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Essays on Credit and Goods Market Imperfections in the U.S. Economy: 1895 - 1940

DISSERTATION

Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the

Graduate School of The Ohio State University

By

Margaret Mary McConnell, M.S.

*****

The Ohio State University

1997

Dissertation Committee: Approved by

Stephen G. Cecchetti, Adviser Pok Sang Lam y Adviser Alan D. Viaxd y r Department of Economics UMI Number: 9721138

Copyright 1997 by McConnell, Margaret Mary

All rights reserved.

UMI Microform 9721138 Copyright 1997, by UMI Company. All rights reserved.

This microform edition is protected against unauthorized copying under Title 17, United States Code.

UMI 300 North Zeeb Road Ann Arbor, MI 48103 © Copyright by

Margaret Mary McConnell

1997 ABSTRACT

In this dissertation, I examine theories of credit and goods market imperfections using U.S. economic data from the period 1913 to 1940. Chapter 1 provides a brief overview of the dissertation.

Chapter 2 is the historical background for the third chapter. In it, I focus on those aspects of the period that make it an ideal one for studying the effects of shifts in bank credit and balance sheet positions on credit constrained firms.

The third chapter uses panel data on the number and liabUities of business failures from 1895 to 1935, by industry, to test theories of monetary transmission based on asymmetric information. These theories imply that a contraction will increase the relative likelihood of default for credit constrained firms. I find that movements in the number of failures are better explained by the variation in aggregate prices and bank failures than by changes in real output. I also find that while cychcal downturns tended to decrease size in trade industries and had little effect in manufacturing industries, periods of deflation and bank failure were accompanied by increases in the size of failed firms within an industry. These findings suggest the importance of financial variables in explaining firm failure and that moderately-sized, more highly leveraged firms had a greater difficulty withstanding deflation and banking crises.

In Chapter 4, I examine the correlation between the mean and the skewness of wholesale price changes across industries from 1913 to 1940 to test for price rigidity.

ii Just as others have found for the post-war period, I find a positive and statistically significant correlation. In contrast to the findings of other authors for the post-war period, however, the pre-war correlation cannot be explained by sampling bias in the correlation estimator. These results are consistent with the presence of price rigidity in the pre-war period. The difference between the pre- and post-war results is shown to be a smaller degree of heterogeneity in the variance of price changes across industries in the pre-war period. Finally, I look for evidence that the direction of price rigidity is a function of the underlying inflationary trend.

m To my parents.

IV ACKNOWLEDGMENTS

I am grateful to many people for their help in the completion of this dissertation.

I am particularly indebted my advisor, Stephen Cecchetti, for the example he sets as an economist and a scholar; his encouragement and direction have been invaluable.

I thank Pok Sang Lam and Alan Viaxd. Both spent long hours helping me to clarify my ideas and my exposition, primarily by listening and offering suggestions, rather than lecturing. They are both wonderful teachers, and I appreciate and admire their patience and insight.

I am grateful to Nelson Mark and Mario Crucini, who at different points in my graduate career, gave me some very important advice that I needed, more than wanted, to hear. I thank Jim Butkiewicz for stimulating my interest in macroeco­ nomic research and for his advisement and ffiendship.

My family provided constant support and encouragement. Without them, I could not have finished this dissertation. Finally, I thank Melaney, Cristina, Wajme, Cor- rina, Sven, Monica, Christina, Carolyn, Michelle and Larry for always listening and reminding me to laugh. VITA

June 12, 1967 ...... Bom - Belmax, NJ

1989 ...... B.A. Economics, University of Delaware. 1992 ...... M.S. Economics, University of Delaware. 1991-1993 ...... Graduate Teaching Associate, The Ohio State University. 1993-1996 ...... Graduate Research Associate, The Ohio State University. 1996-Present ...... Economist, Federal Rjeserve Bank of New York.

PUBLICATIONS

Research Publications

Butkiewicz, James L. and Margaret Mary McConnell “The Stability of the Demand for Money and Ml Velocity; Evidence from the Sectoral Data”. Quarterly Review of Eœnomics and Finance., 35(2):223-243, Fall 1995.

FIELDS OF STUDY

Major Field: Economics

Studies in: Monetary and Macroeconomics Profs. Stephen G. Cecchetti and Pok Sang Lam Public Finance Prof. Alan Viard

VI TABLE OF CONTENTS

P age

A bstract ...... ii

Dedication...... iv

Acknowledgments ...... v

V i t a ...... vi

List of T ables ...... ix

List of Figures ...... x

Chapters:

1. Overview ...... 1

2. Historical Background ...... 5

2.1 Introduction ...... 5 2.2 Aggregate Fluctuations: 1985 to 1940 ...... 6 2.2.1 The National Banking System...... 7 2.2.2 Aggregate Fluctuations In the National Banking Era .... 9 2.2.3 The Federal Reserve System...... 11 2.2.4 Aggregate Fluctuations Under the Early Years of the Federal Reserve System...... 14 2.3 Business Financing and the Use and Availability of Bank Credit . . 18 2.3.1 Business Financing and the Use of Bank C re d it ...... 19 2.3.2 The Availability of Bank Credit ...... 23 2.4 C onclusion ...... 25

vu 3. Business Failures and Credit Market Imperfections: Debt-Deflation and Bank Panics in the Pre-WWII U.S ...... 27

3.1 Introduction ...... 27 3.2 The D a t a ...... 30 3.3 Business Failures and Alternative Views of the Monetary Transmis­ sion Mechanism...... 37 3.4 E stim a tio n ...... 42 3.4.1 Aggregate Shocks and the Number of Business Failures . . . 42 3.4.2 Aggregate Shocks and the Size of Business Failures 51 3.4.3 Summary of Empirical Findings...... 57 3.5 Conclusion ...... 58

4. Inflation and Price Rigidity: Evidence From Wholesale Prices -1913 to 1940 60

4.1 Introduction ...... 60 4.2 Price Rigidity and the Moments of Inflation...... 62 4.2.1 Menu C o sts ...... 62 4.2.2 Sector-Speciflc Shocks and Input-Output R elations 63 4.3 The Mean-Skewness Correlation: 1913 to 1940 ...... 66 4.4 Monte Carlo Tests for Bias ...... 69 4.4.1 Small-Sample Bias: T h e o ry...... 69 4.4.2 Small Sample Bias: Evidence from the Pre-War Data .... 70 4.5 Asymmetric Price Adjustment...... 77 4.5.1 Inflation and Relative Price Variability...... 77 4.5.2 The Mean-Variance Correlation: 1913 to 1940 ...... 81 4.6 frnphcations for Models of Price S e ttin g...... 89 4.7 C onclusion ...... 90

Appendices:

A. Chapter 3: Additional Results...... 94

B. D a ta ...... 100

B.l Chapter 3 Data...... 100 B.2 Chapter 4 D a ta...... 101

vm LIST OF TABLES

Table P age

3.1 Business Failures: 1895:1935 ...... 34

3.2 Response of Number of Failures to Aggregate Shocks ...... 45

3.3 Cross-Industry Regressions...... 50

3.4 Response of Real Mean Size of Failures to Aggregate S h o ck s ... 54

3.5 Response of N ominal Mean Size of Failures to Aggregate Shocks . . . 56

4.1 Correlation Between Mean and Skewness of Price C hanges ...... 68

4.2 Bias and Bias-Adjusted Mean-Skewness Correlation CeoflScients . . . 72

4.3 Bias and Bias-Adjusted Estimates Using Modified Covariance Matrix 76

4.4 Correlation between Mean and Variance and |Mean| and Variance: 1913 - 1940 ...... 82

A.l Response of Number of Manufacturing Failures to Aggregate Shocks . 95

A.2 Response of Number of Trade Failures to Aggregate Shocks .... 96

A.3 Response of Mean Real Liabilities to Deflationary S h o c k...... 97

A.4 Response of Mean Real Liabilities to Industrial Production Shock . . 98

A.5 Response of Mean Real Liabilities to Bank Liabilities S h o c k . 99

IX LIST OF FIGURES

Figure Page

3.1 Business Failures: 1895 to 1935 ...... 32

3.2 Aggregate Variables: 1895 to 1935 ...... 35

3.3 Impulse Responses to Bank Failure Shocks ...... 52

4.1 Positively Skewed Distribution of Relative Price Shocks ...... 64

4.2 Variance Inequality Across Industries ...... 74

4.3 Positive Inflationary Expectations...... 80

4.4 Negative Inflationary Expectations...... 80

4.5 Correlation Coefficient: Mean-Variance, 3-Yr W indow...... 85

4.6 Correlation Coefficient: |Mean]-Variance, 3-Yr W indow ...... 86 CHAPTER 1

OVERVIEW

Our ability to distinguish alternative macroeconomic theories reUes on the occur­ rence of ‘natural’ experiments; changes in the economic environment that give rise to variation in economic data. These experiments may take the form of differences across regions, political structures or time periods. Our capacity to interpret the resulting variation depends on our understanding of the experiment, i.e., our understanding of the changes in underlying economic conditions across regions or periods.

In this dissertation, I use two sets of U.S. economic data from the pre-WWII period to examine theories of credit and goods market imperfections. Against the backdrop of episodes of severe price deflation, deep, protracted declines in output and widespread and relatively frequent instability in the banking system, I use a monthly record of business failures from 1895 to 1935 to test for credit market imperfections, and 41 components of the Wholesale Price Index from 1913 to 1940 to test for price rigidity.

In Chapter 2, I provide an overview of the economy during this period as back­ ground for the study of credit market imperfections in Chapter 3. I focus on those aspects of the period that make it an ideal one for studying the effects of shifts in bank credit and balance sheet positions on credit constrained Arms. In particular, I

1 describe the major episodes of deflation and financial panic and attempt to give the reader a sense of the features of the institutional structure of commercial banking that may have contributed to their severity. In addition, I describe certain aspects of flrm financing patterns during this period and discuss the extent to which there were differences in borrowing, based upon firm size, that suggest that small and medium­ sized firms were more susceptible to the effects of financial crises and deflations than either very large or very small firms.

Chapter 3 is an empirical study of the effects of credit market imperfections on business failures over the period 1895 to 1935. The failure data was collected from

Dun’s Review magazine, which reported the number and total liabilities of failed firms in 28 manufacturing and trade industries on a monthly basis.

In this chapter, I examine the effects of bank failures, deflations and output on both the number and size of business failures. I find that downturns in real activity per se had httle relationship to business failures, while deflations and banking crises appear to have had important effects. I also find differences across industries in their response to changes in aggregate conditions. The effects of bank failures show up most prominently in manufacturing industries, and deflations seemed to have been more destructive in trade industries.

Finally, I find that deflations and bank failures tended to increase the average size of a failure, while output fluctuations tended either to have no effect or to slightly decrease the size. A potential explanation for these findings is that manufacturing firms experienced a greater degree of distress during banking crises because they made greater use of bank credit, and that larger trade firms and medium-sized manufac­

turing firms were forced into bankruptcy during deflations and bank panics because

they were more highly leveraged.

In Chapter 4, the focus of the dissertation changes firom imperfections in credit

markets to imperfections in goods markets. I use monthly observations on the 41

components of the Wholesale Price Index firom 1913 to 1940, as reported by the BLS,

to examine the evidence for price rigidity in this period.

I focus first on the ‘stylized fact’ firom post-war data that the cross-sectional mean

and skewness of relative price changes are positively related and that the relationship

is statistically significant. As I note in Chapter 4, this relationship has been subject to

a variety of interpretations, ranging firom price rigidity to price flexibUity to sampling bias in the correlation estimator itself.

I find that even after controlling for the effect of sampling bias, I cannot reject the hypothesis that prices were rigid at short-horizons in the pre-war period. In addition,

I find that models of the mean-skewness correlation that emphasize price flexibility and inter-industry linkages are not likely to be an important part of the story in either the pre- or post-war period.

Finally, I examine changes in the mean-variance relationship over time during the pre-war period and find that there are differences between the severe but brief recession of the early 1920s and the Great Depression. Specifically, the differences in the mean-variance correlation across these periods are consistent with possibility that price-setters expected deflation in the early 1920s, but did not in the early years of the Great Depression. Since our understanding of the importance of credit and goods market imperfec­ tions in the current period is by no means complete, the relevance of this work is not purely historical. Instead, the use of economic data from a period which differs in meaningful ways from the current period should provide insight into the mechanisms that currently generate economic fluctuations. CHAPTER 2

HISTORICAL BACKGROUND

In the effort to strengthen their position, the banks not only refused to make new loans, but closed out the old accounts at maturity in many instances. With business throughout the country generally extended as far as credit would permit, the sudden withdrawal of accommodation by the banks forced many concerns into bankruptcy. This year’s failure record disclosed the fact that the large majority of smaller con­ cerns were able to withstand the strain, having enjoyed many years of prosperity... but a relatively moderate number of large firms were unable to raise funds at any price...

—Dun’s Review, January 11, 1908

2.1 Introduction

Theories of credit market imperfections are based on the belief that asymmetric

information in financial markets causes some firms in the economy to rely on inter­

mediated credit, and in particular, on bank credit, for financing. In addition, these

theories suggest that market imperfections cause external financing to be more ex­

pensive than internal financing for certain firms, and that declines in the value of a

firm’s net worth aSect the price diflferential between external and internal financing.

In Chapter 3 of this dissertation, I use monthly data on business failures from

1895 to 1935 to test for credit market imperfections. In this chapter, I provide an overview of this period as background for the empirical work presented in Chapter 3 by describing certain aspects of the economic environment that make this forty year

5 span, an ideal period for testing theories of credit market imperfections. In particular,

the financial system suffered numerous episodes of stress which are unparalleled in

the post-war economy. In addition, the period was marked by sharp and protracted

declines in both prices and output. Finally, anecdotal evidence on the differences in the financing patterns of firins of different sizes, that suggests that small and medium­ sized firms were more susceptible to the effects of deflations and banking crises than very small and very large firms. The remainder of this chapter proceeds as set forth below.

Section 2.2 describes aggregate fluctuations and the changes in the institutional structure of commercial banking during this period. Section 2.3 characterizes the structure of business financing, focusing on the differences between large and small firm financing and on changes in the use and availability of bank credit during this period. Section 2.4 concludes with a discussion of the implications of this historical survey as a basis for the empirical work in the next two chapters.

2.2 Aggregate Fluctuations: 1985 to 1940

In this section I describe the episodes of financial crisis, price deflation and output declines that provide the backdrop for the studies of market imperfections contained in the next two chapters. A clear understanding of these fluctuations requires a fa- mUiarity with the basic workings of the institutional structure of commercial banking during this period. For this reason, I describe separately the aggregate fluctuations which occurred during the National Banking Era and those which occurred under the Federal Reserve System, and include a discussion of the aspects of each that were likely to have affected the strength and character of contemporaneous economic fluctuations.

2.2.1 The National Banking System

Congress inaugurated the National Banking System through the passage of the

National Bank Act of 1863. The Office of the Comptroller of the Currency was estab­ lished to issue national bank charters and set capital requirements. National banks obtained currency (the flrst national currency) by presenting eligible U.S. government bonds. In the event that a national bank failed, its notes could be redeemed at par by the Federal Government.

The banking system during the National Banking Era designated no official lender of last resort. Instead, the clearinghouse system, which was originally designed to deal with the day to day activity of settling checks across banks, became the mechanism through which the system dealt with financial crises. Clearinghouses would act as emergency lenders by allowing banks in financial distress to deposit bills and bonds as security for loans up to 75% of their face value. These loans, which were known as clearinghouse loan certificates constituted a joint liability of the clearinghouse banks and could be used for local settlements (Klebaner, 1990). Clearinghouse loan certificates were issued on six different occasions during the National Banking Era.

Two basic features of the National Banking System contributed to its inherent instability and to the eventual need for restructuring. The first was the reserve structure. The prevailing reserve structure, known as ‘pyramiding’, led rural and reserve city banks (known as correspondent banks) to hold portions of their reserves as deposits in central reserve cities, with the result that a large fraction of the system’s reserves were concentrated in these cities, particularly New York (Bordo, Rappaport and Schwartz (1991)) Since New York banks loaned heavily on the call loan market, the amount of call loans issued by New York banks was virtually equal to the amount of deposits held with them by correspondent banks (Klebaner). In periods of financial stress, the rural banks would withdraw their deposits fiom the New York banks. Since the New York banks did not generally hold excess reserves, they were unable to meet the demands of the country banks without calling in stock market loans and risking the initiation of a sharp decline in asset values and an increase in bankruptcy. In response to the outflow of correspondent deposits. New York banks were often forced to suspend payments, which in turn caused the suspension of payments by county banks, thus transmitting the financial panic. There were five occurrences of payment suspensions during the National Banking Era (Gorton, 1988).

An additional source of instability was the absence of a lender of last resort to increase the supply of notes during times of seasonal increases in the demand for currency or periods of financial stress. Instead, the stock of notes was tied directly to the stock of government debt. James (1978) refers to this problem as the ‘inelasticity of the money stock’.

Friedman and Schwartz (1963) explain that the money stock was inelastic under this system in the sense that it did not allow banks to accommodate a shift in the pubhc’s portfoUo from deposits towards currency during times of financial stress, not that the money stock as a whole should have been expanded or contracted during periods of rising or declining business activity. The fact that there was a fixed supply

^According to James (1978), this reserve structure essentially already existed and was simply reinforced by the National Banking Act, rather than imposed on the system.

8 of currency meant that increases in the demand for currency, per se, put extra pressure

on the banking system by reducing its abüity to extend credit (i.e., the outflow of

currency necessarily implied an outflow of reserves).

2.2.2 Aggregate Fluctuations In the National Banking Era

There were three episodes of panic in the banking system (as defined by Gorton

1988) during the years 1895 to 1914^. These panics occurred in October 1896, October

1907 and August 1914.

There little is written about the panic of 1896. The recession lasted from 1895:4

to 1897:1, so the panic actually occurred rather late in the recession. The panic

of 1896 was not accompanied by a suspension of convertibility and clearinghouse

loan certificates were drawn in anticipation of a deterioration in the conditions in

the banking system, though the certificates were never actually issued for general

circulation (Klebaner). The contraction in prices and real income during the broader

recession was relatively mild. Rrom the peak to the trough of the NBER business

cycle dates, wholesale prices fell 7.92%, while real GNP fell 7.22%^.

The panic of October 1907 occurred amidst a recession which lasted from May

1907 to June 1908. During this period, real GNP fell by 12.5% and wholesale prices

declined by 6.87%. The panic itself was apparently initiated by the unsuccessful

speculative activities of a group of banks and businessmen which resulted in the banks’

requiring assistance from the New York Clearing House. Friedman and Schwartz note that the involvement of the president of New York’s third largest trust company,

^As noted above, the National Banking Era began in 1863, but I limit my discussion to the years which correspond to the data used in Chapters 2 and 3. ^Wholesale changes are computed from the BLS’s quarterly Wholesale Price Index (WPI), and real GNP changes are calculated using the quarterly series reported in Gordon (1986). Knickerbocker Trust, exacerbated the situation. The National Bank of Commerce

denied Knickerbocker clearing privileges and the New York Clearing House likewise

denied the trust company. The situation in New York led to the withdrawal of the

correspondent deposits of the rural banks. New York banks restricted convertibility

of deposits into currency on October 26, and clearing houses issued loan certificates.

Soon after, the suspension of convertibifity spread nationwide (Klebaner).

According to Klebaner, 2.22% of total commercial deposits were suspended in

1907; this was six times the amount that were suspended during the period 1900 to

1906. The panic of 1907 was accompanied by a stock market crash which yielded

a monthly return for October of -10.9%, which Wilson, SyUa and Jones (1990) note

was the third largest negative return in the period 1834 to 1908.

Friedman and Schwartz refer to the banking system’s suspension of payments as

‘a refusal by the banking system to convert deposits into currency or specie at the

request of depositors’'*. They also discuss the possibihty that one reason for the difference in the severity of the Great Depression relative to the recession of 1907 was that the suspension of payments, which was in some sense an inherent feature of the

national banking system, probably precluded the widespread collapse of the banking system and a subsequent decline in the money supply.

The remainder of the period, from 1908 to 1914, was characterized by relative stability, with the exception of the panic of August 1914 (Gorton). The passage of the Aldrich-Vreeland Act of 1908, which was designed to stop the widespread suspension

■‘Friedman and Schwartz, p 167.

10 of convertibility and to promote interim stability until a larger-scale banking reform could take place, laid the ground work for the passage of the Federal Reserve Act of

1913 (Studenski and Kroos).

2.2.3 The Federal Reserve System

The Federal Reserve System was established in an attempt to address some of the major difficulties of the National Banking System. These deficiencies were viewed as the fixed supply of currency, the pyramiding of reserves, and costly and slow check clearing. The Federal Reserve Act allowed centralized control over the discount rate and the gold stock, and created a lender of last resort for banks by allowing them to transfer certain securities, referred to as ‘eligible paper’, to the district bank as collateral for the extension of credit.

The purpose of the system was three-fold. First, it was to accommodate the demand for credit for productive uses in agriculture, industry and trade. To this end, the only eUgible assets were "notes, drafts, and bills of exchange arising out of commercial transactions; that is, notes, drafts and bills of exchange issued or drawn for agricultural, industrial and commercial purposes, or the proceeds of which have been used or are to be used for such purposes"^. In other words, short-term securities used for commercial transactions, and not for speculative purposes, or for the financing of long-term capital®.

® Chandler, 1971 p 9. ® These types of short-term loans were often referred to as ‘self-liquidating’ loans.

11 Second, the system sought to promote adherence to the gold-standard, and third,

to prevent panics and alleviate those which did occur. During the 1920s, there was

a growing recognition by some that the first two features of the system significantly

hindered its abihty to achieve the third (Kroos and Blyn).

Maintenance of the gold standard implied toleration for fluctuations in the do­

mestic money stock and price level in response to movements in the gold stock and

was increasingly acknowledged as the precipitant of large output and price fluctua­

tions. In addition, the inelasticity of the currency was not fully eliminated because

rediscounting and note issue were linked to the stock of commercial paper and gold

under the provisions of the Act (Studenski and Kroos).

The problems associated with the real bills doctrine were twofold. First, Klebaner

notes that in the early years of the system it simply could not be enforced. He

estimates that only one-third of all bank loans were for commercial purposes in 1916.

Approximately one-half were used for investment purposes and two-thirds for the

financing of fixed and capital, rather than for purposes such as the purchase of raw

materials, payroll or the production of goods already sold.

If some viewed the real bills doctrine as merely unenforceable, others viewed it as

a major hindrance to the Fed’s ability to implement expansionary during periods of

financial crisis. Chandler critiques the claims made by Reserve Bank officials in 1929 that the eUgibility requirements were not overly restrictive, in the sense that banks

had an adequate supply of eligible assets (assets which allowed them to secure loans

from the Federal Reserve). He suggests that banks’ supplies of eligible assets were not only not inadequate to allow banks to extend credit, they not even sufficient to keep banks from contracting their portfolios or from shutting down completely.

12 He further suggests that policy makers’ views stemmed from the fact that they premised their analysis upon a review of aggregate ratios of eligible assets to total borrowings from the Federal Reserve to gauge the adequacy of the supply of eligible assets. As long as these ratios were low, policy makers’ concluded that eligibility requirements were not exerting contractionary pressure on banks^.

Chandler relies, instead, upon evidence on the ratios of eligible assets to total loans and investments of member banks (seen as an overstated measure of the ratio of eligible assets to deposits), and then further refines the data by distinguishing between the various classes of banks. He finds that while the ratio of eligible assets to total loans and investments was 21% for member banks as a whole, 1.2% held no eligible assets and almost 30% held ehgible assets that amounted to less then 10% of their total loans and investments. With ratios this low, withdrawals would likely force these banks to either borrow from the Fed and deplete their supply of eligible assets. At a m inim um , they would prompt banks to liquidate loans to strengthen their liquidity.

In addition, the distribution of eligible assets across banks was very uneven. While only 15% of central reserve city banks held eligible assets of less than 10% of their loans and investments, more than 17% and 22% of reserve city and rural banks, respectively, occupied this extended position. Finally, Chandler cites evidence that the relevant ratios differed dramatically from region to region; the Philadelphia and

Cleveland districts each had over 46% of its banks holding eligible assets of less than

10% of loans and investments, while the Dallas and Kansas City districts had only

2.4% and 1.1% of its banks with ratios that low.

^Throughout the first four years of the depression, borrowings from the Fed never exceeded 10% of the member banks eligible assets.

13 While the establishment of the Federal Reserve System may have laid the foun­ dation for a more stable financial environment, there were clear deficiencies in its conduct of monetary poUcy that either aggravated, or failed to prevent, large price level and output fluctuations and periods of financial panic. The above discussion suggests that adherence to poUcies such as the gold standard and the real bills doc­ trine, as well as basic inexperience, led to the diflSculties experienced by the banking system and the US economy as a whole during the early years of the Federal Reserve

System. The following section is a description of the fluctuations which occurred during those early years.

2.2.4 Aggregate Fluctuations Under the Early Years of the Federal Reserve System

The establishment of the Federal Reserve System was accompanied by the start of the First World War. With the war came economic controls and easy credit pohcies that led to the war-time inflation (Studenski and Kroos). Between the 1915:1 and

1920:2, the cumulative increase in wholesale prices was 89%.

The war was followed by the most severe deflation to occur in the post-Civil

War period up to that time. The NBER cites the beginning of the contraction as

January 1920, though industrial production, manufacturing, employment and payrolls declined only sUghtly from January to September. 'Wholesale prices, which rose until

May 1920, had declined by 56% by June 1921, with over three-quarters of the decline occurring between August 1920 and February 1921 (Friedman and Schwartz). Real

GNP declined by 14.58% from peak to trough. Despite the severity of the recession and deflation, this downturn was relatively short, particularly in comparison to the

Great Depression, and was not accompanied by a financial crisis.

14 The Great Depression of 1929 to 1933 is unrivaled by any other recession in terms of its length and severity. By the end of the downturn, unemployment had risen to 25% (and stayed above 14% throughout the 1930s). The cumulative decrease in real GNP over the course of the downturn was 45%, and the cumulative decline in wholesale prices was 46.6%. Real income did not return again to the level of 1929 until 1937 (Kroos and Blynn)®.

The first banking crisis, which was accompanied by an increase in the deposits of failed banks firom approximately $25 million to $400 million, occurred in October

1930. The crisis was characterized by a struggle for fiquidity which led to massive withdrawals of currency from the banking system and sharp increases in the currency to deposit and excess reserve ratios (Friedman and Schwartz).

Mishkin (1991) documents a sharp increase in the spread between the rate on government securities and the rate on lower grade bonds, reflecting the fact that banks were selling their holdings of lower grade bonds in order to gain liquidity.

Friedm an and Schwartz note that the decline in bond prices probably contributed to the future banking crises by reducing the market value of banks portfolios and hence bank capital.

®The first major financial shock of Depression was the stock market crash of October 1929, and while the crash was associated with an increase in the rate of economic decline, it did not initiate a widespread financial panic Friedman and Schwartz suggest that this was probably due, in part, to the prompt action of the Federal Reserve Bank of New York in supplying reserves and the willingness on the part of the New York banks to extend credit to brokers and dealers.

15 The second banking crisis occurred in March 1931. The rise in the currency to deposit and excess reserve ratios was even more severe in this panic. Interest rate spreads again rose dramatically, in part because of the increased supply of low grade bonds and in part because the yields on government bonds were falling rapidly during this period®.

There were large scale open market purchases in April 1932 that were initiated to offset gold outflows but that eventually served to reduce bank failures by provid­ ing the necessary liquidity to the system. Also in 1932, the Reconstruction Finance

Corporation (RFC) was established to extend emergency assistance to financial in­ stitutions and railroads. Kroos and Blyn note, however, that there were limits to the

RFC’s effectiveness, as it could only make loans against certain classes of assets and it was forced to pubhsh the names of the institutions it was helping. The Glass-Steigall

Act, passed in February 1932, gave district Reserve Banks the right to make loans on a wider range of assets than had been provided for in the Federal Reserve Act.

Chandler questions whether the easy-money policies of 1932 were expansionary enough to relieve the pressure on the banking system^®. He cites two factors suggesting that they were not. First, he notes that while the reserve positions of member banks had increased considerably from early to mid 1932, the mid-1932 positions were still below those which had prevailed in September 1931, following the second banking crisis and preceding the expansionary actions of 1932. His claim is that since the

®The reductions in the yields on government bonds probably reflected both their liquidity on the open market and their eligibility for rediscount. Chandler breaks the year 1932 into two subperiods, each of which correspond to different policies on the part of the Federal Reserve. First, from February to June, gold outflows continued, while the Federal Reserve engaged in expansionary open market operations and the district banks lowered their discount rates. In the second half of the year there was a reversal in the gold outflows, leading to a $585 m illion increase in the US gold stock. During this period, the Federal Reserve did not change rates and engaged in virtually no open market operations.

16 September 1931 positions were not strong enough to oflfeet the decline and induce banks to begin making loans again, then it is likely that the positions in June 1932

(when the Fed ceased its expansionary policy) were also too weak.

The second factor he points to is the reserve positions of individual banks within the system. Even if you could view reserves in the aggregate and conclude that there was no longer any contractionary pressure, large difiEerences in reserves from bank to bank suggest otherwise. In particular he notes that while central and reserve city banks in New York and Chicago were holding significant excess reserves, reserve city banks in other regions, as well as country banks, were deeply indebted to the Fed and h old in g only small quantities of excess reserves. For these banks at least, there was still strong contractionary pressure. This contractionary pressure is reflected in the fact that bank lo a n s and investments continued to decline, albeit at a slower rate, throughout 1932.

In December 1932, the rate of bank failures again began to increase. In early 1932, bank holidays were declared in Louisiana, Michigan and Maryland, with other states imposing restrictions on withdrawals, but not declaring holidays. The Fed engaged in expansionary operations and member banks’ borrowings increased dramatically, but the reserve positions of banks continued to worsen throughout January and February.

Finally, at the urging of the directors of the Federal Reserve Bank of New York and the Board of Governors, a bank holiday was declared. This hohday closed all banks across the country from March 6 through March 9, and suspended gold redemption and gold shipments abroad. The holiday was followed by the passage of the Banking

Acts of 1933 and 1935, which, among other things, established deposit insurance, set limits on the interest rates that commercial banks could pay on time deposits and

17 centralized the control of the Federal Reserve System with the Board of Governors in

Washington, D.C.. Presumably this legislation, which was designed to stabilize the banking system, as well as the fiscal pohcies implemented under the New Deal and the start of WWn, facüitated the slow upturn in economic activity that began in 1933.

The mechanisms by which the economy recovered from the Depression, however, are beyond the scope of this essay.

Against the backdrop of the instabihty of the financial system and the severity of the aggregate fluctuations during this period, the remainder of this chapter provides anecdotal evidence of aspects of firm financing patterns and changes in the availabihty of bank credit during the depression supports the view that certain firms, mainly small and medium-sized ones, were more severely affected by episodes of financial crisis and deflation.

2.3 Business Financing and the Use and Availability of Bank Credit

This section describes two aspects of business financing during this period as back­ ground for the empirical work presented in Chapter 3. First, it describes differences in the structure of financing across large and small firms. Second, it provides a brief discussion of the changes in the use and availability of bank credit during the Great

Depression^ ^.

While the analysis of Chapter 3 is not lim ite d to the period of the Great Depression, the bulk of the data on firm financing and the availability of bank credit pertain to this period. For this reason, I focus on the years surrounding the Depression in the hope that the results indicate a relationship which applies to other periods of instability in the banking system.

18 2.3.1 Business Financing and the Use of Bank Credit

The size distribution of firms in the first half of this century was heavily skewed toward small firms. Of the 3.4 million firms in existence in 1939, close to I million were sole-proprietorships. Jacoby and Saulnier (1947) estimate that 90% of manufacturing firms, 93.7% of wholesale trade firms and 94.5% of retail trade firms had assets under

$250,000, while .8% of manufacturing, .2% of wholesale trade and no retail trade firms had assets over $5 million.

As in the current period, the financing patterns difiered across firms of different size^^. First, Jacoby and Saulnier report that very small firms and large firms tended to finance more heavily out of net-worth than did small and medium sized firms.

The former did so presumably because they had difficulty gaining access to external markets, and the latter did so because of their ability to issue equity. Goldsmith

(1958) indicates that unincorporated enterprises used internal financing (defined as retained earnings or net investment by proprietors) twice as often as external.

Merwin describes the breakdown of leverage by size in 1939^^. For firms with assets of $10,000 to $25,000, the proportion of debt is relatively small. Those firms in the $100,000 to $250,000 asset range hold the highest proportion of debt, and as asset size increases beyond $250,000, the proportion of debt again declines.

^^Most of the information on the financing of large and small firms reported in this section is taken from two NBER studies on the financing of small and large corporations. It should be noted that the failure data used in the next chapter consists primarily of very small firms that were likely to have been sole-proprietorships rather than corporations. Unfortunately, the data on the financing patterns of firms of this size is relatively sparse. The financing information of small corporations is useful as a benchmark, however, because something is known about the direction of the differences between the financing behavior of small and very small firms. *®This information is reported in Merwin, p 144, footnote 11, but was originally obtained from an unpublished NBER study of Industrial and Commercial Debt in 1939.

19 Small Firm Financing

Small corporations during the period from 1926 to 1936 financed the preponder­ ance of their assets through short-term debt". A look across the period, however, reveals a change in the composition of this debt. Specifically, there was a trend away from bank financing and toward the use of trade credit (defined as credit extended by nonfinancial companies, consisting primarily of accounts payable). Merwin compares the proportion of total assets financed through notes payable and accounts payable in the years 1926-28 with that financed through these forms in the years 1934-36. Over this period, the small firms in each of five manufacturing industries increased the proportion of total assets financed through accounts payable, while the proportion of assets financed through notes payable rose in only two of the industries, stayed the same in one and decreased in two.

Goldsmith’s description of the use of financial intermediaries by unincorporated businesses reinforces the findings of Merwin’s survey. Specifically, Goldsmith notes that a minority of the short-term financing used by unincorporated businesses was provided by banks (though banks provided more than any other financial intermedi­ ary) during the period 1920 to 1940, and that this fact can be attributed to a marked shift away from bank credit toward trade credit during the 1929 to 1939 period.

Another distinguishing feature of small firms during this period is that they tended to have lower profit rates, a lower probability of success and less of an ability to

^‘‘The information in this section is taken primarily from Financing Small Corporations in Five Manufacturing Industries, 1926-36, by Charles Merwin (1942). Merwin defines small as a firm with assets of less than $250,000. It is interesting to note that the increase in the number of ‘large’ bankruptcies described in the quote at the opening of this chapter is in reference to failures of firms with liabilities in excess of $100,000. These large failures were not large firms by comparison to the economy-wide size distribution of firms. I will return to this point in Chapter 3, when I characterize the effects of bank failures and deflations on the size of a failed firm.

20 withstand deflationary conditions than did large firms^^. During upturns, the small companies in the study tended to finance the accumulation of fixed and current as­ sets, as well as the payment of cash dividends, with borrowings and net income.

During downturns, they tended to sell current and fixed assets to finance operations and disburse dividends. These patterns have two basic implications. First, there was essentially no net accumulation of fixed assets over the sample period studied.

Second, the current items on the balance sheet tended to be cyclically sensitive; dur­ ing recessions inventories and receivables were liquidated to pay off^ current liabilities and to finance operations. In extreme deflationary conditions, fixed assets were also liquidated.

These patterns imply that since small firms tended to finance heavily out of current obligations, they were unable to accumulate cash balances over the course of the downturn and thus were reliant on banks (or trade credit) during the early stages of the upturn. I discuss below that cash management practices of large firms were quite difierent and worked in a way that decreased their rehance on bank credit.

Large Firm Financing

Large firms used short-term debt in much smaller proportion than small firms during this period^®. In manufacturing, for example, current obligations accounted for 19% of assets when all firms are considered, but for only 9% when large firms

^®Merwin’s study kept track of the discontinuances of the firms used in the sample. ^®The information in this section is largely drawn from The Financing of Large Corporations 1920-39 by Albert Koch (1943). In this study, a large firm is one with assets in excess of $10 million.

21 alone are considered. In addition, large trade firms used short-term debt in higher proportion than large manufacturing firms, though within trade, the proportion of short-term financing still declined with size^^.

Koch’s study finds that both large manufacturing and trade firms reduced their use of short-term instruments to finance both fixed and current assets over the period

1920 to 1939. In manufacturing, the reduction occurred gradually over the 1920s, and then sharply during the Depression, while for trade there was no obvious tendency to reduce short-term financing until the advent of the Depression. For both sectors, the decline which occurred during the depression was followed by an increase firom 1933 to 1939. Elssentially, the bulk of the reduction which occurred over the whole sample can be attributed to the depression.

There was a change in the composition of short-term financing in large indus­ tries similar to the one that occurred in small industries. Specifically, notes payable declined as a portion of total shorL-ierm habihties, accounts payable remained approx­ imately the same, and other current habihties accounted for an increasing proportion over the sample^®. In 1920, notes payable were 42% of current obhgations in manufac­ turing industries, and 61% in trade industries. By 1939, these percentages had fallen to 6% and 5%, respectively. Koch notes that essentially all of the increase in bank borrowing over the sample can be attributed to small and medium-sized companies.

As indicated above, the cash management practices of large firms differed substan­ tially from those of small firms, and in way that suggest large firms were less rehant on bank credit. Since large companies typically had a smaller fraction of their assets

^^Koch notes that large trade firms actually used a higher proportion of trade debt than did small- or medium-sized trade firms. Other current liabilities are largely comprised of obligations to employees, bondholders, stock­ holders and government.

22 financed through current debt and therefore were able to meet those obhgations with relative ease, they tended to accumulate large cash balances during downturns rather than disburse unearned dividends or retire long-term debt. Thus, in the early stages of upturns these firms would transform cash into inventories or other current assets rather than seek external financing. Jacoby and Saulnier suggest that an impfica- tion of this is that while small firms maintained their dependence on banks over the course of the cycle, the structure of large firm financing, and in particular, their cash management practices, allowed them greater independence firom bank financing

2.3.2 The Availability of Bank Credit

The above description of the financing patterns of both small and large firms suggested a movement away firom the use of bank financing during the Depression years. For small firms, this movement was accompanied by a movement toward the use of trade credit. For larger firms, this shift was generally towards equity issuance or the use of retained earnings. This section will discuss anecdotal evidence for the hypothesis that these changes in the composition of financing were in part driven by contractionary shifts in the supply of bank credit, and that these shifts were more important for small and medium-sized firms than for large firms.

Merwin ofiers two potential explanations for the portfolio shifts which occurred during the Depression. First, inventory values declined with the deflation of 1929 to

1933, and with that, the need for short-term financing. Second, the choice to retire bank loans in larger proportion than trade debt may have been due to increased competition among large raw materials suppliers, which presumably bid smaller firms

^^See Hunter (1982) for a discussion of the differential cash management practices of small and large firms during the Depression.

23 away from banks. Chandler suggests that an alternative, or additional, reason for the portfolio shift may have been a decreased willingness or ability on the part of banks to make loans.

Two factors suggest the inadequacy of the former two explanations. The first is the differential behavior of small and large firms. Small firms did not shift away from short-term financing over the period, though large firms did. If declines in inventory values reduced the need for short-term financing for large firms, it did not do so for small firms. Also, while both and large and small firms shifted from bank financing toward trade credit, the latter did so in much larger proportion. However, the increase in trade credit granted to small firms does not seem to have been accompanied by a reduction in the terms on which the credit was offered. Merwin presents evidence that the terms were the same or tighter than those offered in the 1920s^°. It seems likely then that both small and large firms shifted away from the use of bank credit, per se. Large firms shifted toward financing out of cash balances or equity, and small firms, as a result of a lack of alternatives, shifted toward costly, but available, trade credit.

That the shift in the composition of liabilities reflects a decline in the availability of bank credit is consistent with the results of a survey conducted by the National

Industrial Conference Board in 1932. Of the 62% of the respondents who defined themselves as dependent on bank credit to some degree, 22% were either refused credit or issued credit under much tighter terms than they had previously experienced. Of this 22%, approximately 82% were classified as small firms (with small defined either by the size of the capital stock or the number of employees). Chandler notes that 23%

^°See Merwin, p 74.

24 of the credit denials were based not on the financial health of the firm, but rather the health of the bank. Another 43% were a result of bank policies that stated that credit could not be extended if doing so would potentially put the bank in the position of having to borrow firom the Federal Reserve.

Chandler describes two additional studies which suggest a decline in the availabil­ ity of bank credit. The first is a survey of firms in the Chicago Federal Reserve District in 1934 that finds that banks in that district became less willing to make working capital loans (as opposed to self-liquidating loans). As discussed above, while the practice of making only self-hquidating loans was consistent with the stated policy of the Federal Reserve, it was not generally implemented. The second study was undertaken by the Bureau of the Census and finds that of the 45% of the firms which reported having difficulty obtaining credit, 20% were ‘creditworthy’ by standards of the usual financial ratios and credit rating agencies.

2.4 Conclusion

In the time period described in this chapter the banking system suffered multiple incidents of stress, and one incident of virtual collapse. Due to the structure of reserves under the National Banking System and the inadequacies of the policies implemented during the Great Depression, the difficulties in the banking system were not simply regional fiare-ups, but instead were a national phenomenon. In addition to the instability of the banking system, there were severe price defiations and deep, protracted downturns in economic activity. Finally, patterns of financing across firms and anecdotal evidence on the changes in the availability of bank credit during the

Depression suggest that small and medium-sized firms were the primary users of bank

25 credit, and as such, they were more susceptible to the effects of balance sheet effects of deflations and to declines in the availability of bank credit. If theories of credit market imperfections are an important part of the transmission ofnom inal shocks to the real economy, there should be evidence of them during this period.

26 CHAPTER 3

BUSINESS FAILURES AND CREDIT MARKET IMPERFECTIONS: DEBT-DEFLATION AND BANK PANICS IN THE PRE-WWH U.S.

3.1 Introduction

In the closing years of the nineteenth century and the first forty of this century, the United States economy experienced five episodes of sustained price deflation, six banking panics, one so severe as to induce a government-mandated ‘bank holiday’,

and output declines of a magnitude and duration unrivaled by downturns in the post­ war period. The strength and character of aggregate fluctuations, coupled with the widespread instability of the flnancial system, make this a natural period for testing models of monetary transmission based on factors such as the condition of borrowers’ balance sheets and the relative availability of bank credit. In this chapter, I use data on business failures from 1895 to 1935, by industry, to explore the evidence for these alternatives to the standard IS/LM ‘liquidity effect’ model of monetary transmission.

By impUcitly assuming that all firms face the same interest rate, the standard model ignores the effect of financial structure on the firm’s cost of financing. But when borrowers have more information than lenders, the price of external funds will depend on both a firm’s coUateralizable net worth and its dependence on bank credit.

27 If monetary shocks lower net worth or reduce bank loan supply relative to other forms of credit, they will raise the price of external funds for firms which rely on collateral and intermediation to overcome problems of asymmetric information. Since the increase in the cost of financing for these firms is unrelated to the inherent riskiness of their proposed projects, monetary policy will have distributional consequences that result in production inefficiency.

One imphcation of the difierential impact of monetary contractions on the cost of financing across firms is that they will raise the relative probability of default for credit constrained firms. 1 explore this imphcation empiricaUy by using the number and habihties of business failures in 28 manufacturing and trade industries over the period

1895 to 1935. 1 exploit the substantial variabUity in business cycle fluctuations over this period, along with the within- and across-industry variation in failures, to answer two primary questions. First, what is the relative abihty of output, defiations and bank failures to explain the variation in business failures? Second, what factors aSect the size of failures? The answer to each of these questions bears on the fundamental issue of whether monetary pohcy has distributional consequences which suggest an associated loss of productive efficiency.

1 find that movements in the number of failures are better explained by the vari­ ation in aggregate prices and bank failures than by changes in real output. 1 also find that trade industries were more adversely affected by deflations. A preliminary ranking of the industries by their use of bank credit reveals that banking crises led to larger increases in failure rates for industries which made the most use of bank financ­ ing (mainly manufacturing industries). Finally, while cychcal downturns tended to decrease the size of failures in trade industries and had little effect on size in manufac­

28 turing industries, periods of deflation and bank failure were accompanied by increases in the size of failed flnns within an industry. This result suggests that moderately sized, more highly leveraged firms had a greater difficully withstanding deflation and banking crises than did smaller, internally financed firms.

The chapter will proceed as follows. Section 3.2 provides an overview of the data in the context of the period under study. Section 3.3 is a brief discussion of the credit channel of monetary transmission and its impUcatious for business failures. In

Section 3.4,1 describe the empirical tests of these implications and present the results.

Section 3.5 concludes and discusses directions for further research.

29 3.2 The Data

The data used in this study is drawn from Dun's Review magazine for the period 1895 to 1935^ Although Dun's reported failure data before and after these years, this is the longest period over which they reported failures by industry without dramatically changing the definition of the industries. The data consist of monthly observations on the number and the sum of the liabilities of failed businesses in 14 manufacturing and 14 trade industries. To properly interpret the behavior of these series, it is important to understand precisely what they measure. First, the reported liabilities include only current liabilities and mortgage debt and do not include any publicly held long-term obligations. This omission is unlikely to create any important bias, however, since the vast majority of the firms which failed during this period had issued little or no publicly-held debt^.

Second, a firm was classified as failed once it ‘ceased operations following assign­ ment or bankruptcy; ceased with losses to creditors after such actions as execution, foreclosure, or attachment; voluntarily withdrew leaving unpaid obligations; was in­ volved in court actions such as receivership, reorganization, or arrangement; or volun­ tarily compromised with creditors out of court' This description suggests that the lag between the firm's experience of financial difficulty and its appearance as a failure in the Dun's data may vary considerably depending on the circumstances of the fail­ ure'*. Last, the industry classifications used in Dun's do not correspond closely to the

'All data are described in Appendix B of the chapter. ^Private Long-Term Debt and Interest in the United States,(Kuvin (1936)). ^Business Cycle Indicators (1961) Vol. II, pg. 19. ■‘Reading through Dim’s Review reveals just that. I found evidence of lags ranging from one or two months to one year.

30 classifications used in either the Census of Manufactures or the Census of Business.

This makes it diflScult to detrend the series with data from other sources.

Figures 3.1 plots the quarterly number and average real liabilities of failures across all industries from 1895 to 1935. The vertical lines are the NBER dates of business cycle troughs. While both series exhibit cyclicality, that of the average liabilities series is more pronounced. Particularly noteworthy is the tendency of average lia- bihties to peak prior to the end of a recession. By contrast, the number of failures fluctuates around a strong upward trend, presumably reflecting increases in the total number of firms in the economy over this period. In fact, when this series is scaled by linear interpolation of the annual total number of firms in existence, it appears to be stationary with no trend®.

Table 3.1 lists the mean liabilities and number of failures for each industry®. There is substantial variation across industries in both the habihties and number of failures, and there is roughly an inverse relationship between the average habihties of a failure and the number of failures. The average habihties range in size from as small as

$6,000 in the grocery industry (averaging almost 700 failures per quarter) to as large as $100,000 in the cottons industry (which averaged only 6 failures per quarter). It is important to note however, that all of these are, in general, very small firms^. As a point of reference, an NBER study of business financing over this period classifies

®It is possible to detrend the aggregate number of failures because Dun's reports annual total number of all firms in manufacturing and trade over this period. Unfortunately, they report no data at a less aggregated level. ®The mean is computed over the entire sample. ^It might be most appropriate to measure the size of a firm by some characteristic that does not change with prices, such as capital stock or employees. For the purposes of this chapter, however, liabilities will have to be a proxy for size.

31 (a) Aggregate Number of Failures

7753

6753

5753

2753

1895 1900 1905 1910 1920 Year

(b) Aggregate Real Average Liabilities of Failures

27262

24262

21262

18262

15262

1895 1900 1905 1910 1915 1920 1930 1935 Year

Figure 3.1: Business Failures: 1895 to 1935

32 fixms with assets of less than $50,000 as 'very small’. Of the 14 manufacturing

industries reported in Dun’s, eight had average liabilities of less than $50,000. All of

the trade industries were below this amount and most were below $20,000.

Figure 3.2 plots industrial production, prices and the real liabilities of suspended

banks per $1000 of all bank liabilities (note that because of the bank holiday declared

in 1933.1, the bank failure series extends only until 1932.4)®. Though movements in

the price level are dominated by the war-time inflation and the severe deflations of

the early 1920s and the Great Depression, there are three other periods in this sample

during which prices fell for four or more quarters: 1895:3 to 1896:3, 1907:2 to 1908:1

and 1925:4 to 1927:3®. Similarly, the liabilities of failed banks series is dominated by

the variation which occurs during the Depression. But there are a number of other episodes in which the banking system appears to have experienced financial stress.

These include 1896:3 to 1896:4, 1899:4 to 1900:1, 1903:4, 1907:4 to 1908:2, 1914:5,

1921:75, 1923:4 to 1924:1 and 1926:4 to 1927:1. Gorton (1988) identifies 1896, 1907

and 1914 panic dates during the National Banking Era, and 1930, 1931 and 1933 as panics under the Federal Reserve System. As was the practice under the National

Banking System, the panics of 1907 and 1914 were accompanied by a suspension of convertibility of deposits into currency and the issuance of clearinghouse loan certifi­ cates. In 1896, clearinghouse loan certificates were written, but not issued^®. The panic of 1933 culminated in the 'banking holiday’ declared in March of that year.

^An alternative to the liabilities of failed banks is the number. Both series are used in the estimation, though only the results of the liabilities estimation are reported in this chapter. ®These deflations are dated using the NBER’s Index of the General Price Level ratio spliced to the Consumer Price Index (described in the appendix). Presumably these dates are somewhat sensitive to the choice of series. '°See Klebaner (1990) and James (1978) for discussions of the clearinghouse system and other aspects of the structure of commercial banking under the National Banking Era.

33 Industry Mean Mean Liabilities Number Manufacturing Iron, Foundries and Nails 68542 22 Machinery and Tools 54914 64 Woolens, Carpets and Knit Goods 59129 9 Cottons, Lace and Hosiery 99033 6 Lumber, Carpenters and Coopers 36324 119 Clothing and Millinery 15324 125 Hats, Gloves and Furs 18281 25 Chemicals and Drugs 42397 15 Printing and Engraving 20237 45 Milling and Bakers 11488 76 Leather, Shoes and Harness 26553 32 Liquors and Tobacco 28859 26 Glass, Earthenware and Brick 41736 23 All Other Manufacturing 28312 384 Trade General Stores 10104 335 Groceries, Meat and Fish 6325 679 Hotels and Restaurants 13355 159 Liquors and Tobacco 7445 155 Clothing and Furnishing 10574 325 Dry Goods and Carpets 18340 186 Shoes, Rubbers and Trunks 9376 116 Furniture and Crockery 13473 91 Hardware, Stoves and Tools 13145 94 Chemicals and Drugs 7805 127 Jewelry and Clocks 12843 73 Books and chapters 9779 25 Hats, Furs and Gloves 15595 20 All Other Tirade 15319 462

Note: Entries arc the mean of quarterly observations over the entire sample period. Liabilities arc nominal.

Table 3.1: Business Failures: 1895:1935

34 Co) Industrial Production

(b) Price Level

(c) Real Liobilities of Suspended Banks per $1,000 Liabilities of All Banks

Figure 3.2: Aggregate Variables: 1895 to 1935

35 Consideration of the failures presented in Figure 3.1 in the context of the aggregate series shown in Figure 3.2 suggests that some role may exist for each of these vari­ ables in explaining both the number and liabilities of firm failures. If credit-market imperfections have important implications for the incidence of business defaults, such implications should be evidenced by the behavior of failures during periods of severe defiation and near collapse of the financial system. The remainder of the chapter will describe the theoretical justification for these impUcations and examine the extent to which they are home out in the data.

36 3.3 Business EEÛlures and Alternative Views of the Monetary Transmission Mechanism

The standard view of the monetary-transmission mechanism holds that in the absence of immediate price adjustment, a nominal shock such as a decline in bank reserves will have real effects as interest rates rise to compensate people for holding a less liquid portfolio. In this section, I briefly discuss two alternatives to this mechanism, one that involves an explicit role for banks and bank-dependent customers

(referred to as the bank credit channel), and another that focuses on the changes in the condition of borrowers’ balance sheets (termed the broad credit channel). Each of these impUes that credit constrained flrms will experience a disproportionate increase in the price of external funds during downturns and therefore an increased likelihood of default.

The bank credit channel relaxes the implicit assumption of the standard transmis­ sion mechanism that bonds and intermediated loans are perfect substitutes. In the framework of an augmented IS/LM model such as the one presented in Bemanke and

Blinder (1988), relaxation of this assumption imphes that the IS curve is a function of loan supply and so monetary policy shifts both the LM and IS curves. Unless either loan demand or loan supply are perfectly elastic (firms view loans and bonds as per­ fect substitutes in the former case, and banks do in the latter) policy actions which contract bank UabUities must also contract bank assets. The reduction in bank loan supply raises the interest rate on bank loans relative to open market credit and forces a reduction in the spending of bank dependent customers^ This mechanism also

“ Bernanke and Blinder point out that there are two ways in which policy actions can affect output that involve a more complicated mechanism than the standard channel. The first of these is the one described above and the second is essentially some form of loan rationing. Loan rationing is not a necessary condition for the bank credit channel.

37 allows for shocks to credit supply which occur independently of deliberate monetary policy actions. For example, Bemanke (1983) argues that waves of bank failures and the debt crisis which resulted from the large-scale accumulation of debt in the 1920’s led to reductions in loan supply and a leftward shift in the IS curve during the Great

Depression.

There is debate over the extent to which the conditions required for the lending channel hold in the contemporary economy. The debate centers around two points.

First, firms must be dependent on banks and not just on intermediaries in general.

Second, the banking system (or a particular bank, if individual bank-firm relationships are important) must not be able to ofifeet the contraction in deposits by issuing CDs or reducing its securities holdings^^. Kashyap and Stein (1994) describe the extent to which these concerns have been addressed in recent literature.

It is more likely that the conditions described above were present during the time period considered here. Miron, Romer and Weil (1993) conclude that the composition of both bank and firm balance sheets, as well as the structure of reserve requirements, suggest that the bank credit channel was likely to have been more important in the pre-WWII era than after (although their empirical results find little support for this channel in either era). In addition, capital markets were substantially less integrated in the first half of this century and commercial banking was dominated by small banks. Kroos and Blyn (1971) note that, in 1915, 75% of the commercial banks in operation were state or private banks with average assets of less than $650,000

($10 million in 1995 dollars). Kashyap and Stein (1994b) point out that, during a

^^Cecchetti (1995) points out that all that is actually necessary here is for a complimentarity to exist between outside money and loans and that the loans do not have to be the assets of deposit taking institutions. For the institutional environment considered in this chapter, however, the conditions as they are stated above should suffice.

38 contraction, smaller banks should have the same difficulty accessing credit markets as smaller nonbank firms are hypothesized to have (this is essentially an application of the broad credit channel, which is discussed below, to banks). As long as customers are somewhat dependent on the individual banks with which they have developed relationships, the bank credit channel will run through smaller banks^^. This aspect of the bank lending channel emphasized by Kashyap and Stein was likely to have been important during the period under study^**.

The broad credit channel is based on the importance of the condition of bor­ rowers’ balance sheets and the structure of financing in the overall economy. When information is asymmetric, the price of external funds for a marginal investment (or expenditure), holding constant the value of the loan, is aflfected by the potential borrower’s coUateralizable net worth. Shocks such as a decline in bank reserves or unanticipated price declines (or reductions in the rate of inflation), will reduce bor­ rower net worth, and raise the price of external funds relative to internal funds (the price diSerential is referred to as the agency cost of the loan). This reduces the desired level of investment for liquidity-constrained flrms.

Bemanke and Gertler (1989) present a model in which it is costly for lenders to verify that the borrower is truthfully reporting the outcome of the project for which the funds were borrowed. The agency costs of lending in this model vary inversely

one important function of intermediation is to overcome the free-rider problem associated with the production of information by an institution separate from the one granting credit, it is reasonable to think that individual bank-firm relationships matter. See, for example, Peterson and Rajan (1994). ^■‘Chandler (1971) claims that it is likely that bank loan supply decreased during this time as a result of the increase in the perceived riskiness of business loans and a reduction in the preference for risk on the part of bankers. In addition, a National Industrial Conference Board Survey (1932) indicates that about 20% of businesses which had previously experienced no difficulty obtaining bank credit were refused or restricted in their access to credit during the depression. About 80% of these were small firms.

39 with net worth because the borrower’s incentive to misreport his project return and underpay the lender rises as his net worth (the amount he will lose if he lies and is caught) declines. This model has two important implications. First, the procyclicality of net worth and the endogenous response of agency costs introduce a propagation mechanism which Gilchrist, Bemanke and Gertler (1994) refer to as the financial accelerator. This accelerator works through the induced reductions in the spending of those firms for which agency costs rise most significantly; small and low net worth firms^®. Second, exogenous shocks to net worth, such as deflation, can have real eflects. If asymmetric information implies that balance sheets matter, then (if debt contracts are not indexed) inflation that is lower than expected causes more than just a mere redistribution from debtors to creditors. It induces an increase in agency costs and a subsequent decline in the spending of firms most likely to face substantial agency costs; smaller firms or firms with weak balance sheets.

The main hypothesis of this chapter is that all of these mechanisms operate at least partly through their effects on business failures. According to the credit channel, the viabihty of a firm depends not only on the underlying profitability of its investment projects but also on unrelated financial factors. As discussed in the introduction, the main implications of this hypothesis are cross-sectional. If the credit channel is operative, either through banks or balance sheets or both, it should be reflected in an increase in the relative frequency of failure for small or low net worth firms.

Gertler and Gilchrist (1993) discuss three reasons that the accelerator afiFect applies to small firms. First, bankruptcy costs may be proportionately higher for small firms. Second, large borrowers may have proportionately greater coUateralizable net worth (this arises from the fact that they have longer expected lifetimes). Third, since smaU borrowers are likely to be less diversified, they are more susceptible to unobservable idiosyncratic risk.

40 One question arises in the context of firm failure that does have a counterpart in the literature on the cross-sectional implications of the credit cheinnel for investment behavior. Specifically, should a failure that occurs because deflation raises the real burden of debt to the point that a firm becomes insolvent be ascribed to the credit channel, or should this be attributed to the nominal rigidity of non-indexed debt?

Implicit in this question is the related question of what the money channel says about firm failure. Since the money channel is described in terms of individual investment projects and the rate of interest (hence it implicitly assumes that all firms face the same marginal cost of financing), it essentially abstracts from any consideration of the investing firm and the firm’s balance sheet. Because the possibility of default arises only when financial factors are explicitly considered, it seems that failure which occurs as a result of insolvency is an aspect of the credit channel^®. The next section explores the characteristics of the Dun’s failure data in the context of this discussion.

*®In addition, the very use of debt actually arises as a solution to moral hazard and adverse selection problems. The non-indexation of debt, however, is presumably unrelated to problems of asymmetric information.

41 3.4 Estimation

This section examines the relationship between business failures and aggregate

economic variables, examining both the within-industry and across-industry aspects

of these relationships. In Section 3.4.1,1 first estimate the relative ability of output,

deflation and bank failures to explain movements in business failures within industries,

and then compare these estimated relationships eicross industries. In Section 3.4.2,1

examine whether the size of a failure exhibits any systematic response to industrial

production, price or banking shocks. Section 3.4.3 is a brief summary of the empirical

findings.

3.4.1 Aggregate Shocks and the Number of Business Fhilures

In this section I characterize the extent to which business failures are related

to movements in real output, deflation or bank suspensions. The specification used

is an autoregressive model of the form

4 4 4 4 AlnUit = 5/ + ^ PiijAlnriit^j + ^ /32ijAnt-j + ^ 0zij^iPt~j + 53 04ijAibankt-j + i= i j=i j=i j=i (3.1)

where nn is the number of failures in industry I in period t, ip is the growth rate of

industrial production, tt is the rate of inflation and bank is the liabilities of suspended

banks per $1,000 of all bank liabilities and e is a stationary, potentially heteroskedastic

and autocorrelated error^^.

One way this specification can arise is by noting thatnu = puNu where pu is

the probability of failure for a firm in industry I during period t and Nu is the

an alternative to the liabilities of suspended banks, I also use the number of suspended banks. The results are robust to this change of specification. I report the results generated using the liabilities because intuition suggests that this is a more accurate measure of distress in the banking system.

42 number of firms in industry I in period t. If pu % where Xt is a vector of aggregate economic variables which affect the probability of failures and /?/ is a vector of parameters which reflect the responsiveness of the probabUity of failure to changes in Xt, and where t is a time trend and fin is a potentially nonstationary error term, then Innn = XtPt + a/t + /Mt-

If the number of firms follows a stochastic trend, then Nu is nonstationary and the regression must be estimated in first differences^®. For this reason, Ekjuation 3.1 is first differenced. To account for the variabUity of the lag between financial difficulty and appearance in Dun’s Review, I use a two quarter moving average of the number of failures as the dependent variable.

I include as independent variables industrial production, inflation and bank fail­ ures. The latter two variables are included as measures of the credit channel. Other things equal, I expect that deflation and bank failures increase firm failures if net worth considerations and bank lending are important. I interpret the coefficient on industrial production as the direct relationship between changes in the real economy and the number of firm failures, for a given amount of deflation or stress on the banking system^®. Since the timing of the effects of aggregate economic variables on failures is not well-specified, I include 4 lags of the independent variables. All data is quarterly and is seasonally adjusted using quarterly dummy variables.

Augmented Dickey Riller tests on the number of failures in each industry led to a rejection of a unit root at the 5% level in only 9 of the 28 industries, supporting the assumption of the nonstationarity of the number of firms in an industry. did not include an interest rate in the regression because the relationship between interest rates and firm failures is not an explicit component of either the money or the credit channel. Since the money channel abstracts firom ‘firms’, there is no obvious role for default. The alternative channels imply that contractions are transmitted through changes in the cost of financing not reflected in open market interest rates. An estimated relationship between interest rates and firm failures would therefore be difficult to interpret.

43 I estimate Equation 3.1 for each of the 28 industries using OLS and use the coefficients from these equations to compute the response to a one standard deviation shock to each of the independent variables of the number of failures in each industry.

Thus I am assuming no feedback from riit to the aggregate variables. This exercise has two purposes. First, the impulse responses allow a comparison of the sensitivity of failures to alternative shocks indicating whether firms fail because of declines in the real economy or because of unanticipated defiation and bank failures. At this stage,

I answer this question by simply counting the number of industries which exhibit a statistically significant response to each of the shocks. Later in this section, the impulse responses are compared across industries in order to determine whether a relationship exists between an industries’ characteristics (such as size or the use of bank credit) and its sensitivity to certain types of shocks.

Table 3.2 characterizes the 8-quarter impulse response functions imphed by the estimation of equation 1 for each of the 28 industries. Panels A through D report the number of industries (out of 14 for each of manufacturing and trade) for which the impulse response was positive (panel A) or negative (panel B) on impact and the number of industries for which the response was positive (panel C) or negative (panel

D) at some point during the first 8 quarters. Panel E shows the number of industries for which a shock induced a positive cumulative efikct on the number of failures after

8 quarters. I report only those responses which are different from zero at either the

5% or 10% leveP.

^“Robust standard errors for the point estimates for equation (1) are generated using the Newey- West (1987) correction for autocorrelation and heteroskedastidty. I use the delta method to construct the asymptotic standard errors for the estimates of the impulse responses.

44 One Standard Deviation Impulse to [sign of impulse given in parentheses): Prices(-) IP(-) I Bank Liab(+) Significance Level: 10% 5% 10% 5% 10% 5% A. Number of Industries with Positive Initial Impact: Manufacturing 6 3 4 4 Trade 10 7 6 4 B. Number of Industries with Negative Initial Impact: Manufacturing 0 0 0 0 0 0 Tirade 0 0 0 0 0 0 C. Number of Industries with Positive Response During the 1st 8 Quarters: Manufacturing 8 5 1 1 11 11 Tirade 13 12 6 5 7 5 D. Number of Industries with Negative Response During the 1st 8 Quarters: Manufacturing 2 2 1 1 Tirade 1 1 0 0 E. Number of Industries with Positive Cumulative Response after 8 Quarters: Manufacturing 6 6 0 0 8 5 Tirade 12 9 3 0 4 3

Note; The numbers shown are the number of industries out of 14 manufacturing and 14 trade which exhibited a significant response of the indicated level and sign. This table is constructed from the impulse responses generated the coefficient estimates of Equation 3.1. All regressions include seasonal dummy variables. Robust standard errors for the point estimates were used to construct the asymptotic standard errors for the impulse response point estimates. A subset of the impulse responses used to compute this table are shown in Appendix A, Tables 7 and 8.

Table 3.2: Response of Number of Failures to Aggregate Shocks

45 Several aspects of the results are interesting. First, the first row of each of panels

A and C indicate that deflations and bank panics led to increases in failures in more manufacturing industries than did industrial-production downturns. This is partic­ ularly evident in the first row of panel C, which shows that only one manufacturing industry experienced a significant increase in the number of failures in response to an industrial-production shock, while over half experienced a significant (at the 10% level) increase in response to deflation shocks and bank failure shocks. The first row of panel C also indicates a greater sensitivity of manufacturing failures to banking shocks than to deflationary shocks. The second row of panel C (as well as panel

A) shows the opposite to be true for trade firms. At the 10% level, all but one of the trade industries experienced a significant increase in failures during deflations, all but two at the 5% level. As shown in panel E, the number of manufacturing indus­ tries in which shocks led to positive cumulative increases in the number of failures is about the same across the source of shocks (except for industrial production shocks, for which the eflect was not positive in any industry). In contrast, deflations had positive cumulative eflects in almost all the trade industries (at the 10% level) while banking shocks had significant cumulative eflects in less than half the industries.

Second, bank failures aflected manufacturing industries with a lag, while they af­ fected trade firms immediately. This is seen by comparing the entries under banking liabihties in panel A with those in panel C. For example, only 4 manufacturing indus­ tries were aflected by bank failures on impact, while 11 were aflected at some point during the 1st eight quarters. Comparison of the same numbers for trade firms does not reveal such a striking increase in the number of industries aflected.

46 Third, there were no cases in which these shocks led to declines in the number of failures on impact and very few which led to significant declines at any time during the period. Last, there appears to have been slightly more sensitivity to industrial production shocks in trade industries than in manufacturing, though still less than half of the trade industries exhibited a positive response to declines in industrial production. The overall significance of the response of failures to deflations and bank panics and the leick of significance of the response to downturns in real activity suggest the importance of both balance sheets and bank credit in the determination of business failures^^.

One implication of the credit channel is that firms which are reliant on bank credit or which have low coUateralizable net worth should experience an increase in their relative likelihood of failure during periods of deflation or banking crisis. I explore this implication in two ways. First, in the remainder of this section, I attempt to determine whether the responses generated above differ across industries in ways consistent with the operation of the credit channel. Second, I characterize the behavior of the size (as measured by average liabilities) of a failure within an industry in response to shocks to each of the aggregate variables used above. Any systematic relationship between

^^One potential reason for the lack of sensitivity of the number of failures to industrial production shocks is that during business cycle upturns there is an increase in the number of new firms. Since the probability of failure is negatively related to the age of the firm, one might expect upturns to be accompanied by increases in failures because of the preponderance of new entrants. I investigated this possibility using monthly data (Evans (1948), Business Incorporations in the United States, 1800-1943) on business incorporations (this is not the same as new entrants but presumably the two are correlated). I regressed the growth in the number of incorporations (in selected states) on industrial production for 3 separate sample periods corresponding to changes in the states used to construct the incorporations variable. I find a positive relationship between industrial production and incorporations in only two of the periods, and in both cases this relationship is insignificant, with p-values greater than .5.

47 the size of a failure and aggregate economic activity would suggest that firms are failing

for reasons unrelated to the underlying profitability of their investment projects.

These results are discussed in Section 3.4.2.

A Cross-Industry Comparison of Business Failures

The industries in this sample vary greatly in mean firm size, their use of bank

credit and type of assets. In this section I first rank the industries by mean size

and test whether any relationship exists between size and sensitivity to the aggregate

shocks used above. I then discuss alternatives to ranking the industries by size which

are based more directly on credit market access, such as their use of bank credit

and the coUateralizability of their assets, and present preliminary evidence that such

rankings would be useful in explainingcross-industry variation in failure rates.

As a method of summarizing the differences in the responsiveness of failures in

industries of different size to each of the shocks, I estimate 3 sets of weighted least

squares regressions (1 for each of price, industrial production and bank liabilities

shocks) of the form:

where is res{k) is the impulse response after k periods for industry I, msi is the

mean size of a failure in industry I (as shown in Table 3.1), and

standard error of the cumulative impulse response after k periods.

Table 3.3 reports the estimates of 7*, for horizons {k) of 1, 4 and 8 quarters along

with p-values computed from standard errors corrected for heteroskedastidty using

White’s correction. A positive coeflScient on inflation and industrial production indi­ cates that negative shocks to these variables tend to increase failures more for smaller

48 industries. A negative coefficient on the hanking variable has the same interpretation.

No significant (at either the 5% or 10% level) relationship exists between the size of an industry and its sensitivity to industried production shocks. For horizons of one and four quarters there is a significant negative relationship between size and sensitivity to deflations (when the regression is estimated over all industries and manufacturing alone at fc = 1 and for trade alone at fc = 4). This suggests that failures in larger industries were more responsive to deflations. But this relationship no longer exists after 8 quarters. At all horizons, the relationship between industry size and sensi­ tivity to bank failures is positive. Larger industries were more adversely affected by banking panics.

One potential explanation for the finding that industries with larger firms are more sensitive to bank failures than industries with smaller firms is that larger indus­ tries made greater use of bank credit. An alternative strategy, then, is to rank the industries by their use of bank credit. Koch (1943) indicates that small trade firms used very little bank credit, and that chain grocery stores in particular used a small amount of bank credit in comparison with other trade industries. Also, Jacoby and

Saulnier (1947) indicate that iron, leather and textile manufacturing flrms tended to use more bank credit than other manufacturing flrms over this period. Figure 3.3 plots the cumulative impulse responses to banking shocks for iron, leather and two textile manufacturing industries (panels (a) through (d)) along with the responses of the four smallest industries (panels (e) through (h), including grocery stores in panel

(e)). The average increase across trade failures is about 4%, while the increase in manufacturing failures averages about 10%. In addition, three of the manufacturing responses are significant at the 1% level, while only one of the trade responses is

49 Dependent Variable: Cumulative Response k Quarters After Shock Regression Estimated over: Manufacturing Tirade All Industries k = l Price: -4.53 -10.04 -7.08 (.05) (.04) (.13) Industrial Production: .35 .46 -.18 (.52) (.77) (.75) Banking: 1.30 2.66 1.33 (.00) (.00) (.00) k=4 Price: -7.07 -29.33 -31.10 (.50) (.12) (.09) Industrial Production: -.14 -6.88 -4.37 (.96) (.13) (.21) Banking: 6.00 13.72 5.12 (.00) (.00) (.00) k =8 Price: 4.26 -16.64 -26.20 (.65) (.20) (.19) Industrial Production: -3.64 -5.58 -5.03 (.37) (.15) (.20) Banking: 6.76 8.41 6.16 (.00)(.00) (.00)

Note: This table reports the results of the WLS regression shown in equation (3). P-values arc in parentheses. Standard errors were constructed using White’s correction for heteroskedastidty.

Table 3.3: Cross-Industry Regressions

50 significant at the 10% level. These impulse responses axe crude support for the claim

that in industries in which bank loans were an important source of financing, bank

failures had significant implications for the viability of firms while they did not in

industries which made relatively little use of bank credit^^.

3.4.2 Aggregate Shocks and the Size of Business Fsdlures

In this section, I characterize the effects of changes in aggregate conditions

on the size of failures within industries. To do this, I examine the response of the

average liabilities of a failure to deflation, industrial production and banking shocks.

I estimate:

4 4 4 4 Inoit = O!/ + ^ 6iijlnOit-j + ^ ^ 6^ijbankt~j + €t (3.3) j = i j=i i=i j = i

where o,t is the average liabilities of a failure in industry I in period t. I do not take

first differences of this regression on the assumption that size is an 1(0) variable^^.

Equation 3.3 is estimated for both real and nominal liabilities. It is unclear whether nominal or real liabilities is a better measure of firm size when one is inter­ ested in the pattern of the size response. It is possible that as a defiation progresses the average liabilities of failures will decline; not because the firms themselves are smaller, but simply due to the decline in the price level. This would bias the results toward the finding that smaller firms fail during deflations. On the other hand, the use of real liabilities may bias the results upward. It is incorrect to deflate liabilities

Jacoby and Saulnier also note that the Machinery and Tools industry used relatively little bank credit. Though not shown here, this is supported by the comparison of the impulse responses. Augmented Dickey Fhller (ADF) test rejects a unit root in nominal liabilities in 22 of the 28 industries at the 5% level, and Park’s (1990a) J test with J(5,l) rejects in 27 of the 28 at the 5% level. The ADF test rejects a unit root in real liabilities at the 5% level in 24 out of 28 industries, and the Park test rejects in 27 of the 28 at 5%. Plots of the autocorrelation function support these results, the autocorrelation dies out between 1 and 2 lags.

51 I i

* i : ? Î

1 Î 2 I

*

Figure 3.3: Impulse Responses to Bank Failure Shocks

52 acquired prior to a deflation by the price level which prevailed when the firm failed.

In order to correct for this, I deflate the liabihties by the average of the previous two quarters price level, on the presumption that many of the UabUities of these firms were of a maturity of less than 3 quarters. In any case, this should reduce the upward bias created by deflating by the contemporaneous price level and avoid the problem associated with the use of nominal UabUities.

I again compute 8 quarter impulse responses to a shock to each of the exogenous variables for each industry. The plots of the impulse responses are too numerous to show here, but a common feature of virtually all of the responses is a tendency for some cycling to occur, which makes them somewhat difficiUt to interpret. Table 3.4 summarizes the responses of real UabUities by reporting the sign of initial impact of each shock when this impact is significantly diflerent from zero (panels A and B), along with whether the response was ever significantly different from zero after the initial impact and, if so, whether it was positive or negative (panels C and D). I use this as a measure of the ‘tendency’ of size to increase or decrease in response to these shocks.

The results in Table 3.4 indicate that the initial effect of deflations and bank faUures is to increase the average size of a failure rather than decrease it, though in about half the industries the effect is not different from zero. Also, both deflations and bank panics display a greater tendency to increase the size of a faUure after the initial impact than to decrease it. This is particularly true for trade industries, where in almost every industry the size increased in response to bank faUures and deflations at some point during the 8 quarters after the shock^'*.

the majority of cases, the increase occurred within the 1st four quarters. On the other hand, industrial production shocks show no tendency to either increase or decrease the size of

53 One Standard Deviation Impulse to ( sign of impulse giv en in parentheses): Prices(-) IP(-) Bank Liab(+) Significance Level: 10% 5% 10% 5% 10% 5% A. Num ber of Industries w ith Positive Initial Impact: Manufacturing 5 2 1 0 6 4 Tirade 7 7 3 1 7 4 B. Num ler of Industries w th Negative Initial Impact: Manufacturing 0 0 1 1 0 0 Tirade 0 0 1 1 0 0 C. Number of Indi; stries with PositiveÎ Response During the 1st 8 Quarters: Manufacturing 9 7 2 0 9 5 Tirade 13 12 3 1 13 10 D. Number of Indu stries with Negativ e Response During the 1st 8 Quarters: Manufacturing 2 1 2 1 2 1 Trade 2 2 5 5 2 0

Note: The numbers shown are the number of industries out of 14 manufacturing and 14 trade which exhibited a significant response of the indicated level and sign. This table is constructed firom the impulse responses generated the coefficient estimates of Equation 3.3. All regressions include seasonal dummy variables. Robust standard errors for the point estimates were used to construct the asymptotic standard errors for the impulse response point estimates. A subset of the impulse responses used to compute this table are shown in Appendix A, Tables 9, 10, 11

Table 3.4: Response of Real Mean Size of Failures to Aggregate Shocks

54 These results have some intuitive appeal. First, it is likely that leverage during this period, as in the current period, was not a monotonie function of firm size^®.

Since very small and very large businesses tend to finance more firom internal funds than from debt, they are likely to be more insulated from defiation and its effect on the real burden of debt. The tendency of average size to increase, rather than decrease, may reflect this phenomenon. Second, Koch (1943) notes that the pro­ portion of financing obtained through banks and trade credit increases with size in trade industries. Smaller trade firms tended not to use bank financing (presumably they were very small firms and were financed internally). This is consistent with the finding that size increased in almost every trade industry in response to both bank panics and deflations.

As a robustness check I recompute these impulse responses using nominal liabil­ ities as the dependent variable (as well as nominal UabUities of faUed banks as an independent variable). The results of this estimation are reported in Table 3.5. The effects of deflations look roughly simUar across specifications. But bank faUures now tend to decrease size (after impact) only sUghtly less frequently than they increase it. The pattern of response, however, indicates that the size exhibited a significant decline after it had increased. manufacturing failures, while in trade industries they tend to decrease the size (though in less than half the industries). Peterson and Rajan (1994) present data on small businesses collected in 1988 and 1989 which indicate that net worth declines as a fraction of total assets as size increases. Goldsmith (1958) indicates that unincorporated enterprises used twice as much internal financing as external. In a 1939 study, for firms with assets of $10,000 to $25,000, the proportion of debt was relatively small, and firms which held the highest proportion of debt were those in the $100,000 to $250,000 size class. As asset size increased beyond $250,000, the proportion of debt declined again.

55 One Standard Deviation Impulse to ( sign of impulse given in parentheses): Prices(-) n>(-) Bank Liab(+) Significance Level: 10% 5% 10% 5% 10% 5% A. Num ber of Industries with Positive Initial Impact: Manufacturing 5 3 2 2 3 3 Trade 5 5 0 0 3 3 B. Num Der of Industries w th Negative Initia] Impact: Manufacturing 0 0 0 0 0 0 Tirade 3 3 0 0 0 0 C. Number of Indu stries with PositivfÎ Response During the 1st 8 Quarters: Manufacturing 7 7 4 2 5 5 Trade 10 12 0 0 7 7 D. Number of Indu stries with Negativ e Response During the 1st 8 Quarters: Manufacturing 2 2 3 1 4 4 Trade 3 2 2 2 3 3

Note: The numbers shown are the number of industries out of 14 manufacturing and 14 trade which exhibited a significant response of the indicated level and sign. This table is constructed kom the impulse responses generated the coefficient estimates of Equation 3.3. All regressions include seasonal dummy variables. Robust standard errors for the point estimates were used to construct the asymptotic standard errors for the impulse response point estimates. A subset of the impulse responses used to compute this table are shown in Appendix A, Tables 9, 10, 11.

Table 3.5: Response of Nominal Mean Size of Failures to Aggregate Shocks

56 3.4.3 Summary of Empirical Findings

A number of interesting observations emerge from the characterization presented in the preceding three sections. First, the results indicate that downturns in real activity per se have Uttle relationship to firm failures, while defiations or banking crises appear to have important effects. Second, the effects of bank failures show up most prominently in manufacturing industries, either when compared with the effects of deflations on manufacturing failures, or with the effects of bank failures on trade industries. Deflations, on the other hand, led to significant increases in failures in almost all trade industries, as opposed to only about half the manufacturing industries. Third, in over half the manufacturing industries and almost all of the trade industries, deflations led to a significant increase in the average size of a failure during one or more of the eight quarters following the shock. In contrast to the effects of financial variables, cycUcal downturns were tended to decrease in trade industries and had no effect on size in manufacturing industries.

A potential explanation for these findings is that firms in trade industries were more sensitive to deflations because their assets were less coUateralizable, manufactur­ ing firms experienced a greater degree of distress during banking crises because they made greater use of bank credit, and that larger trade firms and mid-sized manufac­ turing firms were forced into bankruptcy during deflations and bank panics because they were more highly leveraged.

57 3.5 Conclusion

In this chapter, I examine movements in the number and size of failed firms over the period 1895 to 1935, and their relationship to production, inflation and bank failures, to determine whether these movements are consistent with the hypothesis that credit-constrained firms experience a higher incidence of financial distress during contractions.

The results indicate that deflations and bank panics had a significant impact on the number of firm failures even after controlling for downturns in real activity, and that in the presence of these two financial variables, movements in aggregate production were not associated with movements in failures. In addition, deflations tended to aSect trade firms more adversely while bank panics had a stronger impact on manufacturing.

One potential reason for the heightened sensitivity of trade industries to defiations may be their lack of coUateralizable assets relative to manufacturing firms. Failures in manufacturing industries may have been more responsive to bank failures because they were relatively heavy users of bank credit. Second, bank failures (as a measure of aggregate financial stress) appear to have had important effects even after accounting for movements in aggregate prices that reduce net worth. Such a finding is consistent with the notion that individual bank-firm relationships were important during this period and hence may offer insight into the empirical importance of the distinction between the bank and the broad credit channels.

FinaUy, the increase in the average size of a failure during banking crises and deflations indicates that sUghtly larger firms were more adversely affected than the smaUest firms. A potential explanation for this finding is that smaUer firms tend to be less leveraged or make relatively less use of bank credit. Instead, it may be

58 more moderately-sized firms which are driven to insolvency or denied access to credit because of the decline in their balance-sheet positions or shifts in bank loan supply.

Though the investigation presented here is by no means exhaustive, the findings are consistent with the hypothesis that monetary policy has distributional consequences by affecting the spending and viability of firms for reasons unrelated to the underlying profitability of their investment opportunities.

59 CHAPTER 4

INFLATION AND PRICE RIGIDITY: EVIDENCE FROM WHOLESALE PRICES - 1913 TO 1940

4.1 Introduction

The observed correlation between the cross-sectional mean and skewness of price changes across industries is positive and statistically significant for the post-war pe­ riod. This finding is fairly robust to the particular index used to compute the cor­ relation. Ball and Mankiw (1995) demonstrate this using the four-digit components of the Producer Price Index (PPI) at an annual frequency, Balke and Wynne (1996) produce this correlation using the 49 components of the Implicit GDP deflator and

Bryan and Cecchetti (1996) use monthly data to document a positive and significant relationship across the 36 components of the Consumer Price Index (CPI).

The interpretation of this ‘stylized fact’, however, varies widely across authors.

Ball and Mankiw (BM) attribute the correlation to a combination of menu costs and skewness in the distribution of desired price changes across industries. Balke and Wynne (BW), on the other hand, show that a model with fully flexible prices can produce this correlation when the model is calibrated to match the input-output structure of the actual economy. Finally, Bryan and Cecchetti (BC) attribute the

60 observed correlation to a small-sample bias in the correlation estimator. After ad­ justing post-war correlations for this bias, they find no evidence of a statistically significant relationship. In their analysis then, the correlation between the mean and the skewness is simply a statistical phenomenon and therefore cannot be interpreted as lending support for any particular model of price setting behavior.

In this paper, I estimate the mean-skewness correlation and compute the small sample bias using the 41 components of the Wholesale Price Index (WPI) for the period 1913 to 1940. In contrast to the results from the post-war data, the pre-war bias-adjusted correlations are positive and statistically significant at short-horizons.

This result is consistent with theories of price-rigidity in the pre-war period.

The remainder of the paper is organized as follows. In Section 4.2,1 briefly discuss the models presented in Ball and Mankiw (1995) and Balke and Wynne (1996). In

Section 4.3, I describe the data and present the observed correlations for the pre- and post-war periods. Section 4.4 is divided into two parts. Section 4.4.1 describes the issue of small-sample bias raised in Bryan and Cecchetti (1996). Section 4.4.2 computes this bias using the pre-war data and adjusts the estimates from Section 4.3.

In this section, I show that the differences between the pre- and post-war results can be attributed to differences in the magnitude of the bias across periods. The bias is smaller in the pre-war period because there is less heteroskedastidty across industries in the variance of price changes.

In Section 4.5,1 characterize the correlation between the mean and the variance of relative price changes for the purpose of identifying changes in the direction of price rigidity during this period. I summarize the impfications of the empirical work in this chapter for models of price setting in Section 4.6. Section 4.7 concludes.

61 4.2 Price Rigidity and the Moments of Inflation

In this section, I briefly describe the models of Ball and Mankiw (1995) and

Balke and Wynne (1996). In both models, the key empirical implication is a positive relationship between the mean and the skewness of relative price changes. The under­ lying economic conditions giving rise to the correlation, however, differ across the two models. In Ball and Mankiw, the crucial elements are costly price adjustment and skewness in the distribution of desired relative price changes. In Balke and Wynne, the important features are a combination of industry speciflc shocks and input-output relationships across sectors.

4.2.1 Menu Costs

In the model presented in Ball and Mankiw, skewness in the population of de­ sired relative price adjustments and small costs of changing prices lead to a positive correlation between the mean and the skewness of observed price changes. The intu­ ition for their model is the following. If prices are perfectly flexible, then relative price changes, which are mean-zero by definition, should not affect the aggregate price level.

In other words, under perfect price flexibility, inflation is a purely nominal phenom­ enon and changes in the real economy lead only to changes in relative prices, not to changes in the aggregate price level. On the other hand, if there are costs to making price adjustments, then asymmetry in the distribution of desired price changes will affect inflation, because firms will adjust only when the cost of doing so exceeds the benefit^. Skewness in the price change distribution implies more adjustments on one tail than the other.

^In Ball and Mankiw, inflation is deSned as the weighted mean of industry price changes.

62 Figure 4.1 illustrates the case of positive skewness in the distribution of desired adjustments, which I denote / (9) ,with 6 being a particulm realization of a firm’s desired price change. In the case shown here, positive and negative price adjustments are equally costly, so |a| = 6 | | . Only those firms which incur a shock 9 such that oo a |0| > b will adjust. As is clear from the picture, f f (9) d9 > f f{9)d9 as long as, 6 —oo as assumed here, |a| = | 6| . Since there are more price increases than price decreases, the mean price change is positive. Hence, relative price shocks are inflationary. In the same way, negative skewness in the distribution of desired price changes will be deflationary. If prices were fully flexible, a = 6 = 0 would hold. In this case, regardless of the shape of the distribution of desired changes, the mean price change would be zero.

The empirical implication of this model then, is a positive correlation between the mean and the skewness of price changes. As discussed above, this implication has been tested using post-war data, and abstracting from the issue sampling bias, the impUcation has been borne out. In this chapter, I test the implication using data from the pre-war period.

4.2.2 Sector-Specific Shocks and Input-Output Relations

Balke and Wynne provide an alternative explanation for the mean-skewness cor­ relation. They calibrate a multiple-sector, flexible-price general equilibrium model to match both the input-output relationships across sectors of the economy and the actual variance of productivity shocks. Their goal is to show that as relationships between sectors become stronger, it becomes easier to generate a positive correlation between the cross-sectional mean and skewness of price changes. Using their model,

63 0 a b

Figure 4.1: Positively Skewed Distribution of Relative Price Shocks

64 they are able to produce correlations of a magnitude similar to those observed in the actual data. Since prices are perfectly flexible in their model, they conclude that the observed correlation is not a unique implication of menu-cost models, and therefore should not be interpreted as evidence for price rigidity.

The Balke and Wynne model produces a positive and statistically significant cor­ relation in three separate cases:

1. the most general case in which they allow for interaction between sectors and

for diflerences in the variance of shocks hitting each sector,

2. the case in which there is interaction between sectors, but the shocks Eire i.i.d.

and serially correlated,

3. the case in which there is no interaction between sectors, but there are dif­

ferences in the variance of shocks to each industry (and no covariance across

shocks).

It should be noted that case 3, in which there is heteroskedasticity in the cross­ industry variance of shocks, but no interaction between sectors, actually produces a larger and more significant correlation than either of the other two cases. I will show below that this result is consistent with the intuition behind the sampling bias described in Bryan and Cecchetti, and that this result suggests that the correlation, at least in the post-war period, can be attributed to heteroskedasticity in the variance of shocks across industries, not the covariance of shocks across industries or the input- output relations across sectors.

65 4.3 The Mean-Skewness Correlation: 1913 to 1940

In this section, I describe the data used in the estimation and present correla­ tions for the pre- and post-war periods. For 1947 to 1995, I use 27 components of the Producer Price Index (PPI). For the early period, I use the 41 components of the Wholesale Price Index (WPI) as reported by the BLS from 1913 to 1940. The moments of these series are computed using the 1982 and 1926 weights, respectively.

Both data series are monthly^. Throughout the paper, I report the results of both the pre- and post-war estimation for the purposes of comparison.

Following Bryan and Cecchetti, I report only the correlation coefficient between the cross-sectional mean and skewness. This is done for two reasons. First, it allows me to refrain from assigning any causal role to either variable. Second, Ball and

Mankiw run OLS regressions of the mean on lagged values of the mean, and contem­ poraneous values of both the skewness and the variance. Theories of asymmetric price adjustment, in which the direction of rigidity is a function of trend inflation, would suggest that the observed skewness of price changes is a function of inflation, and hence OLS estimates of the mean skewness relationship would be inconsistent. For the purposes of the present analysis, I abstract from this issue by computing simple correlations.

The moments are calculated using overlapping observations on inflation at a k month horizon (i.e., inflation in commodity i at the 12 month horizon is defined

^See Appendix B for a description of the construction of the BLS Wholesale Price series from 1913 to 1940.

66 as 7T,t = In — In 12, so that the mean of the distribution of industry infla­ tion is TTt = w.TTit, the variance is u = (^«t - and the skewness is s& = uji , where n indicates the number of commodities across which the moments are being computed.

Table 4.1 reports the observed correlation coeflBcients for horizons ranging from 1 to 84 months. The correlations are computed as the coefficient from a standardized regression of inflation on the skewness. The p-values are computed from robust stan­ dard errors calculated using the Newey-West (1987) correction with lag length set to

1.4*k.

Looking first at horizons 1 though 24, it appears that the mean-skewness rela­ tionship is fairly robust to the sample change. In addition, the correlations in both periods decline in magnitude as the horizon is lengthened, through the 24 month horizon.

Examining the correlations at longer horizons, however, reveals noticeable difler­ ences between the two periods. First, the correlation for the post-war period continues the upward trend that it begins between 12 and 24 months. In contrast, the pre-war correlation continues to decline until somewhere between 48 and 64 months, at which time it begins to increase. In addition, while the post-war correlations remain signif­ icant throughout, the pre-war correlations are statistically insignificant at horizons longer than 24 months.

The correlations reported in Table 4.1 for the post-war period are consistent with either the Ball and Mankiw or the Balke and Wynne story. That the correlation should be initially strong and then die out over time is an implication of the menu cost hypothesis. On the other hand, the Balke and Wynne model predicts that

67 Horizon: WPI: 1947 - 1995 p-value PPI: 1913 - 1940 p-value 1 0.4571 0.00 0.4282 0.00 3 0.4427 0.00 0.3720 0.00 12 0.3821 0.00 0.2831 0.00 24 0.2727 0.10 0.2990 0.00 48 0.1931 0.74 0.4507 0.00 60 0.2032 0.62 0.5418 0.00 72 0.2878 0.78 0.5773 0.00 84 0.3908 0.28 0.5902 0.00

Table 4.1: Correlation Between Mean and Skewness of Price Changes

the correlation should perhaps strengthen as the time horizon increases. The pattern exhibited by the pre-war correlations are broadly consistent with the menu-cost model, and do not seem to offer support for the Balke and Wynne hypothesis. For horizons of I to 24 months, the correlation is significant, but declining. At longer horizons, the correlation is insignificant.

68 4.4 Monte Carlo Tests for Bias

In this section, I describe the issue of small-sample raised in Bryan and Cecchetti

(1996). I then test for this bias using their methodology, and discuss the differences between the pre- and post-war results. To anticipate, 1 find differences in the bias and in the significance of the bias-adjusted estimates across periods. In particular,

1 find that bias cannot account for the observed correlation between the mean and the skewness at short horizons in the pre-war period. In contrast, the entirety of the correlation in the post-war period can be explained by small-sample bias. 1 then show that the differences in the bias result directly firom differences in the heteroskedasticity of shocks across industries in the two periods. The significance of the bias-adjusted correlations at short horizons are consistent with the presence of price rigidity in the pre-war period.

4.4.1 Small-Sample Bias: Theory

As described above. Ball and Mankiw interpret the positive correlation between the mean and the skewness of price changes as evidence of menu-costs and associated price rigidity. Bryan and Cecchetti object to this interpretation on the grounds that since we only observe a fraction of the goods for which prices change each period, the correlation estimator is subject to a small-sample bias. To demonstrate the bias, they conduct a series of Monte Carlo experiments in which a symmetric distribution of relative price shocks is able to generate correlations of a magnitude consistent with those observed in the data.

The intuition for the bias comes from noting that when drawing small samples from a symmetric distribution, draws from the tail wiU move the sample mean and

69 the sample skewness in the same direction. The probability of drawing from the tail of the population distribution is a function of its kurtosis. When a distribution is leptokurtotic, its tails are fatter than the tails of a normal distribution and hence there is a higher probability of drawing from the tail.

Bryan and Cecchetti note that price change distributions for the post-war period tend to be highly leptokurtotic. For instance, the sample kurtosis for the PPI from

1947 to 1995 at k month horizons is 8.554, 7.958, 7.067 and 6.051 for k= l, 3,12 and 24 respectively. Excess kurtosis is also present in the pre-war price change distributions.

The sample kurtosis statistics at the 1, 3, 12 and 24 month horizons for the 1913 to

1940 sample are 8.423, 7.380, 7.142 and 5.938, respectively.

Since each period we examine only a small fraction of total price changes, and since the distribution from which we draw is leptokurtotic, the correlation estimator is biased upward. Bryan and Cecchetti present Monte Carlo experiments which suggest that with levels of kurtosis as high as those observed in the data, the use of extremely large samples is required to overcome the bias.

4.4.2 Small Sample Bias: Evidence from the Pre-War Data

In this section, I compute the small sample bias in the pre-war correlation estima­ tor and adjust the estimates presented in the previous section for this bias. Following

BC, I generate Monte Carlo samples of data of dimension T*N, where T the sample length and N is the number of industries (T = 336, N=41 for the 1913 to 1940 period, and T=585, N=27 for the 1947 to 1995 period). I make the following assumption about the data generating process for each industry:

70 • Industry prices changes are constructed recursively using the autoregressive co­

efficients of the actual data and a matrix of innovations which is N (0, E), where

E is the variance-covariance matrix of the residuals from an OLS regression of

each industry’s price change on the autoregressive coefficients^.

Thus, the null hypothesis in this experiment is that when the population gener­ ating price changes is symmetric, it is not possible to generate correlations of the magnitude of those observed in the data. The significance level of the test is obtained by comparing the observed correlation with the empirical distribution.

Table 4.2 reports the bias (the median of the empirical distribution) and the bias adjusted coefficients for horizons 1 though 24, as computed under the conditions of the experiment described above.

While the actual correlations look very similar across periods (the pre-war corre­ lation is slightly larger at horizons of 1, 3, and 12 months, and slightly smaller at the

24 month horizon), the bias is uniformly smaller in the pre-war period than in the post-war period. The bias adjusted estimates are insignificant at every horizon for the post-war period. In contrast, the bias adjusted estimates for the 1 and 3 month horizons in the pre-war sample are positive and statistically significant. Why is the bias different across the two samples?

The bias is different for the same reason that the bias generated in the exper­ iment with a null hypothesis of homoskedastic shocks, differs from that generated in either of the experiments that allow for heteroskedasticity. The addition of the

conduct two additional expenninents under the following two assumptions: 1. price changes are generated by anN(0, 1) process, and 2. price changes are generated by an iV (0, E) process, with E equal to the variance-covariance matrix of the actual data. Not surprisingly, the first of these is unable to generate correlations of a magnitude of those observed in the data. The second, however, is able to generate large correlations. Each of the experiments is conducted with and without the commodity weights.

71 Horizon Observed Bias Bias-Adjusted Emp p-value WPI: 1913 to 1940 1 0.4571 0.3356 0.1215 0.04 3 0.4427 0.3262 0.1165 0.06 12 0.3821 0.2782 0.1039 0.44 24 0.2727 0.2543 0.0185 0.93 PPI: 1947 to 1995 1 0.4280 0.4368 -0.0086 0.80 3 0.3721 0.4144 -0.0423 0.36 12 0.2831 0.3089 -0.0258 0.81 24 0.2991 0.3301 -0.0310 0.84

Table 4.2: Bias and Bias-Adjusted Mean-Skewness Correlation CeoflBcients

variance-covariance properties of the actual data produces the bias. More precisely, the addition of variance-covariance propertieswhich differ across industries produces the bias. Following the intuition in the previous section, we can see that the bias should be large when the varieinces of the individual industries are very different.

Figure 4.2 illustrates the source of the difference between the pre- and post-war bias. The figure demonstrates, using the Lorenz Curve construction, the inequality of the time-series variance across industries in the pre and post-war periods. If the variance in each industry was identical, the bars would form a 45 degree line. The figure reveals a greater inequaUty in the cross-sectional variance in the post-war

72 period, suggesting that the bias should be larger in the post-war period'*. The hgure indicates, however, that there is inequality in the pre-war period also, hence there is bias in the pre-war correlation estimator®.

The greater inequality of the variances in the post-war period is not the only possible explanation for the fact that the bias is larger in this period. It is important to consider differences in the covariance properties across time periods, since this is also an element of the population DGP. One way to assess the relative importance of these two aspects of the DGP is to modify the experiment in the following way;

• The variance-covariance matrix used in the simulations contains only the vari­

ance terms from the variance-covariance matrix of the data; the off-diagonal

elements are set to zero. This assumes that there is no covariance between

components.

The purpose of this alternative assumption is to help clarify the aspect of the population DGP that generates the bias. As noted above, Balke and Wynne empha­ size a combination of heteroskedastic shocks and input-output relations across sectors as the mechanisms generating the correlation between the mean and the skewness.

The results of the experiment conducted under this alternative suggest that it is the former that is of primary importance, and the latter may actually serve to mitigate the correlation.

■‘The same picture can be generated using the BLS weights to weight the data before computing its variance-covariance matrix. The picture indicates the same qualitative result. The variance inequality is more pronounced in the post-war period. An alternative measure of this might be the coefficient of variation (CV). For the pre-war period, CV = 1.08 for unweighted data and CV=1.S7 for weighted data. For the post-war period, the estimates are 2.00 and 2.64 respectively. 'The actual correlations, small-sample bias and bias adjusted coefficients were also computed for the period 1913-1940 using the more aggregated 10 components of the WPI. Aggregation reduces the heteroskedasticity, and hence reduces the bias and increases the magnitude of the bias adjusted correlations. The qualitative results, however, are not affected by aggregation.

73 PPt IM7-I935

■■■■ml A

»Pt 1913-1:40

Figure 4.2: Variance Inequality Across Industries

74 The results of the simulations are reported in Table 4.3. Column 2 shows the ob­ served correlation. Columns 3 and 4 show the bias adjusted correlations and empirical p-values as reported in Table 4.2. Columns 5 emd 6 report the bias-adjusted estimates and empirical p-values under the alternative assumption that the variance-covariance matrix is diagonal.

The bottom half of the table shows that for the post-war period, the qualitative conclusions are robust to this alternative assumption. To generate a bias of the magnitude observed in the post-war data, it is suflScient to account only for the differences in the variance across components. This is consistent with the results of the

Balke emd Wynne case 3 discussed above. The interaction between the sectors is not necessary to produce the correlation, it is only necessary that the shocks hitting the individual sectors be heteroskedastic. In fact, given that the bias adjusted estimates under the alternative assumption are smaller at each horizon than those generated under the conditions of the original experiment, we can conclude that the covariance properties actually serve to reduce the bias.

While the results are somewhat less clear for the pre-war sample, they still suggest that it is the heteroskedasticty in the variance of shocks across industries that pro­ duces most of the bias. In the pre-war period, however, it appears that the covariance of shocks worked in the opposite direction as the post-war covariances, and tended to exacerbate the bias.

75 Horizon Observed Actual Emp Diagonal Emp Correlation Cov Matrix: p-value Cov Matrix: p-value Bias Adjusted Bias Adjusted Correlation Correlation WPI: 1913 to 1940 1 0.4571 0.1215 0.04 0.1923 0.00 3 0.4427 0.1165 0.06 0.1937 0.00 12 0.3821 0.1039 0.44 0.2260 0.06 24 0.2727 0.0185 0.93 0.1601 0.39 PPI: 1947 to 1995 1 0.4280 -0.0086 0.80 -0.0028 0.91 3 0.3721 -0.0423 0.36 -0.0609 0.21 12 0.2831 -0.0258 0.81 -0.0668 0.52 24 0.2991 -0.0310 0.84 -0.0836 0.57

Table 4.3: Bias and Bias-Adjusted Eîstimates Using Modified Covariance Matrix

76 4.5 Asymmetric Price Adjustment

The empirical analysis up to this point has focused exclusively on the relationship between the mean and the skewness of price change distributions. An extensive literature exists, however, on the implications of theories of price rigidity for the relationship between the mean and relative price variability (as measured by the cross- sectional variance of price changes). In this section I describe these implications and then provide preliminary evidence that is consistent not only with theories of costly price adjustment, but is also with the idea that price adjustment is asymmetric and that the direction of asymmetry changes with agents’ inflationary expectations.

4.5.1 Inflation and Relative Price Variability

I begin by noting that if price setters have complete information and prices are perfectly flexible, there should be no systematic relationship between inflation and relative price variability. This suggests the two basic ways in which a non-zero rela­ tionship can arise. The first is that agents can confuse changes in aggregate prices with changes in relative prices, and the second is that price adjustment is costly and hence is undertaken infrequently. In this paper, I deal only with the empirical impli­ cations of models of costly price adjustment and not with those associated with the first class of models®.

The analysis in this section examines evidence for models of asymmetric price adjustment of the type presented in Ball and Mankiw (1994) and Tsiddon (1991b).

® As Fischer (1981) points out, it is likely that the factors driving the relationship between inflation and relative price variability change over time depending on the source of the disturbance. Many authors have noted that alternative theories of relative price variability and inflation have very similar empirical implications. The focus on menu-cost explanations in this section is for simplicity, but clearly it would be useful to analyze a richer class of models using the pre-war data.

77 In these models the direction of price rigidity is a function of the underlying trend, or expected, rate of inflation^. When inflationary expectations are positive, upward price adjustments take place more quickly than downward adjustments. When inflationary expectations are negative, downward price adjustments are undertaken more quickly than upward adjustments.

In the remainder of this section I describe a simple version of the Ball and Mankiw model and the implications of that model for the relationship between the mean and the variance of component price changes. 1 then examine the behavior of the mean- variance relationship in the pre-war period as a preliminary test of these imphcations.

The M odel

Assume there are multiple industries, each of which contains many firms. In this model, firms set prices for two periods, but can adjust their price in the second period by paying a menu cost, C. Assume also that there is expected inflation of magnitude

7T. Define q as the firm’s desired nominal price, so that q = p + 6, where pt = 7 r t , and 6 is the firm’s desired relative price. In the first period, let firms have a desired relative price of zero, which they expect will prevail in the second period. The desired second period price, then, is just the expected amount of inflation, tt.

Ball and Mankiw assume the firm’s one-period loss function to be of the form

(9 — q*Ÿ + DC, where D is a dummy variable that equals one when the firm pays the

^Ball and ManJdw (1994) and De Abreu Lonrenco and Gruen (1995) both note that an alternative to modeling asymmetric adjustment is simply to assume that price-setters are adverse to nominal price declines by appealing to notions of norms or fairness. I will discuss below there appears to be little evidence of consistent downward nominal rigidity in the pre-war period.

78 menu cost. This specification implies that firms will set their price for each period

to be In the second period, firms receive a shock to their desired relative price of

magnitude where Q is drawn firom a mean zero symmetric distribution.

In this simple setup then, firms enter the second period with an actual price of

\ and a desired price of t t + 0. Thus, a firm’s desired adjustment is equal to | + 0.

Minimisation of the firm’s loss function imphes that the firm does not adjust in the

face of a shock 0 if —y/C — f < 0 < \fC — f .

When TT is positive, the absolute value of the lower bound of nonadjustment is

larger than that of the upper bound; when tt is negative, the absolute value of the

upper bound is larger. In addition, since desired adjustment is given by f +0, positive

inflationary expectations imply that for a given0 , desired price increases are larger

in absolute value than desired price decreases. In the case of expected deflation then,

desired price decreases are larger than desired price increases.

Figure 4.3 shows the case for which tt > 0, so that |6| > |a|. In this case, increases

in the variance of relative price shocks will be inflationary. Figure 4.4 shows the case

in which tt < 0. In this case, since |a| > |6| , increases in the variance of relative price shocks will be deflationary.

The implications of the asymmetric adjustment model for the correlation between the mean and variance of relative price changes are the following. During periods of anticipated price inflation, the correlation should be positive. An increase in the variance of shocks will result in more upward price adjustments than downward ad­ justments, and hence will be inflationary. During periods of anticipated deflation, the correlation should be negative. When there is no underlying inflationary trend, the correlation should be zero.

79 0 a b

Figure 4.3: Positive Inflationary Expectations

0 a b

Figure 4.4: Negative Inflationary Expectations

80 As noted above, an alternative to modeling the direction of price rigidity as arising

endogenously from optimizing behavior on the part of firms is to simply assume that

there is downward rigidity in nominal prices. The implication of this assumption is

that at low or zero rates of inflation, increases in the variance of relative price shocks

win always be inflationary, since the only firms that adjust are those that experienced

positive shocks. Rirther, as trend inflation rises, the relationship between the mean

and the variance should disappear. At high enough rates of inflation, reductions in

relative prices wül no longer imply reductions in nominal prices, but instead that

nominal prices increase less than the trend rate of inflation.

4.5.2 The Mean-Variance Correlation: 1913 to 1940

Table 4.4 reports the correlation between the mean and the variance for both

the pre- and post-war periods, along with the correlation between the absolute value

of the mean and the variance over the sample 1913 to 1940. In contrast to post­

war correlations, the mean-variance relationship is weak in the pre-war period. It is significant only at the 1-month horizon, and the point estimate at this horizon is close

to half that observed in post-war data. On the other hand, the correlation between

the mean and the absolute value is positive, large and significant at every horizon.

The implication of this comparison is straight-forward and not surprising: nominal prices are not always rigid downward. There were multiple episodes of price deflation during the pre-war period, notably the sharp decline in the early 1920s and the sustained decline during the Depression®. The difference between the mean-variance correlation and the absolute value of the mean-variance correlation suggests that in

^Wholesale prices fell 56% from May 1920 to June 1921 and by 46.6% from August 1929 to March 1933.

81 Horizon Mean-Varismce: 1913 to 1940 p-value 1 0.2308 0.08 3 0.1447 0.48 12 0.1287 0.64 24 0.2843 0.16 Horizon Mean-Variance: 1947 to 1995 p-value 1 0.4228 0.00 3 0.3480 0.00 12 0.5956 0.00 24 0.7877 0.00 Horizon |Mean|-Variance: 1913 to 1940 p-value 1 0.4799 0.00 3 0.4754 0.00 12 0.6417 0.00 24 0.5923 0.02

Table 4.4: Correlation between Mean and Variance and |Mean| and Variance: 1913 1940

periods of deflation, increases in the variance of relative price shocks are associated with increases in the rate of deflation. In other words, there were periods when price setters anticipated deflation and were more willing to undertake price decreases than increases.

As a preliminary attempt to identify changes in the direction of price rigidity,

I estimate rolling regressions of the mean on the variance over this period. The regressions were computed using 3-year windows of monthly observations on the mean and the variance of price changes. Each time the regression is estimated, the earliest observation in the 3-year window is dropped from the sample and an additional one

82 is added at the end. The purpose of these regressions is to gain an apprehension of the

way in which the correlation changes over time, and how these changes correspond

with changes in the underlying trend rate of inflation.

Figure 4.5 plots the point estimate of the correlation, along with the empirical

p-value and the ‘trend’ rate of inflation on the date corresponding to the midpoint of

the 3-year window®. The trend inflation rate is computed as the mean of the monthly

inflation rates over the 3-year sample (since the value of trend inflation is quite small

relative to the correlations and p-values, the value plotted is flfty times the actual

value).

Focusing flrst on the correlation coefficient and the trend inflation line, note that

in general, periods of trend inflation are accompanied by a positive correlation and

periods of trend deflation are accompanied by a negative correlation. The rather

striking exception to this pattern is the deflationary years of the Great Depression.

During these years, the correlation is negative for only a short period of time relative

to the other deflationary episodes in this sample, and in particular, relative to the deflation of the early 1920s. In addition, the correlation is actually positive as early

as 1931, despite the fact that the deflation continued for close to two more years.

While the point estimates of the correlation are consistent with the hypothesis that the asymmetry in the range of shocks to which firms do not adjust is a function of the underlying trend or expected rate of inflation, the empirical p-values show that the correlation is statistically significant during only a few periods, namely, the war­ time inflation of the late teens, the recovery from the recession of the early 1920s,

®The empirical p-values were computed using the stationary bootstrap technique as described in Politis and Romano (1994) to generate 1000 sets of simulated data. A correlation coefficient was computed for each set of data, and the actual point estimate of the correlation was compared to the empirical distribution of the correlation coefficient.

83 the years from 1931 to 1936, and during the late 1930s. The insignificance of point

estimates during the deflationary periods can be explained by the fact that most of

the deflations lasted for less than 2 years. Since the regressions are computed using

3-year samples of data, those which include deflations also include inflations. This

serves to reduce the statistical significance of the estimated correlation.

An alternative method of testing for asymmetric adjustment, and one which avoids

the problem of changes in the sign of the correlation, is to estimate the rolling regres­

sions using the absolute value of the mean rather than the mean. In these regressions,

the correlation should be positive and significant whenever there is an inflationary or

deflationary trend, and should be zero when there is no trend. Figure 4.6 plots the

correlation between the absolute value of the mean and the variance, along with the

empirical p>-value and the trend inflation rate. The correlation between the absolute

value of the mean and the variance is small and insignificant during only two periods

in the entire sample; the relatively stable years of the mid-1920s and the early years

of the Depression^".

How can these results be interpreted? First, they are consistent with the hypoth­

esis that the range of non-adjustment to relative price shocks does change with the

underlying trend rate of inflation. The size and insignificance of the correlation dur­

ing the 1920s are consistent with the idea that price setters expected neither inflation

nor deflation during these years. That this should be the case is supported by the

value of trend inflation during this period, which hovers close to zero relative to the rest of the sample. If agents expected no change in inflation during this period, the range of non-adjustment to shocks should be symmetric around zero. In this case,

^°The correlation is insignificant in 1919 also, but the episode is very brief in comparison to either of the two mentioned above.

84 0.9 0.8 Empirical p-value 0.7 Correlation Coefficient 0.6 0.5 0.4 0.3 0.2 0.1

- 0.1 - 0.2

- 0.3

- 0.4

- 0.5

- 0.6 Trend Inflation

- 0.7

- 0.8 1915 1917 1919 1921 1923 1925 1927 1929 1931 1933 1935 1937 1939

Figure 4.5: Correlation Coefficient: Mean-Variance, 3-Yr Window

85 0.9 Empirical p-value Correlation Coefficient 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1

-0.1 ■02 - 0.3 - 0.4 - 0.5 -0.6 - 0.7 - 0.8 Trend Inflation ■0.9

1915 1917 1919 1921 1923 1925 1927 1929 1931 1933 1935 1937 1939

Figure 4.6: Correlation CoefBcient: |Mean|-Variance, 3-Yr Window

86 increases in the variance should lead to no change in inflation. In contrast, during the severe deflation of the early 1920s and the mild deflation of the late 1920s, the correlation is positive and significant.

Second, the results suggest that there were difierences in price setting behavior between the deflation of the early 1920s and the Great Depression. Beginning in the late 1920s, the correlation estimate begins to decline, despite the increase in the rate of deflation during this period. The correlation is close to zero and insignificant firom roughly 1929 through 1932. In contrast, the correlation is positive and significant during the other deflationary episodes (the early 1920s, the deflation of 1926-27, and the deflation of the late 1930s).

The differences across these two periods are consistent with the following. Price- setters expected deflation in the early 1920s and in the other deflationary episodes and hence were more willing to lower their prices than to raise them. During the early years of the depression, on the other hand, the breakdown in the absolute value of the mean-variance relationship suggests that price-setters were not expecting continued deflation, but instead expected price stability.

The purpose of this section has been to characterize the mean-variance relation­ ship in the pre-war period, and to determine whether changes in this relationship are consistent with models such as the one presented in Ball and Mankiw. A number of important theoretical issues, however, have been abstracted from in this charac­ terization. In particular, I have made no attempt to determine the extent to which differences in the frequency of price adjustment across these episodes can explain differences in the correlation. Further, I have not considered signal extraction type models in which prices are flexible, but agents cannot distinguish aggregate from rel­

87 ative shocks. If, for example, the ratio of aggregate to relative shocks was very low during the depression, then the correlation migh t disappear in the event that price setters’ found it less diflScult to distinguish between aggregate and real shocks. It will be necessary to refine the analysis presented here in order to determine which of these theories is ultimately supported by the available data.

88 4.6 Implications for Models of Price Setting

This paper considers three alternative interpretations of the observed mean-skewness correlation. First, the correlation is potential evidence of price rigidity. Second, the correlation may be produced by a combination of inter-industry linkages and het- eroskedastic shocks across industries. Third, the correlation may reflect a sampling bias which results from heterogeneity in the variance of shocks across industries.

The results presented here suggest the following. First, when we compare two periods with differing degrees of variance inequality, we see that the bias cannot fully explain the correlation in the period with greater homogeneity while it can explain the correlation in the period with greater heterogeneity. Second, we see that the bias- adjusted correlations for the pre-war period are larger and significant only at short horizons. This is consistent with a model in which firms don’t immediately adjust to shocks because of costs of changing prices. In such models, all firms will ultimately adjust, and so we expect the correlations to disappear at longer horizons. Third, we see that for the post-war period, the heterogeneity in the variance of shocks is sufficient to generate correlations as large as those observed in the data. This suggests that the inter-industry linkages emphasized by Balke and Wynne are not the driving force in their model, but rather it is the heterogeneity in the shocks that is the essential element.

Finally, there is evidence that not only were prices rigid during this period, but that the direction of rigidity changed over the period. In particular, the movements in the mean-variance relationship over the sample suggest that price-setters anticipated the deflation of the early 1920s, and hence were more willing to undertake price

89 decreases than increases. On the other hand, there is no evidence of asymmetric

adjustment during the early years of the Great Depression, suggesting that price-

setters anticipated price stability.

4.7 Conclusion

In this paper I examine the observed mean-skewness correlation and the small-

sample bias in the correlation estimator over the period 1913 to 1940. I find that even

after adjusting for bias in the correlation estimator, there is evidence of a positive and

statistically significant relationship between the mean and skewness of price changes.

This result is consistent with the hypothesis that prices were rigid in the pre-war

period.

I also find evidence that it is sufficient to aUow only for heteroskedasticity in the

shocks across industries to generate correlations as large as those observed in the

data, suggesting that the mechanisms emphasized in Balke and Wynne are not the

dispositive elements of the story.

Finally, to the extent that the mean-skewness correlations are suggestive of price

rigidity in the pre-war period, there is additional evidence that the direction of rigid­

ity changed with price-setters’ underlying inflationary expectations. In particular, changes in the mean-variance relationship over the period suggest that while the de­ flation of the early 1920s may have been anticipated, the deflation in the early years of the Great Depression was not.

90 References

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91 Gilchrist, Simon, G., Ben S. Bemanke and Mark Gertler, (1994). ‘The Financial Accelerator and the Flight to Quality’. Finance and Economics Discussion Paper No. 93-8, Board of Governors, July 1994. Gorton, Gary, (1988). ‘Banking Panics and Business Cycles’, Oxford Economic Pa­ pers 40: 751-781. US Bureau of the Census, (1975). Historical Statistics of the United States, Colonial Times to 1970, Bicentennial Edition, Part 2. Washington DC Jacoby, Neil H. and Raymond J. Saulnier, (1947). Business Finance and Banking, National Bureau of Economic Research: Financial Research Program, NBER.New York. James, John A, (1978). Money and Capital Markets in Postbellum America, Princeton University Press, Princeton, NJ. Kashyap, Anil K and Jeremy C. Stein, (1994a). ‘Monetary Policy and Bank Lending’, in Monetary Policy, N. Gregory Mankiw, ed., Chicago: University of Chicago Press for NBER. New York. (1994b). ‘Monetary Policy and Bank Balance Sheets’, Working Paper, Grad­ uate School of Business, University of Chicago, (March). Kimmel, Lewis H., (1939). The Availability of Bank Credit 1933-1938, Conference Board Studies, No. 242, National Industrial Conference Board, Inc. New York. Klebaner, Benjamin, J., (1990). American Commercial Banking, A History. Twayne Publishers, Boston. Koch, Albert R., (1943). The Financing of Large Corporations 1920-1939, National Bureau of Economic Research: Studies in Business Financing, NBER. New York. Kroos, Herman E. and Martin R. Blyn, (1971). A History of Financial Intermediaries Random House, New York. Kuvin, Leonard, (1936). Private Long-Term Debt and Interest in the United States, National Industrial Conference Board Studies, No. 230. National Industrial Con­ ference Board, Inc. New York. Miron, Jeflhey and Christina D. Romer, (1990). ‘A New Monthly Index of Industrial Production, 1884-1940’. Journal of Economic History 50: 321-37. and David Weil, (1994a). ‘Historical Perspectives on the Monetary Transmis­ sion Mechanism’, in Monetary Policy, N. Gregory Mankiw, ed., Chicago: University of Chicago Press for NBER. Moore, Geoffirey, (1961). Business Cycle Indicators, Volume II, Basic Data on Cycli­ cal Indicators, Princeton University Press, Princeton.

92 and Victor Zamowitz, (1986). ‘The Development and Role of the National Bureau of Economic Research’s Business Cycle Chronologies’, Appendix A, The American Business Cycle, Robert J. Gordon, ed. Peterson, Mitchell and Ra^uram G. Rajan, (1994). ‘The Benefits of Firm Creditor Relationships: A Study of Small Business Financing’. The Journal of Finance, Vol. XLDC, No. 1. Politis, Dimitris N., and Joseph P. Romano, (1994). ‘The Stationary Bootstrap’, Journal of the American Statistical Association 89: 1303-13 Studenski, Paul and Herman E. Kroos, (1963). The Financial History of the United States, McGraw Hill, Inc. Tsiddon, D., (1991b). ‘The (Mis)behavior of the Aggregate Price Level’, (mimeo) Hebrew University, October.

93 APPENDIX A

CHAPTER 3: ADDITIONAL RESULTS

This appendix contains a summary of the impulse responses from which the tables presented in Chapter 3 are constructed. This is not the complete set of results used to construct these tables, since this would require the presentation of the impulse responses at all lags for all specifications.

94 One Standard Deviation Impulse to: Prices(-) IP(-) Bank Liab(-f-) Industry 1 Qtr [^Qtr 1 Qtr 8 Qtr 1 Qtr 1 8 Qtr Iron, Foundries and Nails .05 .13 -.02 -.12 .01 .10 (.04) (.00) (.38) (.06) (46) (.03) Machinery and Tools .02 .05 -.01 -.05 .03 .07 (.03) (.06) (40) (.35) (.00) (.00) Woolens, Carpents and Knit Goods .04 .10 -.06 -.05 .06 .08 (.17) (. 02) (.15) (.55) (.03) (.11) Cottons, Lace and Hosiery .02 .04 .06 .12 .02 .07 (.42) (41) (.07) (.17) (41) (.46) Lumber, Carpenters and Coopers .01 .02 -.01 -.05 .01 .04 (38) (.50) (22) (22) (.15) (.16) Clothing and Millinery -.01 .05 .01 .01 .01 .10 (.70) (.39) (.60) (.91) (.14) (08) Hats, Gloves and Furs .04 .05 -.02 .01 .02 .10 (.06) (36) (.51) (.91) (.14) (.02) Chemicals and Drugs .06 .07 -.04 -.03 .01 .04 (.02) (.04) (.07) (63) (.30) (.12) Printing and Engraving .03 .07 .01 .03 .00 .07 (.15) (.14) (.58) (.69) (.79) (.06) Milling and Bakers .01 .04 .00 -.01 -.01 .01 (.45) (.21) (.74) (88) (23) (.56) Leather, Shoes and Harness .04 .10 -.01 .05 .00 .06 (.04) (.00) (.55) (.31) (42) (.01) Liquors and Tobacco .02 .06 .03 .10 .04 .09 (08) (.00) (.11) (.01) (.00)(.00) Glass, Earthenware and Brick .01 .04 .01 .02 .01 .05 (22) (32) (52) (.77) (33) (39) All Other Manufacturing .02 .02 .00 .02 .01 .02 (.47) (.47) (.78) (.70) (.39) (36)

Note: This Table is constructed from the impulse responses generated by the estimation of Equation 3.1. The 8 Quarter response is the cumulative response after 8 quarters.

Table A.l: Response of Number of Manufacturing Failures to Aggregate Shocks

95 One Standard Deviation Impulse to: Prices(-) H>(-) Bank Liab(+) Industry 1 Qtr 8 Qtr 1 Qtr 8 Qtr 1 Qtr 8 Qtr General Stores .04 .15 .00 -.01 .00 .00 (.05) (.00) (.84) (92) (.50) (.91) Groceries, Meat and Fish .01 .05 -.01 .06 .01 -.01 (.07) (.03) (.35) (. 12) (.13) (.69) Hotels and Restaurants .01 .04 -.01 -.02 .02 .06 (24) (.08) (41) (58) (.01) (.03) Liquors and Tobacco .02 .10 .01 .01 .01 .01 (.03) (.02) (.49) (82) (24) (.42) Clothing and Furnishing .02 .07 .01 .06 .02 .07 (.07) (.09) (.53) (46) (.00) (.03) Dry Goods and Carpets .03 .11 .00 .05 .01 .03 (.09) .010 (85) (.62) (24) (.52) Shoes, Rubbers and Trunks .01 .02 .03 .15 -.01 .03 (42) (70) (.03) (.12) (.51) (.40) Furniture and Crockery .04 .18 .01 .12 .02 .05 (.02)(.00) (62) (.11) (.00)(.10) Hardware, Stoves and Tools .04 .16 -.01 .01 .01 .03 (.04) (.01) (42) (81) (.05) (.40) Chemicals and Drugs .04 .13 .01 .13 .00 .02 (.06) (.01) (33) (.12) (.65) (.57) Jewelry and Clocks .01 .10 .00 .06 .02 .03 (46) (.01) (.79) (41) (20) (.57) Books and Papers .00 .07 -.01 .13 .02 .07 (.95) (.04) (.52) (.08) (. 01) (.03) Hats, Furs and Gloves .04 .07 .01 .02 .02 -.03 (.17) (.04) (.65) (.81) (34) (.31) All other Trade .02 .05 .00 .00 .00 .06 (.08) (.13) (83) (.99) (73) (. 20)

Note; This Table is constructed from the impulse responses generated by the estimation of Equation 3.1. The 8 Quarter response is the cumulative response after 8 quarters.

Table A.2: Response of Number of Itade Failures to Aggregate Shocks

96 Industry First p-value Number of Quarters: Significant Positive Negative Response Response Response Manufacturing Iron, Foundries and Nails .15 .01 2 0

Machinery and Tools -- 0 0 Woolens, Carpents and Knit Goods .15 .02 2 0

Cottons, Lace and Hosiery -- 0 0 Lumber, Carpenters and Coopers .07 .01 1 0 Clothing and Millinery .04 .05 2 0 Hats, Gloves and Furs .06 .01 2 0 Chemicals and Drugs -.10 .05 2 0 Printing and Engraving .06 .08 2 2

Milling and Bakers -- 0 0 Leather, Shoes and Harness .07 .03 1 1 Liquors and Tobacco .16 .00 1 0

Glass, Earthenware and Brick - - 0 0 All Other Manufacturing .03 .04 2 0 Trade General Stores .03 .01 5 0 Groceries, Meat and Fish .03 .02 2 0 Hotels and Restaurants -.06 .01 3 1 Liquors and Tobacco -.03 .04 0 3 Clothing and Furnishing .03 .00 6 0 Dry Goods and Carpets .02 .10 1 0 Shoes, Rubbers and Trunks .04 .07 3 0 Furniture and Crockery .04 .02 6 0 Hardware, Stoves and Tools .05 .02 4 0 Chemicals and Drugs .03 .03 2 0 Jewelry and Clocks .08 .00 2 0 Books and Papers .06 .02 1 2 Hats, Furs and Gloves .08 .03 1 0 All other Trade .05 .03 1 0

Note: This table is constructed from the impulse responses generated by the estimation of Equation 3.3.

Table A.3: Response of Mean Rjeal Liabilities to Deflationary Shock

97 Industry First p-value Number of Quarters: Significant Positive Negative Response Response Response Manufacturing Iron, Foundries and Nails -.12 .00 0 2

Machinery and Tools - - 0 0

Woolens, Carpents and Knit Goods - - 0 0

Cottons, Lace and Hosiery - - 0 0 Lumber, Carpenters and Coopers -.06 .01 0 2

Clothing and Millinery - - 0 0

Hats, Gloves and Furs -- 0 0

Chemicals and Drugs -- 0 0 Printing and Engraving -.05 .10 0 2

Milling and Bakers -- 0 0

Leather, Shoes and Harness -- 0 0 Liquors and Tobacco .09 .04 1 0

Glass, Earthenware and Brick - - 0 0 All Other Manufacturing .05 .01 2 0 Tirade General Stores .02 .09 0 2

Groceries, Meat and Fish - - 0 0 Hotels and Restaurants .03 .10 1 0 Liquors and Tobacco -.05 .02 0 5 Clothing and Furnishing -.01 .08 0 4

Dry Goods and Carpets -- 0 0

Shoes, Rubbers and Thinks -- 0 0

Furniture and Crockery -- 0 0 Hardware, Stoves and Tools -.03 .02 0 4

Chemicals and Drugs -- 0 0

Jewelry and Clocks -- 0 0

Books and Papers -- 0 0 Hats, Furs and Gloves -.01 .05 0 1

All other Trade -- 0 0

Note: This table is constructed from the impulse responses generated by the estimation of Equation 3.3.

Table A.4: Response of Mean Real Liabilities to Industrial Production Shock

98 Industry First p-value Number of Quarters: Significant Positive Negative Response Response Response Manufacturing Iron, Foundries and Nails .06 .10 1 0 Machinery and Tools .05 .10 0 0 Woolens, Carpents and Knit Goods .05 .03 1 0

Cottons, Lace and Hosiery -- 0 0 Lumber, Carpenters and Coopers .05 .03 1 0 Clothing and Millinery .04 .01 2 0 Hats, Gloves and Furs .04 .09 1 0 Chemicals and Drugs -- 0 0 Printing and Engraving .05 .03 3 0 Milling and Bakers .13 .10 1 0 Leather, Shoes and Harness .07 .01 2 2

Liquors and Tobacco -- 0 0 Glass, Earthenware and Brick .11 .00 1 0

All Other Manufacturing - - 0 0 Trade General Stores .03 .00 2 1 Groceries, Meat and Fish .06 .05 1 0 Hotels and Restaurants .05 .07 4 0 Liquors and Tobacco .03 .06 6 0 Clothing and Fbmishing .01 .10 2 0 Dry Goods and Carpets .03 .10 1 1 Shoes, Rubbers and Trunks .05 .04 1 1 Furniture and Crockery .02 .04 1 0

Hardware, Stoves and Tools - - 0 0 Chemicals and Drugs .04 .00 1 0 Jewelry and Clocks .05 .03 3 0 Books and Papers .05 .04 4 0 Hats, Furs and Gloves .06 .10 1 0 All other Trade .03 .01 2 0

Note: This tabic is constructed from the impulse responses generated by the estimation of Equation 3.3.

Table A.5: Response of Mean Real Liabilities to Bank Liabilities Shock

99 A PPEN D IX B

DATA

B.l Chapter 3 Data

Prices: I use a spliced series of monthly observations. The series from 1893 to

1913 is the Index of the General Price Level as reported in the NBER’s Historical

Data Base. I spliced this with the monthly oflScial BLS CPI from 1913 to 1920 and then to the National Industrial Conference Board’s CLI from 1920 to 1935.

Industrial Production: I use the series collected by Miron and Romer (1989) from

1895 to 1919, and the Federal Reserve’s Industrial Production Series from 1919 to

1935 and use the overlapping observations to ratio splice the two series.

Number of suspended banks: Dun’s Review published quarterly suspensions from

1894.1 to 1924.4. The Federal Reserve published monthly suspensions from 1921.01 to 1933.02 (the series is considered to end here because the bank hohday renders subsequent series incomparable). Annual data on the number of banks in existence is available from Historical Statistics, Series 580. I scaled the number of failures in each of the Dun’s and the Federal Reserve Series by a linear interpolation of this series.

For the 3 years for which the two series overlap I estimated their relationship using

100 an OLS regression. I use the regression coefficients and the Federal Reserve Series to

‘fit’ the values of Dun’s series over the period 1925:1 to 1932:4.

Liabilities of Suspended Banks: Dun’s Review published quarterly liabilities of suspended banks firom 1894.1 to 1924.4. The Federal reserve reported monthly de­ posits of suspended banks fi’om 1921.01 to 1933.02. I scaled each of these series by a linear interpolation of the liabilities or deposits of all banks (Series 581 and 585, respectively). I then used the regression technique described above to generate a consistent series.

Failure Data: The number and liabilities of failed firms are taken firom the monthly series published in Dun and Bradstreet and Dun’s Review entitled ‘Failures by Branches of Business’ over the period 1895 to 1935.

B.2 Chapter 4 Data

Prices (1913-1940): The index numbers for 1913-1925 were taken firom Bulletin

543, pages 11-33. These number appear to correspond to a revision described on page 185 of Historical Statistics of the United States, Colonial Times to 1970. The revision was the adaptation of the year 1926, rather than 1913, as the base period.

The Historical Statistics references pages 2 - 6 of BLS Bulletin 493 for a description of the revision.

The index numbers for 1926 to 1931 were taken firom BLS Bulletin number 572.

According to pg 1 of the bulletin, the index number of wholesale prices was expanded in 1931 to include 784 price series instead of 550. The revision was extended back to and including Jan 1926. The added price series were almost entirely for fully manufactured articles. The 10 major groups were retained, but a rearrangement of

101 commodities within these groups was made in several instances as well as the addition of several subgroups (these are described below). The former base period, the average price for 1926 was retained.

The index numbers for 1932 to 1940 were taken from various issues of the BLS monthly Labor Review.

To constuct a series that runs from 1913 to 1940, the maximum number of com­ modity subgroups is 41 (i.e., 6 subgoups are lost by linking the pre and post 1926 data). The following is a description of changes made to the post 1926 data to make it comparable with the pre 1926 data^:

1. CEREAL and FRUITS AND VEG were added back into OTHER FOODS.

2. CLOTHING and KNIT GOODS were added back into OTHER TEXTILES

3. ELECTRICITY was eliminated.

4. PLUMBING AND HEATING were put into the OTHER subgroup in the Metals

and Metal Products group.

5. STRUCTURAL STEEL was included as its own commodity subgroup, rather

than part of IRON and STEEL.

^Corresponding changes in the weights were also made.

102