WHEN FINANCE LEAVES DOES ENTERPRISE FOLLOW?
The Lasting Impact of Bank Distress on Entrepreneurship
Tania Babina and Elizabeth A. Berger
April 26, 2017
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Contact Information: [email protected] and [email protected]
When Finance Leaves Does Enterprise Follow?:
The Lasting Impact of Bank Distress on Entrepreneurship
Abstract
This paper measures the long-term impact of negative shocks to
external financing on individual entrepreneurship. Using a dataset
that spans 110 years, we examine how county-level bank distress
during the Great Depression affects present-day entrepreneurship.
The results reveal that entrepreneurship rates over the period 1930
to 2010 are 9% lower in high distress counties compared to low
distress counties. The decline comes from entrepreneurs in high
education and high earnings occupations and is associated with
reduced innovative output (patents) in high distress counties
following the Great Depression. Our findings suggest that local
financial distress alters the long-term distribution of
entrepreneurship across the US.
Entrepreneurs are the backbone of enterprise in the US and depend heavily on external financing
(Haltiwanger, Jarmin, and Miranda, 2013 and Robb and Robinson, 2012). And yet, despite an
abundance of evidence showing a positive link between financial access and entrepreneurship,
there is a dearth of evidence about how negative shocks to financial access affect entrepreneurship
both in the short- and long-run (Black and Strahan, 2002; Kerr and Nanda, 2009; and Nguyen,
2015). On one hand, perhaps “where enterprise leads finance follows” (Robinson, 1952) such
that long-run entrepreneurship is impervious to financial fluctuations.
On the other hand,
enterprise might follow finance. To address this gap, we ask, “what happens when finance leaves
- does enterprise follow?”
In this paper, we evaluate the long-term impact of negative financial shocks on the
individual propensity to be an entrepreneur. In a quasi-experimental setting around the Great
Depression, we measure how a severe financial shock affects the long-run supply of entrepreneurs.
We document that lower bank financing leads to a long-term decline in entrepreneurship. In fact,
these negative effects persist for nearly a century and grow more severe with time.
The Great Depression presented a systemic failure of the US financial system that resulted
in a dramatic reduction in financial resources. The historical context of these early banking shocks
combined with our empirical analyses support using the Great Depression as a quasi-experimental
setting. Specifically, bank distress during the Great Depression had large, negative impacts on
local economies (Calomiris and Mason, 2003; Richardson and Troost, 2009; and Nanda and
Nicholas, 2014). Moreover, the segmented nature of the banking system, into state and national
banks, and laws that prevented banks from forming branches, created variation in the initial
intensity of the financial impact of the Great Depression on local economies. Finally, our empirical
analyses suggest that the initial intensity of financial shocks during the Great Depression is
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unrelated to local entrepreneurship and we use an instrumental variables setting to further mitigate
reverse causality concerns. Lastly, the historical nature of the setting produces a time series of
nearly one century over which to study individual entrepreneurship.
Bank distress may have led to long-term, or permanent, reductions in entrepreneurship for
several reasons.
Among many possible explanations, one is that entrepreneurs may have
physically relocated away from distressed areas to areas with more abundant financing.
Additionally, although the financial shocks may have only discouraged contemporaneous
entrepreneurship, indirect effects, via a loss of inter-generational occupational transmission of
entrepreneurship, could reduce future entrepreneurship.
We use cross-sectional variation in the severity of county-level bank distress during the
Great Depression in a difference-in-differences setting to identify the role of bank distress on future
entrepreneurship. Detailed county-level banking data come from the Federal Deposit Insurance
Corporation (FDIC). In the early 1900s, local, state-level banks were the main source of primary
borrowing, such that suspensions of state banks are likely the relevant financing shock to
entrepreneurs (Alhadeff, 1951; Horton, Larsen, and Wall, 1942). In particular, we define a proxy
of county-level financial distress as the ratio of bank deposits at state banks suspended during the
Great Depression to total state-bank deposits prior to the Great Depression. Counties with high
bank distress are counties with the distress ratio above the median county’s financial distress in
our sample. Entrepreneurship data come from individual survey responses from 110 years of
Decennial Censuses of Population, reported for each decade.
Over this period, the data
consistently identify respondents who “work on their own account”. Hence, for the first time, we
are measuring individual entrepreneurship rates for more than a century – longer than possible
using alternative Census data, which start in 1978. In addition, individual survey data allow us to
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directly study individual, rather than aggregate, decisions and to control for individual
characteristics such as age, nativity, ethnicity, and homeownership. We use year fixed effects to
absorb national economic trends in each decade and state fixed effects to absorb state-level
persistent characteristics such that the analysis compares counties within a state over time.
The results reveal a long-lasting decline in the individual propensity to engage in
entrepreneurship in counties with more financial distress during the Great Depression. On average,
we find that entrepreneurship falls by over 1 percentage point in counties with high bank distress,
which amounts to a 9% reduction in the rate of entrepreneurship. Moreover, our results show that
this reduction in entrepreneurship has grown more severe over time. As banking deregulations
increased capital mobility between 1970 and 1990, the propensity to become an entrepreneur fell
by an additional 50% - to 2 percentage points less than low distress counties.
We use an instrumental variables setting to test the robustness of our results to alternative
explanations. In this setting, a good instrumental variable is one that is independent of local
economic conditions, predicts bank distress during the Great Depression, but does not permanently
affect local economic conditions. We find such an instrumental variable in Rajan and Ramcharan
(2015). The authors show that uncertainty surrounding the supply of agricultural commodities
following World War I and the Russian Revolution created a bubble in US farm land prices that
ultimately led to a US credit crisis. A sharp increase and a subsequent retraction in international
agricultural commodity prices drove a collapse in farm land prices in US counties depending on
each county’s exposure to international commodity prices (Yergin, 1992). An attractive feature
of this setting is that these shocks were transitory economic shocks. Farming fully recovered by
1922 (Johnson 1973; Alston, Grove, and Wheelock 1994). But these shocks weakened bank
balance sheets, lowered the resilience of local banking systems to subsequent financial shocks that
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arose throughout the 1920s, and ultimately made these banks particularly susceptible to bank runs
during the Great Depression (Calomiris, 1992; Richardson, 2009). Hence, the change in a county’s
exposure to international commodity prices from 1917 to 1920 serves as an instrument for bank
distress during the Great Depression. This instrument strongly predicts financial distress during
the Great Depression, and the second stage results indicate that financial distress reduced
entrepreneurship throughout the 21st century in distressed counties. The analysis supports the
interpretation that bank distress leads to a long-run reduction in entrepreneurship.
We further explore the robustness of these results to the influence of omitted variables,
alternative variable definitions, and alternative sample selection criteria. We show that high and
low distress counties exhibited parallel trends in entrepreneurship prior to the Great Depression.
We find no evidence that bank distress was predicted by entrepreneurial activity prior to the Great
Depression. Our results are robust to using a continuous measure of bank distress and to using
industry and occupation fixed effects. In terms of sample selection criteria, we show that small,
less-populated counties are not driving our results.
Further analysis examines cross-sectional variation in the effect of bank distress on
entrepreneurship in specific industries, on the quality of future entrepreneurs, and on county-level
innovative output. Our results show that entrepreneurship related to agriculture did not decline,
mitigating the concern that bank distress was correlated with fewer entrepreneurial opportunities
associated with agriculture. Instead, local financial distress reduces entrepreneurship in industries
that are prone to information asymmetry (e.g., services and retail industries). This finding supports
the hypothesis that local finance is critical for entrepreneurship in industries with more soft
information.
Our results also reveal that higher quality entrepreneurship declines, i.e.
entrepreneurship in occupations with higher educational requirements (skill) and higher wages
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(productivity).
Evidence complementary to these findings shows that innovative output
(patenting) declines in areas with high bank distress between 1930 and 1950.
We examine present day outcomes related to entrepreneurship to verify that our measure
of entrepreneurship correlates with other factors related to entrepreneurship. Our results show that
present day employment in young firms is lower in counties with high bank distress during the
Great Depression relative to counties with less distress. Because entrepreneurs are the primary
source of new, young firms, this result suggests that lower rates of entrepreneurship lead to fewer
employment opportunities in young firms (e.g., entrepreneurial firms). Moreover, the results
indicate that economic volatility in the form of income inequality is higher in counties with more
entrepreneurs (lower bank distress) - a result which is consistent with the lottery-like payoffs of
entrepreneurship that influence the tails of the income distribution (Manso, 2016). To complement
these results, we document that present day average population and labor force statistics are similar
across US counties, a result which support the idea that entrepreneurship rather than population
differences drive the differences in other entrepreneurial outcomes.
This paper unites two separate bodies of literature: one that addresses the economic impact
of the Great Depression and another that studies factors affecting entrepreneurship. Our finding
that local bank distress leads to long-term reductions in the propensity of individuals to engage in
entrepreneurship is relevant to both. Our results relate to studies of the shorter-term economic
effects of bank distress during the Great Depression (Calomiris and Mason, 2003; Richardson and
Troost, 2009; and Nanda and Nicholas, 2014). In the wake of the recent US financial crisis, our
paper complements research about the effects of the Great Recession on new businesses and
entrepreneurship, by highlighting the potential long-run effects of large financial shocks (Siemer,
2014).
Our results also add to the literature that explores cross-sectional predictors of
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entrepreneurship. For example, Glaeser, Kerr, and Kerr (2015) show that historical proximity to
natural resources affects present day entrepreneurship, and we show that past financial shocks
influence current entrepreneurship. To the best of our knowledge, we are the first to summarize
individual participation in entrepreneurship in the US over a time series of more than one century.
I.
Data
We construct a panel dataset that spans the decades between 1910 and 2010 from nine
sources: bank data from the FDIC Great Depression files, individual entrepreneurship data from
the Decennial Censuses of the United States’ population, patent data from the United States Patent
and Trademark Office (USPTO) and the Harvard Dataverse, employment and economic data from
the US Census, the Bureau of Economic Analysis, and the Bureau of Labor Statistics, farming data
from the National Historical Geographic Information System (NHGIS), and employment data
from the Quarterly Workforce Indicators (QWI) data.
Credit Availability and Bank Distress
We use US counties as the relevant market for access to credit. The advantage of using
counties over Metropolitan Statistical Areas (MSAs) is that the Census requires that an MSA have
a minimum number of residents to sample that population, whereas counties span rural and urban
parts of the United States. Moreover, myriad state banking regulations at the time meant that banks
could not branch across state lines and that, in most states, banks could not branch within state
boundaries (Alhadeff, 1951; Carlson and Mitchener, 2009; Jayaratne and Strahan, 1996). Hence,
a county-level geography roughly identifies the physical proximity of borrowers and lenders.
During the early 20th century, a significant portion of primary borrowing came from local,
state-level banks where relationship lending was paramount (Alhadeff, 1951; Horton, Larsen, and
Wall, 1942). In contrast, national lenders tended to provide refinancing and other secondary
6
lending activities which require lower information costs (Horton, Larsen, and Wall, 1942). In this
historical context, we consider state banks, which were smaller and engaged in more local,
relationship-based lending, to be capital providers for marginal borrowers, such as entrepreneurs.
The Federal Deposit Insurance Corporation (FDIC) reports county-level data on the
number of active banks, total bank deposits, the number of bank suspensions, and the charter (state
or national) for each bank in each county between 1920 and 1936 as of the end of each year. These
data are unavailable in the states of Wyoming, Hawaii, and Alaska, and in the District of Columbia,
and do not distinguish bank failures from bank suspensions. However, Calomiris and Mason
(2003) argue that these shortcomings do not interfere with identifying bank distress empirically.
We use 1930 as our event date for banking sector distress because 1930 is the year in which
banks began to fail, en masse, and to destroy relationship capital and access to finance (Calomiris
and Mason, 2003; Nanda and Nicholas, 2014). Suspensions and failures of state banks between
January 1, 1930 and February 28, 1933 proxy for the decline in bank finance available for
entrepreneurs (Friedman and Schwartz, 1963). Similar to Nanda and Nicholas (2014), our measure
of the intensity of bank distress in each county is the total deposits in suspended or failed state
banks from 1930 through 1933 divided by the total deposits at state banks in existence in 1929.
𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑖𝑖,𝑠𝑠 =
∑𝑇𝑇=1933
𝑡𝑡=1930 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑎𝑎𝑎𝑎 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 𝑜𝑜𝑜𝑜 𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝑖𝑖,𝑠𝑠,𝑡𝑡
𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷 𝑎𝑎𝑎𝑎 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝑖𝑖,𝑠𝑠,1929
Figure A.1 in Appendix maps the distribution of the suspended deposits variable and shows
that 30% of counties experienced no bank suspensions during this period. This pattern creates a
large mass at zero and makes linear models potentially unattractive. For that reason, we define an
indicator variable, Bank Distress, that equals one for counties with state bank deposits suspensions
7
above the median county’s suspensions, and zero otherwise. 1 Figure 1 maps the geographic
distribution of bank distress and shows that high and low-distress counties are spread broadly
throughout the US.
[Insert Figure 1]
Entrepreneurship
Data from the Decennial Censuses of the United States’ population between 1910 and 2010
provide a measure of entrepreneurship. The Census samples include either 1 or 5 percent of the
US population (for 5% samples, we take a random sample of 20% to get a 1% population sample
for that decade). Following the literature (Evans and Jovanovic 1989; Hamilton 2000), our sample
consists of non-farm males in the labor force who were between the ages of 16 and 65, and
excludes residents of institutional group quarters and unpaid family workers. This dataset provides
a consistent definition of entrepreneurs across the 110-year time series. We define entrepreneurs
as workers who at the time of the Census report “working on own account”, which includes
business owners and self-employed individuals. 2
In addition, the data provide consistent
definitions of each respondent’s age, gender, race, county and state of residence, employment,
industry and occupation of employment, and labor force participation. Figure A.2 in Appendix
maps the geographic distribution of entrepreneurship and shows that entrepreneurs were spread
broadly throughout the US prior to the Great Depression.
The Census Bureau provides a time-consistent, 3-digit industrial classification system and
a time-consistent, 3-digit occupational classification system. Because the Census does not provide
data on individual education and earnings over the full sample period, we construct a proxy for
1
In robustness tests, we verify that our results hold for the continuous variable, and for quartiles and terciles
of the variable.
2
The Census does not consistently separate worker classes into self-employed individuals as opposed to
business owners.
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education and earnings using an individual’s reported occupation.
The Census maps each
occupation into indices of earnings and educational attainment associated with each occupation.
The education score indicates the percentage of people in the respondent's occupational category
who have completed one or more years of college and the occupational earnings scores indicate
the median earned income of persons in each occupation. We define a “High Education” (“High
Earnings”) indicator equal to one for respondents with an education (earnings) score greater than
or equal to the median value of the score within each county-decade. Conditioning the education
and earnings measures within each decade resolves concerns about time trends in these variables,
e.g. increased educational attainment, over the full sample period. We use the 3-digit industries to
identify ten SIC-1 sectors:
Agriculture, Forestry, and Fishing; Mining; Construction;
Manufacturing; Transportation, Communication, and Utilities; Wholesale Trade; Retail Trade;
Finance, Insurance, and Real Estate; Services; and Public Administration.
We construct a proxy for innovative output in each county as the aggregate patenting
activity in each decade between 1880 and 1975 using detailed data from the United States Patent
and Trademark Office (USPTO) and the Harvard Dataverse. Our measure of total patenting
activity is the log of the total number of patents in each county in each decade. The Harvard
Dataverse provides a measure (bounded between zero and one) of data accuracy for each patent in
the dataset. We construct a weighted patent measure as total patents adjusted for the accuracy of
the patent data.
County-level Economic Outcomes
The US Census, the Bureau of Economic Analysis, and the Bureau of Labor Statistics
report county-level economic data in recent decades (1970 – 2010), which include total population,
white, African American, and elderly shares of the population, total personal and per capita
9
incomes, the size of the labor force, the number of employed individuals, the unemployment rate,
and a Gini coefficient of income inequality for each decade between 1970 and 2010. The National
Historical Geographic Information System (NHGIS) provides historical county-level estimates of
farming activity between 1910 and 1940. We examine additional economic and demographic
outcomes following the Great Depression using a dataset from Fishback, Troesken, Kollmann,
Haines, Rhode, and Thomasson (2011).
We combine these data to construct time-consistent,
county-level measures of total farms and farm acreage.
Quarterly Workforce Indicators (QWI) data published by the Longitudinal EmployerHousehold Dynamics (LEHD) program report total employment by firm-age and firm-size at the
county level. We construct an annual measure of employment across firm-size and firm-age
groups in each county. We define young firms as those that are between zero and five years old
and small firms as firms that employ between zero and 49 employees. 3 We measure the fraction
of total employment in young firms or small firms in each county (i) as of the fourth quarter (t) of
each year between 1996 and 2014. We link the Census survey data to county-level banking and
economic data based on an individual’s county of residence. 4
Our final sample is an unbalanced panel of all individuals living in counties that report
data between 1910 and 2010. We winsorize all continuous variables at the 1% and 99% levels.
Table 1, Panel A summarizes person-level data. To the best of our knowledge, we are the first to
summarize individual entrepreneurship over a period that spans more than one century.
More
than 11% of respondents identify themselves as entrepreneurs and almost 39% of respondents
3
The coverage of the LEHD data is limited in the early years, but increases over time, creating an
unbalanced panel of county-level data over the full time-series of data.
4
Starting in 1950, the data do not identify counties with population levels below the Census disclosure
thresholds. Therefore, we drop those counties from the sample when we merge in to the FDIC county-level
bank distress data. To alleviate concerns about sample selection, we repeat our analysis at the state level
and find results that are statistically and economically similar to our main results.
10
reside in counties with above median bank distress during the Great Depression. Panel B reports
the person-level data by decade and reveals time-series trends in the data. Most important for our
analysis, the proportion of entrepreneurs declines between 1910 (17%) to 2010 (11%). We use
year fixed effects in our analyses to control for these time trends.
[Insert Table 1]
Table 2 provides further information about the personal characteristics of entrepreneurs,
compared to other individuals. Panel A assesses whether entrepreneurs are demographically
distinct from other individuals (non-entrepreneurs). Entrepreneurs are more likely to be older,
foreign born, white, and homeowners and these differences are statistically significant. The fact
that entrepreneurs, i.e., business owners, tend to be older than the average population complement
findings that individuals i.e., employees, who work at young, new firms tend to be younger
(Ouimet and Zarutski, 2014). Moreover, the fact that entrepreneurs tend to be homeowners is
consistent with Schmalz, Sraer, and Thesmar (2016) who find that collateralizable assets such as
homeownership are valuable sources of financing for entrepreneurs. Panel B reports these
differences by decade and for the first time shows long-term trends in how entrepreneurs differ
from non-entrepreneurs. Over time, entrepreneurs are increasingly younger, more likely to be born
in the US, white, and less likely to be homeowners, compared to individuals who are not
entrepreneurs.
[Insert Table 2]
II.
Results: Difference-in-Differences
A. Empirical Methodology
We estimate whether bank distress during the Great Depression influences the long-run
trajectory of future entrepreneurship in a county. We use a differences-in-differences setting with
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state and year fixed effects and exploit within-state, county-level variation in the severity of bank
distress. This method removes persistent differences across states, such as state-level banking
laws and state-specific economic drivers, to compare distressed counties within states. The year
fixed effects remove common time trends such as world wars and national economic crises and
booms. Our main specification is:
𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸ℎ𝑖𝑖𝑖𝑖𝑗𝑗,𝑖𝑖,𝑠𝑠,𝑡𝑡 = 𝛽𝛽0 + 𝛽𝛽1 𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑖𝑖,𝑠𝑠 +
(1)
𝛽𝛽2 𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝑖𝑖,𝑠𝑠 𝑥𝑥𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴1920𝑡𝑡 + 𝛾𝛾𝑡𝑡 + 𝜆𝜆𝑠𝑠 + 𝜀𝜀𝑗𝑗,𝑖𝑖,𝑠𝑠,𝑡𝑡
where 𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸ℎ𝑖𝑖𝑖𝑖𝑗𝑗,𝑖𝑖,𝑠𝑠,𝑡𝑡 denotes whether the individual survey respondent j reported
entrepreneurship as his primary occupation in county i at time t in state s. 𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴1920𝑡𝑡 is an
indicator variable that equals one in 1930 and subsequent decades following the Great Depression
and zero otherwise. 𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑖𝑖,𝑠𝑠 denotes the degree of bank distress in county i in state s
during the Great Depression. The estimate of the effect of bank distress on future entrepreneurship
is given by 𝛽𝛽3, which measures differences in the propensity of individuals to engage in
entrepreneurship in areas with higher bank distress over the subsequent nine decades compared to
areas with lower bank distress. To account for correlation between counties within a state, all
specifications include robust standard errors clustered at the state level.
The identifying assumption is that all unobserved heterogeneity comes from state-specific
fixed effects and not correlation in the unobservable variables driving both county-level bank
distress and entrepreneurship. To support this assumption, we examine whether treatment and
control groups exhibit similar trends in the outcome variable prior to the intervention (in our case
the Great Depression). Figure 2 shows that counties with high bank distress (solid line) and low
bank distress (dashed line) exhibit parallel trends in the rate of entrepreneurship prior to the Great
Depression. This pattern suggests that the Great Depression is the driver of differences in
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entrepreneurship rates that arise following the bank distress shocks. The figure also shows that a
decline in the rate of entrepreneurship in distressed counties begins between 1930 and 1940 and
persists through 2010. To further mitigate concerns that an unobservable variable drives both bank
distress and entrepreneurship, we examine the robustness of our results in Section III.
[Insert Figure 2]
B. Main Results
Our main analysis measures how bank distress during the Great Depression affects longterm entrepreneurship. Table 3 reports the estimated coefficients from Equation 1. Columns 1
and 2 report the regression coefficients from our full sample analysis without control variables and
with control variables, respectively. The estimated coefficient on Bank Distress X After reveals
that counties with above-median bank distress during the Great Depression experience a decline
in individual entrepreneurship of about 1 percentage point on average over 1930 through 2010
relative to 1910 and 1920, compared to counties with lower bank distress.
This effect is
statistically significant at the 5% level and predicts that decade-over-decade the supply of
entrepreneurs is 9% lower in counties that experienced higher bank distress during the Great
Depression (9% = 1% / 11.5%, where 11.5% is the mean entrepreneurship rate over the sample
period).
[Insert Table 3]
One potential concern is that counties with higher bank distress have different industrial
composition which may lead to different demand for entrepreneurs or different career paths for
local entrepreneurs. To address this concern we repeat our main analysis but include industry
fixed effects in the regression specifications. Columns 3 and 4 report that our main results are
robust to the inclusion of industry fixed effects. Another concern is that counties with higher bank
13
distress had different patterns of homeownership among residents.
Homeownership is an
important determinant of an individual’s choice to become an entrepreneur.
Because the
homeownership variable is missing during the 1950 Census, we exclude this control variable from
our main analysis. However, Columns 5 and 6 show that our results are robust to including
homeownership as an additional control variable in the regression specifications. Overall, the
results in Table 3 reveal that bank distress during the Great Depression is associated with the
reduced individual propensity to engage in entrepreneurship by 9%.
We hypothesize that the loss of relationship lending is the mechanism through which bank
distress reduces entrepreneurship. Our measure of bank distress uses the suspension of state banks
as the relevant shock to entrepreneurs. This definition is motivated by evidence that in the early
1900s, local, state-level banks were the main source of primary borrowing, such that state banks
supplied capital to entrepreneurs (Alhadeff, 1951; Horton, Larsen, and Wall, 1942). In later test
(Section IV) we will explore whether local financial distress has a differential effect on
entrepreneurship in industries that are prone to information asymmetry (e.g., services and retail
industries) and therefore require more soft information gathering and relationship lending.
C. Time-series effects
We explore time trends in the effect of bank distress on entrepreneurship by including the
interaction of Bank Distress with each decade since 1920 in our sample, such that the coefficients
measure the rate of entrepreneurship relative to the rate in 1910. Table 4 reports the results of this
analysis. The statistically and economically insignificant coefficient on Bank Distress X 1920
shows that high and low distress counties exhibit parallel trends in entrepreneurship prior to the
Great Depression.
These results provide a statistical assessment of the parallel rates of
entrepreneurship in counties with high and low bank distress, depicted in Figure 2, prior to the
14
Great Depression. Consistent with our main results and the trends in Figure 2, the rate of
entrepreneurship declines after the Great Depression and the decline becomes more severe over
time, falling from 0.7% in 1930 to 1.3% by 1950, 1.5% by 1980, and ultimately to 2.1% by 2010. 5
[Insert Table 4]
Historical changes in the banking system potentially explain the accelerated decline in
entrepreneurship after 1990. Between 1910 and 1970, state laws restricted bank branching across
and within states, which led to geographical segmentation of bank deposits and lending activity
(Jayaratne and Strahan, 1996). States lifted bank branching restrictions between 1970 and 1994
and the Reagle-Neal Act of 1994 further increased competition and consolidation in the banking
sector (Stiroh and Strahan, 2003; Black and Strahan, 2002; Kerr and Nanda, 2009). We posit that
as these changes diversified banking markets across state borders, the need for local banks to
resolve information asymmetries became even more important for entrepreneurs’ financial access.
Hence, we expect that the presence of local bank branches should promote local entrepreneurship,
and become more relevant following bank deregulation.
To test this hypothesis, we examine the banking system in the short and long run in counties
with high bank distress compared to low bank distress between 1929 and 2010. The results,
reported in Section V.A. (Table 13), report how the structure of the banking system and the supply
of capital within the banking system changed over time. In the short term (by 1936), counties with
high bank distress had fewer financial institutions and lower levels of bank capital. In the long
term (by 2010), we find that total banking deposits and hence the aggregate volume of capital in
high distress counties converged to that of lower distress counties. This pattern reflects overall
5
Although the interactions in 1960 and 1970 are not statistically significant they are economically
consistent with the other coefficients. A potential explanation for low power is that there are fewer counties
in those decades. In contrast to other decades, the 1960 and 1970 Censuses report results for less than onetenth of all US counties.
15
economic recovery between these areas over the long run. However, our results show that the
Great Depression did alter the structure of the banking sector permanently. The banking sector in
high distress counties has fewer bank branches in the present day.
According to our hypothesis, entrepreneurs require local bank financing to resolve
information asymmetries, and local bank financing is driven, at least in part, by the supply of local
bank branches. Hence, our finding that there are fewer bank branches in high distress counties
supports the hypothesis that entrepreneurs permanently lacked local finance in high distress areas.
III.
Bank Distress and Entrepreneurship in an Instrumental Variables Setting
The identifying assumption in our difference-in-differences setting is that an unobservable
time-varying variable that is correlated with the timing of the Great Depression does not drive both
county-level bank distress and changes in entrepreneurship. In this section, we use an instrumental
variable to test the hypothesis that financial distress, and not an omitted variable, drives lower
entrepreneurship rates. A strong instrumental variable in this setting is exogenous to local
economic conditions but predicts bank distress during the Great Depression. Moreover, the
instrument should be transitory and should not permanently alter economic conditions.. We use
the source of an earlier credit crisis that preceded the Great Depression as an instrument for bank
distress during the Great Depression.
The credit crisis arose after World War I and the Russian Revolution created uncertainty
surrounding the supply of commodities, particularly wheat and other grains. A subsequent
worldwide boom in international commodity prices between 1910 and 1920 drove a bubble in land
prices in US counties depending on the types of crops that traditionally grew in each county (Rajan
and Ramcharan, 2015).
International agricultural production recovered more quickly than
anticipated, reducing agricultural prices to pre-boom levels. This drop in international commodity
16
prices drove a collapse in US land prices in counties with high exposure to international
commodity prices between 1921 and 1922 (Yergin, 1992; Blattman, Hwang, and Williamson,
2007). Although these shocks were transitory and farming fully recovered by 1922 (Johnson 1973;
Alston, Grove, and Wheelock 1994), they weakened bank balance sheets and made these banks
particularly susceptible to bank runs (Richardson, 2009).
This shock provides a strong instrumental variable in our setting for several reasons. First,
international commodity prices are exogenous to county-level demographic, economic, and
financial conditions prior to the shock. The 15-year lag between this credit crisis and the Great
Depression alleviates concerns that the local economic environment during the Great Depression
drives our results. Second, the shocks weakened these local banking systems through the 1920s
and made them less resilient to additional financial shocks that occurred in the early 1930s. Hence,
these shocks are a bellwether for “the straw that broke the camel’s back” in the early 1930s. Third,
the agricultural sector and the local economies recovered following this real estate driven credit
crisis of the 1920s, mitigating the concern that a decline in economic opportunities drives the
permanent drop in entrepreneurship. Because these international agricultural price shocks did not
change local economies, we argue that our results are driven by bank distress and not an omitted
variable.
Rajan and Ramcharan (2015) construct an index of each county’s “agricultural produce
deflator” over the period 1910–1930 by weighting the annual change in each commodity’s price
over the relevant period by the share of agricultural land devoted to that commodity’s production
in each county in 1910. The index consists of the seven commodities for which world prices are
consistently available during this period: cotton, fruits, corn, tobacco, rice, sugar, and wheat.
Following Rajan and Ramcharan (2015) we use the county-level change in the value of the index
17
from 1917 to 1920 - the period with the sharpest increase in international commodity prices - as a
county-level characteristic that predicts bank distress.
To assess the determinants of county-level bank distress, we aggregate personal-level data
to the level of our bank distress variable (county), and include county-level measures of the
banking structure preceding the Great Depression. As an analog to control variables from our
main regression analysis, we construct county-level aggregates of individual-level variables that
control for the existing stock of entrepreneurs in a county and the individual propensity to engage
in entrepreneurship. These variables include: the fraction of entrepreneurs, the average resident
age, foreign born residents, white residents, and home owners. To control for the role of the
existing banking structure on bank distress during the Great Depression, we include county-level
banking structure variables prior to the Great Depression: total bank deposits in 1929 and the total
number of national banks in 1929. Columns 1 and 2 of Table 5 report the cross-section of countylevel characteristics prior to the Great Depression (in 1920) for low distress and high distress
counties, respectively. Column 3 shows that entrepreneurship rates are not statistically different
between counties with high and low bank distress. However, these groups did differ in their
exposure to international commodity prices (i.e., 1917 to 1920 Change in Commodity Index).
[Insert Table 5]
We use these characteristics to predict bank distress during the Great Depression in the
following regression specification where we control for state fixed effects (𝜆𝜆𝑠𝑠 ):
𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑖𝑖,𝑠𝑠 = 𝛽𝛽0 + 𝛽𝛽1 𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸ℎ𝑖𝑖𝑖𝑖𝑖𝑖,𝑠𝑠 + Ψ1 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑠𝑠𝑖𝑖,𝑠𝑠 + 𝜆𝜆𝑠𝑠 + 𝜀𝜀𝑖𝑖,𝑠𝑠
(2)
Table 6 reports the results of this analysis. Neither the fraction of entrepreneurs in 1920
(i.e., “1920 Entrepreneur Share”) nor the changes in the county-level entrepreneurship share from
1910 to 1920 (i.e., “1910 to 1920 Change in Entrepreneur Share”) predict bank distress during the
18
Great Depression. Instead, the results indicate that the international commodity price boom
following WWI strongly predicts financial distress during the Great Depression (Wicker, 1996,
p.7; Brocker and Hanes, 2012; Nanda and Nicholas, 2014; and Rajan and Ramcharan, 2015).
These results support the assertion that our measure of bank distress is exogenous to local
entrepreneurship.
[Insert Table 6]
We explore the robustness of our main results in an instrumental variables setting by using
changes in international commodity prices from 1917 to 1920 as an instrument for bank distress
during the Great Depression. We test the hypothesis that exogenous bank distress reduces countylevel entrepreneurship over the long-term in an IV setting where in the first stage Bank Distress
and interaction of Bank Distress X After 1920 is instrumented with the 1917 to 1920 Change in
Commodity Index and its interaction with the After 1920 indicator. The second stage is:
�
𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸ℎ𝑖𝑖𝑝𝑝𝑖𝑖,𝑠𝑠,𝑡𝑡 = 𝛽𝛽1 𝑋𝑋 𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑠𝑠
𝚤𝚤,𝑠𝑠 +
(4)
�
𝛽𝛽 2 𝑋𝑋 𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑠𝑠
𝚤𝚤,𝑠𝑠 𝑋𝑋𝑋𝑋𝑋𝑋𝑋𝑋𝑋𝑋𝑋𝑋 1920𝑡𝑡 + 𝛾𝛾𝑡𝑡 + 𝜆𝜆𝑠𝑠 + 𝛾𝛾𝑋𝑋𝑖𝑖,𝑠𝑠,𝑡𝑡 + 𝜓𝜓𝑖𝑖𝑖𝑖𝑖𝑖
where 𝑋𝑋𝑖𝑖𝑖𝑖𝑖𝑖 is a vector of county characteristics that include age, foreign population share, the share
of the white population, the share of homeowners, total bank deposits in 1929, and the total number
of national banks in 1929. The county-level change in the international commodity index from
1917 to 1920 is an instrument for bank distress under the assumption that the index has a direct
effect on bank distress during the Great Depression (via the relatively short-lived farm land price
bubble) but only affects entrepreneurship via its effect on bank distress. Evidence that these shocks
were short-lived and driven by large-scale international events supports the claim that this
instrument meets the exclusion restriction requirement.
19
Table 7 Column 1 reports the results of the first stage regression and shows that the change
in the international commodity index from 1917 to 1920 is a statistically and economically
significant predictor of bank distress during the Great Depression. The first-stage partial Rsquared shows that even after controlling for state and year fixed effects, and controls, the
instrument explains 2.5% of variation in the dependent variable and the fully robust, adjusted Fstatistic of 8 reflects the most conservative estimate of the model with standard errors clustered at
the state level. The second stage results (Column 2) show that bank distress plays a causal role in
reducing the number of entrepreneurs. The coefficient on the instrumented bank distress variable
is -0.078 (t = 2.36). Given that bank distress is .38 on average, this coefficient corresponds to an
average decline in the entrepreneurship rate of -0.078 * 0.38 = -0.029, or 2.9 percentage points.
This estimated effect is larger than our OLS estimates (-0.027 * 0.38 = -0.01) using the continuous
bank distress variable, reported in Appendix Table 5. One potential explanation for this difference
is that the IV estimates are derived only from the variation that comes from the instrument (i.e.,
counties that experienced the earlier credit crisis). The IV coefficient measures the local average
treatment effect of bank distress on entrepreneurship in counties that also experienced the earlier
credit crisis and differs from the OLS estimates that capture the effects of bank distress on all
counties that had high bank distress during the Great Depression (Angrist et al., 1996).
[Insert Table 7]
Although the shock to agriculture was short-lived, we provide evidence to mitigate a
concern that farm distress reduced demand for entrepreneurship related to agriculture. We use data
from the NHGIS between 1910 and 1940 to study the agricultural sector following these bank
distress shocks. Table 8 reports that the number of farms and the land allocated to farming did not
20
decline following the commodity shocks or following the Great Depression. Although the
agricultural price shock led to bank distress it did not permanently depress local agriculture.
[Insert Table 8]
IV.
Bank Distress and Types of Entrepreneurship
The results indicate that fewer individuals in areas with more severe bank distress chose
entrepreneurship as a career. This result motivates questions about whether there is variation in
this effect across industries, quality of entrepreneurs, and productivity. In other words, does
financial distress merely chase out lower quality entrepreneurs? The analyses in this section
distinguish the types of entrepreneurs that were influenced by bank distress. First, we assess
whether the effect of bank distress on entrepreneurship is correlated with entrepreneurship in
specific industries. Next, we assess whether bank distress is correlated with the skill (education)
or productivity (wages) of future entrepreneurs following financial distress. Lastly, we assess the
impact of bank distress on innovative output, in the form of patenting activity.
A. Future Entrepreneurship by Industry
We explore whether the decline in individual entrepreneurship varies by industry in
counties with high and low bank distress. This analysis sheds light on the mechanisms through
which financial distress reduces entrepreneurship. For example, external financing needs vary by
industry and negative financial shocks should have a greater impact on entrepreneurship in
industries with higher dependence on external financing. In addition, information asymmetry
varies across industries and increases the need for financiers to engage in costly information
acquisition before making loans. Our measure of bank distress reduces the supply of these
financiers and thereby potentially reduces the financing available to entrepreneurs in industries
21
with higher information asymmetry. In addition, this analysis will provide evidence about how
bank distress shocks affected future entrepreneurship in agriculture-related industries.
Table 9 summarizes the rates of entrepreneurship in ten SIC-1 sectors by decade over the
sample period. Several time-series patterns emerge. Entrepreneurship in retail trade is stable over
the sample period despite the introduction of big-box retailers in the past few decades. In contrast,
entrepreneurship in services has grown from 10% to 30% of the sector’s total employment.
[Insert Table 9]
To test the hypothesis that the decline in individual entrepreneurship varies by industry in
counties with high and low bank distress, we run a triple differences regression in which the triple
interaction term measures the change in entrepreneurship in areas with high bank distress in a
particular industry following the Great Depression relative to all other industries. Table 10,
Column 1 shows no decline in the propensity for entrepreneurs to be involved in agricultural
entrepreneurship, validating our assumption that the agricultural shock did not change future
entrepreneurial opportunities in agriculture.
[Insert Table 10]
The industries with the largest decline in the fraction of entrepreneurs are in Services and
Retail trade. Both sectors have high information asymmetry, a characteristic which increases the
need for local finance to resolve information gaps. In addition, the services sector depends heavily
on external financing (Rajan and Zingales, 1998; Gilje, 2012). Hence, these sectors are more likely
to need external finance and more likely to need bank financing to be local. The effects of bank
distress are economically large. The proportion of entrepreneurs in the service sector declines by
an additional 2.7% per decade following the Great Depression in more distressed counties.
B. Future Entrepreneurship by Education and Earnings
22
We test the hypothesis that bank distress eliminated low-quality or low-skill entrepreneurs
to address concerns that counties with high distress were funding wasteful, less productive
entrepreneurship prior to the bank distress shock.
Under this hypothesis, the remaining
entrepreneurs following the shock should be of higher quality because only the best entrepreneurs
will qualify for financing in a more competitive financial environment.
We measure entrepreneurs’ skill using a proxy for individual education and we measure
individual productivity using a proxy for individual wages (see Section I for a detailed discussion
of these proxy variables). We construct these proxies within each county-decade to resolve
concerns about time trends in these variables, e.g. increased educational attainment, over the full
sample period.
Although approximate, these variables provide the best estimates of an
entrepreneur’s education and earnings that we can construct consistently over the 110-year period.
To test our hypothesis, we use a triple differences analysis to assess whether specific types
of entrepreneurs, i.e. those with low earnings or low education, were more likely to exit
entrepreneurship relative to entrepreneurs with higher earnings or education. Table 11, Columns
1 and 2 report the results and show that individuals in occupations that require higher education
were less likely to be entrepreneurs following the bank distress shock. Individuals in higher
earning occupations (Columns 3 and 4) were also less likely to be entrepreneurs at a higher rate
compared to lower earners. In fact, the magnitude of our main results is almost entirely explained
by a decline in entrepreneurship in occupations with high education or high earnings. In contrast
to the hypothesis, the results suggest that bank distress disproportionately led higher quality
entrepreneurs to quit entrepreneurship.
[Insert Table 11]
C. Future Innovative Output
23
We assess variation in the impact of bank distress on innovative output across counties
following the Great Depression. We use data from the USPTO to construct aggregate county-level
patenting activity between 1880 and 1975 as a proxy for innovative output (see Section I for a
detailed description). Because we cannot measure innovative output separately for entrepreneurs
and non-entrepreneurs, our tests rely on the assumption that there is a correlation between
entrepreneurial activity and the number of patent filings in a county.
Table 12 reports the results of a difference-in-differences analysis on county-level
patenting activity. First, the interactions of bank distress with patenting output prior to the Great
Depression reveal that patenting activity did not differ in counties with high and low bank distress.
The results show that the total number of patents filed in distressed counties declines by more than
12% in the decade following the Great Depression (1930-1939) and throughout the subsequent
decades through 1950. These results are consistent with the hypothesis that bank distress reduced
innovative activity within a county. In addition, these findings extend Nanda and Nicholas (2014)
by showing the long-term effects of financial distress on entrepreneurial activity.
[Insert Table 12]
V.
County-level Banking and Economic Outcomes following the Great Depression
The analysis in this section, addresses the question of whether bank distress during the
Great Depression led to broader economic differences in a long-run equilibrium. Due to data
limitations we are unable to assess the outcomes over the full 110-year period and cannot perform
our main difference-in-differences analysis to assess “causal” effects of entrepreneurship on these
outcomes. Instead this analysis measures the differences in means in banking outcomes between
1970 and 2010 and in economic outcomes between 1970 and 2000. This analysis serves to provide
24
suggesting evidence of how persistently lower entrepreneurship influences the development of a
local economy.
A. Banking Outcomes
The banking literature emphasizes the importance of local deposits and the proximity of
individuals to physical bank locations (Becker, 2007; Berger, Miller, Petersen, Rajan, and Stein,
2005). Explicit in our hypotheses is that the financial sector influenced the immediate reduction in
entrepreneurship. In addition to these immediate effects, permanent changes in the bank sector
present a potential channel through which the Great Depression may have had persistent effects
on local entrepreneurship.
We measure how bank distress during the Great Depression affected short- and long-term
access to finance via local deposits and the number of local bank branches. County-level data on
deposits are available between 1920 and 1936 and 1994 - present. Our proxies for short term
recovery are (1) the difference between the log of total county-level deposits in 1936 and the log
of 1929 deposits and (2) difference between the log of total banks in 1936 and total banks in 1929.
Our proxies for long term recovery are (1) the difference in the log of total county-level deposits
in 2010 and the log of 1929 deposits and (2) the difference between the log of the total number of
bank branches in 2010 and the log of the 1929 total number of bank branches. Bank branches in
2010, rather than number of banks, are the relevant comparison to 1929 banks because banks were
permitted to form branching networks in 2010 and were not able to establish branches in 1929.
Table 13 shows that in the short term (by 1936), deposits and bank branches did not recover
in counties with higher levels of bank distress during the Great Depression. Banking access was
still significantly lower in counties with higher bank distress during the Great Depression. The
long term effects show that by 2010, deposit growth in counties with high and low bank distress
25
was similar. However, the number of banks never recovered. Areas with more bank distress
during the Great Depression did not experience comparable growth in bank branches over the long
run. As discussed in Section II, these long-run differences in the banking sector potentially drive
persistently lower rates of entrepreneurship in distressed counties.
[Insert Table 13]
B. Economic Outcomes
We examine county-level economic indicators between 1970 and 2000 to understand how
lower rates of entrepreneurship influence long-term, out-of-sample economic development. We
examine whether persistently lower rates of entrepreneurship have affected county-level
demographics (e.g., population size, race, and age); county-level economic outcomes (e.g. total
income, per capita income, employment, labor force, and unemployment) and county-level income
inequality (e.g. Gini coefficient). The Gini coefficient is a measure of income inequality that is
bounded between zero and one, where a coefficient of zero denotes perfect equality and a
coefficient of one denotes extreme inequality in a county. We use recent data (1970 – 2000) from
the U.S. Census, the Bureau of Economic Analysis, and the Bureau of Labor Statistics to assess
whether the decline in entrepreneurship caused by bank distress during the Great Depression
influences recent economic outcomes. Section I describes these data in detail.
Table 14 reports the difference-in-means and p-values of each variable between counties
with high and low bank distress for each decade. Each difference represents the variable mean in
a high distress county less the variable mean in the low distress county. High and low distress
counties do not exhibit statistically significant differences in terms of county-level demographics
between 1970 and 2000 meaning that lower entrepreneurship rates are not associated with different
individual characteristics in distressed counties. The economic outcomes reveal that counties with
26
high and low distress do not differ in terms of employment rates and size of the labor force such
that an economic equilibrium with fewer entrepreneurs yields similar average economic outcomes
to an equilibrium with more entrepreneurs. However, the Gini coefficient exhibits variation in the
income distribution depending on bank distress. Income inequality, as measured by the Gini
coefficient, is higher in counties with lower bank distress (i.e., higher entrepreneurship) after the
Great Depression compared to counties with high bank distress and lower entrepreneurship. This
result is consistent with wide variation in entrepreneurial incomes affecting the income
distribution. Bank distress reduced entrepreneurship and reduced the potential for high income
payoffs within a county (Manso, 2016). On average, high and low distress counties converged
economically, however, future entrepreneurship is correlated with variation in income and
economic opportunities.
[Insert Table 14]
We use the QWI data, described in Section I, to test whether the rate of entrepreneurship
affects the distribution of employment opportunities across firms (i.e. small firms and young firms)
in each county. The data categorize firms by size and age and we summarize the number of
employees in each of these categories in each county. This analysis tests whether bank distress
during the Great Depression correlates with other measures of entrepreneurship, i.e., the fraction
of employment in young and small firms. Table A.1 in the Appendix reports the differences-inmeans (p-values in parentheses) of employment opportunities in young firms and in small firms
between counties with high bank distress and low bank distress for each year between 1996 and
2014. The results reveal several features of employment growth across these firm age categories
over the past 20 years.
Overall, counties with high bank distress (i.e., lower rates of
entrepreneurship) have fewer employment opportunities in young firms compared to counties with
27
more entrepreneurship. The relationship between entrepreneurship and job opportunities in young
firms is especially pronounced during periods of high innovation throughout the US. Specifically,
between 1996 and 2002, i.e. the internet boom, and between 2005 and 2008, i.e. the run up to the
financial crisis, entrepreneurs increase the supply of employment opportunities in young firms
during these innovative periods. Hence, counties with fewer entrepreneurs do not experience job
growth in the same types of firms during innovative periods.
VI.
Robustness
We test the robustness of our results to the influence of sample selection, alternative fixed
effects, and alternative variable definitions. Table A.1 in Appendix tabulates these results.
Columns 1 and 2 report results of an analysis on a subsample of counties that excludes less
populated counties and the results show that our results are not driven only by counties with a
small fraction of U.S. population. Columns 3 and 4 report our main results with the inclusion of
occupation fixed effects. The results mitigate concerts that fluctuations in demand for specific
occupations drives our results. We repeat our main analysis using the continuous measure of bank
distress (Columns 5 and 6) and find results that are qualitatively similar to our main results and
are statistically significant at the 1% level.
VII.
Conclusion
This paper shows that financial distress influences individual choices to engage in
entrepreneurship and that these decisions have long-lasting implications on the supply of
entrepreneurs in local economies.
The lack of historical data on entrepreneurship prior to 1978 (when Census data become
available) made it previously impossible to examine long-term determinants of participation in
entrepreneurship. We open the possibility of studying entrepreneurship in the long-term by
28
analyzing a dataset that provides a time-consistent measure of entrepreneurship over the past 110year period. In addition, the endogenous link between financial access and entrepreneurship
presents an empirical challenge for the identification of a treatment effect of negative financial
shocks on future entrepreneurship. Our analysis disentangles finance from entrepreneurship by
using local bank distress during the Great Depression as a quasi-experimental setting, in which
some local economies had less financial access than others. We use the cross-sectional variation
in local financial distress to explore how the individual propensity to become an entrepreneur
changes both immediately following the shock, but also over the subsequent eight decades.
Our question is whether negative financial shocks have long-lasting effects on
entrepreneurship.
Although the academic research provides evidence that entrepreneurship
exhibits concurrent sensitivity to financial access, there is little evidence about the long-term
implications of these decisions on future entrepreneurship.
Our results support the hypothesis that bank distress reduced local entrepreneurship in the
long run. The finding that bank distress had a long-term impact on entrepreneurship is new to the
literature and the historical results highlight the importance of resolving local financial frictions to
avoid a permanent reduction in local entrepreneurship.
We explore cross-sectional variation in future entrepreneurship across industries and across
entrepreneurial quality (earnings and education) following the bank distress shock. The results
indicate that entrepreneurship in occupations associated with more education or higher earnings
falls following episodes of bank distress.
Hence, bank distress drives out high quality
entrepreneurship. In addition, industries that lose more entrepreneurs are those with higher
information asymmetry. These results are consistent with the notion that a reduction in local
29
financial resources reduces financial availability for risky investments that require higher
information-gathering efforts.
We assess the long-term implications of these findings on local economies. The results
show that the average population and wealth in high distress counties does not differ from that in
low distress counties over the long term. However, averages belie the individual heterogeneity
within these local areas. Entrepreneurial output, in the form of patenting activity declines over the
long run in distressed areas. We examine income inequality within counties and find that counties
with more entrepreneurship (i.e., low bank distress during the Great Depression) have higher
income inequality in recent decades. This result is consistent with the lottery-like payoffs of
entrepreneurial activity (Manso, 2016).
Our results open the door for future inquiry into how these financial shocks lower the
propensity for individuals to become entrepreneurs. For example, entrepreneurs may physically
relocate to areas with better financial access.
In addition, a reduction in the number of
entrepreneurs potentially reduces the supply of future generations of entrepreneurs due to
intergenerational occupation transfer - children may not “inherit” entrepreneurship as a viable
career path because they did not have the example set by the older generation. We are currently
investigating these mechanisms.
30
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33
Table 1: Summary Statistics
The table reports summary statistics for the sample of individual survey respondents to Decennial Censuses from 1910 through 2010. The sample
consists of non-farm males in the labor force between the ages of 16 and 65. Panel A reports statistics for the full sample and Panel B reports
means of the variables by decade. Entrepreneur is an indicator variable equal to 1 for respondents who “work on own account” at the time of
the Census. Bank Distress is an indicator variable equal to 1 for counties with state bank deposit suspensions during the Great Depression above
the median county’s suspensions. County-level deposit suspension is the ratio of bank deposits at state banks that were suspended between 1930
and 1933 divided by total deposits at state banks in 1929. Age is the respondent’s age at the time of the survey. Foreign, white, black, asian,
hispanic, other ethnicity, and home owner are indicator variables equal to 1 if the survey respondent self-identifies into each of these categories
and zero, otherwise.
Panel A. Statistics:
N
Mean
St. Dev
Entrepreneur
2,955,671
0.115
0.319
Bank Distress
2,955,671
0.388
0.487
Age
2,955,671
37.947
12.681
Foreign
2,955,671
0.174
0.379
White
2,955,671
0.798
0.401
Black
2,955,671
0.089
0.284
Asian
2,955,671
0.025
0.157
Hispanic
2,955,671
0.080
0.271
Other Ethnicity
2,955,671
0.008
0.087
Home Owner
2,873,326
0.568
0.495
Panel B. Mean by Decade:
1910
1920
1930
1940
1950
1960
1970
1980
1990
2000
2010
Entrepreneur
0.172
0.147
0.131
0.132
0.106
0.099
0.090
0.095
0.108
0.110
0.115
Age
34.937
36.343
37.207
37.999
37.524
39.583
39.046
36.601
37.311
38.702
41.352
Foreign
0.282
0.254
0.212
0.152
0.080
0.092
0.101
0.103
0.147
0.201
0.228
White
0.906
0.912
0.903
0.907
0.871
0.866
0.835
0.787
0.756
0.678
0.638
Black
0.080
0.073
0.077
0.075
0.100
0.090
0.097
0.104
0.089
0.095
0.092
Asian
0.004
0.003
0.003
0.002
0.002
0.005
0.010
0.020
0.034
0.051
0.070
Hispanic
0.009
0.012
0.017
0.015
0.026
0.037
0.055
0.083
0.116
0.152
0.180
Other Ethnicity
0.001
0.001
0.001
0.001
0.001
0.001
0.003
0.005
0.006
0.023
0.020
Home Owner
0.359
0.399
0.446
0.410
NA
0.625
0.629
0.666
0.676
0.673
0.700
34
Table 2: Summary Statistics: Differences-in-Means by Entrepreneurship Status
The table reports summary statistics for the sample of individual survey respondents to Decennial Censuses from 1910 through 2010. The
sample consists of non-farm males in the labor force between the ages of 16 and 65. Panel A reports demographic summary statistics for
population subgroups depending on their individual entrepreneurship status. Column 1 [Column 2] presents means (standard deviations reported
in parentheses) for individuals who are non-entrepreneurs [entrepreneurs]. The entrepreneur variable equals 1 for respondents who “work on
own account”. Column 3 reports the the differences-in-means and the p-value of the t-test adjusted for clustering at the state level (reported in
parentheses). Age is the respondent’s age at the time of the survey. Foreign, white, black, asian, hispanic, other ethnicity, and home owner are
indicator variables equal to 1 if the survey respondent self-identifies into each of these categories and zero, otherwise. Panel B reports differencesin-means by Entrepreneur indicator for each demographic variable by decade (e.g, in 1910, entrepreneurs were on average 7.347 years older than
non-entrepreneurs and were 1.4% less likely to be foreign-born).
(1)
(2)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
(11)
Non-Entrepr
Entrepr
Diff
37.420
(12.621)
0.172
(0.377)
0.785
(0.411)
0.095
(0.293)
0.026
(0.158)
0.086
(0.281)
0.008
(0.090)
0.570
(0.495)
43.810
(11.330)
0.206
(0.404)
0.874
(0.332)
0.040
(0.196)
0.027
(0.162)
0.053
(0.223)
0.006
(0.080)
0.688
(0.463)
6.390
(0.000)
0.034
(0.000)
0.088
(0.000)
-0.055
(0.000)
0.001
(0.353)
-0.034
(0.000)
-0.002
(0.000)
0.117
(0.000)
1910
1920
1930
1940
1950
1960
1970
1980
1990
2000
2010
Age
7.347
Foreign
-0.014
6.784
7.052
6.529
6.071
6.379
6.968
6.582
6.075
6.521
6.198
0.029
0.061
0.072
0.055
0.054
0.021
0.004
0.007
-0.008
White
0.027
0.054
0.049
0.058
0.047
0.078
0.071
0.083
0.108
0.097
0.114
0.091
Black
-0.054
-0.048
-0.052
-0.043
-0.073
-0.060
-0.061
-0.071
-0.058
-0.052
-0.044
Asian
0.004
0.003
0.002
0.003
0.004
0.006
0.004
0.004
0.006
0.003
-0.002
Hispanic
-0.004
-0.005
-0.008
-0.006
-0.008
-0.017
-0.024
-0.039
-0.043
-0.063
-0.042
Other Ethnicity
0.000
0.000
0.000
-0.000
-0.001
-0.001
-0.001
-0.002
-0.002
-0.002
-0.003
Home Owner
0.188
0.162
0.167
0.158
NA
0.147
0.145
0.119
0.125
0.129
0.090
Panel A. Entrepreneurship Status:
Age
Foreign
White
Black
Asian
Hispanic
Other Ethnicity
Home Owner
Panel B. Differences-in-Means by Decade:
(3)
35
Table 3: The Long-Term Effect of Bank Distress on Entrepreneurship
This table reports the results from a difference-in-differences regression of propensity for entrepreneurship
on bank distress during the Great Depression. The identification strategy relies on cross-sectional variation
in bank distress across counties within a state. The sample consists of individual survey respondents to
Decennial Censuses from 1910 through 2010. The selected respondents are non-farm males in the labor force
between the ages of 16 and 65. The dependent variable, Entrepreneur, is the indicator variable equal to 1 for
respondents who “work on own account” at the time of the Census. Bank Distress is an indicator variable
equal to 1 for counties with state bank deposit suspensions during the Great Depression above the median
county’s suspensions. County-level deposit suspension is the ratio of bank deposits at state banks that were
suspended between 1930 and 1933 divided by total deposits at state banks in 1929. After is an indicator
variable equal to 1 for respondents in 1930 or later Censuses. The estimate of the effect of bank distress
on future entrepreneurship is the coefficient on the interaction Bank Distress X After that measures the
relative change between areas with higher bank distress over the subsequent 9 decades (1930 through 2010).
Age is the respondent’s age at the time of the survey. Foreign, white, black, asian, hispanic, other ethnicity,
and home owner are indicator variables equal to 1 if the survey respondent self-identifies into each of these
categories and zero, otherwise. Industry is defined according to the 3-digit 1950 Census Bureau industrial
classification system. Standard errors are clustered at the state level. ∗ , ∗∗ , and ∗∗∗ denote significance at
the 10%, 5%, and 1% levels.
Entrepreneur
Bank Distress
Bank Distress X After
(1)
(2)
(3)
(4)
(5)
(6)
0.014**
(2.29)
-0.010*
(-1.89)
0.014**
(2.39)
-0.013**
(-2.25)
0.010***
(24.47)
-0.000***
(-12.95)
0.013***
(2.70)
-0.068***
(-23.57)
-0.018***
(-2.79)
-0.048***
(-8.72)
-0.024***
(-9.28)
0.014***
(3.38)
-0.009**
(-2.30)
0.014***
(3.62)
-0.010***
(-2.70)
0.012***
(26.04)
-0.000***
(-15.90)
0.009***
(3.90)
-0.061***
(-19.63)
-0.017***
(-5.44)
-0.051***
(-11.30)
-0.025***
(-8.97)
0.013**
(2.15)
-0.013**
(-2.23)
0.009***
(21.58)
-0.000***
(-12.08)
0.019***
(3.66)
-0.061***
(-19.41)
-0.017**
(-2.35)
-0.043***
(-7.18)
-0.020***
(-6.86)
0.041***
(15.60)
0.012***
(3.26)
-0.010**
(-2.61)
0.012***
(28.37)
-0.000***
(-18.22)
0.014***
(5.33)
-0.053***
(-17.61)
-0.016***
(-4.50)
-0.046***
(-9.13)
-0.021***
(-7.72)
0.042***
(24.60)
0.007
2,955,671
47
Yes
Yes
No
0.041
2,955,671
47
Yes
Yes
No
0.123
2,955,671
47
Yes
Yes
Yes
0.158
2,955,671
47
Yes
Yes
Yes
0.045
2,794,355
47
Yes
Yes
No
0.163
2,794,355
47
Yes
Yes
Yes
Age
Age Squared
Foreign
Black
Asian
Hispanic
Other Ethnicity
Home Owner
R-squared
Number of Observations
Number of Clusters
Year FE
State FE
Industry FE
36
Table 4: Time Series Effects of Bank Distress on Entrepreneurship
This table reports the time-series estimates of the effect of bank distress during the Great Depression on
subsequent entrepreneurship. The sample consists of individual survey respondents to Decennial Censuses
from 1910 to 2010. The selected respondents are non-farm males in the labor force between the ages of 16
and 65. The dependent variable, Entrepreneur, is an indicator variable equal to 1 for respondents who “work
on own account” at the time of the Census. Bank Distress is an indicator variable equal to 1 for counties
with state bank deposit suspensions during the Great Depression above the median county’s suspensions.
County-level deposit suspension is the ratio of bank deposits at state banks that were suspended between
1930 and 1933 divided by total deposits at state banks in 1929. The interactions (Bank Distress X 1920, Bank
Distress X 1930, etc.) measure the relative change in entrepreneurship in areas with higher bank distress
during the Great Depression relative to 1910. Column 2 includes the following person-level demographic
controls: age, age squared, foreign, black, asian, hispanic, other ethnicity. Age is the respondent’s age at the
time of the survey. Foreign, white, black, asian, hispanic, and other ethnicity are indicator variables equal
1 if the survey respondent self-identifies into each of these categories and zero, otherwise. Standard errors
are clustered at the state level. ∗ , ∗∗ , and ∗∗∗ denote significance at the 10%, 5%, and 1% levels.
Entrepreneur
Bank Distress
Bank Distress X 1920
Bank Distress X 1930
Bank Distress X 1940
Bank Distress X 1950
Bank Distress X 1960
Bank Distress X 1970
Bank Distress X 1980
Bank Distress X 1990
Bank Distress X 2000
Bank Distress X 2010
R-squared
Number of Observations
Number of Clusters
Controls
State FE
Year FE
(1)
(2)
0.016**
(2.26)
-0.003
(-0.95)
-0.007*
(-1.77)
-0.004
(-0.91)
-0.014*
(-2.00)
-0.011
(-1.60)
-0.010
(-1.10)
-0.012*
(-1.91)
-0.015**
(-2.20)
-0.015**
(-2.02)
-0.017**
(-2.34)
0.016**
(2.35)
-0.003
(-0.95)
-0.008*
(-1.99)
-0.003
(-0.71)
-0.013*
(-1.97)
-0.011
(-1.61)
-0.013
(-1.42)
-0.015**
(-2.44)
-0.019**
(-2.63)
-0.018**
(-2.39)
-0.021**
(-2.69)
0.007
2,955,671
47
No
Yes
Yes
0.041
2,955,671
47
Yes
Yes
Yes
37
Table 5: County-Level Summary Statistics by Bank Distress
The table reports county-level summary statistics grouped by the severity of bank distress during the Great
Depression. Column 1 (Bank Distress=0) presents means (standard deviations) for counties with low bank
distress and Column 2 (Bank Distress=1) for counties with high bank distress. Column 3 reports the the
differences-in-means and the p-value of the t-test adjusted for clustering at the state level (reported in
parentheses). Bank Distress equals 1 for counties with bank distress greater than or equal to the median
county’s bank deposit suspensions. County-level deposit suspensions is the ratio of bank deposits at state
banks that were suspended between 1930 and 1933 divided by total deposits at state banks in 1929. 1920
Entrepreneur Share is the fraction of a county’s respondents who “work on own account” in the 1920
Decennial Census. The selected respondents are non-farm males in the labor force between the ages of
16 and 65. 1920 Average Age, 1920 Foreign Share, 1920 White Share, and 1920 Home Owner Share are
similarly defined. 1917 to 1920 Change in Commodity Index is the county-level change from 1917 to 1920
in the international commodity price index calculated for each county, where weights are the crop share of a
given farm product out of total county farm output and prices are international farm product prices. 1929
Total Bank Deposits is the total, county-level deposits at all banks in 1929. 1929 Number National Banks
is the number of nationally chartered banks in 1929 in each county.
Counties With:
1920 Entrepreneur Share
1920 Average Age
1920 Foreign Share
1920 White Share
1920 Home Owner Share
1917 to 1920 Change in Commodity Index
1929 Total Bank Deposits
1929 Number National Banks
Number of Observations
Bank Distress = 0
Bank Distress = 1
Difference
0.148
(0.056)
26.554
(3.597)
0.065
(0.087)
0.848
(0.212)
0.536
(0.172)
3.278
(2.333)
10073.464
(27325.042)
2.586
(3.333)
0.148
(0.051)
26.695
(3.462)
0.072
(0.081)
0.879
(0.195)
0.565
(0.165)
4.050
(2.584)
9145.964
(20581.783)
2.327
(2.906)
0.001
(0.847)
0.141
(0.696)
0.007
(0.438)
0.032
(0.099)
0.029
(0.049)
0.772
(0.006)
-927.500
(0.600)
-0.259
(0.320)
1354
1375
38
Table 6: Predictors of Bank Distress
The table reports estimates of the causes of county-level bank distress during the Great Depression. In
Columns 1 and 2, bank distress during the Great Depression is regressed on the fraction of entrepreneurs in
a given county in 1920 (i.e., 1920 Entrepreneur Share). In Columns 3 and 4, bank distress is regressed on
the changes in the fraction of county-level entrepreneurship from 1910 to 1920 (i.e., 1910 to 1920 Change in
Entrepreneur Share). Bank Distress is an indicator variable equal to 1 for counties with state bank deposit
suspensions above the median county’s suspensions. County-level deposit suspensions is the ratio of bank
deposits at state banks that were suspended between 1930 and 1933 divided by total deposits at state banks
in 1929. 1920 Entrepreneur Share is the county-level fraction of respondents who “work on own account”
at the time of the 1920 Decennial Census. The selected respondents are non-farm males in the labor force
between the ages of 16 and 65. 1920 Average Age, 1920 Foreign Share, 1920 White Share, and 1920 Home
Owner Share are similarly defined. 1910 to 1920 Change in Entrepreneur Share is the change in the countylevel fraction of respondents who “work on own account” from the 1910 to the 1920 Decennial Censuses.
1910 to 1920 Change in Average Age, 1910 to 1920 Change in Foreign Share, 1910 to 1920 Change in White
Share, and 1910 to 1920 Change in Home Owner Share are similarly defined. 1917 to 1920 Change in
Commodity Index is the county-level change from 1917 to 1920 in the international commodity price index
calculated for each county, where weights are the crop share of a given farm product out of total county farm
output and prices are international farm product prices. 1929 Total Bank Deposits is the total, county-level
deposits at all banks in 1929. 1929 Number National Banks is the number of nationally chartered banks in
1929 in each county. Standard errors are clustered at the state level. ∗ , ∗∗ , and ∗∗∗ denote significance at
the 10%, 5%, and 1% levels.
Bank Distress
1920 Entrepreneur Share
(1)
(2)
-0.141
(-0.58)
0.244
(1.09)
1910 to 1920 Change in Entrepreneur Share
1920 Average Age
(3)
(4)
-0.167
(-0.82)
-0.106
(-0.53)
-0.006
(-1.44)
-0.064
(-0.30)
0.129
(1.53)
0.076
(0.97)
0.017***
(2.87)
0.049***
(4.24)
-0.056**
(-2.33)
1920 Foreign Share
1920 White Share
1920 Home Owner Share
1917 to 1920 Change in Commodity Index
1929 Log(Total Bank Deposits)
1929 Log(Number National Banks)
0.015***
(3.07)
0.038***
(3.09)
-0.051**
(-2.02)
-0.004
(-1.33)
0.244
(1.08)
0.053
(0.46)
-0.024
(-0.31)
1910 to 1920 Change in Average Age
1910 to 1920 Change in Foreign Share
1910 to 1920 Change in White Share
1910 to 1920 Change in Home Owner Share
R-squared
Number of Observations
Number of Clusters
State FE
0.170
2,832
47
Yes
0.190
2,729
47
Yes
0.178
2,752
47
Yes
0.188
2,727
47
Yes
39
Table 7: Instrumental Variables Analysis: The Effect of Bank Distress on Entrepreneurship
This table reports estimates of the bank distress effect on entrepreneurship using instrumental variable
strategy. The sample consists of decennial county-level data aggregated from individual survey respondents
to Decennial Censuses from 1910 through 2010. The selected respondents are non-farm males in the labor
force between the ages of 16 and 65. Column 1 reports the first stage regression results. The instrument,
1917 to 1920 Change in Commodity Index, is the county-level change from 1917 to 1920 in the international
commodity price index calculated for each county, where weights are the crop share of a given farm product
out of total county farm output and prices are international farm product prices. The instrument is interacted
with the indicator variable After to predict the interaction between Bank Distress and After. Bank Distress
is an indicator variable equal to 1 for counties with state bank deposit suspensions above the median
county’s suspensions. County-level deposit suspensions is the ratio of bank deposits at state banks that were
suspended between 1930 and 1933 divided by total deposits at state banks in 1929. After is the indicator
variable equal to 1 for respondents in 1930 or later Censuses. Column 2 reports the results of the second
stage instrumental variables regression where international commodity prices instrument for state bank
failures. The dependent variable, Entrepreneur Share, is the county-level fraction of respondents who “work
on own account” at the time of the Census. The causal estimate of the effect of bank distress on future
entrepreneurship is the coefficient on the interaction Bank Distress X After, which measures the relative
change between counties with higher bank distress over the subsequent decades (from 1930 through 2010).
All columns include the following demographic controls: age, age squared, foreign, black, asian, hispanic,
other ethnicity, all of which are aggregated to county-decade level from the individual survey responses.
Standard errors are clustered at the state level. ∗ , ∗∗ , and ∗∗∗ denote significance at the 10%, 5%, and 1%
levels.
1917 to 1920 Change in Commodity Index
1917 to 1920 Change in Commodity Index X After
1st Stage
2nd Stage
(1)
Bank Distress X After
(2)
Entrepreneur Share
-0.010*
(-1.92)
0.031***
(3.69)
Bank Distress
0.112
(1.12)
-0.078**
(-2.36)
Bank Distress X After
F-statistic in first stage
p-value of F-Statistic
Partial R-squared in first stage
Number of Observations
Number of Clusters
Controls
State FE
Year FE
8
0.001
0.025
12,692
47
Yes
Yes
Yes
12,692
47
Yes
Yes
Yes
40
Table 8: Entrepreneurship Rates by Sector
The table reports mean rates of entrepreneurship by sector for the sample of individual survey respondents to the Decennial Censuses from 1910
through 2010. Industry is defined according to the 3-digit 1950 Census Bureau industrial classification system. We map the 3-digit industry of each
respondent into SIC-1 sectors. The sample consists of non-farm males in the labor force between the ages of 16 and 65. We define entrepreneurs
as respondents who “work on own account” at the time of the Census. Columns 1910 through 2010 report the fraction of respondents defined as
entrepreneurs within each sector by decade (e.g., in 1910 4.7% of all workers in the Mining industry identified as entrepreneurs). Column Total
reports the fraction of respondents defined as entrepreneurs within each sector over the 110 years.
Mean Entrepreneurship Rate by Decade:
1910
1920
1930
1940
1950
1960
1970
1980
1990
2000
2010
Total
Agriculture/Forestry/Fishing
0.073
0.044
0.047
0.040
0.022
0.018
0.012
0.018
0.023
0.024
0.026
0.030
Mining
0.047
0.049
0.038
0.032
0.018
0.008
0.003
0.008
0.005
0.003
0.004
0.017
Construction
0.096
0.074
0.101
0.121
0.100
0.086
0.079
0.094
0.105
0.112
0.112
0.101
Manufacturing
0.293
0.343
0.291
0.288
0.342
0.330
0.299
0.259
0.207
0.170
0.138
0.249
Transp/Commun/Elctr/Gas/Sanitary
0.141
0.143
0.127
0.096
0.102
0.087
0.088
0.085
0.077
0.080
0.074
0.095
Wholesale Trade
0.029
0.034
0.031
0.034
0.048
0.044
0.054
0.054
0.057
0.047
0.039
0.044
Retail Trade
0.140
0.125
0.149
0.154
0.149
0.126
0.138
0.142
0.155
0.163
0.164
0.148
Finance/Insurance/Real Estate
0.025
0.027
0.040
0.034
0.028
0.038
0.049
0.048
0.056
0.058
0.063
0.046
Services
0.107
0.109
0.133
0.130
0.119
0.134
0.183
0.205
0.236
0.276
0.314
0.196
Public Administration
0.025
0.035
0.035
0.049
0.067
0.089
0.092
0.088
0.078
0.067
0.066
0.065
41
Table 9: Heterogeneity in the Effect of Bank Distress on Entrepreneurship by Sector
This table reports heterogeneity in the effect of bank distress on future entrepreneurship by sector. The sample consists of individual survey
respondents to Decennial Censuses from 1910 through 2010. The selected respondents are non-farm males in the labor force between the ages
of 16 and 65. A respondent’s industry is defined according to the 3-digit 1950 Census Bureau industrial classification system. We map these
3-digit industries into SIC-1 sectors. We define an Agriculture/Forestry/Fishing indicator equal to 1 for respondents in agriculture, forestry, or
fishing. Other industry indicators are similarly defined. The dependent variable, Entrepreneur, is the indicator variable equal to 1 for respondents
who “work on own account” at the time of the Census. Bank Distress is an indicator variable that equals 1 for counties with state bank deposit
suspensions above the median county’s suspensions. County-level deposit suspensions is the ratio of bank deposits at state banks that were
suspended between 1930 and 1933 divided by total deposits at state banks in 1929. After is an indicator variable that equals 1 for respondents in
1930 or later Censuses. In column 1, the estimate of the effect of bank distress on future entrepreneurship in non-Agriculture/Forestry/Fishing
sectors is the coefficient on the interaction Bank Distress X After. In column 1, the F -test (and its corresponding p-value) is for the two-sided test
that the sum of the coefficients on Bank Distress X After and Bank Distress X After X Agriculture/Forestry/Fishing equals zero. All columns
include the Bank Distress indicator, a sector indicator and its interactions with Bank Distress and After indicators. Controls are measured at the
individual level and include a person’s age, age squared, foreign, black, asian, hispanic, other ethnicity. Age is the respondent’s age at the time
of the survey. Foreign, white, black, asian, hispanic, and other ethnicity are indicator variables equal to 1 if the survey respondent self-identifies
into each of these categories and zero, otherwise. Standard errors are clustered at the state level. ∗ , ∗∗ , and ∗∗∗ denote significance at the 10%,
5%, and 1% levels.
Entrepreneur
Bank Distress X After
Bank Distress X After X Agriculture/Forestry/Fishing
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
-0.013**
(-2.31)
0.009
(0.73)
-0.016**
(-2.61)
-0.011*
(-1.98)
-0.012**
(-2.19)
-0.014**
(-2.12)
-0.013**
(-2.33)
-0.011**
(-2.41)
-0.013**
(-2.24)
-0.010*
(-1.93)
-0.012**
(-2.10)
Bank Distress X After X Mining
0.028***
(3.49)
Bank Distress X After X Construction
-0.024***
(-2.86)
Bank Distress X After X Manufacturing
0.009*
(1.98)
Bank Distress X After X Transp/Commun/Elctr/Gas/Sanitary
0.005
(0.70)
Bank Distress X After X Wholesale Trade
0.008
(0.57)
Bank Distress X After X Retail Trade
-0.035***
(-3.43)
Bank Distress X After X Finance/Insurance/Real Estate
-0.019
(-1.37)
Bank Distress X After X Services
-0.027**
(-2.26)
Bank Distress X After X Public Administration
42
R-squared
Number of Observations
Number of Clusters
F -Statistic
p-value of F -Statistic
Controls
State FE
Year FE
0.006
(1.03)
0.047
2,955,671
47
0.144
0.706
Yes
Yes
Yes
0.043
2,955,671
47
6.474
0.014
Yes
Yes
Yes
0.047
2,955,671
47
12.315
0.001
Yes
Yes
Yes
0.064
2,955,671
47
1.103
0.299
Yes
Yes
Yes
0.047
2,955,671
47
3.849
0.056
Yes
Yes
Yes
0.041
2,955,671
47
0.159
0.692
Yes
Yes
Yes
0.056
2,955,671
47
14.043
0.000
Yes
Yes
Yes
0.042
2,955,671
47
5.216
0.027
Yes
Yes
Yes
0.053
2,955,671
47
9.438
0.004
Yes
Yes
Yes
0.049
2,955,671
47
1.161
0.287
Yes
Yes
Yes
Table 10: Heterogeneity in the Effect of Bank Distress on Entrepreneurship by Education and Earnings
This table reports heterogeneity of the effects of bank distress on future entrepreneurship across the levels
of education and earnings scores. Columns 1 and 2 report results by education score; columns 3 and 4
– by earnings score. Education score indicates the percentage of people in the respondent’s occupational
category who had completed one or more years of college. Earnings score assigns a measure of the median
earned income for each occupation using the time-consistent 1950 occupational classification. Occupation
is defined according to 3-digit 1950 Census Bureau occupational classification system. We define a High
Education [High Earnings] indicator equal to 1 for respondents with the education [earnings] score greater
than or equal to the median value of the score within each county-decade. The sample consists of individual
survey respondents to Decennial Censuses from 1910 to 2010. The selected respondents are non-farm males
in the labor force between the ages of 16 and 65. The dependent variable, Entrepreneur, is the indicator
variable that equals 1 for respondents who “work on own account” at the time of the Census. Bank Distress
is an indicator variable that equals 1 for counties with state bank deposit suspensions above the median
county’s suspensions. County-level deposit suspensions is the ratio of bank deposits at state banks that were
suspended between 1930 and 1933 divided by total deposits at state banks in 1929. After is the indicator
variable that equals 1 for respondents in 1930 or later Censuses. In columns 1 and 2, the estimate of the
effect of bank distress on future entrepreneurship in low-education occupations is the coefficient on the
interaction Bank Distress X After; the difference in high-education from the low-education occupations is
the coefficient on Bank Distress X After X High Education. Columns 3 and 4 provide similar estimates
by earnings scores. Controls are measured at the individual level and include a person’s age, age squared,
foreign, black, asian, hispanic, other ethnicity. Standard errors are clustered at the state level. ∗ , ∗∗ , and
∗∗∗ denote significance at the 10%, 5%, and 1% levels.
Entrepreneur
Bank Distress
Bank Distress X After
Bank Distress X After X High Education
High Education
Bank Distress X High Education
After X High Education
(1)
(2)
(3)
(4)
0.008*
(1.81)
-0.003
(-0.76)
-0.022***
(-2.90)
0.167***
(44.25)
0.022***
(3.37)
-0.091***
(-17.91)
0.011**
(2.28)
-0.007
(-1.53)
-0.018**
(-2.56)
0.161***
(40.90)
0.016**
(2.68)
-0.100***
(-20.12)
0.010*
(1.75)
-0.005
(-1.08)
0.011*
(1.78)
-0.007
(-1.40)
-0.016**
(-2.17)
0.161***
(23.59)
0.015*
(1.75)
-0.096***
(-18.05)
-0.016**
(-2.24)
0.145***
(21.22)
0.013
(1.58)
-0.104***
(-19.20)
0.024
2,892,733
47
0.051
2,892,733
47
Yes
Yes
Yes
Bank Distress X After X High Earnings
High Earnings
Bank Distress X High Earnings
After X High Earnings
R-squared
Number of Observations
Number of Clusters
Controls
State FE
Year FE
0.029
2,892,733
47
Yes
Yes
0.056
2,892,733
47
Yes
Yes
Yes
Yes
Yes
43
Table 11: Patenting Activity By Bank Distress
This table reports the county-level estimates of innovation (patenting activity) in areas with differential intensity of bank distress during the Great Depression. It reports regression coefficients of a stylized differencesin-differences approach in an OLS framework using decennial county-level data from 1880 through 1970,
inclusively. In column 1, the dependent variable, Log(Patents), is the natural logarithm of total number of
patents in a given county. In column 2, the dependent variable is the Log(Weighted Patents), where weights
denote the accuracy of the historical data for each observation. Bank Distress is an indicator variable that
equals 1 for counties with state bank deposit suspensions above the median county’s suspensions. Countylevel deposit suspensions is the ratio of bank deposits at state banks that were suspended between 1930 and
1933 divided by total deposits at state banks in 1929. 1880 - 1899 (1890 - 1899, etc.) is the indicator variable
that equals 1 for observations in 1880 - 1899 (1890 - 1899, etc.) Census. The estimate of the patent outputs
by counties with higher bank distress during the Great Depression is measured by the interaction coefficients
Bank Distress X 1880 - 1899, Bank Distress X 1890 - 1899, etc. The interactions measure the relative change
in patenting activity in areas with higher bank distress during the Great Depression relative to 1900-1909.
All columns include the following demographic controls: age, age squared, foreign, black, asian, hispanic,
other ethnicity, all of which are aggregated to county-decade level from the individual respondents in the
sample. Standard errors are clustered at the state level. ∗ , ∗∗ , and ∗∗∗ denote significance at the 10%, 5%,
and 1% levels.
Bank Distress
High Bank Distress X 1880 - 1889
High Bank Distress X 1890 - 1899
High Bank Distress X 1910 - 1919
High Bank Distress X 1920 - 1929
High Bank Distress X 1930 - 1939
High Bank Distress X 1940 - 1949
High Bank Distress X 1950 - 1959
High Bank Distress X 1960 - 1969
High Bank Distress X 1970 - 1979
R-squared
Number of Observations
Number of Clusters
Controls
State FE
Year FE
Log(Patents)
Log(Weighted Patents)
(1)
(2)
0.201***
(2.83)
-0.090
(-1.59)
-0.054*
(-1.72)
-0.039
(-1.23)
-0.011
(-0.33)
-0.125**
(-2.46)
-0.139**
(-2.23)
-0.103
(-1.56)
-0.091
(-1.10)
-0.091
0.201***
(2.83)
-0.090
(-1.60)
-0.054*
(-1.74)
-0.039
(-1.24)
-0.011
(-0.34)
-0.126**
(-2.48)
-0.140**
(-2.25)
-0.104
(-1.57)
-0.091
(-1.11)
-0.091
0.391
28,400
47
Yes
Yes
Yes
0.391
28,400
47
Yes
Yes
Yes
44
County Status
High Distress
Average Distress
No Distress
Figure 1:
Map of Bank Distress across US Counties
The Figure denotes county-level bank distress for counties throughout the US. Bank distress is the county-level ratio of bank deposits at state banks that were suspended
between 1930 and 1933 divided by total deposits at state banks in 1929. Counties with bank distress above the median fraction of bank distress are shaded dark grey (red,
in color version). Counties with below median are shaded light grey (yellow, in color version). Counties not in the sample are shaded white.
45
'
0 .2 0 0
1 9 2 9
F r a c t io n o f E n t r e p r e n e u r s
0 .1 7 5
0 .1 5 0
0 .1 2 5
0 .1 0 0
0 .0 7 5
1 9 1 0
1 9 2 0
1 9 3 0
1 9 4 0
1 9 5 0
1 9 6 0
1 9 7 0
1 9 8 0
1 9 9 0
2 0 0 0
2 0 1 0
Y e a r
B a n k D is t r e s s
H ig h
L o w
Figure 2: Entrepreneurship Rate in Counties with High vs. Low Bank Distress
The Figure plots fraction of entrepreneurs in counties with high bank distress (solid, blue line) and low bank distress (dashed,
red line) during the Great Depression. The sample consists of individual survey respondents to Decennial Censuses from 1910
through 2010. The selected respondents are non-farm males in the labor force between the ages of 16 and 65. Entrepreneurs are
respondents who “work on own account” at the time of the Census. High bank distress counties are counties with state bank
deposit suspensions during the Great Depression above the median county’s suspensions. County-level deposit suspension is the
ratio of bank deposits at state banks that were suspended between 1930 and 1933 divided by total deposits at state banks in
1929.
46
'
3 0
P e rc e n t
2 0
1 0
0
0 .0 0
0 .2 5
0 .5 0
0 .7 5
1 .0 0
S u s p e n d e d D e p o s it s
Figure A.1: Fraction of 1929 Deposits in Banks Suspended over 1930-1933
The Figure plots histogram of fraction of deposits in banks suspended during the Great Depression. Suspended Deposits is the
county-level ratio of bank deposits at state banks that were suspended between 1930 and 1933 divided by total deposits at state
banks in 1929. The sample consists of counties existing in 1929 in 47 US states (excludes AK, HI, WY, and DC).
47
County Status
High Entrep
Low Entrep
Figure A.2:
Map of Entrepreneurship across US Counties
This figure denotes county-level entrepreneurship for counties throughout the US. Entrepreneurship is defined as the fraction of respondents in a county that report
entrepreneurship as the primary occupation. Counties with with above median fraction of entrepreneurs (high entrepreneurship) are shaded dark grey (red, in color version).
Counties with below median are shaded light grey (yellow, in color version). Counties not in the sample are shaded white.
48
Table A.1: County-Level Bank Statistics by Bank Distress
The table reports summary statistics of county-level banking characteristics from 1929 through 1936 and
from 1990 through 2010 from the FDIC Summary of Depository Institutions. The sample consists of deposit
balances at all bank branches in a county and the number of bank branches in each county. The variables
measure the growth in deposits from 1929 to 1936, 2000, and 2010 and the growth in bank branches from 1929
to 1936, 2000, and 2010. Column 1 (Bank Distress=0) presents means (standard deviations) for counties
with low bank distress during the Great Depression. Column 2 (Bank Distress=1) presents means (standard
deviations) for counties with high bank distress. Bank Distress equals 1 for counties with bank distress
greater than or equal to the median county’s bank deposit suspensions. County-level deposit suspensions
is the ratio of bank deposits at state banks that were suspended between 1930 and 1933 divided by total
deposits at state banks in 1929. Column 3 (Difference) reports the differences-in-means of this growth and
the p-value of the t-test adjusted for clustering at the state level (reported in parentheses).
Banking Outcomes:
Deposits 1929-1936
Deposits 1929-2000
Deposits 1929-2010
Banks 1929-1936
Banks 1929-2000
Banks 1929-2010
Number of Observations
Bank Distress = 0
Bank Distress = 1
Difference
-0.207
(0.774)
2.953
(3.975)
2.367
(4.953)
-0.277
(0.251)
-0.701
(0.596)
-0.881
(0.621)
-0.690
(1.315)
2.636
(3.901)
2.261
(4.719)
-0.521
(0.315)
-0.889
(0.567)
-1.067
(0.600)
-0.483
(0.000)
-0.317
(0.179)
-0.106
(0.679)
-0.244
(0.000)
-0.188
(0.000)
-0.186
(0.000)
1411
1420
49
Table A.2: The Effects of Bank Distress on Economic Outcomes
This table reports the county-level estimates of economic activity in areas with differential intensity of bank
distress during the Great Depression. It reports regression coefficients of a differences-in-differences framework using decennial county-level data from 1910 through 1940. Bank Distress is an indicator variable that
equals 1 for counties with state bank deposit suspensions above the median county’s suspensions. Countylevel deposit suspensions is the ratio of bank deposits at state banks that were suspended between 1930 and
1933 divided by total deposits at state banks in 1929. 1920 (1930, 1940) is the indicator variable that equals
1 for observations in the 1920 (1930, 1940) Censuses. The dependent variables are the natural logarithm
of: number of farms in a given county (Column 1), land used in farming (Column 2), population (Column
3). The estimate of these differences by areas with higher bank distress during the Great Depression is
measured by the interaction coefficients Bank Distress X 1920, Bank Distress X 1930, etc. The interactions
measure the relative change in these variables in counties with higher bank distress during the Great Depression relative to 1910. All columns include the following demographic controls: age, age squared, foreign,
black, asian, hispanic, other ethnicity, all of which are aggregated to county-decade level from the individual
respondents in the sample. Standard errors are clustered at the state level. ∗ , ∗∗ , and ∗∗∗ denote significance
at the 10%, 5%, and 1% levels.
Bank Distress
Bank Distress X 1920
Bank Distress X 1930
Bank Distress X 1940
R-squared
Number of Observations
Number of Clusters
Controls
State FE
Year FE
Log(Farms)
Log(Farm Land)
Log(Respondents)
(1)
(2)
(3)
0.101***
(3.75)
0.032
(1.52)
0.038
(1.43)
0.053**
(2.10)
0.071***
(2.84)
0.021
(1.10)
0.022
(0.72)
0.031
(0.85)
0.087
(1.54)
0.031
(1.28)
0.035
(1.28)
0.034
(0.94)
0.444
11,156
47
Yes
Yes
Yes
0.539
11,156
47
Yes
Yes
Yes
0.420
11,156
47
Yes
Yes
Yes
50
Table A.3: Differences-in-Means of Employment in Young Firms by Bank Distress (1996 - 2014)
The table reports differences-in-means of employment in young firms between 1996 and 2014 across the sample counties with low and high
bank distress during the Great Depression (high minus low distress). High bank distress counties are defined as counties with state bank
deposit suspensions above the median county’s suspensions. Bank distress is the county-level ratio of bank deposits at state banks that were
suspended between 1930 and 1933 divided by total deposits at state banks in 1929. Quarterly Workforce Indicators (QWI) data published by the
Longitudinal Employer-Household Dynamics (LEHD) program report total employment by firm-age and firm-size in each quarter at the county
level. Employment Fraction in Young Firms is the the fraction of total employment in young firms in each county (i) as of the fourth quarter
(t) of each year between 1996 and 2014. We define young firms as those that are between zero and five years old. Each column reports the
differences-in-means of employment across firm types in each year (e.g., in 1996, counties with high bank distress during the Great Depression
had the fraction of employment in young firms that was 1.4 percentage points lower than in counties with low distress). The p-value of the t-test
for the differences-in-means adjusted for clustering at the state level (reported in parentheses).
Economic Outcomes:
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
-0.014
(0.024)
-0.012
(0.028)
-0.005
(0.199)
-0.009
(0.046)
-0.009
(0.070)
-0.008
(0.053)
-0.007
(0.082)
-0.004
(0.243)
-0.006
(0.100)
-0.007
(0.057)
Number of Observations
1184
1302
1938
2143
2449
2634
2708
2794
2801
2804
Economic Outcomes:
2006
2007
2008
2009
2010
2011
2012
2013
2014
-0.008
(0.016)
-0.008
(0.013)
-0.005
(0.087)
-0.003
(0.292)
-0.005
(0.081)
-0.006
(0.045)
-0.004
(0.198)
-0.007
(0.078)
-0.005
(0.243)
2807
2805
2802
2804
2814
2814
2818
2811
2740
Employment Fraction in Young Firms (0-5 year old )
Employment Fraction in Young Firms (0-5 year old )
Number of Observations
51
Table A.4: Differences-in-Means of County-Level Demographic Statistics by Bank Distress (1970 - 2000)
The table reports differences-in-means of demographic statistics between 1970 and 2000 across the sample
of counties with low and high bank distress during the Great Depression (high minus low distress). High
bank distress counties are defined as counties with state bank deposit suspensions above the median county’s
suspensions. Bank distress is the county-level ratio of bank deposits at state banks that were suspended
between 1930 and 1933 divided by total deposits at state banks in 1929. The Gini coefficient measures
income inequality within a county. The Gini coefficient is bounded between zero and one hundred, with
one hundred denoting highest inequality and zero denoting perfect equality. Population is the log of total
county population. White, African American, and Senior Population fraction are the fraction of total
population that identify in to each of these groups. Total Labor Force is the log number of people who are
either employed or seeking employment. Employed population is the log number of people employed in the
county. Unemployment rate is the fraction of the labor force that is unemployed. Each column reports the
differences-in-means of these outcomes (e.g., in 2000, income inequality was lower by 0.636 in counties with
high bank distress compared to counties with low distress; in 2000, fraction of while population was 0.009
larger in counties with high bank distress compared to counties with low distress). The p-value of the t-test
adjusted for clustering at the state level (reported in parentheses).
Outcomes:
Gini Coefficient
Population
White Population Frac
African American Population Frac
Senior Population Frac
Total Labor Force
Employed Population
Unemployment Rate
Number of Observations
1970
1980
1990
2000
-0.437
(0.224)
0.082
(0.439)
0.012
(0.319)
-0.012
(0.308)
0.004
(0.296)
NA
NA
NA
NA
NA
NA
-0.406
(0.167)
0.055
(0.605)
0.009
(0.452)
-0.008
(0.457)
0.004
(0.193)
NA
NA
NA
NA
NA
NA
-0.640
(0.051)
0.010
(0.923)
0.007
(0.546)
-0.007
(0.549)
0.008
(0.015)
0.037
(0.736)
0.038
(0.727)
-0.001
(0.837)
-0.636
(0.046)
0.009
(0.939)
0.009
(0.461)
-0.007
(0.531)
0.006
(0.029)
0.029
(0.792)
0.031
(0.784)
-0.001
(0.437)
2824
2822
2821
2821
52
Table A.5: Robustness of the Effects of Bank Distress on Entrepreneurship
This table reports results from robustness tests of the treatment effect of bank distress on entrepreneurship.
It reports regression coefficients from a difference-in-differences framework. Columns 1 and 2 exclude smaller
counties (counties with population share less then 0.005% of total population in that decade); columns 3 and
4 include occupation fixed effects (3-digit 1950 Census Bureau occupational classification system); columns
5 and 6 use the continuous bank distress variable (Continuous Bank Distress). The sample consists of
individual survey respondents to Decennial Censuses from 1910 to 2010. The selected respondents are nonfarm males in the labor force between the ages of 16 and 65. The dependent variable, Entrepreneur, is an
indicator variable equal to 1 for respondents who “work on own account” at the time of the Census. Bank
Distress is an indicator variable equal to 1 for counties with state bank deposit suspensions during the Great
Depression above the median county’s suspensions. County-level deposit suspension is the ratio of bank
deposits at state banks that were suspended between 1930 and 1933 divided by total deposits at state banks
in 1929. After is an indicator variable equal to 1 for respondents in 1930 or later Censuses. The estimate
of the effect of bank distress on future entrepreneurship is the coefficient on the interaction Bank Distress
X After that measures the relative change between areas with higher bank distress over the subsequent
9 decades (1930 through 2010). Continuous Bank Distress is the county-level deposit suspensions, which
equals the ratio of bank deposits at state banks that were suspended between 1930 and 1933 divided by total
deposits at state banks in 1929. Age is the respondent’s age at the time of the survey. Foreign, white, black,
asian, hispanic, and other ethnicity are indicator variables equal to 1 if the survey respondent self-identifies
into each of these categories and zero, otherwise. Standard errors are clustered at the state level. ∗ , ∗∗ , and
∗∗∗ denote significance at the 10%, 5%, and 1% levels.
Entrepreneur
Bank Distress
Bank Distress X After
(1)
(2)
(3)
(4)
0.013**
(2.24)
-0.010*
(-1.80)
0.014**
(2.36)
-0.012**
(-2.20)
0.010***
(24.07)
-0.000***
(-12.73)
0.014***
(2.94)
-0.067***
(-23.96)
-0.018***
(-2.92)
-0.048***
(-8.56)
-0.024***
(-9.07)
0.012***
(3.18)
-0.009**
(-2.44)
0.012***
(3.20)
-0.009**
(-2.45)
0.004***
(18.39)
-0.000***
(-6.42)
0.018***
(5.95)
-0.020***
(-6.99)
-0.013***
(-4.46)
-0.015***
(-3.89)
-0.006**
(-2.49)
Age
Age Squared
Foreign
Black
Asian
Hispanic
Other Ethnicity
Continuous Bank Distress
Continuous Bank Distress X After
R-squared
Number of Observations
Number of Clusters
Year FE
State FE
Occupation FE
0.006
2,930,024
47
Yes
Yes
0.040
2,930,024
47
Yes
Yes
0.213
2,955,671
47
Yes
Yes
Yes
0.225
2,955,671
47
Yes
Yes
Yes
(5)
(6)
0.027**
(2.53)
-0.023***
(-2.89)
0.010***
(24.45)
-0.000***
(-12.94)
0.013**
(2.67)
-0.068***
(-24.15)
-0.018***
(-2.78)
-0.048***
(-8.74)
-0.024***
(-9.33)
0.029***
(2.80)
-0.027***
(-3.15)
0.007
2,955,671
47
Yes
Yes
0.041
2,955,671
47
Yes
Yes
53