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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, [email protected] ‡ Johnson Graduate School of Management, Cornell University, [email protected]
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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