ABSTRACT
ALLY OR ANTAGONIST? BANKING AND ANTEBELLUM
AMERICAN AGRICULTURE
by Michael Sotak
Despite the economic advantages of incorporating a new innovation into a
society, there is often a delay between the invention and adoption of the
technology. This is especially evident with agricultural innovations in the
1800s. One of the possible causes of an innovation’s delayed adoption is
credit availability. This paper will look at antebellum farming and bank
data in the Midwest to examine the contribution that banks and financial
depth provide to the adoption of a new technology. I will show evidence
consistent with the hypothesis that banks allow cash-constrained farmers
to adopt the new technology, speeding its incorporation. My results are
also consistent with a downside to the presence of banks, namely that
increased financial leverage can magnify losses from a negative economic
shock.
ALLY OR ANTAGONIST? BANKING AND ANTEBELLUM
AMERICAN AGRICULTURE
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
Michael A. Sotak II
Miami University
Oxford, Ohio
2014
Advisor________________________
(Prof. Chuck Moul)
Reader_________________________
(Prof. George Davis)
Reader_________________________
(Prof. Melissa Thomasson)
Contents
1. Introduction
2. Historical Background
3. Data
4. Methods and Theory
5. Results
6. Robustness Checks
7. Conclusion
8. Works Cited
9. Appendix
1
2
3
5
6
9
10
11
19
‹‹
List of Tables
Table 1 – Summary Statistics
Table 2 – Headcount of Mules
Table 3 – Bushels of Corn
Table 4 – Headcount of Swine
Table 5 – Bushels of Wheat
Table 6 – Wheat with Corn as Independent Variable
A1 – Bank Dummy Robustness Check - Swine and Mules
A2 – Bank Dummy Robustness Check – Wheat and Corn
A3 – Dependent Variable – Headcount of Cattle
A4 – Dependent Variable – Headcount of Horses
A5 – Dependent Variable – Headcount of Sheep
A6 – Dependent Variable – Tons of Hay
A7 – Dependent Variable – Bushels of Oats
A8 – Dependent Variable – Bushels of Potatoes
A9 – Dependent Variable – Bushels of Rye
A10 – Dependent Variables – Log of Mules and Corn
A11 – Dependent Variables – Log of Swine and Wheat
‹‹‹
13
14
15
16
17
18
19
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21
22
23
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29
ACKNOWLEDGMENTS
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‹˜
1. Introduction
Technology and innovation are essential factors in the advancement of any
economy. The basis of supplier theory is that producers will allocate resources to
maximize profits. Part of this is using technologies that minimize average costs of
production, so it seems the rational choice is to adopt any technology that makes the
production process more efficient. However, it is not as simple as this: “Unlike the
invention of a new technology, which often appears to occur as a single event or jump,
the diffusion of that technology usually appears as a continuous and rather slow process”
(Hall and Khan 2003).
In this paper, I examine changes in farming output in the Midwest during the midnineteenth century. I look at the effect of credit availability (proxied by bank counts)
across counties on farming output, as well as the exogenous shock of varying levels of
precipitation and how credit availability magnifies those effects. My results will
demonstrate that banks have real effects on production, presumably by playing a
significant role in the adoption of new innovations and technology.
Others have attempted to explain the delayed adoption of technology by looking
at the manner of conserving grass as winter fodder (silage), which was invented in 1880
in Britain but not widely adopted until 1970 (Brassley 1996). Brassley highlights the
importance of all aspects being in place before speedy adoption can occur. Others have
found that the delay is due to heterogeneity among potential adopters and propose a
generalized cycle that can be applied to the adoption of any innovation (Mahajan et al.
1995). Extensions to this work have incorporated heterogeneity into other previously
proposed diffusion models (Young 2009). All of these works have examined the delayed
adoption of technology by looking at the innovations themselves and the people who use
them. Little work, however, has looked at how intermediaries such as banks can play a
role in the process.
I am hardly the first to point out how credit inefficiencies can contribute to the lag
between the invention and the adoption of innovation. Farmers today often cite lack of
capital as a major reason for not adopting a technology that could improve their
productivity (Croppenstedt et al. 2003). Some have looked at the role banks play to help
mitigate this problem, such as offering supply contracts as collateral for loans (Dries et al.
2004) or the use of reputation as collateral (de Janvry et al. 2010).To my knowledge,
though, none have examined if the presence of banks facilitates the adoption of
technology.
There has, however, been research on the general value that banks provide. Others
have shown that banks provide value to the financial world by reducing transactions costs
between lenders and borrowers as well as alleviating problems of asymmetric
information (Hadlock and James 2002). Some research has also examined the effects of
banks on asset pricing. Previous results find that credit availability magnifies the effects
of shocks (positive and negative) on asset prices (Rajan and Ramcharan 2012). My paper
differs from previous work in that I will look at the effects on farming output rather than
prices. My results confirm that credit availability can not only magnify the output effects
of positive and negative shocks, but also increase the potential output of farmers.
Although it would be ideal to look at this problem with modern data, that task has
proved too difficult. Because the financial system is so fully integrated into our economy,
ͳ
it is impossible to geographically isolate instances of reduced or increased credit
availability. To address this issue, I use pre-Civil War banking and farming data. The
rationale is that in a time when communication and transportation were difficult and
slow, credit availability can be isolated and observed by treating each county as a “subeconomy”. Assuming that farmers can only get loans from their local banks, the
antebellum data are able to capture the effects in a way that complex modern data cannot.
The remainder of my paper proceeds as follows: Section II provides a historical
background for my data. Section III explains the data sources and key variables used in
the analysis. Section IV explains both the economic theory and my model. Section V
explains the results of the analysis, and Sections VI and VII are the conclusion and work
cited. All tables are listed at the end of the paper in the Appendix.
2. Historical Background
In order to understand my analysis, some background of antebellum farming must
be provided. Initially, farming and harvesting were done by hand and were thus very
labor intensive. As described by an article in the Scientific American (1896):
Before the building of the reaper it could be truly said that those who
earned their bread by the labor of the harvest did so by the sweat of their
brows. In the heat of midsummer, without protection from the beating sun,
in a stooping position, the toilers of the world gathered the harvests. So
excessive and trying was the labor that the wages at harvest time were
double those of other seasons of the year, and the farmer engaged his help
months in advance for this rushing period.
About 250-300 labor-hours were required to produce 100 bushels (5 acres) of wheat with
the walking plow, brush harrow, hand broadcast of seed, sickle, and flail. At harvest time,
the sickle and flail were used to individually cut each plant and remove the seeds one by
one. Mules were also made to walk in circles over piles of wheat, threshing the grain.1
Harvesting of corn in a similar manner was significantly more efficient but still required
about 75-90 labor-hours to produce 100 bushels (2 ½ acres) of corn with the walking
plow, harrow, and hand planting.2 The harvest was significantly simpler for corn, as it is
much quicker to remove the ears of corn than to remove each seed of grain.
One of the first significant farming innovations can be specifically traced to both
its invention and its adoption. Cyrus McCormick invented the first mechanical reaper in
1831. The first model did the work of five men reaping by hand. It was then improved
upon for the next twenty years before its widespread adoption in the 1850s. Reaper
production, however, was slow to expand. In 1840 there were only three reapers made. In
1845, 500 machines were made, and by 1850, the production had increased to 3,000, and
in 1855 to 10,000. In 1860 the output had increased to 20,000 machines annually
(Scientific American 1896). The steel plow, invented by John Deere in 1837, followed a
similar timeline, and did not see widespread adoption until 1855 with 10,000 plows sold
annually (Halberstadt 2003).
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There is limited historical information about farming machinery at this time, so
there is no certain explanation as to why it took almost twenty years for the mechanical
reaper to become mainstream. However, in 1849 Jacob J. and Henry F. Mann of Indiana,
modified the reaper to have a series of circular bands for carrying the grain. This was
quickly followed by other improvements, all with the function of more efficiently
carrying or sorting the grain. It is therefore not unreasonable to assume that, at about 50
labor hours per acre of wheat, there was a great demand to improve the efficiency of
harvesting grains. It is likely that these modifications pertaining specifically to grain
made the innovation much more appealing to farmers and the growth of the industry
accelerated (Scientific American 1896).
Farming innovations could lead to increased capacity, an obvious potential benefit
for farmers. Previously, farmers had to harvest their fields by hand. With the mechanical
reaper, farmers could combine capital with labor to do the job far more efficiently and do
so at a fraction of the cost. The reaper also made obsolete prior methods of threshing,
such as mules walking in circles.
The use of the mechanical reaper, though, required a significant amount of
capital. Farmers needed sufficient funds to either purchase the new technology or rent its
use from the owner. The data on banks show that, by the time the use of the mechanical
reaper was possible, there was also varying credit availability across the Midwest,
creating a very real constraint for farmers in the areas with little credit availability. If a
farmer was having a bad year and needed to access more funds, the local bank was an
obvious solution. More banks in a particular area (or county) competed for the provision
of loans and consequently drove down interest rates. This enabled more farmers to use
the mechanical thresher for their crops. It is therefore expected that counties with more
banks produced significantly higher amounts of wheat and that these production
advantages should exceed those from other farm outputs that did not benefit from the
reaper/thresher.
There was a potential downside to this financial depth. The presence of banks
may have also allowed farmers to over-leverage themselves by taking advantage of the
lower interest rates. When a negative shock occurred, farmers who had over-leveraged
themselves (and were therefore unable to borrow more funds) were more likely to need to
sell their livestock early for liquidity. Farmers could raise immediate funds by selling
livestock that was not fully grown, but they could not harvest their crops early. Farmers
who lacked access to credit, though, would have been forced to prepare for inclement
conditions by keeping sizable savings. For that reason, when the area experienced a
negative shock such as a drought, the decrease in output for livestock is expected to be
greater if that area has many banks than in an area where credit availability is limited.
3. Data
The dependent variables for my regressions come from various categories of
farming crops or animals. The farming output data come from state reports from 1853 to
1861. Observations are at the county level from four Midwestern states: Indiana for the
years 1853 and 1858, Illinois for the years 1856 and 1857, Ohio for 18613, and Wisconsin
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͵
for 1857.4 The two main categories on which I focus are livestock (e.g., horses, mules
and donkeys, cattle, sheep, and swine) and crops (e.g., wheat, rye, corn, oats, potatoes,
and hay). Data were available for both quantity and value. Prices were inferred from
these values, but preliminary analysis indicated that available regressors had little
explanatory power.These results are therefore omitted.
The focus of my analysis will be on wheat, corn, swine, and mules. Wheat and
swine were the primary cash crops of this era, and corn was used primarily for hog-feed. I
have already mentioned the significance of mules as a substitute for the mechanical
reaper. Sheep, swine, rye, oats, potatoes, and hay had insignificant results and are omitted
from the discussion, but they are included in the appendix. Because the focus of my
research is on farming, highly populated urban counties (such as Cleveland’s Cuyahoga
County in Ohio) were omitted from the regressions.5
I use the number of banks in each county as a measure of credit availability.
These data are taken from Warren Weber’s bank census dataset from the Federal Reserve
Bank of Minneapolis. I also use a measure for drought levels in the observed years as an
exogenous shock to measure the magnitude of the effect on the dependent variable at
varying levels of credit availability. This drought measure is taken from previous work
that uses gaps in tree-rings as a measure for drought and precipitation (Zhang et al.
2004).6 This measure equals 0 when precipitation is average for that location and is
inversely related to inferred precipitation so that greater positive values indicate less
precipitation and more severe drought. County-level populations are taken from census
data from the University of Virginia Library for the years 1850 and 1860. I linearly
interpolate the populations for 1853, 1856, 1857, and 1858 and extrapolate for 1861.
Acreage measurements are from the United States Census Bureau (2012).7 I also create
interactions between the bank counts, the drought index, acreage and population.
Because the farming data are taken from different states’ documents, they do not
have exactly the same coverage of variables. For that reason, observations of the
dependent variables range from 208 to 523 county-year observations. These documents
are retrieved from Google Books. The original documents have been uploaded quite
recently and are not titled in a way that facilitates search. They are therefore quite
difficult to find. As Google Books expands and the cataloging improves, it may be
possible to add more observations in the future.
Summary statistics for the non-urban counties of all of the variables are listed in
Table 1. Some of these statistics are worth noting. First, the number of banks ranges
from zero to seven banks with a mean of about one. This wide variation makes it possible
to see how banks affect output. Additionally, the drought index ranges from -1 to 3
(where 3 is a very bad drought), allowing for analysis of bank effects during a negative
ͶListed as Annual Reports of their respective state and year in the works cited.
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͸In particular, county drought is determined at the county centroid as the weighted
average of posted measures.
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shock. Of the livestock, swine are the most prevalent; however, horses, cattle, and mules
and donkeys all have higher values. Finally, wheat, corn, and oats make up the majority
of the crop production. Corn is by far the largest crop, and I will focus on wheat and corn
as they make sense within the historical and technological context.
4. Methods and Theory
The focus of my research is to show that credit availability affects both the level
of output and the magnitude of the changes to output from precipitation shocks. I use a
total count and a count per acre of the various farming outputs as separate dependent
variables.8 I use yield as well as count to account for systematic differences in county size
and to reflect that productivity changes should be equally apparent in yields as in totals. I
control for the population (Pop), the drought index (Drought), the number of acres per
county (Acres), and the year (Year) as a proxy for secular technological improvement. I
also include fixed effects for each state (State). If the coefficient on banks is positive,
then more banks (Banks), and consequently greater financial depth, in a county are
associated with greater output. The coefficient is estimated using the following equation:
(1) ܳ௖௧ ൌ ߚଵ ‫ݏ݁ݎܿܣ‬௖ ൅ ߚଶ ܲ‫݌݋‬௖௧ ൅ ߚଷ ‫ݐ݄݃ݑ݋ݎܦ‬௖௧ ൅ ߚସ ‫ݏ݇݊ܽܤ‬௖௧ ൅ ߚହ ܻ݁ܽ‫ݎ‬௧ ൅
ߚ଺ ܵ‫݁ݐܽݐ‬௖ ൅ ߝ௖௧
The quantity of output (ܳ௖௧ ሻ is the dependent variable, and regressions were run
for mules, corn, swine and wheat.9 The focus of the first set of regressions is the
coefficient on the number of banks. The coefficient for the number of banks is expected
to be positive for crops or livestock that benefit from additional credit availability and
negative for those which are damaged by the presence of banks (perhaps a substitute of a
crop or animal has increased benefits from financial depth).
The second set of regressions explains the increase in the magnitude of the effect
on the dependent variable from economic shocks as credit availability increases. I will be
using the same variables as in equation (1), but this time I include all of the interactions
between the acres, population, number of banks, and drought index variables. The
coefficients are estimated using the following equation:
(2) ܳ௖௧ ൌ ߚଵ ‫ݏ݁ݎܿܣ‬௖ ൅ ߚଶ ܲ‫݌݋‬௖௧ ൅ ߚଷ ‫ݐ݄݃ݑ݋ݎܦ‬௖௧ ൅ ߚସ ‫ݏ݇݊ܽܤ‬௖௧ ൅ ߚହ ܻ݁ܽ‫ݎ‬௖௧ ൅
ߚ଺ ܵ‫݁ݐܽݐ‬௖ ൅ ߙଵ ܲ‫݌݋‬௖௧ ‫ݏ݁ݎܿܣ כ‬௖ ൅ ߙଶ ܲ‫݌݋‬௖௧ ‫ݐ݄݃ݑ݋ݎܦ כ‬௖௧ ൅ ߙଷ ܲ‫݌݋‬௖௧ ‫כ‬
‫ݏ݇݊ܽܤ‬௖௧ ൅ ߙସ ‫ݏ݁ݎܿܣ‬௖ ‫ݐ݄݃ݑ݋ݎܦ כ‬௖௧ ൅ ߙହ ‫ݏ݁ݎܿܣ‬௖ ‫ݏ݇݊ܽܤ כ‬௖௧ ൅ ߙ଺ ‫ݐ݄݃ݑ݋ݎܦ‬௖௧ ‫כ‬
‫ݏ݇݊ܽܤ‬௖௧ ൅ ߝ௖௧
The focus of this equation is the coefficient on the interaction of the number of
banks and the drought index (ߙ଺ ). A positive coefficient suggests that banks have a
mitigating effect on poor farming conditions for that particular farming output. In other
words, when there is a drought, the output will not decrease as greatly if there is more
financial depth. This effect is expected for crops that require a significant amount of
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capital to fund the harvest, as the availability of credit allows farmers to borrow the
capital required to harvest the crop and pay back after the crop is sold. In the absence of
financial depth, farmers would simply take the hit of a poor farming year as they would
be unable to fund their harvest. A negative coefficient, on the other hand, says that more
credit availability magnifies the effects of a negative shock on output. It is expected that
farmers increase their leverage when interest rates are lower, possibly over-extending
themselves. Therefore, they would experience larger losses from a negative shock like a
drought, and we expect the interaction coefficient to be negative. It is expected that the
remaining dependent variables will have either negative or insignificant interaction
coefficients for this reason.
In addition to total counts and yields, I also used the natural log of the dependent
variable to see if it could provide a better explanation than the linear model. When
running these regressions, all of the population and acreage variables, including the
interactions, were also in log form, while the rest of the variables remained linear. Later
estimation indicates that linear specifications broadly dominate the log-linear
specifications in goodness-of-fit. Therefore, these results have been largely omitted from
the discussion but are included in the appendix.
5. Results
The results from the regressions described in the previous section align with what
was expected from the economic theory. The dependent variables I have focused on are
mules, corn, swine, and wheat. Regressions were run for all of the other variables as well
and are included in the appendix. First, I will look at the effects on the mules and corn, as
these are typically used as production inputs as opposed to swine and wheat, which are
the “cash crops” and account for most of the farmers’ income. Because neither benefit
from the mechanical reaper and both are required as inputs in production, it is expected
that credit availability will have little effect, with the possibility of a negative effect on
mules if they are a substitute for the new technology.
The results for mules are shown in Table 2. Columns (1)-(2) show the simple
OLS regressions results. It is reassuring to see coefficients for count that are positive and
statistically significant for both acreage and population. This is expected as generally
larger and more populated counties would have more mules. The coefficient on year is
also positive, which is consistent with an upward trend in production over time. The fixed
effect for Illinois is consistently positive across all four regressions, suggesting that
Illinois has more mules than Wisconsin. Finally the drought coefficient is positive,
suggesting an increase in mules when there is a drought. This will further support the
conclusion of mules as an inferior substitute for the mechanical reaper, as they become
more prevalent during poor farming conditions.
The coefficient on number of banks is both significant and negative for both
headcount and yield, which suggests that mules were an inferior substitute for the
mechanical reaper. In other words, without a bank, farmers could not take a loan to afford
the reaper and had to “settle” for mules to perform the same tasks. The coefficient on the
number of banks is -40.3 (from column (1)). The mean number of mules in a county is
͸
223, so estimates suggest that each additional bank reduces the expected headcount by
almost 20%.10
Columns (3)-(4) are the results for the OLS regression with interaction terms.
Both regressions show a negative coefficient for the drought*bank interaction term,
though neither is statistically significant. The negative coefficient suggests that banks
magnify the negative impact of a drought for mules; however, with no significant
evidence, a definitive conclusion cannot be drawn. Still, the negative coefficient is
consistent with the story that farmers with high credit availability over-leveraged
themselves, making their losses greater during a negative economic shock as they needed
to sell mules to get cash to fund their harvest.
Table 3 displays the results of the corn regressions. The simple OLS results are in
columns (1)-(2). Again, there is as statistically significant positive coefficient for
population. In column (2) there is a statistically significant negative coefficient for acres,
but, as this is the regression for yield per acre, this result is not alarming. In fact, it is very
likely that bigger counties have a lower yield per acre due to large areas that are not
farmed. Again there is a highly statistically significant coefficient for year demonstrating
an upward trend over time. Additionally, state fixed effects are consistently positive for
both Indiana and Ohio, with a consistently larger coefficient for Indiana. This suggests
that both Ohio and Indiana produce more corn than Wisconsin, and Indiana produces
more than Ohio. Like for mules, the coefficient on drought for corn is positive (though
not as significant).
The coefficient for the number banks is negative but not significant. This suggests
financial depth does not impact the production of corn, which was expected as corn does
not benefit from the new threshing technologies as greatly as wheat does. To the extent
that farmers shift land from corn to wheat to exploit threshing advances, this substitution
appears to be minor. This is further supported by the OLS results with the interaction
terms Drought*Banks in columns (3)-(4), which are also statistically insignificant. As a
crop, corn cannot be sold early for cash, so we do not expect to see the same magnifying
result as might have been seen with mules.
I will now examine the results for swine and wheat. Table 4 displays the results
for swine. Columns (1)-(2) display the simple OLS results, and again we see statistically
positive coefficients of counts for both acreage and population. The coefficient for year is
slightly negative, though not significant. This suggests little change in the number of pigs
over time. The state fixed effects are consistently positive for Indiana and negative for
Illinois, suggesting Indiana has the most swine, followed by Ohio and then Illinois. The
coefficient on drought is positive and significant for all four regressions, again suggesting
an increase in the number of pigs during a drought.
The coefficients on banks in columns (1)-(2) are all negative but statistically
insignificant. This is still consistent with the reaper story, as swine do not benefit from
the technology and therefore should not be affected by credit availability except for the
minor effect of farmers shifting production from corn and hogs to wheat. The coefficients
on the interaction terms in columns (3)-(4) are not statistically significant so it appears
that banks do not affect the magnitude of the negative effects resulting from a drought.
ͳͲIt is worth noting the results for mules without interactions for the log-linear model had
a better fit; its coefficient on banks was negative but not significant.
͹
Although none of the variables I have focused my discussion on show this
magnifying result that was expected, other variables did produce statistically significant
coefficients on the Drought*Banks interaction term, and these results are listed in the
appendix. Specifically, a look at the cattle results shows a statistically significant negative
coefficient for headcount and yield (See Table A3). This does show evidence that banks
can magnify the losses of some livestock during a drought, which is likely due to farmers
overleveraging themselves when there is credit availability. When a drought occurred,
they would have to sell off some livestock for cash that can be used to fund the harvest of
the more lucrative crops.
The results for wheat are summarized in Table 5. The coefficients on acres,
population and year are again positive and significant, suggesting an upward trend over
time as well as more wheat in larger counties. The negative coefficients on the state
effects suggest that Wisconsin produces the most wheat, followed by Indiana and then
Ohio. The coefficient on drought is both negative and statistically significant, which
suggests less wheat is produced in poor farming conditions.
Unlike corn and swine, estimates of wheat’s coefficients in Columns (1)-(2)
show a highly significant positive coefficient for the number of banks. This suggests that
credit availability does have a positive impact on wheat, which was expected due to the
benefit that wheat production receives from the incorporation of the mechanical reaper.
The coefficient on the number of banks is about 28000. Given that the mean of wheat is
about 175,000, estimates indicate that each bank increases the expected bushels of wheat
by roughly 15% of the mean.11 Columns (3) and (4) show positive and significant
coefficients for banks interacted with the drought variable. This suggests that credit
availability mitigates the negative effects of a drought, which is consistent with the story
that farmers can take loans during a crisis in order to fund their harvest of wheat.
One of the possible issues with these results is the endogeneity of banks. It is
possible that counties have unobservable characteristics, such as rich soil, that cause
higher farming outputs and that attract banks to that county; this would also explain the
correlation between banks and wheat. One solution to this problem is to run a differenced
equation of the same counties across years and look at the changes in banks compared to
the changes in farming output. Unfortunately, due to data limitations previously
discussed, only Illinois and Indiana have observations for two years each. Additionally,
Illinois does not have data on crops, further reducing the number of observations for the
wheat variable, which is the focus of my analysis. Also, Indiana lacks data on mules,
which is another strong and relevant result to my discussion. Therefore the single-state
differenced-data will lack a sufficient sample size to achieve significant results. For this
reason, I have not included any single-state differenced regressions.
There are other ways to address the endogeneity problem besides the differenced
model. As previously described, the models for wheat and corn are as follows:
ܹ௜௧ ൌ ܺ௜௧ ߚௐ ൅ ߦ௜ௐ ൅ ߝ௜௧ௐ ͳͳThe coefficient for the log-linear model is not consistent with these results, but a
comparable goodness-of-fit was calculated and the linear model is a much better fit than
the log-linear model. This inconsistency is therefore not overly problematic. The loglinear results are included in the appendix.
ͺ
‫ܥ‬௜௧ ൌ ܺ௜௧ ߚ஼ ൅ ߦ௜஼ ൅ ߝ௜௧஼
Where ܺ௜௧ are the observed regressors (number of banks, population, etc.) and ߦ௜ௐ and ߦ௜஼
are the county specific unobservable characteristics for wheat and corn. The concern is
that endogenous banking creates a correlation between ܺ௜௧ and ߦ௜ௐ , which would bias
ଵ
ߚௐ upward. Assumingߦ௜஼ ൌ ߦ௜ௐ , or that the relevant part of these county specific
ఏ
unobservables between corn and wheat is proportionate, the model for corn can be
rearranged as follows.
ͳ
‫ܥ‬௜௧ ൌ ܺ௜௧ ߚ஼ ൅ ߦ௜ௐ ൅ ߝ௜௧஼ ߠ
ߠ‫ܥ‬௜௧ ൌ ܺ௜௧ ߠߚ஼ ൅ ߦ௜ௐ ൅ ߠߝ௜௧஼ Then, taking the difference between ܹ௜௧ and ߠ‫ܥ‬௜௧ and rearranging, I can run a simple
OLS regression using the following model.
ܹ௜௧ െ ߠ‫ܥ‬௜௧ ൌ ܺ௜௧ ߚௐ െ ܺ௜௧ ߠߚ஼ ൅ ߦ௜ௐ െ ߦ௜ௐ ൅ ߝ௜௧ௐ െ ߠߝ௜௧஼ ܹ௜௧ ൌ ߠ‫ܥ‬௜௧ ൅ ܺ௜௧ ሺߚௐ െ ߠߚ஼ ሻ ൅ ߥ௜௧ The results for these regressions of wheat with corn as a dependent variable are
displayed in Table 6. The coefficients for year and population are positive and
significant, while the coefficient for drought is negative and significant. The state dummy
coefficients are also negative, suggesting that Wisconsin produces the most wheat,
followed by Indiana and Ohio. The coefficient for bushels of corn is both positive and
statistically significant, suggesting that the more corn a county produces the more wheat
it produces as well. Or, in other words, counties that farm a lot of everything farm a lot of
wheat.
In columns (1) and (2), there is a positive and statistically significant coefficient
on the number of banks. As these are differential results, this positive coefficient suggests
that the presence of banks is more beneficial to wheat than it is to corn. This supports the
original conclusion that credit availability facilitated the adoption of the mechanical
reaper. Additionally in columns (3) and (4) the coefficient on the Drought*Banks
interaction term suggests that increased credit availability mitigates the effects of a
negative shock, like a drought, due to farmers overleveraging themselves. This further
supports the story that bankers would take loans to facilitate the harvesting of wheat, as
the benefits from the mechanical reaper were much greater for wheat than for corn. The
consistency of these results that eliminate the concern of the endogeneity of banks further
supports the role that banks play in the adoption of new technologies.
6. Robustness Checks
A number of robustness checks were run in order to provide further support of my
previous results. One concern was that because most counties have very few or no banks,
that a continuum for number of banks may not be as accurate as a dummy for the
existence of banks. To address this, I re-ran the regressions with a dummy for at least one
bank, again for exactly one bank and more than one bank, and then finally for exactly
ͻ
one, exactly two and more than two banks. The results of this final regression are
included in the appendix.
Looking at the results for mules when all three dummies are included, we see that
the number of banks still has a negative effect on mules. The coefficients on all three
dummies are negative, and the coefficients on two banks and many banks are statistically
significant, which further supports that increased credit availability does negatively affect
the number of mules. The results for swine are somewhat surprising. The coefficient for
all of the bank dummies is negative, but it is highly significant for counties that have
exactly one bank. This is contradictory to the original results that banks have no effect on
the number of swine; however, the inconsistency as the number of banks rises does not
support this so it is difficult to make a conclusion.
The results for wheat are not quite as nice, as the coefficient for one bank is
negative and statistically significant, suggesting that the one bank negatively impacts the
amount of wheat in a county. This is difficult to explain, but the coefficients for two
banks and many banks are both positive and the coefficient for many banks is statistically
significant. Despite the inexplicable negative effect from one bank, we do still see that
increasing the number of banks and credit availability does still have a positive effect on
wheat, suggesting financial depth does help facilitate the adoption of the mechanical
reaper. Furthermore the results for corn are insignificant and inconsistent across the
varying dummies. This is consistent with my original conclusion that banks have no
effect on the output of corn.
7. Conclusion
My analysis has helped to show the important role that banks play in the adoption
of new farming innovations. Namely, the presence of banks allows for greater financial
depth and increases the speed at which new technology is adopted and incorporated into
the economy. I have also shown that there can be a downside to the presence of banks.
The increased leverage from high credit availability leads to magnified losses during
negative shocks. It is therefore difficult to say whether the banks are the ally or the
antagonist in our economy, but it is clear that they play a key role in the speedy adoption
of innovation.
Further research should be applied to extend my results. As previously stated,
there are significant data limitations to my analysis. In the future when the data are more
readily accessible, others could extend my research to provide a statistically significant
differenced model. Others could also look at different time periods and the invention and
adoption of other farming innovations.12 If similar results could be replicated with other
innovations, we could begin to see the impact of banks and financial depth on innovation
adoption across all industries.
ͳʹŠ‡‡š–„‹‰ƒ†˜ƒ…‡‡–‹•–Š‡ˆ‹”•–Š‘”•‡Ǧ†”ƒ™…‘„‹‡ǡ™Š‹…Š™ƒ•‹˜‡–‡†
‹ͳͺͺͶǤ
ͳͲ
8. Works Cited
Brassley, Paul. "Silage in Britain, 1880—1990: The Delayed Adoption of an
Innovation." The Agricultural History Review (1996): 63-87.
Croppenstedt, A., M. Demeke and M. Meschi (2003). "Technology adoption in the
presence of constraints: The case of fertilizer demand in Ethiopia." Review of
Development Economics 7: 58-70.
de Janvry, A., C. McIntosh and E. Sadoulet (2010). "The supply- and demand-side
impacts of credit market information." Journal of Development Economics 93:
173-188.
Dries, L., T. Reardon and J. Swinnen (2004). "The rapid rise of supermarkets in Central
and Eastern Europe: Implications for the agrifood sector and rural development."
Development Policy Review 22(5): 525-556.
Halberstadt, Hans (2003). “The American Family Farm”. MBI Publishing Company. p.
18.
Hadlock, Charles J., and Christopher M. James. "Do banks provide financial slack?" the
Journal of Finance 57.3 (2002): 1383-1419.
Hall, Bronwyn H., and Beethika Khan. “Adoption of new technology”. No. w9730.
National Bureau of Economic Research, (2003).
Mahajan, Vijay, Eitan Muller, and Frank M. Bass. "Diffusion of new products: Empirical
generalizations and managerial uses." Marketing Science14.3_supplement (1995):
G79-G88.
ƒ–‹‘ƒŽ‰”‹…—Ž–—”‡‹–Š‡Žƒ••”‘‘ǡ‹•–‘”‹…ƒŽ‹‡Ž‹‡Ȃƒ”ƒ…Š‹‡”›ƒ†
‡…Š‘Ž‘‰›ǤŠ––’•ǣȀȀ™™™Ǥƒ‰…Žƒ••”‘‘Ǥ‘”‰Ȁ‰ƒȀ–‹‡Ž‹‡Ȁˆƒ”̴–‡…ŠǤŠ–
‡„ǤͲͳƒ”…ŠǤʹͲͳͶ
Rajan, Raghuram and Rodney Ramcharan. “The Anatomy of a Credit Crisis: The Boom
and Bust in Farm Land Prices in the United States in the 1920s” NBER Working
Paper 18027 (2012).
ScientificAmerican,Volume75ǤǤ͹Ͷȋ—Ž›ʹ͹ǡͳͺͻ͸ȌǤ
United States Census BureauǤ"County Totals Datasets: Population, Population Change
and Estimated Components of Population Change: April 1, 2010 to July 1,
2012". 2012 Population Estimates.
Young, H. Peyton. "Innovation diffusion in heterogeneous populations: Contagion, social
influence, and social learning." The American Economic Review (2009): 18991924.
Zhang, Z., Michael Mann and Edward Cook. 2004. Alternative Method United States
Summer PDSI Reconstructions. IGBP PAGES/World Data Center for
Paleoclimatology Data Contribution Series # 2004-046. NOAA/NGDC
Paleoclimatology Program, Boulder CO, USA
ͳͳ
Annual Reports:
Annual Reports of the Officers of State of the State of Indiana (1853) By Indiana Google
Books Link
Annual Report of the Auditor of State By Indiana. Dept. of Audit and Control (1858)
Google Books Link
Reports Made to the General Assembly of Illinois, Volume 1 By Illinois. General
Assembly (1859) Google Books Link
Annual Report of the Commissioner of Statistics, to the Governor of the State of Ohio By
Ohio. Commissioner of Statistics Google Books link
Public Documents of the State of Wisconsin: Being the Reports of the Various By
Wisconsin Google Books Link
ͳʹ
Table 1 - Summary Statistics
Variable
#
Obs.
Mean
St. Dev.
Min
Max
States
Independent Variables:
Year
Population (‘000s)
Acres (000s)
Number of Banks
Drought Index
512
512
512
512
512
1,857
15
282
1
1
2
8
127
1
1
1,853
1
0
0
-1
1,861
54
758
7
3
All
All
All
All
All
Interactions:
Population*Acres
Population*Drought
Population*Banks
Acres*Drought
Acres*Banks
Drought*Banks
512
512
512
512
512
512
4,578
8
11
239
176
0
3,892
-16
25
-393
373
1
0
27
0
370
0
-3
26,610
84
227
1,538
3,723
7
All
All
All
All
All
All
Livestock (Number)
Horses
Cattle
479
448
4,733
13,021
2,953
7,663
25
777
14,655
49,539
Sheep
448
15,627
22,634
192
169,697
Swine
448
23,421
18,478
874
228,692
Mules and Donkeys
275
223
252
3
1,508
All
IL, IN,
OH
IL, IN,
OH
IL, IN,
OH
IL, OH
Crops:
Wheat (Bushels)
291
173,697
175,360
90
1,072,415
Rye (Bushels)
287
6,096
9,651
10
60,936
Corn (Bushels)
287
584,519
560,853
293
3,210,717
Oats (Bushels)
291
141,013
159,110
160
782,394
Potatoes (Bushels)
291
52,294
52,207
1,556
284,457
Hay (Tons)
206
5,809
10,207
8
90,436
ͳ͵
IN, OH,
WI
IN, OH,
WI
IN, OH,
WI
IN, OH,
WI
IN, OH,
WI
IN, WI
Table 2 - Headcount of Mules13
(1)
VARIABLES
Acres
Population
Drought Index
Number of Banks
Illinois
Year
Count
(2)
Count per
1,000 Acres
0.40***
(0.11)
11.11***
(2.77)
273.20***
(33.31)
-40.30***
(12.94)
453.70***
(149.10)
187.70***
(37.57)
-0.35
(0.24)
21.50***
(5.36)
718***
(77.90)
-90.10***
(31.40)
1,172***
(378)
494***
(91.70)
Count
(4)
Count per
1,000 Acres
-919,400***
(170,600)
-1.24***
(0.41)
-13.85**
(6.63)
14.97
(46.32)
59.63
(57.09)
364.60***
(128.90)
200.20***
(34.20)
0.048**
(0.020)
7.94***
(1.72)
-0.023
(1.71)
0.58***
(0.16)
-0.18
(0.11)
-19.59
(17.13)
-371,962***
(63,616)
-2.16**
(0.90)
-5.62
(15.90)
357***
(119)
69.30
(146)
962***
(369)
515***
(91.10)
.032
(.044)
18.30***
(4.62)
-1.97
(4.34)
0.64*
(0.37)
-.059
(0.23)
-66.70
(44.70)
-957,100***
(169,500)
-349,353***
(69,941)
275
0.374
275
0.302
275
0.539
275
0.380
Population*Acres
Population*Drought
Population*Banks
Acres*Drought
Acres*Banks
Drought*Banks
Constant
Observations
R-squared
Robust standard errors in parentheses.
*** p<0.01, ** p<0.05, * p<0.1
(3)
ͳ͵…Ž—†‡•‘„•‡”˜ƒ–‹‘•ˆ”‘ȋǯͷ͸Ƭǯͷ͹Ȍƒ†ȋǯͷ͹ȌǢ‹•„‡…Šƒ”•–ƒ–‡Ǥ
ͳͶ
Table 3 - Bushels of Corn14
(1)
VARIABLES
Acres
Population
Drought Index
Number of Banks
Indiana
Ohio
Year
Bushels
(2)
Bushels per
Acre
338
(329)
24,489***
(5,271)
47,638
(83,738)
-13,932
(20,935)
511,511***
(89,053)
370,816**
(187,310)
78,012***
(13,780)
-3.34***
(1.27)
80.62***
(19.02)
140
(307)
-16.68
(79.43)
2,201***
(349)
867
(667)
388***
(53)
Bushels
(4)
Bushels per
Acre
-719,936***
(97,886)
230
(434)
24,855**
(12,273)
322,518**
(148,510)
25,425
(49,136)
502,274***
(87,545)
356,250**
(180,574)
71,897***
(13,812)
6.93
(36.27)
-10,183
(7,546)
-5,331*
(2,721)
-394
(535)
299
(217)
-32,929
(38,887)
-1.338e+08***
(2.563e+07)
-1.52
(1.87)
166***
(42)
276
(531)
-105
(240)
2,225***
(346)
966
(663)
369***
(53)
-0.25**
(0.12)
-53.16*
(28.52)
-25.46***
(9.33)
2.93
(1.92)
2.31***
(0.81)
-122
(136)
-686,135***
(99,104)
-1.452e+08***
(2.556e+07)
287
0.490
284
0.433
287
0.504
284
0.459
Population*Acres
Population*Drought
Population*Banks
Acres*Drought
Acres*Banks
Drought*Banks
Constant
Observations
R-squared
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
(3)
ͳͶ…Ž—†‡•‘„•‡”˜ƒ–‹‘•ˆ”‘ȋǯͷ͵ƬǯͷͺȌǡȋǯ͸ͳȌƒ†ȋǯͷ͹ȌǢ‹•„‡…Šƒ”
•–ƒ–‡
ͳͷ
Table 4 - Headcount of Swine15
(1)
VARIABLES
Acres
Population
Drought Index
Number of Banks
Illinois
Indiana
Year
Count
(2)
Count per
Acre
18.28***
(6.25)
843***
(132)
6,695***
(1,355)
-1,411
(864)
-18,455***
(5,605)
6,184
(5,207)
-81.01
(674)
-0.099***
(0.019)
2.21***
(0.45)
19.63***
(4.58)
-4.91
(3.50)
-54.48***
(19.87)
42.18*
(22.82)
0.92
(3.16)
Count
(4)
Count per
Acre
-1,636
(5,879)
26.91*
(16.11)
1,160***
(398)
9,772***
(2,465)
-285
(2,859)
-18,318***
(5,961)
6,916
(5,393)
-237
(659)
-0.27
(1.08)
-47.62
(126)
-237*
(122)
-9.51
(7.89)
9.02
(7.16)
794
(880)
442,579
(1.228e+06)
-0.12*
(0.07)
5.77***
(1.53)
19.09*
(9.94)
-8.62
(12.23)
-37.13*
(22.39)
50.43**
(23.53)
0.62
(3.09)
-0.0054
(0.0037)
-0.91**
(0.46)
-1.09**
(0.47)
0.032
(0.034)
0.071***
(0.026)
2.12
(3.62)
-1,099
(5,758)
157,798
(1.254e+06)
448
0.193
448
0.183
448
0.212
448
0.205
Population*Acres
Population*Drought
Population*Banks
Acres*Drought
Acres*Banks
Drought*Banks
Constant
Observations
R-squared
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
(3)
ͳͷ…Ž—†‡•‘„•‡”˜ƒ–‹‘•ˆ”‘ȋǯͷ͸Ƭǯͷ͹Ȍǡȋǯͷ͵ƬǯͷͺȌƒ†ȋǯ͸ͳȌǢ‹•
„‡…Šƒ”•–ƒ–‡
ͳ͸
Table 5 - Bushels of Wheat16
(1)
VARIABLES
Acres
Population
Drought Index
Number of Banks
Indiana
Ohio
Year
Bushels
(2)
Bushels per
Acre
389***
(149)
8,607***
(1,550)
-73,149**
(30,215)
27,692**
(10,706)
-95,631***
(35,249)
-272,288***
(62,055)
34,141***
(4,279)
-1.31**
(0.51)
34.02***
(5.39)
-314***
(99)
56.33**
(27.82)
-297**
(139)
-1,121***
(214)
162***
(15.39)
Bushels
(4)
Bushels per
Acre
-299,318***
(28,494)
170
(119)
12,802***
(3,394)
-125,707***
(46,686)
-39,940**
(19,924)
-49,617
(37,256)
-199,679***
(60,164)
33,039***
(3,941)
-13.06
(9.95)
-3,039
(3,352)
-98.41
(855)
308*
(177)
217***
(76)
55,455***
(14,688)
-6.132e+07***
(7.304e+06)
-0.66
(0.61)
66.86***
(12.81)
-348*
(196)
-113
(92.01)
-149.90
(137)
-867***
(216)
151***
(15.76)
-0.11***
(0.035)
-9.34
(10.40)
-2.25
(2.95)
0.61
(0.73)
0.72**
(0.32)
155***
(53)
-280,262***
(29,202)
-6.336e+07***
(7.929e+06)
291
0.600
288
0.502
291
0.687
288
0.559
Population*Acres
Population*Drought
Population*Banks
Acres*Drought
Acres*Banks
Drought*Banks
Constant
Observations
R-squared
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
(3)
ͳ͸…Ž—†‡•‘„•‡”˜ƒ–‹‘•ˆ”‘ȋǯͷ͵ƬǯͷͺȌǡȋǯ͸ͳȌƒ†ȋǯͷ͹ȌǢ‹•„‡…Šƒ”
•–ƒ–‡
ͳ͹
Table 6 - Wheat with Corn as Independent Variable17
VARIABLES
Bushels of Corn
(thousands)
Acres
Population
Drought Index
Number of Banks
Indiana
Ohio
Year
(1)
Bushels of
Wheat
(2)
Wheat per
Acre
(3)
Bushels of
Wheat
(4)
Wheat per
Acre
79.500***
(23.300)
379.0**
(149.4)
6,643***
(1,590)
-84,893***
(27,814)
26,923**
(10,747)
-156,184***
(36,776)
-329,936***
(58,759)
28,940***
(4,226)
0.107***
(0.0205)
-0.826*
(0.454)
25.17***
(5.371)
-354.3***
(86.50)
48.28*
(27.93)
-575.9***
(139.7)
-1,288***
(198.0)
123.4***
(15.09)
-5.366e+07***
(7.837e+06)
-228,343***
(27,957)
0.0928***
(0.0224)
177.2
(114.8)
10,958***
(3,123)
-163,159***
(43,851)
-47,913**
(19,763)
-114,479***
(38,339)
-258,754***
(58,077)
27,260***
(3,780)
-15.03
(9.540)
-1,834
(3,418)
389.6
(929.5)
340.9**
(157.4)
203.5***
(72.64)
52,689***
(15,375)
-5.055e+07***
(7.009e+06)
0.106***
(0.0210)
-0.364
(0.565)
50.63***
(12.29)
-449.2***
(169.8)
-144.9*
(79.73)
-432.5***
(139.8)
-1,053***
(202.1)
116.8***
(15.05)
-0.0882***
(0.0333)
-2.689
(10.18)
0.833
(3.161)
0.469
(0.618)
0.558*
(0.295)
129.5***
(49.75)
-216,389***
(27,902)
283
0.590
286
0.733
283
0.640
Population*Acres
Population*Drought
Population*Banks
Acres*Drought
Acres*Banks
Drought*Banks
Constant
Observations
286
R-squared
0.640
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
ͳ͹…Ž—†‡•‘„•‡”˜ƒ–‹‘•ˆ”‘ȋǯͷ͵ƬǯͷͺȌǡȋǯ͸ͳȌƒ†ȋǯͷ͹ȌǢ‹•„‡…Šƒ”
•–ƒ–‡
ͳͺ
9. Appendix
A1: Bank Dummy Robustness Check - Swine and Mules18
VARIABLES
Acres
Population
Drought Index
One Bank
Two Banks
Three or More Banks
Illinois
Indiana
Year
Constant
(1)
No. of Swine
(2)
Swine per Acre
(3)
No. of Mules
(4)
Mules per Acre
18.66***
(6.256)
885.8***
(134.4)
6,636***
(1,366)
-3,919**
(1,887)
-2,284
(4,356)
-3,271
(2,821)
-17,772***
(5,701)
6,641
(5,294)
-27.58
(681.2)
57,959
(1.268e+06)
-0.0970***
(0.0191)
2.364***
(0.465)
19.40***
(4.609)
-14.75**
(7.333)
-3.770
(16.29)
-11.91
(11.26)
-51.81**
(20.43)
43.93*
(23.26)
1.142
(3.194)
-2,042
(5,944)
0.400***
(0.108)
11.17***
(2.755)
273.9***
(33.67)
-41.15
(31.67)
-101.7**
(46.76)
-146.5***
(42.11)
456.8***
(149.9)
-0.000346
(0.000238)
0.0224***
(0.00530)
0.718***
(0.0786)
-0.129*
(0.0730)
-0.198
(0.141)
-0.317***
(0.0943)
1.181***
(0.381)
188.2***
(38.14)
-350,381***
(70,991)
0.494***
(0.0929)
-919.4***
(172.9)
448
0.186
275
0.377
275
0.305
Observations
448
R-squared
0.197
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
ͳͺ…Ž—†‡•‘„•‡”˜ƒ–‹‘•ˆ”‘ȋǯͷ͸Ƭǯͷ͹Ȍǡȋǯͷ͵ƬǯͷͺȌƒ†ȋǯ͸ͳȌǢ‹•
„‡…Šƒ”•–ƒ–‡Ǣ‘‘„•‡”˜ƒ–‹‘•ˆ”‘ˆ‘”—Ž‡•Ǥ
ͳͻ
A2: Bank Dummy Robustness Check - Wheat and Corn19
VARIABLES
Acres
Population
Drought Index
One Bank
Two Banks
Three or More Banks
Indiana
Ohio
Year
Constant
(1)
Bushels of Wheat
(2)
Wheat per Acre
(3)
Bushels of Corn
(4)
Corn per Acre
385.1***
(147.7)
10,063***
(1,540)
-70,852**
(28,646)
-38,594**
(18,924)
14,267
(32,111)
82,069**
(40,753)
-101,688***
(35,039)
-285,961***
(59,566)
34,844***
(4,189)
-6.466e+07***
(7.763e+06)
-1.304***
(0.502)
37.03***
(5.456)
-308.6***
(96.09)
-95.50
(67.75)
25.88
(134.0)
175.8
(118.9)
-303.4**
(135.8)
-1,144***
(208.2)
163.4***
(15.16)
-302,656***
(28,083)
358.4
(350.7)
23,601***
(5,374)
38,729
(85,673)
46,707
(76,352)
64,604
(141,172)
-82,791
(86,171)
504,186***
(90,659)
359,187*
(192,258)
77,854***
(13,793)
-1.449e+08***
(2.558e+07)
-3.397***
(1.299)
77.18***
(19.35)
99.73
(313.9)
339.7
(280.6)
94.90
(446.7)
-148.8
(334.0)
2,117***
(349.0)
776.5
(685.7)
384.8***
(52.53)
-714,311***
(97,409)
288
0.505
287
0.493
284
0.438
Observations
291
R-squared
0.603
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
ͳͻ…Ž—†‡•‘„•‡”˜ƒ–‹‘•ˆ”‘ȋǯͷ͵ƬǯͷͺȌǡȋǯ͸ͳȌƒ†ȋǯͷ͹ȌǢ‹•„‡…Šƒ”
•–ƒ–‡
ʹͲ
A3: Dependent Variable - Headcount of Cattle20
VARIABLES
Acres
Population
Drought Index
Number of Banks
Illinois
Indiana
Year
(1)
No. of
Cattle
(2)
Cattle per
Acre
(3)
No. of
Cattle
(4)
Cattle per
Acre
19.06***
(3.655)
533.0***
(57.95)
-2,426***
(502.4)
-249.2
(308.6)
3,038*
(1,764)
-3,466***
(1,067)
-1.809
(111.2)
-0.0522***
(0.00887)
1.490***
(0.161)
-9.461***
(1.655)
-0.939
(0.914)
10.30*
(6.004)
-12.35***
(4.322)
0.135
(0.493)
4,796
(206,897)
-204.4
(916.2)
20.46***
(5.914)
449.2***
(110.4)
-864.1
(859.3)
-1,138
(996.6)
1,007
(1,952)
-4,490***
(1,185)
-77.45
(106.8)
0.173
(0.303)
104.7**
(47.88)
-77.76
(51.20)
-7.947***
(2.852)
9.249*
(4.789)
-640.3**
(248.6)
146,593
(198,768)
-0.0391*
(0.0232)
2.838***
(0.413)
-10.47***
(3.442)
-5.195*
(3.050)
8.261
(7.226)
-15.46***
(4.779)
-0.111
(0.514)
-0.00353***
(0.000946)
0.242
(0.161)
-0.345**
(0.159)
2.49e-05
(0.0117)
0.0396***
(0.0104)
-2.586***
(0.821)
246.1
(954.8)
448
0.479
448
0.725
448
0.509
Population*Acres
Population*Drought
Population*Banks
Acres*Drought
Acres*Banks
Drought*Banks
Constant
Observations
448
R-squared
0.700
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
ʹͲ…Ž—†‡•‘„•‡”˜ƒ–‹‘•ˆ”‘ȋǯͷ͸Ƭǯͷ͹Ȍǡȋǯͷ͵ƬǯͷͺȌƒ†ȋǯ͸ͳȌǢ‹•
„‡…Šƒ”•–ƒ–‡
ʹͳ
A4: Dependent Variable - Headcount of Horses21
VARIABLES
Acres
Population
Drought Index
Number of Banks
Illinois
Indiana
Ohio
Year
(1)
No of Horses
(2)
Horses per Acre
(3)
No of Horses
(4)
Horses per Acre
5.594***
(1.328)
221.1***
(22.08)
-632.8***
(212.1)
-186.2*
(104.1)
2,240***
(480.4)
774.8*
(463.3)
2,955***
(623.1)
-145.6***
(45.80)
-0.0186***
(0.00337)
0.662***
(0.0628)
-2.016***
(0.675)
-0.430
(0.284)
9.859***
(1.399)
6.480***
(1.321)
15.02***
(1.840)
-0.761***
(0.187)
269,035***
(85,014)
1,419***
(346.9)
2.570
(2.279)
201.6***
(48.45)
-219.4
(332.5)
161.6
(261.8)
2,290***
(463.1)
720.4
(497.2)
2,512***
(690.2)
-141.6***
(44.68)
0.208*
(0.119)
-32.52**
(16.28)
-36.97**
(16.09)
-0.196
(1.113)
1.389
(1.360)
-151.5
(103.7)
261,977***
(82,871)
-0.0175**
(0.00823)
1.335***
(0.178)
-2.289*
(1.290)
-1.731*
(0.905)
9.291***
(1.504)
4.210***
(1.626)
11.68***
(2.246)
-0.804***
(0.186)
-0.00138***
(0.000390)
-0.111**
(0.0553)
-0.135***
(0.0392)
0.00587
(0.00432)
0.0132***
(0.00289)
-0.703**
(0.299)
1,496***
(344.7)
474
0.606
479
0.753
474
0.642
Population*Acres
Population*Drought
Population*Banks
Acres*Drought
Acres*Banks
Drought*Banks
Constant
Observations
479
R-squared
0.739
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
ʹͳ…Ž—†‡•‘„•‡”˜ƒ–‹‘•ˆ”‘ȋǯͷ͸Ƭǯͷ͹Ȍǡȋǯͷ͵ƬǯͷͺȌǡȋǯ͸ͳȌƒ†ȋǯͷ͹ȌǢ
‹•„‡…Šƒ”•–ƒ–‡
ʹʹ
A5: Dependent Variable - Headcount of Sheep22
VARIABLES
Acres
Population
Drought Index
Number of Banks
Illinois
Indiana
Year
(1)
No. of Sheep
(2)
Sheep per Acre
(3)
No. of Sheep
(4)
Sheep per Acre
5.158
(6.676)
817.6***
(220.5)
4,828**
(1,985)
189.3
(992.8)
-45,434***
(7,872)
-34,470***
(4,586)
-1,232***
(282.6)
-0.0703***
(0.0243)
2.058***
(0.659)
10.24
(6.824)
1.403
(2.993)
-145.5***
(27.60)
-118.5***
(16.84)
-3.879***
(0.959)
2.320e+06***
(526,061)
7,349***
(1,786)
-24.33
(24.69)
315.9
(492.5)
8,756**
(3,471)
3,407
(3,265)
-40,231***
(10,107)
-30,160***
(6,133)
-1,120***
(292.8)
2.664
(1.898)
-369.1
(282.9)
-237.6
(240.3)
-1.779
(10.62)
3.860
(10.58)
-468.6
(1,043)
2.112e+06***
(545,832)
-0.149*
(0.0859)
4.330**
(1.676)
3.521
(11.99)
4.747
(9.195)
-129.7***
(36.58)
-113.6***
(22.59)
-3.686***
(1.073)
-0.00243
(0.00506)
-0.672
(0.869)
-1.060
(0.720)
0.0506
(0.0425)
0.0570*
(0.0340)
-3.944
(3.197)
6,979***
(2,001)
448
0.444
448
0.491
448
0.455
Population*Acres
Population*Drought
Population*Banks
Acres*Drought
Acres*Banks
Drought*Banks
Constant
Observations
448
R-squared
0.474
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
ʹʹ…Ž—†‡•‘„•‡”˜ƒ–‹‘•ˆ”‘ȋǯͷ͸Ƭǯͷ͹Ȍǡȋǯͷ͵ƬǯͷͺȌƒ†ȋǯ͸ͳȌǢ‹•
„‡…Šƒ”•–ƒ–‡
ʹ͵
A6: Dependent Variable - Tons of Hay23
VARIABLES
Acres
Population
Drought Index
Number of Banks
Indiana
Year
(1)
No. Tons of Hay
(2)
Hay per Acre
(3)
No. Tons of Hay
(4)
Hay per Acre
14.61
(13.57)
327.7***
(110.9)
4,490*
(2,574)
2,038**
(792.3)
-6,334**
(2,578)
-105.0
(361.0)
-0.139
(0.0853)
1.669***
(0.493)
9.267
(11.41)
6.584
(4.010)
-36.78**
(17.56)
1.248
(1.755)
197,349
(670,127)
-2,254
(3,262)
-1.625
(13.41)
474.7
(366.8)
-3,880
(3,535)
3,839
(4,077)
-5,420**
(2,664)
-18.61
(318.3)
0.0119
(1.328)
533.2**
(221.4)
-306.4
(194.8)
-2.411
(14.21)
9.849
(8.728)
4,343
(2,913)
37,876
(589,809)
-0.160
(0.113)
0.756
(2.465)
9.172
(26.23)
37.59
(31.41)
-27.34*
(15.51)
1.312
(1.742)
0.00543
(0.00988)
1.199
(1.020)
-1.608
(1.138)
-0.115
(0.0980)
-0.0176
(0.0597)
29.29
(19.76)
-2,384
(3,232)
203
0.231
206
0.587
203
0.344
Population*Acres
Population*Drought
Population*Banks
Acres*Drought
Acres*Banks
Drought*Banks
Constant
Observations
206
R-squared
0.482
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
ʹ͵…Ž—†‡•‘„•‡”˜ƒ–‹‘•ˆ”‘ȋǯͷ͵ƬǯͷͺȌƒ†ȋǯ͸ͳȌǢ‹•„‡…Šƒ”•–ƒ–‡
ʹͶ
A7: Dependent Variable - Bushels of Oats24
VARIABLES
Acres
Population
Drought Index
Number of Banks
Indiana
Ohio
Year
(1)
Bushels of Oats
(2)
Oats per Acre
(3)
Bushels of Oats
(4)
Oats per Acre
41.91
(106.9)
9,267***
(1,503)
35,673
(29,087)
12,255
(11,658)
-58,949*
(30,523)
42,572
(60,964)
4,147
(3,852)
-1.950***
(0.488)
33.98***
(5.486)
55.37
(100.8)
32.70
(31.29)
-281.3**
(131.0)
-5.343
(216.4)
37.64***
(13.58)
-7.700e+06
(7.138e+06)
-69,298***
(25,158)
-162.0
(132.1)
5,631*
(3,411)
-34,695
(47,921)
-130.1
(23,808)
-31,868
(27,211)
65,352
(54,633)
5,923
(3,648)
10.21
(11.10)
-2,604
(3,055)
731.3
(1,263)
376.1*
(199.9)
-20.48
(107.4)
17,063
(19,351)
-1.096e+07
(6.760e+06)
-1.447***
(0.525)
47.29***
(15.06)
-120.3
(173.3)
10.05
(102.1)
-179.2*
(105.8)
124.5
(195.3)
36.58***
(13.78)
-0.0494
(0.0416)
-10.74
(10.07)
-0.760
(4.005)
1.250*
(0.739)
0.137
(0.410)
71.18
(65.15)
-67,567***
(25,541)
288
0.569
291
0.671
288
0.586
Population*Acres
Population*Drought
Population*Banks
Acres*Drought
Acres*Banks
Drought*Banks
Constant
Observations
291
R-squared
0.650
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
ʹͶ…Ž—†‡•‘„•‡”˜ƒ–‹‘•ˆ”‘ȋǯͷ͵ƬǯͷͺȌǡȋǯ͸ͳȌƒ†ȋǯͷ͹ȌǢ‹•„‡…Šƒ”
•–ƒ–‡
ʹͷ
A8: Dependent Variable - Bushels of Potatoes25
VARIABLES
VARIABLES
VARIABLES
VARIABLES
VARIABLES
Acres
Acres
Acres
Acres
Acres
Population
Population
Population
Population
Population
Drought Index
Drought Index
Drought Index
Drought Index
Drought Index
Number of Banks
Number of Banks
Number of Banks
Number of Banks
Number of Banks
Indiana
Indiana
Indiana
Indiana
Indiana
Ohio
Ohio
Ohio
Ohio
Ohio
Year
Year
Year
Year
Year
Population*Acres
Population*Acres
Population*Acres
Population*Acres
Population*Acres
Population*Drought
Population*Drought
Population*Drought
Population*Drought
Population*Drought
Population*Banks
Population*Banks
Population*Banks
Population*Banks
Population*Banks
Acres*Drought
Acres*Drought
Acres*Drought
Acres*Drought
Acres*Drought
Acres*Banks
Acres*Banks
Acres*Banks
Acres*Banks
Acres*Banks
Drought*Banks
Drought*Banks
Drought*Banks
Drought*Banks
Drought*Banks
Constant
Constant
Constant
Constant
Constant
Observations
R-squared
Observations
R-squared
Observations
R-squared
Robust standard
errors in
parentheses
*** p<0.01, **
p<0.05, * p<0.1
Observations
R-squared
Robust standard
errors in
parentheses
*** p<0.01, **
p<0.05, * p<0.1
Observations
R-squared
Robust standard
errors in
parentheses
*** p<0.01, **
p<0.05, * p<0.1
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
ʹͷ…Ž—†‡•‘„•‡”˜ƒ–‹‘•ˆ”‘ȋǯͷ͵ƬǯͷͺȌǡȋǯ͸ͳȌƒ†ȋǯͷ͹ȌǢ‹•„‡…Šƒ”
•–ƒ–‡
ʹ͸
A9: Dependent Variable - Bushels of Rye26
VARIABLES
Acres
Population
Drought Index
Number of Banks
Indiana
Ohio
Year
(1)
Bushels of Rye
(2)
Rye per Acre
(3)
Bushels of Rye
(4)
Rye per Acre
-7.628
(9.368)
532.8***
(155.5)
-2,580
(2,403)
-967.3
(711.0)
-6,204
(4,285)
-7,462
(6,729)
901.3***
(292.6)
-0.0920**
(0.0377)
1.836***
(0.586)
-10.20
(8.637)
-2.588
(2.434)
-22.76
(16.72)
-32.52
(25.32)
4.222***
(1.084)
-1.669e+06***
(541,311)
-7,802***
(2,004)
-16.00
(12.46)
489.1
(423.6)
-3,283
(3,302)
234.3
(2,330)
-9,886
(6,109)
-11,925
(7,930)
1,035***
(294.3)
0.617
(1.066)
510.5
(370.5)
-49.53
(61.80)
-25.57
(19.72)
-1.049
(8.684)
-2,392
(1,767)
-1.912e+06***
(544,286)
-0.0874*
(0.0449)
3.208**
(1.590)
-13.98
(11.91)
-4.024
(8.524)
-36.55
(22.96)
-45.92
(29.67)
4.403***
(1.104)
-0.00255
(0.00351)
1.784
(1.355)
-0.333
(0.228)
-0.0782
(0.0736)
0.0275
(0.0319)
-8.848
(6.249)
-8,137***
(2,042)
286
0.254
287
0.335
286
0.282
Population*Acres
Population*Drought
Population*Banks
Acres*Drought
Acres*Banks
Drought*Banks
Constant
Observations
287
R-squared
0.303
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
ʹ͸…Ž—†‡•‘„•‡”˜ƒ–‹‘•ˆ”‘ȋǯͷ͵ƬǯͷͺȌǡȋǯ͸ͳȌƒ†ȋǯͷ͹ȌǢ‹•„‡…Šƒ”
•–ƒ–‡
ʹ͹
A10: Dependent Variables - Log of Mules27 and Corn28
VARIABLES
Log Acres
Log Population
Drought Index
Number of Banks
Illinois
(1)
ln(No. of Mules)
(2)
ln(No. of Mules)
(3)
ln(Bushels of Corn)
(4)
ln(Bushels of Corn)
0.592***
(0.155)
0.603***
(0.154)
1.655***
(0.149)
-0.0517
(0.0520)
1.932***
(0.487)
0.0944
(0.791)
0.435
(1.507)
0.617
(1.183)
-0.671
(0.897)
1.764***
(0.548)
0.709***
(0.196)
0.735***
(0.134)
0.419**
(0.173)
0.00384
(0.0442)
1.399***
(0.364)
2.327***
(0.649)
-1.546
(1.796)
-1.164
(0.779)
2.326***
(0.308)
1.749***
(0.429)
0.183***
(0.0255)
Indiana
1.018***
(0.135)
0.0306
(0.252)
0.118
(0.161)
-0.412**
(0.205)
0.141
(0.222)
0.349*
(0.184)
-0.219**
(0.0954)
-1,892***
(251.8)
-335.3***
(47.12)
2.677***
(0.376)
2.197***
(0.463)
0.175***
(0.0239)
-0.318**
(0.127)
-0.638**
(0.262)
-0.0492
(0.165)
0.657
(0.416)
0.239
(0.171)
0.101
(0.0935)
-323.4***
(44.21)
275
0.462
0.488
284
0.448
0.708
284
0.478
0.727
Ohio
Year
1.045***
(0.137)
LogPopulation*LogAcres
LogPopulation*Drought
LogPopulation*Banks
LogAcres*Drought
LogAcres*Banks
Drought*Banks
Constant
-1,945***
(255.2)
Observations
275
Linear Goodness-of-fit
0.453
R-squared
0.475
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
ʹ͹…Ž—†‡•‘„•‡”˜ƒ–‹‘•ˆ”‘ȋǯͷ͸Ƭǯͷ͹Ȍƒ†ȋǯͷ͹ȌǢ‹•„‡…Šƒ”•–ƒ–‡Ǥ
ʹͺ…Ž—†‡•‘„•‡”˜ƒ–‹‘•ˆ”‘ȋǯͷ͵ƬǯͷͺȌǡȋǯ͸ͳȌƒ†ȋǯͷ͹ȌǢ‹•„‡…Šƒ”
•–ƒ–‡
ʹͺ
A11: Dependent Variables- Log of Swine29 and Wheat30
VARIABLES
Log Acres
Log Population
Drought Index
Number of Banks
Illinois
Indiana
(1)
ln(No. of
Swine)
(2)
ln(No. of
Swine)
(3)
(4)
ln(Bushels of Wheat)
ln(Bushels of Wheat)
0.338***
(0.115)
0.617***
(0.0869)
0.531***
(0.0683)
-0.0391
(0.0327)
-1.519***
(0.239)
0.00271
(0.134)
0.651***
(0.243)
1.746***
(0.578)
1.474***
(0.391)
-1.435***
(0.547)
-1.481***
(0.235)
0.00961
(0.138)
0.519*
(0.289)
1.259***
(0.149)
-0.742***
(0.198)
-0.0349
(0.0450)
0.608
(0.416)
2.347***
(0.727)
-2.018
(2.463)
-2.155*
(1.134)
-0.486*
(0.269)
-2.544***
(0.442)
0.391***
(0.0335)
-0.338
(0.309)
-2.297***
(0.449)
0.390***
(0.0343)
-0.0400**
(0.0168)
-0.0475***
(0.0158)
-0.171
(0.108)
-0.0464
(0.0628)
-0.339***
(0.119)
-0.151*
(0.0816)
0.418***
(0.127)
-0.0159
(0.0454)
92.71***
(29.42)
-720.3***
(61.97)
-0.189
(0.140)
-0.0708
(0.246)
-0.125
(0.191)
0.255
(0.517)
0.440*
(0.266)
0.128
(0.101)
-719.7***
(63.93)
80.99***
(31.19)
448
0.188
0.456
288
0.533
0.754
288
0.211
0.777
Ohio
Year
Log
Population*LogAcres
LogPopulation*Drought
LogPopulation*Banks
LogAcres*Drought
LogAcres*Banks
Drought*Banks
Constant
Observations
448
Goodness-of-fit
0.170
R-squared
0.400
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
ʹͻ…Ž—†‡•‘„•‡”˜ƒ–‹‘•ˆ”‘ȋǯͷ͸Ƭǯͷ͹Ȍǡȋǯͷ͵ƬǯͷͺȌƒ†ȋǯ͸ͳȌǢ‹•
„‡…Šƒ”•–ƒ–‡
͵Ͳ…Ž—†‡•‘„•‡”˜ƒ–‹‘•ˆ”‘ȋǯͷ͵ƬǯͷͺȌǡȋǯ͸ͳȌƒ†ȋǯͷ͹ȌǢ‹•„‡…Šƒ”
•–ƒ–‡
ʹͻ