ABSTRACT
RELATING LEVERAGE TO BANKING MARKET STRUCTURE:
THE CASE OF RAILROADS IN
ANTEBELLUM AMERICA
by Nathan Andrew Klyn
The free-banking era provides a unique time period to study bank market structure.
Using data from historical maps and the Inter-University Consortium for Political
and Social Research Censuses of the United States, I build upon previous research
to investigate the relationship between railroads and antebellum banks from
1854-60. I first use simple ordinary least squares models to estimate the equilibrium
relationships between railroads and balance sheet composition. I then estimate an
endogenous market structure model that relates railroads to unobserved bank
profitability through the number of observed banks. I find that railroads increased
bank lending but reduced bank leverage (as related to banknotes), suggesting that
railroads caused banks to shift their business emphases. My results further indicate
that railroads had a net negative effect on banking profitability. I conclude that
reduced bank leverage, as opposed to increased banking activity through lending,
increased antebellum bank stability.
Relating Leverage to Banking Market Structure:
The Case of Railroads in Antebellum America
A Thesis
Submitted to the
Faculty of Miami University
in partial fulfillment of
the requirements for the degree of
Master of Arts
Department of Economics
by
Nathan A. Klyn
Miami University
Oxford, Ohio
2014
Advisor
Dr. Charles Moul
Reader
Dr. William Even
Reader
Dr. Gregory Niemesh
Contents
1 Introduction
1
2 Literature Review
2.1 Free Banking Era . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2 Endogenous Market Structure Review . . . . . . . . . . . . . . . . . .
3
3
6
3 Bank Balance Sheet
3.1 Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.2 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.3 Estimation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7
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4 Endogenous Market Structure
4.1 Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.2 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.3 Estimation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . .
11
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5 Appendicies
5.1 Appendix A: Drought Index . . . . . . . . . . . . . . . . . . . . . . .
5.2 Appendix B: Railroad Map Sources . . . . . . . . . . . . . . . . . . .
20
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6 References
23
7 Tables
26
8 Figures
40
ii
List of Tables
1
2
3
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14
Bank Data Description . . . . . . . . . . . . . . .
Bank Data Summary Statistics . . . . . . . . . .
Levels of Asset, Loan and Specie Models . . . . .
Level of Banknote Circulation Model . . . . . . .
Specie-to-Assets Model . . . . . . . . . . . . . . .
Loan-to-Assets Model . . . . . . . . . . . . . . . .
Circulation-to-Specie Model . . . . . . . . . . . .
Endogenous Market Structure Data Description .
Endogenous Market Structure Summary Statistics
Duopoly-Plus Ordered Probit Models . . . . . . .
Triopoly-Plus Ordered Probit Models . . . . . . .
Cross Section Threshold Estimates . . . . . . . .
Pooled Ordered Probit Models . . . . . . . . . . .
Pooled Cross Section Threshold Estimates . . . .
iii
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List of Figures
1
2
Spread of Railroads Across the United States . . . . . . . . . . . . . .
Spread of Railroads and Banks Across the United States . . . . . . .
iv
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41
1
Introduction
Large bank leverage ratios, such as debt-to-equity, prior to the last recession had
disastrous effects on the global economy. Regulators are consequently working to find
a way to increase stability in the banking sector. A difficult and important question
regarding contemporary banking regulation is: Would added bank regulation lead to
a less competitive banking structure and less competitive banking behavior? Banks
seek high leverage ratios to maximize profits during economic expansions. Some
regulators believe that lower leverage ratios will lead to a more stable banking industry
but United States banks are disinclined to accept lower leverage ratios. Globally,
the fall in domestic profits due to lower leverage ratios may lead to competitively
disadvantaged United States banks. The lower profits may lead to fewer banks with
higher interest rates and less investment.
There is a societal trade off between bank stability and interest rates. All else
equal, a highly leveraged banking sector has higher profits, more banks, and lower interest rates with consequent increased economy-wide investment. Conversely a highly
leveraged banking sector, though, is exposed to major boom and bust cycles. A less
leveraged banking sector has lower bank profits, fewer banks, higher interest rates,
and lower investment, and is not as susceptible to the booms and busts1 . A study on
contemporary banking leverage ratios, regulation and bank stability would be quite
difficult due to the globalization of banking. To perform such a study on the relationship between leverage and bank stability, I turn back the clock to the free banking era.
During the free banking era state, not federal, laws governed banking. States were
either free-banking states or charter-only states. In free-banking states, laws required
bankers to meet capital requirements, but banks could enter and exit markets as they
wished. By contrast state legislatures granted charters on a bank-by-bank basis in
charter-only states.
During the antebellum time period, railroads also began to spread westward. Railroads lowered transportation costs and connected the country in a never-before seen
way. Railroads allowed for an increasingly connected society, increasing population
and bringing economic activity to an area (Atack et al., 2009). The spread of railroads
presents an opportunity to study a shock to the banking sector.
Similarly to contemporary banks, antebellum banks relied on leverage to drive
profits. In this research I build upon previous antebellum bank literature. In a forthcoming book chapter Atack et al. (2014) look at the impact of railroads on antebellum
bank stability. Controlling for other means of transportation and bank financial measures they find railroads decreased bank failures, where a failure is defined as a bank
being unable to redeem notes at par. Atack et al. (2014) also attempt to explain
how railroads increased antebellum bank stability, claiming (through Atack et al.
(2009)),but with little evidence, that the most obvious answer is railroads brought
1
See Aspinwall (1970), Berger and Hannan (1989), Corvoisier and Gropp (2002), Edwards (1964),
Smirlock, Michael, T. Gilligan, and William Marshall (1984), and Stevens (1990) for good discussions
on the relationship between bank market structure and interest rates.
1
about greater urbanization, which led to greater bank scrutiny and activity. To control for urbanization they include total population and the fraction of population
living in an urban area. They find that the fraction of people living in an urban area
reduces bank failures only if bank financial measures are excluded.2 They also find
that higher banknote circulation levels increase the probability of bank failures and
increased lending decreased the probability of bank failure. Finding that bank financial measures affected antebellum bank failure rates, Atack et al. (2014) look into
the relationship between railroads and financial measures, finding railroads lowered
a bank’s specie-to-asset ratio, lowered a bank’s banknote-circulation-to-assets ratio
and increased lending as a percent of assets. They also find that banks diversified
their assets away from bonds to loans and diversified their liabilities away from note
circulation to equity and debt due to railroads. Railroads increased the probability of
banknote redemption, thus making the practice of circulation less profitable. Atack
et al. (2014) claim that railroads increased a bank’s market size, and increased the
demand for loanable funds. On the liability side of the balance sheet, Atack et al.
(2014) note that the decrease in banknote circulation is offset by an increase in interbank lending. Interbank lending became attractive for liquidity reasons. In a similar
paper Jaremski (2010) looks at balance sheet ratios and bank stability, finding that
both increased lending and reduced banknote circulation decreased the probability of
free bank failures during the antebellum era.3
On a similar note, Chabot and Moul (2014) research government guarantees and
bank failures in Indiana. Using data from 1854 and 1860, they use an endogenous
market structure model to relate the banking panics of 1854 and 1857 to the number
of banks in a given market. Chabot and Moul use access to railroads as a secondary control variable in their model, finding that railroads may have decreased
bank profitability. Chabot and Moul’s results only hint at railroads decreasing bank
profitability due to a small data set. This result superficially clashes with Atack, et
al.’s (2014) results that railroads increased bank lending as a percent of assets.
Atack et al.’s lack of explanation of how railroads increased bank stability leaves
the door open to more research. From Atack et al. (2014), railroads increased bank
lending, reduced bond holdings, decreased banknote circulation and increased interbank lending. Atack et al. did not to look at bank’s ratio of banknote circulation-tospecie, which I refer to as leverage. Increased banking stability could have come from
increased banking activity and profitability or through deceased leverage ratios. In
this thesis I attempt to answer how railroads made antebellum banks more stable and
how railroads affected banking profitability. Lower leverage ratios imply less severe
boom-bust cycles while increased banking activity implies a larger banking sector.
A larger banking sector further exposes an economy to danger through boom-bust
cycles.
2
An urban area is defined as a city with a population over 2,500.
Balance sheet ratios include capital-to-assets, specie-to-assets, deposits-to-assets, loans-to-assets,
bonds-to-assets, and banknote circulation-to-assets.
3
2
First, I aim to replicate some of the findings in Atack et al. (2014). I then construct
an antebellum banknote leverage ratio and find the effect of railroads on bank leverage.
Following Chabot and Moul (2014) I then use an endogenous market structure model
to find the net effect of railroads on antebellum bank profits. From the results in the
endogenous market structure model, I draw conclusions on how railroads increased
bank stability. I find that railroads increased the level of antebellum bank lending,
decreased the level banknote circulation, and decreased banknote circulation-to-specie
ratios (leverage). I find railroads decreased antebellum bank profits, increasing the
market size needed to support a given number of market banks by thirty percent. I
conclude that lower leverage ratios increased bank stability.
The paper is organized as follows. First I briefly describe antebellum banking
and the relevant literature. Second I attempt to qualitatively replicate the findings
in Atack et al. (2014). Third I build upon the findings of Chabot and Moul (2014)
by using a endogenous market structure model and relate the number of banks in a
market to bank profitability.
2
Literature Review
2.1
Free Banking Era
During the free banking era (1837-1865), banks in the United States were governed
by state laws. Prior to the free banking era, state legislatures chartered banks on a
bank-by-bank basis. The free banking era began after the Second Bank of the United
States’ charter expired in 1836. In 1837, Michigan became the first state to adopt
free banking laws, and other states followed. Entry was relatively easy during the
time period. In order to open a bank, potential bank owners needed a minimum level
of capital.
Once a bank was set up with the state, the bank began to circulate banknotes.
Banknotes served two of the main functions of money: store of value and a medium of
exchange. Banknotes had a specified par value and operate like checks do today. State
laws required banks to make a security deposit with the state banking authority before
banks could issue notes. State laws specified acceptable assets that could be used as
collateral. Generally, allowable assets included state bonds and U.S. government
bonds.4 Both traded on the New York and Philadelphia Stock Exchanges, making it
easier to value the security deposit. States often required the security deposit bonds
be valued at the lesser of par or market value. Some states required banks to issue
notes at a fraction of the bond’s par or market value (e.g., 90 percent). Once the
bankers made the security deposit, the state banking authority signed the banknotes.
Banknotes circulated just as paper currency does today. As long as the security
deposit’s value was at least as great as the value of the notes, banks received interest
4
Other assets were occasionally allowed. Examples include real estate in Michigan and slaves in
Georgia.
3
on the bonds posted as collateral. Notes would trade outside their city of origin at
a discount as long as the discount was smaller than the transaction cost of returning
the notes to the bank for redemption (Chabot and Moul, 2014).
The law required banks to convert their banknotes into specie at face value on
demand. If a bank failed to convert notes into specie on demand, the noteholder
could protest to the state banking authority. The state banking authority would then
sell some of the bank’s collateral to fulfill the request. If word spread that the bank
failed to convert banknotes into specie on demand, the banknotes would trade below
par value and could trigger a bank run as noteholders realized the bank was not in
sound financial condition.
The free banking system was a fractional reserve system and may have led to
highly leveraged banks. If a bank’s notes circulated near par value, the bank could
use the banknotes to buy more bonds to post as collateral. With the additional notes
gained from posting more collateral, the bank could buy more bonds and profit from
the bonds’ coupon payments. This process could be repeated as long as markets
believed the bank was in sound financial condition. As the circulation of banknotes
increased, bank’s faced the increasing risk that noteholders would redeem the notes
for specie. A way to circumvent this problem was for banks to circulate their notes
at places far from their place of operations, which would decrease the likelihood note
redemption.
If the security deposit’s value fell below the outstanding notes’ value, banks were
required to remedy the situation. Banks could either increase their deposit with the
state or decrease the amount of notes outstanding. If a bank failed to make these
adjustments in a timely manner, the banking authority closed the bank. The bank’s
security deposit with the state was then used to pay noteholders on a pro rated basis.
If the proceeds from selling the bank’s security deposit were less than the notes’ par
value, noteholders could file suit against the bank’s stockholders for the difference.
Dwyer (1996) notes the amount of bank entry and exit during the free banking era.
While some banks certainly failed due to poor operations, some historians believe that
reckless banking pervaded the free banking era. The term “wildcat banking” is often
used to describe reckless banking during this era but an exact definition of a wildcat
bank is elusive. Rockoff (1974) identified a wildcat banking state by five criteria:
(1) the short life span of the free banks-generally less than one year; (2) the large
number of entrants; (3) low liquidity ratios; (4) large number of bank failures; and
(5) large noteholder losses. Rolnick and Weber (1984) and Economopoulous (1988)
have shown that wildcat banking was not as pervasive as many thought during the
free banking era.
Free banking succeeded in some states more than others. New York was one of
the first to adopt free banking laws. New York’s banking system was so successful
that it was imitated by other states. While Ohio also had a successful free banking
system, Michigan’s banking system was a failure. Wildcat banking was rampant
during the free banking era in Michigan. Michigan laws were less stringent than other
4
banks, particularly with regard to collateral requirements. These lax laws encouraged
a banking system that was highly susceptible to fraud, and Michigan banks often
intentionally circulated notes that could not be redeemed. These wildcat banks failed,
leaving noteholders with nothing (Rockhoff and Walton, 2004).
There are other important events that occurred during the free banking era. In
1854 Ohio passed a law prohibiting state residents from holding out-of-state notes
resulting in the Indiana Banking Panic of 1854. Many Ohio residents at the time
held Indiana banknotes, and a run on Indiana banks followed. This run on Indiana
banks led to many failures across Indiana (Chabot and Moul, 2014). A national bank
panic occurred in 1857. The exact cause of the panic of 1857 is unclear. The panic
did however cause widespread financial collapse and bank failures (Calomiris and
Schweikart, 1991). Calomiris and Schweikart (1991) research potential causes of the
1857 panic and favor the Dred Scott decision which ruled that no person of African
decent could claim United States citizenship. The court ruling affected expectations
by increasing the tensions of the North and South and made war more likely.
Railroads from the eastern seaboard to the middle of the country provided a
cheaper means of transportation and paved way for easier migration. Given their
advantage over canals and waterways railroads quickly rose to dominance. By 1840,
there were about as many miles of rail as canals (Atack et al., 2009). By 1850, railroads
mileage exceeded canal mileage by two to one, and by 1860 the United States had
more railway miles than the rest of the world combined (Atack et al., 2009). Much
of the railroad construction focused on the Midwest. Between 1853 and 1856, more
than half the railway building in the United States took place in the Midwest (Atack
et al., 2009).
Railroads reduced travel time and expanded bank markets. Weber (2002) notes
the relationship between banknote discounts and transportation costs. Weber contends that transportation routes played a major role in banknote discount rates and
redemption. Weber also finds that, compared to locations with no railway access but
close proximity to the city of redemption, banknotes will trade at higher discount relative to cities farther way from the redemption city that have railway access. Atack
et al. (2009) look at the impact of railroads on population density and urbanization.
Atack et al. (2009) find railroads had little impact on population density but had
a large impact on urbanization. Atack et al.’s (2009) estimates suggest railroads increased the fraction of people living in urban areas by three to four percentage points
and may account for more than half of the increased urbanization in the Midwest
during the 1850’s. The increased urbanization in the Midwest may have also led
to increased investment in infrastructure. In another paper, Atack, Jaremski, and
Rousseau (2013) consider how railroads spread across the country. Atack, Jaremski,
and Rousseau (2013) find that railroad tracks were laid in counties where at least one
bank was already present. Once tracks were laid in a county, new banks generally
followed within two to three years (Atack, Jaremski, and Rousseau, 2013).
5
2.2
Endogenous Market Structure Review
Drawing conclusions about firm profitability would seem to require firm profits, margins, and costs to be observed. Bresnahan and Reiss (1990, 1991) make it easier
to draw conclusions about firm profitability without observing profits, margins, or
costs. Bresnahan and Reiss infer market competition by looking at endogenous market structure directly. Bresnahan and Reiss assume that all markets are in long-run
equilibrium, i.e., N firms observed in a market earn positive economic profits but
N + 1 would not. Bresnahan and Reiss estimate profit functions using expressions for
revenue and costs that depend on observable market characteristics and allows them
to link easily observed market characteristic to firm profitability. The Bresnahan and
Reiss approach treats profits as a latent variable. Using an ordered probit with the
number of firms as the dependent variable, they are able to infer how revenue and
costs are linked to observable market characteristics.
Bresnahan and Reiss’s approach allows for the derivation of entry thresholds for
markets. Entry thresholds are the market size needed for a given number of firms to
breakeven. An entry threshold is derived from a zero profit condition and is equal
to unobservable fixed costs over variable profits per customer (Bresnahan and Reiss,
1991). Entry thresholds then can be used to measure the level of competition in a
market. Most models of imperfect competition predict that variable profit margins
will decline with the number of firms in a market, and entry thresholds will rise. If
firms engage in collusive behavior though, variable profits will not change and entry
thresholds will stay the same.
Bresnahan and Reiss (1990) use their model to study entry in isolated markets in
the retail automobile industry. They find that the entry of the second firm did not
lower variable profit margins compared to a monopoly. Bresnahan and Reiss (1991)
also research entry in numerous other markets. They find the entry of the second and
third firms both increased competition for markets with doctors, dentists, druggists,
plumbers, and tire dealers.
The Bresnahan and Reiss method is identified by market characteristics affecting
variable profit margins or fixed costs, but not both. In reality many observable market
characteristics are likely to affect both. To address this issue, Abraham, Gaynor, and
Vogt (2007) (henceforth AGV) extend Bresnahan and Reiss’s model. AGV use an
exponential specification instead of Bresnahan and Reiss’s linear specification. AGV’s
exponential specification directly incorporates the identification issue into the model
so that the impacts on variable profit margins and fixed costs cannot be distinguished.
AGV incorporate quantity data into their research to separate the effect on fixed costs
from changes in competition. AGV applies their exponential specification to local
hospital markets and reject that fixed costs can explain the high observed threshold
ratios, concluding that the higher ratios were due to increased competition.
6
3
Bank Balance Sheet
3.1
Model
Railroads may alter bank balance sheet composition and bank behavior. To describe
these equilibrium relationships, I estimate simple OLS models. Specifically I estimate:
Balance Sheet Itemi,t = β0 + α1 RRi,t + βXi,t + i,t
(1)
where Balance Sheet Item includes bank i’s assets, banknote circulation, loans and
specie at time t. Also included as dependent variables are the ratios of specie-to-assets,
loan-to-assets, and a bank’s leverage ratio (defined below). Assets, circulation, loans,
and specie are examined to better understand how railroads changed absolute levels
of bank assets. I attempt to replicate Atack et al’s. (2014) findings looking at specie
to assets and loan to asset ratio. I define leverage to be a bank’s value of notes in
circulation over specie. Banknote circulation was important to antebellum banks,
but not only from a profitability point of view. Banks faced recourse if the public
was unable to redeem notes for specie. Facing the risk of closure, banks had to hold
enough specie to mitigate closure risk. My measure for leverage is therefore highly
pertinent to the time period.
RR is a binary variable indicating if a railroad was present in a bank’s county of
operations.5 X is a vector of control variables that includes a bank’s age, assets, town
population, state fixed effects and year fixed effects. State fixed effects are included
to capture regulatory differences between states. Log linear and linear models were
considered for each separate model. Using goodness-of-fit tests, the model with the
best fit is presented in the tables.
3.2
Data
I combine existing data sets to provide bank, county and town level information for
1854-1860. Table one provides a brief description of the variables. Table two provides
summary statistics. Warren Weber’s antebellum balance sheet database provided the
balance sheet variables (Weber, 2011). Weber’s database is impressive, as he provides
a detailed source of bank information. Balance sheet data may appear multiple times
during a year for a given bank, and balance sheet data was not available in every
year for every bank. Railroad information was gathered using eighteen historical
maps. The maps contained the location of railroads across states. If a railroad was
present in a bank’s county of operation, the railroad binary takes a value of one.6
Forty seven percent of sample bank observations have a railroad in their county, but
only five percent gain access to a railroad over the time period. The average amount
5
Atack et al. (2013) results indicate that railroads may not be an exogenous variable. The results
that follow in the rest of this paper should be interpreted with this in mind.
6
See appendix for details.
7
of leverage for the sample is 12.84 and railroad banks have lower average leverage
(banknote circulation-to-specie) at 5.77, compared to 19 for non-railroad banks. This
is a priori evidence that railroads were associated with lower bank leverage. Mean
asset values for sample banks is $501,310 with railroad banks being bigger from an
asset point of view. The average amount of loans outstanding for sample banks is
$274,010. Railroad banks have a higher mean value of loans outstanding by a large
margin. The average level of specie on hand is $49,690. The average level of bank
note circulation is $132,354, with railroads banks displaying a much higher average
level of note circulation.
I assume the first available date for which balance sheet data were available was
the first day of bank operation. The mean age for sample banks is 8.64 years, with
a wide range. Railroad banks tend to be older banks, with an average age of 10.82
years. The difference in average age supports the findings of Atack et al. (2009) that
railroads followed established banks. Town population data were taken from the InterUniversity Consortium for Political and Social Research (ICPSR) machine-readable
1850 and 1860 Censuses of the United States. Population data is only available for
1850 and 1860. Population data for 1854-1859 were linearly interpolated. Average
town population for sample banks is 47,430. Railroad banks have a larger town
population compared to non-railroad banks. I follow Zhang et al.’s (2004) paper and
use the Palmer Drought Severity Index (PDSI) to control for weather. Zhang et al.
make use of large-scale and nonlocal covariance information to reconstruct patterns of
continental drought from tree-ring records in the conterminous Unites States. They
specifically look at the ‘Dust Bowl’. Following Zhang et al. I use town latitude
and longitude to assign PDSI values to each town by year. A more positive PDSI
value indicates wet conditions.7 I restrict my sample to banks in Illinois, Indiana,
Iowa, Missouri, Ohio, Pennsylvania, and Wisconsin due to the availability of data.8
Observations with no assets, negative balance sheet ratios, and a value of zero for age
are dropped from the sample9 . After all criteria are applied, 1,726 bank observations
are left for the sample. The sample includes 201 unique city/town observations and
320 unique banks.
3.3
Estimation Results
Table three displays results for models with a bank’s balance sheet level of assets,
loans, and specie as the dependent variables. State fixed effects are included. I
am interested in how railroads affected bank balance sheet behavior. Railroads are
associated with higher asset levels only if railroad access is the only control variable,
7
See appendix for details.
Michigan is excluded from sample because of its wildcat banking history.
9
Some cities have changed names since the 19th century. If latitude and longitude could not be
identified, the bank observations were dropped. Some banks had two town names for their location.
Under these circumstances, midpoint coordinate were used. Balance sheet ratios include banknote
circulation-to-specie, specie-to-assets and loans-to-assets.
8
8
but railroads have little effect on a bank’s level of assets once other control variables
are included. A bank’s age and town population are significant and have the expected
sign. Older banks have higher asset levels and banks in larger towns have higher
asset levels. The loan specification provides evidence consistent with bank behavior
changing due to railroads. If a railroad was present in a bank’s county banks increased
lending. This increase in lending was offset by a decrease in bond holdings(Atack
et al., 2014). Railroads presumably increased the size of a bank’s lending market
through lower transportation costs. The other variables show their expected signs
in the loan specification. Older banks, banks in higher population towns, and banks
in more wet areas all display higher lending levels. The effect of drought decreases
with town population and lending decreases as time passes. I recognize that assets
is an endogenous regressor. Assets as an explanatory variable is included as a proxy
for bank size and bank financial condition. The results in table three are robust to
the exclusion of assets with regard to the railroad coefficient. If assets are excluded
in the specie specification the coefficient on age becomes significant and the size of
the coefficients on age and population increase. If assets are excluded in the loan
specification the coefficient on drought becomes significant and the coefficients on
railroads, age, and population increase. Railroads also have little effect on a bank’s
level of specie. As I will show later, railroads decreased bank leverage through a
decrease in note circulation, not through an increase in specie levels.
Table four presents eight different specifications with banknote circulation as the
dependent variable. Once state fixed effects are added, the coefficient on railroads
becomes negative and significant. Railroads were associated with lower banknote
circulation, presumably due to the increased probability of a large number of noteholders redeeming their notes. Previously, I showed that railroads were associated
with greater bank lending. An increase in stability through lower bank leverage ratios
is also plausible. A bank’s age has a positive significant relationship with banknote
circulation, but the relationship goes away once state fixed effects are added. The
coefficient on town population displays a curious negative sign once state fixed effects
are added. While intuition may lead to the belief that banks in higher populated
towns will have a higher level of banknote circulation(due to increased demand), it
is not difficult to imagine scenarios in which banks in higher populated areas face
higher probabilities of banknote redemption. Assets is again included as a measure of
bank size and health. The positive and significant coefficient on the railroad binary
variable is robust to the exclusion of assets. Year fixed effects are also included to
capture explanatory elements specific to certain years. The reference group is 1854.
Circulation in subsequent years is below circulation in 1854 and displays a decreasing
then increasing pattern, relative to banknote circulation in 1854.
Table five displays the results from seven models with a bank’s specie-to-asset
ratio as the dependent variable. Atack, et al. (2014) find that railways lowered
a bank’s specie-to-asset ratio and believe a banks specie-to-assets ratio measures a
bank’s ability to withstand a bank run. I replicate their findings qualitatively once
9
state fixed effects are added. Presumably, the decrease in a bank’s specie-to-asset ratio
is due to the increase in bank lending. Recall that I find no significant relationship
between railways and a bank’s level of assets or specie. I do find a positive relationship
between bank lending and railroads. Log of age is significantly positive with a banks
specie-to-asset ratio in all specifications besides specification five and seven. While
the results in table three show a negative relationship between years and loans, banks
may have reorganized their asset portfolios to include other assets not examined in
this research. Examples include bonds, real estate and stocks.
I am unable to replicate Atack, Jaremski, and Rousseau (2014) finding that railroads increased a bank’s loan-to-asset ratio in table six. This is surprising due to my
earlier finding that railroads increased a banks level of loans but not assets. Atack
et al, have a larger data set and use different dependent variables, including a more
precise railroad variable. I do find the same positive relationship with regard to the
log of a bank’s age. I also find a negatively significant relationship with the log of
population, but the relationship goes away once state fixed effects are added. Neither
precipitation nor the precipitation interaction term is significant. I do find that as
time passes in years, the ratio of loans to assets declines which goes along with my
findings in table three that the level of bank lending decreases as time passes.
Table seven displays results from eight models with the note circulation-to-specie
ratio as the dependent variable. Recall I have defined leverage as banknote circulation
over specie, and state laws required banks to redeem banknotes at par value on demand. This definition of leverage explains how banks changed the ratio of banknote
circulation to specie which is more in line with the time period. I do find that railroads are associated with different bank banknote circulation-to-specie ratios across
all specifications. Banks with access to railroads in their county of operation had
lower leverage ratios, consistent with the increased probability of note redemption, a
reasonable decision. Leverage ratios decline with log of age, but the effect goes away
if log of assets is omitted. Log of assets is positively significant. Banks in towns with
higher populations have lower leverage ratios, perhaps due to the increased possibility of note redemption. Banks located in drier areas have higher leverage ratios, and
leverage ratios decline with the interaction term.10 As time goes on, banks decreased
their banknote circulation-to-specie ratios.11
So far I have shed light on changing bank behavior due to the expansion of railroads. Banks had more lending and lower leverage. Railroads made note circulation
more risky. The increase in population due to railroads also may have increased the
10
To control for a non-linear relationship between a bank’s note circulation-to-specie ratio and
weather, a quadratic PDSI term was added in a model not included in this paper. The coefficient
on the quadratic palmer variable was negative and significant indicating that extreme rainfall has a
large negative effect on note circulation-to-specie levels.
11
The note circulation-to-specie model was also estimated with town fixed effects. When town
fixed effects were added, the coefficient on the railroad variable becomes insignificant. This result
is most likely due to a lack of variation in railroad access. Only five percent of sample banks gain
railroad access from 1854 to 1860.
10
demand for loanable funds, raising the returns on loans, and have increased the liquid
funds a bank could access. Presumably, all else equal, banks enjoyed an increase in
their profits through increased lending. But what is the net effect on bank profits
with the two opposite effects of railways? Recall that Atack et al. (2014) find that
railroads are associated with lower bank failure rates. The next step is to find how
railroads affect antebellum bank profitability. Antebellum banks either experienced
increased stability through rising activity or through lower leverage ratios. To answer
this question, I will use an endogenous market structure model relating bank profitability to the number of banks in a market. If railroads made banks more stable
through increased bank activity, railroads will have a positive effect on banking profits. If railroads made banks more stable through lower banknote circulation-to-specie
ratios, railroads will have a negative effect on banking profits.
4
4.1
Endogenous Market Structure
Model
Since profits, prices and cost data are unavailable, I use an alternative method to
measure the net impact of railroads on antebellum bank profitability. I employ techniques similar to Bresnahan and Reiss (1990, 1991) and extended by AGV to examine
the determinants of bank market structure and profitability. In particular, I employ
a discrete dependent variable model that relates the number of banks in a market
to characteristics of that market. Although bank profits, prices, and costs are unavailable, this estimation technique allows for the estimation of the impact of market
characteristics on bank profitability. The inclusion of market size as an explanatory
variable allows for the derivation of more readily understandable market thresholds.
Comparisons of market thresholds when a railroad is/is not present in a market’s
county allow me to draw conclusions about the relationship among bank market
structure, profitability and railroads.
A bank’s entry decision depends upon expected profits given entry. I assume that
long-run bank profits can be expressed as a function of the number of active banks
in the market and characteristics of the market. In other words, bank i’s profit in
market k is given by:
Πi (Nk , yk , xk )
(2)
where Nk is the number of banks in a given market, yk is the total population of
consumers in market k, and xk is a vector of variables that can affect both costs and
demand. Equation two can be interpreted as a reduced form discounted long-run
profit function reflecting the outcome of competition between the banks in market k.
If Nk equals one, this function describes the equilibrium profits of a monopolist. If
Nk equals two, equation two function describes the equilibrium profits of a duopolist
and so on.
I view banks’ equilibrium post-entry profits as unobserved random variables. I
11
also impose strong restrictions on bank profits in a given market. I do not allow for
heterogeneous banks. I assume equilibrium profits for bank i in market k are given
by
Πi (Nk , yk , xk ) = πi (Nk , yk , xk ) + k
(3)
Notice the components of the bank i’s profits only depend on characteristics of market
k. Several useful implication follow from the aforementioned assumptions. If expected
profits decline in the number of banks in a market, the equilibrium number of banks
in a market is the maximum number of sustainable banks. Second, the assumption
that there are no unique variables to bank i in market k allows for the derivation
of thresholds that characterize the equilibrium number of firms in a market. The
equilibrium number of firms in a market, denoted Nk∗ , can be characterized as
Nk∗ = 0 if πk1 + εk < 0
Nk∗ = N if πkN + εk ≥ 0 and πkN +1 + εk < 0
There will be N banks in market k if it is profitable for N banks to enter given market
conditions and unprofitable for additional banks to enter.
I further assume that the random error term is i.i.d. normal across markets. Thus
the probabilities of observing N banks in market k are
P (Nk∗ = 0) = 1 − Φ(πk1 )
P (Nk∗ = 1) = Φ(πk1 ) − Φ(πk2 )
P (Nk∗ = 2) = Φ(πk2 ) − Φ(πk3 )
P (Nk∗ ≥ 3) = Φ(πk3 )
where Φ(•) is the cumulative density function of a standard normal random variable
with the variance of the error term standardized to one. My strong restrictions on
bank heterogeneity are not harmless. The restrictions imply that the variation in
market outcomes is based solely on differences in market characteristics. Without
this restrictive assumption, it would be difficult to discern the effect of unobserved
heterogeneity from market characteristics. The ease of entry during the free banking
era makes the homogeneity restriction less bothersome. I assume expected bank
profits can be broken down into variable profits and fixed costs. I additionally allow
fixed costs to vary across markets due to endogenous barriers to entry.
Formally, expected bank profits for a single bank in a market with N banks is
ΠN =
1
SdN VN − FN
N
(4)
where ΠN denotes long-run profits per bank, S denotes market size, dN denotes percapita demand for banking services, VN is average variable profit per transaction and
FN denotes fixed costs. I assume, as AGV do, that variable profits and fixed costs
12
can be represented using exponential functions of N and variables that affect demand
and costs:
(5)
v(N, yk , xk ) = exp(yk λ + xk δx + δN + εvk )
F (N, xk ) = exp(xk γx + γN + εfk )
(6)
where δN and γN are coefficients on binary variables for market structure. δN and γN
capture differences in variable profits and fixed costs in markets with 1 to N banks.
The errors εv and εF are assumed to be normally distributed with zero mean and
constant variance. The exponential specification alleviates the identification problems
that occur when estimating a linear profit function12 . Following AGV I use the log
of population, so variable profits can be written as
v(N, yk , xk ) = P OPkλ exp(xk δx + δN + εvk )
(7)
Substituting the exponential specification of variable profits and fixed costs into the
inequalities that determine market structure yields that the N th bank enters when its
variable profits exceed fixed costs:
1
exp(yk λ + xk δx + δN + εvk ) − exp(xk γx + γN + εfk ) > 0
N
(8)
and rearranging and taking logs yields
yk λ + xk δx + δN + εvk − ln(N ) > xk γx + γN + εFk
(9)
Let µx = δx − γx , µN = γN − δN , and εΠ = εv − εF . εΠ is distributed N (0, σ 2 ). Then

0



1
N=
2



3+
if
if
if
if
yk λ + xk µx + Π
k < µ1
µ1 ≤ yk λ + xk µx + εΠ
k < µ1 + µ2 + ln(2)
µ2 + ln(2) ≤ yk λ + xk µx + εΠ
k < µ1 + µ3 + ln(3)
µ3 + ln(3) ≤ yk λ + xk µx + εΠ
k
The normality assumption of εΠ allows for an ordered probit model where µn are
the threshold values that can be estimated using maximum likelihood techniques.
The dependent variable of the ordered probit is the number of banks in market k. All
parameters are rescaled by σ, which I normalize to one.
Different model specifications can be estimated. There is a tradeoff between
tighter parameter estimates and information obtained from the model. Estimating
more specific market structures (such as triopoly-plus) places more restrictions on the
model, but yields more information such as market thresholds (discussed later). The
information gained from added threshold estimates is useful, and this thesis focuses
12
See Abraham, Gaynor and Vogt (2007) for details.
13
on the duopoly-plus and triopoly plus specifications.
All of the above derivations work well for cross sectional analysis. I am mainly
interested in the net effect of railroads on bank market structure and profitability
to illuminate the causes of on bank stability. Recall that Chabot and Moul (2014)
hint at the possibility that railroads decreased bank profits but do not have enough
observations to draw a solid conclusion. My data consists of market observations from
1854-1860. I estimate a pooled cross section to better estimate the effect of railroads.
In order to pool the cross sections, I impose that the parameters are identical across
1854-1860, and allow the scalars that represent the distribution variance to differ. I
normalize σ54 to one, and estimate σ55 , σ56 , σ57 , σ58 , σ59 , σ60 as free parameters.
During the free banking era, state-charter-only banks still operated. The model I
have outlined implicitly assumes that all markets are subject to free entry. I resolve
this issue by estimating a probit model for state chartered banks, where the dependent
variable is dichotomous representing markets with zero banks or at least one bank.
Little information is lost since there are few market observations where more than
one state chartered bank is observed.
The ordered profit model can be difficult to interpret. I use the parameter estimates to derive easier to understand market thresholds. Specifically, the market sizes
for a monopoly Y M , duopoly Y D , and triopoly Y T can be formed from the zero profit
conditions. For example:
µ1 − xµx
)
(11)
Y M = exp(
λ
µ1 + µ2 + ln(2) − xµx
)
(12)
Y D = exp(
λ
µ1 + µ3 + ln(3) − xµx
Y T = exp(
)
(13)
λ
where the estimated parameters from the endogenous market structure model are
used. The thresholds of Y M , Y D , and Y T provide estimates for their respective
market population required to support the long-run equilibrium number of firms. I
also construct market thresholds ratios.
A more readily understandable estimate of how railroads altered bank profitability
is the percentage change in the market population needed to support a given number
of firms if a railroad entered the county. This change is given by:
θ = exp(
4.2
−βRR
)−1
λ
(14)
Data
Data were compiled from 1854-1860 cross sections. Potential markets consist of places
found in the Inter-University Consortium for Political and Social Research (ICPSR)
machine-readable 1850 and 1860 Censuses of the United States. Included in the
ICPSR are cities, towns and villages. The ICPSR data are not complete, and this
14
study focuses on states for which population data appear to be complete. States
included in the analysis are Iowa, Illinois, Missouri, Ohio, Western Pennsylvania
and Wisconsin. The endogenous market structure model assumes that markets are
isolated. Relying on the 1860 census, markets are assumed to be isolated if the
market satisfies either of two criteria. First, observations that are at least ten miles
from the nearest neighboring observation are included. Second, observation pairs that
are near one another but far away from other observations are included. To satisfy
this requirement, two observations must be within five miles of each other and at
least ten miles away from the next closest observation. If the second requirement is
met, a new observation is created that combines all features of the two observations.
Similar conditions are used to construct the other cross sections if both 1850 and 1860
populations are observed.
Applying these criteria reduces the sample to 154 market observations for the
1854-1857 cross sections, 277 market observations for the 1858-1859 cross sections,
and 362 for the 1860 cross sections.13 Henceforth these observations are referred to
as markets. Once the market is identified, relevant variables can be constructed.
The 1850 and 1860 censuses provide population data. Population data are linearly
interpolated for 1854-1860. Many markets suffered drought conditions in the years
1854-1860. Weather shocks are controlled for using an annual rainfall index derived
from tree-ring records. This weather information is compiled using the annual time
series of the PDSI by taking a weighted average of the nearest grid points’ values from
Zhang, Mann, and Cook (2004) and is different from the precipitation measurement
used in the bank regressions in previous sections14 . The measure considers past data,
ignores higher than average rainfall, puts more weight on recent time periods and
exhibits a convex relation with the PDSI.
The variable of concern to this research is of course railway access. The railroad
binary takes on a value of one if a railroad was operating in the bank’s county during
the observation year. Railroad data was taken from eighteen historical maps, as was
done in the previous bank model data set. Recall that if railroads bank failures
through increased activity, railroads will have a positive effect on bank profitability.
If railroads increased bank stability through lower leverage ratios, the coefficient on
railroads will be negative. Following Chabot and Moul’s research on Indiana banking I
include various iterations of Indiana control variables as binary variables. Chabot and
Moul (2014) and Calomiris and Schweikart (1991) note that the antebellum banking
situation in Indiana differed than the rest of the country. The panics of 1854 and
1857 had large effects on Indiana banking. I run different pooled regressions to look
at the effect of those panics on Indiana banking.
Table eight reports definitions for variables used in the endogenous market structure model. Table nine reports summary statistics. Market data is from December,
13
St. Louis and Cincinati are excluded due to their size and easy transportation access. Iowa did
not have free banking laws until 1858 and so are excluded until that year.
14
See appendix for details.
15
31 from 1854-1860. Chabot and Moul’s (2014) data is from August 1, 1854 and December, 31 1860. Overall, the data shows an increase in banking and railroad access
from 1854-1860. In 1954 58 percent of all sample markets had railroad access. In 1860
60 percent of sample markets had railroad access. The number of markets with at
least one bank increases from 1854-1860, as does the number of markets with railroad
access and at least one bank. Also, the number railroad markets with at least one
bank increases over time. The average population of markets in the sample increases
from 1854 to 1859 and drops in 1860. This drop is most likely due to the increase in
number of observed markets. For a given market structure the number of consumers
in a market increases if a railroad is present. There are no market observations with
three or more banks that do not have a railroad present in the county.
4.3
Estimation Results
Recall the purpose of the endogenous market structure model is to infer the net
effect of railroads on banking profitability. From the sign of the effect of railroads on
banking profitability, I then can draw conclusions on the effect of increased lending
and decreased leverage ratios on antebellum bank profitability and stability. Table
10 provides the results for the duopoly-plus cross section models. Population is a
significant determinant of the number of banks in a market. A positive relationship
is exactly what theory suggests. Railroads are significant for the 1855 and 1860 cross
sections, displaying a negative relationship with the number of banks in a market.
The negative effect of railroads in the 1855 and 1860 cross sections is evidence that
railroads were associated with lower bank profits; the drop in profits due to lower
leverage ratios is greater than the increase in profits from lending. The drought
measurement is negative and significant. Drier areas have fewer banks. Charter-only
states display significantly lower levels of banking, as is expected. Indiana displays a
higher level of banking compared to other states in the sample in 1854, but Indiana has
a lower level of banking in 1860, compared to other states. This finding is consistent
with Chabot and Moul (2014).
Table 12 displays the estimated market thresholds implied by equations (11)-(13)
for the duopoly-plus and triopoly-plus cross sections. The market thresholds represent
the market size needed to sustain a given number of banks. Also listed are the ratios
of market thresholds relating the market size needed to support a duopolists to the
market size needed to support a monopolist. If an additional firm in a market has no
effect on the competitiveness of the market, the ratio for a duopolist to a monopolist
will be equal to two. The last row displays the percentage change in market size
needed to support a given market structure. Percentage changes are given by equation
(14). The null hypothesis is the percentage change is equal to zero. To obtain any
threshold or ratios if there is a railroad operating a market’s county, multiply by the
number listed in the percentage change row. For example, a monopolist in a railroad
county needs 10,608 (6310·1.681) people to break even in the duopoly plus model.
Table 11 presents cross section model results for the triopoly-plus specification.
16
The triopoly-plus specification places added restrictions on the model by estimating
the triopoly-plus cutoff, µ3 . The added parameter estimate increases the amount
of information that can be inferred from the model, specifically the triopoly-plus
market threshold and its ratio to a duopoly’s market threshold. The triopoly-plus
model is the focus of this thesis since there are 18 markets with at least three banks.
Even though the parameter estimates in the triopoly-plus model are less exact, the
parameter estimates are significant. Higher population levels lead to higher levels
of banking, and population’s estimated coefficient is similar across cross sections.
Railroads display the same negative relationship with the number of banks in 1854
and 1860. Again drier areas have fewer banks. State-charter-only states have fewer
banks, but there is no effect of state-charter-only laws in 1860. Indiana also has a
higher level of banking in 1854 relative to other sample states. In the 1855-1859 cross
sections no such effect is significant. Indiana has a lower banking level compared to
other states in 1860. Added in table 11 are the p-values from a Wald Test with the
null hypothesis being µ3 = µ2 . This form of the test is a conservative way to see if a
triopoly plus market structure significantly differs from a duopoly market structure.
I am able to reject the null in the 1855-1859 cross sections. Again, the threshold and
percentage change information is presented below the parameter estimates. In 1854
4,326 people were needed to support a monopolist, compared to 1,990 in 1860. The
threshold estimates appear realistic. The standard errors of the threshold estimates
are large due to a small number of observations.
Table 13 presents six different pooled cross section estimates. Specifications one
through three estimate parameters for the triopoly-plus model. Specifications four
through six provide estimates for a duopoly-plus model. All specifications provide
evidence that railroads lowered bank profitability through lowering the number of
equilibrium banks in an isolated markets. The finding that railroads lowered bank
profitability says that while railroads increased bank lending, the lowering of leverage
ratios dominated the increased profits from lending. My findings imply that it was
lowered leverage ratios, not increased banking activity, that provided greater stability
by way of lower failure rates. At the bottom of table 14 is the percentage increase
in market size needed to sustain a given market structure due to railroad access. My
estimates suggest that railroads increase the market size needed to support a given
market structure by 30 percent.15
Population is also significant across all specifications. The population parameter
estimates show that population was a big factor in determining a bank’s location
in antebellum United States. Drier markets also experienced less banking, and the
drought parameter estimate is significant across specifications. The focus should be on
the tripoly plus model, as the results indicate that market structure under a triopoly
15
Since the 1860 cross section adds market observations that were not available in 1854-59, pooled
cross sections for 1854-1859 were estimated. The significance of the coefficient on the railroad binary
variable does not change but the magnitude of the coefficient estimate does fall. The estimated
increase in market size needed due to railroad access is still thirty percent and significant if the 1860
cross section was dropped from the pooled cross section.
17
plus is significantly different than a duopoly-plus market structure.
The last three control variables are included due to the fact that banking in
Indiana differed from the rest of the sample states (Calomiris and Schweikart, 1991
and Chabot and Moul, 2014). I estimate three different models describing how Indiana
banking changed over time. Indiana bank guarantees were not honored in 1854, and
Chabot and Moul show that banking in Indiana changed due to the failure of the
state to honor those guarantees. Another bank panic hit the United States in 1857,
with Indiana sustaining a large majority of bank failures (Calomiris and Schweikart,
1991). I estimate three models for Indiana banking in 1855-1860, Indiana banking in
1857-1860 and a time trend model. All models show a decline in the level of banking
in Indiana. Specifications one and four show that the Indiana banking panic of 1854
significantly reduces Indiana banking levels. Specifications two and five show that
the national banking panic in 1857 also significantly reduced Indiana banking level,
supporting Calomiris and Schweikart’s finding. Both banking panics had a lasting
effect on Indiana banking levels. Specifications three and six show that banking levels
in Indiana declined overtime with Indiana banking levels below other sample state’s
banking levels by 1858. The time trend model fits the data best for the triopoly plus
model, while the Indiana banking after 1857 specification fits the best in the duopoly
plus model.
Table 14 displays the threshold estimates for the pooled cross section models.
For all specifications about 2,800 people are needed to support a monopolist, with
roughly 3,600 needed to support a monopolist if a railroad is present in the county. It
is unlikely that bank fixed costs(Collateral requirement and building costs) changed
drastically over the free banking era, so the increase in market thresholds can be
interpreted as a fall in variable profit margins. About 8,800 people are needed to
support a duopolist with no railroad is present, with about 11,600 people needed if
a railroad is present. For a triopoly plus market structure, roughly 25,500 people
are needed with no railroad present, and about 33,500 are needed if a railroad is
present. The threshold ratios are significant as well. Recall that, if the competitive
conduct of firms in a market does not change when a market goes from a monopolist
to a duopolist, the threshold ratio should equal two. The ratio of a triopoly market
threshold to duopoly market threshold should be equal to one and a half if the market
is perfectly competitive.
There are other possible interpretations of the effect of railroads on the number
of banks in a market. One explanation is that the presence of railroads altered they
type of loans made by banks. Railroad tracks were built in high population areas
with developed cities. If a bank is in a more industrialized area, banks may specialize
in manufacturing loans instead of agricultural loans. Bank specialization may have
increased the returns to scale, and my railroad control variable may be picking up
the effect of such specialization.
18
Conclusion
The free banking era was a much simpler environment compared to contemporary
banking. Railroads provided a shock to antebellum banking. My results provide
evidence that railroads increased bank lending and decreased banknote circulationto-specie ratios. The net effect lowered antebellum bank profits. The decrease in
banknote circulation-to-specie ratios led to an increase in banking stability. My results
suggest a possible way to increase the stability of contemporary banks would be to
pass legislation requiring banks to lower leverage ratios.
As antebellum banking profits declined and stability increased, interest rates may
have increased. Further research should be done regarding antebellum bank stability
and interest rates. A more stable banking sector is less open to the boom and bust
cycles but may have higher interest rates. Regulators face a trade off between stability and national investment. This is not an easy choice, and investment is a large
component of a national economy. Regulators face a choice between a stable banking
system and a steady level of investment or periods of high investment followed by
periods of low investment. Neither choice is a clear favorite.
19
5
5.1
Appendicies
Appendix A: Drought Index
The Palmer Drought Seversity Index is used to construct the drought variable in
the endogenous market structure models. The PDSI is standardized to local climate
and standardized to zero. Negative numbers indicate drought conditions, with -2
indicating moderate, -3 indicating severe, and -4 indicating extreme drought. I use
the reconstructed measures of the PDSI provided by Zhang, Mann and Cook (2004).
Zhang, Mann and Cook (2004) study employs tree-ring data over the conterminous
United States to estimate PDSI measures for a system of latitude-longitude grid
points back to the year 1700. My market appropriate PDSI measure then use market
latitude-longitude coordinates to construct a weighted average of the gridpoints.
My market-level measure is constructed for the 1854-1860 cross sections. Given
the cumulative impact of yearly precipication, the measure more heavily weights
recent years. The measure also builds on the intuition that droughts are worse for
agriculture than overly wet seasons and that agricultural costs of drought are non
linear Chabot and Moul (2014). Each market-year’s drought measure is
(P DSIj,t−k | P DSIj,t−k < 0)2
(15)
so that a market-year’s drought measure is positive if there as any degree of drought,
and zero otherwise. The weight applied to k years ago follows the formula for a look
back of L years
k−1
(16)
wk = 1 −
L
and the full drought measure for market j at time t is
DROU GHTj,t =
L
X
wk ((P DSIj,t−k | P DSIj,t−k < 0)2 )
(17)
k=1
While a different measure of drought is used in the balance sheet models, the above
drought measure fits the endogenous market structure models best. This measure is
also used in Chabot and Moul (2014).
20
5.2
Appendix B: Railroad Map Sources
Atack, Jeremy and Robert A. Margo. ‘‘The Impact of Access to Rail Transportation on Agricultural Improvement: The American Midwest as a test
case, 1850-1860. Journal of Transport and Land Use 4.2 (2011): 5-18.
Print.
Brockman, Paul. ‘‘Evansville & Illinois Railroad Records, 1850: Collection
Information.’’ Indiana Historical Society-Manuscripts and Archives.
Posted: 3 April 1997. Accessed : 14 May 2013, from: http://www.indi
anahistory.org/our-collections/collection-guides/evansville-illinois
-railroad- records-1850.pdf
Colton, George Woolworth. ‘‘Coltons Railroad & Township Map of the State of
Ohio.’’ Map. Library of Congress. H. H. Colton, 1854. Web. 14 May
2013, from: http://hdl.loc.gov/loc.gmd/g4081p.rr002790.
Colton, George Woolworth. ‘‘Indiana, Illinois, Missouri & Iowa with Parts of
Adjoining States.’’ Map. Library of Congress. G. Woolworth Colton,
1858. Web. 14 May 2013,from: http://hdl.loc.gov/loc.gmd/g4061p.rr001210.
Duncan, Jacob M. ‘‘Barringtons New and Reliable Railroad Map and Shippers &
Travelers Guide of Pennsylvania, Engrd. By Ths. Leonhardt.’’ Map.
Library of Congress. Barringtons, 1860. Web. 14 May 2013, from:
http://hdl.loc.gov/loc.gmd/g3821p.rr002950.
King, S. D. ‘‘Map of the state of Indiana Compiled from the United Surveys’’
Map. Library of Congress. J. H. Colton, 1852. Web. 14 May 2013,
from: http://hdl.loc.gov/loc.gmd/g4090.rr002090.
‘‘Map of a Railroad Route from Phenixville to Pinegrove.’’ Map. Library
of Congress. Unknown, 1852. Web. 14 May 2013, from: http://hdl.loc.
gov/loc.gmd/g3821p.rr005320.
‘‘Map of the Williamsport and Elmira Railroad with its connections.’’ Map.
Library of Congress. P.S. Duval & Company, 185-. Web. 14 May 20
13, from: http://hdl.loc.gov/loc.gmd/g3791p.rr006180.
‘‘Map Showing the Location of the Chicago & Northwestern Railway with its
Branches and Connections through Illinois, Iowa, Nebraska, Wisconsin, Minnesota, Michigan’’ Map. Library of Congress. Chicago & N
orthwestern Railway, 1862. Web. 14 May 2013, from: http://hdl.loc.
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21
‘‘Railroad and County Map of Illinois Showing the Internal Improvements.’’
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22
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25
7
Tables
Variable Name
LEVERAGE
RR
ASSETS
CIRCULATION
LOAN
SPECIE
AGE
POP
PALMER
PALPOP
YEAR
Y1854
Y1855
Y1856
Y1857
Y1858
Y1859
Y1860
WI
IL
OH
IA
MO
PA
IN
Table 1: Bank Data Description
Description
Bank leverage ratio defined as value of notes in circulation over specie
Dummy variable indicating railroad access within county
Value of bank assets
Value of banknote circulation
Value of bank loans
Value of bank specie on hand
Age of bank in years
Total (free and slave) population from U.S. Census:
linear interpolation for years 1854-1859
Palmer Drought Severity Index
Interaction of PALMER and population
Observation year
Dummy variable indicating observation year was 1854
Dummy variable indicating observation year was 1855
Dummy variable indicating observation year was 1856
Dummy variable indicating observation year was 1857
Dummy variable indicating observation year was 1858
Dummy variable indicating observation year was 1859
Dummy variable indicating observation year was 1860
Dummy variable indicating observation is in Wisconsin
Dummy variable indicating observation in in Illinois
Dummy variable indicating observation is in Ohio
Dummy variable indicating observation is in Iowa
Dummy variable indicating observation is in Missouri
Dummy variable indicating observation in in Pennsylvania
Dummy variable indicating observation is in Indiana
26
27
Whole Sample (1,726)
RR=1 (804)
Std. Dev. Min
Max
Mean Std. Dev. Min
61.53
0.04
1647.99
5.77
12.41
0.04
0.50
0
1
1
0
0
903.94
9.89
8,822.45 718.28
1,103.96
9.89
183,914
216
2,805,660 152,902 232,264
216
479.53
0
4,444.52 421.33
644.99
9.89
116.42
0.08
1,384.96
79.81
160.99
0.25
10.43
0.08
46
10.82
10.55
0.34
132.80
0.26
565.53
97.62
182.09
0.41
0.70
-2.79
1.15
-0.43
0.70
-2.79
5.89
-32.80
9.18
-3.95
6.28
-32.80
2.07
1854
1860
1858
2.07
1854
0.33
0
1
0.14
0.35
0
0.20
0
1
0.05
0.21
0
0.37
0
1
0.18
0.38
0
0.24
0
1
0.07
0.25
0
0.37
0
1
0.18
0.38
0
0.37
0
1
0.16
0.36
0
0.45
0
1
0.24
0.43
0
0.30
0
1
0.09
0.28
0
0.31
0
1
0.11
0.31
0
0.30
0
1
0.10
0.29
0
0.41
0
1
0.23
0.42
0
0.11
0
1
0.03
0.16
0
0.42
0
1
0.30
0.46
0
0.42
0
1
0.16
0.37
0
RR=0 (922)
Max
Mean Std. Dev. Min
Max
202.35
19.00
82.92
0.06
1647.99
1
0
0
0
0
8,822.45 312.11
625.20
32.65 8,237.07
2,805,660 114,436 125,008
1,197 1,357,605
4,444.52 145.54
180.28
0
995.76
1,384.96
23.42
36.13
0.08
713.50
45.98
6.74
9.94
0.08
46
565.53
3.66
2.81
0.26
14.05
0.98
-0.48
0.71
-2.52
1.15
8.16
-3.77
5.53
-21.15
9.18
1860
1858
2.06
1854
1860
1
0.12
0.32
0
1
1
0.03
0.18
0
1
1
0.16
0.37
0
1
1
0.05
0.22
0
1
1
0.15
0.36
0
1
1
0.18
0.38
0
1
1
0.31
0.46
0
1
1
0.11
0.32
0
1
1
0.11
0.32
0
1
1
0.11
0.31
0
1
1
0.19
0.39
0
1
1
0.00
0.05
0
1
1
0.17
0.38
0
1
1
0.29
0.45
0
1
Assets, circulation, loans and specie are in thousands of dollars. Palmer is Palmer Drought Severity Index.
LEVERAGE
RR
ASSETS
CIRCULATION
LOANS
SPECIE
AGE
POP
PALMER
PALPOP
YEAR
Y1854
Y1855
Y1856
Y1857
Y1858
Y1859
Y1860
IA
IL
IND
OH
MO
PA
WI
Mean
12.84
0.47
501.31
132,354
274.01
49.69
8.64
47.43
-0.46
-3.85
1858
0.13
0.04
0.17
0.06
0.16
0.17
0.28
0.10
0.11
0.10
0.21
0.01
0.23
0.23
Table 2: Bank Data Summary Statistics
Table 3: Asset, Loan and Specie Models
Dependent Variable Assets
Loans
Specie
RR
State Fixed Effects
28.100*** 0.133
(10.330)
(2.513)
3.226***
0.073
(0.663)
(0.155)
0.824***
0.172***
(0.153)
(0.0376)
0.310***
0.068***
(0.051)
(0.013)
258.600
116.000*
16.080
(188.900) (61.870)
(21.980)
-29.790
-15.290** -2.533
(22.630) (7.624)
(2.820)
-12.220
-14.510*** -1.250
(8.610)
(3.771)
(0.867)
Yes
Yes
Yes
R-squared
0.430
AGE
POP
ASSETS
PALMER
PALPOP
YEAR
8.908
(29.130)
6.385***
(1.363)
2.750***
(0.280)
0.760
0.690
Notes: The dependent variable is described in the column heading. Results from OLS Models.
(N=1,726). Assets, loans and specie are in thousands of dollars. Robust standard errors are listed
below the coefficients in parentheses. * denotes significance at 10%; ** at 5% level; and *** at 1%
level.
28
Table 4: Banknote Circulation Model
Specification
(1)
RR
38.470*** 9.091
(9.167)
(7.605)
6.559***
(0.498)
0.027
(0.034)
AGE
POP
(2)
ASSETS
PALMER
PALMERPOP
(3)
(4)
0.503
(6.835)
4.484***
(0.553)
-0.350***
(0.089)
0.123***
(0.027)
33.610
(80.310)
-5.123
(10.350)
-3.443
-15.100***
(6.688)
(5.553)
4.266***
0.685
(0.526)
(0.491)
-0.344***
-0.319***
(0.088)
(0.06)
0.123***
0.097***
(0.027)
(0.019)
54.070
39.070
(78.020)
(60.460)
-8.183
-5.762
(10.550)
(7.828)
-2.853
(31.000)
-58.080***
(22.180)
-107.800***
(20.020)
-66.880***
(12.650)
-86.910***
(12.370)
-74.260***
(13.820)
y1855
Y1856
Y1857
Y1858
Y1859
Y1860
(5)
(6)
(7)
(8)
-16.440***
(5.453)
0.723
(0.465)
-0.318***
(0.058)
0.095***
(0.019)
56.220
(58.740)
-8.148
(8.107)
22.940
(26.130)
-41.180**
(19.690)
-63.070***
(17.140)
-68.880***
(12.640)
-59.710***
(12.540)
-52.510***
(12.640)
-15.530***
(5.590)
1.304**
(0.529)
-0.058*
(0.033)
-17.190***
(5.493)
0.771*
(0.467)
-0.321***
(0.059)
0.097***
(0.019)
51.72(60.460)
-6.607
(7.786)
86.200
(70.190)
-11.06
(9.704)
40.770
(33.440)
-30.150
(22.450)
-63.960***
(20.200)
-75.720***
(15.060)
-65.660***
(14.180)
-51.770***
(14.080)
YEAR
State Fixed Effects No
No
No
No
Yes
Yes
Yes
-10.170***
(1.845)
Yes
R-squared
0.150
0.397
0.423
0.601
0.618
0.494
0.611
0.011
Notes: The dependent variable is described in the column heading. Results of an OLS Model.
(N=1,726). Assets and loans are in thousands of dollars. Robust standard errors are listed below
the coefficients in parentheses. * denotes significance at 10%; ** at 5% level; and *** at 1% level.
29
Table 5: Specie-to-Assets Model
Specification
(1)
RR
0.0028
-0.0002
(0.0052) (0.0060)
0.0122***
(0.0025)
-0.0025
(0.0018)
LAGE
LPOP
(2)
PALMER
PALPOP
(3)
(4)
(5)
(6)
(7)
-0.0010
(0.0060)
0.0128***
(0.0025)
-0.0012
(0.0018)
-0.0299
(0.0218)
0.0016
(0.0026)
-0.0048
(0.0057)
0.0154***
(0.0025)
-0.00012
(0.0017)
-0.0433**
(0.0220)
-0.0002
(0.0026)
-0.0879***
(0.0115)
-0.1130***
(0.0130)
-0.1440***
(0.0108)
-0.0575***
(0.0115)
-0.0616***
(0.0114)
-0.0858***
(0.0109)
-0.0131***
(0.0048)
0.0003
(0.0026)
0.0031*
(0.0016)
-0.0133
(0.0201)
0.0021
(0.0025)
-0.0139***
(0.0047)
0.0049**
(0.0025)
0.0012
(0.0017)
-0.0096
(0.0201)
0.0020
(0.0025)
-0.0226**
(0.0114)
-0.0109
(0.0123)
-0.0348***
(0.0107)
-0.0208*
(0.0116)
-0.0242**
(0.0107)
-0.0460***
(0.0110)
-0.0136***
(0.0047)
0.0038
(0.0025)
0.0019
(0.0017)
-0.0062
(0.0200)
0.0017
(0.0025)
Y1855
Y1856
Y1857
Y1858
Y1859
Y1860
YEAR
State Fixed Effects
No
No
No
No
Yes
Yes
-0.0065***
(0.0016)
Yes
R-squared
0.000
0.017
0.029
0.112
0.372
0.388
0.383
Notes: The dependent variable is described in the column heading. Results of an OLS Model.
(N=1,726). Assets and specie are in thousands of dollars. Robust standard errors are listed below
the coefficients in parentheses. * denotes significance at 10%; ** at 5% level; and *** at 1% level.
30
Table 6: Loan-to-Assets Model
Specification
(1)
RR
0.057** 0.041
0.039
(0.027) (0.029)
(0.029)
0.192*** 0.194***
(0.011)
(0.011)
-0.058*** -0.055***
(0.009)
(0.010)
-0.065
(0.130)
0.002
(0.016)
LAGE
LPOP
PALMER
PALPOP
(2)
(3)
Y1855
Y1856
Y1857
Y1858
Y1859
Y1860
(4)
(5)
(6)
(7)
0.015
(0.027)
0.197***
(0.011)
-0.053***
(0.010)
-0.008
(0.131)
-0.012
(0.016)
-0.496***
(0.059)
-0.442***
(0.079)
-0.607***
(0.064)
-0.463***
(0.061)
-0.451***
(0.052)
-0.53***
(0.058)
-0.020
(0.021)
0.057***
(0.012)
-0.0026
(0.008)
0.0312
(0.080)
-0.003
(0.010)
-0.021
(0.021)
0.075***
(0.013)
-0.008
(0.008)
0.071
(0.078)
-0.006
(0.010)
-0.143***
(0.043)
-0.074
(0.067)
-0.124**
(0.051)
-0.215***
(0.049)
-0.144***
(0.041)
-0.177***
(0.048)
-0.022
(0.021)
0.072***
(0.013)
-0.008
(0.008)
0.062
(0.080)
-0.005
YEAR
-0.143***
State Fixed Effects
No
No
No
No
Yes
Yes
-0.028***
(0.006)
Yes
R-squared
0.003
0.150
0.154
0.240
0.565
0.577
0.573
Notes: The dependent variable is described in the column heading. Results from an OLS Model.
(N=1,726). Assets and loans are in thousands of dollars. Robust standard errors are listed below
the coefficients in parentheses. * denotes significance at 10%; ** at 5% level; and *** at 1% level.
31
Table 7: Circulation-to Specie Model
Specification
(1)
(2)
(3)
(4)
(5)
(6)
(7)
RR
-13.230***
(2.766)
-6.037***
(1.679)
-2.577***
(0.887)
6.633**
(3.375)
-5.624***
(1.571)
-5.962***
(1.703)
-3.306***
(1.058)
7.247**
(3.506)
-7.342***
(2.076)
43.280***
(14.830)
-4.327***
(1.563)
-5.783***
(1.832)
-4.402***
(1.264)
7.904**
(3.649)
-6.921***
(1.949)
42.790***
(14.710)
-3.662**
(1.428)
4.148*
(2.314)
19.500***
(4.462)
9.799***
(2.594)
19.160***
(6.973)
1.023
(1.309)
21.000***
(4.343)
-7.263***
(2.029)
-1.837*
(1.038)
11.470**
(5.403)
-8.071***
(2.172)
50.970***
(15.230)
-5.257***
(1.598)
-6.941***
(2.058)
-4.111***
(1.260)
12.190**
(5.447)
-7.593***
(2.113)
48.510***
(15.070)
-5.232***
(1.529)
9.364***
(3.277)
4.321
(3.709)
11.910***
(3.976)
11.550**
(5.830)
12.620***
(2.864)
21.840***
(4.696)
-6.293***
(1.868)
-2.318
(1.472)
No
0.012
No
0.030
No
0.041
No
0.059
Yes
0.125
Yes
0.136
LAGE
LASSETS
LPOP
PALMER
PALMERPOP
Y1855
Y1856
Y1857
Y1858
Y1859
Y1860
YEAR
State Fixed Effects
R-squared
-7.090***
(2.015)
-3.760***
(1.209)
12.380**
(5.461)
-5.380*** -7.608***
(1.330)
(2.114)
44.130*** 47.770***
(13.930)
(14.720)
-4.703*** -5.092***
(1.400)
(1.561)
10.31***
(3.391)
4.896
(3.812)
11.860***
(3.966)
10.190*
(5.616)
9.981***
(2.331)
19.960***
(4.459)
3.274***
(0.607)
Yes
Yes
0.120
0.134
Notes: Assets are in thousands of dollars. Results from an OLS Model. (N=1,726). Dependent
variable is banknote circulation-to-specie. Robust standard errors are listed below the coefficients
in parenthesis. * denotes significance at 10%; ** at 5% level and *** at 1% level.
32
(8)
Table 8: Endogenous Market Structure Data Description
Variable Name Description
N
Number of state banks registered in market
RR
Dummy variable indicating railroad access within county
LPOP
Log of total (free and slave) population from U.S. Census:
linear interpolation for years 1854-1859
Measure of drought severity, constructed from Palmer Drought Severity Index
DROUGHT
DRPOP
Interaction of drought and population
Binary variable indicating that market is in state of Indiana
IN
SCO
Binary variable indicating that market in state that requires banks to have
charter from state legislature
IN55-60
Binary variable indicating that market is in Indiana, but not in 1854
Indiana time trend variable
INTT
IN58-60
Binary variable indicating market is in Indiana and 1857-1860
33
34
#Mkts
#Mkts| N = 0
#Mkts| N = 1
#Mkts| N = 2
#Mkts | N ≥ 3
AvgPop
AvgPop| N = 0
AvgPop| N = 1
AvgPop| N = 2
AvgPop| N ≥ 3
Drought
DRPOP
1855
1856
RR=0 RR=1 RR=0 RR=1
97
157
90
164
85
125
75
128
9
23
12
28
3
6
3
6
0
3
0
2
1,264 2,300 1,334 2,333
1,248 1,457 1,333 1,512
1,495 4,011 1,414 3,769
1,016 7,978 1,056 8,122
NA
12,976 NA
17,362
0.23
-0.06
0.57
-0.22
1.70
2.26
6.43
8.70
1857
RR=0 RR=1
77
177
66
131
9
35
2
10
0
1
1,391 2,341
1,393 1,556
1,532 3,216
692
7,972
NA
18,352
1.19
-0.42
9.11
11.26
1858
1859
RR=0 RR=1 RR=0 RR=1
88
189
86
191
77
140
73
140
9
39
10
41
2
9
3
9
0
1
0
1
1,470 2,473 1,510 2,565
1,476 1,600 1,452 1,617
1,583 3,751 2,194 3,982
715
8,749 628
9,092
NA
18,420 NA
18,487
0.57
-0.27
0.41
-0.19
7.04
9.34
5.14
6.83
Table 9: Endogenous Market Structure Summary Statistics
1854
RR=0 RR=1
107
147
94
117
12
19
1
6
0
5
1,296 2,219
1,206 1,420
1,970 3,856
1,685 7,555
NA
8,291
0.05
0.02
0.54
1.00
1860
RR=0 RR=1
143
219
113
162
20
46
6
10
4
1
997
2,241
832
1,475
1,813 3,541
1,584 8,118
683
7,822
0.82
-0.53
3.12
4.13
Table 10: Duopoly-Plus Ordered Probit Models. Number of Banks as Dependent
Variable.
µ1 /σ54
µ2 /σ54
LP OP/σ54
RR/σ54
DR/σ54
DRP OP/σ54
SCO/σ54
IN/σ54
1854
5.812***
(2.319)
0.643***
(.247)
0.664**
(.317)
-0.345
(.267)
-2.574***
(.944)
0.942***
(.361)
-1.231***
(.401)
1.015***
(.295)
| LogLikelihood | 80.504
Observations 254
1855
5.814***
(1.903)
0.394
(.203)
0.702***
(.263)
-0.400
(.244)
0.376
(.279)
0.215**
(.103)
-0.849***
(.324)
0.342
(.260)
1856
1857
1858
6.723*** 5.962*** 6.305***
(1.773)
(1.456)
(1.462)
0.515*** 0.383**
0.438**
(.193)
(.170)
(.177)
0.862*** 0.738*** 0.772***
(.247)
(.202)
(.201)
-0.508** -0.120
-0.129
(.229)
(.231)
(.225)
-0.076
-0.110** -0.152**
(.070)
(.050)
(.061)
0.017
0.005
0.007
(.017)
(.010)
(.012)
-1.239*** -0.790*** -0.686***
(.328)
(.273)
(.247)
0.2912
0.213
0.112
(.248)
(.237)
(.233)
1859
6.747***
(1.465)
0.349**
(.164)
0.839***
(.20)
-0.204
(.219)
-0.176**
(.081)
0.006
(.016)
-0.488**
(.233)
-0.059
(.235)
1860
4.886***
(1.039)
0.315**
(.127)
0.641***
(.149)
-0.501***
(.179)
-0.151**
(.071)
0.017
(.022)
0.291
(.196)
-0.488***
(.218)
96.288
254
112.993 131.891
254
254
143.149
277
201.570
362
137.248
277
Notes: Table presents the estimates of an Ordered Probit Maximum Likelihood Model. Standard
errors are listed below the coefficients in parentheses. Thresholds and ratios shown are if no railroad
is present in the county. * denotes significance at 10%; ** at 5% level; and *** at 1% level.
35
Table 11: Triopoly-Plus Ordered Probit Models. Number of Banks as Dependent
Variable.
µ1 /σ54
µ2 /σ54
µ3 /σ54
LP OP/σ54
RR/σ54
DR/σ54
DRP OP/σ54
SCO/σ54
IN/σ54
1854
8.210***
(1.940)
0.584**
(.234)
1.133***
(.375)
0.981***
(.265)
-0.225
(.259)
-1.591**
(.716)
0.379**
(.183)
-1.143***
(.395)
1.095***
(.286)
P V alue H0 : µ3 = µ2 0.096
| LogLikelihood | 89.833
Observations 254
1855
6.712***
(1.672)
0.370
(.197)
1.906**
(.789)
0.820***
(.233)
-0.395
(.245)
-0.238
0.228**
0.150
(.067)
-0.828***
(.326)
0.432
(.252)
1856
6.464***
(1.697)
0.521***
(.193)
1.675**
(.598)
0.829***
(.236)
-0.510***
(.229)
-0.089
(.068)
0.022
(.016)
-1.259***
(.327)
0.238
(.246)
1857
1858
5.697*** 6.071***
(1.427)
(1.434)
0.389**
0.444**
(.171)
(.178)
1.554*** 1.589***
(.548)
(.563)
0.702*** 0.740***
(.198)
(.197)
-0.208
-0.134
(.231)
(.225)
-0.118** -0.161***
(.049)
(.061)
0.008
0.010
(.009)
(.011)
-0.793*** 0.688***
(.273)
(.247)
0.205
0.108
(.237)
(.233)
1859
6.535***
(1.441)
0.353**
(.165)
1.556***
(.563)
0.811***
(.197)
-0.208
(.219)
-0.187**
(.081)
0.010
(.016)
-0.490**
(.233)
-0.0641
(.235)
1860
4.857***
(1.011)
0.316**
(.127)
0.543***
(.198)
0.640***
(.145)
-0.527***
(.178)
-0.145**
(.070)
0.012
(.020)
-0.285
(.195)
-0.470**
(.217)
0.047
100.450
254
0.047
115.953
254
0.021
134.087
254
0.030
145.339
277
0.179
213.696
362
0.037
139.388
277
Notes: Table presents the results of an Ordered Probit Maximum Likelihood Model. Standard
errors are listed below the coefficients in parentheses. Thresholds and ratios shown are if no railroad
is present in the county. * denotes significance at 10%; ** at 5% level; and *** at 1% level.
36
Table 12: Cross Section Threshold Estimates
1854
Duopoly-Plus Model
Thresholds
Y M 6,310
(4,920)
Y D 47,210
(80,580)
Ratios
Y D /Y M 7.479
(7.552)
RR Percentage
Percent Change 0.681
(.807)
Triopoly-Plus Model
Thresholds
Y M 4,326
(1,631)
Y D 15,911
(10,936)
Y T 42,093
(41,768)
Ratios
Y D /Y M 3.678
(1.450)
Y T /Y D 2.646
(2.493)
RR Percentage
Percent Change 0.258
(.354)
1855
1856
1857
1858
1859
1860
3,947
(1,957)
18,553
(19,467)
2,444
(692)
9,926
(6,312)
3,243
(1,246)
13,941
(10,038)
3,540
(1,314)
15,332
(10,696)
3,108
(968)
10,759
(5,969)
2,037
(557)
9,809
(5,641)
4.701
(3.046)
4.062
(1.815)
4.299
(1.911)
4.332
(1.869)
3.462
(1.202)
4.815
(1.956)
0.789
(.798)
0.803
(.640)
0.311
(.441)
0.182
(.358)
0.275
(.355)
1.183
(.811)
3,610
(1,390)
13,200
(9,510)
141,050
(255,610)
2,440
3,330
3,640
3,160
1,990
(710)
(1,350)
(1,420)
(1,020)
(536)
10,540
15,540
16,890
11,480
9,642
(6,860) (11,990) (12,470) (6,660)
(5,423)
69,200
145,480
137,120
83,490
25,916
(99,790) (228,050) (201,870) (102,910) (20,660)
3.661
(1.564)
10.686
(21.603)
4.327
(1.993)
6.565
(9.406)
4.665
(2.230)
9.362
(11.679)
4.643
(2.125)
8.118
(10.071)
3.633
(1.321)
7.273
(10.724)
4.844
(1.935)
2.689
(1.758)
0.619
(.594)
0.851
(.685)
0.345
(.479)
0.199
(.379)
0.292
(.374)
1.279
(.857)
Standard errors are in parentheses. The null hypothesis for the threshold ratios are Y D /Y M = 2
and Y T /Y D = 1.5.
37
Table 13: Pooled Ordered Probit Models. Number of Banks as Dependent Variable.
µ1 /σ54
µ2 /σ54
µ3 /σ54
LP OP/σ54
RR/σ54
DR/σ54
DRP OP/σ54
SCO/σ54
IN/σ54
IN N 54 − 60/σ54
(1)
9.292***
(1.001)
0.649***
(.136)
1.472***
(.250)
1.169***
(.131)
-0.312***
(.105)
-0.104***
(.033)
0.002
(.006)
-0.903***
(.149)
0.760***
(.227)
-0.706***
(.248)
IN 58 − 60/σ54
IN T T /σ54
P V alue : H0 µ3 = µ2 0.000
| LogLikelihood | 978.538
Observations 1,847
(2)
8.877***
(.945)
0.597***
(.129)
1.386***
(.238)
1.118***
(.124)
-0.303***
(.10)
-0.103***
(.031)
0.002
(.006)
-0.878***
(.143)
0.490***
(.145)
(3)
(4)
(5)
(6)
9.279*** 10.210*** 9.601*** 10.067***
(.984)
(1.131)
(1.051)
(1.104)
0.648*** 0.793*** 0.723*** 0.744***
(.135)
(.158)
(.148)
(.151)
1.486***
(.248)
1.169*** 1.287*** 1.209*** 1.258***
(.129)
(.148)
(.138)
(.144)
-0.310*** -0.349*** -0.332*** -0.298***
(.104)
(.116)
(.111)
(.112)
-0.110*** -0.122*** -0.121*** -0.093***
(.033)
(.038)
(.036)
(.035)
0.002
0.003
0.005
0.002
(.006)
(.007)
(.007)
(.007)
-0.919*** -1.023*** -0.977*** -0.920***
(.149)
(.171)
(.161)
(.166)
0.731*** 0.793*** 0.510*** 0.783***
(.176)
(.236)
(.158)
(.190)
-0.775***
(.265)
-0.628***
-0.704***
(.202)
(.226)
-0.207***
-0.218***
(.054)
(.059)
0.000
977.059
1,847
0.000
973.669
1,847
935.917
1,847
934.424
1,847
938.572
1,847
Notes: Specifications (1)-(3) are for triopoly-plus. Specifications (4)-(6) are for duopoly-plus.
Standard errors are listed below the coefficients in parentheses. σ55 − σ60 estimated but not shown.
The null hypothesis for percentage change is that percentage change is equal to zero (e.g. 1.258=1).
* denotes significance at 10%; ** at 5% level; and *** at 1% level.
38
Table 14: Pooled
Specification (1)
Thresholds
Y M 2,823
(268)
D
Y
8,890
(1,381)
T
Y
25,422
(6,027)
Ratios
Y D /Y M 3.150
(.304)
Y T /Y D 2.860
(.936)
RR Percentage
Percent Change 0.306
(.136)
Cross Section Threshold Estimates
(2)
(3)
(4)
(5)
(6)
2,816
2,808
2,797
(268)
(264)
(265)
8,932
8,851
8,884
(1,398) (1,363) (1,397)
26,005 25,649
(6,256) (6,067)
2,993
2,817
(288)
(271)
9,381
9,088
(1,503) (1,465)
3.172
(.309)
2.911
(.965)
3.152
(.302)
2.898
(.955)
3.176
(.313)
3.134
(.307)
3.226
(.326)
0.311
(.137)
0.303
(.134)
0.312
(.139)
0.267
(.129)
0.316
(.141)
Standard errors are in parentheses. The null hypothesis for the threshold ratios are Y D /Y M = 2
and Y T /Y D = 1.5.
39
8
Figures
Figure 1: Spread of Railroads Across the United States. Taken from Atack et al.
(2009)
40
Figure 2: Spread of Railroads and Banks Across the United States. Taken from Atack
et al. (2014)
41