Great Depression of Wages:
An Investigation of the Impact of Entering the Labor Market
During the Great Depression Using a Regression Discontinuity
Approach
Jeremy Grant Moulton∗
University of North Carolina, Chapel Hill
October 26, 2011
Abstract
This paper investigates the impact of entering the labor market during the Great Depression
employing a regression discontinuity approach with a discontinuity at the age of labor market
entry. Using the 1940 and 1950 U.S. Censuses I investigate the medium and long-term effects of
a poor labor market on labor market entrant’s earnings. Less educated individuals entering the
labor market during the Great Depression experienced a seven to ten percent negative shock to
their earnings ten years after the onset of the Great Depression in 1940 in comparison to those
entering just prior. This negative effect diminishes, but is still evident ten more years later in
1950. I do not find a negative effect for more educated individuals and instead find a relatively
noisy, positive effect.
JEL Classification: J60, N32
∗
I am grateful to Ann Stevens, Colin Cameron, Hilary Hoynes, and Marianne Page for their helpful comments and
ongoing support on this project. Any remaining errors or omissions are my own. Contact: [email protected]
1
1
Introduction
Much recent work has examined the impact of entering the labor market during a recession. Current
empirical work by Kahn (2010) and Oreopoulos et al. (2006) finds that graduating college during
a recession results in large, negative wage effects that persist for several years. These papers shed
light on the added cost of high unemployment; not only are those that lose their job during a
recession affected, but young workers entering the market for the first time experience long-term
negative effects as well. The recent Great Recession has only increased interest in this question.
Considering that the Great Recession is the worst and longest lasting recession since the Great
Depression, understanding how a depressed labor market affects recent college graduates and those
entering the labor market is important for both those affected and policy makers. Knowing the
long term impact of entering the labor market during a recession may induce policy makers to
shape new policy and/or strengthen current policy to provide assistance to individuals searching
for work in a bad labor market to improve national productivity through better job-matching and
reduce cross-cohort inequality.
All prior papers that estimate the impact of entering the labor market during a recession
have used shocks to the unemployment rate driven by recessions in the 1970s to early 2000s that
lasted a relatively short time period. Kahn uses changes to the national unemployment index that
changed from a low of 5.3 percent to a high of 9.7 percent, but were relatively short lived.1 The
unemployment rate thus far in the Great Recession peaked at 9.6 percent, but has remained high
for a longer time period than the years considered by Kahn and Oreopoulos et al. The Great
Recession bears more similarity to the Great Depression than more recent recessions in its severity
and duration. These differences may lead to corresponding differences in the severity and duration
of negative wage effects. This paper attempts to answer the question of how a severe and longlasting recession impacts wages utilizing the immense, sudden, and prolonged negative shock to the
labor market during the Great Depression using a regression discontinuity framework.
The question posed in this paper will also be of interest to economic historians interested in
understanding the impact of the Great Depression as well as researchers employing cohort based
estimation strategies. Cohort based designs rely on similarity of non-treatment characteristics
of individuals across birth cohorts, such as papers that investigate returns to education using
the World War II G.I. Bill or compulsory schooling laws. This paper provides evidence that
birth cohorts entering the labor market during the Great Depression experienced a negative shock
1
Kahn also uses state changes to unemployment that exhibit larger differences, but these shocks should not be as
detrimental to college graduates as a shock to the entire nation.
2
that persisted for over ten years and this should be considered and addressed when using cohort
estimation strategies employing these birth cohorts. I find that there is a seven to ten percent
shock to income for less educated labor market entrants in the medium-term in 1940 (roughly ten
years after the start of the Great Depression) which diminishes in the longer-term in 1950. There
does not however appear to be a negative effect for the more educated in the medium or long-term
which is consistent with Oreopoulos et al. (2006) that finds stronger effects for workers at the lower
end of the wage and ability distribution. They find that the higher ability workers are more able
to mitigate the negative effects of entering a bad labor market. The estimated effects in this paper
using the Great Depression for more educated entrants may even be positive, however I discuss
the possibility that this an effect of World War II service or a positive shock to the employment
possibilities of high educated workers during the Great Depression.
2
Literature Review
Oreopoulos et al. (2006) provides a thorough review of the theoretical and empirical research
investigating the effects of entering a bad labor market. Starting with basic theory, the neo-classical
model predicts that any short-term wage effects for entrants of a good or bad labor market should
not persist in the long-run (the Perfect Spot Market models). However if laborers invest in human
capital while on the job, there are differences in job mobility based on first job, or differences in
the types of jobs hiring in pro or counter cyclical industries then the theory suggests that unlucky
entrants will constantly lag the lucky (the Permanent Scarring Models). The results found in this
paper, using the Great Depression, suggest that the more educated are not permanently scarred,
however the less educated while not permanently scarred have scars that last more than ten years.
The empirical literature provides strong support of the theory that there is a long-term scarring
of entrants of a bad labor market. Kahn (2010) investigates the effect of unemployment at the
time of college completion on wages using NLSY panel data from 1979 to 1989 and March CPS
data from 1987 to 2006. She estimates the wage effect for the initial, fifth, tenth, and fifteenth year
after graduation. She finds a six to seven percent reduction in wages for each 1 percentage point
increase in the unemployment rate which diminishes to 2.5 percent 15 years after the shock. Kahn
and several others (Bertrand and Mullainathan (2001) and Oyer (2006) for example) focus on the
more educated portion of the labor distribution because they feel that these individuals’ income
could be more affected by differences in on-the-job training (a theorized primary driver of the wage
differential).
3
Oreopoulos et al. (2006) investigates both higher and lower ability labor market entrants using
university-employer matched data from Canada for 1982 to 1999. They find that for a two standard
deviation shift in the unemployment rate, there is an initial ten percent shock to earnings that slowly
fades to zero in ten years. Because of their rich data, they are able to show that 30 to 40 percent
of the wage shock is due to labor entrants in bad times joining smaller, lower paying firms. While
they investigate college graduates, they separate the upper and lower distributions of ability and
identify a larger effect for the lower ability group.
3
Methodology
As mentioned above, Kahn (2010) and Oreopoulos et al. (2006) employ national, regional, and
state level variation in the unemployment rate and panel data to estimate the impact of graduating
in a recession on earnings over time. Their methods work well to utilize their data that include both
positive and negative shocks to the unemployment rate. The Great Depression is different from
the recessions used previously in that it went from a stable, low unemployment rate to a stable,
high unemployment rate. Figure 1 shows this sudden change to the labor market by showing
real GNP, with a 1972 base year, using data from Balke and Gordon’s (1986) data appendix and
two unemployment indices using data from Margo(1993), Romer(1986) and the BLS Employment
Status of the Civilian Noninstitutional Population, 1940 to Date. The Lebergott unemployment
index includes individuals working in “work relief” jobs as unemployed and is higher than the Darby
unemployment index which considers them employed. All three of these indices indicate that in
1930 (the vertical, black dashed line) there was a discontinuous change in the trend of aggregate
productivity and employment. Aside from a positive blip in 1921, unemployment was relatively flat
from 1918 to 1929. The blip in 1921 is the result of the Depression of 1921 following World War
I. The unemployment rate stayed extremely high for the first 5 years of the Great Depression and
then fell slightly, but remained higher than the pre-Great Depression trend until the early 1940s as
the United States ramped up production for World War II. The difference in unemployment for the
period 1922 to 1929 (average 3.72 percent) in relation to 1930 to 1934 (average 18.96 percent) was
15.24 percentage points, an incredibly large shock to unemployment. The shock occurred quickly
and with enough force to vastly change the entire labor market.
The perfect sample to estimate the impact of the Great Depression on income would include
panel level data on earnings for individuals from the 1920s until their retirement, however panel
level earnings data does not begin until 1937 with data from the Social Security Administration.
Kopczuk, Saez, and Song (2007) use this data to investigate inequality and mobility over time, but
4
access to the data is very restricted. The best public data available for this time period comes from
the United States Census. Income data is first included in the 1940 census, which works perfectly
for this paper since the Great Depression started in 1930 and the earnings data from the 1930 census
would not include the impact of the Great Depression. Using a regression discontinuity framework,
I compare earnings for individuals reaching working age just prior to the Great Depression in
the Roaring Twenties to those reaching working age during the Great Depression using the 1940
Census. This strategy should estimate the medium-term effects of entering a severely depressed
labor market. Since prior research indicates that the earnings impact will diminish over time, this
effect will be smaller than the initial effect, but data does not exist to estimate this effect. I then
compare the earnings of these same birth cohorts using the 1950 Census to determine if the impact
is long lasting.
I use Equation 1 to estimate the regression discontinuity or effect of entering the labor market
during the Great Depression. The model includes a linear time trend consisting of a birth year
cutoff (C) subtracted from the individual’s birth year (BirthY eari − C) which estimates the trend
in income over time for all birth cohorts. β1 is the slope of the pre-cutoff trend and when combined
with the constant α can be used to graph the pre-cutoff trend. The second time trend (BirthY eari −
C)1(BirthY eari ≥ C) is identical to the first trend except it is interacted with an indicator variable
equal to 1 if the individual was born at or past the cutoff. β2 estimates the difference in the slope
of the income birth year relationship after the cutoff and when combined with the first trend,
constant, and the effect variable (described below) can be used to construct the post-cutoff trend.
The most important variable is an indicator variable equal to 1 if the individual was born at or
past the cutoff 1(BirthY eari ≥ C) which is used to estimate β3 , the shift in the intercept of
the post-cutoff trend, or as interpreted in the regression discontinuity framework, the effect of
the treatment. If individuals reaching working age during the Great Depression were negatively
affected, then β3 should be negative and will represent the dollar difference in income as a result of
entering the labor market in the Great Depression. Graphically β3 is the vertical distance between
the pre-Great Depression cohort’s trend line and the post-Great Depression cohort’s trend line at
the cutoff.
Incomei = α + β1 (BirthY eari − C) + β2 (BirthY eari − C)1(BirthY eari ≥ C)+
β3 1(BirthY eari ≥ C) + StateF Ei + i
5
(1)
I include state fixed effects (StateF Ei ) in some specifications to better control for across state
variation in labor markets, but this does not appear to affect the estimates. I also include a
squared pre and post-cutoff trend, as seen in Equation 2 to potentially increase the fit of the pre
and post-cutoff trends.
Incomei = α + β1 (BirthY eari − C) + β2 (BirthY eari − C)1(BirthY eari ≥ C)
+β3 1(BirthY eari ≥ C) + StateF Ei
(2)
+β4 (BirthY eari − C)2 + β5 [(BirthY eari − C)1(BirthY eari ≥ C)]2 + i
To provide evidence that it is the Great Depression and not some other factor that is causing
the wage effect, I stratify the sample based on the intensity of the treatment. I create an indicator
variable to stratify the sample using Wallis’ (1989 Table 2A) employment indices at the state-year
level (each state is normalized to have a base employment level equal to 100 in 1929). This indicator
variable is equal to one if the individual currently lives in a state that was relatively hard hit by the
Great Depression (this stratification is shown at the bottom of the results tables as UE Shock =
High or Low). I define a state as hard hit if the state had an employment index level lower than the
median employment index for all states for at least 3 years out of the years 1930 to 1935. I classify
22 states out of 48 as hard hit states.2 It should be noted that there was a great deal of migration
in response to the Great Depression. Because the intensity is assigned by the individual’s current
state of residence, it may not perfectly stratify the sample since the individual may have moved
to a state with higher employment. Even though the intensity may not be perfectly assigned, the
estimates presented in the Results section corroborate the hypothesis that less educated entrants
in harder hit states suffer larger effects.
3.1
What Discontinuity?
Prior research using college graduates faced a relatively straightforward job of determining when
individuals enter the labor market, it was simply when the entrant graduated college. Because
college attendance was not nearly as prevalent in the 1930s as it is today, the assignment of labor
force entry takes slightly more work. Goldin and Katz (2003) provide my starting point by showing
that the modal labor law in the 1920s allowed individuals to obtain a work permit at age 14 if
2
High UE states = Alabama, Arkansas, California, Colorado, Idaho, Illinois, Indiana, Louisiana, Michigan, Missouri, Montana, Nevada, New Mexico, Ohio, Oklahoma, Pennsylvania, Tennessee, Texas, Utah, Washington, Wisconsin, and Wyoming.
6
they completed six years of education. Figure 2 is a probability density function of education for
the 1940 and 1950 samples used in this paper and indicates that a large portion of the sample
left school in the 8th grade when they would have been 14 years old. The 1940 and 1950 Census
collected income data from individuals aged 14 and older, showing that they considered 14 the
first age individuals would be eligible to work. Additionally, the Historical Statistics of the United
States’ chapter on Labor Force under the Unemployment section indicates that the Bureau of
Labor Statistics calculated the unemployment rate including individuals age 14 or older up until
1947 when it was changed to 16 and older due to changing labor laws. This evidence points to using
a 14 year old labor force entry age, which I assign to individuals earning an 8th grade degree.3
The proportion of individuals completing 8th grade is high (roughly 20 percent in the 1940
census), but there is reason to believe that the proportion may be even larger, Yamashida (2008)
and Goldin (1998) show that there is an overstatement of education at the high school level in the
1940 census, especially for those born prior to 1916. For the regression discontinuity estimates in
this paper, I initially estimate effects assuming that the labor market entry age was 14 years old
for the entire sample and then limit the 14 year old age cutoff for those achieving eight years of
education or 18 years old for those individuals earning 12 years of education. This labor market
entry age means that 1916 is the birth year discontinuity for those earning eight years of education
and 1912 is the discontinuity for those completing high school.
An identifying assumption of regression discontinuity method is that the running variable (year
of birth trends) absorb everything that is not due to the exogenous discontinuity and that being on
either side of the discontinuity is exogenous. This may be a problem for this paper if the individual
can change their position in relation to the cutoff. The individual obviously does not have control
over their birth year, but they could have changed their education in response to the bad labor
market, thus changing their labor market entry age. Prior research from Yamashita (2008) and
Goldin and Katz (2003) finds although high school attendance was increasing from the early 1900s
the Great Depression had negligible effects on student’s decisions to prolong schooling, meaning
students were most likely not remaining in high school to escape a bad job market. To further
investigate this potential problem I also estimate the effect of the Great Depression on educational
attainment using the same regression discontinuity method and show that the Great Depression
had a very small effect on educational attainment. The results indicate that there may be slight
positive movement in the decision to attend high school at the onset of the Great Depression. This
3
Using an 8th grade and below sample does not significantly change the majority of results, but restricting to only
8th graders should include those individuals that would have entered the labor market at 14 rather than at a slightly
younger age.
7
could potentially drive the results to be positively biased since those individuals that prolong their
education came from more well-off households (since their family could afford to have a non-working
child), but could also negatively bias the results because those individuals that previously invested
in high school did so with the opportunity cost of not joining a booming labor market while those
investing in high school during the Great Depression made the decision to “escape” a bad market
and thus faced a lower opportunity cost meaning that they may be lower ability laborers.
4
Data
I use the 1 percent samples from the 1940 and 1950 decennial censuses from the Integrated Public
Use Microdata Series (IPUMS). I retain white, male individuals born between 1908 and 1920.4
I drop those with missing or NA education or had their education, gender, or race imputed and
those with missing or top coded income. In the 1940 census sample there are only 25 top coded
observations and 266 in the 1950 census. These restrictions leave a sample of 96,518 individuals
in the 1940 census and 29,590 in the 1950 census. The 1950 census contains a smaller number of
observations because education and income data are only available for sample line observations,
while these questions were asked of all respondents in the 1940 census.
I estimate effects for three separate samples; first the sample I refer to as the Total Sample
includes all individuals that received 13 years or less of education (I restrict to 13 because that
is the maximum education an individual born the last year of my sample could attain), second
a sample of people stopping their schooling at the 8th grade, and lastly a sample stopping their
schooling at the 12th grade. Figure 3 depicts the proportion of each birth cohort working at
the 1940 census for birth cohorts 1900 to 1925 for the total, 8th grade, and 12th grade samples.
An individual is considered working if they have non-missing, positive earnings. There are a few
interesting aspects of this figure. As described in the Method section, the primary discontinuity
used in this paper is 1916 and it is clear that by 1916 there is a reduction in the percent working
for the 8th grade and total sample. This is interesting because those born in 1916 were 23 to
24 years old at the 1940 census and eligible to begin working in 1930, 9 to 10 years earlier so
this drop in unemployment cannot be entirely driven by an age effect. It is more likely that the
lower proportion working is driven by the poor labor market prevalent in the Great Depression.
While I want to retain those cohorts negatively affected by the Great Depression, I drop those born
after 1920 because these individuals would have been relatively young (less than 20) and have less
4
Birth year is calculated as: CensusY ear − Age − 1 to compensate for the collection of census data in the early
part of the year.
8
than 50 percent of their birth cohort employed. Additionally, I drop those individuals reaching
working age during and before the Recession of 1921 to retain a pre-sample that experienced a
similar, favorable labor market. As previously discussed, Figure 1 shows that the unemployment
rate spikes in 1921 due to the Recession of 1921 which would have affected those individuals born
14 years prior, in 1907. The shaded portion of Figure 3 reflects the restricted sample used in this
paper 1908-1920. Another interesting aspect of Figure 3 is that for high school graduates there is
actually an increase in employment following the 1912 cutoff, this may explain why I find positive
effects for the high school sample and may indicate that the Great Depression had differential
effects on individuals based on their education. By 1950 this differential in employment by age is
gone and the relationship appears to be positively sloped, as seen in Figure 4. This indicates that
there was not a permanent scar to employability caused by the Great Depression, but could also
reflect the transformation of the labor market during the boom before and after World War II.
5
5.1
Results
Income
Figure 5 depicts the graphical results of the regression discontinuity for the entire 1940 census using
a 1916 birth year cutoff, represented by the vertical black line. The horizontal axis is the birth
year, while the vertical axis is income. The linear pre-cutoff and post-cutoff trends (black lines) fit
very closely with the birth year means (circles). There does not appear to be a quadratic nature
to the data, the gray lines represent the quadratic fit of the data. The vertical distance between
the two trends at the cutoff is the estimated effect of entering the labor market during the Great
Depression. There is a $34.52 vertical drop in yearly income at birth year 1916 using a linear fit
and $40.88 using the quadratic fit that are both statistically significant at the one percent level, as
seen in the numerical results in Table 1. Including state fixed effects, columns 2 and 4, does not
move the estimated effect. The average income for the 1940 sample is $815.81 and the median is
$720, so the linear fit effect represents a 4.2 percent difference in average income or 4.8 percent of
median income. This effect is relatively large in comparison to Oreopoulos et al. who find an initial
loss of ten percent that fades to zero in eight years for a change from an economic boom to bust
(two standard deviation shift in the unemployment rate). I provide results using logged income for
the 8th and 12th grade samples, making the conversion to percentages easier. The stratification
of the sample by high and low state employment levels, columns 5 to 8 in Table 1 indicate that
there is a much larger effect in those states harder hit by the Great Depression, $41.89 for the high
9
UE states compared to only $25.08 for the low UE states using a linear fit (the quadratic fit is in
columns 7 and 8). These results are for the entire sample and many of these students completed
high school and some attended college, so age 14 may not be the most suitable cutoff to use for the
entire sample.
Figure 6 displays the graphical results for the sample restricted to those that completed 8 years
of education. The 8th grade restricted sample results are roughly the same in magnitude, $39.80
for the linear fit and $36.63 for the quadratic and statistically significant at the 1 percent level as
seen in Table 2. The fit also appears better than the total sample. Columns 5 through 8 in Table 2
show that the harder hit states are more affected by the Great Depression, with a negative effect of
$60.22 significant at the 1 percent level for the hard hit states and a negative $15.61 effect for the
less hard hit states, that is not significant at even the 10 percent level. This is the strongest evidence
that the Great Depression is driving the difference in income. Table 3 presents the estimates from
columns 1, 3, 5, and 6 from Table 2 except using logged income in columns 1 to 4 and omitting
the cutoff birth year, 1916 in columns 5 to 8. Both of these alternate specifications reflect similar
results to the those found in Table 2. The log income results show that the percentage effect on
income is larger for the 8th grade sample than the full sample (6.92 percent). While the point
estimates are relatively similar for the full and 8th grade sample, the average income is $762.35 for
the 8th grade sample which is lower than the full sample. The estimate falls a bit using quadratic
trends in column 2. The estimates for the high and low shock states provide strong evidence that
it is the unemployment shock and not some other cohort effect that is driving these estimates.
Omitting the cutoff year in columns 5 to 8 increases the estimates slightly from the full 8th grade
sample, but reflect similar estimates.
While there is a strong negative effect for the 8th grade sample, there does not appear to be
a negative effect for those that stopped their education at high school. The results restricting
the sample to 12th graders are in Figure 7. If anything there might be a positive $49.64 effect of
graduating in the Great Depression significant at the 5 percent level, in Table 4, which actually gets
bigger in the harder hit states (columns 7 and 8). There are a couple of possible reasons for this
result. First, the employment rate for 12th grade graduates actually rose after the 1912 birth year
cutoff (see Figure 3) so they may have faced a slightly better labor market. Second, an inspection
of Figure 7 indicates that a more realistic fit of the data may be a linear pre-cutoff trend and a
quadratic post-cutoff trend. When this method is used, there is no difference in income for those
born on either side of the cutoff, as seen in columns 3 and 4 of Table 4. These results fit well
with findings of Oreopoulos et al. (2006) who find that more educated, higher skilled individuals
are more capable of pulling themselves out of the negative effects of a bad labor market. When I
10
convert to logged income in Table 5, the estimates are actually quite large (13.4 percent in column
1), but when the logged income is used with a quadratic for pre and post-trend, the effect falls
to 0.7 percent. The results omitting the cutoff year (1912), as with the 8th grade sample, get
larger than the full 12th grade sample. It seems that the 12th grade graduates entering the labor
market during the Great Depression actually experienced a positive wage effect or at the very least
experience no effect.
I move forward ten years to 1950 to estimate the longer-term effects of the Great Depression.
Figure 8 contains the graphical regression discontinuity for the full sample in 1950. Using the
linear fit and quadratic fit there is no statistically or economically significant difference in income
for those born on either side of the cutoff, as seen in Table 6. The linear fit in column 1 indicates
that there is a $8.33 difference in yearly average income at the discontinuity. The average income
for the total 1950 sample is $2,914 which is much higher than the 1940 sample, so the small effects
estimated in Table 6 are even smaller in percentage of yearly income terms. The total sample results
provide some evidence that the negative effect of the Great Depression diminished significantly in
the long-term. It should be noted that for all the 1950 results, the graphical fit does not appear
as clean as the 1940 sample nor are the table results as statistically significant as those using the
1940 sample. This is potentially the result of income and education only being collected of those
respondents that were sample-line persons. There is a large difference in observations, from 96,518
in the 1940 census to 29,590 in the 1950 census and this smaller number of observations may be
driving the more noisy estimates.
As with 1940, the total sample may not be the best sample to estimate the regression discontinuity because they include those that entered the labor market at age 14, but also those that
entered later. Restricting the sample to only those that have an 8th grade education leads to
opposing results for the linear and quadratic methods as seen in Figure 9. Table 7 indicates that
although the sign of the effect flips depending on the method, both methods provide statistically
insignificant results. The effects are relatively similar for the high and low shocked states, seen
in columns 5 to 8. Even if we assume that the true effect was $80.42, from Table 7 column 1,
average income in 1950 was $2,666. Table 8 contains the estimates using log income in the first
four columns. The log results indicate that the effect is a noisy negative 4.9 percent in 1950 using
the linear fit and a noisy, positive 3.59 percent using a quadratic fit. Omitting the cutoff year
1916 from the analysis serves to boost the effect for column 1 in Table 7 to $207.8 and increases
the statistical significance substantially. The quadratic fit effect also turns negative, however the
high shocked states experienced a smaller long-run effect than the low shock states. The last four
columns of this table will be discussed in the World War II subsection.
11
Figure 10 depicts the results for the sample restricted to 12th grade graduates. Interestingly,
it appears that there may be a positive effect from graduating in the Great Depression, with the
linear fit estimating a $117.5 effect, significant at the 5 percent level in Table 9. However this effect
is wiped away as quadratic trends are used instead of linear. The log results in Table 10 shows
that there may be a 3.42 percent increase in yearly income using the linear fit. Although the high
shock states appear to actually have a positive effect on the earnings, while the low shock states
have a negative effect. A visual inspection of the graphical regression discontinuity in Figure 10
indicates that there may be some truth to the positive effect. The post-1912 trend appears to be
above the pre-1912 trend for all years, but 1912. When 1912 is omitted from the sample, as in
Table 10 columns 5 to 8, the effects get more economically and statistically significant. To better
understand what is happening with these oddly positive results of the Great Depression for high
school graduates in 1950, I explore the impact of World War II on the birth cohorts used in this
paper.
5.2
World War II
Figure 11 from Larsen et al. (2011) shows that World War II mobilization rates increased for
cohorts born around 1912. Bound and Turner (2002) find an increase in college attendance for those
birth cohorts that had higher mobilization rates and Lemieux and Card (2001) find an increase
in earnings for Canadians that had higher mobilization rates in World War II. However, Angrist
and Krueger (1994) find little evidence that World War II veterans earned more than non-veterans
and there may instead be a negative effect. The prior research on the effect of the World War
II GI Bill focused on those veterans that attended college, but this sample is restricted to those
earning only 12 years of education. Two possible explanations are that there is a positive veteran
effect that diminished and was not previously found in prior research that has focused on the 1960
and more recent censuses or the GI Bill was used to complete high school and what we see is
the impact of high school completion. The last point seems less plausible because although the
percent of individuals in the 1950 sample earning an 8th grade education fell from the 1940 level,
the 12th grade education proportion does not move from the 1940 level. All of the action of the GI
Bill appears to occur in the college portion of the distribution. Additionally, since this sample is
restricted to those individuals that earned a 12th grade degree, we would think that the pre-World
War II cutoff high school graduates would be higher ability workers since they chose to invest in 12
years of education paying the full price, while the post-World War II graduates were subsidized by
the GI Bill to complete high school and thus we should see an even larger negative effect in Figure
10.
12
Table 8 provides estimates in columns 9 to 12 that include an indicator variable for World War
II veteran status. The veteran status is actually always negative and the point estimates do not
deviate substantially from those found in Table 7. While there was not change for the 8th grade
sample, the 12th grade sample goes from positive effects of the Great Depression in Table 9 to
slightly negative effects in Table 10 column 1 that controls for World War II participation. The
effect stays positive when using a quadratic fit however, although the high and low effected states
results indicate that individuals in the hard hit states experience a negative shock to income, while
the less affected states have no effect. These results seem to indicate that, at least for the more
educated, World War II may have been a contributing factor in the positive wage effects.
5.3
Education
The starkly different results by education level suggest that any effect of the Great Depression on
education could confound the results found in this paper. As mentioned in the Method section,
prior research has not found a significant increase in education as a result of the Great Depression.
Using a regression discontinuity framework, I find that there may have been a slight increase.
Figure 12 depicts the estimates of a regression discontinuity of total education for the total sample
used in this paper. This figure shows there was a change in slope at the 1916 birth year cutoff,
but there is little evidence that there was a discontinuous shift in delaying job market entry at the
discontinuity. Table 11 indicates that for the linear fit there is a 0.0504 year increase in education
in response to the Great Depression. Although not statistically significant, the effects are actually
larger for the low UE states than the high UE states, 0.0932 years for the low UE compared to only
0.0175 for the high UE states indicating something other than the Great Depression may be driving
the small difference in education. The quadratic fit actually indicates a negative effect of the Great
Depression on education, however a visual inspection indicates that a pre-linear and post-quadratic
trend is most likely a better fit, in which case the effect is negligible.
Similar to the full education results, Figures 13 and 14 show that there is not a large discontinuity
for the probability of earning an 8th grade or 12th grade education. As before, there is a slight
effect if using linear trends on either side, but quadratic trends fit the data more smoothly. Column
1 of Table 12 shows there is a 1.5 percentage point reduction in the probability of earning an 8th
grade degree, out of a base of 22.1 percent, but this falls to a 0.685 percentage point reduction
when using quadratic trends. The low UE states are increasing the value of the aggregate estimate,
as seen in columns 5 and 6. Table 13 indicates there is a corresponding 1.51 percentage point
increase in stopping at high school graduation, out of a base of 31 percent, but again falls to 0.541
13
percentage point when using quadratic trends. The base linear fit estimate for total education is
not statistically significant, the 8th grade effect is statistically significant at the 1 percent level,
and the 12th grade estimates are significant at the 10 percent level. I include the estimates for the
1950 sample in Tables 14 - 16 which show very similar results. The results do not entirely rule out
a small effect, but it does not seem that there is evidence that substantial proportion of people
delayed their labor market entry as a result of the Great Depression by staying in high school.
6
Conclusion
I have shown that young, less educated workers facing a horrendous labor market during the Great
Depression did not significantly delay their entry by remaining in high school and suffered negative
long-term effects to their income. The medium-term effects, 10 years later, for the less educated
ranged from a negative 7 to 10 percent shock to earnings, with a likely higher initial shock. These
effects appear to dissipate to roughly half the medium-term amount another 10 years later in 1950.
The more educated group does not appear to be affected in the medium-term and in the long-run
actually experienced a positive shock to income, although this is likely the result of either a more
favorable market during the Great Depression for high ability workers or another shock, World War
II. This makes interpretation of the long-term effects difficult. Like Oreopoulos et al. (2006), I find
that the lesser educated are the real victims of a poor labor market. Policy makers should keep
this in mind when forming policy to get workers back on their feet during recessions.
14
7
References
Angrist, Joshua and Alan Krueger (1994). “Why Do World War II Veterans Earn More than
Nonveterans?” Journal of Labor Economics, Vol, 12, No. 1, pp. 74-97.
Balke, Nathan and Gordon, Robert J. (1986). “Appendix B Historical Data”, The American
Business Cycle: Continuity and Change, pp. 781-850.
Bertrand, Marianne and Sendhil Mullainathan (2001). “Are CEOs Rewarded for Luck? The Ones
without Principals Are.” The Quarterly Journal of Economics, August 2001, Vol. 116, No. 3, pp.
901-932.
Bound, John and Sarah E. Turner (2002). “Going to War and Going to College: Did World War
II and the G.I. Bill Increase Educational Attainment for Returning Veterans?” Journal of Labor
Economics, Vol. 20 No. 4, pp. 784-815.
Carter, Susan B. (2006). “Labor Force”, Historical Statistics of the United States, Millenial Edition
On Line, pp. 2:13-35.
Goldin, Claudia (1998). “Americas Graduation from High School: The Evolution and Spread of
Secondary Schooling in the Twentieth Century.” Journal of Economic History, Vol. 58, No. 2, pp.
345-74.
Goldin, Claudia, and Laurence Katz (2003). “Mass Secondary Schooling and the State: The Role
of State Compulsion in the High School Movement.” NBER Working Paper No. 10075.
Kahn, Lisa (2010). “The Long-term Labor Market Consequences of Graduating from College in a
Bad Economy.” Labour Economics, Vol. 17, No. 2, pp. 303-316.
Kopczuk, Wojciech, Emmanuel Saez, and Jae Song (2007). “Uncovering the American Dream:
Inequality and Mobility in Social Security earnings Data Since 1937”, NBER Working Paper No.
13345.
Larsen, Matthew, T.J. McCarthy, Jeremy G. Moulton, Marianne Page, and Ankur Patel (2011).
“War and Marriage: Assortative Mating and World War II G.I. Bill”, Working Paper, University
of California, Davis.
Lemieux, Thomas, and David Card (2001). “Education, Earnings and the Canadian G.I. Bill,”
Canadian Journal of Economics, Vol. 34, No.2, pp. 313-344.
Margo, Robert A. (1993). “Employment and Unemployment in the 1930s”, The Journal of Economic Perspectives, Vol. 7, Issue 2, pp. 41-59.
15
Oreopoulos, Philip, Till von Wachter, and Andrew Heisz (2006). “The Short- and Long-Term
Career Effects of Graduating in a Recession: Hysteresis and Heterogeneity in the Market for College
Graduates.” NBER Working Paper No. 12159.
Oyer, Paul (2006). “The Macro-Foundations of Microeconomics: Initial Labor Market Conditions
and Long- Term Outcomes for Economists.?” NBER Working Paper No. 12157.
Romer, Christina (1986). “Spurious Volatility in Historical Unemployment Data”, The Journal of
Political Economy, Vol. 94, No. 1, pp. 1-37.
U.S. Bureau of Labor Statistics (BLS) Internet site and BLS Handbook of Methods, Bulletin
number 2490 (April 1997).
Wallis, John Joseph (1989). “Employment in the Great Depression: New Data and Hypotheses”,
Explorations in Economic History, Vol. 26, pp. 45-72.
Yamashita, Takashi (2008). “The Effects of the Great Depression on Educational Attainment”,
Working Paper, Reed College.
16
8
Figures
Figure 1: Great Depression - Unemployment and GNP
$575
30
$525
25
20
$425
$375
15
$325
Unemployment Rate
Real GNP/$1 billion (1972 dollars)
$475
10
$275
5
$225
1950
1949
1948
1947
1946
1945
1944
1943
1942
1941
1940
1939
1938
1937
1936
1935
1934
1933
1932
1931
1930
1929
1928
1927
1926
1925
1924
1923
1922
1921
1920
1919
0
1918
$175
Year
Real GNP
Unemployment (Lebergott)
Unemployment (Darby)
This
graph depicts real GNP, 1972 base year and the Lebergott and Darby unemployment indices over time. The vertical
black dashed line at 1930 indicates the discontinuity in unemployment used in this paper. The real GNP data is from
Balke and Gordon (1986). Unemployment data are from Margo (1993), Romer (1986) and the BLS Employment
Status of the Civilian Noninstitutional Population, 1940 to Date.
17
Figure 2: Education PDF
30
Proportion Achieving Education Level = x
25
20
15
10
5
0
None
1st
2nd
3rd
4th
5th
6th
7th
8th
9th
10th
11th
12th
1st
2nd
3rd
4th
5th
Education Level
1940
1950
This
graph depicts the proportion achieving different education levels for the 1940 census (gray) and 1950 census (black).
18
Figure 3: Percent Working in 1940 Census by Education Level
0.9
0.8
0.7
Proportion with > 0 Income
0.6
0.5
0.4
0.3
0.2
0.1
High School Cutoff
8th GradeCutoff
1923
1922
1921
1920
1919
1918
1917
1916
1915
1914
1913
1911
1912
1910
1909
1908
1907
1906
1905
1904
1903
1902
1901
1900
0
Birth Year
Total
8th Grade
High School
This
graph depicts the percent of each birth cohort working in the 1940 census. An individual is considered to be working
if they have non-missing, positive income. The shaded portion of the graph indicates the sample used in this paper,
1908-1920. There are cohort averages for the total (solid), 8th grade (big dashed), and 12th grade samples (small
dashed). The vertical lines represent the cutoffs used for the high school and 8th grade samples.
19
Figure 4: Percent Working in 1950 Census by Education Level
0.9
Proportion with > 0 Income
0.85
0.8
0.75
0.7
High School Cutoff
8th GradeCutoff
1923
1922
1921
1920
1919
1918
1917
1916
1915
1914
1913
1912
1911
1910
1909
1908
1907
1906
1905
1904
1903
1902
1901
1900
0.65
Birth Year
Total
8th Grade
High School
This
graph depicts the percent of each birth cohort working in the 1950 census. An individual is considered to be working
if they have non-missing, positive income. The shaded portion of the graph indicates the sample used in this paper,
1908-1920. There are cohort averages for the total (solid), 8th grade (big dashed), and 12th grade samples (small
dashed). The vertical lines represent the cutoffs used for the high school and 8th grade samples.
20
Figure 5: 1940 Census: Regression Discontinuity for Total Sample
$1,175
$1,075
$975
Income
$875
$775
$675
$575
$475
1920
1919
1918
1917
1916
1915
1914
1913
1912
1911
1910
1909
1908
$375
Birth Year
This figure depicts the results of a regression discontinuity around a 1916 birth year cutoff (vertical black line) for
individuals below 13 years of education from the 1940 census. The circles are the birth year averages of income,
the black lines represent the linear fit of the pre and post trends, and the gray lines represent the quadratic fit of
the pre and post trends. Note: the gray lines in some figures are difficult to see due to their similarity with the linear fit.
21
Figure 6: 1940 Census: Regression Discontinuity for 8th Grade Graduates
$1,075
$975
$875
Income
$775
$675
$575
$475
1920
1919
1918
1917
1916
1915
1914
1913
1912
1911
1910
1909
1908
$375
Birth Year
This figure depicts the results of a regression discontinuity around a 1916 birth year cutoff (vertical black line) for
individuals with an 8th grade education from the 1940 census. The circles are the birth year averages of income,
the black lines represent the linear fit of the pre and post trends, and the gray lines represent the quadratic fit of
the pre and post trends. Note: the gray lines in some figures are difficult to see due to their similarity with the linear fit.
22
Figure 7: 1940 Census: Regression Discontinuity for 12th Grade Graduates
$1,375
Income
$1,175
$975
$775
$575
1920
1919
1918
1917
1916
1915
1914
1913
1912
1911
1910
1909
1908
$375
Birth Year
This figure depicts the results of a regression discontinuity around a 1912 birth year cutoff (vertical black line) for
individuals with an 12th grade education from the 1940 census. The circles are the birth year averages of income,
the black lines represent the linear fit of the pre and post trends, and the gray lines represent the quadratic fit of
the pre and post trends. Note: the gray lines in some figures are difficult to see due to their similarity with the linear fit.
23
Figure 8: 1950 Census: Regression Discontinuity for Total Sample
$3,050
$3,000
$2,950
Income
$2,900
$2,850
$2,800
$2,750
1920
1919
1918
1917
1916
1915
1914
1913
1912
1911
1910
1909
1908
$2,700
Birth Year
This figure depicts the results of a regression discontinuity around a 1916 birth year cutoff (vertical black line) for
individuals below 13 years of education from the 1950 census. The circles are the birth year averages of income,
the black lines represent the linear fit of the pre and post trends, and the gray lines represent the quadratic fit of
the pre and post trends. Note: the gray lines in some figures are difficult to see due to their similarity with the linear fit.
24
Figure 9: 1950 Census: Regression Discontinuity for 8th Grade Graduates
$2,900
$2,800
Income
$2,700
$2,600
$2,500
$2,400
1920
1919
1918
1917
1916
1915
1914
1913
1912
1911
1910
1909
1908
$2,300
Birth Year
This figure depicts the results of a regression discontinuity around a 1916 birth year cutoff (vertical black line) for
individuals with an 8th grade education from the 1950 census. The circles are the birth year averages of income,
the black lines represent the linear fit of the pre and post trends, and the gray lines represent the quadratic fit of
the pre and post trends. Note: the gray lines in some figures are difficult to see due to their similarity with the linear fit.
25
Figure 10: 1950 Census: Regression Discontinuity for 12th Grade Graduates
$3,550
$3,450
Income
$3,350
$3,250
$3,150
$3,050
1920
1919
1918
1917
1916
1915
1914
1913
1912
1911
1910
1909
1908
$2,950
Birth Year
This figure depicts the results of a regression discontinuity around a 1912 birth year cutoff (vertical black line) for
individuals with an 12th grade education from the 1950 census. The circles are the birth year averages of income,
the black lines represent the linear fit of the pre and post trends, and the gray lines represent the quadratic fit of
the pre and post trends. Note: the gray lines in some figures are difficult to see due to their similarity with the linear fit.
26
Figure 11: World War II Mobilization Rates
This figure from Larsen et al. (2011) depicts the proportion of each birth cohort serving in World War II using the
1960, 1970, and 1980 census.
27
Figure 12: 1940 Census: Regression Discontinuity for Total Education
9.90
Education (in years)
9.70
9.50
9.30
9.10
1920
1919
1918
1917
1916
1915
1914
1913
1912
1911
1910
1909
1908
8.90
Birth Year
This figure depicts the results of a regression discontinuity around a 1916 birth year cutoff (vertical black line) for all
individuals with an education below 13 years from the 1940 census. The circles are the birth year averages of income,
the black lines represent the linear fit of the pre and post trends, and the gray lines represent the quadratic fit of
the pre and post trends. Note: the gray lines in some figures are difficult to see due to their similarity with the linear fit.
28
Figure 13: 1940 Census: Regression Discontinuity for 8th Grade Education
0.30
0.28
Prob(Education = 8)
0.26
0.24
0.22
0.20
0.18
1920
1919
1918
1917
1916
1915
1914
1913
1912
1911
1910
1909
1908
0.16
Birth Year
This figure depicts the results of a regression discontinuity around a 1916 birth year cutoff (vertical black line)
for all individuals with an education below 13 years from the 1940 census. The vertical axis is the proportion of
the birth cohort earnings exactly an 8th grade education. The circles are the birth year averages of income, the
black lines represent the linear fit of the pre and post trends, and the gray lines represent the quadratic fit of
the pre and post trends. Note: the gray lines in some figures are difficult to see due to their similarity with the linear fit.
29
Figure 14: 1940 Census: Regression Discontinuity for High School Education
0.38
0.36
0.34
Prob(Education = 12)
0.32
0.30
0.28
0.26
0.24
0.22
1920
1919
1918
1917
1916
1915
1914
1913
1912
1911
1910
1909
1908
0.20
Birth Year
This figure depicts the results of a regression discontinuity around a 1916 birth year cutoff (vertical black line)
for all individuals with an education below 13 years from the 1940 census. The vertical axis is the proportion of
the birth cohort earnings exactly a 12th grade education. The circles are the birth year averages of income, the
black lines represent the linear fit of the pre and post trends, and the gray lines represent the quadratic fit of
the pre and post trends. Note: the gray lines in some figures are difficult to see due to their similarity with the linear fit.
30
9
Tables
31
32
86716
782.7∗∗∗
(3.429)
39752
Low
749.1∗∗∗
(9.252)
-30.74∗∗∗
(3.680)
-46.02∗∗∗
(1.745)
-25.08∗
(12.21)
(6)
income
46964
High
782.4∗∗∗
(5.356)
-5.580∗∗∗
(0.641)
-0.0215
(0.411)
-20.44∗∗∗
(3.982)
-43.30∗∗∗
(3.390)
-52.14∗∗∗
(5.489)
(7)
income
39752
Low
738.4∗∗∗
(14.67)
-5.482∗∗
(2.377)
-0.734
(0.883)
0.285
(11.84)
-52.56∗∗∗
(8.053)
-26.38
(15.56)
(8)
income
The results in this table are estimated using a regression discontinuity framework with a 1916 birth year cutoff on the full 1940 sample (¡= 13 years of
education). Standard errors clustered on the birth year in parenthesis.
*p <0.1, ** p <0.05, *** p <0.01
86716
x
904.0∗∗∗
(55.04)
46964
86716
762.8∗∗∗
(4.735)
N
x
908.9∗∗∗
(55.07)
High
86716
767.6∗∗∗
(3.560)
-42.52∗∗∗
(3.470)
-43.11∗∗∗
(0.886)
-41.89∗∗∗
(9.235)
(5)
income
UE Shock
State FE
cons
-4.889∗∗∗
(1.389)
-10.82∗
(5.721)
-5.510∗∗∗
(1.159)
-11.25∗
(5.277)
-47.49∗∗∗
(1.550)
Post Trend2
-34.70∗∗∗
(2.859)
-37.08∗∗∗
(3.223)
Post Trend
-47.32∗∗∗
(2.758)
-40.75∗∗∗
(3.357)
-0.367∗
(0.176)
-44.23∗∗∗
(0.557)
-44.40∗∗∗
(0.598)
Trend
-40.88∗∗∗
(5.109)
(4)
income
-0.329
(0.292)
-36.05∗∗∗
(7.287)
-34.52∗∗∗
(8.344)
Effect
(3)
income
Trend2
(2)
income
(1)
income
Table 1: 1940 Regression Discontinuity - Total Sample
33
19168
660.4∗∗∗
(12.27)
9094
Low
652.8∗∗∗
(13.18)
-12.79∗∗∗
(3.321)
-49.16∗∗∗
(2.572)
-15.61
(14.76)
(6)
income
10074
High
682.9∗∗∗
(13.50)
-0.0624
(0.903)
1.454
(0.843)
-28.76∗∗∗
(7.450)
-30.17∗∗∗
(7.325)
-80.23∗∗∗
(13.50)
(7)
income
9094
Low
628.7∗∗∗
(21.17)
4.031∗∗
(1.786)
-1.554
(1.382)
-8.327
(13.27)
-63.26∗∗∗
(12.54)
13.00
(21.48)
(8)
income
The results in this table are estimated using a regression discontinuity framework with a 1916 birth year cutoff on the 8th grade 1940 sample. Standard
errors clustered on the birth year in parenthesis.
*p <0.1, ** p <0.05, *** p <0.01
19168
x
992.5∗∗∗
(179.4)
10074
19168
657.0∗∗∗
(5.923)
N
x
984.8∗∗∗
(180.5)
High
19168
656.9∗∗∗
(2.132)
-10.15∗∗∗
(2.132)
-43.38∗∗∗
(1.993)
-60.22∗∗∗
(12.43)
(5)
income
UE Shock
State FE
cons
0.0550
(0.900)
-16.70∗∗
(6.053)
1.800∗∗
(0.687)
-18.29∗∗∗
(4.697)
-39.83∗∗∗
(4.838)
Post Trend2
-9.197∗∗∗
(1.081)
-11.22∗∗∗
(1.389)
Post Trend
-46.04∗∗∗
(4.246)
-41.92∗∗∗
(7.646)
0.562
(0.517)
-44.94∗∗∗
(0.832)
-46.09∗∗∗
(0.517)
Trend
-36.63∗∗∗
(6.168)
(4)
income
0.00573
(0.456)
-34.34∗∗∗
(5.908)
-39.80∗∗∗
(4.436)
Effect
(3)
income
Trend2
(2)
income
(1)
income
Table 2: 1940 Regression Discontinuity - 8th Grade Sample
34
-0.0750∗∗∗
(0.00841)
-0.0544∗∗∗
(0.0130)
-0.0597∗∗∗
(0.00206)
-0.0786∗∗∗
(0.00322)
Trend
Post Trend
9094
Low
6.233∗∗∗
(0.0323)
-0.0844∗∗∗
(0.00708)
-0.0622∗∗∗
(0.00557)
-0.0261
(0.0356)
(4)
loginc
17845
656.9∗∗∗
(2.139)
-8.456∗∗∗
(0.572)
-46.09∗∗∗
(0.518)
-47.83∗∗∗
(2.177)
(5)
income
17845
657.0∗∗∗
(5.944)
-0.424
(0.503)
0.00573
(0.457)
-6.447
(4.386)
-46.04∗∗∗
(4.261)
-49.94∗∗∗
(6.012)
9351
High
660.4∗∗∗
(12.31)
-8.605∗∗∗
(2.097)
-43.38∗∗∗
(2.000)
-64.71∗∗∗
(12.52)
Omit 1916
(6)
(7)
income
income
8494
Low
652.8∗∗∗
(13.23)
-8.438∗∗
(2.748)
-49.16∗∗∗
(2.581)
-28.27∗
(13.57)
(8)
income
The results in this table are estimated using a regression discontinuity framework with a 1916 birth year cutoff on the 8th grade 1940 sample. The first four
columns use log income and the last four use income and omit the cutoff year 1916. Standard errors clustered on the birth year in parenthesis.
*p <0.1, ** p <0.05, *** p <0.01
10074
19168
N
19168
High
6.241∗∗∗
(0.0158)
-0.0742∗∗∗
(0.00519)
-0.0576∗∗∗
(0.00253)
-0.106∗∗∗
(0.0173)
UE Shock
6.212∗∗∗
(0.0155)
-0.000618
(0.00272)
Post Trend2
cons
-0.00168∗
(0.000852)
Trend2
6.238∗∗∗
(0.0121)
-0.0473∗∗
(0.0172)
-0.0692∗∗∗
(0.0140)
Effect
(1)
loginc
Log Income
(2)
(3)
loginc
loginc
Table 3: 1940 Regression Discontinuity - 8th Grade Sample - Robustness Checks
35
-22.27∗∗∗
(6.737)
-21.26∗∗
(6.984)
Post Trend
26893
26893
26893
26893
11554
Low
15339
High
1081.6∗∗∗
(21.15)
11554
Low
1140.8∗∗∗
(18.86)
-0.0982
(4.312)
-3.883
(4.357)
13.46
(22.28)
-69.84∗∗∗
(21.61)
-6.985
(20.83)
(10)
income
The results in this table are estimated using a regression discontinuity framework with a 1912 birth year cutoff on the 12th grade 1940 sample. Standard
errors clustered on the birth year in parenthesis.
*p <0.1, ** p <0.05, *** p <0.01
26893
15339
26893
x
1387.7∗∗∗
(115.5)
-13.98∗∗
(4.910)
89.90∗∗∗
(24.74)
-138.4∗∗∗
(24.36)
74.31∗∗∗
(23.27)
(9)
income
-9.063∗∗
(3.824)
1141.2∗∗∗
(6.525)
-18.91∗∗∗
(5.120)
-69.35∗∗∗
(2.327)
31.55
(19.61)
(8)
income
8.426
(4.931)
1148.4∗∗∗
(14.14)
-22.90∗∗
(9.408)
-70.08∗∗∗
(7.813)
63.42∗∗
(27.84)
(7)
income
4.431
(3.849)
59.33∗∗
(19.47)
-112.8∗∗∗
(19.13)
40.12∗∗
(17.02)
(6)
income
N
x
1106.3∗∗∗
(20.52)
-8.144
(4.731)
3.239
(4.747)
58.02∗∗
(23.80)
-109.7∗∗∗
(23.58)
40.05∗
(20.89)
(5)
income
High
x
1430.4∗∗∗
(113.1)
-4.631∗∗∗
(0.421)
-4.904∗∗∗
(0.388)
1145.5∗∗∗
(8.122)
14.79∗∗
(6.215)
-68.29∗∗∗
(5.061)
-3.531
(9.610)
(4)
income
18.00∗∗
(5.985)
-69.70∗∗∗
(5.049)
0.814
(9.003)
(3)
income
UE Shock
State FE
cons
Trend2
1430.7∗∗∗
(113.1)
-68.30∗∗∗
(5.052)
-69.70∗∗∗
(5.049)
Trend
1145.5∗∗∗
(8.122)
42.57∗
(21.96)
49.64∗∗
(22.32)
Effect
Post Trend2
(2)
income
(1)
income
Table 4: 1940 Regression Discontinuity - 12th Grade Sample
36
0.157∗∗
(0.0711)
0.00733
(0.0271)
-0.0987∗∗∗
(0.0151)
0.0955∗∗∗
(0.0215)
0.134∗
(0.0682)
-0.0535∗∗∗
(0.00494)
-0.0874∗∗∗
(0.0182)
Effect
Trend
Post Trend
11554
Low
6.873∗∗∗
(0.00571)
24980
1145.5∗∗∗
(8.150)
24980
1106.3∗∗∗
(20.60)
14287
High
1148.4∗∗∗
(14.19)
-27.12∗∗
(9.872)
-70.08∗∗∗
(7.841)
86.70∗∗
(33.48)
10693
Low
1141.2∗∗∗
(6.548)
-22.98∗∗∗
(4.795)
-69.35∗∗∗
(2.335)
54.49∗∗
(17.74)
(8)
income
The first four columns use log income and the last four use income and omit the cutoff year 1912. Standard errors clustered on the birth year in parenthesis.
*p <0.1, ** p <0.05, *** p <0.01
26893
15339
26893
N
6.877∗∗∗
(0.0157)
High
6.831∗∗∗
(0.0131)
UE Shock
6.876∗∗∗
(0.00923)
2.936
(4.778)
-0.00803∗∗
(0.00359)
Post Trend2
cons
-8.144
(4.748)
61.10∗∗
(24.17)
-109.7∗∗∗
(23.66)
-69.70∗∗∗
(5.067)
-25.50∗∗∗
(7.088)
33.40
(21.77)
Omit 1916
(6)
(7)
income
income
73.25∗∗
(24.45)
(5)
income
-0.00918∗∗
(0.00303)
-0.0848∗∗∗
(0.0175)
-0.0521∗∗∗
(0.00285)
0.104
(0.0659)
(4)
loginc
Trend2
-0.0893∗∗∗
(0.0191)
-0.0548∗∗∗
(0.00699)
Log Income
(2)
(3)
loginc
loginc
(1)
loginc
Table 5: 1940 Regression Discontinuity - 12th Grade Sample - Robustness Checks
37
25179
3031.4∗∗∗
(26.90)
11026
Low
2800.7∗∗∗
(19.10)
-36.97∗∗∗
(7.142)
-16.45∗∗∗
(2.914)
8.372
(29.17)
(6)
income
14153
High
2986.9∗∗∗
(25.67)
5.593
(5.059)
-3.126
(2.443)
-22.18
(27.31)
-35.23
(20.42)
19.81
(26.28)
(7)
income
11026
Low
2790.5∗∗∗
(23.17)
2.311
(5.010)
-0.712
(1.346)
-37.17
(23.07)
-22.73∗
(11.93)
21.97
(30.67)
(8)
income
The results in this table are estimated using a regression discontinuity framework with a 1916 birth year cutoff on the full 1950 sample (¡= 13 years of
education). Standard errors clustered on the birth year in parenthesis.
*p <0.1, ** p <0.05, *** p <0.01
25179
x
2877.8∗∗∗
(205.5)
14153
25179
2904.4∗∗∗
(15.94)
N
x
2908.7∗∗∗
(212.5)
-39.95∗∗∗
(5.071)
-7.637
(4.432)
-29.67
(28.05)
(5)
income
High
25179
2931.6∗∗∗
(19.59)
3.796
(3.664)
-2.300
(1.578)
-22.69
(19.42)
-31.58∗∗
(12.32)
16.63
(17.72)
(4)
income
UE Shock
State FE
cons
3.177
(3.263)
-29.88
(18.70)
Post Trend2
-36.99∗∗∗
(5.466)
-41.57∗∗∗
(4.382)
Post Trend
-27.92∗
(14.45)
-1.904
(1.798)
-11.27∗∗∗
(3.466)
-11.11∗∗∗
(2.989)
Trend
21.48
(17.47)
(3)
income
Trend2
-19.30
(21.87)
-8.330
(21.00)
(2)
income
Effect
(1)
income
Table 6: 1950 Regression Discontinuity - Total Sample
38
5038
2269
Low
2665.3∗∗∗
(85.32)
2769
High
2559.1∗∗∗
(43.93)
46.80∗∗∗
(7.945)
-7.545∗∗∗
(2.223)
-88.50∗∗
(38.60)
-76.09∗∗
(32.11)
2269
Low
2507.3∗∗∗
(101.6)
25.92
(20.33)
-10.49∗
(5.451)
-42.77
(89.65)
-100.3∗
(46.73)
107.9
(123.1)
98.78∗∗
(45.30)
-99.77∗∗∗
(21.74)
(8)
income
(7)
income
-6.085
(18.05)
-79.03
(123.7)
(6)
income
The results in this table are estimated using a regression discontinuity framework with a 1916 birth year cutoff on the 8th grade 1950 sample. Standard
errors clustered on the birth year in parenthesis.
*p <0.1, ** p <0.05, *** p <0.01
5038
2672.9∗∗∗
(29.28)
2769
5038
x
2831.5∗∗∗
(418.7)
N
5038
2539.6∗∗∗
(61.29)
High
x
2970.6∗∗∗
(427.0)
-4.457
(28.58)
-31.76∗∗∗
(7.873)
-82.93
(55.84)
(5)
income
UE Shock
State FE
2669.6∗∗∗
(48.80)
36.88∗∗∗
(10.52)
36.62∗∗∗
(9.585)
Post Trend2
cons
-8.427∗∗∗
(1.867)
-8.625∗∗∗
(2.430)
Trend2
-90.44∗∗∗
(19.29)
84.13
(57.98)
(4)
income
-70.87
(44.92)
-37.29
(22.96)
Post Trend
-97.59∗∗∗
(24.62)
99.71
(66.38)
(3)
income
-68.99
(43.01)
-14.62
(9.121)
-19.97∗
(9.492)
Trend
-35.58
(23.23)
-93.80
(81.93)
-80.42
(77.94)
(2)
income
Effect
(1)
income
Table 7: 1950 Regression Discontinuity - 8th Grade Sample
39
99.16
(58.74)
5.528
(11.36)
2269
Low
7.742∗∗∗
(0.0600)
4653
2669.6∗∗∗
(48.97)
4653
2539.6∗∗∗
(61.51)
2546
High
2672.9∗∗∗
(29.38)
32.86
(28.49)
-31.76∗∗∗
(7.901)
2107
Low
2665.3∗∗∗
(85.62)
-25.44
(37.68)
-6.085
(18.11)
-230.8∗
(127.9)
(8)
income
4683
2756.2∗∗∗
(47.68)
-109.5∗∗
(49.61)
-38.26
(22.19)
-14.13∗
(7.212)
-92.27
(69.82)
4683
2658.4∗∗∗
(51.24)
-107.2∗
(49.72)
36.09∗∗∗
(7.264)
2576
High
2767.1∗∗∗
(47.08)
-125.6∗
(63.23)
-8.934
(28.66)
-95.86∗∗
(35.17)
-6.441∗∗
(2.354)
-26.20∗∗
(10.24)
-80.64
(50.11)
-72.10∗∗∗
(22.54)
56.80
(56.56)
2107
Low
2738.9∗∗∗
(73.31)
-87.13
(68.52)
-72.06∗
(33.68)
-0.671
(14.35)
-107.7
(122.9)
Control for WWII Vet Status
(9)
(10)
(11)
(12)
income
income
income
income
The results in this table are estimated using a regression discontinuity framework with a 1916 birth year cutoff on the 8th grade 1950 sample. The first four
columns use log income, the second four use income and omit the cutoff year 1916, and the last four columns include an indicator variable control for World
War II veteran status. Standard errors clustered on the birth year in parenthesis.
*p <0.1, ** p <0.05, *** p <0.01
5038
2769
5038
7.703∗∗∗
(0.0212)
N
7.665∗∗∗
(0.0616)
High
7.721∗∗∗
(0.0368)
UE Shock
cons
WWII Veteran
5.409
(10.91)
-0.0349
(0.0271)
0.0204∗∗∗
(0.00584)
0.00688
(0.00982)
-97.59∗∗∗
(24.71)
-19.97∗
(9.525)
Post Trend2
-0.0444
(0.0324)
0.00901
(0.0129)
-0.0141∗∗∗
(0.00406)
-193.4∗∗∗
(61.13)
-207.8∗∗∗
(53.39)
-93.82
(77.56)
Omit 1916
(6)
(7)
income
income
(5)
income
-8.625∗∗∗
(2.439)
-0.0122
(0.0143)
Post Trend
-0.0366
(0.0257)
-0.0758
(0.0985)
-0.0288
(0.0236)
(4)
loginc
-0.00367
(0.00265)
-0.00356
(0.00737)
Trend
0.0359
(0.0638)
Log Income
(2)
(3)
loginc
loginc
Trend2
-0.0494
(0.0523)
Effect
(1)
loginc
Table 8: 1950 Regression Discontinuity - 8th Grade Sample - Robustness Checks
40
8166
8166
3392.9∗∗∗
(40.11)
8166
x
3290.7∗∗∗
(298.0)
8.352
(5.786)
-14.51∗∗
(6.227)
3404.8∗∗∗
(58.69)
2.929
(26.25)
3339
Low
3323.7∗∗∗
(56.92)
-8.564
(34.32)
-49.31
(32.92)
87.35
(77.51)
(8)
income
4827
High
3339
Low
3050.5∗∗∗
(103.4)
-56.90∗∗
(23.75)
50.92∗∗∗
(1.460)
3644.0∗∗∗
(6.114)
47.81∗
(23.87)
345.8∗∗
(120.2)
-328.2∗∗
(118.4)
264.9∗∗
(109.0)
(10)
income
-55.76∗∗∗
(3.379)
-204.4∗∗∗
(32.13)
191.9∗∗∗
(7.180)
-158.9∗∗
(71.86)
(9)
income
The results in this table are estimated using a regression discontinuity framework with a 1912 birth year cutoff on the 12th grade 1950 sample. Standard
errors clustered on the birth year in parenthesis.
*p <0.1, ** p <0.05, *** p <0.01
8166
x
3250.8∗∗∗
(298.2)
4.966
(9.422)
-11.85
(9.669)
17.45
(34.74)
-55.78∗∗
(24.22)
133.1
(81.22)
(7)
income
4827
8166
3369.3∗∗∗
(18.61)
-6.157∗∗∗
(1.921)
-6.885∗∗∗
(2.171)
31.59
(51.86)
-21.12
(29.84)
29.56
(57.04)
(6)
income
N
x
3251.6∗∗∗
(298.8)
58.19∗∗
(20.95)
55.82∗∗
(23.40)
-29.21
(46.60)
19.87
(65.59)
(5)
income
High
8166
3369.3∗∗∗
(18.61)
-61.87∗∗∗
(5.057)
69.18
(48.77)
(4)
income
-53.44∗∗∗
(5.429)
43.42
(55.13)
(3)
income
UE Shock
State FE
cons
Trend2
Post Trend2
6.940
(10.02)
-61.85∗∗∗
(5.024)
-53.44∗∗∗
(5.429)
Trend
-1.454
(10.85)
135.4∗∗
(46.71)
117.5∗∗
(53.31)
Effect
Post Trend
(2)
income
(1)
income
Table 9: 1950 Regression Discontinuity - 12th Grade Sample
41
-0.00912
(0.00590)
Post Trend
3339
Low
7658
3369.3∗∗∗
(18.67)
-12.06
(6.987)
-0.0133∗
(0.00653)
7.999∗∗∗
(0.00745)
-53.44∗∗∗
(5.448)
179.3∗∗∗
(25.22)
(5)
income
-0.00322
(0.00382)
-0.0212
(0.0244)
(4)
loginc
7658
3392.9∗∗∗
(40.25)
-7.603
(9.562)
4.966
(9.455)
-12.19
(48.47)
-29.21
(46.76)
114.8∗∗
(46.61)
4534
High
3404.8∗∗∗
(58.90)
-8.085
(24.83)
-55.78∗∗
(24.30)
197.0∗∗∗
(63.43)
Omit 1916
(6)
(7)
income
income
3124
Low
3323.7∗∗∗
(57.12)
-18.13
(34.16)
-49.31
(33.03)
143.4∗
(77.08)
(8)
income
5038
2869.5∗∗∗
(43.48)
-91.51∗
(44.59)
-67.27∗∗∗
(16.23)
17.64
(14.86)
-39.61
(54.12)
5038
2719.2∗∗∗
(16.14)
-91.23∗
(44.53)
30.30∗∗∗
(4.269)
-29.83∗∗∗
(2.941)
77.46∗∗
(30.56)
-130.8∗∗∗
(14.39)
114.9∗∗
(40.34)
2769
High
2955.6∗∗∗
(55.73)
-137.3∗
(64.44)
-55.94∗∗
(18.88)
13.44
(16.01)
-94.80
(53.90)
2269
Low
2785.0∗∗∗
(65.17)
-32.31
(62.04)
-87.46∗
(40.21)
29.07
(37.93)
3.170
(96.48)
Control for WWII Vet Status
(9)
(10)
(11)
(12)
income
income
income
income
The results in this table are estimated using a regression discontinuity framework with a 1912 birth year cutoff on the 12th grade 1950 sample. The first four
columns use log income, the second four use income and omit the cutoff year 1916, and the last four columns include an indicator variable control for World
War II veteran status. Standard errors clustered on the birth year in parenthesis.
*p <0.1, ** p <0.05, *** p <0.01
4827
8166
N
8166
High
7.978∗∗∗
(0.0189)
-0.00604
(0.00747)
-0.0111∗
(0.00525)
0.0727∗∗
(0.0317)
UE Shock
cons
7.990∗∗∗
(0.0241)
-0.00448
(0.00577)
Post Trend2
WWII Veteran
0.000529
(0.00567)
Trend2
7.987∗∗∗
(0.00914)
-0.00518
(0.0281)
-0.00776∗∗
(0.00321)
Trend
0.0212
(0.0298)
-0.0109
(0.0311)
0.0342
(0.0258)
Log Income
(2)
(3)
loginc
loginc
Effect
(1)
loginc
Table 10: 1950 Regression Discontinuity - 12th Grade Sample - Robustness Checks
42
86716
39752
Low
9.606∗∗∗
(0.0534)
-0.0995∗∗∗
(0.0254)
0.0947∗∗∗
(0.0174)
0.0932
(0.0637)
(6)
education
46964
High
10.03∗∗∗
(0.0165)
-0.0341∗∗∗
(0.00508)
0.00363∗∗∗
(0.00104)
-0.0465∗
(0.0221)
0.139∗∗∗
(0.00979)
-0.0925∗∗∗
(0.0191)
(7)
education
39752
Low
9.773∗∗∗
(0.0709)
-0.0417∗∗∗
(0.00933)
0.0114∗
(0.00574)
-0.0814
(0.0531)
0.196∗∗∗
(0.0448)
-0.132
(0.0753)
(8)
education
The results in this table are estimated using a regression discontinuity framework with a 1916 birth year cutoff on the total 1940 sample (¡=13 years of
education). Standard errors clustered on the birth year in parenthesis.
*p <0.1, ** p <0.05, *** p <0.01
86716
9.977∗∗∗
(0.0156)
46964
86716
x
9.653∗∗∗
(0.143)
N
86716
9.918∗∗∗
(0.0391)
High
x
9.561∗∗∗
(0.140)
-0.134∗∗∗
(0.0174)
0.107∗∗∗
(0.00377)
0.0175
(0.0415)
(5)
education
UE Shock
State FE
9.811∗∗∗
(0.0310)
-0.0332∗∗∗
(0.00636)
-0.0379∗∗∗
(0.00637)
Post Trend2
cons
0.00657∗∗∗
(0.00211)
0.00739∗∗
(0.00291)
0.163∗∗∗
(0.0164)
Trend2
-0.111∗∗∗
(0.0172)
-0.120∗∗∗
(0.0199)
Post Trend
0.168∗∗∗
(0.0232)
-0.120∗∗∗
(0.0316)
-0.115∗∗
(0.0421)
-0.0643∗∗
(0.0283)
0.104∗∗∗
(0.00798)
0.102∗∗∗
(0.00967)
Trend
(4)
education
(3)
education
-0.0662∗
(0.0320)
0.0262
(0.0409)
0.0504
(0.0476)
(2)
education
Effect
(1)
education
Table 11: 1940 Total Education Regression Discontinuity
43
86716
0.186∗∗∗
(0.00248)
39752
Low
0.216∗∗∗
(0.00441)
0.00482∗∗∗
(0.00112)
-0.0107∗∗∗
(0.000946)
-0.0289∗∗∗
(0.00494)
(6)
grade8
46964
High
0.184∗∗∗
(0.00304)
0.00370∗∗∗
(0.000468)
-0.000159
(0.000298)
-0.00523∗
(0.00254)
-0.0135∗∗∗
(0.00213)
0.00519
(0.00326)
(7)
grade8
39752
Low
0.210∗∗∗
(0.00598)
0.00113
(0.00117)
-0.000361
(0.000324)
0.00501
(0.00536)
-0.0139∗∗∗
(0.00289)
-0.0221∗∗∗
(0.00624)
(8)
grade8
The results in this table are estimated using a regression discontinuity framework with a 1916 birth year cutoff on the total 1940 sample (¡=13 years of
education). Grade8 is an indicator variable equal to 1 if the individuals has 8 years of education. Standard errors clustered on the birth year in parenthesis.
*p <0.1, ** p p <0.01
86716
x
0.298∗∗∗
(0.0349)
46964
86716
0.196∗∗∗
(0.00296)
N
x
0.304∗∗∗
(0.0362)
High
86716
0.199∗∗∗
(0.00258)
0.00718∗∗∗
(0.00229)
-0.0121∗∗∗
(0.000836)
-0.00378
(0.00494)
(5)
grade8
UE Shock
State FE
cons
0.00285∗∗∗
(0.000613)
0.00116
(0.00283)
0.00251∗∗∗
(0.000719)
-0.000261
(0.00329)
-0.0153∗∗∗
(0.00173)
Post Trend2
0.00664∗∗∗
(0.00148)
0.00614∗∗∗
(0.00142)
Post Trend
-0.0139∗∗∗
(0.00197)
-0.00583∗
(0.00275)
-0.000444∗
(0.000234)
-0.0113∗∗∗
(0.000727)
-0.0115∗∗∗
(0.000807)
Trend
-0.00685∗
(0.00326)
(4)
grade8
-0.000268
(0.000271)
-0.0169∗∗∗
(0.00410)
-0.0150∗∗∗
(0.00381)
Effect
(3)
grade8
Trend2
(2)
grade8
(1)
grade8
Table 12: 1940 8th Grade Regression Discontinuity
44
86716
0.384∗∗∗
(0.00392)
39752
Low
0.329∗∗∗
(0.00675)
-0.0195∗∗∗
(0.00404)
0.0159∗∗∗
(0.00209)
0.0195∗
(0.00966)
(6)
highschool
46964
High
0.394∗∗∗
(0.00778)
39752
Low
0.337∗∗∗
(0.0128)
-0.00650∗∗∗
(0.00162)
0.000594
(0.000934)
0.000740∗
(0.000359)
-0.00571∗∗∗
(0.000678)
-0.00155
(0.00917)
0.0212∗∗
(0.00760)
-0.000626
(0.0135)
(8)
highschool
-0.0150∗∗∗
(0.00423)
0.0272∗∗∗
(0.00360)
-0.00821
(0.00792)
(7)
highschool
The results in this table are estimated using a regression discontinuity framework with a 1916 birth year cutoff on the total 1940 sample (¡=13 years of
education). Highschool is an indicator variable equal to 1 if the individuals has 12 years of education. Standard errors clustered on the birth year in
parenthesis.
*p <0.1, ** p p <0.01
86716
x
0.305∗∗∗
(0.0269)
46964
86716
0.369∗∗∗
(0.00947)
N
x
0.295∗∗∗
(0.0256)
High
86716
0.359∗∗∗
(0.00435)
-0.0278∗∗∗
(0.00300)
0.0207∗∗∗
(0.000785)
0.0119
(0.00729)
(5)
highschool
UE Shock
State FE
cons
-0.00582∗∗∗
(0.000984)
-0.00914
(0.00542)
-0.00612∗∗∗
(0.00103)
-0.00937
(0.00584)
0.0247∗∗∗
(0.00429)
Post Trend2
-0.0235∗∗∗
(0.00315)
-0.0244∗∗∗
(0.00333)
Post Trend
0.0248∗∗∗
(0.00469)
-0.00687
(0.00911)
0.000663
(0.000456)
0.0188∗∗∗
(0.00101)
0.0186∗∗∗
(0.00111)
Trend
-0.00541
(0.00983)
(4)
highschool
0.000702
(0.000505)
0.0126
(0.00732)
0.0151∗
(0.00776)
Effect
(3)
highschool
Trend2
(2)
highschool
(1)
highschool
Table 13: 1940 High School Regression Discontinuity
45
25179
11026
Low
9.601∗∗∗
(0.0759)
-0.0364
(0.0281)
0.0965∗∗∗
(0.0146)
0.00570
(0.0879)
(6)
education
14153
High
10.15∗∗∗
(0.0863)
11026
Low
9.460∗∗∗
(0.0716)
0.00739
(0.0262)
-0.00986
(0.00656)
0.0109∗
(0.00512)
0.000203
(0.0121)
0.0608
(0.121)
0.00937
(0.0496)
0.142
(0.0867)
(8)
education
-0.169∗∗
(0.0660)
0.216∗∗∗
(0.0501)
-0.323∗∗∗
(0.0910)
(7)
education
The results in this table are estimated using a regression discontinuity framework with a 1916 birth year cutoff on the total 1950 sample (¡=13 years of
education). Standard errors clustered on the birth year in parenthesis.
*p <0.1, ** p <0.05, *** p <0.01
25179
9.997∗∗∗
(0.0560)
14153
25179
x
10.27∗∗∗
(0.209)
N
25179
9.854∗∗∗
(0.0626)
-0.0285
(0.0188)
0.120∗∗∗
(0.0131)
-0.190∗∗
(0.0714)
(5)
education
High
x
10.26∗∗∗
(0.212)
0.00266
(0.0122)
0.000886
(0.00341)
-0.0538
(0.0593)
0.120∗∗∗
(0.0325)
-0.128∗∗
(0.0558)
(4)
education
UE Shock
State FE
cons
9.826∗∗∗
(0.0249)
-0.0684
(0.0702)
0.00182
(0.0150)
-0.0318∗∗
(0.0115)
-0.0358∗∗
(0.0143)
Post Trend
0.128∗∗∗
(0.0347)
Post Trend2
0.112∗∗∗
(0.00627)
0.110∗∗∗
(0.00732)
Trend
-0.120∗
(0.0648)
0.00197
(0.00356)
-0.122∗∗∗
(0.0296)
-0.0994∗∗
(0.0331)
Effect
(3)
education
Trend2
(2)
education
(1)
education
Table 14: 1950 Total Education Regression Discontinuity
46
-0.0113∗∗∗
(0.00104)
-0.0116∗∗∗
(0.000960)
0.00137
(0.00323)
Trend
Post Trend
25179
25179
11026
Low
0.175∗∗∗
(0.00486)
0.000269
(0.00272)
-0.0120∗∗∗
(0.00153)
0.0219∗∗
(0.00877)
(6)
grade8
14153
High
0.170∗∗∗
(0.00533)
0.00406∗∗
(0.00181)
11026
Low
0.170∗∗∗
(0.0108)
0.00261
(0.00156)
-0.000367
(0.000843)
-0.00560
(0.00903)
-0.0177∗∗
(0.00802)
0.000279
(0.000397)
-0.0153∗
(0.00737)
0.0319∗∗
(0.0117)
(8)
grade8
-0.00868∗∗
(0.00349)
0.0251∗∗
(0.00877)
(7)
grade8
The results in this table are estimated using a regression discontinuity framework with a 1916 birth year cutoff on the total 1950 sample (¡=13 years of
education). Grade8 is an indicator variable equal to 1 if the individuals has 8 years of education. Standard errors clustered on the birth year in parenthesis.
*p <0.1, ** p p <0.01
25179
14153
25179
0.166∗∗∗
(0.00339)
N
x
0.162∗∗
(0.0551)
High
x
0.170∗∗∗
(0.00605)
0.00207
(0.00380)
-0.0111∗∗∗
(0.000669)
0.0204
(0.0124)
(5)
grade8
UE Shock
State FE
0.163∗∗
(0.0558)
0.00271
(0.00189)
0.00342∗
(0.00157)
Post Trend2
cons
-0.000127
(0.000510)
-0.00823
(0.00850)
-0.0124∗∗
(0.00437)
0.0279∗∗
(0.00961)
(4)
grade8
-0.0000161
(0.000492)
-0.0122
(0.00736)
-0.0117∗∗
(0.00420)
0.0281∗∗∗
(0.00850)
(3)
grade8
Trend2
0.170∗∗∗
(0.00345)
0.0208∗
(0.0106)
0.0209∗
(0.0105)
Effect
0.00101
(0.00315)
(2)
grade8
(1)
grade8
Table 15: 1950 8th Grade Regression Discontinuity
47
0.0181∗∗∗
(0.00144)
0.0179∗∗∗
(0.00157)
-0.000947
(0.00294)
Trend
Post Trend
25179
25179
11026
Low
0.335∗∗∗
(0.0150)
-0.00246
(0.00486)
0.0147∗∗∗
(0.00253)
-0.00849
(0.0203)
(6)
highschool
14153
High
0.410∗∗∗
(0.0148)
-0.000525
(0.00169)
0.00146
(0.001000)
-0.0151
(0.0115)
0.0331∗∗∗
(0.00993)
-0.0510∗∗∗
(0.0155)
(7)
highschool
11026
Low
0.324∗∗∗
(0.0220)
-0.00318
(0.00251)
-0.000746
(0.00116)
0.0200
(0.0142)
0.00813
(0.0106)
-0.00611
(0.0239)
(8)
highschool
The results in this table are estimated using a regression discontinuity framework with a 1916 birth year cutoff on the total 1950 sample (¡=13 years of
education). Highschool is an indicator variable equal to 1 if the individuals has 12 years of education. Standard errors clustered on the birth year in
parenthesis.
*p <0.1, ** p p <0.01
25179
14153
25179
0.389∗∗∗
(0.00615)
N
x
0.384∗∗∗
(0.0642)
0.00144
(0.00312)
0.0203∗∗∗
(0.00239)
-0.0321∗∗∗
(0.00699)
(5)
highschool
High
x
0.373∗∗∗
(0.0145)
-0.00183
(0.00160)
0.000358
(0.000770)
0.00191
(0.00953)
0.0213∗∗
(0.00740)
-0.0306∗
(0.0145)
(4)
highschool
UE Shock
State FE
0.379∗∗∗
(0.0630)
-0.00200
(0.00180)
Post Trend2
cons
0.000510
(0.000792)
0.000528
(0.0103)
0.0224∗∗
(0.00767)
-0.0311∗
(0.0159)
(3)
highschool
Trend2
0.366∗∗∗
(0.00718)
-0.0224∗∗
(0.00975)
-0.0208∗
(0.0106)
Effect
-0.000826
(0.00268)
(2)
highschool
(1)
highschool
Table 16: 1950 High School Regression Discontinuity