The Great Depression Of Income: Historical Estimates of the LongTerm Effect of Entering the Labor Market During a Recession
Jeremy Grant Moulton
University of North Carolina, Chapel Hill
Department of Public Policy
Abernethy Hall, CB #3435
Chapel Hill, NC 27599-3435
[email protected]
919-962-1002
In this paper I use three different approaches to estimate the long-run impact of variation in labor market
entry conditions, driven by the Great Depression, on income and other labor outcomes in the 1940 Census.
I find a strong relationship between income and state level wage worker employment and wages (from the
Census of Manufacturers) measured in the year an individual became working aged, using a birth state and
birth cohort fixed effects model. In the second approach I use a regression discontinuity and find that 10
years after entry, less educated men entering the labor market at the beginning of the Great Depression in
1930 earned 8.1 percent less than entrants just one year prior. Lastly, I combine the first and second
approaches to estimate the regression discontinuity for two samples stratified by severity of the Great
Depression in the observation’s birth state (data from Wallis, 1989). I find that the effect is larger (14.6
percent) for those born in states more negatively affected by the Great Depression and close to zero for
those born in states relatively less affected.
Keywords: Labor Market Entry; Great Depression
JEL Classification: J60, N32
I. INTRODUCTION
The Great Depression shifted the United States from an era of opulence to widespread desolation.
This transformation took most by surprise as the economy plunged following the stock market
crash in late 1929 and monetary/banking shocks of the early 1930s. While GDP sank,
unemployment skyrocketed to record levels. Millions of Americans lost their jobs and struggled
to survive for nearly a decade. The Great Depression has been studied extensively, with many
modern researchers and journalists comparing it to the Great Recession. One of the Great
Depression’s more renowned scholars, Ben Bernanke (1986), once remarked: “Seismologists learn
more from one large earthquake than from a dozen small tremors. On the same principle, the Great
Depression of the 1930's would appear to present an important opportunity for the study of the
effects of business cycles on the labor market.” In this paper I employ the “large earthquake” of
the Great Depression as a natural experiment in investigating an important question in the literature
– what is the impact of labor market conditions at first entry on long-term labor outcomes? This
general question is not new (see Ellwood, 1982; Beaudry and Dinardo, 1991; Baker, Gibbs, and
Holmstrom, 1994; Devereux, 2002a; 2002b; Kahn, 2010; Oyer, 2006; 2008; Genda, Kondo, and
Ohta, 2010; Oreopoulos, von Wachter, and Heisz, 2012; Schmieder and von Wachter, 2010; Abel
and Dietz, 2016), but until now no one has used the Great Depression to glean new insights into
this particular question. The closest is recent research from Thomasson and Fishback (2014) who
used this economic shock to investigate the impact of conditions at birth (driven by the Great
Depression) on income and disability much later in life (1970 and 1980) and Feigenbaum (2015)
who used it to investigate intergenerational mobility and inequality. This question is of interest to
economic historians who study the Great Depression specifically; but, more generally, the results
present fresh insights for labor economists interested in comparing the estimates to those using
1
modern recessions (that are smaller and shorter lived), and for researchers conducting cohort-based
studies that rely on similarity of individuals across the cohorts studied in this paper.
I investigate the long-run impact of the Great Depression on income, labor force participation at
the extensive and intensive margins, and whether they were employed by the Works Progress
Administration (WPA). I use the 1940 Census that measures these outcomes roughly 10 years after
the onset of the Great Depression.1 I employ three approaches to investigate this question. The first
is a common method in the literature – a birth state and birth cohort fixed effects model. This
model uses variation in state level employment and wages from the Census of Manufacturers
(COM) at the time of initial labor market entry. I next use a regression discontinuity (RD) model
that focuses the analysis on the Great Depression’s large negative labor market shock between
1929 and 1930. RD has not previously been used to study the impact of labor market entry
conditions on long-run labor outcomes, most likely because modern recessions (aside from the
Great Recession) have not been as large, immediate, or long-lasting. Lastly, I estimate if the
discontinuity varies with the severity of the labor market shock by combining the first two
approaches. To preface the main results, I find that entering the labor market during the Great
Depression had a strong, negative impact on less educated entrants’ income that persisted for at
least 10 years. I also find larger effects in states more negatively affected by the Great Depression.
Although the estimates are quite large, the treatment effect in relation to the shock’s size is not
significantly different from the current literature that uses modern recessions.
1
Most of the labor outcomes in the 1940 Census are actually for the prior year.
2
The paper proceeds as follows: Section II includes a brief review of the literature, Section III
describes the data, Section IV outlines the methodology, Section V examines the results, and
Section VI concludes.
II. LITERATURE REVIEW
The literature review focuses on prior research investigating the impact of recessions on labor
market outcomes, given the theoretical and methodological parallels in common with this study.
Oreopoulos et al. (2012) provides a thorough review of the theoretical and empirical research
concerning the impact of entering a bad labor market. In short, the neo-classical model predicts
that any short-term negative wage or income shock should not persist in the long run because the
workers’ productivity will be observed and they will eventually be paid according to their marginal
productivity (often referred to as perfect spot market models). However, if laborers invest in
human capital while on the job, there are differences in mobility based on first job, or differences
in the types of jobs hiring in pro- or counter-cyclical industries, then theory suggests that unlucky
entrants will constantly lag the lucky (i.e. permanent scarring models).
The empirical literature investigating modern recessions provides strong support for long-term
scarring for entrants into poor labor markets. Kahn (2010) investigates the effect of unemployment
at the time of college completion on wages for white males using the National Longitudinal Survey
of Youth (NLSY) for those graduating college between 1979 and 1989 and the March Current
Population Survey (CPS) for those graduating between 1986 and 1996. She finds an initial six to
seven percent reduction in wages for each one percentage point increase in the unemployment rate,
that diminishes to 2.5 percent 15 years after the shock. Oreopoulos et al. (2012) also investigate
3
college educated labor market entrants graduating between 1982 and 1999 using universityemployer matched administrative data from Canada. They find that a two standard deviation shift
in the unemployment rate initially causes a 10 percent income shock that slowly fades to zero in
10 years. Because of their rich data, they show that 40 to 50 percent of the wage shock is from
entrants during recessions joining smaller, lower paying firms. They also stratify their sample by
ability and identify a larger effect for the lower ability group.
Kahn (2010), Oreopoulos et al. (2012), and several others focus on the highly educated, as they
are hypothesized to be more affected by differences in on-the-job training. While the most recent
literature uses college graduates, prior literature focused on workers in general and in the case of
Devereux (2002a) found that recessions have a larger impact on the lesser skilled. Mansour (2009)
reevaluated work by Kahn (2010) and found even larger effects after controlling for worker ability.
Genda et al (2010) finds that the impact for less educated men born in the United States is
temporary, while it is prolonged for Japanese men, although this can be attributed to different
hiring practices. What is clear from the prior literature is that labor market conditions at a worker’s
entry cause an immediate and often persistent effect on income.
III. DATA
I use the one percent 1940 decennial Census sample from the Integrated Public Use Microdata
Series (IPUMS, Ruggles et al 2010). I retain white males not enrolled in school, living in group
quarters, or on a farm. For most specifications I include those born between 1911 – 1920. Birth
year is calculated as Census Year - Age - 1 to compensate for the collection of data on April 1,
4
1940.2 I drop those with missing, NA, or zero education, and imputed education, gender, age,
income, group quarter, school attendance, or race. I also drop those with top coded wage and salary
annual income.3 Consistent with the literature and to limit the influence of outliers, I cull those
with income below the 1st percentile or above the 99th and also log the value.
In addition to income, I also include outcomes for whether the individual worked one or more
weeks in the prior year, the number of weeks that they worked, the number of hours they worked
in the prior week, and whether they are a WPA worker (designated in the Census as “at work,
public emergency”). There are some concerns with the hours worked variable as it only measures
the hours worked in the prior week rather than the usual hours worked per week. WPA status is
included as a labor outcome since the WPA primarily employed unemployed, unskilled workers
to construct New Deal projects. Their compensation was different from the private sector in that
they were paid a monthly wage equivalent to the prevailing wage for the county in which they
worked, but they were required to work 30 hours a week with any overtime remunerated as leave
time (Howard, 1943, pp. 213-218). Possibly because of this requirement, the hours worked
measure is also not available for WPA workers in the Census.
2
Failing to compensate for this will overestimate the birth year. There is a 75 percent chance that the individual was
born in the Year – Age – 1 birth year and a 25 percent chance it was Year – Age. Angrist and Krueger (1991, p. 991)
discuss this characteristic of Census data, Fagernäs (2014) uses this calculation for the 1910 – 1930 Censuses,
Snyder and Evans (2006, p. 486) use this strategy with the CPS, Stanley (2003, p. 694 in table notes) use it with the
OCG, and numerous genealogy websites recommend using it with the Census (for instance
http://familyhistorydaily.com/family-history/a-little-more-accuracy-estimating-the-year-of-birth/).
3
I hereafter omit “wage and salary” from references to income.
5
III. METHODS
The following approaches estimate the impact of labor market conditions at initial entry on labor
market outcomes in 1940.4 Because of the distance between labor entry and 1940, these models
estimate a relatively long-run effect that is similar in length to prior research (for instance Kahn,
2010; Oreopolous et al 2012). The prior literature focusing on college graduates assigns the labor
market conditions by the individual’s graduation year and usually birth state. In this paper I instead
assign the labor market condition measure by birth state and the first age an individual could enter
the labor market. Goldin and Katz (2008) show that the modal labor law in the 1920s allowed
individuals to obtain a work permit at age 14 if they completed at least six years of education, with
age being a more important factor than education. Between 1923 – 1940 41 states had a 14-yearold entry age, five were age 15, and two were age 16. While research finds that minimum age
working laws had some effect on child labor force participation in the early 20th century (Moehling,
1999; Lleras-Muney 2002; Manacorda 2006; Goldin and Katz 2011), enforcement was not perfect.
For instance, Fagernäs (2014) found that birth registration and school laws reduced the incidence
of child labor force participation in the 1910 and 1920 Census, but found no evidence of a reduction
in 1930, although she claims this is partly due to low incidence. I also find no difference in the
labor force participation of 14-year-olds in states with a minimum labor entry age of 15 or 16
compared to those using age 14 in the 1930 Census (see Appendix Tables A1 and A2). Further
evidence supporting an entry age of 14 comes from the 1940 Census, that collected income data
from those aged 14 and older, indicating that they considered 14 the first age individuals were
4
While there is interest in estimating the impact in later years, there are serious issues with using the 1950 Census.
By 1950 the labor market experienced a colossal positive shock brought about by World War II. World War II also
increased education (Bound and Turner, 2002) and flattened wages (Goldin and Margo, 1992). Additionally, the
1950 Census only collected income and education information from “sample-line” respondents, meaning there are
only one-third as many observations compared to the 1940 Census.
6
eligible to work. Additionally, the Unemployment section of the Historical Statistics of the United
States' chapter on Labor Force (Gartner, et al. 2006, pp. 2-29) explains that the Bureau of Labor
Statistics calculated the unemployment rate including individuals aged 14 and older until 1947
when the requirement was changed to 16 due to changing labor laws. If laws were perfectly
enforced, then the estimates using an age 14 minimum working age should be attenuated in relation
to using the actual labor laws, however, I instead find the opposite to be true (see Appendix Tables
A3 and A4).5 This indicates that these laws were not perfectly enforced, so I proceed by assuming
that age 14 is the minimum entry age for all states.
In most states individuals could have entered the labor market with six years of education, but very
few individuals have six years of education. There are instead two modal education exit points
during the 1920s and 1930s: the 8th and 12th grades. In most of the specifications discussed below
I include estimates for the full sample and the 8th grade sample, but I focus most of the discussion
of results on the 8th grade sample. I do this because those leaving school upon 8th grade completion
were 14 years old, coinciding both with the labor entry laws in most states and the labor market
condition measure I assign to each observation.6 The 8th grade sample is then closest to prior
research that assigned labor market conditions using the individual’s year of college graduation.7
5
Compare Tables A3 and A4 with Tables 2, 3, and 4. I also find that the effect is larger when states with a 15 or 16
minimum entry age law are omitted.
6
Goldin and Margo (1992) include 8th and 12th grade graduates in their Table II and separate 8th grade and 12th grade
from other educational groups in many of their tables. A prior version of this paper included results for the 12th
grade sample, but none of the estimates were significant and minimum entry age does not coincide with graduating
from the 12th grade, so they have been omitted.
7
I have also conducted the analysis in Equation 2 for all grade levels separately, using cutoffs at age 14 and also age
at graduation. I find statistically significant evidence only for the 8th grade sample (see Table A5 in the Appendix).
This does not mean that other education groups were unaffected in the short term since this cannot be estimated with
existing data (income data did not become available until the 1940 Census). It could also be that more educated
groups are better equipped to adjust in the long run (consistent with the high ability results in Oreopoulos et al,
2012) or simply that groups other than the 8th and 12th grade do not include enough observations to find statistically
significant results; for instance the 7th grade estimate using age 14 entry is similar in magnitude to the 8th grade
estimate, but is statistically insignificant.
7
IV.1 First Approach
The first approach (Equation 1) is similar in design to the prior literature (for instance Oreopolous
et al, 2012) in that it estimates the impact of labor market entry conditions on long-term labor
outcomes using a birth state (BPLs) and birth cohort (Agec) fixed effects model. The model uses
variation in initial labor market entry conditions (COMmeasuresc) within birth states both before
and during the Great Depression.8 The prior literature used the unemployment rate as the key
explanatory variable, but this measure is not available at the state level for the 1920s or 1930s, so
I instead use three different employment measures from the COM.9 The first measure is the total
number of employed wage workers divided by 100 thousand, the second is the total wages paid to
these wage workers divided by $100 million, and lastly the total wages divided by the total number
of wage workers, then divided by $100. The first measure serves as the closest proxy to
unemployment, while the last is a better measure of wages per capita and/or the amount of hours
and weeks worked for those that were employed. All measures are positively correlated with the
labor market, so q1 should be positive for all outcomes except WPA worker.
Yisc = α + q1COMmeasuresc + BPLs + Agec + εi
(1)
IV.2 Second Approach
The first approach estimates the impact of labor market entry conditions on long-term labor
outcomes, while the second approach (Equation 2) differs by using regression discontinuity (RD)
to answer a slightly different question – what is the impact of entering the labor market at the
beginning of the Great Depression in relation to just prior? RD requires a “running” variable that
8
9
Results are relatively similar when also using variation across states (i.e. no birth state fixed effects).
For information on the Great Depression and the COM see Ziebarth (2015).
8
determines treatment status, with values above a cutoff receiving treatment and those below
serving as a control (or vice versa). RD works by modeling the trends in an outcome variable
across values of this running variable, with the difference in the intercepts of these trends at the
cutoff estimating the treatment effect.10 The Great Depression serves as the treatment, which in
1930 immediately changed the market from a boom to bust. Visually the discontinuity in treatment
can be seen in Figures 1 and 2. Figure 1 depicts GNP per capita and two national unemployment
measures. Figure 2 depicts discontinuities in the COM state level employment measures used in
the first approach. Both figures clearly reveal that the market immediately shifted downward in
1930 (vertical dashed lines).
0
200
5
GNP ($1972)
250
300
10
15
20
Unemployment Rate
25
350
Figure 1: Great Depression
1920
1925
1930
Year
GNP ($1972)
Unemployment (Darby)
1935
1940
Unemployment (Lebergott)
Source: GNP ($1972) from Balke and Gordon (1986). Unemployment from Margo (1993), Romer (1986) and the
BLS Employment Status of Civilian Noninstitutional Population, 1940 to Date.
Notes: The vertical dashed line at 1930 indicates the discontinuity in unemployment and productivity used as the labor
market shock in this paper.
10
Please see Imbens and Lemieux (2008) for a more thorough review of RD. As time (birth year) is the running
variable in this model, it could also be referred to as an event study or interrupted time series. I instead refer to the
model throughout as RD as I am more interested in estimating the discontinuity than trend changes.
9
14
COM Wages/Employment
6
8
10
12
0
1
4
.5
COM Wages
1
1.5
2
COM Employment
1.2
1.4
1.6
1.8
2
2.5
Figure 2: Great Discontinuity of Labor Market Conditions
1900
1910
1920
Year
1930
1940
1900
1910
1920
Year
1930
1940
1900
1910
1920
Year
1930
1940
Source: Census of Manufacturers (1899, 1904, 1909, 1914, 1919, 1921, 1923, 1925, 1925, 1925, 1927, 1929, 1931,
1933, 1933, 1935, 1937, 1939)
Note: COM Employment is the total number of wage workers in a state divided by 100,000, COM Wages is the total
wages paid to these workers divided by $100,000,000, and COM Wages/Employment is total wages paid in the state
divided by total wage employment, and then divided by $100. Figures depict an RD with a cutoff at 1930 (the
beginning of the Great Depression).
For the RD in this paper I use birth year as the running variable11 and assign 1916 as the cutoff,
since observations born before 1916 (assuming an age 14 entry) could have entered the labor
market before the Great Depression, with those born in 1916 or thereafter entering during. The
first variable in Equation 2 is an indicator variable equal to one for observations born in 1916 or
thereafter. The coefficient (β1) measures the size of the discontinuity in the outcome between those
entering the labor market in 1929 in relation to 1930. This measure should be negative for all
outcomes, aside from WPA worker. The second coefficient (β2) measures the trend in the
relationship between the outcome variable and running variable. The third coefficient (β3)
measures whether the trend changes above the cutoff. Because the outcome is measured in 1940
11
The standard errors presented in the paper are Huber-White heteroskedasticity robust. Lee and Card (2008)
suggest clustering on the running variable if it is discrete so I have also estimated the standard errors clustering on
birth year. There is a small cluster problem since the models only include 10 birth years, so I use the Cameron,
Gelbach, and Miller (2008) wild bootstrap with Judon Caskey’s cgmwildboot ado program (1,000 reps and impose a
null of zero for all coefficients). These p-values are similar to not clustering for most outcomes and specifications,
for instance the p-value for the primary discontinuity estimates discussed in the paper are very small (0.004 in Table
3, column 6 and 0.006 in Table 4, column 1).
10
and the running variable is birth year, these trends control for the age-earnings profile, or returns
to experience. This means that the discontinuity is estimated holding age and experience constant.
Consistent with the first approach, the model also includes birth state fixed effects.
Yisc = α + β11(BirthYearisc ≥ 1916) + β2(BirthYearisc – 1916) + β3(BirthYearisc – 1916) ×
1(BirthYearisc ≥ 1916) + BPLs + εi
(2)
Contrary to the first approach, RD does not use all the variation in labor market conditions across
time to estimate the treatment effect, but instead focuses on the difference in the outcome for two
birth cohorts that are similar in all respects, aside from the treatment. This is important because
RD can be used to address two potential concerns with the first approach. First, since I rely on the
1940 Census for the labor outcomes, the first approach suffers from comparing outcomes for
cohorts that are up to 12 years apart in age. The model controls for age flexibly, but the outcome
is measured in different distances from labor entry for each cohort; for some the outcome is five
years from entry (1921 birth cohort) and for others it is 17 (1909 cohort). This is less of an issue
with RD because the estimate is the difference between two neighboring birth cohorts (1915 and
1916) that entered 11 and 10 years from the measurement of the outcome measures. Secondly,
changes in the labor market measures may not have the same impact on labor outcomes in a boom
and bust. Consider the common trends in the COM measures before and after 1930. These trends
indicate that the market improved in a relatively similar fashion before and after the downward
shock in 1930, at the onset of the Great Depression. The first approach assumes that the effect of
increased employment in 1929 is similar to the same increase in 1933. However, it may be that
improvements to the labor market in the Great Depression have more (or less) of an impact than
11
the same increase during the Roaring Twenties. If the estimated effects using RD are larger than
the estimates using the first approach, then it is possible that there are differential effects in a boom
and bust.
IV.3 Third Approach
In an effort to incorporate some of the state level variation in the severity of the Great Depression
(Wallis, 1989; Rosenbloom and Sundstrom, 1999) that the first approach employed, I also include
a third approach that combines the first and second. I estimate a modified RD that allows the
discontinuity to differ for observations born in states more affected by the Great Depression.12 This
approach also offers a validity check of the RD, by estimating if there are larger discontinuities in
states more affected by the Great Depression. I use the Wallis (1989, Table 2A) employment index
to designate states as High Shock if their index was below the annual median employment index
for at least four years between 1930 – 1934.13 I estimate the RD models separately for the high and
low shock samples and hypothesize that the discontinuities should be larger in high shock states.
These indices begin at 100 in 1929 for all states, so the high shock assignment reflects states that
significantly deviated from their prior employment due to the Great Depression rather than states
with poor labor markets in general. The Wallis index was chosen over the COM since it is available
annually after 1929 (the COM is only available biennially), there are more variables incorporated
in its creation (the COM was actually one of its benchmarks), and the COM only includes
12
Due to potential problems of migration in response to the Great Depression (for instance Fishback et al, 2006), I
assign these measures based on birth state rather than a more current residency measure. While there is reason to
worry that migration to low shock states potentially biases the estimates, the coefficients are similar when using
current state of residence or residence five years prior.
13
I classify 22 states out of 48 as high shock states: Alabama, Arizona, California, Colorado, Idaho, Illinois, Indiana,
Louisiana, Michigan, Missouri, Montana, Nevada, New Mexico, Ohio, Oklahoma, Pennsylvania, Tennessee, Texas,
Utah, Washington, Wisconsin, and Wyoming.
12
manufacturing employment (even in the Great Depression manufacturing only accounted for 2530 percent of GDP).14
In addition to stratifying the sample by the high and low shock birth states, I also estimate the
impact along the distribution of treatment (Wallis index). I do this by including the Wallis index
for the observation’s birth state (averaged over 1930 – 1934) interacted with the discontinuity
variable in Equation 2. The coefficient for this interaction variable estimates if the discontinuity
varies with the level of the employment index. If the effect is larger in areas more negatively
shocked by the Great Depression then the coefficient on the interaction variable will be positive,
since the employment index is a positive measure of the market. The relationship between the
discontinuity and the index may not be linear, so I also estimate several RD models using
observations within a moving window along the Wallis index. I plot these coefficients and 95
percent confidence intervals and hypothesize that the discontinuities should be negative and larger
at low levels of the index.
IV.4 RD Assumptions
An identifying assumption of RD is that the running variable absorbs everything except the
exogenous discontinuity in treatment and that there is no discontinuous change in other factors
occurring at the cutoff. Individuals do not have control over their birth year, but labor market
conditions may have altered their education decisions. For modern estimates of the relationship
between labor markets and education see Card and Lemieux, 2001 and Clark, 2011. However,
research from Yamashita (2008), Goldin (1998), Goldin and Katz (2008), and Kisswani (2008)
14
Results are relatively similar assigning this measure using the COM – having an employment index below the
annual median at least twice for years 1931, 1933, and 1935.
13
finds that while high school attendance increased from the early 1900s, the Great Depression had
a small effect on students’ educational decisions. Additionally, Tyack et al (1984) does not find a
discontinuous change in enrollment, school term, or average daily attendance between 1920 –
1940. There are also only relatively small differences in the percent changes in term length, staff,
and total school expenditures from 1930 – 1934 between the states I designate high and low
shock.15 This provides evidence that there was not a discontinuous change in the number of
students escaping a bad labor market by attending high school. Additionally, consider that the
market was very different from today - education loans were not available and lending in general
was extremely tight, so borrowing and/or prolonging education to escape a poor job market was
not an option for many people. I investigate this concern by estimating the effect of the Great
Depression on years of education and an indicator variable equal to one for those terminating their
education in the 8th grade using the RD methodology in Equation 2. I do not find that the Great
Depression had a large or statistically significant discontinuous impact on educational attainment,
aside from a reduction in the probability of exiting school at the 8th grade for the low shock sample
(see Table A6 and Figure A1 in the Appendix). Even with no or limited evidence that the Great
Depression significantly altered education, not all individuals with eight years of education entered
the labor market at age 14.16 Additionally, all those continuing their education beyond the 8th grade
would not have entered full time employment at age 14. Because of this, the 8th grade sample is
the closest to full compliance with the assignment of labor market conditions, but we should still
15
Data from Tyack et al (1984) Table 2, originally from NEA, “Current Conditions in the Nation’s Schools.”
Research Bulletin 11 (November 1933): 111. In rural schools, there was only a 2.26 percent difference in term
length and a 4.3 percent difference in the change of total expenditures for high shock states compared to low shock.
16
Because of uncertainty over entry age I also estimate a donut RD (Barreca, Guldi, Lindo, and Waddell, 2011 their paper was concerned with heaping, but the approach works to address this concern as well). I remove 1916,
1915 – 1916, or 1916 – 1917 and the results are similar in sign for all donuts and are statistically significant for the
first two.
14
think of the effects estimated in this paper as an “intention to treat” effect and thus attenuated in
relation to the average treatment effect if treatment and compliance were perfectly assigned.
V. RESULTS
Table 1 reports the summary statistics for the analytic sample used for the second approach (RD).
The sample for the first approach is very similar and differs by only including odd birth years
(COM is biennial) and the addition of two extra birth years (1909 and 1921). The summary
statistics are separately reported for the full sample, those completing their education in the 8th
grade, and the high and low shock 8th grade samples.
Table 1: Summary Statistics
Income
Worked Last Year
Weeks Worked Last Year
Hours Worked Last Week
WPA Worker
Age
Education
N
Full Sample
(1)
856
(536)
0.898
(0.302)
40.69
(14.21)
37.42
(18.37)
0.055
(0.228)
23.88
(2.74)
10.35
(2.70)
52,066
8th Grade
(2)
761
(469)
0.894
(0.307)
38.95
(14.31)
35.01
(19.16)
0.080
(0.271)
24.23
(2.75)
8
8th Grade
High Shock
(3)
750
(468)
0.884
(0.321)
38.16
(14.51)
34.54
(19.18)
0.085
(0.279)
24.22
(2.75)
8
8th Grade
Low Shock
(4)
772
(469)
0.904
(0.294)
39.66
(14.09)
35.44
(19.13)
0.075
(0.264)
24.25
(2.74)
8
9,061
4,299
4,762
Source: 1940 US Census - Integrated Public Use Microdata Series (IPUMS 1% sample)
Notes: The sample is composed of white men born 1911 – 1920, not living on a farm or in group quarters. Summary
statistics are provided separately for those completing their education in the 8th grade and by severity of the Great
Depression in the observation’s birth state (High and Low Shock). The income distribution has been culled below the
1st and above the 99th percentile for all but Worked Last Year. WPA Worker is equal to 1 if the observation identified
their employed status as “at work, public emergency” and 0 otherwise. Standard deviations are shown in parenthesis.
15
V.1 First Approach
The estimates for the first approach (Equation 1) are reported in Table 2. Each row and column
represent a different regression, with the birth state and birth year estimates suppressed for brevity.
The first three panels include the coefficients for the three measures of labor market entry
conditions (assigned by birth state and when age 14) for the full sample, with the last three for the
8th grade sample. The estimated effect of labor market conditions on logged income is positive for
almost all measures. Expectedly, the effects are larger for the 8th grade sample, as these individuals
had a higher chance of entering the labor market at the same time the labor market conditions are
assigned (at age 14) compared to the full sample. The probability of working in the prior year
increased and the number of weeks worked in the prior year or hours worked in the prior week
also increased, but are not as significant across all measures and samples. Only the
wage/employment measure reflects a decrease in the probability of WPA worker status.
16
Table 2: Effect of Labor Market Entry Conditions
Log
(Income)
(1)
Worked
Last Year
(2)
Weeks
Worked
(3)
Hours
Worked
(4)
WPA
Worker
(5)
COM Employment/100k
0.022**
(0.009)
0.018***
(0.006)
0.368*
(0.200)
0.474***
(0.147)
-0.000
(0.003)
N
34,684
41,845
34,684
32,732
34,684
(6)
(7)
(8)
(9)
(10)
COM Wages/$100m
0.009**
(0.004)
0.009***
(0.003)
0.151*
(0.090)
0.168**
(0.072)
-0.001
(0.001)
N
34,684
41,845
34,684
32,732
34,684
(11)
(12)
(13)
(14)
(15)
(COM Wages/Employment)/$100
0.020
(0.015)
0.021***
(0.006)
0.468**
(0.203)
0.579**
(0.243)
-0.006*
(0.003)
N
34,684
41,845
34,684
32,732
34,684
(16)
(17)
(18)
(19)
(20)
0.034**
(0.017)
0.030***
(0.011)
0.569
(0.408)
0.738*
(0.391)
-0.015
(0.011)
N
6,482
7,761
6,482
5,936
6,482
8th Grade Sample
(21)
(22)
(23)
(24)
(25)
COM Wages/$100m
0.019**
(0.008)
0.015***
(0.005)
0.303*
(0.161)
0.297
(0.178)
-0.008
(0.005)
N
6,482
7,761
6,482
5,936
6,482
8th Grade Sample
(26)
(27)
(28)
(29)
(30)
0.054*
(0.031)
0.037***
(0.011)
0.868**
(0.406)
0.397
(0.702)
-0.020*
(0.011)
6,482
7,761
6,482
5,936
6,482
Full Sample
Full Sample
Full Sample
8th Grade Sample
COM Employment/100k
(COM Wages/Employment)/$100
N
Source: 1940 US Census - Integrated Public Use Microdata Series (IPUMS 1% sample) and Census of Manufacturers
1923, 1925, 1927, 1929, 1931, 1933, and 1935
Notes: The sample is composed of white men born 1909 – 1921 (odd years), not living on a farm or in group quarters.
The model is a birth state and age fixed effects model using the value of the explanatory variable for the year the
observation turned 14. COM Employment is the total number of wage workers in the individual’s birth state and COM
Wages is total wages paid to these workers. Robust standard errors clustered on birth state are included (in parenthesis).
*, **, and *** indicate the statistical significance at 10%, 5%, and 1% level, respectively.
17
To put these results in context, if a state experienced an increase of 100 thousand employed wage
workers in the year an individual could enter the labor market (i.e. turned 14), their income would
increase by 2.2 percent.17 The increase would be larger at 3.4 percent for the 8th grade sample. To
allow comparison with the RD results, I estimate an RD similar to Equation 2 for the different
COM labor market measures. Using state level data I find that the number of employed workers
in a state fell by 67,200 at the onset of the Great Depression (see Figure 2 and Table A7 in the
Appendix), meaning the impact of the Great Depression would be less than the 2.2 percent estimate
above (as it would be multiplied by 0.672). If the RD is instead limited to birth years 1909 – 1921
and weighted by the number of observations in each state (i.e. using the individual level data) then
the discontinuity is larger at 128,397, making the effect of the Great Depression roughly doubled
in relation to using the shock estimated using the state level data.
V.2 Second Approach
The estimates from the second approach (Equation 2) are included in Table 3. The first coefficient
in each panel is the estimated discontinuity for the full sample (top panel) and 8th grade sample
(bottom). Consistent with the first approach, there is strong evidence that labor market conditions
at first entry have an impact on long-run income. There is a 5.2 percent reduction for the full
sample and a larger 8.4 percent reduction for the 8th grade sample. This estimate is the difference
in income for someone born in 1915, entering the labor market in 1929, in comparison to someone
born a year later in 1916, entering at the onset of the Great Depression (controlling for age and
work experience). In contrast to the results in the first approach the impact on income is roughly
17
Technically these coefficients should be interpreted as an increase in log units or converted to percent changes
using eβ – 1. The values are very similar from this conversion, so I refer to the coefficients in the tables as percent
changes.
18
doubled, but there is less evidence of reduced likelihood of working and limited evidence of fewer
weeks and hours worked, or WPA worker status.
Table 3: Regression Discontinuity
Full Sample
1(BirthYear ≥ 1916)
BirthYear - 1916
1(BirthYear ≥ 1916) × (BirthYear - 1916)
N
Log
(Income)
(1)
Worked
Last Year
(2)
Weeks
Worked
(3)
Hours
Worked
(4)
WPA
Worker
(5)
-0.052***
(0.012)
-0.055***
(0.003)
-0.105***
(0.005)
0.003
(0.004)
-0.006***
(0.001)
-0.035***
(0.002)
-0.753***
(0.238)
-0.533***
(0.053)
-1.106***
(0.091)
-0.386
(0.323)
-0.414***
(0.075)
-0.456***
(0.122)
0.002
(0.004)
-0.000
(0.001)
0.011***
(0.002)
52,066
61,870
52,066
49,212
52,066
(7)
(8)
8th Grade
(6)
1(BirthYear ≥ 1916)
-0.084***
(0.029)
-0.052***
(0.006)
-0.069***
(0.011)
0.016
(0.012)
-0.012***
(0.002)
-0.024***
(0.005)
-0.929
(0.598)
-0.576***
(0.127)
-0.454**
(0.225)
-1.506*
(0.837)
-0.235
(0.182)
-0.619**
(0.315)
0.013
(0.011)
-0.000
(0.002)
0.011***
(0.004)
9,061
10,703
9,061
8,337
9,061
BirthYear - 1916
1(BirthYear ≥ 1916) × (BirthYear - 1916)
N
(9)
(10)
Source: 1940 US Census - Integrated Public Use Microdata Series (IPUMS 1% sample
Notes: The sample is composed of white men born 1911 – 1920, not living on a farm or in group quarters. The model
is an RD that allows the trend to differ on either side of the cutoff. I use a 1916 cutoff, which corresponds to the birth
year for those turning 14 at the onset of the Great Depression. Birth state fixed effects are included, but not reported.
The top panel includes results for the full sample, while the bottom includes only those completing their education in
the 8th grade. Huber-White robust standard errors (in parenthesis) are reported. *, **, and *** indicate the statistical
significance at 10%, 5%, and 1% level, respectively.
V.3 Third Approach
The results in Table 4 merge the first two approaches to determine if the impact differs for states
more negatively shocked by the Great Depression and tests if the model is properly identified. The
top panel includes the estimates for the 8th grade sample that were born in states more heavily
19
shocked by the Great Depression, with the bottom panel for those born in less shocked states. The
results are stark; there is a 14.6 percent income reduction for the high shock sample and no
difference for the low shock sample. This result provides additional evidence for a causal
interpretation of the findings, where the treatment effect varies with intensity of the treatment in a
predictable way. While there are large differences in the discontinuities for income across the high
and low shock samples, the differences are much smaller for the other outcomes, aside from the
increase in WPA worker for the high shock group.
Table 4: Regression Discontinuity for 8th Grade by Severity of Treatment
8th Grade High Shock
1(BirthYear ≥ 1916)
BirthYear - 1916
1(BirthYear ≥ 1916) × (BirthYear - 1916)
N
(1)
(2)
(3)
(4)
(5)
-0.146***
(0.044)
-0.048***
(0.010)
-0.064***
(0.016)
0.008
(0.017)
-0.012***
(0.004)
-0.026***
(0.007)
-1.575*
(0.873)
-0.656***
(0.186)
-0.275
(0.328)
-1.420
(1.219)
-0.237
(0.264)
-1.004**
(0.459)
0.039**
(0.017)
-0.001
(0.004)
0.005
(0.006)
4,299
5,145
4,299
3,934
4,299
8th Grade Low Shock
1(BirthYear ≥ 1916)
BirthYear - 1916
1(BirthYear ≥ 1916) × (BirthYear - 1916)
N
(6)
(7)
(8)
(9)
(10)
-0.028
(0.040)
-0.056***
(0.009)
-0.073***
(0.015)
0.023
(0.015)
-0.012***
(0.003)
-0.023***
(0.006)
-0.323
(0.819)
-0.503***
(0.175)
-0.625**
(0.309)
-1.608
(1.152)
-0.231
(0.251)
-0.267
(0.432)
-0.011
(0.014)
0.000
(0.003)
0.017***
(0.006)
4,762
5,558
4,762
4,403
4,762
Source: 1940 US Census - Integrated Public Use Microdata Series (IPUMS 1% sample
Notes: The sample is composed of white men born 1911 – 1920, completing their education at the 8th grade, not living
on a farm or in group quarters. The model is an RD using a 1916 cutoff. Birth state fixed effects are included, but not
reported. The top panel includes results for those born in states relatively more affected by the Great Depression (High
Shock), while the bottom includes those living in states less affected (Low Shock). Huber-White robust standard errors
(in parenthesis) are reported. *, **, and *** indicate the statistical significance at 10%, 5%, and 1% level, respectively.
20
Figure 3 depicts the logged income RD for the high and low shock 8th grade samples.18 The trends
above and below the cutoff are quite similar for both samples, but there is a visible negative
intercept shift for the high shock sample at the cutoff, which is the treatment effect (discontinuity)
estimated in the model. The trends are visibly steeper after the cutoff, which is likely a product of
the age-earnings profile since it affects both the high and low shock samples in a similar manner.
Because the post-cutoff trends are similar, the cohorts following the 1916 cohort in the high shock
sample also lagged behind the low shock sample. This means that the effect was relatively similar
for all cohorts in the high shock sample that entered during the Great Depression.
5.8
6
Log(Income)
6.2
6.4
6.6
6.8
Figure 3: Regression Discontinuity of Log(Income) for 8th Grade Sample
1911
1912
1913
1914
1915 1916
Birth Year
Low Shock
1917
1918
1919
1920
High Shock
Source: 1940 US Census - Integrated Public Use Microdata Series (IPUMS 1% sample)
Note: This figure is a graphic depiction of the RD in Table 4. The marker size designates relative cohort size.
18
When creating the RD figures I omit the birth state fixed effects since their inclusion does not affect the estimates
and if included would require either averaging the state coefficients or the selection of a representative state to plot
the intercept.
21
Figure 4 depicts the other labor outcomes and reveals some differences in the two samples above
the cutoff even if the estimated discontinuities are not significant. For instance, the weeks worked
measure for the high shock sample has a visible negative intercept shift at the cutoff and the hours
worked trend becomes more steep following the cutoff. The changes in the other outcomes in
Figure 4 led me to investigate if the income reduction in Table 4 is explained in part by changing
weeks worked or WPA status. I do not include hours worked in this specification because it is
missing for WPA workers, the coefficient estimate for WPA worker was significant in Table 4
while hours was not, hours worked in the prior week is a very noisy proxy for usual hours worked,
and the coefficients are similar when using hours worked rather than WPA. I provide the estimates
for the full sample, 8th grade, and the high and low shock 8th grade samples in Table 5. The size of
the discontinuity falls by roughly half when controlling for weeks worked and WPA worker,
indicating that part of the income reduction was caused by reduced work weeks, but a significant
portion was reduced wages. The conclusion is the same if I instead use logged weekly wages in
Equation 2 (not reported), as the coefficients are similar to those in Table 5.
22
32
.75
Worked Last Year
.8
.85
.9
Weeks Worked
34 36 38 40
42
.95
Figure 4: Regression Discontinuity of Other Outcomes for 8th Grade Sample
1911 1912 1913 1914 1915 1916 1917 1918 1919 1920
Birth Year
High Shock
Low Shock
High Shock
28
.05
WPA Worker
.1
Hours Worked
30 32 34 36
38
.15
Low Shock
1911 1912 1913 1914 1915 1916 1917 1918 1919 1920
Birth Year
1911 1912 1913 1914 1915 1916 1917 1918 1919 1920
Birth Year
Low Shock
High Shock
1911 1912 1913 1914 1915 1916 1917 1918 1919 1920
Birth Year
Low Shock
High Shock
Source: 1940 US Census - Integrated Public Use Microdata Series (IPUMS 1% sample)
Note: This figure is a graphic depiction of the RD in Table 4. The marker size designates relative cohort size.
23
Table 5: Regression Discontinuity Controlling for Weeks Worked and WPA
1(BirthYear ≥ 1916)
BirthYear - 1916
1(BirthYear ≥ 1916) × (BirthYear - 1916)
Weeks Worked
WPA
N
8th Grade
8th Grade
High
Shock
8th Grade
Low Shock
Log
(Income)
(1)
Log
(Income)
(2)
Log
(Income)
(3)
Log
(Income)
(4)
-0.025***
(0.009)
-0.036***
(0.002)
-0.063***
(0.003)
0.035***
(0.000)
-0.397***
(0.010)
-0.050**
(0.021)
-0.034***
(0.005)
-0.048***
(0.008)
0.033***
(0.000)
-0.353***
(0.020)
-0.077**
(0.031)
-0.027***
(0.007)
-0.052***
(0.012)
0.034***
(0.001)
-0.381***
(0.029)
-0.025
(0.029)
-0.040***
(0.006)
-0.044***
(0.011)
0.033***
(0.001)
-0.324***
(0.027)
52,060
9,067
4,301
4,766
Full
Sample
Source: 1940 US Census - Integrated Public Use Microdata Series (IPUMS 1% sample
Notes: The sample is composed of white men born 1911 – 1920, completing their education at the 8th grade, not living
on a farm or in group quarters. The model is an RD using a 1916 cutoff. The model includes weeks worked and WPA
worker. Birth state fixed effects are included, but not reported. Huber-White robust standard errors (in parenthesis)
are reported. *, **, and *** indicate the statistical significance at 10%, 5%, and 1% level, respectively.
The results in Table 6 are from a model that more flexibly estimates if the discontinuity varies with
the size of the labor market shock. This model is the same RD, but includes the Wallis employment
index (averaged 1930 – 1934) interacted with the discontinuity variable. The first coefficient in
Table 6 estimates if there is a discontinuity when the Wallis index equals zero. The second
coefficient, the interaction, measures how the discontinuity varies as the index changes. There is a
strong positive relationship between the discontinuity and the Wallis index, meaning the
discontinuity increases with the severity of the Great Depression. I further investigate this
relationship by estimating several discontinuities using data within a window of size 10, moving
in steps of one along the majority of the Wallis index distribution, from 73 to 91. Figure 5 depicts
24
the discontinuity coefficients and 95 percent confidence intervals from these regressions. The
negative effect is primarily experienced by states with an index below the median, with a much
larger effect at very low levels. Consistent with Table 4, the discontinuities are closer to zero and
not statistically significant at the upper end of the distribution.
Table 6: Regression Discontinuity Interacted with Severity of Treatment
1(BirthYear ≥ 1916)
1(BirthYear ≥ 1916) × Employment Index
BirthYear - 1916
1(BirthYear ≥ 1916) × (BirthYear - 1916)
N
Log
(Income)
(1)
Worked
Last Year
(2)
Weeks
Worked
(3)
Hours
Worked
(4)
WPA
Worker
(5)
-0.735***
(0.218)
0.008***
(0.003)
-0.052***
(0.006)
-0.069***
(0.011)
0.002
(0.089)
0.000
(0.001)
-0.012***
(0.002)
-0.024***
(0.005)
-8.776**
(4.448)
0.094*
(0.053)
-0.576***
(0.127)
-0.455**
(0.225)
-10.230
(6.435)
0.105
(0.076)
-0.235
(0.182)
-0.619**
(0.315)
0.100
(0.091)
-0.001
(0.001)
-0.000
(0.002)
0.011***
(0.004)
9,061
10,703
9,061
8,337
9,061
Source: 1940 US Census - Integrated Public Use Microdata Series (IPUMS 1% sample) and Wallis (1989, 2A)
employment index
Notes: The sample is composed of white men born 1911 – 1920, completing their education at the 8th grade, not living
on a farm or in group quarters. The model is an RD using a 1916 cutoff. The model includes an interaction of the
Wallis employment index (averaged over 1930 – 1934) with the discontinuity variable to determine if the treatment
varies with the severity of the Great Depression in the observation’s birth state. Birth state fixed effects are included,
but not reported. Huber-White robust standard errors (in parenthesis) are reported. *, **, and *** indicate the statistical
significance at 10%, 5%, and 1% level, respectively.
25
.1
Figure 5: Effect Across Distribution of Shock
-.3
-.2
Discontinuity
-.1
0
Median
73
78
83
Wallis Index
88
Source: 1940 US Census - Integrated Public Use Microdata Series (IPUMS 1% sample) and Wallis (1989, 2A)
employment index
Note: Figure depicts the discontinuity coefficients and 95% confidence intervals from the RD for the 8th grade
sample using data within a moving window (10 wide) along the state employment index (averaged over 1930-1934)
from 73 to 91 in steps of one. The median state employment index is designated with a vertical dashed line.
V.4 Robustness checks
Lastly, I address three common concerns associated with RD models, with the results depicted in
Figure 6. The first concern is that the estimated discontinuity is an artifact of noise and is no
different from using a randomly assigned cutoff (Imbens and Lemieux, 2008). The second and
third concerns are that the results are not robust to altering the bandwidth (amount of data included
on either side of the cutoff) or trend specification (Lee and Lemieux, 2010). I address the first
concern by comparing the discontinuity using the 1916 cutoff to several placebo cutoffs. The 1916
discontinuity should be the largest, with the placebo discontinuities smaller and less statistically
significant. I estimate these discontinuities for the 8th grade high and low shock samples using
Equation 2 with a bandwidth of five birth years, centered on the discontinuity. The top plots in
26
Figure 6 depict the discontinuity coefficients and 95% confidence intervals from this exercise. As
expected, the discontinuity using 1916 for the high shock sample is the largest, with all the placebo
discontinuities smaller and not statistically significant. The only exception is the 1917 placebo
discontinuity, which is smaller, but statistically significant. The 1917 placebo discontinuity is not
entirely unexpected as there is contamination from the discontinuity at the true cutoff, one year
prior, that is still present when using a linear RD with a bandwidth overlapping the true
discontinuity. Additionally, all discontinuities from the low shock sample are close to zero and not
statistically significant.
I next address the last two concerns – whether the results are robust to changing the bandwidth or
functional form of the RD trends. A larger bandwidth requires more care in modeling the trends,
so I follow a common practice of using higher order polynomials in the trend (in this case I use
quadratic trends that can change slope on either side of the cutoff).19 The bottom four plots in
Figure 6 include the coefficients and 95 percent confidence intervals for the linear trends RD
(middle plots) and a specification that allows for different quadratic trends on either side of the
cutoff (bottom plots). The figure includes bandwidths from three to nine years, or the smallest at
1913 – 1918 to the largest 1907 – 1924. I also estimate the model using data from 1900 – 1924
and the results are similar to the 1907 – 1924 bandwidth estimates. As expected, the results using
the linear trends are similar for smaller bandwidths and the results for the quadratic model are
similar in magnitude to the linear RD for larger bandwidths. This indicates that using the linear
trends RD and a five-year bandwidth does not result in estimates that are significantly different
from alternative bandwidth or trend modeling choices.
19
Results are similar using cubic trends that can differ on either side of the cutoff.
27
Figure 6: RD Robustness Checks
.1
Discontinuity
0
-.1
-.2
-.2
-.1
Discontinuity
0
.1
.2
Placebo: Low Shock
.2
Placebo: High Shock
1910
1912
1914
Birth Year
1916
1918
1912
1914
Birth Year
1916
1918
-.2
Discontinuity
-.1
0
.1
.175
BW (Linear): Low Shock
-.35 -.3
-.35 -.3
-.2
Discontinuity
-.1
0
.1
.175
BW (Linear): High Shock
1910
3
4
5
6
7
Bandwidth (in Years)
8
9
3
5
6
7
Bandwidth (in Years)
8
9
-.2
Discontinuity
-.1
0
.1
.175
BW (Quadratic): Low Shock
-.35 -.3
-.35 -.3
-.2
Discontinuity
-.1
0
.1
.175
BW (Quadratic): High Shock
4
3
4
5
6
7
Bandwidth (in Years)
8
9
3
4
5
6
7
Bandwidth (in Years)
8
9
Source: 1940 US Census - Integrated Public Use Microdata Series (IPUMS 1% sample)
Note: The top two figures depict the discontinuities and 95 percent confidence intervals from the RD using placebo
cutoffs, using data within a window of 10 years (bandwidth = 5). The vertical dashed line designates the true cutoff
(1916). The middle two figures depict the coefficients and 95 percent confidence intervals using varying bandwidth
sizes for an RD with linear trends that can differ in slope on either side of the cutoff. The bottom two figures depict
28
the same exercise but uses an RD with quadratic trends that can differ on either side of the cutoff. The vertical dashed
lines in the bottom four figures are the default bandwidth used throughout the paper.
V. CONCLUSION
Prior research reveals that the Great Depression changed those that lived through it in many ways.
Using variation driven by the Great Depression as a large scale (and large magnitude) natural
experiment I find that labor market conditions at labor force entry significantly affected income in
the long run. I also find some evidence that these individuals worked fewer weeks and were
employed by the federal government as WPA workers.
The results from all approaches fit within the range of estimates using modern recessions. The
effects are relatively large and long-lasting in comparison to Oreopoulos et al. (2012) who find an
initial earnings loss of nine percent that fades to zero in ten years for a labor market moving from
an economic boom to bust (two standard deviation shift in the unemployment rate). However, the
employment shock in this paper is of a much larger and sustained magnitude and the relatively
generous policies governing the Canadian labor market may result in a smaller long-term effect in
comparison. On the other hand, the effect is smaller than Kahn (2010), who found a 2.5 percent
wage reduction for each one-percentage point increase in unemployment 15 years after a labor
market shock. I find that 10 years after entry, the Great Depression reduced the 8th grade sample’s
income by roughly half a percent for each one percentage point increase in the unemployment
rate.20 I also find a relatively similar estimate using the third approach when interacting the Wallis
index with the discontinuity variable; the discontinuity or effect is 0.8 percent larger for every one
20
The unemployment rate increased by 15.8 percentage points when comparing 1925 – 1929 to 1930 – 1934 and
income fell by 8.4 percent for the 8th grade sample in Table 3. The effect is closer to one percent for the high shock
sample.
29
point drop in the employment index. Care must be taken when comparing these estimates as the
labor market entry assignment may be more precise using college graduation as Kahn did,
compared to using age 14 for the full sample or even the 8th grade sample as I use. Therefore the
results in this paper are likely attenuated and should be scaled up to take this into consideration.
In any event, these estimates reflect that the Great Depression caused large cross-cohort and crossstate inequality that arose simply from birthplace and birth year.
30
Acknowledgements
I am grateful to Ann Stevens, Colin Cameron, Hilary Hoynes, Marianne Page, Gabriel Mathy,
John Parman, Rowena Gray, Matthew Larsen, Christine Durrance, Scott Wentland, Steven
Hemelt, anonymous referee comments, and seminar participants at the Society of Labor
Economists Annual Meeting for their helpful comments. Any remaining errors or omissions are
my own.
31
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35
Appendix
Table A1: Labor Force Participation Among 14 and 15-Year-Olds in 1930
Age 14 LFP
State
Alabama
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
Florida
Georgia
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Age 15 LFP
Legal Labor
Entry Age
Full
Out of
School
Full
Out of
School
14
14
14
15
14
14
14
14
14
14
14
14
14
14
14
14
15
14
14
15
14
14
14
16
14
14
14
14
14
14
14
14
16
14
0.048
0.033
0.055
0.037
0.047
0.044
0.012
0.080
0.101
0.016
0.026
0.028
0.044
0.053
0.033
0.082
0.064
0.048
0.032
0.025
0.022
0.069
0.049
0.078
0.035
0.038
0.019
0.033
0.075
0.024
0.091
0.019
0.029
0.045
0.202
0.063
0.208
0.095
0.375
0.377
0.000
0.171
0.373
0.000
0.145
0.053
0.047
0.200
0.167
0.415
0.314
0.339
0.278
0.172
0.097
0.217
0.301
0.091
0.045
0.000
0.273
0.191
0.231
0.159
0.427
0.143
0.108
0.327
0.098
0.105
0.172
0.044
0.109
0.134
0.164
0.130
0.217
0.062
0.054
0.055
0.082
0.079
0.088
0.187
0.067
0.169
0.109
0.051
0.039
0.114
0.138
0.112
0.068
0.091
0.037
0.101
0.118
0.104
0.215
0.054
0.065
0.115
0.328
0.424
0.636
0.143
0.558
0.633
0.474
0.483
0.694
0.333
0.276
0.230
0.366
0.289
0.348
0.548
0.234
0.623
0.569
0.276
0.250
0.387
0.506
0.267
0.233
0.143
0.313
0.527
0.650
0.477
0.581
0.500
0.306
0.341
36
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming
14
14
15
14
14
14
15
14
14
14
14
14
14
14
0.072
0.025
0.011
0.192
0.017
0.044
0.082
0.037
0.008
0.065
0.044
0.021
0.020
0.000
0.294
0.235
0.091
0.543
0.159
0.369
0.000
0.000
0.309
0.087
0.161
0.088
0.000
0.063
0.102
0.081
0.183
0.337
0.046
0.103
0.149
0.105
0.078
0.132
0.081
0.065
0.042
0.063
0.414
0.427
0.667
0.780
0.333
0.488
0.490
0.296
0.438
0.387
0.595
0.340
0.168
0.143
Source: 1930 US Census - Integrated Public Use Microdata Series (IPUMS 1% and 5% samples) and Goldin and Katz
(2008) data appendix
Notes: The sample is composed of white men not living on a farm or in group quarters. The table includes the minimum
age for labor entry and the proportion of 14 and 15-year-olds from each state that were in the labor force in 1930.
Results are provided for the full sample and those not in school.
37
Table A2: Effect of Legal Labor Entry Age Laws on LFP of 14-Year-Olds
15 or 16 Labor Entry Age
Constant
N
Full
LFP
(1)
0.003
(0.009)
0.037***
(0.004)
44,195
Out of
School
LFP
(2)
-0.007
(0.066)
0.230***
(0.021)
2,621
Source: 1930 US Census - Integrated Public Use Microdata Series (IPUMS 1% and 5% samples) and Goldin and Katz
(2008) data appendix
Notes: The sample is composed of white men, 14 years old, not living on a farm or in group quarters. The model
includes an indicator variable for whether their current state of residence had a 15 or 16-year-old minimum labor entry
age. Robust standard errors clustered on the state are included (in parenthesis). *, **, and *** indicate the statistical
significance at 10%, 5%, and 1% level, respectively.
38
Table A3: Effect of Labor Market Entry Conditions Using Labor Laws
Log
(Income)
(1)
Worked
Last Year
(2)
Weeks
Worked
(3)
Hours
Worked
(4)
WPA
Worker
(5)
COM Employment/100k
0.022**
(0.009)
0.019***
(0.006)
0.321
(0.200)
0.427***
(0.155)
-0.002
(0.003)
N
34,127
41,055
34,127
32,213
34,127
(6)
(7)
(8)
(9)
(10)
COM Wages/$100m
0.008**
(0.004)
0.009***
(0.003)
0.125
(0.085)
0.149**
(0.072)
-0.001
(0.001)
N
34,127
41,055
34,127
32,213
34,127
(11)
(12)
(13)
(14)
(15)
(COM Wages/Employment)/$100
0.007
(0.011)
0.013*
(0.006)
0.130
(0.218)
0.224
(0.181)
-0.006**
(0.002)
N
34,127
41,055
34,127
32,213
34,127
(16)
(17)
(18)
(19)
(20)
0.027*
(0.015)
0.027**
(0.010)
0.428
(0.374)
0.477
(0.458)
-0.012
(0.010)
N
6,384
7,621
6,384
5,853
6,384
8th Grade Sample
(21)
(22)
(23)
(24)
(25)
COM Wages/$100m
0.017**
(0.007)
0.013***
(0.004)
0.252*
(0.149)
0.224
(0.212)
-0.006
(0.005)
N
6,384
7,621
6,384
5,853
6,384
8th Grade Sample
(26)
(27)
(28)
(29)
(30)
0.021
(0.019)
0.019**
(0.009)
0.338
(0.326)
-0.083
(0.591)
-0.007
(0.008)
6384
7621
6384
5853
6384
Full Sample
Full Sample
Full Sample
8th Grade Sample
COM Employment/100k
(COM Wages/Employment)/$100
N
Source: 1940 US Census - Integrated Public Use Microdata Series (IPUMS 1% sample) and Census of Manufacturers
1923, 1925, 1927, 1929, 1931, 1933, and 1935
Notes: The sample is composed of white men born 1909 – 1921 (odd years), not living on a farm or in group quarters.
The model is a birth state and age fixed effects model using the value of the explanatory variable for the year the
observation would have first been legally eligible to enter the labor market. COM Employment is the total number of
wage workers in the birth state and COM Wages is total wages paid in the birth state. Robust standard errors clustered
on birth state are included (in parenthesis). *, **, and *** indicate the statistical significance at 10%, 5%, and 1%
level, respectively.
39
Table A4: Regression Discontinuity Using Labor Laws
Log
(Income)
(1)
Worked
Last Year
(2)
Weeks
Worked
(3)
Hours
Worked
(4)
WPA
Worker
(5)
-0.034***
(0.012)
-0.015
(0.009)
-0.005
(0.011)
0.004
(0.004)
-0.412
(0.276)
52,066
52,230
42,172
61,870
61,870
(6)
(7)
(8)
(9)
(10)
-0.065**
(0.029)
-0.031
(0.022)
-0.038
(0.029)
0.012
(0.011)
-0.229
(0.687)
N
9,061
9,080
6,864
10,703
10,703
8th Grade High Shock
(11)
(12)
(13)
(14)
(15)
-0.105**
(0.043)
-0.058*
(0.032)
-0.020
(0.040)
0.005
(0.017)
-1.014
(0.996)
N
4,299
4,322
3,237
5,145
5,145
8th Grade Low Shock
(16)
(17)
(18)
(19)
(20)
-0.024
(0.040)
-0.006
(0.030)
-0.055
(0.040)
0.018
(0.015)
0.574
(0.954)
4,762
4,758
3,627
5,558
5,558
Full Sample
1(Yearborn ≥ C)
N
8th Grade
1(Yearborn ≥ C)
1(Yearborn ≥ C)
1(Yearborn ≥ C)
N
Source: 1940 US Census - Integrated Public Use Microdata Series (IPUMS 1% sample
Notes: The sample is composed of white men not born 1911 – 1920, living on a farm or in group quarters. The model
is an RD using a cutoff that corresponds to the birth year for those turning their state’s minimum legal entry age at the
onset of the Great Depression. Birth state fixed effects are included, but not reported. Results are provided for the full
sample, 8th grade sample, and high and low shock 8th grade samples. Huber-White robust standard errors (in
parenthesis) are reported. *, **, and *** indicate the statistical significance at 10%, 5%, and 1% level, respectively.
40
Table A5: Regression Discontinuity by Education
Education Level:
1(BirthYear ≥ 1930 - Ed - 6)
BirthYear - (1930 - Ed - 6)
1(BirthYear ≥ 1930 - Ed - 6)
× [BirthYear - (1930 - Ed 6)]
N
1(BirthYear ≥ 1916)
BirthYear - 1916
1(BirthYear ≥ 1916) ×
(BirthYear - 1916)
N
6th
7th
8th
9th
10th
11th
12th
Log
(Income)
(1)
0.045
(0.086)
-0.054***
(0.018)
Log
(Income)
(2)
-0.020
(0.061)
-0.060***
(0.013)
Log
(Income)
(3)
-0.084***
(0.029)
-0.052***
(0.006)
Log
(Income)
(4)
0.010
(0.041)
-0.069***
(0.010)
Log
(Income)
(5)
-0.039
(0.034)
-0.055***
(0.008)
Log
(Income)
(6)
0.037
(0.044)
-0.053***
(0.011)
Log
(Income)
(7)
0.004
(0.020)
-0.049***
(0.005)
-0.175***
(0.035)
-0.104***
(0.023)
-0.069***
(0.011)
-0.062***
(0.015)
-0.048***
(0.012)
-0.030**
(0.015)
-0.027***
(0.007)
1,290
2,474
9,061
4,405
5,998
3,985
15,042
(8)
(9)
(10)
(11)
(12)
(13)
(14)
0.009
(0.075)
-0.053***
(0.017)
-0.080
(0.056)
-0.040***
(0.013)
-0.084***
(0.029)
-0.052***
(0.006)
-0.046
(0.042)
-0.067***
(0.010)
-0.043
(0.037)
-0.068***
(0.008)
-0.052
(0.042)
-0.044***
(0.010)
-0.003
(0.020)
-0.064***
(0.005)
-0.038
(0.027)
-0.075***
(0.020)
-0.069***
(0.011)
-0.069***
(0.016)
-0.067***
(0.013)
-0.116***
(0.016)
-0.128***
(0.007)
1,512
2,678
9,061
4,276
5,758
3,923
17,003
Source: 1940 US Census - Integrated Public Use Microdata Series (IPUMS 1% sample
Notes: The sample is composed of white men not living on a farm or in group quarters, born in a 10 year window
around the cutoff determined by education in the top panel and 1911 – 1920 in the bottom panel. The model is an RD
using a cutoff equal to the birth year the individual would have entered the labor market at the onset of the Great
Depression based on their education (birth year + education + 6) in the top panel and the year they would have been
14 (1916) in the bottom panel. Birth state fixed effects are included, but not reported. Results are reported for different
levels of terminal education. Huber-White robust standard errors (in parenthesis) are reported. *, **, and *** indicate
the statistical significance at 10%, 5%, and 1% level, respectively.
41
Table A6: Education Regression Discontinuity
1(BirthYear ≥ 1916)
Full
Sample
Education
(1)
Full
Sample
Education
(2)
High
Shock
Education
(3)
Low Shock
Education
(4)
0.004
(0.046)
0.055
(0.063)
-0.049
(0.068)
0.053***
(0.012)
-0.202***
(0.016)
-0.288
(0.310)
0.004
(0.004)
0.053***
(0.012)
-0.202***
(0.016)
0.036**
(0.016)
-0.200***
(0.023)
0.071***
(0.017)
-0.204***
(0.024)
52,066
52,066
26,591
25,475
8th Grade
(5)
8th Grade
(6)
8th Grade
(7)
8th Grade
(8)
-0.012*
(0.006)
-0.001
(0.009)
-0.024**
(0.010)
-0.011***
(0.002)
0.011***
(0.002)
0.053
(0.041)
-0.001
(0.000)
-0.011***
(0.002)
0.011***
(0.002)
-0.012***
(0.002)
0.012***
(0.003)
-0.010***
(0.002)
0.010***
(0.003)
52,066
52,066
26,591
25,475
1(BirthYear ≥ 1916) × Employment Index
BirthYear - 1916
1(BirthYear ≥ 1916) × (BirthYear - 1916)
N
1(BirthYear ≥ 1916)
1(BirthYear ≥ 1916) × Employment Index
BirthYear - 1916
1(BirthYear ≥ 1916) × (BirthYear - 1916)
N
Source: 1940 US Census - Integrated Public Use Microdata Series (IPUMS 1% sample) and Wallis (1989, 2A)
employment index
Notes: The sample is composed of white men born 1911 – 1920 not living on a farm or in group quarters. The model
is an RD using a 1916 cutoff. The model also includes an interaction of the Wallis employment index (averaged over
1930 – 1934) with the discontinuity variable to determine if the treatment varies with the severity of the Great
Depression in the observation’s birth state in columns 2 and 6. Birth state fixed effects are included, but not reported.
Huber-White robust standard errors (in parenthesis) are reported. *, **, and *** indicate the statistical significance at
10%, 5%, and 1% level, respectively.
42
.1
9.8
10
.15
Education
10.2
10.4
8th Grade Graduate
.2
.25
10.6
.3
10.8
Figure A1: Education Regression Discontinuity
1911
1912
1913
1914
1915 1916
Birth Year
Low Shock
1917
1918
1919
1920
High Shock
1904
1906
1908
1910
1912
1914
Birth Year
Low Shock
1916
1918
1920
High Shock
Source: 1940 US Census - Integrated Public Use Microdata Series (IPUMS 1% sample)
Note: This figure is a graphic depiction of the RD in Table A6 for years of education and a longer bandwidth for
whether the individual left school at 8th grade completion. I use the longer bandwidth for the 8th grade outcome to
show that the two samples deviate in 1908 and return to a relatively similar point in 1916.
43
Table A7: Regression Discontinuity of Labor Market Conditions
COM Employment/100k
1(Year ≥ 1930)
Year - 1930
1(Year ≥ 1930) × (Year - 1930)
Full
Sample
(1)
High
Shock
(2)
Low
Shock
(3)
-0.672***
(0.061)
0.028***
(0.003)
0.027***
(0.009)
-0.799***
(0.094)
0.038***
(0.005)
0.030**
(0.014)
-0.565***
(0.079)
0.019***
(0.003)
0.023**
(0.011)
720
330
390
N
COM Wages/$100m
(4)
(5)
(6)
1(Year ≥ 1930)
Year - 1930
1(Year ≥ 1930) × (Year - 1930)
-1.382***
(0.120)
0.077***
(0.007)
0.012
(0.015)
-1.717***
(0.203)
0.094***
(0.012)
0.032
(0.028)
-1.099***
(0.139)
0.063***
(0.009)
-0.005
(0.015)
720
330
390
N
(COM Wages/Employment)/$100
(7)
(8)
(9)
1(Year ≥ 1930)
Year - 1930
1(Year ≥ 1930) × (Year - 1930)
-3.872***
(0.201)
0.322***
(0.006)
-0.215***
(0.028)
-4.193***
(0.336)
0.332***
(0.009)
-0.206***
(0.049)
-3.601***
(0.241)
0.314***
(0.007)
-0.222***
(0.033)
720
330
390
N
Source: Census of Manufacturers (1899, 1904, 1909, 1914, 1919, 1921, 1923, 1925, 1925, 1925, 1927, 1929, 1931,
1933, 1933, 1935, 1937, 1939)
Note: Coefficients are from an RD using the beginning of the Great Depression (1930) as the cutoff. COM Employment
is the total number of wage workers in the birth state and COM Wages is total wages paid in the birth state. State fixed
effects are included, but not reported. Huber-White robust standard errors (in parenthesis). *, **, and *** indicate the
statistical significance at 10%, 5%, and 1% level, respectively.
44