The Real Effects of Fed Intervention: Revisiting the 1920-1921 Depression

The Real Eﬀects of Fed Intervention: Revisiting the 1920-1921 Depression∗

Bruce Carlin William Mann UCLA UCLA

September 7, 2018

Abstract

We provide causal evidence that discount rate changes by the Federal Reserve affected lending and economic output during the 1920-1921 depression. Our identification strategy exploits county-level variation in access to the Fed’s discount window. We implement this strategy with hand-collected data on banking and agriculture in Illinois. Intervention by the Fed temporarily lowered agricultural output, but caused debt-to-output levels to be persistently lower, lasting into the Great Depression. The underlying mechanism appears to be consolidation of farms and efficient reallocation of resources. Our findings call into question the conventional narrative that Fed policy was misguided during the 1920-1921 depression.

∗[email protected], [email protected]. We thank faculty and sem- inar participants at the University of Chicago, UC Berkeley, UCLA, the University of Colorado, Indiana University, the UBC Summer Finance Conference, and the University of Queensland for valuable comments and suggestions. Darren Tan, Joseph Needle, and Alex Simon provided excel- lent research assistance. Mann gratefully acknowledges ﬁnancial support from the Laurence and Lori Fink Center for Finance and Investments. 1 Introduction

Establishing the causal effect of monetary policy interventions on the real economy poses identification problems that are often insurmountable (e.g., Nakamura and Steinsson, 2018). The primary stumbling block is simultaneity: Interventions are just as much a reaction to as a catalyst for the fluctuations in economic outcomes. For this reason, historical natural experiments are an attractive setting to assess what a central bank can do to manage shocks and affect real outcomes (e.g., Friedman and Schwartz, 1963, Richardson and Troost, 2009). But such opportunities are scarce. In this paper, we revisit an important natural experiment in the history of the Federal Reserve around the deflationary depression of 1920-1921. This episode has long attracted attention and debate, especially as to whether the Fed made poor decisions. Following the end of World War I, global demand for American products plummeted, especially in the agricultural sector. During this contraction, and contrary to what modern conventional wisdom would prescribe, Federal Reserve banks raised discount rates aggressively. Many economists and historians have asserted that this policy exacerbated the depression and hurt subsequent economic activity. But this issue remains inconclusive even to this day, due to the empirical challenges noted above.1 The results in this paper support some aspects of conventional wisdom, but contradict others. We find that the Fed’s discount rate increases in 1920 did indeed have a large contractionary impact on the real economy, reducing both bank lending and agricultural

1 Friedman and Schwartz(1963) argue that rate increases worsened the depression by restricting the money supply. Kuehn(2012) argues that the rate increases decreased aggregate demand via government spending, not the money supply, while Soule(1947) and Romer(1988) emphasize supply instead of demand shifts as the main culprit. Sage(1983) emphasizes the removal of credit from highly-indebted farmers. Meltzer(2000) echoes all of these mechanisms. Frederico(2005) compares the “real” and “monetary” views of the episode.

1 output. But when the Fed subsequently lowered rates back to their original levels, the effect on output reversed, while lower levels of indebtedness persisted. This intervention led to lower debt-to-output ratios that lasted through 1929, accompanied by consolidation and efficient reallocation of resources in the farming sector. Lower indebtedness has been shown to be an important predictor of farms’ survival and recovery during the Great Depression. We thus challenge the view that the Fed’s actions during this episode were naive. A plausible alternative interpretation is that the Fed correctly identified and tried to manage a credit bubble that arose during World War I. To establish these findings, we focus on lending activity and agricultural output during 1916-1929 in Illinois, which was the preeminent farming state at the time. Our identification strategy to address simultaneity concerns is based on variation in the county-level composition of national versus state banks within Illinois. Upon the Federal Reserve’s founding in 1913, national banks were required to join, whereas state banks were given the choice. Virtually none did in Illinois, due to the offsetting costs of membership such as minimum reserve ratios and required ownership of Fed stock.2 This gave rise to regional variation in credit availability and exposure to the Fed’s discount rate changes, based on whether local banks had been chartered as state or national banks in the pre-Fed era.3 We hand-collect county-level data from several sources on banking and agriculture in Illinois. Statistics on lending by national banks come from Fed call reports and from Comptroller’s reports of condition for national banks. We augment these with statistics on state banks, from reports published by Illinois on the condition of state banks during the

2Even by 1929, only 5% of Illinois state banks were members of the Federal Reserve. See page 26 of the Federal Reserve’s Banking and Monetary Statistics 1914–1941 , published in 1943. 3While the discount window was intended only as a liquidity facility for times of stress, during its early years member banks used it much more liberally, with aggregate borrowing from the discount window exceeding $2 billion by 1920. See Gorton and Metrick(2013), Figure 1, which was provided by Ellis Tallman.

2 same time period. Finally, we collect county-level statistics on crop production for the same years from Illinois agriculture reports. We first show that, as the Fed aggressively raised discount rates during 1920, national banks decreased their lending activity. To demonstrate this finding, we use the number of national banks per capita and the fraction of national banks in each county in 1916 as proxies for county-level exposure to Fed policy. Importantly, we confirm that these variables are not correlated with observables such as credit conditions or agricultural output as of 1916. However, counties with more exposure to the Fed exhibited a significantly smaller volume of loans outstanding in 1920, after removing county and year fixed effects. The magnitude is large: County-level loans outstanding would have been 41.9% lower in 1920 if a county had 100% Fed membership compared to the same county if it had zero membership. We next provide causal evidence that the Fed’s discount rate changes in the 1920s affected the real economy: Both measures of Fed influence predict significantly lower agricultural output in 1920, accounting for both county and year fixed effects. Combining the credit and output effects into an IV specification, we estimate that a marginal dollar of bank credit led to an additional $0.73 to $0.79 of annual agricultural output during 1916-1920. The increase in interest rates by the Fed during 1920 thus had a significant contractionary effect on the real economy. While this has been generally believed to be true, to our knowledge this is the first causal evidence. The findings so far might suggest that the Fed’s discount rate increase during 1920 was a naive decision that damaged the real economy. Indeed, this has been the conventional narrative. However, we challenge this view by studying the longer-term effects of the Fed intervention on credit and output. We find that agricultural output in counties exposed to Fed policy rebounded quickly when interest rates were lowered back to pre-1919 levels. However,

3 the negative effect of the Fed’s discount rate changes on bank lending was persistent. As a result, the ratio of bank loans outstanding to agricultural output was much lower in counties with high Fed membership following the 1920-1921 Depression. This ratio remained lower through 1929, up to the eve of the Great Depression. Finally, we investigate the underlying mechanism that accounts for this change. We hand-collect county-level farm and acreage data from the United States Census of Agricul- ture. Counties with little exposure to national banks lost both farms and acreage in the aftermath of the 1920-1921 depression. In contrast, counties with more exposure to the Fed experienced consolidation in which acreage was preserved and reallocated for efficient use. We show that this effect persisted well into the 1930’s. As we discuss in the paper, the likely explanation for this difference is an avoidance of debt overhang problems. In counties with less austerity, farms that failed likely had higher debt, which prevented successful acquisi- tion and reallocation. By providing more discipline through higher discount rates, the Fed enabled consolidation and higher efficiency. In sum, our findings suggest that agricultural regions that were more exposed to the Fed’s actions in 1920 were better prepared for the long-term, and in particular for the coming trauma of the Great Depression. Commentators have often faulted the Fed for its aggressive rate hikes in 1920, but our identification strategy suggests an interpretation that is more positive. The Fed may deserve credit for successfully identifying and, in some regions, managing the credit expansion brought on by World War I.

Related Literature The most similar paper to ours is Richardson and Troost(2009), who identify the eﬀect of monetary policy on bank failures during the 1930’s by comparing banks located in Mississippi at the boundary of more- and less-interventionist Fed districts. They ﬁnd that banks in the

4 6th district (Atlanta) survived at higher rates and had faster recoveries than those in the 8th district (St. Louis) due to the monetary activism extended by the Atlanta Fed. We share the goal of using cross-sectional variation to identify the effect of policy in the early 20th century, but we exploit different, within-county variation. Also, their focus is on how emergency lending impacted bank survival, whereas we abstract from the individual bank and focus on how discount rates affected county-level credit provision. Our paper is also related to Calomiris, Jaremski, Park, and Richardson(2015), who hand- collect data on state banks in New York over a similar time period in order to understand why some state banks did not join the Federal Reserve. Their focus on New York was appropriate because New York state banks joined the Fed at an unusually high rate: By 1920, they find that 62% of state banks in New York City, and 23% outside the city, were members of the Fed. Since our goal is to understand the transmission of discount rate policy to the agricultural sector, we focus on Illinois, where few state banks joined the Fed during 1916-1926. Indeed, by 1929, only 5% of Illinois state banks were members of the Federal Reserve, which is in line with the national average of 7.5%.4 This is advantageous because the lower membership rate allows us to study differential exposure to discount rate policy by county and assess its local effect on the real economy. A broader literature studies the structure and real effect of the banking industry. Rajan and Ramcharan(2015) use nationwide data on the number of banks per county to study bank failures in the early 20th century as a function of real estate prices. Jayaratne and Strahan(1996) demonstrate that bank credit is also constrained by branching restrictions, such as Illinois’s complete prohibitions on bank branching in the early 20th century. Petersen and Rajan(1995) show that small firms borrow from local lenders, even in recent history.

4 See page 26 of the Federal Reserve’s Banking and Monetary Statistics 1914–1941 , published in 1943.

5 Our paper also contributes to a growing literature on spatial heterogeneity in the impacts of monetary policy (e.g. Gabriel and Lutz, 2014).

2 Historical background

This section provides a brief historical background for our study, drawing on several sources, most significantly White(1983), Friedman and Schwartz(1963), and the 1922 Report of the Joint Commission of Agricultural Inquiry to Congress. The National Banking Act of 1864 created a system of national banks, chartered by the federal government, to exist alongside state-chartered banks. By 1870, national banks had almost completely displaced state banks due to a punitive tax enacted by Congress on state bank notes. However, the declining importance of note issuance and high capital requirements for national banks led to a comeback by state banks in the 1870s-80s. The two systems continued to fluctuate in relative size thereafter, as their regulators took turns loosening requirements to attract entrants (White, 1983). By 1914, both types were scattered throughout the country. When the Federal Reserve system began operations in 1914, national banks were required to join. State banks could not be forced to join, and very few did in Illinois, especially outside of Chicago. The benefits of access to the discount window were offset by higher reserve ratios and the requirement to own stock in the Fed, so that membership in the Fed was often not a net benefit for a state bank. Just as the Fed began operations in 1914, World War I disrupted European production, creating massive demand for US exports. This also led to inflation, initially due to gold inflows as payments for exports, and later (after the US joined the war) due to growth

6 in Fed notes and reserves, as the government borrowed to extend credit to its allies. The Fed maintained low interest rates to accommodate this ﬁscal policy, delaying its intended operations until after the war. Low interest rates fueled growth in bank lending. Member banks used the Fed’s discount window for long-term borrowing, contrary to its intentions (Gorton and Metrick, 2013). Much of this credit went to the agricultural sector, as the government encouraged farmers to expand production (Sage, 1983). Frederico(2005) shows that farms’ aggregate indebtedness roughly tripled to $140 billion during 1910-1920 and was only paid down slowly thereafter (see his Figure 4), which he argues contributed to the struggles of the agricultural sector during the Great Depression. With the war’s end, government borrowing and European demand dropped sharply during 1919 and 1920. After some consideration, Federal Reserve banks across the country raised discount rates sharply during 1920, partly to avoid violating gold reserve requirements, and partly to contain credit growth. Friedman and Schwartz(1963) also cite a desire to demonstrate independence from President Harding, who advocated continued low rates to aid farmers. Figure1 shows a timeline of discount rates for the Chicago Fed and the St. Louis Fed, the two districts covering Illinois. Based on this, banks in rural Illinois that had been drafted into the Fed system were ultimately left with less access to capital. Immediately afterward, in the second half of 1920 and throughout 1921, the agricultural and manufacturing sectors exhibited sharp declines in prices, production, and employment. To some extent, this was the inevitable result of declining demand from Europe. However, the Fed’s decision to raise rates has commonly been perceived to have worsened the problem. Early analysis focused on credit conditions for bank-dependent industries, especially agriculture, as the mechanism for the Fed’s eﬀect. For example, Link(1946) says:

7 A period of ruinous deﬂation such as the farmers experienced in 1920-1921 ne- cessitates immediate credit if the farmers are to survive as independent farmers; and of course it was credit that they demanded [...] The insurance companies, trust companies, etc., which had normally provided this intermediate credit, had largely withdrawn from the lending market and as a consequence the chief burden of “carrying the farmers through” fell upon the commercial banks — and ultimately upon the Federal Reserve System.

The earliest proponent of this view seems to have been the 1922 Report to Congress of the Joint Commission of Agricultural Inquiry, which also highlighted the diﬀerential eﬀects on member and non-member banks:

Whatever restraining inﬂuence was exercised could be exercised only against member banks, and could be exercised only by restriction of credit, either through refusing loans to member banks in individual cases, or by pressure of discount rates applied to those member banks whose necessities required them to borrow from the Federal reserve banks. Restraint was exercised in both ways, and there were cases where restraint resulted in hardship not only upon the member banks but also upon the member banks’ customers.

In contrast to this bank-credit view, Friedman and Schwartz(1963) agreed that the Fed worsened the problem, but argued that the mechanism was mainly through deflationary effects of a restricted money supply. Commentators ever since have struggled to disentangle the separate roles of demand shocks, credit conditions, and monetary policy in this episode. A theme in much commentary has been criticism of the Fed’s actions, as reflected both in the Joint Report and in Friedman and Schwartz(1963). However, these analyses are based on aggregate figures, complicating inference about the mechanism of the Fed’s impact. Our study provides causal evidence, and therefore an opportunity to re-evaluate the popular critique of the Fed’s actions.

8 3 Data

3.1 Sources

We exploit several hand-collected datasets on banking and agricultural output in Illinois in the early 20th century. This section describes each dataset in detail. Federal reserve member bank call reports: Data on lending by member banks come from two sources. The first is call reports that were filed with the Federal Reserve. Selected dates of these reports were microfilmed in 1946 and later scanned into PDF format and posted on the website of the FRASER library at the Federal Reserve Bank of St. Louis. The remaining dates were lost or destroyed after 1946. These details are discussed in Mason (1998) and Calomiris and Mason(2003), who coded the microfilmed call reports for 1930– 1933. For our window of interest, the call report dates that survived are June 1916, June 1921, December 1926, and December 1929. From each call report for each Illinois member bank at each of these dates, we hand-collect the name, city, county, and charter number, as well as total loans outstanding (item 1A in the report). The middle panel of Figure2 shows an example call report from July 1916 for the Farmers National Bank in the city of Cambridge, Henry County. Comptroller’s reports of condition for national banks: This is the second source of data for member banks. We collected reports for the years 1913-1929, all of which were issued on December 31 of each year. For each national bank in Illinois, we collect the name, city, county, and total loans outstanding. For the years 1915, 1916, 1919, and 1920 we also collect total assets, demand deposits, and time deposits, which are useful for our robustness checks in later analysis. The bottom panel of Figure2 shows an example of the entry for national banks located in Cambridge, Illinois in 1916, including the same bank identified in

9 the middle panel. The first column records total loans outstanding as of December 31, 1916. The dates of the Fed call reports and the Comptroller’s reports of condition of national banks coincided precisely in December 1926 and 1929. We compared the entries in these two sets of reports and verified that the differences in reported numbers were negligible. As such, there did not appear to be any meaningful differences in accounting or reporting standards between the sources. State bank reports of condition: Data on lending by state banks come from State- ments of Condition published by the State of Illinois from the years 1915-1929. Each report contains a list of all state banks, their cities and counties of location, and basic financial information including total loans outstanding and total assets, both of which we collect. The top panel of Figure2 shows an example report on the Cambridge State Bank. However, the timing of these reports were more erratic: in some years, multiple reports were filed throughout the year, while in other years there were none. As we describe below, we only analyzed data from these reports that were closely matched to the data described above about the member banks.5 Timing details across banking datasets: Figure1 compares the timing of each dataset with the time series of Fed discount rates for Chicago and St. Louis, the two reserve districts covering Illinois. Since we are most interested in the time period from 1920-1921 when the Fed abruptly raised interest rates, we analyze the reports with highest frequency around this time. Therefore, we compare the two systems of banks in each year between 1919-1922 and add in 1916, 1926, and 1927. Note that in 1916, the Fed call report and the report on the condition of the state banks are dated to the end of June and beginning of July, while the

5The following are report dates for the condition of state banks: April 1915, July 1916, May 1917, June 1917, July 1918, March 1919, June 1919, June 1920, February 1920, January 1921, December 1921, June 1922, December 1922, June 1924, December 31 for 1926, 1927, and 1929.

10 remainder occur at the year-end.6 County-level crop data: Data on county-level agricultural output come from Statis- tical Reports of the Illinois State Board of Agriculture for the years 1916-1929. For a given year, these reports provide the yields of major crops by county in bushels or tons, as well as their dollar values at the prices prevailing in each county on the reporting date. We collect the yields and dollar values for corn, oats, hay, wheat, and barley. Figure3, reproduced from the crop report for 1926-1927, shows that these crops collectively accounted for over 90% of the gross value of Illinois crops. We sum the dollar amounts to construct a county-year measure of agricultural output. The statistics we analyze are for the years 1916, 1919-1922, and 1926-1927.7 County-level farm and acreage data: Data on the number of farms and acreage put to use in each Illinois county come from reports from the United States Census of Agriculture. We obtained reports for the years 1920, 1925, 1930, and 1935. For each of these years, we hand-collected information on the number of farms in existence and the acreage used for farming. We use data from 1920 and 1925 to investigate the effect of monetary policy on the organization and efficiency of farming around the Fed intervention during 1920-1921. Then, we use data from 1930 and 1935 to analyze the persistence of these effects. Population data: We collect the county-level population from census numbers for 1910, 1920, and 1930 from the US Census website (census.gov). We interpolate these figures to the dates we analyze by fitting a log-linear model of population growth for each county. In all the analysis that follows, we exclude Cook County, which contains Chicago and is

6For all of the years, the reports of national and state banks occur within 1-3 days of each other, except for 1919 where the Comptroller’s report occurs on December 31, 1919 and the report on the condition of the state banks occurs on February 28, 1920. 7 Many other crops and agricultural products were also recorded, but we do not analyze these because they collectively constitute only a negligible share of aggregate agricultural output for Illinois.

11 an extreme outlier on every dimension in our data.

3.2 Stylized facts and motivation

Since our data are largely novel, we start by demonstrating several basic facts of interest to motivate our analysis. Table1 summarizes the county-level banking and agricultural data as of 1916. The median county had 4 national banks and 6 state banks, and the median county-level fraction of national banks was 40%. The median county had $2.5 million in bank loans outstanding as of 1916 (worth $61 million in 2018 dollars), and 24 thousand residents (using our interpolation between the 1910 and 1920 censuses). The mean (median) dollar value of total agricultural output was $3.4m ($3.1m). This output was dominated by corn, which accounted for 57% of total value in the median county, and for 48% even at the lower quartile. For state banks in a given county, deposits were about 75% of total liabilities on average, while for national banks the comparable figure was only 67%. For state banks, the residual is mostly explained by paid-in-capital, as deposits plus paid-in-capital averaged over 90% of total liabilities. In contrast, for national banks this fraction was only 79%. The differences in these fractions across state and national banks are statistically significant at any conventional level. They reflect a substantial amount of national bank funding coming from non-deposit sources, such as borrowing at the discount window. Figure4 examines trends in the county-level statistics over time. The years 1916–1920 were a time of rising credit growth nationwide, and this fact is reflected in our sample. Bank loans outstanding in Illinois roughly doubled during this time. Subsequently, the level of credit reached a plateau and very little deleveraging took place in the aggregate during the 1920s. Deposits at national banks similarly grew in the years leading up to 1919 and

12 remained stable through the end of 1920. During 1916-1920, there was also explosive growth in agricultural output because of the demand for food products in Europe during World War I. However, as the war ended, Euro- pean production rebounded, which led to less demand for American products. In response, there was a sharp decline in the value of agricultural output. Consistent with this, all crop prices rose during the war and subsequently fell, reflecting the broad deflation experienced throughout the economy around 1920-1921. In the aggregate trends in Figure4, changes in discount rates are negatively associated with agricultural output, but not bank loans, which might seem to support the view that the Fed affected the local economy through channels other than through bank lending (for example, through a deflationary channel, as emphasized in Friedman and Schwartz, 1963). However, as we show in the remainder of the paper, this is not the whole story. Using state banks as counterfactuals for Fed member banks, we will provide novel evidence that there was indeed a strong lending channel for the impact of Fed policy on the local economy.

4 Onset of the 1920-1921 depression

4.1 Empirical strategy

To study how exposure to Fed policy affected the real economy, we use two specifications in our main analysis and then explore other variations as robustness checks. Our first specification studies how the number of national banks per capita in a given county affected loan volumes and agricultural output around the interest rate increases in 1920 and in the aftermath of the 1920-1921 depression. We regress county-year outcomes

Xct against county fixed effects, year fixed effects, and interactions between year dummies

13 and the number of national banks per 10,000 people in 1916:

X National banksc,1916 X = α + γ + β × × 1{y = t} + ε , (1) ct c t y Population ct y c,1916

where Xct = Yct for agricultural output or Xct = Lct for county-level loans (summing across the two systems of banks). While this first specification is straightforward, the interpretation of the magnitudes of the regression coefficients is not completely clear. They reflect a counterfactual shift from zero national banks to a large presence of these banks. To provide a better idea of the magnitudes involved, we employ a second specification based on the fraction of banks in each year that were national banks in a given county:

X National banksc,y X = α + γ + β × × 1{y = t} + ε , (2) ct c t y Total banks ct y c,y

8 where Xct is again the economic variable of interest. In this regression, the magnitudes of the coefficients reflect a counterfactual shift from zero to 100% membership by county banks in the Fed for the average county. However, this latter specification raises a concern of reverse causality. During 1916-1920, many new state banks opened, while there was very little change in the presence of national banks. This caused the fractional Fed membership rate of county banks in some counties to fall sharply. For example, in a region of Southeast Illinois known as Little Egypt, six counties experienced declines of 40 percentage points or more in the Fed membership rate from 1916 to 1920 because of the entry of state banks. This entry could be a response to the

8Time t runs across the years 1916, 1919, 1920, 1921, 1922, 1926, and 1927, while y can take all of these values except 1916. As such, βy represents the time-varying effect of the 1916 county-level Fed membership rate on economic outcomes. The 1916 effect is subsumed by county fixed effect αc.

14 discount rate changes that we study, which might make the time-varying Fed membership rate a confounded treatment measure. In contrast, the first of our two specifications was not subject to this concern, as the number of national banks per capita was extremely stable throughout our sample (the correlation is above 90% between any two years). To combine the stability of the first specification with the interpretation of the second, we use the 1916 per-capita number of national banks to instrument the time-varying membership rate in specification (2). This instrument is not subject to reverse causality, since it is measured before the changes to discount rates occurred in 1920, and remains extremely stable throughout the time period we study. As we discuss below, it is also strongly predictive of the fraction of banks that were national banks in each subsequent year. To employ this instrument, we augment specification (2) with the following system of first-stage regressions, one for each year y:

National banksc,y National banksc,1916 = δy + + εcy. (3) Total banksc,y Populationc,1916

4.2 Identifying assumptions and instrument properties

Before exploring the results of our speciﬁcations, it is important to point out that a key identifying assumption in our analysis is that systematic correlation between the presence of member banks and relative changes in county lending and agricultural output can be attributed only to discount rate changes. To address this exclusion restriction, we investigate whether counties with a higher number or fraction of national banks look systematically diﬀerent from those with lower values (in the cross section as of 1916). If they do not, then this suggests that they would have followed similar average trends if not for the rate increases.

15 Figure5 maps out the geographic distribution of the county-level fraction of national banks as of 1916, 1919, 1921, and 1926 and Figure6 shows the same for the number of national banks per-capita. Encouragingly, neither shows any particular geographic clustering or pattern, with high and low values scattered all across the state. In Figures7 and8, we further demonstrate that the 1916 number of national banks per capita and the 1916 fraction of national banks per county do not predict any county-level diﬀerences in the total number of banks, loan volumes, or agricultural output. While one cannot explicitly test the exclusion restriction, these ﬁndings are encouraging. They are consistent with the view that the relative composition of state versus national banks in a given county was mostly driven by historical trends in the late 1800s (as was described in Section2), which were no longer relevant to relative county-level trends in 1916–1920. Finally, our IV analysis requires the per-capita number of national banks in 1916 to be a strong instrument for predicting the fraction of banks that were national banks in later years. The top-left panel of Figure7 addresses this relevance assumption for the initial year of the analysis, 1916. In Table2 and Figure9, we similarly analyze the relevance of this instrument for predicting this fraction in future years. Table2 shows that the instrument strongly predicts this fraction in every later year in our sample, and explains between 40-50% of its cross-sectional variation. Figure9 illustrates these patterns visually with scatter plots similar to the top-left panel of Figure7. 9

4.3 Discount rate eﬀects

Table3 summarizes the results of our estimation of speciﬁcation (1). The outcome variable is listed at the top of each column. The main eﬀects of the time dummies are listed in

9For space reasons, the ﬁgure omits 1929, which looks very similar to the other years.

16 the lower rows of the table. These reflect the average change in the outcome variable since 1916 for a hypothetical county with no national banks. The key terms of interest are the interaction terms listed at the top of the table. These reflect the additional growth in each outcome variable since 1916 exhibited by a hypothetical county with one more national bank per ten thousand residents. The nearly zero coefficients in the top row show that all counties grew equally in the expansion from 1916-1919, during which time the discount rate was approximately 4%. There does not appear to be a difference for counties that had more member banks. This is a useful placebo test that shows that Fed membership does not correlate with differential trends across counties during the time period before discount rates were changed. However, when the Fed raised discount rates sharply at the end of 1919, the interaction terms in the table demonstrate that the two types of counties exhibited very different responses: Agricultural output in a hypothetical county with two national banks per ten thousand residents (the upper quartile in the data) plunged immediately to approximately 1916 levels. When discount rates were subsequently lowered, the discrepancy in agricultural output across counties with more national banks quickly vanished. The interaction coefficients are not statistically significant in any year after 1920. In Column 2, we examine the patterns for the county-level volume of loans outstanding. As with the output results in column 1, there is no differential effect of national bank presence in 1919, but then a strong effect in 1920. This suggests that the mechanism for the relatively lower agricultural output in this year was a restriction of bank credit, an interpretation that we continue to develop below. However, even after the differential effect on agricultural output disappears in the following year (as shown in the third and lower rows of Column 1), the differential effect on

17 loans outstanding persists (as shown in Column 2). Indeed, there is a statistically significant difference across counties with more national banks in every subsequent year in the sample. This important difference in the findings is the basis for our interpretation of the long-term effects of the Fed’s intervention, and we will return to it later. Table4 reports the results of specification (2) augmented with the first-stage regression in (3). The trends are similar to what we documented with specification (1), but allow for a clearer interpretation of the economic magnitudes. The coefficient of −0.871 implies that agricultural output would have been about 58% lower (e−0.871 − 1 = −0.581) in 1920 for a hypothetical county with 100% membership rate in the Fed, compared to one with zero membership. A more salient way to assess this magnitude is to compare the upper and lower quartiles of national-bank fractions in Table1 (0.50 and 0.30, respectively). The Fed’s actions would then be expected to cause agriculture to decline by 11.6% more for a county at the upper as compared to the lower quartile (calculated as 0.58 × [0.50 − 0.30] = 0.116). Based on Table4, there was also an economically significant drop in lending. As before, there was no differential effect across counties between 1916 and 1919. However, in 1920, the coefficient of −0.543 implies roughly a 41.9% drop in loans, going from zero to 100% membership in the Fed (calculated as e−0.543 − 1 = −0.419). Comparing the upper and lower quartiles as before, this translates to a 8.4% drop at higher membership rates. The magnitudes are indeed large and appear to grow over during the later years in the table. While we use the 1916 number of national banks per capita as the proxy for national bank presence in specification (1), our results are robust to using alternative measures. Table 5 shows that our results regarding county-level loans are qualitatively unchanged if we use the 1916 dollar value of national bank deposits, assets, or loans at the county level.

18 4.4 Additional characterization of the results

In the next few results we focus more closely on the years 1916 and 1920. Figure 10 depicts the underlying variation behind our credit and output results from 1916- 1920. The two panels plot county-level changes of the outcome variables in the regressions against the 1916 number of county banks per ten thousand residents, differencing away the county fixed effects and allowing the intercept to pick up aggregate time trends. The slopes of the fitted lines correspond to the coefficients in the second row of Table3. The figure demonstrates that the variation driving those regressions is spread out across the state, and importantly, is not driven by any small subset of observations or by any outlier counties. There was a smooth connection between exposure to the Fed’s discount rate changes (measured by 1916 per-capita number of national banks) and county-level responses to those changes. This helps address identification concerns: Any confounding variable would have to vary quite smoothly with both Fed membership rates in 1916 and with credit and output growth during 1916–1920. Figure 10 also clarifies that the effects in Table3 are largely due to relatively high credit growth from 1916–1920 in counties with low Fed membership rates, during a time in which credit generally expanded statewide. This suggests a specific interpretation of our findings: The Fed’s discount rate increases constrained credit growth among member banks at a time of rapid expansion, the latter years and immediate aftermath of World War I. In Column 3 of Table3, we build on this interpretation by showing that the Fed’s actions led to lower long-run ratios of loans outstanding to agricultural output, which arguably reflected the exact tradeoff that the Fed was trying to make. To pin down the Fed’s discount rate as the mechanism for the loan effects, we next match the changes in bank assets (loans) with changes on the liabilities side of the balance sheet. In

19 particular, since bank loans in the cross-section are mostly explained by deposits, we would like to confirm that there is no confounding decrease in bank deposits among counties with more national banks during 1916–1920. While there is no reason to expect such a change, this exercise is useful to confirm that the variation we capture is driven by the heavy usage of the discount window during the time, as described in Gorton and Metrick(2013). In Figure 11, we examine which component of the county-level bank funding structure shrank in the counties with more national banks. We decompose total county-level bank liabilities into state bank liabilities, national bank deposit liabilities, and national bank non- deposit liabilities. The latter category includes discount window borrowing from the Fed. We then examine the changes in each of these categories as a fraction of total county-level bank liabilities from 1916–1920, plotting this change against the 1916 per-capita number of national banks as in Figure 10. The top panel of Figure 11 shows national bank deposit liabilities rose marginally (but insignificantly) as a fraction of total bank liabilities for counties with more national banks relative to other counties. In contrast, national bank non-deposit liabilities fell significantly, accounting for a ten to twenty percent decrease in total bank funding at the upper end of the range of the horizontal axis.10 In short, the change in county bank loans seems to be accounted for by the non-deposit category of national bank liabilities, consistent with our empirical design, which isolates the impact of variation discount window availability via the Fed’s discount rate changes during this time. Finally, we can use our results to derive and compute a credit-output multiplier for agriculture in 1920. We continue to restrict our sample to the years 1916 and 1920 and construct an instrumental-variables estimate of the effect of credit on output. We use the

10By construction, state bank liabilities must also rise in the same counties. In unreported results, we conﬁrm this result but show that it is not statistically signiﬁcant.

20 following two-stage system.

Yct = ηc + δt + θ × Lcct + νct. (4)

National banksc,1916 Lct = αc + γt + β × × 1{y = 1920} + ct, (5) Populationc,1916

In the first stage regression (5), we predict county-level loans as a function of the number of national banks per ten thousand residents in 1916, and rely on the identifying assumption that changes in county loans from 1916-1920 should not correlate with the 1916 fraction of national banks except due to changes in the Fed’s policy. Then we substitute the predicted loan values from specification (5) into (4) and obtain a consistent estimate of θ. Table6 implements this approach. The IV estimate of the effect of a marginal dollar of credit in 1920 is $0.73 in additional agricultural output in the level specification (Column 3). The log specification also yields results that are consistent with these magnitudes (Column 1). The coefficient of 1.371 implies that a 1% increase in credit led to a 1.4% increase in output. The average county-level loan-to-output ratio in 1920 was approximately 2, so dividing the 1.4% estimate by 2 suggests a dollar multiplier of $0.7, which is in line with the other specification. Table6 also includes analysis weighted by the inverse of population and yields similar magnitudes ($0.75 and $0.76, respectively), suggesting that the effects are not confined to large-population counties, but are also present in the rural counties where banking frictions are likely to be particularly important.

21 5 Long-term eﬀects: 1920–1927

5.1 Debt and output

The discount rate increases in 1920 were short-lived. The Chicago Fed lowered rates back to pre-1919 levels by the end of 1921 and the St. Louis Fed did the same in early 1922. According to Tables3 and4, the negative effect of exposure to Fed policy on agricultural production in 1920 was similarly short-lived. As soon as the Fed lowered rates back to pre-1920 levels, production rebounded in relative terms: The regression coefficients for the interaction terms in years after 1920 are economically small and statistically insignificant. This is demonstrated visually in Figure 12, which plots the regression coefficients and standard errors from Column 1 of Table4 across the years in the regression. In contrast, the effect of the change in discount rates on bank loans was more persistent. This is illustrated by the regression coefficients from Column 2 in Tables3 and4, and also in Figure 13, which plots the regression coefficients and standard errors from Column 2 of Table4 across time. As can be seen, there is a permanent relative drop in leverage after the Fed intervention of 1920. Intuitively, this pattern is consistent with the evidence from Figure4, which showed that the rapid debt buildup during 1916–1920 was followed by a much slower process of deleveraging in the years leading up to the Great Depression. Taking these findings together, we form a ratio of (bank) credit to (agricultural) output for Illinois counties during our sample period. This ratio serves as a useful economic indicator of the debt burden facing rural regions in Illinois in the years leading to the Great Depression. We then repeat the specifications from the first two columns of Tables3 and4 with this ratio as the dependent variable. Column 3 of each table reports the results. To illustrate the results visually, the regression coefficients from Table4 are plotted in Figure 14. The figure

22 shows that intervention by the Fed led to lower loans-to-output ratios in the long term. More precisely, there is no departure across counties in the loans-to-output ratio in 1919. This is in line with our earlier placebo results showing that exposure to the Fed’s discount rate changes does not predict cross-sectional differences before those discount rate changes actually happened. Furthermore, there is still no cross-sectional effect as of 1920 either. At this point in time, counties with more exposure to those discount rate changes experienced relative decreases in both output and lending, so the ratio of the two does not appear different for those counties. However, in 1921, relative differences in agricultural output disappeared (as depicted in Figure 12), while differences in loans outstanding remained (Figure 13). These effects combine to yield a lower loans-to-output ratio, captured in Figure 14, that persisted through the beginning of the Great Depression.

5.2 Underlying mechanisms

At this point, one may wonder why agricultural output rebounded quickly, while changes in debt levels did not. To study this further, we investigate whether a structural change arose in counties with more exposure to the Fed. We use data from the United States Census of Agriculture, which provides county-level data on the number of farms in operation and the total acreage devoted to farmland in each county. This data source was described in detail in Section3 above. Using statistics from 1920 and 1925, we repeat our regression in (1) above, using measures of utilization as dependent variables. The results are demonstrated in a series of plots, in which the change in key county-level outcome variables from 1920 to 1925 is depicted as a function of the 1916 per-capita number of national banks. Figures 15 and 16 show that the number of farms decreased signiﬁcantly across Illinois

23 from 1920 to 1925, but that exposure to the Fed’s intervention did not have a differential effect on this rate of change across counties. However, Figures 17 and 18 show that counties with few national banks experienced a drop in the land devoted to farming, whereas counties with more exposure to the Fed did not experience this drop. The contrast between these two sets of results suggests that consolidation took place within counties exposed to the Fed and not in other areas. This trend appears to be long-lasting: Figures 19-22 show that the larger acreage levels persisted, and if anything slightly increased, through 1929 and 1935. Figures 23-25 show that there was no long-term relative difference in the number of farms. Based on these findings, it seems that intervention by the Fed allowed for efficient consolidation in which farmland was reallocated to more efficient farmers while less efficient, more debt-dependent farms closed. We conjecture that the underlying mechanism is the avoidance of debt overhang problems. When farms were starved of credit during the 1920-1921 episode, less efficient owners in areas with more national banks were prevented from taking on debt. When they eventually went into financial distress, potential acquirers could take over properties with a lower debt burden. In contrast, in counties where debt levels were higher, takeovers may not have occurred as easily, due to the higher burden that would need to be born by potential acquirers. These findings point to a mechanism by which the Fed’s effect on credit markets had a long-term real effect on economic conditions in the counties that were more affected.

6 Conclusion

In this paper, we ﬁnd that Fed policy during 1920-1921 aﬀected both credit and agricultural output in rural areas. We study farming because it was a major part of the U.S.

24 economy at the time, and we focus on the state of Illinois due to its central role in farming, and due to the availability of novel county-level data on bank lending and agricultural output. We use the divide between state and national banks, which pre-dated the Fed, to study the effect of Fed policy on member and non-member banks and their local economies. We find that the Fed’s increase in discount rates in 1920 caused counties with greater proportions of member banks to suffer a relative drop in credit and agricultural output. Our findings provide evidence of a bank credit channel by which Fed policy affected local economies. Further, we show that the subsequent lowering of interest rates restored counties’ relative output levels, but led to lower debt levels among counties with high membership. These events resemble the conventional idea of policymakers attempting to stabilize the economy during a volatile period. We document a consolidation channel for these findings, in which efficient reallocation of resources took place in counties exposed more to Fed policy. As we discuss in the paper, a likely explanation was an avoidance of debt overhang frictions imposed when starving farms of credit. In counties with more exposure to the Fed, less efficient farms failed with lower debt levels, which allowed better use of resources: higher output per farm and per acre. In sum, our results call into question a popular view that the Fed did not understand the impact of its actions in 1920. Counties more exposed to the Fed’s discount rate changes experienced a transitory drop in agicultural output that recovered rather quickly, and a long- term decrease in bank indebtedness that lasted until the beginning of the Great Depression. Slow and painful deleveraging has been cited as a source of farm distress during this time, and as an amplifying factor in the impact of the Depression. Thus, while we cannot assess the optimality of the Fed’s actions or perform a welfare analysis, we can say that the counties more exposed to its actions in 1916 seemed to be in

25 better financial shape on at least one important dimension through the end of the 1920s. Our findings are consistent with the Fed having anticipated, and in some regions successfully mitigated, the effects of a temporary credit boom.

26 References

Board of Governors of the Federal Reserve System, 1943, Banking and Monetary Statistics, 1914–1941, Technical report.

Calomiris, Charles, Matthew Jaremski, Haelim Park, and Gary Richardson, 2015, Liquidity Risk, Bank Networks, and the Value of Joining the Fed, Working paper.

Calomiris, Charles, and Joseph R. Mason, 2003, Fundamentals, Panics, and Bank Distress During the Depression, The American Economic Review 93, 1615–1647.

Frederico, Giovanni, 2005, Not Guilty? Agriculture in the 1920s and the Great Depression, Journal of Economic History 65, 949–976.

Friedman, Milton, and Anna Schwartz, 1963, A Monetary History of the United States (Princeton University Press).

Gabriel, Stuart, and Chandler Lutz, 2014, The Impact of Unconventional Monetary Policy on Real Estaet Markets, UCLA working paer.

Gorton, Gary, and Andrew Metrick, 2013, The federal reserve and ﬁnancial regulation: The ﬁrst hundred years, NBER Working paper.

Jayaratne, Jith, and Philip Strahan, 1996, The Finance-Growth Nexus: Evidence from Bank Branch Deregulation, The Quarterly Journal of Economics 111, 639–670.

Kuehn, Daniel, 2012, A note on America’s 1920-21 depression as an argument for austerity, Cambridge Journal of Economics 36, 155–160.

Link, Arthur S., 1946, The Federal Reserve Policy and the Agricultural Depression of 1920- 1921, Agricultural History 20, 166–175.

Mason, Joseph R., 1998, American Banks During the Great Depression: A New Research Agenda, Federal Reserve Bank of St. Louis Review 80, 151–152.

Meltzer, Allan H., 2000, Lessons from the Early History of the Federal Reserve, Presidential Address to the International Atlantic Economic Society.

Nakamura, Emi, and J´onSteinsson, 2018, Identiﬁcation in Macroeconomics, Journal of Economic Perspectives 32, 59–86.

Petersen, Mitchell, and Raghuram Rajan, 1995, The Eﬀect of Credit Market Competition on Lending Relationships, Quarterly Journal of Economics 110, 407–443.

27 Rajan, Raghuram, and Rodney Ramcharan, 2015, The Anatomy of a Credit Crisis: The Boom and Bust in Farm Land Prices in the United States in the 1920s, The American Economic Review 105, 1439–1477.

Richardson, Gary, and William Troost, 2009, Monetary Intervention Mitigated Banking Panics during the Great Depression: Quasi-Experimental Evidence from a Federal Reserve District Border, 1929–1933, Journal of Political Economy 117, 1031–1073.

Romer, Christina, 1988, World War I and the Postwar Depression: A Reinterpretation Based on Alternative Measures of GNP, Journal of Monetary Economics 22, 91–115.

Sage, Leland L., 1983, Rural Iowa in the 1920s and 1930s, The Annals of Iowa 47, 91–103.

Soule, George, 1947, The Prosperity Decade: From War to Depression, 1917-29 (Routledge).

White, Eugene Nelson, 1983, The Regulation and Reform of American Banking, 1900–1929 (Princeton University Press).

White, Halbert, 1980, A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity, Econometrica 48, 817–838.

28 A Tables and ﬁgures 8 6 4 Discount rate 2 0

Jul 1916 Dec 1919Dec 1920Dec 1921Dec 1922 Dec 1926Dec 1927 Date

Chicago St. Louis

Figure 1: Discount rates at Federal Reserve Banks of Chicago and St. Louis, 1915-1930. Dashed lines are dates for which we have data on both state and national banks.

29 Figure 2: Example statement of condition for a nonmember state bank as of June 30, 1916 (top); call report for a member national bank as of July 1, 1916 (middle); and entry for the same national bank in the Comptroller’s report of condition as of December 31, 1916 (bottom), all from Cambridge, Illinois.

30 15

GROSS FARM VALUE OF ILLINOIS CROPS DECEMBERJ927

UTILIZATION OF CULTIVATED ACREAGE ILLINOIS -I927

«»fl8a-6570 26%

SPRING WHEAT I.J % 216,000 ACRES BARLEY E.3 •/•> 453,000 ACRES

Figure 3: Page 15 of the 1926-1927 Illinois crop report.

31 mean min p25 p50 p75 max National banks 4.376 0 2 4 6 15 State banks 6.307 0 3 6 9 24 Fraction national banks 0.429 0 0.300 0.400 0.542 1 National banks per 10k residents 1.521 0 0.775 1.448 2.010 4.408 Bank loans, $1,000 3734.9 0 1192.3 2460.2 4480.6 19213.8 1916 Population 33016.0 7299.7 16492.0 24020.4 38034.5 128898.7 Bank loans / 1916 pop. 100.6 0 57.96 93.21 136.4 230.1 Deposits/liabilities, nat’l banks 0.671 0.455 0.622 0.662 0.734 0.831 Deposits/liabilities, state banks 0.754 0.487 0.725 0.765 0.801 0.870 (Deposits+capital)/liabilities, nat’l 0.785 0.571 0.745 0.789 0.829 0.956 (Deposits+capital)/liabilities, state 0.902 0.598 0.887 0.915 0.941 0.977 Corn output (bushels, 1k) 2383.7 222.5 935.9 2050.3 3417.8 8131.6 Hay output (tons, 1k) 62.43 5.635 29.41 50.68 83.28 189.6 Oats output (bushels, 1k) 1773.3 31.25 615.8 1303.4 2365 9124.9 Wheat output (bushels, 1k) 109.4 0.171 31.58 72.96 161.3 742.5 Barley output (bushels, 1k) 13.23 0 0 0 5.220 307.9 Corn price per bushel 0.844 0.750 0.810 0.840 0.860 1 Hay price per ton 9.911 6 8.250 10 11 15 Oats price per bushel 0.370 0.300 0.350 0.360 0.380 0.500 Wheat price per bushel 1.116 1 1.080 1.120 1.150 1.300 Barley price per bushel 0.646 0.500 0.600 0.650 0.650 0.900 Corn value (dollars) 2008.7 178.0 792.7 1742.7 2909.1 6505.3 Hay value (dollars) 618.2 56.35 303 506.8 771.9 2843.4 Oats value (dollars) 648.6 11.56 227.3 515.2 893.0 3193.7 Wheat value (dollars) 123.8 0.186 35.68 77.70 185.5 891 Barley value (dollars) 8.361 0 0 0 3.393 184.8 Corn value / ag. output 0.563 0.200 0.483 0.569 0.654 0.814 Hay value / ag. output 0.210 0.0310 0.126 0.197 0.277 0.527 Oats value / ag. output 0.175 0.0255 0.113 0.163 0.218 0.382 Wheat value / ag. output 0.0497 0.0000795 0.00932 0.0336 0.0673 0.246 Barley value / ag. output 0.00231 0 0 0 0.00110 0.0418 Ag. output, $1,000 3407.7 445.2 1669.0 3148.9 4644.9 10846.3 Bank loans / ag. output 1.150 0 0.524 0.795 1.222 6.803 Observations 101

Table 1: County-level summary statistics as of 1916 for banking and agricultural data in Illinois. The sample is all counties in Illinois excluding Cook County.

32 oprle ftecrec o ainlbns giutrldt r rmco eot by reports the crop by from reports are and data Agriculture. reports Agricultural of call banks. Department with national Illinois banks for are the state currency data Banking the for of 1916-1930. reports Comptroller statistics, banking agricultural state and combining banking Illinois from in Trends 4: Figure eoisa ainlbns o liosdrn 1915-1929. and during output, Illinois for agricultural banks, banks), national state at and deposits (national loans Total (a)

Price per bushel, dollars $ millions .5 1 1.5 2 2.5 200 300 400 500 600 700 1916 1916 b vrg rcso on ha,adoats. and wheat, corn, of prices Average (b) g uptBankloansoutstanding National bankdeposits Ag. output onOt Wheat Oats Corn 1920 1920 33 Date Date 1925 1925 1929 1929 (.6666667,1] (.5,.6666667] (.5625,1] (.4,.5] (.4166667,.5625] (.3333333,.4] (.3333333,.4166667] (.2352941,.3333333] (.2631579,.3333333] [0,.2352941] (.2,.2631579] No data [0,.2]

(.4375,.6666667] (.4516129,.8] (.3571429,.4375] (.368421,.4516129] (.2666667,.3571429] (.25,.368421] (.2068966,.2666667] (.2105263,.25] (.1578947,.2068966] (.1388889,.2105263] [0,.1578947] [0,.1388889]

Figure 5: Fraction of banks that were national banks for each Illinois county as of four dates during our sample period: July 1, 1916 (top left); December 31, 1919 (top right); December 31, 1921 (bottom left); December 31, 1926 (bottom right). National banks are compiled from call reports ﬁled with the Federal Reserve for 1916, and from annual reports of the Comptroller of the Currency for other years. State banks are compiled from statements of condition published by the Illinois state government. In each year, counties are divided into six bins by number of national banks per capita, with darker shading corresponding to a higher fraction.

34 (.8876663,3.333589] (.6911818,2.479012] (.3425684,.8876663] (.3710714,.6911818] (−.092067,.3425684] (−.0318009,.3710714] (−.5504376,−.092067] (−.5412058,−.0318009] (−.9625897,−.5504376] (−.994536,−.5412058] [−1.767335,−.9625897] [−1.767335,−.994536]

(.7930215,2.589747] (.7993507,2.593969] (.3537657,.7930215] (.3576038,.7993507] (.0357715,.3537657] (.0598235,.3576038] (−.4969825,.0357715] (−.5798016,.0598235] (−.9088264,−.4969825] (−1.003063,−.5798016] [−1.767335,−.9088264] [−1.767335,−1.003063]

Figure 6: Number of national banks per capita for each Illinois county as of four dates during our sample period: July 1, 1916 (top left); December 31, 1919 (top right); December 31, 1921 (bottom left); December 31, 1926 (bottom right). National banks are compiled from call reports ﬁled with the Federal Reserve for 1916, and from annual reports of the Comptroller of the Currency for other years. Population statistics are interpolated by county between decennial Census numbers. In each year, counties are divided into six bins by number of national banks per capita, with darker shading corresponding to a higher fraction.

35 1 β

= 0.177; p = 0.00 30 β = −0.003; p = 1.00 .8 20 .6 .4 10 Fraction national banks, 1916 .2 Number of national and state banks, 1916 0 0 0 1 2 3 4 0 1 2 3 4 1916 national banks per 10k residents 1916 national banks per 10k residents 4 17 β = −0.130; p = 0.29

β = 0.082; p = 0.30 16 3 15 2 14 Ln(Bank loans), 1916 1 13 Ln(National and state banks), 1916 0 12 0 1 2 3 4 0 1 2 3 4 1916 national banks per 10k residents 1916 national banks per 10k residents

β β = 0.035; p = 0.68

.016 = 0.000; p = 0.19 16 .015 15 .014 Ln(Ag. output), 1916 14 Ln(Corn output), 1916 .013 13 .012 0 1 2 3 4 0 1 2 3 4 1916 national banks per 10k residents 1916 national banks per 10k residents

Figure 7: Balance checks. Each dot corresponds with a county in Illinois (excluding Cook), and the horizontal axis plots the number of national banks per ten thousand residents as of 1916. The top-left figure plots the fraction of national banks as of 1916, which demonstrates the relevance of the instrument for predicting the Fed membership rate. The other figures plot (from left to right, top to bottom): The number of banks in the county as of July 1916 (both national and state banks); the log of this number; the log of total bank loans outstanding as of July 1916; the log of total bushels of corn produced during 1916; and the log of the total value of corn, hay, oats, wheat, and barley produced during 1916. Each figure also reports a best-fit line and its slope, with a p-value calculated from White(1980) standard errors. 36 3 β = −0.742; p = 0.11 2 1 0 −1 Number of national and state banks, 1916 −2 0 .2 .4 .6 .8 1 1916 fraction national banks

2 β = −0.763; p = 0.11 β = −0.485; p = 0.31 2 1 1 0 0 −1 −1 Ln(Bank loans), 1916 −2 −2 Ln(National and state banks), 1916 −3 −3 0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1 1916 fraction national banks 1916 fraction national banks

2 2 β = −0.484; p = 0.31 β = −0.247; p = 0.61 1 1 0 0 −1 Ln(Ag. output), 1916 −1 Ln(Corn output), 1916 −2 −2 −3 0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1 1916 fraction national banks 1916 fraction national banks

Figure 8: Balance checks. Each dot corresponds with a county in Illinois (excluding Cook), and the horizontal axis plots the fraction of banks that were national banks as of 1916. The figures plot (from left to right, top to bottom): The number of banks in the county as of July 1916 (both national and state banks); the log of this number; the log of total bank loans outstanding as of July 1916; the log of total bushels of corn produced during 1916; and the log of the total value of corn, hay, oats, wheat, and barley produced during 1916. Each figure also reports a best-fit line and its slope, with a p-value calculated from White(1980) standard errors. 37 (1) (2) (3) (4) (5) (6) (7) Frac. Frac. Frac. Frac. Frac. Frac. Frac. 1916 p.c. 0.163∗∗∗ 0.123∗∗∗ 0.120∗∗∗ 0.119∗∗∗ 0.131∗∗∗ 0.132∗∗∗ 0.131∗∗∗ (0.0177) (0.0149) (0.0147) (0.0147) (0.0157) (0.0158) (0.0187) Year 1919 1920 1921 1922 1926 1927 1929 Obs 101 101 101 101 101 101 101 R2 0.508 0.483 0.476 0.470 0.471 0.451 0.416 Standard errors in parentheses ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

Table 2: First stage of the instrument. In each regression, the explanatory variable is the number of national banks per ten thousand residents in each county as of July 1916. The outcome variable is the fraction of county banks that were national banks as of the year listed in each column. White(1980) standard errors are reported in parentheses. 1 1 1 .8 .8 .8 .6 .6 .6 .4 .4 .4 Fraction national banks, 1919 Fraction national banks, 1920 Fraction national banks, 1921 .2 .2 .2 0 0 0 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 1916 national banks per 10k residents 1916 national banks per 10k residents 1916 national banks per 10k residents 1 1 1 .8 .8 .8 .6 .6 .6 .4 .4 .4 Fraction national banks, 1922 Fraction national banks, 1926 Fraction national banks, 1927 .2 .2 .2 0 0 0 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 1916 national banks per 10k residents 1916 national banks per 10k residents 1916 national banks per 10k residents

Figure 9: Visual illustration of the ﬁrst stage of the instrument: The horizontal axis in each ﬁgure is the number of national banks per ten thousand residents as of July 1916. The vertical axis plots the fraction of banks that were national banks at the end of (left to right, top to bottom) 1919, 1920, 1921, 1922, 1926, and 1927.

38 (1) (2) (3) Ln(Output) Ln(Loans) Loans/Output 12/31/19 × 1916 nat’l banks -0.0210 0.0125 0.0888 (0.0302) (0.0241) (0.0575) 12/31/20 × 1916 nat’l banks -0.105∗∗∗ -0.0654∗ -0.158 (0.0324) (0.0339) (0.137) 12/31/21 × 1916 nat’l banks -0.0492 -0.0736∗∗ -0.594∗∗ (0.0353) (0.0365) (0.265) 12/31/22 × 1916 nat’l banks -0.0468 -0.0746∗∗ -0.272∗ (0.0338) (0.0358) (0.144) 12/31/26 × 1916 nat’l banks 0.00501 -0.116∗∗∗ -0.566∗∗∗ (0.0314) (0.0408) (0.193) 12/31/27 × 1916 nat’l banks -0.00876 -0.119∗∗∗ -0.536∗∗∗ (0.0333) (0.0428) (0.184) 12/31/29 × 1916 nat’l banks 0.00257 -0.163∗∗∗ -0.456∗∗∗ (0.0318) (0.0394) (0.132) 12/31/19 0.764∗∗∗ 0.457∗∗∗ -0.382∗∗∗ (0.0565) (0.0446) (0.133) 12/31/20 0.221∗∗∗ 0.798∗∗∗ 1.117∗∗∗ (0.0598) (0.0666) (0.295) 12/31/21 -0.269∗∗∗ 0.763∗∗∗ 2.698∗∗∗ (0.0710) (0.0713) (0.558) 12/31/22 0.113∗ 0.753∗∗∗ 1.256∗∗∗ (0.0640) (0.0702) (0.305) 12/31/26 -0.0170 0.868∗∗∗ 2.012∗∗∗ (0.0624) (0.0740) (0.410) 12/31/27 -0.0319 0.868∗∗∗ 2.025∗∗∗ (0.0673) (0.0758) (0.402) 12/31/29 0.121∗∗ 0.876∗∗∗ 1.469∗∗∗ (0.0590) (0.0720) (0.282) Fixed eﬀect County County County Obs 808 807 808 Standard errors in parentheses ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

Table 3: County-level regressions of total loans outstanding, and agricultural output, as a function of the 1916 number of national banks in a county per ten thousand residents. The excluded year in each column is 1916, so all effects are relative to that date. County fixed effects are included in all regressions. Standard errors are clustered by county.

39 (1) (2) (3) Ln(Output) Ln(Loans) Loans/Output 12/31/19 × frac. nat’l -0.144 0.0676 0.430 (0.194) (0.146) (0.418) 12/31/20 × frac. nat’l -0.871∗∗∗ -0.543∗∗ -1.433 (0.299) (0.262) (1.186) 12/31/21 × frac. nat’l -0.431 -0.627∗∗ -5.118∗∗ (0.320) (0.288) (2.283) 12/31/22 × frac. nat’l -0.414 -0.640∗∗ -2.447∗ (0.292) (0.300) (1.318) 12/31/26 × frac. nat’l 0.0200 -0.897∗∗∗ -4.460∗∗∗ (0.252) (0.311) (1.590) 12/31/27 × frac. nat’l -0.0846 -0.919∗∗∗ -4.212∗∗∗ (0.264) (0.327) (1.525) 12/31/29 × frac. nat’l 0.00139 -1.260∗∗∗ -3.635∗∗∗ (0.255) (0.343) (1.187) 12/31/19 0.785∗∗∗ 0.451∗∗∗ -0.396∗∗ (0.0812) (0.0598) (0.201) 12/31/20 0.312∗∗∗ 0.855∗∗∗ 1.297∗∗∗ (0.0952) (0.0891) (0.431) 12/31/21 -0.219∗∗ 0.832∗∗∗ 3.275∗∗∗ (0.108) (0.0972) (0.818) 12/31/22 0.163∗ 0.825∗∗∗ 1.557∗∗∗ (0.0966) (0.100) (0.470) 12/31/26 -0.0142 0.956∗∗∗ 2.471∗∗∗ (0.0880) (0.102) (0.588) 12/31/27 -0.0189 0.960∗∗∗ 2.471∗∗∗ (0.0947) (0.106) (0.578) 12/31/29 0.125 1.010∗∗∗ 1.886∗∗∗ (0.0870) (0.111) (0.441) Fixed eﬀect County County County Instrument 1916 nat’l p.c. 1916 nat’l p.c. 1916 nat’l p.c. Obs 807 807 807 Standard errors in parentheses ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

Table 4: County-level regressions of total loans outstanding, and agricultural output, as a function of the fraction of national banks in a county. The excluded year in each column is 1916, so all effects are relative to that date. County fixed effects are included in all regressions. The interaction terms are instrumented with interactions between time dummies and the 1916 number of national banks per capita, following specification (3). Standard errors are clustered by county. 40 (1) (2) (3) Ln(Loans) Ln(Loans) Ln(Loans) 12/31/19 × proxy -0.336 -0.264 -0.950 (0.560) (0.368) (0.603) 12/31/20 × proxy -2.517∗∗∗ -1.766∗∗∗ -3.684∗∗∗ (0.822) (0.533) (0.829) 12/31/21 × proxy -3.162∗∗∗ -2.168∗∗∗ -4.429∗∗∗ (0.894) (0.579) (0.903) 12/31/22 × proxy -3.047∗∗∗ -2.099∗∗∗ -4.324∗∗∗ (0.853) (0.553) (0.870) 12/31/26 × proxy -3.606∗∗∗ -2.567∗∗∗ -4.893∗∗∗ (0.884) (0.561) (0.914) 12/31/27 × proxy -3.867∗∗∗ -2.743∗∗∗ -5.318∗∗∗ (1.019) (0.651) (0.993) 12/31/29 × proxy -3.573∗∗∗ -2.558∗∗∗ -4.813∗∗∗ (0.985) (0.639) (1.023) 12/31/19 0.494∗∗∗ 0.497∗∗∗ 0.523∗∗∗ (0.0412) (0.0412) (0.0419) 12/31/20 0.834∗∗∗ 0.841∗∗∗ 0.882∗∗∗ (0.0618) (0.0609) (0.0588) 12/31/21 0.820∗∗∗ 0.825∗∗∗ 0.871∗∗∗ (0.0689) (0.0675) (0.0653) 12/31/22 0.803∗∗∗ 0.809∗∗∗ 0.854∗∗∗ (0.0653) (0.0643) (0.0623) 12/31/26 0.885∗∗∗ 0.898∗∗∗ 0.934∗∗∗ (0.0677) (0.0653) (0.0641) 12/31/27 0.894∗∗∗ 0.907∗∗∗ 0.950∗∗∗ (0.0729) (0.0703) (0.0673) 12/31/29 0.819∗∗∗ 0.834∗∗∗ 0.867∗∗∗ (0.0720) (0.0701) (0.0686) Fixed effect County County County Proxy 1916 nat’l deposits 1916 nat’l assets 1916 nat’l loans Obs 807 807 807 Standard errors in parentheses ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

Table 5: Robustness to diﬀerent measures of national bank presence: In column 1, the measure is national bank deposits per capita; in column 2, it is national bank assets per capita; and in column 3, it is national bank loans outstanding per capita. All are measured in 1916 and in thousands of dollars. The speciﬁcations are as in Column 2 of Table3.

41 1

β = −0.10 (p=0.00) .5 0 1916−1920 −.5 Change in Ln(agricultural output), −1 0 1 2 3 4 1916 national banks per 10k residents

β = −0.06 (p=0.07) 1.5 1 1916−1920 .5 Change in Ln(bank loans outstanding), 0 0 1 2 3 4 1916 national banks per 10k residents

Figure 10: Each dot in the figures corresponds with a county in Illinois, excluding Cook County. The figures plot the change in the log of county-level loans outstanding (top panel) and agricultural output (bottom panel) from 1916 to 1920. The explanatory variable on the horizontal axis is the county’s 1916 number of national banks per capita. The slope and p-values are reported for a best-fit line with White(1980) standard errors.

42 β = 0.006; p = 0.413 .1 0 −.1 over total county bank liabilities −.2 1916−1920 change in nat’l bank deposit liabilities −.3 0 1 2 3 4 1916 nat’l banks / 10k residents .1

β = −0.020; p = 0.008 0 −.1 over total county bank liabilities −.2 1916−1920 change in nat’l bank non−deposit liabilities −.3 0 1 2 3 4 1916 nat’l banks / 10k residents

Figure 11: Each dot in the figures corresponds with a county in Illinois, excluding Cook county. The figures plot the change in two components of total county-level bank liabilities: in the top panel, national bank deposits, and in the bottom panel, national bank liabilities other than deposits. Both are scaled by total county bank liabilities (summing across national and state banks). The explanatory variable on the horizontal axis in each figure is the county’s 1916 number of national banks per ten thousand residents. The slope and p-values are reported for a best-fit line with White(1980) standard errors.

43 (1) (2) (3) (4) ∆ Ln(Ag. output) ∆ Ln(Ag. output) ∆ Ag. output ∆ Ag. output ∆ Ln(Loans), 1916-1920 1.371∗∗∗ 1.508∗∗ (0.520) (0.621) ∆ Loans., 1916-1920 0.734∗∗ 0.764∗∗∗ (0.319) (0.289) 1916 Ln(Loans) 0.792∗∗∗ 0.919∗∗∗ 648503.9 370001.5 (0.245) (0.286) (490909.4) (407439.9) 1916 Ln(Ag. output) -0.846∗∗∗ -0.970∗∗∗ -2442943.1∗∗ -1845073.0∗∗∗ (0.259) (0.308) (957076.6) (645092.8) 1916 Loans/Ag. output -0.249∗∗ -0.286∗∗ -1207070.9∗ -757125.4∗ (0.101) (0.115) (654675.4) (428208.3) Weighting None (1916 pop.)-1 None (1916 pop.)-1 Obs. 100 100 100 100 Standard errors in parentheses

44 ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

Table 6: Instrumental-variables regressions for the marginal effect of bank credit on agricultural output during 1920. The sample is each county in Illinois excluding Cook County. Each regression models the within-county change in agricultural output from 1916 to 1920 as a function of the within-county change in bank loans outstanding from 1916 to 1920. This differenced specification removes a county fixed effect in the levels of output and loans. In columns 1 and 2, both output and loans are measured in logs, while in columns 3 and 4 they are measured in levels. In each regression, the change in bank lending volume is instrumented with the county’s number of national banks per ten thousand residents as of 1916. This follows the identification strategy pursued throughout the paper, which exploits the differential response of national banks to the Fed’s discount rate increases in 1920. The other explanatory variables are measured as of 1916 and left as exogenous. Columns 1 and 3 are unweighted regressions. In columns 2 and 4, observations are weighted by the inverse of the county’s 1916 population. oun1. Column , 4 Table from errors standard and Coefficients 12: Figure oun2. Column 4, Table from errors standard and Coefficients 13: Figure

Regression coefficient on interaction term Regression coefficient on interaction term between year dummy and fraction nat’l banks between year dummy and fraction nat’l banks −2 −1.5 −1 −.5 0 .5 −1.5 −1 −.5 0 .5 1919 1919 1920 1920 1921 1921 1922 1922 45 Date Date 1926 1926 1927 1927 1929 1929 oun3. Column , 4 Table from errors standard and Coeﬃcients 14: Figure

Regression coefficient on interaction term between year dummy and fraction nat’l banks −10 −5 0 1919 1920 1921 1922 46 Date 1926 1927 1929 β = 6.87 (p=0.62) 200 0 −200 Change in number_farms, 1920−−1925 −400