Emerging market corporate leverage and global financial conditions

Journal Pre-proof Emerging market corporate leverage and global financial conditions

Adrian Alter, Selim Elekdag PII:

S0929-1199(20)30034-1

DOI:

https://doi.org/10.1016/j.jcorpfin.2020.101590

Reference:

CORFIN 101590

To appear in:

Journal of Corporate Finance

Received date:

6 August 2018

Revised date:

5 February 2020

Accepted date:

6 February 2020

Please cite this article as: A. Alter and S. Elekdag, Emerging market corporate leverage and global financial conditions, Journal of Corporate Finance(2020), https://doi.org/ 10.1016/j.jcorpfin.2020.101590

This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

© 2020 Published by Elsevier.

Journal Pre-proof

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 M egginson, Camelia M inoiu, Steve Phillips, Garence Staraci, Yan Sun, Kai Yan, Ling Zhu, and participants at IM F seminar, IFABS 2017 Oxford Conference; 2017 Workshop on Systemic Risk, organized by CRM M ontreal; and “Unconventional M onetary Policy: Lessons Learned,” a 2017 conference organized by the Hong Kong M onetary Authority, Federal Reserve Board, and Federal Reserve Bank of Atlanta for helpful comments and discussions. The views expressed in this paper are those of the authors and do not necessarily represent the views of the IM F, its Executive Board, or IM F management.

ro

Conditions

of

Emerging Market Corporate Leverage and Global Financial

re

International Monetary Fund

-p

Adrian Alter [email protected], Selim Elekdag [email protected]

lP

Abstract

Jo

ur

na

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 Keywords: Corporate capital structure, leverage, global financial conditions, U.S. monetary policy, emerging market economies, small- and medium-sized enterprises (SMEs)

Journal Pre-proof 2 I. 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 (Figure 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 percent. 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)

of

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

ro

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

-p

bond issuance is related to global financial conditions therefore runs the risk of overlooking the

re

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

lP

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

na

Torre, Martinez Peria, and Schmukler 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

ur

still predominantly bank-based (Brauning and Ivashina 2019; Cihak et al. 2012; IMF 2015). Together, SMEs and other (non-listed) firms—which have limited access to capital markets and

Jo

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. 1

See, for example, Feyen et al. (2015), Gozzi et al. (2015), and Caballero, Fernandez, and Park (2019).

2

Brauning and Ivashina (2019) 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 (La Porta, Lopez-de-Silanes, and Schliefer 1999; Morck, Wolfenzon, and Yeung 2005). 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 percent of firms, 66.4 percent of employment (with an upper range of over 85 percent), and 56.8 percent of value added (with an upper range of over 81 percent), on average (European Commission 2018).

Journal Pre-proof 3 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

of

firm size to be a reasonable indicator of capital market access, with the implication that SMEs

ro

are more likely to face borrowing constraints (and thereby more sensitive to interest rate

-p

fluctuations).

We also consider a complementary proxy to measure borrowing constraints based on Rajan and

re

Zingales (1998) which differentiates firms based on their intrinsic dependence on external financing. Greater leverage can confer important benefits, such as facilitating productive

lP

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-

na

Ozcan, Laeven, and Moreno 2018). Even if borrowing by individual SMEs is small, a collective increase in leverage could raise economy-wide vulnerabilities. For example, rapid increases in

ur

corporate leverage have often been associated with boom-bust cycles (Mendoza and Terrones

Jo

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 4

Studies include Calvo, Leiderman, and Reinhart (1993, 1996), Rey (2015), Miranda-Agrippino and Rey (2015), Fratzscher, Lo Duca, and Straub (2013), and Bruno and Shin (2015).

Journal Pre-proof 4 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

of

accommodative global financial conditions, and would be more inclined to increase their

ro

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

-p

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

re

28 EMs.

lP

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,

na

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

ur

further sharpens our identification strategy; it presumes that global financial conditions can be

Jo

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 5

Building on the work of Kiyotaki and Moore (1997), Bernanke, Gertler, Gilchrist (1996), and Iacoviello (2005), open-economy models were developed by Gertler, Gilchrist, and Natalucci (2007), Elekdag and Tchakarov (2007), and Fernandez 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 colla teral valuation, jointly interact and determine the demand for capital and debt. See also Cardarelli, Elekdag, and Lall (2011).

Journal Pre-proof 5 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

of

ratio of up to 0.1 percentage point per year, but it can be up to twice as high when using

ro

alternative measures of U.S. monetary policy. Considering the large peak-to-trough decline in the shadow rate of more than 1,000 basis points from 2007 to 2013, and the protracted nature of

re

increases in leverage can build up over time.

-p

these exceptionally loose global financial conditions, it is clear how even seemingly incremental

This impact is more pronounced for firms facing financial constraints. In particular, we find that

lP

leverage growth for SMEs is 50 percent more sensitive to global financial conditions relative to larger firms. Likewise, our results indicate that the impact of U.S. shadow-rate fluctuations is

na

greater for firms that are more dependent on external financing. In this case, a one-standarddeviation loosening of the U.S. shadow rate results in an increase in leverage ratio that is on

ur

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

Jo

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 more pronounced in EMs whose monetary policy is more

Journal Pre-proof 6 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

of

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

ro

corporate finance literature that focuses on listed firms (predominantly in the United States), we

-p

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

re

considering SMEs and other non-listed firms is emphasized by Kalemli-Ozcan, Sorensen, and

lP

Yesiltas (2012), who spotlight leverage dynamics across advanced economies. Second, and relatedly, unlike recent papers that focus on bond issuances (Caballero, Fernandez,

na

and Park 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

ur

how EM corporate leverage growth is influenced by global financial conditions. Third, this paper provides novel cross-country empirical evidence that financial frictions play an

Jo

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.

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).

Journal Pre-proof 7 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-

of

level leverage dynamics. We proceed as follows: Section II discusses the empirical framework, hypotheses, and data,

-p

ro

Section III presents the main results of the paper, and Section IV provides concluding remarks.

re

II. M ETHODOLOGY AND DATA

lP

A. Regression Specifications and Hypotheses

Our empirical approach is to test whether global financial conditions influence corporate

na

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

ur

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

Jo

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 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).

Journal Pre-proof 8 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, longer-term 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

of

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,

ro

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

-p

alternative channels may be driving the results, all regressions include country-time and firm

re

fixed effects (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).

lP

To investigate the relationship between EM corporate leverage growth and global financial conditions, we start by estimating the following equation:

na

∆𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒𝑖,𝑠,𝑐,𝑡 = 𝛼 ∗ 𝐺𝑙𝑜𝑏𝑎𝑙 𝐹𝑖𝑛𝑎𝑛𝑐𝑖𝑎𝑙 𝐶𝑜𝑛𝑑𝑖𝑡𝑖𝑜𝑛𝑠𝑡

ur

+𝛿 ∗ 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠𝑖,𝑠,𝑐,𝑡−1 + 𝜀𝑖,𝑠,𝑐,𝑡

(1)

Jo

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 I for details). Furthermore, firm-specific fixed effects are included to account for unobserved firm-level factors (as are combinations of time, country-time, sectortime, and country-sector-time fixed effects). In the baseline specifications, to make statistical 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.

Journal Pre-proof 9 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 firm when using the SME dummy, or by sector when using the Rajan-

of

Zingales metric):

∆𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒𝑖,𝑠,𝑐,𝑡 = 𝛼 ∗ 𝐺𝑙𝑜𝑏𝑎𝑙 𝐹𝑖𝑛𝑎𝑛𝑐𝑖𝑎𝑙 𝐶𝑜𝑛𝑑𝑖𝑡𝑖𝑜𝑛𝑠𝑡

ro

(2)

-p

+𝛽 ∗ 𝐺𝑙𝑜𝑏𝑎𝑙 𝐹𝑖𝑛𝑎𝑛𝑐𝑖𝑎𝑙 𝐶𝑜𝑛𝑑𝑖𝑡𝑖𝑜𝑛𝑠𝑡 ∗ 𝐹𝑖𝑛𝑎𝑛𝑐𝑖𝑎𝑙 𝐶𝑜𝑛𝑠𝑡𝑟𝑎𝑖𝑛𝑡𝑠𝑖,𝑠

re

+𝛿 ∗ 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠𝑖,𝑠,𝑐,𝑡−1 + 𝜀𝑖,𝑠,𝑐,𝑡 The slope coefficient on the interaction term, 𝛽, captures the extent to which the effect of global

lP

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

na

𝛽 > 0. Any evidence would suggest that a loosening in global financial conditions fosters faster

ur

EM leverage growth by relaxing borrowing constraints. Lastly, to shed further light on underlying transmission channels, we investigate whether the

Jo

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 = 𝛼 ∗ 𝐺𝑙𝑜𝑏𝑎𝑙 𝐹𝑖𝑛𝑎𝑛𝑐𝑖𝑎𝑙 𝐶𝑜𝑛𝑑𝑖𝑡𝑖𝑜𝑛𝑠𝑡

(3)

+𝛽 ∗ 𝐺𝑙𝑜𝑏𝑎𝑙 𝐹𝑖𝑛𝑎𝑛𝑐𝑖𝑎𝑙 𝐶𝑜𝑛𝑑𝑖𝑡𝑖𝑜𝑛𝑠𝑡 ∗ 𝐹𝑖𝑛𝑎𝑛𝑐𝑖𝑎𝑙 𝐶𝑜𝑛𝑠𝑡𝑟𝑎𝑖𝑛𝑡𝑠𝑖 ,𝑠 +𝛾 ∗ 𝐺𝑙𝑜𝑏𝑎𝑙 𝐹𝑖𝑛𝑎𝑛𝑐𝑖𝑎𝑙 𝐶𝑜𝑛𝑑𝑖𝑡𝑖𝑜𝑛𝑠𝑡 ∗ 𝑃𝑜𝑙𝑖𝑐𝑦 𝑆𝑦𝑛𝑐ℎ𝑟𝑜𝑛𝑖𝑧𝑎𝑡𝑖𝑜𝑛𝑐 +𝛿 ∗ 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠𝑖,𝑠,𝑐,𝑡 −1 + 𝜀𝑖,𝑠,𝑐,𝑡 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

Journal Pre-proof 10 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.

B. Data and Variable Definitions This section summarizes the main variables and data sources used in the analysis, with details

of

relegated to the Appendix.

ro

ORBIS

The firm-level dataset used is this paper comes from ORBIS (Bureau van Dijk Electronic

-p

Publishing), an annual global panel dataset with over 280 million public and private companies.

re

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

lP

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

na

context of EMs.10 As explained in detail in the Appendix, our sample covers about 800,000

Measures of leverage

ur

nonfinancial EM firms over 2004-2017, totaling 5 million firm-year observations.

Jo

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 I, circumvents the issue of missing debt data for certain firms (especially SMEs). Furthermore, motivated by the clear upward trends in leverage documented in Figure 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, Leary, and Roberts (2015) also consider growth of leverage, 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, Brauning and Ivashina (2019) and Barunik and Kocenda (2019) highlight the role of loose global financial conditions on foreign currency bank lending and, more generally, on forex markets.

Journal Pre-proof 11 but in the context of the U.S., a mature economy, thus motivating our focus on leverage growth in faster growing EMs. 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

of

country-time fixed effect terms (see Appendix for details and discussions).

ro

C. Descriptive Statistics

-p

Table 1 reports summary statistics for selected variables and complements Figure 1. The estimated shadow rate seems to reasonably reflect monetary policy events in the unconventional

re

policy regime. We use the U.S. shadow rate estimated by Krippner (2014), which entered

lP

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

na

percent in July 2007 to a trough of less than –5 percent in May 2013 is especially staggering. The shadow rate bottomed out in when the Fed raised the possibility of tapering its purchases of

ur

Treasury and agency bonds, and has continued to increase since then, and has been hovering around 2 percent more recently. To facilitate the interpretation of the results, we use the inverted

Jo

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 percent of the firms in our sample (Appendix Table 1). 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, Opler, and Titman (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.

Journal Pre-proof 12 III. 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 RajanZingales 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.

of

A. Baseline Results

ro

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

-p

positive and statistically significant coefficient (0.054). This initial result suggests that expansionary global monetary conditions are associated with faster EM corporate leverage

re

growth. Specifically, an increase in the inverted U.S. shadow rate (looser monetary conditions)

lP

of 1 percentage point corresponds to an increase in the leverage ratio of about 0.1 percentage point per year. Recalling the 1,000 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

na

increases in leverage can accumulate over time.14

ur

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

Jo

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 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).

Journal Pre-proof 13 findings suggest that leverage growth for SMEs is 60 percent more sensitive to global financial conditions relative to larger firms, confirming our hypothesis. 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 percent confidence level, with an estimated coefficient value of 0.033.

of

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

ro

macroeconomic conditions variable, which is therefore omitted from the regression. In column 5,

-p

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,

re

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

lP

(statistically significant at the 1 percent level).

na

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

ur

measure and the inverted U.S. shadow rate.15 We find that the impact of U.S. shadow rate

Jo

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 country15

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.

Journal Pre-proof 14 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.

of

B. Are looser global financial conditions associated with credit misallocation? The upswing in EM corporate leverage amid accommodative global financial conditions over the

ro

past decade has raised concerns that borrowing may have been excessively allocated to risky

-p

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

lP

re

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

na

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 Marques 2006). Likewise, the

ur

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

Jo

getting 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, 16

The alternative SME dummy takes the value of unity for firms with operating revenue below €20 ($26) million, implying that 80 percent of the firms in the sample are SMEs. 17

See also Bekaert, Hoerova, and Lo Duca (2013), Cerutti, Claessens, and Puy (2019), and Cerutti, Claessens, and Ratnovski (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. 18

Journal Pre-proof 15 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.

of

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

-p

associated with potential credit misallocation.19

ro

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

re

We have been arguing that the effect of global financial conditio ns on leverage growth depends on firms’ financial constraints. However, other mechanisms may be at work, including, for

lP

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

na

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

ur

include it in our regression model. The results are shown in the middle two columns (3-4) of

Jo

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, 19

Likewise, Baumohl, Iwasaki, and Kocenda (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 La Porta, Lopez-de-Silanes, and Schliefer (1999) and Morck, Wolfenzon, and Yeung (2005).

Journal Pre-proof 16 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. C. 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

of

remain statistically significant with the expected sign across various alternative leverage ratios.

ro

D. 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

-p

proxy for global financial conditions. We now consider two complementary measures. First, we

re

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

lP

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

na

As a result, an increase in the inverted U.S. 10-year Treasury yield (looser global financial conditions) of 1 percentage point corresponds to an increase in leverage growth of about 0.2

ur

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

Jo

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 inverted 10-year Treasury yield of one standard deviation—corresponding to more 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.

Journal Pre-proof 17 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, Rivolta, and Stracca 2015). Using such a measure strengthens our case for treating U.S. monetary conditions as exogenous, since U.S.

of

monetary policy is unlikely to be affected in a systematic way by idiosyncratic EM shocks.22 As

ro

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

-p

leverage growth.

re

E. What explains the link between global financial conditions and leverage growth across

lP

EMs?

As noted earlier, whereas large firms make greater use of capital markets, SMEs have more

na

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

ur

(Brauning and Ivashina 2019; Cihak et al. 2012; IMF 2015). Taken together, we therefore suspect that changes in global financial conditions affect leverage through its impact on domestic such as SMEs.

Jo

interest rates. Moreover, this effect should be more pronounced for financially constrained firms,

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 maximu m (minimu m) 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.

Journal Pre-proof 18 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 equation (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

of

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

ro

proxied by the U.S. shadow rate—affect EM corporate leverage growth through domestic

-p

interest rates.23

In a complementary exercise, we further examine the transmission of global financial conditions

lP

re

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

na

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).

ur

In Table 8, the first two columns show that the inverted domestic interest rate enters with a

Jo

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.

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.

Journal Pre-proof 19

IV. 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

of

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

-p

debt (rather than, for example, only bond issuances).

ro

including and focusing on SMEs (and other non-listed firms) and by considering total firm-level

To the best of our knowledge, this is the first papers to demonstrate that global financial

re

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

lP

synchronized with the United States, suggesting that global financial conditions influence corporate leverage growth through domestic interest rates.

na

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

ur

solvent during episodes of accommodative global financial conditions. Although rising corporate leverage could be accompanied by productive investment and economic growth, these results

Jo

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.

Journal Pre-proof 20

APPENDIX I. ORBIS DATASET AND VARIABLE DEFINITIONS This appendix provides further details on the data and variables used in the analysis. 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.

of

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

ro

data is based on book values. Although ORBIS has the advantage of being comprehensive, with

-p

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.

re

As with other large micro data sets, the data need to be managed carefully before they can be

lP

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

na

purposes, we are guided by the methods laid out in Gopinath et al. (2015), Kalemli-Ozcan et al. (2015), Kalemli-Ozcan, Laeven, and Moreno (2018), Kalemli-Ozcan, Sorensen, and Yesiltas

ur

(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

Jo

non-financial corporations with total assets in excess of $1 million. As a result, about 70 percent our sample are considered SMEs. Finally, all variables are winsorized at 2.5 percent to account for outliers, especially owing to input errors. 24 The ORBIS-based firm- level dataset is then merged with a country-specific measure of macroeconomic conditions (ICRG index) and global factors (for example, a measure of the U.S. monetary policy stance, both which are discussed below. In sum, the dataset comprises about 800,000 firms for 28 EMs during 2004-2017, 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.

Journal Pre-proof 21 resulting in an unbalanced panel comprising nearly 5 million firm-year observations (Appendix Table 1). 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

of

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

ro

larger datasets that include non-listed firms, which is the reality we face when using ORBIS.

-p

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

re

Zingales 1995). Later, we also consider alternative definitions of leverage, including the ratios of

lP

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

Financial dependence index

na

consider variations of these ratios where cash is netted out. 25

ur

As measure of a sector’s intrinsic dependence on external finance, we use the financial

Jo

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: 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 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.

Journal Pre-proof 22 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 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

of

opposing theoretical predictions.27

ro

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

-p

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

re

acute free cash flow problems (Jensen 1986). In contrast, the pecking order theory argues that

lP

firms prefer internal finance over external funds, implying that profitability and leverage are negatively correlated.

na

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.

ur

Conversely, the pecking order theory is usually interpreted as implying an inverted relationship

Jo

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

26

We thank Hui Tong for sharing their data (see Tong and Wei 2011 for details).

27

Departures from the M odigliani-M iller (1958) irrelevance proposition regarding firm capital structure can be categorized into three broad alternative theories. The first is the trade-off theory in which firms issue debt until the benefits (tax incentives) and costs (bankruptcy) of debt are balanced (the “tax-bankruptcy trade-off”). The second is the pecking order theory (M yers and M ajluf 1984), which governs the order of financing sources: firms prefer to finance themselves first by using internal funds, then by issuing debt, and, as a last resort, by issuing equity. The third is the market timing theory, in which managers are more likely to tap markets with the most favorable conditions. 28

If capital expenditures and dividend payouts are fixed, then more profitable firms will become less levered over time.

Journal Pre-proof 23 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 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

of

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,

ro

inflation, government budgets, and the current account. The ICRG financial risk indicator is

-p

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

re

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

lP

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-

na

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

Jo

ur

depending on their location and period in question.

29

Furthermore, tangibility makes it difficult for shareholders to substitute high -risk assets for low-risk ones, and few debt-related agency problems also predict that leverage and tangibility are positively correlated. 30

However, as noted in Frank and Goyal (2009), if adverse selection is about assets in place, tangibility increase s adverse selection and results in higher debt (and we are back to a prediction that tangibility and leverage are positively related). This ambiguity under the pecking order theory reflects the fact that tangibility can be used as a proxy for different economic factors. Likewise, as note by Berger, Ofek, and Yermack (1997), for example, amid agency problems, the relationship between corporate governance and leverage is also ambiguous. 31

For further details: https://www.prsgroup.com/about-us/our-two-methodologies/icrg.

Journal Pre-proof Figure 1. Emerging Market Corporate Debt and Global Financial Conditions Emerging Markets Nonfinancial Corporate Debt (Stocks, in $US billions) 30

Emerging Markets Nonfinancial Corporate Debt to GDP

(Ratios, in percent) 185

China (LHS) Other Emerging Market Economies (LHS) China Nonfinancial Corporate Debt to GDP (RHS) Other Emerging Market Economy Nonfinancial Corporate Debt to GDP (RHS)

25

(Percent) 120

165

100 145

20

125

15

105

80

60

85

10

40 65

45

0

25

20

of

5

0

2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

ro

2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

U.S. Interest Rates

Global Shadow Rates (Percent)

-p

(Percent)

10

10 10-Year Treasury Note Yield

8

Federal Funds Rate

U.K. SSR Japan SSR

6

Euro Area SSR

re

6

Global Shadow Rate

8

2-Year Treasury Note Yield U.S. Shadow Rate

U.S. SSR

4

4

2

lP

2 0

-4 -6 -8 1997

2000

2002

2005

2007

2010

2012

2015

ur

1995

na

-2

2017

0 -2 -4 -6 -8 -10 1995

1997

2000

2002

2005

Jo

Sources: Haver Analytics; Krippner (2014); Reserve Bank of New Zealand; Authors’ calculations.

2007

2010

2012

2015

2017

Journal Pre-proof Table 1. Summary Statistics: Key Variables (2004-2017)

Key variables

Observations

Mean

Median

Standard deviation

First quartile

Third 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

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

e

r P

Sources: Orbis database; Reserve Bank of New Zealand; PRS Group; Authors’ calculations.

o r p

f o

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. “ T angibility” 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 T ong and Wei (2011). “ Macroeconomic conditions” are measured by the ICRG economic and financial index. T he “ inverted U.S. shadow rate” is estimated from the term-structure model based on Krippner (2014). “ SIC2 level” refers to the 2-digit (major group) Standard Industrial Classification code.

l a

Jo

n r u

Journal Pre-proof Table 2. Baseline: EM Corporate Leverage and Global Financial Conditions

Macroeconomic conditions Inverted shadow rate (ISR)

(4)

(5)

(6)

-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)

ISR * SME dummy

Observations R-squared (within)

YES

YES

na

Source: Authors’ calculations.

2,953,719 0.009

lP

Fixed effects Firm Time Country-time Sector-time

2,953,719 0.009

of

Tangibility

(3)

0.0334*** (0.00933)

0.0302*** (0.00791)

0.0282*** (0.00912)

0.0253*** (0.00813)

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

-p

Profitability

(2)

re

Sales

(1)

ro

Leverage

YES

Jo

ur

Note: ***, **, and * denote statistical significance at the 1, 5, and 10 percent levels, respectively. T he 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) m illion, €20 ($26) million, and 150, respectively. T he “ 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.

Journal Pre-proof Table 3. Robustness: Alternative Financial Constraint Measures

Profitability Tangibility Macroeconomic conditions ISR * Financial dependence

(2)

(3)

(4)

-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)

2,403,879 0.014

2,953,719 0.010

2,954,202 0.014

YES

YES YES

YES

0.0364*** (0.0119)

-p

ISR * Alternative SME dummy

of

Sales

(1)

ro

Leverage

2,403,456 0.010

re

Observations R-squared (within)

lP

Fixed effects Firm Time Country-time Source: Authors’ calculations.

na

YES YES

YES

YES

Jo

ur

Note: ***, **, and * denote statistical significance at the 1, 5, and 10 percent levels, respectively. T he 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 T ong 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.

Journal Pre-proof Table 4. Global Financial Conditions and Credit Misallocation

(1)

(2)

(3)

(4)

(5)

(6)

-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)

VARIABLES Sales Profitability Tangibility Inverted shadow rate (ISR) * Low Profitability dummy ISR * Low interest coverage ratio dummy

0.0718*** (0.0129) 0.0303*** (0.00779)

of

ISR * SME dummy

0.0719*** (0.0130)

0.0323** (0.0135) 0.203 (0.273)

0.292 (0.216)

0.268 (0.224)

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

ro

ISR * Financial dependence

0.298 (0.213)

2,954,202 0.014

2,954,202 0.014

re

Observations R-squared (within)

-p

ISR * Growth opportunity

Fixed effects Firm Country-time

YES YES

lP

YES YES

na

Source: Authors’ calculations.

Jo

ur

Note: ***, **, and * denote statistical significance at the 1, 5, and 10 percent levels, respectively. T he 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 T ong and Wei (2011). Growth opportunities are measured using sector-specific median sales growth similar to Fisman and Love (2007). T he “ low profitability” and “ low interest rate coverage ratio” dummies take a value of unity for firms in the lower quartiles of these respective distributions. T he “ 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.

Journal Pre-proof Table 5. Robustness: Alternative Leverage Ratios

Leverage

Sales Profitability Tangibility Inverted shadow rate (ISR) * SME dummy

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

TLTE

TATE

NTLTA

NTLTE

NTATE

TLTE

TATE

NTLTA

NTLTE

NTATE

-0.307*** (0.0182) 2.775*** (0.136) 0.631*** (0.0751) 0.00479*** (0.00160)

-0.281*** (0.0234) 2.366*** (0.150) 0.805*** (0.0663)

-0.286*** (0.0241) 2.416*** (0.153) 0.815*** (0.0677)

-0.0214*** (0.00121) 0.179*** (0.00457) 0.0353*** (0.00665)

-0.320*** (0.0199) 2.903*** (0.135) 0.626*** (0.0932)

-0.320*** (0.0199) 2.902*** (0.136) 0.626*** (0.0932)

0.00873*** (0.00273)

0.00947*** (0.00288)

0.000271* (0.000156)

0.0103*** (0.00317)

0.0103*** (0.00316)

-0.272*** (0.0197) 2.280*** (0.131) 0.797*** (0.0552) 0.00539*** (0.00129)

-0.277*** -0.0202*** -0.307*** (0.0203) (0.00114) (0.0182) 2.328*** 0.180*** 2.775*** (0.135) (0.00398) (0.136) 0.808*** 0.0349*** 0.631*** (0.0565) (0.00579) (0.0752) 0.00557*** 0.000413*** 0.00480*** (0.00135) (0.000126) (0.00159)

ISR * Financial dependence

Observations R-squared (within) Fixed effects Firm Country-time

Source: Authors’ calculations.

2,954,130 0.009

2,954,130 0.009

YES YES

YES YES

rn

u o

J

l a

2,277,776 0.016

YES YES

o r p

e

r P

f o

2,277,722 0.011

2,277,722 0.011

2,403,827 0.010

2,403,827 0.009

1,846,655 0.016

1,846,615 0.012

1,846,615 0.012

YES YES

YES YES

YES YES

YES YES

YES YES

YES YES

YES YES

Note: ***, **, and * denote statistical significance at the 1, 5, and 10 percent levels, respectively. T he dependent variable is now either the ratio of total liabilities to total equity (T LT E) or ratio of total assets to total equity (T AT E), or their net versions (NT LTE, NT ATE, as well as the ratio of net total liabilities to total assets, or NT LT A, 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 T ong and Wei (2011). T he “ 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.

Journal Pre-proof Table 6. Robustness: Alternative Measures of Global Financial Conditions

Leverage

Sales Profitability Tangibility Macroeconomic conditions Inverted Federal funds rate (IFFR)

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

-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)

IFFR * SME dummy

0.0895*** (0.0211)

IFFR * Financial dependence

0.0947** (0.0375)

Inverted 10-year bond yield (I10YR)

l a

I10YR * Financial dependence

GK * SME dummy

Fixed effects Firm Country-time

e

r P

0.132*** (0.0146) 0.299*** (0.0540)

n r u

Gertler-Karadi monetary policy shocks (GK)

Observations R-squared (within)

o r p

0.155*** (0.0364)

I10YR * SME dummy

GK * Financial dependence

f o

Jo

2.696*** (0.544) 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 percent levels, respectively. T he 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 T ong and Wei (2011). “ IFFR” and “ I10YR” denote the inverted Federal funds rate and the 10-year U.S. T reasury 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 Gertler and Karadi (2015) shocks. Standard errors are clustered by sector. Fixed effect terms are not reported.

Journal Pre-proof Table 7. Global Financial Conditions and Monetary Policy Synchronization

Profitability Tangibility Macroeconomic conditions Inverted shadow rate (ISR) ISR * SME dummy

(2)

(3)

(4)

-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)

-1.851*** (0.0932) 9.516*** (0.475) 6.940*** (0.392) 0.145*** (0.0171) 0.0250** (0.0101)

-1.820*** (0.0946) 9.645*** (0.486) 7.022*** (0.402) 0.0818*** (0.0137)

0.0359*** (0.0132) 0.0424*** (0.0132)

0.0312*** (0.00925)

ISR * Policy synchronization

0.0479*** (0.0161)

0.0351* (0.0183)

0.0342** (0.0136) 0.0475*** (0.0150)

Observations R-squared (within)

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

lP

re

-p

ISR * Financial dependence

of

Sales

(1)

ro

Leverage

Source: Authors’ calculations.

ur

na

Fixed effects Firm Time

YES

Jo

Note: ***, **, and * denote statistical significance at the 1, 5, and 10 percent levels, respectively. T he 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 T ong and Wei (2011). T he “ 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.

Journal Pre-proof Table 8. Robustness: Monetary Policy Synchronization

(1)

(2)

Leverage

(3)

(4)

(5)

(6)

Policy Synchronization

Profitability Tangibility Inverted domestic interest rate (IDR)

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)

1,170,362 0.010

1,444,022 0.018

946,475 0.010

YES YES

YES YES

YES YES

IDR * SME

1,767,112 0.011

1,170,362 0.006

YES

YES

YES YES

re

Fixed effects Firm Country-time

1,767,112 0.017

-p

Observations R-squared (within)

ro

IDR * RZ

of

Sales

High

lP

Source: Authors’ calculations.

Jo

ur

na

Note: ***, **, and * denote statistical significance at the 1, 5, and 10 percent levels, respectively. T he 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 T ong 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.

Journal Pre-proof Appendix Table 1. Country and Firm Coverage Panel A.

Panel B. Country Observations Share (%)

Panel C.

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

48

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

760,038

100.0

612 5,712 18,901 295 249,033 30,144 19,750 24,998 10,263 296 2,275 116,081 4,320 2,152 8,789 489 423 8,413 39,797 30,552 132,246 8,674 132 62 42 15,821 29,718

Size Category Observations Share (%) Very large company Large company Medium sized company Small company

Total

4.3 25.8 54.8 15.2

32,493 196,137 416,184 115,224

760,038

BvD Major Sector Observations Share (%)

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

ro

-p

re

Jo

r u

a n

P l

f o Total

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

760,038

100.0

80,757 68,368 10,756 42,883 14,808 11,087 117,590 52,490 71,502 2,983 44,099 11,357 41,909 25,449 146,198

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.

Journal Pre-proof REFERENCES Berger, Allen N., and Gregory F. Udell. 2004. "The Institutional Memory Hypothesis and the Procyclicality of Bank Lending Behavior." Journal of Financial Intermediation 13 (4): 458-495. Baker, Malcolm, and Jeffrey Wurgler. 2002. "Market Timing and Capital Structure." Journal of Finance 57 (1): 1-32.

of

Baruník, Jozef, and Evzen Kočenda. 2019. “Total, Asymmetric and Frequency Connectedness Between Oil and Forex Markets.” The Energy Journal 40 (SI2): 15774.

ro

Baumöhl, Eduard, Ichiro Iwasaki, and Evzen Kočenda. 2019. “Institutions and Determinants of Firm Survival in European Emerging Markets.” Journal of Corporate Finance 58 (October): 431-53.

re

-p

Beck, Torsten, Asli Demirgüç-Kunt, Luc Laeven, and Vadim Maksimovic. 2006. “The Determinants of Financing Obstacles.” Journal of International Money and Finance 25 (6): 932–52

lP

Bekaert, Geert, Campbell R. Harvey, Christian T. Lundblad, and Stephan Siegel. 2014. “Political Risk Spreads.” Journal of International Business Studies 45 (4): 471–493.

na

Bekaert, Geert, Marie Hoerova, and Marco Lo Duca. 2013. “Risk, Uncertainty and Monetary Policy.” Journal of Monetary Economics 60 (7): 771–788.

ur

Berger, Philip G., Eli Ofek, and David L. Yermack. 1997. “Managerial Entrenchment and Capital Structure Decisions.” Journal of Finance 52 (4): 1411–1438.

Jo

Bernanke, Ben and Mark Gertler. 1989. Agency costs, net worth and business fluctuations. In Business Cycle Theory. Edward Elgar Publishing Ltd. Bernanke, Ben, Mark Gertler, and Simon Gilchrist. 1996. “The Financial Accelerator and the Flight to Quality.” Review of Economics and Statistics 78 (1): 1–15. Bräuning, Falk, and Victoria Ivashina. 2019. “US Monetary Policy and Emerging Markets Credit Cycles.” Journal of Monetary Economics, forthcoming. Bruno, Valentina, and Hyun Song Shin. 2015. “Capital Flows and the Risk-Taking Channel of Monetary Policy.” Journal of Monetary Economics 71 (C): 119–32. Caballero, Julian, Andres Fernandez, and Jongho Park. 2019. “On Corporate Borrowing, Credit Spreads and Economic Activity in Emerging Economies: An Empirical Investigation.” Journal of International Economics 118 (May): 160-178.

Journal Pre-proof Calvo, Guillermo A., Leonardo Leiderman, and Carmen M. Reinhart. 1993. “Capital Inflows and Real Exchange Rate Appreciation in Latin America: The Role of External Factors.” IMF Staff Papers 40 (1) 108–151. ———. 1996. "Inflows of Capital to Developing Countries in the 1990s." Journal of Economic Perspectives 10 (2): 123–139. Cardarelli, Roberto, Selim Elekdag, and Subir Lall. 2011. "Financial Stress and Economic Contractions." Journal of Financial Stability 7 (2): 78–97.

of

Cerutti, Eugenio, Stijn Claessens, and Damien Puy. 2019. “Push Factors and Capital Flows to Emerging Markets: Why Knowing Your Lender Matters More Than Fundamentals.” Journal of International Economics 119 (July): 133-149.

ro

Cerutti, Eugenio, Stijn Claessens, and Lev Ratnovski. 2017. “Global Liquidity and Drivers of Cross-border Bank Flows.” Economic Policy 32 (89): 81–125.

re

-p

Cihak, Martin, Asli Demirgüç-Kunt, Erik Feyen, and Ross Levine. 2012. “Benchmarking Financial Systems Around the World.” Policy Research Working Paper 6175, World Bank, Washington, DC.

lP

Cook, Douglas O., and Tian Tang. 2010. "Macroeconomic Conditions and Capital Structure Adjustment Speed." Journal of Corporate Finance 16 (1): 73-87.

na

Dell'Ariccia, Giovanni, and Robert Marquez. 2006. "Lending Booms and Lending Standards." Journal of Finance 61 (5): 2511-2546.

ur

DeAngelo, Harry, and Richard Roll. 2015. "How Stable Are Corporate Capital Structures?" Journal of Finance 70 (1): 373–418.

Jo

Dedola, Luca, Giulia Rivolta, and Livio Stracca. 2015. “If the Fed Sneezes, Who Gets a Cold?” Mimeo. de la Torre, Augusto, Maria Soledad Martinez Peria, and Sergio Schmukler. 2008. “Bank Involvement with SMEs: Beyond Relationship Lending.” Policy Research Working Paper 4649, World Bank, Washington, DC. Donaldson, Gordon. 2000. "Corporate Debt Capacity: A Study of Corporate Debt Policy and the Determination of Corporate Debt Capacity." Beard Books. Elekdag, Selim, and Ivan Tchakarov. 2007. "Balance Sheets, Exchange Rate Policy, and Welfare." Journal of Economic Dynamics and Control 31 (12): 3986-4015. European Commission. 2018. Annual Report on European SMEs 2017/2018. Brussels: European Commission.

Journal Pre-proof Fernández, Andrés, and Adam Gulan. 2015. "Interest Rates, Leverage, and Business Cycles in Emerging Economies: The Role of Financial Frictions." American Economic Journal: Macroeconomics 7 (3): 153–188. Feyen, Erik H. B., Swati R. Ghosh, Katie Kibuuka, and Subika Farazi. 2015. “Global Liquidity and External Bond Issuance in Emerging Markets and Developing Economies.” Policy Research Working Paper 7363, World Bank, Washington, DC. Fisman, Raymond, and Inessa Love. 2007. "Financial Dependence and Growth Revisited." Journal of the European Economic Association 5 (2-3): 470-479.

ro

of

Fons-Rosen, Christian, Sebnem Kalemli-Ozcan, Bent E. Sørensen, Carolina VillegasSanchez, and Vadym Volosovych. 2013. “Quantifying Productivity Gains from Foreign Investment.” NBER Working Paper 18920, National Bureau of Economic Research, Cambridge, MA.

-p

Frank, Murray Z., and Vidhan K. Goyal. 2003. “Testing the Pecking Order Theory of Capital Structure.” Journal of Financial Economics 67 (2): 217–48.

lP

re

———. 2009. "Capital Structure Decisions: Which Factors are Reliably Important?" Financial Management 38 (1): 1-37.

na

Fratzscher, Marcel, Marco Lo Duca, and Roland Straub. 2013. “On the International Spillovers of U.S. Quantitative Easing.” ECB Working Paper 1557, European Central Bank, Frankfurt.

ur

Gertler, Mark, and Simon Gilchrist. 1993. “The Role of Credit Market Imperfections in the Monetary Transmission Mechanism: Arguments and Evidence.” Scandinavian Journal of Economics 95 (1): 43–64.

Jo

———. 1994. “Monetary Policy, Business Cycles, and the Behavior of Small Manufacturing Firms.” Quarterly Journal of Economics 109 (2): 309–40. Gertler, Mark, Simon Gilchrist, and Fabio M. Natalucci. 2007. “External Constraints on Monetary Policy and the Financial Accelerator.” Journal of Money, Credit and Banking 39 (2–3): 295–330. Gertler, Mark, and Peter Karadi. 2015. "Monetary Policy Surprises, Credit Costs, and Economic Activity." American Economic Journal: Macroeconomics 7(1): 44–76. Ghosh, Atish R., Mahvash S. Qureshi, Jun Il Kim, and Juan Zalduendo. 2014. "Surges." Journal of International Economics 92 (2): 266–285.

Journal Pre-proof Gopinath, Gita, Sebnem Kalemli-Ozcan, Loukas Karabarbounis, and Carolina VillegasSanchez. 2015. “Capital Allocation and Productivity in South Europe.” NBER Working Paper 21453, National Bureau of Economic Research, Cambridge, MA. Gozzi, Juan Carlos, Ross Levine, Maria Soledad Martinez Peria, and Sergio L. Schmukler. 2015."How Firms Use Corporate Bond Markets under Financial Globalization." Journal of Banking & Finance 58 (September): 532–551. Graham, John R., Mark T. Leary, and Michael R. Roberts. 2015. "A Century of Capital Structure: The Leveraging of Corporate America." Journal of Financial Economics 118 (3): 658–683.

ro

of

Graham, John R., and Campbell R. Harvey. 2001. “The Theory and Practice of Corporate Finance: Evidence from the Field.” Journal of Financial Economics 60 (2-3): 187243.

-p

Greenwood, Robin, and Samuel G. Hanson. 2013. "Issuer Quality and Corporate Bond Returns." Review of Financial Studies 26 (6): 1483-1525.

lP

re

Hovakimian, Armen, Tim Opler, and Sheridan Titman. 2001. "The Debt-Equity Choice." Journal of Financial and Quantitative Analysis 36 (1): 1-24.

na

Iacoviello, Matteo. 2005. "House Prices, Borrowing Constraints, and Monetary Policy in the Business Cycle." American Economic Review 95 (3): 739–764.

ur

IMF (International Monetary Fund). 2015. Global Financial Stability Report. April. Washington, DC: International Monetary Fund.

Jo

———. 2018. Global Financial Stability Report. April. Washington, DC: International Monetary Fund. Jensen, Michael C. 1986. "Agency Costs of Free Cash Flow, Corporate Finance, and Takeovers." American Economic Review 76 (2): 323-329. Kalemli-Ozcan, Sebnem, Bent Sorensen, and Sevcan Yesiltas. 2012. “Leverage Across Firms, Banks, and Countries.” Journal of International Economics 88 (2): 284–98. Kalemli-Ozcan, Sebnem, Bent Sorensen, Carolina Villegas-Sanchez, Vadym Volosovych, and Sevcan Yesiltas. 2015. “How to Construct Nationally Representative Firm Level Data from the ORBIS Global Database.” NBER Working Paper 21558, National Bureau of Economic Research, Cambridge, MA. Kalemli-Ozcan, Sebnem, Luc Laeven, and David Moreno. 2018. “Debt Overhang, Rollover Risk, and Corporate Investment: Evidence from the European Crisis.” NBER Working Paper 24555, National Bureau of Economic Research, Cambridge, MA.

Journal Pre-proof Kersten, Renate, Job Harms, Kellie Liket, and Karen Maas. 2017. “Small Firms, Large Impact? A Systematic Review of the SME Finance Literature.” World Development: 97 (September): 330–48. Kiyotaki, Nobuhiro, and John Moore. 1997. “Credit Cycles.” Journal of Political Economy 105 (2): 211–48. Krippner, Leo. 2014. “Documentation for United States Measures of Monetary Policy.” Reserve Bank of New Zealand and Centre for Applied Macroeconomic Analysis. http://www.rbnz.govt.nz/research_and_publications/research_programme/additional_ research/5892888.pdf.

ro

of

Korajczyk, Robert A., and Amnon Levy. 2003. "Capital Structure Choice: Macroeconomic Conditions and Financial Constraints." Journal of Financial Economics 68 (1): 75109.

-p

Laeven, Luc, and Hui Tong. 2012. “US Monetary Shocks and Global Stock Prices.” Journal of Financial Intermediation 21 (3): 530–547.

lP

re

La Porta, Rafael, Florencio Lopez‐ de‐ Silanes, and Andrei Shleifer. 1999. "Corporate Ownership Around the World." Journal of Finance 54 (2): 471-517.

na

Lang, William W., and Leonard I. Nakamura. 1995. "‘Flight to Quality in Banking and Economic Activity." Journal of Monetary Economics 36 (1): 145-164.

ur

Mendoza, Enrique G., and Marco E. Terrones. 2008. “An Anatomy of Credit Booms: Evidence from Macro Aggregates and Micro Data.” NBER Working Paper 14049, National Bureau of Economic Research, Cambridge, MA.

Jo

Miranda-Agrippino, Silvia, and Hélène Rey. 2015. “World Asset Markets and the Global Financial Cycle.” NBER Working Paper 21722, National Bureau of Economic Research, Cambridge, MA Morck, Randall, Daniel Wolfenzon, and Bernard Yeung. 2005 "Corporate Governance, Economic Entrenchment, and Growth." Journal of Economic Literature 43 (3): 655720. Modigliani, Franco, and Merton Miller. 1958. “The Cost of Capital, Corporation Finance and the Theory of Investment.” American Economic Review 48 (3): 261–97. Myers, Stewart C. 1984. “The Capital Structure Puzzle.” Journal of Finance 39 (3): 574-592. Myers, Stewart C., and Nicholas S. Majluf. 1984. “Corporate Financing and Investment Decisions when Firms Have Information that Investors Do Not Have.” Journal of Financial Economics 13 (2): 187–221.

Journal Pre-proof Rajan, Raghuram G. 2006. “Has Finance Made the World Riskier?” European Financial Management 12 (4): 499–533. Rajan, Raghuram G., and Luigi Zingales. 1995. “What Do We Know about Capital Structure? Some Evidence from International Data.” Journal of Finance 50 (5): 1421–60. ———. 1998. "Financial Dependence and Growth." American Economic Review 88 (3): 559–86.

of

Rey, Hélène. 2015. “Dilemma not Trilemma: The Global Financial Cycle and Monetary Policy Independence.” NBER Working Paper 21162, National Bureau of Economic Research, Cambridge, MA.

ro

Schularick, Moritz, and Alan M. Taylor. 2012. “Credit Booms Gone Bust: Monetary Policy, Leverage Cycles, and Financial Crises.” American Economic Review 102 (2): 1029– 61.

re

-p

Tong, Hui, and Shang-Jin Wei. 2011. "The Composition Matters: Capital Inflows and Liquidity Crunch During a Global Economic Crisis." Review of Financial Studies 24 (6): 2023–2052.

lP

Highlights:  U.S. financial conditions are found reliably associated with faster corporate leverage growth in emerging markets (EMs) A rich panel data from more than 800,000 firms (including small- and medium-sized enterprises, SMEs) across 28 EMs is considered



A loosening of monetary conditions of 1 percentage point corresponds to an increase in leverage ratio of up to 0.1 percentage point per year



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



We find evidence that looser global financial conditions are also associated with greater credit misallocation

Jo

ur

na

