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Call for Papers
2010 Conference on
Bank Structure and
Competition

Federal Reserve Ban
of Chicago

Fourth Quarter 2009

RESEARCH uBRARY
Federal Reserve Bank
of St. Louis

JAN 1 9 2010

perspectives
2

How will baby boomer retirements affect teacher
labor markets?
Daniel Aaronson and Katherine Meckel

16

The recession of 1937—A cautionary tale
Francois R. Velde

40

Employment growth: Cyclical movements
or structural change?
Ellen R. Rissman

58

Index for 2009

Economic.___
perspectives

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Contents

Fourth Quarter 2009, Volume XXXIII, Issue 4

2

How will baby boomer retirements affect teacher labor markets?
Daniel Aaronson and Katherine Meckel
The authors estimate teacher demand and supply through 2020 to gauge the impact of baby boomer
retirements on the demand for new teachers. They find that the projected demand will accelerate
through at least 2020, and a good portion of this increase will be due to retirements. Still, this demand,
once it has been adjusted for the size of the potential work force, will not be considerably different
from that of the past five decades.

16

The recession of 1937—A cautionary tale
Francois R. Velde
This article reviews the competing explanations offered for the recession of 1937, which interrupted
the recovery from the Great Depression. One explanation, increases in labor costs due to the New
Deal’s industrial policies, fails to account for the full extent of the downturn and for the ensuing
recovery. In contrast, monetary policy and fiscal policy seem to capture the downturn—although
not its precise timing—and the recovery.

38

Conference on Bank Structure and Competition: Call for papers

40

Employment growth: Cyclical movements or structural change?
Ellen R. Rissman
In judging the degree of slack in the economy, policymakers must determine the origin of any increase
in the unemployment rate—specifically, how much of it is due to a cyclical slowdown (driven by the
broader economy) as opposed to a structural realignment in production (driven by a shift in production
from declining industries to expanding ones). The model developed in this article provides some
insight into the sources and magnitude of structural change and its impact on the unemployment rate.

58

Index for 2009

How will baby boomer retirements affect
teacher labor markets?
Daniel Aaronson and Katherine Meckel

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 problems3—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 how 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

2

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 teachers4 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
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.

4Q/2009, Economic Perspectives

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

Federal Reserve Bank of Chicago

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 year8 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

3

BOX 1

A very simple numerical illustration

Assume that there are five types of teachers in time t
(that is, T1t , …, T5t) distinguished solely by their age.
Age determines turnover propensity. For example,
the youngest group, T1t, exits with probability p1. = 0.2
in every year; groups T2t, T3t, and T4t exit with probability
p2. = p3. = p4. = 0.1; and the oldest group T5t leaves with
probability p5.= 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
5

year, total turnover Exit.1= ∑ Ti1 × pi. = 200 × 0.2 +
i=1

200 × 3 × 0.1 + 200 × 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.1=ΣTi1 × 0.01 = 1000 × 0.01 = 10 teachers.
Together, this implies that Demand for New
Teachers.1 = Exit.1 + Expand.1 = 210, the number of
teachers that must be hired prior to the second year.

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 flat 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
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

4

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
(z1. = 0.5) and the other half in age group 2 (z2. = 0.5).
Therefore, we add 105 teachers to T12 and T22. 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: Ti2=Ti1 × (1 – pi) + Exit.1 ×
zi. + Expand.1 × 0.01 × zi.. 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 T12= 200 ×
(1 – 0.2) + 200 × 0.5 + 1000 × 0.01 × 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.

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.

4Q/2009, Economic Perspectives

Third, we do not focus solely on exits because transitions between part-time
Projected demand for public school teachers, 2009–20
and full-time teaching positions clearly
millions
affect changes in the total number of teacher
5
hours. Specifically, figure 3 (p. 8) shows
that part of the growth in teacher hours between 2003–04 and 2004–05 arises from
4
a larger fraction of teachers switching
from part-time to full-time positions than
vice versa. The exit rate alone is, on aver3
age, 7.8 percent; however, accounting for
changes in hours from switches between
part-time and full-time positions effec2
tively lowers the overall hours turnover
rate to 6.5 percent.13
To this point, we have described how
1
2009 ’10 ’11 ’12 ’13 ’14 ’15 ’16 ’17 ’18 ’19 ’20
we project staffing levels due to the work
choices of the existing cohort of 2003–04
Notes: The solid line is our preferred forecast. The shaded region encompasses
teachers. But, each year, demand for classthe plausible range of estimates. Demand for public school teachers was
estimated for 2004–08 (not shown), since actual hiring data for those years
rooms exceeds the number of returning
are not available, and then carried through 2020. See the text for details.
Sources: Authors’ calculations based on data from the U.S. Census Bureau,
teachers; therefore, new instructors must
population estimates and projections; U.S. Department of Education, Institute
be added to account for those hours. In
of Education Sciences, National Center for Education Statistics, 2003–04 Schools
and Staffing Survey and compilation of yearly national student counts by grade
the simulation, this deficit is filled by addand type of school, 1999–2003; and Hussar and Bailey (2007), table 33.
ing 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 distriTable 2 reports the results of our baseline regression.
bution of characteristics of new teachers in the most
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
		
Table 1
characteristics. This computation provides us with a
Average
public
school
grade progression rates
forecast of the number of returning teacher hours in
2004–05. We then add a year to each returning teacher’s
	
Progression rate
age, experience, and tenure. We continue to project
Kindergarten to 1st grade	
1.064
this cohort through the forecast horizon (2020–21),
1st to 2nd grade	
0.987
using the same procedures (appropriately adding a year
2nd to 3rd grade	
1.008
to each returning teacher’s age, experience, and tenure).
3rd to 4th grade	
1.003
Three points about the simulations thus far are worth
4th to 5th grade	
1.004
noting. First, it is well known that exits are especially
5th to 6th grade	
1.016
high in the first few years of teaching. That pattern is
6th to 7th grade	
1.015
clearly evident in figure 2, panel A, which displays the
7th to 8th grade	
0.997
hump in exits for women in their late twenties and early
8th to 9th grade	
1.133
thirties. A similar pattern exists when exits are plotted
9th to 10th grade	
0.892
against tenure or experience. We include a dummy for
10th to 11th grade	
0.910
the first five years of experience to account for this
11th to 12th grade	
0.934
nonlinearity.
Second, because of data limitations, tenure is
Note: A number above 1 implies a net influx of students coming
into public schools in that particular grade from either private
measured as consecutive years as a public school
schools or schools without grade levels or home schooling; or
teacher, whereas experience is measured as the total
it implies an influx of children entering the U.S. school system
for the first time (recent immigrants).
number of years as a public school teacher. UnsurSource: Authors’ calculations based on data from the U.S.
prisingly, our results are nearly identical if we exDepartment of Education, Institute of Education Sciences,
National Center for Education Statistics, compilation of yearly
clude the tenure measure.
national student counts by grade and type of school, 1999–2003.
figure 1

Federal Reserve Bank of Chicago

5

recent SASS.14 We then update all of the
figure 2
returning teacher characteristics by makTeacher transition rates, by age
ing them older an additional year (and
giving them an additional year of total
A. Women
percent
work experience and tenure) and rerun
100
the simulations to the following year, using the transition probabilities inferred from
80
table 2. We continue this algorithm through
to part-time
the 2020–21 school year, continuously replacing missing teacher hours with repre60
sentative entrants and updating the tenure,
to full-time
total experience, and part-time/full-time
40
status of those remaining. This methodology assumes that schools will continue to
exit
20
hire teachers from the same demographic
(that is, gender, age, experience, and tenure)
background as they have in the recent
0
22 25 28 31 34 37 40 43 46 49 52 55 58 61
past and that the part-time and full-time
age
fractions by age and gender stay constant.15
We can ascertain the importance of
B. Men
teacher retirements in two ways. First,
percent
the follow-up survey asks the reason why
100
teachers exit the profession. Retirement
is listed as the reason for roughly 32 perto part-time
80
cent of all exits at the end of the 2003–04
school year, including 70 percent or higher
60
among exits of teachers aged 55 and older.
to full-time
However, our prediction methods cannot
distinguish between reasons for exiting
40
the teaching profession. Therefore, we comexit
pute the probability a future exit is due to
20
retirement based on the actual gender and
age exit rates displayed in panels A and B
0
of figure 2 and their correlation with the
22
26
30
34
38
42
46
50
54
58
reason for exit in the 2003–04 SASS. Exage
its rise by 1.2 percentage points, on averNotes: The transition rates are computed as five-year weighted moving
age, per year for ages 50–60, with overall
averages. To full-time and to part-time rates encompass teachers who
switch their work time commitment as well as those who keep the same
turnover rates hitting close to 30 percent
work time commitment as the previous year.
shortly after age 60. Note that turnover is
Sources: Authors’ calculations based on data from the U.S. Department
of Education, Institute of Education Sciences, National Center for Education
also high among young and inexperienced
Statistics, 2003–04 Schools and Staffing Survey and 2004–05 Teacher
teachers, especially women, who represent
Follow-up Survey.
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 inBasic estimates of demand for new teachers
troducing high-turnover employees into the system.
Figure 4 provides several estimates of demand
for
new
teachers through 2020. First, concentrate on
Demand for new teachers
the
solid
line, which is our preferred estimate of future
Lastly, for each year, we compare returning
demand
for
new teachers. In this scenario, just under
teacher supply (that is, how many teachers are left
280,000
teachers
are added in the 2009–10 school
from the 2003–04 cohort and each subsequent cohort
year,
or
about
9
percent
of the projected 3.2 million
of new teachers) with demand. The additional teachteacher
work
force.
Over
the coming decade, the total
ers needed to fill the gap between supply and demand
number
of
new
teachers
needed
to fill growth in demand,
are what we call the demand for new teachers.

6

4Q/2009, Economic Perspectives

		

Table 2

Impact of age, gender, experience, and tenure on teacher labor market transitions
	
	
Age 22, Male	
Age 23, Male	
Age 24, Male	
Age 25, Male	
Age 26, Male	
Age 27, Male	
Age 28, Male	
Age 29, Male	
Age 30, Male	
Age 31, Male	
Age 32, Male	
Age 33, Male	
Age 34, Male	
Age 35, Male	
Age 36, Male	
Age 37, Male	
Age 38, Male	
Age 39, Male	
Age 40, Male	
Age 41, Male	
Age 42, Male	
Age 43, Male	
Age 44, Male	
Age 45, Male	
Age 46, Male	
Age 47, Male	
Age 48, Male	
Age 49, Male	
Age 50, Male	
Age 51, Male	
Age 52, Male	
Age 53, Male	
Age 54, Male	
Age 56, Male	
Age 57, Male	
Age 58, Male	
Age 59, Male	
Age 60, Male	
Age 61, Male	
Age 62, Male	
Age 63, Male	
Age 64, Male	
Age 65, Male	
Age 66, Male	
Age 67, Male	

Coefficient	
	
8.093646	
0.024761	
–0.10479	
–0.15153	
1.350913	
0.206976	
0.930092	
0.804145	
0.392089	
0.176934	
0.563326	
–0.11639	
0.486308	
0.060937	
0.456112	
0.591015	
–0.27538	
–0.05095	
1.000113	
-0.64211	
0.492821	
0.462676	
0.017997	
0.784613	
–0.00701	
0.305273	
–0.79063	
0.209362	
0.480825	
0.014518	
0.126609	
0.047295	
0.361217	
0.007383	
0.214801	
0.510085	
0.77383	
–0.23118	
0.055861	
0.262981	
–9.97802	
–1.72697	
–1.89667	
–9.83992	
–1.72607	

Standard error		
29105.29	
0.000281	
0.000203	
0.000187	
0.000296	
0.000176	
0.000201	
0.000193	
0.000191	
0.000179	
0.000184	
0.000198	
0.00019	
0.000193	
0.000205	
0.000249	
0.000178	
0.000223	
0.000365	
0.000175	
0.000233	
0.000204	
0.000201	
0.000246	
0.000216	
0.000236	
–0.00018	
0.000194	
0.000239	
0.000198	
0.000162	
0.000181	
0.000156	
0.000149	
0.000159	
0.000205	
0.000189	
0.000199	
0.000245	
0.00029	
19421.77	
0.000889	
0.000572	
54831.4	
0.000544	

Age 22 	
Age 23 	
Age 24 	
Age 25 	
Age 26 	
Age 27 	
Age 28 	
Age 29 	
Age 30 	
Age 31 	
Age 32 	
Age 33 	
Age 34 	
Age 35 	
Age 36 	
Age 37 	
Age 38 	
Age 39 	
Age 40 	
Age 41 	
Age 42 	
Age 43 	
Age 44 	
Age 45 	
Age 46 	
Age 47 	
Age 48 	
Age 49 	
Age 50 	
Age 51 	
Age 52 	
Age 53 	
Age 54 	
Age 55 	
Age 57 	
Age 58 	
Age 59 	
Age 60 	
Age 61 	
Age 62 	
Age 63 	
Age 64 	
Age 65 	
Age 66 	
Age 67 	

Male				
≤5 years of total experience			
	
≤3 years of current experience				
4–32 years of current experience				
≥33 years of current experience				
Full-time in 2003				

Coefficient	

Standard error	

0.584441	
0.50437	
0.561063	
0.34095	
0.133373	
0.11059	
–0.19276	
–0.10115	
–0.02861	
0.266742	
–0.10671	
0.37932	
0.04453	
0.220451	
0.318921	
0.394561	
0.198718	
0.027583	
0.10183	
0.614629	
0.279795	
0.161943	
0.039813	
0.10397	
0.528427	
0.645033	
0.37092	
0.390664	
0.257623	
0.247397	
0.003844	
0.049014	
–0.19105	
–0.10263	
–0.24693	
–0.4417	
–0.58112	
–0.33222	
–0.84614	
–1.05536	
–0.05081	
–1.13825	
0.190768	
–0.24195	
–0.83847	

0.000148	
0.00011	
0.000106	
0.000103	
0.000095	
9.81E-05	
8.95E-05	
8.75E-05	
8.89E-05	
0.000106	
0.000104	
0.000102	
8.92E-05	
0.000109	
0.000109	
0.000106	
0.000121	
0.000117	
0.000102	
0.000113	
0.000106	
9.89E-05	
9.88E-05	
9.84E-05	
0.000114	
0.000105	
0.000106	
0.000109	
0.000102	
9.66E-05	
8.52E-05	
9.13E-05	
8.79E-05	
0.000082	
9.67E-05
0.000124	
9.96E-05	
0.000104	
0.000143	
0.000138	
0.000185	
0.000121	
0.000311	
0.000197	
0.000181	

–0.11158	
–0.17221	
–0.15405	
0.057591	
–0.86998	
0.969324	

0.000108
3.26E-05
3.08E-05
3.17E-05
8.49E-05
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.

Federal Reserve Bank of Chicago

7

figure 3

Transition rates to full-time and part-time
teaching positions, by age
percent
10

8

6

4

2
0
22

26

30

34

38

42
age

46

50

54

58

Part-time to full-time
Full-time to part-time
Note: These transitions rates are computed as five-year weighted moving
averages.
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.

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.
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

8

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.

4Q/2009, Economic Perspectives

But it also shows that the 1970s was a
time when hiring was brisk. The reasons,
Projected demand for new public school teachers, 2009–20
of course, differ. In the 1970s, 72 percent
thousands
of our new teacher demand measure was
450
necessitated by growing populations of
school-age children. By contrast, between
400
2010 and 2020, we expect that only around
350
31 percent of this measure of the demand
300
for new teachers will be due to student
population growth. The remainder will
250
be due to teacher retirements.
200
We recognize that comparing abso150
lute numbers is misleading because the
100
size of the aggregate population, and consequently the potential and actual teacher
50
work pool, has grown over time. There0
fore, the black line normalizes our new
2009 ’10 ’11 ’12 ’13 ’14 ’15 ’16 ’17 ’18 ’19 ’20
teacher numbers by the population aged
Notes: Demand for new teachers was estimated for 2004–08 (not shown),
25–54. Here, we find that this ratio is not
since actual hiring data for those years are not available, and then carried
through 2020. The solid line is our preferred forecast. The shaded region
unusually high right now, nor do we exencompasses the plausible range of estimates. The dashed line shows
pect it to become unusually high in the
openings created by retirements. See the text for further details.
Sources: Authors’ calculations based on data from U.S. Census Bureau,
near term. Demand for new teachers as a
population estimates and projections; U.S. Department of Education,
percent of the labor force aged 25–54 is
Institute of Education Sciences, National Center for Education Statistics,
2003–04 Schools and Staffing Survey, 2004–05 Teacher Follow-up Survey,
expected to average 0.91 percent between
and compilation of yearly national student counts by grade and type of
2010 and 2020—just above the 0.83 perschool, 1999–2003; and Hussar and Bailey (2007), table 33.
cent 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
Are these projections historically high?
in the aggregate.
Of course, there are always retirements. The key
question is how unusual hiring might be given the
Demand for new teachers by school
baby boomer retirements. We provide some historical
demographics
context by comparing U.S. Census-based estimates of
Obviously, not all schools face the same future
future changes in new full-time public school teacher
hiring requirements; the effect of the baby boomer
demand with past changes.19
retirements could put particular strain on some more
Because of data limitations, we provide very rough
than others. We explore this issue by looking at the key
approximations of changes in demand for new full-time
parameters in our forecasts when schools are stratified
public school teachers during a decade by adding growth
by region,21 urban or rural status, share of students rein the full-time teacher labor force to the number of
ceiving free or reduced price lunch, and share of students
teachers who are of retirement age. The idea behind
who are minorities. For each of these categories, table 3
this calculation, which clearly understates year-to-year
reports the share of teachers over age 50 and 55 (first
hiring, is that it consistently measures all well-observed
and second columns of data), the hours turnover rate
new full-time public school teachers that 1) fill newly
for new and experienced teachers (third, fourth, and
created positions and 2) replace retirees. The number
fifth columns), the student–teacher ratio (sixth column),
of retirees is conservatively estimated as those who are
and the growth rate of the student population (seventh
at least age 55 at the beginning of the decade (and thus
column).22 The eighth column provides results from
retire by age 65). The demand for new full-time public
simulations of new teacher demand, using the same
school teachers is plotted in figure 5. We make compamethodology as in figure 4, but assuming that the enrable projections for 2010 and 2020, which vary from
tire teacher labor market takes on parameters of a subour more detailed projections reported previously but
population (as described in the leftmost column).
are consistent with the historical data.20
Those numbers are reported relative to the baseline
The red line again shows the rise in new full-time
forecasts of the nationally representative population.
public school teachers needed in the coming decade.
figure 4

Federal Reserve Bank of Chicago

9

figure 5

Demand for new full-time public school teachers
percent

thousands

1.4

1,200

1.2

1,000

1.0

800

0.8
600
0.6
400

0.4

200

0.2
0

0
1950

’60

’70

’80

’90

2000

’10

Total new teachers (left-hand scale)
New teachers as percentage of labor
force aged 25–54 (right-hand scale)

’20

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
Notes: All part-time teachers are dropped from our calculations. Dashed
teachers increases beyond historical norms.
lines indicate forecasts.
Source: Authors’ calculations based on data from U.S. Census Bureau,
Obviously, there are many factors that
population estimates and projections.
affect teacher labor supply—a short list
of which would include salaries, pension
systems, classroom and school conditions,
On average, schools that should expect to see
and certification requirements and other barriers to
unusually high demand for new teachers are in the
entry. We concentrate on teacher financial compensaWest and South; they are located in large cities and
tion because of its relevance to policy discussions
small towns; and they educate high shares of minority
and because of the attention that has been paid to its
and low-income students. The particular explanations
estimation in the literature.
vary somewhat by school characteristic. However, reThat attention in the literature certainly does not
tirements do not seem to be driving any of the results
imply a consensus. A number of recent papers have
in an economically significant way. For example, we
established a link between teacher salary, outside
stratified schools into quartiles based on the fraction
work alternatives, and turnover (for example, Dolton
of minority students (the bottom two quartiles are agand van der Klaauw 1995, 1999; Murnane and Olsen
gregated for simplicity).23 While the top quartile has
1989, 1990; Stinebrickner, 1998; and Harris and Adams,
a higher fraction of teachers aged over 55, there is no
2007). But others (for example, Scafidi, Sjoquist, and
statistical difference in the share of teachers aged over
Stinebrickner, 2006; Hanushek, Kain, and Rivkin, 2004;
50 across the racial minority representation quartiles.
Clotfelter et al., 2008; and Ondrich, Pas, and Yinger,
If we leave all parameters at the top quartile’s level
2008) cast doubt on these findings. We concentrate on
but switch the age distribution of the teachers so that
the larger estimates of the impact of salary on turnover
it matches the schools at the bottom half of the miin the literature and therefore consider our results to
nority representation distribution, overall new teacher
be a lower bound estimate of the effect of raising
demand increases by only 4 percent. Consequently,
teacher salaries on future demand for new teachers.
there is little evidence that baby boomer retirements
We mechanically introduce the impact of an
will affect schools with a high proportion of minority
across-the-board salary adjustment to our transition
students any more than other schools.
probabilities by adjusting potential exit rates using
Additional hiring demands in schools that have
salary–turnover elasticities from various studies.25 For
student populations with high minority representation
our original cohort of 2003 public school teachers,
or those with many low-income members (who receive
we use the “overall” (that is, representative of the
free or reduced price lunches) are driven almost entirely
entire public school teacher labor force) elasticity
by higher turnover propensity and expected student
estimate from Harris and Adams (2007). For the

10

4Q/2009, Economic Perspectives

Federal Reserve Bank of Chicago

		

Table 3

Key parameters and projected demand for new teachers, by school characteristics
				
		
Percent change	
	
Age distribution	
Teacher hours turnover rate,				
	
Student–	
of student	
	
of teachers, 2003–04	
2003–04 to 2004–05		
	
teacher	
population	
	
	

Age	
>50	

Age		
>55	
New	

Age 25	
to 35	

Age	
>50	

	
	

Percent change
in new teachers
ratio,	
growth rate,	
relative to
2003–04	
2008–20	
baseline	
			

Northeast	
0.317***	
0.135***	
0.063	
0.073	
0.129	
12.8***	
–1.9	
4.4
Midwest	
0.313***	
0.121*	
0.000*	
0.071	
0.108	
14.2***	
0.6	
–10.3
South	
0.273***	
0.118***	
0.093	
0.055	
0.104	
14.2***	
25.4	
27.4
West	
0.304	
0.140***	
0.054	
0.107*	
0.102	
17.9***	
24.8	
6.2
									
Percentile of free/reduced price
lunch students
>75 percentile	
0.279***	
0.120**	
0.136**	
0.124	
0.123	
14.4***	
—	
53.7
50–75 percentile	
0.297	
0.124	
0.029	
0.062	
0.125	
14.3***	
—	
13.8
<50 percentile	
0.305***	
0.130**	
0.018	
0.045	
0.098	
15.2***	
—	
–20.9
									
Large or mid-sized central city	
0.301	
0.131**	
0.137**	
0.103*	
0.130*	
15.4***	
—	
23.7
Urban fringe	
0.296	
0.128	
0.000**	
0.058*	
0.096	
15.3***	
—	
–10.4
Small town/rural	
0.293	
0.114***	
0.104	
0.063	
0.124**	
13.3***	
—	
26.5
										
Percentile of minority students
>75 percentile	
0.296	
0.141**	
0.203***	 0.114*	
0.122	
15.5***	
21.6	
65.4
50–75 percentile	
0.291	
0.118***	
0.034	
0.069	
0.106	
15.1***	
10.0	
23.0
<50 percentile	
0.300	
0.122***	
0.000**	
0.045*	
0.106	
14.3***	
–0.8	
–26.2
									
Observations	
42,310		
675	
1,629	
1,673				
*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.

11

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

12

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 forecasts30 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.

4Q/2009, Economic Perspectives

NOTES
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.

1

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.

2

Hanushek, Kain, and Rivkin (2004); and Jacob (2007).

3

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 in our calculations.

4

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.

Detailed calculations and data are available from the authors
upon request.

6

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 influx 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.

7

These are available at www.census.gov/ipc/www/usinterimproj/.
These projections are an interim revision of more detailed forecasts
released in 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.

8

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.
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.

11

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.

12

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.

13

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.

14

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 fraction 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.

15

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).

16

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.

9

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.
10

Federal Reserve Bank of Chicago

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

17

13

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.
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.
18

For these calculations, we restrict the U.S. Census sample to fulltime teachers only. The U.S. Decennial Censuses include short-term
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.
19

More specifically, these forecasts are computed by applying agespecific 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.
20

Unfortunately, sample sizes preclude reliable results at smaller
levels of geography.
21

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.

22

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 free or reduced price
lunch students, and the second quartile comprises schools with
37 percent to 61 percent free or reduced price lunch students.

23

Another example where inner-city schools might be disadvantaged
is described in Boyd et al. (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.

24

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.

25

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.

26

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.

27

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.

28

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).

29

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/.

30

REFERENCES

Aaronson, Daniel, Lisa Barrow, and William
Sander, 2007, “Teachers and student achievement in
the Chicago public high schools,” Journal of Labor
Economics, Vol. 25, No. 1, January, pp. 95–135.
Aaronson, Daniel, and Daniel Sullivan, 2001,
“Growth in worker quality,” Economic Perspectives,
Federal Reserve Bank of Chicago, Vol. 25, No. 4,
Fourth Quarter, pp. 53–74.
Boyd, Donald, Hamilton Lankford, Susanna Loeb,
and James Wyckoff, 2005, “The draw of home: How
teachers’ preferences for proximity disadvantage urban
schools,” Journal of Policy Analysis and Management,
Vol. 24, No. 1, pp. 113–132.

14

Capps, Randy, Michael Fix, Julie Murray, Jason
Ost, Jeffrey Passel, and Shinta Herwantoro, 2005,
“The new demography of America’s schools: Immigration and the No Child Left Behind Act,” Urban
Institute, report, September 30.
Clotfelter, Charles, Elizabeth Glennie, Helen Ladd,
and Jacob Vigdor, 2008, “Teacher bonuses and
teacher retention in low-performing schools: Evidence
from the North Carolina $1,800 teacher bonus program,”
Public Finance Review, Vol. 36, No. 1, pp. 63–87.
Corcoran, Sean, William Evans, and Robert Schwab,
2004, “Women, the labor market, and the declining
relative quality of teachers,” Journal of Policy Analysis
and Management, Vol. 23, No. 3, pp. 449–470.

4Q/2009, Economic Perspectives

Dillon, Sam, 2007, “With turnover high, schools fight
for teachers,” New York Times, August 27, available
at www.nytimes.com/2007/08/27/education/
27teacher.html.
Dolton, Peter, and Wilbert van der Klaauw, 1999,
“The turnover of teachers: A competing risks explanation,” Review of Economics and Statistics, Vol. 81,
No. 3, August, pp. 543–550.
__________, 1995, “Leaving teaching in the UK:
A duration analysis,” Economic Journal, Vol. 105,
No. 429, March, pp. 431–444.
Gordon, Robert, Thomas Kane, and Douglas
Staiger, 2006, “Identifying effective teachers using
performance on the job,” Hamilton Project, Brookings
Institution, discussion paper, No. 2006-1, April.
Hanushek, Eric, John Kain, and Steven Rivkin,
2004, “Why public schools lose teachers,” Journal
of Human Resources, Vol. 39, No. 2, pp. 326–354.
Harris, Douglas, and Scott Adams, 2007, “Understanding the level and causes of teacher turnover:
A comparison with other professions,” Economics of
Education Review, Vol. 26, No. 3, June, pp. 325–337.
Hussar, William, 1999, “Predicting the need for
newly hired teachers in the United States to 2008–09,”
U.S. Department of Education, Institute of Education
Sciences, National Center for Education Statistics,
research and development report, No. NCES 1999026,
August.
Hussar, William, and Tabitha Bailey, 2007,
Projections of Education Statistics to 2016, U.S.
Department of Education, Institute of Education
Sciences, National Center for Education Statistics,
compendium, No. NCES 2008060, December.
Jacob, Brian, 2007, “The challenges of staffing urban
schools with effective teachers,” Future of Children,
Vol. 17, No. 1, pp. 129–153.

Murnane, Richard, and Randall Olsen, 1990,
“The effects of salaries and opportunity costs on
length of stay in teaching: Evidence from North
Carolina,” Journal of Human Resources, Vol. 25,
No. 1, Winter, pp. 106–124.
__________, 1989, “The effects of salaries and opportunity costs on duration in teaching: Evidence from
Michigan,” Review of Economics and Statistics, Vol. 71,
No. 2, May, pp. 347–352.
Ondrich, Jan, Emily Pas, and John Yinger, 2008,
“The determinants of teacher attrition in upstate
New York,” Public Finance Review, Vol. 36, No. 1,
pp. 112–144.
Podgursky, Michael, Ryan Monroe, and Donald
Watson, 2004, “The academic quality of public school
teachers: An analysis of entry and exit behavior,”
Economics of Education Review, Vol. 23, No. 5,
October, pp. 507–518.
Rivkin, Steven, Eric Hanushek, and John Kain,
2005, “Teachers, schools, and academic achievement,”
Econometrica, Vol. 73, No. 2, March, pp. 417–458.
Rockoff, Jonah, 2004, “The impact of individual
teachers on student achievement: Evidence from
panel data,” American Economic Review, Vol. 94,
No. 2, pp. 247–252.
Scafidi, Benjamin, David Sjoquist, and Todd
Stinebrickner, 2006, “Do teachers really leave for
higher paying jobs in alternative occupations?,”
Advances in Economic Analysis and Policy, Vol. 6,
No. 1, article 8, available at www.bepress.com/cgi/
viewcontent.cgi?article=1604&context=bejeap.
Stinebrickner, Todd, 1998, “An empirical investigation of teacher attrition,” Economics of Education
Review, Vol. 17, No. 2, April, pp. 127–136.

Murnane, Richard, 1975, The Impact of School
Resources on the Learning of Inner City Children,
Cambridge, MA: Ballinger.

Federal Reserve Bank of Chicago

15

The recession of 1937—A cautionary tale
François R. Velde

Introduction and summary
The U.S. economy is beginning to emerge from a
severe economic downturn precipitated by a financial
crisis without parallel since the Great Depression. As
thoughts turn to the appropriate path of future policy
during the recovery, a number of economists have proffered the recession that began in 1937 as a cautionary
tale. That sharp but short-lived recession took place
while the U.S. economy was recovering from the Great
Depression of 1929–33.1
According to one interpretation, the 1937 recession
was caused by premature tightening of monetary policy and fiscal policy prompted by inflation concerns.
The lesson to be drawn is that policymakers should
err on the side of caution. An alternative explanation
is that the recession was caused by increases in labor
costs due to the industrial policies that formed part of
the New Deal—the policies of social and economic
reform introduced in the 1930s by President Franklin
D. Roosevelt. If a policy lesson can be drawn from
this, it might have more to do with the dangers of interfering with market mechanisms.
The goal of this article is to present the relevant
facts about the recession of 1937 and assess the competing explanations. Although overshadowed by its
more dramatic predecessor, the recession of 1937 has
received some attention before, in particular Roose
(1954) and Friedman and Schwartz (1963). Then, as
now, the competing explanations centered on fiscal
policy, that is, the impact of taxation and government
spending on the economy; monetary policy, or the
management of currency and reserves; and labor relations policy, or more broadly government policy toward businesses.
The rest of this article is organized as follows.
I first present the salient facts about the 1937 recession. I then review the competing explanations and finally provide a quantitative assessment of their likely

16

contributions to the recession. I find that monetary
policy and fiscal policy do not explain the timing of
the downturn but do account well for its severity and
most of the recovery. Wages explain little of the downturn and none of the recovery.
The recession
Before describing the salient features of the 1937
recession, I first take up the issue of its timing. The traditional National Bureau of Economic Research (NBER)
business cycle dates put the peak of the recession in
May 1937 and the trough in June 1938. Romer (1994)
argues that there are inconsistencies in the way these
dates were established over time, devises an algorithm
that closely reproduces the dates of post-war business
cycles, and applies it to the Miron and Romer (1990)
industrial production series to produce new dates. In
the case of the 1937 recession, Romer identifies August
1937 as the start of the recession. Cole and Ohanian
(1999) implicitly use the same starting date when they
state that industrial production peaked in that month.
I will stick to the traditional date for several reasons.
One is that Romer (1994) directs her argument mostly
at cycles before 1927, when a shift in NBER methodology occurred. Another is that the NBER dating process
considers a broader set of series than just industrial
production. Roose (1954) lists the peaks of 40 monthly
series and shows that 27 series peaked before August.
Finally, industrial production as measured by the Board
of Governors of the Federal Reserve System peaked
in May 1937. There is no controversy over the end
date of the recession, set by the NBER and Romer
(1994) in June 1938.
François R. Velde is a senior economist in the Economic
Research Department at the Federal Reserve Bank of
Chicago. The author thanks Ross Doppelt and Christian
Delgado de Jesus for research assistance.

4Q/2009, Economic Perspectives

figure 1

figure 2

Gross domestic product per capita, 1900–2000

Industrial production per capita, 1919–42

thousands of 1996 dollars
45
40
35
30

index, 1929 = 100
180
160

25

120

20

100

140

15

80

10

60

5
1900 ’10 ’20 ’30 ’40 ’50 ’60 ’70

’80 ’90 2000

Notes: The population is age 16 and older; gross domestic
product per capita is measured on an annual basis over the
period 1900–2000. The trend line (black) grows at the average
growth rate over the periods 1919–29 and 1947–97.
Source: Author’s calculations based on data from Carter et al.
(2006), tables Aa125–144 and Ca9–19.

Figure 1 plots real annual gross domestic product
(GDP) per capita (population aged 16 years and older)
over the twentieth century. The trend line follows that
series’ average growth rate over the periods 1919–29
and 1947–97, and is set to coincide with the series in
1929. This is the metric by which Cole and Ohanian
(2004) show that the recovery after the Great Depression
was weak, since the series does not return to trend until
1942. The exceptional nature of the Great Depression
and the ensuing recovery is starkly evident, but the 1937
recession barely registers in the annual series. The
reason is that the recession is so short, beginning in
mid-1937 and ending in mid-1938.
To get a better sense of the importance of this
episode, we need to look at higher-frequency data. The
national income and product accounts (NIPAs) are not
available at the usual quarterly frequency before 1946,
however, so we have to resort to other series. Figure 2
plots a monthly index of industrial production, which
will be the main focus of my analysis in the final section.
Again, a trend line has been added, growing at the average rate of growth for the period from January 1919
to August 1929. The severity of the 1937 recession is
now apparent. In particular, it is striking to see that the
speed at which industrial production contracted is greater
than during the Great Depression. From its peak in
July 1937 to its trough in May 1938, industrial production declined 32 percent. By comparison, it took two
full years for industrial production to fall as much from

Federal Reserve Bank of Chicago

40
1919

’25

’30

’35

’40

Notes: The population is age 16 and older; industrial production
per capita is measured on a monthly basis over the period
January 1919–December 1942. The trend line (black) is the
average growth rate over the period January 1919–August
1929. The shaded areas indicate official periods of recession
as identified by the National Bureau of Economic Research.
Source: Author’s calculations based on data from the Board
of Governors of the Federal Reserve System, G.17 statistical
release, various issues.

its July 1929 peak. Other measures confirm the severity of the 1937 recession—for example, employment
fell by 22 percent and stock prices declined by over
40 percent (Carter et al., tables Cb46 and Cb53).
Another striking aspect of the 1937 recession is the
recovery that ensued. The rate of growth of industrial
production was slightly higher than that which prevailed
over the period 1933–37 (22 percent per year compared
with 21 percent), and the recovery proceeded smoothly,
without the pauses and reversals that marked 1934. Had
it not been for the 1937 recession, industrial production
would have returned to its trend three or four years earlier.
Although official NIPA data are not available on
a quarterly basis during that period, Balke and Gordon
(1986) have estimated the components of gross national product (GNP), using regression-based interpolation. Although these estimates should be taken with
care, I show them in figure 3; I present the growth rates
in table 1 for the period of interest, with the averages
for the preceding expansion as the point of comparison.
They display some interesting differences of timing
with industrial production. Nondurables consumption
growth, strong in the last three quarters of 1936, stalled
in early 1937 and collapsed in the third quarter. The
various components of investment do not show such a
clear pattern until the fourth quarter of 1937, when all
growth rates turn negative. In contrast, the recovery is
firm across all sectors in the third quarter of 1938.

17

Fiscal policy

figure 3

In the 1930s, total government was still a relatively
small but growing share of the economy: In 1929 total
government consumption and investment represented
9 percent of GDP, and by 1939 it had reached 16 percent. During the same period, the federal government
grew in importance relative to the states and local government: Federal spending grew from 1.6 percent to 6.4
percent of GDP.2 However, figure 4 shows that the
stance of fiscal policy at the state and local level did
not change much during the period under consideration. I will therefore concentrate on federal finances.
Until the Great Depression, the traditional fiscal
policy had been one of balanced budgets. During the
early stages of the New Deal, the vast expansion of
the federal government was financed through debt, but
by the middle of the 1930s, concerns were growing
over the size of the public debt, which had gone from
16 percent of GDP in 1929 to 40 percent in 1936.
In 1936, there was a deliberate attempt to return
to a balanced budget. Figure 5 shows the components
of federal revenues by source and also plots expenditures. On the expenditures side, there is little to note
except a very large spike in the second quarter of 1936.
This represents the payment of bonuses to World War I
veterans, which Congress decided to accelerate that
year before the November elections. This probably
boosted demand in the last three quarters of 1936 well
above its earlier levels (table 1), but it is hard to see
how it could have precipitated a recession on its own.

Components of gross national product, 1919–41
billions of 1972 dollars
400
350
300
250
200
150
100
50
0
1932 ’33 ’34 ’35 ’36 ’37 ’38 ’39 ’40 ’41
Net exports
Change in business inventories
Government purchases
Residential structures
Nonresidential structures
Producers’ durable equipment
Durable goods
Nondurable goods and services
Note: Data are quarterly over the period 1919:Q1–1941:Q4.
Source: Balke and Gordon (1986).

		

Table 1

Growth rates of components of gross national product, annualized, 1933–38
	

	
	
	
1933:Q1–1935:Q4	
1936:Q1	
1936:Q2	
1936:Q3	
1936:Q4	
1937:Q1	
1937:Q2	
1937:Q3	
1937:Q4	
1938:Q1	
1938:Q2	
1938:Q3	
1938:Q4	

Nondurable		
Producers’	
goods and	
Durable	
durable	
Nonresidential	
Residential 	
Government
services	
goods	
equipment	
structures	
structures	
purchases
( - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - percent - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - )
3.8	

17.2	

32.5	

8.9	

27.5	

5.0

2.5	
10.9	
10.5	
14.1	
– 0.4	
– 0.3	
– 7.8	
– 2.5	
– 2.7	
– 4.9	
15.9	
7.7	

18.4	
18.1	
21.5	
11.6	
7.9	
–7.4	
10.8	
– 47.4	
– 65.6	
– 17.4	
25.7	
44.0	

4.5	
29.6	
44.6	
29.5	
26.5	
0.5	
3.9	
– 106.0	
– 73.0	
– 45.2	
56.8	
43.5	

7.1	
–  21.8	
59.4	
47.7	
21.9	
112.0	
– 96.0	
– 54.5	
– 3.4	
– 71.3	
41.9	
21.2	

– 26.9	
0.7	
93.0	
– 15.7	
15.7	
36.2	
– 63.5	
– 63.6	
0.0	
13.6	
114.1	
45.4	

39.8
11.2
3.8
– 2.0
– 19.9
– 2.4
2.8
7.1
19.3
6.9
4.0
3.8

Source: Author’s calculations based on data from Balke and Gordon (1986).

18

4Q/2009, Economic Perspectives

figure 4

figure 5

Federal and state and local receipts
and expenditures, 1929–41

Federal government revenues, by source,
and expenditures, 1934–41

billions of dollars

billions of dollars
7

15

6
5

10

4
3

5

2
1

0

1930

’32

’34

’36

’38

’40

0
1934

’35

’36

’37

’38

’39

’40

Federal receipts

Other

Federal expenditures

Social Security

State and local receipts
State and local expenditures

Miscellaneous internal

Note: Data are annual.
Source: U.S. Bureau of Economic Analysis, National Income
and Product Accounts of the United States, Historical Tables,
tables 3.2 and 3.3.

On the revenue side, it is apparent that revenues
increased sharply in the first quarter of 1937. There are
two main factors. The most important one is the increase
in income tax revenue, which grew by 66 percent from
1936 to 1937. This was due to a significant increase
in income tax rates in the Revenue Act passed in June
1936. The rates previously ranged from 4 percent
(starting at $4,000) to 59 percent (above $1 million).
They remained unchanged for income brackets below
$50,000, but were increased above that threshold, to
reach 75 percent on the top earners. As a result, the
average marginal tax rate for incomes above $4,000
almost doubled, from 6.4 percent to 11.6 percent.3
The second factor, of lesser quantitative importance,
is the beginning of Social Security taxation. The Social
Security tax rate was 2 percent, with half paid by the
employer, and the ceiling was $3,000. Collection began in January 1937, and represented 10.5 percent of
total federal tax receipts for the year 1937.
The undistributed profits tax
One interesting component of fiscal policy in that
period was the introduction of a tax on undistributed
profits (Lent, 1948). The motivation for the tax was not
so much to raise revenue as to encourage firms to pay
out dividends. The government saw this as desirable

Federal Reserve Bank of Chicago

’41

Income and profits taxes
Expenditures
Note: Data are quarterly over the period 1934:Q1–1941:Q4.
Source: Board of Governors of the Federal Reserve System
(1943), table 150, pp. 513–515.

for two reasons. First, the accumulation of earnings by
corporations allowed some earnings to avoid income
taxation. Second, it was thought that firms did not know
the best uses of the capital they were retaining and could
possibly spend it on wasteful projects. According to this
view, it would be better to send the earnings to the shareholder and flowing back into general capital markets.
The tax was announced, without warning, by
President Roosevelt in March 1936, and enacted in the
summer as part of the Revenue Act of 1936. Earnings
that were not distributed as dividends were subjected
to an additional tax. Lent (1948) found that the tax generated little revenue because most corporations, especially the large ones, simply paid out larger dividends.
Also, smaller corporations were able to use legal mechanisms to require their shareholders to reinvest the dividends into shares of the corporation. The firms that were
the most affected (as shown by the increase in their
tax liability) were the medium-sized corporations.
The tax, although it had little effect in terms of
revenues, could have had two effects on the economy.
First, to the extent that small and medium firms find it
difficult to access credit and capital markets, they have
to rely on internal sources of funds to finance investment. The tax would obviously increase the cost of

19

investment for those firms. Second, the tax was reflective
of a changed political climate and increasingly populist rhetoric coming from politicians and the Roosevelt
administration. At the same time as the tax was announced, the Roosevelt administration was becoming
increasingly vocal against “economic royalists,” alleged
monopolists, and business in general. Although the
tax was widely considered a failure and was repealed
in all but name after two years, it may have played a
psychological role in increasing uncertainty about the
profitability of investment. This assessment must be
tempered by the fact that, as table 1 (p. 18) shows,
there was a surge in investment in the second half of
1936, and all components of investment do not start
falling uniformly until late in 1937.
By early 1938, the severity of the recession prompted a turnaround in fiscal policy. This was manifested
in a dramatic announcement by President Roosevelt
on April 14, 1938, of a new “spend–lend” program
with a $2 billion increase in spending.
To sum up, fiscal policy became tighter in early
1937, with a brief return to a balanced budget due to
tax increases. The stance was reversed in early 1938,
shortly before the trough of the recession.
Monetary policy
Most of the recent discussions of the 1937 recession have centered on the monetary policy carried out
by the Federal Reserve System. Because the 1930s were
a period of great change for monetary policy, I will
first provide some background on this change to show
that the Fed abandoned its traditional instruments and
adopted a passive attitude during the first half of the
1930s. When policy became active again in 1935, it
was through the use of a new instrument, namely, changes
in reserve requirements, coupled with actions by the
U.S. Department of the Treasury. The stance of monetary policy, like that of fiscal policy, reversed as the
1937 recession took its toll. I will then examine in
more detail the response of the banking system to
monetary policy during the recession.
Background
The 1930s were a period of considerable change
for U.S. monetary policy. The turning point was the
Gold Reserve Act, passed on January 30, 1934. It nationalized all gold in the United States, including the
gold reserve held by the Fed. It authorized the president to devalue the dollar, which he did immediately,
changing the dollar price of an ounce of gold from $20.67
(its price since the 1830s) to $35. This implied that the
Treasury made a capital gain of 60 percent, or about
$2 billion, on its newly acquired gold holdings. The

20

proceeds were used to create an Exchange Stabilization
Fund under the sole discretionary control of the Treasury.
The existence of the fund gave the Treasury a strong
hand in its dealings with the Fed, and for the next
17 years the Treasury dominated monetary policy.
From its foundation to the early 1930s, the Fed’s
balance sheet had consisted essentially of its gold reserve, which backed the currency (subject to a 40 percent reserve requirement) and private debt. Monetary
policy consisted of managing the portfolio of private
debt, either through discounting or, since the 1920s,
through open-market purchases and sales of private
debt. The debt was short-term, either commercial
paper or bankers’ acceptances, with typically 90 days
or less to maturity.
Figure 6, panels A and B show the rates at which
the Fed bought commercial paper and bankers’ acceptances, compared with open-market rates. The Fed’s
rate in panel A is somewhat lower than the market
rate because the latter pertains to paper of four to six
months maturity, whereas the Fed purchased shorter
maturities. Both panels in figure 6 show that, until
1932, the Fed’s rate was close to the market rate; in
other words, the Fed was active in the open market.
After 1934, the Fed’s rates are above market rates, indicating that the Fed had ceased to use interest rates
for the conduct of monetary policy.
In the years that followed, the stance of monetary
policy was dictated by actions of the Treasury. This
can be seen in figure 7, which plots the sources of reserve funds—that is, the existing and potential sources
of legal tender. Treasury currency (that is, currency
issued directly by the Treasury) and Federal Reserve
credit—the first two components—played no role in
the 1930s, as they remained essentially constant. The
Fed’s portfolio during that period consisted of gold certificates (issued by the Treasury in 1934 in exchange for
the Fed’s gold reserve) and government bonds. Private
debt had completely disappeared. The portfolio was
kept constant throughout the period, with a few minor
exceptions. The gold stock, the third component, was
the main source of variation in the monetary base.
The Treasury did not immediately monetize the
capital gain it had made on gold. The source of growth
in the monetary base is to be found elsewhere. From
1934 on, persistent gold inflows into the United States
account for the growth in the gold component. There
were two reasons for the inflows. After the devaluation
of 1934, foreigners bought dollars because they had
become cheaper (and U.S. domestic prices had not
adjusted fully). Later, gold inflows continued because
increasing political instability in Europe induced
long-term capital flows into the United States.

4Q/2009, Economic Perspectives

figure 6

figure 7

New York Reserve Bank (NYRB) rates
and prevailing open-market rates, 1919–39

Monetary base and components of the supply
of reserve funds, 1934–40

A.	NYRB discount rate and open-market prevailing rate
	 on 4–6 month prime commercial paper
percent
9

billions of dollars

35
30

8

25

7
20

6
5

15

4

10

3

5

2

0
1934 ’35

1
0

1920 ’22 ’24 ’26 ’28 ’30 ’32 ’34 ’36 ’38
NYRB discount rate
Prime commercial paper rate

B.	NYRB buying rate and open-market prevailing rate
	 for 90-day bankers’ acceptances

percent
7

6
5
4
3
2
1
0

1920 ’22 ’24 ’26 ’28 ’30 ’32 ’34 ’36 ’38
NYRB buying rate
Open-market rate
Note: Data are weekly over the period 1919–39.
Source: Board of Governors of the Federal Reserve System
(1943), tables 115, 117, and 121, pp. 442–445, 452–459.

When foreigners offered gold for sale, the Treasury
issued gold certificates and deposited them at the Fed,
increasing its account’s balances. The Treasury then used
the increase to pay for the gold. Thus, gold inflows
translated one for one into increases in the monetary
base. In other words, gold inflows were monetized. This
accounts for the steady increase in the monetary base.

Federal Reserve Bank of Chicago

’36

’37

’38

’39

’40

Monetary base
Gold
Reserve Bank credit
Treasury currency
Note: Data are weekly over the period January 3, 1934–
December 31, 1940.
Source: Board of Governors of the Federal Reserve System
(1943), table 103, pp. 378–394.

The growth in reserves
Then, as now, the U.S. banking system comprised a
variety of banks depending on supervisory jurisdiction.
Banks incorporated under federal law were all members
of the Federal Reserve System and the Federal Deposit
Insurance Corporation (FDIC). Banks incorporated under state law could be members of the Federal Reserve
System and the FDIC, the FDIC only, or neither. Unincorporated banks could be members of the FDIC. In June
1936, member banks represented 70 percent of all bank
deposits. In this section I focus on member banks’ statistics, since they were more frequently collected by the
Federal Reserve System and were directly affected by
the System’s changes in reserve requirements.4
Reserves (which can take the form of currency
or balances at Federal Reserve Banks) were required
by law since 1917. The quantity of required reserves
depended on the total amount of demand deposits (net
of deposits of other banks) or time deposits that a bank
held, as well as its location (see table 2). Reserves above
the required amount are excess reserves. If a bank located in a central reserve city held $1 in excess reserves,
it could potentially increase its demand deposits by
an additional $7.69.
For all member banks, reserves grew from
$2,235 million in June 1933, near the trough of the

21

Great Depression, to $6,613 million by March 1937,
on the eve of the 1937 recession, a 300 percent increase.
Demand deposits during the same period grew only from
$26,564 million to $41,114 million—a 150 percent
increase. With constant reserve requirements, this meant
that excess reserves grew considerably: In January
1934, they were estimated to be $827 million, but by
March 1935, when the Fed began to be concerned, they
had reached $2,200 million, or 48 percent of total reserves. By comparison, before the banking panics of
1931, excess reserves were typically 2 percent or 3 percent of total reserves (Board of Governors of the Federal
Reserve System, 1943).
Why did banks hold such large reserves? Friedman
and Schwartz (1963) propose a shift in banks’ preferences for reserves as a consequence of the banking
panics of 1931–33. This shift took place gradually over
the period 1933–36, and subsided slowly only in the
late 1930s and early 1940s as experience with the FDIC
and general economic recovery made banks more comfortable holding lower levels of reserves.5 In contrast,
Frost (1971) has argued that banks’ demand for reserves
was stable throughout the period. With fixed costs of
adjusting reserves, that demand for reserves behaves
differently at low levels of interest rates than at higher
levels. Below a certain threshold, the demand rises much
more rapidly as rates fall. The reason is that, when shortterm rates are low, it is less costly to hold large amounts
of reserves than repeatedly to incur the fixed cost of
adjusting. Frost’s explanation for high reserves in the
1930s is solely the low level of interest rates.
Policy reaction (1935–37)
Beginning in March 1935, the Federal Open Market
Committee (FOMC), which determines monetary policy
and interest rates, became increasingly concerned with
the growth in excess reserves. Its members feared that
such reserves could ultimately lead to an uncontrolled
credit expansion, once banks decided to increase their
deposits. At that date, the Fed staff prepared a background memo titled “Excess reserves and Federal
Reserve policy.” The authors believed that increasing
government debt supplied the bonds that led to reserve
growth. But the memo concluded that neither past experience nor central bank theory gave any guidance
for a policy response in the current circumstances
(Meltzer, 2003, pp. 492–493).
Yet in spite of mounting concerns, it took the Fed
over a year to take action. This was due partly to the
uncertainty presented in the March 1935 memo and
partly to the need to avoid antagonizing the Treasury.
Concerns over potential inflation were balanced against
concerns over the recovery and the federal government’s
desire for low interest rates when it was financing its debt.

22

By October 1935, excess reserves in the banking
system exceeded the Fed’s portfolio of government
bonds, and the FOMC decided to analyze the distribution
of excess reserves across banks to make sure that increases in requirements would not fall disproportionately on some banks. It also decided to coordinate policy
with the Treasury. The ultimate result of this coordination was that the policy actions in 1936–37 took two
forms: increases in reserve requirements by the Fed
and sterilization of gold inflows by the Treasury
(explained in more detail later).
Friedman and Schwartz (1963, p. 544) see monetary policy (that is, the increase in reserve requirements
and, “no less important,” the gold sterilization program)
as “a factor that significantly intensified the severity
of the decline and also probably caused it to occur
earlier than otherwise.”
Reserve requirements
The Banking Act of 1935, passed in August 1935,
made important changes to the structure of the Federal
Reserve.6 One of the changes concerned reserve requirements. Since 1917, reserve requirements had been set
in section 19 of the Federal Reserve Act at various levels
depending on the location of the member bank.7 The
Board of Governors was now given the authority to
change the reserve requirements “in order to prevent
injurious credit expansion or contraction,”8 but the
requirements could be no lower than they had been
since 1917 and no higher than twice those levels.
The purpose of increasing the reserve requirements
was to pave the way for a return to the Fed’s traditional
policy tools, namely, rediscounting (buying privately
issued debt at a discount to reflect the time to maturity)
and open-market operations. The Fed thought that, as
long as excess reserves were so large, it could have no
effect on the banking sector’s lending activities. Only
if banks became borrowers again would the Fed be
able to ease or tighten policy; until then, in the famous
phrase of Marriner Eccles, Chairman of the Board of
Governors, the Fed would be “pushing on a string”
(Meltzer, 2003, p. 478).
Why were policymakers worried about inflation
in 1936? The answer is twofold. First, there were objective signs of inflation. Wholesale prices, which had
been stable in the early part of 1936, began to rise in
late 1936 and early 1937. The annualized six-month
change in wholesale prices rose steadily from 0.5 percent in August 1936 to 10.2 percent in March 1937.
Retail prices as measured by the National Industrial
Conference Board’s (NICB) cost-of-living index did
not rise as fast, but the 12-month change was nevertheless 5.4 percent by March 1937.9

4Q/2009, Economic Perspectives

met from excess reserves, leaving over
$600 million in excess reserves.
Member bank reserve requirements, 1917–41
Figure 8, panel A shows total and
		
Percent of net		
Percent of
estimated required reserves at weekly re		 demand deposits		
time deposits
porting member banks. The four vertical
	
Central 	
		
dotted lines on this panel and on panels B
	
reserve 	 Reserve		
Effective date	
city	
city	
Country	
All
and C mark the four changes in reserve
requirements (three increases and one
June 21, 1917	
13	
10	
7	
3
decrease).
August 16, 1936	
19.5	
15	
10.5	
4.5
Two things are apparent from figure 8.
March 1, 1937	
22.75	
17.5	
12.25	
5.25
May 1, 1937	
26	
20	
14	
6
One is that, in the aggregate, the increase
April 16, 1938	
22.75	
17.5	
12	
5
in reserve requirements did not reduce excess reserves to zero (see panel B): The
Source: Board of Governors of the Federal Reserve System (1943), table 107,
p. 400.
estimated excess reserves on May 5, 1937,
the first reporting date after the last increase,
were $887 million—28 percent of what they
The other answer is that some policymakers were
were on August 12, 1936, before the increases began.
still worried about repeating what they saw as the misThe second point to make is that the growth of
take of the 1920s. In that view, the Great Depression
total reserves paused during 1937, and then resumed,
was partly a result of the speculative excesses of the
mirroring the behavior of the monetary base (see
1920s, which the Fed had not done enough to prefigure 8, panel A). The two lines in the graph thus
vent. Whether they saw incipient signs of a speculasummarize the two prongs of monetary policy: The
tive boom developing (or whether they wanted to
lower line (required reserves) reflects the Fed’s acprevent such a boom from getting started in the first
tions, while the upper line (total reserves) reflects
place), there was for some an inclination toward preTreasury’s sterilization of gold inflows.
emptive action. Although this view was perhaps not
Gold sterilization
dominant in the FOMC, it nevertheless supported the
As explained previously, since 1934 the Treasury
move toward action in early 1937 and slowed the rehad
let
gold inflows increase the monetary base. Starting
versal of policy later on during the downturn.
in
December
1936, the Treasury changed its procedure.
That said, it should be emphasized that the Fed did
Instead
of,
in
effect, converting gold inflows into the
not see the increase in reserve requirements as contracmonetary
base,
it used proceeds from bond sales to
tionary, and its public pronouncements insisted that the
pay
for
the
gold
that was brought to the Treasury at
stance of policy had not changed. In the Fed’s view,
the
price
of
$35
per
ounce. As a result, the gold stock
mopping up excess reserves through the increase in
in
the
United
States
continued
to increase but the monerequirements should have had no effect. Recent authors
tary
base
remained
roughly
constant
(see figure 9, p. 26).
such as Currie (1980), Calomiris and Wheelock (1998),
From
December
1936
to
February
1938,
the gold stock
and Telser (2001) have argued that the increase in reincreased
15
percent,
but
the
monetary
base
grew by
serve requirements did not cause the recession.
only
4
percent.
The
policy
was
halted
in
February
Table 2 shows the changes in reserve requirements.
1938 and reversed over the ensuing months.
The first increase in reserve requirements was announced
on July 14, 1936, and went into effect a month later. At
The response of banks
the time, total reserves stood at $5.87 billion, split almost
How did banks respond to the increase in reserve
exactly between required reserves of $2.95 billion and
requirements? Figure 8, panel B plots excess reserves.
excess reserves of $2.92 billion. Thus, an increase of
It is apparent that, in the aggregate, excess reserves
50 percent in reserve requirements could be easily met
were sufficient to meet the new requirements, and
by banks with the excess reserves.
panel B shows no sign of banks scrambling to keep
The second and third increases were announced
their excess reserves at high levels, contrary to what
on January 30, 1937: The first was to take effect on
is occasionally asserted.
March 1; the second on May 1. At the time of the anThe picture is somewhat different, however, if one
nouncement, total reserves had increased to $6.78
looks at more disaggregated data. Figure 8, panel C shows
billion, and required reserves were $4.62 billion, slightly
the proportion of required reserves out of total reserves
higher than they had been after the first increase. The
by class of member banks. Recall that member banks
increase of 33 percent in requirements could again be
were classified according to their location. Banks in
Table 2

Federal Reserve Bank of Chicago

23

figure 8

Reserves of member banks, 1934–38
A. Total and estimated required reserves at member banks
billions of dollars
10

B.	Estimated excess reserves at member banks
billions of dollars
3.5

3.0

8

2.5
6

2.0
1.5

4

1.0
2

0.5

0
1934

’35

’36

’37

Total

’38

0
1934

’35

’36

’37

’38

Required

C.	Proportion of required reserves out of total reserves,
	 by class of member banks

percent
100

90
80
70
60
50
40
30
1934

’35

’36

’37

’38

Central reserve city banks—New York
Central reserve city banks—Chicago
Reserve city banks
Country banks
Notes: Data for panels A and B are weekly over the period January 3, 1934–December 28, 1938; data for panel C are monthly over the
period January 1934–December 1938. The four vertical dotted lines in each panel mark the four changes in reserve requirements (three
increases and one decrease) noted in table 2. The shaded areas indicate official periods of recession as identified by the National Bureau
of Economic Research.
Source: Author’s calculations based on data from the Board of Governors of the Federal Reserve System (1943), tables 103 and 105,
pp. 378–394, 396–398.

New York City and Chicago (the two central reserve
cities) were considerably closer to their limit than banks
in reserve cities and country banks.
A comparison of table 3 and table 4 confirms that
the reaction of member banks in New York City was
markedly different from that of the banking system

24

overall. From June 1936 to June 1937, total deposits
grew by 2.3 percent overall but only 0.3 percent in New
York City member banks. Loans increased by 8.6 percent overall, and by 21.2 percent among New York
City banks; but the latter banks reduced their holdings
of government bonds by 23.8 percent and other securities

4Q/2009, Economic Perspectives

		

Table 3

All banks: Main assets and total deposits, 1936–38
	
Call dates	
	
June 1936	
December 1936	
June 1937	
December 1937	
June 1938	
December 1938	

Investments		
Loans	
Total	
U.S. government bonds	
Other securities	
Deposits
( - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - millions of dollars - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - )
20,636	
21,359	
22,410	
22,065	
20,982	
21,261	

27,776	
28,086	
27,155	
26,362	
26,230	
27,570	

17,323	
17,587	
16,954	
16,610	
16,727	
17,953	

10,453	
10,499	
10,201	
9,752	
9,503	
9,617	

57,884
60,619
59,222
58,494
58,792
61,319

Source: Board of Governors of the Federal Reserve System (1943), table 2.

		

Table 4

New York City member banks: Main assets and total deposits, 1936–38
	

Investments		

Call dates	
	

Loans	
Total	
U.S. government bonds	
Other securities	
Reserves	
Deposits
( - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - millions of dollars - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - )

June 30, 1936	
December 31, 1936	
March 31, 1937	
June 30, 1937	
December 31, 1937	
March 7, 1938	
June 30, 1938	
September 28, 1938	
December 31, 1938	

3,528	
3,855	
3,961	
4,276	
3,673	
3,532	
3,172	
3,146	
3,262	

6,028	
5,426	
5,140	
4,730	
4,640	
4,785	
4,841	
5,209	
5,072	

4,763	
4,209	
3,829	
3,630	
3,595	
3,611	
3,740	
3,987	
3,857	

1,265	
1,217	
1,311	
1,100	
1,045	
1,174	
1,101	
1,222	
1,215	

2,106	
2,658	
2,719	
2,749	
2,738	
2,941	
3,517	
3,743	
4,104	

11,387
11,824
11,400
11,421
10,759
10,570
11,192
11,410
11,706

Source: Board of Governors of the Federal Reserve System (1943), table 23.

by 13.0 percent, while banks overall reduced these
holdings 2.1 percent and 2.4 percent, respectively.
Although banks did not suddenly increase their
reserve holdings, they did reduce the rate of growth
of deposits. Figure 9 shows that demand deposits, which
had been growing steadily at 20 percent per year since
March 1933, grew more slowly starting in July 1936
and peaked in March 1937. Over the next 12 months,
they fell by 6 percent, reached a low in December 1937,
and began growing again after the end of the recession.
This describes the size of the banking sector from
the liability side. Which assets shrank to meet this fall
in liabilities? Figure 10 shows that member bank assets
fell into three broad categories: reserves, investments
(U.S. bonds and other securities), and loans. Reserves
grew, as we saw. Loans were not affected much, although
they grew more slowly than before. Among reporting
member banks, the 12-month growth rate of loans peaks
on August 4, 1937, at 19.1 percent, and the absolute
level peaks on September 15, 1937, at $10.05 billion,
16 percent higher than a year before. Loans then fall
4.5 percent in the next three months as the recession

Federal Reserve Bank of Chicago

deepens. The category that bore the brunt of the
reduction was investments, particularly government
debt, simply because those were the most liquid assets.
This can be seen for weekly reporting member banks
in table 5, which shows the composition of assets for
the week following each change in reserve requirements. From the second to the third increase (March–
May 1937), total assets fell by $1.3 billion: Loans
actually increased by $0.5 billion, and most of the
reduction came from U.S. bonds.
Looking at interest rates confirms that the impact
of reserve requirements manifested itself on tradable
securities rather than loans. Table 6 shows that rates
charged by banks on loans were little affected (and in
some locations fell) after the reserve requirements increased, while short-term commercial paper rates rose.
The impact of the second round of reserve requirement increases was felt immediately in the U.S. bond
market. There is in fact a particular day, March 15, when
U.S. long-term bonds, whose yields had remained
very stable, went up by 2 basis points, prompting the
Secretary of the Treasury to get on the phone and

25

figure 9

figure 10

Monetary base, demand deposits at
commercial banks, and money stock, 1919–39

Components of member banks’
balance sheets, 1932–38

billions of 1929 dollars
45
40
35
30

billions of dollars
50

40

25
20

30

15

20
10

10
5
1920 ’22 ’24 ’26 ’28 ’30 ’32 ’34 ’36 ’38
Monetary base
Demand deposits
Money stock
Notes: Data are monthly over the period December 1919–
December 1939 and adjusted for cost of living. The shaded
areas indicate official periods of recession as identified by
the National Bureau of Economic Research.
Source: Friedman and Schwartz (1963), appendix A.

complain to Eccles, the Chairman of the Fed, that the
Fed had bungled the increase in reserve requirements.
Figure 11, panels A and B show clearly how bond
rates increased in March 1937, before the recession
began. Figure 12, which shows that corporate issues
declined sharply in March 1937, suggests a channel
through which the increase in reserve requirements
could have affected the economy, namely, by reducing
the banking sector’s demand for (government and)
corporate liabilities. The fall in lending translated into
higher interest rates and a lower volume of issues.
Prices
The behavior of prices (see figure 13) during the
recession is broadly consistent with the notion that monetary policy was contractionary. Whichever indicator
one uses, it is apparent that prices peaked in mid-1937,
as the recession was under way (recall that there is some
ambiguity as to the exact starting date, either May or
August). The aggregate indexes normally used (the
deflators for gross domestic product and personal consumption expenditures) are only available annually
for this period. The monthly indexes such as the
National Industrial Conference Board’s cost-of-living
index and the Wholesale Price Index, closely watched at

26

0
1932

’33

’34

’35

’36

’37

’38

Other
Loans
Other securities
U.S. bonds
Reserves
Note: Data are quarterly over the period 1932–38.
Source: Board of Governors of the Federal Reserve System
(1943), table 18.

the time for evidence of inflation, both peaked in
September 1937; the Consumer Price Index did too,
although it is not a very good measure for this period
because data were not collected on a monthly basis,
and missing data are interpolated.
What is rather puzzling is that the trend in prices,
having turned deflationary during the recession, continued well after the end of the recession. The National
Industrial Conference Board’s index bottoms out in
June 1939, having declined by 2.2 percent since the end
of the recession, while wholesale prices, having fallen
3 percent, bottom out in August 1939 just before the
outbreak of the European war sets off speculative buying.
Cole and Ohanian (1999) have used the recession
of 1920–21, during which output fell sharply after a
steep drop in prices and recovered strongly once the
deflation ended, to highlight the puzzling nature of
the slow recovery after the end of deflation in 1933.
The recovery of 1938 adds to this puzzle: The economy
rebounded as sharply as in 1921—an analogy noted
by Friedman and Schwartz (1963) in spite of continued deflation (Steindl, 2007).
The following conclusions emerge from the foregoing discussion. First, monetary policy was as much

4Q/2009, Economic Perspectives

		

Table 5

Assets of weekly reporting member banks after reserve requirement changes, 1936–38
			
			
Balances with
		
Government	
Other		
Vault	
domestic	
Total
	
Loans	
bonds	
securities	
Reserves	
cash	
banks	
assets
	
( - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - millions of dollars - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - )
August 19, 1936	
March 3, 1937	
May 5, 1937	
April 20, 1938	

8,369	
9,054	
9,533	
8,585	

10,564	
10,303	
9,499	
9,156	

3,323	
3,318	
3,208	
3,068	

4,884	
5,291	
5,307	
5,980	

373	
398	
337	
330	

2,288	
2,055	
1,797	
2,188	

32,315
33,677
32,362
31,938

Source: Board of Governors of the Federal Reserve System (1943), table 48.

		

Table 6

Short-term rates and lending rates, 1936–38
	

1936	

1937	

December	

June	

1938

	

June	

December	

June	

	

( - - - - - - - - - - - - - - - - - - - - - - - - - - percent - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - )

December

Short-term open-market rates
in New York City
Prime commercial paper, 4–6 months	
Prime bankers’ acceptances, 90 days	

0.75	
0.13	

0.75	
0.19	

1.00	
0.47	

1.00	
0.44	

0.88	
0.44	

0.63
0.44

Rates charged on customers’ loans
by banks in principal cities
Total (19 cities)	
New York City	
North and East (7 cities)	
South and West (11 cities)	

2.71	
1.71	
3.02	
3.51	

2.58	
1.74	
2.94	
3.14	

2.57	
1.73	
2.79	
3.29	

2.52	
1.70	
2.72	
3.23	

2.56	
1.70	
2.70	
3.31	

2.60
1.70
2.95
3.31

Source: Board of Governors of the Federal Reserve System (1943), tables 2, 120, and 124.

a consequence of Fed actions as Treasury actions. The
fall in the money stock was a direct consequence of
the gold sterilization program. Second, the increases
in reserve requirements began to bite only in early
1937. Finally, the channel through which monetary
policy affected the banking system is not as straightforward as sometimes asserted. Lending did not begin
to fall until the recession was under way. The reaction
of the banking system was to liquidate securities
holdings, primarily U.S. bonds, but also private sector securities. The impact on market rates is evident
starting in March 1937.
Labor costs
Roose (1954) and other authors have cited alternative explanations to the monetary and fiscal policy
stories. A number of these stories center on increased
labor costs, which I now take up.

Federal Reserve Bank of Chicago

New Deal policies and the slow recovery
As Friedman and Schwartz (1963, p. 493) put it,
“the most notable feature of the revival after 1933 was
not its rapidity but its incompleteness”: Unemployment
remained high throughout the 1930s, the revival was
erratic and uneven, and private investment (particularly construction) remained very low compared with
the 1920s. They go on to note that prices rose much
more than in earlier expansions despite the large resource gaps that remained. The reason was “almost
surely the explicit measures to raise prices and wages
undertaken with government encouragement and assistance. ... In the absence of the wage and price push,
the period 1933–37 would have been characterized
by a smaller rise in prices and a larger rise in output
than actually occurred” (Friedman and Schwartz,
1963, pp. 498–499).
Cole and Ohanian (2004) develop a general equilibrium model to address this question. Specifically,
they document that output, consumption, investment,

27

figure 11

figure 12

Rates on bonds, 1929–41

New corporate security issues, total and
proceeds proposed as new money, 1934–40

A.	Rates on government bonds and corporate bonds,
	 by rating
percent
12

billions of dollars
0.7
0.6

10

0.5

8

0.4

6

0.3

4

0.2
0.1

2
0
1929

0
1934

’31

’33

’35

’37

’39

’41

U.S. government
Aaa
Baa

’35

’36

’37

’38

’39

’40

Total
New money
Notes: Data are monthly over the period January 1934–
December 1940. The shaded area indicates an official period
of recession as identified by the National Bureau of Economic
Research.
Source: Board of Governors of the Federal Reserve System
(1943), table 138, pp. 491–492.

B.	Rates on corporate bonds, by sector
percent
10
9
8
7
6
5
4
3
2
1929

’31

’33

’35

’37

’39

’41

Industrial
Railroad
Public utility
Notes: Data are monthly over the period January 1929–
December 1941. The shaded areas indicate official periods
of recession as identified by the National Bureau of Economic
Research.
Source: Board of Governors of the Federal Reserve System
(1943), table 128, pp. 468–471.

and hours worked remained far below trend from 1934
to 1939, while total factor productivity was back to
trend in 1936 and wages were 10 percent to 20 percent
above trend. They also document the history of the
National Industrial Recovery Act (NIRA), which

28

gave industries protection from antitrust legislation
as long as firms raised prices and shared their profits
with workers in the form of higher wages. The NIRA
was struck down by the Supreme Court in May 1935,
but the National Labor Relations Act (NLRA), or
Wagner Act, was passed in July 1935 to increase the
bargaining power of unions; furthermore, according
to Cole and Ohanian (2004), the Roosevelt administration did not enforce antitrust laws, allowing firms
to continue to collude in raising prices. The combined
effect of firm collusion and worker bargaining power
is shown in their equilibrium model to result in lower
output than in a competitive version of the same economy. Starting from 1934 conditions and assuming the
same changes in total factor productivity (that is, changes
not due to changes in inputs) as in the data, the competitive version of the economy returns to trend by
1936 or so, while the cartelized version of the economy,
calibrated so that wages are 20 percent above their
normal level, displays lower consumption, investment,
and employment, along with higher wages.
New Deal policies and the 1937 recession
The Wagner Act, which is still in force today, immediately generated legal challenges. Several cases
involving that act were taken up by the Supreme Court
in January 1937.

4Q/2009, Economic Perspectives

figure 13

figure 14

Various measures of prices, 1929–41

Indexes of nominal and real wages, 1929–40

index, 1929 = 100
105

index, 1936 = 1

100

1.3

1.4

95

1.2

90
1.1

85

1.0

80
75

0.9

70

0.8

65

1930

’32

’34

’36

’38

’40

0.7
1929

’31

’33

’35

’37

’39

National Industrial Conference Board
cost-of-living index (monthly)

National Industrial Conference Board
25 industries (nominal)

Consumer Price Index (monthly)

U.S. Bureau of Labor Statistics
89 industries (nominal)

Wholesale Price Index (monthly)
Gross domestic product deflator (annual)

National Industrial Conference Board
25 industries (real)

Personal Consumption Expenditures
deflator (annual)

U.S. Bureau of Labor Statistics
89 industries (real)

Note: The shaded areas indicate official periods of recession
as identified by the National Bureau of Economic Research.
Sources: Beney (1936); National Industrial Conference
Board (1938, 1939, 1940); U.S. Bureau of Labor Statistics
from National Bureau of Economic Research, Macrohistory
Database, series m04169a; and U.S. Bureau of Labor
Statistics and U.S. Bureau of Economic Analysis from Haver
Analytics.

In an earlier version of their work, Cole and
Ohanian (2001, p. 49) commented that “while more
work is required to assess the 1937–38 downturn, our
theory raises the possibility that an increase in labor
bargaining power may have been an important contributing factor to the downturn of 1937–38.” In recent testimony to the Senate, Ohanian (2009) went
further to assert: “Wages jumped in many industries
shortly after the NLRA was upheld by the Supreme
Court in 1937, and our research shows that these higher
wages played a significant role in the 1937–38 economic contraction.”
The behavior of wages
Figure 14 shows the behavior of wages, as measured by the U.S. Bureau of Labor Statistics (BLS)
and the National Industrial Conference Board, both
in nominal terms and deflated by the monthly cost-ofliving index computed by the National Industrial
Conference Board.
Nominal average hourly earnings as measured by
the BLS had been close to constant from January to

Federal Reserve Bank of Chicago

Notes: Data are monthly over the period January 1929–
December 1940. The shaded areas indicate official periods
of recession as identified by the National Bureau of Economic
Research.
Source: U.S. Bureau of Economic Analysis, Survey of Current
Business, 1934–41, various issues.

August 1936, oscillating between $0.571 and $0.575.
They fell to $0.569 in September and then began to rise.
By April 1937, they had reached $0.638; they peaked at
$0.667 in November of that year, 17 percent higher than
in September 1936. But most of that increase (70 percent) had taken place before the Supreme Court handed
down its decision on April 12, 1937. The picture is not
much different if we look at the National Industrial
Conference Board’s index: According to this measure,
67 percent of the rise had occurred before the decision.
Stock prices (shown in figure 15) do not support
the notion that the release of the Supreme Court decision suddenly shifted bargaining power within existing collusive arrangements from firms to workers. If
that had been the case, then the net present value of
the firms’ share of the collusive rents should have fallen
upon receipt of the news. In fact, April 12, 1937, was
a quiet day on stock markets, and the next day the Wall
Street Journal commented that the decision “caused
little more than a ripple in the markets,” an initial
sell-off in steels having been recovered before day’s
end, and trading before and after the decision “hardly

29

figure 15

figure 16

Stock prices, 1934–40

Nominal wages and man-days lost
to strikes, 1932–40

index, 1935−39 = 100

index, 1936 = 1, deflated
by NICB cost-of-living index

180
160

index, 1936 = 1

1.25

10

140
120

1.15

100
1.05

80

5

60

0.95

40
20

0.85

0
1934

’35

’36

’37

’38

’39

’40

Total
Industrial
Railroad
Public utility
Notes: Data are weekly over the period January 3, 1934–
December 25, 1940. The dashed vertical line marks
April 13, 1937, the day after the Supreme Court decision
on the Wagner Act was released.
Source: Board of Governors of the Federal Reserve System
(1943), table 134.

better than dull.” The paper went on to assert that “had
the decisions ... gone the other way, there is little doubt
that the share market would have responded with a show
of active buying; but as the reverse was true, the stock
market’s following managed to be philosophic about
it” (Dow Jones and Company, 1937b, p. 37).
The commentary, as well as a quotation by Henry
Ford the following day that “we thought the Wagner
Act was the law right along” (Dow Jones and Company,
1937a, p. 2), suggests that the decision had been correctly anticipated all along. Surprisingly, however, the
stock market had been rising steadily over the previous
two years. From May 1935, when the NIRA was struck
down, to April 1937, it shot up 70 percent (see figure 15).
Why did wages rise in 1936–37?
There is little doubt that the rise in wages was
linked to the Wagner Act and the resulting increase
in labor union activity. Figure 16 compares the level
of wages with the number of man-days lost to strikes.
While strikes were recurrent throughout the period,
a sustained increase in strikes is noticeable from the
end of 1936 to a peak in June 1937 of 5 million mandays—four times the decade’s average.

30

0.75
1932

0
’34

’36

’38

’40

National Industrial Conference Board (NICB)
25 industries (left-hand scale)
U.S. Bureau of Labor Statistics (BLS)
89 industries (left-hand scale)
Idle man-days (right-hand scale)
Notes: Data are monthly over the period January 1932–
December 1940 (the BLS series starts in January 1934).
Factory hourly average earnings are measured on the lefthand scale, and idle man-days due to strikes are measured
on the right-hand scale.
Sources: National Bureau of Economic Research,
Macrohistory Database, series m08257; and U.S. Bureau
of Economic Analysis, Survey of Current Business, 1934–41,
various issues.

To test the claim that the Wagner Act caused wage
increases in manufacturing, I looked at wages in railroads and farming, sectors to which the act did not
apply. Figure 17 shows data collected by the National
Industrial Conference Board for wage rates in 25 industries, all wage earners in Class I railroads, and farm
labor. Wages did not rise for railroad employees when
they were rising in industry: In fact, railroad wages
fell during the same period. However, farm wages
follow the same pattern as industrial wages throughout the whole period.
I have looked at industry data to see if industries
that saw a greater increase in wages also saw a larger
drop in employment. To do this, I regressed the percentage change in employment from July 1937 to June
1938 (the peak and trough of total employment) on
the percentage change in average hourly earnings
from September 1936 to November 1937 (the trough and
peak of nominal wages in figure 14, p. 29). The results
for the 31 industries are shown in figure 18. The relationship is negative and significantly different from 0

4Q/2009, Economic Perspectives

figure 17

figure 18

Average hourly earnings in 25 industries and
in railroads and wage rates of farm labor, 1932–40

Impact of wage increases on employment
across 31 industries
percent change in employment, July 1937–June 1938,
annualized
10

index, 1936 = 1
1.4

1.3

0
–10

1.2

–20

1.1

–30

1.0

–40

0.9

–50

0.8

Y = −1.77X + 0.03
(0.66) (0.10)

–60

R 2 = 0.22

0.7
1932 ’33

–70

’34

’35

’36

’37

’38

’39

’40

National Industrial Conference Board
25 industries
Farm
Railroads
Notes: The average hourly earnings data for the 25 industries
and the railroads are monthly and the monthly wage rates
of farm labor data are quarterly, seasonally adjusted, over the
period 1932–40. The shaded areas indicate official periods
of recession as identified by the National Bureau of Economic
Research.
Sources: National Industrial Conference Board (1938, 1939,
1940).

at the 1 percent confidence level. The coefficient is
large: A 1 percent increase in wages leads to a 1.8 percent
fall in employment, but it is imprecisely estimated.
More problematic is the fact that these results
are not robust to slight changes in the dates at which
the changes are measured. For example, just changing
the end date for wage increases from November 1937
to June 1937 reduces the R2 statistic from 22 percent
to 7 percent; and the estimated coefficient is three
times smaller and not significantly different from 0
at the 5 percent confidence level.
Quantitative assessment
I have described the main explanations proposed
for the recession of 1937. To assess which one (or more)
of these explanations is the most plausible, it is necessary to go beyond theoretical plausibility and pure
issues of timing. Ideally, one would construct a wellspecified economic model that encompasses the competing explanations, estimate the parameters of the model
using actual data, and then carry out experiments in
the model. This is a difficult task, if only because there
is not an agreed-upon model to use.

Federal Reserve Bank of Chicago

0

5
10
15
20
percent change in wages, September 1936–
November 1937, annualized

25

Source: Author’s calculations based on data from the
U.S. Bureau of Economic Analysis, Survey of Current
Business, 1936–38, various issues.

It is nevertheless possible to make a quantitative
assessment, using the techniques of vector autoregression (VAR) analysis. The basic idea behind VAR
analysis is to construct a statistical model of the relations between any number of variables of interest.
The variables are all interrelated, both over time
(one variable’s current value affects another variable’s
future value) and within a single time period. That is
because, typically, all of the variables are determined
simultaneously by the economy. Prices do not explain
quantities any more than quantities explain prices: Both
are determined jointly by supply and demand relationships. In a dynamic setting, where variables evolve
over time, an additional complication is that the future
may influence the past. Suppose that it is known with
certainty that a certain event will take place a year
from now; individuals will plan ahead accordingly
and alter their decisions today. The future is embedded
in the present to the extent that it is anticipated. Only
surprises may reveal to us what the effect of a particular
variable might be.
VAR analysis is in some ways a generalization
of standard regression analysis but acknowledges that
the left-hand-side variable in one relation is the righthand-side variable in another. Therefore all regressions
are computed simultaneously. Each regression has a
residual, which represents the effect of unexpected or
unexplained variations. For example, we may specify
that output is a function of past values of money growth,

31

		

Table 7

Variance–covariance matrix of the residuals of the vector autoregression
	
M1	
Surplus	
AHE	
CP rate	
WPI	
IP	

M1	

Surplus	

AHE	

CP rate	

WPI	

IP

1.000
– 0.026	
– 0.203	
– 0.454	
0.070	
0.355	

1.000
0.087	
– 0.041	
– 0.064	
– 0.032	

1.000
0.041	
– 0.405	
– 0.475	

1.000
0.059	
– 0.163	

1.000
0.327	

1.000

Note: M1 is the money stock; surplus is the federal surplus; AHE is an index of real average hourly earnings in industries, adjusted for changes
in output per man-hour; CP rate is the interest rate on commercial paper; WPI is the Wholesale Price Index; and IP is industrial production.
Sources: Author’s calculations based on data from Friedman and Schwartz (1963); Board of Governors of the Federal Reserve System (1943),
tables 115, 117, 121, and 150; Board of Governors of the Federal Reserve System, G.17 statistical release, various issues; and National
Bureau of Economic Research, Macrohistory Database, series 08142 and 01300.

fiscal surplus, wages, and other variables of interest.
In each time period, we will have the predicted value
of output based on the past histories of these variables,
and the actual value will differ to some extent: That is
the error term, or innovation. Statistical theory tells
us that a system of variables can be represented as the
sum of the responses to current and past innovations.
If the innovations are properly identified, it becomes
possible to say, for example, that output responds in
a certain way to an unexpected change in money
growth or fiscal policy.
The problem is to identify the innovations to each
variable. If we allow that, say, monetary policy can
be affected within the current period by fiscal policy,
then the error term in the money equation will combine innovations to money as well as innovations to
fiscal policy. In that case, output responses to this innovation will be responses to both monetary policy
and fiscal policy, and statistics are of no use in disentangling the two. In general, one must make identifying assumptions guided by economic theory to interpret
a VAR.
The VAR I run includes a small set of variables
to describe the state of the economy: industrial production (IP), the rate on commercial paper to measure shortterm interest rates, and the Wholesale Price Index
(WPI) to represent prices (Sims, 1980; and Burbidge
and Harrison, 1985). Furthermore, it includes one variable for each competing explanation: the money stock
(M1), the federal surplus, and an index of real average
hourly earnings in industries (AHE), adjusted for changes
in output per man-hour.10 The data are monthly and
extend from January 1919 to December 1941.11
The main identifying assumption is that money
does not respond within the month to other contemporary variables. Thus, innovations to the regression
of money growth on other variables represent only
innovations to money growth itself. This is a common

32

identification assumption for post-World War II data
(Christiano, Eichenbaum, and Evans, 1999). I also
assume that surpluses do not react within the month
to anything but money growth, and that the wage
does not respond to anything but money growth and
the fiscal variable.
As it turns out, this particular ordering matters
little. Table 7 shows the variance–covariance matrix
of the residuals from the VAR. Note how it is close to
diagonal in the first three elements, which means that
the residuals of the monetary, fiscal, and wage equations are uncorrelated. Therefore, the residuals of the
regressions can be interpreted as fundamental innovations, that is, exogenous and unpredictable changes in
the monetary, fiscal, and wage conditions.12
Table 8 shows that the percentage of forecast
error at various horizons is attributable to the innovations in each of the variables. The three factors together
explain about half of the unpredictable movements in
industrial production, but wages alone explain little.
The other half is attributable to innovations to the other
variables (interest rate, prices, and industrial production itself) to which I do not try to attach any particular meaning.
To understand how much each factor (monetary
policy, fiscal policy, and wages) contributed to the
recession of 1937, I examine a historical decomposition. The method is as follows. Using the estimated
statistical relationships between the variables and data
up through December 1935 only, I predict what industrial production will be in all succeeding months.
Then, for each of the three factors, I compute the effect of its innovations on all the variables in each of
the succeeding months from January 1936 to the end
of the sample in December 1941. An innovation to
money growth affects future money growth, but also
all other variables. The effect of the sequence of innovations that I have computed from January 1936

4Q/2009, Economic Perspectives

		

Table 8

Variance decomposition of industrial production at various horizons
	
Months	

Standard
error	

M1	

Surplus	

AHE	

CP rate	

WPI	

IP

	
( - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - percent - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - )
							
6	
0.097	
15.5	
4.8	
16.1	
4.3	
6.8	
52.6
12	
0.136	
30.3	
7.9	
11.2	
2.5	
5.0	
43.0
18	
0.165	
39.1	
8.5	
7.7	
2.7	
3.5	
38.4
24	
0.187	
41.2	
8.3	
6.9	
3.2	
2.8	
37.7
30	
0.203	
40.4	
8.0	
6.8	
3.3	
2.5	
38.9
36	
0.216	
39.2	
8.1	
6.7	
3.4	
2.2	
40.4
Note: M1 is the money stock; surplus is the federal surplus; AHE is an index of real average hourly earnings in industries, adjusted for changes
in output per man-hour; CP rate is the interest rate on commercial paper; WPI is the Wholesale Price Index; and IP is industrial production.
Sources: Author’s calculations based on data from Friedman and Schwartz (1963); Board of Governors of the Federal Reserve System (1943),
tables 115, 117, 121, and 150; Board of Governors of the Federal Reserve System, G.17 statistical release, various issues; and National
Bureau of Economic Research, Macrohistory Database, series 08142 and 01300.

onward can be traced out for each factor and added to
the baseline projection of industrial production, representing how much that factor explains.
The results are shown in figure 19. The black line
represents the actual path of industrial output over the
period. The baseline represents the forecast for industrial output based on information up through December
1935. This forecast essentially sees industrial output
growing at a 4.5 percent trend. To the baseline I add
successively the effect of innovations to the money
stock (M1), to the surplus, and to wages.
The figure supports the following conclusions.
First, the effect of changes in wages starting in early
1937 was to depress output, but by a small amount.13
Moreover, the effect remains negative until late 1941,
which is not surprising, since, as we saw, wages remained relatively high. Second, fiscal shocks and monetary shocks between them do a good job of accounting
for the recession, with the following nuances. If monetary and fiscal shocks had been the only forces at play,
the economy should have peaked in late 1936. Also, the
monetary shock alone does not explain the full depth
of the recession; the fiscal and monetary shocks explain the economy’s turning point in mid-1938, but
not the full extent of the recovery.
This indicates that other forces at work in the economy sustained the expansion from late 1936 to mid1937, in spite of the contractionary impact of monetary
policy and fiscal policy. Likewise, other forces contributed to the recovery, even as monetary policy and
fiscal policy turned expansionary in mid-1938.
The results from the VAR need to be interpreted
with caution. In particular, they do not disprove the
importance of wages. In the Cole and Ohanian (2004)
story, the change in workers’ bargaining power is not
a temporary shock to an otherwise stationary system,

Federal Reserve Bank of Chicago

but a shift from one steady-state equilibrium to another.
The VAR is not designed to capture such changes.
The results do suggest, however, that as a quantitative
matter the monetary and fiscal shocks are sufficient to
account for the general pattern of the recession. Furthermore, the extent to which these factors fail to reproduce
the data is by predicting an earlier and more prolonged
downturn; in other words, the factor that is missing is an
expansionary one, not a contractionary one like wages.
Conclusion
The recession of 1937 has been cited as a cautionary tale about the dangers of premature policy tightening on the way out of a deep downturn. In contrast,
some authors have downplayed the role of monetary
policy suggested by Friedman and Schwartz (1963).
In particular, Cole and Ohanian (1999) dismiss the role
of reserve requirements in the 1937 recession for two
reasons. One is timing: “we would expect to see output fall shortly after” the changes in reserve requirements; but, they write, industrial production peaked
in August 1937, 12 months after the first change (Cole
and Ohanian, 1999, p. 10). The other is that interest
rates did not increase: Commercial loan rates remained
in the same range, and rates on corporate bonds “were
roughly unchanged between 1936 and 1938” (Cole
and Ohanian, 1999, p. 10).
I have shown in this article that tightened monetary policy consisted in the joint action of increased
reserve requirements that were staggered from August
1936 to May 1937 and gold sterilization that started
in December 1936. Gold sterilization turned the growth
rate of money negative, and banks responded to increased reserve requirements by curtailing the financing
of firms, with visible effects on interest rates. Industrial
production peaked only a few months later, in May 1937.

33

figure 19

Historical decomposition of industrial output,
1936–41
index, 100 = January 1936

180
160
140
120
100
80
1936

’37

’38

’39

’40

’41

ensuing two years and resulted in 10 percent wage increases over a short period in early 1937. Although it
has no particular timing advantage over the monetary
and fiscal policy explanations, the labor costs story
would plausibly account for the onset of the recession
but not for the recovery, since the wage increases
were not reversed.
Finally, a simple VAR shows that monetary and
fiscal factors account fairly well for the pattern of industrial production and, in particular, for the depth of
the recession, although other factors are needed to explain why the economy did not contract earlier and why
it rebounded so strongly. Wages cannot account for
much of the downturn. Naturally, there are limits to
the persuasiveness of an essentially statistical exercise. But, in the absence of a full-fledged economic
model, this exercise suggests no additional explanation may be needed.

Actual
Baseline
Baseline + M1
Baseline + M1 + surplus
Baseline + M1 + surplus + wages
Notes: Data are monthly over the period January 1936–
December 1941. M1 is the money stock; surplus is the
federal surplus.
Sources: Author’s calculations based on data from Friedman
and Schwartz (1963); Board of Governors of the Federal
Reserve System (1943), tables 115, 117, 121, and 150;
Board of Governors of the Federal Reserve System, G.17
statistical release, various issues; and National Bureau of
Economic Research, Macrohistory Database, series 08142
and 01300.

Moreover, monetary policy went into reverse: The
New York Fed lowered its discount rate from 1.5 percent to 1 percent on September 27, 1937; the gold
sterilization program ended in February 1938 and
was reversed from February to April; and reserve requirements were lowered on April 16, 1938, just as
the federal government announced a large increase in
spending. The recession ended in June 1938.
An alternative story would rely on increased labor
costs due to the effects of the Wagner Act. The act was
passed in 1935, but labor activism built up over the

34

4Q/2009, Economic Perspectives

NOTES
1

See Blinder, (2009), Krugman (2009), and Romer (2009).

Author’s calculations based on data from the U.S. Bureau of
Economy Analysis, National Income and Product Accounts of the
United States.

2

These were computed as the marginal tax rate weighted by the
number of returns in each bracket above $4,000, using numbers in
U.S. Department of the Treasury (1938, p. 88; 1940, pp. 119, 193).
Barro and Sahasakul (1983) find much lower average marginal tax
rates because the number of filers was only 20 percent of all households, and they assign a zero marginal tax rate to the other 80 percent.

3

Member banks made quarterly reports, whereas nonmember banks
reported twice a year. Furthermore, some member banks, representing
82 percent of all member banks by assets, reported statistics on a
weekly basis.

4

5
The Federal Deposit Insurance Corporation, which began operations
in January 1934, levied a premium on participating banks based
on total deposits, and insured deposits up to $5,000 per depositor.
In 1936, insured deposits amounted to $22,230 million, representing
68 percent of the deposits of participating banks and 47 percent of
all bank deposits (Board of Governors of the Federal Reserve
System, 1943, p. 401).
6

Banking Act of 1935 (49 Stat. 706).

There were two central reserve cities, namely, New York and
Chicago, and 60 reserve cities (see the list in Board of Governors
of the Federal Reserve System, 1943, p. 401).

7

Federal Reserve Bank of Chicago

8

Banking Act of 1935 (49 Stat. 706).

9

Beney (1936) and National Industrial Conference Board (1938).

The data come from the NBER Macrohistory Database, series 08142
and 01300. Wages are deflated by the Wholesale Price Index.
10

The VAR is monthly; all variables are in logs except the surplus,
which can be negative. A time trend and seasonal dummies are added
because the surplus is not seasonally adjusted. Lag length, chosen
to minimize the Akaike information criterion, is 3. As an alternative,
I have also used (the log of) man-days idle due to strikes instead of
wages.

11

12
There is some negative correlation between wages adjusted for labor
productivity and money. An alternative specification in which average
hourly earnings are not adjusted for productivity yields a nearly
diagonal matrix, and the results of the historical decomposition are
quite similar.

The impulse response function of wages on output is negative at
first but turns positive after ten months. This suggests that the
shock identified as a wage shock is more complex than a shock to
workers’ bargaining power and probably includes a productivity
component. If man-days idled by strikes is used instead of wages,
the impulse response function is consistently negative, but of smaller magnitude: The percentage of IP variance explained by innovations to man-days at the three-year horizon is 2.5 percent instead of
6.5 percent for average hourly earnings.

13

35

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Research Department, working paper, No. 597, May.
__________, 1999, “The Great Depression in the
United States from a neoclassical perspective,”
Federal Reserve Bank of Minneapolis Quarterly
Review, Vol. 23, No. 1, Winter, pp. 2–24.
Currie, Lauchlin B., 1980, “Causes of the recession,”
History of Political Economy, Vol. 12, No. 3, Fall,
pp. 316–335.
Dow Jones and Company, 1937a, “Wagner Act
decisions to cause no change in Ford labor policies,”
Wall Street Journal, April 14, p. 2.
__________, 1937b, “Financial markets,” Wall
Street Journal, April 13, p. 37.
Federal Deposit Insurance Corporation, 1984,
Federal Deposit Insurance Corporation: The First
Fifty Years—A History of the FDIC, 1933–1983,
Washington, DC.
Friedman, Milton, and Anna J. Schwartz, 1963,
A Monetary History of the United States, 1867–1960,
Princeton, NJ: Princeton University Press.
Frost, Peter A., 1971, “Banks’ demand for excess
reserves,” Journal of Political Economy, Vol. 79,
No. 4, July/August, pp. 805–825.
Krugman, Paul, 2009, “Stay the course,” New York
Times, June 14, available at www.nytimes.com/2009/
06/15/opinion/15krugman.html.
Lent, George E., 1948, The Impact of the Undistributed Profits Tax, 1936–37, Studies in History,
Economics, and Public Law, No. 539, New York:
Columbia University Press.

4Q/2009, Economic Perspectives

Meltzer, Allan H., 2003, A History of the Federal
Reserve, Volume 1:1913–51, Chicago: University
of Chicago Press.

__________, 1994, “Remeasuring business cycles,”
Journal of Economic History, Vol. 54, No. 3,
September, pp. 573–609.

Miron, Jeffrey A., and Christina D. Romer, 1990,
“A new monthly index of industrial production,
1884–1940,” Journal of Economic History, Vol. 50,
No. 2, June, pp. 321–337.

Roose, Kenneth D., 1954, The Economics of
Recession and Revival: An Interpretation of 1937–38,
Yale Studies in Economics, Vol. 2, New Haven, CT:
Yale University Press.

National Industrial Conference Board, 1940,
“The cost of living in the United States in 1939,”
Conference Board Economic Record, Vol. 2, No. 3,
pp. 25–31.

Sims, Christopher A., 1980, “Comparison of
interwar and post-war business cycles: Monetarism
reconsidered,” American Economic Review, Vol. 70,
No. 2, May, pp. 250–257.

__________, 1939, “The cost of living in the United
States in 1938,” Conference Board Management
Record, Vol. 1, No. 1, p. 1.

Steindl, Frank G., 2007, “What ended the Great
Depression? It was not World War II,” Independent
Review, Vol. 12, No. 2, Fall, pp. 179–197.

__________, 1938, “Cost of living in the United
States, July 1936–December 1937,” Conference
Board Service Letter (supplement), Vol. 11, No. 3,
pp. 3–11.

Telser, Lester G., 2001, “Higher member bank reserve
ratios in 1936 and 1937 did not cause the relapse into
depression,” Journal of Post Keynesian Economics,
Vol. 24, No. 2, pp. 205–216.

Ohanian, Lee E., 2009, “Lessons from the New
Deal,” testimony before the U.S. Senate Committee
on Banking, Housing, and Urban Affairs, Washington,
DC, March 31.

U.S. Department of the Treasury, 1940, Statistics
of Income for 1937, Washington, DC: Government
Printing Office.

Romer, Christina D., 2009, “The lessons of
1937,” Economist, June 18, available at www.
economist.com/businessfinance/displaystory.cfm?
story_id=13856176.

Federal Reserve Bank of Chicago

__________, 1938, Statistics of Income for 1936,
Washington, DC: Government Printing Office.

37

CALL FOR PAPERS and SAVE THE DATE
May 5-7,2010

T

he Federal Reserve Bank of Chicago invites the submission of research- and policy-

oriented papers for the 46th annual Conference on Bank Structure and Competition

to be held May 5-7, 2010, at the InterContinental Hotel in Chicago. Since its inception, the

conference has fostered a dialogue on current public policy issues affecting the
financial services industry. As in past years, the program will highlight a conference theme

(to be announced), but will also feature sessions on other topical financial issues. We wel­
come submissions of high-quality research on all topics related to financial services, their

regulation, and industry structure. Possible session topics include, but are not limited to:

■

Regulatory reform in the financial sector;

■

Lessons from the recent credit market crisis;

■

Failure resolution for large complex financial institutions;

■

Mortgage loan modification programs;

■

Systemic risk regulation;

■

Asset bubbles;

■

The future role of Fannie Mae, Freddie Mac, and other
government-sponsored enterprises;

■

The future of the originate-and-distribute model (securitization);

■

The financial safety net;

■

The Basel Capital Accord;

■

The shadow banking sector;

■

Fair lending and the Community Reinvestment Act;

■

The future of low-income homeownership programs;

■

Market value accounting issues;

■

Deposit insurance reform;

■

The mixing of banking and commerce;

■

Financial industry consolidation;

■

Small business finance;

■

Competitive strategies of financial institutions;

■

Derivative markets;

■

Consumer financial protection/education;

■

The burden of bank regulation; and

■

Retirement finance.

If you would like to present a paper at the conference, please submit your completed paper

or a detailed abstract (the more complete the paper, the better), along with your name,
address, affiliation, telephone number, and email address, and those of any co-authors, by
December 19, 2009. Manuscripts should be submitted via email to:
BSC_2010_submissions @ frbchi.org
Information will be updated on the conference website as it becomes available:

www.chicagofed.org/BankStructureConference
For additional information, contact the conference chairman:
Douglas Evanoff at 312-322-5814 or devanoff@frbchi.org

Employment growth: Cyclical movements
or structural change?
Ellen R. Rissman

Introduction and summary
The Federal Reserve, in its policy analysis, must carefully weigh incoming data and evaluate likely future
outcomes before determining how best to obtain its
twin goals of employment growing at potential and
price stability. It is tempting to regard high or rising
unemployment as a sign of a weak economy. And, normally, a weak economy is one with little inflationary
pressure and, therefore, room for expansionary monetary policy to stimulate growth. But unemployment is
influenced by more than simply aggregate conditions.
In a dynamic economy that responds to changing opportunities, some industries are shrinking while others
are growing. Labor must flow from declining industries
to expanding ones. This adjustment takes time. It takes
time for employees in declining sectors to learn about
new opportunities in other industries, acquire necessary
skills, apply for job openings, and potentially relocate.
And during this period of adjustment, the unemployment rate rises as waning industries lay off workers.
Thus, the unemployment rate may increase or decrease,
even though the aggregate state of the economy remains
stable, simply because the labor market adjusts to
shifting patterns of production.
For policymakers, it is essential to decipher what
portion of a rising unemployment rate is due to a cyclical slowdown in which many sectors of the economy
are simultaneously affected, as opposed to a structural
realignment in production in which particular sectors
of the economy are affected. The two factors ideally
should result in different policy responses. If unemployment is rising because of a weak economy, the textbook
response is for the Fed to take a more accommodative
policy stance. If, instead, the unemployment rate is
rising because of underlying compositional shifts in
employment, an easing of monetary policy may discourage declining industries from contracting by keeping
them marginally profitable, impeding the adjustment
process. Furthermore, this policy may also encourage

40

inflation as employers across a broad spectrum of industries compete for scarce labor resources. Thus, comprehending the underlying sources of movements in the
unemployment rate is more than just a theoretical exercise: It has practical implications for monetary policy.
As a first step toward evaluating the role of structural change, I need to be able to measure it. Lilien (1982)
suggests a dispersion measure that is a weighted average
of squared deviations of industry employment growth
rates from aggregate employment growth. Abraham
and Katz (1986) argue that Lilien’s measure does not
properly account for cyclical shifts in employment across
industries, instead conflating cyclical variation with structural change. When aggregate economic conditions are
weak, certain sectors are affected more than others because demand for their products is more cyclically sensitive, but as soon as economic conditions improve, these
sectors will also recover more quickly. The Lilien measure more accurately captures both cyclical variation
in employment responses and structural changes in the
composition of employment across industries, making
it impossible to disentangle the importance of the two
effects on the measure of dispersion.
The sectoral shifts hypothesis has been revisited
more recently by Phelan and Trejos (2000) and Bloom,
Floetotto, and Jaimovich (2009). Phelan and Trejos
(2000) calibrate a job creation/job destruction model
to data from the U.S. labor market to suggest that permanent changes in sectoral composition can precipitate aggregate economic downturns. Bloom, Floetotto,
and Jaimovich (2009) examine the effect of what they
term “uncertainty shocks” on business cycle dynamics,

Ellen R. Rissman is an economist in the Economic Research
Department at the Federal Reserve Bank of Chicago. The
author thanks Gadi Barlevy for helpful comments and
suggestions and Zachary Seeskin for his valuable research
assistance.

4Q/2009, Economic Perspectives

arguing that increases in uncertainty lead to a decline
in economic activity in affected industries, followed by
a rebound. Increasing uncertainty, in their view, causes
firms to be more cautious in their hiring and investment
decisions and impedes the reallocation of capital across
sectors. Thus, structural change and recessions are simultaneous events, implying that distinguishing structural
change from cyclical downturns is problematic.
As noted by Bloom, Floetotto, and Jaimovich (2009),
structural realignment (in other words, sectoral reallocation) may be concurrent with economic downturns.
Businesses on the brink of downsizing or disappearing
altogether may find that they are tipped over the edge
during a recession. To the extent that whole industries
are affected, the downturn will then occur at the same
time as sectoral reallocation. Recessions are followed
by expansions, whereas sectoral reallocation tends to
have a long-term impact on the composition of employment. Therefore, shifts in production that are cyclical
in nature tend to be transitory, but those that are the result of structural realignment are more long lasting.
Previous studies, including Loungani, Rush, and
Tave (1990) and Rissman (1993), have employed a
variety of techniques to distinguish between sectoral
shifts that are driven by structural change and those that
are driven by cyclical swings. Loungani, Rush, and
Tave (1990), for example, suggest that stock market
prices efficiently reflect the future stream of business
profits. They employ measures based on stock prices to
create a dispersion measure that reflects structural shifts
rather than short-term cyclical fluctuations. In Rissman
(1993), I note that structural change is long lasting,
whereas cyclical swings are of a shorter duration. I use
this observation to distinguish between compositional
shifts in employment that are due to cyclical fluctuations, which are short term, and those that are due to
structural realignment, which are long term. Rissman’s
(1993) measure cannot be produced in real time because current changes in employment patterns may
be either temporary or permanent. Thus, this measure
offers little guidance for policymakers who need to
make decisions based on current information. In contrast, the Loungani, Rush, and Tave (1990) measure
has the benefit of being based on stock price data that
are available at high frequency. However, stock prices
are noisy, and it may be difficult to disentangle the persistence of shocks from them. In particular, a given
decline in a stock price may be a reflection of shortrun factors or may instead be interpreted as a small
permanent decline in an industry’s fortunes. Having
a supplementary employment-based measure that
does not require the use of leading data, in contrast to
Rissman (1993), would provide a useful benchmark.

Federal Reserve Bank of Chicago

This problem of optimally inferring the current
state has been widely studied in economics and in
related statistical literature. Stock and Watson (1989)
employ the Kalman filter to create an index of coincident economic indicators. They formally operationalize
the idea that the business cycle “refers to co-movements
in different forms of economic activity, not just fluctuations in GNP [gross national product].”1 Stock and
Watson (1989) examine several different economic
time series, including employment, and try to extract
a common factor. I use the same approach here to identify a common factor in the labor market based on how
it affects employment in different industries. This common factor is permitted to have different loadings in
each industry, giving some context to the notion that
some sectors are more cyclically sensitive than others.
This framework has the added benefit of creating a common factor that can be interpreted as a measure of the
employment cycle, focusing only on the industry cross
section of employment growth. This is particularly
relevant, since it is widely thought that the labor market
typically lags the business cycle. Thus, a measure of the
business cycle based only on cross-sectional employment
growth helps clarify the relationship between more traditional measures of the cycle, such as real gross domestic product (GDP) growth, and employment growth.
This measure of the cycle may help shed light on the
phenomenon of the jobless recoveries that we have experienced during the two most recent expansions following the contractions ending in 1991:Q1 and 2001:Q4.
Furthermore, the model is based upon quarterly data,
giving policymakers a more timely tool for evaluating
the relative importance of cyclical and structural factors to the labor market than other measures. There is
little reason why the model cannot be estimated on a
monthly basis as well. Finally, the model provides some
insight into the sources and magnitude of structural
change in the economy.
To summarize the results, most industries exhibit
cyclical employment growth, which accounts for the
majority of the variation in employment in those industries. However, structural shifts are also important
and account for most of the variation in employment
growth in the finance, insurance, and real estate (FIRE)
sector and in the government sector. Perhaps not surprisingly, given the well-chronicled declines in the
housing market, the construction industry has undergone a structural reduction in employment after a
notably long period of structural expansion. Recent
structural employment declines in finance, insurance,
and real estate are particularly large when compared
with past episodes. Careful measurement of structural
change suggests that sectoral reallocation may have been

41

on the rise in the past few quarters. However, structural
realignment cannot account for much of the recent increase we have observed in the unemployment rate.
In the next section, I examine employment growth
for nine industries comprising most of total nonfarm
employment. Then, I introduce the estimation framework. I present my results using this framework. Finally,
I develop a measure of sectoral reallocation and investigate its impact on the unemployment rate.
Industry employment growth
The U.S. Bureau of Labor Statistics collects detailed industry employment data for workers on nonfarm payrolls. Over the years the industry classification
system has changed to reflect the changing industrial
composition of the economy. Because of this, it is difficult to compare earlier industry data, which were
collected using the Standard Industrial Classification
(SIC) System, with more recent industry data, which
were collected using the North American Industry
Classification System (NAICS). For example, nine new
service sectors and 250 new service industries are recognized in the NAICS data, but they are not in the SIC
data. The problem of comparability over time is less of
an issue with the broadest industry aggregates. Earlier
estimates of sectoral reallocation were computed using
SIC data. To facilitate comparison with earlier work,
the NAICS data were converted as closely as possible
to be consistent with SIC classifications.
Figure 1 shows annualized quarter-to-quarter
employment growth from 1950 through the second
quarter of 2009 for the following nine sectors: construction; durable manufacturing; nondurable manufacturing; transportation and utilities; wholesale trade;
retail trade; finance, insurance, and real estate; services;
and government.2 Business cycle contractions, as
determined by the National Bureau of Economic
Research (NBER), have been shaded for reference.
The figure also shows the average annual industry
employment growth rate over this period.
Given the current focus on the housing market
as the source of some of our economic problems, it is
interesting to examine employment in the construction
sector. Employment growth in construction is highly
volatile and, not surprisingly, quite cyclical as well.
Construction employment growth appears to decline in
advance of business cycle peaks and reaches its bottom
at or just past the trough of a recession. Although
employment growth in construction was above average during the most recent expansion, which peaked
in December 2007, the strong employment growth
does not appear abnormally large in comparison with
earlier recoveries. Nonetheless, the most recent quarters

42

show a very strong drop in construction employment,
surpassing even the large declines of the mid-1970s.
It is an open question as to what part of this observed
decline in construction is structural in nature and what
part is cyclical (and will therefore rebound when aggregate conditions improve).
The finance, insurance, and real estate sector tells
a somewhat different story. Like most industries, FIRE
experiences reduced employment growth during recessions. Yet, while FIRE’s employment growth has
dipped below average during recessions, historically,
employment in this sector has very rarely declined.
The steep drop in employment in the early 1990s seems
to be the harbinger of a change in employment growth
in this sector, with average employment growth falling
below the 3 percent growth of earlier decades. Furthermore, the steep job losses of the past few quarters are
unprecedented in the past 60 years. The key question
is whether the sharp employment declines are cyclical,
with employment likely to rebound as the economy
moves into the expansionary phase of the business cycle,
or structural and, therefore, likely to linger. Later, I will
show that employment growth in this industry tends
to be highly persistent, suggesting that these declines
are likely to last for quite a while. Yet, these job losses
in FIRE may not transfer directly into increased unemployment. Since workers in FIRE may have skills that
are more easily transferred to other areas, they may be
more likely to find employment in expanding sectors;
therefore, the adjustment out of this sector may not involve much of an increase in the unemployment rate.
The services sector is also interesting to consider.
At one time, this sector was thought to be the engine
of employment growth, as can be seen by the high
average employment growth rates since the 1950s. Yet,
more recently, employment growth here has been weak
as well. And employment growth in services over the
past couple of quarters is the lowest it has been since
the late 1950s.
Taken as a whole, these data suggest several important facts. First, average growth rates differ across
industries, with some sectors of the economy barely
growing at all, such as durable and nondurable manufacturing, and others exhibiting more robust growth,
such as FIRE and services. Second, some industries
are far more volatile than others. Construction, durable and nondurable manufacturing, and transportation
and utilities have wide swings in employment growth
compared with the other industries. Third, unsurprisingly, employment growth is highly cyclical, dropping
during contractions and rising during expansions. However, some industries appear more cyclically sensitive
than others. Focusing on the period since the onset

4Q/2009, Economic Perspectives

figure 1

Employment growth: Selected industries, 1950:Q1–2009:Q2
A. Construction
percent

B. Durable manufacturing
percent

30

30

15

15

0

0

–15

–15

–30
1950 ’56 ’62

’68 ’74

’80 ’86 ’92

’98 2004 ’10

–30
1950 ’56 ’62

’68 ’74

’80 ’86 ’92

C. Nondurable manufacturing
percent

D. Transportation and utilities
percent

20

20

10

10

0

0

–10

–10

–20
1950 ’56 ’62

’68 ’74

’80 ’86 ’92

’98 2004 ’10

–20
1950 ’56 ’62

E. Wholesale trade
percent

F. Retail trade
percent

10

10

5

5

0

0

’98 2004 ’10

’68 ’74

’80 ’86 ’92

’98 2004 ’10

’68 ’74

’80 ’86 ’92

’98 2004 ’10

’68 ’74

’80 ’86 ’92

’98 2004 ’10

–5

–5
–10
1950 ’56 ’62

’68 ’74

’80 ’86 ’92

’98 2004 ’10

–10
1950 ’56 ’62

G. Finance, insurance, and real estate
percent

H. Services
percent

10

10

5

5

0

0

–5

–5

–10
1950 ’56 ’62

’68 ’74

’80 ’86 ’92

’98 2004 ’10

’68 ’74

’80 ’86 ’92

’98 2004 ’10

–10
1950 ’56 ’62

I. Government
percent
10
5
0
–5
–10
1950 ’56 ’62

Notes: These are quarterly annualized growth rates calculated on an SIC (Standard Industry Classification system) conformable basis.
The dashed horizontal line in each panel is the average annual industry employment growth rate. The shaded areas indicate official periods
of recession as identified by the National Bureau of Economic Research; the dashed vertical line in each panel indicates the most recent
business cycle peak.
Source: Author’s calculations based on data from the U.S. Bureau of Labor Statistics from Haver Analytics.

Federal Reserve Bank of Chicago

43

44

3.41
2.45	
3.37	
2.08	
Note: Data are seasonally adjusted.
Source: Author’s calculations based on data from the U.S. Bureau of Labor Statistics from Haver Analytics.

1.93	
0.52	
0.58	
3.54	
1950s	

3.31	

4.15
3.70	
3.38	
3.03	
2.42	
1.30	
1.23	
2.08	
1960s	

2.50	

2.65
3.62	
3.59	
3.33	
2.97	
1.30	
0.09	
2.64	
1970s	

0.98	

1.13
3.72	
2.97	
2.52	
1.57	
1.32	
– 0.25	
1.63	
1980s	

– 0.90	

1.28
3.24	
1.56	
1.44	
1.20	
1.76	
– 0.78	
– 0.04	
2.42	
1990s	

1.04
1.16	
0.18	
– 0.18	
– 0.41	
– 0.29	
– 3.41	
– 0.42	
2000s	

– 3.82	

2.29
3.00	
2.53	
2.06	
1.63	
1.00	
– 0.40	
0.37	
2.00	

Government

( - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - percent - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - )

Total	

	

where git is employment growth in sector i at time t,
i = 1, …, I, and t = 1, …, T ; ai is average employment growth in the industry; Cit is the cyclical portion of industry employment growth (and it varies
across time and industry); and Xit is the idiosyncratic
part of industry employment growth (and it also
varies across time and industry). This construction
is similar to the problem analyzed by Stock and
Watson (1989), in which they noted that individual
aggregate time series depend upon a common cyclical
component and an idiosyncratic component.

		

1)	 git = ai + Cit + Xit ,

Finance,
insurance,	
and real estate	

Services	

The discussion in the previous section suggests
that industry employment growth, in addition to
having a long-term average, can be described by
two additional components: a cyclical component
and an idiosyncratic component that reflects other
noncyclical factors. Let

				
							
		
Durable	
Nondurable	
Transportation	
Wholesale	
Retail	
	
Construction	
manufacturing	
manufacturing	
and utilities	
trade	
trade	

Table 1

A model of industry employment growth

Average annualized quarterly employment growth, in total and by decade

of the current recession in the fourth quarter of 2007,
employment has declined precipitously in most industries. If most of the recent declines in employment growth are cyclical, then employment growth
should rebound and return to normal as the economy
moves into the expansionary phase of the business
cycle. However, a portion of the recent declines in
employment growth may be the result of other factors such as structural realignment in the economy.
If this is indeed the case, then it may indicate that
some industries will likely experience more permanent reductions in employment or employment
growth. An accurate assessment of whether employment data are driven by the business cycle or structural
change is important for formulating policy and for
projecting the future path of employment growth.
Table 1 shows the same employment growth data
for the entire sample in the first row and divided
into ten-year increments in the subsequent rows.3
Construction employment has averaged 2.0 percent
annualized quarterly growth over the entire sample
period. However, over the past decade the average
quarterly growth in construction employment has
been –0.42 percent. Durable and nondurable manufacturing have experienced large declines in employment over the past decade, with job losses or
stagnant growth since the late 1970s. Employment
growth has been weak for the past decade in transportation and utilities, as well as in wholesale and
retail trades. In fact, all sectors have exhibited weaker
average employment growth over the past decade
than they have averaged over the past 60 years.4

4Q/2009, Economic Perspectives

As currently specified, equation 1 cannot be estimated because there is no way to distinguish between
the cyclical and idiosyncratic components. To address
this issue, I assume that the cycle is a common component affecting all industries. However, the cycle may
have a differential impact across sectors. Specifically,
1
i

2
i

2)	 Cit = b Ct + b Ct −1 ,
where bi1 and bi2 are parameters indicating the sensitivity
of the i-th sector to current and lagged values of the business cycle. Furthermore, it is assumed that the cycle itself follows a second-order autoregressive process with:
3)	 Ct = φ1 Ct–1 + φ2 Ct–2 + ut  .
Here ut is independent and identically normally distributed with unit variance. The φ1 and φ2 are unknown
parameters that are to be estimated. Setting σu2 = 1
determines the scale of the business cycle. For example,
an alternative estimate of the cycle Ct* = δCt would
result in estimates of the bi values scaled by 1/δ. Two
sets of estimates are possible, both Ct and −Ct  , depending upon the initial values of the parameters.
For ease of interpretation, it is assumed that the business cycle has a positive impact on durable manufacturing employment growth.
The idiosyncratic component of industry employment growth Xit is assumed to follow an AR(1)
process. Specifically,
4)	 Xit = γi   Xit–1+ eit ,
where γi is a sector-specific parameter that indicates the
degree of persistence of sectoral shocks. It is assumed
that the εit values are uncorrelated over time and across
industries. Note that E(eit ) = 0 and E (εit2 ) = σi2 for all
i, t. Furthermore, the εit values are assumed to be uncorrelated with the cyclical shock ut for all i, t. This
specification allows for a common unobserved cycle
that has a differential impact across industries. It also
permits structural change to occur through the idiosyncratic component Xit. Thus, changes in an industry’s
employment growth are due to either cyclical factors
or factors that are specific to that particular industry.
Estimation is accomplished using the Kalman
filter, details of which are discussed in box 1. The
state vector z t is given by z t = [Ct , Ct −1 , Ct −2 , X 1t ,
X 2t , , X It ]'. The Kalman filter algorithm enables
estimates of the state vector z t and the underlying parameters to be estimated. These parameters include the values
for ai , bi1, bi2, γi , σi  , and φ1 and φ2. The shocks ut and εit
can also be obtained for i =1, …, I and t =1, …, T.

Federal Reserve Bank of Chicago

The Kalman filter is a way of optimally updating the
underlying state vector as new information becomes
available each quarter. A Kalman smoothing algorithm is used to optimally backcast for final estimates
of the state vector and model parameters.
Estimation results
∧

The estimate of the cycle Ct obtained from the
Kalman filter exercise is shown in figure 2.5 The 2×
standard error bands are also shown. These standard
error bands indicate whether the estimate is significantly
different from zero. Defining a recession as the period
during which the estimated employment cycle is significantly below zero, the estimate indicates that we are
currently in the midst of a deep recession. The cyclical
point estimate in 2009:Q1 measures the recession to be
the most severe since 1950. However, because of parameter uncertainty, this point estimate is not significantly
worse than earlier recessions in a statistical sense. The
estimate for 2009:Q2 indicates that aggregate employment continues to deteriorate, albeit at a slower pace.
Employment failed to rebound as quickly as other
sectors of the economy during the two most recent
recoveries following the NBER-dated recessions of
1990–91 and 2001. This lack of improvement in the
labor market, termed the “jobless recovery,” drew
commentary from both the popular press and economists. As computed here, the employment-based measure
of the cycles indicates that the contractions lasted seven
and eleven quarters, respectively—significantly longer
than the length of the NBER’s contractionary periods
of three and four quarters, respectively—indicating
that the labor market experienced a delayed recovery
relative to other measures of economic activity that
the NBER’s Business Cycle Dating Committee examines in determining business cycle peaks and troughs.
Shortly after the 2001 recession, Groshen and Potter
(2003) suggested that the abnormally slow recovery
was the result of sectoral reallocation (in other words,
structural factors) rather than cyclical factors. The
evidence provided here shows that the slow growth in
employment was likely attributable to weak cyclical
activity.6 Using a similar methodology, Aaronson,
Rissman, and Sullivan (2004) reach a similar conclusion. Furthermore, findings presented in the next
section regarding the role of Xit appear to show that
sectoral shocks do not play a major role in accounting
for unemployment. Recall that the employment cycle
is defined by co-movement in employment growth
rates across many industries simultaneously. As such,
the model interprets the lengthy employment contraction during these two episodes as broad-based; that
is, a wide spectrum of industries are negatively affected,

45

BOX 1

The Kalman filter

The Kalman filter is a statistical technique that is useful in estimating the parameters of the model specified
in equations 1–4 (pp. 44–45). In addition, the Kalman
filter enables the estimation of the processes ut and
εit and the construction of the unobserved cyclical
variable Ct and the idiosyncratic components Xit.
The Kalman filter consists of a state equation and a
measurement equation. The state equation describes
the evolution of the possibly unobserved variable(s)
of interest, z t , while the measurement equation relates observables gt to the state. The vector gt is
related to the m × 1 state vector, z t , via the measurement equation:
B1)	 gt = Bzt + Dηt + Hwt ,
	
where t = 1, …, T ; B is an N × m matrix; h t is an N × 1
vector of serially uncorrelated disturbances with mean
zero and covariance matrix IN ; and wt is a vector of
exogenous (possibly predetermined) variables with
H and D being conformable matrices.
In general, the elements of z t are not observable.
In fact, it is this very attribute that makes the Kalman
filter so useful to economists. Although the z t elements
are unknown, they are assumed to be generated by a
first-order Markov process as follows:
B2)	 z t = Az t −1 + Fu t + Gw t ,

	

for t = 1, …, T, where A is an m × m matrix, F is an
m × p matrix, and u t is a p × 1 vector of serially uncorrelated disturbances with mean zero and covariance
matrix Ig. This equation is referred to as the state or
transition equation.
The definition of the state vector z t for any particular model is determined by construction. In fact,
the same model can have more than one state-space
representation. The elements of the state vector may
or may not have a substantive interpretation. Technically, the aim of the state-space formulation is to set
up a vector z t in such a way that it contains all the

and the contraction is not concentrated in only a few
industries, as would be the case if sectoral reallocation
were the underlying cause of low aggregate employment growth.
Table 2 provides parameter estimates with associated standard errors. Focus on the coefficient estimates
of the bi1 values (second column): All sectors of the economy are affected by cyclical variation, as constructed
here. However, the degree of cyclical sensitivity varies across industries, with durable manufacturing
∧

46

relevant information about the system at time t and
that it does so by having as small a number of elements
as possible. Furthermore, the state vector should be
defined so as to have zero correlation between the
disturbances of the measurement and transition
equations, u t and h t .
The Kalman filter refers to a two-step recursive
algorithm for optimally forecasting the state vector z t ,
given information available through time t – 1, conditional on known matrices B, D, H, A, F, G. The
first step is the prediction step and involves forecasting z t on the basis of z t-1. The second step is the updating step and involves updating the estimate of the
unobserved state vector z t on the basis of new information that becomes available in period t. The results
from the Kalman filtering algorithm can then be used
to obtain estimates of the parameters and the state
vector z t by employing traditional maximum likelihood techniques.1
The model of employment growth proposed
here can be put into state-space form, defining the
state vector z t = [Ct , Ct−1 , Ct−2 , X 1t , X 2t , , X It ]'. The
Kalman filter technique is a way to optimally infer
information about the parameters of interest and, in
particular, the state vector z t , which in this case is
simply the unobserved cycle, Ct, and its two lags and
the unobserved structural components Xit. The cycle,
as constructed here, represents that portion of industry
employment growth that is common across the industries while allowing the cycle to differ in its impact
on industry employment growth in terms of timing
and magnitude through the parameters bi1 and bi2 .
The model is very much in the spirit of Burns and
Mitchell’s (1946) idea of cycles entailing co-movement, but the estimation technique permits the data
to determine which movements are common and
which are idiosyncratic.2
The interested reader may obtain further details in Harvey (1989)
and Hamilton (1994).

1

Stock and Watson (1989) employ the Kalman filter in constructing
leading and current economic indicators.

2

employment being the most contemporaneously cyclically sensitive, followed by construction. The estimated
intercept term  ai (first column) is not significantly different from zero in construction, durable manufacturing, nondurable manufacturing, and transportation and
utilities. The estimated parameter γ i (fourth column)
gives the degree of persistence of the idiosyncratic component. There is a great deal of variation in the persistence
of these idiosyncratic shocks εi , with finance, insurance, and real estate exhibiting the most persistence.
∧

∧

4Q/2009, Economic Perspectives

figure 2

Estimated employment cycle, 1950:Q1–2009:Q2
6
4
2
0
–2
–4
–6
–8
1950

’55

’60

’65

’70

’75

’80

’85

’90

’95

2000

’10

’05

Note: The dashed lines indicate the 2× standard error bands, indicating whether the estimate is significantly different from zero.
Source: Author’s calculations based on data from the U.S. Bureau of Labor Statistics from Haver Analytics.

		

Table 2

Parameter estimates, 1950:Q1–2009:Q2
ˆ1i
b
	

ˆ2i 	
b

ˆγi

s
ˆi

ˆi	
a
	
		
Construction	
1.8435	
	
(1.1963)	

1.8695***	
(0.3407)	

1.5357***	
(0.4730)	

0.4240***	
(0.0741)	

20.1902***
(1.8060)

Durable manufacturing	
	

0.2714	
(1.5350)	

3.7417***	
(0.3549)	

0.8463	
(0.6397)	

0.612***	
(0.0552)	

9.9197***
(0.9951)

Nondurable manufacturing	
	

– 0.4657	
(0.6295)	

1.5231***	
(0.1537)	

0.4054	
(0.2385)	

0.6461***	
(0.0479)	

1.9574***
(0.2371)

Transportation and utilities	
	

0.9105	
(0.5921)	

1.2185***	
(0.2354)	

0.7769**	
(0.3036)	

0.0933	
(0.0883)	

3.8068***
(0.4611)

Wholesale trade	
	

1.5546***	
(0.4448)	

0.8004***	
(0.1135)	

0.6365***	
(0.1888)	

0.5516***	
(0.0673)	

1.2072***
(0.1134)

Retail trade	
	

1.9921***	
(0.4377)	

1.2430***	
(0.1589)	

0.2449	
(0.2255)	

0.1727*	
(0.0818)	

1.7845***
(0.2004)

Finance, insurance,	
and real estate	

2.3220***	
(0.6162)	

0.2073*	
(0.0913)	

0.2340***	
(0.0862)	

0.8978***	
(0.0361)	

0.7583***
(0.0786)

Services	
	

2.9343***	
(0.4061)	

1.0738***	
(0.0994)	

0.3221	
(0.1661)	

0.1728	
(0.1119)	

0.4891***
(0.0804)

Government	
	

2.2712***	
(0.3532)	

0.0890	
(0.1280)	

0.1438	
(0.1031)	

0.5748***	
(0.0639)	

2.9139***
(0.2494)

	

*Significant at the 5 percent level.
**Significant at the 2 percent level.	
***Significant at the 1 percent level.
Note: Standard errors are in parentheses.
Source: Author’s calculations based on data from the U.S. Bureau of Labor Statistics from Haver Analytics.			

Federal Reserve Bank of Chicago

47

BOX 2

Calculating the variance

Rewriting the model as a vector AR(1) process,
define
B3)	 yt = [ g1t , g 2t ,, g It , Ct , Ct −1 , Ct −2 , X 1t , X 2t ,, X It ]'.
	
Then
B4)	 yt = Πyt −1 + νt , 	

B5)	 Ω = ΠΩΠ '+ Σ. 	

B6)	 vec(Ω) = [ I − Π ⊗ Π]−1 vec(Σ), 	
where ⊗ is the Kronecker product of ∏ with itself
and vec(x) is the vector constructed by stacking the
columns of an n × m matrix into a single column
vector. The matrix ∏ is given by
Γ I×I 

03×I  ,

Γ I×I 

Shocks to both services and transportation and utilities
are not statistically persistent. Furthermore, variation in
these shocks differs across industries, reflecting in part
the variation in employment growth noted in figure 1
(p. 43). Shocks to the idiosyncratic portion of industry
employment growth are more variable in construction,
durable manufacturing, and transportation and utilities
than in other sectors of the economy (fifth column).
Using the model, it is straightforward to calculate
the portion of the variation in an industry’s employment
growth that is attributable to cyclical activity and that
which is attributable to industry-specific factors. Details
of the calculations are found in box 2, and the results
are presented in table 3. As noted previously, some
industries exhibit much more variation in employment
growth than others. Construction and durable manufacturing are the two most volatile sectors of the economy,
exhibiting large swings in employment growth. By
comparison, the variance of employment growth in
nondurable manufacturing and transportation and
utilities is about one-fifth that of the most volatile

48

b12
b22

bI2

0 
0 
,
 

0 
0

0 ,

0

and

This can be solved as:

BI×3
A3×3
0 I×3

 b11

 b1
B8)	 B =  2
	

 1
 bI

φ1 φ2

B9)	 A =  1 0

	
0 1


which has a variance

0 I×I

B7)	 Π =  03×I
0
 I×I

and the submatrices are given by

 γ1 0 … 0 
0 γ
0 
2
B10)	 Γ = 
.





	
 0 … 0 γI 
The error term n t is given by
B11)	 v t = [ε1t ,ε 2t ,…, ε It , ut , 0, 0,ε 1t , ε 2t ,…,ε It ]'. 		

industries, and the least volatile sectors have about
one-tenth the variance. The model attributes this volatility to either cyclical variation or the idiosyncratic
structural component. Within construction, for example,
about half the total variance in employment growth
stems from the structural component and half is the
result of cyclical variation. The cyclical component
accounts for most of the variation in employment
growth in durable manufacturing, nondurable manufacturing, transportation and utilities, wholesale trade,
retail trade, and services. In contrast, the structural
component carries the most weight in two sectors—
FIRE and government.
In addition to examining the estimated cycle, it
is also useful to consider the idiosyncratic portion of
employment growth. Figure 3 shows the idiosyncratic
component Xit for each of the nine industries from
1950:Q1 through 2009:Q2. Positive values suggest
that employment growth is stronger in these industries
than explained by either normal cyclical variation Cit
or long-term averages ai. Note that the scale differs

4Q/2009, Economic Perspectives

of the coefficients on the contemporaneous estimate of the cycle, bi1, being smaller
Effect of cyclical and structural components on variation,
in magnitude for the 1984:Q1–2009:Q2
1950:Q1–2009:Q2
sample period than for the entire sample.
	
	
Fraction	
Fraction
For example, in the full sample a one
		
of total	
of total
standard deviation increase in the cycle
	
Total	
variance	
variance
	
variance	
due to C	
due to Xi
increased durable manufacturing employment growth by 3.7 percent per annum,
Construction	
46.9245	
0.4754	
0.5246
whereas in the 1984:Q1–2009:Q2 sample,
Durable manufacturing	
58.7445	
0.7300	
0.2700
Nondurable manufacturing	
10.8692	
0.6909	
0.3091
the impact was a much smaller 1.2 perTransportation and utilities	
11.5470	
0.6674	
0.3326
cent (see second row, second column of
Wholesale trade	
5.7097	
0.6961	
0.3039
tables 2 and 4, respectively). Furthermore,
Retail trade	
6.3833	
0.7119	
0.2881
Finance, insurance,
generally, estimates of the variance of the
and real estate	
4.2831	
0.0874	
0.9126
idiosyncratic shocks in each industry, σi ,
Services	
4.4125	
0.8857	
0.1143
are much smaller for the 1984:Q1–2009:Q2
Government	
4.4569	
0.0236	
0.9764
sample, with the exception of finance, inSource: Author’s calculations based on data from the U.S. Bureau of Labor Statistics
surance, and real estate (compare the fifth
from Haver Analytics.
column in tables 2 and 4). For example,
the estimate of the standard deviation in
the shock to construction is 20.2 for the entire sample,
from one industry to the next. Upon closer inspection
but a much smaller 4.3 for the 1984:Q1–2009:Q2 sample.
of the construction sector (figure 3, panel A), the estiThere is also evidence that for the 1984:Q1–2009:Q2
mates suggest that employment growth in this industry
sample, industry shocks are more persistent, as can be
was higher than could be explained from the business
seen by comparing the estimated γ i values for the entire
cycle or sectoral trends over most of the 1990s through
sample and those for the 1984:Q1–2009:Q2 sample,
the first half of 2006, when the trend abruptly reversed,
with government being a notable exception (see the
reflecting the unfolding crisis in the housing market.
fourth column in tables 2 and 4). Nonetheless, the inThe sharp drop in Xit shows that construction employterpretation of the results seems to hold. In particular,
ment seems to be taking a bigger hit in the current
when estimated on the 1984:Q1–2009:Q2 sample, Xit
episode than can be explained based on the usual prior
in construction shows the run-up in construction emcyclical patterns for this sector. Perhaps even more
ployment starting in the mid-1990s and the abrupt denoteworthy is the recent experience in finance, insurcline in 2006 that cannot be explained by the typical
ance, and real estate (figure 3, panel G) that shows a
cyclical patterns of the past. The estimated Xit values
marked decline in recent years, suggesting this sector
are shown in figure 4 for the two samples.
is in the midst of a restructuring that is unexplained by
either the normal cyclical pattern or long-term trends.
Sectoral reallocation
How this downsizing of FIRE affects the unemployIn his original paper, Lilien (1982) presented a
ment rate is an open question.
dispersion measure as a way to quantify the degree of
As table 1 (p. 44) suggested, the parameters of
sectoral reallocation occurring in the economy at any
the model may change over time. A test of parameter
given time. His measure is given by
stability can be done using a likelihood ratio test. The
test statistic compares the log likelihood of the model
1/ 2
estimated using the full sample, from 1950:Q1 through


5)	 σ Lt ≡  ∑ sit ( git − gt ) 2  , 	
2009:Q2, with the sum of the log likelihoods from

 i
	
the model estimated on two smaller samples—the
1950:Q1–1983:Q4 period and the 1984:Q1–2009:Q2
where sit is industry i’s employment share at time t ;
period. The resulting test statistic is distributed X 2(46),
git is employment growth in i at time t; and gt is total
and its value is 498.22, rejecting the hypothesis that
employment growth at time t. Abraham and Katz
at normal confidence levels the parameter vector is
(1986) demonstrate that this dispersion measure will
the same for the two smaller sample periods.
increase even if no sectoral reallocation is present,
Table 4 presents parameter estimates from the
simply because some industries are more cyclically
1984:Q1–2009:Q2 sample period. In comparing the
sensitive than others.
estimates found in table 2 (p. 47) and table 4, there is
some evidence of “The Great Moderation,”7 with most
Table 3

∧

∧

∧

Federal Reserve Bank of Chicago

49

figure 3

Noncyclical employment growth: Selected industries, 1950:Q1–2009:Q2
A. Construction
percent

B. Durable manufacturing
percent

20

20

10

10

0

0

–10

–10

–20
1950 ’56 ’62

’68 ’74

’80 ’86 ’92

’98 2004 ’10

C. Nondurable manufacturing
percent

–20
1950 ’56 ’62

8

5

4

0

0

–5

–4
’68 ’74

’80 ’86 ’92

’98 2004 ’10

E. Wholesale trade
percent

–8
1950 ’56 ’62

6

3

3

0

0

–3

–3
’68 ’74

’80 ’86 ’92

’98 2004 ’10

G. Finance, insurance, and real estate
percent

–6
1950 ’56 ’62

’68 ’74

’80 ’86 ’92

’98 2004 ’10

’68 ’74

’80 ’86 ’92

’98 2004 ’10

’68 ’74

’80 ’86 ’92

’98 2004 ’10

H. Services
percent

8

4

4

2

0

0

–4

–2

–8
1950 ’56 ’62

’98 2004 ’10

F. Retail trade
percent

6

–6
1950 ’56 ’62

’80 ’86 ’92

D. Transportation and utilities
percent

10

–10
1950 ’56 ’62

’68 ’74

’68 ’74

’80 ’86 ’92

’98 2004 ’10

’68 ’74

’80 ’86 ’92

’98 2004 ’10

–4
1950 ’56 ’62

I. Government
percent
6
3
0
–3
–6
1950 ’56 ’62

Notes: The panels show the estimated Xit values. The shaded areas indicate official periods of recession as identified by the National Bureau
of Economic Research; the dashed vertical line indicates the most recent business cycle peak.
Source: Author’s calculations based on data from the U.S. Bureau of Labor Statistics from Haver Analytics.

50

4Q/2009, Economic Perspectives

		

Table 4

Parameter estimates, 1984:Q1–2009:Q2
ˆi
a
	

	

ˆ1i
b
	

ˆ2i
b
	

ˆγi

	

s
ˆi

Construction	
	

1.1703	
(5.8890)	

2.0239***	
(0.4471)	

0.1505	
(0.7041)	

0.7837***	
(0.1066)	

4.3028***
(1.5117)

Durable manufacturing	
	

−2.0404	
(5.8171)	

1.2233***	
(0.3697)	

0.9706***	
(0.3687)	

0.7809***	
(0.1028)	

1.4940***
(0.3755)

Nondurable manufacturing	
	

−1.7455	
(2.8657)	

0.6885***	
(0.2296)	

0.3827	
(0.2630)	

0.7261***	
(0.1025)	

0.8580***
(0.2103)

Transportation and utilities	
	

0.9551	
(2.8641)	

0.6666*	
(0.3072)	

0.4383	
(0.3680)	

0.0793	
(0.1213)	

2.0082***
(0.3299)

Wholesale trade	
	

0.5980	
(2.6710)	

0.7366***	
(0.1784)	

0.2876	
(0.2258)	

0.7551***	
(0.0807)	

0.6228***
(0.1714)

Retail trade	
	

1.0153	
(2.5684)	

0.7983***	
(0.2299)	

0.1708	
(0.3379)	

0.3190*	
(0.1487)	

1.0826***
(0.2504)

Finance, insurance,	
and real estate	

1.1042	
(1.5869)	

0.2289	
(0.1927)	

0.2021	
(0.1860)	

0.8818***	
(0.0883)	

0.9104***
(0.2466)

Services	
	

2.5218	
(2.2840)	

0.6012***	
(0.1699)	

0.2775	
(0.1683)	

0.0339	
(0.2188)	

0.3434***
(0.0882)

Government	
	

1.3070***	
(0.4682)	

− 0.1437	
(0.2637)	

0.2893	
(0.2858)	

0.2061*	
(0.1003)	

1.4475***
(0.2520)

*Significant at the 5 percent level.
**Significant at the 2 percent level.
***Significant at the 1 percent level.
Note: Standard errors are in parentheses.
Source: Author’s calculations based on data from the U.S. Bureau of Labor Statistics from Haver Analytics.

Keep in mind the Abraham and Katz (1986)
criticism that Lilien’s (1982) dispersion measure reflects
cyclical movements: The framework presented previously provides a way to eliminate the impact of the
cycle on employment shares, industry employment
growth, and aggregate employment growth so as to
create a dispersion measure that is purged of cyclical
variation. This measure is given by
1/ 2

6)	



σ t ≡  ∑ sit ( g it − g t ) 2  ,
 i


where x indicates that the variable x is purged of the
cycle. To create the purged series, first, let g it = X it .
Then, assuming that the cycle was zero in some reference
year, taken here to be 1964, it is simple to calculate
eit , et , sit , and g t , where eit is noncyclical employment
in industry i at time t and et is total noncyclical employment at time t. Figure 5 shows the results of these calculations. The red line is Lilien’s (1982) measure as
given in equation 5, and the black line is calculated

Federal Reserve Bank of Chicago

as in equation 6. The noncyclical measure of dispersion
is far less volatile than the original measure, as Abraham
and Katz (1986) argued. Nonetheless, there has been
a modest uptick in this measure of structural realignment over the past couple of quarters. Figure 6 shows
the noncyclical measure in panel A and another measure
that is based only on the shocks εit in panel B. In this
figure you can see the recent uptick more clearly. The
most recent quarter shows a decline in these dispersion
measures, reflecting industry shocks that are smaller in
magnitude than those of the previous few quarters. However, while it suggests a potential role for industrial
realignment in explaining recent increases in unemployment, this simple summary measure may not be too
informative in explaining recent changes in the unemployment rate. To put it more succinctly, structural
realignment in and of itself may have little impact on
the unemployment rate. Workers laid off in one sector
may be readily absorbed into other industries, particularly if real wages adjust to encourage the flow of
workers from declining industries to expanding ones.

51

In order to determine whether the
structural component of employment
growth plays a role in unemployment
dynamics, I ran regressions of the following form:

figure 4

Estimated idiosyncratic component in construction:
Full sample versus 1984:Q1–2009:Q2 sample
percent
8

7)	 ∆urt = α ( L)∆urt −1 + δ ( L)Cyclet +

6

λ ( L)Σt + cWt + υt ,

4
2
0
–2
–4
–6
–8
1984 ’86 ’88 ’90 ’92 ’94 ’96 ’98 2000 ’02 ’04 ’06 ’08 ’10
1950:Q1–2009:Q2 (full) sample
1984:Q1–2009:Q2 sample
Notes: See the text for further details on the idiosyncratic component (Xit)
of industry employment growth, which is estimated on the two samples.
The shaded areas indicate official periods of recession as identified by the
National Bureau of Economic Research; the dashed vertical line indicates
the most recent business cycle peak.
Source: Author’s calculations based on data from the U.S. Bureau of Labor
Statistics from Haver Analytics.

∧

figure 5

Dispersion measures, 1950:Q1–2009:Q2
16
14
12
10
8
6
4
2

∧

0
1950 ’55

’60

’65 ’70

’75 ’80 ’85 ’90

’95 2000 ’05

’10

σ t (equation 6)
σLt (Lilien, 1982)
Note: The shaded areas indicate official periods of recession as identified by
the National Bureau of Economic Research; the dashed vertical line indicates
the most recent business cycle peak.
Source: Author’s calculations based on data from the U.S. Bureau of Labor
Statistics from Haver Analytics.

52

where α(L), δ(L), and λ(L) are polynomials in the lag operator L; ∆urt is the
change in the unemployment rate at time t;
Cyclet is a measure of the cycle at time t;
Σt is a measure of sectoral reallocation at
time t, including the constructed dispersion
measures or, more broadly, the individual
estimated Xit and εit values; and Wt is other
variables that potentially influence changes
in the unemployment rate. The variable υt
is a random shock assumed to be independent and identically normally distributed.
Two separate measures of the cycle
were examined, namely, deviations of
real GDP growth from its long-term
average ( gGDPt - gGDP) and Ct . Several
different measures of Σt were considered,
including the two noncyclical measures
computed as in equation 6, as well as the
estimated Xit values and the εit values individually. Regression results are shown
in table 5. Three lags of changes in the
unemployment rate are included in each
regression, as is a demographic variable
that is calculated as the change in the
female labor force participation rate of
white women aged 20 and above. (Other
demographic variables that reflected changes
in the age, race, and sex composition of
the labor force were also investigated but
were statistically insignificant and are not
reported in these results.) Of the two cyclical variables considered, the measure of
the employment cycle Ct performed better
than deviations of real GDP growth from
its long-term average, in that those regressions had higher R 2 values. Generally, the
two dispersion measures of sectoral reallocation did poorly in explaining changes
to the unemployment rate. The third and
fourth columns examine the impact of
adding dispersion measures of sectoral
reallocation to the regressions. These

4Q/2009, Economic Perspectives

figure 6

Noncyclical measures of sectoral reallocation, 1984:Q1–2009:Q2
A. Noncyclical measure
3.6

dispersion measures are statistically significant, but enter with the opposite sign
anticipated by the sectoral reallocation
hypothesis; that is, increasing reallocation, as measured here, tends to reduce
the unemployment rate.8 The last two regressions omit the cyclical variable, Ct  ,
and include the two dispersion measures.
Only in the results of the sixth column, in
which the cyclical variable is omitted,
does dispersion enter significantly positive. The weak results suggest that sectoral
reallocation as measured here may be positively associated with changes in the unemployment rate. However, once cyclical
effects are properly accounted for, the
impact disappears or changes sign.
One possibility is that these dispersion
measures, being summary statistics, are
not very good at capturing the effects of
reallocation in the labor market. The dispersion measure treats all employment
shifts of the same magnitude as identical,
regardless of the industry. This ignores the
possibility that human capital may differ
across industries, suggesting that unemployment responses should differ across
sectors as well. Specifically, some industries may require industry-specific human
∧

3.2
2.8
2.4
2.0
1984 ’86 ’88 ’90 ’92 ’94 ’96 ’98 2000 ’02 ’04 ’06 ’08 ’10

B. Measure based only on the shocks εit
5.0
4.0
3.0
2.0
1.0
1984 ’86 ’88 ’90 ’92 ’94 ’96 ’98 2000 ’02 ’04 ’06 ’08 ’10
Note: The shaded areas indicate official periods of recession as identified by
the National Bureau of Economic Research; the dashed vertical line indicates
the most recent business cycle peak.
Source: Author’s calculations based on data from the U.S. Bureau of Labor
Statistics from Haver Analytics.

		

Table 5

Regression results: Dependent variable is urt   , 1984:Q1–2009:Q2 sample
	

3 lags urt–1	
Current and two lags 	
of gGDPt – gGDP	

1	

2	

3	

4	

5	

6

Yes	

Yes	

Yes	

Yes	

Yes	

Yes

– 0.0433***	
—	
—	
—	
—	
(0.0074)					

—

Current and two lags 	
—	
– 0.1674***	
∧
of Ct 		
(0.0264)	
	
		
Change in female 	
0.1126	
0.0690	
participation rate	
(0.0749)	
(0.0652)	
∧

σ based on Xit	

—	
—	
			

– 0.1771***	
(0.0264)	
0.1240	
(0.0696)	

∧

2

0.6714	

0.7904	

0.1270	
(0.0686)	

– 0.0131*	
—	
(0.0065)		

—	
—	
—	
σ based on εit	
				
R 	

– 0.1980***	
(0.0291)

0.7970	

—	
0.0639	
(0.0950)	

0.0362
(0.0911)

0.0073	
(0.0077)	

—

– 0.0175*	
—	
(0.0077)		
0.7993	

—

0.5987	

0.0182*
(0.0079)
0.6162

*Significant at the 5 percent level.
**Significant at the 2 percent level.
***Significant at the 1 percent level.
Notes: Estimating over the full sample did not materially change the results. The full sample was estimated from 1954:Q2 through 2009:Q2,
since the female labor force participation rate data are not available prior to 1954:Q2. The estimate of the employment cycle employed in the
analysis is from the 1950:Q1–2009:Q2 Kalman filter exercise. Standard errors are in parentheses.
Sources: Author’s calculations based on data from the U.S. Bureau of Labor Statistics and U.S. Bureau of Economic Analysis from Haver Analytics.		
		
	

Federal Reserve Bank of Chicago

53

		

Table 6

Effect of idiosyncratic components and shocks on changes in the unemployment rate, 1954:Q2–2009:Q2
		

εit
Xit				

	
	
	

Coefficient		
and standard		
2
error	
R 	

Construction	
	

– 0.0111***	
0.7910	
(0.0025)		

Durable manufacturing	
	

Coefficient		
and standard		2
R 	
error	

Coefficient	
and standard
error	

– 0.0117***	
(0.0027)	

– 0.0093***	
0.7836	
(0.0027)		

–0.0093***
(0.0027)	

– 0.0174***	
0.7876	
(0.0044)		

– 0.0191***	
(0.0045)	

– 0.0156**	
0.7818	
(0.0050)		

– 0.0102
(0.0062)	

Nondurable manufacturing	
	

– 0.0022	
0.7718	
(0.0076)		

– 0.0013	
(0.0082)	

0.0010	
0.7718	
(0.0105)		

0.0074		
(0.0129)	

Transportation and utilities	
	

– 0.0146*	
0.7770	
(0.0065)		

– 0.0041	
(0.0070)	

– 0.0119	
0.7756	
(0.0062)		

– 0.0043		
(0.0064)	

Wholesale trade	
	

0.0161	
0.7743	
(0.0104)		

0.0180	
(0.0103)	

0.0159	
0.7735	
(0.0125)		

0.0166		
(0.0126)	

Retail trade	
	

0.0242**	
0.7781	
(0.0098)		

0.0108	
(0.0103)	

0.0212*	
0.7766	
(0.0099)		

0.0132		
(0.0111)	

Finance, insurance,	
and real estate	

0.0001	
0.7718	
(0.0074)		

0.0031	
(0.0072)	

0.0171	
0.7734	
(0.0139)		

0.0201	
(0.0130)	

Services	
	

0.0596***	
0.7799	
(0.0212)		

0.0228	
(0.0252)	

0.0508***	
0.7790	
(0.0192)		

0.0448		
(0.0243)	

Government	
	

0.0126*	
0.7761	
(0.0062)		

0.0146**	
(0.0059)	

0.0224***	
0.7806	
(0.0076)		

0.0227***		
(0.0072)	

			

Coefficient	
and standard	
error	

2

R = 0.8179			

2

R = 0.8064	

*Significant at the 5 percent level.
**Significant at the 2 percent level.
***Significant at the 1 percent level.
Notes: Dependent variable is ur t . Also included in the regressions are three lags of the dependent variable, one current and two lags of the
estimated employment cycle, and changes in the labor force participation rate of white women aged 20 and above. See the text for further details.
Source: Author’s calculations based on data from the U.S. Bureau of Labor Statistics from Haver Analytics.

capital. Sectoral reallocation away from those industries will take time and cost more for those who have
become displaced. To examine this possibility, I have
entered the idiosyncratic components both individually and together. The results are found in tables 6 and
7, which differ only in their sample periods. Table 6
provides results for the period from 1954:Q2 through
2009:Q2, and table 7 provides results from 1984:Q1
through 2009:Q2.9
The first two columns of table 6 examine the
effect of including each idiosyncratic component
separately in a regression having both cyclical and
demographic variables. The R 2 values are reported
from each of these regressions in the second column.
The sectors of the economy in which the idiosyncratic
component of employment growth is statistically
significant are construction, durable manufacturing,
transportation and utilities, retail trade, services, and

54

government. The signs of these effects are also interesting to consider. Specifically, as noncyclical employment grows above trend in construction, durable
manufacturing, and transportation and utilities, it reduces the unemployment rate. However, it has the opposite effect in retail trade, services, and government,
in that shifts toward these industries tend to raise the
unemployment rate. The third column reports the coefficients from a single regression in which all idiosyncratic industry components are included, in addition
to current and lagged employment cycle and demographic variables. Noncyclical shifts in construction,
durable manufacturing, and government are still
statistically significant, entering with the same sign
as in the single variable regressions. However, transportation and utilities, retail trade, and services are
no longer statistically significant.

4Q/2009, Economic Perspectives

		

Table 7

Effect of idiosyncratic components and shocks on changes in the unemployment rate, 1984:Q1–2009:Q2
εit

		
Xit	
	
	
Coefficient		
	
and standard		
2
R 	
	
error	

Coefficient	
and standard	
error	

Coefficient		
and standard		2
R 	
error	

Coefficient	
and standard
error	

Construction	
	

– 0.0101*	
0.7990	
(0.0045)		

– 0.0129*	
(0.0063)	

– 0.0085	
0.7935	
(0.0055)		

– 0.0125*
(0.0059)

Durable manufacturing	
	

– 0.0025	
0.7884	
(0.0070)		

– 0.0054	
(0.0087)	

– 0.0128	
0.7912	
(0.0110)		

– 0.0123
(0.0132)

Nondurable manufacturing	
	

0.0130	
0.7919	
(0.0101)		

0.0031	
(0.0145)	

0.0152	
0.7898	
(0.0175)		

0.0139
(0.0208)

Transportation and utilities	
	

– 0.0254***	
0.8048	
(0.0090)		

– 0.0272**	
(0.0108)	

– 0.0246***	
0.8050	
(0.0086)		

– 0.0243**
(0.0094)

Wholesale trade	
	

0.0108	
0.7987	
(0.0113)		

– 0.0001	
(0.0137)	

0.0007	
0.7881	
(0.0162)		

– 0.0024
(0.0172)

Retail trade	
	

0.0252*	
0.7981	
(0.0117)		

0.0206	
(0.0143)	

0.0239	
0.7966	
(0.0121)		

0.0221
(0.0146)

– 0.0048	
0.7892	
(0.0070)		

– 0.0043	
(0.0079)	

0.0130	
0.7901	
(0.0139)		

0.0107
(0.0137)

Services	
	

0.0025	
0.7882	
(0.0305)		

0.0102	
(0.0376)	

0.0003	
0.7881	
(0.0274)		

0.0149
(0.0346)

Government	
	

0.0087	
0.7997	
(0.0099)		

0.0019	
(0.0099)	

0.0005	
0.7881	
(0.0101)		

– 0.0023
(0.0099)

Finance, insurance, 	
and real estate	

			
	

2

R = 0.8153			

2

R = 0.8104

*Significant at the 5 percent level.
**Significant at the 2 percent level.
***Significant at the 1 percent level.
Notes: Dependent variable is ur t . Also Included in the regressions are three lags of the dependent variable, current and two lags of the
estimated employment cycle, and changes in the labor force participation rate of white women aged 20 and above. See the text for further details.
Source: Author’s calculations based on data from the U.S. Bureau of Labor Statistics from Haver Analytics.

The fourth, fifth, and sixth columns of table 6 repeat the regression exercise but instead employ idiosyncratic shocks εit as explanatory variables. The results
are consistent with the results using Xit. Shocks to construction and durable manufacturing tend to reduce
unemployment, whereas shocks to retail trade, services,
and government tend to raise unemployment (fourth
column). The transportation and utilities industry does
not meet the 5 percent significance criterion. However,
its marginal significance level is close to 10 percent.
Table 7 reestimates the equations of the preceding
table, but with the 1984:Q1–2009:Q2 sample period.
Most of the results disappear for this sample period.
To obtain estimates of the effect of sectoral reallocation on the unemployment rate, I assume that the
economy was in equilibrium in 2007:Q4, with an unemployment rate of 4.8 percent. Furthermore, I assume

Federal Reserve Bank of Chicago

that the cycle is set equal to its expected value from
2007:Q4 through 2009:Q2. In this analysis, that implies that Ct = 0. I also assume that there are no demographic changes in the female labor force participation rate over this period.
Table 8 provides estimates of the effect of Xit on
the civilian unemployment rate as estimated from the
equation used in the third column of table 7, using the
1984:Q1–2009:Q2 sample period. The first column
gives the estimated total effect of the Xit on the unemployment rate, given the assumptions in the preceding
paragraph. The impact of sectoral reallocation in this
model is negligible. The remaining columns compute
the impact on the equilibrium unemployment rate of
having idiosyncratic employment growth shocks in the
specified industry given by the estimated shocks. For
example, although equilibrium employment remained

55

56

Table 8

4.68	

4.63	

4.82	

4.80	

4.80	

4.80	

4.80	

4.80	

4.71	

4.70	

4.69	

4.73	

4.75	

4.94	

4.90	

4.87	

4.85	

4.83	

4.82	

4.79	

4.79	

4.79	

4.79	

4.79	

4.80	

Notes: Results are for the regression in table 7, third column. Calculations assume that the equilibrium unemployment rate was equal to its value of 4.8 percent in 2007:Q4 and that the employment cycle
is in equilibrium from 2007:Q4 through 2009:Q2, so that Xit = 0 from 2007:Q4 through 2009:Q2. The first column calculates the unemployment rate that would have occurred had the industry idiosyncratic
components been as estimated from 2007:Q4 through 2009:Q2 and the employment cycle been in equilibrium. The second through tenth columns reflect the impact of the idiosyncratic components in each
of the individual industries. For example, in the second column the estimated impact of idiosyncratic shifts in construction on the unemployment rate in 2009:Q2, assuming all other industry components to
be as given by the estimated Xit values, is to raise the unemployment rate by 0.24 percentage points (calculated by subtracting the value in the last row, first column, from the value in the last row, second
column, 5.06 – 4.82). Results differ if the unemployment rate regression is estimated using the entire sample period. 	
Source: Author’s calculations based on data from the U.S. Bureau of Labor Statistics from Haver Analytics.

5.06	

4.64	

4.82	

4.68	

4.81	

2009:Q2	

5.01	

4.68	

4.73	

4.70	

4.82	

2009:Q1	

4.94	

4.69	

4.69	

4.73	

4.81	

2008:Q4	

4.91	

4.72	

4.81	

4.79	

4.79

4.79

4.79

4.80

4.80

4.80

4.80

4.71	

4.76	

4.80	

4.80	

2008:Q3	

4.89	

4.76	

4.80	

4.80	

4.76	

4.78	

4.80	

2008:Q2	

4.84	

4.80	

4.79	

4.80	

4.80	

2008:Q1	

4.80	

4.80	

4.80	

( - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - percent - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - )

Government

2007:Q4	

Services	

	

Finance,
insurance,	
and real estate	

Estimated impact of idiosyncratic industry employment growth on unemployment, 1984:Q1–2009:Q2

				
								
	
All		
Durable	
Nondurable	
Transportation	
Wholesale	
Retail	
	
Xit	
Construction	
manufacturing	
manufacturing	
and utilities	
trade	
trade	

		

largely unchanged, by 2009:Q2 the
shocks to construction raised the unemployment rate by approximately 25 basis points (see the notes in table 8). This
rise was offset by declines elsewhere.
As a whole, these models suggest
that idiosyncratic shifts in industry employment growth account for very little
of the observed increase in the unemployment rate over the past several quarters.
On its own, this would imply that there
is room for accommodative policy as
a response to the current increase in unemployment, but bringing to bear additional evidence on dispersion would
help us gain a better sense of whether
the conclusions implied by the empirical
model discussed here are robust. There
is a great deal of uncertainty surrounding the estimates presented here. As
noted before, the parameters of the statespace model appear to differ between
the 1950:Q1–1983:Q4 period and the
1984:Q1–2009:Q2 period. Because of
parameter and model uncertainty, these
estimates of the impact of sectoral reallocation on the unemployment rate must
be viewed somewhat skeptically. To
underscore this fact, results of the same
exercise that estimate the unemployment
equation using the full sample suggest a
decline in unemployment since 2008:Q1
attributable to sectoral reallocation.

Conclusion	

The labor market appears to have a
cycle that is well described by co-movements in employment growth. The estimate of the employment cycle that results
from my model seems to agree with anecdotal evidence about jobless recoveries. The model also does a good job of
capturing turning points in the business
cycle, suggesting that it may be a useful
tool for understanding labor market dynamics and may help in predicting future
employment. The idiosyncratic component that the methodology yields may
also provide some additional insight into
the impact of structural realignment on
changes in the unemployment rate. Structural change favoring construction, durable manufacturing, and transportation

4Q/2009, Economic Perspectives

and utilities seems to be associated with decreasing
unemployment; this suggests that there may be some
impediments to displaced workers in these sectors
finding jobs in other industries. Even with the downsizing of finance, insurance, and real estate, the overall
impact on the unemployment rate is not statistically
significant. One possibility is that employees from

finance, insurance, and real estate are better able to
find alternative employment in other sectors of the
economy because the skills they possess are more
readily transferable to employment in other industries. Conversely, employees in construction, durable
manufacturing, and transportation and utilities may
be less readily absorbed into other sectors.

NOTES
Stock and Watson (1989), p. 353.

1

The services sector includes information services, professional
and business services, education and health services, leisure and
hospitality, and other services. Mining has been omitted from the
analysis for two reasons. First, because of the incidence of strikes,
employment growth in this industry is quite volatile. Second, mining
accounts for a small fraction of total employment.

2

Averages for the current decade are based on data through 2009:Q2.

3

The only exception, unreported here, is the mining sector.

4

The hat symbol (^) indicates an estimate.

5

There is another notable discrepancy when comparing the NBER
business cycle recession dates with those estimated here. The two
NBER recessions in the early and mid-1970s were longer by two
and three quarters, respectively, than those proposed here. Instead,

6

the employment-based measure of the cycle shows a labor market
that was quick to return to more normal activity during those times.
7
The Great Moderation is a term used to describe the period usually
thought to have begun in 1984 and lasting through the present, during which many economic time series exhibited less volatility than
in previous years. The validity of this concept as a permanent shift
has been called into question by the recent financial crisis.

The coefficients reported here are for contemporaneous measures
of dispersion. Including a number of leads and lags did not substantively change the results. Altering the specification so that the dispersion measure was in changes or log changes had no bearing on
the results either.

8

The full sample period is slightly shortened by starting in 1954:Q2
because earlier data for female labor force participation were not
available.

9

REFERENCES

Aaronson, Daniel, Ellen R. Rissman, and Daniel
G. Sullivan, 2004, “Can sectoral reallocation explain
the jobless recovery?,” Economic Perspectives, Federal
Reserve Bank of Chicago, Vol. 28, No. 2, Second
Quarter, pp. 36–49.
Abraham, Katharine G., and Lawrence F. Katz,
1986, “Cyclical unemployment: Sectoral shifts or
aggregate disturbances?,” Journal of Political Economy,
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Bloom, Nicholas, Max Floetotto, and Nir Jaimovich,
2009, “Really uncertain business cycles,” Stanford
University, mimeo, January, available at http://aida.
econ.yale.edu/seminars/macro/mac09/bloom-floetottojaimovich-090219.pdf.
Burns, Arthur F., and Wesley C. Mitchell, 1946,
Measuring Business Cycles, New York: National
Bureau of Economic Research.
Groshen, Erica L., and Simon Potter, 2003, “Has
structural change contributed to a jobless recovery?,”
Current Issues in Economics and Finance, Federal
Reserve Bank of New York, Vol. 9, No. 8, August, pp. 1–7.
Hamilton, James D., 1994, Time Series Analysis,
Princeton, NJ: Princeton University Press, pp. 372–408.

Federal Reserve Bank of Chicago

Harvey, Andrew C., 1989, Forecasting, Structural
Time Series Models, and the Kalman Filter, Cambridge,
UK, and New York: Cambridge University Press.
Lilien, David M., 1982, “Sectoral shifts and cyclical
unemployment,” Journal of Political Economy, Vol. 90,
No. 4, August, pp. 777–793.
Loungani, Prakash, Mark Rush, and William Tave,
1990, “Stock market dispersion and unemployment,”
Journal of Monetary Economics, Vol. 25, No. 3, June,
pp. 367–388.
Phelan, Christopher, and Alberto Trejos, 2000,
“The aggregate effects of sectoral reallocations,”
Journal of Monetary Economics, Vol. 45, No. 2,
April, pp. 249–268.
Rissman, Ellen R., 1993, “Wage growth and sectoral
shifts: Phillips curve redux,” Journal of Monetary
Economics, Vol. 31, No. 3, June, pp. 395–416.
Stock, James H., and Mark W. Watson, 1989, “New
indexes of coincident and leading economic indicators,”
in NBER Macroeconomics Annual 1989, Olivier J.
Blanchard and Stanley Fischer (eds.), NBER Macroeconomics Annual, Vol. 4, Cambridge, MA: National
Bureau of Economic Research, pp. 351–394.

57

Index for 2009
Issue

Pages

Economic Perspectives special issue on payments fraud: An introduction
Gene Amromin and Richard D. Porter

First Quarter

2-6

Payments Fraud: Perception Versus Reality—A conference summary
Tiffany Gates and Katy Jacob

First Quarter

7-13

Fraud containment
Bruce J. Summers

First Quarter

17-21

Data security, privacy, and identity theft: The economics behind
the policy debates
William Roberds and Stacey L. Schreft

First Quarter

22-30

Perspectives on retail payments fraud
Steve Malphrus

First Quarter

31-36

Divided we fall: Fighting payments fraud together
Mark N. Greene

First Quarter

37^42

An examination of the fraud liability shift in consumer card-based
payment systems
Duncan B. Douglass

First Quarter

43^49

Vulnerabilities in first-generation RFID-enabled credit cards
Thomas S. Heydt-Benjamin, Daniel V. Bailey, Kevin Fu,
Ari Juels, and Tom O’Hare

First Quarter

50-59

Comparing patterns of default among prime and subprime mortgages
Gene Amromin and Anna L. Paulson

Second Quarter

18-37

Investing over the life cycle with long-run labor income risk
Luca Benzoni and Olena Chyruk

Third Quarter

2-16

Preannounced tax cuts and their potential influence on the 2001 recession
R. Andrew Butters and Marcelo Veracierto

Third Quarter

17-32

How will baby boomer retirements affect teacher labor markets?
Daniel Aaronson and Katherine Meckel

Fourth Quarter

2-15

Employment growth: Cyclical movements or structural change?
Ellen R. Rissman

Fourth Quarter

40-57

Policymaking under uncertainty: Gradualism and robustness
Gadi Barlevy

Second Quarter

38-55

The recession of 1937—A cautionary tale
Franyois R. Velde

Fourth Quarter

16-37

Second Quarter

2-17

Title and author(s)
BANKING, CREDIT, AND FINANCE

ECONOMIC CONDITIONS

MONEY AND MONETARY POLICY

REGIONAL ISSUES
From tail fins to hybrids: How Detroit lost its dominance
of the U.S. auto market
Thomas H. Klier

To order copies of any of these issues or to receive a list of other publications, please telephone (312) 322-5111 or write to: Federal Reserve
Bank of Chicago, Public Information Center, P.O. Box 834, Chicago, IL 60690-0834. The articles are also available to download in PDF
format at www.chicagofed.org/economic_research_and_data/economic_perspectives.cfm .

58

4Q/2009, Economic Perspectives