Inflation and Disintermediation*

Isha Agarwal† Matthew Baron‡

November 2019

Abstract

In a country-level panel from 1870 to 2016, large increases in inflation are associated with lower future bank credit-to-GDP, even in the absence of monetary tightening. The lending contraction is primarily driven by banks with balance sheets most negatively exposed to inflation increases. To better understand how inflation shocks transmit to the macroeconomy through a banking channel, we study an unexpected inflation increase in the U.S. in early-1977. Our identification strategy exploits differences in reserve requirements across U.S. states for Fed nonmember banks, leading banks to be differentially exposed to unexpected inflation increases. More exposed banks reduce lending, which in turn reduces local house prices, construction employment, and capital expenditure at bank-dependent firms. Our results suggest that an important consequence of inflation is its distortion of the banking sector.

* The authors would like to thank Yevhenii Usenko for extraordinary research assistance and to the following people for their comments and feedback: Olivier Darmouni, Daniel Dieckelmann, Ernest Liu, Christian Moser, Kris Nimark, Eswar Prasad, Wei Xiong, Scott Yonker, and seminar participants at Cornell and Princeton. The authors would also like to thank Felipe Silva and the librarians at the Harvard Business School Historical Collections for their assistance with archival material. † Sauder School of Business, University of British Columbia, isha.agarwal@sauder.ubc.ca ‡ Johnson Graduate School of Management, Cornell University, baron@cornell.edu

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A central and still-unresolved question in monetary economics is to what extent, and

through which channels, does an increase in inflation lead to short-run fluctuations in the

macroeconomy. Prior work has typically highlighted nominal rigidities in labor markets and in

nonfinancial firms as key frictions leading increases in inflation to have real effects. For example,

New Keynesian models generally imply that small unexpected increases in inflation may actually

increase output and employment by relaxing nominal wage rigidity constraints (Ball, Mankiw, and

Romer, 1988). Macroeconomic models with financial frictions also predict that small unexpected

increases in inflation can increase output by reducing the real debt burden of financially

constrained agents (e.g., Bernanke, Gertler, and Gilchrist, 1999). On the other hand, in models

stressing investment uncertainty, tax distortions, or non-indexation of contracts, inflation increases

can have a negative impact on economic performance (e.g., Auerbach, 1979; Ball and Cecchetti,

1990; Feldstein, 1997). In extreme cases, hyperinflations can lead to a complete breakdown of the

price mechanism, leading to severe macroeconomic consequences.1

This paper, in contrast, is the first to introduce and explore a bank credit channel through

which an unexpected increase in inflation leads to short-run macroeconomic fluctuations. The

intuition is that banks can be inflation-exposed because of inflation asset-liability mismatch, which

we show can lead to quantitatively important net negative consequences for aggregate lending and

the nonfinancial economy. Prior academic and policy work has typically not considered distortions

to the banking sector arising from inflation to be a first-order concern for short-run macroeconomic

performance.2 In this paper, we demonstrate how a banking channel is quantitatively important

both in the U.S. and in a variety of international settings, especially in emerging market economies

where rising inflation is a recurring problem.3

1 Another possibility is that rising inflation may in itself cause little macroeconomic harm but may simply be a symptom of other underlying problems, such as fiscal imbalances, supply-side contractions, or currency depreciations, which themselves affect the macroeconomy. Other traditionally-cited costs include “menu costs” (e.g., Sheshinski and Weiss, 1977) and “shoeleather costs” (e.g., Pakko, 1998; English, 1999), though the literature is generally skeptical whether these costs can be large in magnitude for moderate inflations. As Shiller’s (1997) survey approach shows, households may simply dislike inflation for behavioral reasons, or they may see it as an implicit tax that transfers wealth from households to the government. 2 One exception, that focuses on long-run development, is Boyd, Levine, and Smith (2001), who show that high inflation countries tend to have lower financial sector development in the long-run. 3 For example, according to the IMF’s World Economic Outlook, there have been recent large jumps in inflation in many large emerging market economies: Argentina (20% to 55% in 2018-9), Brazil (6% to 10% in 2015-6), Egypt (10% to 32% in 2016-7), India (6% to 11% in 2007-9), and Turkey (10% to 20% in 2018-9).

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Our hypothesis of a bank credit channel through which inflation shocks are transmitted to

the real economy is motivated by two broad macroeconomic patterns that we uncover. First, by

analyzing an unbalanced country-level panel from 1870-2016, we find that large increases in

inflation are associated with lower levels of future bank credit-to-GDP. The bank credit-to-GDP

ratio falls by one percentage point on average, relative to trend, one year following an inflation

increase greater than 10 percentage points, and more for larger inflations. The decline in credit is

slightly larger for emerging economies, though also present for developed economies.

Furthermore, it does not appear to be simply driven by monetary tightening as a reaction to the

inflation, as the effect is present even excluding episodes when policy interest rates rise.

Second, within prominent historical inflation episodes, the lending contraction is driven

primarily by banks whose balance sheets are most negatively exposed to inflation increases. This

part of the analysis focuses on a subset of prominent high inflation episodes—France and Germany

in the 1920s; Argentina, Brazil, Indonesia, Mexico, Turkey, Venezuela, and other economies in

recent decades—for which we are able to collect detailed balance sheet data of individual banks.4

Our analysis relies on the idea that banks are differentially exposed to unexpected changes in

inflation. This evidence suggests that the lending contraction is not entirely driven by an aggregate

factor, such as a supply-side contraction or investment uncertainty, as these factors do not easily

explain why the lending contraction is driven primarily by those banks whose balance sheets are

most negatively exposed to inflation.

To show this result, we construct an inflation-exposure measure for each bank, constructed

by classifying individual balance sheet items as either inflation-protected or inflation-exposed

(coded as -1 or +1, respectively) and then taking a weighted average across all assets and liabilities

to get a total inflation exposure that ranges between -1 and +1. This measure is constructed so that

a “high” bank-level inflation exposure (close to +1) means that an inflation increase would

presumably have a large negative effect on bank value. For example, a bank holding mostly

nominal long-term bonds and funded by market-rate short-term debt would see its value very

4 Writing about the German hyperinflation that peaked in 1924, Balderston (1991) confirms much of our intuition through narrative analysis. He reports that the six largest German banks (the Grossbanken) lost over two-thirds of their capital. Furthermore, “a general credit famine developed in 1922. This reflected, on the side of demand for credit, the rising desire to exploit inflation…but on the supply side, it reflected not only the banks’ shrinking real resources, but perhaps also a growing reluctance to give their capital away in mark-denominated loans” (p. 561). We quantitatively analyze this inflation episode as part of our analysis in Section III.

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negatively affected by an inflation increase (since bond value would be eroded by inflation, while

nominal funding costs would rise). In general, a bank that has mostly inflation-exposed assets and

liabilities (e.g., cash, long-term nominal bonds, market-rate short-term debt) will receive an

average score close to +1, while a bank that has mostly inflation-protected assets and liabilities

(e.g., real and inflation-index assets, fixed-rate liabilities) will receive a score close to -1.5

From an endogeneity point of view, one may still be concerned that in the above analysis,

the inflation-exposed banks might be systematically different from other banks in a way that is

correlated with their subsequent lending. We therefore examine the effects on the banking sector

of a sudden and unexpected inflation increase in the U.S. in early-1977, exploiting across-state

differences in reserve requirements for Fed nonmember banks, along with within-state differences

between Fed nonmember versus member banks. We show these regulatory differences

substantially affect banks’ cash-to-deposit ratios and in turn their inflation exposure.6,7

Specifically, we turn to a sudden and unexpected inflation shock in the U.S. in early-1977,

in which inflation (as measured by the year-over-year change in the monthly CPI for all urban

consumers) saw a one-time increase from 5% to 7%, where it remained for the subsequent year.

The cause of this burst of inflation is generally attributed to an increase in energy prices early in

the year, which filtered into non-energy prices and led to a broad rise in the price index. We

instrument banks’ inflation exposure using state-level differences in reserve requirements for

Federal Reserve nonmember banks. Nonmember banks are all state-chartered and have reserve

requirements set at the state level; in contrast, Federal Reserve member banks (which may be either

5 It is important to note that some banks may actually see their value increased by inflation, and such banks would receive an inflation-exposure measure that is negative (close to -1). For example, a bank holding all real assets and funded by non-interest-bearing deposits will benefit from rising inflation, since the assets will keep up with inflation, while the real value of the liabilities will be eroded. This is true of many large banks in developing countries, in which the costs of inflation are passed to depositors through low deposit rates (i.e. financial repression). Effectively, such a bank is earning seignorage. 6 Why does the cash-to-demand-deposit ratio from reserve requirements affect banks’ inflation exposure? To satisfy the reserve requirement, a bank may hold more non-interest-bearing cash (the numerator), which negatively affects inflation exposure, since banks lose money on non-interest-bearing cash; alternatively, to satisfy reserve requirement ratios, a bank may fund itself through fewer non-interest-bearing demand deposits (the denominator), which also negative affects inflation exposure. Thus, both the numerator and denominator of a higher reserve requirements ratio go in the same direction to make the bank more negatively exposed to rising inflation. 7 Banks with higher required ratios may, of course, try to hedge their inflation exposure through other offsetting balance sheet choices, but we find they can only imperfectly do so, presumably because reserve requirements in many states in the 1970s were highly constraining, sometimes requiring as high as 30% cash-to-deposits.

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nationally- or state-chartered) have uniform, nationally-set reserve requirements. In a first stage

regression, we find that states with higher reserve requirements induce nonmember banks in those

state to hold higher cash-to-deposit ratios and therefore to have higher balance sheet-based

inflation-exposure measures. As a placebo test, we run the same state-level analysis with Federal

Reserve member banks, which have uniform national reserve requirements that do not vary across

states; as expected, our tests return null results, as differences across states should not affect the

cash holdings or inflation exposures of member banks.

In a second stage, we then conduct a difference-in-differences analysis of bank lending by

nonmember banks: comparing before and after the inflation increase and across high- versus low-

inflation-exposed banks (as instrumented by state-level reserve requirement). Subsequent to the

inflation shock, inflation-exposed banks reduce lending in various forms (e.g., total loans, C&I

loans, loans to households). We estimate that loan growth is reduced by 8.7 percentage points

(compared to average loan growth of ~20% in 1977) for the most highly inflation-exposed banks.

These results are robust to controlling for bank- and state-level characteristics.

One may worry that these results may spuriously reflect other potential differences across

states correlated with state-level differences in reserve requirements. We show that this is unlikely

to be the case for two reasons. First, in placebo tests with Fed member banks, which have uniform

reserve requirements across states, we see no systematic difference in lending across states. These

placebo results confirm that the lending reduction is not driven by other, potentially unobservable,

differences across states. Second, we test whether a variety of observable differences across

states—including oil production, prior GDP or lending growth, other state-level macroeconomic

differences, and other bank and nonfinancial firm characteristics—are correlated with state-level

reserve requirements across states, but do not find evidence of this. We also control for the above-

mentioned variables in all regression analyses.

We then provide evidence on potential channels through which an increase in inflation

affects bank lending. In the case of the U.S. in 1977 with only a modest rise in inflation, we do not

find that a “net wealth channel” (in the sense of Holmstrom and Tirole, 1997, or Rampini and

Viswanathan, 2019) can quantitatively account for the magnitude of the lending reduction.

Although more inflation-exposed nonmember banks do indeed earn lower net interest margins (as

higher inflation forces these banks to pay higher interest expenses on interest-bearing liabilities

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without receiving similarly higher interest income from their assets), the decrease in profitability

is only around 1.5 percentage point, compared to an average return-on-equity of 12%, and is short-

lived.8 Using a multiplier from the literature, it is difficult to reconcile this decrease in bank value

with the magnitude of the lending decline for affected banks. However, in international episodes

with large increases in inflation, international bank stock evidence suggests that net wealth effects

can be large and able to explain the lending contractions in these episodes.

We next investigate a second “deposits outflow” channel in which rising inflation can lead

to an aggregate outflow of deposits due to regulated deposit rates, as savers take their funds outside

the banking system to market-rate investments that earn higher nominal rates. However, we find

no evidence for this channel during the specific U.S. episode in 1977.

What accounts for the large reduction in lending? We argue that, for the U.S., the evidence

points towards a third “flight-to-inflation-protection” channel, as rising inflation generates

increased uncertainty about future inflation, forcing banks with high inflation exposure to sharply

reallocate their assets by shifting away from long-term nominal loans and into interest-bearing

short-term securities. Consistent with this channel, we show that, after the increase in inflation,

inflation-exposed banks tend to shift their asset allocation to minimize their exposure to subsequent

inflation shocks: away from cash and towards short-term interest-bearing securities and real assets.

Lastly, we show how inflation shocks are transmitted to the real economy through a lending

contraction. We study the real effects of the contraction in bank credit by analyzing outcomes of

publicly traded nonfinancial firms within each state, distinguishing “bank-dependent” versus

“non-bank-dependent firms” using the methodology of Almeida and Campello (2007). Comparing

“bank-dependent” versus “non-bank-dependent” firms helps isolate firm-level effects due to the

bank credit supply shock. In states with high reserve requirements, we find reduced investment

expenditure and debt subsequent to the inflation shock for only bank-dependent firms, consistent

with a credit supply channel. The above results are robust to controlling for firm- and state-level

characteristics. However, we do not observe any effect on firm sales or profits with our data.

8 This result is consistent with Drechsler, Savov, and Schnabl (2018) who show that, as U.S. banks try to minimize their asset-liability nominal interest rate mismatch, banks in the aggregate are only modestly but negatively affected by higher nominal rates.

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We then use state-level aggregate data from the Federal Housing Finance Agency and the

Bureau of Economic Analysis Regional Accounts to show that states with inflation-exposed banks

see decreases in aggregate house prices and construction employment. These results suggest that

the credit supply decrease from inflation seems to be transmitted to the macroeconomy in part

through a housing effect. Our results are consistent with a large literature demonstrating that bank

credit and housing effects are important transmission mechanisms for monetary policy.

Our results thus isolate how an unexpected increase in inflation can transmit to the

macroeconomy through the banking sector. To sum up some of the advantages of our approach:

our identification strategy addresses endogeneity concerns that inflation and declining output

might be correlated due to other reasons. To do this, it relies only on cross-sectional comparisons,

which control for any aggregate factors that often coincide with inflation—for example, a supply-

side contraction, investment uncertainty, currency depreciation, or expectations of future monetary

tightening—since these other channels are not easily able to explain the cross-sectional differences

in lending across banks with different inflation exposures. By comparing nonmember versus

member banks or bank-dependent firms versus non-bank-dependent firms within the same state,

it is unlikely that our results are driven by different macroeconomic conditions across states.

Are our results driven by expectations of future monetary tightening as a reaction to higher

inflation? In the U.S. case in early-1977, this is unlikely, as there is no evidence from the narrative

accounts of Romer and Romer (1989) that the Fed was considering tightening policy in 1977,

despite the rise in inflation. In addition, long-term Treasury rates remained nearly constant during

this period, indicating that markets were also not expecting increased nominal rates in the future.

There were also no expectations of future higher inflation in 1977 (despite the ex-post higher

inflation in 1979-1981): according to Cochrane (2011), “the Fed expected further moderation [in

inflation], and surveys and long-term interest rates did not point to expectations of higher

inflation.” It is also unlikely that our results are driven by currency effects, as most Fed nonmember

banks are small local lenders and thus presumably had minimal exposures to foreign currency

assets.

Our paper proceeds as follows. Section II presents the data, Section III analyzes global

high inflation episodes, Section IV analyzes the U.S. 1977 episode, and Section V concludes.

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II. Data

We gather data for two settings. The first setting examines global high inflation episodes,

including a subset for which we are able to collect detailed balance sheet data of individual banks.

The second setting examines an unexpected inflation increase in the U.S. in early-1977 and

compares Fed nonmember versus member banks. In both settings, we construct balance sheet-

based inflation expose measures for each bank.

Global high inflation episodes

To examine the aggregate credit implications of rising inflation, we analyze a country-level

panel of annual macroeconomic data covering 47 advanced and economies countries over the

period 1870-2016. This dataset, which contains annual country-level data on inflation, interest

rates, GDP growth, currency returns, and bank credit-to-GDP, is taken from Baron, Verner, and

Xiong (2019).9 For the purposes of the analysis, we define high inflation episodes as years with an

increase in the inflation rate of at least 10 percentage points (with a positive level of inflation over

the entire episode). We only record the first year, if there are successive such years in a given

country. These episodes are reported in Appendix Table A1.

We then gather individual bank balance sheets for the subset of the global high inflation

episodes from Appendix Table A1 for which such data is available. The balance sheet data comes

from two sources. The first is the Bankscope financials database, which starts in the late-1980s to

late-1990s (depending on the country) and provides standardized information on bank balance

sheet and income statement variables for cross-country comparison. The second is individual

financial statements for French and German banks during the interwar period from the Harvard

Business School historical collections (for French banks and larger German banks) and from the

Der Deutsche Oekonomist (for smaller German banks, 1919 and 1924 balance sheets only). We

transcribe and assemble these French and German banks’ historical financial statements into

standardized bank balance sheet and income statement formats for analysis.

9 Their macroeconomic and financial data come, in turn, from sources such as the Maddison database, the Jorda-Schularick-Taylor macro-history database, Global Financial Data, and the OECD, IMF, and World Bank datasets. Baron, Verner, and Xiong (2019) also gather additional data on bank credit-to-GDP from the BIS’s long credit series, newly-transcribed IMF statistical manuals from the 1940s and 1950s, League of Nations’ Money and Banking Statistics (volumes from 1925-1939), and other country-specific sources, allowing them to form aggregate bank credit-to-GDP series going back to at least 1918 for nearly all the countries in their sample and back to 1870 for a subset of countries. The authors document data sources for each variable and country in extensive detail in their Data Appendix.

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We gather data on the following variables for each bank: total assets, gross loans, a foreign-

owned indicator, and all individual balance sheet items listed in Appendix Table A5. We winsorize

all Bankscope variables at 5 and 95 percent level to remove outliers.

The subset of high inflation episodes for which we have individual bank financial statement

data is listed in Appendix Table A3, along with summary statistics of individual banks

characteristics in each episode. This list covers high inflation episodes in the last 30 years in

Argentina, Brazil, Indonesia, Mexico, Turkey, Uruguay, and Venezuela, in addition to Germany

in 1922 and France in 1926. Figure A1 plots inflation rates during these episodes. We exclude all

high inflation episodes occurring in 1998 and 2008 from this list, as it would be difficult to

disentangle the impact of inflation from the global financial crises occurring in those years. In

subsequent robustness analysis, we will also exclude all banking crises, sovereign debt crises, and

balance-of-payment crises from this analysis.

The early-1977 U.S. episode

For the early-1977 U.S. sudden inflation increase, we gather annual data on bank-level

variables from the U.S. Report of Condition and Income (commonly known as Call Reports) filed

by financial institutions regulated by the Federal Reserve System, the Federal Deposit Insurance

Corporation (FDIC), and the Office of the Comptroller of Currency (OCC). We download the data

from the Bank Regulatory Database on Wharton Research Data Services for December 1976 and

December 1977.

Since our identification strategy relies on differences in reserve requirements across states,

we retain only “depository institutions” that are subject to reserve requirements: these include

commercial banks, savings banks, savings and loan associations, credit unions, and U.S. branches

and agencies of foreign banks.10 More than 90 percent of the observations correspond to

commercial banks. We also exclude banks that do not have state or national charters. These include

“non-U.S. entities chartered by non-U.S. authorities, pseudo entities, individuals, or charter types

other than U.S. banking”. Our sample is also restricted to banks that have observations for both

years. Finally, we drop banks that change their authority charter (from national to state or vice

versa) between 1976 and 1977. The Call Reports data allows us to divide banks into Federal

10 Source: https://www.federalreserve.gov/monetarypolicy/reservereq.htm

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Reserve member banks (which can be either nationally or state-chartered) or nonmember banks

(which are all state-chartered), as reserve requirements are determined by Fed membership status.

For detailed information on reserve requirements in 1977—state-level reserve

requirements for nonmember banks, along with the uniform national reserve requirements for

member banks—we use data from Gilbert and Lovati (1978). Gilbert and Lovati (1978) report

state-by-state nonmember bank reserve requirements in detail, not just the various reserve ratios

required to back different types of deposits (demand deposits, time deposits, etc.), but detailed

state-level rules including whether cash in the process of collection (CIPC) or “due from” balances

count towards required reserves, whether certain types of government deposits can be excluded

from reserve requirements, and whether reserves can be partially invested in interest-bearing

securities, among other rules. We adjust for these finer state-level differences in all our analysis.

A sample of Gilbert and Lovati’s (1978) data is shown in Appendix Figure A6.

To study the real consequences of inflation, we download data on publicly traded

nonfinancial firms from Compustat. We examine the variables total assets, investment (defined as

capital expenditures divided by last year’s plant, property and equipment), employment, income,

sales, cash, long- and short-term debt, and bond ratings for the universe of nonfinancial firms for

the period 1975-1977. We drop firms with an annual growth in assets or sales of over 100 percent.

This rule, based on Almeida and Campello (2007), ensures we do not include firms which

experience large jumps in fundamentals, as these can be indicative of mergers or reorganizations.

We also drop firms with a greater than 100 percent annual growth in investment. To be included

in the sample, a firm should have observations for the entire 1975-1977 period.

Data on state-level unemployment rates are from the Bureau of Labor Statistics; state-level

data on an all-transactions house price index are from the Federal Housing Finance Agency; state-

level employment data for various sectors are from the Bureau of Economic Analysis Regional

Economic Accounts; data on oil production for each state in 1977 are from the U.S. Energy

Information Administration; U.S. data on oil prices, real GDP growth, and short- and long-term

interest rates are from the FRED database.

Constructing bank-level inflation exposure measures

To study bank-specific inflation exposure in both the global episodes and the U.S. setting,

we create three different measures of exposure to inflation for each bank: an asset-based exposure,

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liability-based exposure, and total inflation exposure. To construct the asset-based exposure, we

assign a value +1 to a balance sheet item on the asset side if its value is “inflation exposed”, and -

1 if it is “inflation protected”. For instance, if a bank has a high proportion of cash holdings, it

should be hurt by inflation; hence, non-interest-bearing cash gets a value +1. In contrast, real assets

or foreign assets are generally thought to be inflation protected and are assigned a value of -1. We

calculate the asset-based inflation exposure of each bank as a weighted average (as a proportion

of total assets of each balance sheet item) of the +1 and -1’s. Hence, for this measure, the more

positive the value of asset exposure (closer to +1), the greater the bank should be harmed by

increasing inflation.

Similarly, we create a measure of inflation exposure for the liability side of the balance

sheet. Items that harm the bank when inflation rises (which, analogous to the asset side, we call

“inflation exposed” liabilities) receive a value of +1. Such items include short-term money market

funding, which come with higher interest payments as inflation rises. We assign a value of -1 to a

liability item if inflation erodes the real liability, meaning that inflation helps this bank by reducing

the real value of what it owes. For instance, demand deposits or current accounts are generally

non-interest-bearing in our sample of countries, so the bank benefits when inflation increases, since

the costs of inflation are passed through to depositors; we thus assign a -1 to demand deposits or

current accounts. Using these categorizations, we calculate the liability-based inflation exposure

of each bank as a weighted average (as a proportion of total assets) of the +1 and -1’s. The complete

categorization of asset and liability balance sheet items into “inflation exposed” or “inflation

protected” is reported in Appendix Table A5.

We then calculate a total inflation exposure measure for each bank by a simple average of

the asset and liability measures. In this total measure, which nets the total inflation exposure from

both the asset and liability side, a positive value (closer to +1) implies that an increase in inflation

hurts bank value, while a negative value (closer to -1) implies that it helps bank value. The total

inflation exposure measure is sometimes simply referred to as the “inflation exposure measure”

later in this paper and is the default measure used.

III. Global high inflation episodes

A. Evidence from macroeconomic aggregates

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Using our macroeconomic panel of 47 countries covering 1870-2016, we start by

investigating the response of bank credit-to-GDP in the aftermath of episodes of high increases in

inflation. We define high inflation episodes as years with an increase in the inflation rate of at least

10 percentage points. We exclude those episodes where inflation jumps from a negative number

to a positive number. For instance, a 14-percentage point jump in inflation calculated as a jump

from -12 percent inflation to 2 percent inflation is not included in our list of high inflation episodes.

We further exclude the two world wars (and their immediate aftermath, due to the severe supply-

side disruptions) and other major country-specific wars for our analysis. We only record the first

year, if there are successive years with a 10-percentage point increase in a given country. This

definition results in 241 unique high inflation episodes (listed in Appendix Table A1), with a

median value of 11 episodes per country over the sample period 1870-2016.

We then analyze aggregate bank credit-to-GDP outcomes subsequent to these high

inflation increases. Figure 1 plots the average one- to three-year ahead difference in the credit-to-

GDP ratio, relative to time 0, where time 0 refers to the start of an inflation episode.11 The credit-

to-GDP ratio is detrended based on a past-10-year log-linear trend within a country.

In Panel A, the baseline specification (solid blue line) shows the one-year ahead credit-to-

GDP ratio is approximately one percentage point lower following a high inflation episode. The red

dashed line corresponds to the same analysis but only for those inflation episodes for which

monetary policy was not contractionary, defined to mean that policy rates did not subsequently

rise. The fall in one-year ahead credit-to-GDP ratio is almost unchanged. Thus, the lending

contraction is not entirely due to contractionary monetary policy as a response to the rising

inflation. Similarly, one may worry that changes in bank lending might be mainly driven by

concurrent problems related to banking crises, balance-of-payment crises (i.e. sudden current

account reversals), or sovereign debt defaults. However, the green line shows the results are robust

to excluding various types of crises, meaning episodes that are approximately contemporaneous

with banking crises (from combining Reinhart and Rogoff, 2009, and Laeven and Valencia, 2014),

balance-of-payment crises (from combining Kaminsky and Reinhart, 1999; Catao, 2007; and

Calvo, Izquierdo and Mejia, 2008) and sovereign debt defaults (from Reinhart and Rogoff, 2009).

11 We use the change in bank credit-to-GDP instead of real credit growth (defined as nominal credit growth deflated by the inflation rate), because the latter is likely to be distorted by measurement error in the inflation rate, which can be substantial during high inflation episodes.

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Panel B differentiates between emerging and advanced economies, and shows that there is

a decline for both, though the initial decline is somewhat larger for emerging economies. Appendix

Table A2 shows that the larger inflation increases are associated with larger reductions in credit-

to-GDP: for example, 30 and 40 percentage point increases in inflation are associated with 1.9 and

2.1 percentage point subsequent declines in credit-to-GDP, respectively.

Table 1 confirms these results in a formal regression framework using the following

econometric specification:

𝛥𝑦#$ = 𝛼# + 𝛽𝐼𝑛𝑓𝑙𝑎𝑡𝑖𝑜𝑛𝐸𝑝𝑖𝑠𝑜𝑑𝑒#$ + 𝜸𝑿𝒊𝒕 +𝜖#$ (1)

where 𝛥𝑦#$ is the one- to three-year ahead change in the bank credit-to-GDP ratio,

𝐼𝑛𝑓𝑙𝑎𝑡𝑖𝑜𝑛𝐸𝑝𝑖𝑠𝑜𝑑𝑒#$ is a dummy variable that identifies the starting year of inflation episodes

from Appendix Table A1, and 𝑿𝒊𝒕 is a vector of country-level control variables that can include

lags of real GDP growth, change in the short-term interest rate, and currency returns.

Column 1 suggests that the one-year ahead mean credit-to-GDP ratio is about 1.5

percentage lower in inflation episodes relative to non-inflation episodes. Columns 2 adds as

controls just one-year lags: real GDP growth, change in the short-term interest rate, and currency

returns between t-1 and t. Column 3 adds two more additional lags of all these variables plus

contemporaneous changes in these variables (i.e. from t to t+h, where h is the same horizon as the

dependent variable). Controlling for these variables helps to account for other contemporaneous

factors that may decrease lending, such as a supply-side contraction, currency decline, or

contractionary monetary policy. Even with these controls, the coefficient on the inflation episode

dummy remains negative and statistically significant, suggesting a robust negative correlation

between inflation and future bank credit. Subsequent columns report similar results at longer

horizons.

There are many reasons why a large inflation increase might be associated with a lower

subsequent bank credit-to-GDP ratio, including greater investment uncertainty or reduced

aggregate loan demand. In the following section, we show that the lending contraction is not

entirely driven by an aggregate factor but instead primarily by those banks whose balance sheets

are most negatively exposed to inflation.

B. Evidence from bank-level financial statement data

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We now turn to analyzing individual bank balance sheets for the subset of global high

inflation episodes from Appendix Table A1 for which we are able to collect detailed balance sheet

data of individual banks—France and Germany in the 1920s, and Argentina, Brazil, Indonesia,

Mexico, Turkey, Uruguay, and Venezuela in recent decades. Since Bankscope data for most

countries is available after 1990 (and only after 1998 for some countries), we include in this next

analysis all inflation episodes from Appendix Table A1 for which we find balance sheet data for

at least 5 banks for those episodes in Bankscope database.

Appendix Figure A1 plots the inflation rates (year-over-year change in the price index)

over time for these countries. To provide an initial sense of bank lending in these countries,

Appendix Figure A2 shows the evolution of bank lending (normalized by assets) for the large

inflation episodes in these countries. The rectangles in each panel correspond to the interquartile

range of gross loans to assets. The horizontal line within each rectangle is the median value of

gross loans to assets. We see that following each inflation episode, there is a significant and

persistent contraction in the median value of lending. For instance, in Brazil after the inflation

episode starting in 1992, the median value of gross loans to assets decreases by 20 percentage

points and does not recover to the pre-shock level even after five years from the shock. A similar

persistent decline can be observed for the other countries.

Using the total inflation exposure measure described in Section II, Figure 2 shows the

scatterplots of change in loans-to-assets plotted against the inflation exposure measures for each

individual inflation episode.12 The subsequent change in gross loans to assets is computed through

the trough of the aggregate lending decline in each episode. We use the loans-to-assets ratio, as

opposed to the real change in loans, as the latter might be affected by measurement error of the

inflation rate, especially when inflation is extremely high. Nevertheless, one advantage of bank

cross-sectional analysis is that, by analyzing only relative differences between banks, our results

are not confounded by potential measurement error of aggregate quantities like the overall inflation

rate.

The plots in Figure 2 show a strong negative relationship between the total bank inflation

exposure and bank lending for all individual episodes, implying that banks that are more inflation

exposed reduce lending more. Figure 3 pools all the above inflation episodes and shows

12 Appendix Figures A3 and A4 show change in loans-to-assets plotted against the asset-based and liability-based inflation exposure measures, separately. The results are qualitatively similar to those in Figure 2.

14

scatterplots for changes in gross loans to assets as a function of the three inflation exposure

measures. Table 2 confirms these results in a formal regression framework. In particular, we

estimate the following equation for each inflation episode individually:

𝛥 < =>?@A?AAB$A

CD= 𝛼 + 𝛽 ∙ 𝐼𝑛𝑓𝑙𝑎𝑡𝑖𝑜𝑛𝐸𝑥𝑝𝑜𝑠𝑢𝑟𝑒D + 𝜽J ∙ 𝑿D +𝜖D (2)

where the dependent variable is the subsequent change in gross loans-to-assets for bank b around

the inflation episode, and Inflation Exposureb is one of the three bank exposure measures described

in Section II. The vector Xb consists of bank-level controls at time t, described below. The

coefficients on Inflation Exposureb are reported in Table 2. For each inflation episode, the

coefficient is reported for the asset-based, liability-based, and total inflation exposure measure

(estimated separately). For each episode, the first row reports the coefficients estimated without

controls, and the second row is estimated with controls. The coefficients are negative in nearly all

cases.

The coefficients from a regression that pools all the episodes together (with episode fixed

effects) are reported at the bottom of Table 2 and are all significant at the 1 percent level. The

magnitude of the coefficient (without controls) suggests that an increase in total inflation exposure

of a bank from 0 to 1 is associated with a decrease in loans-to-assets of 9.4 percentage points,

subsequent to a large inflation episode.

We include a variety of controls to examine the various ways in which banks may be

differentially affected by inflation increases, to help us understand through which channels

inflation may reduce banking lending. We control for government securities to total assets, as

governments running high deficits during inflation episodes may force certain banks to buy

government debt, thus crowding out private lending. We also control for non-demand deposits to

total deposits, as demand depositors may take their (i.e. non-interest-bearing) savings outside the

banking system when inflation is high and put their funds directly into interest-bearing or inflation-

protected assets, which would affect banks that rely more on demand deposit funding.

Other control variables help address concerns that inflation-exposed banks might be

systematically different from other banks in a way that is correlated with their subsequent lending.

We thus include total assets and an indicator variable of foreign-ownership as control variables,

since larger and foreign-owned banks may have greater foreign share of assets and deposits, which

in turn affects their exposure to local currency inflation. We also include the ratio of equity-to-

assets, as the profitability of more leveraged banks will be more affected by an inflation shock;

15

total securities-to-assets and non-deposit funding to total funding (defined as the sum of total

deposits, short-term funding, and long-term debt funding), as banks with high ratios may have

different banking business models than traditional deposit-taking institutions; and lagged loan

growth to control for past conditions. We report the full regression results with coefficient

estimates for all control variables in Appendix Table A4.

Finally, one may worry, as before, that the change in bank lending might be driven mainly

by concurrent banking crises, balance-of-payment crises, or sovereign debt defaults. We perform

a robustness test excluding such episodes.13 Note that we have already excluded episodes in 1998

and 2008, due to the global banking crises in these years. The pooled results excluding banking

crises, balance-of-payment crises, and sovereign debt defaults are reported at the bottom of Table

2 and are also plotted in Appendix Figure A5. The negative relationship between inflation exposure

and subsequent change in loans-to-assets remains robust and significant.

IV. Evidence from the U.S. in 1977

A. Background on the inflation increase

We study an unexpected inflation shock in the U.S. in early-1977, in which inflation (as

measured by the year-over-year change in the monthly CPI for all urban consumers) increased in

the first quarter of 1977 from 5% to 7%, where it remained stable at 7% for the subsequent year

until the middle of 1978. This episode was separate from the better-known and larger inflation

increases that came before and after it. The inflation rate over time is plotted in Figure 4 Panel A.

To put this period in context, this particular inflation episodes was one of four distinct rises

in inflation in the 1970s.14 In the first episode (1969-1972), the rise in inflation is generally

attributed to a booming economy, high government spending in part due to the Vietnam war, and

aggressive union wage demands. In the second phase (1973-4), the rise in inflation is generally

13 As in Section III.A, our sources are: banking crises (Laeven and Valencia, 2014), balance-of-payment crises (Kaminsky and Reinhart, 1999; Catao, 2007; and Calvo, Izquierdo and Mejia, 2008) and sovereign debt defaults (Reinhart and Rogoff, 2009). Within our sample, the excluded episodes are: banking crises (Argentina 1989, 1995, & 2001; Brazil 1990 & 1994; Indonesia 1997; Mexico 1994; Turkey 2000; Uruguay 2002; and Venezuela 1994), balance-of-payment crises (Argentina in 1995 & 1999-2001; Brazil 1995 & 1998, Indonesia 1997-1999, Mexico 1994-1995, Turkey 1994 & 1997) and sovereign debt defaults (Argentina in 1989 & 2001, Brazil 1987 & 1990; Indonesia 1998, 2002; Mexico 1995; Turkey 2000-1; and Venezuela 1990, 1995-8, & 2004). This leaves the inflation episodes of Argentina 2013, France 1926, Germany 1922, Indonesia 2005, and Venezuela 2013 to be analyzed. 14 This paragraph on the 1970s inflations draws on Blinder (1982), DeLong (1997), and Reed (2014).

16

attributed to surging energy and food prices due to the “oil shock”. Inflation levels then decreased

in 1975 and 1976 back to more moderate levels due to a weak economy, and the Fed as a result

abandoned its anti-inflationary stance and focused mainly on lowering unemployment. However,

in a third phase in 1977, which is the focus of this study, inflation rose markedly in the first half

of the year but stabilized by the middle of the year, remaining roughly constant at that level for an

entire year until the middle of 1978. The fourth and most drastic phase was started by a fresh burst

of energy inflation in 1979, pushing the general inflation rate over 13 percent by the end of 1979.

What caused inflation to increase in early 1977 from 5% to 7% and then to stabilize there?

This particular rise in inflation is generally attributed to an increase in energy prices in the first

quarter of the year (see Figure 4 Panel D), which filtered into non-energy prices and led to a broad

rise in the price index. Casson (1977) writes that energy prices surged in winter 1976-7, driven by

abnormally cold weather, which led to a marked rise in heating oil and natural gas prices and

critical energy supply shortages. As a secondary factor, he also notes “concerns about the possible

impact on prices of the President's stimulative fiscal proposals [announced in January 1977] […]

and the well-publicized warnings at that time that the administration’s fiscal proposals would cause

an accelerated rise in prices.”15

It is important to note that the rise in prices we study in 1977 was generally unanticipated,

as the median inflation forecast of National Association of Business Economists members was

5.9% for 1977, compared to a realized inflation rate of 5.8% in 1976 (Casson, 1977). Similarly,

the even larger surge in inflation in 1979 was generally unanticipated in 1977, so that the effects

on the banking sector in 1977 are unlikely to be due to expectations of future inflation. Consistent

with the lack of expectation of future inflation increases, Figure 4 Panel C shows that the 10-year

Treasury yield was roughly constant at 8% for all of 1976 and 1977.

Finally, we want to emphasize that the Fed did not tighten monetary policy as a reaction to

the increase in inflation in early 1977. As described by Romer and Romer (1989) in their analysis

of the FOMC’s minutes, other than a slight lowering of “target annual monetary growth rates,

which were not the central focus of policy … little other explicit anti-inflationary action was

taken,” and it was not until August 1978 that the Fed abandoned its expansionary policy and took

15 “Predictions that the stimulative tax and expenditure package would lead to sharply higher prices began to appear shortly after the proposals were first set forth in some detail on January 7, 1977. Such expectations gained considerable momentum in the weeks that followed as many bank and brokerage reports, as well as newspaper and magazine articles… repeated the charges.” (Casson, 1977).

17

a strongly anti-inflationary stance. Consistent with this lack of monetary tightening, Figure 4 Panel

C shows that the nominal Fed Funds rate was generally constant at around 5% in 1976 and rose

one-to-one with inflation (from 5% to 7%) in 1977, implying that the real rate was roughly constant

during the period of study. Thus, it is unlikely that the effect we find on bank lending in 1977 is

driven by monetary tightening.

B. Measuring banks’ inflation exposure

We use balance sheet items from the call reports in 1976 to build an inflation-exposure

measure for each bank. As described in Section II, we assign on the asset side a value +1 to a

balance sheet item if its value is “inflation exposed”, and -1 if it is “inflation protected”; on the

liability side, assign a value of -1 to a liability item if inflation erodes the liability, and a value of

1 if it does not; and then average the two measures to calculate the total inflation-exposure

measure. The complete categorization of each asset and liability balance sheet item is reported in

Appendix Table A5.

When classifying liability items into inflation-exposed or -protected, it is important to note

some institutional background on which types of deposits were interest-bearing, as non-interest-

bearing deposits are considered inflation protected (-1), while short-term interest-bearing deposits

are inflation exposed (+1), as nominal interest rates increase with inflation. In the period of study,

Regulation Q (“Reg Q”) imposed various restrictions on the payment of interest, including

prohibiting banks from paying interest on demand deposits. While Req Q only imposed this

requirement on member banks, the FDIC established identical rules in practice for insured

nonmember banks (Friedman, 1970).16 In contrast, time and savings deposits were interest-

bearing. Though time and savings deposits in principle had interest rate ceilings imposed by Reg

Q, those interest rate ceilings did not bind in practice in 1976-77, having been loosened over the

previous decade. In practice, average time and savings deposit rates were almost exactly equal to

the three-month T-bill rate (see Gilbert, 1986, Chart 3). Thus, we consider time and savings

deposits as fully interest-bearing during this period.

16 Workarounds of Reg Q, such as automatic transfer service (ATS) and negotiable order of withdrawal (NOW) accounts, which effectively created interest-bearing demand deposits, were not in widespread use in 1977, as they were only approved nationwide in 1980.

18

Our inflation-exposure measure is similar, but not identical, to an interest-rate exposure

measure. It is not identical, since certain balance sheet items such as real estate holdings, might be

exposed to real interest rate risk but less so to inflation risk. However, we are in effect measuring

the nominal interest rate exposure of banks—just that in our setting we focus on the inflation

component of the nominal interest rate, since the real short-term interest rate was roughly constant

in 1976 and 1977. We do not argue any fundamental difference if a rise in real rates were driving

an increase in nominal rates. As we show, a rise in inflation (and thus in the nominal interest rate)

leads to greater interest income for inflation-protected assets and greater interest expense for

inflation-exposed liabilities.17

C. Identification

To identify the effects of the 1977 U.S. increase in inflation on bank lending, we use state

reserve requirements, which apply to Fed nonmember banks, as an instrument for inflation-

exposure of banks in different states. In contrast, Fed member banks have uniform reserve

requirements across all states and thus should not be differentially affected across states in the

same way.18

Why should differences in state-level reserve requirements affect banks’ inflation

exposures? We conjecture that reserve requirements have large effects on non-interest-bearing

cash holding of banks.19 If this is true, then since cash is a non-interest-bearing nominal asset,

banks with higher cash holdings should be negatively affected by inflation as compared to banks

17 This result is thus related to the large literature on the interest-rate sensitivity of banks. Flannery and James (1984); English, Van den Heuvel, and Zakrajšek (2018); and Gomez, Landier, Sraer, and Thesmar (2019) report relatively large sensitivities of bank earnings and equity prices to interest rate changes. For example, English, Van den Heuvel, and Zakrajšek (2018) reports that a 100-basis point increase in interest rates increases interest income-to-assets of the median bank by almost nine basis points and decreases its stock price by 7%. Dreschler, Savov, and Schnabl (2018), in contrast, find a much more modest effect, that a 1 percentage point nominal interest rate increase is associated with a 2.4% decline in bank equity index prices, though their bank equity index is weighted towards the largest U.S. commercial banks, which they show are able to hedge their interest rate exposure using the market power of their deposit franchise. Hoffmann, Langfield, Pierobon, and Vuillemey (2019), like us, show wide heterogeneity in banks’ interest rate exposure, with many banks even being helped by interest rate increases. 18 It is important to note that the distinction between being a Fed member versus a nonmember bank is related but not exactly the same thing as being nationally-charted or state-chartered. A nationally-chartered commercial bank is required by law to be a member of the Federal Reserve System. However, a state-chartered commercial bank can choose to be a Fed member or a nonmember. Reserve requirements are determined based on being a Fed member or nonmember. 19 By “cash”, we mean all non-interest-bearing cash-like assets, including vault cash, demand deposits at other commercial banks, and non-interest-bearing reserves at Federal Reserve banks.

19

with a higher proportion of inflation protected assets. Our identification strategy thus relies on the

fact that differences in reserve requirements across states for nonmember banks should affect their

cash holdings and, hence, inflation exposure only of nonmember banks. However, we should not

see the same state-level differences from the inflation increase for member banks, as state-level

reserve requirements should not affect cash holdings of member banks, since they face uniform

national reserve requirements imposed by the Federal Reserve.

Are differences in reserve requirements across states large enough to matter? In 1976-77,

the answer is yes. Reserve requirements were quite high in some states (e.g., 15% on demand

deposits in Florida; 20% in Maryland, Massachusetts, Texas; 27% in Vermont), while in other

states they were low (e.g., 0% in Illinois).20 We show in first-stage regression results that high

reserve requirements in some states are quite constraining, forcing banks to have high cash-to-

deposit ratios and high inflation-exposure measures. One concern, motivated by Dreschler, Savov,

and Schnabl (2018), is that banks with higher reserve requirements may offset their higher cash

holdings by reducing their inflation exposure in other asset classes or on the liability side (e.g.,

with more demand deposits). However, it is an empirical question to what extent banks can do

this. Our results show that, while banks can partly offset their inflation exposure, they imperfectly

do so, as higher reserve requirements are associated with higher net balance sheet exposure

measures and lower net interest margins after the inflation shock.

Might there be other differences across states affecting bank lending that are correlated

with state-level reserve requirements? Importantly, we run placebo tests of all our results on

member banks—which are not affected by state-level reserve requirements—and find null results.

20 There is little indication in primary sources or the subsequent literature why such large differences exist between states. However, these across-state differences have been generally persistent over several decades, suggesting that the state-level reserve requirements in the 1970s were not determined in response to economic or banking conditions of the 1970s. The correlation between reserve requirements in 1948 and 1977 is 68%; the correlation between 1962 and 1977 is 78%; and the correlation between 1974 and 1977 is 99%. Even the correlation between reserve requirements in 1910 and 1977 is 35%, and for many states, such as Colorado, Florida, Idaho, Illinois, and Maryland, reserve requirements on demand deposits or time deposits have not changed since at least 1910. On average, though, across states, there has been a small downward trend in reserve requirements since the 1940s. Sources for this information include Bartlett (1911), Rodkey (1934), Harrison (1964), and Knight (1974), which analyze the history of state reserve requirements from the 1860s to the 1970s.

Instead of focusing on why such large differences exist between states, the large literature on state-level reserve requirements focuses mainly on state reserve requirements being less stringent that Federal Reserve member requirements, which led to a steady conversion of banks from member to nonmember between 1945 and 1980 (see, for example, Rose and Rose, 1979). The Monetary Control Act of 1980 abolished state-level reserve requirements and made nonmember banks subject to the same reserve requirements as member banks.

20

This suggests that changes in lending are not due to other state differences but due to something

specific to nonmember banks. Furthermore, all our results control for state-level observables, such

as GDP growth, unemployment, being an oil-producing state, regional differences, differences in

bank characteristics, and lagged lending. In Appendix Table A6, we furthermore test differences

in these observable variables across high vs. low reserve requirement states but do not find large

significant differences.

Lastly, given that a state-chartered bank may choose to be either a member or nonmember

bank, one may wonder whether there are systematic differences between member and nonmember

banks that might affect our results.21 It is important to point out that, although there might be

differences between member and nonmember banks, what matters to us is whether these

differences between member and nonmember banks are differentially affected by state reserve

requirements. This could potentially be a concern, since banks may be more likely to choose Fed

membership in states with high state-level reserve requirements. However, Appendix Table A6

analyzes, within member banks, observable differences of banks between states with high reserve

requirements versus states with low reserve requirements and finds little difference on variables

other than asset size. This result is consistent with a large literature (e.g., Prestopino, 1976; Gambs

and Rasche, 1978; Gilbert; 1978) showing surprisingly little correlation between state reserve

requirements and the mix of member and nonmember banks in a state (or the outflow rate from

Fed membership). Similarly, we perform placebo tests, as mentioned before, of all our results using

member banks, looking at potential differential effects across high vs. low reserve requirement

states, but find no effects on lending and other outcome variables, suggesting that any selection

effects into Fed membership do not cause systematic differences in outcomes due to the inflation

shock.

D. First stage regression results

21 We analyze observable differences between member and nonmember banks in our sample and find in Appendix Table A6, consistent with a large literature (e.g., Knight, 1974; Rose and Rose, 1979), that the main difference is simply bank size. Gilbert and Lovati (1978) and Rose and Rose (1979), along with many other papers, discuss the motivations for being a member or nonmember bank and attribute the choice mainly to the tradeoff between the generally stricter reserve requirements for member banks versus access to Federal Reserve services (its payments system and discount window)—also noting that banks are more likely to be member banks when geographically-close banks are members too, as payments and correspondence relationships are facilitated.

21

In the first-stage, we show that higher state-level reserve requirements affect the inflation

exposure measures of nonmember banks. Given that reserve requirements mandate minimum

levels of cash-to-deposit ratios, the purpose of this first-stage is to verify that reserve requirements

are influential enough to lead to large aggregate differences in cash-to-deposit ratios, and thus in

inflation exposure, across states. We then show in the second-stage that higher fitted values of

inflation exposure predict lower subsequent lending after the inflation increase in early 1977 and,

in turn, consequences for real economic activity. We also show that there is no first-stage or

second-stage results for member banks, which have a uniform national reserve requirement and

thus should not differ systematically by state.

Before we formally estimate the first stage regression, we present Figure 5, which shows

reserve requirements by state and how they are influential in driving large differences in cash-to-

deposit ratios across states. Panel A of Figure 5 plots, for each state, the state-level reserve

requirement for demand deposits (the red dot), along with the “adjusted” cash-to-deposit ratio for

each nonmember bank (the blue X’s), while Panel B does the same for member banks. State-level

reserve requirements for demand deposits in 1976-77 are taken, as mentioned, from Gilbert and

Lovati (1978). We construct an “adjusted” cash-to-deposit ratio for each bank because different

states may have different reserve requirements for different types of deposits (e.g., time and

savings deposits), different rules regarding eligible cash items allowed as reserves (e.g., cash in

the process of collection (CIPC) and “due from” balances), higher marginal reserve requirements

for the largest banks, or allowances that a certain percentage of reserves may be held as interest-

bearing securities. The “adjusted” cash-to-deposit ratio thus appropriately scales different types of

deposits, or adjusts what counts as eligible reserves, to allow a visual comparison of each bank’s

cash-to-deposit ratio to demand deposit reserve requirements (represented by the red dot).22 It is

important to note that these adjustments are only made for visualization purposes in Figure 5 and

are not used in computing cash-to-deposit ratios or other metrics in any other part of the paper.

22 We do the adjustments as follows. If time deposits and demand deposits have reserve requirements of 3% and 6% respectively in a given state, for example, then time deposits for each bank in that state are scaled by two when calculating total deposits, so that both demand and time deposits will both effectively look to have reserve requirements of 6%. Applying the same general principle, if states have increasing marginal reserve requirements, then deposit balances above each threshold are scaled similarly. (If time deposit reserve requirements are 0% in a state, then the adjusted cash-to-deposit ratio is simply calculated at cash-to-demand-deposits.) Similarly, if, for example, one-third of reserves may be held as securities, then the total deposits of each bank are scaled by a factor of (1 – 1/3) = 2/3. Finally, “cash” is defined according to each state’s eligible cash assets, such as CIPC and “due from” balances.

22

As one can see from Panel A in Figure 5, state-level reserve requirements are binding for

many, though not all banks, in each state, as demonstrated by the fact that many banks tend to be

bunched just to the right of the reserve requirements. Many other banks, though, hold excess

reserves. Some banks may even fall below reserve requirements, either due to lax enforcement

penalties (Gilbert and Lovati, 1978) or measurement error. However, most relevant to us is the fact

that, even though some banks do choose to hold excess reserves, Panel A shows that reserve

requirements appear influential, as the typical cash-to-deposit ratio of nonmember banks is further

to the right for states with higher reserve requirements. In contrast, Panel B shows that member

banks are not differentially affected across states, as they are driven by uniform national Federal

Reserve requirements.

Because many banks hold excess reserves, in all the subsequent analyses, we restrict our

sample to those banks for whom the reserve requirement constraint is most likely to be binding:

specifically, banks with adjusted cash-to-deposit ratios less than 5 percentage points from the

constraint. (For states with fewer than 5 banks satisfying this restriction, we include all the banks

in those states.) The purpose of this restriction is to analyze the banks that are most strongly

affected by the “treatment” (differences in reserve requirements), thus strengthening the power of

the first-stage. Although the choice to hold excess reserves is endogenous, the relevant comparison

is not between banks with excess reserve reserves and those at the constraint, but between

otherwise similar banks at the constraint in different states. The identification assumption is that

these are similar banks in different states that would have ordinarily chosen to hold similar cash-

to-deposit ratios, but cannot in some states, since reserve requirements force them away from their

unconstrained choices.23, 24

With this sample of banks, we formally estimate the following first-stage regression. We

estimate it separately for member and member banks to show that state reserve requirements affect

23 It may seem mechanical that banks within 5 percentage points of the constraint will have higher cash holdings in higher reserve requirement state, but that is precisely the point. The identification assumption is that, in the absence of reserve requirements, these banks would not have chosen such a cash-to-deposit ratio: the reserve requirements are mechanically forcing these banks to hold more cash than they would otherwise. 24 In a robustness test, we re-estimate all our results but using all banks and find, as expected, that while the first-stage results are weaker, our second stage results are similar in magnitude. Adding in banks that are far from the constraint dilutes the strength of the first-stage, since we are diluting the treatment effect with banks far from the constraint. We do not use this specification as our main one because it has a weak instrument problem.

23

the inflation exposure measures of nonmember banks but do not have any impact on inflation

exposure of member banks.

(𝑖𝑛𝑓𝑙𝑎𝑡𝑖𝑜𝑛𝑒𝑥𝑝𝑜𝑠𝑢𝑟𝑒)#,A = 𝛼 + 𝛽(𝐷𝑒𝑚𝑎𝑛𝑑𝐷𝑒𝑝𝑅𝑅)A + 𝜸J𝑿A+𝜀#,A (3)

The regression is estimated at the bank level for either all nonmember banks or all member

banks in December 1976. The endogenous variable is the inflation exposure measure for bank i in

state s. This variable is instrumented using (Demand Dep RR)s, the reserve requirement for demand

deposits of nonmember banks in state s. The control variables represented by the vector Xs adjust

for the nuances of state reserve requirements. These controls include: (Demand Dep RR)s

interacted with indicators for whether federal and state government demand deposits are exempt

from reserves, the percentage to which securities holdings are eligible as reserves for demand

deposits, and whether CIPC and “due from” balances are eligible as reserves for demand deposits;

an indicator variable of whether demand deposit reserve requirements are gradated (i.e. having

higher marginal reserve requirements for banks with larger aggregate demand deposits); and (Time

Dep RR)s, the time deposit reserve requirement for nonmember banks in state s, along with that

variable interacted with indicators of whether CIPC & “Due From” balances or Federal Funds Sold

& Certificate of Deposit balances held at other institutions count towards reserves backing time

deposit. Gilbert and Lovati (1978) provide the data and argue these variables are important in

practice.

The results are plotted in Figure 6 collapsed by state: the total inflation exposure measure

(computed here based on the aggregate balance sheet of all nonmember or member banks in each

state) is plotted as a function of the nonmember bank demand deposit reserve requirement, along

with an OLS line-of-best-fit. The left panel is for nonmember banks, and the right panel is for

members banks. Figure 6 shows a moderately strong positive relationship between state reserve

requirements and inflation for nonmember banks, while there does not seem to be such as

correlation for member banks.25

The full bank-level results are reported in Table 3. The odd-numbered columns correspond

to nonmember banks, while the even-numbered columns correspond to member banks. Columns

25 Reserve requirements, which mandate a ratio of cash to demand deposits, can increase a bank’s inflation exposure in two ways: by increasing cash or decreasing demand deposits. By repeating the first-stage analysis but substituting the total inflation exposure with the asset-based or liabilities-based inflation exposure measures, we show that most of the higher total inflation exposure is due to the asset side (higher cash holdings), though a small part of the effect is on the liability side (due to a shift away from demand deposits to other forms of funding, such as time deposits).

24

1 and 2 do not include control variables, while columns 3 through 6 include various subsets of

controls. Comparing the nonmember bank (odd-numbered) to the member bank (even-numbered)

columns, we see that the coefficient on (Demand Dep RR)s is significant and positive for

nonmember banks while it is close to zero and not significant for member banks. These results

provide support to our hypothesis that state level reserve requirements affect inflation exposure of

nonmember banks but do not have any effect on the inflation exposure of member banks. After

including controls, the coefficient on (Demand Dep RR)s approaches 1, meaning that a 1

percentage point increase in reserve requirements increases the proportion of inflation-exposed

assets (or decreases the proportion of inflation-protected liabilities) by about 1 percentage point.

Thus, banks with higher reserve requirements do not, in practice, seem to be able to hedge their

inflation exposure due to higher cash holdings by either reducing their holdings of other inflation-

exposed asset classes or by making an offsetting change on the liability side (for example, by

increasing their funding share from long-term bonds).

E. Second stage regression results: effects on bank lending

We next investigate how inflation exposure affects banks’ lending, using the fitted inflation

exposure measure from the first stage regression. We estimate the following second-stage equation

at the bank level:

∆(𝑙𝑜𝑎𝑛𝑠)#,A = 𝛼 + 𝛽1TU + 𝛽TU1TU × (𝚤𝑛𝑓𝑙. 𝑒𝑥𝑝. )Y,AZ +𝛽U1U × (𝚤𝑛𝑓𝑙. 𝑒𝑥𝑝. )Y,AZ

+𝜸J𝑿#,A+ 𝜀#,A (4)

where, D(loans)i,s is the one-year growth rate of gross loans (or other outcome variables) between

end-1976 and end-1977 for bank i and state s. 1NM is an indicator variable taking a value of one if

bank i is a nonmember banks, 1M is an analogous indicator variable for member banks, and

(𝚤𝑛𝑓𝑙. 𝑒𝑥𝑝. )Z#,A is the fitted value of the inflation exposure measure of bank i from the first-stage

regression estimated. The vector Xs consists of the following controls: state GDP growth between

1976 and 1977, the state unemployment rate in 1976, dummy variables for oil-producing states

and different U.S. regions, and bank variables (log asset size and lagged lending growth). The

main coefficients of interest are bNM and bM, which captures how subsequent loan growth varies

with inflation exposure for nonmember and member banks, respectively. Our specification

estimates the effect on nonmember (bNM) and member (bM) banks separately to assess the

magnitudes individually and demonstrate the lack of effect for member banks, but we also test

25

their difference.

The results, collapsed by state, are first visualized in Figure 7, which plots loan growth of

nonmember (left plot within each panel) or member banks (right plot within each panel) as a

function of the fitted inflation exposure measure of nonmember banks.26 The main dependent

variable is growth of gross loans between 1976 and 1977, which is the divided into components:

commercial and industrial loans and loans to households. (Presumably, loans to households are

mostly mortgage loans, though the Call Reports in 1977 unfortunately do not provide breakdowns

into mortgage loans, consumer loans, etc. We are thus limited to analyzing just these two categories

of loans due to data available.) Panels A and D plot the results for total loans, Panels B and E for

C&I loans, and Panels C and F for loans to households. All panels show a negative relationship

between loan growth and inflation exposure for nonmember banks but no relationship for member

banks, as expected.

Table 4 reports the results from the full bank-level regression. The main dependent

variables are the growth of gross loans (in columns 1-2) and the percentage point difference in

gross loans-to-assets (in column 3) between end-1976 and end-1977. Other columns decompose

gross loans into components: C&I loans (column 4-6) and loans to households (columns 7-9); and

columns 10-11 report growth of total assets as the dependent variable.

In column 1 (without controls), the coefficient on 1TU × [𝚤𝑛𝑓𝑙. 𝑒𝑥𝑝.Z \ is significant and

negative (-0.3671, s.e. = 0.067), while on 1U × [𝚤𝑛𝑓𝑙. 𝑒𝑥𝑝.Z \ it is insignificant and approximately

zero (0.1207, s.e. = 0.067). Thus, nonmember banks with high inflation exposure (due to high

state-level reserve requirements) reduce their lending more than banks with low inflation exposure;

however, lending growth of member banks is unaffected by differences in state inflation exposure,

as expected. The difference between nonmember and member banks, reported at the bottom of the

table, is statistically significant (-0.4877, s.e. = 0.095). The results with controls in column 2 are

similar in magnitude and significance.

To interpret the magnitudes, a bank with a high inflation-exposure measure of 0.260 (i.e.

the 95th percentile of inflation exposure among all banks in 1976) would thus reduce its lending

by 0.260 * 0.415 = 10.8% [using the coefficient from column 2 with controls], compared to the

26 Loan growth is computed by first aggregating all nonmember or member banks in each state. The fitted inflation exposure measure of nonmember banks is taken from the first-stage estimate plotted in Figure 6, left panel.

26

average loan growth rate of 18.0%.27 One can also estimate the aggregate effect: the average bank

(weighted by loan amount) has an inflation exposure of 0.161, implying an aggregate lending

contraction of 0.161 * 0.415 = 6.7% (relative to the counterfactual with all banks having an

inflation exposure of zero).

We find similar results for C&I loans (column 4-6) and loans to households (column 7-9),

with the effect being stronger for loans to households (0.5841 in column 8 compared to 0.3126 in

column 5). All the estimates are relatively robust to adding control variables. The regressions are

also robust to estimating them as a share of assets (columns 3, 6, and 9), demonstrating that the

reduction in loans is not simply driven by a decrease in total assets, which is also verified in

columns 10-11. Thus, there is a within-portfolio shift by affected banks away from loans towards

other assets.

F. Evidence on potential channels

What channels might be important in driving the lending reduction? We examine three

relevant possibilities. The first is a net wealth channel in which impaired earnings due to rising

inflation leads banks to reduce loan growth. The second is a flight to inflation-protection, as

banks—fearing even higher future inflation—reduce their holdings of long-term nominal loans

and shift towards more inflation-protected assets, such as short-term interest-bearing securities.

The third is a deposits outflow channel, in which depositors shift their savings from non-market-

rate deposits to market-rate interest-bearing securities outside the banking system in response to

higher inflation; the outflow of deposits forces banks to reduce their loans.

Our evidence on channels tends to support the flight to inflation-protection channel being

the most important in magnitude in the case of the U.S. in 1977. We argue that the net wealth

channel has a difficult time accounting for the magnitude of the lending decline, given that the

increase in inflation is relatively small in this case (5% to 7%). In contrast, we show using

international bank stock data for the other global inflation episodes that the net wealth channel can

indeed potentially account for the magnitudes of the lending declines in these cases. As for the

deposits outflow channel, we argue it was likely not a key channel for the U.S. in 1977, though it

may be important in other episodes.

27 These back-of-the-envelope calculations assume that banks with inflation exposure of 0 are unaffected.

27

Net wealth channel

To assess a net wealth channel, we first test whether bank inflation exposure (the fitted

value from the first stage) affects banks’ net interest margins after the inflation increase. To do

this, we repeat the second-stage regressions but with D(net interest margin) = D(interest

income/equity) - D(interest expense/equity) as the dependent variable, where D is the percentage

point difference between 1976 and 1977. The results are reported in columns 1 and 2 of Table 5.

We find that bank inflation exposure affects subsequent earnings after the inflation increase. For

more inflation-exposed nonmember banks, we find lower relative interest income (consistent with

more cash-heavy asset portfolios that do not keep up with inflation), relatively similar interest

expenses (as inflation-exposure differences between banks are mainly driven by the asset side),

which, in turn, leads to lower net interest margins.

However, according to column 1 of Table 5, the decrease in profitability is only around 1.5

percentage point, compared to an average return-on-equity of 12%.28 Using a multiplier from the

literature, it is difficult to reconcile this decrease in bank value with the magnitude of the lending

decline for affected banks. Even if the reduction in return-on-equity were assumed to be

permanent, bank value would thus be reduced by 1.5/12 = 12.5%.29 Combined with a very rough

point estimate from Baron, Verner, and Xiong (2019), who report that a 30% drop in bank market

equity values is followed by an average credit-to-GDP reduction of 3.2 percentage points, a 12.5%

drop in bank value should roughly correspond to a lending reduction of 1.3% (though the

assumption here of a linear response probably leads to overestimation of the magnitude, given that

28 A simple back-of-the-envelope calculation confirms that a 1.5 percentage point reduction is roughly the right magnitude. Consider the following highly-simplified high-inflation-exposed bank, which has the following asset composition (30% cash, 60% long-term fixed-rate nominal loans, and 20% short-term loans and interest-bearing securities with an average repricing maturity of one year) and the following liability composition (10% equity, 60% demand deposits, and 30% time deposits with an average repricing maturity of year). Note that such a bank has a total inflation exposure measure of 0.8 – 0.2 + 0.3 – 0.6 = 0.3. Then, if nominal short-term interest rates increase by 2%, after a year the return-on-assets will have changed by 0.20*(2) – 0.30*(2) = 0.2 percentage points, due to the repricing of the securities and time deposits (and assuming the interest paid or received on cash, loans, and demand deposits does not change). Given the bank’s 10-to-1 leverage, the return-on-equity thus decreases by 2 percentage points. Given that the securities and time deposits are repriced uniformly over the course of the year, the average return-on-equity over the course of the year would be half of that, or 1 percentage point. 29 This estimate is broadly consistent with a prior literature examining the effect of inflation on banks net worth in the aggregate. For example, Cao (2014) finds that a one percentage-point permanent increase in the U.S. inflation rate leads to an average 15 percent loss of Tier 1 capital for U.S. commercial banks. Similarly, Santoni (1986) and Lajeri and Dermine (1999) both find that unexpected inflation is inversely correlated with subsequent changes in the bank stock index, independently of changes in the interest rate.

28

the response of bank lending is probably nonlinear in the net wealth shock). A net wealth effect

thus has difficulty accounting for the 5 to 10 percentage point relative reduction in loan growth for

the most inflation exposed banks.

However, in international episodes with large increases in inflation, international bank

stock evidence suggests that net wealth affects can be large and able to explain the lending

contractions in these episodes. Appendix Table A7 analyzes bank equity index returns across a

panel of 47 countries over the period 1870-2016 using data taken from Baron, Verner, and Xiong

(2019). The table shows that an “inflation episodes” (as defined as a year with >10 percentage

point increase in the inflation rate, as in Section III.A) is associated with a cumulative 33.4%

decline in bank real returns between t-1 and t+3 in the case with the full set of controls (relative to

a 16.6% decline in nonfinancial real returns). Using the very rough point estimate from Baron,

Verner, and Xiong (2019), who report that a 30% drop in bank market equity values is followed

by an average credit-to-GDP reduction of 3.2 percentage points, a 33.4% drop in bank value should

roughly correspond to a lending reduction of 3.6%, which is roughly the magnitude of the lending

decline across these episodes reported in Table 1.

Flight to inflation-protection

Since inflation erodes the real value of long-term nominal assets but has much less effect

on short-term interest-earning assets or real assets, it is natural to expect a shift in portfolios of

banks with greater inflation exposure towards more inflation-protected assets. As our first-stage

results suggest that most banks in our sample are initially unhedged to inflation in 1976, banks

may decide to mitigate their subsequent inflation exposure after inflation increases in early-1977.

Inflation increases are often persistent and associated with higher future inflation uncertainty (Ball,

1992), giving banks—most especially, those who are initially highly exposed to inflation—good

reasons to fear even higher future inflation.

To formally test a shift towards greater inflation protection, we estimate Equation 4 with

alternative dependent variables: the change in cash holdings, which are nominal assets, and the

change in holdings of interest-bearing securities, which are inflation-protected assets. As before,

all changes are computed from end-1976 to end-1977. After the unexpected increase in inflation

in early-1977, we expect a shift away from the former and a shift towards the latter in more

inflation-exposed banks.

29

The results are visualized in Figure 8, collapsed at the state level. Panel A and C show

variation in one-year growth of cash and securities for nonmember and member banks as a function

of bank inflation exposure. Panel B and D show the same for one-year differences in cash-to-assets

and securities-to-assets. We see a negative relationship between cash holdings and inflation

exposure for nonmember banks, but not for member banks. Similarly, we see a positive

relationship between holdings of securities and inflation exposure for nonmember banks, but not

for member banks.

Bank-level results are reported in Table 5. Columns 3-4 and 6-7 examine the one-year

differences in the ratios of cash-to-assets and securities-to-assets, respectively, and columns 5 and

8 examine the one-year growth rates in cash and securities, respectively. From column 3, we see

that the coefficient on 1TU × [𝚤𝑛𝑓𝑙. 𝑒𝑥𝑝.Z \ is significant and negative, which implies that

nonmember banks reduce cash holdings in states that are more exposed to inflation. In fact, they

insulate their balance sheets by shifting towards inflation-protected assets, such as securities: the

coefficient on securities in column 6 is positive and significant for nonmember banks.

These shifts away from cash and towards interest-bearing securities are large in magnitude.

A bank with a high inflation-exposure measure of 0.260 (i.e. the 95th percentile of inflation

exposure), as in Section III.E, would increase its securities-to-asset ratio by 0.260 * 0.2730 = 7.1

percentage points (column 6). These magnitudes are important because they demonstrate how even

a small inflation increase from 5% to 7% can induce large changes in lending through a large

rebalancing of banks’ portfolios. The implication is that, in this large rebalancing, banks that are

the most unhedged to inflation will need to reduce the most their exposure to long-term nominal

lending, which can thus in turn account for the large sudden lending decline.

The deposit outflows channel

The deposit outflows channel, in which depositors may shift funds from non-market-rate

deposits to market-rate securities outside the banking system, first came to prominence in the

second half of 1966 and again in 1968-69 in response to a rapid rise in interest rates (Ruebling,

1970). It was then that the term “disintermediation” was commonly used to describe this

phenomenon when interest rate ceilings on time deposits effectively prevented both banks and

other thrift institutions from competing for funds.

30

There are institutional reasons to believe that the deposits outflow channel would probably

not be of primary importance in the U.S. 1977 setting. The deposit outflows in the 1966-69 episode

were mainly of time deposit, which were previously subject to an interest rate cap (Ruebling,

1970). However, by 1977, the interest rate cap was removed in practice and, in fact, time and

savings deposits did get the same yield as three-month T-bills (see Gilbert, 1986, Chart 3).30 Thus,

we should not expect time and savings deposit outflows in 1977. As for demand deposits, in the

1966-69 episode, even though demand deposits could not pay interest due to Reg Q, they ended

up being “sticky”, since they were typically used for transactional purposes or by small depositors.

Similarly, in 1977, we also expect demand deposits to be “sticky”.

Turning to the data, the evidence suggests that the deposits channel did not seem to be of

primary importance in the U.S. 1977 setting. Figure 9 shows that both demand deposits and time-

and savings- deposits did not seem to react specifically to the inflation increase in 1977. For

example, in Panel A, which plots aggregate demand and time deposits to total bank assets, time

deposits are flat and, while demand deposits are declining, this seems to be part of a long-run

downward trend with no apparent deviation around the 1977 inflation increase. Panel B shows the

same series, but scaled by the CPI instead of total assets, and similarly shows that a long-run

similar downward trend for both types of deposits, with almost no short-term fluctuations in or

around 1977.31 Similarly, Table 5 estimates the second-stage regression again but with demand-

deposits-to-assets (columns 9-10) or other-deposits-to-assets (columns 11-12) as dependent

variables. The coefficients on 1TU × [𝚤𝑛𝑓𝑙. 𝑒𝑥𝑝.Z \ in Table 5 are close to zero and not statistically

significant, suggesting there is no differential effect on deposits subsequent to the inflation

increase.

G. Transmission to nonfinancial firms and the macroeconomy

Results from the previous sections suggest that the early-1977 inflation shock had a

negative effect on bank credit supply, especially in states with nonmember banks more exposed to

the inflation shock. In this section, we investigate how the contraction in lending propagates to the

30 After June 1970, many types of time deposits of denominations greater than $100,000 became exempt from interest rate ceilings. Chart 3 in Gilbert (1986) shows that the average time and savings deposit rates across all insured commercial banks tracked the three-month T-bill rate. 31 Similarly, money market mutual funds, which were beginning to become a substitute for time depositors searching for higher yield, were flat, even in nominal terms, throughout all of 1977 (see Gilbert, 1984, Chart 4).

31

real economy.

First, we examine consequences for the state-level macroeconomy in terms of housing and

construction employment. To do so, we re-estimate Equation 4, the second-stage regression,

collapsed at the state level, with two new state-level dependent variables: house price growth and

construction employment growth. In Figure 10, we find that the fitted values of inflation exposure

(from the state-level first-stage results in Figure 6) are negatively correlated with house price

growth and construction employment growth at the state level, consistent with the decreases in

bank lending reported in the previous subsection. The results are formally estimated in Table 6,

which shows the results for house price growth and construction employment growth are

significant. Table 6 also estimates similar results for other state-level variables such the one-year

growth rate (December 1976 to December 1977) in manufacturing employment, retail

employment, service-sector employment, state-level GDP, and state-level CPI, but finds no

significant changes. Thus, the aggregate state-level effects seem mostly concentrated in the

housing and construction sectors, as these sector might be expected to be most affected by changes

in credit-supply.

We next test whether the reduction in bank lending has an effect on nonfinancial firms.

Bank credit can be an important source of financing for capital expenditures for nonfinancial firms,

and, hence, a decline in bank lending should affect investment and other related quantities. Our

empirical strategy differentiates between bank-dependent and non-bank-dependent nonfinancial

firms, which we classify following Almeida and Campello (2007), as described below. The reason

for this approach is that bank-dependent firms should be more affected by the reduction in bank

lending, as non-bank dependent firms can access public debt as a substitute for the reduced bank

lending. Effectively, this comparison also provides us with another placebo test, as state-level

differences in banks’ inflation exposure should not affect non-bank-dependent firms. Furthermore,

comparing the effect on bank-dependent versus non-bank-dependent firms allows us to focus on

the effect coming from a reduction in the supply of bank lending to firms, rather that the effect

coming from the demand-side (e.g., credit-constrained consumers may purchase less from firms

in affected states).

Turning now to our data on publicly-listed nonfinancial firms from Compustat, we estimate

the following model to estimate the real effects of the fall in bank lending:

𝛥(𝐼𝑛𝑣𝑒𝑠𝑡𝑚𝑒𝑛𝑡)#,A = 𝛼 + 𝛽1D?@^_`BaB@`B@$

32

+𝛽bc1D?@^_`BaB@`B@$ × (𝚤𝑛𝑓𝑙. 𝑒𝑥𝑝. )Z#,A + 𝛽Tbc1@>@_D?@^_`Ba × (𝚤𝑛𝑓𝑙. 𝑒𝑥𝑝. )Z

#,A

+𝜸J ∙ 𝑿𝒊,𝒔 + 𝜖#,A (5)

where Investmenti,s is defined as the ratio of capital expenditures to lagged capital stock for

nonfinancial firm i headquartered in state s.32 Δ(Investment)i,s is one-year growth in investment

between end-1976 and end-1977. We also look at other dependent variables, such as the growth

of nonfinancial firm debt-to-asset, sales, cash-to-assets, and return-on-equity over that period.

1D?@^_`BaB@`B@$ is a dummy variable that take on the value of one if firm i is bank-dependent. The

main coefficient of interest is bBD, which shows how investment of bank-dependent firms varies

with bank inflation exposure.

Following Almeida and Campello (2007), we classify a firm as bank-dependent if meets

either of two conditions: 1) it does not have an S&P bond rating in COMPUSTAT, or 2) long-term

debt is less than 10% of assets. Almeida and Campello (2007) argue that the first condition is a

good proxy for whether a firm has access to bond markets and that the second condition captures

the fact that bank debt is mostly short-term (used for financing working capital) while public debt

is often long-term. Thus, 1D?@^_`BaB@`B@$ takes a value 1 if either of the two conditions are met

and 0 otherwise. The regression [𝚤𝑛𝑓𝑙. 𝑒𝑥𝑝.Z \is the fitted value of inflation exposure from the first-

stage (i.e. first-stage results in Figure 6 collapsed by state). Xi,s is a vector of firm- and state-level

controls.

Figure 7 visualizes the results collapsed at the state-level by plotting the change in

investment in 1977 against state-level (fitted) inflation exposure for nonmember banks. The left

panel is for bank-dependent firms, and the right panel is for non-bank-dependent firms. The

vertical axis measure percent growth in investment. We see that the growth rate of investment for

bank dependent firms is lower in states with high inflation exposure, while the growth rate of firms

which are not bank dependent is relatively uncorrelated with inflation exposure.

The full regression results are also reported in Table 5. We see that the coefficient on the

interaction term between bank dependence and inflation exposure is negative and significant for

growth in investment (column 1) and percentage point change in debt-to-assets (column), while it

is not significant for non-bank dependent firms. However, there is no significant change in firm

32 Of course, the location of a firm’s headquarters may be a crude proxy for the location of the entire firm and the location of its main lending banks. However, for bank-dependent firms, which are typically small and thus more localized, especially in 1977, firm headquarters may be a relatively good proxy.

33

sales, cash-to-assets, and return-on-equity, suggesting that the bank credit channel in this setting

mainly acts through affecting investment and debt, as might be expected, but has little effect on

other aspects of nonfinancial firm operations.

V. Conclusion

Our paper hypothesizes a previously neglected bank credit channel through which inflation

shocks are transmitted to the real economy. By analyzing a sudden and unexpected inflation shock

in the U.S. in early-1977, we show how inflation-exposed banks reduce their lending and how this

affects household and nonfinancial firm investment in a quantitatively important way.

Understanding the consequences of higher inflation is important for several reasons. First,

and most importantly, even though inflation is currently subdued in most countries around the

world, rising inflation is a recurring problem in emerging economies. For example, according to

the IMF’s World Economic Outlook, there have been recent large jumps in inflation in many large

emerging market economies: Argentina (20% to 55% in 2018-9), Brazil (6% to 10% in 2015-6),

Egypt (10% to 32% in 2016-7), India (6% to 11% in 2007-9), and Turkey (10% to 20% in 2018-

9), just to name a few examples. It is important to understand the macroeconomic costs of rising

inflation, especially to the financial system.

Second, this issue is important for understanding the optimal level of inflation in a low

interest rate environment where higher steady-state inflation would allow central bankers more

room to lower rates. As policy makers discuss transitioning to a higher inflation target of 3% or

more, it is important to understand the stresses to the banking sector of moving to this new long-

run inflation target. Our work suggests that even an increase to a moderately higher level of

inflation might induce a sudden pullback in lending due the flight-to-inflation-protection channel.

While our work focuses on demonstrating a causal effect of inflation on bank lending in

the U.S. setting, one limitation is that we cannot directly assess the magnitudes of particular

channels in international inflation episodes (e.g., Argentina, Brazil), due to lack of detailed data.

For stress testing the banks in these countries to sudden changes in inflation, one would need more

detailed regulatory data of bank balance sheet exposure—such as more detailed data differentiating

between indexed or non-indexed, short-term or long-term, local- or foreign-currency denominated,

or specific asset classes such as government bonds and real estate—which we hope regulators who

have such data will be able to better understand the inflation exposure of banks in their country.

34

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Shiller, R. J. (1997). Why Do People Dislike Inflation?. In Reducing inflation: Motivation and strategy. University of Chicago Press, 13-70.

Figure 1: Bank credit-to-GDP after large increases in inflation

Using a country-level panel of 47 countries over the period 1870-2016, the figure plots the average one-to three-year ahead aggregate bank credit-to-GDP ratio, subsequent to the start of an “inflation episode”.Bank credit-to-GDP is detrended using a past-10-year log-linear trend within a country and plotted relativeto time 0, where time 0 refers to the start of an “inflation episode”. Inflation episodes are defined as yearswith an increase in the inflation rate of at least 10 percentage points (with a positive level of inflation overthe entire episode). In Panel A, the solid blue line shows the baseline specification. The dashed red lineshows the results only for those inflation episodes in which monetary policy is non-contractionary, definedas inflation episodes with no subsequent increase in policy rates. The solid green line excludes inflationepisodes which coincide with banking crises, balance-of-payment crises, or sovereign defaults (as defined inthe text). Panel B shows the average one-, two-, and three-year ahead difference in credit to GDP ratio forthe subsamples of emerging market and developed economies.

Panel A: Baseline specification

Panel B: Country subsets

Figure 2: Bank inflation exposure and bank lending

This figure plots the change in gross-loans-to-assets of individual banks against the total bank inflationexposure measure (as defined in Section II) for each individual inflation episode (Panels A through N) listedin Table A3. The bank-level data comes from Bankscope and new historical sources. The subsequent changein loans-to-assets is computed through the trough of the aggregate lending decline in each episode.

Panel A: Argentina 2002

-.6-.4

-.20

.2.4

Δ (l

oans

-to-a

sset

s)

-1 -.5 0 .5 1inflation exposure

Panel B: Argentina 2013

-.3-.2

-.10

.1.2

.3Δ

(loan

s-to

-ass

ets)

-1 -.5 0 .5 1inflation exposure

Panel C: Brazil 1992-93

-.50

.5Δ

(loa

ns-to

-ass

ets)

-1 -.5 0 .5 1inflation exposure

Panel D: France 1926

-.03

-.02

-.01

0.0

1Δ

(loa

ns-to

-ass

ets)

-1 -.5 0 .5 1inflation exposure

Panel E: Germany 1922

-.50

.5Δ

(loan

s-to

-ass

ets)

-1 -.5 0 .5inflation exposure

Panel F: Indonesia 2005

-.3-.2

-.10

.1Δ

(loa

ns-to

-ass

ets)

-1 -.5 0 .5 1inflation exposure

Panel G: Mexico 1995-.4

-.20

.2.4

Δ (l

oans

-to-a

sset

s)

-1 -.5 0 .5 1inflation exposure

Panel H: Turkey 1994

-.20

.2.4

.6.8

Δ (l

oans

-to-a

sset

s)

-1 -.5 0 .5 1inflation exposure

Panel I: Turkey 1997

-.4-.2

0.2

.4Δ

(loa

ns-to

-ass

ets)

-1 -.5 0 .5 1inflation exposure

Panel J: Turkey 2001

-.4-.2

0.2

Δ (l

oans

-to-a

sset

s)

-1 -.5 0 .5 1inflation exposure

Panel K: Uruguay 2002

-.4-.2

0.2

.4Δ

(loa

ns-to

-ass

ets)

-1 -.5 0 .5 1inflation exposure

Panel L: Venezuela 1996

0.0

5.1

.15

.2Δ

(loa

ns-to

-ass

ets)

-1 -.5 0 .5 1inflation exposure

Panel M: Venezuela 2002-.8

-.6-.4

-.20

.2Δ

(loa

ns-to

-ass

ets)

-1 -.5 0 .5 1inflation exposure

Panel N: Venezuela 2013

-.2-.1

0.1

.2.3

Δ (l

oans

-to-a

sset

s)

-1 -.5 0 .5 1inflation exposure

Figure 3: Bank inflation exposure and bank lending: all episodes pooled together

This figure plots the change in gross-loans-to-assets against the asset-based (Panel A), liability-based (PanelB), and the total (Panel C) bank inflation exposure measures (as defined in Section II) for all banks pooledtogether from all the inflation episodes listed in Table A3. The subsequent change in gross loans-to-assets iscomputed through the trough of the aggregate lending decline in each episode.

Panel A: Asset Exposure

-1-.5

0.5

1Δ

(loa

ns-to

-ass

ets)

-1 -.5 0 .5 1asset-based inflation exposure

Panel B: Liability Exposure

-1-.5

0.5

1Δ

(loa

ns-to

-ass

ets)

-1 -.5 0 .5 1liability-based inflation exposure

Panel C: Total Exposure

-1-.5

0.5

1Δ

(loa

ns-to

-ass

ets)

-1 -.5 0 .5 1inflation exposure

Figure 4: The U.S. 1977 inflation episode: macroeconomic indicators

This figure plots several macroeconomic variables for the U.S. around the early-1977 inflation episode. PanelA plots inflation, measured as the year-over-year change in the monthly CPI for all urban consumers, PanelB plots the annualized growth rate of quarterly real GDP, Panel C plots the Federal Funds rate (solid redline) and the 10-year Treasury constant maturity rate (blue dashed line), and Panel D plots the West TexasIntermediate crude oil price (USD per barrel) and heating oil price (cents per gallon), both normalized to1 in December 1974. The vertical green lines indicate the beginning and end of the period studied, fromDecember 1976 to December 1977.

Panel A: Inflation

56

78

9C

PI In

flatio

n (%

)

Jan1976 Jan1977 Jan1978 Jan1979

Panel B: Real GDP Growth

34

56

7R

eal G

DP

Gro

wth

, ann

ualiz

ed (%

)

Jan1976 Jul1976 Jan1977 Jul1977 Jan1978 Jul1978

Panel C: Interest Rates

46

810

Jan1976 Jan1977 Jan1978 Jan1979

10-Year Treasury Yield Fed Funds Rate

Panel D: Energy Prices

.91

1.1

1.2

1.3

Jan1976 Jan1977 Jan1978 Jan1979

WTI Crude Heating Oil

Figure 5: Reserve requirements and adjusted cash-to-deposit ratios across states

This figure plots state-level reserve requirements for demand deposits and the “adjusted” cash-to-depositratio (as defined in section IV) of each bank in 1976. Panel A plots, for each state, the state-level reserverequirement for demand deposits (the red squares) for nonmember banks along with the “adjusted” cash-to-deposit ratio for each nonmember bank in the state (the blue X’s). Panel B plots the same but for memberbanks in each state. State-level reserve requirements for demand deposits in 1976 are taken from Gilbert andLovati (1978).

Panel A: Nonmember Banks

IllinoisNew YorkArkansas

CaliforniaDelaware

IowaKansas

KentuckyMinnesota

MississippiMissouri

NevadaNew Jersey

OhioOklahoma

South CarolinaUtah

WashingtonWest Virginia

LouisianaMontana

North CarolinaNorth Dakota

AlabamaArizonaIndianaMaine

TennesseeVirginia

MichiganHawaii

New HampshireNew Mexico

OregonPennsylvania

WisconsinConnecticut

ColoradoGeorgia

IdahoMaryland

MassachusettsNebraska

Rhode IslandTexas

South DakotaAlaskaFlorida

WyomingVermont

0 .1 .2 .3adjusted cash-to-deposit ratio

Panel B: Member Banks

IllinoisNew YorkArkansas

CaliforniaDelaware

IowaKansas

KentuckyMinnesota

MississippiMissouri

NevadaNew Jersey

OhioOklahoma

South CarolinaUtah

WashingtonWest Virginia

LouisianaMontana

North CarolinaNorth Dakota

AlabamaArizonaIndianaMaine

TennesseeVirginia

MichiganHawaii

New HampshireNew Mexico

OregonPennsylvania

WisconsinConnecticut

ColoradoGeorgia

IdahoMaryland

MassachusettsNebraska

Rhode IslandTexas

South DakotaAlaskaFlorida

WyomingVermont

0 .3.1 .2 adjusted cash-to-deposit ratio

Figure 6: First stage regressions: state-level reserve requirements and bank inflation exposure

This figure visualizes the first stage regression from equation (3) but collapsed at the state level. The totalinflation-exposure measure (constructed from the aggregate balance sheets of all nonmember or memberbanks within each state in December 1976) is plotted against state-level reserve requirements for demanddeposits for nonmember banks. The left panel is for nonmember banks, and the right panel is for memberbanks. State-level reserve requirements for demand deposits in 1976 are taken from Gilbert and Lovati (1978).

AL

AK

AZ

AR

CA CO

CT

DE

FLGA

HI

ID

IL

IN

IA

KSKY

LA

ME

MD

MA

MI

MN

MS

MO

MTNE

NV

NH

NJ

NM

NY

NC

NDOH

OK

ORPA

RI

SC

SD

TNTX

UT

VT

VA

WA

WV

WIWY

AL

AK

AZ

ARCA

CO

CT

DE

FL

GA

HI

ID

IL

INIA

KSKY

LA

ME

MD

MA

MI

MN MSMO

MT NE

NV

NH

NJ

NM

NY

NC

ND

OH

OK

ORPA

RI

SC

SD

TN

TX

UT

VT

VAWA

WV

WI

WY

-.2

0

.2

.4

-.2

0

.2

.4

0 .1 .2 .3 0 .1 .2 .3

Nonmember Member

Infla

tion-

expo

sure

mea

sure

Nonmember reserve requirements for demand deposits

Figure7:

Second

stageregression

s:ba

nkinfla

tion

expo

sure

andba

nklend

ing

Thisfig

urevisualizes

thesecond

stageregression

from

equa

tion

(4),

colla

psed

atthestatelevel,an

dexam

ines

how

bank

s’infla

tion

expo

sure

inDecem

ber1976

affects

their

lend

ingover

thesubsequent

year.

Pan

elsA

throug

hF

plot

theon

e-year

chan

gein

state-levellend

ingou

tcom

evariab

les(D

ecem

ber1976

toDecem

ber1977)againstthe

state-levelinfla

tion

expo

sure

measure

(the

fittedvaluefrom

thefirst

stage,

takenfrom

Figure6).Pan

elsA,B,an

dC

plot

theon

e-year

grow

thin

grossloan

s,commercial

and

indu

strial

loan

s,an

dloan

sto

households,respectively,while

Pan

elsD,E,an

dFplot

thepe

rcentage

pointchan

gein

loan

s-to-assets,

commercial

andindu

strial

loan

s-to-assets,

andho

useholdloan

s-to-assets.

Astheseplotsarecolla

psed

onthestagelevel,theba

lancesheets

ofallba

nksarefirst

aggregated

atthestate-level(for

mem

beran

dno

nmem

ber

bank

ssepa

rately)be

fore

compu

ting

both

theloan

grow

thratesan

dtheba

nkinfla

tion

expo

sure

measuresformem

beran

dno

nmem

berba

nks.

The

left

plot

withineach

pane

lis

forno

nmem

berba

nks,

while

therigh

tplot

isformem

berba

nks.

Pan

elA:T

otal

loan

s

AL

AK

AZ

ARC

A

CO

CT

DE

FL

GA

HI

ID

ILIN

IA

KS

KY

LA ME

MD MA

MI

MN M

S

MO

MT

NE

NVNH

NJ

NM

NY

NC

ND

OH

OK

OR

PA

RI

SCSD

TN

TX

UT

VT

VA

WA

WV

WI

WY

AL

AK

AZ

ARC

A

CO

CT

DE

FL

GA

HI

ID

IL

IN

IA

KS

KY

LA ME

MD MA

MI

MNM

S

MO

MT

NE

NV NH

NJ

NM

NY

NC

ND

OH

OK

OR

PA

RI

SC

SD

TN

TX

UT

VT

VA

WA

WV

WI

WY

0.1.2.3

0.1.2.3

0.1

.2.3

0.1

.2.3

Non

-mem

ber

Mem

ber

Growth of Gross Loans, 76-77

Infla

tion

expo

sure

Pan

elB:C

&Iloan

s

AL

AK

AZ

AR C

A

CO

CT

DE

FL

GA

HI

ID

IL

IN

IA

KS

KY

LA ME

MD MA

MI

MN M

S

MO

MT

NE

NVNH

NJ

NM

NY

NC

ND

OH

OK

OR

PA

RI

SCSD

TN

TXU

T

VT

VA

WA

WV

WI

WY

AL

AK

AZ

ARC

A

CO

CT

DE

FL

GA

HI

ID

IL

IN

IA

KS

KY

LA ME

MD MA

MI

MN M

S

MO

MT

NE

NV NH

NJ

NM

NY

NC

ND

OH

OK

OR

PA

RI

SCSD

TN

TX

UT

VT

VA

WA

WV

WI

WY

0.2.4

0.2.4

0.1

.2.3

0.1

.2.3

Non

-mem

ber

Mem

ber

Growth of Commercial and Industrial Loans, 76-77

Infla

tion

expo

sure

Pan

elC:L

oans

toho

useh

olds

AL

AK

AZ

ARC

A

CO

CT

DE

FL

GA

HI

ID

IL

IN

IA

KS

KY

LAME

MD MA

MI

MN M

S

MO

MT

NE

NVNH

NJ

NM

NY

NC

ND

OH

OK

OR

PA

RI

SC

SD

TN

TX

UT

VT

VA

WA

WV

WI

WY

AL

AK

AZ

ARC

A

CO

CT

DE

FL

GA

HI

ID

IL

IN

IAK

S

KY

LAME

MD MA

MI

MNM

S

MO

MT

NE

NV NH

NJ

NM

NY

NC

ND

OH

OK

OR

PAR

I

SC

SD

TN

TX

UT

VT

VA

WA

WV

WI

WY

0.1.2.3.4

0.1.2.3.4

0.1

.2.3

0.1

.2.3

Non

-mem

ber

Mem

ber

Growth of Personal Loans, 76-77

Infla

tion

expo

sure

Pan

elD:T

otal

loan

s/assets

AL

AK

AZ

ARC

AC

O

CT

DE

FL

GA

HI

ID

IL

IN

IA

KS

KY

LA ME

MD MA

MI

MN M

S

MO

MT

NE

NVNH

NJ

NM

NY

NC

ND

OH

OK

OR

PA

RI

SC

SD

TNTX

UT

VT

VA

WA

WV

WI

WY

AL

AK

AZ

AR C

A

CO

CT

DE

FLG

A

HI

ID

IL

IN

IA

KS

KY

LA ME

MD MA

MI

MN M

S

MO

MT

NE

NV NH

NJ

NM

NY

NC

ND

OH

OK

OR

PA

RI

SC

SD

TN

TX

UT

VT

VA

WA

WV

WI

WY

-.050.05.1

-.050.05.1

0.1

.2.3

0.1

.2.3

Non

-mem

ber

Mem

ber

Difference in Loans to Assets, 76-77

Infla

tion

expo

sure

Pan

elE:C

&Iloan

s/assets

AL

AK

AZ

AR C

A

CO

CT

DE

FL

GA

HI

ID

IL

IN

IA

KS

KY

LA ME

MD MA

MI

MN M

S

MO

MT

NE

NVNH

NJ

NM

NY

NC

ND

OH

OK

OR

PA

RI

SCSD

TN

TX

UT

VT

VA

WA

WV

WI

WY

AL

AK

AZ

ARC

AC

O

CT

DE

FL

GA

HI

ID

IL

IN

IA

KS

KY

LA ME

MD MA

MI

MN M

S

MO

MT

NE

NV NH

NJ

NM

NY

NC

ND

OH

OK

OR

PA

RI

SC

SD

TN

TX

UT

VT

VA

WA

WV

WI

WY

-.04-.020.02

-.04-.020.02

0.1

.2.3

0.1

.2.3

Non

-mem

ber

Mem

ber

Difference in Industiral Loans to Assets, 76-77

Infla

tion

expo

sure

Pan

elF:L

oans

toho

useh

olds

/assets

AL

AK

AZ

ARC

A

CO

CT

DE

FL

GA

HI

ID

IL

IN

IA

KS

KY

LAME

MD MA

MI

MN M

S

MO

MT

NE

NV NH

NJ

NM

NY

NC

ND

OH

OK

OR

PA

RI

SC

SD

TN

TX

UT

VT

VA

WA

WV

WI

WY

AL

AK

AZ

ARC

A

CO

CT

DE

FLG

A

HI

ID

IL

IN

IA

KS

KY

LA ME

MD MA

MI

MNM

S

MO

MT

NE

NV NH

NJ

NM

NY

NC

ND

OH

OK

OR

PAR

I

SC

SD

TN

TX

UT

VT

VAW

A

WV

WI

WY

-.020.02.04

-.020.02.04

0.1

.2.3

0.1

.2.3

Non

-mem

ber

Mem

ber

Difference in Persoanl Loans to Assets, 76-77

Infla

tion

expo

sure

Figure 8: Change in cash and securities holdings

This figure visualizes results from equation (4), collapsed at the state level as in Figure 7, with the change incash holdings and the change in holdings of interest-bearing securities as dependent variables. Panels A andC plot one-year growth in state-level cash and securities holdings for nonmember (left) and member banks(right) as a function of the fitted value of inflation exposure of nonmember banks (taken from Figure 6).Panels B and D show the same plots but for the one-year difference in cash-to-assets and securities-to-assets.

Panel A: Cash

AL

AK

AZ

AR

CA

COCT

DE

FL

GA

HI

ID

IL

IN

IA KS

KY

LA

ME

MD

MA

MI

MN

MS

MO

MT

NE

NV

NH

NJ

NM

NYNC

ND

OH

OK

OR

PA

RI

SC

SD

TN

TX

UT

VT

VA

WA

WV

WI

WY

AL

AK

AZ

AR

CA

CO

CT

DE

FL

GA

HI IDILIN

IA

KS

KY

LA

ME

MD

MA

MIMN

MS

MO

MT

NENV

NH

NJ

NMNY

NC

ND

OH

OK

OR

PA

RI

SC

SD

TN

TX

UT

VTVA

WA

WV

WI

WY

0.5

1

0.5

1

0 .1 .2 .3 0 .1 .2 .3

Nonmember Member

Gro

wth

of c

ash,

76-

77

Inflation exposure

Panel B: Cash/Assets

AL

AK

AZ

AR

CA

COCTDE FL

GA

HI

ID

IL

IN

IA

KS

KY

LA

ME

MD

MA

MI

MN

MS

MO

MT

NE

NV

NH

NJ

NM

NYNC

ND

OH

OK

OR

PARI

SC

SD

TN

TX

UT

VT

VA

WA

WV

WI

WY

AL

AK

AZ

ARCA

CO

CT

DE

FL

GA

HI

ID

IL

IN

IA

KS

KY

LA

ME

MD

MA

MIMN

MS

MO

MT

NENV

NH

NJ

NMNY

NC

ND

OH

OK

OR

PA

RI

SC

SD

TN TX

UT

VT

VAWA

WV

WI

WY-.0

10

.01

.02

.03

-.01

0.0

1.0

2.0

3

0 .1 .2 .3 0 .1 .2 .3

Nonmember Member

Diff

eren

ce in

cas

h to

ass

ets,

76-7

7

Inflation exposure

Panel C: Securities

ALAK

AZ

AR

CACO

CT

DE

FL

GA

HI

ID

IL

IN

IA

KS

KYLA

ME

MD

MA

MIMN

MS

MO

MT

NE

NV

NH

NJ

NM

NY

NC

ND

OH

OK

ORPA

RI

SC

SD

TN

TXUT

VT

VA

WA

WV

WI

WY

AL

AK

AZ

ARCA

CO

CT

DE

FL

GA

HI

IDIL

INIA

KS

KY

LA

ME

MD

MA

MI

MN

MS

MO

MT

NE

NV

NH

NJNM

NY

NC

ND

OH

OK

OR

PA

RISC

SD

TN TX

UT

VT

VA

WAWV

WI

WY

-.2-.1

0.1

.2.3

-.2-.1

0.1

.2.3

0 .1 .2 .3 0 .1 .2 .3

Nonmember Member

Gro

wth

of s

ecur

ities

, 76-

77

Inflation exposure

Panel D: Securities/Assets

AL

AK

AZ

ARCA

COCT

DE

FL

GAHI

ID

IL

IN

IAKS

KY

LA

ME

MD

MA

MI

MN

MS

MO

MT

NE

NV

NH

NJ

NM

NYNC

ND

OH

OK

OR

PA

RI

SCSD

TN

TX

UT

VT

VA

WAWV

WI

WY

AL

AK

AZ

AR

CA

CO

CT

DEFLGA

HI

IDIL IN

IA

KS

KY

LAME

MDMA

MI

MN

MS

MO

MT

NE

NV

NH

NJ

NMNY

NC

ND

OH

OK

ORPARI

SC

SD

TN

TX

UT VT

VA

WAWV

WI

WY

-.1-.0

50

.05

-.1-.0

50

.05

0 .1 .2 .3 0 .1 .2 .3

Nonmember Member

Diff

eren

ce in

secu

ritie

s to

asse

ts, 7

6-77

Inflation exposure

Figure 9: Aggregate changes in deposits

This figure plots the aggregate changes in deposits for all U.S. commercial banks. Panel A plots the ratio ofaggregate demand deposits to total assets (solid blue line) and aggregate time and savings deposits to totalassets (solid red line) between 1975 and 1979. Panel B plots real demand deposits (solid blue line) and realtime and savings deposits of commercial banks (solid red line) in USD billion. Real deposits are computedas nominal deposits deflated by the CPI index for all urban consumers. The vertical green lines representthe start and end of the period analyzed, from December 1976 to December 1977.

Panel A: Deposits to Total Assets

.43

.44

.45

.46

.47

.48

time

depo

sits

to a

sset

s

.18

.2.2

2.2

4de

man

d de

posi

ts to

ass

ets

01jan1975 01jul1976 01jan1978 01jul1979

demand deposits time deposits

Panel B: Real Deposits

760

780

800

820

840

860

real

tim

e de

posi

ts

320

340

360

380

400

real

dem

and

depo

sits

01jan1975 01jul1976 01jan1978 01jul1979

demand deposits time deposits

Figure 10: Effects on house prices and construction employment

This figure shows how the contraction in lending in states with nonmember banks more exposed to inflationpropagates to the real economy. It presents results from equation (4), with state-level house price growthand state-level construction employment growth between December 1976 and December 1977 as dependentvariables. Panels A and B plot state-level house price growth and state-level growth in construction employ-ment, respectively, against the fitted values of state-level inflation exposure of nonmember banks (taken fromFigure 6). Growth in state-level house prices is constructed as the year-over-year growth in quarterly alltransactions house price index from the Federal Housing Finance Agency. State-level data on constructionemployment is from the Bureau of Economic Analysis Regional Accounts.

Panel A: House Price Growth

AL

AKAZ

AR

CA

CO

CT

DE

FL

GA

HI

IDIL

INIA KS

KY

LA

ME

MD

MA

MI

MN

MS

MO

MT

NE

NV

NH

NJ

NMNY NC

ND

OH

OK

OR

PARI

SC

SD

TN

TX

UT

VT

VA

WA

WV

WI

WY

-.2-.1

0.1

.2H

ouse

Pric

e G

row

th, 7

6-77

0 .1 .2 .3Inflation exposure

Panel B: Construction Employment

AL

AK

AR

CA

CO

CT

DE

FL

GA

HI

ID

IL

IN

IA

KS

KY

LA

ME

MD

MA

MI

MN

MS

MO

MT

NE

NVNH

NJ

NM

NY

NC

NDOH

OK

ORPA

RISCSD

TN

TX

UT

VT

VA

WA

WV

WIWY

-.1-.0

50

.05

Con

stru

ctio

n Em

ploy

men

t Gro

wth

, 76-

77

0 .1 .2 .3Inflation exposure

Figure 11: Effects on nonfinancial firms’ investment

This figure shows the effects of the reduction in bank credit on investment of nonfinancial firms. Specifically,the figure visualizes results from equation (5) collapsed at the state-level, with one-year aggregate growthin investment of nonfinancial firms plotted against the fitted values of state-level inflation exposure ofnonmember banks (taken from Figure 6). The left panel is for bank-dependent firms and the right panelis for firms that are not bank-dependent. We following Almeida and Campello (2007) to classify a firm asbank-dependent (see definition in the main text). The dependent variable is constructed by first aggregatinginvestment at the state-level for bank-dependent and non-bank-dependent firms separately and thencomputing one-year growth rates between 1976 and 1977. Investment is defined as capital expendituresdivided by the previous year’s plant, property, and equipment.

Tab

le1:

Highinfla

tion

episod

esarefollo

wed

bycontractionin

cred

it

Using

acoun

try-levelpa

nelof

47coun

triesover

thepe

riod

1870-2016,

this

tableestimates

theaverageon

e-to

three-year

ahead

aggregateba

nkcredit-to-GDPratio,

subsequent

tothestartof

an“in

flation

episod

e”.Infla

tion

episod

esaredefin

edas

yearswithan

increase

intheinfla

tion

rate

ofat

least10

percentage

points

(withapo

sitive

levelo

finfla

tion

over

theentire

episod

e).Ban

kcredit-to-GDP

isdetrende

dusingapa

st-10-year

log-lin

ear

trendwithinacoun

try.

Colum

ns(1),

(4),

and(7)do

notinclud

ean

ycontrols,column(2),

(5),

and(8)controlforchan

gesin

threemacroecon

omic

variab

les(realG

DP,

interest

ratesan

dcurrency

returns),a

ndcolumn(3),(6),an

d(9)ad

dtw

omorelags

ofallc

ontrol

variab

les,tw

olags

ofinfla

tion

,an

don

e-,tw

o-,an

dthree-year

aheadchan

gein

allcontrolvariab

les.

Allcolumns

includ

ecoun

tryfix

edeff

ects.Stan

dard

errors

aredo

uble

clustered

atthecoun

tryan

dyear

level.***p<

0.01,*

*p<

0.05,*

p<0.1.

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

Dep.Var.

∆(credit-to-G

DP) i,t,t+1

∆(credit-to-G

DP) i,t,t+2

∆(credit-to-G

DP) i,t,t+3

InflationEpisodes

i,t

-0.015***

-0.013**

-0.014***

-0.020**

-0.017

-0.018*

-0.030**

-0.034**

-0.036**

(0.004)

(0.005)

(0.005)

(0.008)

(0.012)

(0.010)

(0.013)

(0.016)

(0.015)

Rea

lGDP

growth

i,t−

1,t

0.183***

0.159***

0.37

4***

0.226***

0.568***

-0.072

(0.045)

(0.037)

(0.088)

(0.082)

(0.133)

(0.253)

Curren

cyreturni,t−

1,t

0.017

0.041***

0.065**

0.064**

0.079*

-0.037

(0.015)

(0.012)

(0.029)

(0.027)

(0.043)

(0.075)

∆InterestRate

i,t−

1,t

0.000

-0.001

-0.011*

-0.012

-0.030***

-0.023

(0.002)

(0.002)

(0.006)

(0.009)

(0.010)

(0.017)

Observation

s3,890

2,921

2,722

3,792

2,850

2,654

3,696

2,780

2,587

Num

berof

grou

ps47

3838

4738

3847

3838

Cou

ntry

FE

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Add

itiona

lcontrols

No

No

Yes

No

No

Yes

No

No

Yes

Table 2: Bank-level inflation exposure and subsequent bank lending, global inflation episodes

This table reports coefficients from bank-level regressions (equation 2) estimating subsequent changes ingross loans-to-assets as a function of the inflation exposure measure for each bank. The table estimatesthis regression separately for each inflation episode listed in Table A3. Column (1) is for the asset-basedinflation exposure measure, column (2) is for the liability-based inflation exposure measure, and column(3) is for the total bank exposure measure. For each episode, the first row reports results from equation(2) without controls and the second row reports results with bank-level controls (log assets, log commonequity, government securities to assets, non-demand deposits to total deposits, non-deposit funding to totalfunding, lagged loan growth, and a dummy variable for foreign banks). The last four rows reports resultspooling all episodes together (with episode fixed effects) and for the subsample excluding banking crises,balance-of-payments crises, and sovereign debt defaults crises (as defined in the main text).

Inflation Episode Inflation Controls Asset Exposure Liability Exposure Total Exposure(1) (2) (3)

Argentina 2002 42.5% No -0.2916*** -0.3040*** -0.4304***Yes -0.3799*** -0.5577*** -0.7572***

Argentina 2013 353.9% No -0.0844** -0.0184 -0.0884**Yes -0.1236*** 0.0271 -0.0869*

Brazil 1992-93 2004.4% No -0.1868*** -0.1578* -0.2672***Yes -0.1973*** -0.2288 -0.3640*

France 1926 14.7% No 0.0103 -0.0069 -0.0117Yes

Germany 1922 2.2 · 109% No -0.1341* -0.1579** -0.2468***Yes

Indonesia 2005 10.7% No -0.0996* -0.1282* -0.1836**Yes -0.1919 -0.5520 -0.7820

Mexico 1995 46.0% No -0.3515*** -0.1797 -0.3571***Yes -0.8650*** 0.4795 -1.2162**

Turkey 1994 54.4% No -0.3319 -0.2507* -0.4439**Yes -0.1878 0.0682 -0.0320

Turkey 1997 19.3% No -0.0904 -0.1431 -0.1814Yes -0.0092 -0.0346 -0.0156

Turkey 2001 29.5% No -0.1959 -0.0379 -0.1679Yes -0.4029 -0.1777 -0.0845

Uruguay 2002 22.4% No -0.0712 -0.0539 -0.0882Yes -0.1828 -0.1789 -0.3496

Venezuela 1996 46.6% No -0.0493 0.1341 -0.0989Yes 0.1631 0.1654 -0.2960

Venezuela 2002 18.9% No -0.2144*** -0.1805* -0.3797***Yes -0.1535 -0.2730 -0.3125

Venezuela 2013 36.1% No 0.1349*** 0.0649 -0.1416Yes -0.1569*** 0.1216 -0.1157

All No -0.1238*** -0.0816*** -0.1291***Yes -0.1195*** -0.1237*** -0.1945***

Excluding other No -0.0228 -0.0534** -0.0183crises Yes -0.2399** -0.2521*** -0.4250***

Tab

le3:

First

stag

eregression

s:state-levelreserve

requ

irem

ents

andba

nkinfla

tion

expo

sure

Thistablerepo

rtsestimates

from

thefirst

stageregression

(equ

ation3)

estimated

attheba

nk-le

velforthesampleof

allmem

beror

nonm

embe

rba

nksin

theU.S.in

1976-77.

The

depe

ndentvariab

leis

thetotalinfla

tion

expo

sure

ofeach

bank

regressedon

state-levelreserverequ

irem

enton

deman

ddepo

sits

ofno

nmem

berba

nksin

that

state(D

eman

ddepo

sitRR).

The

controlvariab

les(non

-instruments)ad

just

forthenu

ancesof

state

reserverequ

irem

ents.These

controlsinclud

e:an

interactionwithindicators

forwhether

federala

ndstategovernmentdeman

ddepo

sits

areexem

pted

from

reserverequ

irem

ents

(Gov

tdepo

sits

RR),thefraction

ofsecurities

eligible

asreserveassets

(Securitieseligible),whether

CIP

Can

d“D

ueFrom

"ba

lances

areeligible

asreserves

assets

(CIP

Celigible).

Other

controls

includ

ean

indicatorvariab

leof

whether

deman

ddepo

sitreserverequ

irem

ents

aregrad

ated

(Dem

anddep.

grad

ated

sche

dule);

timedepo

sitreserverequ

irem

ents

forno

nmem

berba

nks(T

imeDep

RR)an

dtheirinteractionwith

whether

CIP

Can

d“D

ueFrom

"ba

lances

areeligible

asreserves

assets

fortimedepo

sits

(CPIC

eligible)an

dwhether

FederalF

unds

sold

andcertificate

ofdepo

sitba

lances

held

atotherinstitutions

areeligible

towards

timedepo

sitrequ

ired

reserves

(Fed

Fund

ssold

andCDseligible).

Colum

n(1),

(3),

and(5)areforno

nmem

berba

nks,

andcolumn(2),

(4),

and(6)areformem

berba

nks.

Stan

dard

errors

clusteredat

thestatelevelarerepo

rted

inpa

rentheses.

***p<

0.01,*

*p<

0.05,*

p<0.1.

Non

mem

ber

Mem

ber

Non

mem

ber

Mem

ber

Non

mem

ber

Mem

ber

(1)

(2)

(3)

(4)

(5)

(6)

Dem

andde

positRR

0.671***

0.068

1.018***

0.131

1.112***

0.136

(0.102)

(0.073)

(0.122)

(0.088)

(0.120)

(0.090)

×Govtde

posits

RR

-0.034

-0.237***

(0.089)

(0.066)

×Se

curities

eligible

-0.620***

-0.146

(0.125)

(0.094)

×CIP

Celigible

-0.952***

-0.151

(0.171)

(0.127)

Dem

andde

p.grad

ated

sche

dule

-0.033***

0.009

(0.011)

(0.008)

Tim

eDep

RR

-0.007***

-0.001

-0.005***

-0.001

(0.001)

(0.001)

(0.002)

(0.001)

×Fe

dFu

ndsSo

ldan

dCDselig.

-0.022***

-0.003

(0.004)

(0.003)

×CIP

Celigible

0.013**

-0.001

(0.005)

(0.004)

Con

stan

t0.015

0.093***

0.007

0.092***

0.039**

0.116***

(0.013)

(0.009)

(0.013)

(0.009)

(0.015)

(0.011)

Observation

s1367

1542

1367

1542

1367

1542

Adj.R

20.030

-0.000

0.047

0.000

0.123

0.016

F-statistic

43.0

0.9

34.7

1.2

25.0

4.1

Tab

le4:

Second

stag

eregression

s:ba

nkinfla

tion

expo

sure

andba

nklend

ing

Thistablerepo

rtsresultsfrom

thesecond

stageregression

(equ

ation4)

estimated

attheba

nk-le

vel.The

depe

ndentvariab

les(listedin

thetoprowof

thetable)

are

theon

e-year

grow

thratesforgrossloan

s,commercial

andindu

strial

loan

s,loan

sto

househ

olds,a

ndassets,a

ndon

e-year

diffe

rencein

loan

sto

assets,c

ommercial

andindu

strial

loan

sto

assets,an

dho

useh

oldloan

sto

assets.Colum

ns(1),

(4),

(7),

and(10)

dono

tinclud

econtrols

while

allothe

rcolumns

includ

econtrols.

Con

trol

variab

lesinclud

estateGDPgrow

thbe

tween1976

and1977,the

stateun

employ

mentrate

in1976,d

ummyvariab

lesforoil-p

rodu

cing

states

anddiffe

rent

U.S.region

s,an

dlags

ofba

nkvariab

les(size,

lend

inggrow

th,liq

uidassets).1N

Mis

anindicatorvariab

leforno

nmem

berba

nksan

d1M

isan

indicatorvariab

leformem

berba

nks.

(InfExp)isthefittedvalueof

theinfla

tion

expo

sure

measure

forno

nmem

berba

nksfrom

thefirst-stage

regression

.Stan

dard

errors

clustered

atthestate-levela

rerepo

rted

inpa

renthe

ses.

***p<

0.01,*

*p<

0.05,*

p<0.1.

Dep.Variable:

%∆(T

otal

Loans)

∆(T

otLoans-

%∆(C

&ILoans)

∆(C

&ILoans

%∆(L

oans

toHou

seho

lds)

∆(L

oans

toHHs

%∆(A

ssets)

-to-Assets)

-to-Assets)

-to-Assets)

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

1N

M0.0839***

0.0839***

0.04

36***

0.0767***

0.0767***

0.0083***

0.0928***

0.0928***

0.0154***

-0.0011

-0.0011

(0.013)

(0.012)

(0.005)

(0.025)

(0.024)

(0.003)

(0.021)

(0.021)

(0.003)

(0.022)

(0.022)

(InfExp)×1M

0.1207*

0.0726

0.0248

0.1929

0.2039

0.0203

-0.0855

-0.1503

-0.0384***

0.0639

0.0750

(0.067)

(0.068)

(0.026)

(0.133)

(0.134)

(0.017)

(0.111)

(0.113)

(0.014)

(0.088)

(0.099)

(InfExp)×1N

M-0.3671***

-0.4151***

-0.2686***

-0.3236**

-0.3126**

-0.0522*

**-0.5193***

-0.5841***

-0.124

1***

0.1361

0.1472

(0.067)

(0.068)

(0.026)

(0.133)

(0.134)

(0.017)

(0.111)

(0.113)

(0.014)

(0.114)

(0.108)

Con

stan

t0.1404***

0.1717***

0.0419***

0.1061***

0.2215***

0.0215***

0.1936***

0.1942

***

0.0126***

0.1202***

0.0768***

(0.009)

(0.013)

(0.005)

(0.018)

(0.026)

(0.003)

(0.015)

(0.022)

(0.003)

(0.013)

(0.019)

Difference

-0.4877***

-0.4877***

-0.2935***

-0.5165***

-0.5165***

-0.0726***

-0.4338***

-0.4338***

-0.0857***

0.0722

0.0722

(0.095)

(0.094)

(0.036)

(0.188)

(0.186)

(0.023)

(0.157)

(0.156)

(0.019)

(0.144)

(0.145)

Con

trols

No

Yes

Yes

No

Yes

Yes

No

Yes

Yes

No

Yes

Observation

s2909

2909

2909

2873

2873

2873

2905

2905

2905

2909

2909

Adj.R

20.022

0.054

0.071

0.004

0.034

0.051

0.018

0.025

0.041

0.006

0.032

Tab

le5:

Effe

ctson

bank

profi

tability,

cash

andsecu

rities

holdings,a

ndde

positfund

ing

Thistablepresents

evidence

onpo

tentialchan

nels

throug

hwhich

infla

tion

-exp

osed

bank

sredu

ceba

nklend

ing.

The

tableis

constructedsimila

rly

toTab

le4bu

twithalternatedepe

ndentvariab

les.

The

depe

ndentvariab

lesaretheon

e-year

diffe

rencein

netinterest

margin(colum

n1an

d2),

one-year

diffe

renc

ein

cash-to-assets

ratio(colum

n3an

d4),o

ne-yearpe

rcentdiffe

renc

ein

cash

(colum

n5),o

ne-yeardiffe

renc

ein

securities-to-assets

ratio(colum

n8an

d9),on

e-year

percentdiffe

rencein

securities

(colum

n10),

one-year

diffe

rencein

deman

ddepo

sits-to-assets

ratio(colum

n11

and

12),on

e-year

diffe

rencein

otherdepo

sits-to-assets

ratio(colum

n13

and14).

Con

trol

variab

lesinclud

estateGDPgrow

thbe

tween1976

and1977,the

stateun

employmentrate

in1976,d

ummyvariab

lesforoil-p

rodu

cing

states

anddiffe

rent

U.S.regions,a

ndlags

ofba

nkvariab

les(size,

lend

inggrow

th,

liquidassets).

Stan

dard

errors

clusteredat

thestate-levela

rerepo

rted

inpa

rentheses.

***p<

0.01,*

*p<

0.05,*

p<0.1.

Dep.Variable:

∆(N

etInt.

Margin)

∆(C

ash-to-A

ssets)

%∆(C

ash)

∆(Securities-to-A

ssets)

%∆(Securities)

∆(D

eman

dDep.-to-A

ssets)

∆(O

ther

Dep.-to-A

ssets)

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

1N

M-0.0026

-0.0017

0.0141***

0.0141***

0.6665***

-0.0534***

-0.0534***

-0.1165**

-0.0057

-0.0057

0.00

150.0015

(0.007)

(0.007)

(0.003)

(0.003)

(0.160)

(0.014)

(0.013)

(0.048)

(0.007)

(0.007)

(0.009)

(0.009)

(InfExp)×1M

0.0256

-0.0129

-0.0016

-0.0028

0.1339

-0.079

6-0.0599

-0.2461

-0.0112

0.0011

-0.0004

-0.0069

(0.037)

(0.037)

(0.017)

(0.017)

(0.530)

(0.055)

(0.041)

(0.209)

(0.036)

(0.038)

(0.049)

(0.050)

(InfExp)×1N

M-0.0157

-0.0596

-0.0629***

-0.0641***

-3.7639*

**0.2730***

0.2927***

0.8175***

0.0315

0.0438

-0.0118

-0.0183

(0.037)

(0.037)

(0.019)

(0.019)

(1.059)

(0.071)

(0.074)

(0.240)

(0.031)

(0.031)

(0.040)

(0.042)

Con

stan

t-0.0142***

0.0354***

0.0058***

0.0046

0.2714**

-0.0109*

-0.0292**

-0.0115

0.0010

-0.024

3***

0.0005

0.0056

(0.005)

(0.007)

(0.002)

(0.004)

(0.118)

(0.006)

(0.013)

(0.043)

(0.006)

(0.007)

(0.006)

(0.012)

Difference

-0.0413

-0.0467

-0.0613**

-0.0613**

-3.8978***

0.3526***

0.3526***

1.0636***

0.0426

0.0426

-0.0114

-0.0114

(0.091)

(0.051)

(0.025)

(0.025)

(1.163)

(0.090)

(0.082)

(0.319)

(0.048)

(0.047)

(0.063)

(0.063)

Con

trols

No

Yes

No

Yes

Yes

No

Yes

Yes

No

Yes

No

Yes

Observation

s2903

2903

2908

2908

2908

2902

2902

2902

2909

2909

2909

2909

Adj.R

20.004

0.032

0.054

0.055

0.08

80.056

0.067

0.039

0.002

0.042

0.000

0.004

Table 6: Effects on local employment growth and housing

This table shows how the the inflation exposure of nonmember banks (state-level fitted value from thefirst-stage regressions taken from Figure 6) affects state-level macroeconomic variables during the 1977 U.S.inflation episode. The dependent variables are the one-year growth in state-level construction employment,manufacturing employment, retail employment, service-sector employment, state-level GDP, house prices,and state-level CPI between 1976 and 1977. Control variables include contemporaneous and lagged stateGDP growth, unemployment rate, and dummy variables for oil-producing states and different U.S. regions.Robust standard errors are reported in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

Construction Manufacturing Retail Services State GDP House Price State CPIempl. growth empl. growth empl. gr. empl. gr. growth growth growth(1) (2) (3) (4) (5) (6) (7)

(Inf Exp) -0.3856** 0.1196 0.0089 -0.0100 -0.1425 -0.5366** 0.0025(0.184) (0.072) (0.036) (0.048) (0.129) (0.228) (0.017)

Constant 0.0155 -0.0544* -0.0585*** 0.0143 0.1068** 0.1596** 0.0611***(0.043) (0.028) (0.013) (0.010) (0.042) (0.066) (0.005)

Controls Yes Yes Yes Yes Yes Yes YesObservations 50 50 50 50 50 50 50Adj. R2 0.908 0.866 0.852 0.793 0.109 0.186 0.223

Table 7: Effects on nonfinancial firms

This table shows the effects of the reduction in bank lending on nonfinancial firms in the same state. Thedependent variables are the one-year change in investment (column 1), debt-to-assets ratio (column 2), cash-to-assets ratio (column 4), return-on-equity (column 5), and one-year growth in sales (column 3). 1BD isan indicator variable for bank-dependent firms and 1NBD is an indicator variable for firms that are notbank-dependent. (Inf Exp) is the fitted value of the inflation exposure measure for nonmember banks fromthe first-stage regression. All columns control for firm-level characteristics, including assets, common equity,long-term debt to total debt, cash-to-current assets, and state-level control variables including GDP growthbetween 1976 and 1977, the 1976 unemployment rate, and dummy variables for oil-producing states and U.S.regions. Standard errors clustered at the state level are reported in parenthesis. *** p<0.01, ** p<0.05, *p<0.1.

∆Investment ∆(Debt/Assets) ∆Sales ∆(Cash/Assets) ∆ROE(1) (2) (3) (4) (5)

1BD 0.0293** 0.0196*** 0.0093 0.0023 -0.0132(0.0141) (0.00410) (0.0255) (0.0025) (0.0196)(Inf Exp) × 1NBD 0.0358 0.0136 0.150 -0.0045 -0.0126(0.0301) (0.0129) (0.100) (0.0044) (0.0793)(Inf Exp) × 1BD -0.215* -0.0934*** -0.0062 -0.0016 0.144(0.110) (0.0289) (0.225) (0.0145) (0.159)

Constant 0.0036 0.0342*** 0.328*** -0.0036 -0.0411(0.0215) (0.00862) (0.0561) (0.0037) (0.0322)

Difference -0.251** -0.107*** -0.156 0.0029 0.156(0.122) (0.0366) (0.180) (0.0150) (0.181)

Bank & state controls Yes Yes Yes Yes YesObservations 1812 1829 1823 1792 1822Adj. R2 0.001 0.083 0.032 0.026 0.219

Figure A1: International inflation episodes

This figure shows inflation episodes for Argentina, Brazil, France, Germany, Indonesia, Mexico, Turkey,Uruguay, and Venezuela. These are the countries and inflation episodes for which we were able to findindividual bank balance sheet data for at least 5 banks. See Table A1 for the precise start year for eachinflation episode and the corresponding jump in inflation for each episode.

Panel A: Argentina 2002

010

2030

40In

flatio

n (%

)

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Panel B: Argentina 2013

1020

3040

50In

flatio

n (%

)

2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

Panel C: Brazil 1992

050

010

0015

0020

0025

00In

flatio

n (%

)

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

Panel D: France 1926

-10

010

2030

40In

flatio

n (%

)

1924 1925 1926 1927 1928 1929 1930 1931

Panel E: Germany 1922

05.

00e+

091.

00e+

101.

50e+

102.

00e+

102.

50e+

10In

flatio

n (%

)

1918 1919 1920 1921 1922 1923 1924 1925 1926

Panel F: Indonesia 2005

05

1015

20In

flatio

n (%

)

2001 2003 2005 2007 2009 2011 2013 2015

Panel G: Mexico 1995

1020

3040

50In

flatio

n (%

)

1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

Panel H: Turkey 1994 and 1997

4060

8010

012

0In

flatio

n (%

)

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

Panel I: Turkey 2001

020

4060

80In

flatio

n (%

)

1998 1999 2000 2001 2002 2003 2004 2005 2006

Panel J: Uruguay 2002

510

1520

25In

flatio

n (%

)

1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018

Panel K: Venezuela 1996

2040

6080

100

Infla

tion

(%)

1993 1994 1995 1996 1997 1998 1999 2000

Panel L: Venezuela 2002

1015

2025

30In

flatio

n (%

)

2000 2001 2002 2003 2004 2005 2006 2007

Panel M: Venezuela 20130

5010

015

0In

flatio

n (%

)

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

Figure A2: Bank lending falls during high inflation episodes

This figure shows the evolution of bank lending (normalized by assets) following large inflation episodes listedin Table A3. The rectangles in each box represent the quartile range and the horizontal line within eachrectangle is the median value of loans to assets. This figure is based on bank-level data from Bankscope andnewly-uncovered historical records.

Panel A: Argentina 2002

0.2

.4.6

.81

gros

s lo

ans

to a

sset

s

2000 2001 2002 2003 2004 2005

Panel B: Argentina 2013

.2.4

.6.8

1gr

oss

loan

s to

ass

ets

2011 2012 2013 2014 2015 2016 2017

Panel C: Brazil 1992-93

0.2

.4.6

.81

gros

s lo

ans

to a

sset

s

1991 1992 1993 1994 1995 1996 1997 1998 1999

Panel D: France 1926

.2.4

.6.8

1gr

oss

loan

s to

ass

ets

1923 1924 1925 1926 1927 1928 1929 1930

Panel E: Germany 1922

0.2

.4.6

.81

gros

s lo

ans

to a

sset

s

1919 1920 1921 1922 1923 1924 1925 1926 1927 1928

Panel F: Indonesia 2005

0.2

.4.6

.81

gros

s lo

ans

to a

sset

s

2003 2004 2005 2006 2007 2008 2009 2010

Panel G: Mexico 19950

.2.4

.6.8

1gr

oss

loan

s to

ass

ets

1993 1994 1995 1996 1997 1998 1999 2000

Panel H: Turkey 1994 and 1997

0.2

.4.6

.81

gros

s lo

ans

to a

sset

s

1992 1993 1994 1995 1996 1997 1998 1999 2000

Panel I: Turkey 2001

0.2

.4.6

.81

gros

s lo

ans

to a

sset

s

2000 2001 2002 2003 2004 2005 2006 2007

Panel J: Uruguay 2002

0.2

.4.6

.81

gros

s lo

ans

to a

sset

s

2000 2001 2002 2003 2004 2005 2006

Panel K: Venezuela 1996

0.2

.4.6

.8gr

oss

loan

s to

ass

ets

1994 1995 1996 1997 1998 1999 2000

Panel L: Venezuela 2002

0.2

.4.6

.8gr

oss

loan

s to

ass

ets

2000 2001 2002 2003 2004 2005 2006 2007

Panel M: Venezuela 2013

0.2

.4.6

.8gr

oss

loan

s to

ass

ets

2011 2012 2013 2014 2015 2016 2017

Figure A3: The asset-based inflation exposure measure and bank lending

This figure is similar to Figure 2 but uses the asset-based inflation exposure measure instead of the totalinflation exposure measure.

Panel A: Argentina 2002

-.6-.4

-.20

.2.4

Δ (l

oans

-to-a

sset

s)

-1 -.5 0 .5 1asset-based inflation exposure

Panel B: Argentina 2013

-.3-.2

-.10

.1.2

.3Δ

(loan

s-to

-ass

ets)

-1 -.5 0 .5 1asset-based inflation exposure

Panel C: Brazil 1992-93

-.50

.5Δ

(loa

ns-to

-ass

ets)

-1 -.5 0 .5 1asset-based inflation exposure

Panel D: France 1926

-.03

-.02

-.01

0.0

1Δ

(loa

ns-to

-ass

ets)

-1 -.5 0 .5 1asset-based inflation exposure

Panel E: Germany 1922

-.50

.5Δ

(loan

s-to

-ass

ets)

-1 -.5 0 .5 1asset inflation exposure

Panel F: Indonesia 2005

-.4-.2

0.2

Δ (l

oans

-to-a

sset

s)

-1 -.5 0 .5 1asset-based inflation exposure

Panel G: Mexico 1995-.4

-.20

.2.4

Δ (l

oans

-to-a

sset

s)

-1 -.5 0 .5 1asset-based inflation exposure

Panel H: Turkey 1994

-.20

.2.4

.6.8

Δ (l

oans

-to-a

sset

s)

-1 -.5 0 .5 1asset-based inflation exposure

Panel I: Turkey 1997

-.4-.2

0.2

.4Δ

(loa

ns-to

-ass

ets)

-1 -.5 0 .5 1asset-based inflation exposure

Panel J: Turkey 2001

-.4-.2

0.2

Δ (l

oans

-to-a

sset

s)

-1 -.5 0 .5 1asset-based inflation exposure

Panel K: Uruguay 2002

-.50

.51

Δ (l

oans

-to-a

sset

s)

-1 -.5 0 .5 1asset-based inflation exposure

Panel L: Venezuela 1996

0.0

5.1

.15

.2Δ

(loa

ns-to

-ass

ets)

-1 -.5 0 .5 1asset-based inflation exposure

Panel M: Venezuela 2002-.8

-.6-.4

-.20

.2Δ

(loa

ns-to

-ass

ets)

-1 -.5 0 .5 1asset-based inflation exposure

Panel N: Venezuela 2013

-.2-.1

0.1

.2.3

Δ (l

oans

-to-a

sset

s)

-1 -.5 0 .5 1asset-based inflation exposure

Figure A4: The liability-based inflation exposure measure and bank lending

This figure is similar to Figure 2 but uses the liability-based inflation exposure measure instead of the totalinflation exposure measure.

Panel A: Argentina 2002

-.6-.4

-.20

.2.4

Δ (l

oans

-to-a

sset

s)

-1 -.5 0 .5 1liability-based inflation exposure

Panel B: Argentina 2013

-.3-.2

-.10

.1.2

.3Δ

(loan

s-to

-ass

ets)

-1 -.5 0 .5 1liability-based inflation exposure

Panel C: Brazil 1992-93

-.50

.5Δ

(loa

ns-to

-ass

ets)

-1 -.5 0 .5 1liability-based inflation exposure

Panel D: France 1926

-.03

-.02

-.01

0.0

1Δ

(loa

ns-to

-ass

ets)

-1 -.5 0 .5 1liability-based inflation exposure

Panel E: Germany 1922

-.50

.5Δ

(loan

s-to

-ass

ets)

-1 -.5 0 .5 1liability inflation exposure

Panel F: Indonesia 2005

-.3-.2

-.10

.1Δ

(loa

ns-to

-ass

ets)

-1 -.5 0 .5 1liability-based inflation exposure

Panel G: Mexico 1995-.4

-.20

.2.4

Δ (l

oans

-to-a

sset

s)

-1 -.5 0 .5 1liability-based inflation exposure

Panel H: Turkey 1994

-.20

.2.4

.6.8

Δ (l

oans

-to-a

sset

s)

-1 -.5 0 .5 1liability-based inflation exposure

Panel I: Turkey 1997

-.4-.2

0.2

.4Δ

(loa

ns-to

-ass

ets)

-1 -.5 0 .5 1liability-based inflation exposure

Panel J: Turkey 2001

-.4-.2

0.2

Δ (l

oans

-to-a

sset

s)

-1 -.5 0 .5 1liability-based inflation exposure

Panel K: Uruguay 2002

-.4-.2

0.2

.4Δ

(loa

ns-to

-ass

ets)

-1 -.5 0 .5 1liability-based inflation exposure

Panel L: Venezuela 1996

0.0

5.1

.15

.2Δ

(loa

ns-to

-ass

ets)

-1 -.5 0 .5 1liability-based inflation exposure

Panel M: Venezuela 2002-.8

-.6-.4

-.20

.2Δ

(loa

ns-to

-ass

ets)

-1 -.5 0 .5 1liability-based inflation exposure

Panel N: Venezuela 2013

-.2-.1

0.1

.2.3

Δ (l

oans

-to-a

sset

s)

-1 -.5 0 .5 1liability-based inflation exposure

Figure A5: Bank inflation exposure and bank lending: all episodes pooled together, but excludingcrises

This figure is similar to Figure 3 but excludes banking crises, balance-of-payment crises, and sovereign debtdefaults. The excluded episodes are: banking crises (Argentina 1989, 1995, & 2001; Brazil 1990 & 1994;Indonesia 1997; Mexico 1994; Turkey 2000; Uruguay 2002; and Venezuela 1994), balance-of-payment crises(Argentina in 1995 & 1999-2001; Brazil 1995 & 1998, Indonesia 1997-1999, Mexico 1994-1995, Turkey 1994& 1997) and sovereign debt defaults (Argentina in 1989 & 2001, Brazil 1987 & 1990; Indonesia 1998, 2002;Mexico 1995; Turkey 2000-1; and Venezuela 1990, 1995-8, & 2004). This leaves the inflation episodes ofArgentina 2013, France 1926, Germany 1922, Indonesia 2005, and Venezuela 2013 in the sample.

Panel A: Asset Exposure

-.50

.5Δ

(loa

ns-to

-ass

ets)

-1 -.5 0 .5 1asset-based inflation exposure

Panel B: Liability Exposure

-.50

.5Δ

(loa

ns-to

-ass

ets)

-1 -.5 0 .5 1liability-based inflation exposure

Panel C: Total Exposure

-.50

.5Δ

(loa

ns-to

-ass

ets)

-1 -.5 0 .5 1inflation exposure

Figure A6: Excerpt from Gilbert and Lovati (1978)

This figure is an excerpt from Gilbert and Lovati (1978). It illustrates the differences in reserve requirementsfor nonmember banks across states and other nuances of reserve requirements. For instance, in Arkansas,Idaho and Illinois securities can not be used to satisfy reserve requirement on demand deposits. There aredifferences in terms of eligible reserve assets, deposits that need to be backed by reserves, uniform vs gradatedreserve requirements, etc.

Table A1: Inflation episodes and credit contraction by country

This table lists all “inflation episodes” in our sample, reporting the subsequent credit contraction (the changein the detrended credit-to-GDP ratio). See section III for the definition of high inflation episodes. The listbelow excludes episodes during the two world wars and other major country-specific wars.

country startyear

jumpinfl.

∆(credit/gdp)t,t+1 country startyear

jumpinfl.

∆(credit/gdp)t,t+1

Argentina 1872 0.104 Chile 1932 0.236 -0.017Argentina 1885 0.203 Chile 1936 0.137 -0.002Argentina 1890 0.268 Chile 1953 0.718 -0.024Argentina 1905 0.195 -0.004 Chile 1959 0.155 0.016Argentina 1951 0.281 0.012 Chile 1962 0.357 -0.027Argentina 1954 0.167 0.008 Chile 1972 4.859 -0.039Argentina 1958 0.760 -0.096 Chile 1982 0.112 -0.157Argentina 1962 0.142 -0.003 China 1988 0.190 0.032Argentina 1965 0.201 0.032 China 1993 0.100 -0.126Argentina 1970 0.575 -0.001 Colombia 1872 0.161Argentina 1975 3.074 -0.055 Colombia 1881 0.536Argentina 1981 6.003 0.025 Colombia 1886 0.109Argentina 1987 48.41 -0.017 Colombia 1912 0.143Argentina 2002 0.425 -0.043 Colombia 1925 0.440 -0.040Argentina 2013 3.539 -0.024 Colombia 1928 0.237 -0.025Australia 1882 0.111 -0.012 Colombia 1936 0.125 -0.023Australia 1951 0.130 -0.005 Colombia 1938 0.281Austria 1951 0.245 -0.017 Colombia 1950 0.249 -0.020Belgium 1876 0.207 Colombia 1953 0.137 -0.019Belgium 1886 0.123 -0.022 Colombia 1957 0.123 -0.022Belgium 1894 0.245 -0.006 Colombia 1963 0.261 -0.029Belgium 1908 0.139 0.006 Colombia 1979 0.100 0.031Belgium 1923 0.211 -0.040 Colombia 2008 0.266 -0.009Belgium 1926 0.348 0.011 Denmark 1873 0.122 0.026Belgium 1935 0.117 -0.017 Egypt 1898 0.114Brazil 1889 0.177 Egypt 1905 0.180Brazil 1891 0.251 Egypt 1980 0.107 0.112Brazil 1897 0.117 Egypt 1986 0.165 -0.064Brazil 1952 0.159 Egypt 1991 0.186 0.002Brazil 1959 0.202 Egypt 2008 0.115 -0.063Brazil 1961 0.483 -0.016 Egypt 2016 0.122Brazil 1974 0.198 0.015 Finland 1950 0.175 -0.029Brazil 1976 0.136 -0.027 Finland 1956 0.137 -0.037Brazil 1979 0.610 -0.077 Finland 1969 7.022 0.016Brazil 1983 1.374 -0.007 France 1920 0.286 0.064Brazil 1987 18.93 0.870 France 1926 0.147 0.007Brazil 1992 20.04 0.439 France 1951 0.137 -0.014Chile 1886 0.152 Germany 1873 0.121Chile 1892 0.233 Germany 1922 2.2 · 109

Chile 1894 0.267 Germany 1951 0.159 0.010Chile 1909 0.139 Greece 1932 0.147 -0.081Chile 1924 0.101 -0.028 Greece 1953 0.200Chile 1928 0.124 -0.002 Greece 1973 0.241 0.007

country startyear

jumpinfl.

∆(credit/gdp)t,t+1 country startyear

jumpinfl.

∆(credit/gdp)t,t+1

Greece 1979 0.132 -0.021 Mexico 1892 0.170Hong Kong 1951 0.147 Mexico 1896 0.115Hungary 1951 0.346 Mexico 1900 0.199Hungary 1990 0.164 Mexico 1905 0.166Iceland 1950 0.675 Mexico 1954 0.169 -0.008Iceland 1958 0.114 0.008 Mexico 1957 0.303 -0.018Iceland 1972 0.503 0.006 Mexico 1973 0.158 -0.035Iceland 1978 0.167 0.031 Mexico 1976 0.159 -0.155Iceland 1982 0.275 0.016 Mexico 1982 0.702 -0.009Iceland 1985 0.173 -0.061 Mexico 1986 0.954 0.018Iceland 1987 0.109 0.033 Mexico 1995 0.460 -0.098Iceland 2008 0.106 -0.340 New Zealand 1920 0.106 0.126India 1885 0.113 Norway 1920 0.200 0.271India 1920 0.159 Norway 1924 0.115 0.006India 1964 0.103 0.006 Norway 1950 0.104 -0.051India 1973 0.157 -0.008 Norway 1970 0.103 0.021Indonesia 1951 0.451 Peru 1905 0.274Indonesia 1955 0.242 0.000 Peru 1967 0.112 -0.007Indonesia 1957 0.572 0.010 Peru 1976 0.207 -0.012Indonesia 1960 1.570 0.008 Peru 1978 0.412 0.003Indonesia 1972 0.231 0.008 Peru 1981 0.119 -0.003Indonesia 1979 0.140 -0.044 Peru 1983 0.521 -0.017Indonesia 1998 0.673 -0.352 Peru 1985 0.468 -0.013Indonesia 2005 0.107 0.011 Peru 1987 75.86 0.013Israel 1951 0.654 0.003 Philippines 1903 0.244Israel 1976 0.145 0.065 Philippines 1907 0.105Israel 1979 0.848 -0.014 Philippines 1923 0.119Israel 1982 3.434 -0.012 Philippines 1927 0.313Italy 1920 0.550 0.024 Philippines 1930 0.238Italy 1924 0.158 -0.038 Philippines 1970 0.199 -0.012Italy 1974 0.124 -0.007 Philippines 1973 0.366 0.008Italy 1976 0.109 -0.029 Philippines 1979 0.160 -0.006Japan 1874 0.339 Philippines 1983 0.423 -0.127Japan 1879 0.219 0.007 Portugal 1973 0.268 -0.037Japan 1889 0.329 -0.026 Portugal 1981 0.119 0.016Japan 1892 0.129 0.014 Portugal 1983 0.150 -0.006Japan 1897 0.236 -0.020 Russia 1932 0.214Japan 1900 0.212 -0.035 Russia 1935 0.114Japan 1932 0.119 -0.113 Russia 1998 0.733 -0.034Japan 1951 0.128 0.050 Singapore 1950 0.244Japan 1973 0.123 -0.059 Singapore 1973 0.215 -0.096Korea 1956 0.155 0.004 South Africa 1910 0.105Korea 1963 0.130 -0.044 South Africa 1920 0.343 -0.022Korea 1974 0.179 -0.094 Spain 1874 0.123Korea 1980 0.110 0.024 Spain 1882 0.114Luxembourg 1923 0.198 Spain 1909 0.126 0.005Luxembourg 1926 0.363 Spain 1930 0.159 -0.020Malaysia 1950 0.244 Sweden 1951 0.157 -0.021Malaysia 1973 0.145 -0.020 Taiwan 1973 0.214

country startyear

jumpinfl.

∆(credit/gdp)t,t+1 country startyear

jumpinfl.

∆(credit/gdp)t,t+1

Thailand 1960 0.103 -0.002 Uruguay 1965 0.526 -0.035Thailand 1969 7.105 0.004 Uruguay 1967 0.865 -0.012Thailand 1973 0.112 0.011 Uruguay 1972 0.591 -0.040Turkey 1958 0.123 -0.016 Uruguay 1974 0.296 0.124Turkey 1977 0.276 -0.049 Uruguay 1977 0.173 0.021Turkey 1979 0.445 -0.006 Uruguay 1979 0.371 0.006Turkey 1984 0.126 -0.002 Uruguay 1983 0.625 -0.103Turkey 1987 0.355 -0.031 Uruguay 1989 0.600 -0.052Turkey 1991 0.107 0.007 Uruguay 2002 0.224 -0.318Turkey 1994 0.544 0.027 Venezuela 1906 0.139Turkey 1997 0.193 -0.101 Venezuela 1924 0.135Turkey 2001 0.295 -0.006 Venezuela 1979 0.132 -0.016United Kingdom 1920 0.155 0.043 Venezuela 1987 0.276 -0.002Uruguay 1889 0.322 Venezuela 1989 0.455 -0.031Uruguay 1898 0.176 Venezuela 1993 0.390 -0.058Uruguay 1951 0.187 Venezuela 1996 0.466 0.065Uruguay 1957 0.125 Venezuela 2002 0.189 -0.003Uruguay 1959 0.290 Venezuela 2013 0.361Uruguay 1963 0.324 0.019 Venezuela 2015 0.938

Table A2: Distribution of the credit contraction following inflation episodes

This table shows the distribution of changes in one-year ahead detrended credit-to-GDP ratio followinginflation episodes, based on different inflation thresholds. The top panel shows the distribution of changes indetrended credit-to-GDP ratio following inflation episodes for the full sample. The middle panel shows thedistribution of detrended credit-to-GDP ratio only for those inflation episodes where there was no change ininterest policy rates. The bottom panel shows the distribution of changes in detrended credit-to-GDP ratioafter controlling for country fixed effects and changes in macroeconomic variables (real GDP, interest rates,exchange rate) during inflation episodes.

Measure InflationThreshold

Mean p5 Median p95 N

Baseline 2% -0.003 -0.067 -0.002 0.066 6495% -0.003 -0.063 -0.004 0.071 32610% -0.009 -0.103 -0.009 0.064 16120% -0.019 -0.096 -0.015 0.039 6430% -0.019 -0.098 -0.013 0.046 3640% -0.021 -0.098 -0.009 0.046 29

No monetary 2% 0.002 -0.041 0.004 0.071 49tightening 5% -0.002 -0.051 0.001 0.071 24

10% -0.007 -0.051 0.001 0.016 820% 0.010 0.004 0.01 0.016 230% 0.010 0.004 0.01 0.016 240% 0.010 0.004 0.01 0.016 2

After controls 2% -0.007 -0.085 -0.004 0.06 4885% -0.007 -0.071 -0.004 0.057 22410% -0.002 -0.110 -0.005 0.062 8920% -0.018 -0.110 -0.003 0.05 2530% -0.016 -0.307 0 0.061 1340% -0.011 -0.307 0.001 0.093 12

Table A3: Summary statistics: banks during high inflation episodes

This table documents the summary statistics of bank level data for the most international high inflationepisodes.

N Mean SD p25 Median p75

Argentina 2002log (assets) 77 5.91 1.85 4.45 5.64 7.09log (common equity) 77 4.17 1.49 3.04 3.91 5.00govt. securities to assets 77 0.06 0.08 0.01 0.04 0.08demand deposits to total deposits 77 0.17 0.15 0.08 0.13 0.22total deposits to total funding 77 0.70 0.22 0.59 0.73 0.87term deposits to total deposits 77 0.46 0.26 0.30 0.46 0.64lagged loan growth 77 -0.19 28.50 -13.70 -2.04 14.93foreign 77 0.44 0.50 0.00 0.00 1.00asset-based inflation exposure 77 0.44 0.33 0.26 0.54 0.68liability-based inflation exposure 77 0.13 0.30 -0.03 0.21 0.36inflation exposure 77 0.29 0.27 0.14 0.33 0.51

Argentina 2013log (assets) 72 20.14 1.91 19.12 19.94 21.78log (common equity) 72 18.27 1.62 17.18 17.94 19.82earning assets to total assets 72 0.77 0.14 0.74 0.79 0.85non-interest funding to total liabilities 72 0.05 0.05 0.02 0.03 0.06∆ (loans-to-assets) 72 -0.03 0.09 -0.09 -0.03 0.01asset-based inflation exposure 72 0.51 0.33 0.32 0.56 0.77liability-based inflation exposure 72 0.16 0.31 -0.05 0.18 0.39inflation exposure 72 0.34 0.25 0.17 0.34 0.49

Brazil 1992-93log (assets) 74 6.39 1.92 4.93 6.32 7.94log (common equity) 74 4.41 1.51 3.30 4.41 5.36govt. securities to assets 74 0.29 0.23 0.12 0.19 0.40demand deposits to total deposits 74 0.05 0.08 0.00 0.01 0.07total deposits to total funding 74 0.66 0.27 0.43 0.69 0.88term deposits to total deposits 74 0.62 0.27 0.44 0.63 0.86foreign 74 0.31 0.47 0.00 0.00 1.00asset-based inflation exposure 74 0.28 0.45 0.06 0.44 0.62liability-based inflation exposure 74 0.04 0.32 -0.18 0.04 0.27inflation exposure 74 0.16 0.32 -0.05 0.23 0.42

France 1926log (assets) 5 15.45 0.55 15.23 15.61 15.83log (common equity) 5 12.42 0.50 12.43 12.43 12.43∆ (loans-to-assets) 5 -0.01 0.02 -0.02 -0.00 -0.00asset-based inflation exposure 5 0.92 0.09 0.88 0.97 0.97liability-based inflation exposure 5 -0.62 0.23 -0.75 -0.61 -0.53inflation exposure 5 0.30 0.19 0.14 0.37 0.46

N Mean SD p25 Median p75

Germany 1922∆ (loans-to-assets) 61 -0.08 0.19 -0.17 -0.11 0.03asset-based inflation exposure 61 0.05 0.35 -0.23 0.05 0.36liability-based inflation exposure 61 -0.66 0.31 -0.83 -0.75 -0.64inflation exposure 61 -0.31 0.25 -0.50 -0.31 -0.15

Indonesia 2005log (assets) 21 9.01 1.37 8.20 8.82 9.28log (common equity) 21 6.94 1.29 6.47 6.67 7.30govt. securities to assets 21 0.09 0.10 0.01 0.04 0.12demand deposits to total deposits 21 0.24 0.18 0.12 0.18 0.27total deposits to total funding 21 0.86 0.17 0.80 0.94 0.98term deposits to total deposits 21 0.55 0.22 0.40 0.57 0.73lagged loan growth 21 33.04 33.29 10.28 25.56 46.48foreign 21 0.52 0.51 0.00 1.00 1.00asset-based inflation exposure 21 0.72 0.24 0.62 0.79 0.90liability-based inflation exposure 21 0.42 0.26 0.31 0.45 0.60inflation exposure 21 0.57 0.19 0.46 0.55 0.72

Mexico 1995log (assets) 19 9.31 1.79 7.61 9.87 10.42log (common equity) 19 6.74 1.21 5.47 6.80 7.53govt. securities to assets 19 0.12 0.11 0.03 0.07 0.18demand deposits to total deposits 19 0.15 0.12 0.06 0.12 0.22total deposits to total funding 19 0.72 0.18 0.58 0.72 0.89term deposits to total deposits 19 0.54 0.26 0.28 0.57 0.69foreign 19 0.32 0.48 0.00 0.00 1.00asset-based inflation exposure 19 0.41 0.36 0.16 0.45 0.73liability-based inflation exposure 19 0.12 0.31 -0.18 0.16 0.34inflation exposure 19 0.26 0.30 0.02 0.31 0.56

Turkey 1994log (assets) 20 8.82 1.42 7.90 8.75 9.76log (common equity) 20 6.20 1.65 5.13 6.66 7.27govt. securities to assets 20 0.11 0.09 0.04 0.10 0.16demand deposits to total deposits 20 0.00 0.00 0.00 0.00 0.00total deposits to total funding 20 0.89 0.18 0.88 0.97 1.00term deposits to total deposists 20 0.48 0.29 0.21 0.53 0.67foreign 20 0.10 0.31 0.00 0.00 0.00asset-based inflation exposure 20 0.56 0.23 0.40 0.60 0.73liability-based inflation exposure 20 0.44 0.28 0.34 0.52 0.64inflation exposure 20 0.50 0.19 0.44 0.55 0.62

N Mean SD p25 Median p75

Turkey 1997log (assets) 20 11.01 1.52 9.98 11.54 11.90log (common equity) 20 8.66 1.15 7.69 8.67 9.50govt. securities to assets 20 0.13 0.11 0.04 0.09 0.21demand deposits to total deposits 20 0.00 0.00 0.00 0.00 0.00total deposits to total funding 20 0.95 0.10 0.95 1.00 1.00term deposits to total deposits 20 0.48 0.26 0.32 0.53 0.65lagged loan growth 20 138.00 48.78 105.57 130.90 188.00foreign 20 0.15 0.37 0.00 0.00 0.00asset-based inflation exposure 20 0.47 0.27 0.31 0.48 0.69liability-based inflation exposure 20 0.65 0.21 0.51 0.77 0.82inflation exposure 20 0.56 0.18 0.43 0.64 0.69

Turkey 2001log (assets) 31 14.63 2.08 13.61 14.41 16.52log (common equity) 31 12.33 2.43 11.06 12.23 14.03govt. securities to assets 31 0.20 0.11 0.13 0.20 0.25demand deposits to total deposits 31 0.44 0.32 0.12 0.46 0.74total deposits to total funding 31 0.79 0.18 0.69 0.81 0.92term deposits to total deposits 31 0.07 0.23 0.00 0.00 0.00foreign 31 0.26 0.44 0.00 0.00 1.00asset-based inflation exposure 31 0.34 0.28 0.14 0.32 0.52liability-based inflation exposure 31 -0.12 0.45 -0.51 -0.12 0.19inflation exposure 31 0.11 0.23 -0.04 0.06 0.27

Uruguay 2002log (assets) 42 7.29 2.05 5.89 7.11 8.93log (common equity) 42 4.75 1.76 3.66 5.16 5.60govt. securities to assets 42 0.07 0.10 0.00 0.04 0.09demand deposits to total deposits 42 0.00 0.00 0.00 0.00 0.00total deposits to total funding 42 0.96 0.12 0.96 1.00 1.00term deposits to total deposits 42 0.67 0.34 0.51 0.75 0.97foreign 42 0.71 0.46 0.00 1.00 1.00asset-based inflation exposure 42 0.72 0.28 0.63 0.82 0.90liability-based inflation exposure 42 0.66 0.34 0.54 0.69 0.84inflation exposure 42 0.69 0.26 0.56 0.77 0.85

N Mean SD p25 Median p75

Venezuela 1996log (assets) 13 7.96 3.18 5.06 7.29 11.33log (common equity) 13 6.12 2.91 3.40 4.87 8.72govt. securities to assets 13 0.41 0.27 0.16 0.34 0.68demand deposits to total deposits 13 0.40 0.26 0.35 0.44 0.49total deposits to total funding 13 0.94 0.14 0.96 0.98 0.99term deposits to total deposits 13 0.13 0.12 0.04 0.11 0.20foreign 13 0.23 0.44 0.00 0.00 0.00asset-based inflation exposure 13 0.15 0.43 0.01 0.17 0.35liability-based inflation exposure 13 0.04 0.20 -0.07 0.06 0.16inflation exposure 13 0.10 0.22 0.08 0.15 0.23

Venezuela 2002log (assets) 47 6.29 2.71 4.13 6.14 8.06log (common equity) 47 4.51 2.67 2.40 4.03 6.10govt. securities to assets 47 0.16 0.16 0.03 0.12 0.20demand deposits to total deposits 47 0.32 0.21 0.09 0.37 0.47total deposits to total funding 47 0.97 0.08 0.98 1.00 1.00term deposits to total deposits 47 0.26 0.20 0.12 0.22 0.37lagged loan growth 47 36.79 52.88 3.35 21.43 49.14foreign 47 0.17 0.38 0.00 0.00 0.00asset-based inflation exposure 47 0.36 0.41 0.23 0.44 0.59liability-based inflation exposure 47 0.16 0.22 -0.00 0.13 0.29inflation exposure 47 0.26 0.23 0.15 0.28 0.40

Venezuela 2013log (assets) 28 12.10 2.15 10.65 11.72 13.95log (common equity) 28 9.86 1.84 8.59 9.61 11.21earning assets to total assets 28 0.74 0.10 0.68 0.73 0.80non-interest funding to total liabilities 28 0.03 0.03 0.01 0.02 0.04∆ (loans-to-assets) 28 0.07 0.10 0.02 0.08 0.14asset-based inflation exposure 28 0.28 0.35 0.01 0.31 0.54liability-based inflation exposure 28 -0.38 0.29 -0.60 -0.48 -0.20inflation exposure 28 -0.05 0.20 -0.19 -0.05 0.06

Table A4: Inflation Exposure and Bank Lending: Pooled International Inflation Episodes

This table presents the full regression results from Table 2, as estimated using equation (2). The dependentvariable is the bank-level change in loans-to-assets around each inflation episode, and the main independentvariable is, alternatively, the asset-based inflation exposure measure (column 1 and 2), liability-based inflationexposure measure (column 3 and 4), and total inflation exposure measure (column 5 and 6). The odd-numbered columns do not include bank-level controls while the even numbered columns include controls.All columns include inflation episode fixed effects. Robust standard errors are reported in parentheses. ***p<0.01, ** p<0.05, * p<0.1.

Dep. var.: ∆(loans-to-assets)b,n (1) (2) (3) (4) (5) (6)

asset-based inflation exposureb,n -0.1238*** -0.1195**(0.021) (0.060)

liability-based inflation exposureb,n -0.0816*** -0.1237**(0.024) (0.062)

inflation exposureb,n -0.1291*** -0.1945**(0.025) (0.082)

log(assets)b,n 0.0127 0.0142 0.0132(0.012) (0.012) (0.013)

log(common equity)b,n 0.0093 0.0089 0.0105(0.013) (0.013) (0.014)

govt. securities to assetsb,n 0.0972 0.2941*** 0.1216(0.132) (0.069) (0.110)

demand deposits to total depositsb,n 0.1921*** 0.0740 0.0973(0.071) (0.088) (0.072)

total deposits to total fundingb,n -0.0270 0.0306 0.0536(0.059) (0.085) (0.075)

term deposits to total depositsb,n 0.0561 0.0811 0.0754(0.054) (0.058) (0.055)

lagged loan growthb,n 0.0005** 0.0005** 0.0005**(0.000) (0.000) (0.000)

foreign bank indicatorb,n 0.0329 0.0332 0.0343(0.025) (0.024) (0.024)

Inflation episode fixed effects Yes Yes Yes Yes Yes YesObservations 436 179 375 179 436 179Inflation episodes (clusters) 14 12 14 12 14 12Adj. R2 0.075 0.294 0.033 0.283 0.068 0.299

Table A5: Classification of balance sheet items into inflation-exposed or inflation-protected

This table shows how we classify each item on the balance sheet as as inflation-exposed or inflation-protected.

Assets Liabilities

Inflation exposed Cash and due from banks Saving deposits(coded as +1) Government securities Term deposits

Fixed-income securities Deposits from banksResidential mortgage loans Short term borrowingNonresidential mortgage loans Subordinated borrowingOther loans Other funding

DerivativesTrading liabilities

Inflation protected C&I loans (non-mortgage) Current or demand deposits(coded as -1) Consumer loans (non-mortgage) Long-term debt

All other securities Other liabilitiesOther earning assetsInvestment in propertyForeclosed real estateFixed assetsGoodwill and other intangiblesOther assets

Table A6: Differences in observable bank characteristics

This table reports the differences in observed characteristics across banks by Fed membership status (PanelA), across low and high inflation exposure states (Panel B), and across low and high inflation exposurestates by nonmember and member banks separately (Panel C). Columns reports the mean of each variablein December 1976.

Panel A: Nonmember versus member banksNonmember Member Diff(1) (2) (3)

Total Assets 23.54 56.43 -32.88***Share of demand deposits 0.3459 0.3575 -0.0116***Deposits to Assets 0.9031 0.8969 0.0062***Loans to Assets 0.5444 0.5268 0.0175***Loan Growth 0.1563 0.1407 0.0156***Securities to Assets 0.3096 0.3038 0.0057**Capital to Assets 0.0833 0.0797 0.0035***Earnings to Equity 0.1134 0.1134 0.0000

Panel B: Banks in low versus high inflation-exposure states

Low High Difference(1) (2) (3)

Total Assets 36.10 36.42 -0.3210Share of demand deposits 0.3439 0.3568 -0.0130***Deposits to Assets 0.9014 0.9000 -0.0014**Loans to Assets 0.5303 0.5448 -0.0146***Loan Growth 0.1544 0.1459 0.0085***Securities to Assets 0.3251 0.2898 0.0353***Capital to Assets 0.0811 0.0827 -0.0015***Earnings to Equity 0.1164 0.1105 0.0058***Share of member banks 0.4033 0.4657 -0.0624Northeast 0.0869 0.2592 -0.1720Midwest 0.3043 0.1851 0.1190South 0.3478 0.2962 0.0515West 0.2608 0.2592 0.0016Oil State 0.0869 0.1851 -0.0982Unemployment (%) 7.02 7.08 -0.0583GDP growth 0.1169 0.1256 -0.0087House price growth 0.0919 0.0624 0.0296

Panel C: Banks in low versus high inflation-exposure states, by Fed membership status

Nonmember Member

Low High Difference Low High Difference(1) (2) (3) (1) (2) (3)

Total Assets 22.19 25.02 -2.83*** 61.39 52.25 9.13***Share of demand deposits 0.3414 0.3507 -0.0092*** 0.3482 0.3653 -0.0171***Deposits to Assets 0.9039 0.9022 0.0017*** 0.8969 0.8969 0.0000Loans to Assets 0.5350 0.5547 -0.0196*** 0.5216 0.5312 -0.0096***Loan Growth 0.1637 0.1481 0.0156*** 0.1380 0.1430 -0.0049Securities to Assets 0.3289 0.2883 0.0407*** 0.3180 0.2919 0.0261***Capital to Assets 0.0825 0.0841 -0.0015*** 0.0785 0.0808 -0.0022***Earnings to Equity 0.1161 0.1105 0.0055*** 0.1169 0.1105 0.0063***

Tab

leA7:

Infla

tion

episod

esan

dsubseque

ntba

nkequity

inde

xreturns

Thistableshow

sthechan

gein

bank

equity

index(pan

elA)an

dthebroadstockmarketindex(pan

elB)du

ring

infla

tion

episod

es.Colum

ns(1)-(3)

show

resultsforthechan

gein

bank

equity

index(and

thebroadmarketindex)

forthepe

riod

t−1to

twhere

tisthestartyear

ofan

infla

tion

episod

e.Colum

ns(4)-(5)show

thechan

gein

theseindexesover

thepe

riod

t−1to

t+1,

columns

(6)-(9)show

resultsforthepe

riod

t−1to

t+2an

dcolumns

(10)-(12)show

resultsforthepe

riod

t−

1to

t+

3.The

mainindepe

ndentvariab

leis

thedu

mmyforan

infla

tion

episod

e(Inflation

Episod

e i,t).

Con

trol

variab

lesinclud

econtem

poraneou

schan

gein

real

GDP,

interest

rates,

andcurrency

return

andtw

olags

ofeach

control.

Stan

dard

errors

inpa

renthesisaredo

uble

clusteredat

coun

tryan

dyear

level.

***p<

0.01,*

*p<

0.05,*

p<0.1.

Pan

elA:Ban

kequity

inde

x

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

Dep.Var.

∆(ban

kreal

ret.) i,t−1,t

∆(ban

kreal

ret.) i,t−1,t+1

∆(ban

kreal

ret.) i,t−1,t+2

∆(ban

kreal

ret.) i,t−1,t+3

InflationEpisodes

i,t

-0.171***

-0.117**

-0.133***

-0.311***

-0.253***

-0.258**

-0.291**

-0.232**

-0.218

*-0.334**

-0.249

*-0.255**

(0.051)

(0.049)

(0.041)

(0.095)

(0.093)

(0.099)

(0.112)

(0.114)

(0.113)

(0.138)

(0.140)

(0.118)

Rea

lGDP

growth

i,t−

1,t

1.109***

1.204***

0.939**

0.984**

0.973**

-2.574***

1.107**

-4.802***

(0.273)

(0.274)

(0.463)

(0.476)

(0.425)

(0.694)

(0.522)

(1.265)

Curren

cyreturni,t−

1,t

0.239

0.257

0.097

0.111

0.080

-0.378

0.34

00.024

(0.171)

(0.197)

(0.262)

(0.269)

(0.313)

(0.266)

(0.351)

(0.335)

∆InterestRate

i,t−

1,t

-2.790***

-3.270***

-3.676***

-4.453***

-3.462***

-3.595***

-3.767***

-3.706***

(0.480)

(0.652)

(0.708)

(0.849)

(0.690)

(0.609)

(0.765)

(1.139)

Constant

0.086***

0.045***

0.047**

0.180***

0.142***

0.132***

0.269***

0.229***

0.172***

0.363***

0.319***

0.277***

(0.012)

(0.014)

(0.019)

(0.023)

(0.026)

(0.035)

(0.032)

(0.036)

(0.054)

(0.041)

(0.045)

(0.068)

Observation

s2,628

2,628

2,497

2,547

2,547

2,418

2,470

2,470

2,338

2,394

2,394

2,264

Num

berof

grou

ps37

3737

3737

3737

3737

3737

37Add

itiona

llags

No

No

Yes

No

No

Yes

No

No

Yes

No

No

Yes

Pan

elB:Broad

equity

marketindex

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

Dep.Var.

∆(ban

kreal

ret.) i,t−1,t

∆(ban

kreal

ret.) i,t−1,t+1

∆(ban

kreal

ret.) i,t−1,t+2

∆(ban

kreal

ret.) i,t−1,t+3

InflationEpisodes

i,t

-0.083*

-0.019

-0.027

-0.222**

-0.150*

-0.142

-0.196**

-0.130

-0.138*

-0.166

-0.090

-0.099

(0.050)

(0.046)

(0.041)

(0.091)

(0.085)

(0.094)

(0.086)

(0.086)

(0.082)

(0.112)

(0.111)

(0.094)

Rea

lGDP

growth

i,t−

1,t

0.908***

1.112***

0.405

0.629

-0.051

-3.457***

-0.368

-5.973***

(0.234)

(0.262)

(0.372)

(0.386)

(0.423)

(0.760)

(0.646)

(1.321)

Curren

cyreturni,t−

1,t

0.232*

0.245

0.024

0.004

-0.177

-0.616**

-0.150

-0.251

(0.136)

(0.162)

(0.181)

(0.196)

(0.254)

(0.275)

(0.361)

(0.274)

∆InterestRate

i,t−

1,t

-2.902***

-3.506***

-4.170***

-4.966***

-4.548***

-3.553***

-4.890***

-3.521***

(0.354)

(0.517)

(0.495)

(0.649)

(0.690)

(0.708)

(0.660)

(0.772)

Observation

s2,769

2,769

2,623

2,701

2,701

2,556

2,633

2,633

2,521

2,566

2,566

2,456

Num

berof

grou

ps37

3737

3737

3737

3737

3737

37Add

itiona

llags

No

No

Yes

No

No

Yes

No

No

Yes

No

No

Yes