alter2020

Journal of Corporate Finance 62 (2020) 101590
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Journal of Corporate Finance
journal homepage: www.elsevier.com/locate/jcorpfin
Emerging market corporate leverage and global financial conditions☆
Adrian Alter , Selim Elekdag
⁎
T
International Monetary Fund, Washington DC, United States of America
ARTICLE INFO
ABSTRACT
Keywords:
Corporate capital structure
Leverage
Global financial conditions
U.S. monetary policy
Emerging market economies
Small- and medium-sized enterprises (SMEs)
This paper explores how global financial conditions influence corporate leverage growth in
emerging markets (EMs). Using a sample of 800,000 listed and non-listed firms across 28 EMs, we
find that accommodative global financial conditions—initially proxied with a measure of U.S.
monetary policy—are associated with faster leverage growth. The impact is more pronounced for
financially constrained firms, such as small- and medium-sized enterprises (SMEs), and for EMs
whose domestic monetary policy is more aligned with that of the United States. The findings
suggest that global financial conditions affect EM firms' leverage growth by influencing domestic
interest rates and by relaxing corporate borrowing constraints. Finally, leverage increases disproportionately more for firms that are either relatively less profitable or less solvent when global
financial conditions become looser.
JEL classification numbers:
F21
F32
G32
1. Introduction
Corporate leverage in emerging markets (EMs) has risen sharply over the past decade amid favorable global financial conditions.
The corporate debt of nonfinancial EM firms has increased fivefold from about $5 trillion in 2006 to more than $25 trillion in 2018
(Fig. 1). Likewise, the EM nonfinancial corporate-debt-to-GDP ratio has risen by more than 50 percentage points over the same
period, reaching a peak of over 100%.
Most of the recent literature has emphasized the surge in EM corporate bond issuance.1 However, only large firms typically have
access to capital markets, as noted in Gertler and Gilchrist (1993) and Beck et al. (2006). Whereas large firms make greater use of
capital markets (including for bond issuance), small- and medium-sized enterprises (SMEs) are more restricted in their external
financing options and rely heavily on loans—in particular, credit intermediated through banks (Gertler and Gilchrist, 1994; Kersten
et al., 2017).2 Focusing exclusively on how EM corporate bond issuance is related to global financial conditions therefore runs the risk
☆
We are thankful to Douglas Cumming (the editor), three anonymous referees, Andreas Adriano, Prasad Ananthakrishnan, Soner Baskaya, Luis
Brandao, Sophia Chen, Hunter Clark, Gaston Gelos, Neesha Harnam, Sebnem Kalemli-Ozcan, Peter Lindner, Bill Megginson, Camelia Minoiu, Steve
Phillips, Garence Staraci, Yan Sun, Kai Yan, Ling Zhu, and participants at IMF seminar, IFABS 2017 Oxford Conference; 2017 Workshop on Systemic
Risk, organized by CRM Montreal; and “Unconventional Monetary Policy: Lessons Learned,” a 2017 conference organized by the Hong Kong
Monetary Authority, Federal Reserve Board, and Federal Reserve Bank of Atlanta for helpful comments and discussions. This paper builds on the
analysis presented in Chapter 3 of the IMF's October 2015 Global Financial Stability Report. The views expressed in this paper are those of the
authors and do not necessarily represent the views of the IMF, its Executive Board, or IMF management.
⁎
Corresponding author.
E-mail addresses: [email protected] (A. Alter), [email protected] (S. Elekdag).
1
See, for example, Feyen et al. (2015), Gozzi et al. (2015), and Caballero et al. (2019).
2
Bräuning and Ivashina (forthcoming) note the small share of bond and equity financing across EMs. Likewise, concentrated corporate ownership
structures (owing to majority shareholding by the state or families) reduces recourse to equity finance (Morck et al., 2005; Porta et al., 1999).
https://doi.org/10.1016/j.jcorpfin.2020.101590
Received 6 August 2018; Received in revised form 5 February 2020; Accepted 6 February 2020
Available online 10 February 2020
0929-1199/ © 2020 Elsevier B.V. All rights reserved.
Journal of Corporate Finance 62 (2020) 101590
A. Alter and S. Elekdag
Fig. 1. Emerging market corporate debt and global financial conditions.
Haver Analytics; Krippner (2014); Reserve Bank of New Zealand; Authors' calculations.
of overlooking the important role of a large segment of the corporate sector, including financially constrained firms such as SMEs.
Unlike the United States, the bulk of real economic activity in EMs is not accounted for by large firms (Kalemli-Ozcan et al., 2015). In
particular, SMEs account for a vast majority of firms, employment, and value added across countries' corporate landscapes (de la
Torre et al., 2008).3 At the same time, bank loans remain the main source of financing for the EM corporate sector, and, more
generally, most financial systems are still predominantly bank-based (Bräuning and Ivashina, forthcoming; Cihak et al., 2012; IMF,
2015). Together, SMEs and other (non-listed) firms—which have limited access to capital markets and thereby are more dependent
on bank-based financing—are likely to be key drivers of aggregate EM corporate leverage dynamics. Therefore, studies that focus
solely on bond issuances cover only a very narrow aspect of how, and why, the EM corporate landscape has been evolving so
markedly over the past 15 years.
This paper aims to fill this gap in the literature by investigating whether global financial conditions differentially affect EM firms'
leverage dynamics using a more comprehensive and thereby more representative sample of firms. We cast a wide net, and in addition
to SMEs, use a sample that includes other non-listed firms (which are likely to face financing constraints) as well as large listed firms
(which have typically attracted greater attention in many studies).
The emphasis of the paper is initially on a specific aspect of firm heterogeneity: financial (borrowing) constraints. The main
conjecture of the paper is that leverage across SMEs and/or firms facing borrowing constraints is likely to be disproportionately
sensitive to global financial conditions (relative to large enterprises). Following Gertler and Gilchrist (1994), we consider firm size to
3
Although SME definitions vary to account for country-specific circumstances, consider the European Union, which has a common SME definition
across its member states (which includes both advanced and emerging market economies). The statistics are striking: SMEs account for 99.8% of
firms, 66.4% of employment (with an upper range of over 85%), and 56.8% of value added (with an upper range of over 81%), on average
(European Commission, 2018).
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Journal of Corporate Finance 62 (2020) 101590
A. Alter and S. Elekdag
be a reasonable indicator of capital market access, with the implication that SMEs are more likely to face borrowing constraints (and
thereby more sensitive to interest rate fluctuations).
We also consider a complementary proxy to measure borrowing constraints based on Rajan and Zingales (1998) which differentiates firms based on their intrinsic dependence on external financing. Greater leverage can confer important benefits, such as
facilitating productive investment and thereby faster growth. However, rapid increases in leverage could be associated with credit
misallocation (Greenwood and Hanson, 2013) and debt overhang problems (Kalemli-Ozcan et al., 2018). Even if borrowing by
individual SMEs is small, a collective increase in leverage could raise economy-wide vulnerabilities. For example, rapid increases in
corporate leverage have often been associated with boom-bust cycles (Mendoza and Terrones, 2008; Schularick and Taylor, 2012).
Conceptually, global financial conditions can influence EM leverage growth via several related and mutually reinforcing channels.
In a world of integrated financial markets, direct interest-rate linkages reflect forces of cross-border arbitrage on rates of return.
Changes in global interest rates can induce portfolio shifts, which affect capital flows, exchange rates, and asset prices. Many papers
have documented a link between EM capital flows and global financial conditions—where U.S. monetary policy takes center stage.4
For instance, EM central banks may react to episodes of large capital inflows—possibly associated with a loosening of U.S. policy
rates—by lowering their policy rates more than they would otherwise to alleviate currency appreciation pressures (in an effort to
protect their export-oriented sectors). Thus, these lower domestic rates would be further transmitted to the real economy and encourage additional borrowing. Moreover, we would expect this channel to be stronger in EMs whose monetary policy is more closely
aligned with U.S. monetary policy. At the same time, the subsequent reduction of domestic interest rates would raise the value of
collateral, and therefore would relax borrowing constraints. In turn, greater access to capital sets in motion a feedback loop where
increased borrowing, leverage, investment, and output boost asset prices yet again, thereby further relaxing borrowing constraints.5
Taken together, firms that are especially dependent on external financing for their business operations (such as SMEs) stand to benefit
the most from accommodative global financial conditions, and would be more inclined to increase their borrowing relative to other
types of firms given their less binding financial constraints.
Our empirical analysis focuses on the relationship between corporate leverage growth and a measure of global financial conditions, standard firm-level determinants of leverage, and other controls using panel data from about 800,000 listed and non-listed
firms, including SMEs, across 28 EMs, covering 2004-2017. First, we proxy global financial conditions using an estimate of the U.S.
shadow rate, which helps account for unconventional monetary policies—but we consider other indicators as well, such as the Federal
funds rate itself, and U.S. bond yields at various maturities. In addition, we use a measure of (unanticipated) monetary policy shocks
(based on Gertler and Karadi, 2015) that further sharpens our identification strategy; it presumes that global financial conditions can
be seen as exogenous to any individual EM firm. Second, to help distinguish the role of global financial conditions from other global
factors, we differentiate firms based the degree of financial constraints they face using an SME dummy or the Rajan and Zingales
(1998) index of external financial dependence. Exploiting this type of firm heterogeneity facilitates identification because it is more
likely that global financial conditions would disproportionately affect financially constrained firms as compared to, for instance,
global growth or commodity-price fluctuations. Motivated by concerns that—in the context of accommodative global financial
conditions—borrowing may have been excessively allocated to risky enterprises (reflecting, inter alia, poorer screening by loan
officers), we also differentiate firms based on their profitability (i.e., return on assets) and their solvency (i.e., interest coverage ratio).
This differentiation provides some initial impressions on the prevalence of credit misallocation associated with looser global financial
conditions.
We find compelling evidence that accommodative global financial conditions are positively associated with faster EM corporate
leverage growth. An increase in the inverted U.S. shadow rate (looser monetary conditions) of 1 percentage point corresponds to an
increase in the leverage ratio of up to 0.1 percentage point per year, but it can be up to twice as high when using alternative measures
of U.S. monetary policy. Considering the large peak-to-trough decline in the shadow rate of more than 1000 basis points from 2007 to
2013, and the protracted nature of these exceptionally loose global financial conditions, it is clear how even seemingly incremental
increases in leverage can build up over time. This impact is more pronounced for firms facing financial constraints. In particular, we
find that leverage growth for SMEs is 50% more sensitive to global financial conditions relative to larger firms. Likewise, our results
indicate that the impact of U.S. shadow-rate fluctuations is greater for firms that are more dependent on external financing. In this
case, a one-standard-deviation loosening of the U.S. shadow rate results in an increase in leverage ratio that is on average up to 0.1
percentage point larger for firms whose financial dependence is at the 75th percentile (i.e., Chemicals and Pharmaceuticals) relative
to firms whose financial dependence is at the 25th percentile (i.e., Construction sector firms).
We also uncover some evidence that looser global financial conditions are associated with credit misallocation. In particular, our
exploratory analysis reveals that leverage appears to increase disproportionately for firms that are either relatively less profitable or
less solvent when global financial conditions become more accommodative. Next, to better understand the relationship between
global financial conditions and leverage growth, we differentiate EMs based on their degree of monetary-policy synchronization with
the U.S. (as measured by the correlation between U.S. and individual EM interest rates). We show that the effect on leverage growth is
4
Studies include Calvo et al. (1993, 1996), Rey (2015), Miranda-Agrippino and Rey (2015), Fratzscher et al. (2013), and Bruno and Shin (2015).
Building on the work of Kiyotaki and Moore (1997), Bernanke et al. (1996), and Iacoviello (2005), open-economy models were developed by
Gertler et al. (2007), Elekdag and Tchakarov (2007), and Fernández and Gulan (2015) to include financial frictions, which can take the form of
borrowing constraints, thus prohibiting some firms from implementing their desired investment projects. These frictions underpin a financial
accelerator mechanism whereby the cost of debt, asset prices (including the exchange rate), and collateral valuation, jointly interact and determine
the demand for capital and debt. See also Cardarelli et al. (2011).
5
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A. Alter and S. Elekdag
more pronounced in EMs whose monetary policy is more closely aligned with the United States, which suggests that global financial
conditions affect EM corporate leverage growth through domestic interest rates. To highlight the robustness of our main results, an
extensive set of sensitivity analyses is summarized throughout the paper. Moreover, to help reduce concerns that alternative channels
may be driving the results, all regressions include country-time and firm fixed effects (unless otherwise noted). In sum, the results
suggest that financial frictions play an important role in the transmission of global financial conditions that affect domestic interest
rates and disproportionately influence the leverage growth of financially constrained firms such as SMEs across EMs.
This paper contributes to the literature along several dimensions. First, unlike much of the corporate finance literature that
focuses on listed firms (predominantly in the United States), we consider SMEs and other private firms in addition to listed firms. This
provides a more comprehensive picture of corporate leverage dynamics across countries. The importance of considering SMEs and
other non-listed firms is emphasized by Kalemli-Ozcan et al. (2012), who spotlight leverage dynamics across advanced economies.
Second, and relatedly, unlike recent papers that focus on bond issuances (Caballero et al., 2019; Feyen et al., 2015; Gozzi et al.,
2015), this paper considers total debt (which encompasses both bond- and bank-based debt), thus providing a more comprehensive
picture of how EM corporate leverage growth is influenced by global financial conditions. Third, this paper provides novel crosscountry empirical evidence that financial frictions play an important role in the transmission of monetary policy to the real economy,
namely to the nonfinancial corporate sector across a wide range of EMs. In particular, we emphasize the links between global
financial conditions (proxied by the U.S. shadow rate), domestic monetary policy rates, and firm-level financial constraints.6 The use
of U.S. short-term rates is supported by many studies, including Rey (2015), which argue that U.S. monetary policy greatly influences
global financial conditions. Fourth, our suggestive findings are broadly consistent with the literature that the riskiness of corporate
credit allocation increases during favorable macroeconomic environments (Greenwood and Hanson, 2013; IMF, 2018; Lang and
Nakamura, 1995). Fifth, and in the spirit of Frank and Goyal (2009), by emphasizing the impact of global financial conditions on
corporate leverage, we uncover a new, quantitatively important, and reliable determinant of capital structure that is likely to be of
relevance for any small, financially integrated emerging or advanced economy.7 Relatedly, to the best of our knowledge, this paper is
the first study to demonstrate that global financial conditions are a reliable determinant of firm-level leverage dynamics.
We proceed as follows: Section II discusses the empirical framework, hypotheses, and data, Section III presents the main results of
the paper, and Section IV provides concluding remarks.
2. Methodology and data
2.1. Regression specifications and hypotheses
Our empirical approach is to test whether global financial conditions influence corporate leverage growth across EMs, and
whether this effect is more pronounced for firms that are more financially constrained. In line with many studies, including Rey
(2015), we use a measure of the U.S. monetary policy stance to gauge global financial conditions. Initially, we use an estimate of the
U.S. shadow rate based on Krippner (2014) which accounts for unconventional monetary policies in wake of the global financial
crisis. One advantage of this shadow rate is that it can capture the monetary policy stance even when quantitative easing and forward
guidance effectively push the short end of the yield curve into negative territory, which was particularly relevant in the case of U.S.
monetary policy over our sample period. Throughout the paper we assume that global financial conditions are exogenous to any
individual firm. To address any residual concerns regarding endogeneity and simultaneity, we also use U.S. monetary policy shocks
based on Gertler and Karadi (2015).
To distinguish the role of global financial conditions from other global factors, we differentiate firms based on the degree of
financial constraints they face using two complementary approaches. First, as discussed by, for example, Gertler and Gilchrist (1993),
firm size is a reasonable indicator of capital market access, where a strong correlation exists between size and access to external
finance. Specifically, SMEs on average rely heavily on intermediary credit, whereas large firms make far greater use of equity, longerterm debt, and commercial paper. In other words, SMEs have a greater tendency to face borrowing constraints rendering them more
sensitive to interest rate fluctuations. Likewise, in the presence of legal reserve requirements, monetary policy can influence the pool
of funds available to bank-dependent borrowers such as SMEs (Gertler and Gilchrist, 1994).8 Therefore we construct a dummy
variable that takes a value of unity if a firm is an SME.9 Second, we use the financial dependence measure proposed by Rajan and
Zingales (1998), which gauges a sector's intrinsic demand for external finance.
These two approaches exploit firm-level heterogeneity, which should help sharpen identification, as financially constrained firms
are more likely impacted by global financial conditions than global growth or commodity prices fluctuations. Moreover, to help
reduce concerns that alternative channels may be driving the results, all regressions include country-time and firm fixed effects
6
Our results provide additional support for the vast literature noted above on the role of global financial conditions and EM capital flows, and on
the important role of financial accelerator mechanisms. This literature can be traced back to Bernanke and Gertler (1989) and Kiyotaki and Moore
(1997); see also Forbes and Warnock (2012) and Ghosh et al. (2014).
7
These results are consistent with the vast literature on capital flows. For example, global factors such as risk aversion and economic uncertainty
are found to be associated with periods of extreme capital flows (Forbes and Warnock 2012). In the same vein, capital flow surges to EMs synchronize internationally and are driven by global push factors, including U.S. interest rates and investor risk aversion (Ghosh et al., 2014).
8
At the same time, SMEs are more likely to be young firms (e.g., startups), with less collateral and a higher degree of idiosyncratic risk. This
typically amplifies informational frictions and augments external finance costs (underpinned by domestic interest rate fluctuations).
9
SMEs are firms with operating revenues, total assets, and staffing below €10 ($13) million, €20 ($26) million, and 150 employees, respectively.
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A. Alter and S. Elekdag
(unless otherwise noted), thereby effectively controlling for alternative channels that affect firms differently (depending on their
country of location and the time period in question).
To investigate the relationship between EM corporate leverage growth and global financial conditions, we start by estimating the
following equation:
Leveragei, s, c, t =
Global _Financial _Conditionst +
Controlsi, s, c, t
1
+
(1)
i, s , c , t
where i, s, c, and t, are indices of firms, sectors, countries, and time. Note that this is an annual panel regression, where firm-level
leverage growth is regressed on Global Financial Conditionst, firm-specific controls (which are lagged first differences; e.g., profitability, size, and tangibility), and macroeconomic conditions (the ICRG index) in some specifications (see Appendix A for details).
Furthermore, firm-specific fixed effects are included to account for unobserved firm-level factors (as are combinations of time,
country-time, sector-time, and country-sector-time fixed effects). In the baseline specifications, to make statistical inference, we
report standard errors clustered at the sector level, which corrects for the complex correlation structure. The slope coefficient, α,
measures the extent to which the global financial conditions (initially proxied by the U.S. shadow rate) affects EM leverage growth.
Given the uptrend over the past decade in EM leverage growth amid favorable global financial conditions, we expect α > 0.
To better identify the transmission of global financial conditions on corporate leverage, we differentiate firms based on the degree
of financial constraints they face. Therefore, we introduce the interaction between the Global Financial Conditionst and Financial
Constraintsi, s (which differ across firms when using the SME dummy, or by sector when using the Rajan-Zingales metric):
Leveragei, s, c, t =
Global _Financial _Conditionst +
Controlsi, s, c , t
1
+
Global _Financial _Conditionst
Financial _Constraintsi, s +
(2)
i, s, c, t
The slope coefficient on the interaction term, β, captures the extent to which the effect of global financial conditions depends on
the nature of firms' financial constraints. We anticipate that favorable global financial conditions will matter more for financially
constrained firms, that is β > 0. Any evidence would suggest that a loosening in global financial conditions fosters faster EM leverage
growth by relaxing borrowing constraints.
Lastly, to shed further light on underlying transmission channels, we investigate whether the impact of global financial conditions
on EM corporate leverage varies across countries depending on the degree of monetary policy synchronization. We do so by adding a
country-level interaction term:
Leveragei, s, c, t =
Global _Financial _Conditionst +
Global _Financial _Conditionst
Global _Financial _Conditionst
Policy _Synchronizationc +
Financial _Constraintsi, s +
Controlsi, s, c, t
1
+
i, s , c , t
(3)
where the slope coefficient on the additional interaction term, γ, captures the degree to which the effect of shadow rate fluctuations depends on EMs degree of monetary policy synchronization with the U.S. (as measured by the correlation of U.S. and
individual EM interest rates). We expect that global financial conditions will be more pronounced in countries whose monetary policy
is more closely aligned with U.S. monetary policy in general, that is, γ > 0. Such a finding would indicate that U.S. monetary policy
shocks affect firms' leverage growth through domestic interest rates.
2.2. Data and variable definitions
This section summarizes the main variables and data sources used in the analysis, with details relegated to the Appendix.
2.2.1. ORBIS
The firm-level dataset used is this paper comes from ORBIS (Bureau van Dijk Electronic Publishing), an annual global panel
dataset with over 280 million public and private companies. Relative to other firm-level cross-country databases, a key advantage of
ORBIS is its wider coverage of both listed and non-listed firms—which includes SMEs. Although ORBIS has the advantage of being
more comprehensive, with millions of firms represented in the database, more detailed information on financial statements (such as
debt) is harder to come by in the context of EMs.10 As explained in detail in the Appendix, our sample covers about 800,000
nonfinancial EM firms over 2004–2017, totaling 5 million firm-year observations.
2.2.2. Measures of leverage
Although we later consider alternative definitions, our baseline measure of EM corporate leverage is the ratio of total (non-equity)
liabilities to total assets (TLTA), which is consistent with, for example, Rajan and Zingales (1995). This is the broadest definition of
leverage, and as discussed in detail in Appendix A, circumvents the issue of missing debt data for certain firms (especially SMEs).
Furthermore, motivated by the clear upward trends in leverage documented in Fig. 1, we focus on the growth of EM corporate
leverage, rather than its level. We appear to be in good company: DeAngelo and Roll (2015) note that “capital structure stability is the
exception, not the rule.” Graham et al. (2015) also consider growth of leverage, but in the context of the U.S., a mature economy, thus
10
For example, we are not able to analyze risks owing to net foreign exchange exposures because ORBIS does not contain information on foreign
currency positions. Among other studies, Bräuning and Ivashina (forthcoming), Cecchetti et al. (2020) and Baruník and Kočenda (2019) highlight
the role of loose global financial conditions on foreign currency bank lending and, more generally, on forex markets.
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A. Alter and S. Elekdag
motivating our focus on leverage growth in faster growing EMs.
2.2.3. Controls
The drivers of EM firms' leverage dynamics, other the measures of financial constraints and global financial conditions, are based
on the literature. Following Rajan and Zingales (1995) and Frank and Goyal (2009), we control for size (log sales), profitability
(return on assets), and asset tangibility (ratio of net property, plant, and equipment to total assets).11 We also include a measure of
overall macroeconomic conditions in regressions which are otherwise captured by country-time fixed effect terms (see Appendix for
details and discussions).
2.3. Descriptive statistics
Table 1 reports summary statistics for selected variables and complements Fig. 1. The estimated shadow rate seems to reasonably
reflect monetary policy events in the unconventional policy regime. We use the U.S. shadow rate estimated by Krippner (2014),
which entered negative territory in November 2008, when the Federal Reserve started the Large Scale Asset Purchases program. The
shadow rate further declined as the Fed adopted additional unconventional policies. The cumulative decline of over 1000 basis points
from a peak of over 5% in July 2007 to a trough of less than −5% in May 2013 is especially staggering. The shadow rate bottomed
out in when the Fed raised the possibility of tapering its purchases of Treasury and agency bonds, and has continued to increase since
then, and has been hovering around 2% more recently. To facilitate the interpretation of the results, we use the inverted shadow
rate—the shadow rate multiplied by −1.12 It is also noteworthy that the Rajan-Zingales measure of financial dependence ranges from
a low of −2.2 for the Tobacco and Cigarettes sectors, an industry that has been in decline over the last decades, to a high of 3.8 for
the Electronic Repair and Related Services, an industry that has seen large growth. Based on our initial definition, SMEs account for
about 70% of the firms in our sample (Appendix Table 1).
3. Empirical results
We initially consider the changes in the U.S. shadow rate as a source of exogenous variation in global financial conditions for EMs,
and assess whether such variations disproportionately affect leverage growth in financially constrained firms (proxied by the SME
dummy or the Rajan-Zingales measure). After presenting the baseline results, we conduct some exploratory analysis to uncover any
evidence of greater credit misallocation amid looser global financial conditions. We then seek to explain the link between global
financial conditions and EM leverage growth. Throughout, we summarize the findings of an extensive set of sensitivity exercises to
showcase the robustness of our main results.
3.1. Baseline results
The baseline results are presented in Table 2.13 In Column 1, as a first pass, we examine the impact of changes in the inverted U.S.
shadow rate on EM corporate leverage. We obtain a positive and statistically significant coefficient (0.054). This initial result suggests
that expansionary global monetary conditions are associated with faster EM corporate leverage growth. Specifically, an increase in
the inverted U.S. shadow rate (looser monetary conditions) of 1 percentage point corresponds to an increase in the leverage ratio of
about 0.1 percentage point per year. Recalling the 1000 basis point peak-to-trough decline in the shadow rate over the course of
2007–2013 and the persistence of policy accommodation, it is clear how incremental increases in leverage can accumulate over
time.14
Column 2 introduces an interaction term between the inverted U.S. shadow rate and the SME dummy, which is central to this
paper. Indeed, in contrast to other firms, we expect that SMEs are in a better position to borrow more amid favorable global financing
conditions because of less binding financial constraints. We find that the coefficients associated with the shadow rate and its interaction term are statistically significant at 0.038 and 0.023, respectively. These findings suggest that leverage growth for SMEs is
60% more sensitive to global financial conditions relative to larger firms, confirming our hypothesis.
11
These controls are based on the three capital structure theories (trade-off, pecking order, market timing) and their empirical predictions as
discussed in Frank and Goyal (2009). See Baker and Wurgler (2002), Donaldson (2000), Graham and Harvey (2001), Myers (1984), Myers and
Majluf (1984), as well as Baker and Wurgler (2002), Cook and Tang (2010), Hovakimian et al. (2001), and Korajczyk and Levy (2003).
12
The multiplication of the shadow rate by −1 implies that when the shadow rate is in positive territory, the inverted shadow rate is negative.
13
Throughout the analysis firm fixed effects were included and standard errors were clustered at the sector level. As documented in the working
paper version, the results are robust to an array of sensitivity tests using alternative ways to cluster the standard errors (including at the firm and
country levels, country and time, sector and time, as well as country and sector).
14
Although not the focus of the paper, we find that leverage growth is negatively related to sales growth, but positively related to changes in
profitability and tangibility. The latter result is generally consistent with the literature: tangible assets are easier to value and tend to lower expected
distress costs. The positive link between leverage and profitability growth suggests that more profitable firms have lower expected financial distress
costs and therefore take on more debt. The inverse relationship between leverage and firm size is usually interpreted as being consistent with the
pecking order theory (Frank and Goyal, 2003). As for macroeconomic conditions, the results also indicate that leverage is procyclical: sounder
country-level fundamentals co-vary positively with EM leverage growth, in line with, for example, the theoretical models of Kiyotaki and Moore
(1997).
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Journal of Corporate Finance 62 (2020) 101590
A. Alter and S. Elekdag
Table 1
Summary statistics: Key variables (2004–2017).
Key variables
Leverage
Sales
Profitability
Tangibility
Financial dependence (SIC2 level)
Macroeconomic conditions
Correlation with U.S. monetary policy (Country level)
Growth opportunities (SIC2 level)
Inverted U.S. shadow rate
Standard
First
Third
Observations
Mean
Median
deviation
quartile
quartile
Minimum
Maximum
4,998,394
4,925,309
4,820,130
4,701,554
56
459
28
67
15
0.54
15.27
0.07
0.32
0.14
37.20
0.42
0.13
−0.15
0.56
15.25
0.04
0.27
0.06
37.31
0.54
0.13
−0.15
0.27
1.69
0.10
0.26
0.67
3.65
0.35
0.04
2.81
0.32
14.29
0.01
0.09
−0.08
35.13
0.12
0.11
−1.55
0.76
16.28
0.10
0.52
0.30
39.44
0.68
0.15
1.70
0.06
0.00
−0.06
0.00
−2.19
20.88
−0.34
−0.02
−5.33
0.98
32.97
0.34
0.85
3.84
45.71
0.81
0.28
5.37
Sources: Orbis database; Reserve Bank of New Zealand; PRS Group; Authors' calculations.
Note: Leverage is the ratio of total liabilities to total assets. “Sales” is the logarithmic transformation of total sales. “Profitability” is measured by the
return-on-assets. “Tangibility” is the ratio of net property, plant, and equipment to total assets. “Financial dependence” is an updated version of the
original Rajan and Zingales (1998) index based on Tong and Wei (2011). “Macroeconomic conditions” are measured by the ICRG economic and
financial index. The “inverted U.S. shadow rate” is estimated from the term-structure model based on Krippner (2014). “SIC2 level” refers to the 2digit (major group) Standard Industrial Classification code.
Table 2
Baseline: EM corporate leverage and global financial conditions.
Leverage
(1)
(2)
(3)
(4)
(5)
(6)
Sales
−1.776***
(0.107)
9.648***
(0.487)
6.905***
(0.356)
0.157***
(0.0192)
0.0535***
(0.00883)
−1.778***
(0.107)
9.651***
(0.487)
6.907***
(0.356)
0.158***
(0.0191)
0.0380***
(0.0116)
0.0234**
(0.00970)
−1.761***
(0.109)
9.779***
(0.490)
6.971***
(0.364)
0.101***
(0.0204)
−1.815***
(0.106)
9.952***
(0.479)
6.964***
(0.375)
−1.765***
(0.110)
9.862***
(0.477)
6.980***
(0.365)
0.0836***
(0.0200)
−1.811***
(0.107)
10.03***
(0.474)
6.988***
(0.377)
0.0334***
(0.00933)
0.0302***
(0.00791)
0.0282***
(0.00912)
0.0253***
(0.00813)
2,953,719
0.009
2,953,719
0.009
2,953,719
0.010
2,954,202
0.014
2,953,719
0.011
2,954,202
0.014
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
Profitability
Tangibility
Macroeconomic conditions
Inverted shadow rate (ISR)
ISR * SME dummy
Observations
R-squared (within)
Fixed effects
Firm
Time
Country-time
Sector-time
YES
Source: Authors' calculations.
Note: ***, **, and * denote statistical significance at the 1, 5, and 10% levels, respectively. The dependent variable is the ratio of total liabilities to
total assets (first differenced). “Sales” is the logarithmic transformation of total sales. “Profitability” is measured by the return-on-assets, while
“Tangibility” is the ratio of net property, plant, and equipment to total assets. Firm-specific regressors are first differenced and lagged.
“Macroeconomic conditions” are measured by the ICRG economic and financial index. “SME dummy” is a dummy variable that takes the value of
unity for firms with operating revenues, total assets, and employees below €10 ($13) million, €20 ($26) million, and 150, respectively. The
“inverted U.S. shadow rate” (ISR) is estimated from the term-structure model based on Krippner (2014). Note that a positive value of the inverted
shadow rate corresponds to a negative value of the shadow rate. Standard errors are clustered by sector. Fixed effect terms are not reported.
In Column 3, we include dummies for each year to control for other contemporaneous time effects. Importantly, these time
dummies capture fluctuations in other global factors such as global growth and commodity prices. Accordingly, the inverted shadow
rate is now fully absorbed by the year dummies (time fixed effect terms) and is therefore dropped from this specification. The
interaction term is still positive and now statistically significant at the 1% confidence level, with an estimated coefficient value of
0.033.
In Column 4, we include dummies for country-time fixed effects to account for unobserved country-specific factors (such as the
business cycle). These dummies absorb country-specific macroeconomic conditions variable, which is therefore omitted from the
regression. In Column 5, we introduce sector-time fixed effect terms to account for unobserved factors that vary over time for each
sector. Finally, in Column 6, we consider sector-time and country-time pairs jointly, which further sharpens our identification
strategy. Notice that the coefficient on the interaction term is similar in magnitude across these specifications and quite precisely
estimated (statistically significant at the 1% level).
7
Journal of Corporate Finance 62 (2020) 101590
A. Alter and S. Elekdag
Table 3
Robustness: Alternative financial constraint measures.
Leverage
(1)
(2)
(3)
(4)
Sales
−1.816***
(0.0951)
9.625***
(0.487)
7.026***
(0.401)
0.0847***
(0.0132)
0.0372**
(0.0139)
−1.860***
(0.0951)
9.842***
(0.504)
7.027***
(0.413)
−1.761***
(0.109)
9.773***
(0.490)
6.970***
(0.364)
0.101***
(0.0204)
−1.816***
(0.106)
9.950***
(0.479)
6.963***
(0.375)
0.0195**
(0.00878)
0.0193**
(0.00823)
2,403,456
0.010
2,403,879
0.014
2,953,719
0.010
2,954,202
0.014
YES
YES
YES
YES
YES
YES
Profitability
Tangibility
Macroeconomic conditions
ISR * Financial dependence
ISR * Alternative SME dummy
Observations
R-squared (within)
Fixed effects
Firm
Time
Country-time
0.0364***
(0.0119)
YES
YES
Source: Authors' calculations.
Note: ***, **, and * denote statistical significance at the 1, 5, and 10% levels, respectively. The dependent variable is the ratio of total liabilities to
total assets (first differenced). “Sales” is the logarithmic transformation of total sales. “Profitability” is measured by the return-on-assets, while
“Tangibility” is the ratio of net property, plant, and equipment to total assets. Firm-specific regressors are first differenced and lagged.
“Macroeconomic conditions” are measured by the ICRG economic and financial index. “ISR” refers to the inverted U.S. shadow rate and is estimated
from the term-structure model based on Krippner (2014). Note that a positive value of the inverted shadow rate corresponds to a negative value of
the shadow rate. “Financial dependence” is an updated version of the original Rajan and Zingales (1998) index based on Tong and Wei (2011).
“Alternative SME dummy” is a variable that takes the value of unity for firms with operating revenue below €20 ($26) million. Standard errors are
clustered by sector. Fixed effect terms are not reported.
Table 3 differentiates firms based on the degree of financial constraints they face using two complementary measures. The first is
the Rajan-Zingales measure and the second is a broader definition of SMEs. Column 2 introduces an interaction term between the
Rajan-Zingales measure and the inverted U.S. shadow rate.15 We find that the impact of U.S. shadow rate fluctuations is statistically
significant and larger for sectors which depend more on external finance. Based on the estimated coefficient in Column 2 (0.037), an
increase in the inverted U.S. shadow rate of one standard deviation—corresponding to more accommodative monetary conditions—is
associated with leverage growth that is on average about 0.04 percentage points greater for firms whose financial dependence is at
the 75th percentile (Chemicals and Pharmaceuticals) relative to firms whose financial dependence is at the 25th percentile (the
Construction sector). Column 2 indicates that these results are robust to the inclusion of country-time fixed effects. In Column 3 and
Column 4, we consider a complementary SME dummy based on a broader definition of SMEs.16 The estimated coefficients on the
interaction terms using this alternative SME dummy remain positive and statistically significant.
Overall, these results support our first two hypotheses: (1) we find that accommodative global financial conditions are reliably
associated with faster EM corporate leverage growth, and (2) that this impact is more pronounced for firms that are relatively more in
need of external financing, such as SMEs.
3.2. Are looser global financial conditions associated with credit misallocation?
The upswing in EM corporate leverage amid accommodative global financial conditions over the past decade has raised
concerns that borrowing may have been excessively allocated to risky firms (for example, those which are less profitable and
solvent). Directing a growing share of lending to riskier firms may be rational and reflect the normal functioning of a sound
financial system in some phases of the business cycle, or it may be a result of improvements in banks' risk management technologies. Alternatively, it may reflect poorer screening of borrowers, excessive risk taking (or neglect of risk), and a misallocation of financial resources that could undermine financial and economic stability. In particular, if looser global financial
conditions bring about a significant expansion of credit, the capacity and incentives of banks to screen borrowers are likely to
deteriorate, reinforcing the procyclical nature of lending standards and of lending to relatively riskier firms (Berger and Udell,
2004; Dell'Ariccia and Marquez, 2006). Likewise, the risk appetite of financial intermediaries with long-term liabilities and
short-term assets is likely to make them search for yield when monetary conditions are loose, resulting in riskier firms getting
15
The regression does not include the financial dependence variable on its own, as this is fully captured by the firm fixed effect terms (as would
the SME dummies on their own). Recall that time fixed effect terms capture global factors, including fluctuations in the shadow rate.
16
The alternative SME dummy takes the value of unity for firms with operating revenue below €20 ($26) million, implying that 80% of the firms
in the sample are SMEs.
8
Journal of Corporate Finance 62 (2020) 101590
A. Alter and S. Elekdag
easier access to credit (Rajan, 2006).17
We attempt to gauge these effects by considering two standard metrics of corporate vulnerability: profitability and solvency.
Regarding the former, we use firms' return-on-assets (ROA) for practical and substantive reasons: in practice, this measure is readily
available for a predominance of firms in our sample; in terms of substance, recall that ROA is not influenced by leverage, unlike other
commonly used measures such the return on equity.18 Regarding solvency, and for broadly similar reasons, we use the interest
coverage ratio (ICR), which complements other debt-based measures of solvency. In both cases, firms are considered riskier if their
ROA or ICR is in the lower quartile of the sample. We construct two dummies accordingly, which are then interacted with the
inverted shadow rate. Recall that the dummy on its own would be absorbed by the firm fixed effect terms. A positive coefficient on
any of these two interaction terms would suggest that, for example, a loosening of global financial conditions is associated with a
disproportionate increase in leverage growth for firms which are relatively less profitable or less solvent.
The first two columns of Table 4 present an initial set of results. Notice that the estimated coefficients on the interaction terms are
positive and statistically significant. Leverage growth appears to increase disproportionately for firms that are either relatively less
profitable or less solvent when global financial conditions loosen. Such results hint at the role that more stringent supervisory
standards and enhanced governance frameworks could play in managing the risks associated with potential credit misallocation.19
We have been arguing that the effect of global financial conditions on leverage growth depends on firms' financial constraints.
However, other mechanisms may be at work, including, for example, the demand channel. For instance, when a tightening of global
financial conditions is associated with a contraction in demand, firms with superior growth opportunities may suffer relatively more.
To account for this alternative channel, we use sector-specific median sales growth as a proxy for growth opportunities, in line with
Fisman and Love (2007). Specifically, we interact this measure of global growth opportunities with the inverted shadow rate and
include it in our regression model. The results are shown in the middle two columns (3–4) of Table 4. Although the estimated
coefficient on the interaction term is positive—hinting that global financial conditions may weakly affect leverage growth via the
demand channel—it is not statistically significant. Importantly, the main results on the interactions between the inverted shadow rate
and our two measures of corporate financial constraints are not altered.
In the last two columns (5–6) of Table 4, we test whether our suggestive findings regarding credit misallocation hold after
controlling for this demand channel. Importantly, while we do not attempt to disentangle the precise mechanisms at play, we note
that the coefficients associated with interaction terms involving the profitability and solvency dummy variables are unaltered,
whereas the interaction terms between the measure of global growth opportunities and the shadow rate remain statistically insignificant. Overall, our exploratory analysis provides evidence of riskier credit allocation given that less profitable and less solvent
firms borrow relatively more when global financial conditions loosen.
3.3. Are the results robust to alternative leverage ratios?
Alternative leverage ratios (discussed in the Appendix) are now considered to assess the robustness of our main findings. In sum,
the results shown in Table 5 demonstrate that the interaction of the inverted shadow rate with the SME dummy or the Rajan-Zingales
measure remain statistically significant with the expected sign across various alternative leverage ratios.
3.4. Are the results robust to alternative measures of global financial conditions?
Thus far we have used a measure of the U.S. monetary policy stance—the shadow rate—as a proxy for global financial conditions.
We now consider two complementary measures. First, we consider both the Federal funds rate and the 10-year Treasury bond yield.
These are among the more familiar measures of the U.S. monetary policy stance, but, as in the case of the Federal funds rate,
potentially constrained by the zero lower bound. In Table 6, the inverted U.S. shadow rate is replaced with the inverted Federal funds
rate and the 10-year U.S. Treasury bond yield.20 As a result, an increase in the inverted U.S. 10-year Treasury yield (looser global
financial conditions) of 1%age point corresponds to an increase in leverage growth of about 0.2 percentage point per year. Of note, the
new interaction terms have the expected signs and are all statistically significant at the 1 percent level. Interestingly, it appears that
the strength of the linkage between EM corporate leverage growth and U.S. monetary conditions increases when we use the yields
from longer-dated securities. Consider the interaction with the Rajan-Zingales metric. Notice the monotonic increase in the estimated
coefficient as we move from the Federal funds rate (0.095) to the 10-year bond yield (0.229). This would imply that an increase in the
17
See also Bekaert et al. (2013), Cerutti et al. (2019), and Cerutti et al. (2017).
ROE = ROA ∗ λ, where ROE = NI/E, ROA = NI/A, λ = A/E, and ROE, NI, E, and A denote return on equity, net income, equity, and assets,
respectively.
19
Likewise, Baumöhl et al. (2019) note the link between corporate leverage, governance, and institutions and quantify the degree to which lower
corporate debt can improve the chances of firm survival. For more on the importance of governance more broadly, see Porta et al. (1999) and Morck
et al. (2005).
20
Although not reported, we also considered the two- and five-year bond yield, which results in similar findings. These results are available upon
request. In addition, in earlier work, we considered a global shadow rate (the first principal component of the shadow rates associated with the Bank
of England, Bank of Japan, European Central Bank, and the U.S. Federal Reserve). Given the highly synchronized nature of these rates—as
documented in Figure 1—it is not surprising that we found similar results. Likewise, using lagged U.S. and global shadow rates yield similar
conclusions. For brevity, these results are not shown. We also consider the role of the VIX (which has been used as a complementary measure of
global financial conditions) and find a positive, statistically significant relationship between global financial conditions and EM leverage growth.
18
9
Journal of Corporate Finance 62 (2020) 101590
A. Alter and S. Elekdag
Table 4
Global financial conditions and credit misallocation.
Variables
(1)
(2)
(3)
(4)
(5)
(6)
Sales
−1.815***
(0.106)
9.953***
(0.479)
6.961***
(0.375)
0.0251**
(0.0108)
−1.814***
(0.106)
9.955***
(0.480)
6.959***
(0.375)
−1.820***
(0.106)
9.967***
(0.480)
6.962***
(0.375)
−1.860***
(0.0953)
9.842***
(0.504)
7.026***
(0.414)
−1.820***
(0.106)
9.968***
(0.480)
6.959***
(0.375)
0.0253**
(0.0108)
−1.819***
(0.106)
9.970***
(0.481)
6.957***
(0.375)
0.298
(0.213)
0.0323**
(0.0135)
0.203
(0.273)
0.292
(0.216)
0.268
(0.224)
Profitability
Tangibility
Inverted shadow rate (ISR) * Low Profitability dummy
ISR * Low interest coverage ratio dummy
ISR * SME dummy
0.0718***
(0.0129)
ISR * Financial dependence
ISR * Growth opportunity
Observations
R-squared (within)
Fixed effects
Firm
Country-time
0.0303***
(0.00779)
0.0719***
(0.0130)
2,954,202
0.014
2,954,202
0.014
2,947,155
0.014
2,403,466
0.014
2,947,155
0.014
2,947,155
0.014
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
Source: Authors' calculations.
Note: ***, **, and * denote statistical significance at the 1, 5, and 10% levels, respectively. The dependent variable is the ratio of total liabilities to
total assets (first differenced). “Sales” is the logarithmic transformation of total sales. “Profitability” is measured by the return-on-assets, while
“Tangibility” is the ratio of net property, plant, and equipment to total assets. Firm-specific regressors are first differenced and lagged.
“Macroeconomic conditions” are measured by the ICRG economic and financial index. “Financial dependence” is an updated version of the original
Rajan and Zingales (1998) index based on Tong and Wei (2011). Growth opportunities are measured using sector-specific median sales growth
similar to Fisman and Love (2007). The “low profitability” and “low interest rate coverage ratio” dummies take a value of unity for firms in the lower
quartiles of these respective distributions. The “inverted shadow rate” (ISR) is estimated from the term-structure model based on Krippner (2014).
Note that a positive value of the inverted shadow rate corresponds to a negative value of the shadow rate. Standard errors are clustered by sector.
Fixed effect terms are not reported.
inverted 10-year Treasury yield of one standard deviation—corresponding to more accommodative global financial conditions—is
associated with leverage growth that is on average about 0.1 percentage point greater for firms whose financial dependence is at the
75th percentile relative to firms whose financial dependence is at the 25th percentile.
Second, we use a measure of U.S. monetary policy shocks in place of the shadow rate. The data is based on Gertler and Karadi
(2015).21 The advantage of this measure is that it abstracts from monetary policy actions that were already anticipated by market
participants, and like the shadow rate, it allows for the inclusion of the protracted episode when U.S. short-term rates were close to
the zero lower bound (see also Dedola et al., 2015). Using such a measure strengthens our case for treating U.S. monetary conditions
as exogenous, since U.S. monetary policy is unlikely to be affected in a systematic way by idiosyncratic EM shocks.22 As shown in
Table 6 (Columns 7–9), the results once again reconfirm our earlier findings: there is a positive and statistically significant relationship between the U.S. monetary shocks and EM leverage growth.
3.5. What explains the link between global financial conditions and leverage growth across EMs?
As noted earlier, whereas large firms make greater use of capital markets, SMEs have more limited choices for external financing
and therefore rely heavily on intermediary credit—in particular, banks. Likewise, bank loans remain the main source of financing for
the EM corporate sector, and, more generally, most EM financial systems are still predominantly bank-based (Bräuning and Ivashina,
forthcoming; Cihak et al., 2012; IMF, 2015). Taken together, we therefore suspect that changes in global financial conditions affect
leverage through its impact on domestic interest rates. Moreover, this effect should be more pronounced for financially constrained
21
We thank Peter Karadi for sharing an updated version of the monetary policy shocks data. For consistency (as was done for the shadow rates),
we again multiply these shocks by −1 so that a positive shock corresponds to a looser monetary stance. The shocks measured by one-year-ahead
futures on 3-month Eurodollar deposits were the most reliable in the context of this paper. Note that the Gertler-Karadi shocks are available at
monthly frequency, as are other measures in the literature. However, we have an annual panel dataset, and frequency conversion is not trivial. As
the Gertler-Karadi estimates are shocks, it would not be surprising to find that the average (or sum) within each year is virtually zero. Therefore, to
capture the variation inherent in the shocks, we take the maximum (minimum) monthly value when the shock is positive (negative) in a given year
as the annual measure of the shock in this final robustness check.
22
An exception would be shocks that are global in nature and monetary authorities around the global respond in a similar manner.
10
11
2,954,130
0.009
YES
YES
2,954,130
0.009
YES
YES
−0.277***
(0.0203)
2.328***
(0.135)
0.808***
(0.0565)
0.00557***
(0.00135)
TATE
TLTE
−0.272***
(0.0197)
2.280***
(0.131)
0.797***
(0.0552)
0.00539***
(0.00129)
(2)
(1)
YES
YES
2,277,776
0.016
−0.0202***
(0.00114)
0.180***
(0.00398)
0.0349***
(0.00579)
0.000413***
(0.000126)
NTLTA
(3)
YES
YES
2,277,722
0.011
−0.307***
(0.0182)
2.775***
(0.136)
0.631***
(0.0752)
0.00480***
(0.00159)
NTLTE
(4)
YES
YES
2,277,722
0.011
−0.307***
(0.0182)
2.775***
(0.136)
0.631***
(0.0751)
0.00479***
(0.00160)
NTATE
(5)
YES
YES
2,403,827
0.010
0.00873***
(0.00273)
−0.281***
(0.0234)
2.366***
(0.150)
0.805***
(0.0663)
TLTE
(6)
YES
YES
2,403,827
0.009
0.00947***
(0.00288)
−0.286***
(0.0241)
2.416***
(0.153)
0.815***
(0.0677)
TATE
(7)
YES
YES
1,846,655
0.016
0.000271*
(0.000156)
−0.0214***
(0.00121)
0.179***
(0.00457)
0.0353***
(0.00665)
NTLTA
(8)
YES
YES
1,846,615
0.012
0.0103***
(0.00317)
−0.320***
(0.0199)
2.903***
(0.135)
0.626***
(0.0932)
NTLTE
(9)
YES
YES
1,846,615
0.012
0.0103***
(0.00316)
−0.320***
(0.0199)
2.902***
(0.136)
0.626***
(0.0932)
NTATE
(10)
Source: Authors' calculations.
Note: ***, **, and * denote statistical significance at the 1, 5, and 10% levels, respectively. The dependent variable is now either the ratio of total liabilities to total equity (TLTE) or ratio of total assets to
total equity (TATE), or their net versions (NTLTE, NTATE, as well as the ratio of net total liabilities to total assets, or NTLTA, all first differenced). “Sales” is the logarithmic transformation of total sales.
“Profitability” is measured by the return on assets, while “Tangibility” is the ratio of net property, plant, and equipment to total assets. Firm-specific regressors are first differenced and lagged.
“Macroeconomic conditions” are measured by the ICRG economic and financial index. “Financial dependence” is an updated version of the original Rajan and Zingales (1998) index based on Tong and
Wei (2011). The “inverted U.S. shadow rate” (ISR) is estimated from the term-structure model based on Krippner (2014). Note that a positive value of the inverted shadow rate corresponds to a negative
value of the shadow rate. Standard errors are clustered by sector. Fixed effect terms are not reported.
Observations
R-squared (within)
Fixed effects
Firm
Country-time
ISR * Financial dependence
Inverted shadow rate (ISR) * SME dummy
Tangibility
Profitability
Sales
Leverage
Table 5
Robustness: Alternative leverage ratios.
A. Alter and S. Elekdag
Journal of Corporate Finance 62 (2020) 101590
Journal of Corporate Finance 62 (2020) 101590
A. Alter and S. Elekdag
Table 6
Robustness: Alternative measures of global financial conditions.
Leverage
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
Sales
−1.747***
(0.104)
9.647***
(0.497)
6.889***
(0.355)
0.154***
(0.0210)
0.0818***
(0.0254)
−1.815***
(0.106)
9.960***
(0.479)
6.966***
(0.375)
−1.860***
(0.0951)
9.848***
(0.506)
7.027***
(0.414)
−1.727***
(0.105)
9.659***
(0.488)
6.912***
(0.358)
0.132***
(0.0175)
−1.817***
(0.106)
9.974***
(0.479)
6.967***
(0.375)
−1.862***
(0.0952)
9.876***
(0.509)
7.030***
(0.415)
−1.750***
(0.121)
10.61***
(0.666)
7.278***
(0.359)
0.134***
(0.0124)
−1.828***
(0.124)
11.22***
(0.723)
7.362***
(0.376)
−1.872***
(0.117)
10.97***
(0.751)
7.430***
(0.410)
Profitability
Tangibility
Macroeconomic conditions
Inverted Federal funds rate (IFFR)
IFFR * SME dummy
IFFR * Financial dependence
0.0895***
(0.0211)
Inverted 10-year bond yield
(I10YR)
I10YR * SME dummy
0.0947**
(0.0375)
0.155***
(0.0364)
I10YR * Financial dependence
0.132***
(0.0146)
Gertler-Karadi monetary policy
shocks (GK)
GK * SME dummy
0.299***
(0.0540)
2.696***
(0.544)
GK * Financial dependence
Observations
R-squared (within)
Fixed effects
Firm
Country-time
0.124
(0.692)
4.223***
(1.458)
2,953,719
0.009
2,954,202
0.014
2,403,879
0.014
2,953,719
0.009
2,954,202
0.014
2,403,879
0.014
2,063,165
0.009
2,063,377
0.014
1,685,386
0.014
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
Source: Authors' calculations.
Note: ***, **, and * denote statistical significance at the 1, 5, and 10% levels, respectively. The dependent variable is the ratio of total liabilities to
total assets (first differenced). “Sales” is the logarithmic transformation of total sales. “Profitability” is measured by the return on assets, while
“Tangibility” is the ratio of net property, plant, and equipment to total assets. Firm-specific regressors are first differenced and lagged.
“Macroeconomic conditions” are measured by the ICRG economic and financial index. “Financial dependence” is an updated version of the original
Rajan and Zingales (1998) index based on Tong and Wei (2011). “IFFR” and “I10YR” denote the inverted Federal funds rate and the 10-year U.S.
Treasury yield. Note that a positive value of the inverted FFR or 10YR (denoted with IFFR and I10YR) corresponds to a negative value of the FFR and
10YR. “GK” denotes monetary policy shocks based on Gertler and Karadi (2015). Standard errors are clustered by sector. Fixed effect terms are not
reported.
firms, such as SMEs.
To test this hypothesis, we consider the role of monetary policy synchronization. Specifically, following Laeven and Tong (2012),
we use the correlation of quarterly money market rates between the U.S. and the individual EMs over 2004–2017 as a measure of
monetary policy synchronization. As shown in eq. (3), this correlation coefficient is interacted with the inverted U.S. shadow
rate—our proxy for global financial conditions—and introduced in our baseline specification. If the effect of global financial conditions on leverage growth is more pronounced in EMs whose monetary policy is more closely aligned with U.S. monetary policy, this
would lend support to the relevance of the domestic interest rate channel.
Table 7 depicts two important and interrelated results. First, the interaction terms of the SME dummy and Rajan-Zingales metric
with the shadow rate remain unaltered. Second, the coefficient associated with the interaction of the shadow rate and policy synchronization measure is positive and statistically significant. This result suggests that global financial conditions—as proxied by the
U.S. shadow rate—affect EM corporate leverage growth through domestic interest rates.23
In a complementary exercise, we further examine the transmission of global financial conditions via domestic interest rates by
performing sample splits. The sample of EMs is divided into two groups accordingly to our measure of policy synchronization (high
versus low). We then test whether the domestic interest rate channel has a stronger effect on corporate leverage growth in the group
of EMs whose monetary policy is more aligned with that of the U.S. Accordingly, we now introduce the inverted domestic interest rate
and its interaction with our two measures of firm-level financial constraints (SME and Rajan-Zingales measures).
23
We also find that the transmission of global financial conditions is stronger for EMs with more open capital accounts. Again, for brevity, this
result is not shown, but available upon request. Likewise, in earlier work, we found suggestive evidence that the transmission may be even stronger
for EMs with more rigid exchange rate regimes and more open capital accounts.
12
Journal of Corporate Finance 62 (2020) 101590
A. Alter and S. Elekdag
Table 7
Global financial conditions and monetary policy synchronization.
Leverage
(1)
(2)
(3)
(4)
Sales
−1.788***
(0.107)
9.675***
(0.481)
6.903***
(0.356)
0.158***
(0.0191)
0.0186
(0.0140)
0.0198**
(0.00936)
−1.764***
(0.109)
9.794***
(0.485)
6.969***
(0.364)
0.0988***
(0.0215)
−1.820***
(0.0946)
9.645***
(0.486)
7.022***
(0.402)
0.0818***
(0.0137)
0.0312***
(0.00925)
−1.851***
(0.0932)
9.516***
(0.475)
6.940***
(0.392)
0.145***
(0.0171)
0.0250**
(0.0101)
ISR * Policy synchronization
0.0479***
(0.0161)
0.0351*
(0.0183)
0.0342**
(0.0136)
0.0475***
(0.0150)
0.0359***
(0.0132)
0.0424***
(0.0132)
Observations
R-squared (within)
Fixed effects
Firm
Time
2,953,719
0.009
2,953,719
0.010
2,403,456
0.009
2,403,456
0.010
YES
YES
YES
YES
YES
YES
Profitability
Tangibility
Macroeconomic conditions
Inverted shadow rate (ISR)
ISR * SME dummy
ISR * Financial dependence
Source: Authors' calculations.
Note: ***, **, and * denote statistical significance at the 1, 5, and 10% levels, respectively. The dependent variable is the ratio of total liabilities to
total assets (first differenced). “Sales” is the logarithmic transformation of total sales. “Profitability” is measured by the return on assets, while
“Tangibility” is the ratio of net property, plant, and equipment to total assets. Firm-specific regressors are first differenced and lagged.
“Macroeconomic conditions” are measured by the ICRG economic and financial index. “Financial dependence” is an updated version of the original
Rajan and Zingales (1998) index based on Tong and Wei (2011). The “inverted shadow rate” (ISR) is estimated from the term-structure model based
on Krippner (2014). Note that a positive value of the inverted shadow rate corresponds to a negative value of the shadow rate. “Policy synchronization” is measured using the correlation of money market rates between the U.S. and individual EMs. Standard errors are clustered by sector.
Fixed effect terms are not reported.
In Table 8, the first two columns show that the inverted domestic interest rate enters with a positive and highly statistically
significant coefficient for EMs in the high policy synchronization group (in contrast to the EMs in the low synchronization group).
Likewise, as depicted in the remaining columns, the interaction terms for EMs whose monetary policy is more synchronized with that
of the U.S. obtain larger coefficient values and are statistically significant. For example, in the case of the interaction with the SME
dummy, notice the estimated coefficients of 0.078 and 0.034 for the high and low policy synchronization groups, respectively. This
suggests that SME leverage growth is twice as sensitive to domestic interest rate fluctuations in EMs where monetary policy closely
tracks that of the U.S.
4. Conclusions
This paper is motivated by the sharp rise in emerging market (EM) corporate debt over the past decade, when global financial
conditions were exceptionally accommodative. Accordingly, it investigates whether there is a reliable relationship between global
financial conditions—initially proxied by a measure of the U.S. monetary policy stance—and firm-level leverage growth across a wide
range of EMs. Our findings suggest an economically meaningful and statistically robust relationship whereby accommodative global
financial conditions are associated with faster EM corporate leverage growth. Moreover, this effect is more pronounced for firms that
face financial constraints, such as SMEs. Importantly, this paper differs from other studies by explicitly including and focusing on
SMEs (and other non-listed firms) and by considering total firm-level debt (rather than, for example, only bond issuances).
To the best of our knowledge, this is the first papers to demonstrate that global financial conditions are a reliable determinant of
firm-level leverage dynamics. We also show that the effect on leverage growth is more pronounced in EMs whose monetary policy is
more synchronized with the United States, suggesting that global financial conditions influence corporate leverage growth through
domestic interest rates.
To exploit the cross-sectional heterogeneity, we conduct further exploratory analysis, which reveals that leverage growth is higher
for firms that are either relatively less profitable or less solvent during episodes of accommodative global financial conditions.
Although rising corporate leverage could be accompanied by productive investment and economic growth, these results seem to
validate the concerns that some borrowing may have been excessively allocated to riskier firms during such episodes. The results
warrant the consideration of higher supervisory standards, stronger corporate governance frameworks, and more active macroprudential policies.
13
Journal of Corporate Finance 62 (2020) 101590
A. Alter and S. Elekdag
Table 8
Robustness: Monetary policy synchronization.
Leverage
(1)
(2)
(3)
(4)
(5)
(6)
Policy Synchronization
Sales
Profitability
Tangibility
Inverted domestic interest rate (IDR)
IDR * SME
High
Low
High
Low
High
Low
−2.117***
(0.0856)
10.16***
(0.549)
6.269***
(0.513)
0.0707***
(0.0229)
−0.967***
(0.0843)
8.385***
(0.897)
7.780***
(0.331)
−0.00289
(0.0216)
−2.234***
(0.0968)
10.85***
(0.515)
6.373***
(0.546)
−1.084***
(0.0805)
8.368***
(0.845)
7.783***
(0.320)
−2.276***
(0.0828)
10.95***
(0.585)
6.469***
(0.570)
−1.143***
(0.0917)
7.829***
(0.812)
7.841***
(0.360)
0.0772***
(0.0149)
0.0341***
(0.00946)
0.158***
(0.0366)
−0.000266
(0.0106)
IDR * RZ
Observations
R-squared (within)
Fixed effects
Firm
Country-time
1,767,112
0.011
1,170,362
0.006
1,767,112
0.017
1,170,362
0.010
1,444,022
0.018
946,475
0.010
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
Source: Authors' calculations.
Note: ***, **, and * denote statistical significance at the 1, 5, and 10% levels, respectively. The dependent variable is the ratio of total liabilities to
total assets (first differenced). “Sales” is the logarithmic transformation of total sales. “Profitability” is measured by the return on assets, while
“Tangibility” is the ratio of net property, plant, and equipment to total assets. Firm-specific regressors are first differenced and lagged.
“Macroeconomic conditions” are measured by the ICRG economic and financial index. “RZ” (financial dependence) is an updated version of the
original Rajan and Zingales (1998) index based on Tong and Wei (2011). “IDR” is the inverted domestic interest rate for individual EMs. Note that a
positive value of the inverted domestic interest rate corresponds to a negative value of the domestic interest rate. EMs are split into a high and low
policy synchronization groups based on the correlation of money market rates between the U.S. and individual EMs. Standard errors are clustered by
sector. Fixed effect terms are not reported.
Appendix A. ORBIS dataset and variable definitions
This appendix provides further details on the data and variables used in the analysis.
A.1. ORBIS
The firm-level dataset used is this paper is ORBIS (Bureau van Dijk Electronic Publishing, BvD), an annual global panel dataset for
over 280 million public and private (non-listed) companies. A notable advantage of ORBIS is that it includes non-listed firms, such as
SMEs. Data on firms' financial positions and productive activities is sourced from their balance sheets and income statements. Because
ORBIS includes non-listed firms, by construction, all available data is based on book values. Although ORBIS has the advantage of
being comprehensive, with millions of firms represented in the database, more detailed information on financial statements is harder
to come by in the context of EMs. For example, debt is not reported by many EM firms.
As with other large micro data sets, the data need to be managed carefully before they can be used for formal econometric
analysis. Kalemli-Ozcan et al. (2015) discuss challenges of the ORBIS data base and methods to overcome them. Accordingly, when
cleaning ORBIS for our purposes, we are guided by the methods laid out in Gopinath et al. (2015), Kalemli-Ozcan et al. (2015),
Kalemli-Ozcan et al. (2018), Kalemli-Ozcan et al. (2012), and Fons-Rosen et al. (2013). For instance, to avoid double counting and to
improve comparability across countries, consolidated accounts are considered. We focus on private EM non-financial corporations
with total assets in excess of $1 million. As a result, about 70% our sample are considered SMEs. Finally, all variables are winsorized
at 2.5% to account for outliers, especially owing to input errors.24 The ORBIS-based firm-level dataset is then merged with a countryspecific measure of macroeconomic conditions (ICRG index) and global factors (for example, a measure of the U.S. monetary policy
24
Some additional details are as follows: All companies categorized as “Public authority/State/Government” are excluded from our sample. We
drop company-years observations with missing information on total assets, total shareholder funds, total liabilities, and sector. We also drop
company-year observations with negative total assets, cash holdings, total equity, total fixed assets, current assets, current liabilities, total liabilities,
loans, or depreciation and amortization. Moreover, several accounting checks were considered. For example, if the sum of fixed assets and current
assets exceeds total assets (by a notable margin) those observations are dropped. Another accounting relationship was to check whether the sum of
non-current liabilities, current liabilities, and total equity exceeds total liabilities and shareholder funds.
14
Journal of Corporate Finance 62 (2020) 101590
A. Alter and S. Elekdag
stance, both which are discussed below. In sum, the dataset comprises about 800,000 firms for 28 EMs during 2004–2017, resulting
in an unbalanced panel comprising nearly 5 million firm-year observations (Appendix Table 1).
Table 1
Country and firm coverage.
Panel A.
Panel B.
Panel C.
Country
Observations
Share (%)
Size Category
Observations
Share (%)
BvD Major Sector
Observations
Share (%)
Argentina
Brazil
Bulgaria
Chile
China
Colombia
Croatia
Hungary
India
Indonesia
Kazakhstan
Korea
Lithuania
Mexico
Morocco
Pakistan
Peru
Philippines
Poland
Romania
Russia
Serbia
South Africa
Sri Lanka
Thailand
Turkey
Ukraine
Venezuela
Total
612
5712
18,901
295
249,033
30,144
19,750
24,998
10,263
296
2275
116,081
4320
2152
8789
489
423
8413
39,797
30,552
132,246
8674
132
62
42
15,821
29,718
48
760,038
0.1
0.8
2.5
0.0
32.8
4.0
2.6
3.3
1.4
0.0
0.3
15.3
0.6
0.3
1.2
0.1
0.1
1.1
5.2
4.0
17.4
1.1
0.0
0.0
0.0
2.1
3.9
0.0
100.0
Very large company
Large company
Medium sized company
Small company
32,493
196,137
416,184
115,224
4.3
25.8
54.8
15.2
Total
760,038
100.00
Chemicals, rubber
Construction
Education, health
Food, beverages
Gas, water, electricity
Hotels & restaurants
Machinery, equipment
Metals & metal prod.
Other services
Post & telecommunication
Primary sector
Publishing, printing
Textiles, wearing apperal
Transport
Wholesale & retail
Wood, cork, paper
80,757
68,368
10,756
42,883
14,808
11,087
117,590
52,490
71,502
2983
44,099
11,357
41,909
25,449
146,198
17,802
10.6
9.0
1.4
5.6
1.9
1.5
15.5
6.9
9.4
0.4
5.8
1.5
5.5
3.3
19.2
2.3
Total
760,038
100.0
Sources: Orbis database; Authors' calculations.
Note: “BvD” denotes Bureau van Dijk Electronic Publishing, which produces the ORBIS firm-level dataset used is this paper. Cross-sectional statistics
are shown for 2012; total number of firms exceeds 800,000 in several years.
A.2. Measures of leverage
Leverage, or financial leverage, is the degree to which a company uses fixed-income securities such as debt. A high degree of
financial leverage entails larger interest payments, which negatively affect firm's profitability. Leverage is usually presented as a
ratio, such as debt to assets. The broadest definitions of leverage consider total non-equity liabilities. An advantage of using total
liabilities is that it implicitly recognizes that some firms can use trade credit as a means of financing, rather than purely for transactions (Rajan and Zingales, 1995). Another benefit of using total liabilities is its availability. For some countries, debt may not be
reported in larger datasets that include non-listed firms, which is the reality we face when using ORBIS.
For these reasons, we initially consider the ratio of total (non-equity) liabilities to total assets (TLTA) as our measure of EM
corporate leverage (consistent with, for example, Rajan and Zingales, 1995). Later, we also consider alternative definitions of
leverage, including the ratios of total liabilities to total equity and total assets to total equity. Furthermore, to account for the fact that
leverage may have risen owing to the accumulation of precautionary cash buffers, we consider variations of these ratios where cash is
netted out.25
A.3. Financial dependence index
As measure of a sector's intrinsic dependence on external finance, we use the financial dependence measure proposed by Rajan
and Zingales (1998). Conceptually, the Rajan and Zingales index aims to identify sectors that are naturally more dependent on
external financing for their business operation. They compute a sector's dependence on external finance as:
25
Studies have also singled out leverage ratios using long-term debt, given that it has a closer link to investment. However, relative to total debt
statistics, data on long-term debt is even more difficult to come by in ORBIS. Likewise, ORBIS does not contain information on foreign currency
positions.
15
Journal of Corporate Finance 62 (2020) 101590
A. Alter and S. Elekdag
Financial Dependence = (Capital Expenditures
Cash Flow )/ Capital Expenditures
where cash flow = cash flow from operations + decreases in inventories + decreases in receivables + increases in payables. The
index is computed using data on publicly listed U.S. firms, which are judged to be least likely to suffer from financing constraints
relative to generally smaller firms in other countries, including EMs. We use an updated version of the original Rajan and Zingales
(1998) index based on Tong and Wei (2011) over 1990–2006, which allows us to consider over 50 sectors.26
A.4. Firm-level controls
Building on the literature (for example, Rajan and Zingales, 1995) and based on data availability, size (log sales), profitability
(return on assets), and asset tangibility (the ratio of net property, plant, and equipment to total assets) are firm-level controls used in
the baseline specification. As noted by Frank and Goyal (2009), the expected signs of these controls are ambiguous based on opposing
theoretical predictions.27
Leverage and profitability: Profitable firms face lower expected costs of financial distress (and find interest tax shields more valuable), and therefore the tax and bankruptcy costs perspective predicts that profitable firms take on more debt.28 Moreover, the
agency costs perspective predicts that the discipline provided by debt is more valuable for profitable firms with more acute free cash
flow problems (Jensen, 1986). In contrast, the pecking order theory argues that firms prefer internal finance over external funds,
implying that profitability and leverage are negatively correlated.
Leverage and size: Large and potentially more diversified firms face lower default risk. Therefore, the trade-off theory predicts that
larger firms will have relatively more debt. Conversely, the pecking order theory is usually interpreted as implying an inverted
relationship between leverage and firm size (Frank and Goyal, 2009).
Leverage and asset tangibility: Tangible assets, such as property, plant, and equipment, are easier for outsiders to value than
intangibles, such as goodwill. Therefore, a greater share of tangible assets relative to total assets lowers expected distress costs, and
therefore suggests a positive relationship between tangibility and leverage.29 The pecking order theory makes the opposite prediction. Low information asymmetry associated with tangible assets makes equity issuance less costly, and therefore leverage ratios
should be lower for firms with a greater share of tangible assets.30
A.5. Country-specific controls
In some specifications, we explicitly attempt to account for country-specific macroeconomic conditions. In particular, we follow
Bekaert et al. (2014), and take the average of the International Country Risk Guide (ICRG) economic and financial risk ratings. The
ICRG economic risk indicator is designed to capture a country's current economic strengths and weaknesses. It combines information
on five economic statistics: GDP levels, GDP growth, inflation, government budgets, and the current account. The ICRG financial risk
indicator is designed to assess a country's ability to finance its official, commercial, and trade debt obligations. It combines data from
five statistics: foreign debt as a percentage of GDP, foreign debt as a percentage of exports, the current account as a percentage of
exports, official reserves, and exchange rate stability. In each case, a higher value indicates stronger fundamentals.31 Recall that
various theoretical studies have differing predictions regarding the cyclicality of leverage, further motivating our empirical analysis.
Although we use the ICRG to control for country-specific macroeconomic conditions, we also consider regressions that include
country-time fixed effects, thereby controlling for a wider array of factors that may be affecting firm-level leverage depending on
their location and period in question.
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