Harvests and Financial Crises in Gold-Standard America
Christopher Hanes,
State University of New York at Binghamton
[email protected]
Paul W. Rhode,
University of Michigan and NBER
[email protected]
March, 2010
Abstract: The American financial crises of 1884, 1893 and 1896 were ultimately caused
by fluctuations in the cotton harvest due to economically exogenous factors such as
weather. The transmission channel did not run through financial institutions specifically
associated with the cotton trade or cottongrowing regions. Rather, it was through the
effect of exogenous export-revenue fluctuations on American financial markets under the
pre-1914 gold standard regime. As a poor cotton harvest depressed export revenues, it
reduced international demand for American assets. That depressed American stock
prices, raised long- and short-term interest rates and weakened the balance sheets of
financial-center banks. This precipitated a business-cycle downturn with the usual time
lag of six months to a year in the effect of monetary shocks on real activity. The business
cycle downturn had further effects on stock prices and bankruptcies, which made the
financial system vulnerable to panics. Both the financial crises and business cycles
caused by cotton harvest shocks could have been avoided by actions of a central bank,
even under gold-standard constraints.
Thanks to Michael Bordo, Joseph Ferrie, Hugh Rockoff and Eugene White for
comments, and to Eugene White and Marc Weidenmier for data.
In the pre-1914 gold standard era, the U.S. suffered several financial crises with
all the symptoms that have become unpleasantly familiar recently: stock price crashes,
tighter credit rationing, runs on financial intermediaries and payment-system
breakdowns. Most pre-1914 crises were roughly coincident with sharp downturns in real
economic activity, suggesting that they were either causes of business cycles or part of
the transmission mechanism linking the business cycles’ ultimate causes to employment
and output. Thus, it is important to identify the crises' causes as part of the business-cycle
history of the U.S. The crises are also of interest because the subsequent development of
American monetary institutions was affected by contemporary views of the crises' causes.
Contemporary observers of pre-1914 crises believed that many were caused partly
by shocks to demand for high-powered money and/or its supply originating in
agriculture. Rural households and businesses made relatively little use of bank accounts,
so payments associated with autumn harvests and spring planting boosted demand for
cash in those seasons. To meet surges in local cash demand, rural banks withdrew
deposits from correspondent money-center banks. Unlike most large gold-standard
countries, America had no central bank. An increase in high-powered money demand
could be accommodated only by an international gold inflow or by a boost to the supply
of Treasury-backed nongold currency. The latter was “inelastic,” insensitive to seasonal
or business-cycle factors. Thus, contemporaries argued, the fall and spring peaks in cash
demand left money-center banks with low reserves. A random decrease in deposits or
increase in loan defaults that would be shrugged off in other seasons could touch off a
financial crisis (Sprague, 1910, p. 127; Kemmerer, 1911, p. 48). Contemporaries also
noted that, apart from these regular seasonal effects on money demand, harvests of export
crops such as cotton and wheat could affect financial conditions through the potential
relation between net export revenue and the high-powered money supply under the gold
standard. Harvard professor A. Piatt Andrew (1906, p. 326) claimed:
the size of the crops will exert a considerable influence upon the balance of trade and the
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international movement of gold. The extent of bank reserves in the great financial centres
and the contraction or expansion of general credit may in consequence depend most
importantly upon the output of the season’s harvests...When the American crops are
abundant, our exports very naturally tend to increase, and gold imports are apt to occur.
That in turn means large cash holdings in the banks, with, under normal conditions, the
accompaniment of expanding credit and bouyant trade. When, on the other hand, the
movement of exports and of gold swings in the contrary direction, and in that event we
are apt to be confronted with dwindling bank reserves, a contingent contraction of the
general credit, declining business, and less activity in trade.
Sprague (1903, 1915) pointed to specific incidents in which harvests affected moneymarket conditions through gold flows.
In theory, all of these agriculture-related disturbances to money markets could
have been counteracted by a central bank, even under gold-standard constraints. Through
discounting or open-market operations, a central bank could adjust the supply of nongold
high-powered money (currency and central-bank reserve balances) to accommodate
regular seasonal shocks to high-powered money demand, or to smooth transitory shocks
to the balance of payments. Thus, proponents of a central bank for the U.S. frequently
invoked the purported links between agricultural shocks and financial crises in the policy
debates leading up to the foundation of the Federal Reserve system.
In modern economics literature, Miron (1986) supports this old view, arguing that
the regular seasonal peaks in rural activity raised short-term interest rates, decreased
money-center bank reserves and raised the probability of panics; that realized panics were
probably the causes of the associated business-cycle fluctuations; and that the creation of
the Fed eliminated these problems. But more common is another view of pre-1914 bank
panics, in which monetary shocks from agriculture play little role. Gorton (1988),
Wilson, Sylla and Jones (1990), Mishkin (1991), and Calomiris and Gorton (1991) argue
that the mass deposit withdrawals marking financial crises were caused by business-cycle
downturns and stock-price crashes occuring weeks before the harvest season: when
depositors perceived that a downturn was likely, they feared resulting loan defaults would
endanger banks’ solvency. Calomiris and Gorton (1991, pp. 130-38) allow that asset-side
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shocks may have been more likely to provoke mass withdrawals when deposits had been
withdrawn for seasonal reasons, but observe that panics were not generally coincident
with seasonal deposit withdrawals, and “years in which panics occurred in the fall..were
not years of unusually large harvests for corn, wheat and cotton” (p. 137). They conclude
that “seasonal money-demand shocks originating in the countryside cannot possibly be
the cause of panics” (p. 148), and discount the argument that money-supply adjustments
by a central bank could have prevented the crises: “The problem is not that depositors
want cash for its own sake..but are concerned that their bank will fail” (p. 165).
In this paper, we re-examine the link between agriculture and American financial
crises of the pre-1914 gold standard era. Our conclusions differ from the old view that
agricultural shocks caused bank panics and hence cyclical downturns. They also differ
from the new view that cyclical downturns, unrelated to harvests’ monetary effects,
caused bank panics. We find that most panics were indeed effects, not ultimate causes, of
cyclical downturns. But downturns that bred panics were a result of earlier financialmarket disturbances caused by fluctuations in harvests, specifically cotton harvests, due
to exogenous factors such as weather.
The transmission channel from the cotton harvest to American financial markets
did not run through financial institutions specifically associated with the cotton trade or
cottongrowing regions. Rather, it was through the effect of exogenous export-revenue
fluctuations on American financial markets under the pre-1914 gold standard regime. A
poor cotton harvest was an exogenous shock to American export revenues. Under the pre1914 American gold standard regime, a negative shock to American export revenues
caused an immediate decrease in international demand for American financial assets. The
decrease in asset demand was manifested in low prices for stock and long-term bonds,
high short-term interest rates, and withdrawals of correspondent deposits from New York
banks. Six months to a year later - the usual time-lag in the effect of monetary shocks on
real activity - the harvest-season financial shock was followed by a downturn in industrial
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activity, continued low stock prices and an increase in business failures. Thus, a poor
cotton harvest created the preconditions for a financial crisis, not within the harvest
season itself, but in the following year.
Importantly, wheat harvest fluctuations did not have these effects on American
financial markets, even though wheat harvests had similar effects on American export
revenues. That was because wheat harvests also had a strong effect on high-powered
money demand. Thus, wheat-harvest fluctuations affected international gold flows and
exchange rates, but not international demand for American financial assets. The fact that
wheat harvests affected export revenues and gold flows but not financial markets shows
that central bank proponents were right: even under gold standard constraints, some pre1914 crises - the ones caused by poor cotton harvests - could have been prevented by
open-market operations.
To begin, we review some of the existing literature on pre-1914 American
financial crises. Next we describe the causes of harvest fluctuations and their possible
effects on financial markets in the pre-1914 monetary regime. Then we examine harvests'
effects on stock markets, bond yields, short-term interest rates, bank deposits and lending,
and failures of banks and other firms.
1) Financial crises in the gold-standard era
Most studies (e.g. Sprague, 1910; Wilson, Sylla and Jones, 1990; Wicker, 2000)
identify five national financial crises in the postbellum era: in 1873, 1884, 1890, 1893
and 1907. These were the five occasions when the New York Clearing House (NYCH)
issued clearinghouse loan certificates. On three of these occasions - 1873, 1893 and 1907
- NYCH members also restricted cash payments. Gorton (1988) identifies a sixth crisis in
1896, when the NYCH authorized clearinghouse loan certificates but did not issue any.1
1
A few studies have defined financial crises differently. To the set of dates defined above, DeLong and
Summers (1986) add months of extraordinary volatility in commercial paper rates. Jalil (2009) defines
crises to be times when contemporary newspaper articles said there were crises. He identifies "major" and
"minor" panics. 1893 and 1907 are on his list of major panics. All of the remaining events listed above are
4
We are particularly interested in crises that occurred while America was part of
the international gold standard regime: between January 1879, when the U.S. Treasury
began to redeem legal-tender currency dollars (“greenbacks”) in gold, and the beginning
of the First World War when most countries suspended gold convertibility. Table 1
shows dates given by various authors for the crises that occured within 1879-1913. The
first column shows months of “panic onset” given by Wicker (2000, p. 9). These are the
same months “panic began” or “crisis began” according to the frequently-cited
chronology of Kemmerer (1910, p. 222). The second column is from Gorton (1988). The
third gives months of NYCH actions.
In this section of the paper explain why it is sensible to define crises in this way.
We argue that existing literature has identified plausible exogenous causes for some of
the crises, but three big ones - 1884, 1893, and 1896 - remain unexplained.
1.1) New York's role in financial crises
New York City was the country's central financial market. As a pre-1914
contemporary observed, “New York is the cheapest market for loans in the United States
and is consequently resorted to by large borrowers from all sections” (Sprague, 1910, p.
221). New York banks were the main conduit of funds to private banks and brokerage
houses, the "shadow banking system" of the era, which engaged in many forms of
financial intermediation.2
New York was also the intermediary between the central financial market of the
gold standard world, London, and the hinterland of America beyond the Hudson.3
Payments for imports and exports were settled through claims to sterling in London, even
if the trade counterparty was outside the British Empire. American importers and
included in minor panics. But his list of minor panics includes a number of other events that were regional
bank panics or clusters of bank failures, not threats to the national financial system.
2
These firms bought long-term bonds and stocks with funds raised from deposits and short-term loans,
made mortgage loans and sold them on or packaged them into bonds, marketed commercial paper, and
bought firms’ receivables (Wicker, p. 63, 95; Redlich, 1947, pp. 74-80).
3
Bordo and MacDonald (2005) show that short-term interest rates in continental financial centers were
tightly linked to London’s, less tightly linked from one continental center to another.
5
exporters purchased London sterling claims from, or sold them to, financial institutions in
New York City (Myers 1931, pp. 338-50). There was very active arbitrage between New
York and London at the long maturity of stocks and bonds. American equities and dollarpayment bonds were held by European investors, who traded them in London as well as
New York (Wilkins, 1989, pp. 191, 202, 208, 229). There was also active arbitrage at the
three-month to a year maturity. The most important American debt instrument at this
maturity was commercial paper, which was actively traded in New York, brokered by
commercial paper houses in many other cities, and held by American banks and investors
all over the country. Though Europeans did not hold much commercial paper, American
financial institutions borrowed in London at the same maturity by issuing "finance bills."
Also, American foreign exchange dealers held larger or smaller stocks of London bills
depending on their expected dollar returns relative to American commercial paper and
New York stock-exchange term loans (Goodhart, 1969). At the overnight or call
maturity, however, there was no arbitrage between New York and London. (Today, the
London overnight Eurodollar market is tightly linked to the New York overnight fed
funds and overnight repo markets).
New York banks were central in America's payments system and held the
ultimate cash reserves for all the nation’s banks. Banks all over the country held demand
deposits in New York correspondent banks, or in large regional banks which in turn held
New York deposits. New York banks paid interest on these deposits and provided
payment services for their respondents. Respondent banks used their New York accounts
to clear payments, including those associated with check clearing, and as the first source
of cash to cover net redemptions of deposits. A substantial increase in demand for cash
anywhere in the country, on the part of the nonbank public or by banks to hold as
reserves, caused withdrawals of respondent banks' deposits in New York banks, and
shipments of cash out of New York.
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Because the U.S. had no central bank to supply cash to New York banks, mass
withdrawals of cash by out-of-town respondents could force New York banks to restrict
or "suspend" cash payments. A restriction of payments in New York triggered
suspensions by banks and city clearinghouses throughout the country, which had at least
two macroeconomic effects. First, it blocked the national payments system, which raised
transactions costs of employment and trade, especially interregional trade (James,
McAndrews and Weiman [2009]). Second, it hindered the operation of financial
intermediaries, which raised the cost of credit and tightened credit rationing to potential
borrowers at any given level of market interest rates (Mishkin, 1991). Credit supply was
most obviously restricted at times when many financial intermediaries failed. Failure
rates of national banks, state banks, trust and mortgage companies (in raw numbers or
weighted by assets) were highest around the crises listed in Table 1 (Grossman, 1993).
There are no time-series data on failures of private banks and brokerage houses, but there
were conspicuous failures of such firms around these dates.
The NYCH had one device to free up cash for shipment out of New York, and
hence avoid or at least loosen a restriction of payments: the issuance of "clearinghouse
loan certificates." These could be used to settle negative clearinghouse balances, and thus
substituted for cash in local payments. They were also associated with verification of a
bank's solvency, as they were created as loans to a bank after clearinghouse
representatives had examined the bank’s books and deemed it a good risk. As Wicker
(2000, p. 116) explains, restriction of cash payments and/or the issuance of clearinghouse
loan certificates were the two responses of the NYCH to a “threat, either real or alleged,
to the reserve position of the NYCH banks” caused by “withdrawal of bankers’ balances
of the interior banks eroded the reserves of the NYCH banks.”
Thus, national financial crises are commonly identified with the NYCH actions
listed in Table 1 because they indicate a general run on the nation's central financial
institutions. But what caused these mass withdrawals of correspondent deposits from
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New York?
1.2) General causes of financial crises
Theoretical models point to two mechanisms for runs on short-term loans to
financial intermediaries. One is illustrated by Diamond and Dybvig (1983). Absent a
lender of last resort, a financial intermediary that borrows short-term to finance illiquid
assets must suspend payment or fail if its creditors withdraw funding. Because the first
creditors to cash out are most likely to be paid (the “first-come-first-served” or
“sequential-service” constraint), a creditor will withdraw if he expects others to
withdraw. This creates the possibility of multiple equilibria: a fundamentally irrelevant
"sunspot" signal, or an exogenous shock that causes some creditors to withdraw for their
own reasons, can cause all creditors to withdraw. Another mechanism hinges on
creditors' inability to observe and value the assets on intermediaries' balance sheets. An
event that signals a general drop in asset prices can spur withdrawals from all
intermediaries, including solvent ones, because creditors cannot immediately observe
whether any particular intermediary is solvent. Morris and Shin (2000) point out that
these two mechanisms can interact. A signal of a small deterioration in intermediaries'
balance sheets might be enough to set off a panic, because of the illiquidity multipleequilibrium mechanism: a creditor withdraws not just because he fears the intermediary is
insolvent, but because he expects the signal to spur others to withdraw.
Both mechanisms have been cited as possible explanations of prewar financial
crises. Chari (1989) argues that seasonal fluctuations in cash demand associated with
agriculture - country banks withdrew cash from New York during fall harvest and spring
planting to satisfy local cash demand - set off Diamond-Dybvig-style runs. Calomiris and
Gorton (1991) argue that prewar crises were instead set off by signals of asset-price
decline. Two important signals, they argue, were stock-price declines and hikes in
business failure rates. Both indicated economic conditions that could cause businesses to
default on bank loans. Examining time-series data, Calomiris and Gorton find crises (as
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defined above) occurred whenever both stock price declines and business failures had
passed threshholds. Mishkin (1991) makes a similar argument. We add that a stock price
decline could create fears for New York banks' solvency not just because it signaled
future defaults on ordinary business loans, but also because many New York banks
engaged in stock speculation at one remove, by lending to nonbank intermediaries that
held stocks – “off balance-sheet” enterprises closely linked to the bank.4
Certainly, all five crises were preceded by stock-price declines. This is obvious in
Figure 1, which plots the Cowles commission New York stock price index on a monthly
frequency. All the crises but 1890 were preceded by cyclical downturns. This is shown by
the last two columns of Table 1, which gives the months of NBER business cycle peaks
preceding the crises and the alternative peak dates identified by Romer (1994) based a
monthly IP index (Miron and Romer, 1990). (The monthly IP index begins with January
1884, so it cannot be used to date the 1884 downturn.)
But what caused the stock-price declines and cyclical downturns?
1.3) Causes of specific crises
The crises of 1890 and 1907 have been well explained by existing literature.
Events exogenous to the American economy boosted London interest rates and widened
the spreads over European returns that Europeans required to hold American assets.
4
A contemporary observed that New York Banks “make advances to speculators, who in some instance are
in control of the banks extending the loans,” pointing to the National City Bank (which became Citibank)
“as an illustrattion of an institution dominated by financiers rather than bankers, proper” (Youngman, 1906,
p. 439). About 1884, Sprague (1910) observes that "the strain of successive breaks in prices on the stock
exchange brought about the downfall of a number of speculators whose plans might have proved successful
if general conditions had been such as to lead to a rise in security values” (p. 110). One of these was the
brokerage of Grant and Ward. The failure of Grant and Ward brought down the Marine National bank,
which had lent to the brokerage (Wicker, 2000, p. 36); "the Metropolitan National Bank was also forced to
close because of a serious run caused..by allegations that its president was speculating in railroad securities
on funds borrowed from the bank" (p. 37). The Newark Savings Bank, with deposits of "over six million,"
failed because of the failure of the brokerage Fisk and Hatch, which "owed the Newark Savings Bank over
$1 million" (p. 38). In 1890, a stock price decline caused the failure of the brokerage Decker, Howell and
Co.; "As a result, Decker, Howell and Co.'s banker, the Bank of North America, was faced with a..shortage
at the NYCH..that prompted the NYCH to call an emergency meeting on the same day..for the explicit
purpose of providing direct assistance to the bank..through the issue of loan certificates" (Wicker, p. 45). In
1907, the brokerage house of Moore and Schley had lent to customers buying shares in a firm, taking the
shares as collateral; Moore and Schley used the same shares as collateral to borrow from banks; the stock
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Essentially, there was a decrease in international demand for American financial assets.
This drove down New York stock and bond prices and raised New York short-term
interest rates.
In 1890, developments in South America caused European investors to fear
extensive defaults on Argentine bonds. November saw a large hike in the Bank of
England’s discount rate and the first Baring's failure. These events decreased European
demand for American assets through the channels of international “financial contagion”
described in modern economics literature, particularly contagion through the channel of
common leveraged creditors (for surveys see Claessens and Forbes, 2001; Kaminsky,
Reinhart and Vegh, 2003).5 Fels (1959, p. 167) puts it this way: “British financial houses
were caught with a large amount of Argentine securities which they could neither sell,
except at ruinous prices, nor obtain advances upon. In order to raise funds to keep their
businesses going, they sold their good securities, American as well as British.”
Prior to the 1907 crisis, the Bank of England responded to an outflow of gold to
the U.S. by raising its discount rate and draining reserve supply to push up London shortterm rates. It also took steps to make it harder for American firms to borrow in London,
at any given interest rates (Friedman and Schwartz, 1963, p. 156; Sayers, 1976, pp. 5456). Odell and Weidenmier (2004) argue that these actions caused a cyclical downturn
and stock-market decline, and that the U.S.-bound gold flow reflected payments by
British insurers to victims of the San Francisco earthquake - an event that took place
within the U.S., but was probably not a response to American financial-market
developments.
That leaves 1884, 1893 and 1896. Existing literature has identified a factor that
price fell, and a contemporary judged that "If Moore and Schley go there is no telling...how many banks
might be included in the consequences" (Wicker, 2000, p. 96).
5
Bordo (2006) observes that 1890 was a “sudden stop” of capital flow to many countries which had been
selling financial assets in European capital markets. Sprague (1910) described 1890 as “a crisis in which
the United States participated..as a passive sharer in a disturbance the causes of which were in other parts
of the world” (p. 127). See also Friedman and Schwartz (1963, p. 104), Wilkins (p. 194 and fn. 39, p. 222).
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contributed to these crises, but cannot explain why they occurred when they did: "silver
risk." This was the danger perceived by contemporaries that the U.S. would link the
dollar to silver and float against gold. Silver risk obviously affected the required spread
between U.S. and European asset returns, though it does not seem to have caused
American depositors to withdraw dollar deposits for conversion into gold coin [Sprague,
1910, pp. 165, 169; Friedman and Schwartz, 1963, pp. 108-09]). It disappeared after the
1896 presidential election, which was taken to signal a firm U.S. commitment to the gold
standard (Friedman and Schwartz, 1982, pp. 513-517).
By itself, silver risk cannot account for the 1884, 1893 and 1896 crises because
the more-or-less exogenous political events that created it - Bland-Allison Act of 1878
and the Sherman Silver Purchase Act of 1890 - occurred years before crises. Indeed,
existing literature does not claim that changes in silver risk touched off these crises, but
rather that the crises occurred when other events crystallized the potential risk. Sprague
(1910, pp. 109-110), Fels (1959, pp. 130-131) and Friedman and Schwartz (1963, pp.
100-101) all argue that an increase in silver risk caused foreign sales of U.S. assets in
1884, but that the increase in perceived risk was triggered by a preceding cyclical
downturn. With respect to 1893, Sprague (1910, pp. 162, 165, 168, 179) concluded that
the effects of perceived devaluation risk did not come into play until the financial crisis
was already underway. Fels (1959, pp. 184-187) describes devaluation fears as a factor
that interacted with a cyclical downturn, but did not cause it. Friedman and Schwartz
(1963) argue that the passage of the Sherman Silver Purchase Act in 1890 had an
immediate effect on capital inflow, creating persistent deflationary pressure, but that the
occurrence of the crisis in 1893, rather than an earlier or later year, was due to a series of
exogenous shocks to American net exports and international gold flows (pp. 106-109).
2) Harvest shocks to export revenues
The specific events that Friedman and Schwarz identify as the cause of American
net export fluctuations around 1893, and hence the crisis of 1893, were fluctuations in
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wheat harvests. They argue that wheat harvests affected American economic conditions
on this and several other occasions in the gold standard era (pp. 97-99) because the
resulting high export revenues boosted high-powered money growth through
international gold flow. Fels (1959, pp. 60, 87, 181, 220) makes a similar argument.
Oddly, neither Fels nor Friedman and Schwartz mention the other great American
export crop of the gold-standard era, cotton, in this context. But pre-1914 contemporaries
mentioned both cotton and wheat harvests as affecting financial conditions through
international gold flows (e.g. Sprague 1903, p. 50; 1915, p. 500; Andrew 1906). The
nation’s third staple crop, corn, was a relatively unimportant export. Most corn was used
as an input to further agricultural production, to feed draft and meat animals. As animal
feed, corn contributed to exports of pork, beef, and cattle on the hoof, but any effect of
corn harvests on these revenues would be drawn out over time.
Production of all three crops was fundamentally seasonal. Cotton was planted in
the spring months from March through May, harvested beginning in August and
continued through November (Covert 1912, p. 93). In the 1900s, over sixty percent of the
cotton harvest was completed by the end of October (U.S. Bureau of the Census 1909, p.
13). Wheat was planted in two seasons: “winter wheat" from September through October,
"spring wheat" from March through May; but both wheat plantings were harvested in late
summer and early autumn, mostly in July and August (Covert 1912, pp. 30, 41; Monthly
Crop Reporter Sept.1920, p. 100). Corn was planted in the spring, harvested from early
September through November (Covert 1912, p. 17).
For all three crops, agents of the U.S. Agriculture Department estimated annual
production throughout the gold-standard era. (Unfortunately, there are no reliable annual
data on livestock production in this era.) The estimates indicate that year-to-year
fluctuations in the size of the harvest were of similar magnitude for all three crops. Data
on quantities and values of crop-related exports are also available on an annual basis for
twelve-month spans ending in June, which corresponds nicely to the exports associated
12
with one harvest season. These data show that cotton and wheat harvest fluctuations both
affected export revenues, with very similar magnitudes; corn harvests had smaller effects,
as one would expect.
Table 2 illustrates these patterns. Panel A shows statistics on volatility in the three
crops’ harvests starting with the harvests of autumn 1878, through 1913. Harvest
fluctuations are defined to be deviations in the log of crop output from trend. For the first
two columns of panel A, the trend is a quadratic in time over 1869-1913; for the last two
columns, it is an HP trend with the smoothing parameter at the value (100) used by most
studies of annual historical data.6 Using either trend, there is about the same degree of
volatility in the cotton and wheat harvests (measured by the range between maximum and
minimum values, or by the standard deviation); the corn harvest may be slightly more
volatile. Panel B shows results of regressions where the LHS variable is the log of total
export revenue from raw cotton, wheat, wheat flour, corn and corn flour, over the twelve
months ending June 30th of the year following the harvest. The RHS includes harvest
deviations from trend, quadratic time trend terms, and (for columns (2) and (4) the log of
the best available price index (the WPI excluding farm products and foods, described
below) averaged over the twelve months ending with the June preceding the harvest
season. Coefficients on the cotton and wheat harvests are of very similar magnitude; corn
coefficients are smaller.
Year-to-year harvest fluctuations could be exogenous shocks to the American
economy, because crop production was strongly affected by factors essentially
independent from nonagricultural production or financial markets: weather, plant diseases
and pests. Plant diseases and pests had long-run effects on production (mitigated by
innovations in agricultural technique [Olmstead and Rhode 2002]) but also year-to-year
effects that depended on interactions with weather. For example, the boll weevil, which
6
Ravn and Uhlig (2002) show that a smoothing-parameter value of 6.25 for annual-frequency data would
be more consistent with the value of 1600 conventionally applied to quarterly- data. That does not mean the
13
began to affect U.S. cotton production in 1892, caused most damage in years of
especially wet, warm weather (Henry 1925, p. 523; Kincer 1928). In the early twentieth
century, a number of pioneering statistical studies showed that harvest fluctuations were
systematically related to available time-series data on the weather (e.g. Moore, 1917).
3. Harvests and American business cycles
In a recent paper, Davis, Hanes and Rhode (2009), hereafter referred to as DHR,
examine the relation between the three crops' harvests and business cycles before 1914,
as indicated by annual indices of industrial production. To establish causation, DHR use
weather data to create instruments for annual harvest fluctuations. They find strong
evidence that cotton harvest fluctuations affected IP within the postbellum gold-standard
era. IP was not affected by wheat or corn harvests. There is no evidence that cotton or
corn harvests were affected by industrial business cycles or by third factors that affected
both agriculture and industry, though there is some weak evidence that wheat harvests
may have been affected by such third factors. Friedman and Schwartz were right about
the importance of crop fluctuations but wrong about the crop that mattered: it was cotton,
not wheat.
DHR account for the different IP effects of the three crops as the result of moneydemand effects interacting with the gold-standard export-revenue effect. Though the
wheat harvest affected export revenue, it also had a strong positive effect on rural
demand for cash over the calendar year following an autumn harvest season. Thus, a poor
wheat harvest depressed both export revenue and high-powered money demand: that
reduced gold inflows but did not affect interest rates or economic activity. The cotton
harvest's cash-demand effect was weaker, relative to its export-revenue effect, partly
because Southern tenancy and credit systems economized on the use of cash. Because a
conventional value for quarterly data is correct. Setting the annual-data smoothing parameter at 6.25 rather
than 100 would create trends that are extremely sensitive to relatively short-term fluctuations in output.
14
poor cotton harvest depressed export revenue but had little effect on money demand, it
tended to raise American interest rates and depress real activity.
To test their hypothesis, DHR examine harvests' effects on gold flows, the dollarsterling exchange rate, high-powered money growth, the commercial paper rate and price
inflation, as annual averages over the twelve months after the harvest season. They show
that cotton harvests affected gold flows, the foreign exchange value of the dollar, highpowered money supply growth, commercial paper rates and price inflation. Wheat
harvests had similar or stronger effects on gold flows, the exchange rate and money
growth, but wheat harvests did not affect commercial paper rates or inflation.
DHR do not address the issue of financial crises or examine harvests’ effects on
financial variables other than the commercial paper rate. But their argument suggests
cotton-harvest fluctuations could have been a shock to American financial markets. The
crises of 1884, 1893 and 1896, in particular, appear related to cotton harvests. This is
shown by Figure 2. The solid line in the figure plots the “IP gap:” the deviation from
trend in the log of the IP index. The dashed lines on the lower scale plot “cotton harvest
fluctuations” lagged one year, similarly defined as the deviation from trend in the log
(quantity in pounds). (The particular trends used for this figure are quadratic in time over
a span 1869-1913 to match DHR. DHR show that one- or two-sided HP trends, and other
specifications of the relation between cotton harvests and IP, give very similar results.)
The dashed lines on the upper scale plot IP gaps accounted for entirely by two lagged
cotton harvest fluctuations (the harvests of the autumn preceding the IP year, and the
autumn before that). That is, they are forecasts from a regression of the IP gap on the
previous two autumn’s cotton harvest fluctuations. Vertical lines mark the five crisis
years. Cotton harvests account for the IP downturns in 1884, 1893 and 1896.
Moreover, the poor cotton harvests of 1883, 1892 and 1895 were associated with
extraordinarily low cotton export revenues. This is shown by Figure 3. The solid line
plots the residual from a regression of the year's cotton export revenue (in logs) on
15
quadratic time trend terms and the log of the WPI over the twelve months preceding the
harvest. Thus it indicates export-revenue fluctuations in a way that corresponds to the
regressions in Table 2B. Revenues over the twelve months ending in June of 1884, 1893
and 1896 were the three lowest values of the export-revenue residual. Revenues were also
extraordinarily low following the bad autumn 1909 harvest, which was followed by a
recession (see figure 2) but not a financial crisis. To be sure, exogenous natural events
like cotton harvests were not the only cause of export revenue fluctuations. The low
export revenues following the 1898 and 1908 harvests (1899 and 1909 in the solid line)
were not caused by poor cotton harvests. Presumably, export revenues were affected by
American real activity and other factors affecting American real activity, such as foreign
incomes. For example, cotton exports might be larger when U.S. activity was low,
reducing demand for American cotton textiles, and foreign activity was high, raising
demand for British-produced textiles. Cotton export revenue itself cannot be thought of
as an exogenous shock. But the figure shows that the bad cotton harvests in 1883, 1892
and 1895, which were exogenous shocks, were associated with low export revenues as
our hypothesis requires.
4) Possible effects of harvests on financial markets
Did cotton harvests really affect American financial markets in a way that could
lead to crises? Did wheat and corn fail to affect financial markets in the same way? To
answer these questions, we must spell out in more detail the possible links between
harvest fluctuations and financial markets under the pre-1914 American monetary
regime. Three characteristics of the regime are key to our argument.
First, for purposes of foreign trade the U.S. had a practically fixed exchange rate.
Under the international gold standard, a country’s monetary authorities exchanged
domestic high-powered money for gold at a fixed rate. Flows of monetary gold balanced
a gap between net exports and capital outflow. Rates of exchange between financial
centers were tied to the "parity" value defined by currencies’ gold content. An exchange
16
rate could not remain outside the bounds – the “gold points” - that just covered costs of
transporting gold between the locations. The London-New York gold points were narrow.
They held the exchange rate within a range that was too small to make a difference for
the relative price of foreign goods.7
Second, though there was active arbitrage between London and New York, the
bulk of historical evidence indicates that uncovered interest-rate parity (UIP) did not hold
across the Atlantic, even for stocks and bonds. There was imperfect international capital
mobility between New York, on the one hand, and the rest of the gold-standard world
through London, on the other.8
Third, because the United States (unlike most large gold-standard countries) had
no institution that acted as a central bank, international gold flows had a direct effect on
the American high-powered money supply. The supply consisted of monetized gold and
nongold currency issued by the Treasury. The Treasury held a stock of gold and currency
in its vaults, removed from the supply to banks and the public. Thus, the high-powered
money supply was proximately determined by two factors in addition to the balance of
payments: the rate of growth of nongold currency, and the change in Treasury vault cash
(Friedman and Schwartz 1963, pp. 124-134). The Treasury did not actively manage the
stock of nongold currency. On occasion, Treasury officials deliberately managed
Treasury vault cash to affect money-market conditions (Myers, 1931, pp. 370-86), but
7
Canjels, Prakash-Canjels and Taylor (2004) observe that from 1879 through 1913 the largest deviation
from parity in the New York-London rate on any day was 1.06 percent (p. 870).
8
Studies have examined whether New York-London interest-rate spreads were related to probable future
change in exchange rates in a manner consistent with uncovered interest-rate parity. At times, the spread
obviously exceeded the range of possible future exchange-rate variation assuming the dollar remained tied
to gold (Giovannini 1993, pp. 133-136). But financial market participants may have factored in a risk that
the dollar would be devalued or floated, at least before 1896. Foster (1994) finds clear evidence against
uncovered interest-rate parity in seasonal patterns: month to month, New York interest rates were relatively
low just when just when financial market participants should have been expecting a regular seasonal
depreciation of the dollar. Studies of the covered differential - the spread between London interest rates and
New York investments that were not subject to the risk of a dollar devaluation - generally indicate that
covered interest-rate parity failed to hold at business-cycle frequencies (Obstfeld and Taylor 1998, pp.
361-363; Juhl, Miles and Weidenmier, 2005). Calomiris and Hubbard (1996) conclude that "Clearly,
interest rate parity did not hold perfectly across the Atlantic" (p. 195). With respect to long-term assets,
Edelstein (1982, pp. 157-58) shows that realized risk-adjusted rates of return were consistently higher for
London-traded American bonds and shares than for British.
17
they did not do so systematically to counteract the effects of international gold flows.9
Under these conditions, an exogenous negative shock to American export
revenues, such that that associated with a bad cotton or wheat harvest, had to be balanced
by one or more of the following: an increase in net foreign holdings of American assets that is increased capital inflow (or smaller outflow); a gold outflow (or decreased inflow);
an increase in net exports due to other factors. A gold outflow meant a reduction in the
high-powered money supply. A gold outflow could take place only if some factor acted
to reduce high-powered money demand.
A bad wheat harvest was an exogenous negative shock to net exports and a
negative shock to high-powered money demand. Thus, the export-revenue effect of a bad
wheat harvest could be balanced by a gold outflow, with no other adjustments in
international capital markets or the American economy.
A poor cotton harvest was a negative shock to net exports but had weaker effects
on money demand. Thus, a poor cotton harvest required an increase in capital inflow
and/or an increase in net exports apart from cotton. With imperfect capital mobility,
increased capital inflow required an increase in expected returns to American assets,
relative to European assets. That meant lower prices for American bonds and stocks, and
perhaps higher short-term interest rates. Of course, this shock to American stock prices
and interest rates would tend to reduce real activity.
Meanwhile, with an effectively fixed exchange rate, an increase in net exports
apart from cotton required a decrease in American wages and prices. That required a
decline in real activity, because the pre-1914 American economy, like postwar
economies, had an imperfectly flexible wage and price level. A decline in real activity
would also tend to reduce American expenditure on imports. Thus, the decline in real
9
Nongold money consisted of greenbacks, silver notes, national banknotes and silver coins. The quantity of
greenbacks was simply fixed; the rate at which the Treasury created new silver notes was governed by
longstanding political factors (Myers, 1931, pp. 396-398; 402); and the rate at which banks created national
bank notes was remarkably insensitive to variations in interest rates and business activity (Myers, 1931, p.
403; Cagan, 1965, p. 91).
18
activity resulting from the asset-price shock helped balance the export-revenue shock
through net exports of other goods.
Finally, as the decline in real activity tended to reduce money demand, perhaps
assisted by an increase in short-term interest rates, it allowed a decrease in high-powered
money demand and hence gold outflow.
Overall, a poor cotton harvest had to be accompanied by some combination of a
cyclical downturn, lower stock prices, higher bond yields, and perhaps higher short-term
interest rates - just the conditions that set the stage for a financial crisis. Essentially, a
poor cotton harvest was a shock that decreased international demand for American assets,
not because of events in foreign financial markets as in 1890 and 1907, but because an
exogenous negative shock to net exports had to be balanced by capital inflow under the
pre-1914 American regime.
A complication to this story is the possible relation between gold flows and
expected future changes in exchange rates. Though exchange rates did not vary enough to
affect relative prices significantly, they did vary somewhat as international gold flows
drove the rate to (or perhaps a bit beyond) a gold point.10 Between London and New
York, exchange-rate variations were large enough to make a difference to asset returns at
short maturities. Some exchange-rate movements were predictable, and arbitragers
responded to these predictable movements as well as to relative interest rates (Goodhart,
1969). While gold was flowing out, the exchange rate was at or near the gold export
point, which is to say the exchange value of the dollar was especially low (high exchange
rate in dollars per pound sterling). As exchange rates had to remain within the gold points
in the long run, that could in turn be associated with expectations of future dollar
appreciation (future decrease in the exchange rate).
10
Canjels, Prakash-Canjels and Taylor (2004) argue that the marginal costs of shipping gold at a particular
point in time increased with the amount of gold shipped. If so, the average exchange rate over a period
could indicate the direction of gold flow not only because the rate was driven to a gold point, but also
because the rate on a particular day could range further from parity – in effect, the gold points widened when the gold flow was bigger.
19
The following model keeps track of this complication and makes one more point
that will be important to keep in mind when we turn to data. Harvests of a crop that affect
financial markets and real activity, because the crop’s money-demand effect is relatively
small, may be expected to affect interest rates and asset prices first, real activity later.
Model
There are two assets, a short-term asset with an expected dollar return per period i
(a short-term interest rate) and a long-term asset with an expected dollar return per period
j (an expected return to holding a bond or equity). In each period t, the nonagricultural
output gap y is negatively related to the lagged output gap and expected real asset returns
in the previous period:
(1)
yt = - γi (i - Eπ )t-1 - γj (j - Eπ )t-1 + γlag yt-1
where real asset returns are normalized to trend and Eπ is the average expected rate of
price inflation over a period. The lag in the effect of real asset returns on real activity
accords with conventional wisdom and many empirical studies of modern
macroeconomic data (e.g. Christiano, Eichenbaum and Evans, 2005), which show that an
exogenous shock to interest rates affects real activity with a lag of six months to a year.
There is no reason to believe this lag was shorter in the postbellum era.
The expected long-run asset return is equal to the short-term interest rate plus a
possibly-variable term premium:
(2)
jt = τt + it
The change in the domestic price level p from the previous period is positively
related to the output gap:
(3)
πt = α yt
This expression is consistent with empirical studies of price inflation in the pre-1914 U.S.
(e.g. Gordon, 1990; Hanes, 1999), which find a relation between the level of real activity
20
and the rate of inflation along the lines of the original Phillips curve (rather than the
“accelerationist” Phillips curve apparent in post-1950s data). It is also consistent with a
standard expectations-augmented aggregate supply function assuming that prices were
expected to return to a PPP level in the long run (Alogoskoufis and Smith, 1991). For the
same reasons, a higher current price level is generally associated with expectations that
the price level will remain stable or fall:
(4)
Eπ = - φπ pt
where φπ ≥
0
The change in the high-powered money supply m is (in a log approximation)
equal to the change in the nation’s monetary gold stock g plus the change in nongold
currency n:
(5)
∆mt = ∆gt + ∆ nt
Gold inflow is equal to the balance of international payments from capital flows and net
exports. Holding the foreign price level fixed, a log-approximation is:
(6)
∆gt = CAt - θy yt - θp pt + xt
where CA is the change in net international holdings of American assets and the
remaining terms on the right-hand side describe determinants of net exports: the
nonagricultural output gap y (which affect domestic demand for imported goods), the
domestic price level p (which determines the relative price of foreign goods). x represents
exogenous shocks such as harvest fluctuations.
Holding fixed expected foreign-currency returns to foreign assets, desired foreign
holdings of American assets are positively related to expected returns on American
assets. Thus:
(7)
CAt = θi ∆ ( i - Eė )t + θj ∆ ( j - Eė )t
where θj > 0, θi ≥ 0
where Eė is the expected future change in the log exchange rate to the next period. Given
the obvious international trade in American stocks and bonds, one should assume the
coefficient on the long-term asset return θj is relatively large, but as there also appears to
21
have been arbitrage at the bill maturity, θi may also be positive.
The average exchange rate over the period is negatively related to gold flow:
(8)
et = - η ∆ gt
where e is the average dollar price of London sight exchange, and expectations are that
future exchange rates will remain at the current level or return to a long-term trend:
(9)
Eė= - φe e
where φe ≥
0
Finally, high-powered money demand is:
(10)
mt - pt = µy yt - µi it + µlag (mt-1 - pt-1 ) + zt where µi
> 0,
High-powered money demand is affected by the output gap and the nominal return to the
short-term asset. The last term represents exogenous shocks to high-powered money
demand, such as the effect of harvest fluctuations on demand for cash.
Now consider a harvest fluctuation h which boosts export revenue in the "harvest"
period 1 , and perhaps also in the immediate "post-harvest" period 2:
∂x1 / ∂h > 0,
∂x2 / ∂h ≥ 0
The harvest fluctuation may also affect high-powered money demand in the two periods:
∂z1 / ∂h ≥ 0,
∂z2 / ∂h ≥ 0
The relation between a harvest fluctuation and asset returns within the harvest
period is described by:
(11) - ( µi + βθi )∂∆i1 /∂h - βθj )∂∆j1/∂h = β ∂x1/∂h - ∂z1/∂h + ∂∆n1/∂h
where β = 1/[1+(θi + θj ) φe η ]
This expression says that a harvest fluctuation may or may not affect asset prices,
depending on the strength of its money-demand effect relative to its export-revenue
effect, and on the relation between the harvest fluctuation and nongold currency. In
America without a central bank, there was no systematic relation between harvest
fluctuations and nongold currency: ∂∆n1/∂h= 0 .
22
A poor cotton crop had a relatively small effect on money demand. It created a
negative value on the right-hand side of (11). Thus, a poor cotton crop caused an increase
in the expected return to the long-term asset, which is to say a decrease in its price, and
perhaps an increase in the short-term interest rate. In the post-harvest period, these events
will be followed by a decrease in output and inflation.
A poor wheat crop had a stronger money-demand effect. It did not, on net, affect
the right-hand side of (11). Thus, a poor wheat crop had no effect on financial markets or
post-harvest output. At the same time, the wheat harvest would affect gold flow, as DHR
found to be the case. For wheat,
(12) ∂∆g1/∂h = β ∂x1/∂h
while for cotton:
(13) ∂∆g1/∂h = β ( ∂x1/∂h + θi ∂∆i1 /∂h + θj ∂∆j1/∂h )
As the size of the cotton harvest has a negative effect on expected asset returns, its effect
on gold flows may actually be smaller than the effect of wheat.
What if America had a central bank to manage currency supply? Then the central
bank could increase (decrease) the supply in response to a poor (good) cotton harvest:
(14)
∂∆n1/∂h = - β ∂x1/∂h + ∂z1/∂h
If the central bank followed (14), a poor cotton harvest would be accompanied by a boost
in nongold currency. That would reduce the country’s monetary gold demand to match
the decline in export revenue. A poor cotton harvest would have the same effect on the
economy as a poor wheat harvest. It would cause an international gold outflow (or lower
inflow), but it would not affect financial markets in the harvest period, or output and
inflation in the post-harvest period. A central bank could make such an adjustment
through open-market operations in domestic assets, which would not affect the balance of
payments. Alternatively, the central bank could set its discount rate at (or slightly above)
the interest rate prevailing before the harvest shock: as the interest rate rose to this
23
ceiling, discounting by the central bank would automatically supply the right quantity of
currency.
5) Harvests' effects on American IP, price level, New York and London financial
markets
DHR showed that cotton harvests, but not wheat or corn harvests, affected IP, the
price level and New York commercial paper rates in the year following the harvest. They
examined annual data only. Here, we want to examine harvest effects on a larger set of
financial-market variables. We also want to pin down the timing of the cotton harvests'
effects: did they occur immediately within the harvest season, or unfold over the course
of the following year? But we cannot try to pin down the timing too precisely. The
calendar timing of the harvest varied somewhat from year to year: the bulk of the cotton
harvest, for example, could come in September one year, October the next. There were
also long-term trend changes in harvest timing over the gold standard era, as the centers
of cultivation moved west to areas with different climates (for cotton, to Texas and
Mississippi; for wheat, to the plains states). To allow for some year-to-year variation in
the exact monthly timing, we average monthly series up to a quarterly frequency.
To definitively establish the direction of causality we use two-stage least squares
with weather data as instruments for harvest fluctuations. DHR give details on the
weather data, the first-stage specification and the quality of the instruments.
We want to distinguish between effects specifically associated with financial
crises, and effects that occurred whether or not there was a financial crisis. To do this, we
look at two sets of samples. The first includes all years 1879-1913. The second excludes
harvests that were followed by a financial crisis in the next calendar year. The results we
present treat 1896 as a crisis year, following Gorton, but results were similar if 1896 was
classed as a non-crisis year.
Finally, we must we must specify time trends for many variables. To maintain
comparabilitry with DHR (2009) we present results using harvest deviations from
24
quadratic time trends over 1869-1913, but HP trends or quadratic trends over 1879-1913
gave similar results.
Our results match the model. Cotton harvests had an immediate effect, within the
harvest season, on New York financial markets. The effects are most obvious in stock
and bond markets. A poor harvest immediately depressed stock prices and raised bond
yields. It also raised commercial paper rates. But cotton harvests had no immediate effect
on IP or the price level. The cotton harvest’s effects on IP and inflation began to appear
in the calendar year following the harvest. They were strongest about twelve months after
the harvest season. These effects occurred whether or not there was a financial crisis in
the following year. Wheat harvests or corn harvests did not have these effects. None of
the harvests affected London interest rates, bond prices or stock prices.
Sources and details for all variables are given in the data appendix.
5.1) American IP and price level
To observe the timing of the cotton harvest's effect on real activity, we use the
Miron-Romer monthly IP index, which is the best available indicator of real activity on a
shorter-than-annual frequency. Unfortunately it is unavailable for months before 1884
(because important components are absent before then). We calculate the change in the
log of the IP index from the year preceding the harvest season (the twelve-month average
of the log from July of the preceding calendar year through the June just before the
harvest) to each quarter of the harvest season, that is Q3 and Q4 of the harvest year, and
the change from the pre-harvest period to each quarter of the following calendar year.11
Table 3, panel A) shows results of regressing these IP changes on cotton harvest
fluctuations. (We repeated all of these regressions with wheat and corn harvest
fluctuations added to the right-hand side. This had little effect on values of cotton
coefficients, and the wheat and corn coefficients were never significantly different from
11
The Miron-Romer series lacks an observation for March 1902. To define a value for the quarter including
this month, we set the log of the missing month's IP equal to the average of the adjacent months.
25
zero at conventional levels.) The first row shows results of OLS regressions when all
years are in the sample. The second show results of 2SLS, with all years in the sample.
The last shows results from OLS and the edited sample. In all cases, for Q3 of the harvest
year, cotton coefficients are close to zero and not significantly different from zero at
conventional levels. For Q4 of the harvest year, only the 2SLS coefficient is marginally
significant. Coefficients are largest in the third quarter of the following year.
We can use Davis' annual IP series, which cover the early 1880s, to confirm one
important result from the monthly data: that the cotton harvest affected the following
year's IP whether or not a crisis occurred in the following year. Panel B) of the table
shows these regressions. The regressions make a further point: output was especially low
in the year following a crisis, even accounting for the general effect of cotton harvests.
For the first columns of the table, the IP gap was regressed on the lagged IP gap and the
previous autumn's cotton harvest, with all years in the sample for (1) and crisis years
excluded for (2). Either way the cotton-harvest coefficient is positive and significantly
different from zero at the one percent level. Columns (3) – (5) add to the RHS dummy
variables that mark the year of a crisis and the year following a crisis. The dummy for a
crisis year is not significantly different from zero; the dummy for a post-crisis year is
negative and significantly different from zero at the one percent level.
For the price level the best measure is the Warren and Pearson (1932) wholesale
price index before 1890, linked to the BLS WPI. These series are monthly and together
they cover all of the gold-standard era. The standard version of the series puts heavy
weight on prices of raw cotton, wheat and corn, so we constructed a WPI from the
standard series’ components excluding raw farm products and foods. We calculated the
change in the log WPI to the quarter from the pre-harvest Q2.
Table 4 shows results of regressing these WPI changes on cotton harvest
fluctuations. Coefficients indicate that, as for output, effects were largely absent within
the harvest season, strongest in the third or fourth quarter of the following year.
26
5.2) New York financial markets
To observe effects on the stock market we look at the Cowles Foundation stock
price index. We also look at stock trading volume, that is the number of shares sold on
the New York Stock Exchange, because many studies have shown that, for whatever
reason, high trading volume is associated with increasing stock prices (Karpoff, 1987).
For bonds, we look at Macaulay's index of railroad bond yields. Finally, we look at
Macaulay's series for New York commercial paper rates, which includes sixty to ninety
day paper. We calculate changes to each quarter from a pre-harvest period. For long-term
bond yields and stock prices, which appear to have been relatively unaffected by seasonal
factors, the pre-harvest period is the second quarter of the harvest year. Short-term
interest rates and trading volume had strong seasonals in the gold-standard era. (On
seasonals in trading volume see Owens and Hardy, 1925). For those variables, the preharvest period is the average over the twelve months preceding the harvest season (from
July of the previous year through June).
To make it easy to understand the magnitude of estimated coefficients, in the
following tables we scale coefficients to the standard deviation of a cotton harvest
fluctuation (0.12 for harvests 1878-1912), which was about the same as those for the
other two crops (0.13 for wheat; 0.14 for corn).
Table 5, panel a) shows results for stock prices. In OLS, cotton harvest
coefficients indicate an effect beginning in the third quarter of the harvest year and
continuing into the following year. Next we show results of three separate 2SLS
regressions, one regression for each crop. Again cotton harvest coefficients indicate an
effect beginning in the third quarter of the harvest year and continuing into the following
year. In 2SLS, wheat gives a coefficient significantly different from zero at the four
percent level in the harvest-season third quarter. Otherwise, wheat and corn coefficients
are not significantly different from zero in either OLS or 2SLS. In panel b), cotton's
effects on stock trading volume mirror those on stock prices.
27
For bond yields, in c), OLS coefficients on cotton are negative and significantly
different from zero in the fourth quarter of the harvest year; 2SLS are similar.
For commercial paper, in d), cotton coefficients are negative and significantly
different from zero at the five percent level or better, for the two quarters within the
harvest season through the third quarter of the following year. Wheat and corn
coefficients are positive, not significantly different from zero. In 2SLS, for cotton only,
the coefficient is significant at the three percent level in the harvest-year third quarter.
Figures 4-6 are scatterplots of cotton harvest fluctuation against changes to the
harvest-season fourth quarter in the log stock price index, railroad bond yields, and
commercial paper rates. The relations between these variables are obvious, especially if
one views 1890 and 1907 as outliers.
Table 6 shows results of OLS regressions with the cotton harvest alone on the
RHS, for the edited samples excluding crisis years as well as the full sample. These
results show that the cotton crop affected stock prices, stock trade volumes, railroad
bonds and commercial paper rates within the harvest season, whether or not a crisis
occurred in the following year. Looking beyond the harvest season, the cotton harvest
appears to have affected stock prices, trading volumes and bond yields in the post-harvest
calendar year whether or not a crisis occurred. That may not be true for commercial
paper. For this short-term rate, cotton coefficients in the post-harvest quatyers are not
different from zero unless the sample includes pre-crisis harvests.
What was the economic significance of the cotton harvest’s effects on New York
interest rates and stock prices? To get a handle on this, consider the cotton harvest
fluctuations that preceded 1884, 1893 and 1896. The harvest deviation-from-trend in
these years (1883, 1892, 1895) was about two standard deviations lower than it had been
in the previous harvest. The 2SLS estimates imply that by the end of the harvest year this
depressed American equity prices about 7 percent, raised railroad bond yields about 10
basis points, and raised commercial paper rates about one percent.
28
Is the magnitude of the effect on the bond yield reasonable relative to the change
in the commercial paper rate? Yes. The bonds in the index are of maturity ten years or
greater. If market participants expected the short-term rate hike to persist for about a year,
the change in their yield should be about one-tenth of the short-term rate change
assuming no change in the total premium for the bond’s term, relative liquidity and
default risk.12
The stock-price drop is about 1.75 percent for each 25 basis point increase in the
commercial paper rate. That is not out of line with the response of stock prices to
unexpected changes in short-term interest rates nowadays. Rigobon and Sack (2004)
examine the effect on stock prices of surprise changes in the Fed’s interest-rate target.
They estimate that “an unanticipated 25-basis point increase..results in a 1.7% decline in
the S&P index” (p. 1567). Using different methods, Bernanke and Kuttner (2005) find
that find that an “unanticipated 25-basis-point cut in the Federal funds rate target is
associated with about a 1% increase in broad stock indexes” (p. 1221). In the period they
examined, changes in the target fed funds rate affected bill rates about one-to-one and
could be expected to persist for about a year (their figure 6). Bernanke and Kuttner
further observe that the stock-price response is stronger than can be accounted for by the
apparent effects of a policy change on future real interest rates or dividends.
Given the apparent effects of cotton harvests on stock and bond prices in the
harvest season, financial market participants had incentive to trade in anticipation of the
effects, if they understood the phenomenon and had sufficiently good forecasts of the
cotton crop. Certainly, the size of the coming cotton crop was of great interest to many
financial market participants. A contemporary observed:
On many, if not upon all the Cotton Exchanges of the country, the daily variations of
rainfall and temperature in the states of the Cotton Belt, during the growing season, are,
for the information of brokers, plotted on a large map. The Government crop reports
describe the variations in weather during the interval covered in their crop survey. The
12
Commercial paper rates were (and are) quoted on a 360-day “money market yield” basis. The “bond
equivalent yield” is that rate multiplied by (365/360).
29
leading newspaper give daily reports of the “Weather in the Cotton Belt” (Moore, 1917,
p. 6)
On the other hand, it is not at all clear that anyone had the information or techniques
needed to forecast the cotton crop's size before the beginning of the third quarter. Moore
(1917) examined the accuracy of government reports on crop conditions in forecasting
the cotton crop in the pre-1914 era. He concluded that the reported information available
before the beginning of July “is so erroneous that any forecast from it is spurious. Any
money that changes hands as a result of the report is the gain or loss of a simple gamble.”
Information available in July was better but still had “negligible” value (p. 166).
We looked for effects of cotton harvest fluctuations on financial markets in
periods prior to the harvest season, before harvest-year July. We found no evidence of
them. Table 7 shows some results of OLS and 2SLS regressions on cotton harvest
fluctuations. For the first two columns of the table, the RHS variables are changes to the
second quarter of the harvest year, from the first quarter or from the twelve months
ending with March. For (3) and (4), RHS variables are changes from the same periods to
the month of June. Changes to July, the first harvest-season month, are shown in (5) and
(6) for comparison. For changes to the second quarter or to June, none of the coefficients
is significantly different from zero at conventional levels. For changes to July, the
coefficients are larger in magnitude and several are significant at conventional levels.
5.3) London financial markets
We look at open-market bill rates, consol yields and an index of London equity
prices. Table 8 shows results of OLS regressions with all three crops' harvests on the
RHS. For bill rates and consol yields, no coefficient is significantly different from zero at
conventional levels. For the equity price index, the coefficient on cotton in the third
quarter of the harvest year is significantly different from zero at the five percent level, but
its sign is negative: cotton harvests may have been negatively related to equity prices in
London, the opposite of their effect in New York.
30
6) Bank balance sheets
Given the cotton harvest's effects on asset prices and real activity, one would
expect also to observe effects on bank balance sheets. A decrease in real activity and/or
an increase in interest rates should tend to reduce demand for bank deposits. A decrease
in deposits would depress loan supply (pre-1914 banks had almost no managed
liabilities). Bank loan supply could also be depressed by an increase in commercial paper
rates and expected bond returns - the opportunity cost of loaned funds. Finally, a decrease
in real activity would depress loan demand.
New York banks were subject to special factors. Banks outside New York
actively managed their deposits with New York correspondents. For whatever reason, the
interest rate New York banks paid on correspondent deposits remained nearly fixed
across the pre-1914 era (James, 1978). Thus, changes in market interest rates could have
strong effects on the quantity of balances out-of-town banks chose to leave on deposit in
New York.
In this section of the paper we examine harvest effects on bank balance sheets,
distinguishing between New York City and other banks. We find that cotton harvests had
strong immediate effects on New York banks. Within the harvest season, a poor cotton
harvest reduced New York deposits, especially correspondent deposits, and caused New
York banks to cut back on lending. Over the following year, New York banks continued
to show strong effects on deposits and lending, whether or not a crisis occurred. Outside
New York, banks showed few if any immediate effects of the cotton harvest, within the
harvest season. Over the following year they were mainly affected on the deposit side:
unless a crisis occurred, the cotton harvest had little or no effect on their lending.
We can observe effects on national banks only, as there are no available data on
the balance sheets of state-chartered banks or trust companies at a less-than-annual
frequency. For national banks, data are available for all of the gold-standard era from call
reports. Call reports were collected on five dates spread across each year, at somewhat
31
irregular intervals. (The exact dates of a call report were supposed to be unpredictable to
banks, in order to discourage window-dressing.) We took the reports falling into seasonal
spans of months and treated them as if they were ordinary quarterly time-series data. The
spans are described on the tables. We calculate variables as the log of “quarterly” values
less the log of average values over the four “quarters” preceding the harvest.
For New York banks only, we look at deposits due to other banks. For non-new
York banks only, we look at “net deposits,” that is deposits less deposits due from other
banks. This gives an idea of the volume of deposits non-New York banks used to fund
assets other than deposits at correspondent banks, such as loans and securities.
For both sets of banks, we look at total loans plus discounts (one number), and deposits
due to nonbanks, which unfortunately includes deposits due to some types of financial
intermediary as well as to individuals and ordinary businesses. To look at reserves we use
two measures: first, banks’ holdings of all forms of high-powered money; second,
excluding national bank notes. National bank notes did not satisfy regulatory reserve
requirements but were otherwise perfect substitutes for other types of high-powered
money.
Table 9 shows results of regressions with all three crops on the right-hand side.
For New York banks, shown in panel A), cotton harvests are positively related to loans
and discounts, and to interbank deposits, within the harvest season. They are more
strongly related to nonbanks’ deposits over the year following the harvest season. They
are more or less unrelated to either measure of cash reserves. Wheat and corn harvests are
unrelated to any New York bank variables.
For banks outside New York, panel B), cotton harvests are unrelated to loans and
discounts within the harvest season, but positively related in the last two quarters of the
following year. Cotton harvests are weakly related to nonbanks’ deposits within the
harvest season, more strongly related (larger coefficients) over the following year.
Patterns are similar for net deposits. There is little relation between cotton harvests and
32
either measure of cash reserves. Corn harvests are positively related to non-New York
banks’ reserves at the end of the following calendar year.
Table 10, panel A shows results for New York banks and cotton alone on the
right-hand side, for various samples and 2SLS. In all variants, cotton harvests are
positively related to loans and discount, and to interbank deposits, within the harvest
season. They appear positively related to New York cash reserves only if crisis-year
harvests are excluded from the sample. Figures (7) through (10) are scatterplots of the
same data.
Results for non-New York banks, in panel B, indicate that their loans and
discounts were generally unrelated to cotton harvests within the harvest season. Cotton
harvests appear positively to loans and discounts in the following year only if the sample
includes harvests followed by crisis years.
7) Bankruptcies and bank failures
Our results with respect to bankruptcies and bank failures are tentative because
we are uncertain about the quality of the data we have so far, but for what they are worth
they indicate that cotton harvests were unrelated to bankruptcy rates within the harvest
season. Cotton harvests were negatively related to bankruptcies over the following
calendar year but only to the degree that cotton harvests were related to financial crisies.
That is, in regressions cotton harvests appear related to bankruptcies only if the sample
includes harvests followed by financial crises. Bank failure rates, similarly, appear related
to cotton harvests only if the sample includes crisis years.
Bankruptcies
We calculated the quarterly bankruptcy rate for nonbank businesses as the ratio of
failures of nonbank firms to the number of active firms (see the appendix). We doubt
these data because the bankruptcy rate takes very extreme values in the early 1880s, and
we do not know why. We are looking into this. Anyway, Table 11, panel A shows results
of regressing this variable on all three crops' harvests. The cotton coefficient is essentially
33
zero for the harvest-season quarters; in the third quarter of the post-harvest year it is
negative and significantly different from zero. Wheat harvest coefficients are negative but
not significantly different from zero for any quarter. Panel B shows results of regressions
on the cotton harvest alone. Coefficients in the second or third quarter of the post-harvest
year are significantly different from zero only if the sample includes harvests preceding
crisis years.
Bank failures
Bank failure data refer to banks that were permanently closed and put into
receivership. For institutions other than national banks (state banks, trust companies and
private banks), we have annual-frequency data on assets of institutions that failed within
twelve-month periods ending with June, and values of assets held by all such banks
around June. From these data we calculated assets of institutions that failed over JulyJune as a percent of all institutions’ assets as of the previous June. This is an assetweighted failure rate.
For national banks only, records give the exact date that a receiver was appointed.
From these data we calculated assets of failed national banks annually July-June, and
quarter-by-quarter. Unfortunately, the date of receivers’ appointment could be weeks
after the day a failed bank closed its doors, so the exact dating may be subject to
substantial error; this will have a bigger effect on the quarterly data of course. Using the
value of all national bank’s assets in the preceding June (that is, the call report closest to
the preceding June), we calculated asset-weighted failure rates for national banks, and for
all banks – national and other – aggregated.
Table 12, panel A shows results of regressions where the LHS variables are
annual July-June asset-weighted failure rates. RHS variables are crop fluctuations in the
harvest season within the first three months of this July-June period. For all banks
together, the cotton-harvest coefficient is significantly different from zero on both OLS
and 2SLS if the sample includes crisis years, but not if the sample excludes crisis years in
34
column (7). Looking at national and non-national banks separately, it is the non-national
bank failures that appear more strongly related to cotton harvests.
Panels (B) and (C) show results for the quarterly, national-bank only data. The
cotton-harvest coefficient is significantly different from zero only in OLS, only for the
third quarter of the year following the harvest, and only if the sample includes crisis
years.
8) Conclusion
In the postbellum gold-standard era, cotton harvest fluctuations had economically
significant effects on New York financial markets. Within the harvest season, a poor
cotton harvest depressed New York stock and bond prices and raised commercial paper
rates. It caused an outflow of deposits from New York banks, especially correspondent
bank deposits, and caused New York banks to cut back on lending. This financial shock
precipitated a downturn in real activity over the year following the harvest. Thus, a poor
cotton harvest set the stage for a financial crisis, not within the harvest season as believed
by contemporaries, but in the calendar year following the harvest. Poor cotton harvests
were responsible for the crises of 1884, 1893, and 1896, specifically. Wheat and corn
harvests had none of these effects.
Though contemporaries were wrong about the timing of the relation between
cotton harvests and financial crises, they were right to argue that the financial effects of
harvest shocks could have been forestalled by a central bank. An American central bank
could have responded to a poor cotton harvest by boosting the supply of nongold highpowered money through open-market operations or discounting. This would effectively
substitute currency for monetary gold, reducing America’s demand for monetary gold in
line with the decline in export revenue. A poor cotton harvest would then cause a gold
outflow but need not affect expected returns to American assets. Such a policy would not
jeopardize the dollar’s gold convertibility, because a cotton harvest’s effects on export
35
revenues were fundamentally transient. A good cotton harvest in the following year
would call for the opposite operation.
Proof that such a policy would work can be found in the absence of apparent
effects from wheat harvest fluctuations. Like cotton harvests, wheat harvests affected
export revenues but they also affected high-powered money demand in a way that tended
to counteract possible effects on financial markets. As a poor wheat harvest reduced rural
cash demand, it reduced American demand for high-powered money. Given the inelastic
supply of nongold money, this reduced American demand for monetary gold in line with
the decline in export revenue. Thus, a poor wheat harvest reduced international gold
inflows, but it did not create the preconditions for a financial crisis.
Appendix: Data sources
General note: “NBER” refers to National Bureau of Economic Research Macro History database
(http://www.nber.org/databases/macrohistory/)
U.S. high-powered money supplys: Monthly data on components of the high-powered money
stock begin with June 1878 (Friedman and Schwartz 1970, pp. 205-211): high-powered money
outside the Treasury (NBER 14135); gold held in the Treasury (NBER 14137). Nongold money
outside the Treasury calculated as money outside the Treasury minus gold (gold coins and gold
certificates) outside the Treasury (14131). Total monetary gold, in or outside the Treasury, is
NBER 14076.
WPI: Individual components of the Warren and Pearson (1932) wholesale price index, excluding
farm products and foods, were weighted using Warren and Pearson’s 1889 weights (p. 184).
International gold inflow to U.S.: Monthly net gold export from NBER 14112.
Commercial paper rate: NBER 13002, from Macaulay (1938, appendix Table 10).
Railroad bond yield index: NBER 13019A, from Macaulay (1938, appendix Table 10, col. 4)
Stock price index: NBER 11025, from Cowles Commission.
Number of shares traded on New York Stock Exchange: NBER 11002, from Commercial and
Financial Chronicle.
Bond yield spreads: Series originally constructed by Mishkin (1991), also used by Mishkin and
White (2006), from data in Macaulay (1938). Series kindly provided to us by Eugene White.
Business failure rates: Data originally from Dun’s Review. Quarterly or monthly number of
business failures from NBER 09029, divided by a figure for number of active firms. The latter is
from Carter et. al. (2006), series Ch408. This is an annual figure for the number of active firms
36
from the same original sources as the number of business failures, dating from around July of a
year. For Q3 and Q4 failure rates, we used this July figure. For Q1, we used the average of the
July figure and the figure for the preceding year. For Q4, we used the average of the July figure
and that from the following year.
New York stock exchange shares sold: starting January 1881, from Owens and Hardy (1925)
appendix D.
London equity price index: Smith and Horne (1934), from NBER 11012.
London consol yield. NBER series 13041 (2) linked to 13041 (3) at April 1884.
London bill rate: From weekly data in The Economist, taken from NBER series 13016.
Bank failures. Assets of failed national banks from U.S. Comptroller of the Currency (1921),
Table 39. Assets of failed “other” banks and bank assets as of preceding June from U.S.
Comptroller of the Currency (1932).
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Figure 1
Stock Prices and Crisis Dates
Monthly, January 1879 - December 1913
May
1884
4.6
Nov. May Oct.
1890 1893 1896
Oct.
1907
Log (Stock price index)
4.4
4.2
4.0
3.8
3.6
3.4
3.2
80
85
90
95
42
00
05
10
Figure 2
IP gaps and Cotton Harvest fluctuations
(quadratic trends)
1878 -1913
0.2
IP gap
actual
from cotton
0.1
0.0
0.3
-0.1
0.2
0.1
-0.2
0.0
-0.1
Cotton
harvest
(previous fall)
-0.2
-0.3
80
85
90
95
43
00
05
10
Figure 3
Value of cotton exports and cotton harvest fluctuations
1880 - 1913
0.4
Cotton exports
value, residual*
0.2
0.0
0.3
-0.2
0.2
0.1
-0.4
0.0
-0.1
Cotton harvest
fluctuation
previous fall
-0.2
-0.3
80
85
90
95
00
05
10
* Residual from regression
Ln($Cotton exports) on time trends and price level
44
Figure 4
Change in Stock Price Index and Cotton Harvests
1879-1913
Change in log of stock price index to Q4
0.3
0.2
0.1
0.0
1896
1884
1893
-0.1
1890
-0.2
1907
-0.3
-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
Figure 5
Change in Railroad Bond Yields and Cotton Harvests
1879-1913
Change in yield index to Q4, percent
0.4
1907
0.2
1890
1896
0.0
1893 1884
-0.2
-0.4
-0.3
1880
-0.2
-0.1
0.0
45
0.1
0.2
0.3
Figure 6
Change in Commercial Paper Rates and Cotton Harvests
1879-1913
Change in CP rate to Q4, percent
3
2
1907
1890
1896
1
0
1884
1893
-1
-2
-3
-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
Autumn cotton harvest
deviation of log from quadratic trend
Figure 7
Change in New York Bank Loans and Cotton Harvests
1879-1913
Change in log loans to November-December
0.3
0.2
0.1
1907
0.0
1896
1890
-0.1
1893
1884
-0.2
-0.3
-0.2
-0.1
0.0
0.1
0.2
Autumn cotton harvest
deviation of log from quadratic trend
46
0.3
Figure 8
Change in log deposits to November-December
Change in New York Deposits due to Banks and Cotton Harvests
1879-1913
0.4
0.2
1893
1896
0.0
1884
1890
1907
-0.2
-0.4
-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
Autumn cotton harvest
deviation of log from quadratic trend
Figure 9
Change in log deposits to November-December
Change in New York Deposits due to Nonbanks and Cotton Harvests
1879-1913
0.3
0.2
1893
1907
0.1
1890
1884
0.0
1896
-0.1
-0.2
-0.3
-0.2
-0.1
0.0
0.1
0.2
Autumn cotton harvest
deviation of log from quadratic trend
47
0.3
Figure 10
Change in log reserves to November-December
Change in New York Bank Reserves and Cotton Harvests
1879-1913
(Including National Bank notes of other banks)
0.8
0.6
1893
0.4
1884
0.2
1896
0.0
1890
1907
-0.2
-0.4
-0.3
-0.2
-0.1
0.0
0.1
0.2
Autumn cotton harvest
deviation of log from quadratic trend
48
0.3
Table 1 Panic dates
Panic onset
(Wicker,
Kemmerer)
Gorton
1884 May
1884 June
1890 Nov.
1890 Nov.
1893 May
1893 May
1896 Oct.
1907 Oct.
1907 Oct.
NY Clearinghouse
actions
(Wicker [2000])
1884 May1
1890 Nov.1
1893 June1, August2
1907 Oct.1,2
Preceding cyclical peak
NBER
Romer (1994)
1882 March
NA
1890 July
none (trough 1887 July)
1893 Jan.
1893 January
1895 Dec.
1896 January
1907 May
1907 July
1Issuance of clearinghouse certificates
2 Suspension of cash payments
Table 2 Harvests and Crop-related Export Revenue
A) Harvest volatility, 1878-1913
Deviation from trend
Quadratic
Cotton
Wheat
Corn
Cotton
0.20
0.23
0.16
0.19
Maximum
-0.22
-0.20
-0.37
-0.22
Minimum
0.12
0.13
0.14
0.11
Std. Dev.
HP
Wheat
0.24
-0.19
0.11
Corn
0.17
-0.37
0.14
B) Export revenue and harvests 1879-1913
LHS variable: Ln($Exports cotton+wheat+wheat flour + corn + corn flour)
Crop trends
Quadratic
HP
(1)
(2)
(3)
(4)
Cotton(-1)
0.481
0.492
0.488
0.504
[0.158]
[0.149] [0.163]
[0.151]
0.01
0.00
0.01
0.00
Wheat(-1)
0.452
[0.151]
0.01
0.469
[0.143]
0.00
0.463
[0.161]
0.01
0.490
[0.150]
0.00
Corn(-1)
0.221
[0.131]
0.10
0.227
[0.124]
0.08
0.213
[0.136]
0.13
0.241
[0.127]
0.07
WPI (12 months
preceding harvest,
exc. farm & foods)
Time
Time2/100
0.454
[0.211]
0.04
-0.020
[0.012]
0.10
0.070
[0.021]
0.00
0.005
[0.016]
0.74
0.026
[0.028]
0.37
49
0.511
[0.213]
0.02
-0.034
[0.011]
0.01
0.093
[0.020]
0.00
-0.005
[0.016]
0.74
0.043
[0.027]
0.12
Table 3 Cotton Harvests and Industrial Production
Coefficient
[SE]
p-value
A) Quarterly 1885-1913 Miron-Romer IP index
LHS variable: Log(quarterly IP) - Log(annual average July to June preceding harvest season)
Harvest year
Q3
Q4
-0.010
0.142
[0.148] [0.194]
0.95
0.47
Following year
.
Q1
Q2
Q3
0.356
0.317
0.595
[0.160] [0.146] [0.157]
0.03
0.04
0.00
Q4
0.399
[0.214]
0.07
2SLS, all years
0.169
[0.203]
0.41
0.489
[0.273]
0.08
0.647
[0.220]
0.01
0.410
[0.190]
0.04
0.705
[0.206]
0.00
0.456
[0.279]
0.11
OLS, exc. pre-crisis
harvests
0.047
[0.179]
0.80
0.233
[0.232]
0.33
0.397
[0.198]
0.06
0.299
[0.178]
0.11
0.443
[0.164]
0.01
0.373
[0.216]
0.10
OLS, All years
B) Annual 1880-1913 Davis IP index
LHS variable: IP gap
Excluding
All years crisis years
(1)
(2)
(3)
Cotton(-1) 0.372
0.340
0.305
[0.080] [0.093]
[0.070]
0.00
0.00
0.00
Crisis
-0.023
[0.023]
0.34
Crisis(-1)
-0.092
[0.023]
0.00
IP(-1)
0.564
[0.120]
0.00
0.575
[0.130]
0.00
0.565
[0.100]
0.00
50
All years
.
(4)
(5)
0.372
0.323
[0.083] [0.067]
0.00
0.00
-0.001
[0.028]
0.96
-0.087
[0.022]
0.00
0.565
[0.124]
0.00
0.547
[0.099]
0.00
Table 4 Cotton harvests and WPI
Quarterly 1879-1913
LHS variable: Log(quarterly WPI) – Log(quarterly WPI, Q2 preceding harvest season)
Harvest year
Following year
.
Q3
Q4
Q1
Q2
Q3
Q4
OLS, All years
-0.023
-0.002
0.085
0.156
0.235
0.250
[0.037] [0.076] [0.108] [0.110] [0.111]
[0.127]
0.54
0.98
0.43
0.16
0.04
0.06
2SLS, all years
0.051
[0.052]
0.34
0.148
[0.108]
0.18
0.269
[0.148]
0.08
0.344
[0.150]
0.03
0.407
[0.151]
0.01
0.410
[0.170]
0.02
OLS, exc. pre-crisis
harvests
-0.006
[0.044]
0.89
0.004
[0.092]
0.97
0.076
[0.131]
0.57
0.155
[0.132]
0.25
0.232
[0.134]
0.09
0.245
[0.152]
0.12
51
Table 5 U.S. Financial Variables and Harvests 1879-1913
Number of observations: 35 for Harvest year Q3, Q4; 34 for following year.
a) Stock prices
Log index
(Change to quarter
from pre-harvest Q2)
Coefficient scaled to one SD log harvest fluctuation = 0.12
[SE] also rescaled
p-value
Harvest year
Following year
Q3
Q4
Q1
Q2
Q3
Cotton
0.022
0.038
0.056
0.068
0.088
[0.010] [0.016] [0.020] [0.023] [0.027]
0.03
0.03
0.01
0.01
0.00
Wheat
0.011
0.009
0.012
0.000 -0.016
[0.093] [0.015] [0.019] [0.022] [0.025]
0.25
0.56
0.52
0.99
0.52
-0.019
[0.030]
0.53
Corn
0.013
0.031
0.034
0.024
0.018
[0.008] [0.013] [0.017] [0.019] [0.022]
0.12
0.02
0.05
0.23
0.43
0.023
[0.027]
0.39
Cotton
0.036
0.059
0.072
0.064
0.067
[0.014] [0.023] [0.027] [0.030] [0.034]
0.01
0.01
0.01
0.04
0.06
0.049
[0.041]
0.24
Wheat
0.028
0.032
0.034
0.032
0.017
[0.013] [0.022] [0.028] [0.032] [0.037]
0.04
0.16
0.22
0.31
0.65
0.023
[0.041]
0.57
Corn
0.005
0.015
0.019
0.009 -0.001
[0.011] [0.018] [0.023] [0.027] [0.031]
0.67
0.40
0.41
0.75
0.97
0.008
[0.034]
0.82
Cotton
0.110
0.166
0.176
0.236
0.148
[0.052] [0.050] [0.070] [0.067] [0.061]
0.04
0.00
0.02
0.00
0.02
0.167
[0.070]
0.02
Wheat
-0.034 -0.080
0.026 -0.069 -0.090
[0.049] [0.047] [0.066] [0.063] [0.058]
0.49
0.10
0.69
0.29
0.14
-0.070
[0.066]
0.30
Corn
0.066
0.091
0.095
0.017
0.007
[0.042] [0.041] [0.058] [0.055] [0.051]
0.13
0.03
0.11
0.76
0.89
0.060
[0.058]
0.31
Cotton
0.207
0.238
0.188
0.243
0.166
[0.073] [0.074] [0.091] [0.085] [0.080]
0.01
0.00
0.05
0.01
0.05
0.149
[0.090]
0.11
OLS, all 3 crops on LHS
2SLS
(each crop is a separate
regression)
b)Stock trading volume
Log number of shares
(Change to quarter from
pre-harvest July to June)
OLS, all 3 crops on LHS
2SLS
.
Q4
0.069
[0.032]
0.04
Wheat
0.042
52
0.009
0.113
-0.023
-0.065
0.060
(each crop is a separate
regression)
c) Railroad bonds yield
(Change to quarter
from pre-harvest Q2)
[0.068] [0.073] [0.094] [0.093] [0.078]
0.54
0.91
0.24
0.81
0.42
[0.093]
0.52
Corn
0.058
0.096
0.096
0.023
0.043
[0.054] [0.058] [0.077] [0.079] [0.068]
0.29
0.11
0.22
0.77
0.54
0.090
[0.076]
0.25
Cotton
-0.027 -0.041 -0.050 -0.047 -0.075
[0.015] [0.021] [0.025] [0.033] [0.040]
0.08
0.05
0.06
0.17
0.07
-0.041
[0.044]
0.36
Wheat
-0.008 -0.011 -0.005 -0.008 -0.002
[0.014] [0.020] [0.024] [0.032] [0.038]
0.55
0.56
0.83
0.81
0.97
-0.004
[0.042]
0.93
Corn
-0.011 -0.016 -0.022 -0.024 -0.023
[0.012] [0.017] [0.021] [0.028] [0.034]
0.35
0.35
0.30
0.39
0.50
-0.018
[0.036]
0.62
Cotton
-0.052 -0.066 -0.057 -0.040 -0.055
[0.020] [0.027] [0.032] [0.042] [0.051]
0.01
0.02
0.09
0.34
0.29
-0.031
[0.055]
0.58
Wheat
-0.014 -0.008 -0.010 -0.018 -0.019
[0.018] [0.026] [0.032] [0.040] [0.050]
0.44
0.75
0.75
0.67
0.71
-0.027
[0.052]
0.61
Corn
-0.008 -0.022 -0.028 -0.032 -0.018
[0.015] [0.022] [0.027] [0.034] [0.043]
0.60
0.32
0.30
0.36
0.67
-0.015
[0.044]
0.73
Cotton
-0.433 -0.455 -0.422 -0.408 -0.654
[0.204] [0.179] [0.187] [0.219] [0.290]
0.04
0.02
0.03
0.07
0.03
-0.061
[0.238]
0.80
Wheat
0.133
0.240
0.349
0.340
0.348
[0.192] [0.169] [0.178] [0.208] [0.276]
0.49
0.17
0.06
0.11
0.22
0.278
[0.227]
0.23
Corn
0.079
0.246
0.166
0.078
0.100
[0.167] [0.147] [0.155] [0.182] [0.241]
0.64
0.10
0.29
0.67
0.68
0.228
[0.198]
0.26
Cotton
-0.587 -0.365 -0.189 -0.033 -0.141
[0.267] [0.247] [0.255] [0.294] [0.386]
0.03
0.15
0.46
0.91
0.72
0.299
[0.319]
0.36
Wheat
-0.010
0.205
0.332
0.220
0.186
[0.255] [0.236] [0.238] [0.270] [0.366]
0.123
[0.287]
OLS, all 3 crops on LHS
2SLS
(each crop is a separate
regression)
d) Commercial paper
(Change to quarter
from pre-harvest
July-to-June avg.)
OLS, all 3 crops on LHS
2SLS
(each crop is a separate
53
regression)
0.97
Corn
0.39
0.17
0.42
0.61
0.67
-0.017
0.152
0.082
0.000
0.126
[0.215] [0.196] [0.209] [0.236] [0.315]
0.94
0.44
0.70
1.00
0.69
0.185
[0.243]
0.45
Table 6 U.S. Financial Variables and Cotton Harvests, 1879-1913
Number of observations for Harvest year Q3, Q4 (Following year):
All years
35 (34)
Excluding harvests followed by crises (inc. 1896)
30 (29)
a) Stock prices
Log index
(Change to quarter
from pre-harvest Q2)
Coefficient scaled to one SD log harvest fluctuation = 0.12
[SE] also rescaled
p-value
Harvest year
Following year
Q3
Q4
Q1
Q2
Q3
All years
0.025
0.042
0.061
0.071
0.086
[0.010] [0.017] [0.020] [0.023] [0.026]
0.02
0.02
0.01
0.00
0.00
.
Q4
0.067
[0.031]
0.04
Exc.
pre-crisis harvests
0.034
0.049
0.070
0.077
0.079
[0.012] [0.020] [0.024] [0.025] [0.027]
0.01
0.02
0.01
0.00
0.01
0.069
[0.031]
0.03
b)Stock trading
volume
Log number of shares
(Change to quarter
from pre-harvest
July to June)
All years
0.108
0.157
0.190
0.224
0.131
[0.051] [0.053] [0.069] [0.065] [0.061]
0.04
0.01
0.01
0.00
0.04
0.159
[0.068]
0.03
Exc.
pre-crisis harvests
c) Railroad bonds
(Change to quarter
from pre-harvest Q2)
All years
0.148
0.212
0.264
0.293
0.175
[0.060] [0.062] [0.081] [0.073] [0.069]
0.02
0.00
0.00
0.00
0.02
-0.029 -0.045 -0.054 -0.051 -0.078
[0.014] [0.020] [0.025] [0.032] [0.039]
0.05
0.03
0.04
0.12
0.05
0.192
[0.077]
0.02
-0.043
[0.042]
0.31
Exc.
pre-crisis harvests
-0.050 -0.066 -0.071 -0.066 -0.071
[0.015] [0.023] [0.028] [0.037] [0.043]
0.00
0.01
0.02
0.09
0.11
-0.058
[0.044]
0.21
d) Commercial paper All years
(Change to quarter
from pre-harvest
July-to-June avg.)
Exc.
pre-crisis harvests
-0.400 -0.387 -0.339 -0.336 -0.577
[0.196] [0.184] [0.193] [0.218] [0.284]
0.05
0.04
0.09
0.13
0.05
0.014
[0.238]
0.95
-0.596 -0.445 -0.203 -0.127 -0.136
[0.229] [0.218] [0.206] [0.238] [0.267]
0.01
0.05
0.33
0.60
0.61
0.086
[0.260]
0.74
54
Table 7 U.S. Financial variables and cotton harvests, spring and early summer of harvest year
Coefficient scaled to one SD log harvest fluctuation = 0.12
[SE] also rescaled
p-value
a) Stock prices
Log index
(Change
from pre-harvest Q1)
b)Stock trading vol.
Log shares (Change
from pre-harvest
April to March average)
c) Railroad bonds
(Change
from pre-harvest Q1)
d) Commercial paper
(Change
from pre-harvest
April to March avg.)
Q2
June
July
OLS
2SLS
OLS
2SLS
OLS
2SLS
0.001
0.028 -0.257 -0.298 -0.328
-0.392
[0.010] [0.015] [0.174] [0.233] [0.158]
[0.212]
0.89
0.07
0.15
0.21
0.05
0.07
-0.050 -0.001 -0.022
0.056 -0.029
[0.049] [0.066] [0.060] [0.082] [0.054]
0.31
0.99
0.71
0.50
0.59
0.013
[0.073]
0.86
-0.004 -0.018 -0.257 -0.298 -0.328
[0.011] [0.015] [0.174] [0.233] [0.158]
0.73
0.26
0.15
0.21
0.05
-0.392
[0.212]
0.07
-0.170 -0.281 -0.258 -0.441 -0.437
[0.177] [0.238] [0.211] [0.286] [0.263]
0.34
0.25
0.23
0.13
0.11
-0.732
[0.359]
0.05
55
Table 8 London Financial Variables and Harvests, 1879-1913
Number of observations: 35 for Harvest year Q3, Q4; 34 for following year.
a) Stock prices
Log index
(Change to quarter
from pre-harvest Q2)
b) Consol yield
(Change to quarter
from pre-harvest Q2)
c) Bills rate
(Change to quarter
from pre-harvest
July-to-June avg.)
Coefficient scaled to one SD log harvest fluctuation = 0.12
[SE] also rescaled
p-value
Harvest year
Following year
.
Q3
Q4
Q1
Q2
Q3
Q4
Cotton
-0.007 -0.003 -0.004 -0.013 -0.012
-0.013
[0.003] [0.006] [0.010] [0.012] [0.014]
[0.015]
0.05
0.64
0.70
0.28
0.38
0.38
Wheat
-0.003 -0.005 -0.006 -0.013 -0.021
[0.003] [0.006] [0.010] [0.011] [0.013]
0.32
0.41
0.56
0.25
0.12
-0.025
[0.014]
0.08
Corn
0.004
0.011
0.015
0.014
0.012
[0.003] [0.005] [0.009] [0.010] [0.011]
0.18
0.04
0.10
0.17
0.29
0.010
[0.012]
0.42
Cotton
0.009 -0.001 -0.004
0.008
0.015
[0.007] [0.010] [0.013] [0.016] [0.021]
0.19
0.90
0.76
0.63
0.46
0.013
[0.022]
0.56
Wheat
0.002 -0.003
0.006
0.001
0.006
[0.007] [0.010] [0.012] [0.016] [0.020]
0.73
0.78
0.62
0.93
0.75
0.005
[0.021]
0.83
Corn
0.002
0.007
0.016
0.013
0.018
[0.006] [0.008] [0.010] [0.014] [0.017]
0.79
0.42
0.15
0.35
0.29
0.018
[0.018]
0.34
Cotton
-0.074 -0.228 -0.183 -0.141 -0.166
[0.138] [0.193] [0.166] [0.173] [0.212]
0.60
0.25
0.28
0.42
0.44
-0.054
[0.247]
0.83
Wheat
-0.223 -0.065
0.074
0.085
0.111
[0.130] [0.182] [0.158] [0.165] [0.201]
0.10
0.72
0.64
0.61
0.58
-0.075
[0.235]
0.75
Corn
0.025
0.193
0.254
0.117
0.181
[0.113] [0.158] [0.138] [0.144] [0.176]
0.83
0.23
0.08
0.42
0.31
0.318
[0.205]
0.13
56
Table 9 National Bank Balance Sheets and Harvests 1879-1913
Number of observations: 35 for Harvest year; 34 for following year through summer;
33 for following year fall
Coefficient scaled to one SD log harvest fluctuation = 0.12
[SE] also rescaled
p-value
A) New York Banks
a) Loans and Discounts
b) Deposits due to
Banks
c) Deposits due to
Nonbanks
Cotton
Harvest year
Following year
.
Aug.-Oct. Nov.-Dec. Feb.-Mar. May-Jul. Aug.-Oct. Nov.-Dec.
0.034
0.052
0.058
0.063
0.061
0.044
[0.013] [0.016] [0.018]
[0.019] [0.021]
[0.022]
0.02
0.00
0.00
0.00
0.01
0.06
Wheat
-0.019
[0.013]
0.14
-0.018
[0.015]
0.24
-0.015
[0.017]
0.38
-0.012
[0.018]
0.48
-0.022
[0.020]
0.28
-0.015
[0.021]
0.48
Corn
0.000
[0.011]
0.97
-0.003
[0.013]
0.82
0.000
[0.015]
0.98
0.001
[0.015]
0.94
0.007
[0.017]
0.70
0.009
[0.019]
0.65
Cotton
0.061
[0.020]
0.00
0.079
[0.020]
0.00
0.070
[0.027]
0.01
0.079
[0.030]
0.01
0.070
[0.033]
0.04
0.030
[0.030]
0.34
Wheat
-0.027
[0.019]
0.15
-0.017
[0.019]
0.38
-0.021
[0.025]
0.42
-0.011
[0.028]
0.70
-0.022
[0.031]
0.49
-0.021
[0.029]
0.47
Corn
0.009
[0.016]
0.54
0.000
[0.016]
0.98
0.014
[0.022]
0.53
0.021
[0.025]
0.41
0.033
[0.027]
0.23
0.023
[0.025]
0.37
Cotton
0.025
[0.013]
0.07
0.031
[0.016]
0.06
0.043
[0.019]
0.03
0.068
[0.023]
0.01
0.040
[0.025]
0.11
0.033
[0.025]
0.19
Wheat
-0.016
[0.012]
0.21
-0.012
[0.015]
0.44
-0.020
[0.018]
0.27
-0.014
[0.022]
0.54
-0.016
[0.023]
0.50
-0.006
[0.024]
0.78
Corn
-0.018
[0.011]
0.10
-0.017
[0.013]
0.21
-0.019
[0.016]
0.24
-0.014
[0.019]
0.49
-0.012
[0.021]
0.55
-0.019
[0.021]
0.38
57
d) Cash reserves
including other banks'
notes
e) Cash reserves
not including other
banks' notes
Cotton
0.026
[0.022]
0.23
0.035
[0.031]
0.27
0.020
[0.037]
0.60
0.057
[0.042]
0.18
-0.009
[0.042]
0.83
-0.031
[0.039
0.44
Wheat
-0.031
[0.020]
0.14
-0.003
[0.029]
0.92
-0.014
[0.036]
0.70
0.013
[0.040]
0.74
0.013
[0.040]
0.74
0.015
[0.037]
0.69
Corn
-0.008
[0.018]
0.67
-0.007
[0.026]
0.78
0.001
[0.031]
0.97
0.005
[0.035]
0.89
0.006
[0.035]
0.87
-0.015
[0.033]
0.66
Cotton
0.027
[0.022]
0.22
0.036
[0.031]
0.27
0.020
[0.038]
0.61
0.056
[0.043]
0.20
-0.009
[0.042]
0.83
-0.031
[0.040]
0.44
Wheat
-0.030
[0.021]
0.15
-0.002
[0.030]
0.94
-0.013
[0.036]
0.73
0.013
[0.040]
0.76
0.015
[0.040]
0.72
0.015
[0.038]
0.69
Corn
-0.008
[0.018]
0.68
-0.006
[0.026]
0.80
0.002
[0.031]
0.95
0.006
[0.035]
0.88
0.006
[0.035]
0.86
-0.014
[0.033]
0.68
B) Banks outside New York
a) Loans and Discounts
Harvest year
Following year
.
Aug.-Oct. Nov.-Dec. Feb.-Mar. May-Jul. Aug.-Oct. Nov.-Dec.
Cotton
0.005
0.006
0.010
0.021
0.032
0.033
[0.008] [0.009] [0.011]
[0.012] [0.013]
[0.014]
0.55
0.50
0.39
0.08
0.02
0.02
Wheat
0.009
[0.008]
0.24
0.012
[0.009]
0.18
0.017
[0.010]
0.11
0.017
[0.011]
0.14
0.015
[0.013]
0.25
0.016
[0.013]
0.23
Corn
-0.005
[0.007]
0.47
-0.001
[0.008]
0.91
0.001
[0.009]
0.90
0.004
[0.010]
0.70
0.005
[0.011]
0.62
0.008
[0.012]
0.50
58
b) Deposits due to
nonbanks
c) Total deposits
less deposits due from
other banks
("net deposits")
d) Cash reserves
including other banks'
notes
e) Cash reserves
not including other
banks' notes
Cotton
0.016
[0.010]
0.11
0.020
[0.010]
0.05
0.027
[0.011]
0.02
0.043
[0.012]
0.00
0.053
[0.014]
0.00
0.048
[0.014]
0.00
Wheat
0.007
[0.009]
0.46
0.012
[0.009]
0.21
0.015
[0.010]
0.13
0.020
[0.012]
0.10
0.013
[0.013]
0.32
0.013
[0.014]
0.34
Corn
-0.004
[0.008]
0.65
-0.001
[0.008]
0.90
0.003
[0.009]
0.75
0.007
[0.010]
0.47
0.011
[0.011]
0.35
0.012
[0.012]
0.32
0.014
[0.010]
0.19
0.018
[0.012]
0.13
0.024
[0.012]
0.05
0.041
[0.013]
0.00
0.054
[0.015]
0.00
0.051
[0.016]
0.00
Wheat
0.010
[0.010]
0.32
0.015
[0.011]
0.18
0.017
[0.011]
0.14
0.016
[0.013]
0.21
0.015
[0.014]
0.29
0.016
[0.016]
0.30
Corn
-0.005
[0.009]
0.55
-0.003
[0.010]
0.78
0.001
[0.010]
0.90
0.007
[0.011]
0.55
0.010
[0.013]
0.41
0.014
[0.014]
0.30
0.007
[0.008]
0.36
0.015
[0.010]
0.15
0.007
[0.010]
0.50
0.019
[0.011]
0.09
0.011
[0.010]
0.31
0.012
[0.011]
0.32
Wheat
-0.003
[0.007]
0.71
0.001
[0.009]
0.94
-0.008
[0.010]
0.40
0.003
[0.010]
0.81
0.007
[0.010]
0.48
0.012
[0.011]
0.27
Corn
0.008
[0.006]
0.19
0.002
[0.008]
0.79
0.012
[0.009]
0.17
0.016
[0.009]
0.08
0.028
[0.009]
0.00
0.029
[0.010]
0.01
0.009
[0.008]
0.29
0.015
[0.010]
0.14
0.009
[0.011]
0.42
0.020
[0.012]
0.09
0.014
[0.011]
0.24
0.010
[0.011]
0.38
Wheat
-0.001
[0.007]
0.86
-0.001
[0.009]
0.91
-0.006
[0.011]
0.58
0.004
[0.011]
0.70
0.008
[0.011]
0.44
0.013
[0.011]
0.25
Corn
0.007
[0.006]
0.29
0.004
[0.008]
0.63
0.010
[0.009]
0.28
0.014
[0.010]
0.15
0.027
[0.009]
0.01
0.029
[0.010]
0.01
Cotton
Cotton
Cotton
59
Table 10 National Banks and Cotton Harvests 1879-1913
Number of observations for Harvest year Q3, Q4 (Following year):
All years
35 (34)
Excluding harvests followed by crises (inc. 1896)
30 (29)
Coefficient scaled to one SD log harvest fluctuation = 0.12
[SE] also rescaled
p-value
A) New York City
a) Loans
and discounts
b) Deposits due
to banks
c) Deposits due
to nonbanks
OLS, all years
Harvest year
Following year
.
Aug.-Oct. Nov.-Dec. Feb.-Mar. May-Jul. Aug.-Oct. Nov.-Dec.
0.030
0.048
0.055
0.061
0.057
0.042
[0.013] [0.015]
[0.017] [0.018]
[0.020]
[0.022]
0.03
0.00
0.00
0.00
0.01
0.06
OLS, exc.
pre-crisis harvests*
0.043
[0.015]
0.01
0.062
[0.017]
0.00
0.059
[0.020]
0.01
0.057
[0.020]
0.01
0.041
[0.021]
0.06
0.031
[0.025]
0.22
2SLS, all years
0.047
[0.018]
0.01
0.049
[0.020]
0.02
0.063
[0.022]
0.01
0.040
[0.024]
0.11
0.033
[0.027]
0.24
0.003
[0.029]
0.91
OLS, all years
0.056
[0.020]
0.01
0.076
[0.019]
0.00
0.067
[0.026]
0.01
0.079
[0.029]
0.01
0.069
[0.032]
0.04
0.028
[0.029]
0.35
OLS, exc.
pre-crisis harvests*
0.066
[0.024]
0.01
0.083
[0.023]
0.00
0.059
[0.029]
0.05
0.050
[0.029]
0.10
0.033
[0.033]
0.32
0.026
[0.034]
0.44
2SLS, all years
0.078
[0.027]
0.01
0.076
[0.026]
0.01
0.053
[0.034]
0.13
0.034
[0.039]
0.38
0.027
[0.043]
0.54
-0.008
[0.039]
0.85
OLS, all years
0.020
[0.013]
0.15
0.027
[0.016]
0.10
0.037
[0.019]
0.06
0.064
[0.023]
0.01
0.036
[0.024]
0.14
0.030
[0.024]
0.21
OLS, exc.
crisis-year harvests*
0.018
[0.015]
0.21
0.039
[0.016]
0.03
0.049
[0.020]
0.02
0.091
[0.020]
0.00
0.064
[0.020]
0.00
0.053
[0.021]
0.02
2SLS, all years
0.027
[0.018]
0.14
0.022
[0.021]
0.31
0.034
[0.025]
0.17
0.041
[0.030]
0.18
0.004
[0.032]
0.89
-0.009
[0.032]
0.79
60
d) Cash reserves OLS, all years
including
other banks' notes
0.020
[0.021]
0.36
0.034
[0.030]
0.26
0.017
[0.036]
0.63
0.060
[0.040]
0.14
-0.006
[0.040]
0.88
-0.029
[0.038]
0.44
OLS, exc.
pre-crisis harvests*
0.013
[0.025]
0.62
0.026
[0.035]
0.47
-0.006
[0.041]
0.88
0.023
[0.046]
0.61
-0.033
[0.046]
0.48
-0.001
[0.040]
0.98
2SLS, all years
0.008
[0.029]
0.77
0.012
[0.040]
0.77
-0.014
[0.047]
0.77
-0.001
[0.055]
0.98
-0.060
[0.053]
0.27
-0.060
[0.050]
0.24
0.021
[0.022]
0.34
0.035
[0.030]
0.26
0.017
[0.036]
0.63
0.059
[0.041]
0.15
-0.006
[0.040]
0.89
-0.030
[0.038]
0.44
OLS, exc.
pre-crisis harvests*
0.014
[0.025]
0.58
0.026
[0.035]
0.46
-0.006
[0.041]
0.89
0.021
[0.046]
0.65
-0.033
[0.046]
0.48
-0.002
[0.040]
0.96
2SLS, all years
0.011
[0.029]
0.71
0.013
[0.040]
0.75
-0.014
[0.048]
0.77
-0.003
[0.055]
0.96
-0.060
[0.054]
0.28
-0.060
[0.050]
0.24
e) Cash reserves OLS, all years
not including
other banks' notes
B) Outside New York City
a) Loans
and discounts
b) Deposits due
to nonbanks
OLS, all years
Harvest year
Following year
.
Aug.-Oct. Nov.-Dec. Feb.-Mar. May-Jul. Aug.-Oct. Nov.-Dec.
0.006
0.009
0.013
0.024
0.036
0.037
[0.008] [0.009]
[0.011] [0.012]
[0.013]
[0.014]
0.43
0.35
0.24
0.04
0.01
0.01
OLS, exc.
pre-crisis harvests*
0.008
[0.010]
0.42
0.009
[0.011]
0.42
0.010
[0.013]
0.45
0.016
[0.014]
0.26
0.017
[0.014]
0.22
0.018
[0.015]
0.22
2SLS, all years
0.014
[0.011]
0.20
0.020
[0.012]
0.11
0.029
[0.015]
0.06
0.034
[0.015]
0.03
0.039
[0.017]
0.03
0.040
[0.018]
0.03
OLS, all years
0.018
[0.010]
0.08
0.022
[0.010]
0.03
0.030
[0.010]
0.01
0.048
[0.012]
0.00
0.056
[0.014]
0.00
0.052
[0.014]
0.00
OLS, exc.
pre-crisis harvests*
0.022
[0.012]
0.07
0.025
[0.012]
0.04
0.028
[0.013]
0.04
0.038
[0.014]
0.01
0.038
[0.014]
0.01
0.037
[0.015]
0.02
0.032
0.037
0.046
0.053
0.058
0.055
2SLS, all years
61
[0.013]
0.02
[0.013]
0.01
[0.014]
0.00
[0.016]
0.00
[0.018]
0.00
[0.019]
0.01
0.015
[0.010]
0.14
0.020
[0.011]
0.08
0.027
[0.012]
0.03
0.045
[0.013]
0.00
0.045
[0.013]
0.00
0.058
[0.015]
0.00
0.020
[0.012]
0.12
0.024
[0.014]
0.09
0.026
[0.014]
0.08
0.035
[0.015]
0.03
0.038
[0.016]
0.02
0.040
[0.017]
0.03
0.026
[0.014]
0.07
0.033
[0.015]
0.04
0.041
[0.016]
0.01
0.049
[0.017]
0.01
0.056
[0.020]
0.01
0.053
[0.021]
0.02
0.007
[0.007]
0.34
0.015
[0.009]
0.12
0.007
[0.010]
0.52
0.021
[0.011]
0.06
0.015
[0.012]
0.21
0.017
[0.013]
0.20
OLS, exc.
pre-crisis harvests*
0.000
[0.008]
0.96
0.010
[0.010]
0.32
-0.008
[0.011]
0.45
0.006
[0.012]
0.61
0.011
[0.014]
0.43
0.015
[0.016]
0.33
2SLS, all years
-0.007
[0.010]
0.51
-0.003
[0.013]
0.81
-0.011
[0.014]
0.44
0.007
[0.015]
0.65
0.003
[0.015]
0.87
0.003
[0.017]
0.87
0.009
[0.008]
0.26
0.015
[0.009]
0.12
0.009
[0.011]
0.41
0.022
[0.011]
0.06
0.018
[0.012]
0.16
0.015
[0.013]
0.24
OLS, exc.
pre-crisis harvests*
0.002
[0.009]
0.86
0.011
[0.010]
0.32
-0.006
[0.011]
0.61
0.007
[0.012]
0.59
0.014
[0.015]
0.34
0.015
[0.016]
0.35
2SLS, all years
-0.003
[0.011]
0.78
0.002
[0.013]
0.86
-0.009
[0.015]
0.56
0.009
[0.015]
0.54
0.010
[0.016]
0.55
0.006
[0.017]
0.73
c) Total deposits OLS, all years
less deposits
due from
other banks
("net deposits") OLS, exc.
pre-crisis harvests*
2SLS, all years
d) Cash reserves OLS, all years
including
other banks' notes
e) Cash reserves OLS, all years
not including
other banks' notes
62
Table 11 Bankruptcy Rates and Harvests, 1879-1913
Number of observations: 35 for Harvest year Q3, Q4; 34 for following year.
Coefficient scaled to one SD log harvest fluctuation = 0.12
[SE] also rescaled
p-value
A) All crops on RHS
Bankruptcy rate
(quarterly average)
Cotton
Harvest year
Following year
.
Q3
Q4
Q1
Q2
Q3
Q4
-0.029
0.013 -0.063 -0.103 -0.213
-0.098
[0.078] [0.099] [0.093] [0.058] [0.067]
[0.092]
0.71
0.89
0.50
0.09
0.00
0.29
Wheat
-0.142 -0.060 -0.121 -0.095 -0.103
[0.073] [0.093] [0.088] [0.056] [0.064]
0.06
0.52
0.18
0.10
0.12
-0.130
[0.087]
0.15
Corn
0.017
0.009 -0.046 -0.057 -0.050
[0.064] [0.081] [0.077] [0.049] [0.056]
0.79
0.91
0.56
0.25
0.38
-0.040
[0.076]
0.61
B) Cotton only on RHS
Bankruptcy rate
(quarterly average)
OLS, all years
Harvest year
Following year
.
Q3
Q4
Q1
Q2
Q3
Q4
-0.056
0.002 -0.090 -0.126 -0.237
-0.127
[0.078] [0.094] [0.091] [0.060] [0.068]
[0.091]
0.48
0.98
0.33
0.04
0.00
0.17
OLS, exc.
pre-crisis harvests
-0.072
0.067 -0.045 -0.069 -0.110
[0.094] [0.112] [0.105] [0.070] [0.066]
0.45
0.56
0.67
0.33
0.11
-0.062
[0.104]
0.56
2SLS, all years
-0.108
0.078 -0.212 -0.185 -0.242
[0.106] [0.128] [0.123] [0.079] [0.089]
0.31
0.54
0.09
0.03
0.01
-0.241
[0.122]
0.06
63
Table 12 Bank Failures and Harvests, 1879-1913
RHS variables: Assets of Failed Banks/Assets of Banks previous June, in percent
Coefficient scaled to one SD log harvest fluctuation = 0.12
[SE] also rescaled
p-value
A) Annual data, covering July of harvest year through following June
Cotton(-1)
OLS
All
National
(1)
(2)
-0.092 -0.049
[0.043] [0.039]
0.04
0.22
Excluding pre-panic harvests
2SLS
OLS
Other
All
National Other
All
National
Other
(3)
(4)
(5)
(6)
(7)
(8)
(9)
-0.256
-0.143
-0.105
-0.323
-0.045
-0.038
-0.086
[0.119] [0.055] [0.050] [0.151]
0.042
0.047
0.093
0.04
0.01
0.04
0.04
0.30
0.42
0.36
Wheat(-1)
-0.002 -0.029
[0.041] [0.037]
0.97
0.43
0.026
[0.114]
0.82
0.017
[0.055]
0.75
0.002
[0.047]
0.96
0.050
[0.153]
0.75
-0.025
0.036
0.49
-0.039
0.040
0.34
-0.044
0.080
0.59
Corn(-1)
-0.027 -0.011
[0.036] [0.032]
0.46
0.73
-0.093
[0.099]
0.36
-0.033
[0.046]
0.48
0.015
[0.041]
0.71
-0.076
[0.128]
0.56
-0.009
0.033
0.79
0.003
0.036
0.93
-0.035
0.072
0.63
B) Quarterly data, National Banks only, All crops on RHS
Harvest year
Following year
.
Q3
Q4
Q1
Q2
Q3
Q4
Cotton
-0.015 -0.004 -0.009 -0.021 -0.025
-0.009
[0.012] [0.022] [0.010] [0.014] [0.011]
[0.022]
0.23
0.86
0.37
0.15
0.03
0.68
Wheat
-0.014 -0.003 -0.013
0.000 -0.004
[0.114] [0.021] [0.010] [0.013] [0.011]
0.23
0.87
0.19
0.95
0.67
-0.015
[0.021]
0.48
Corn
-0.015
0.021 -0.009 -0.022 -0.008
[0.099] [0.018] [0.008] [0.012] [0.009]
0.88
0.25
0.28
0.07
0.41
-0.012
[0.018]
0.52
Cotton
-0.028 -0.029 -0.022 -0.020 -0.016
[0.016] [0.029] [0.013] [0.018] [0.014]
0.09
0.32
0.11
0.28
0.26
0.018
[0.029]
0.55
Wheat
-0.016
0.026 -0.003 -0.005
0.000
[0.014] [0.027] [0.012] [0.018] [0.014]
0.28
0.33
0.84
0.79
0.98
-0.030
[0.026]
0.26
OLS
2SLS
(each crop is a separate
regression)
Corn
0.000
64
0.031
-0.005
-0.014
0.005
0.007
[0.013] [0.022] [0.011] [0.015] [0.012]
0.95
0.16
0.63
0.34
0.71
C) Quarterly data, National Banks only,Cotton only on RHS
Harvest year
Following year
Q3
Q4
Q1
Q2
Q3
Bankruptcy rate
OLS, all years
-0.018 -0.003 -0.012 -0.023 -0.027
(quarterly average)
[0.012] [0.021] [0.010] [0.014] [0.011]
0.14
0.90
0.23
0.12
0.02
OLS, exc.
pre-crisis harvests
-0.021 -0.010 -0.006 -0.009
0.001
[0.014] [0.026] [0.012] [0.013] [0.006]
0.16
0.70
0.61
0.48
0.86
65
[0.023]
0.77
.
Q4
-0.013
[0.021]
0.54
0.020
[0.018]
0.28