How will baby boomer retirements affect teacher labor markets?
Aaronson, Daniel ; Meckel, Katherine
Introduction and summary
Teachers play a vital role in their students' educational
performance. In addition, there is a correlation between a
teacher's experience and her effectiveness in the classroom--at
least in the first few years of her career. These intuitive outcomes are
supported by a large body of research literature. (1) With this in mind,
it is reasonable to view rising rates of teacher turnover (since the
early 1990s) as a cause for concern. Further, we expect that
retirements, which have driven some of this increase, will accelerate to
record levels in the coming decade as growing numbers of baby boomers
reach retirement age. (2) This pattern will inevitably necessitate a
significant increase in the demand for new teachers. Some
communities--for example, poor urban districts, which tend to have
especially high teacher turnover rates and severe recruitment problems
(3)--might be particularly susceptible to declining teacher quality as a
result of increased retirements.
In this article, we use a simple model of teacher demand and supply
in order to gauge the implications of baby boomer retirements on the
projected demand for new teachers. Our forecast links estimates of
demand for all teachers with the expected supply of returning teachers
through 2020 (that is, the 2020-21 school year). We assume any shortfall
would have to be addressed by hiring additional teachers. We discuss how
projected demand for new teachers compares with the past half century
and what types of schools are likely to have to augment their teacher
hiring over the coming decade. We also calculate bow much teacher
salaries would have to increase in order to fill the gap between teacher
supply and demand. To compute the supply and demand of the teacher
market, we use a variety of data sets and sources--for example, the U.S.
Census Bureau's Decennial Census and Current Population Survey
(CPS) and various publications of the U.S. Department of
Education's National Center for Education Statistics (NCES),
including its 2003-04 Schools and Staffing Survey (SASS) and the
accompanying 2004-05 Teacher Follow-up Survey (TFS).
We estimate the number of new full-time public school teachers (4)
needed from 2009 through 2020 will be between 2.3 million and 4.5
million, with the range encompassing reasonable assumptions about
fertility rates, student-teacher ratios, and turnover propensity. Our
preferred calculations--based partly on the latest teacher data
available from the 2003-04 school year (and therefore not accounting for
the economic downturn that began in late 2007)--predict roughly 277,000
new full-time public school teachers needed in 2009--10, rising to
303,000 new teachers by 2020-21, or 3.5 million for all school years
between 2009-10 and 2020-21. Retirements account for about one-third of
the teachers who leave the teaching work force over this period. Adding
the private school sector to these calculations raises the number of new
teachers needed by about 20 percent, to 4.2 million, but lowers the
fraction due to retirements by roughly 3 percentage points.
These numbers, in isolation, are difficult to assess without some
historical context. Therefore, we provide rough estimates of projected
demand for new teachers over the past six decades using U.S. Decennial
Censuses, combined with analogous hiring projections for the years 2010
and 2020. We find that more teachers will retire between 2010 and 2020
than in any other decade since the end of World War II. But because of
relatively slower projected growth in the school-age population, the
total number of new teachers needed for all reasons (including
retirements) is within historical norms. Indeed, normalized by the size
of the aggregate labor force (one rough measure of the potential teacher
work force), demand for new teachers will be similar in magnitude in the
coming decade to that in past decades. Therefore, we would not expect
the increase in forthcoming retirements, in the aggregate, to have a
significant impact on national levels of teacher hiring much beyond the
variation in teacher hiring needed in the past.
However, it is still possible that certain areas will be especially
hard hit by teacher retirements. Therefore, we explore how demand for
new teachers is likely to vary based on a school's regional
location, urban or rural status, share of free or reduced price school
lunch recipients, and racial composition. We find that the need for new
teachers is likely to be notably elevated in schools with a high
fraction of minority or low-income students. However, this is not driven
by an abnormal number of upcoming retirements in these schools, but
rather by a combination of elevated teacher turnover rates and expected
student population growth. For example, schools with minority
representation in the top quartile of the distribution are expected to
require 65 percent more new teachers than schools with average minority
representation, but only 4 percent of this difference is due to
retirements.
It is important to emphasize that our estimates are based on a
mechanical model of teacher labor markets that assumes some key factors
related to the propensity to enter and exit the teaching
profession--such as compensation, pension packages, certification
requirements, and tenure decisions--will look like they have in the
recent past. (5) Difficulty in hiring or retaining teachers could lead
local communities to change policies in a way that influences the supply
of available teachers. However, many communities, especially those that
face the most significant change in hiring over the coming decade, could
find making the necessary policy changes challenging. To quantify this
difficulty, we provide a very simple calculation of how compensation
would have to change in order to offset elevated hiring requirements and
keep teacher quality relatively stable; this exercise assumes that
salary adjustment is the sole tool schools use to satisfy their growing
demand for teachers. We find that real salaries would have to rise by an
additional 10 percent beyond historical averages between 2009 and 2020.
Pay would have to be particularly bolstered in heavily poor and minority
schools in order to offset their expected demand for new teachers over
the coming decade.
This article is organized as follows. In the next section, we
explain our algorithm for projecting the demand for new teachers in the
coming decade. Then we describe the results and provide some historical
context by comparing our projections with similar estimates from the
past half century. We also explore how our estimates differ by various
school characteristics. Next, we ask how compensation policy might have
to be adjusted, given estimates of the labor supply elasticity of
teachers, to account for any additional hiring requirements in the
future. We acknowledge that our data do not cover the period since the
current economic downturn began: therefore, we briefly explore some
channels in which the current recession might affect short- and long-run
demand for new teachers.
A mechanical model of demand for new teachers
In this section, we describe the algorithm we use to forecast
demand for new teachers. (6) To provide further intuition for our
methodology, we also present a very simple numerical example in the
accompanying box that, for expository reasons, strips the model to its
bare minimum. Similar models are presented in Hussar (1999).
Demand for teachers
We estimate future demand for teachers by projecting student
enrollment through 2020 (that is, the 2020-21 school year). Student
enrollment is forecasted based on projections of the five-year-old
population, estimates of the propensity to attend public school
kindergarten, and estimates of grade progression rates. We then apply a
student-teacher ratio to get the total number of teachers needed to fill
classrooms to accommodate these students.
We begin with a baseline of the most recent count of students,
broken down by grade, compiled by the National Center for Education
Statistics for the 2003-04 school year. Each of these students is
assumed to advance through the public school system based on estimated
grade-specific progression rates calculated by the NCES for the
1999-2000 school year through the 2002-03 school year and displayed in
table 1. (7)
New cohorts are added each year to kindergarten based on U.S.
Census projections of five year olds corresponding to that school year
(8) and the average fraction of five year olds that have attended public
school kindergarten in the recent past. Since the mid- 1980s, the share
of five year olds attending public school kindergarten has varied a bit
over time, but not in a way that suggests a trend. (9) Therefore, we
project forward using 0.878, which is the average share of five year
olds who attend public school between 1999 and 2003, the last four years
for which data are available.
To get a final count of classrooms, we apply student--teacher
ratios to our student totals based partly on forecasts from table 33 of
Hussar and Bailey (2007), which include high, middle, and low scenarios.
In the middle scenario, student--teacher ratios in public schools
decline by roughly 1.2 students per teacher between 2004 and 2016.
However, because of concern that these trend projections overweight the
substantial decline in student-teacher ratios seen in the 1990s, our
baseline assumption uses the average of the high scenario and a fiat
student-teacher ratio. Empirically, this is roughly equivalent to using
the average decline between the school years 1999-2000 and 2003-04, the
most recent years of data available. (10)
BOX 1
A very simple numerical illustration
Assume that there are five types of teachers in time t (that is,
[T.sub.1t], ..., [T.sub.5t]) distinguished solely by their age. Age
determines turnover propensity. For example, the youngest group,
[T.sub.1t], exits with probability [p.sub.1] = 0.2 in every year;
groups [T.sub.2t], [T.sub.3t], and [T.sub.4t] exit with probability
[p.sub.2] = [p.sub.3] = [p.sub.4] = 0.1; and the oldest group
[T.sub.5t] leaves with probability [p.sub.5] = 0.5. We simplify the
example by assuming that all teachers work full time and are of the
same gender and that experience, tenure, and age are perfectly
collinear; but in the article, probabilities for various
transitions, including exits, are estimated by age, experience,
tenure, and gender (see table 2, p. 7). Further, assume there are
1,000 teachers in the initial year (t = 1), split evenly across the
age groups. At the end of that initial year, total turnover
[Exit.sub..1] = [5.summation over (i=1)] [T.sub.i1] x [p.sub.i] =
200 x 0.2 + 200 x 3 x 0.1 + 200 x 0.5 = 200, where i = 1 to 5 is
the age group. The overall turnover rate is 200/1000 = 0.2.
Further, assume that the demand for teachers grows by 1 percent per
year because of changes in student--teacher ratios and growth in
the new kindergarten cohorts. We abstract from issues related to
grade progression rates for simplicity. Prior to the second year,
the teacher work force must therefore expand by
[Expand.sub..1]=[summation of][T.sub.i1] x 0.01 = 1000 x 0.01 = 10
teachers.
Together, this implies that Demand for New [Teachers.sub..1] =
[Exit.sub..1] + [Expand.sub..1] = 210, the number of teachers that
must be hired prior to the second year.
These 210 new teachers are assumed to have certain characteristics
estimated from earlier cohorts of new teachers. To keep the example
simple, assume that half the new teachers fall in age group 1
([z.sub..1]= 0.5) and the other half in age group 2 ([z.sub.2] =
0.5). Therefore, we add 105 teachers to [T.sub.12] and [T.sub.22].
Note, that in our simulations, all returning teachers are made
older by a year between the first and second years. We abstract
from that in this example, but it would imply that teachers would
move between age types each year.
In the second year, the new distribution of teachers for group i
is: [T.sub.i2]=[T.sub.i1] x (1 - [p.sub.i]) + [Exit.sub..1] x
[z.sub.i.] + [Expand.sub..1] x 0.01 x [z.sub.i.]. The first term is
the number of returning teachers of type i, the second term is the
number of new teachers of type i replacing any who exit, and the
third term is the number of new teachers of type i hired because of
expansions in the teacher work force. So, for example, the number
of young teachers in the second year is [T.sub.12]= 200 x (1 - 0.2)
+ 200 x 0.5 + 1000 x 0.01 x 0.5 = 265. Once we have a new
distribution of teacher types, we again apply turnover
propensities, add in growth to demand, and then compute the number
of new teachers needed prior to the third year. This algorithm
continues through the forecast horizon.
Figure 1 summarizes the projected demand for public school
teachers. The solid line provides our best guess estimate, with the
shaded range allowing for plausible deviations for the student-teacher
ratio and the population growth rate of five year olds, as explained
previously.
Supply of teachers
To project the supply of teachers, we begin again with the latest
detailed accounting of market size--this time taken from the 2003-04
SASS. That survey tells us there were just over 3 million full-time
public school teachers. (11) From the 2004-05 TFS, we can compute that
7.8 percent left public school teaching the following year. An
additional 89.6 percent continued as full-time teachers and 2.6 percent
as part-time teachers. Note that the latter two rates encompass teachers
who switch their work time commitment (full-time versus part-time
positions), as well as those who keep the same work time commitment as
the previous year.
But all of these rates differ substantially by age, gender,
experience, and tenure in the current job. (12) Examples of this
heterogeneity are displayed in figures 2's panels A and B, which
show how rates of exiting the teaching profession and staying as
full-time or part-time teachers differ by age and gender. For example,
not surprisingly, exit rates rise monotonically after age 50, and
part-time status appears to be higher among women throughout most of the
age distribution.
To compute future changes in teacher hours, we simulate the
fraction of hours that are likely to return each year by using a simple
ordered probit model that allows for three possible transitions--exit,
full-time to part-time, and part-time to full-time--and accounts for
differences by age, gender, experience, and tenure.
[FIGURE 1 OMITTED]
Table 2 reports the results of our baseline regression. We use the
coefficient estimates from this regression to assign an
end-of-school-year outcome for all individuals in the 2003-04 cohort
based on their personal characteristics. This computation provides us
with a forecast of the number of returning teacher hours in 2004-05. We
then add a year to each returning teacher's age, experience, and
tenure. We continue to project this cohort through the forecast horizon
(2020-21), using the same procedures (appropriately adding a year to
each returning teacher's age, experience, and tenure).
Three points about the simulations thus far are worth noting.
First, it is well known that exits are especially high in the first few
years of teaching. That pattern is clearly evident in figure 2, panel A,
which displays the hump in exits for women in their late twenties and
early thirties. A similar pattern exists when exits are plotted against
tenure or experience. We include a dummy for the first five years of
experience to account for this nonlinearity.
Second, because of data limitations, tenure is measured as
consecutive years as a public school teacher, whereas experience is
measured as the total number of years as a public school teacher.
Unsurprisingly, our results are nearly identical if we exclude the
tenure measure.
Third, we do not focus solely on exits because transitions between
part-time and full-time teaching positions clearly affect changes in the
total number of teacher hours. Specifically, figure 3 (p. 8) shows that
part of the growth in teacher hours between 2003-04 and 2004-05 arises
from a larger fraction of teachers switching from part-time to full-time
positions than vice versa. The exit rate alone is, on average, 7.8
percent; however, accounting for changes in hours from switches between
part-time and full-time positions effectively lowers the overall hours
turnover rate to 6.5 percent. (13)
To this point, we have described how we project staffing levels due
to the work choices of the existing cohort of 2003-04 teachers. But,
each year, demand for classrooms exceeds the number of returning
teachers; therefore, new instructors must be added to account for those
hours. In the simulation, this deficit is filled by adding the
appropriate number of "missing" hours to the model each year,
while assigning them the age, gender, experience, tenure, and
part-time/full-time status that replicates the distribution of
characteristics of new teachers in the most recent SASS. (14) We then
update all of the returning teacher characteristics by making them older
an additional year (and giving them an additional year of total work
experience and tenure) and rerun the simulations to the following year,
using the transition probabilities inferred from table 2. We continue
this algorithm through the 2020-21 school year, continuously replacing
missing teacher hours with representative entrants and updating the
tenure, total experience, and part-time/full-time status of those
remaining. This methodology assumes that schools will continue to hire
teachers from the same demographic (that is, gender, age, experience,
and tenure) background as they have in the recent past and that the
part-time and full-time fractions by age and gender stay constant. (15)
[FIGURE 2 OMITTED]
We can ascertain the importance of teacher retirements in two ways.
First, the follow-up survey asks the reason why teachers exit the
profession. Retirement is listed as the reason for roughly 32 percent of
all exits at the end of the 2003-04 school year, including 70 percent or
higher among exits of teachers aged 55 and older. However, our
prediction methods cannot distinguish between reasons for exiting the
teaching profession. Therefore, we compute the probability a future exit
is due to retirement based on the actual gender and age exit rates
displayed in panels A and B of figure 2 and their correlation with the
reason for exit in the 2003-04 SASS. Exits rise by 1.2 percentage
points, on average, per year for ages 50-60, with overall turnover rates
hitting close to 30 percent shortly after age 60. Note that turnover is
also high among young and inexperienced teachers, especially women, who
represent the bulk of the new teachers. Again, because we tend to
exchange exiting teachers with these high-turnover replacements,
retirements further amplify demand for new teachers by temporarily
introducing high-turnover employees into the system.
Demand for new teachers
Lastly, for each year, we compare returning teacher supply (that
is, how many teachers are left from the 2003-04 cohort and each
subsequent cohort of new teachers) with demand. The additional teachers
needed to fill the gap between supply and demand are what we call the
demand for new teachers.
Basic estimates of demand for new teachers
Figure 4 provides several estimates of demand for new teachers
through 2020. First, concentrate on the solid line, which is our
preferred estimate of future demand for new teachers. In this scenario,
just under 280,000 teachers are added in the 2009-10 school year, or
about 9 percent of the projected 3.2 million teacher work force. Over
the coming decade, the total number of new teachers needed to fill
growth in demand, as well as replace exiting teachers, grows by just
over 2,000 per year, hitting 303,000 by 2020. From 2009 through 2020,
roughly 3.5 million net teachers need to be added.
[FIGURE 3 OMITTED]
The shaded region provides alternative estimates, with the outer
ranges suggesting plausible upper and lower bounds of hiring required
when we adjust three key factors: the U.S. Census's assumed
fertility rate, the estimated teacher turnover rate, and the estimated
student-teacher ratio. The fertility rate is allowed to vary by plus or
minus 1 percentage point from our baseline, with this range determined
by U.S. Census's high and low population projections. (16) The
teacher exit rate is also allowed to vary by plus or minus 1 percentage
point from our estimated average 7.8 percent baseline rate. (17) This
encompasses several alternative estimates, including the 2004-05 TFS
turnover rate that weights full-time and part-time teachers equally (8.4
percent) and turnover rates from the Current Population Survey's
2003-04 outgoing rotation file for full-time teachers (8.4 percent) and
full-time college-educated teachers with annual incomes between $10,000
and $150,000 (6.9 percent). Finally, the bounds on the student teacher
ratio are allowed to range between the NCES's high assumption
projection and a constant ratio based on values from the 2003-04 SASS.
The edges of the shaded region use all three assumptions that result in
the highest or lowest projection of the demand for new teachers. Taken
together, these adjustments broaden the range of plausible new teacher
demand to between 2.3 million and 4.5 million from 2009 through 2020.
Approximately 42 percent of this range is due to changes in the assumed
birth rate, 33 percent to changes in the assumed turnover rate, and 25
percent to changes in the assumed student-teacher ratio.
The dashed line shows the number of new teachers arising from
retirements. We find that roughly 30 percent to 35 percent of demand for
new teachers between 2009 and 2020 is due to openings created by
retirements. Retirements rise from about 82,000 in 2003-04 to just under
96,000 in 2009-10 and average around 96,000 per year over the next
decade.
Including private schools
Thus far, we have only included public school teachers. We can
compute simple projections for private school new teachers by applying
the overall turnover rate of 10.7 percent among private school teachers
in the SASS to current staffing levels and NCES projections of the
demand for private school classrooms through 2015. (18) In our baseline
scenario, private school demand for new teachers rises from roughly
55,000 in 2009 to almost 62,000 in 2015. Projecting this trend forward
to 2020 would imply about 725,000 new private school teachers between
2009 and 2020--about a fifth of the public school net demand for new
teachers over the same time period.
The ratio of private school students to public school students is
about 13 percent, significantly less than the ratio of projected private
school to public school new teacher demand. That is mostly explained by
a higher overall teacher turnover rate in the private sector (roughly 3
percentage points higher). One consequence of these sector-specific
dynamics is that under 10 percent of net private school hiring through
2020 is driven by retirements, suggesting that the retirements of baby
boomers will have significantly less impact in private schools over this
period. If we aggregate the public school and private school sectors, 29
percent of net teacher hiring is due to retirements (in the SASS)--which
is less than the 32 percent of net teacher hiring due to retirements
among public schools alone.
[FIGURE 4 OMITTED]
Are these projections historically high?
Of course, there are always retirements. The key question is how
unusual hiring might be given the baby boomer retirements. We provide
some historical context by comparing U.S. Census-based estimates of
future changes in new full-time public school teacher demand with past
changes. (19)
Because of data limitations, we provide very rough approximations
of changes in demand for new full-time public school teachers during a
decade by adding growth in the full-time teacher labor force to the
number of teachers who are of retirement age. The idea behind this
calculation, which clearly understates year-to-year hiring, is that it
consistently measures all well-observed new full-time public school
teachers that 1) fill newly created positions and 2) replace retirees.
The number of retirees is conservatively estimated as those who are at
least age 55 at the beginning of the decade (and thus retire by age 65).
The demand for new full-time public school teachers is plotted in figure
5. We make comparable projections for 2010 and 2020, which vary from our
more detailed projections reported previously but are consistent with
the historical data. (20)
The red line again shows the rise in new full-time public school
teachers needed in the coming decade.
But it also shows that the 1970s was a time when hiring was brisk.
The reasons, of course, differ. In the 1970s, 72 percent of our new
teacher demand measure was necessitated by growing populations of
school-age children. By contrast, between 2010 and 2020, we expect that
only around 31 percent of this measure of the demand for new teachers
will be due to student population growth. The remainder will be due to
teacher retirements.
We recognize that comparing absolute numbers is misleading because
the size of the aggregate population, and consequently the potential and
actual teacher work pool, has grown over time. Therefore, the black line
normalizes our new teacher numbers by the population aged 25-54. Here,
we find that this ratio is not unusually high right now, nor do we
expect it to become unusually high in the near term. Demand for new
teachers as a percent of the labor force aged 25-54 is expected to
average 0.91 percent between 2010 and 2020--just above the 0.83 percent
average between 1960 and 2000 (and below the 0.96 and 1.20 percent
levels reached in 1970 and 1980). This suggests modest concerns about
filling teacher vacancies in the aggregate.
Demand for new teachers by school demographics
Obviously, not all schools face the same future hiring
requirements; the effect of the baby boomer retirements could put
particular strain on some more than others. We explore this issue by
looking at the key parameters in our forecasts when schools are
stratified by region, (21) urban or rural status, share of students
receiving free or reduced price lunch, and share of students who are
minorities. For each of these categories, table 3 reports the share of
teachers over age 50 and 55 (first and second columns of data), the
hours turnover rate for new and experienced teachers (third, fourth, and
fifth columns), the student-teacher ratio (sixth column), and the growth
rate of the student population (seventh column). (22) The eighth column
provides results from simulations of new teacher demand, using the same
methodology as in figure 4, but assuming that the entire teacher labor
market takes on parameters of a subpopulation (as described in the
leftmost column). Those numbers are reported relative to the baseline
forecasts of the nationally representative population.
[FIGURE 5 OMITTED]
On average, schools that should expect to see unusually high demand
for new teachers are in the West and South; they are located in large
cities and small towns; and they educate high shares of minority and
low-income students. The particular explanations vary somewhat by school
characteristic. However, retirements do not seem to be driving any of
the results in an economically significant way. For example, we
stratified schools into quartiles based on the fraction of minority
students (the bottom two quartiles are aggregated for simplicity). (23)
While the top quartile has a higher fraction of teachers aged over 55,
there is no statistical difference in the share of teachers aged over 50
across the racial minority representation quartiles. If we leave all
parameters at the top quartile's level but switch the age
distribution of the teachers so that it matches the schools at the
bottom half of the minority representation distribution, overall new
teacher demand increases by only 4 percent. Consequently, there is
little evidence that baby boomer retirements will affect schools with a
high proportion of minority students any more than other schools.
Additional hiring demands in schools that have student populations
with high minority representation or those with many low-income members
(who receive free or reduced price lunches) are driven almost entirely
by higher turnover propensity and expected student population growth.
Putting all these pieces together, we would predict that if all schools
had the characteristics of schools with a high proportion of minority
students, the demand for new teachers would be 65 percent higher than
the baseline forecasts over the forecast horizon. Over 60 percent of
this gap is explained by differences in turnover rates across the
age/experience distribution, and just under half by differences in
expected student population growth rates. Similar issues arise for
schools with high fractions of free or reduced price lunch program
participants or for those in urban areas, many of which are also schools
with a high fraction of minority students. (24)
What can policy do? The case of teacher compensation
Finally, we ask how policy can respond if community demand for new
teachers increases beyond historical norms. Obviously, there are many
factors that affect teacher labor supply--a short list of which would
include salaries, pension systems, classroom and school conditions, and
certification requirements and other barriers to entry. We concentrate
on teacher financial compensation because of its relevance to policy
discussions and because of the attention that has been paid to its
estimation in the literature.
That attention in the literature certainly does not imply a
consensus. A number of recent papers have established a link between
teacher salary, outside work alternatives, and turnover (for example,
Dolton and van der Klaauw 1995, 1999; Murnane and Olsen 1989, 1990;
Stinebrickner, 1998; and Harris and Adams, 2007). But others (for
example, Scafidi, Sjoquist, and Stinebrickner, 2006; Hanushek, Kain, and
Rivkin, 2004; Clotfelter et al., 2008; and Ondrich, Pas, and Yinger,
2008) cast doubt on these findings. We concentrate on the larger
estimates of the impact of salary on turnover in the literature and
therefore consider our results to be a lower bound estimate of the
effect of raising teacher salaries on future demand for new teachers.
We mechanically introduce the impact of an across-the-board salary
adjustment to our transition probabilities by adjusting potential exit
rates using salary-turnover elasticities from various studies. (25) For
our original cohort of 2003 public school teachers, we use the
"overall" (that is, representative of the entire public school
teacher labor force) elasticity estimate from Harris and Adams (2007).
For the cohorts of new teachers introduced into our model, we use the
"new teacher" salary-turnover elasticities (calculated for
teachers during their first five years of teaching) reported in Dolton
and van der Klaauw (1995), Stinebrickner (1998), and Harris and Adams
(2007). (26) For those new teachers that survive past their fifth year,
we switch them to the "overall" exit elasticity once they
complete that fifth year. We continue to assume that the fraction
transitioning to part-time or full-time teaching positions remains the
same; that is, these transitions are unaffected by new salary levels. We
also assume that these salary effects do not differ across school types,
as described in Hanushek, Kain, and Rivkin (2004).
In the aggregate, we calculate that annual wage growth about 0.8
percentage points beyond average pay growth would offset much of the
additional net new demand for teachers over the coming decade, relative
to the early 1990s. Specifically, the ratio of teacher hiring to the
size of the general labor force between 1988 and 1995 was about 0.00144.
We project that this ratio will fluctuate between 0.00178 and 0.00184
during the 2010s. To get the ratio back to 0.00144 by 2020 would require
roughly an additional cumulative wage growth of 10 percent between 2009
and 2020. By comparison, cumulative real weekly wage growth of teachers
in the Current Population Survey was 9 percent between 1989 and 2004,
suggesting that the pay hike needed to reach this fairly ambitious
target is relatively large.
Teacher pay would have to be especially bolstered in schools with
high proportions of poor and minority students in order to offset their
expected teaching needs over the coming decade. For example, if a goal
was to reduce the demand for new teachers in schools with a high
fraction of minority students from 65 percent to 30 percent above
baseline national needs by 2020 (thereby offsetting the turnover and
retirement rate differences in these schools), average real pay for
teachers would have to rise by well over 25 percent.
How does the recent recession impact our estimates?
The data underlying our projections are not available for the
current downturn; this is unfortunate, since our predictions could be
affected by a significant decline in economic activity. Economic theory
predicts at least three ways in which demand for new teachers might be
altered by a recession: through changes to the fertility rate,
immigration, and teacher attrition (including retirements). (27)
Both fertility rates and net migration flows are commonly observed
to fall during recessions, and early indications are that both measures
have fallen during the current downturn. (28) Lower migration will
reduce demand for teachers now, and lower fertility will reduce demand
five years hence. Moreover. children of immigrant parents tend to be
disproportionately from low-income families and clustered in a few large
urban areas. (29) Therefore, it is possible that lower net migration
will help to relieve some constraints in schools where the demand for
new teachers is projected to be relatively high.
Lower teacher attrition, and consequently lower replacement hiring,
is also possible as household wealth declines and alternative labor
market opportunities evaporate. Because of these factors, we would
speculate that some additional weight should be placed on our lower
bound projections in figure 4 (p. 9) for the next couple of years.
Beyond that, we think that consensus economic forecasts (30) imply that
these cyclical effects will fade away.
Conclusion
In this article, we provide a simple model of teacher demand and
supply in order to gauge the implications of baby boomer retirements on
demand for new teachers over the coming decade. We find that the demand
for new teachers will rise over the coming decade--and a good portion of
this will be due to retirements. That said, we do not expect that this
increase in teacher demand will be significantly different from that of
past decades, especially relative to the size of the aggregate labor
force. However, the added hiring requirements are likely to play out
longer than they have in the past, and they will not be equally
dispersed across the nation. Moreover, simply raising pay, unless
substantially unanchored from past trends, is unlikely to keep teacher
quality constant, especially at schools that have traditionally had the
most difficulty recruiting and retaining teachers.
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NOTES
(1) See, for example, Murnane (1975); Rockoff (2004); Rivkin,
Hanushek, and Kain (2005); and Aaronson, Barrow, and Sander (2007).
Unsurprisingly, experience is correlated with productivity across a
variety of professions Recent estimates (for example, Aaronson and
Sullivan, 2001) suggest that the loss of human capital from the baby
boom generation, primarily through lost experience, will be enough to
lower the potential rate that the economy can grow during the 2000s by
one-tenth to two-tenths of a percentage point per year from its 1990s
levels.
(2) Over the past two decades, the share of teachers that have left
the profession has been increasing, from roughly 5 percent of teachers
in the early 1990s to over 8 percent a decade later. For an example of
popular press concern about teacher turnover, see Dillon (2007). See
Gordon, Kane, and Staiger (2006) for a discussion of the impact of baby
boomer retirements on the teaching profession.
(3) Hanushek, Kain, and Rivkin (2004); and Jacob (2007).
(4) We define a full-time public school teacher as one who works 35
hours per week. Teachers who work fewer hours are counted as fractions
of full-time teachers. Unless otherwise indicated, this is how we count
teacher demand and supply m our calculations.
(5) Our model also assumes that variability in the business cycle
looks similar to that of the past. Given the current deep recession,
there may be some concern that this assumption is inaccurate.
Unfortunately, key data from recent years are unavailable. Later in the
article, we briefly discuss how our estimates might change as a result
of the current recession.
(6) Detailed calculations and data are available from the authors
upon request.
(7) We thank William Hussar at the NCES for providing us with
student counts, by type of school and grade, for the years 1970-2003. We
used these data to calculate the progression rates. A number above 1
implies a net influx of students coming into public schools in that
particular grade either from private schools or schools without grade
levels or home schooling; or it implies an reflux of children entering
the U.S. school system for the first time (recent immigrants) This is
particularly notice able in the transition between eighth and ninth
grades and kindergarten and first grade In order to put heavy weight on
more recent history, we used the average from 1999-2003 only Our results
do not change appreciably if we take into account the longer time
series.
(8) These are available at www.census.gov/ipc/www/usinterimproj/.
These projections are an interim revision of more detailed forecasts
released m 2000; the forecasts have been updated to take into account
the 2000 U.S. Census. In the sensitivity, analysis to follow, we use the
high and low series from the original projections released in 2000. We
have also tried using the five- and six-year-old population, but this
resulted in kindergarten projections that were less accurate when
compared with similar NCES student projections.
(9) We calculate the share of five year olds attending public
school over time, using population estimates by age from the U.S. Census
and the student counts by grade from the NCES. Some of the changes in
the share of five year olds attending public school may line up with the
business cycle. We found some very mild, but not particularly robust,
evidence of procyclicality. But that appears to be driven primarily by a
correlated drop in both gross domestic product (GDP) growth and public
school attendance of five year olds in the early 1990s.
(10) The average annual drop in the public school student-teacher
ratio for the school years 1991-92 through 1998-99 was -0.15, according
to Hussar and Bailey (2007), whereas the average annual drop for the
school years 1999-2000 through 2003-04 was -0.06. The average decline
predicted by the NCES's high assumption for the school years
2004-05 through 2015-16 is -0.10. Mechanically, we compute the
student-teacher ratio in our SASS data for 2003 and then apply the
year-to-year differences in the average of the NCES's high
assumption and a flat student--teacher ratio. Because the NCES ratio is
projected out to 2016, we use the rate of growth since 2004 to project
ratios further into the future.
(11) This count excludes pre-kindergarten teachers and short-term
substitutes We find that the count of full-time and part-time teachers
in the 2003-04 SASS is consistent with similar counts in the U.S. Census
Bureau's 2004 American Community Survey (ACS) and NCES Common Core
of Data (CCD), To make the ACS sample comparable, it is necessary to
exclude teachers categorized as "other," which includes
short-term substitutes and instructors working outside of public
elementary and secondary education, and to restrict the sample based on
education and salary. Doing so provides a sample that is similar not
only in count to the SASS but also in the distribution of education,
age, and earnings.
(12) Obviously, there are other teacher characteristics that can
affect turnover. To take one important example, Podgursky, Monroe, and
Watson (2004) and Corcoran, Evans, and Schwab (2004) estimate the impact
of teacher ability on teacher retention. Later, we discuss how the
fraction of low-income students, racial composition, urban or rural
status, and geographical location of a school affect teacher turnover
propensities.
(13) In order to count the number of teacher hours lost in this
transition, it is necessary to quantify the "amount of
teacher" added or subtracted when a teacher switches from part-time
to full-time status or vice versa. The average part-time teacher in the
sample works roughly 60 percent of a full-time teacher's hours
(which again is defined as 35 hours per week). Therefore, a teacher who
switches from full-time to part-time status is counted as 0.6 of her
original weight, and a teacher who switches from part-time to full-time
status is counted as 1.67, or 1/0.6, of her original weight.
(14) In particular, we use the demographic distribution of teachers
who were not teaching in public schools in the prior year. On average.
these teachers are eight years younger (and eight years less
experienced) than returning public school teachers. Gender composition
is nearly identical between the two groups of teachers.
(15) Since the labor force--both teacher and overall labor
force--has been growing older, this could reflect the distribution of
new teachers as well. In order to examine how demographic change affects
our forecasts, we used U.S. Census population projections by age and
gender to project the age and gender distribution of new teachers from
2004 through 2020. We found that adjusting our estimates to take into
account an increasing traction of older teacher hires does not make a
large difference in our projections The baseline of new teachers
increases by just under 2 percent and retirements increase by 4 percent
by school year 2020-21.
(16) The U.S. Census does not report revised interim high and low
projections. Instead, we applied the growth rates of the high and low
projections from the detailed U.S. Census projections released in 2000
to recalibrate new high and low projections that are in line with the
revised middle forecast (see note 8 for further details).
(17) A component of this assumption is the age of retirement over
time. We currently assume that the age of retirement stays constant at
the 2003-04 level. The median age of teacher retirements in the 2003-04
outgoing rotation files from the Current Population Survey is 60.0,
quite close to the average median age of 60.3 between 1994 and 2005. We
see little evidence of a trend in this series. However, if the
retirement age declined by one year during the period 2004-20, this
would be equivalent to an increase of 0.6 percentage points in the exit
rate over this time period; this implies a 2.2 percent change in net new
teachers between 2009 and 2020 if we assume uniform year-to-year
increases in the exit rate.
(18) By using the NCES projections, we explicitly accept
NCES's assumption that factors influencing private school and
public school enrollments, such as transfers to and from private and
public schools, migration, dropouts, deaths, and grade promotion will
display future patterns consistent with past patterns. The NCES uses
past grade progression rates to project enrollment.
(19) For these calculations, we restrict the U.S. Census sample to
full-time teachers only. The U.S. Decennial Censuses include short-team
substitutes and other instructors who do not teach in public schools.
Moreover, education requirements and salary ranges change over time.
Therefore, it is not clear how to restrict the historical Census samples
to make them comparable to SASS counts.
(20) More specifically, these forecasts are computed by applying
age-specific teacher retention rates using the U.S. Census Bureau's
American Community Survey from 2001 through 2006 and the student-teacher
ratios and U.S. Census projections of the school-age population as
discussed previously As with calculations using U.S. Census data for new
teacher demand in previous decades, estimates for new teacher demand
from 2000 through 2010 and from 2010 through 2020 are computed by adding
the number of teachers over 65 to the growth in the demand for full-time
public school teachers.
(21) Unfortunately, sample sizes preclude reliable results at
smaller levels of geography.
(22) We take the student growth rate from U.S. Census population
projections, which do not report results by urban or rural status or by
free or reduced price student lunch status. Consequently, the rural
versus urban status and lunch status simulations assume average student
growth over the entire population for all school types. This
mechanically mutes the comparison with minority or regional
subpopulations. Note also that the hours turnover rate accounts for
changes in part-time and full-time status, as well as exits. Because the
transition from part-time to full-time is relatively common (see figure
3, p. 8), especially for new teachers, that transition can more than
offset hours lost from exit and from full-time to part-time switches In
such cases, we report the hours turnover rate as 0.
(23) For racial composition, the top quartile comprises schools
with at least 75 percent minority representation. The second quartile
comprises schools with 29 percent to 75 percent minority representation
For free/reduced price school lunch composition, the top quartile
comprises schools with at least 61 percent tree or reduced price lunch
students, and the second quartile comprises schools with 37 percent to
61 percent free or reduced price lunch students.
(24) Another example where inner-city schools might be
disadvantaged is described in Boyd et at (2005). That paper shows that
teachers often work in areas near where they grew up. This can make it
more difficult to hire teachers for districts with alumni who do not go
into the teaching profession.
(25) Of course, an across-the-board salary increase affects the
transition decisions of both high- and low-quality teachers. As much as
it creates an incentive for the low-quality teachers to stay longer, it
may not necessarily improve overall teacher productivity.
(26) It is important to note that in our model, some teachers may
be returning to teaching after a break. In the literature, the
elasticities tend to be computed for the first five years of teaching
experience.
(27) Another channel is larger class sizes. However, we find little
evidence of countercyclical movements in class size during the previous
two business cycles. That said, pressures on state and local governments
have been notably more severe during this recession.
(28) Analysis using the March Current Population Survey shows that
the number of recent immigrants in 2008 (that is, foreign-born residents
who moved to the United States last year) was down 7 percent from 2007
and 30 percent from 2006. The Centers for Disease Control and
Prevention's National Center for Health Statistics reports a
decline of 0.5 percentage points in fertility rates during early 2008
relative to the previous few years.
(29) Roughly one-fifth of school-age children are from immigrant
families, up from 6 percent in 1970, and these children are concentrated
in states with the largest urban school districts, for example,
California, New York, Texas, Florida, Illinois, and New Jersey (Capps et
al., 2005).
(30) See, for example, the Federal Reserve Bank of
Philadelphia's Survey of Professional Forecasters, available at
www.phil.frb.org/research-and-data/real-time-center/
survey-of-professional-forecasters/.
Daniel Aaronson is a vice president and economic advisor and
Katherine Meckel is a former associate economist in the Economic
Research Department at the Federal Reserve Bank of Chicago.
TABLE 1
average public school grade progression rates
Progression rate
Kindergarten to 1st grade 1.064
1st to 2nd grade 0.987
2nd to 3rd grade 1.008
3rd to 4th grade 1.003
4th to 5th grade 1.004
5th to 6th grade 1.016
6th to 7th grade 1.015
7th to 8th grade 0.997
8th to 9th grade 1.133
9th to 10th grade 0.892
10th to 11th grade 0.910
11th to 12th grade 0.934
Note: A number above 1 implies a net influx of students coming
into public schools in that particular grade from either private
schools or schools without grade levels or home schooling; or
it implies an influx of children entering the U.S. school system
for the first time (recent immigrants).
Source: Authors' calculations based on data from the U.S.
Department of Education, Institute of Education Sciences,
National Center for Education Statistics, compilation of yearly
national student counts by grade and type of school, 1999-2003.
TABLE 2
Impact of age, gender, experience, and tenure on teacher
labor market transitions
Standard
Coefficient error
Age 22, Male 8.093646 29105.29 Age 22
Age 23, Male 0.024761 0.000281 Age 23
Age 24, Male -0.10479 0.000203 Age 24
Age 25, Male -0.15153 0.000187 Age 25
Age 26, Male 1.350913 0.000296 Age 26
Age 27, Male 0.206976 0.000176 Age 27
Age 28, Male 0.930092 0.000201 Age 28
Age 29, Male 0.804145 0.000193 Age 29
Age 30, Male 0.392089 0.000191 Age 30
Age 31, Male 0.176934 0.000179 Age 31
Age 32, Male 0.563326 0.000184 Age 32
Age 33, Male -0.11639 0.000198 Age 33
Age 34, Male 0.486308 0.00019 Age 34
Age 35, Male 0.060937 0.000193 Age 35
Age 36, Male 0.456112 0.000205 Age 36
Age 37, Male 0.591015 0.000249 Age 37
Age 38, Male -0.27538 0.000178 Age 38
Age 39, Male -0.05095 0.000223 Age 39
Age 40, Male 1.000113 0.000365 Age 40
Age 41, Male 0.64211 0.000175 Age 41
Age 42, Male 0.492821 0.000233 Age 42
Age 43, Male 0.462676 0.000204 Age 43
Age 44, Male 0.017997 0.000201 Age 44
Age 45, Male 0.784613 0.000246 Age 45
Age 46, Male -0.00701 0.000216 Age 46
Age 47, Male 0.305273 0.000236 Age 47
Age 48, Male -0.79063 -0.00018 Age 48
Age 49, Male 0.209362 0.000194 Age 49
Age 50, Male 0.480825 0.000239 Age 50
Age 51, Male 0.014518 0.000198 Age 51
Age 52, Male 0.126609 0.000162 Age 52
Age 53, Male 0.047295 0.000181 Age 53
Age 54, Male 0.361217 0.000156 Age 54
Age 56, Male 0.007383 0.000149 Age 55
Age 57, Male 0.214801 0.000159 Age 57
Age 58, Male 0.510085 0.000205 Age 58
Age 59, Male 0.77383 0.000189 Age 59
Age 60, Male -0.23118 0.000199 Age 60
Age 61, Male 0.055861 0.000245 Age 61
Age 62, Male 0.262981 0.00029 Age 62
Age 63, Male -9.97802 19421.77 Age 63
Age 64, Male -1.72697 0.000889 Age 64
Age 65, Male -1.89667 0.000572 Age 65
Age 66, Male -9.83992 54831.4 Age 66
Age 67, Male -1.72607 0.000544 Age 67
Male
[less than or equal to] 5 years of total experience
[less than or equal to] 3 years of current experience
4-32 years of current experience
[greater than or equal to] 33 years of current experience
Full-time in 2003
Standard
Coefficient error
Age 22, Male 0.584441 0.000148
Age 23, Male 0.50437 0.00011
Age 24, Male 0.561063 0.000106
Age 25, Male 0.34095 0.000103
Age 26, Male 0.133373 0.000095
Age 27, Male 0.11059 9.81E-05
Age 28, Male -0.19276 8.95E-05
Age 29, Male -0.10115 8.75E-05
Age 30, Male -0.02861 8.89E-05
Age 31, Male 0.266742 0.000106
Age 32, Male -0.10671 0.000104
Age 33, Male 0.37932 0.000102
Age 34, Male 0.04453 8.92E-05
Age 35, Male 0.220451 0.000109
Age 36, Male 0.318921 0.000109
Age 37, Male 0.394561 0.000106
Age 38, Male 0.198718 0.000121
Age 39, Male 0.027583 0.000117
Age 40, Male 0.10183 0.000102
Age 41, Male 0.614629 0.000113
Age 42, Male 0.279795 0.000106
Age 43, Male 0.161943 9.89E-05
Age 44, Male 0.039813 9.88E-05
Age 45, Male 0.10397 9.84E-05
Age 46, Male 0.528427 0.000114
Age 47, Male 0.645033 0.000105
Age 48, Male 0.37092 0.000106
Age 49, Male 0.390664 0.000109
Age 50, Male 0.257623 0.000102
Age 51, Male 0.247397 9.66E-05
Age 52, Male 0.003844 8.52E-05
Age 53, Male 0.049014 9.13E-05
Age 54, Male -0.19105 8.79E-05
Age 56, Male -0.10263 0.000082
Age 57, Male -0.24693 9.67E-05
Age 58, Male -0.4417 0.000124
Age 59, Male -0.58112 9.96E-05
Age 60, Male -0.33222 0.000104
Age 61, Male -0.84614 0.000143
Age 62, Male -1.05536 0.000138
Age 63, Male -0.05081 0.000185
Age 64, Male -1.13825 0.000121
Age 65, Male 0.190768 0.000311
Age 66, Male -0.24195 0.000197
Age 67, Male -0.83847 0.000181
Male -0.11158 0.000108
[less than or equal to] 5 years of total
experience -0.17221 3.26E-05
[less than or equal to] 3 years of current
experience -0.15405 3.08E-05
4-32 years of current experience 0.057591 3.17E-05
[greater than or equal to] 33 years of
current experience 0.00000 8.49E-05
Full-time in 2003 0.969324 3.59E-05
Notes: There are three types of transitions from year to year:
exiting out of the teacher work force; becoming or remaining a
part-time teacher; and becoming or remaining a full-time teacher.
The model is estimated using an ordered probit with the data
sources.
Sources: Authors' calculations based on data from the U.S.
Department of Education, Institute of Education Sciences. National
Center for Education Statistics, 2003-04 Schools and Staffing
Survey and 2004-05 Teacher Follow-up Survey.
TABLE 3
Key parameters and projected demand for new teachers,
by school characteristics
Age distribution
of teachers, 2003-04
Age Age
>50 >55
Northeast 0.317 *** 0.135 ***
Midwest 0.313 *** 0.121 *
South 0.273 *** 0.118 ***
West 0.304 0.140 ***
Percentile of free/reduced
price lunch students
>75 percentile 0.279 *** 0.120 **
50-75 percentile 0.297 0.124
<50 percentile 0.305 *** 0.130 **
Large or mid-sized central city 0.301 0.131 **
Urban fringe 0.296 0.128
Small town/rural 0.293 0.114 ***
Percentile of minority students
>75 percentile 0.296 0.141 **
50-75 percentile 0.291 0.118 ***
<50 percentile 0.300 0.122 ***
Observations 42,310
Teacher hours turnover
rate, 2003-04 to 2004-05
Age 25 Age
New to 35 50
Northeast 0.063 0.073 0.129
Midwest 0.000 * 0.071 0.108
South 0.093 0.055 0.104
West 0.054 0.107 * 0.102
Percentile of free/reduced
price lunch students
>75 percentile 0.136 ** 0.124 0.123
50-75 percentile 0.029 0.062 0.125
<50 percentile 0.018 0.045 0.098
Large or mid-sized central city 0.137 ** 0.103 * 0.130 *
Urban fringe 0.000 ** 0.058 * 0.096
Small town/rural 0.104 0.063 0.124 **
Percentile of minority students
>75 percentile 0.203 *** 0.114 * 0.122
50-75 percentile 0.034 0.069 0.106
<50 percentile 0.000 ** 0.045 * 0.106
Observations 675 1,629 1,673
Percent Percent
change change
of student in new
Student- population teachers
teacher growth relative
ratio, rate, to
2003-04 2008-20 baseline
Northeast 12.8 *** -1.9 4.4
Midwest 14.2 *** 0.6 -10.3
South 14.2 *** 25.4 27.4
West 17.9 *** 24.8 6.2
Percentile of free/reduced
price lunch students
>75 percentile 14.4 *** -- 53.7
50-75 percentile 14.3 *** -- 13.8
<50 percentile 15.2 *** -- -20.9
Large or mid-sized central city 15.4 *** -- 23.7
Urban fringe 15.3 *** -- -10.4
Small town/rural 13.3 *** -- 26.5
Percentile of minority students
>75 percentile 15.5 *** 21.6 65.4
50-75 percentile 15.1 *** 10.0 23.0
<50 percentile 14.3 *** -0.8 -26.2
Observations
* Statistically significantly different from the remaining
population at the 10 percent level.
** Statistically significantly different from the remaining
population at the 5 percent level.
*** Statistically significantly different from the remaining
population at the 1 percent level.
Notes: Student growth rates are from U.S. Census Bureau's
population projections. All other parameters are computed from the
2003-04 Schools and Staffing Survey and 2004-05 Teacher Follow-up
Survey. Hours turnover accounts for changes between part-time
status and full-time status. If switches from part-time to
full-time more than offset lost hours through exits and switches
from full-time to part-time, we report the hours turnover rate as
0. The final column reports the percent change in net demand for
new teachers relative to the baseline if the full population had
the subpopulation characteristics. Sources: Authors' calculations
based on data from U.S. Census Bureau, population estimates and
projections; U.S. Department of Education. Institute of Education
Sciences. National Center for Education Statistics, 2003-04 Schools
and Staffing Survey, 2004-05 Teacher Follow-up Survey, and
compilation of yearly national student counts by grade and type of
school. 1999-2003; and Hussar and Bailey (2007), table 33.
