (0.3)

34.5 (0.7)

41.0 (0.3)

36.3 (0.8)

44.7 (0.1)

44.7 (0.2)

43.1 (0.1)

42.5 (0.2)

42.4 (0.1)

40.4 (0.3)

38.0 (0.8)

41.5 (0.3)

39.8 (0.9)

44.7 (0.1)

45.0 (0.3)

43.2 (0.1)

42.8 (0.2)

42.6 (0.1)

41.7 (0.4)

Full Sample

Nonteachers

Teachers

School Year

Nonteachers

Teachers

Juin, Juillet, Août

Nonteachers

Teachers

38.1 (0.6)

21.5 (1.7)

39.4 (0.6)

23.2 (1.7)

44.4 (0.1)

43.6 (0.3)

42.9 (0.1)

41.6 (0.5)

41.7 (0.2)

33.3 (1.2)

Remarques: Mean (SE) for various measures of hours of work. Diary hours are calculated using equation
1. Usual hours and hours of work last week are occupational averages. Teachers include elementary,
middle, high school, and special education teachers. Nonteachers include all other occupations.
Standard errors are calculated using the replicate weights provided by the ATUS. The sample includes
all ATUS respondents with at least a bachelor’s degree who are full-time workers and have reported
weekly earnings. School year includes all months other than June, Juillet, and August. Interview month
is based on the relevant survey for each column.

an average of 38.0 (SE = 0.8) hours per week during the school year. Non-
teachers also work slightly more hours during these months, averaging 40.4
(SE = 0.3) hours per week. During the summer, teachers average 21.5 (SE =
1.7) hours per week and nonteachers average 38.1 (SE = 0.6) hours per week.20
Chiffre 2 shows teachers’ diary hours of work for each calendar month. Dans
the top panel I compare teachers and nonteachers, in the bottom panel I distin-
guish between secondary and elementary/middle school teachers. The dip in
teachers’ hours of work over the summer is evident, as is the fact that secondary
teachers are more likely to work during the summer. This is likely because
more secondary students attend summer school (Morisi 2010; Forgeron 2011).

Tableau 4 presents results by demographic characteristics and teaching as-
signment. En outre, ﬁgure 3 visually summarizes the differences in di-
ary hours of work by demographic characteristics. For each demographic
subgroup, nonteachers’ hours of work exceeds teachers’ hours of work. De-
mographic differences are potentially very important because teachers are
demographically dissimilar from nonteachers. En particulier, they are much
more likely to be female than non-teachers. To investigate the importance of

20. The school year generally ends in mid-June and begins in mid-August so summer hours are an
average of some full work weeks and some vacation weeks. Aussi, some teachers work summer
school and almost all teachers spend at least some time engaged in planning and professional
development activities during the summer months. Recall that the estimate of summer hours is
likely an upper bound because there may be teachers who are missing or miscategorized during
the summer and I assume these teachers spend no more time on activities related to teaching than
their peers who are continuously categorized as teachers.

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TEACHERS’ WORK HOURS AND WAGES

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Chiffre 2. Weekly Diary Hours of Work, by Month. Remarques: Each bar shows the diary hours of work
per week (on the respondent’s main job). The ﬁrst panel shows hours for teachers and nonteachers
by the month of their ATUS interview. The second panel shows hours for secondary teachers and
elementary/middle school teachers by the month of their ATUS interview. Diary hours are calculated
using equation 1. Conﬁdence intervals are calculated using the replicate weights provided by the
ATUS. The sample includes all ATUS respondents with at least a bachelor’s degree who are full-time
workers and have reported weekly earnings.

demographic characteristics, table 5 reports results from ordinary least squares
estimates of equation 2

D Hr si = α + β(cid:3)

Xi + γ Teacher i + Dayi

+ (cid:5)je .

(2)

Respondents are indexed by i; X is a set of demographic and geographic
characteristics; Teacher is an indicator for elementary/middle, secondaire, ou
special education teachers; and Day is a set of variables that describe the ATUS
diary day.21 Results are presented in table 5 for three different sets of X. Le

21. Speciﬁcally, I control for the day of the week, month, and year, as well as whether the ATUS interview
was conducted on a holiday. Holidays include New Year’s Day, Easter, Memorial Day, Independence
Day, Thanksgiving, and Christmas. The coefﬁcients on the variables in Day are omitted from the
table to save space but all are as expected—people work fewer hours on Saturdays, Sundays,

242

Kristine L. West

Tableau 4. Weekly Diary Hours of Work, by Subgroups

Nonteachers

All Teachers

Secondary
Teachers

Elem/Middle
Teachers

Full Sample

Femelle

Male

Age 20–40 Years

Age 40–60 Years

Blanc

Nonwhite

Bachelor’s

Master’s

Has Kids

No Kids

Public Sector

Private Sector

N = 16,646

39.8 (0.3)

37.8 (0.4)

41.3 (0.3)

39.4 (0.4)

40.1 (0.4)

39.8 (0.3)

39.8 (0.7)

39.4 (0.3)

40.0 (0.6)

38.9 (0.4)

40.4 (0.4)

37.3 (0.6)

40.5 (0.3)

N = 2,129

34.5 (0.7)

33.9 (0.9)

36.4 (1.7)

34.0 (1.1)

35.1 (1.2)

34.7 (0.8)

32.8 (2.4)

33.8 (1.1)

35.1 (1.2)

33.8 (1.0)

35.1 (1.1)

34.5 (0.8)

34.5 (2.2)

N = 600

37.6 (1.6)

35.6 (2.2)

39.8 (2.3)

36.3 (2.1)

39.5 (2.2)

37.3 (1.6)

36.7 (5.1)

35.0 (2.3)

40.7 (2.2)

35.1 (1.9)

39.3 (2.2)

37.6 (1.7)

37.8 (3.8)

N = 1,338

33.3 (1.0)

33.9 (1.0)

30.4 (2.7)

34.3 (1.3)

32.4 (1.5)

33.5 (1.1)

31.7 (3.0)

33.4 (1.4)

32.9 (1.4)

33.5 (1.3)

33.2 (1.5)

33.5 (1.0)

32.4 (2.7)

Remarques: Mean (SE) of diary hours of work calculated using equation 1. Sample includes ATUS respon-
dents with at least a bachelor’s degree who are full-time workers with reported weekly earnings. “All
teachers” includes elementary, middle, high school, and special education teachers. “Nonteachers”
includes all other occupations. The N counts refer to the full sample, there are fewer observations
in each subgroup (c'est à dire., 77% of teachers are female so there are 1,640 female teachers [2,219 ×
0.77]).

ﬁrst column shows results with no demographic controls. In this speciﬁcation,
teachers work an average of 5.37 (SE = 0.7) hours per week less than non-
teachers. This drops to 4.31 (SE = 0.7) hours per week in the second column
when gender is controlled for and 3.94 (SE = 0.7) hours per week in the third
column when a full set of demographic and geographic controls is included.22
Secondary school teachers differ from elementary/middle and special ed-
ucation teachers in their work patterns, as well as their demographic makeup.
Thus the ﬁnal three columns of table 5 repeat the analysis, breaking Teacher
into the three different teaching assignments. In each speciﬁcation, secondaire
school teachers are much closer to nonteachers (the omitted group). When a

Mondays, Fridays, and holidays. The coefﬁcients on the indicators for year are not statistically
different from zero. One might worry that the years included, 2003-dix, include the recent Great
Recession and this could impact hours worked systematically. To this point, it is reassuring that
none of the coefﬁcients on the indicators for interview year are statistically signiﬁcant. To further
investigate this concern, I also tested a single indicator for 2008–2010 as well as an interaction
between that indicator and Teacher, neither of which was statistically signiﬁcant. Dernièrement, I include
ﬁgure 4, which shows DHrso for teachers and nonteachers by year and does not reveal any time
trends.

22. The full set of controls includes gender, âge (and age squared), indicators for degrees beyond a
bachelor’s, and indicators for race, multiple jobs, own children in the house, metro area, state, et
census region (coefﬁcients for state and region are omitted from the table to save space).

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TEACHERS’ WORK HOURS AND WAGES

Chiffre 3. Weekly Diary Hours of Work, by Subgroups. Remarques: Each bar shows the diary hours of
work per week (on the respondent’s main job) for teachers and nonteachers for various demographic
groupes. Diary hours are calculated using equation 1. Conﬁdence intervals are calculated using the
replicate weights provided by the ATUS. The sample includes all ATUS respondents with at least a
bachelor’s degree who are full-time workers and have reported weekly earnings.

full set of demographic and geographic controls is included, I ﬁnd that sec-
ondary school teachers work an average of 2.33 (SE = 1.3) hours per week less
than nonteachers and elementary/middle school and special education teach-
ers work an average of 4.43 (SE = 0.9) et 5.93 (SE = 2.2) hours per week less
than nonteachers, respectivement.

Over-reporting

Recall that all ATUS respondents are also CPS respondents and CPS respon-
dents are asked about both their usual hours of work per week and their hours
of work last week.23ATUS respondents are asked about their usual hours of
work again at the time of their ATUS interview. Chiffre 5 shows the distribution
of usual hours and hours of work last week for teachers and nonteachers.24 The
modal response for questions about usual hours and hours of work last week
est 40 hours per week for both teachers and nonteachers. The most noteworthy
point made by this ﬁgure is how incredibly similar teachers and nonteachers
are in their self-reported hours of work.25

23. These questions were revised in 1994. Prior to 1994, respondents were only asked about their work
last week and the CPS attempted to correct for time off and overtime to ﬁgure out a usual work
week.

24. For this ﬁgure I use the responses from the ﬁnal CPS interview because the “hours of work last
week” question is not asked as part of the ATUS. A few respondents switch between teacher and
nonteacher but otherwise the sample is identical to the one described in the data section.

25. For usual hours of work, respondents can report that their hours vary. CPS documentation indicates
that if the respondent asks for a deﬁnition of “usual,” interviewers are instructed to deﬁne the term
as more than half the weeks worked during the past four or ﬁve months. Fait intéressant, despite
the fact that teachers’ work weeks look very different in the summer than during the school year,
teachers are less likely than others to report that their hours vary: 4 percent of teachers report that
their hours vary compared with 5 percent of nonteachers. This is calculated from responses in the
ﬁnal CPS interview and the difference is statistically signiﬁcant at the 1 percent level. When asked
about their usual hours of work per week in the ATUS, only 2.5 percent of teachers report that their

244

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Kristine L. West

Tableau 5. The Teacher Hours Gap: Results for Diary Hours of Work

Teacher

Elem/Middle

Secondary

Special Ed

Femelle

Age

Age2

Master’s

PhD

Professional

Blanc

Mult Jobs

Has Kids

Metro

Month

Year

State

Region

Obs.

(1)

(2)

(3)

(4)

(5)

(6)

−5.373∗∗∗
(0.705)

−4.314∗∗∗
(0.721)

−3.942∗∗∗
(0.741)

−6.152∗∗∗
(0.838)

−3.088∗∗
(1.322)

−7.312∗∗∗
(2.272)

−4.826∗∗∗
(0.873)

−2.759∗∗
(1.307)

−5.959∗∗∗
(2.231)

−3.329∗∗∗
(0.402)

−3.395∗∗∗
(0.4)

−3.458∗∗∗
(0.4)

0.14
(0.142)

−0.002
(0.002)

0.687
(0.564)

3.268∗∗∗
(1.148)

2.630∗∗
(1.234)

−0.333
(0.622)

−1.897∗∗
(0.759)

−1.973∗∗∗
(0.463)

0.736
(0.775)

Oui

Non

Oui

Non

Oui

Non

Oui

Non

−4.426∗∗∗
(0.891)

−2.331∗
(1.306)

−5.930∗∗∗
(2.21)

−3.390∗∗∗
(0.403)

0.146
(0.142)

−0.002
(0.002)

0.69
(0.56)

3.277∗∗∗
(1.15)

2.607∗∗
(1.233)

−0.337
(0.623)

−1.916∗∗
(0.76)

−1.975∗∗∗
(0.464)

0.747
(0.775)

Oui

18,775

0.449

18,775

0.452

18,775

0.458

18,775

0.45

18,775

0.453

18,775

0.458

Remarques: Dependent variable is diary hours of work. Each observation is weighted using the weights
provided by the ATUS and standard errors are calculated with successive difference replication (SDR)
variance estimation using the replicate weights provided by the ATUS. Sample includes ATUS respon-
dents with at least a bachelor’s degree who are full-time workers with reported weekly earnings.
∗Signiﬁcant at the 10% level; ∗∗signiﬁcant at the 5% level; ∗∗∗signiﬁcant at the 1% level.

hours vary and only 2.7 percent of nonteachers report that their hours vary. This difference is not
statistically signiﬁcant.

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Chiffre 4. Weekly Diary Hours of Work, by Year. Remarques: This ﬁgure shows diary hours of work per
week (on the respondent’s main job) for teachers and nonteachers disaggregated by year of the
respondents ATUS interview. Diary hours are calculated using equation 1. The sample includes all
ATUS respondents with at least a bachelor’s degree who are full-time workers and have reported
weekly earnings.

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Chiffre 5. Self Reported Hours of Work, Teachers and Nonteachers. Remarques: This ﬁgure shows the
distribution of responses to the questions about respondents’ usual hours of work per week and
hours of work last week. Data for both are from the ﬁnal CPS interview. The sample includes all ATUS
respondents with at least a bachelor’s degree who are full-time workers and have reported weekly
earnings. En plus, respondents who changed occupations between the time of their ﬁnal CPS
interview and their ATUS interview are excluded. Teachers include elementary, middle, high school,
and special education teachers. Nonteachers include all other occupations.

The third and fourth columns of table 3 report the average “usual hours
of work” for teachers and nonteachers. The ﬁnal column reports the average
“hours of work last week” for teachers and nonteachers. For both teachers and
nonteachers, responses for usual hours of work exceed responses for hours
of work last week, which in turn exceed estimates of diary hours. Speciﬁcally,
usual hours and hours last week appear to be overestimates of work when
compared with the more restrictive diary measure that includes only time spent
working on the respondent’s main job. When the more inclusive deﬁnition

246

Kristine L. West

of work—one that includes other work related activities—is used, le moi-
reported data appear slightly more accurate.

The average over-report across all full-time workers with at least a bachelor’s
degree is about ﬁve hours per week when comparing usual hours of work in
the ATUS with time diary hours and about two and a half hours per week
when comparing hours of work last week in the CPS with time diary hours.
These magnitudes are larger than those found by Frazis and Stewart (2004).
They ﬁnd that among all workers, the average over-reporting for usual hours of
work is three hours per week and the average over-reporting for hours of work
last week is one hour per week. They also report that education is positively
related to over-reporting, cependant, which may explain why my sample of only
college-educated workers shows more pronounced over-reporting.26

The key point made by table 3 is that teachers differ very little from non-
teachers in their self-reported hours of work but they differ considerably in
their diary hours of work. Both teachers and nonteachers report usual hours
of work in excess of 40 hours per week (even during the summer months).
When asked about their hours of work last week, teachers report an average
de 41.7 (SE = 0.4) hours per week during the school year and 33.3 (SE = 1.2)
hours per week during the summer. These estimates are 3.7 et 11.8 hours
more than their diary hours, respectivement. Nonteachers report hours of work
last week that average 42.6 (SE = 0.1) during the school year and 41.7 (SE =
0.2) during the summer. These are only 2.2 et 3.6 hours more than their
diary hours, respectively.27

In what follows, I deﬁne over-reporting ORo in occupation o as:

ORo =

(cid:2)

je = 1[U Hr sio − (D Hr sio ∗ 7)](wi )
N

(3)

where UHrsio is the usual hours of work reported by the N respondents in
occupation o. For this analysis, teachers are split into secondary, elementary/

26.

In support of this assertion, when I calculate over-reporting for all workers with at least a high
school diploma, I ﬁnd that usual hours are 4.4 hours per week larger than diary hours, and hours
of work last week are 2.1 hours per week larger than diary hours. This sample is still more educated
than the full population because it excludes workers with less than a high school diploma and
part-time workers. It is likely that the fact that education is positively associated with over-reporting
is driven by the fact that more education is positively associated with being a salaried employee
rather than an hourly employee and salaried workers are more likely to over-report than hourly
workers. It is also likely that because I did not limit the sample to respondents interviewed on a
reference week that I am slightly overestimating the discrepancy between hours of work last week
and diary hours.

27. Frazis and Stewart (2004) argue that diary hours and hours of work last week are much closer
when the sample is limited to ATUS respondents who are interviewed on a CPS reference week.
I replicate this analysis, although the sample size is much reduced (there are only 411 teachers
interviewed on a CPS reference week) so I prefer the estimates presented here. Restricting the
sample does not change the main result, namely, that teachers are more likely to overestimate their
hours of work than are nonteachers.

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TEACHERS’ WORK HOURS AND WAGES

Chiffre 6. Over-reporting of Usual Hours of Work, by Occupation. Remarques: Each bar shows over-
reporting from equation 4; conﬁdence intervals are calculated using replicate weights provided by
the ATUS. The occupations included are the most common in the ﬁnal sample.

middle, and special education. As before, DHrsio is the diary hours for each
respondent and wi are weights provided by the ATUS. I focus on usual hours
of work rather than hours of work last week because usual hours are re-asked
at the time of the ATUS interview. Malheureusement, hours of work last week is
only asked at the time of the ﬁnal CPS interview and this is separated from the
ATUS interview by two to four months.28

It is more interesting to look at over-reporting as a percent of total hours

so next I modify the deﬁnition of over-reporting to:

ORper cent o

(cid:3)

(cid:2)

N
je = 1

U Hr sio −(D Hr sio ∗7)
U Hr sio
N

(cid:4)
(wi )

∗ 100.

(4)

Chiffre 6 shows ORpercento by occupation. The occupations shown are
the most common in the ﬁnal sample, c'est, they are common occupations
for full-time workers with at least a bachelor’s degree.29 Over-reporting is
quite common, although teachers over-report their usual hours of work more
dramatically. Secondary, elementary/middle, and special education teachers
over-report their usual hours of work in the ATUS by 17 pour cent (SE = 3), 25
pour cent (SE = 2), et 23 pour cent (SE = 6), respectivement. The average across
all teachers is 22 pour cent (SE = 1.6), whereas the average across all other
occupations is 10 pour cent (SE = 0.6).

In sum, teachers are more likely than others to over-report their usual
hours of work and hours of work last week. It is unlikely that teachers are,
on average, more dishonest or forgetful than other workers. Plutôt, I suspect
that teachers have a more difﬁcult time answering questions about their usual
hours of work because their work weeks vary across the calendar year. Pour

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28.

I attempted to match ATUS weeks and CPS reference weeks so that I could compare teachers
who were interviewed at the same point in time (rather than the sample respondent interviewed
at different points in time). As noted in the previous footnote, cependant, the sample was severely
impacted.

29. Each occupation has at least 190 respondents in the ﬁnal sample.

248

Kristine L. West

instance, if a teacher is asked in November about her usual hours of work, elle
likely thinks about her usual hours of work for the last month or so rather than
rolling her summer hours into the calculation. For hours of work last week,
the CPS practice of choosing a reference week to avoid holidays may make it
more likely that teachers are interviewed for the CPS after a full week of work
whereas their ATUS interview falls on a week with time off. If teachers have
more time off than other workers, this would bias the hours of work last week
variable without any malfeasance on the part of teachers.

5. IMPLICATIONS FOR WAGE CALCULATIONS
This analysis has important implications for researchers and policy makers
interested in wage comparisons. Consider a researcher using CPS data to com-
pare wages across occupations. The CPS reports weekly earnings Earni for each
worker i. Most researchers will use this and the usual hours of work variable
or hours of work last week to calculate an hourly wage, Wagei, according to the
simple formula:

Wag ei = E ar ni
Hr si

(5)

I have shown that over-reporting of hours of work is systematically biased
by occupation and that the usual hours of work variable is particularly problem-
atic for workers, such as teachers, whose hours vary throughout the calendar
année. If the weekly earnings data for teachers (and other workers with uneven
schedules) already accounts for the variable hours, cependant, equation 5 will
yield an accurate hourly wage. Par exemple, consider a hypothetical worker
who works 40 hours/week every other week for 52 weeks and earns $52,000. If he reports that he usually works 40 hours/week and earns $1,000 weekly his
hourly wage will be $25/hour, half what it should be. Si, on the other hand, he reports that he usually works 40 hours but adjusts his weekly earnings to $500, then the hourly wage will accurately reﬂect $50/hour. If teachers do the mental arithmetic needed to adjust their weekly earnings to account for variable hours, or if the CPS makes this adjustment to the data after the interview, then the over-reporting of usual hours of work is of little concern to labor economists interested in calculating an hourly wage. I show, cependant, that the weekly earnings data does not properly account for teachers’ variable work hours and thus equation 5 is measured with error. CPS respondents are asked to report earnings in the time period they prefer, Par exemple, hourly, weekly, biweekly, monthly, or annually. Sur 70 percent of teachers elect to report annual earnings. Allowing respondents to report in a periodicity with which they were most comfortable was added to the CPS in 1994. This improved on the previous procedure which only gave respondents the option to report hourly wages or weekly earnings and was l D o w n o a d e d f r o m h t t p : / / direct . m je t . / F / e du e d p a r t i c e – pdlf / / / / / 9 3 2 3 1 1 6 9 1 3 6 8 e d p _ a _ 0 0 1 3 3 pd . f f par invité 0 7 Septembre 2 0 2 3 249 TEACHERS’ WORK HOURS AND WAGES Figure 7. Weeks Worked. Remarques: This ﬁgure shows the distribution of responses to the question about how many weeks the respondent worked. This question is only asked of respondents who report earnings annually. The ATUS extract builder that I used to obtain ATUS data does not include the number of weeks worked data so this ﬁgure is based on data from the 2010 CPS which I downloaded separately for this analysis. The sample includes all full-time college-educated workers in the January 2010 CPS. Teachers include elementary, middle, high school, and special education teachers. Nonteachers include all other occupations. introduced to give the CPS a better chance at calculating accurate weekly earnings since they do not rely on respondents to do the necessary mental arithmetic. Despite this improvement, I show that problems remain. Respondents who elect to report annual earnings are then asked how many weeks they worked for this salary. Using this information, the CPS converts the annual salaries into weekly earnings. Looking at the ﬁnal earnings per week variable reported in the CPS, I ﬁnd that teachers who report weekly earnings average $1,036.3 (dans 2010 USD) per week and teachers who report
annual salaries average $1,131.6 (dans 2010 USD) per week.30 It is unclear in either case, cependant, whether this refers to weekly earnings for the school year or weekly earnings for the entire calendar year. C'est, are teachers and/or the CPS accounting for summers off? I ﬁnd evidence that they are not. Chiffre 7 shows the distribution of responses for the number of weeks worked for teachers and nonteachers who report annual earnings.31 The modal response for both teachers and nonteachers is 52 weeks. This indicates that most teachers view summers as time working. A minority of teachers report between 36 et 44 weeks. These responses more accurately reﬂect the school year. One reason most teachers report working 52 weeks may be that most teachers are given the option to receive their pay spread over the school year or the calendar year and anecdotal evidence suggests the majority choose the 30. These are statistically identical, Prob(>t) = 0.9180. 31. The ATUS extract builder that I used to obtain ATUS data does not include the number of weeks worked variable so these ﬁgures are based on data from the 2010 CPS, which I downloaded separately for this analysis. The sample includes all full-time, college-educated workers in the January 2010 CPS. 250 l Téléchargé à partir du site Web : / / direct . m je t . / F / e du e d p a r t i c e – pdlf / / / / / 9 3 2 3 1 1 6 9 1 3 6 8 e d p _ a _ 0 0 1 3 3 pd . f f par invité 0 7 Septembre 2 0 2 3 Kristine L. West latter.32 I suspect most teachers are reporting weeks paid rather than weeks worked. A simple back of the envelope calculation supports this assertion. The average salary for teachers in 2009–10 was $55,350 (National Center for
Education Statistics) which is 53 weeks at $1,036.6 per week and 49 weeks at $1,131.6 per week.

Because the vast majority of teachers report working 52 weeks, if re-
searchers calculate a wage using equation 5, they are implicitly (and erro-
neously) assuming that hours per week are consistent across all weeks during
the calendar year. Instead of using equation 5 to calculate hourly wages, j'utilise:

Wag ei = E ar ni
D Hr so

(6)

where Earni is the CPS weekly earnings variable adjusted to account for inﬂa-
tion using the Consumer Price Index and base year 2010. DHrso is the time
diary measures of hours of work described in equation 1 for occupation o.
This replaces the individual’s estimate of his or her hours of work with the
estimate I obtain using the ATUS time diaries for all workers within a given
occupation.33

Chiffre 8 compares the distribution of hourly wages calculated using diary
hours and usual or actual hours of work for all full-time college-educated
workers. The distributions are not that different. The mean hourly wage is
slightly higher when calculated using diary hours of work. This reﬂects the
fact that almost all workers over-report their hours of work. In the next section I
show that, although the overall distribution of hourly wages is not dramatically
impacted by substituting DHrso for Hrsi, systematic biases by occupation make
this adjustment very important when calculating the teacher wage gap. À
preview this result, ﬁgure 9 shows the same distributions for teachers. Notice
that the shift in the distribution is much more evident for teachers.

6. THE TEACHER WAGE GAP REVISITED
I estimate a simple Mincer style wage equation controlling for a vector of
demographic and geographic variables, Xi. The demographic variables are
a quadratic in age, additional education beyond a bachelor’s degree (a set of

32. This may surprise some economists because spreading pay over the calendar year delays receipt of
the ﬁnal pay checks and thus decreases the net present value of the salary. Teachers may receive
a beneﬁt from spreading pay over the summer because they do not have to plan for uneven cash
ﬂows. Choosing to spread pay over the calendar year is rational if this beneﬁt outweighs the small
loss of potential interest income.

33. As previously noted, this includes zero hours during the summer for many teachers but positive
hours during the summer if the teachers are working summer school or engaged in planning and
so on.

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251

TEACHERS’ WORK HOURS AND WAGES

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Chiffre 8. Hourly Wages, Comparing Usual Hours, and Hours Last Week to Diary Hours. Remarques: Ce
ﬁgure compares the distribution of hourly wages calculated using usual hours of work and hours of
work last week to hourly wages calculated using diary hours of work for all full-time workers with at
least a bachelor’s degree and reported weekly earnings. I top code wages at $100/hour. indicator variables for master’s, PhD, and professional degrees), and indicators for white and female.34 The geographic variables are state and census-region indicators. I include an indicator variable, Teacheri, to calculate the teacher 34. The omitted category is nonwhite males with no more than a bachelor’s degree. 252 Kristine L. West l D o w n o a d e d f r o m h t t p : / / direct . m je t . / / f edu ed p a r t i c e – pdlf / / / / / 9 3 2 3 1 1 6 9 1 3 6 8 e d p _ a _ 0 0 1 3 3 pdf . Chiffre 9. Hourly Wages, Comparing Teachers’ Usual Hours and Hours Last Week with Diary Hours. Remarques: This ﬁgure compares the distribution of hourly wages for teachers calculated using usual hours of work and hours of work last week to hourly wages calculated using diary hours of work for all full-time workers with at least a bachelor’s degree and reported weekly earnings who are categorized as teachers at the time of their ATUS interview. I top code wages at $100/hour.

wage gap, which equals 1 if the respondent is an elementary/middle, secondaire,
or special education teacher:

ln(Wag e i ) = α + β(cid:3)

Xi + γ Teacher i + (cid:5)je .

(7)

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253

TEACHERS’ WORK HOURS AND WAGES

The coefﬁcient of interest is γ , the difference in wages between teachers
and other demographically similar workers. The semilogarithmic nature of
equation 7 necessitates a transformation of γ in order to discuss the difference
between teachers’ wages and nonteachers’ wages in percentage terms, c'est,
the teacher wage gap. Speciﬁcally, in the text I transform the results using
equation 8:

Teacher Wage Gap = e

γ − 1.

(8)

I compare estimates of the teacher wage gap when Wagei is calculated
using usual hours of work or hours of work last week to estimates of the
teacher wage gap when Wagei is calculated using diary hours of work. Chaque
observation is weighted using the weights provided by the ATUS and standard
errors are calculated using the replicate weights provided by the ATUS.

Results are presented in columns (1), (2), et (3) of table 6. I ﬁnd that
when I use usual hours of work to calculate hourly wages, the teacher wage
gap is 13.0 pecent (SE = 1.6), c'est, teachers earn 13.0 percent less than
demographically similar workers in other occupations. When hours of work
last week are used, this drops to 7.1 pour cent (SE = 1.6). When I use diary hours
of work to calculate hourly wages I do not ﬁnd a signiﬁcant difference between
teachers and nonteachers. C'est, the teacher wage gap disappears.

I also report the teacher wage gap separately for elementary/middle school,
secondary school, and special education teachers, in columns (4), (5), et (6).
I do this because secondary school teachers are less likely to over-report their
usual hours of work than are elementary/middle school or special education
teachers. From a policy perspective, differentiating between secondary school,
elementary/middle school, and special education teachers is important be-
cause the license and training requirements differ for these groups, et le
alternative labor market opportunities differ because most secondary school
teachers have subject-speciﬁc training that is more applicable to other sectors.
When I use diary hours of work to calculate hourly wages, secondary school
teachers earn an hourly wage that is 13.6 pour cent (SE = 2.3) less than demo-
graphically similar workers. Elementary/middle school and special education
teachers, on the other hand, do not earn hourly wages that are less than de-
mographically similar workers. En fait, elementary/middle school and special
education teachers appear to earn higher hourly wages than demographically
similar workers.35

35. Special education teachers often need advanced certiﬁcation which may not be captured by the
educational controls included in this model. As an additional robustness test, I treat all special
education teachers as if they have a master’s degree. In this speciﬁcation, the statistically signiﬁcant
coefﬁcient on special education disappears.

254

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Kristine L. West

Tableau 6. The Teacher Wage Gap: Results for Hourly Wages Using Usual Hours, Hours Last Week, and Diary
Hours of Work—ATUS sample

(1)

(2)

Usual

Last Week

Teacher

−0.122∗∗∗
(0.016)

−0.069∗∗∗
(0.016)

(3)

Diary

0.016
(0.016)

(4)

(5)

Usual

Last Week

(6)

Diary

−0.111∗∗∗
(0.019)

−0.188∗∗∗
(0.024)

0.008
(0.036)

0.063∗∗∗
(0.004)

−0.082∗∗∗
(0.019)

−0.081∗∗∗
(0.029)

0.04
(0.053)

0.061∗∗∗
(0.005)

0.065∗∗∗
(0.018)

−0.128∗∗∗
(0.023)

0.145∗∗∗
(0.039)

0.067∗∗∗
(0.004)

0.063∗∗∗
(0.004)

0.061∗∗∗
(0.005)

0.068∗∗∗
(0.004)

−0.0006∗∗∗
(0.00005)

−0.0007∗∗∗ −0.0006∗∗∗
(0.00005)
(0.00005)

−0.0006∗∗∗
(0.00005)

−0.0007∗∗∗
(0.00005)

0.143∗∗∗
(0.014)

0.227∗∗∗
(0.027)

0.321∗∗∗
(0.028)

0.054∗∗∗
(0.013)

0.127∗∗∗
(0.016)

0.24∗∗∗
(0.029)

0.238∗∗∗
(0.026)

0.089∗∗∗
(0.019)

0.165∗∗∗
(0.014)

0.277∗∗∗
(0.027)

0.371∗∗∗
(0.027)

0.075∗∗∗
(0.014)

0.143∗∗∗
(0.014)

0.226∗∗∗
(0.027)

0.321∗∗∗
(0.028)

0.054∗∗∗
(0.013)

0.127∗∗∗
(0.016)

0.239∗∗∗
(0.029)

0.238∗∗∗
(0.026)

0.089∗∗∗
(0.019)

0.165∗∗∗
(0.015)

0.276∗∗∗
(0.027)

0.374∗∗∗
(0.027)

0.075∗∗∗
(0.014)

−0.153∗∗∗
(0.011)

−0.196∗∗∗
(0.013)

−0.170∗∗∗
(0.012)

−0.156∗∗∗
(0.011)

−0.197∗∗∗
(0.013)

−0.176∗∗∗
(0.012)

Oui

17,734

0.149

0.118

0.15

0.119

0.152

Elem/Middle

Secondary

Special Ed

Age

Age2

Master’s

PhD

Professional

Blanc

Femelle

Month

Year

State

Region

Obs.

Remarques: Dependent variable is ln(hourly wage) calculated from equation 5 using usual hours of work or
equation 6 using hours of work last week or diary hours of work. Each observation is weighted using
the weights provided by the ATUS and standard errors are calculated with successive difference
replication (SDR) variance estimation using the replicate weights provided by the ATUS. Sample
includes ATUS respondents with at least a bachelor’s degree who are full-time workers with reported
weekly earnings.
∗∗∗Signiﬁcant at the 1% level.

Tableau 7 applies my estimates of diary hours to all respondents in the CPS
outgoing rotations (CPS-ORG) for 2003–10 rather than only those who were
selected for and responded to the ATUS. Ce (much) larger sample (N =
308,538), yields very similar results. It is again that case when using usual
hours of work or hours of work last week, teachers appear to be paid less

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255

TEACHERS’ WORK HOURS AND WAGES

Tableau 7. The Teacher Wage Gap: Results for Hourly Wages Using Usual Hours, Hours Last Week, and Diary
Hours of Work—CPS-ORG sample

(1)

(2)

Usual

Last Week

(3)

Diary

(4)

(5)

Usual

Last Week

(6)

Diary

Teacher

−0.100∗∗∗
(0.004)

−0.077∗∗∗
(0.004)

0.013∗∗∗
(0.004)

Elem/Middle

Secondary

Special Ed

Age

Age2

Master’s

PhD

Professional

Blanc

Femelle

Month

Year

State

Region

Obs.

−0.098∗∗∗
(0.005)

−0.113∗∗∗
(0.006)

−0.075∗∗∗
(0.012)

0.064∗∗∗
(0.0008)

−0.074∗∗∗
(0.005)

−0.089∗∗∗
(0.006)

−0.053∗∗∗
(0.012)

0.068∗∗∗
(0.0009)

0.048∗∗∗
(0.005)

−0.073∗∗∗
(0.006)

0.055∗∗∗
(0.012)

0.068∗∗∗
(0.0009)

0.064∗∗∗
(0.0008)

0.068∗∗∗
(0.0009)

−0.0007∗∗∗
(0.00001)

−0.0007∗∗∗ −0.0007∗∗∗
(0.00001)
(0.00001)

−0.0007∗∗∗
(0.00001)

0.145∗∗∗
(0.003)

0.22∗∗∗
(0.007)

0.222∗∗∗
(0.006)

0.088∗∗∗
(0.003)

0.156∗∗∗
(0.003)

0.248∗∗∗
(0.007)

0.231∗∗∗
(0.006)

0.111∗∗∗
(0.003)

0.162∗∗∗
(0.003)

0.259∗∗∗
(0.007)

0.277∗∗∗
(0.007)

0.113∗∗∗
(0.004)

0.145∗∗∗
(0.003)

0.22∗∗∗
(0.007)

0.222∗∗∗
(0.006)

0.088∗∗∗
(0.003)

0.156∗∗∗
(0.003)

0.248∗∗∗
(0.007)

0.231∗∗∗
(0.006)

0.112∗∗∗
(0.003)

0.162∗∗∗
(0.003)

0.26∗∗∗
(0.007)

0.277∗∗∗
(0.007)

0.113∗∗∗
(0.004)

−0.177∗∗∗
(0.002)

−0.194∗∗∗
(0.003)

−0.179∗∗∗
(0.003)

−0.177∗∗∗
(0.002)

−0.194∗∗∗
(0.003)

−0.182∗∗∗
(0.003)

Oui

308,538

0.121

0.129

0.121

0.129

0.121

Remarques: Dependent variable is ln(hourly wage) calculated using usual hours of work, hours of work last
week, or diary hours of work. Each observation is weighted using the weights provided by the CPS
and standard errors are calculated with successive difference replication (SDR) variance estimation
using the replicate weights provided by the CPS. Sample includes CPS-ORG respondents with at
least a bachelor’s degree who are full-time workers with reported weekly earnings.
∗∗∗Signiﬁcant at the 1% level.

than demographically similar workers. When diary hours are used, cependant,
teachers’ wages are actually 1.3 pour cent (SE = 0.4) higher than wages for de-
mographically similar workers. When I disaggregate teachers into secondary,
elementary/middle, and special education, I again ﬁnd that this result is driven
by elementary/middle and special education teachers. The teacher wage gap
persists for secondary teachers. In this sample, I ﬁnd secondary teachers are

256

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Kristine L. West

paid 7.6 pour cent (SE = 0.6) less than demographically similar workers when
hourly wages are calculated using diary hours of work.

Tableau 8 disaggregates the result into demographic subgroups.36 The ﬁrst
column reproduces column (3) from table 6 for comparison. The second and
third columns limit the sample to female and male respondents, respectivement.
I ﬁnd that, on average, female teachers are paid hourly wages that are slightly
more than demographically similar females in other occupations, but male
teachers are paid 12.5 pour cent (SE = 3.1) less than demographically similar
males in other occupations. The fourth and ﬁfth columns limit the sample to
respondents whose highest degree is a bachelor’s or master’s. Teachers whose
highest degree is a bachelor’s degree earn slightly more than workers whose
highest degree is a bachelor’s in other occupations, whereas teachers with a
master’s degree appear to be paid on par with workers with a master’s degree
in other occupations. Enfin, the sixth column disaggregates teachers into
public and private sector workers. Here I ﬁnd that in contrast to public school
teachers, private school teachers are paid hourly wages that are 9.4 pour cent (SE
= 3.6) less than demographically similar workers in other occupations when
hourly wages are calculated using diary hours of work.

Dernièrement, table 9 reproduces the subgroup analysis for public sector teachers
only because their wages are determined by public policy. En moyenne, public
sector teachers earn 3.5 pour cent (SE = 1.6) more than demographically similar
workers when hourly wages are calculated using diary hours of work. The dif-
ference between female and male teachers is more pronounced in this sample.
As was the case with the full sample, public sector teachers whose highest de-
gree is a bachelor’s are paid slightly more than workers with only a bachelor’s
degree in other occupations, whereas those with a master’s degree are paid
wages similar to workers with a master’s degree in other occupations. Peut-être
most importantly, I again ﬁnd that secondary school teachers are paid less, dans
this sample 11.8 pour cent (SE = 2.6) less, than demographically similar workers
in other occupations and elementary/middle and special education teachers
are paid more than demographically similar workers in other occupations
when hourly wages are calculated using diary hours of work.

These subgroup results provide a slightly more nuanced story to com-
plement the main result of this paper. The fact that a wage gap exists for
male teachers is likely because male nonteachers are concentrated in higher
paying occupations than female nonteachers. It suggests that lower wages
for teachers are, at least in part, due to occupational segregation. The results
by degree suggest that teaching is a relatively attractive option for recent

36.

I calculate DHrso separately by subgroup, although the sample size on which the estimates are
based falls and more measurement error is introduced. Noting this caveat, I reestimate equation 6
using subgroup speciﬁc DHrso to calculate Wagei.

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TEACHERS’ WORK HOURS AND WAGES

Tableau 8. The Teacher Wage Gap, Results for Hourly Wages Using Diary Hours of Work, by Subgroup

(1)
Full Sample

(2)
Femelle

(3)
Male

(4)

(5)

Bachelor’s Master’s

(6)
By Sector

0.016
(0.016)

0.044∗∗
(0.018)

−0.118∗∗∗
(0.031)

0.041∗∗
(0.02)

−0.019
(0.026)

0.033∗∗
(0.016)

−0.090∗∗
(0.035)

0.063∗∗∗
(0.007)

0.071∗∗∗
(0.005)

0.074∗∗∗
(0.005)

0.059∗∗∗
(0.009)

0.067∗∗∗
(0.004)

−0.0007∗∗∗ −0.0007∗∗∗ −0.0008∗∗∗ −0.0006∗∗∗ −0.0007∗∗∗
(0.00005)
(0.00006)
(0.00008)

(0.00006)

(0.0001)

0.211∗∗∗
(0.017)

0.295∗∗∗
(0.055)

0.372∗∗∗
(0.05)

0.031∗
(0.018)

0.121∗∗∗
(0.024)

0.254∗∗∗
(0.028)

0.351∗∗∗
(0.03)

0.101∗∗∗
(0.023)

Oui

8,721

0.142

0.165∗∗∗
(0.014)

0.276∗∗∗
(0.027)

0.373∗∗∗
(0.027)

0.075∗∗∗
(0.014)

0.045
(0.028)

−0.113∗∗∗
(0.024)

−0.174∗∗∗
(0.012)

Oui

0.086∗∗∗
(0.017)

−0.192∗∗∗
(0.015)

Oui

9,013

0.115

11,252

0.127

4,758

0.094

17,734

0.151

Teacher

Public Teacher

Private Teacher

Age

Age2

Master’s

PhD

Professional

Blanc

Femelle

Month

Year

State

Region

Obs.

0.068∗∗∗
(0.004)

−0.0007∗∗∗
(0.00005)

0.165∗∗∗
(0.014)

0.277∗∗∗
(0.027)

0.371∗∗∗
(0.027)

0.075∗∗∗
(0.014)

−0.170∗∗∗
(0.012)

Oui

17,734

0.15

Remarques: Dependent variable is ln(hourly wage) calculated from equation 6 using diary hours of work.
Each observation is weighted using the weights provided by the ATUS and standard errors are cal-
culated with successive difference replication (SDR) variance estimation using the replicate weights
provided by the ATUS. Column (1) replicates the result from table 6 for comparison. Columns (2)–(5)
limit the sample to only female, male, highest degree is a bachelor’s, highest degree is a master’s,
respectivement. Column (6) repeats column (1) disaggregating teachers into public and private sector
workers.
∗∗Signiﬁcant at the 5% level; ∗∗∗signiﬁcant at the 1% level.

graduates with only a bachelor’s degree. Most salary schedules are structured
with strong incentives for teachers to pursue a master’s degree mid-career.
For teachers with master’s degrees, wages are on par with nonteachers with
master’s degrees. The fact that a wage gap persists for private school teachers
provides evidence that private school wages are below the market wage.
Private school wages that are below the market wage may be easily explained,
cependant, by compensating differentials.

258

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Tableau 9. The Teacher Wage Gap, Results for Hourly Wages Using Diary Hours of Work, by Subgroup—Public
Sector Teachers Only

(1)

(2)

(3)

(4)

(5)

(6)

Full Sample

Femelle

Male

Bachelor’s Master’s By Assignment

Public Teacher

0.034∗∗
(0.016)

0.157∗∗∗ −0.224∗∗∗
(0.019)

(0.029)

0.069∗∗∗
(0.02)

0.036
(0.027)

0.085∗∗∗
(0.018)

−0.112∗∗∗
(0.026)

0.151∗∗∗
(0.043)

0.067∗∗∗
(0.004)

−0.0007∗∗∗
(0.00005)

0.164∗∗∗
(0.014)

0.277∗∗∗
(0.027)

0.374∗∗∗
(0.027)

0.075∗∗∗
(0.014)

Public Elem/Middle

Public Secondary

Public Special Ed

Age

Age2

Master’s

PhD

Professional

Blanc

Femelle

Month

Year

State

Region

Obs.

0.164∗∗∗
(0.014)

0.277∗∗∗
(0.027)

0.372∗∗∗
(0.027)

0.074∗∗∗
(0.014)

−0.172∗∗∗
(0.012)

Oui

0.068∗∗∗
(0.004)

0.063∗∗∗
(0.007)

0.07∗∗∗
(0.005)

0.073∗∗∗
(0.005)

0.06∗∗∗
(0.009)

−0.0007∗∗∗ −0.0006∗∗∗ −0.0007∗∗∗ −0.0008∗∗∗ −0.0006∗∗∗
(0.00006)
(0.00005)

(0.00006)

(0.00008)

(0.0001)

0.211∗∗∗
(0.017)

0.3∗∗∗
(0.056)

0.373∗∗∗
(0.05)

0.03
(0.018)

0.121∗∗∗
(0.024)

0.254∗∗∗
(0.028)

0.357∗∗∗
(0.03)

0.102∗∗∗
(0.022)

0.087∗∗∗
(0.017)

0.043
(0.028)

−0.194∗∗∗ −0.110∗∗∗
(0.015)

(0.025)

−0.177∗∗∗
(0.012)

Oui

17,734

0.15

8,721

0.149

9,013

0.119

11,252

0.127

4,758

0.092

17,734

0.152

Remarques: Dependent variable is ln(hourly wage) calculated from equation 6 using diary hours of work.
Each observation is weighted using the weights provided by the ATUS and standard errors are cal-
culated with successive difference replication (SDR) variance estimation using the replicate weights
provided by the ATUS. Column (1) includes the entire sample. Columns (2)–(5) limit the sample
to only female, male, highest degree is a bachelor’s, highest degree is a master’s, respectivement.
Column (6) repeats column (1) disaggregating teachers into elementary/middle, secondaire, et
special education teachers.
∗∗Signiﬁcant at the 5% level; ∗∗∗signiﬁcant at the 1% level.

7. CONCLUSION
Teachers, like all workers, make labor market decisions based on a complex set
of information. They consider each job as a bundle of characteristics including
amenities, such as location, work schedule, and level of personal satisfaction.
Nonpecuniary factors certainly play a role in a college graduate’s decision to

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TEACHERS’ WORK HOURS AND WAGES

pursue and subsequently remain in teaching. To ignore the obvious impact that
wages have on the number and quality of potential teachers would be a mistake,
cependant. If teaching pays less than other occupations, schools will be left with
a small pool of low-quality candidates from which to choose. If teaching pays
more than other occupations, there will be a surplus of potential teachers.

To properly compare wages for teachers to wages in other occupations,
researchers must take care to properly account for hours of work. The time
diary data in the ATUS provide a unique opportunity to move beyond warring
anecdotes about teachers who work short contract days/years and teachers
who work extraordinary hours staying late to provide extra help, taking work
home to grade, and planning and attending extracurricular activities on the
weekend. Time diaries provide a clearer picture of hours of work for teachers
and nonteachers than either administrative data or recall data from surveys.
Using the ATUS, I ﬁnd that teachers work more than is contractually required.
They bring work home, and work on the weekends and during the summer
mois. Cependant, teachers work less than they self-report when asked to
recollect a usual week of work. En effet, all workers work less than they self-
report when asked to recollect a usual week, but teachers are more likely to
overestimate their hours than are workers in other occupations.

I construct measures of diary hours per week by occupation and ﬁnd that
teachers work an average of 38.0 hours/week during the school year and
21.5 hours/week during the summer. When I use this measure, rather than a
measure of usual hours of work per week or hours of work last week, I ﬁnd that
teachers’ hourly wages are no more or less than workers in other occupations.
In many ways, this is exactly as economists would predict and is conﬁrmation
of the power of market forces to set wages across occupations.

I ﬁnd that averaging across all teachers obscures some interesting details,
cependant. Secondary school teachers appear to have notably different work
schedules and are less likely to overestimate their usual hours of work than
elementary/middle school and special education teachers. When diary mea-
sures of hours of work are used to calculate hourly wages, secondary school
teachers earn in the range of 7–14 percent less than similar workers in other
occupations, whereas elementary, middle, and special education teachers are
paid on par with or more than similar workers in other occupations.37 I take
this as evidence that policy makers should consider abandoning the single
salary schedule that forces districts to pay all teachers the same regardless
of the grade and subject they teach. Licensing and alternative labor market
opportunities differ for these categories of teachers and I have shown that

37. Male elementary school teachers earn hourly wages that are less than other demographically
similar men. If occupational segregation accounts for the teacher wage gap, then this indicates that
elementary teachers are also underpaid relative to other occupations.

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work schedules differ as well. With the current system, it is likely that schools
face a surplus of elementary school teachers and a shortage of high-quality
secondary school teachers. Raising secondary school teacher wages could help
alleviate this problem. Disaggregating by subject may be important as well. Un-
fortunately, ATUS data only allow for disaggregation by elementary/middle,
secondaire, and special education, so this is left for future research.

Interdisciplinary doctoral fellowship support from the Graduate School at the Univer-
sity of Minnesota and the Minnesota Population Center is gratefully acknowledged.
Thanks to Joe Ritter, Paul Glewwe, Aaron Sojourner, Elton Mykerezi, Katie Genadek,
and two anonymous referees for comments. Any errors are my own.

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