Work 758
Work 758
Work 758
No 758
Foreign currency
borrowing, balance sheet
shocks and real outcomes
by Bryan Hardy
November 2018
© Bank for International Settlements 2018. All rights reserved. Brief excerpts may be
reproduced or translated provided the source is stated.
Abstract
Emerging market firms frequently borrow in foreign currency (FX), but their assets are
often denominated in domestic currency. This behavior leads to an FX mismatch on firms
balance sheets, which can harm their net worth in the event of a depreciation. I use a large,
unanticipated, and exogenous depreciation episode and a unique dataset to identify the
real and financial effects of firm balance sheet shocks. I construct a new dataset of all listed
non-financial firms, matched to their banks, in Mexico over 2008q1-2015q2. This dataset
combines firm-level balance sheets and real outcomes, currency composition of both assets
and liabilities, and firms’ loan-level borrowing from banks in peso and FX. This data allows
me to control for shocks to firms’ credit supply to identify the balance sheet shock and ex-
amine its real consequences. I find that non-exporting firms that have a larger FX mismatch
experience greater negative balance sheet effects following the depreciation. Among these,
smaller firms see a decrease in loan growth, resulting in stagnant employment growth and
decreased growth in physical capital relative to firms with smaller FX mismatch. Larger
firms with a large FX mismatch also have lower growth in FX loans following the shock,
but are able to increase borrowing in peso loans, resulting in relatively higher growth in
employment and physical capital. My results imply that firms are subject to net worth
based borrowing constraints, and that these constraints are more binding on smaller firms
and for loans in FX.
∗I am grateful to Şebnem Kalemli-Özcan for her continued guidance and advice on this project, as well as to
Felipe Saffie and Ethan Kaplan. I thank Carolina Villegas-Sanchez and Vadym Volosovych for their help working
with the data. I am thankful to John Shea, Ina Simonovska, Michael Faulkender, and Stefan Avdjiev for their
support and helpful comments, and participants at seminars at the 84th International Atlantic Economic Society
Conference, Brigham Young University, FDIC, U.S. Treasury OCC Credit RAD, the Federal Reserve Bank of
Dallas, the Federal Reserve Board of Governors, and the Bank for International Settlements. I benefitted from
discussions with numerous other individuals. All errors are my own. The views expressed here are those of the
author and not necessarily those of the Bank for International Settlements.
† bryan.hardy@bis.org
1 Introduction
Much of the credit extended to emerging market firms is denominated in foreign curren-
cies.1 In this paper, I study the impact that foreign currency (FX) borrowing has on firms
following a large depreciation. More generally, I address how negative shocks to firm net
worth (balance sheet shocks) affect firm activity. I construct a novel dataset of currency ex-
posures and loan-level borrowing and examine both the financial and real consequences of
Standard theory predicts that balance sheet shocks, with no offsetting changes to firm
revenue, will lead to tighter borrowing constraints and a consequent decline in real activity.
I find that firm size and the currency denomination of debt are two important characteristics
that determine the impact of these constraints. Borrowing constraints are more binding
following adverse balance sheet shocks for smaller firms, indicating a net worth or size-
based borrowing constraint, and for foreign currency loans, suggesting an additional tighter
constraint on a firm’s foreign currency debt. The interaction of these two constraints leads
large firms with a negative shock to decrease their foreign currency borrowing, but allows
them to increase their local currency borrowing and thus remain unconstrained in their real
activity. Small firms who are constrained in their total borrowing contract their real activity
Balance sheet effects are difficult to identify empirically because it is hard to separate
changes in outcomes due to firm balance sheet shocks from other channels. For example,
shocks to the supply of bank credit (the bank lending channel) have been shown to be quan-
titatively large and important for real outcomes (Chodorow-Reich, 2014). Firm specific de-
mand shocks are also hard to separate from the effects of firm-specific balance sheet shocks.
Existing empirical work in both macro and finance cannot cleanly identify balance sheet
shocks.
I address these challenges in this paper. I construct a dataset that consists of firm bal-
1 See Caballero, Panizza, and Powell (2014); Chui, Kuruc, and Turner (2016); Du and Schreger (2015); Maggiori,
Neiman, and Schreger (2017); McCauley, McGuire, and Sushko (2015); Shin (2013).
1
ance sheets and loan level outcomes for all listed non-financial firms in Mexico, matched
to their banks. This dataset allows me to capture developments on both the financial and
real sides of firm activity, connecting balance sheet effects to real outcomes. The dataset has
two unique features that are crucial to the identification of a balance sheet shock. First, it
includes data on both firms’ FX assets and FX liabilities. This allows me to construct a mea-
sure of true balance sheet FX exposure (currency mismatch) for each firm and to compare
firms with differing levels of exposure, as larger exposure should result in larger shocks to
a firm’s balance sheet for a given sized depreciation.2 Second, the data includes loan-level
information for each of the banks that the firm borrows from, in both foreign and domestic
currency. To my knowledge, this paper is the first to employ such matched firm-bank data
to identify the impacts on the firm of balance sheet shocks, controlling for credit supply
shocks.3
The matched nature of the data makes it possible to compare firms who borrow from
the same bank in the same currency at the same time and are thus exposed to the same
bank-level shocks to credit supply in each currency. This comparison isolates differences
in credit outcomes due to idiosyncratic shocks to firms. Controlling for shocks to credit
supply is crucial because such shocks directly affect the channel by which the balance sheet
effect operates, through the credit available to the firm. Failure to control for bank credit
supply shocks can bias estimates of balance sheet effects if, for instance, firms who borrow
more in foreign currency also borrow more from stronger banks. I show that, for regressions
estimating the impact of the balance sheet shock on FX loan borrowing, failure to control for
credit supply shocks can bias the estimated coefficient downward (toward zero) by 40%.
I analyze the effect of a shock to the exchange rate initiated by the collapse of Lehman
2 Most datasets used in these studies only have data on debt dollarization, but not assets. Exceptions include
Kalemli-Özcan, Kamil, and Villegas-Sanchez (2016), Cowan, Hansen, and Óscar Herrera (2005b), and Alvarez
and Hansen (2017).
3 Niepmann and Schmidt-Eisenlohr (2017a) use loan level data to show that firms with a higher share of foreign
currency loans are more likely to default on their loans, though they do not examine changes in credit or real
outcomes for these firms. Gan (2007) uses matched firm-bank data in Japan to study if banking relationships
affect the impact of a real estate balance sheet shock, but does not fully control for shocks to credit supply on
lending.
2
Brothers in 2008. This depreciation was large, unanticipated, and exogenous to Mexico’s
fundamentals. An endogenous exchange rate shock, such as currency crises used in pre-
vious literature, is problematic because the cause of the shock likely also caused changes
in outcomes through other channels. If the shock is anticipated, firms may endogenously
adjust their FX borrowing and behavior in advance of the shock, leading to mismeasure-
ment of the balance sheet effect. Thus, an exogenous, unanticipated depreciation is ideal to
My analysis focuses on the interaction of the firm’s pre-shock balance sheet exposure
(FX mismatch) with an indicator variable for the period following the depreciation shock:
ferences in outcomes post-depreciation for firms with different exposure (and thus different
size of balance sheet shock). Importantly, I study both the financial and real outcomes of
the firms, which has been seldom done in the literature. For financial outcomes, I focus
on loan growth in foreign and domestic currency, and for real outcomes, I examine growth
tify the channel by which balance sheet shocks operate, via loss of credit, while examining
real outcomes is important to understand the impacts on firm behavior and real economic
activity.
In addition to controlling for correlated credit supply effects, I take several steps to con-
trol for changes in credit demand from the firm that are not driven by balance sheet shocks.
First, I focus on non-exporting firms, which do not have significant foreign currency rev-
enues that would increase with the favorable terms-of-trade change. Second, I control for
shocks to broadly defined sectors (such as changes in demand or production costs) either by
including sector interactions (with the shock) or sector*year fixed effects. Third, I control for
time-varying characteristics of the firm that might affect loan demand, including firm size,
leverage, sales, cash, derivatives, exports, and bond credit. Fourth, I compare the interaction
of the shock with FX exposure with other interacted firm characteristics that may affect firm
3
credit demand following the shock. Fifth, I compare the responses of large vs. small firms in
my sample;4 large and small firms should both respond to changes in demand, but smaller
firms are more likely to be constrained following an adverse balance sheet shock.
Real outcomes vary at the firm level rather than the loan level. In order to control for
shocks to bank credit supply in regressions on real outcomes, I construct a firm-level mea-
sure of bank credit shocks from the loan level data. I show that this measure can be used
as a time varying control when time fixed effects are included in the regression, enabling
me to dynamically control for shocks to credit supply at the firm level. I then proceed with
the same difference-in-difference estimator as before, controlling for time varying firm char-
acteristics and firm-specific credit supply shocks, comparing different interactions with the
For loan outcomes, I find the expected balance sheet effect on foreign currency loans:
firms (non-exporters) with higher currency mismatch see lower loan growth than less ex-
posed firms following the shock. Large firms with higher mismatch, however, compensate
with an even larger increase in local currency borrowing. Smaller firms do not see this in-
crease in their peso borrowing. Uncovered interest rate parity (UIP) fails such that foreign
currency loans have lower interest rates and are more attractive to borrowers. However, the
switch from foreign to domestic currency loans by large firms is not driven by changes in
the interest rate differential following the shock. Foreign currency loans remain consistently
cheaper than local currency loans, even comparing within-firm and within-bank variation
in interest rates. This suggests that the switch to peso loans is driven by borrowing con-
straints, where firms are subject to a borrowing constraint on their total borrowing and an
At the firm level, the impact of the shock is largely insignificant when large and small
firms are pooled together. Consistent with results found with loan outcomes, I find that
4 Smallis defined as being below the sample median in total assets. My sample consists of listed firms, which
tend to be much larger than other firms in the economy, so “small” is a relative term. Nevertheless, both large
and small firms in my sample will be subject to similar demand shocks, particularly those in the same sector in
the same year, so the difference in size will be a salient characteristic in their response.
4
large, exposed non-exporters (who are able to increase their total borrowing by switching
to peso) increase their employment and investment, while small, exposed non-exporters
have no change in employment growth and decrease their physical capital growth relative
to firms with lower mismatch. These results together suggest that balance sheet shocks
can trigger financial constraints that affect a firm’s ability to borrow, which can then have
real effects. The curious finding of an increase for large firms, also found previously in the
literature, could be due to a reallocation of capital towards safer borrowers (in this case
My results have two implications for policy. First, domestic currency liquidity and the
health of the domestic banking system may be a relevant factor for risk assessment of firm
balance sheet shocks, as domestic currency loans provide a substitute for credit lost by large
firms who experience a negative balance sheet shock. This further implies that negative
balance sheet effects will be stronger when a banking crisis accompanies a currency crisis,
the so-called “twin crises” (Kaminsky & Reinhart, 1999). Second, negative real effects from
balance sheet shocks are more likely to come from small firms, so the joint distribution of
size and FX mismatch is important to understand the risk to the economy. Opposite the
conventional wisdom that large firms are important for aggregate effects, small and medium
firms may contribute significantly to the observed negative aggregate outcomes if their FX
My empirical results are relevant for the theoretical literature. First, I show how firms
may face an additional borrowing constraint on their foreign currency borrowing in addi-
tion to the typically modelled borrowing constraint on total debt. Second, my results suggest
that firm heterogeneity in size matters for the impact of the shock through these two con-
straints. Accounting for and explaining the different behavior of large and small firms, and
the general equilibrium implications, will be important in order to understand the aggre-
gate effects. Theoretical research on balance sheet effects should thus account for the joint
5
The remainder of the paper proceeds as follows: Section 2 reviews the literature and
further clarifies the contribution of this paper; Section 3 presents and describes the data
and the context for Mexico; Section 4 describes the identification strategy and presents re-
sults for outcomes at the firm-bank level; Section 5 describes the identification strategy and
presents results for outcomes at the firm level; Section 6 discusses implications for theory;
2 Literature
Much of the empirical work studying firm balance sheet shocks has been done in the context
of exchange rate shocks. A couple of papers, notably Gan (2007) (for Japan) and Chaney,
Sraer, and Thesmar (2012) (for the U.S.), find evidence of a balance sheet channel affecting
firm investment in the context of a real estate price shock. The more expansive FX literature
largely uses firm-level data and examines the effect on investment of an interaction of firm
FX debt with exchange rate changes.5 Most of these papers draw on periods involving a
crisis, with some explicitly using a difference-in-difference approach around the crisis.
Evidence of negative effects from exchange rate related balance sheet shocks have been
found in studies for Mexico (Aguiar, 2005; Pratap, Lobato, & Somuano, 2003), as well as
other emerging markets (Carranza, Cayo, & Galdon-Sanchez, 2003; Cowan et al., 2005b;
Echeverrya, Fergussona, Steinerb, & Aguilara, 2003; Gilchrist & Sim, 2007). Firms with
more FX debt reduce investment following the depreciation, though exporters fare better.6
However, several studies find either zero or positive balance sheet effects (Benavente, John-
son, & Morande, 2003; Bleakley & Cowan, 2008; Bonomo, Martins, & Pinto, 2003; Lueng-
naruemitchai, 2003). These positive effects are sometimes attributed to firms matching their
FX debt with FX revenues, FX assets, or FX derivatives. Very few of these studies have
5 See Table 1 of Cowan, Hansen, and Óscar Herrera (2005a) for a useful comparison of FX exposure measures,
countries, samples, outcomes, and controls for FX assets and derivatives across papers in the literature.
6 Cross country evidence is sparse, but includes Bleakley and Cowan (2008); Caballero (2018); Kalemli-Özcan
et al. (2016); Serena Garralda and Sousa (2017). Serena Garralda and Sousa (2017) and Caballero (2018) use bond
borrowing in FX by firms in many countries to show that FX borrowing is correlated with reduced investment
following an exchange rate shock.
6
data on FX assets or derivatives. Exceptions include Kalemli-Özcan et al. (2016), which uses
a dummy variable indicator for holdings of FX assets in a sample of Latin American firms,
and Cowan et al. (2005b) and Alvarez and Hansen (2017), which find that Chilean firms with
FX liabilities match with FX assets, FX revenues, and FX derivatives. Cowan et al. (2005a)
shows that controlling for FX assets can cause the positive and insignificant coefficient on FX
debt (interacted with depreciation) to become negative and insignificant. On the extensive
margin, Kim, Tesar, and Zhang (2015) shows that negative balance sheet shocks due to FX
debt can increase the probability of firm exit. Similar top this paper, they also highlight that
large firms, who are often used in this literature due to data availability, actually increase
their investment and survival probability following a negative balance sheet shock, while
The existing literature largely relies on variation due to crisis episodes without the abil-
ity to control for shocks to credit supply. Variation in the exchange rate during non-crisis
periods is also problematic, as it is less sudden and likely driven by the economy’s fun-
damentals. Estimates using this variation are thus more prone to bias from forward look-
ing behavior regarding future exchange rate realizations and simultaneity of past borrow-
ing and investment affecting future realizations of the exchange rate. Kalemli-Özcan et al.
(2016) provides an identification strategy to separate the balance sheet shock from credit
supply shocks. Using a cross-country dataset on listed firms, they compare outcomes of
exporting firms during currency crises with those in countries experiencing simultaneous
currency and banking crises (the “twin crises”). They find that during a depreciation, all ex-
porting firms increase investment, but when the depreciation is accompanied by a banking
crisis, only foreign-owned exporters (who have better access to capital) increase investment.
Desai, Foley, and Forbes (2008) similarly conclude that affiliate firms of US multinationals
in emerging markets are able to bypass credit constraints following sharp depreciations,
whereas domestic firms cannot, further illustrating the importance of accounting for credit
7
This paper contributes to and harmonizes the existing empirical literature in several
ways. In addition to controlling for the value of FX assets, FX revenues, and net derivatives
position, I directly control for credit supply shocks using matched firm-bank data. This al-
lows me to use a sharp depreciation episode to measure a clear shock to the balance sheet
while controlling for correlated changes in credit conditions. This identification of the bal-
ance sheet effect of depreciations is unique to the literature. My results confirm those in Kim
et al. (2015), finding that the conflicting results in the literature can be driven by the behavior
of large firms. By comparing domestic vs. foreign currency borrowing, I can further explain
how large firms are able to increase their investment, which is precisely because they are
able to access domestic currency debt, despite the negative balance sheet shock. This cor-
roborates the evidence shown in Kalemli-Özcan et al. (2016), as a concurrent banking crisis,
which reduces domestic currency liquidity, is more likely to generate negative effects even
for large firms. Thus, crisis episodes in emerging markets are likely to generate negative
balance sheet effects, but these effects measured on data from large firms could be zero or
Most of the existing literature does not directly examine how balance sheet shocks affect
access to credit, focusing rather on firm level outcomes like profitability and investment.
In addition to examining real outcomes, I test the mechanism of the balance sheet channel
directly by examining borrowing outcomes for these firms, cleaned of credit supply shocks,
and additionally differentiate the effects by currency of borrowing. Niepmann and Schmidt-
Eisenlohr (2017a) examines the effects of balance sheet shocks on credit from the perspective
of lending banks. They show indirect evidence of balance sheet effects on loan repayment
using loan-level data from US banks to firms in many emerging markets, finding that a US
dollar appreciation is associated with a higher likelihood of default (becoming past due on
loan payments) for firms with a higher share of loans in FX. This provides direct evidence
that firm risk due to FX mismatch can transfer to banks, even if the bank has no FX mis-
match. My research complements theirs by matching the loan-level data to firm FX expo-
8
sures, balance sheets, and studying the real outcomes of firms. Gan (2007) uses a real estate
bubble in Japan as a shock to firm asset value, concurrently examining banking relation-
ships. In addition to decreased investment, she finds that firms with larger shock exposure
see a decrease in their long term bank loans. While the paper examines the propensity of
banks to lend to more exposed firms, it does not fully control for shocks to credit supply.
Chaney et al. (2012) uses variation in local real estate prices in the US as a shock to firm col-
lateral value. They find that firms issue more debt when the value of local real estate where
This paper is also related to the literature on the determinants of foreign currency bor-
rowing.7 I contribute to this literature by examining how exchange rate balance sheet shocks
affect the currency composition of firm borrowing.8 Methodologically, this paper is in line
with much of the recent literature on the bank lending channel, which uses credit registry
and other matched firm-bank data (Chodorow-Reich, 2014; Cingano, Manaresi, & Sette,
2016; Jiménez, Ongena, Peydró, & Saurina, 2014; Khwaja & Mian, 2008). These papers ex-
ploit the matched nature of their datasets for identification, often by including various sets
bank fixed effects to control for possible time varying characteristics of firms and banks and
time invariant characteristics of a particular firm-bank match. Several of these papers specif-
ically analyze the international transmission of shocks via the banking system (Baskaya, di
& Ulu, 2017; Morais, Peydró, & Ruiz, 2015; Ongena, Peydró, & van Horen, 2015; Ongena,
Schindele, & Vonnak, 2016; Schnabl, 2012). While my analysis relies on an international
shock (namely, the dollar appreciation due to the 2008 financial crisis), I focus on the effect
9
Further, the construction of firm level bank shocks from loan level data is related to Al-
faro, Garcia-Santana, and Moral-Benito (2016); Amiti and Weinstein (in press); Greenstone,
Mas, and Nguyen (2014); Niepmann and Schmidt-Eisenlohr (2017b). My work makes an
important contribution here by proving that these bank shock estimates can be included dy-
namically in panel regressions when properly demeaned. This result can be potentially use-
ful in any application of using granular data (e.g. credit registries, student-teacher datasets,
In the theoretical literature, balance sheet effects are central to many macroeconomic
and international finance models (Bernanke, Gertler, & Gilchrist, 1999; Kiyotaki & Moore,
1997). These models rely on a borrowing constraint that depends on the firm’s collateral or
net worth. Krugman (1999) adapted this mechanism to study the impact of exchange rates
and foreign currency debt. Recently the theoretical literature has incorporated currency
mismatch and balance sheet shocks into general equilibrium environments (Bianchi, 2011;
Céspedes, Chang, & Velasco, 2004; Korinek, 2011; Mendoza, 2010). These papers generally
assume that firms only borrow in FX. This paper contributes to the theoretical literature
firm heterogeneity in size and shock exposure. This necessitates considering balance sheet
shocks in an environment where firms can choose the currency of their debt. Salomao and
Varela (2016) constructs a two period model of firm investment dynamics in which firms
can choose a mix of foreign and domestic currency debt. They find that more productive
firms select into larger FX mismatches, but they do not explore the consequences of balance
sheet shocks for these firms. In the appendix, I show that a simple model with borrowing
in both local and foreign currency and separate constraints on total and FX borrowing can
from one instrument, bank lending, to another instrument, bond finance. Rather than switching between types
of debt, my paper shows a shift between currencies of debt.
10
3 Data
3.1 Data Description
The source of my data is quarterly financial reports of firms listed on the Mexican stock
exchange, the Bolsa Mexicana de Valores (BMV). Non-financial listed firms are required to
submit quarterly financial reports to the BMV, which are published on the BMV website as
well as distributed by the individual firms.10 These reports come in pdf form and contain
tables for balance sheet statements, income statements, and cash flow statements. In ad-
dition, several annex tables include more detailed information on sales, sources of credit,
and currency composition of the balance sheet, among other things. These reports are con-
solidated, and so include the positions of any subsidiaries, whether foreign or domestic.
The data from these reports are scraped from the pdf files, harmonized across different pdf
The reports include standard balance sheet variables, notably the value of property,
plant, and equipment (physical capital) and the market value of on-balance sheet deriva-
tives positions. In addition to standard balance sheet variables, a couple of pieces of in-
formation reported are worth noting. Firms report the volume of external sales, which is
exports plus sales by foreign subsidiaries, which gives a more comprehensive measure of
foreign currency revenue for the firm than exports alone.11 Also, firms include a separate
line item for total employment in each quarter. Thus, I can connect financial outcomes from
the balance sheet with real outcomes like employment and investment.
The two most important and unique features of this dataset are the data on currency
composition of the balance sheet and the data on sources of credit. The annex on currency
10 The
Mexican National Banking and Securities Commission (CNBV) requires reporting of relevant corporate
information (i.e. may influence its stock price) to the regulators and public for all listed issuers on the BMV.
Circular 11-28 establishes these reporting requirements, the dissemination of which is managed by the BMV
(Ritch, 2001). Under the new Securities Market Law established in 2006, “listed companies are required to prepare
consolidated financial statements following the standards of the CNBV...The CNBV has established procedures to
review financial statements of the regulated entities in order to enforce compliance with accounting and auditing
requirements...The CNBV is empowered to impose sanctions for the violation of the reporting requirements.”
(OECD, 2008)
11 Sales by these firms’ foreign subsidiaries to buyers in Mexico are assumed to be negligible.
11
composition lists the assets and liabilities on the balance sheet in foreign currency, split into
US dollar and other currencies. On average, about 90% of all foreign currency liabilities
for my sample are denominated in USD. As I cannot determine which foreign currency
a given loan is in, I make the simplifying assumption that all FX balance sheet items are
denominated in USD for the remainder of the paper. The currency composition of both
sides of the balance sheet is used to give a more complete picture of a firm’s on-balance
The second unique feature of this data is the detail on credit to the firms. Firms list
every loan product that they have outstanding, as well as bonds and trade credit extended
by other firms. For each loan, the firm indicates the name of the bank extending the loan,
the interest rate on the loan, the currency of the loan (either peso or FX), and the remaining
maturity structure on the loan (how much of the loan is due within 1 year, within 2 years,
etc.). Loans are listed both from banks resident in Mexico as well as cross-border banks. The
combination of data on a firm’s on-balance sheet foreign currency positions with loan level
data, split by currency, is a unique data contribution that is crucial to identifying the impact
However, the firms list only the name of the lending bank for each loan, with no common
identifiers. I harmonize by hand all of the bank names reported in the data, taking account
of nicknames, abbreviations, different spellings, different languages, and name changes for
the bank.13 5% of loans by volume are identified only by generic names or grouped together
as “Others” or “Various”. These observations are dropped from the main estimation sample.
Of the remaining loans, 30% (by volume) either list multiple banks as the lenders or indicate
that the loan is a syndicated loan without identifying the bank. In these cases, I reference
12 I consider also the on-balance sheet derivatives positions, though I cannot tell the notional amounts of the
derivatives or the type (currency or foreign exchange derivatives, etc.).
13 Information on each bank (location, ownership, mergers, names and nicknames, etc.) was obtained from
banks’ individual web pages, wikipedia, and Bloomberg pages. I further match these banks up to information
in Bankscope, when possible, and use that information and notes in the Foreign Bank Ownership database,
provided by Claessens and Van Horen (2014), to further identify the banks and match them up appropriately for
each firm.
12
information on syndicated loans for these firms from the Thompson One database. Where
it is obvious who the lead bank is, I match the loan to the lead bank. When I cannot tell
who the lead bank is, I match the loan to the largest bank by assets that I can identify as
part of the syndicate. For the few cases in which the participating banks are unclear, the
loan is given its own unique bank identifier.14 With the banks uniquely identified, loans are
All data is presented in thousands of pesos.16 All FX loans are cleaned of valuation effects
and all series are deflated to 2010 pesos using Mexico’s CPI.17 The resulting dataset covers
3.2 Representativeness
Listed firms in Mexico make up an important part of the economy. The market capitaliza-
tion of these firms fluctuates around 30-40% of GDP (source World Bank, BMV). The vast
majority of listed firms in Mexico are non-financial firms. Between 2008-2014, the total share
of GDP from non-financial firms (both listed and unlisted) was around 62%.20
Table A7 plots the share of overall GDP, share of GDP in the non-financial sector, and share
of total credit to the private non-financial sector made up by my full sample of firms. Listed
firms make up around 10% of GDP, and up to a quarter of all non-financial output in 2009.
These firms also absorb a large volume of formal credit (defined as loans + bonds) in the
14 Results are robust to excluding sydicated loans.
15 While care has been taken to accurately match firms to banks, note that any error in the matching process
will add noise to the dependent variable, loans. This measurement error works against my results by attenuating
the estimates.
16 A few financial reports are presented in thousands of US dollars. These are converted into peso using end of
by nominal bonds which shifted the inflation risk to the government. Such lending was primarily used for
mortgages (Karaoglan & Lubrano, 1995).
18 Balance sheet data for these firms is available from 2005q1, but I am unable to examine loan-level trends
before 2008.
19 For perspective, there are about 130 firms listed on the BMV at any given time.
20 Source for Market capitalization of listed firms is from the World Bank and BMV. Source for GDP share of
employment.
13
economy, usually around 60% of all credit to the private non-financial sector.
The firms in my data account for a large portion of the foreign currency debt in Mexico.
Non-banks in Mexico (which includes government, households, etc.) had US dollar debt
outstanding of $117.7 Billion USD on average in 2008.22 In that same period, the firms in my
data accounted for $55.5 Billion USD in FX debt (mostly US dollar), which is about 47% of
Relative to the largest 1000 firms in Mexico, firms in my dataset are at the top end of
the size distribution. Table A8 shows the average size, employment, sales, equipment, and
operating margin of firms in Mexico in 2008, with data in the first two columns drawn from
the 2009 Economic Census in Mexico.23 While my sample is not necessarily representative
of all firms in Mexico, it does represent an important segment of the overall economy, so
their outcomes have ramifications for the aggregate, as well as potential spillover effects to
smaller firms, such as through production network shocks or credit spillovers. These firms
may also be similar to large firms in other emerging markets, so their behavior could be
For my regression analysis, I drop state owned/controlled firms, utilities, and non-financial
firms that provide auxiliary financial services.24 I also drop a few firms that are controlled
by a parent company in the sample and all firms with either no loans or no loans from an
identifiable bank.25
I split the sample into exporters and non-exporters, where exporters are defined as hav-
ing their median share of external sales to total sales over the sample greater than 15%. I
focus my analysis in this paper on the non-exporter sample, so as to isolate the balance
22 Source: BIS global liquidity indicators.
23 Note that I remove the financial firms from the “All Firms” and “1000 Largest Firms” samples. The 1000
largest firms are then the 921 largest non-financial firms.
24 The only quasi public firm is PEMEX, while the only auxiliary financial firm is American Express Mexico.
25 Some firms group smaller loans into ”various” or ”others” categories, and some loans are identified with too
generic a name for the bank in order to identify which bank it is. This drops 5% of loan volume from the sample.
14
sheet shock from changes in export revenues, but results for exporters are in the appendix
for comparison. I also split the sample by firm size, where “small’ is defined as having av-
erage size (measured by log assets) below the sample median.26 These splits break the firms
roughly in half for each group in the regression sample, as shown in Table A1. While large
firms are split evenly between the exporter and non-exporter samples, fewer small firms are
exporters.
These firms are spread across a variety of (broadly defined) sectors,27 shown in Table A2,
though half of the firms and observations are in the manufacturing sector. These sectoral
differences may be relevant for how firms are affected by and respond to the exchange rate
same bank, Table A3 summarizes the banking relationships in the regression sample. The
vast majority of firms and loan volume in the sample are covered by firms that maintain
multiple banking relationships, with firms averaging close to 7 simultaneous bank relation-
ships. On the bank side, there are many more banks in this sample than there are firms. This
is due to the sample being large listed firms that borrow both domestically and internation-
ally. In addition to borrowing from banks resident in Mexico, each firm may borrow from
any one of a wide variety of cross-border banks. This makes it more likely that these banks
will lend to just one firm in the sample. Despite having a large number of banks with only
one relationship with a firm in the sample, between 73-90% of total loan volume is covered
by banks with multiple borrowers in sample. The average number of lending relationships
in the sample for the full set of banks is around 3, but that number doubles when single
relationship banks (which are dropped with the inclusion of bank-quarter fixed effects) are
excluded.
Including the extensive set of fixed effects in separate samples reduces the firm sample
size to 93 firms. Table A4 shows how the full sample, regression sample (after dropping
26 Resultsare robust to adjusting the cutoffs for both exporter and small designations.
27 Sectorsare broad categories: Construction, Energy, Health, IT, Manufacturing, Real Estate, Restaurants and
Hotels, Retail and Wholesale, Telecom, and Transportation.
15
firms with no bank debt, and fixed effects sample compare. There are a few mild differ-
nces across samples, the most significant of which are that the fixed effect sample firms are
slightly larger on average (assets, employees) than the main regression sample. Otherwise,
the fixed effects do not change the composition of the sample of firms.
Table A5 summarizes the loan observations of the regression sample, aggregated to the
firm-bank-currency level. Interest rates are loan weighted averages up to the firm-bank-
currency level. Non-exporters tend to have slightly more and larger loan relationships in
peso than they do in FX, whereas exporting firms have substantially more and larger loan
relationships in FX. However, both exporter and non-exporter firms have lower interest
rates on their FX borrowing than their peso borrowing, on average.28 Across both groups
and both currencies, firms tend to have about half of their outstanding loans due within 1
year. These firms thus may need to roll over both their peso and FX bank debt frequently.
A key variable in my analysis is the firm’s foreign currency exposure (mismatch). I define
this exposure as
FXLiabilities f ,t − FXAssets f ,t
Exposure f ,t = (1)
Assets f ,t
which captures the net share of assets that is exposed to foreign currency mismatch. As a
firm increases its FX exposure, it makes itself more vulnerable to a depreciation that will
have larger negative effects on the balance sheet. Table A6 explores the characteristics of
firms that have more exposure prior to the shock. In the left panel, firms in the telecom
sector have the largest mismatch, while the manufacturing sector, which accounts for the
largest share of firms, has the second highest exposure. Since exposure is not even across
sectors, it will be important to make sure that the effects are driven by exposure and not
by sectoral differences. The right panel presents correlation coefficients for Exposure f ,t with
other firm characteristics. Exposure is higher for firms that are larger in terms of assets and
physical capital, and that have higher leverage, less cash holdings, and a higher share of ex-
ports.29 Leverage is the strongest correlate. I control for all of these variables in my regres-
28 These are simple averages of the interest rates calculated at the firm-bank-currency level. I formally test the
difference between FX and peso interest rates in loan weighted regressions in Table 6.
29 Note that my non-exporter sample can still have non-zero FX revenues. While these revenues are still small
16
sion analysis, and allow for interactions of these attributes with the shock period dummy to
The comparison between exporting and non-exporting firms highlights the degree of ex-
posure in the non-exporting firms. Figure 1 plots the time series for the average share of
foreign sales in total shares, with scale on the left axis, and the average on-balance sheet
FX exposure, with scale on the right axis. Exporters on average receive 40-45% of their rev-
enues from external buyers, whereas the non-export sample average is closer to 5% of their
non-exporting firms still have a relatively high exposure to FX, between 5-10% as compared
to the exporter average of 10-15%. Hence while exporters may have their balance sheet
positions sufficiently hedged by their FX revenues, it is less likely that the balance sheet
positions of non-exporting firms are adequately hedged. Despite having little revenue de-
for my firms against the share of their loans denominated in FX. As is evident in the figure,
the amount of FX loan borrowing does not always give an accurate picture of the currency
exposure of the firm. Some firms with 100% of their loans in FX have a negative exposure
due to their holdings of FX assets, while some firms with 0% of their loans in FX have posi-
The source of the balance sheet shock comes from a sharp depreciation of the exchange rate
in late 2008. The collapse of Lehman brothers in the US precipitated the global financial cri-
sis. One important effect that accompanied this crisis was an appreciation of the US dollar
vis-a-vis almost every other currency. The US Dollar Mexican peso exchange rate is plot-
ted in Figure 3. The depreciation of the peso was both sudden and unexpected. This is
and infrequent, I control for them directly in the empirical analysis.
17
important for my identification because firms were not adjusting their currency positions
in anticipation of a depreciation, and the exchange rate shock was not driven by Mexico’s
fundamentals. The currency movement was also large, as the dollar appreciated by 55%
The shaded area of the graph is the shock period, which captures the aftermath of the
shock for 8 quarters.31 There is also a large depreciation at the end of the sample, beginning
with the Taper Tantrum in 2013.32 However, this depreciation is a long and protracted event
that was likely to be anticipated and possibly connected to Mexico’s fundamentals, making
While the Lehman-induced exchange rate shock is plausibly exogenous, there are other
consequences of the global financial crisis that could potentially also affect the firms in my
sample, particularly because of Mexico’s close proximity and ties to the United States. Fig-
ure 4 shows some of the macroeconomic trends in Mexico around this same period. Around
the crisis, there was a clear slow down in growth in Mexico, as well as a mild decrease in ex-
ports relative to GDP. The drop in exports occurred despite the terms-of-trade improvement,
which reflects decreased demand from its primary trading partner, the US.34 This movement
in exports directly affects the foreign currency revenues in the economy, so export status and
Panel (b) of Figure 4 examines trends in financial variables. Debt inflows to the banking
and corporate sectors both dropped significantly in the aftermath of the crisis, followed by
a strong recovery. Also plotted is the growth of total US dollar credit to non-banks through-
out Latin America, which highlights the general trends of dollar liquidity over the period,
30 SeeSidaoui, Ramos-Francia, and Cuadra (2010) for a more detailed description of Mexico’s experience with
and response to the global financial crisis.
31 Results are robust to adjusting this period to end earlier or start earlier.
32 The Taper Tantrum was a panic in emerging markets that was initiated on May 22, 2013 when the Federal Re-
serve announced that it would begin tapering its bond purchases. This sparked the panic because an anticipated
US dollar appreciation and tighter US monetary policy meant that the FX debt accumulated during quantitative
easing would inflate and become difficult to service.
33 Results are robust to extending the sample to 2015q2, the last period in my data.
34 80% of Mexico’s exports are to the US, and 50% of its imports are from the US over the sample period (UN
COMTRADE database). For the remaining trade, recent evidence from Gopinath (2015) shows that most trade is
invoiced in USD, even if the US is not involved in the trade.
18
matching the capital inflows. Changes in these flows could affect the price and availability
of foreign currency credit. Key to my identification is the ability to control for shocks to
Despite the growth slowdown, drop in exports, and drying up of external and USD fi-
nancing, Mexico was able to recover fairly quickly from the crisis. Mexico’s banking system
was well capitalized ahead of the shock (Sidaoui et al., 2010).35 It is dominated by several
large foreign banks, but the Credit Institutions Law restricts the amount of capital a sub-
sidiary can transfer abroad to their parent bank to less than 50% of Tier 1 capital, which
helped keep the domestic banking sector more stable during the crisis (Sidaoui et al., 2010).
The strong position of domestic banks could potentially help to absorb the loss of external
financing and smooth out the credit results for borrowing firms. Further, banks in Mexico
are required to keep their open FX position below 15% of Tier 1 capital maintained on their
balance sheet, to limit their on-balance sheet currency mismatch (IMF, 2016). This addition-
ally may have helped prevent trouble arising in these banks. However, firms have no such
regulation. My sample consists of large firms who borrow substantially in FX from banks
both within and outside of Mexico, making them a pertinent sample to study the effects of
It is possible that firms in my sample have derivatives positions that hedge their expo-
sure. Anecdotally the use of derivatives by emerging market firms to hedge FX exposure is
quite limited, however, and the market value of their on-balance-sheet net derivatives posi-
tions appear to be small. Figure 5 plots the sample average net derivatives position relative
to total assets. Derivatives positions that would hedge against exchange rate movements
would be reflected after the exchange rate depreciates at the end of 2008, as the sudden de-
preciation would cause their value to change. For non-exporters, the average market value
of their derivatives positions did jump to about half a percent of assets following the depre-
35 The Basel III regulatory framework released in 2010 suggests a capital adequacy ratio (CAR) of about 8-10%,
whereas Mexico’s aggregate CAR has been around 16% over the whole sample period (Banco de Mexico).
36 Of loans made by domestic banks, the share denominated in foreign currencies was historically just below
20% prior to 2003, but has since dropped to just under 10% since 2005 (Hardy, 2018).
19
ciation, indicating some potential hedging, but anecdotally firms did not use derivatives to
hedge and the market value remained small compared to the nearly 10% of assets exposure
(on average) that these firms had at the time. Exporters may have a natural hedge of FX rev-
enues, but their derivatives positions turn negative on average following the shock. This is
due to several listed firms engaging in risky derivatives contracts that essentially bet against
a large depreciation of the peso (Chui, Fender, & Sushko, 2014; Sidaoui et al., 2010).
Why would non-exporting firms take the risk of unhedged FX exposure on their balance
sheet? As is common in many emerging markets, deviations from uncovered interest rate
parity (UIP) make FX loans relatively attractive despite the risk.37 Figure 6 plots deviations
from UIP, where = 1 means UIP holds, and > 1 indicates that FX loans are relatively cheaper
than peso loans. There are consistent deviations from UIP that make FX loans attractive for
even unhedged firms to borrow in. This incentivizes firms tol take unhedged FX positions,
sheets. The sharp depreciation of the peso at the end of 2008 provides such a shock, as
discussed earlier and shown in Figure 3. While this shock provides a movement in the
exchange rate that is exogenous to Mexico’s fundamentals, there could be other macroe-
conomic effects that occurred simultaneously with the global financial crisis. Of particular
concern are changes in trade, which affect foreign currency revenues, and capital inflows,
which affect the credit supply.38 To address the first concern, I split the sample into export-
ing firms (defined as those whose median sales share of exports is above 15%) and non-
37 See
Salomao and Varela (2016) for evidence of UIP deviations in European countries and the correlation of
FX loans with UIP.
38 While a depreciation is usually associated with increased exports due to the terms-of-trade improvement,
the recession in the US (Mexico’s primary trading partner) led to a reduction in demand that overpowered the
improved competitiveness.
20
exporting firms. Non-exporting firms are of particular interest because they do not have the
Financial markets worldwide were shocked following the collapse of Lehman Brothers
(concurrent with the depreciation). Credit supply shocks to a firm’s bank could bias the
estimated effect of the shock if banks that lend more in foreign currency or lend more to
exposed firms are affected differently from the shock. My identification strategy addresses
this by exploiting the matched nature of my dataset between firms and banks. Firms often
maintain multiple bank relationships, and banks lend to many firms. By comparing mul-
tiple firms that borrow from the same bank in the same currency, I am able to control for
credit supply shocks to a specific bank in that currency. In particular, I estimate separate re-
gressions for FX and peso loans, and control for bank-time fixed effects, which accounts for
all variation in outcomes from observed and unobserved time-varying bank factors. This
leaves variation in loan outcomes coming from firm characteristics, with FX mismatch as
The shock period is from 2009q1-2010q4, capturing the 2 years following the large peso
depreciation.39,40 Shock t takes a value of 1 during this period and 0 otherwise. Defining the
shock in this manner allows for flexibility in the timing of the impact for each firm, as firms
may not need to roll over debt or adjust their investment in every quarter. I take the average
of my FX exposure measure ((FX Liabilities - FX Assets)/Total Assets) over 2008 to get a time
invariant measure of exposure just prior to the shock period. I winsorize this measure for
two outlier firms, which have unusually large stocks of FX assets.41 I interact this measure
with the shock dummy to capture the balance sheet shock. Using a time-invariant pre-shock
steady, prolonged episode, and so it is less plausible as an unexpected shock unrelated to Mexico’s fundamentals.
Hence, my main sample of interest ends before that period, spanning 2008q1-2013q1.
41 Results are stronger with the inclusion of non-winsorized outliers. I prefer a winsorized specification to
21
in response to the shock.
time-varying firm controls, firms with different FX exposure who borrow from the same
bank in the same currency do not differ from each other in a way that is correlated with
the difference in their loan growth outcomes following the shock. This improves on the
existing literature, which assumes that firms are exposed to the same credit supply shocks.
The primary threat to this identification will be latent firm characteristics that are correlated
with exposure and that affect loan outcomes through some other channel during the shock
I implement my empirical strategy using the following baseline regression for non-exporting
∆ log( Loancf ,b,t ) = α f + αb,t + β 0 Exposure f × Shock t + ΦX f ,t−1 + ecf ,b,t (2)
where log( Loancf ,b,t ) is the log value of the loans outstanding at firm f from bank b at
time t (quarterly data) in currency c. The dependent variable is loan growth, measured
by ∆ log( Loancf ,b,t ) = log( Loancf ,b,t ) − log( Loancf ,b,t−1 ), which compares the loans outstand-
ing between the same firm-bank pair in the same currency over time.42 Bank-quarter αb,t
and firm α f fixed effects control for time-varying credit supply factors and time-invariant
sector-year fixed effects to account for trends in each sector that could be correlated with the
X f ,t−1 is a vector of time varying firm controls, lagged one period to avoid simultaneity,
which captures any remaining determinants of loan outcomes not associated with the bal-
ance sheet shock. These include firm size measured by log assets, the ratios of cash to assets,
bond debt to assets, total liabilities to assets, sales to assets, and net derivatives position
42 This is winsorized at 1% to reduce the influence large outliers in terms of loan outcomes, but results are
robust to not winsorizing.
43 Any common effects from macroeconomic conditions varying at the quarterly level are subsumed in the
22
relative to liabilities, as well as the share of sales to foreigners (which includes both exports
and sales by foreign subsidiaries).44 Since my independent variable of interest varies only
at the firm-time level, but my outcome variable varies at the firm-bank-time level, I cluster
the standard errors at the firm level.45 The regressions are weighted by the lagged value of
It is possible that we would not observe a significant effect because firms may receive
a balance sheet shock but not hit their borrowing constraint. The effect of a given shock
should be more relevant for firms that are more vulnerable or have less collateral, such as
smaller firms. Thus, I add an interaction of the shock with a dummy for small firms, defined
as having average size (measured by log assets) below the sample median.48,49
In this specification, β 1 represents the impact of the shock for large firms, while β 1 + β 3 is
the impact of the shock for small firms. Note that the sample consists of some of the largest
firms in the economy, so small is a relative term, but it is useful to separate out these firms
from the ultra-large firms since extreme size may enable such firms to access capital readily
44 These variables are winsorized as necessary to avoid the influence of outliers, but results are robust to either
including non-winsorized controls and excluding controls.
45 While clustering may be appropriate, some of the regressions have a lower number of clusters (e.g. 34) which
casts doubt on the asymptotic properties of the estimator. However, results are robust to pooling the exporters
and non-exporters together (for more clusters) and including an exporter dummy interaction with the main
variables of interest. For presentational convenience, results are presented separately by export status. Results
are robust to two way clustering on firm and time. Results are also robust to using Huber-White robust errors
instead of clustered errors.
46 This weighting allows larger loans to be given more weight in the results, so the movements of smaller, less
meaningful loans do not drive the results, but with a decreasing returns to size, so idiosyncrasies in ultra large
loans are not given undue influence on the estimates. Results are robust to not weighting.
47 All regressions are produced in STATA using reghdfe (Correia, 2016).
48 Results are robust to defining the small firm dummy as being in the bottom third instead of the bottom half.
49 While leverage seems like a better candidate to classify more vulnerable firms, the capacity for leverage
increases non-linearly with firm size (Gopinath, Kalemli-Özcan, Karabarbounis, & Villegas-Sanchez, 2017). Thus,
some firms may have a large amount of leverage and not be near their borrowing constraint, while other will have
less leverage and have their constraint be binding. Thus, when working with a sample of firms at the larger end
of the firm-size distribution, size may be a better measure of nearness to a borrowing constraint than leverage. I
consider leverage in conjunction with size and FX exposure in Table A11.
23
despite increased risk.
lidity of this approach by examining pre-period placebos (to check the parallel trends as-
sumption), and firm specific time trends (to control for any differential trends for each firm).
I next present results for loan outcomes at the firm-bank level. I focus on non-exporters
in my analysis, but results for exporters can be found in the Appendix in Tables A15 and
A24.
4.2 Results
Table 1 presents my main results at the firm-bank level. In columns (1)-(4), I find that firms
with a higher level of FX mismatch have lower growth in FX loans following the depreci-
ation. This result holds after including bank-quarter fixed effects in column (3). Of note is
the difference between columns (2) and (3). Column (2) uses the same sample as column
(3), but does not include the bank-quarter fixed effects.50 Failing to control for changes in
bank credit supply can bias the main coefficient of interest downward because firms that
have a currency mismatch and borrow in FX are likely to be borrowing from larger, stronger
banks. Omitting this control in column (2) results in an estimate that is nearly 40% smaller
in absolute value, though still significant. The drop in FX loan growth appears to be general
among both small and large firms, as seen in column (4). The JointTest row at the bot-
tom of the table shows the p-value on the joint significance test of Exposure f × Shock t and
Exposure f × Shock t × Small f (H0 : β̂ 1 + β̂ 3 = 0). Thus, smaller firms have a statistically sig-
nificant, though smaller in magnitude, drop in their FX credit growth, though the smaller
magnitude is not statistically different from the larger effect on the large firms.
Columns (5)-(8) shows the results for peso loans. In Columns (5)-(7), firms with more
exposure have a higher loan growth than less exposed firms following the shock. Here,
accounting for credit supply shocks does not appear to be as important, as reflected in the
50 Includingthe bank-quarter fixed effects reduces the sample size for FX loans because there are many foreign
banks that lend only to one firm in the sample, so their observations are wiped out with the bank-quarter fixed
effects.
24
coefficients in columns (6) and (7). The interesting difference comes in column (8), where we
see that the large increase in peso borrowing is driven by larger firms, while smaller firms
see a mild (though insignificant) decrease in peso loan growth. Thus while all mismatched
firms have lower loan growth in FX, only the large firms increase their peso borrowing to
compensate. Results are robust to alternate specifications of loan growth and of exposure,51
adjusting the length of the shock period, and adjusting the cutoff for exporter and small firm
designations.52
How large are these effects? I use columns (4) and (8) of Table 1 to calculate the estimated
effects for small and large firms separately. For small firms, the net impact on their FX loan
growth following the shock from the FX exposure is −0.264 and the net impact on their peso
loan growth is −0.121. If a small firm increases their FX exposure by 10% of assets (about
equivalent to increasing from the median to the 75th percentile), then their FX loan growth
will fall by 2.64% and their peso loan growth will fall by 1.21%. For a small firm with 33% of
its loans in FX (the pre-shock average), this results in a 1.68% drop in total loan growth. For
a large firm, the estimated impact of the shock is −0.691 for FX and 0.899 for peso. A 10%
increase in exposure for a large firm results in a drop of 6.91% in their FX loan growth and
an increase of 8.99% in their peso loan growth. For a large firm with 56.5% of its loans in FX,
these effects will cancel out. The pre-shock average large non-exporting firm had 27% of its
loans in FX, which would result in a total increase in loan growth of 4.7%.
To put the 1.68% drop for small firms and 4.7% increase for large firms in perspective, the
average loan growth rates in 2008 were 11% and 25% for small and large firms, respectively,
while the median rates were 5% and 2.8%, respectively.53 Thus, for the typical small firm
(in terms of loan growth), increasing their initial FX exposure could completely stall their
loan growth after the depreciation shock. The increase for large exposed firms is large, more
than doubling loan growth for the typical large firm. These effects are thus important to the
51 See Table A16, which examines exposure measured by short term FX liabilities over assets, standard growth
measures, and growth measures that admit entry and exit of firm-bank relationships.
52 Available upon request.
53 These numbers for 2011 were 16.8% and 9.5% for small and large average, and -0.2% and 0.8% for small and
large median.
25
outcomes of the firm.
It could be the case that the the FX and peso results for large firms are driven by different
sets of firms, rather than the same firms moving from FX to peso. In Table 2, I pool FX
and peso loans together in the same regression, and add an interaction with an FX dummy
variable to examine the relative difference between FX and peso borrowing for each firm.
In this pooled specification, I can include firm-quarter fixed effects in order to compare the
relative loan growth of FX vs peso within firm. The regression takes the form:
log(loancf ,b,t ) = α f ,t + αb,t,c + δ0 Exposure f × FXc + δ1 Exposure × Shock t × FXc + ecf ,b,t (4)
where c indexes currency (domestic or foreign). While this specification can control for all
time-varying firm heterogeneity, it relies on variation only from firms who borrow both in
FX and peso. In columns (1) and (2), I include firm fixed effects and bank-quarter-currency
fixed effects, the latter to account for different credit supply shocks for each currency, and
I add in the firm-quarter fixed effects in columns (3) and (4). These results, while more
difficult to interpret with the extra interactions, reveal that there is a significant within firm
difference between peso and FX borrowing for large exposed firms following the shock.
Note that the difference for small firms (the sum of the coefficients on Exposure f × Shock t ×
FXc and Exposure f × Shock t × Small f × FXc ) is close to zero and statistically insignificant,
Is the overall effect on loan outcomes positive or negative for large and small firms? Ta-
ble 3 presents results with FX and peso loans pooled together.54 Controlling for bank supply
shocks in column (1), we see that large exposed firms do have a large and positive impact
on their loan growth, whereas small exposed firms have a negative, though not statistically
significant, impact. Controlling for credit supply shocks by currency in columns (2) and (3)
reveals a significant decline in loan growth for small firms. Thus, it appears that, after con-
trolling for supply shocks, small firms hit with a balance sheet shock indeed appear to hit
26
In addition to affecting the net worth of the firm, the exchange rate shock could also
impact the firm by affecting the firm’s ability to repay short term debt coming due. I exam-
ine and compare the impact on borrowing of the firm’s short term FX exposure with total
FX exposure to attempt to separate the net worth effect from the liquidity effect. These two
measures are highly correlated, so results should be interpreted with caution. Short term ex-
posure is defined as the firm’s 2008 average of short term FX liabilities minus total FX assets,
divided by total assets. For my sample of firms, I have data on the maturity composition of
FX assets only from 2012 onward. However, examination of the post-2012 data reveals that
the average firm holds over 90% of its FX assets as short term assets (e.g. FX deposits, etc.).
Thus, I make the assumption that all FX assets are short term, which allows me to construct
Table 4 reports these results. Comparing just the effect on all firms, columns (1) and (4)
illustrate that the variation from the total FX exposure drives the decrease in FX borrow-
ing and increase in peso borrowing, whereas the short exposure is insignificant. Splitting
by firm size in columns (2) and (5) indicate that small firms may be more sensitive to their
short exposure. Large firms still show the decrease in FX borrowing due to the net worth
shock, but those with a large shock to their short term positions increase their FX borrow-
ing. This likely reflects large firms who have short exposure, but are not fully constrained,
borrowing relatively more in FX to meet their short term FX obligations. Smaller firms do
not appear to have this luxury. While the net effect for small firms is not significant for ei-
ther total exposure or short exposure, the negative net outcome for FX loans in Table 1 is
reflected more in the net coefficient on the short term exposure (−0.916). Column (5) shows
the same increase in peso borrowing by large firms as before, driven by their total FX ex-
posure, but smaller firms with higher short term exposure show a decrease in their peso
borrowing. Thus, smaller firms appear to be more sensitive to the illiquidity aspect of the
balance sheet shock. Columns (3) and (6) present results with just the short term exposure
by itself. These results likewise suggest that large firms are not as affected by their short
27
term exposure in the amounts that they borrow, but smaller firms with higher short term
FX exposure decrease both their FX and peso borrowing following the exchange rate shock.
This element of maturity mismatch and rollover risk may be an important aspect to analyze
when accounting for the responses of firms in the lower end of the size distribution to an
The mechanism for the effects of the balance sheet shock on loan volume could work
through changes in the interest rates charged on firm borrowing. Table 5 presents the base-
line results with the log of (1+ the real or nominal interest rate) as the dependent variable.56
Interest rates are loan weighted within a firm-bank-currency triplet in each period (when
aggregating the data to the firm-bank-currency level), and the regressions are weighted by
where α f ,b captures any time invariant variation in interest rates that is specific to a given
firm-bank pair. This controls for any preferential or unusual banking relationships that may
determine the interest rate. A caveat to this analysis is that interest rates reflect all outstand-
ing loans in the period, not just newly granted loans. The regressions return insignificant
results. The coefficients point in the right direction for small firms with high exposure to
the shock, who should experience higher interest rates if they are more risky, but we cannot
to FX loans. Both forecast series are from the Bank of Mexico’s survey of inflation and exchange rate forecasts.
57 There may not be enough variation in interest rates (as measured by outstanding loans) to capture these
developments. Note that regressions with weaker fixed effects yield similar results.
28
If there is a change in the interest rate differential, this could affect firm borrowing in FX
relative to peso (and thus potentially explain the finding that large exposed firms switch to
peso). Table 6 pools the FX and peso loans together, and considers the following regression:
where r is the real interest rate. In this specification, I can control for all time varying firm
and bank characteristics, and time-invariant firm-bank match characteristics that may deter-
mine the terms of these loans. In columns (1)-(2), I find a decrease in the differential price of
FX vs. peso loans following the depreciation, though this effect is not significantly different
for firms who are more exposed following the shock. The significant and negative FX coef-
ficient indicates that there is a premium on the interest rates for peso loans at the individual
level, even after controlling for all observable and unobservable time varying characteristics
of both firm and bank. This premium is only reduced by 30% following the shock. This
confirms the failure of UIP seen at the aggregate level, and suggests that FX loans are still
attractive for firms (relative to peso) following the shock if they are able to obtain such a
loan.
In column (3), we see that the increase in the real interest rate on FX loans is driven by
loans to small firms. That is, firms in the smaller half of the sample face more expensive FX
borrowing in real terms following the shock.58 This is important as it means that a change
in the interest rate differential cannot explain why large firms switch to peso borrowing
following the shock. Indeed, given that the increase in the FX interest rate is driven mainly
by small firms, we would expect that those firms would have a higher propensity to switch
to the local currency. Column (4) controls for time-vayring bank-specific factors in each
currency via bank-quarter-currency fixed effects. Fully controlling for credit supply shocks
in both currencies removes the significance of the effect for small firms and reduces the
coefficient by nearly two-thrids. This may be due to soaking up too much variation with
58 This result is highly significant with weaker sets of fixed effects.
29
a heavy fixed effect specification, but shocks to bank credit supply in each currency may
play more of a role in determining the change in the interest rate differential than does firm-
specific risk. Columns (5) and (6) include the full interactions, which are not significant
Given my empirical setup, the primary threats to identification are firm characteristics that
are correlated with FX mismatch and are affected by macroeconomic changes that occur
of interest, Exposure f × Shock t with competing interactions of Shock t with other firm char-
results, I focus on the overall effect for FX loans and the small vs large split for peso loans.
Tables A9 and A10 show these regressions, for FX and peso loans respectively, for six firm
characteristics that are correlated with exposure or potentially determine firm outcomes fol-
lowing the depreciation: ratios of exports to sales,59 cash holdings to assets, sales to assets,
net derivatives to liabilities, and leverage (liabilities to assets), as well as firm size (log as-
sets). Exports and size affect the main coefficient of interest the most, but in every case the
sign and significance of the coefficient on Exposure × Shock t are robust to including these
competing interactions.
As noted earlier, firms in some sectors tend to be more exposed to currency shocks than
others. It is possible that firms in different sectors are impacted differently during the shock
period for other reasons, either due to differences in the change in demand, the change
in input costs, or the change in investment opportunities, so the exposure measure could
simply be capturing differences in outcomes by sector. In Tables A12 and A13, I explicitly
include interactions of Shock t and Exposure f × Shock t with sector dummies, in order to see
if see if the balance sheet shocks differ by sector or if a single sector is driving the results.
These regressions include sector dummies one by one, with the column heading indicating
59 Note that since non-exporters are defined as having their median share of sales to foreigners as less than 15%
of total sales, some firms in the non-exporting sample will have some export revenue.
30
which sector is in the interaction term Sector f . While some of the sectors do appear to be
differentially affected during the shock period, none of the interactions appreciably affect
Table 7 further tests for robustness to sectoral differences using alternative fixed effects
specifications. In columns (1) and (4), I include sector-year fixed effect as a more compre-
hensive way to account for trends that may affect certain sectors and thus contaminate my
identification.61 Alternatively, it is possible that banks may differentially adjust their credit
supply following the shock depending on the sector of the firm. This would violate my
identification assumption that firms borrowing from the same bank in the same currency
are exposed to the same credit supply shock in each period. Columns (2) and (5) include
bank-sector-year fixed effects to account for this possibility. Additionally, there could be
unobservable characteristics of each firm-bank match that are correlated with exposure and
affect lending outcomes. For instance, higher mismatch firms may match with banks that
are more exposed to exchange rate shocks. Columns (3) and (6) address this possibility
by including firm-bank fixed effects. In all of these cases, the main results concerning the
Differences in the effect of exposure between large and small firms could be driven by by
some other firm characteristic instead of size. For instance, high leverage could make a firm
more vulnerable to a balance sheet shock. Also, many of the large manufacturing firms are
exporting firms, while the small manufacturing firms are largely non-exporters. Table A11
examines if these characteristics determine the observed differential behavior between small
60 The exception is column (6) of Table A13. Firms in the construction sector appear to have larger impacts
on their peso borrowing (larger positive for large firms, larger negative for small firms) than firms generally.
Nevertheless, the results for construction and non-construction firms point in the same direction. Note that some
of the triple and quadruple interactions in Table A13 are missing due to collinearity.
61 My non-exporter sample largely is not exposed to changes in export revenues associated with the exchange
rate change. However, they could be negatively exposed if they import intermediate goods which would become
more expensive with the change in terms-of-trade. Exporting firms do a lot of importing (see Blaum (2017) for
evidence of this from Mexico), so the exporter sample would be more affected by this issue, but the sector-year
fixed effects do capture sector wide changes in import cost over the shock period. For a very limited sample
of firms, I compute the share of production costs accounted for by imported inputs. Including this measure as
a control captures relevant variation (as indicated by the increase in the within-R2 ), but does not change the
estimated coefficient. These results are available upon request.
31
vs. large firms.62 Columns (1) and (3) compare interactions with a dummy for having pre-
Assets
2009 leverage (defined as Equity ) above the sample median. Leverage appears to generate
more noise in the FX regression in column (1), though the coefficients remain sizable and
point in the same directions. Still, leverage itself does not appear to explain the observed
patterns for either FX or peso loans. Columns (2) and (4) introduce a competing interaction
with a manufacturing dummy. Here, the potential selection effect of manufacturing firms
of the parallel trends assumption underlying this approach in Tables A14 for loan outcomes.
The first two columns in either table highlight that the pre-periods show no significant dif-
ferences in outcomes by level of exposure leading up to the shock. The second two columns
show that the results are robust to the inclusion of firm-specific linear time trends.
Table A18 presents results from a few alternative specifications. First, 42% of loan vol-
ume for sample firms originates from cross-border banks. Thus, these changes in loan out-
comes may be driven by cross-border banks reacting more strongly to the firm balance sheet
shocks, as cross-border banks may differ in their access to FX financing and exposure to the
financial crisis. In columns (1) and (3), I restrict my firm-bank sample to just banks resident
in Mexico and find that the results are robust. Second, the period following the deprecia-
tion was characterized by higher volatility of the exchange rate. Thus, the results could be
driven by an increase in volatility and uncertainty about the exchange rate, rather than the
actual depreciation shock. Restricting the sample to include just the period after the shock,
comparing the immediate aftermath of the depreciation with the later post period, delivers
the same results as shown in columns (2) and (5). Lastly, I conduct a placebo test, replacing
the original shock variable with a dummy that equals 1 from 2010q3-2011q2, a period in
which there were no large exchange rate movements, when firms should not be differen-
tially affected by the exchange rate. This specification delivers the expected null result.
62 Notethat these serve as competing interactions with the small firm dummy, unlike in Tables A12 and A13
where the variables are competing with the exposure measure.
32
Overall, I find strong evidence for a balance sheet effect, whereby a deterioration in net
worth affects firms’ ability to borrow. This constraint on borrowing appears to be tighter for
loans in FX, and more binding generally on smaller firms. The bite of the binding borrowing
constraint on small firms may be amplified if the firm has a larger shock to their short term
positions. This is important, as my small firms are still quite large, so the negative effects
could be larger still for out of sample firms. My results are further suggestive that liquidity
in the domestic currency may be an important factor to offset the negative impact of FX mis-
match shocks for larger firms, though the general equilibrium repercussions of the switch
When analyzing balance sheet shocks, we are ultimately interested in their effects on real
outcomes. Real economic activity does not vary at the loan level, so analysis of real outcomes
necessitates working at the firm level. This section presents the empirical approach and
results for my firm level analysis. I focus on employment and investment outcomes for the
Working at the loan level allows me to control for bank shocks (via bank-time fixed effects)
controlling for bank shocks would be equally valuable. In order to do so, I construct a
control for variation in bank credit supply that varies at the firm level. This is in line with
the work of Alfaro et al. (2016); Amiti and Weinstein (in press); Greenstone et al. (2014);
Niepmann and Schmidt-Eisenlohr (2017b). I first estimate the following regression at the
firm-bank level:63
33
This regression separates loan growth into bank- and firm-specific factors.64 Note that if the
firm-time fixed effects are not included, the bank-time effects will be biased, as they will
attribute all of the time-variation in loan growth to the bank; certain banks may have high
loan growth because they are lending to high loan growth firms.
I construct a firm-specific bank shock as the (loan) weighted sum of the estimated bank
shocks α̂b,t for each bank that the firm borrows from. Formally,65
!
L f ,b,t−1
BS f ,t = ∑ α̂b,t (8)
b∈ B ∑b∈ B f ,t L f ,b,t−1
f ,t
where Y f ,t is either physical capital, measured as property, plant, and equipment (PPE),
firm fixed effect; αt is a time fixed effect; and the other variables and controls are defined
as in the firm-bank level regressions. Similar to those regressions, the firm-level regressions
compare outcomes for firms with differing levels of exposure following the large deprecia-
tion shock.
There is an important econometric issue to address when using the bank shock control.
∆ log( L f ,b ) = α f + αb + e f ,b (11)
64 These effects are computed using the felsdvreg command in STATA (Cornelissen, 2008). See Alfaro et al.
(2016) for more discussion on this approach, which extends methodology originally developed in Abowd, Kra-
marz, and Margolis (1999).
65 This formulation is similar to the Bartik instrument.
34
When both firm and bank fixed effects are included, each set of fixed effects will span
the whole space. Thus, one individual fixed effect must be omitted due to collinearity, and
the remaining fixed effects in this set are then measured relative to the omitted group. This
would be true for each period in which we run this regression. If we expand back to the
multiple period regression in Equation 7, we see that in each period, one fixed effect group
will be omitted, and so the remaining fixed effects will all be estimated relative to the omit-
ted group. Since the effects in each period are measured relative to their own omitted group,
This means that my constructed bank shock measure is also not comparable over time.
Proposition 5.1. Time demeaned values of the estimated α̂ f ,t and α̂b,t are the same as the time
demeaned values of a hypothetical α̂∗f ,t and α̂∗b,t which have all of the fixed effects measured relative
to the same benchmark (e.g. 0). Further, the constructed BS f ,t , when time demeaned, has the same
Proposition 5.1 indicates that by including time fixed effects in the firm level regression
(and thus time demeaning the data), the coefficient on the bank shock in Equation 9 is exactly
the same as it would be if all of the fixed effects were estimated relative to 0 rather than
relative to an omitted category. This result is useful generally when using connected datasets
(such as credit registry data or bilateral trade data) to construct similar shock estimates for
35
a time fixed effect,67 the fixed effect estimates from the matched data can be used in that
regression.68
5.2 Results
I first examine potential substitution at the firm-level to other sources of funding besides
loans (such as bonds and trade credit). These results are presented in Table 8. Columns (1)-
(3) present results where the dependent variable is non-bank liabilities (either total, FX, or
peso). These results mirror the bank borrowing results: large firms increase their funding,
whereas small firms do not. The increase for large firms is driven by their peso borrowing.
One specific area of concern is that the large firms may be switching to FX bond debt in order
to replace their lost FX bank debt (in addition to using more peso borrowing). Columns
(4)-(6) shows that this is not the case. Though not significant, the coefficient on the main
interaction is negative for bond debt, particularly FX bond debt, indicating that the effect of
Table 9 presents my main results at the firm level. Consistent with the firm-bank level
results, I find that while there is no measured effect of the balance sheet shock across all
firms on average (as found elsewhere in the literature), there is a difference in outcomes
for large vs small firms. In columns (1) and (2), I show results for total bank borrowing of
these firms. Large exposed firms see an increase in their bank borrowing relative to large,
less exposed firms, reflecting the increased access to peso credit, while small exposed firms
have a net negative effect, though not statistically significant. In columns (3) and (4), the
difference in employment is similar, with exposed large firms seeing a mild increase while
small firms do not. Columns (5) and (6) examine growth in physical capital. Here, large
exposed firms again see an increase, but smaller exposed firms see a decrease in growth.
While the total effects for small exposed firms measure as a statistical zero for bank credit
67 Or more generally, a fixed effect that aligns with each connected group.
68 Thisdoes not absolve more general issues associated with using an estimated factor in the regression, such
as measurement error. A relatively small sample size makes bootstrapping the errors less feasible, but the results
are robust to excluding the bank shock control, so any measurement error in the bank shock does not appear to
be biasing the coefficients of interest.
36
and employment, there is a significant decrease in growth of physical capital for these firms.
An increase in exposure of 10% of assets for a small firm would result in a decrease in
physical capital growth of 1.14%. For the median small firm, pre-shock capital growth was
on the order of 0.2%, so this shock could represent a substantial decline for some firms, or a
These results are again robust to horseraced interactions with other firm characteristics.
These results are shown in Tables A19 and A20 for employment and capital respectively. The
results are further largely robust to alternative specifications of exposure and growth mea-
surement, shown in the appendix in Table A25, and to interactions with sector dummies,
shown in Tables A21 and A22.69 Thus for smaller firms with a large currency mismatch,
balance sheet shocks can have negative real consequences as well as the negative financial
consequences documented earlier. This provides corroborating evidence that currency mis-
match and balance sheet effects can lead to negative real outcomes via binding borrowing
constraints.
Table A23 checks the validity of the difference-in-difference design for real outcomes.
The first two columns in either table highlight that the pre-periods show no significant dif-
ferences in outcomes by level of exposure leading up to the shock. The second two columns
examine robustness to the inclusion of firm-specific linear time trends. Investmen outcomes
are robust. The employment outcomes in column (3) are no longer significant after includ-
ing firm specific time trends, nevertheless the coefficients are of approximately the same
The 75th percentile firm in terms of FX exposure (for either small or large) experienced
a drop in net worth of 3.33% of assets. The median firm (either small or large) experienced
a 1.1% drop in net worth. Using the estimates from Table 9, a small firm that experiences
a drop in net worth of 1% of assets experiences a decline in physical capital of 0.34%. For
69 The effects on employment appear to be driven in part by the construction sector.
In column (6) of Table A21,
balance sheet shocks to large construction firms result in positive outcomes, but balance sheet shocks to small
construction firms result in large negative outcomes. The direction of the effect for other sectors remains the
same, but is statistically insignificant.
37
a large firm, a drop in net worth of 1% of assets results in an increase in employment of
0.48% and an increase in physical capital of 0.38%. If FX debt in the economy at large is
primarily concentrated among the listed firms, then the aggregate implication is that there
is not much of a net effect of the balance sheet shock on aggregate investment, as the smaller
firms decrease investment while the larger firms increase investment.70 However, direct and
indirect impacts on firms outside of my sample may be important sources of negative real
How important is it to capture the firm’s full on-balance sheet exposure to FX, rather than
relying on more limited measures (e.g. FX debt only)? Table 10 reports coefficients from the
umn (1) augments the main measue used in this paper with an estimate of FX hedging.
This is done by taking the value of the net derivatives position just after the depreciation
(2009q1) and subtracting the net derivatives position just before the depreciation (2008q3).
This captures the fact that if firms were using derivatives to hedge the exchange rate shock,
the market value of these positions would turn positive (into assets) with the sharp depre-
ciation of the peso (as shown earlier in Figure 5). Although this measure does not fully
capture derivatives hedging, comparing columns (1) and (2) suggests that accounting for
derivatives usage for these firms does not appreciably alter the estimates.
Column (3) removes FX assets from the measure, as many studies rely on just informa-
tion about FX liabilities. Here the magnitude for the effect on employment at large firms
decreases, while for physical capital the magnitude for both large and small is halved. This
suggests that firms holding FX liabilities may often also hold some FX assets, so we would
measure a smaller than true effect because we over estimate their exposure. Some stud-
ies rely just on one source of debt to get FX exposure, such as loans or bonds. Column
(4) uses just FX loans in the numerator of the exposure measure. The measured effects on
employment in Panel A are attenuated downwards and all estimates lose significance. The
70 Asshown in Table A15, exporting firms with FX exposure are largely unaffected in their real outcomes,
suggesting their positions are fully hedged.
38
estimates for investment remain similar to those of column (3), still underestimating the im-
pact, but recording a negative net impact for small firms. Column (5) uses only FX bond
debt in the numerator of the exposure measure. With just this piece, we lose all significance
for the investment regression in Panel B. Panel A on employment, however, shows a large
positive (though statistically insignificant) effect for large firms, and a large negative and
significant effect on small firms. These results together highight the importance of having a
more comprehensive measure of firm FX exposure in order to accurately measure the bal-
The result that large firms with a negative balance sheet shock actually have higher
growth in terms of debt, employment, and physical capital than less exposed firms has
been found previously in the empirical literature, yet is contrary to the standard model. We
would expect either a negative effect, if the firm is constrained, or a null effect, if the firm is
unconstrained. The positive effect of a balance sheet shock suggests that there may be some
other factors at play, although a large variety of observable firm characteristics fail to explain
this relationship. The next section discusses implications for theory which could rationalize
these findings.
The evidence presented in this paper is consistent with firms being subject to a constraint
on their total borrowing as well as facing a second, tighter constraint on their FX borrowing.
These constraints, when binding, change the allocation of credit (differently by currency)
and lead to differences in real outcomes. Appendix C presents a simple 3-period model to
illustrate how including this second borrowing constraint on foreign currency debt can gen-
erate the observed patterns in borrowing following the exchange rate shock. The presence
of both borrowing constraints, dependent on net worth or size, is further validated in the
data by Figure 7, which plots the bank debt of non-exporting firms in my sample in peso
and FX against their size (log assets). As firms get larger, they increase their leverage in peso
39
before increasing their leverage in FX.71 This is striking as the lower price of FX debt and
failure of UIP suggests that risk-neutral firms would desire to do the opposite.
In many models, the constraint on total borrowing that the firm faces can be derived
(implicitly or explicitly) from an incentive compatibility constraint in which the firm should
not have an incentive to default on their debt (under most realizations of the exchange rate).
The additional constraint on FX borrowing reflects the risks faced by the bank. Niepmann
and Schmidt-Eisenlohr (2017a) provide evidence that firms that borrow more in FX have a
higher probability of defaulting on their loans (both FX and peso) in the event of a depre-
ciation. Further, most collateral backing loans to emerging market firms is denominated
in local currency (see Calomiris, Larrain, Liberti, and Sturgess (2017) and Fleisig, Safavian,
and de la Peña (2006) for evidence that immovable collateral is frequently required to secure
lending in emerging markets). That means that when a loan is made in FX and the exchange
rate depreciates, the bank recovers a smaller share of the loan value in the event of default,
increasing their downside risk. Thus, the bank has an incentive to limit FX borrowing in
The differential behavior of large vs small firms poses another challenge to existing the-
ory. While standard theory would suggest that a negative balance sheet shock would at
best have no effect on the real activity of the firm (if the firm is away from its borrowing
constraint), my results show that for very large such firms, they are able to increase their
borrowing and investment.73 The model in the appendix considers selection into FX debt
by more productive firms as one possible explanation, as in Salomao and Varela (2016).
Another possible explanation is that the exchange rate movement itself changes the oppor-
tunity set of large vs small firms. For example, large firms may have their revenues tied to
the US dollar via production chains where they serve as suppliers to exporting firms. For
71 Size based borrowing constraints (as in Gopinath et al. (2017)) match the data better, but are not necessary to
generate the qualitative results observed in my analysis.
72 This incentive may strengthen if the bank faces higher penalties for not repaying its FX creditors as compared
affect results implicitly in many other papers (as many studies rely on data from listed firms and have shown
both positive and negative results).
40
firms in my sample, the large non-exporting firms with large FX exposure tend to be in ser-
vices or the construction industry. Thus, this explanation is possible in principle, though
General equilibrium effects may thus play an important role to understand the results.
Carabarı́n, de la Garza, and Moreno (2015) find for Mexico that as alternative sources of
funding (FX bond markets) open up for these large firms, capital in the banking sector is
freed up to lend more to small and medium sized firms. The converse could certainly be
the case, where these large firms shift away from their FX borrowing and towards peso
borrowing, which crowds out smaller firms. Negative aggregate effects, often documented
in the aggregate data following a large depreciation or currency crisis, could occur due to
a misallocation of capital, as banks may reallocate resources from risky borrowers to safe
(large) borrowers in the event of a negative capital shock. Negative effects could also arise
if FX borrowing is pervasive prior to the shock among the small and medium sized firms
who are more likely to be constrained in the event of a shock. Even if the large firms are
unaffected, the decline in investment by smaller firms together may make a larger impact.
General equilibrium effects could also operate through changes in demand during the re-
cession that favor larger firms.Thus, further incorporating firm heterogeneity and currency
of borrowing, modeling the joint distribution of FX debt and firm size, into our macroeco-
nomic models will be important to capture the behavior of the economy and the aggregate
7 Conclusion
In this paper, I estimate the effect of balance sheet shocks on firm borrowing and real activity.
I construct a unique dataset of listed non-financial firms in Mexico that combines firm bal-
ance sheet data, including data on real outcomes, export revenues, and currency exposures,
with loan level data for each firm that includes the currency of borrowing as well as the iden-
tity of the lending bank. I exploit an exogenous and sudden depreciation episode connected
41
with the financial crisis in the US as an experiment. Using matched firm-bank data, I control
for bank credit supply shocks with bank-quarter-currency fixed effects and isolate the im-
to the depreciation. I thus directly examine the mechanism of the balance sheet shock (via
credit outcomes), and differentiate these effects by currency. I estimate bank credit supply
shocks at the firm level, and show how to include this measure as a time-varying control
in firm-level regressions. I then examine the effect of the balance sheet shock for the real
I find that non-exporting firms with a higher currency mismatch on their balance sheet
have slower loan growth in FX following the depreciation shock. However, large firms with
higher FX exposure compensate for this by increasing their peso borrowing, while smaller
exposed firms do not. These results are robust to numerous alternative specifications and
controls. While the borrowing costs for FX loans relative to peso increase following the
shock, compressing the interest rate differential, this was driven by the small firms who did
not switch into peso borrowing. FX loans remain cheaper in real terms for all firms, but this
result suggests that FX loans were still as attractive as before to large firms in terms of the
At the firm level, I find that total bank borrowing by large non-exporters with a mis-
match increases, while smaller non-exporters with a mismatch do not increase the growth
of their bank debt. Larger firms consequently see higher growth in their investment and
employment, while smaller firms do not see higher employment growth and experience
lower investment growth. Together, these results suggest that balance sheet effects can lead
to binding borrowing constraints, that these constraints may bind more tightly on FX loans
and smaller firms, and that these binding constraints can affect real outcomes.
This paper helps to harmonize and complement existing research by identifying and
highlighting the roles of firm size and currency of debt for borrowing constraints. I show
that the null or positive impact of FX related balance sheet shocks found in some studies
42
could be due to their focus on large firms that are able to substitute lost FX credit for do-
mestic currency credit after the shock. This suggests that some firms can avoid a binding
borrowing constraint after a shock if they are able to switch to local currency credit, but oth-
erwise balance sheet effects can have real impacts on these firms. The stability and liquidity
of the domestic banking sector could be a factor for emerging market policy makers to con-
sider when assessing the risk posed by corporate borrowing in foreign currencies. Further,
risk assessment should focus on the exposure of small and medium sized firms, as that is
firms into the local currency credit market could have spillover effects for smaller firms
(especially those not in my sample) by crowding them out of local currency borrowing.
The converse result has been found for listed firms in Mexico by Carabarı́n et al. (2015), who
show that as alternative sources of funding (FX bond markets) open up for these large firms,
capital in the banking sector is freed up to lend more to small and medium sized firms. Thus,
negative effects could occur due to a misallocation of capital from risky to safe borrowers.
Negative real effects could also arise if FX borrowing is pervasive prior to the shock among
the small and medium sized firms who are more likely to be constrained in the event of a
shock. As most existing research relies on large firms for data and analysis of their FX debt,
firm level empirical studies may fail to examine the portion of the economy where negative
debt among the universe of firms and analysis accounting for general equilibrium channels
43
References
Abowd, J., Kramarz, F., & Margolis, D. (1999). High wage workers and high wage firms.
Econometrica, 67(2), 251–333.
Adrian, T., Colla, P., & Shin, H. S. (2012). Which financial frictions? Parsing the evidence from
the financial crisis of 2007-9. NBER Macroeconomics Annual, 27.
Aghion, P., Bacchetta, P., & Banerjee, A. (2001). Currency crises and monetary policy in an
economy with credit constraints. European Economic Review, 45(7), 1121–1150.
Aguiar, M. (2005). Investment, devaluation, and foreign currency exposure: the case of Mex-
ico. Journal of Development Economics, 78, 95–113.
Alfaro, L., Garcia-Santana, M., & Moral-Benito, E. (2016). Credit supply shocks, network
effects, and the real economy. working paper.
Allayannis, G., Brown, G., & Klapper, L. (2003). Capital structure and financial risk: evidence
from foreign debt use in East Asia. The Journal of Finance, 58(6), 2667–2710.
Alvarez, R., & Hansen, E. (2017). Corporate currency risk and hedging in Chile: real and
financial effects. IDB Working Paper, 769.
Amiti, M., & Weinstein, D. (in press). How much do bank shocks affect investment? Evidence
from matched bank-firm loan data. Journal of Political Economy.
Avdjiev, S., Hardy, B., Kalemli-Özcan, Şebnem., & Servén, L. (2017). Gross capital inflows to
banks, corporates and sovereigns. NBER Working Paper, No. 23116.
Barajas, A., & Morales, R. A. (2003). Dollarization of liabilities: beyond the usual suspects.
IMF Working Paper, 03/11.
Baskaya, Y. S., di Giovanni, J., Kalemli-Özcan, Şebnem., Peydró, J., & Ulu, M. (in press).
Capital flows and the international credit channel. Journal of International Economics.
Baskaya, Y. S., di Giovanni, J., Kalemli-Özcan, Şebnem., & Ulu, M. (2017). International
spillovers and local credit cycles. NBER Working Paper 23149.
Basso, H., Calvo-Gonzalez, O., & Jurgilas, M. (2011). Financial dollarization: the role of
foreign-owned banks and interest rates. Journal of Banking and Finance, 35(4), 794–806.
Benavente, J., Johnson, C., & Morande, F. (2003). Debt composition and balance sheet effects
of exchange rate depreciations: a firm-level analysis for Chile. Emerging Markets Review,
4, 397–416.
Bernanke, B., Gertler, M., & Gilchrist, S. (1999). The financial accelerator in a quantitative
business cycle framework. Handbook of Macroeconomics, 1(C), 1341–1393.
Bianchi, J. (2011). Overborrowing and systemic externalities in the business cycle. American
Economic Review, 101(7), 3400–3426.
Blaum, J. (2017). Importing, exporting and aggregate productivity in large devaluations.
working paper.
Bleakley, H., & Cowan, K. (2008). Corporate dollar debt and depreciations: much ado about
nothing? Review of Economics and Statistics, 90(4), 612–626.
Bonomo, M., Martins, B., & Pinto, R. (2003). Debt composition and exchange rate balance
sheet effect in Brazil: a firm level analysis. Emerging Markets Review, 4, 368–396.
Brown, M., & de Haas, R. (2012). Foreign banks and foreign currency lending in emerging
Europe. Economic Policy, 69, 57–98.
Brown, M., Kirschenmann, K., & Ongena, S. (2014). Bank funding, securitization, and loan
terms: evidence from foreign currency lending. Journal of Money, Credit and Banking,
46(7), 1501–1534.
Brown, M., Ongena, S., & Yeşin, P. (2011). Foreign currency borrowing by small firms in
44
transition economies. Journal of Financial Intermediation, 20(3), 285–302.
Caballero, J. (2018). Corporate dollar debt and depreciations: all’s well that ends well? mimeo.
Caballero, J., Panizza, U., & Powell, A. (2014). Balance sheets and credit growth. In A. Powell
(Ed.), Global recovery and monetary normalization: escaping a chronicle foretold? (chap. 4).
Inter-American Development Bank.
Calomiris, C., Larrain, M., Liberti, J., & Sturgess, J. (2017). How collateral laws shape lending
and sectoral activity. Journal of Financial Economics, 123(1), 163-1-88.
Carabarı́n, M., de la Garza, A., & Moreno, O. (2015). Global liquidity and corporate financing
in mexico. mimeo, Banco de México.
Carranza, L., Cayo, J., & Galdon-Sanchez, J. (2003). Exchange rate volatility and economic
performance in Peru: firm level analysis. Emerging Markets Review, 4, 472–496.
Céspedes, L., Chang, R., & Velasco, A. (2004). Balance sheets and exchange rate policy. Ameri-
can Economic Review, 94(4), 1183–1193.
Chaney, T., Sraer, D., & Thesmar, D. (2012). The collateral channel: how real estate shocks
affect corporate investment. American Economic Review, 102(6), 2381–2409.
Chodorow-Reich, G. (2014). The employment effects of credit market disruptions: firm-level
evidence from the 2008-09 financial crisis. Quarterly Journal fo Economics, 129(1), 1–59.
Chui, M., Fender, I., & Sushko, V. (2014). Risks related to EME corporate balance sheets: the
role of leverage and currency mismatch. BIS Quarterly Review, September 2014.
Chui, M., Kuruc, E., & Turner, P. (2016). A new dimension to currency mismatches in the
emerging markets: non-financial companies. BIS Working Papers, 550.
Cingano, F., Manaresi, F., & Sette, E. (2016). Does credit crunch investment down? new
evidence on the real effects of the bank-lending channel. The Review of Financial Studies,
29(10), 2737–2773.
Claessens, S., & Van Horen, N. (2014). Foreign banks: Trends and impact. Journal of Money,
Credit and Banking, 46(1), 295–326.
Cornelissen, T. (2008). The stata command felsdvreg to fit a linear model with two high-
dimensional fixed effects. Stata Journal, 8(2), 170–189.
Correia, S. (2016). Linear models with high-dimensional fixed effects: An efficient and feasible estima-
tor (Tech. Rep.). (Working Paper)
Cowan, K., Hansen, E., & Óscar Herrera, L. (2005a). Currency mismatches, balance-sheet
effects and hedging in Chilean non-financial corporations. Inter-American Development
Bank working paper, 521.
Cowan, K., Hansen, E., & Óscar Herrera, L. (2005b). Currency mismatches in Chilean non-
financial corporations. In R. Caballero, C. Calderón, & L. F. Céspedes (Eds.), External
vulnerability and preventative policies (Vol. 10). Santiago: Banco Central de Chile.
Desai, M., Foley, C. F., & Forbes, K. (2008). Financial constraints and growth: multinational
and local firm responses to currency depreciations. Review of Financial Studies, 21(6),
2859–2888.
Du, W., & Schreger, J. (2015). Sovereign risk, currency risk, and corporate balance sheets.
mimeo.
Echeverrya, J., Fergussona, L., Steinerb, R., & Aguilara, C. (2003). Dollar debt in Colombian
firms: are sinners punished during devaluations. Emerging Markets Review, 4, 417–449.
Fleisig, H., Safavian, M., & de la Peña, N. (2006). Reforming collateral laws to expand access to
finance. Washington D.C.: World Bank.
Gan, J. (2007). Collateral, debt capacity, and corporate investment: evidence from a natural
experiment. Journal of Financial Economics, 85, 709–734.
45
Gilchrist, S., & Sim, J. (2007). Investment during the Korean financial crisis: a structural
econometric analysis. NBER Working Paper, No. 13315.
Gopinath, G. (2015). The international price system. Economic Policy Symposium - Jackson Hole.
Gopinath, G., Kalemli-Özcan, Şebnem., Karabarbounis, L., & Villegas-Sanchez, C. (2017). Cap-
ital allocation and productivity in Southern Europe. Quarterly Journal of Economics, 132(4),
1915–1967.
Greenstone, M., Mas, A., & Nguyen, H. (2014). Do credit market shocks affect the real
economy? Quasi-experimental evidence from the Great Recession and normal economic
times. NBER Working Paper, 20704.
Hardy, B. (2018). Foreign currency bank lending and the global financial cycle. mimeo.
IMF. (2016). Mexico: financial system stability assessment. IMF Country Report, 16/361.
Ize, A., & Levy Yeyati, E. (2003). Financial dollarization. Journal of International Economics,
59(2), 323–347.
Jiménez, G., Ongena, S., Peydró, J., & Saurina, J. (2014). Hazardous times for monetary policy:
what do twenty-three million bank loans say about the effects of monetary policy on
credit risk taking? Econometrica, 82(2), 463–505.
Kalemli-Özcan, Şebnem., Kamil, H., & Villegas-Sanchez, C. (2016). What hinders investment
in the aftermath of financial crises? Insolvent firms or illiquid banks? Review of Economics
and Statistics, 98(4), 756–769.
Kaminsky, G., & Reinhart, C. (1999). The twin crises:the causes of banking and balance-of-
payments problems. American Economic Review, 89(3), 473–500.
Karaoglan, R., & Lubrano, M. (1995). Mexico’s banks after the December 1994 devaluation–A
chronology of the government’s response. Northwestern Journal of International Law and
Business, 16(1), 24–43.
Khwaja, A. I., & Mian, A. (2008). Tracing the impact of banking liquidity shocks: evidence
from an emerging market. American Economic Review, 98(4), 1413–1442.
Kim, Y. J., Tesar, L., & Zhang, J. (2015). The impact of foreign liabilities on small firms: firm-
level evidence from the Korean crisis. Journal of International Economics, 97, 209–230.
Kiyotaki, N., & Moore, J. (1997). Credit cycles. Journal of Political Economy, 105(2), 211–248.
Korinek, A. (2011). Excessive dollar borrowing in emerging markets: balance sheet effects and
macroeconomic externalities. IMF Economic Review, 59(3), 523–561.
Krugman, P. (1999). Balance sheets, the transfer problem, and financial crises. International Tax
and Public Finance, 6(4), 459–472.
Luca, A., & Petrova, I. (2008). What drives credit dollarization in transition economies. Journal
of Banking and Finance, 32(5), 858–869.
Luengnaruemitchai, P. (2003). The asian crisis and the mystery of the missing balance sheet
effect. mimeo, UC Berkeley.
Maggiori, M., Neiman, B., & Schreger, J. (2017). International currencies and capital allocation.
Columbia Business School Research Paper, No. 17-96.
Martı́nez, L., & Werner, A. (2002). The exchange rate regime and the currency composition of
corporate debt: the Mexican experience. Journal of Development Economics, 69(2), 315–334.
McCauley, R. N., McGuire, P., & Sushko, V. (2015). Dollar credit to emerging market
economies. BIS Quarterly Review, December 2015.
Mendoza, E. (2010). Sudden stops, financial crises, and leverage. American Economic Review,
100(5), 1941–1966.
Morais, B., Peydró, J., & Ruiz, C. (2015). The international bank lending channel of monetary
policy rates and quantitative easing: credit supply, reach-for-yield, and real effects. World
46
Bank Policy Research Working Paper, 7216.
Niepmann, F., & Schmidt-Eisenlohr, T. (2017a). Foreign currency loans and credit risk: evi-
dence from U.S. banks. working paper.
Niepmann, F., & Schmidt-Eisenlohr, T. (2017b). No guarantees, no trade: How banks affect
export patterns. Journal of International Economics, 108, 338–350.
OECD. (2008). The role of institutional investors in promoting good corporate governance
practices in Latin America: the case of Mexico. mimeo, OECD.
Ongena, S., Peydró, J., & van Horen, N. (2015). Shocks abroad, pain at home? bank-firm level
evidence on the international transmission of financial shocks. IMF Economic Review,
63(4), 698–750.
Ongena, S., Schindele, I., & Vonnak, D. (2016). In lands of foreign currency credit, bank lending
channels run through? CFS Working Paper, 474.
Pratap, S., Lobato, I., & Somuano, A. (2003). Debt composition and balance sheet effects of
exchange rate volatility in Mexico: a firm level analysis. Emerging Markets Review, 4,
450–471.
Ritch, J. (2001). Public offerings of securities: Mexican law issues. U.S.-Mexico Law Journal,
9(133).
Rosenberg, C., & Tirpák, M. (2008). Determinants of foreign currency borrowing in the new
member states of the EU. IMF Working Paper, No. 08/173.
Salomao, J., & Varela, L. (2016). Exchange rate exposure and firm dynamics. mimeo.
Schnabl, P. (2012). The international transmission of bank liquidity shocks: evidence from an
emerging market. Journal of Finance, 67(3), 897–932.
Serena Garralda, J., & Sousa, R. (2017). Does exchange rate depreciation have contractionary
effects on firm level investment? BIS Working Papers, No. 624.
Shin, H. S. (2013). The second phase of global liquidity and its impact on emerging economies.
Asia Economic Policy Conference, Keynote address at the Federal Reserve Bank of San Francisco.
Sidaoui, J., Ramos-Francia, M., & Cuadra, G. (2010). The global financial crisis and policy
response in Mexico. BIS Papers, no. 54, December.
47
Figure 1: Exports and Exposure
Source: Author’s calculations. FX Exposure is (FX Liabilities - FX Assets)/Total Assets, right axis. Exports is
share of external sales relative to total sales, left axis. Exporting firms are defined as having the share of external
sales to total sales above 15%.
Source: Author’s calculations. % of Loans in FX is FX denominated loans divided by total loans. Balance Sheet
Mismatch is (FX Liabilities - FX Assets)/Total Assets. 2008q1-2015q2.
48
Figure 3: US Dollar - Mexican peso Exchange Rate
49
Figure 4: Macroeconomic Trends of Mexico
(a) Exports and GDP Growth (b) Capital Inflows and Dollar Liquidity
Source: World Bank WDI, Avdjiev et al. (2017), BIS. Debt inflows is defined as portfolio debt inflows (e.g. bonds)
plus other investment debt inflows (e.g. loans) capital flows from external creditors to resident banks or
non-bank firms. USD credit to LA non-banks is total credit provided to non-bank institutions resident in Latin
American countries.
Source: Author’s calculations based on on-balance sheet derivatives positions. Figures expressed as percent.
50
Figure 6: UIP Deviations
Source: Banco de Mexico, FRED. UIP Deviation defined as (st /E[st+1 ]) ∗ ((1 + rt )/(1 + rt∗ )), where st is the
exchange rate expressed as dollars per peso, E[st+1 ] is the year ahead expected exchange rate (from survey of
professional forecasters), and r and r ∗ are the the interest rates on 1 year treasury bills for Mexico and the U.S.,
respectively. All rates are period averages over each quarter.
51
Table 1: Growth in Bank Loans (%), Firm-Bank Level
FX Peso
(1) (2) (3) (4) (5) (6) (7) (8)
53
Table 3: Growth in Bank Loans (%), Firm-Bank Level - All Loans
54
Table 4: Growth in Bank Loans (%), Firm-Bank Level - Short vs Total FX Exposure
FX Peso
(1) (2) (3) (4) (5) (6)
55
Table 5: Interest Rates, Firm-Bank Level
Exposure f × Shockt 0.00994 -0.00420 0.00989 -0.00366 -0.00277 -0.0121 -0.00276 -0.0120
(0.0109) (0.00953) (0.0112) (0.0103) (0.00919) (0.0138) (0.00974) (0.0145)
Shockt × Small f 0.00497 0.00453 0.00177 0.00201
(0.00611) (0.00632) (0.00255) (0.00263)
Exposure f × Shockt × Small f 0.0194 0.0188 0.0185 0.0182
(0.0226) (0.0233) (0.0194) (0.0202)
Observations 691 691 691 691 2250 2250 2239 2239
56
FX Peso
(1) (2) (3) (4) (5) (6)
59
Table 9: Growth in Firm Level Outcomes (%)
60
Table 10: Measures of FX Exposure and Growth in Firm Outcomes (%)
Panel A: Employment
FXL− FXA− FXD FXL− FXA FXL BankFXL BondFXL
Exposure Measure Assets Assets Assets Assets Assets
61
A Appendix Tables
Non-
Exporters Exporters Total
Small 44 20 64
Large 28 32 60
Total 72 52 124
Firms are from the regression sample, which in-
cludes just firms with loan data from identifiable
banks over 2008q1-2013q1. Exporters are defined as
having their median share of external sales to total
sales over the sample greater than 15%. Small firms
are defined as having their average size (measured
by log assets) below the sample median.
Number Firm-Bank
Sector of Firms Observations
Construction 14 2106
Energy 1 8
Health 5 294
IT 1 105
Manufacturing 60 7194
Real Estate 6 339
Restaurants 8 669
Retail and Wholesale 11 578
Telecom 12 1313
Transportation 6 464
Total 124 13 070
Firms are from the regression sample, which includes
just firms with loan data from identifiable banks over
2008q1-2013q1.
62
Table A3: Firm-Bank Relationships
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)
Firms: Multiple Bank Rels. Banks: Multiple Firm Rels.
Av. No. Av. No.
Num Firm Loan Rel. per Num Bank Loan Rel. per
Firms Firms Share Share Firm Banks Banks Share Share Bank
2008 94 80 0.851 0.995 7.280 221 94 0.425 0.732 3.095
2009 89 81 0.910 0.991 6.831 204 82 0.402 0.797 2.980
2010 94 77 0.819 0.957 6.638 220 84 0.382 0.742 2.836
2011 90 73 0.811 0.955 6.644 202 67 0.331 0.760 2.960
2012 89 77 0.865 0.943 6.798 186 82 0.441 0.876 3.253
2013 87 75 0.862 0.941 6.782 180 82 0.456 0.900 3.278
2014 88 75 0.852 0.936 6.898 191 93 0.487 0.902 3.178
This table presents annual (quarter 4) summary statistics on the frequency of different types of firm-bank relationships
within the loan data using end-of-year data for the regression sample. Column (1) lists the number of firm; columns
(2)-(4) deal with firms who borrow from multiple banks, listing the number of them, the share of firms, and the share of
loans accounted for, respectively; column (5) gives the average number of bank relationships each firm in sample has;
column (6) lists the number of banks; columns (7)-(9) deal with banks that lend to multiple firms, listing the number,
the share of banks, and the share of loans accounted for, respectively; and column (10) gives the average number of
firms each bank lends to in sample.
63
Table A5: Firm-Bank Level Loan Summary
64
Table A7: Aggregate Representativeness
Share of
Total Credit
Share of to Private
Share of NFC Value Non-Financial
Year GDP Added Sector
2006 9.34 14.73 55.75
2007 9.19 14.42 56.05
2008 12.67 19.77 62.02
2009 14.52 24.41 61.13
2010 11.90 19.64 63.06
2011 10.85 17.49 61.47
2012 8.34 13.19 61.98
2013 7.05 11.31 60.40
2014 6.24 9.96 60.28
Source: World Bank WDI, INEGI, BIS, author’s cal-
culations. Total credit is loans + bonds. Value added
from my sample calculated as sales - cost of goods
sold. Credit to non-financial sector series from BIS is
to the private non-financial sector, so PEMEX is ex-
cluded from those calculations.
65
Table A9: Growth in FX Loans (%), Firm-Bank Level, Horseraces
66
Table A10: Growth in peso Loans (%), Firm-Bank Level, Horseraces
peso
Dependent Variable = log(loan f ,b,t )
(1) (2) (3) (4) (5) (6)
Horse Variable Exports Cash Derivatives Size Leverage Sales
Exposure f × Shockt 0.854∗∗∗ 0.998∗∗∗ 0.924∗∗∗ 0.898∗∗∗ 1.103∗∗∗ 0.885∗∗∗
(0.279) (0.267) (0.277) (0.293) (0.238) (0.266)
Shockt × Small f 0.0838∗∗ 0.264∗∗∗ 0.0610 -0.409 -0.116 0.115
(0.0384) (0.0646) (0.0407) (1.012) (0.140) (0.0845)
Exposure f × Shockt × Small f -1.332 ∗∗∗ -1.305∗∗∗ -0.975∗∗∗ -0.846∗∗ -1.210∗∗∗ -1.012∗∗∗
(0.402) (0.307) (0.307) (0.330) (0.267) (0.290)
Horse f × Shockt 0.00654 ∗ 0.00694∗∗ -0.00723 0.00124 -0.00403∗∗ 0.00101
(0.00331) (0.00283) (0.0154) (0.0502) (0.00165) (0.00224)
Horse f × Shockt × Small f -0.00140 -0.0304∗∗∗ -0.0841 0.0309 0.00376 -0.00225
(0.00506) (0.00949) (0.0538) (0.0600) (0.00288) (0.00330)
Observations 2377 2377 2377 2377 2377 2377
67
FX Peso
(1) (2) (3) (4)
68
Table A12: Growth in FX Loans (%), Firm-Bank Level - Robustness To Sectors
Exposure f × Shockt -0.581∗∗∗ -0.577∗∗∗ -0.364∗∗ -0.561∗∗∗ -0.428∗∗∗ -0.424∗∗∗ -0.576∗∗∗ -0.575∗∗∗
(0.117) (0.171) (0.166) (0.181) (0.111) (0.106) (0.107) (0.107)
Shockt × Sector f 0.0633 0.0677 -0.119 -0.252 0.130∗ 0.136∗∗ -0.389∗∗∗ -0.543∗∗∗
(0.0778) (0.114) (0.114) (0.192) (0.0767) (0.0648) (0.109) (0.0802)
Exposure f × Shockt × Sector f -0.0180 0.630 -0.113 1.449
(0.364) (0.508) (0.646) (1.003)
69
Exposure f × Shockt 0.855∗∗ 1.614∗∗∗ 0.934∗∗∗ 0.936∗∗∗ 0.889∗∗∗ 0.417∗∗ 0.908∗∗∗ 0.907∗∗∗
(0.318) (0.205) (0.277) (0.276) (0.278) (0.159) (0.279) (0.278)
Shockt × Small f 0.0784∗ 0.0966∗∗ 0.0754∗ 0.0912∗∗ 0.0836 0.0561 0.0705∗ 0.0680∗
(0.0403) (0.0393) (0.0425) (0.0437) (0.0502) (0.0515) (0.0400) (0.0395)
Exposure f × Shockt × Small f -0.963∗∗∗ -1.703∗∗∗ -1.163∗∗∗ -2.132∗∗ -1.111∗∗∗ -0.551∗∗ -1.042∗∗∗ -0.998∗∗∗
(0.334) (0.218) (0.360) (0.841) (0.308) (0.228) (0.300) (0.298)
Shockt × Sector f 0.0531 0.125∗ 0.153∗∗∗ 0.173∗∗∗ -0.0261 -0.0692 0.0777∗ 0.0512
(0.0741) (0.0675) (0.0496) (0.0531) (0.0537) (0.0510) (0.0411) (0.0402)
Shockt × Small f × Sector f 0.184∗ 0.173∗ -0.117 -0.169∗ -0.0709 0.0300 0.0403 0.251∗∗∗
(0.109) (0.102) (0.0871) (0.0993) (0.0820) (0.0687) (0.0947) (0.0714)
70
71
Table A15: Growth in Bank Loans (%), Firm-Bank Level - Exporters
FX Peso
(1) (2) (3) (4) (5) (6)
L − L −1 L − L −1
L −1 0.5∗( L+ L−1 )
73
Table A17: Growth in Bank Loans by Remaining Maturity (%), Firm-Bank Level
FX Peso
(1) (2) (3) (4) (5) (6) (7) (8)
Short Short Long Long Short Short Long Long
Exposure f × Shockt -0.811∗∗∗ -0.768∗ -0.00147 0.599 0.154 0.129 0.864∗∗ 1.326∗∗
(0.259) (0.446) (0.466) (0.356) (0.169) (0.277) (0.389) (0.538)
Shockt × Small f -0.366∗∗∗ 0.189 -0.0411 0.0524
(0.112) (0.153) (0.0568) (0.0712)
Exposure f × Shockt × Small f 0.316 -1.038∗∗∗ 0.0962 -1.157∗
(0.490) (0.362) (0.372) (0.626)
Observations 560 560 397 397 2002 2002 1422 1422
R2 0.448 0.457 0.505 0.513 0.150 0.150 0.206 0.208
74
Firms 28 28 25 25 47 47 42 42
FirmFE Yes Yes Yes Yes Yes Yes Yes Yes
BankQuarterFE Yes Yes Yes Yes Yes Yes Yes Yes
FirmControls Yes Yes Yes Yes Yes Yes Yes Yes
JointTest 0.0430 0.284 0.333 0.640
Sample spans 2008q1-2013q1, Firms reports the number of firms in each regression. Dependent variable is the log difference of loans
outstanding in FX at the firm-bank level in each period, winsorized at 1%. Exposure is the firm’s average 2008 net FX position to
assets, with 2 outlier firms winsorized. Small is a dummy equal to one if the firm’s average size (measured by log assets) is below the
sample median. Risky is a dummy equal to 1 if the firm is a small firm whose average leverage is above the sample median. Shock is a
dummy variable taking a value of 1 in 2009 and 2010 and 0 otherwise. Firm Controls include one quarter lags of firm size (log assets),
cash to assets ratio winsorized at 1%, total liabilities to assets ratio winsorized at 2%, bond credit to assets, share of sales to foreigners
(including exports and sales by foreign subsidiaries), sales to assets ratio, and net derivatives position to total liabilities winsorized at
3%. Regressions are weighted by the lagged value of log loan. Errors are clustered at the firm level. JointTest reports the p-value of
the F-test that the sum of the coefficients of Exposure*Shock and Exposure*Shock*Small is equal to 0. * p < 0.10, ** p < 0.05, *** p <
0.01
Table A18: Growth in Bank Loans (%), Firm-Bank Level, Alternate Samples and Placebos
FX Peso
(1) (2) (3) (4) (5) (6)
Sample: Sample: Placebo: Sample: Sample: Placebo:
Domestic 2009q1- 2010q3- Domestic 2009q1- 2010q3-
Banks 2013q1 2011q2 Banks 2013q1 2011q2
Exposure f × Shockt -0.583∗∗∗ -0.407∗∗∗ -0.257 0.896∗∗∗ 1.253∗∗ -0.440
(0.162) (0.134) (0.265) (0.277) (0.522) (0.327)
Shockt × Small f 0.0723∗ 0.0945∗∗ -0.0158
(0.0388) (0.0454) (0.0454)
Exposure f × Shockt × Small f -1.012∗∗∗ -1.340∗∗ 0.0847
(0.297) (0.539) (0.380)
Observations 493 634 764 2371 2075 2377
75
Exposure f × Shockt 0.405∗∗∗ 0.412∗ 0.376∗∗∗ 0.379∗∗∗ 0.414∗∗∗ 0.400∗∗∗ 0.393∗∗∗ 0.393∗∗∗
(0.115) (0.215) (0.0986) (0.0986) (0.0870) (0.0428) (0.102) (0.102)
Shockt × Small f -0.00101 -0.000992 0.0150 0.0214 -0.00290 -0.00748 0.00208 0.00220
(0.0214) (0.0214) (0.0197) (0.0221) (0.0132) (0.0129) (0.0206) (0.0207)
Exposure f × Shockt × Small f -0.419∗∗ -0.425∗ -0.230 -0.439 -0.367∗∗∗ -0.268∗∗ -0.413∗∗ -0.417∗∗
(0.163) (0.243) (0.151) (0.280) (0.130) (0.102) (0.154) (0.160)
Shockt × Sector f -0.0171 -0.0164 -0.0209∗ -0.0207∗ 0.0422 0.0414 0.0127 0.0127
(0.0239) (0.0194) (0.0114) (0.0115) (0.0291) (0.0280) (0.0121) (0.0121)
Shockt × Small f × Sector f 0.0168 0.0166 -0.0540∗∗ -0.0683∗∗∗ 0.0301 0.0708∗∗ -0.00242 -0.00917
(0.0307) (0.0298) (0.0217) (0.0238) (0.0436) (0.0322) (0.0176) (0.0265)
78
Exposure f × Shockt 0.213∗∗ 0.276∗ 0.218∗∗ 0.219∗∗ 0.202∗∗∗ 0.176∗∗∗ 0.220∗∗ 0.220∗∗
(0.0935) (0.139) (0.0845) (0.0851) (0.0634) (0.0453) (0.0864) (0.0864)
Shockt × Small f 0.00978 0.0100 0.0167 0.0200 -0.00560 -0.00616 0.0108 0.0109
(0.0125) (0.0124) (0.0129) (0.0130) (0.0129) (0.0126) (0.0124) (0.0124)
Exposure f × Shockt × Small f -0.257∗ -0.319∗ -0.208 -0.311 -0.232∗∗ -0.204∗∗ -0.281∗∗ -0.286∗∗
(0.134) (0.169) (0.148) (0.221) (0.115) (0.0993) (0.129) (0.137)
Shockt × Sector f 0.00934 0.0163 0.0195 0.0219∗ -0.0316 -0.0319 0.0124 0.0123
(0.0184) (0.0171) (0.0122) (0.0126) (0.0213) (0.0210) (0.00999) (0.0100)
Shockt × Small f × Sector f 0.0342 0.0332 -0.0459∗ -0.0555∗ 0.0573∗∗ 0.0582∗∗ 0.00943 0.00266
(0.0216) (0.0208) (0.0272) (0.0298) (0.0284) (0.0267) (0.0160) (0.0245)
79
80
Table A24: Growth in Firm Level Outcomes (%) - Exporters
81
Table A25: Growth in Firm Level Outcomes (%), Alternate Specifications
∗ ∗ ∗ ∗
Note that the time average of α̂re f ,t is α̂re f ,t . Thus α̂b,t − α̂t = α̂b,t − α̂t , where α̂t is the time
f f∗ f
average of α̂b,t . By the same logic, α̂ f ,t − α̂t = α̂∗f ,t − α̂t where α̂t is the time average of α̂ f ,t .
c ∗f ,t − α̂∗ − 1 c ∗f ,t − α̂∗
= BS re f ,t
Ft ∑ BS re f ,t
f ∈ Ft
∗
c ∗f ,t − α̂∗ − ( BS
= BS c t − α̂∗ )
re f ,t re f ,t
∗
c ∗f ,t − BS
= BS ct
C Model
My results suggest that firms are subject to a constraint on their total borrowing and a sec-
ond, tighter constraint on their FX borrowing, which gives the balance sheet shocks real
impacts. Here, I present a stylized 3 period model which serves to illustrate qualitatively
83
how this mechanism can generate the behavior observed in the empirical results. The model
The key to the model is that firms, in addition to being constrained in their total debt,
are subject to a second borrowing constraint specifically on their FX borrowing. These con-
straints both depend on the net worth of the firm, which in this model is directly related
to firm size. This assumption is justified in Figure 7, which plots the bank debt of non-
exporting firms in my sample in peso and FX against their size (log assets). As firms get
larger, they increase their leverage in peso before increasing their leverage in FX.75 This is
striking as the lower price of FX debt and failure of UIP suggests that firms would desire to
do the opposite.
The constraint on total borrowing that the firm faces can be derived from an incentive
compatibility constraint, in which the firm should not have the incentive to default on their
debt (under most realizations of the exchange rate). The additional constraint on FX borrow-
ing reflects the risks faced by the bank. Niepmann and Schmidt-Eisenlohr (2017a) provide
evidence that firms that borrow more in FX have a higher probability of defaulting on their
loans (both FX and peso) in the event of a depreciation. Further, most collateral backing
loans to firms is denominated in local currency (see Calomiris et al. (2017) and Fleisig et
al. (2006) for evidence that immovable collateral is frequently required to secure lending in
emerging markets). That means that when a loan is made in FX and the exchange rate depre-
ciates, the bank recovers a smaller share of the loan value in the event of default, increasing
their downside risk. Thus, the bank has an incentive to limit FX borrowing in addition to
to domestic peso creditors. In this model, I leave the explicit problem generating this constraint un-modeled.
84
C.1 General Framework
There are 3 periods t ∈ {0, 1, 2}. The economy is populated by firms (or entrepreneurs) who
seek to maximize their period 2 wealth. Firms are endowed with initial wealth w0 . Firms
are risk neutral and produce using technology yt = f (k t ) = zkαt .77 Capital depreciates fully
upon use.
The timing works as follows: at t = 0, firms inherit their initial wealth (their size) and
is realized. Firms produce and repay their debt (which may be affected by the depreciation),
or default and exit if they are unable to repay, and then use the remaining profits to make
exchange rate is again resolved, firms produce, repay their debt or default, and consume
their profits.
Firms can borrow in peso and FX, but the rate of currency depreciation is uncertain, and
UIP fails such that FX debt is attractive.78 UIP failure takes the following form: E[1 + φ] =
1+r 1
1+r ∗ γ , where γ > 1 captures the deviation from UIP, r > r ∗ are the interest rates on local and
foreign currency loans, respectively, and φ is the rate of depreciation of the local currency.
Firms are subject to constraints on their total borrowing and on their FX borrowing.
The problem is solved recursively. At the end of t = 1, firms take as given wealth w1 and
s.t.
k2 = w1 + d2 + d2∗ (14)
77 Iabstract from employment decisions of the firm for simplicity.
78 UIP failure is shown in the aggregate in Figure 6 and in the microdata at the firm level in Table 6.
79 This formulation is similar to that in Aghion, Bacchetta, and Banerjee (2001).
85
0 ≤ d2 + d2∗ ≤ κ0 w1 (15)
0 ≤ d2∗ ≤ κ1 w1 (16)
where d is peso debt, d∗ is FX debt, z is the (potentially firm specific) productivity, and
k is investment in physical capital. κ1 < κ0 , which means that the borrowing constraint on
FX loans is tighter than for the firm’s overall borrowing. Solving the t = 1 problem leads to
decision rules d2 (w1 ), d2∗ (w1 ), and k2 (w1 ), which depend on wealth carried intro period 1.
Note that the firm maximizes expected period 2 profit, where the only source of uncertainty
The solution for the t=1 decision breaks into 6 cases (denoted by cutoffs W0 to W4 ), whose
Case 0: w1 ≤ 0 = W0
z1 kα1 −(1+r )d1
Pr (Case0) = 1 − G ( (1+r ∗ )d1∗
)
k2 = 0, d2∗ = 0, d2 = 0
Π2 = 0
1
( 1z+2 αr ) 1−α
Case 1: 0 < w1 ≤ 1+κ 0 = W1
z kα −(1+r )d z kα −(1+r )d −W
Pr (Case1) = G ( 1 (11+r∗ )d∗ 1 ) − G ( 1 1 (1+r∗ )d∗1 1 )
1 1
86
Π2 = z2 ((1 + κ1 )w1 )α − (1 + r ∗ )(1 + φ2 )κ1 w1
1
z2 αγ 1−α
1
z2 αγ 1−α
Case 4: (1+1+ r)
κ ≤ w 1 < (1+r )
= W4
1
z kα −(1+r )d −W z kα −(1+r )d −W
Pr (Case4) = G ( 1 1 (1+r∗ )d∗1 3 ) − G ( 1 1 (1+r∗ )d∗1 4 )
1 1
z2 αγ 1−1 α ∗ z2 αγ 1−1 α
k 2 = 1+r , d 2 = 1+r − w1 , d 2 = 0
z2 αγ 1−αα
∗ z2 αγ 1−1 α
Π 2 = z 2 1+r − (1 + r )(1 + φ2 ) 1+r − w1
1
1− α
Case 5: (z12+αγr) ≤ w1
z1 kα1 −(1+r )d1 −W4
Pr (Case5) = G ( (1+r ∗ )d1∗
)
k2 = w1 , d2∗ = 0, d2 = 0
Π2 = z2 w1α
Figure C1 illustrates the relationship between wealth w1 and investment k2 . The differ-
ent cases are determined by which constraints are binding and the funding source (peso, FX,
or own wealth) with which the marginal unit of investment is financed. Starting from 0 in
Figure C1, as a firm increases in w1 , investment k2 increases since higher wealth relaxes the
total borrowing constraint. While the marginal debt is denominated in pesos, the optimal
1
investment level is 1zα
+r
1−α . Once wealth is sufficiently large, the firm can make this level
of investment, so investment is flat though FX debt increases with increasing wealth, which
relaxes the FX borrowing constraint. Once the marginal unit of debt switches to FX, the
1
optimal level of investment increases to 1zαγ
+r
1−α , so firms increase FX debt with increas-
ing wealth (which relaxes their FX debt constraint). Once wealth is sufficiently large, the
firm makes the new optimal level of investment. When the marginal unit of investment is
purchased solely with wealth, then investment increases one-for-one with wealth.
The purpose of this model is to rationalize the patterns of borrowing and investment
outcomes for small firms and large firms after a balance sheet shock. Small firms are con-
strained in their total borrowing, while large firms may be constrained only in their FX
borrowing. Therefore, I focus my analysis on the first two cases, given by wealth cutoffs W1
and W2 corresponding to the first increasing slope and flat segment of the investment curve
87
Figure C1: Size and Investment
k2 (1+κ0)w1 (1+κ1)w1
w1
Peso
Debt
!
𝑧αγ !!!
! !
1+𝑟
FX
! Debt
𝑧α !!!
! !
1+𝑟
Own
Wealth
w1
Total FX
Constrained Constrained Unconstrained
in Figure C1.80,81
For illustration, consider two firms that have the same initial wealth w0 and investment
d1∗ 82
k1 , but for random reasons differ in terms of the FX share of initial debt d1 +d1∗ . A large
depreciation will lead to a larger decrease in w1 for the more exposed firm. Proposition C.1
summarizes the response of borrowing and investment to a shock to w1 for firms in the first
two cases.
Proposition C.1. If 0 < w1 ≤ W1 , then a negative shock to w1 results in lower FX debt, peso debt,
∂d2∗ ∂d2 ∂k2
and investment. That is, ∂w1 > 0, ∂w1 > 0, and ∂w1 > 0.
If W1 < w1 ≤ W2 , then a negative shock to w1 (such that w1 remains above the lower threshold)
results in lower FX debt, higher peso debt, higher total debt, and unchanged investment. That is,
80 Note, however, that the pattern from the other cases matches the data plotted in Figure 7: as the firm gets
bigger, the firm levers up in peso, decreases total borrowing while shifting to FX debt, then levers up in FX debt,
and finally decreases bank debt as firm size becomes extremely large.
81 There is also a case 0, where firms default in period 1 and exit, and so does not involve any decisions for
period 2.
82 The depreciation is quite unexpected, so this assumption could be justified that small and random differences
88
∂d2∗ ∂d2 ∂(d2 +d2∗ ) ∂k2
∂w1 > 0, ∂w1 < 0, ∂w1 < 0, and ∂w1 =0
Proof:
Proof of Proposition C.1. If 0 < w1 ≤ W1 , the constrained optimal debt and investment choices
∂d2∗
are d2∗ = κ1 w1 , d2 = (κ0 − κ1 )w1 , and k2 = (1 + κ0 )w1 . It follows that ∂w1 = κ1 > 0,
∂d2 ∂k2
∂w1 = (κ0 − κ1 ) > 0, and ∂w1 = 1 + κ0 > 0. Hence, a negative shock to w1 leads to lower FX
debt, peso debt, and investment.
If W1 < w1 ≤ W2 , the semi-constrained optimal debt and investment choices are d2∗ =
1 1 1
κ1 w1 , d2 = 1z+
2 α 1− α
r − ( 1 + κ 1 ) w 1 , d 2 + d ∗ = z2 α 1−α − w , and k = z2 α 1−α . It then
2 1+r 1 2 1+r
∂d2∗ ∂d2 ∂(d2 +d2∗ ) ∂k2
follows that ∂w1 = κ1 > 0, ∂w1 = −(1 + κ1 ) < 0, ∂w1 = −1 < 0, and ∂w1 = 0. Hence,
a negative shock to w1 which leaves w1 > W1 , results in lower FX debt, higher peso debt,
The intuition for the first case is straightforward: the firm is constrained in their borrow-
ing, and a negative shock to net worth causes that constraint to bind more tightly, so the
firm must borrow and invest less. The intuition for the second case is as follows: the firm
is constrained in their FX debt, so the negative shock forces them to reduce their FX debt.
They remain unconstrained in their total debt. So, the firm makes up for the lost wealth and
lost FX debt with an increase in peso debt. The increase in peso debt is thus larger than the
This matches most of my key empirical results shown in Table 1 and Table 9. However,
the model does not explain why large exposed non-exporters have higher investment and
employment following the shock, rather than unchanged real outcomes.83 Further, I have
assumed firms of the same size randomly have different levels of FX mismatch. If I relax
this assumption, firms of the same size would choose exactly the same exposure in period 0.
To address these two issues, I allow firms to differ from each other in terms of their period
83 This is also found empirically elsewhere in the literature. See for example Kim et al. (2015).
89
1 and 2 productivity (z1 , z2 ).84 I next describe the firm’s period 0 problem and the role of
At t = 0, firms solve the following problem, taking the decision rules d2 (w1 , z1 , z2 ), d2∗ (w1 , z1 , z2 ),
s.t.
k1 = w0 + d1 + d1∗ (19)
d1 + d1∗ ≤ κ0 w0 (20)
d1∗ ≤ κ1 w0 (21)
(z1 , z2 ) are known at t = 0. The solution for d1 and d1∗ depends on the distribution of
1 + φ and may not have a closed form depending on the functional form of the CDF, G (·).
Using the probabilities of being in case and the expected profit from each case derived
earlier, we can express the period 0 decision as maximizing the expected period 2 profit,
5
max∗ ∑ Pr (Casei |z2 ) ∗ Π2i (w1 , z2 ) (22)
d1 ,d1 i =0
Differences in productivity have a couple of key effects that can generate the patterns
observed in the empirical analysis. The first concerns the cross-sectional difference in firm
90
Proposition C.2. For a given initial wealth w0 , firms that are more productive in period 1 borrow
∂d1∗
more in FX in period 0 than firms that are less productive in period 1: ∂z1 ≥ 0.
Proof:
Proof of Proposition C.2. The proof proceeds in several steps: First, I show that E1 [ Pi2 ] is
default.
Next, I show that E0 [w1 ] is increasing in FX debt, holding k1 (and thus d1 + d1∗ ) constant
Next, I show that the default probability is increasing in d1∗ , again holding investment
constant:
∂Pr (w1 <W0 ) z1 kα1 −(1+r )(k1 −w0 )
∂d1∗ |k1 =k̄ = G 0 (·) (1+r ∗ )(d1∗ )2
> 0 for all values of debt d1 + d1∗ such that the
firm does not default with probability 1 (prevented by borrowing constraint).
maintaining their original default probability and thus have higher expected wealth w1 and
∂d1∗
then higher expected period 2 profits Π2 . So, ∂z1 >0
The intuition is that higher d1∗ increases your probability of being constrained, but higher
z1 decreases your probability of being constrained or defaulting. So, firms that have higher
91
z1 can borrow more in the cheaper currency while maintaining an equal or lower probabil-
ity of default than firms with lower z1 .85 This mechanism is modeled more fully in Salomao
and Varela (2016), which presents a model of firm dynamics that generates more produc-
tive firms selecting into FX borrowing. They confirm this prediction with data for firms in
Hungary.86
The second effect of productivity differences concerns the increase in productivity over
time. Increased future productivity increases the optimal scale of current investment. If
the firm is unconstrained in period 1 and future productivity is higher than current pro-
ductivity (z2 > z1 ), the firm will increase investment k2 up to the new optimal level. Note
that, all things equal, the probability of being constrained increases with higher future pro-
∂Pr (w1 <Wi )
ductivity as the optimal investment size gets larger, requiring more debt: ∂z2 >0∀
i ∈ {0, 1, 2, 3, 4}, where Wi ’s are the cutoffs for the different cases of the solution, detailed
Combining the cross-sectional and dynamic differences in productivity generates the de-
sired results. Firms with higher productivity in period 1 select into FX debt in period 0, but
if there is a negative balance sheet shock in period 1, only the firms who initially had more
wealth will be unconstrained. These unconstrained firms will be able to increase their in-
vestment k2 up to a higher optimal level, relative to firms who are less productive in period
1 (and so chose less FX exposure). I assume that Corr (z1 , zz2 ) > 0, so that currently more
1
productive firms are also more likely to have productive future investment opportunities.
Formally, I consider two types of firms: unproductive firms who have productivity z̄ in both
periods, and productive firms who have productivity z1 and z2 such that z̄ < z1 < z2 .87
85 Since borrowing decisions made in period 0 affect how binding constraints will be for period 1 borrowing
decisions, the FX borrowing constraint may be slack in period 0 for lower productivity firms.
86 In my data, large non-exporting firms with higher income and more productive capacity (higher levels of
physical capital) tend to have larger FX mismatches. However, I do not have data on hours worked or wage bill,
so I cannot compute standard measures of total factor productivity directly. While exposed firms tend to have
higher absolute income and higher levels of physical capital, those characteristics do not explain the positive
results for exposed large firms following the depreciation. Thus, modeling this as an unobserved future oppor-
tunity is appropriate and is one possibility that rationalizes the fact found here, and elsewhere in the literature,
that large exposed firms sometimes do better following a depreciation.
87 The results are similar if firms differ in their initial productivity z , while all firms face the same productivity
1
growth rate: z2 = (1 + gz )z1 .
92
Proposition C.3 gives the conditions whereby a firm with increasing productivity would
Proposition C.3. Let z̄ be the productivity level of unproductive firms in both periods and z1 , z2
be the productivity of highly productive firms, such that z̄ < z1 < z2 . Then d1∗ (w0 , z1 , z2 ) ≥
1 1
z21−α −z̄ 1−α
d1∗ (w0 , z̄, z̄) when z1 −z̄ < X1 kα1 , for a given constant X1 .
Proof:
Proof of Proposition C.3. From Proposition C.2, we know that the probability of default in
period 1 does not depend on z2 and is decreasing in z1 . For the remaining thresholds, it is
sufficient to find conditions for W4 such that Pr (w1 < W4 |z1 , z2 ) < Pr (w1 < W4 |z̄) :
Assuming this condition holds, then Pr (w1 < Wi |z1 , z2 ) < Pr (w1 < Wi |z̄) ∀ i ∈ {1, 2, 3, 4}.
From the logic in the proof to Proposition C.2, this implies that d1∗ (w0 , z1 , z2 ) > d1∗ (w0 , z̄, z̄).
This condition implies that the increase in z2 over z1 cannot be too large, or the firm
will avoid FX debt in period 0 because their constraint (for the higher level of investment)
would be more likely to bind in period 1. Under these conditions, highly productive firms
will borrow more in FX in period 0. Thus, the result in the data that large exposed firms do
better following the depreciation can be explained in the model by selection into exposure
in period 0 by firms with higher current productivity and increasing future productivity
93
(that is, they have productive future investments to make). These firms borrow more in FX
initially and experience a large balance sheet shock. Highly productive but small firms (in
terms of initial wealth w0 , which implies smaller k1 ) are constrained as before, while larger
firms are unconstrained, and so they can increase their investment up to the new optimal
level.
For illustration, suppose that the realized depreciation is large enough that the produc-
tive firms (who borrow more in FX in period 0) end up with lower w1 than unproductive
firms of the same initial w0 .88 This is not necessary, but serves as a useful demonstration that
these results are not due to more productive firms making more money in period 1 than their
less productive counterparts. The effects on period 1 decisions are illustrated in Figure C2.
Consider 4 firms with high or low productivity and high or low initial wealth: {(w H , z H ),
(w H , z L ), (w L , z H ), (w L , z L )}. For firms with lower initial wealth, the drop in net worth that
the productive firms experience (given their higher FX exposure) leads to lower borrowing
and investment, relative to less exposed firms, due to the binding borrowing constraint. For
large (high wealth) firms, the negative shock to net worth leaves them in the unconstrained
range, and so they are able to increase borrowing and investment up to the new optimal
level k2 , but decrease FX borrowing and increase peso borrowing to do so. Thus, comparing
exposed firms to less exposed firms of the same w0 size following the shock, the large firms
tion for the increase in real outcomes for more exposed large firms, one important caveat
with the preceeding discussion is that these differences imply that more productive large
firms would increase their real activity regardless of the exchange rate shock. This would
violate the parallel trends assumption in the empirical section. Thus, while the proposed
model may be a useful framework, especially for understanding the reallocation of debt
by currency (which does not require the assumptions about productivity differences), other
∂d
kα1 −(1+r ) ∂z 1
88 This occurs when (1 + φ) > 1
.
∂d∗
(1+r ∗ ) ∂z1
1
94
Figure C2: Size and Investment: Difference by Productivity
(1+κ0)w1
k2
(1+κ1)w1
!
𝑧 ! 𝛼𝛾 !!!
! !
! 1+𝑟
𝑧 ! 𝛼𝛾 !!!
! !
1+𝑟 !
𝑧 ! 𝛼 !!!
! !
! 1+𝑟
𝑧 ! 𝛼 !!!
! !
1+𝑟
(wL,zH) (wH,zH) w1
(wL,zL) (wH,zL)
95
Previous volumes in this series
757 Explaining Monetary Spillovers: The Matrix Jonathan Kearns, Andreas Schrimpf
November 2018 Reloaded and Fan Dora Xia
756 Financial structure and income inequality Michael Brei, Giovanni Ferri and
November 2018 Leonardo Gambacorta
751 Exchange rates and prices: evidence from the Raphael Auer, Ariel Burstein and
October 2018 2015 Swiss franc appreciation Sarah M Lein
750 Forward guidance and heterogeneous beliefs Philippe Andrade, Gaetano Gaballo,
October 2018 Eric Mengus and Benoit Mojon
749 Whatever it takes. What's the impact of a Carlo Alcaraz, Stijn Claessens,
October 2018 major nonconventional monetary policy Gabriel Cuadra, David Marques-
intervention? Ibanez and Horacio Sapriza
748 Domestic and global output gaps as inflation Martina Jašová, Richhild Moessner
September 2018 drivers: what does the Phillips curve tell? and Előd Takáts
747 How Do Credit Ratings Affect Bank Lending Stijn Claessens, Andy Law and
September 2018 Under Capital Constraints? Teng Wang
746 What drives local lending by global banks? Stefan Avdjiev, Uluc Aysun and
September 2018 Ralf Hepp
745 Financial stress in lender countries and capital Ilhyock Shim and Kwanho Shin
September 2018 outflows from emerging market economies
744 Why you should use the Hodrick-Prescott Mathias Drehmann and
September 2018 filter - at least to generate credit gaps James Yetman