The Teachers Who Leave:
Pulled by Opportunity or Pushed by Accountability?
Sara Champion (Stanford University)
Annalisa Mastri (Mathematica Institute)
Kathryn Shaw (Stanford University)
May 6, 2011
Draft: please do not quote.
There is clear evidence that teacher quality is very important in increasing student
performance (Rivkin, Hanushek, and Kain, 2005). In this era of increasing emphasis on
accountability for students and teachers, what policies should be adopted to increase teachers’
quality? One option is higher pay levels. A second option is to increase the variance of pay
across teachers. It is likely that many high quality college students avoid the teaching profession
because pay is compressed across teachers: star people may not enter teaching because there are
few upside gains to performing well in teaching. Hoxby and Leigh (2004) show that pay
compression is correlated with a lowering of teachers’ SAT scores from 1963 to 2000.
In what follows, three key questions are addressed. How much is teachers’ pay compressed
and what is causing the compression? Are teachers leaving teaching for better opportunities
elsewhere? Did moving to accountability standards in some districts alter quit rates and pay in
those districts?
These questions are addressed by using more comprehensive longitudinal data on teacher’s
careers and employers than has ever been available previously. The data set is the Longitudinal
Employer Household Dynamics (LEHD) data that contains complete earnings records for the
population of employees from 1992 to 2003 for all employed people in seven U.S. states,
Florida, Virginia, North Carolina, Texas, Illinois, Wisconsin, and Pennsylvania. The earnings
records are the reports that each employer submits to the state Unemployment Insurance office
for every employer in the firm. 1
The data set used herein follows 288,186 male teachers during their teaching careers and
after they leave teaching if they leave. Therefore, the data set contains 1,749,878 person-year
observations of the population of all male teachers in the seven states. These teachers are
employed by xx school districts. The full population of male and female teachers is xxx teachers
in these data for these states. One key advantage of this large data set is that there are enough
male teachers so that post-teaching careers can be examined. Women are dropped in most of
1
The authors obtained access to these data, that were constructed by the Census group, by
working through the confidential Census center.
1
this analysis because women are more likely to take time off for family reasons, and thus their
post-teaching pay is difficult to interpret.
The data is described in more detail in the next section, followed by sections that address
each of the three key questions listed above. The conclusion summarizes and interprets the
results.
I.
The Data Set on Teachers and Those Leaving Teaching
The data set is an employee-employer matched data set that follows the careers of teachers.
The data records teachers’ incomes when they enter teaching, get promoted, or leave. The data
comes from the earnings data for each employee that is reported by firms to the state-level
unemployment insurance system. Therefore, the data matches employee records to firm records.
A group of economists at the Census put together this data set by contacting all the state
Unemployment Insurance agencies, and this data set is labeled the Longitudinal Employer
Household Dynamics (LEHD).2
The data set used for this analysis is the population of all employees working in the
Elementary and Secondary Education industry for seven U.S. states from 1992 to 2003.3 The
states are Florida, Virginia, North Carolina, Texas, Illinois, Wisconsin, and Pennsylvania. These
states were chosen because they are diverse in many aspects, including collective bargaining
laws, regional characteristics, and educational policies. For every individual who ever worked in
the state, the data contains a quarterly earnings history for each job held during the sample
period. The data are derived from mandatory reporting to state-level unemployment insurance
systems, and thus the non-response rate is very low. Because employers report earnings for
every employee, the earnings of every person working for a given employer are known precisely:
the income measures are not responses from employees answering surveys on past earnings.
There is limited individual demographic information (age, race, and gender) and the identity and
characteristics of each employer the individual worked for over the sample period are known.
The sample used in this paper is restricted to men. It is limited to men because the LEHD
data does not contain information on hours worked, so female labor supply choices that are
unobserved would dramatically limit of modeling of the income dynamics of teachers.
2
We obtained access to these confidential data by through U.S. Census Research Data Centers. The research in this
presentation was conducted while the authors were Special Sworn Status researchers of the U.S. Census Bureau at
the University of California Berkeley Census Research Data Center. Any opinions and conclusions expressed
herein are those of the author and do not necessarily represent the views of the U.S. Census Bureau. All results have
been reviewed to ensure that no confidential information is disclosed. This research uses data from the Census
Bureau's Longitudinal Employer Household Dynamics Program, which was partially supported by the following
National Science Foundation Grants SES-9978093, SES-0339191 and ITR-0427889; National Institute on Aging
Grant AG018854; and grants from the Alfred P. Sloan Foundation.
3
This corresponds to NAICS industry code 611110. It is additionally required that the employer be publicly owned;
thus this analysis focuses entirely on public school teachers, even though it is also possible to analyze private
schools.
2
Therefore, the data contains 288,186 male full-time public school teachers, or 1,749,878 personyear observations. The larger data set on all teachers contains xxx person-year observations.
This data set follows teachers and those who leave teaching, the “leavers.” Leavers are
defined as individuals who initially worked as full-time public school teachers and then changed
careers. A leaver’s earnings and industry of employment are known even after this career
change.
These data on individual teachers’ incomes is also matched to data on school district
information. This data is from the Schools and Staffing Survey that is done by the Department
of Education.
What are the unique advantages of these data? First, other researchers have used data sets on
teachers within individual states, but not across multiple states. Second, most importantly, these
data follows teachers after they leave teaching, which has not been done for a large data set on
teachers.[footnote the studies] Third, given the size of the data set, it is possible to limit the
analysis to men. While that may seem like a disadvantage, given the labor supply concerns, it
can be an advantage. All of the analysis below can be replicated for all teachers, and in some
instances, the replications for all teachers are summarized in footnotes. Lastly, the income of the
teacher can be the full income on all jobs, not just the income from the school (thus including
second jobs and summer jobs).
What are the disadvantages of these data? There are two. First, there is no information on
the person’s occupation or education. To focus on teachers, very careful lower limits on
earnings have been imposed on the data. Second, the lack of hours of work is a drawback.
II. Rising Relative Pay Compression Over Time
Could rising pay compression contribute to the low aptitudes of teachers? Rising pay
compression has been cited by Hoxby and Leigh (2004) as a primary source of declining aptitude
of teachers. The detailed panel data available here can be used to show whether pay is
compressed, but also why pay is compressed.
Basic means for the data on teachers’ pay are in Table 1. Average pay is about $45,000 in
2002 dollars. Unionized teachers are paid more than nonunion teachers, as a reflection of the
nonunion southern states.
One key result is that the variance of teachers’ pay, across teachers, has not risen over time.
Consider the 90th, 50th, and 10th percentiles for teachers’ log(earnings). Figure 1a shows that
these percentiles are unchanged across academic years 1992/93-2002/03. Table 2a presents the
underlying numbers, as well as the standard deviations of earnings by year.
In contrast, the variance of pay has risen over time for the college-educated workforce. The
college educated earnings come from the Panel Study of Income Dynamics (PSID) data. Figure
3
1a plots the 90th, 50th, and 10th earnings percentiles for the college educated sample of male
heads of households for the PSID data.4 The data underlying Figure 1b are in Table 2b.
Are these results sensitive to the changing composition of the teaching workforce? If there
are more older teachers retiring than younger teachers entering, the variance of earnings would
fall over time, because the variance of pay is greater for older workers. Therefore, wage
regressions are estimated to control for changes in the composition of the teaching workforce.
Adjusting for shifts in the composition of the teachers’ population does not change the
results. Figure 2 plots the time series of the 90th/50th/10th percentiles of the residuals from a
regression of pay on age dummies, district, and year dummies by union and nonunion districts.5
The level of pay rises slightly for nonunion states, but the variance of pay is unchanged for these
earnings residuals that capture the average within age group and within district variance of pay.
Similarly, turning to the PSID data to compare to the college-educated, introducing controls
for changes in the age or occupational composition of the college educated workforce does not
change the conclusions there on the rising variance of pay.6 Most notably, the rising variance is
not because some occupations, like finance, pay star workers exceedingly large sums. The rising
variance occurred within all occupations, on average. Using the LEHD data for all workers (not
just teachers or the college educated), Bryson and Freeman (2009) show that the rising variance
of pay occurred more across firms than within firms across workers.
Therefore, after controlling for changing demographics, from 1992-2003, the highest paid
teachers’ earnings fell nearly 3% over time relative to the median teacher, while the highest paid
college-educated individuals’ earnings rose 12%. Figure 3 displays the 90th / 50th ratios for
teachers versus college-educated men after controlling for changes in the age composition of the
workforce and occupation.
The focus throughout this paper is on the upper tail of the income distribution for teachers –
on the 90th/50th percentile pay cutoffs. This focus arises for two reasons. First, the theoretical
focus of this paper is that teachers are not rewarded with upside gains when they perform well.
Therefore, the focus is on the 90th/50th ratio. That ratio has risen for the college educated, but not
for teachers. Among the college educated, all the action in the last twenty years has been in the
rising upper tail. Second, the data set has high quality data for the upper tail of income for
teachers, but not for the lower tail. Recall that the data set is of all employees in schools who
earn above a minimum teachers’ pay. At the lower end of the income distribution, this may
contain non-teachers who work in schools, like cafeteria staff. That’s a drawback. The
advantage of the data is that it follows the careers of people who move from teaching to
administration, so these data measure the upside income gains for those entering teaching. Thus,
4
Panel Study of Income Dynamics (PSID) data for male household heads for survey years 1991-1999, 2001, 2003,
2005, and 2007 are used, but the income data refers to the year before the survey year. The PSID is a nationally
representative longitudinal sample of approximately 9,000 U.S. families. The sample is restricted to male household
heads with 16 years of education (a college degree but no more) and the income measure used is the head’s income
from wages and salaries. The PSID data has been found to be comparable to the CPS, but is used here for its panel
a
s
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e
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t
.
5
In the figures, the residuals are scaled by the mean of log earnings from the corresponding regression sample.
6
S ee w orking paper, C ham pion, M aastri, and S haw (2009).
4
the upside 90th percentile will be higher than past research papers using data only on teachers,
and thus in some sense, the data here is more accurate. The administrative jobs are what teachers
could aspire to do over the course of their careers.
In sum, while the rest of the economy has seen a dramatic increase in the level of pay for the
top 90th percentile of the pay distribution, teachers have not. The most highly paid teachers earn
nearly the same in 2003 that they did in 1993, relative to the median teacher. Is the rigidity of
the pay distribution over time due to unions? Remarkably, no. The within district wage variance
regressions confirm this point – for all districts, union or nonunion, the variance of pay does not
change over time. There is, however, a slight rise in the level of pay for those in non-union
districts over time. Since all public school districts have pay standards set by public bodies,
there appears to be little difference between union and nonunion pay practices.
III. What are the Forces Compressing Teachers’ Pay?
It is very well known that teachers’ pay is set by a formula. Within a unionized district, a
teacher’s pay is determined solely by educational level of the teacher, and seniority, and in some
districts, by field of teaching (science versus English) and grade level of teaching, and perhaps
school conditions (like city versus suburban).
An example of a typical teacher’s pay schedule is displayed below. The displayed table is
for the Milwaukee, Wisconsin school district. It shows that the level of pay can vary across
districts, but within a district, pay is a fixed function of the education and age (or seniority) or the
teacher.
5
No existing papers have studied the extent and nature of pay compression using detailed
panel data. The contractual formulas like the one above seem to compress pay, just as union
contracts typically do (Freeman, 1982). But how do pay profiles change and what are the
underlying implications for the investments in human capital and effort? Pay regression results
from the LEHD panel data on pay for teachers are compared to the panel data for the PSID data
set on the college educated workforce.
The Structure of Pay
The sources of pay compression are assessed by decomposing the variance of pay. Start with
the wage regression. The basic wage regression is as follows:
(1)
ln Yit = β0t + β1iAgeit + α0i + eit
6
where (1) contains person-specific entry level wages, α0i, that are a function of talent and effort
and human capital investment. It also contains a person specific growth rate, β1i, because it is
likely true that the growth rates of learning differ between teachers and non-teachers. And there
are random shocks, which are assumed to be i.i.d.. The risky shocks, eit are independent of other
variables, like effort or investment β1iAgeit. The risky shocks, eit are not serially correlated: an
unexpected wage cut today does not persist for future years.7
Start with the simplest possible wage regression, for teachers and non-teachers college
educated. Using the traditional Age and Age-squared, it is clear that the teachers profiles are
much flatter. These results are displayed in Figure 4, though that figure introduces age dummies
for every age because the 1.7 million observations in the Teachers data permit semi-parametric
estimation.
Now, consider the structure of pay more carefully by decomposing the variance of pay across
people. Assuming that the age growth rate is not person-specific, replacing β1i with β1 , then the
variance at each time t is equal to:
(2)
var(ln Yit ) = var(β1Ageit) + var(α0i) + 2cov(α0i,β1Ageit) + var(eit)
given the assumptions of the wage equation above (such as i.i.d for the residual shock).
The variance decomposition of (2) produces marked differences between the composition of
pay for teachers in the LEHD versus the college educated in the PSID. These results are
displayed in Table 4 and a series of figures.
First, the age-earnings profiles are much flatter for teachers than non-teachers, thereby
displaying pay compression across age groups. Recall that based on the regression results for (2)
with a person-specific fixed effect, Figure 4 plots the pay profiles of teachers and college
educated to display the steeper profile for teachers. To measure the low variance of pay across
age groups, the regression results of Table 4 compare the estimated values of the the variance of
pay across ages, var(β1Ageit), for teachers and for non-teacher college educated: this variance
is.20 for teachers and is .55 for non-teachers (Table 4, row 6).
Second, the variance of pay across people due to person-specific fixed effects is much
smaller for teachers than non-teachers. That is, var(α0i), is also half as great for teachers than for
the general college educated; .29 versus .76 (Table 4, row 6. columns 1 and 9). Pay is
compressed within age groups.8
7
Research has shown the latter is not true: wage shocks do persist for short periods (Gottschalk and Moffitt, and
Luigi). But the estimation of persistent wage shocks is beyond the basic objectives of this paper.
8
Based on the work of Hoxby and Leigh (2004), it is clear that a good portion of the smaller variance for
teachers is that the range of their aptitudes, based on test score data, is much lower for teachers than for the general
population of college educated. They show that the most highly talented sort away from the teaching profession,
and this sorting has increased over time.
7
And lastly, but importantly, the variance of residual pay is much lower for teachers than for
the college educated. This variance, var(eit), is about one-fifth as large for teacher as for the
college population: it is .1 for teachers and .54 for non-teachers (Table4, row 7, columns 1 and
9). 9
Demand Shocks Responses in Teachers’ Pay
Is teachers’ pay responsive to the conditions in the external labor market that surrounds
teachers? The goal of pay is to retain high quality teachers – to retain and reward those who are
the skills and desire to teach well – to form a good job match, relative to teachers’ outside
options. How should the management practices for teachers’ pay achieve high quality matches
between the firm and the employee? One option is to offer pay packages that are responsive to
demand conditions, so pay adjusts in response to changes in alternative wages in non-teaching
jobs. A second option is to offer pay packages that are not responsive to external conditions,
because teachers prefer to be insured from outside shocks. Teachers are choosing the second
option in pay, conditional on employment.
Teachers’ pay is statistically responsive to local demand shocks, but the effect is
quantitatively small. The details of the regression results are in the working paper Champion,
Maastri, and Shaw (2010). Demand shocks are the county-level wages and employment levels.
However, the size of the effects is very small.
Has pay changed over time, with changing market conditions from 1992 to 2003? It has
slightly. Figure 5 displays the predicted wages from wage regressions that interact each age
dummy with a time trend. As shown, pay for younger workers rose slightly over these booming
years.
Teachers’ Pay: Where You Start is Where You Stay
For teachers, where you start is where you stay. The results above show that there is very
little mobility to other income levels. The variance decomposition of pay above also shows that
teachers are highly insured against shocks—the variance of residual shocks to pay, var(eit), is
very low. 10 Putting these together, the R-squared for the teachers’ regression is exceedingly
high – the regression predicts 91 percent of the variance in pay! This is because there are no
random shocks to pay – pay is set as a function of the person’s age and education. Because age
and person fixed effects can serve as proxies for age and education, the R-squared is very high,
even though the sample size is large 1.7 million data points of male pay in teaching. For the
9
The R-squared in the person fixed effects regression is very high for teachers because there are no shocks, eit, for
teachers. In contrast, the R-squared in the person fixed -effects regression for college educated is lower because the
variance of the person effects α0i is much higher than for non-teachers, but the residual shocks eit have a much higher
variance. Note also that the corr(α0i,β1Ageit) in Table xx is not informative because the only personal variation in
β 1 Age it is in Age, so the correlation is picking up differences in sorting or sampling in the data set.
10
These income numbers do not contain income shocks from permanent layoffs: when men in the sample don’t
t e a c h f o r a y e a r , w e a s s u m e t h e y h a v e l e f t t e a c h i n g .
8
college educated, the fixed effects regression predicts 64 percent of the variance of pay – there
are many determinants of pay other than the person fixed effects.11
Thus, teachers’ are highly insured against shocks, but in addition to being insured against
random idiosyncratic shocks, they are insured against pay outcomes that could be under the
individual teacher’s control. That is, teachers are not rewarded for individual-specific
differences in human capital investment or effort.
Consider the likely differences between teachers and non-teachers. The drawing below
displays hypothetical age-earnings profiles. As in the regressions above, for three hypothetical
people, Persons 1 through 3, the variance of pay is lower and the growth rates are flatter, for
teachers versus non-teachers. The variance in growth rates is lower, compared to the nonteachers. In addition, for Person 4 among non-teachers, his pay starts low and grows through
human capital or effort. As a result, the variance of pay should be high for the young and then
for the old.
Hypothetical Age-Earnings Profiles
The regression results can be used to mimic these drawings, by comparing the variance of
pay by age for teachers and non-teachers. In all data sets, the variance of pay rises with age, as
some people at the high end are working harder, or investing more, or finding better matches
with an employer, as in the drawing above. In the raw data on teachers, there is rising variance
of pay with age: Figure 6 displays it for union and union teachers, by showing the 90th/50th/10th.
How does this compare to non-teachers? Figure 7 shows the 90th/50th ratios by age. For the
teachers, the variance of pay is stable after the mid-30s age range. For non-teachers, it begins to
11
Regression results in working paper Champion, Maastri, and Shaw (2010) also show that regressing current
income on lagged income produces and R-squared of xx percent for teachers, and xx percent for non-teachers. In
addition, the coefficients on lagged income are xx and xx, respectively.
9
rise at that point. Of course, keep in mind that the non-teachers PSID data has much smaller
sample sizes.
The bottom line is that for male teachers, the lifecycle phase when promotions and pay raises
should increase the variance of pay across workers – in the late 30’s and 40’s – they don’t.
Are teachers are making lower investments in human capital investment and in effort than are
other occupations? Age earnings profiles are flatter for teachers, as described above. However,
this could reflect wage bargaining, not human capital investment, and there may be less scope for
on-the-job training among teachers. What other evidence is there?
There is also less variance in person-specific growth rates for teachers than for the collegeeducated in the PSID sample. Using the longitudinal PSID data and the LEHD data, we can
estimate person-specific growth rates by estimating regression (1) in growth rates and letting the
person effect becomes the person growth rate. Our results (in the Appendix), show that the
variance of growth rates is lower for teachers than college educated PSID, though these person
growth rates are not estimated very precisely given the short length of the panel for each person.
Overall, these results suggest that teachers are over-insured and under-rewarded. Flat pay
may have been appropriate in the years when data on performance was rare. In those days, pay
and promotions could have been idiosyncratic. Today, data is easily kept on teachers based on
students’ output on tests, or files of subjective performance evaluations. Therefore, teachers can
be paid based on their input – on the effort that they put into teaching. If their input is poor, they
can be fired. Teachers can also be paid on output – on either objective tests or subjective
evaluations. Other occupations have increased their performance pay on both fronts and the
variance of pay across individuals has risen (Lazear and Shaw, 2009, and Lemiuex, Parent,
MacLeod, QJE).
IV. The Teachers Who Leave: Seeking Opportunity?
There are two unique features of these data on teachers. It is possible to follow the careers of
teachers after they exit teaching—few other data sets do this. And, this data set covers seven
states and 1.7 million male teachers, so it is possible to follow large numbers of men as they exit.
No past studies have been able to achieve this. Men are important for post-teaching careers,
because they are less likely to exit the labor force or reduce hours of work post-teaching. In
these data, 5% of male teachers leave permanently, and 36% leave and then return within our
panel period.
Why do teachers leave teaching? A simple model is the following:
(3)
pr(exit) = EPV(pay in alternative job) – EPV (pay in teaching job)
where we focus on the expected present value (EPV) of pay, but admittedly, many factors other
than pay enter the decision to leave.
10
The pay regressions above provide predictions on who is likely to leave for better
opportunities outside teaching. The focus is on the gap between alternative pay and teachers’
pay that was evident in the regressions and figures.
Consider young teachers first. Figure 4 (above) and Table 4 show that young might have
a great deal to gain by leaving teaching. From age 22 to age 40, the income of teachers grows by
4.2 percent a year (from $22,026 to $46,630), whereas the pay of non-teachers college educated
grows by 6.3 percent a year (from $19,930 to $58874) (Figure 4). This point is reinforced in the
decomposition of variance of Table 4. The variance of the income growth rates is only .1 for
teachers, but .55 for non-teachers (row 5). And the variance of the person effect explains 87
percent of the total pay variance for teachers but 64 percent for non-teachers. Of course, teachers
also have a much lower variance of residual shocks (.1) compared to non-teachers (.54), which
could be luck or could be effort related.
In sum, the young will leave for upside potential, based on several features of the
teachers’ market. The highly compressed age-earnings profiles in teaching make alternative
occupations, with greater returns to human capital investment and effort on the job more
attractive; the lower overall variance of pay will induce younger to leave for greater upside
gains; and the rising pay compression over time will lead them to leave for upside gains. These
influences depend on the assumption that the young were surprised after entering teaching: either
they discovered that their “match” in teaching was poor, or that they did not fully realize that
teaching offered such poor pay options as they age. If these young had been fully would never
have entered. And of course, that is one-underlying point of the research on teachers: high
quality young people are not entering (Hoxby and Leigh, 2004).
The same points follow for older workers. By the time a teacher is 40 years old, the
average teacher earns $46,630 and the average college-educated worker earns $59,874 (Figure
4). This is both due to a steeper age-earnings profile in non-teaching jobs and a rising variance
of pay with age in non-teaching jobs. Assuming they are equally talented, the teacher may find
at that point that the gains from moving exceed the loss of rents from staying in teaching
(including the pension effects, which are not estimated in these data).
Pay for Leavers
Are teachers pulled out of teaching for better opportunities? The data suggests they are
not. No matter how you cut the data, those who leave teaching typically earn much less after
leaving. After several years, pay rebounds for the young; it never does for the old. A series of
graphs and tables display answers to key questions about those who leave teaching.
The LEHD data set contains data on 44,490 men*years of data, or on about 11,000 men who
leave teaching. Overall, for both men and women, there are 288,186 leavers, with 1,749,879
data points on their post-teaching earnings patterns. But the focus is on men. In these LEHD
data, 5 percent of men leave teaching. The annual exit rate for a man age 22 to 26 is 7 percent.
For women, the turnover rates are xx and xx, respectively for the all women and young women.
These turnover rates are typical for those college educated (the PSID data shows similar rates).
11
For those “leavers,” pay after leaving teaching is on average much lower than while teaching.
As shown in Table 5, two years post-teaching, only 45 percent make at least 80 percent of what
they made while teaching. After four years out, 54 percent make 80 percent or more of what
they made while teaching.
Where do the leavers go? A significant percent – 29 percent – stay within the educational
services industry (Table 6). Otherwise, they scatter to all industries. Though not shown in the
table, young leavers are more likely to leave the educational services industry.
Younger leavers do better than older leavers. The way to see this is in Figure 8, displaying
predicted age earnings profiles by age group. These profiles are the true wage growth, based on
within-person fixed effects earnings regressions (Table 7). The age-specific results are very
clear – the young have higher pay growth.
The key result from the age-earnings profiles of Figure 8 is that male leavers in their prime
earnings years do poorly. In these LEHD data, men could be returning to school or retiring. But
during the ages 30 to 44, both are unlikely. Yet, the prime-age men who leave teaching earn
very low incomes post-teaching when they should be fully employed in peak earnings years.
Are there some “star” leavers who obtain high-paying jobs after they leave? The answer is
largely no. Figure 9 shows that the 90th percentile earnings level at each age is a log(earnings) of
almost 11.0, or almost $60,000 dollars. It rises slightly with age. The bottom 10th percentile
earn a very low 8.5, or $4,915, but keep in mind that the lower earnings group might not be
teachers prior to leaving. They might be older janitors. Therefore, focusing on the upper tail, all
those who leave Elementary and Secondary Education are not high performers after leaving.
Are the leavers lower “quality” than the stayers in teaching? That’s impossible to determine
because teachers’ pay is based on pay scales, not on the teacher’s productivity while teaching.
So, we can’t see if the star teachers stay in teaching. However, Figure 10a shows that the
average pay of the leavers while teaching is only slightly lower than those who stay. Rather than
look at average pay levels, Figure 10b looks at the within-person age-earnings profiles. It also
shows that the predicted pay profiles of leavers after leaving is markedly lower and steeper than
when they were teachers (following the same people longitudinally). [add info to tables on how
these predictions were done.]
These results using the LEHD data suggest that those who leave are of very low quality, as
measured by their post-teaching pay. They may be leaving teaching because they don’t “match”
there. If peoples’ skills are multi-dimensional, people must match their talents to the job.
However, the post-leaving pay suggests they don’t match any where else soon. Alternatively, it
might be the case that teachers who leave are low quality: skills in the labor market are onedimensional so there are high performers and low performers due to effort or intelligence. The
poor post-teaching earnings suggest that leavers are not high performers by any measure. They
are below average performers.
These results are restricted to male teachers, because their work lives are less likely to be
altered by maternity leave or child-rearing. However, the results are comparable for women,
12
though harder to establish given the absence of hours of work information in the LEHD data.
Lacking hours information, for women (and men), those with very low earnings are dropped
from the data during the low-earning years. The two key results for male teachers are replicated
are replicated for female teachers. First, women’s earnings post-leaving are persistently lower.
Second, across all ages, including those post-child-rearing years, there is no noteworthy fraction
of female leavers who increase their earnings. Very few women move to high earnings jobs post
teaching (add results from working paper).
V. The Teachers Who Leave: Pushed by Accountability?
The result on leavers pose a striking puzzle: no economic model would predict that teachers
would leave teaching for pay cuts, but the data show that the vast majority of leavers do see their
pay fall. Pay post-teaching remains low for the four years that we follow them after leaving.
The question is clear: why do teachers leave for lower pay? The answer would seem to be
that they are pushed out of teaching into lower paying jobs. What pushes them out?
The Introduction of Accountability Reforms
During the period of 1995 and beyond, three states in these data, Florida, North Carolina,
and Illinois, introduce accountability reforms. In these reforms, the districts that have test scores
that are below the mandated values will experience sanctions, which could be as strong as the
closing of schools. The details of the reforms and sanctions are described in the Appendix.
The Accountability Reform Treatment Effect
The goal is to estimate an exit rate model as a function of the accountability treatment.
The model is as follows:
(4)
emit = Xit β2 + Zct β3 + Ddt β4 + δpred + λpret + γ1 Iafterdt + γ2 Tdt + uit
where emit is the exit rate out of teaching , and the model of exit rates in (4) contains a district
fixed effects.
In these models, i indexes teachers, t indexes school years, d indexes districts, and c
indexes counties. λpret controls for a pre-period time trend which is allowed to vary for the
treatment and control groups. Iaftert is an indicator equal to 0 for all person-year observations in a
state before accountability reform was enacted and equal to 1 in all person-year observations
after accountability reform was enacted. Tdt represents the percent of a district’s teachers that
were working in schools classified as low performing and were thus threatened by accountability
sanctions.
The matrix Xit contains person specific controls: race, sex, and age. The matrix Ddt
contains time-varying district level controls: the median percentile of teacher earnings and the
13
number of teachers. The matrix Zct contains time-varying county specific controls that measure
local labor market conditions: the unemployment rate12, the Bureau of Economic Analysis’s
average wage13, wage and salary employment, and population.
The γ2 is the parameter of interest as it reflects the change in exit resulting from
accountability. γ2 is expected to be positive if accountability increases exits.
It is not straightforward to compare the treatment and control groups in this sample. A
teacher-year observation is "treated" if that teacher worked in a district that had at least one low
performing school that year. A teacher-year observation is part of the "control" sample if that
teacher worked in a district with no low performing schools that year. (There are no "treated"
observations before accountability was introduced since the low-performing distinction did not
exist.) The percent of a district’s teachers working in low performing schools changes over time
as a district’s schools enter and exit from the low performing schools category. The only
comparison group that it is possible to identify over the whole sample period is those districts
that were never treated and those districts that were ever treated. Districts that were ever treated
had higher unemployment rates, but lower average wages. Additionally, ever-treated districts had
a much higher percentage of black teachers (25 percent vs. 6 percent) and were less likely to be
urban. Last, for ever-treated districts, median home values and median incomes were lower while
the percentage of residents below the poverty line was higher. The control group used in the
estimation of the treatment effect is actually more similar than these means suggest as treated
districts are also compared to themselves if they enter and exit treatment after accountability
reforms are introduced. (The only district that does not enter and exit treatment in the post period
is the Chicago Public School District.)
The Impact of Accountability on Teachers’ Exit Rates: Pushed Out by Accountability?
Preliminary results demonstrate that exit rates are significantly higher in accountability
districts. Moreover, the magnitude of the effect is also large. [Results will be described in the
seminar.]
The Impact of Accountability on Teachers’ Pay and Effort
Existing research shows that accountability had two effects. The first effect is that it raised
the test scores for students.
A second effect of accountability reforms of the 1990s is that they raised the effort levels of
teachers working in low-performing schools. Champion (2011) shows that teachers’ effort levels
rose by showing that their moonlighting hours decreased. Using the same LEHD data that is
used in this paper, Champion shows that when accountability was imposed on districts that had
12
The county year level unemployment rate is from the Bureau of Labor Statistic’s Local Area Unemployment
t a t i s t i c s
( L A U S )
p r o g r a m .
13
Average wage is equal to a county’s total wage and salary disbursements divided by a county’s wage and salary
employment. It is an annual measure that should be correlated with changes in the wage rate for available
m o o n l i g h t i n g
o p p o r t u n i t i e s .
S
14
higher levels of low-performing schools, the moonlighting in those districts dropped. The only
sensible explanation is that effort rose. Did effort rise because teachers were monitored more
closely, or because they were given performance pay?
The data suggests that teachers were not given performance pay. Since teachers’ wage
contracts are quite visible, it is well-known that few districts introduced performance pay
changes.14
Regressing individual pay data on a measure of school accountability, teachers in reform
districts did not experience higher wage growth. Individual teachers were not paid more; the
mean level of pay did not rise in these districts; the upside gains to effort measured by increasing
90/50 or 50/10 ratios did not rise.
[results will be described in the seminar]
The Impact of Accountability on Leavers’ Pay
[to be added]
VI. Conclusion
As widely suspected, teachers’ pay is very compressed. It is compressed across age
groups: the age-earnings profile of teachers is flatter than that of a typical college-educated
workers. Pay is also compressed within every age group: the variance of pay for middle-aged
teachers is lower than that of middle-aged college educated. Putting these together, the typical
rising variance of pay with experience is weaker for teachers than the college educated. And
thus, the expected present value of earnings has a much lower variance for teachers than nonteachers. Part of the lower variance of pay for teachers is that they have far fewer random
shocks to pay (given employment) than non-teachers. This could be desirable—they are insured
against typical risks that other employees feel. But they are also potentially over-insured. The
pay compression implies that the returns to human capital investment on the job and effort are
very low.
The pay compression for teachers has risen over time relative to non-teachers. The
variance of pay for the college educated has rose over time; the variance for teachers did not rise.
These features of teachers’ pay suggest that a subset of teachers may leave for better
opportunities elsewhere. The data shows that they do not. Four years after leaving, only 54
percent of leavers make as much as they did while teaching. The earnings for those in the 90th
percentile of earnings for leavers are also not high.
14
See
the
discussion
and
references
15
in
the
Conclusion
section
below.
Thus, most teachers who leave are not pulled by opportunity. Those who leave teaching
tend to be very low quality (as measured by future pay).
A puzzle then exits: why do teachers leave for lower pay? The data here suggests that a
subset of lower quality teachers are being forced out by accountability reforms. The exit rate of
teachers is considerably higher when accountability rules are tighter. Overall, an average
increase in accountability reform is likely to have accounted for a xx percent increase in the exit
rate of teachers. Moreover, the data shows that these are not high quality performers: their postteaching earnings are very low.
These results, on the impact of accountability on teachers’ exit rates and post exit
performance, are surprising. The literature on the impact of the accountability movement on
education has reached several conclusions. One is that accountability has appeared to increase
the quality of education – test scores have risen. While some improvement may be the result of
changes in testing procedures, some of the improvement in student performance is real. What
caused the changes? Teachers may be working harder, as described above (Champion, 2011).
Several things have not changed: pay formulas are unchanged, as shown above, and hiring
practices (outside charter schools) are unchanged. But there is considerable evidence that
management quality has gotten better – principals are managing their schools more tightly.
A potential management tool that principals may use is forcing out the lower quality
teachers. Union contracts do not permit performance-based layoffs. Therefore, there is
widespread perception that firing is not used to improve teachers’ quality. But principals have
other mechanisms for forcing out lower quality teachers; imposing greater teaching loads and
following rules on hours of work.
In sum, the puzzle is only partially resolved. The puzzle is, why do the vast majority of
those who leave teaching move to lower paying jobs? A portion of the teachers who leave are
forced out by accountability reforms. The remainder may be forced out as well due to poor
performance, but we have no measures of the mechanisms that are used to achieve this.
These results have implications for the future of the accountability reforms that are now
well underway. The empirical results herein show that reforms can raise teachers’ average
quality by pushing out poor performing teachers. But unless class size is rising, these teachers
need to be replaced.
Accountability reforms may have pushed out poor performers, but the next step needed to
attract good performers. The stock of teachers needs to be replenished with higher quality
performers.15 A key innovation is also the use of greater performance based pay. Performance
pay can take either of two forms. It can be rewards on the job for superior performance. As the
data on pay compression shows, higher pay variance could attract better teachers. Performance
pay can also be implemented by raising hiring standards. If teachers are hired more carefully,
then paid in accordance with their initial talent, that too is performance pay. The innovations
15
As the baby-boom generation retires in the next ten years, teachers exit rates will be especially high, because the
w o r k
f o r c e
i s
a n
o l d e r
w o r k
f o r c e .
16
that should follow from the Race to the Top are aimed at both uses of performance pay.16 Given
the tight budgets that exist in states, raising the average pay of teachers may not be likely. The
alternative to attract better average teachers is to raise the variance of pay.
The press often states that in education, scarce resources need to be well-spent. All
resources in the world are scarce and need to be spent to reflect that scarcity. The point for
education is that state and Federal fiscal deficits are making expenditures in education relatively
more scarce at a time when the returns to education are rising. How should the declining relative
educational dollars be spent? Empirical results using the longitudinal LEHD herein suggest that
the aptitudes of teachers are stunningly low relative to the college educated population and
enhanced pay for performance through either careful hiring or output based pay may be
warranted.
16
The Department of Education created the Race to the Top tournament in which states with the best reform plans
will share a total of $4 billion to implement their plans. The competition ended in August 2010: in the first round of
grants, 40 states applied and tw o w on, and in the second round, 46 applied and ten w on.
17
Appendix: Accountability Reforms17
Education policy aimed at improving student achievement has changed markedly over
the last three decades. Initially, most education policies focused on more careful regulation of the
inputs required for education production. For example, more stringent certification requirements
were placed on individuals wishing to teach in public schools, and class size quotas were
imposed. Though some of these changes, such as smaller class size, appear to have been
successful, the approach of regulating inputs overall has not been effective at improving student
performance broadly. This has resulted in a shift toward accountability systems.
Accountability systems leave much more in the hands of the district leaders and
principals, allowing them to determine how best to improve student performance. Rather than
regulating specific things like class size or teacher certification at the state level, accountability
sets a performance target and allows administrators and teachers to decide how to achieve it.
This is perhaps the preferred method, as it seems plausible that the individuals who are in the
school from day to day might better know the challenges, as well as the best solutions.
The success of such a method depends very much on the ability of the local administrators to
observe and to know not only where student performance is lacking, but also what will and will
not improve student performance (Hanushek and Raymond 2001). Furthermore, they must be
empowered to act on the changes they know they need to make (Loeb and Strunk 2007). It is
also important that improvements in student performance can be measured reliably over time so
that credit (or blame) can be assigned appropriately.
Accountability so far can be divided into two types, test-based and market-based systems.
Many reforms are a mixture of both systems. The economic theory rationalizing these two
different systems is distinct, though both should lead to improved efficiency in education
production. Market-based systems, which allow increased school choice via passing out
vouchers to students attending failing schools, should improve efficiency due to increased
competitive pressures.
Test-based systems are expected to work via increased information (a school’s average
student performance on achievement tests) made available to teachers, administrators, parents,
and the community in general. This information increases pressure on local schools as members
of the local community have educational incentives, as well as financial incentives. Financial
incentives exist because quality of schooling is capitalized into housing prices (Figlio and Lucas
2004; Rouse, Hannaway, Goldhaber, and Figlio 2007).
Researchers have done much work to assess the impacts of accountability systems. Most
of the literature has focused on the impact of reforms on student achievement (as measured by
changes in standardized test scores). Two influential nation-wide studies that use data at the
state-year level concluded that accountability significantly increased educational achievement.
(Carnoy and Loeb 2003; Hanushek and Raymond 2005). Like the results presented here, these
studies used variation in accountability reforms during the 1990s.
17
T h i s
a p p e n d i x
i s
a n
e x c e r p t
18
f r o m
C h a m p i o n
( 2 0 1 1 ) .
Other studies, many state-specific, have suggested that not all of the observed gains in
student achievement are “real” and that the measured gains in student performance are being
distorted via manipulation of testing conditions (Jacob and Levitt 2003), reclassification of
students into special education (Figlio and Getzler 2002; Hanushek and Raymond 2005; Jacob
2005; Cullen and Reback 2006), poorly designed tests and test scoring methodologies, and
increased school dropout rates.
There are fewer studies that analyze the effects of accountability on teacher behavior.
Influential research published thus far studies how teacher turnover is influenced by
accountability reforms. (Boyd et al. 2005; Clotfelter et al. 2004; Feng, Figlio and Sass 2009)
These papers focus on teachers in North Carolina, New York, and Florida and the results are
somewhat mixed. Boyd et al. find that in response to the introduction of testing, teacher turnover
decreased among those teachers most likely to be affected. Clotfelter et al. find that introduction
of accountability has led to increased teacher turnover in schools serving low-performing
students, but that it is not evident that accountability reforms led to less qualified teachers in
these schools. Feng et al. find that teachers in schools with an unanticipated shock in
accountability pressure (due to a major rule change in Florida’s accountability system) are more
likely to move to another district, and are more likely to exit the teaching labor force, than
schools not experiencing an unanticipated accountability shock.
Importantly, the strength of the accountability system implemented does affect potential
for student achievement gains. To be considered strong, an accountability system must have
well-defined performance measures and targets. It must also have consequences for those who
perform poorly and rewards for those who perform well. The best systems focus on an accurately
computed measure of a teachers’ value-added. Though inferior to value added, a performance
target defined by test score gains is preferable to one defined by test score levels. Using an
accountability strength index that ranks states according to the strength of their implemented
accountability systems, Loeb and Carnoy show that achievement test gains are higher when
accountability is stronger (2003).
Consequences in strong accountability systems can take many forms. Reconstitution of a
school, intervention in the management of a school (for example principal transfers), and loss of
students (through vouchers) are some examples. Further examples include required principal and
teacher evaluations and required training sessions. Training sessions are intended to improve
teacher and administrator skills, so that these individuals become better at the delivery of
education. Often, these sessions occur after regular school hours or on weekends, thus teachers
required to attend have less hours available for moonlighting.
Strangely, one common consequence for poor performance is increased funding. The idea
is that the additional funding can be used to improve teacher skills or to provide educational
resources (inputs) that are important but lacking. (Often, low performing schools have lower per
student funding than other schools in the local area or state.)
Rewards in strong accountability systems are typically monetary and include cash
bonuses to teachers in schools that perform especially well. (Ideally these bonuses would be
19
individualized, rather than at the school level, but this is not at all common.) Recognition as a top
school is perhaps also a reward in its own right. There are not very many examples of rewards
because accountability systems thus far have tended not to focus on rewards nearly as much as
on negative consequences for low performing schools.
The analysis in this paper exploits variation in moonlighting behavior resulting from the
introduction of three separate accountability systems in Florida, North Carolina, and the Chicago
Public School District in Illinois. North Carolina and Illinois’s systems were implemented in
1996 and are primarily test-based. Florida’s system, implemented in 1998, is a mixed system
with a market-based component, vouchers for students in failing schools, and a test-based
component, publication of school report cards, as well as specific consequences for schools
receiving two F’s in a row. All systems studied rely on a mixture of mandated student
achievement levels, as well as expected achievement growth rates for classification of low
performing schools from year to year. Both Florida and North Carolina assign performance
ratings to each school.18 Chicago simply set a minimum acceptable threshold for student
achievement and classified schools as failing or not failing.19
Florida Accountability Reforms
Florida’s performance ratings are based on student achievement results from the Florida
Comprehensive Assessment Tests (FCAT) that were administered for the first time in 1998.
Performance grades were assigned to schools based on these test results beginning in the summer
of 1999. Because teachers in 1998-99 knew that students test scores from that school year could
be considered for accountability purposes, 1998-99 is considered the year of accountability
introduction from the perspective of teachers. FCAT tests are aligned to state standards. Reading
and writing tests were administered to students in grades 4, 8, and 10 annually. Math tests were
administered to students in grades 5, 8, and 10 annually.20 See Chiang 2009 for a more detailed
description of the criteria used to determine school grades and of Florida’s accountability system
generally.
Students in Florida schools that received a failing grade two years in a row were entitled
to vouchers for private school tuition, or the option to transfer to a better performing (C graded
or higher) public school.21 The take-up rate for these school choice options is hard to determine
due to lack of appropriate data, but has been estimated to be 5-10 percent per year according to
Florida Department of Education personnel (Rouse, Hannaway, Goldhaber, and Figlio 2007).
School districts in which at least one school received a D or an F faced a required evaluation by a
community assessment team consisting of local and state administrators, parents, and business
representatives. These teams made recommendations to the state and local school boards on how
schools could improve performance, and technical assistance was made available by the state.
18
Categories in North Carolina include exemplary, meets expectations, no recognition, and low performing. Florida
r a d e d
s c h o o l s
o n
a n
A - F
s c a l e .
19
That 15 percent of a school’s students meet national norms as measured by the Iowa Test of Basic Skills (ITBS)
reading test.
20
Beginning in 2001-02, students in grades 3-10 all took math and reading FCAT tests. Test scores from all grades
were not linked to accountability ratings until 2002-03, however.
g
21
The private school voucher option was ruled unconstitutional by the Florida Supreme Court in 2006.
20
Most importantly, failing schools were given access to highly trained reading coaches and teams
from the Assistance Plus program. These teams provided a host of support to failing schools
including recommendations regarding professional development for teachers and assistance to
administrators in analyzing critical needs and developing plans for addressing those needs.
Rouse et al. survey Florida principals who report that schools lengthened instructional
hours and increased planning and professional development time so that teachers could improve
their instruction. It has been demonstrated in numerous studies that student achievement
improved as a result of this accountability system (Greene 2001; Figlio and Rouse, 2006; West
and Peterson 2006; Chakrabarti 2007 and 2008).
North Carolina Accountability Reforms
North Carolina introduced achievement tests aligned with state standards in 1993, but did
not tie these test results to accountability consequences and rewards until 1996 for elementary
and middle schools, and 1997 for high schools. All students in grades 3-8 were tested in math
and reading, and students in grades 4-7 were tested in writing. High schools students were tested
at the end of fundamental courses such as Algebra I, English I, U.S. History, and Biology. Tests
across subjects are combined into a composite score. Schools are classified as low performing if
they do not have sufficiently high levels of student achievement (50 percent of students scoring
at grade level) or if they do not have sufficient improvement in student achievement since the
previous year.22
Of the three systems in this sample, North Carolina is the only one with significant
positive incentives or rewards. Teachers in schools designated as exemplary are given a $1500
bonus. (The amount is $750 for teachers in schools that merely meet expectations.) Because
these financial incentives are tied only to improvements in student test scores, teachers in low
performing schools can seriously compete for these rewards - not just teachers in the best
schools. North Carolina also has significant sanctions for low performing schools. Low
performing schools are assigned state assistance teams that perform a comprehensive needs
assessment, evaluating teachers and administrators. Also this team aids the school in
implementing (or revising) a school improvement program.
In a survey of North Carolina principals, 2 out of three respondents said that the
accountability system helped them to make their teachers more effective (Ladd 2004). Principals
chose to allocate relatively more resources to low-performing students and asked teachers to
volunteer extra hours after school and on Saturdays to tutor students. It is clear that student
achievement in North Carolina rose after this accountability system was introduced, but it is
difficult to attribute it entirely to accountability because other educational policies were changed
simultaneously. These programs, legislated in the early nineties a few years before the
accountability system was introduced, increased teacher salaries from below the national average
to the national average, and invested in teacher training programs (Ladd 2004).
22
The required improvement is school and year specific. Given the composition of students in the previous year and
the composition of students in the current year, an expected gain in student achievement is predicted at the school
l
e
v
e
l
.
21
Chicago Public School District Accountability Reforms
In 1995, Illinois legislation was passed which gave Chicago’s mayor, Richard M. Daley,
considerable control over the administration of Chicago Public School District (CPS).
Furthermore, it gave CPS administrators the ability to impose sanctions on schools that
performed poorly and did not improve over time. This legislation also diminished the Chicago
Teachers Union’s power to influence working conditions, hours, and hiring and firing rules via
collective bargaining (Quality Counts 1998). In 1996, a high stakes accountability system was
introduced with several components. Under the new system, in hopes of improving student
effort, students in the 3rd, 6th, and 8th grades were no longer “socially promoted.” Instead they
were required to score at or above a certain level on the Iowa Test of Basic Skills (ITBS) in order
to be promoted to the next grade (Jacob 2005).
Furthermore, to increase teacher and principal effort, schools having 15 percent or fewer
students at or above national norms on the ITBS reading exam were put on academic probation.
A school on academic probation was given significant financial resources to be used on
professional development activities for the school’s faculty. (Funding provided for these
activities was 100 percent in the first year and decreased to zero percent by the third year.)
Schools that did not improve adequately on the ITBS from year to year faced direct intervention
or reconstitution (Hess 2002).
Reconstitution typically involved the firing or reassignment of teachers and school
administrators. For the school year beginning in 1996, 109 schools were put on probation. Seven
low-performing high schools on academic probation were reconstituted and 30 percent of
teachers in these seven schools were replaced (Hess 2002). “…[A]s early as 1997 teachers and
administrators in probation schools reported being extremely worried about their job security,
and staff in other schools reported a strong desire to avoid probation” (Jacob 2005). Jacob
demonstrates that the accountability reforms implemented in 1996 led to marked improvement in
the math and reading achievement test scores of Chicago students with gains averaging 0.2 to 0.4
standard deviations on the ITBS. Performance improved disproportionately for students in lowperforming schools.
22
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23
11.8
11.6
11.4
Log Earnings
11.2
11
p90
10.8
p50
10.6
p10
10.4
10.2
10
9.8
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
Year
th
Figure 1a: Log Earnings 90 /50th/10th Percentiles for Teachers
11.8
11.6
11.4
Log Earnings
11.2
11
p90
10.8
p50
10.6
p10
10.4
10.2
10
9.8
1990 1991 1992 1993 1994 1995 1996 1998 2000 2002 2004 2006
Year
Figure 1b: Log Earnings 90th/50th/10th Percentiles for College-Educated
24
11.8
Log Earnings Residuals (Scaled by Mean)
11.4
11
Union p90
Non-­‐Union p90
Union p50
Non-­‐Union p50
10.6
Union p10
Non-­‐Union p10
10.2
9.8
1997
1998
1999
2000
2001
2002
Year
Figure 2: Log Earnings 90th/50th/10th Percentiles for Teachers, Controlling for Composition
Changes
Data in predicted values from yearly regressions of ln(earnings) that take out the on age, state,
and district/occupation effects.
25
1.08
1.07
Log Earnings Residual Ratios
1.06
1.05
p90/p50 PSID
1.04
p90/p50 Teachers
1.03
1.02
1.01
1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
Year
Figure 3: Log Earnings Ratios for 90th/50th Percentiles
Teachers are College Educated (PSID)
Residuals are from regression of 1n(earnings) on age and state dummies.
26
11.2
11
Predicted L og Earnings
10.8
10.6
College-­‐Educated
10.4
Teachers
10.2
10
9.8
22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
Age
Figure 4: Age-Earnings Profiles for Teachers and College-Educated
(Results are from a person fixed-effects regression of ln(earnings) on age and age-squared.)
27
11.2
11.0
Predicted L og Earnings
10.8
10.6
2003
10.4
1993
10.2
10.0
9.8
23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
Age
Figure 5: Shifts in Age Earnings Profiles Over Time
Profiles are predicted from a regression of 1n(earnings) on age dummies interacted with a time
trend.
28
11.8
11.6
11.4
11.2
Log Earnings
11.0
Union p90
Non-­‐union p90
10.8
Union p50
Non-­‐union p50
10.6
Union p10
Non-­‐union p10
10.4
10.2
10.0
9.8
22
24
26
28
30
32
34
36
38
40
42
44
46
48
50
52
54
56
58
60
62
64
Age
Figure 6: Log Earnings 90th/50th/10th Percentiles by Age Group
1.09
1.08
Log Earnings Ratios
1.07
1.06
1.05
PSID p90/p50
Teachers p90/p50
1.04
1.03
1.02
1.01
23
25
27
29
31
33
35
37
39
41
43
45
47
49
Age
Figure 7: Log Earnings Ratios for 90th/50th Percentiles for Teachers and College-Educated
(Results shown here are for union teachers.)
29
10.4
10.2
Predicted L og Earnings
10
9.8
Age 22-­‐29
Age 3 0-­‐34
Age 35-­‐39
9.6
Age 40-­‐44
Age 4 5-­‐50
9.4
9.2
9
1
2
3
4
5
6
Years Since Leaving Teaching
Figure 8: Post-Leaving Experience-Earnings Profiles by Age Group
Log Earnings Regressions include person fixed effects, experience dummies, and state dummies.
Age Group is classified by the age a leaver is when leaving teaching.
30
11.5
11
10.5
Log Earnings 10
9.5
p90
p50
9
p10
8.5
8
7.5
7
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
Age
Figure 9: 90th/50th/10th in Leavers’ Earnings by Age Group
31
49
50
11
10.8
10.6
Predicted L og Earnings
10.4
10.2
10
Always Teachers
Pre-­‐Leave
9.8
Post-­‐Leave 9.6
9.4
9.2
9
29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
Age
Figure 10a: Age Earnings Profiles for Leavers and Stayers in Teaching
Age-Earnings for OLS Regressions
These profiles compare those stayers, who are always teachers, to leavers after they leave
teaching and before they leave. The Age-Earnings Profiles are the coefficients on age dummy
variables.
11
10.8
10.6
Predicted L og Earnings
10.4
10.2
10
Always Teachers
Pre-­‐Leave
9.8
Post-­‐Leave 9.6
9.4
9.2
9
30
35
40
45
Age
Figure 10b: Age-Earnings Profiles for Fixed Effect Regression
Regressions include age and state dummies as well as a person specific fixed effect.
32
Table 1: Variable Means
Union
All
age≤ 50
Annual
Wage
Log Annual
Wage
SD
N
NT
Non-union
Age
22-29
Age
30-39
Age
40-50
All
age≤ 50
Age
22-29
Age
30-39
$45,252
$34,201
10.72
(0.34)
165,751
1,099,879
10.44
(0.22)
38902
137502
Age
40-50
$40,946
$50,011
$40,135
$32,860
$37,798
$43,915
10.62
(0.29)
70277
31484
10.82
(0.33)
117,307
647893
10.6
(0.28)
122,435
649,999
10.4
(0.18)
32228
101451
10.54
(0.24)
56782
219055
10.69
(0.30)
71963
329493
Annual wage is earnings over the 4 quarters of the school year, adjusted by CPI-U 2003 by
region.
33
Table 2a: Variances by Year for Teachers
Variance of Earnings*
Variance of Earnings within Age, State**
School Year
Mean
σ
p10
p50
p90
σ
p10
p50
p90
1992
10.7
0.33
10.26
10.706
11.12
0.28
-0.38
0.0098
0.35
1993
10.72
0.34
10.26
10.725
11.16
0.29
-0.39
0.0134
0.36
1994
10.7
0.34
10.25
10.703
11.14
0.29
-0.39
0.0132
0.36
1995
10.7
0.34
10.24
10.701
11.14
0.29
-0.38
0.0134
0.36
1996
10.61
0.33
10.2
10.615
11.04
0.27
-0.37
0.0131
0.34
1997
10.63
0.33
10.21
10.632
11.07
0.28
-0.38
0.0164
0.34
1998
10.65
0.32
10.24
10.654
11.07
0.28
-0.38
0.0145
0.34
1999
10.67
0.32
10.26
10.670
11.08
0.28
-0.38
0.0188
0.34
2000
10.66
0.31
10.26
10.660
11.07
0.28
-0.38
0.0145
0.34
2001
10.68
0.31
10.28
10.681
11.08
0.28
-0.37
0.0128
0.34
2002
10.7
0.3
10.32
10.700
11.09
0.28
-0.36
0.0095
0.34
*Statistics on actual Earnings by year
** Statistics on the residual from a yearly regression of in(earnings) on age dummies and state dummies.
*** Statistics on the residual from a yearly regression of in(earnings) on age dummies, state dummies, and district
dummies.
Variance of Earning
σ
0.26
0.26
0.27
0.26
0.26
0.26
0.26
0.27
0.26
0.26
0.25
p10
-0.35
-0.36
-0.37
-0.36
-0.35
-0.36
-0.36
-0.36
-0.35
-0.34
-0.33
Table 2b: Variances by Year for College-Educated
Variance of E
Variance of Earnings*
Variance of Earnings within Age, State**
O
Year
Mean
σ
p10
p50
p90
σ
p10
p50
p90
σ
p10
1991 10.75091 0.5557839 9.987393 10.78703 11.42113 0.5136402
10.1579 10.86526 11.41661 0.4466349 10.265
1992 10.78543 0.5496808 10.09448 10.82428 11.45156 0.5017742 10.24274 10.88042
11.4164
0.415579 10.289
1993 10.82942 0.5653179 10.08832
10.8691 11.50059
0.513446 10.18024 10.87557
11.404 0.4311212 10.344
1994
10.8216
0.557568 10.04288 10.83013 11.48998 0.4903298 10.24808 10.84508 11.39477 0.4050586 10.335
1995 10.79393 0.5678223 10.08301 10.78289 11.47334 0.5152859 10.23203 10.86149 11.46513 0.4056807 10.354
1996 10.80212 0.5777761 10.03749 10.81369 11.50344 0.5335431 10.16292 10.84212 11.49627 0.4291796 10.292
1997 10.83648 0.5720847 10.09838 10.84084 11.55461 0.5127651 10.16131 10.86064 11.47698 0.3809198 10.351
1999 10.88962 0.5952824 10.13997 10.87052 11.58901 0.5280824 10.17226 10.84296 11.45105 0.4435315 10.319
2001 10.97726 0.6017324 10.27531 10.95631 11.71994
0.545901 10.19999 10.80458 11.52415 0.4490683
10.33
2003 10.88443 0.6513262 10.15815 10.85032 11.61386 0.5651613 10.18115 10.81176 11.54094 0.4849509 10.314
2005 10.89781 0.6372501 10.16572 10.88948 11.64015 0.5478667 10.13751
10.8336 11.45301 0.4580235 10.289
2007 10.93888 0.6588384 10.16768 10.91332 11.67534 0.5761228 10.09541 10.83155 11.47491 0.4928971 10.272
*Statistics on actual Earnings by year
** Statistics on the residual from a regression of in(earnings) on state dummies and age dummies interacted with
year dummies.
*** Statistics on the residual from a regression of in(earnings) on state dummies, 1-digit occupation dummies, and
age dummies interacted with year dummies.
34
Teachers
Age
Age
2
Constant
R2
NT
N
PSID Sample
0.0830
0.1512
(298.49)
(21.36)
-0.0007
-0.0015
-(200.70)
-(16.25)
8.5060
7.3444
(1527.39)
(55.62)
0.8976
0.7737
1,749,878
8,544
288,186
1,558
Table 3: Quadratic Age-Earnings Profiles for Teachers and College-Educated
35
Table 4: Decomposition of Variance
Teachers - Union
2
3
22-29
30-39
1
All
1
Dependent Variable: σy
0.34
0.22
4
40-50
5
All
Teachers - Non-union
6
7
22-29
30-39
8
40-50
9
All
College Educated PSID
10
11
22-29
30-39
12
40-50
0.29
0.33
0.28
0.18
0.24
0.3
0.91
0.98
0.74
0.95
A: ln(Earnings) Regression with Age and State Dummies
2
σxβage
3
σeit
4
R
2
0.16
0.07
0.07
0.09
0.12
0.06
0.04
0.05
0.24
0.29
0.06
0.04
0.3
0.21
0.29
0.32
0.26
0.17
0.24
0.29
0.88
0.94
0.73
0.95
0.21
0.09
0.05
0.07
0.17
0.1
0.03
0.03
0.073
0.086
0.008
0.002
B: ln(Earnings) Regression Adding Persons Fixed Effects (with Age and State Dummies) - XTREG
5
σxβage
0.2
0.1
0.08
0.05
0.33
0.11
0.13
0.11
0.55
0.37
0.20
0.13
6
σαi
0.29
0.19
0.28
0.32
0.33
0.16
0.24
0.29
0.76
0.70
0.65
0.80
7
σeit
0.1
0.1
0.1
0.09
0.1
0.09
0.09
0.09
0.54
0.63
0.37
0.53
8
σxβ+αi
9
10
R
0.32
0.2
0.28
0.32
0.27
0.16
0.22
0.28
0.73
0.76
0.64
0.80
2
0.91
0.8
0.89
0.92
0.88
0.75
0.85
0.9
0.64
0.59
0.75
0.79
2
0.87
0.7
0.85
0.91
0.79
0.6
0.76
0.86
R for regression with αi only
C: ln(Earnings) Regression Adding District Fixed Effects (with Age and State Dummies)
11
σxβage
0.15
0.06
0.04
0.06
0.12
0.04
0.04
0.05
12
σηD
0.13
0.1
0.13
0.14
0.05
0.06
0.06
0.06
13
σeit
0.28
0.19
0.26
0.3
0.26
0.16
0.23
0.29
14
σxβ+ηD
0.2
0.12
0.14
0.16
0.13
0.08
0.07
0.08
0.33
0.28
0.22
0.22
0.2
0.19
0.08
0.07
10.72
10.44
10.62
10.82
10.6
10.4
10.54
10.69
1,099,879
137,502
314,484
647,893
649,999
101,451
219,055
329,493
15
R
2
D: Means of ln(Earnings)
16
17
Number of Observations
18
Number of Persons
165,751
38,902
70,277
117,307
122,435
32,228
56,782
71,963
19
Number of Districts
1,431
1,263
1,366
1,379
645
582
624
630
36
Table 5: Fraction of Leavers’ in Earnings Growth Categories Post-Teaching
Years Since Leaving
2
3
4
$0 to annual minimum wage
0.28
0.23
0.21
min wage to.8*Teaching Earnings
0.27
0.25
0.25
.8*Teaching Earnings and Higher
0.45
0.52
0.54
Panel A: after teaching Pay
Table 6: Distribution of Leavers by Industry
Leavers' Industries (2 digit NAICS codes)
Percent
Educational Services
29%
Public Administration
9%
Administrative and Support
7%
Construction
7%
Health Care and Social Assistance
5%
Professional, Scientific, and Technical
Services
5%
Wholesale Trade
4%
Retail Trade
4%
Manufacturing
4%
Finance and Insurance
3%
Teacher Exit Rate - men
5%
- women
8%
37
Table 7a: Leavers’ Post Teaching Experience-Earnings Profiles
Age Group
Years
Since
1
2
3
4
5
Leaving
1
ommitted category
2
0.127
0.081
0.014 -0.103 -0.121
(4.06) (3.24) (0.55)
-3.91
-4.51
3
0.295
0.206
0.172 0.056
0.116
(9.33) (7.87) (6.31) (2.11)
(3.86)
4
0.355
0.276
0.212 0.115
0.1
(9.95) (9.63) (7.04) (3.90)
(2.47)
5
0.462
0.349
0.263
0.16
0.15
(10.74) (10.47) (7.97) (4.88)
(3.07)
6-10
0.551
0.353
0.359
0.17
0.154
(12.04) (10.33) (10.53) (4.90)
(1.80)
Constant
9.75
9.81
9.74
9.75
9.65
2
R
0.644
0.666
0.675 0.687
0.709
NT
6,514
9,767 10,748 10,293
7,618
N
1,485
2,175
2,431 2,410
2,500
Regressions are ln(earnings) for each age group after
leaving teaching, with person fixed effects, state
dummies, experience dummies, and clustered standard
errors.
Table 7b: Persistence in Leavers’ Post-Teaching and Pre-Teaching Earnings
All Teachers
Union
Non-union
Cons
γi
R2
Cons
γi
R2
Cons
All
-0.13
0.78 0.03
0
0.73 0.03
-0.09
-(11.26) (17.76)
-(12.98) (13.20)
-(5.80)
20-29
0.57
0.38 0.004
0.53
0.32 0.004
0.47
(26.07)
(3.18)
(15.81)
(2.03)
(14.15)
30-39
0.13
0.98 0.04
-0.19
1 0.05
-0.14
-(7.60) (14.54)
-(8.30) (11.64)
-(5.74)
40-50
-0.62
0.82 0.05
-0.65
0.95 0.06
-0.53
-(31.84) (13.20)
-(25.36) (11.97)
-(17.04)
Regressions are αipostTeach=γ0+γ1 αipreTeach by Age Group of Leaver
38
γi
1.54
(23.70)
0.71
(3.85)
1.37
(13.37)
0.9
(18.88)
R2
0.11
0.01
0.09
0.06
Appendix Table A1: Median, 10th, 90th Percentiles (for Figure 4)
Age
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
p10
10.00
10.07
10.10
10.15
10.18
10.19
10.21
10.21
10.22
10.23
10.23
10.24
10.24
10.24
10.24
10.25
10.25
10.26
10.27
10.29
10.30
10.31
10.33
10.35
10.37
10.39
10.41
10.42
10.43
10.44
10.44
10.42
10.40
10.36
10.32
10.27
10.24
10.22
10.19
10.17
10.17
10.17
10.16
Union Teachers
p50
10.29
10.33
10.36
10.41
10.44
10.47
10.50
10.52
10.54
10.57
10.59
10.60
10.62
10.64
10.65
10.66
10.68
10.69
10.72
10.75
10.77
10.80
10.82
10.84
10.87
10.89
10.90
10.91
10.93
10.94
10.94
10.94
10.94
10.94
10.92
10.90
10.87
10.83
10.79
10.75
10.74
10.71
10.70
p90
10.54
10.56
10.60
10.64
10.69
10.73
10.78
10.82
10.85
10.89
10.92
10.94
10.97
10.99
11.01
11.03
11.05
11.08
11.10
11.13
11.15
11.17
11.19
11.21
11.23
11.25
11.26
11.28
11.29
11.30
11.31
11.32
11.33
11.33
11.33
11.32
11.32
11.30
11.28
11.27
11.27
11.26
11.27
Non-union Teachers
p10
p50
p90
10.05
10.25
10.47
10.12
10.30
10.53
10.15
10.34
10.57
10.17
10.37
10.60
10.18
10.39
10.62
10.19
10.41
10.64
10.20
10.44
10.67
10.21
10.46
10.70
10.22
10.48
10.73
10.23
10.51
10.75
10.23
10.52
10.78
10.22
10.53
10.80
10.23
10.55
10.82
10.22
10.56
10.84
10.22
10.57
10.86
10.22
10.59
10.88
10.23
10.60
10.90
10.24
10.61
10.92
10.24
10.62
10.94
10.24
10.64
10.96
10.25
10.65
10.98
10.26
10.67
11.00
10.27
10.69
11.02
10.28
10.71
11.03
10.29
10.72
11.06
10.29
10.74
11.08
10.30
10.76
11.10
10.32
10.78
11.12
10.32
10.79
11.14
10.32
10.80
11.16
10.32
10.81
11.17
10.32
10.81
11.18
10.31
10.81
11.18
10.28
10.89
11.18
10.26
10.80
11.18
10.24
10.78
11.17
10.22
10.77
11.17
10.19
10.76
11.15
10.18
10.73
11.14
10.16
10.71
11.12
10.16
10.70
11.12
10.16
10.70
11.12
10.16
10.70
11.10
39
PSID College-Educated
p10
p50
p90
9.31
9.76
10.45
9.60
10.12
10.64
9.56
10.24
10.87
9.66
10.33
10.92
9.85
10.44
11.05
10.04
10.57
11.11
10.05
10.59
11.11
10.13
10.68
11.27
9.99
10.68
11.33
10.12
10.75
11.40
10.19
10.73
11.39
10.20
10.87
11.37
10.26
10.80
11.35
10.30
10.84
11.40
10.23
10.88
11.39
10.38
10.95
11.48
10.34
10.98
11.52
10.21
10.91
11.49
10.22
10.99
11.63
10.18
10.94
11.57
10.06
10.95
11.54
10.16
10.94
11.62
10.25
11.02
11.57
10.27
11.00
11.71
10.08
10.97
11.65
10.29
11.02
11.81
10.29
10.99
11.73
10.32
11.01
11.79
Appendix Table A2: Teacher Age-Earnings Profiles (for Figure 7)
Union
FE
OLS
Non-Union
FE
OLS
Union
Age
FE
OLS
0.74
(181.13)
0.76
(185.55)
0.78
(189.82)
0.8
(193.86)
0.81
(197.78)
0.83
(201.37)
0.84
(204.46)
0.86
(207.37)
0.87
(210.58)
10.05
0.91
1,099,879
165,751
0.46
(114.63)
0.48
(120.45)
0.41
(126.43)
0.53
(133.04)
0.55
(138.76)
0.57
(143.94)
0.59
(148.37)
0.6
(152.45)
0.62
(156.92)
10.29
0.21
Non-Union
FE
OLS
Age
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
0.07
(19.90)
0.12
(35.79)
0.18
(51.61)
0.23
(64.71)
0.28
(75.74)
0.32
(86.66)
0.37
(96.50)
0.41
(104.87)
0.44
(113.77)
0.48
(121.62)
0.51
(129.44)
0.54
(136.27)
0.57
(142.80)
0.6
(148.84)
0.62
(154.65)
0.65
(160.24)
0.67
(165.71)
0.7
(171.02)
0.72
(176.19)
0.04
(11.81)
0.07
(19.90)
0.12
(31.52)
0.15
(40.13)
0.18
(47.28)
0.21
(53.84)
0.24
(59.12)
0.26
(63.97)
0.28
(68.98)
0.3
(73.04)
0.31
(76.45)
0.33
(80.10)
0.34
(83.54)
0.35
(86.48)
0.37
(89.96)
0.38
(93.64)
0.4
(97.80)
0.42
(102.74)
0.44
(108.92)
0.07
(17.30)
0.13
(31.03)
0.18
(44.62)
0.24
(57.30)
0.3
(70.56)
0.35
(83.13)
0.41
(95.40)
0.46
(106.97)
0.52
(118.66)
0.57
(128.66)
0.61
(138.24)
0.66
(148.16)
0.71
(157.35)
0.75
(166.28)
0.8
(174.83)
0.84
(182.69)
0.88
(190.94)
0.92
(198.48)
0.96
(205.75)
0.05
(12.49)
0.08
(20.05)
0.11
(25.70)
0.13
(30.21)
0.15
(35.38)
0.17
(39.98)
0.19
(45.04)
0.22
(50.11)
0.23
(54.24)
0.25
(57.15)
0.26
(59.42)
0.27
(62.03)
0.28
(63.96)
0.29
(67.08)
0.31
(69.86)
0.32
(72.53)
0.33
(75.82)
0.35
(78.94)
0.36
(81.17)
42
43
44
45
46
47
48
49
50
Constant
2
R
NT
N
Corr αiXβ
40
-0.2
1
(213.05)
1.03
(220.15)
1.07
(227.57)
1.11
(234.66)
1.15
(241.79)
1.18
(248.13)
1.22
(254.17)
1.25
(261.55)
1.29
(268.43)
9.76
0.88
649,999
122,435
-0.67
0.37
(84.79)
0.39
(88.20)
0.41
(92.19)
0.42
(95.66)
0.44
(99.48)
0.45
(102.56)
0.47
(106.37)
0.49
(111.45)
0.52
(116.40)
10.26
0.17
Appendix Table A3: Shifts in Age Earnings Profiles (for Figure 5)
Age
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
AgeD
0.03
(1.78)
0.05
(3.83)
0.09
(6.61)
0.12
(8.79)
0.14
(10.51)
0.17
(12.56)
0.18
(13.54)
0.20
(14.83)
0.22
(16.31)
0.23
(17.17)
0.24
(17.79)
0.25
(18.83)
0.27
(20.43)
0.29
(22.21)
0.32
(24.14)
0.35
(26.45)
0.38
(28.99)
Teachers
AgeD*Trend
0.00
(1.12)
0.00
(1.45)
0.01
(2.09)
0.01
(2.68)
0.01
(3.36)
0.01
(3.61)
0.01
(4.55)
0.01
(4.99)
0.01
(5.22)
0.01
(5.88)
0.01
(6.51)
0.01
(6.65)
6.65
(6.14)
0.01
(5.23)
0.01
(4.34)
0.01
(3.06)
0.00
(1.71)
Age
40
cons
R2
NT
0.23
1,749,878
41
42
43
44
45
46
47
48
49
50
Trend
41
Teachers
AgeD*Trend
0.00
(0.64)
0.00
-(1.10)
-0.01
-(2.65)
-0.01
-(3.35)
-0.01
-(3.37)
-0.009
-(4.27)
-0.009
-(4.34)
-0.008
-(3.90)
-0.004
-(2.94)
-0.004
-(1.65)
-0.003
-(1.55)
AgeD
0.41
(31.50)
0.45
(34.90)
0.50
(37.92)
0.52
(40.25)
0.54
(42.02)
0.58
(44.69)
0.6
(46.42)
0.62
(47.61)
0.62
(48.10)
0.62
(48.11)
0.64
(49.27)
0.008
(5.02)
10.22
Appendix Table 3a: Age Earnings Profiles for Leavers (Pre and Post) and Stayers
OLS
Age
Pre-Leave
10.38
10.4394306
10.4486324
10.4569959
10.4578956
10.4785894
10.4837154
10.49571
10.5053604
10.5068612
10.5191545
10.5433652
10.5634443
10.5794796
10.5940586
10.6070146
10.6251845
10.6308881
10.6323305
10.5979566
Always Teachers
29
10.46
30
10.5596178
31
10.5828384
32
10.6008531
33
10.6187747
34
10.6338445
35
10.6482017
36
10.6612072
37
10.6752858
38
10.6894303
39
10.7056055
40
10.7215608
41
10.7391032
42
10.7575455
43
10.7759345
44
10.7951725
45
10.812766
46
10.8297859
47
10.84385
48
10.8566047
49
10.8682992
50
10.8883277
Regressions include age and state dummies.
Fixed Effect
PostLeave
9.68
9.8079566
9.8191523
9.8114592
9.8043682
9.8272173
9.806554
9.7463151
9.7393795
9.7101704
9.6975798
9.7354441
9.6432778
9.6594254
9.6908348
9.6709354
9.6188126
9.6373112
9.6143838
9.6002846
9.6300995
9.6305652
42
Pre-Leave
10.37838
10.4239924
10.4406
10.4576045
10.4712803
10.4904483
10.4992685
10.5090135
10.5203972
10.5301307
10.5385907
10.5546096
10.5618461
10.5729185
10.5809696
10.5885832
10.5938806
10.5939378
10.5877578
10.5611644
Always Teachers
10.29108
10.4089081
10.4503449
10.4872465
10.5254865
10.5594798
10.5939122
10.625246
10.6574417
10.6874661
10.7169881
10.7441951
10.7710941
10.7968341
10.8215489
10.8449961
10.8670486
10.8888993
10.9089478
10.9280318
10.9475045
10.9681172
Post-Leave
9.100986
9.3321296
9.3857908
9.4973442
9.5577929
9.65897
9.7205213
9.7373135
9.7805532
9.8162208
9.8553936
9.9624878
9.9931438
10.0590687
10.105786
10.131622
10.149444
10.177534
10.205419
10.231192
10.295569
10.291559
Appendix Table A4: Earnings Response to Demand Shocks (Fixed Effects Results)
Wage and Salary
Employment
Age
Age2
Union
0.0656239
(200.20)
0.0005249
-(143.56)
Personal Income
Non-union
0.0909133
(192.61)
Union
0.068161
(219.16)
Non-union
0.0897903
(184.54)
-0.0006113
-(112.00)
-0.00055
-(157.52)
-0.0006091
-(108.09)
0.0150163
(1.99)
-0.0430391
-(7.22)
0.043048
(5.23)
0.0278254
(3.75)
-0.0606676
-(10.04)
0.0290106
(3.79)
0.0489078
(6.41)
-0.0004057
-(1.11)
-0.0008276
-(2.30)
-0.0017989
-(4.88)
-0.0484043
-(7.04)
0.0021213
(7.50)
0.0028508
(9.92)
0.0017101
(5.22)
0.0223515
(3.11)
-0.0030331
-(7.55)
-0.0020193
-(5.42)
-0.0014257
-(4.11)
0.00000486
(1.13)
-0.0000232
-(7.06)
0.0000381
(8.03)
0.000012
(2.84)
-0.0000278
-(8.32)
0.0000266
(6.05)
0.0000216
(5.00)
7.901164
(783.58)
-0.0000126
-(3.29)
8.859302
(1299.95)
0.0000217
(5.32)
7.956833
(768.33)
0.9014
0.8821
0.9014
0.8822
1,638,053
899,923
1,638,053
899,923
Q1: Shock Quantile (omitted)
Q2: Shock Quantile
Q3: Shock Quantile
Q4: Shock Quantile
Q2 Shock * Age
Q3 Shock * Age
Q4 Shock * Age
Q2 Shock * Age2
Q3 Shock * Age2
Q4 Shock * Age2
Constant
R2
NT
0.1092603
-(17.77)
0.0922004
-(14.42)
0.0628521
-(8.54)
0.0047362
(16.16)
0.0040232
(13.22)
0.0019899
(5.68)
0.0000499
-(14.64)
0.0000403
-(11.41)
0.0000141
-(3.45)
8.924096
(1231.47)
N
215,034
157,505
215,034
157,505
Columns (1) and (2) are ln(earnings) regressions introducing demand shocks from countylevel annual personal income. Columns (3) and (4) introduce demand shocks from countylevel wage and salary employment numbers. The Q1 through Q4 variables are formed by
categorizing the county-level annual percent changes in the shock variable over all years and
all counties.
43