The Influence of the U.S. Postal Savings System on Bank Runs

1 Introduction

Until the creation of the Federal Deposit Insurance Corporation (FDIC) in 1935, the U.S. postal savings system was the only bank to have its deposits fully insured by the federal government. Given that the U.S. experienced widespread bank runs throughout the 1920s and 1930s, culminating in the Great Depression, it is important to understand how the existence of postal banks may have played a role in altering depositor behavior and preventing bank failures.

The existing literature on the postal savings system is limited but has generally pointed to two conclusions. First, in response to a bank failure, individuals move their savings to the postal bank (Kuwayama 2000; Sissman 1936). Second, the postal savings system failed to limit the bank runs of the Great Depression since it did not redeposit its holdings (Friedman and Schwartz 1963; O’Hara and Easley 1979). However, most of the previous research relies on historical accounts and stylized statistics rather than econometric models. In this paper, I attempt to address this gap in the literature by gathering the first county level dataset on postal savings and the first state-level dataset on redeposits from 1911 to 1945. By constructing these datasets, I am able to perform a detailed statistical analysis of the postal savings system.

I find that previous research has not adequately addressed endogeneity issues in the relationship between postal deposits and bank failures. Using fixed effects, I reveal a negative correlation between the two variables. Through an instrumental variables approach structured around the rules in the Postal Depository Act of 1940, I also obtain evidence that the postal savings system had a statistically significant effect on bank runs during the 1920s and 1930s. Lastly, I find that the effects of bank failures on the demand for postal deposits are highly localized. All three conclusions contradict previous results in the postal bank literature.

The paper proceeds in seven sections. Section 2 provides historical background on the postal savings system’s development and highlights its unique institutional features, which are employed in the instrumental variables strategy later on. Section 3 uses previous research to motivate the questions under consideration. Section 4 describes the panel datasets on postal deposits, redeposits, and bank failures. Section 5 outlines an empirical strategy using instrumental variables to analyze the relationship between bank failures and postal deposits as well as the relationship between redeposits and bank failures. Section 6 presents the regression results, and Section 7 offers some conclusions.

2 Historical Background

2.1 The Founding of the Postal Savings System

During the late 19th century, many European countries successfully developed postal savings systems as a way to increase household savings (Schewe 1971). In contrast, the United States was a relatively late adopter. Commercial bankers perceived the postal savings system as a competitive threat, claiming that it could eventually lead to a government takeover of the entire financial sector. It was only after the Panic of 1907 that public support for the postal savings system overcame private sector resistance. The panic left lawmakers to figure out how to restore public confidence in banks and credibility to the financial system. Congress chose to pass the Postal Savings Depository Act of 1910, which authorized the conversion of post offices into government- backed banks where all deposits would be fully insured by the government.

2.2 Institutional Features of the Postal Bank

In response to the bank lobby’s concerns, the postal savings system was designed with several unique institutional features to limit its competition with private banks and ensure that the deposits would remain local. The interest rate on postal deposits was fixed at 2%, significantly less than the 3.5% paid out by most commercial banks in 1910 (O’Hara and Easley 1979). A strict deposit limit of $500 was imposed, later raised to$2,500 in 1918, so the government could argue that the postal bank’s deposits were from poor, rural savers and would not have been placed in private banks anyways.

The Postal Depository Act ordered the conversion of post offices to banks to proceed from first-class post offices, or those with the highest gross annual revenue, to fourth-class offices. Emphasis was placed on keeping higher classification postal banks open over the years; fourth-class postal banks were most likely to be closed in the event of a downturn.

2.3 Redepositing Mechanism

A second major fear was that the system would redeposit its money in large, urban financial markets, depriving the local areas where the savings were generated of investment funds. To avoid this possibility, the Postal Savings Depository Act stated that 95% of postal deposits were to be redeposited in solvent local banks. Only when no local banks were willing to pay the legally required interest rate of 2.25% could the deposits be offered to other banks within the same state or, eventually, placed in federal government securities.

In theory, the postal savings system would have the capacity to stop bank runs through redepositing. As depositors transfer savings from commercial banks to postal banks, the postal savings system could redistribute the new deposits back to the local banks. Even if a commercial bank were hit by a wave of withdrawals, the postal savings system could stymie a potential run by redepositing its funds into the threatened bank, serving as a backstop to the banking system by slowing the vicious cycle between deposit withdrawals and bank failures.

3 Related Literature and Theory

3.1 The Causes of Bank Failures during the Great Depression

While little research has been done on the postal savings system, there exists substantial work on the causes of bank failures during the Great Depression. General theories on Depression-era bank failures disagree over whether failures were due to illiquidity or insolvency.

The first line of reasoning, formalized as the Diamond-Dybvig model, argues that banking panics occurred because depositors were concerned about other depositors’ actions (Diamond 1983; Carlson 2002). If depositors believe that withdrawal demand will exceed the bank’s supply of liquid reserves, they will rush to the bank and precipitate a bank run. Friedman and Schwartz (1963) argue that the bank failures of the Great Depression resulted from a sudden, unwarranted crisis of confidence among depositors rather than a fundamental deterioration in bank health. They point to the fall of the large, national Bank of United States as a signal for depositors to initiate a bank run, and because banks are inherently susceptible to runs, a banking crisis became a self- fulfilling prophecy.

The second line argues that bank failures are primarily motivated by economic shocks. In a regression analysis of Great Depression-era bank failures, Calomiris and Mason (2010) find that most national banking panics can be explained by bank fundamentals. Similarly, Wicker (1980; 1996) claims that the perceived banking crisis was the product of regional and bank- specific events rather than the spread of a nationwide panic. In summary, this view argues that real shocks caused banks to become insolvent during the Great Depression.

Each theory contains different implications for the postal bank and its depositors, raising two major questions: What is the effect of bank failures on the level of postal deposits, and what is the effect of redepositing on bank failures? In the illiquidity theory, depositors move their savings to the safety of the postal bank, which can prevent bank runs by providing commercial banks temporary liquidity. In the insolvency theory, depositors may draw on their postal savings to cushion their income if bank failure is part of a larger economic downturn. Furthermore, redepositing will have a limited impact on a fundamentally insolvent bank.

3.2 Postal Bank-Related Literature

Friedman and Schwartz claim that widespread fear in the Great Depression led to a great shift of deposits from the banking system to the postal bank. They argue that the postal bank failed to use the redepositing mechanism to provide temporary liquidity and limit further bank runs. Instead of serving as potential reserves for the banking system, postal deposits were mostly diverted into Treasury bonds. As a result, Friedman and Schwartz believe that the postal bank allowed banks that were illiquid but not insolvent to fail. However, they rely mainly on a qualitative description of the events in question to reach this conclusion.

Sissman (1936) provides a slightly more quantitative analysis of the postal savings system, noting little correlation between nationwide postal deposits and bank failures from 1911 to 1930, though he attributes this to regional variation. O’Hara and Easley (1979) focused specifically on its effect on the savings and loan (S&L) industry. They find that postal deposits increased exponentially from 1929 to 1934, while S&L and commercial bank deposits declined significantly. Correlation metrics also show that states with high bank failures had low redeposits. They conclude that the postal savings system had a very negative impact on the banking system since postal savings were not redeposited at local banks.

Kuwayama (2000) constructs a time-series model to describe the demand for postal savings. Using national-level postal savings data from 1911 to 1967, the regression analysis finds a statistically significant positive correlation between bank failures and postal savings. However, Kuwayama does not address the potential for endogeneity in the bank failures variable. Both the demand for postal savings and the number of suspended banks may be correlated with exogenous shocks, such as crop failures or stock market crashes, and the model ignores these unobserved changes. Kuwayama also does not include the level of redeposits in any of her models even though it is correlated with both postal deposits and bank failures.

4 Dataset

The problem with most of the existing analyses is that they are largely qualitative in nature, built on a broad range of historical judgments rather than formal theory or empirical tests. Even the ones that employ quantitative measures do not fully address potential endogeneity, making it difficult to determine the effect of bank failures on postal deposits and the effect of redeposits on bank failures. Additionally, previous research has only focused on national-level and state-level postal savings data. By constructing the first county-level dataset, I attain a richer picture of the interaction between postal deposits and bank runs.

The data used in this study were compiled from the “Annual Report of the Operations of the Postal Savings System” which was published by the Postmaster General for every year of the postal bank’s existence. Since bank runs became less frequent in the years following the introduction of the FDIC in 1935, the reports were only digitized for the years from 1911 to 1945. The data contain both the number of postal depositors and the total amount of postal deposits for every operating postal bank branch in the United States, covering an average of 20,000 towns per year. The postal data were then geocoded so that distances could be calculated between towns and counties. The average county contains approximately three postal banks, holding about $200,000 in deposits; by comparison, the average county contains eight commercial banks, holding about$20,000,000 in deposits.

The annual reports also contain state-level data on redeposits (the amount of postal deposits being held by a state’s commercial banks) and bond purchases (the amount of postal deposits used to purchase Treasury bonds). Redeposit data were unavailable at the level of individual postal banks, just at the state level; thus, statistics on postal deposits are calculated again to reflect the longer time span of the bank failure dataset available at the state- level. The average state held about $13 million in postal deposits, representing about 1% of total bank deposits in that state. Only about$5 million of that was redeposited in local banks, with the remainder used to buy government bonds; the averages suggest that the postal bank did not redeposit nearly as much as it was required to by law (95% of postal deposits), though the standard deviation on redeposits is fairly large.

Data on postal bank classification from 1915 to 1940 were available through studies of the postal savings system by the American Bankers Association. In addition to including the postal bank data from the Postmaster General’s annual report, these studies record each postal bank’s classification (first-, second-, third-, or fourth-class). The post office emphasized opening postal banks at locations of a higher classification level, which explains why there are so few fourth-class postal banks on average. It is also clear that a higher class postal bank had significantly more depositors and postal deposits than a lower class postal bank. Statistics on the classifications of all post offices (including non- postal banks) were unavailable.

Bank failure data were gathered from two sources. The FDIC collected data on the total number of banks active, total deposits in all banks, number of suspended banks, and the total deposits of suspended banks in every U.S. county for every year from 1921 to 1936. County-level population data were interpolated from U.S. census data (U.S. Census 1910, 1920, 1930, 1940, 1950).2 The “United States Historical Data on Bank Market Structure” contains data on the number of banks active, total deposits in all banks, number of suspended banks, and the total deposits of suspended banks in every state for the years from 1921 to 1940, a slightly longer period of time than the FDIC dataset. This second dataset also contains estimates of per capita income by state. However, the series is largely incomplete and interpolated, resulting in a smaller number of observations (480, compared to 929 in the rest of the dataset). Also, some data on suspended deposits was also missing, making the number of suspended banks the more complete time series dataset at the state level.

5 Empirical Strategy

The arguments in the existing literature suggest two key issues to examine. First, I will analyze whether the redepositing mechanism helped to prevent bank failures. Second, I will examine the effect of bank failures on the demand for postal savings. Along the way, I will present some of the basic endogeneity issues that were unaddressed by previous research. To resolve these issues, I propose an instrumental variables approach based on the unique institutional features of the postal savings system, which include the use of postal classification to locate banks and the requirement that redepositing occur within state lines.

5.1 The Impact of Redeposits on Bank Failures: Postal Bank Bond Purchases IV and Postal Classification IV

The first question is whether or not the redepositing mechanism served to prevent bank failures. O’Hara and Easley use rank correlation to show that states with high numbers of bank failure had low rates of redeposit. A basic OLS specification to study the same effect follows:

\mathit{F}_{it}= \beta _{1}\mathit{R}_{it}+ \beta _{2}\mathit{O}_{it}+ \beta_{3}\mathit{I}_{it}+ \beta _{4}\mathit{T}_{it}+ \gamma \textrm{X}_{it}+ \omega_{t}+ \nu_{i}+ \mu_{it}

where i indicates the state, t denotes the year, Fit is bank failures (either the number of suspended banks or suspended deposits), Rit is redeposits, Oit is population, Iit is per capita income, Tit is total bank deposits, and Xit is a vector that includes measures of bank soundness and general economic conditions. omegat represents the year fixed effects, and nui represents the county fixed effects.

The main problem with this specification is the potential for endogeneity from the redeposits variable. Redeposits are likely to be correlated with unobserved changes in bank fundamentals. For example, it is possible that the banks were not sound to begin with and therefore would have failed anyways. This was the conclusion reached by Calomiris and Mason (2000), who used regression analysis to show that bank failures were mostly explained by economic conditions. In this case, it would make sense for the government to avoid redepositing in areas with bank failures. Low redepositing is correlated with bank failures because the postal bank would simply want to avoid throwing good money at bad banks. We cannot resolve this issue by adding controls as there are few proxies for economic conditions at the county level.

Instead, there are two ways to instrument for redeposits by the postal bank. First, we can use the number of postal banks per state (fixed at a year before the extensive bank failures of the Great Depression) that interacted with the amount of government bonds purchased by the entire postal savings system. The number of postal banks per state has to be fixed to a year with few bank failures to avoid the possibility that new postal banks were opened in response to the bank failures. This interaction term uses the variation in the number of postal banks per state and the national level of bond purchases by the postal bank to fix the amount of postal savings left for redeposit in each state. Using the interaction term to instrument for redeposits, we can then analyze bank failures in each state. The first and second stage regressions for this IV specification are:

\mathit{F}_{it}= \beta _{1}\mathit{\widehat{R}}_{it}+ \beta _{2}\mathit{O}_{it}+ \beta_{3}\mathit{I}_{it}+ \beta _{4}\mathit{T}_{it}+ \gamma \textrm{X}_{it}+ \omega_{t}+ \nu_{i}+ \mu_{it}

\mathit{R}_{it}= \alpha _{i}\mathit{D}_{it}+ \alpha _{2}\mathit{O}_{it}+ \alpha_{3}\mathit{I}_{it}+ \alpha _{4}\mathit{T}_{it}+ \delta \textrm{X}_{it}+ \omega_{t}+ \nu_{i}+ \mu_{it}

where Dit denotes our instrument, the number of state postal banks in 1919 interacted with the postal bank bond purchases (i indicates the state, t denotes the year). It is possible that states with lots of postal banks in 1919 are differentially sensitive to nationwide economic shocks and that these shocks are also correlated with both postal bank bond purchases and the number of suspended banks. However, this is unlikely given that postal banks were located by classification level.

A second way to measure the effect of redepositing is to instrument for postal deposits using postal bank classification. The key here is that the Postal Depository Act of 1910 introduced postal banks to different places at different times; first- and second-class post offices were converted to postal banks before third- and fourth-class post offices. The classification level also influences whether a postal bank is likely to stay open, as the post office prioritized keeping higher classification postal banks open. Thus, the classification determines how long an area has had a postal bank, which in turn determines the level of postal deposits and the magnitude of the redepositing effect. The first- and second-stage regressions for this IV specification follow below:

\mathit{F}_{it}= \beta_{1}\mathit{\widehat{P}}_{it}+ \beta_{2}\mathit{O}_{it}+ \beta_{3}\mathit{I}_{it}+ \gamma \textrm{X}_{it}+ \omega_{t}+ \nu_{i}+ \mu_{it}

\mathit{P}_{it}= \alpha_{1}\mathit{C}_{it}+ \alpha_{2}\mathit{O}_{it}+ \alpha_{3}\mathit{T}_{it}+ \delta \textrm{X}_{it}+ \omega_{t}+ \nu_{i}+ \mu_{it}

where Cit is a vector that denotes our instrument, the number of first-class, second-class, third-class, and fourth-class postal banks, and Pit is the level of postal deposits. Here, i now indicates the county (not state). The rest of the variables remain the same as in the simple OLS specification.

Therefore, we can use post office classification as an instrument for postal deposits and then regress bank failures on postal deposits in the second stage. The advantage of this approach is that it utilizes the discontinuity between classifications created by the Postal Depository Act. Presumably, the classification bands were set arbitrarily such that small differences around the cutoffs in annual postal revenue determined whether two otherwise similar towns received a postal bank or not. However, one of the practical difficulties in implementing this approach is that postal bank classification data are not available for the entire time period. In addition, there is a possibility for concern that areas with larger post offices might be differentially susceptible to national shocks. For instance, larger post offices might be more closely connected with national financial institutions than smaller post offices, in which case the instrument may suffer from endogeneity.

5.2 The Impact of Bank Failures on Postal Deposits: Nearby State Redepositing IV

The second key question under consideration is whether or not bank failures lead to an increase in postal savings. Kuwayama’s regression analysis suggests that the demand for postal savings rises in response to more bank failures; Sissman reaches a similar conclusion, finding that postal deposits are positively correlated with bank failures. A basic OLS specification to study the link between postal deposits and bank failures follows:

\mathit{P}_{it}= \beta_{1}\mathit{F}_{it}+ \beta_{2}\mathit{O}_{it}+ \beta_{3}\mathit{T}_{it}+ \gamma \textrm{X}_{it}+ \omega_{t}+ \nu_{i}+ \mu_{it}

where i indicates the county, t denotes the year, Pit is postal deposits, Fit is bank failures (either the number of suspended banks or suspended deposits), Rit is redeposits, Oit is population, Tit is total bank deposits, and Xit is a vector that includes measures of bank soundness and general economic conditions. ωit represents the year fixed effects, and vi represents the county fixed effects. The model is similar to the time-series model of aggregate demand for postal savings proposed by Kuwayama but adds in fixed effects and population controls (Kuwayama 2000).

As with the redeposit model, the key concern here is endogeneity. Both postal deposits and bank failures are likely to be correlated with unobserved changes in economic conditions. An unobserved event such as a real economic shock (e.g. a stock market crash) might both create bank failures and cause depositors to withdraw their savings from the postal bank in order to supplement their earnings. The causality could also run in the opposite direction; due to the redepositing mechanism, high levels of postal savings will lead to fewer bank failures. Altogether, it is still difficult to establish causality due to the endogeneity of the bank failures variable.

Moreover, any observed effect between postal deposits and bank failures cannot necessarily be attributed to contagion. When a bank fails, there is a direct displacement effect where depositors of the failing bank must move their savings elsewhere, in addition to a contagion effect where depositors move their money to the postal bank in expectation of other bank failures. So even though Friedman and Schwartz argue that fear drove money into the safety of the postal savings system, it is not entirely accurate to attribute the resulting increase in postal deposits to sudden changes in expectations.

To accurately evaluate Friedman and Schwartz’s claim that fear of bank failures caused depositor flight to the postal savings system, we must identify a source of variation in depositors’ perceptions of bank risk that is uncorrelated with both local shocks and the direct displacement effect. One potential source of this variation is redepositing in nearby states. In the Postal Depository Act, it was decreed that money was “to be invested in government securities and not deposited in other states” in the event that no banks in the same state could satisfy the interest rate requirements of the postal bank. Because redeposits cannot cross state lines, they can only contribute to bank failures in the state from which they originated. Thus, for any given county, we can identify states that are within a certain geographical distance of the county and then use redeposit levels in the nearby states to instrument for bank failures in that state. The first and second stage regressions for this IV specification are:

\mathit{P}_{it}= \beta _{1}\mathit{\widehat{FN}}_{it}+ \beta _{2}\mathit{O}_{it}+ \beta_{3}\mathit{T}_{it}+ \gamma \textrm{X}_{it}+ \omega_{t}+ \nu_{i}+ \mu_{it}

\mathit{FN}_{it}= \alpha_{1}\mathit{Z}_{it}+ \alpha_{2}\mathit{O}_{it}+ \alpha_{3}\mathit{T}_{it}+ \delta \textrm{X}_{it}+ \omega_{t}+ \nu_{i}+ \mu_{it}

where Zit denotes our instrument, redeposits in nearby states, and FNit is bank failures in nearby states. The remaining variables remain the same as in the OLS specification, with i representing county and t representing year. This approach isolates the contagion effect from the displacement effect by assuming that redepositing in nearby states affected the number of bank suspensions in nearby states but is uncorrelated with bank suspensions in the same state as the county being analyzed.

6 Results

6.1 OLS Models

What is the effect of bank failures on the demand for postal savings? Table 1 reports the results of an OLS regression of postal deposits on the number of suspended banks. One of the more interesting results from this model comes from the use of fixed effects. Covering more years and more regions, the dataset also allows for county and year fixed effects to be used for the first time to analyze postal savings. The first column shows a naive regression without fixed effects and indicates that for every suspended bank, postal deposits increase by about $110,000. This positive correlation is what Sissman reported qualitatively and O’Hara and Easley calculated. However, once county and year fixed effects are applied in Columns 2 and 3, the correlation becomes negative. It appears that there are unobserved time- invariant, county-specific factors that are correlated with postal deposits and bank failures (e.g. culture). The use of fixed effects neutralizes these factors and reveals a negative correlation between postal deposits and bank failures, which is a reversal of the effect observed in previous literature. The result remains statistically significant even after controls for population and total bank deposit are added in Column 4. Column 5 tests for a lag effect where bank failures in the previous time period may still be affecting postal deposits; however, none of the lagged bank failures are statistically significant. Columns 6 and 7 offer alternate ways of understanding the effect of bank failures on postal deposits. One indicates that for every suspended bank, postal deposits decline by 2%. The other says that for every suspended bank, postal deposits as a share of total bank deposits declines by 0.006. For comparison, Kuwayama’s regression model found that each additional suspended bank caused postal deposits as a share of total bank deposits to increase by 0.05. What is the effect of redepositing on bank failures? Table 2 shows the results of an OLS regression of bank failures on the level of redeposits. The naive regression in Columns 1 and 2 shows that the effect of redepositing on the number of suspended banks is statistically insignificant. However, performing a log transformation on the redeposits variable corrects for large outliers in the data. The subsequent regression in Column 3 shows that a 1% increase in redeposits decreases the number of suspended banks by 0.056 (statistically significant at the 5% level). Column 4 tests for the effect of redeposits from previous time periods. In all three time periods (t, t 1, t 2), redeposits are inversely correlated with suspended banks but only redeposits from two time periods ago are statistically significant; this is likely due to the decreased number of observations (336, rather than 480 in the previous regressions). Columns 5-8 perform the same regressions as described in Columns 1-4 but with suspended deposits as the dependent variable. There are slightly fewer observations of suspended deposits than suspended banks at the state-level, but the results from Columns 5-8 are similar overall. The regressions imply that a 1% increase in redeposits decreases suspended deposits by about$5,600,000 (statistically significant at the 5% level). Regressing on lagged redeposits proves to be insignificant in Column 8. We cannot take logs of suspended deposits since there are many zeroes in the data.

Table 3 performs a Poisson distribution to account for the fact that the number of suspended banks is zero in most cases. The main results are similar to Table 2 but the statistical significance is strengthened to the 1% level. Column 3 regresses suspended banks on a log of redeposits and indicates that a 1% increase in redeposits decreases the number of suspended banks by about 0.005.

6.2 Models

Although the results above are statistically significant, they do not allow us to conclude that the relationships are causal. In the regression of postal deposits on bank failures, the use of county and year fixed effects mitigates omitted variable bias, but could still be vulnerable to endogeneity from variables that vary within counties over time. As discussed earlier, crop failures in a specific county may cause banks to fail and lead depositors to withdraw their savings from the postal bank in an attempt to supplement their earnings. This income effect story is one way to explain the results observed in Table 1. At the same time, reverse causality remains a problem, as areas with more postal deposits available for redepositing will likely experience fewer bank failures. The regression of bank failures on redeposits, though promising, does not prove that the redeposits actually prevented bank failures. Thus, an instrumental variables approach is still needed given the potential for endogeneity.

Table 4 presents the second stage results of regressing bank failures on redeposits, instrumenting for redeposits by using the number of state postal banks in 1919 interacted with postal bank bond purchases. Column 1 shows a positive but insignificant correlation between redeposits and the number of suspended banks. As in the OLS regression of suspended banks on postal deposits in Table 2, logs were needed to transform outliers in the redeposit data. Columns 2 and 3 show the results of using log of redeposits instead of outright redeposits as an independent variable. The results indicate that a 1% increase in redeposits decreases the number of suspended banks by about 1.0 (5% significance). Columns 4-6 perform the same regressions as described in Columns 1-3 but with suspended deposits as the dependent variable. The results are fundamentally similar and offer an alternate interpretation of the effect of redepositing: a 1% increase in redeposits decreases suspended deposits by about $10,000,000 (5% significance). Table 5 provides the first stage results, which show that states with more postal banks have redeposit levels that are more sensitive to postal bank bond purchases. The F-statistics were greater than 10 in all cases, suggesting that the instruments were strong. Table 6 contains the results of the postal mode 1. The model regresses bank failures on postal deposits, instrumenting for postal deposits using postal bank classification. The results strongly indicate that postal deposits are inversely correlated with bank failures. Columns 1-3 use the number of suspended banks as the dependent variable and are all statistically significant at the 1% level. Column 1 indicates that a$1 increase in postal deposits decreases the number of suspended banks by 0.00000003. Columns 2 and 3 use log of postal deposits as an independent variable, which makes sense given that postal deposits are often in the millions. Columns 2 and 3 show that a 1% increase in postal deposits decreases the number of suspended banks by about 0.004. Columns 4-6 replicate Columns 1-3 but with suspended deposits as a dependent variable. Column 6 is statistically significant at the 5% level and shows that a 1% increase in postal deposits decreases suspended deposits by $418,000. Table 7 provides the first stage results. Overall, the results are consistent with the hypothesis that postal deposits are limiting bank failures through the redepositing effect. This hypothesis may also help explain why postal deposits are negatively correlated with bank failures in the OLS regressions in Table 1. Finally, I test for the contagion effect caused by bank failures. Table 8 regresses postal deposits on bank failures in nearby states, instrumenting for bank failures in a state by using redeposits in nearby states. First stage results are shown in Table 9. In the first stage, we see that redepositing in nearby states is a significant predictor of bank failures in a state. Column 1 analyzes states within a 25-mile radius of any given county. This was the smallest distance tested for and thus had the most statistical significance (significant at 1% level). It states that for every bank failure in a nearby state, postal deposits increased by about$14,000. Column 2 adjusts the distance constraint to a 50-mile radius. Bank failures within a 50-mile radius cause postal deposits to increase by \$3,500 (significant at the 5% level). Extending the distance constraint even further to 75-miles and 100-miles in Columns 3 and 4 subsequently caused the sign on the postal deposits coefficient to become negative but the results were not statistically significant. These regressions were also run with the log of postal deposits as the dependent variable, but the results were also insignificant. The results suggest that while there is an increase in the demand for postal savings in response to bank failures, it remains highly isolated within a 25- or 50-mile radius. Within this small radius, we may also be observing the direct displacement effect rather than the contagion effect of bank failures since depositors may cross state lines to move their deposits to the postal bank.

7 Conclusion

Altogether, there are several key findings that contradict the existing theories on the postal savings system. First, the use of fixed effects reverses the positive correlation between postal deposits and bank failures observed by Kuwayama and Sissman. Fixed effects neutralize the time-invariant factors at the county level, revealing a negative correlation. One way to interpret this result is that bank failures were indicative of real economic shocks that decreased postal depositors’ incomes and caused them to draw on their savings. Alternately, due to the redepositing mechanism, it could be a case of reverse causality.

Second, bank failures and redeposits are negatively correlated. On the surface, the OLS regression confirms the result from O’Hara and Easley’s analysis, which uses rank correlation to show that states with many bank failures also had low levels of redepositing. However, this result does not prove that low redepositing caused certain states to experience more bank failures. The two instrumental variables approaches help to establish the causal relationship that redepositing did have a significant effect in preventing bank failures.

Most significantly, the results from the instrumental variables regressions refute Friedman and Schwartz’s as well as O’Hara and Easley’s criticisms of postal bank redepositing during the Great Depression. The results from the postal bank bond purchases IV indicate that every 1% increase in redeposits prevented 1 bank failure; in the OLS regression, a 1% increase in redeposits only prevented of 0.056 bank failures. The large discrepancy between the IV and OLS specifications may be explained by the fact that many bank failures occurred due to fundamental reasons, so the effect of redeposits on bank failures in a naive regression is low. Furthermore, the result suggests that when the postal bank chose to redeposit, it did so strategically and to great effect. Although there may be some endogeneity issues with postal classification IV, results are consistent with the first IV. Given the strength of the redepositing mechanism, it also appears that the OLS regression of postal deposits on bank failures is likely to be a case of reverse causality.

Third, the contagion effects of bank failures on the demand for postal savings appear to be limited. The nearby state redepositing IV finds that only bank runs in nearby states within a 25 or 50-mile radius from a given county caused postal deposits to increase. This result contradicts Friedman and Schwartz’s description of a “contagion of fear” spreading across the country during the Great Depression and more closely aligns with Wicker’s description of bank runs spreading within small, regional areas.

In summary, there are three main results from the regression analysis. First, the correlation between postal deposits and bank failures is actually negative and likely signals a case of reverse causality due to the redepositing effect. Second, redeposits had a statistically significant effect in preventing bank failures. Third, the contagion effect of bank failures on postal deposits only exists in a localized area. Overall, the analysis refutes many hypotheses from previous research on the postal bank and complements the literature on the causes of bank failures during the Great Depression.

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