Why do Indian Firms Borrow in Foreign Currency?

Abstract

We investigate why firms in emerging economies such as India borrow in foreign currency. The results of a dynamic panel regression approach suggest that both firm-specific factors and macroeconomic factors are significant in explaining the corporate sector dollarization. Export revenue and tangible assets are primary drivers of the external commercial borrowings (ECBs) of non-financial firms, whereas the ECBs of financial firms are sensitive to interest rates in global markets. The policy measures to relax restrictions on firm borrowing in foreign currency facilitate the denomination of corporate debt in foreign currency, but such exposure was adversely affected by the global financial crisis. The findings of the study suggest the vital need to develop the domestic bond market to reduce firms’ dependency on external finance. The results also call for competitive interest rates in the domestic fixed income segment through monetary policy intervention.

JEL Classification: G300, G320, F310

Keywords

Foreign Currency Borrowings Dollar Debt Non-financial Firms Interest Rate Exchange Rate Depreciation

1. Introduction

Emerging and developing economies (EDEs) pursuing financial liberalization with an aim to achieve faster economic growth. As a part of this liberalization process, EDEs have been allowing firms to borrow from international markets to meet both short-term and long-term financial needs. Over the decades, EDEs have built up high stocks of dollar liabilities (Eichengreen & Hausmann, 2005). Such a staggering rise of corporate borrowings and involvement of firms in foreign currency lending have raised concerns in policymaking circles. In this light, we probe factors influencing borrowings of the firms in foreign currency.

The issue is especially important for EDEs such as India, which are witnessing a surge in dollar loans and real exchange rate depreciation. Recently, Shin and Zhao (2013) raised concerns about the increased exposure of corporates in India and China. The growth of corporate borrowings makes these economies ideal applicants for investigation, and therefore calls for a fresh study. To the best of our knowledge, no study has so far explored Indian firms’ willingness for external commercial borrowings (ECBs)¹. The available literature has mostly focused on Latin America and East Asia in the aftermath of the currency crises in these regions, and these studies are more of a post-mortem analysis of the crisis. Moreover, India’s ECB framework is unique compared to its emerging market economy (EME) peers, and therefore a country-specific analysis with firm-level data is essential to understand the heterogeneity. A study pertaining to India is also important because of increased borrowings in foreign currency by Indian financial and non-financial companies and a rise in the percentage of ECBs in total external debt to 37.33. A study of the ECBs framework also offers policy lessons for other EDEs.

This study contributes to the extant literature in several ways. First, it explains why Indian firms borrow in foreign currency. The question assumes significance against the backdrop of recent policy measures to liberalize the norms for ECBs further, and the recent crisis in Argentina and Turkey due to foreign corporate debt. Recently, Vonnák (2018) using a Hungarian micro-level dataset found that creditworthy firms borrowing in foreign currency worsened their loan performances during the time of crisis. Second, a study of the determinants of ECBs in India has important implications for sustainable economic growth and financial stability, and policy lessons for other EDEs in Asia and Europe. Although the introduction of rupee-denominated bonds (Masala Bonds) is a vital step in reducing exchange rate risks, lower-rated companies still find it difficult to raise funds from this source (Roy, 2016). Firms in EDEs depend on external sources of finance to generate growth-enhancing investments. Excessive controls or restrictions on ECBs in these economies hurt growth and firms’ prospects of potential investments. External finance is positively associated with firms’ core innovations, including new product lines, opening of new plants, establishment of foreign joint ventures and new licensing agreements (Ayyagari, Demirgüç-Kunt, & Maksimovic, 2011). At the same time, growing ECBs threaten financial stability since they are susceptible to global shocks.

Third, a vast literature on corporate finance in India has focused on the role of the sources of finance to corporates. However, studies seldom analyse the currency composition of corporate debt, despite the growing significance of ECBs as a source of finance and the potential repercussions of such debt on the Indian economy in the event of shocks. Therefore, this study is a valuable addition to corporate finance literature. Fourth, most of the studies are limited to a couple of firm-specific factors (e.g., Gelos, 2003; Kedia & Mazumdar, 2003) and ignore the role of macroeconomic factors. In this study, we include both macroeconomic and firm-level factors, and attempt to fill the gap in the literature. This comprehensive analytical structure which is in line with third-generation crisis models, offers vital insights into corporate debt composition. Additionally, we present a novel disaggregated analysis to provide fresh evidence on financing strategies. Finally, the outcome of this empirical study offers inputs into macroeconomic policy design and prudential measures for domestic markets. The findings also help in understanding corporate strategies related to dollar loans.

The rest of the article is divided into the following sections. In Section 2, we provide a brief review of related literature on foreign currency borrowings (FCBs) and the policy framework. Section 3 focuses on the data and methodology. In Section 4, we discuss the empirical results on the determinants of ECBs and we conclude the article in the last section.

2. Related Literature

2.1 Theoretical Studies

A firm’s choice of currency to borrow depends on the expected costs of borrowing. The choice of currency depends on several factors. The extant literature discusses various determinants of FCBs that can be classified into supply-side and demand-side incentives. The demand-side incentives can be further bifurcated into firm-specific factors and macroeconomic factors. Other observers classify factors based on the motives of the firms raising FCBs.

2.1.1 Firm-Specific Factors

Various firm-specific factors influence firms’ decisions to borrow in foreign currency. The size of the firm is a significant factor determining the currency composition of corporate debt. Theory suggests that larger the firm, more significant is its ability to hold debt in foreign currency. Therefore, the expected sign of firm size and FCBs is positive. Aguiar (2000), Gelos (2003) and Kesriyeli, Özmen, and Yiğit (2005) find a statistically significant and positive relationship between firm size and dollar debt. Benavente, Johnson, and Morande, (2003) also show the tendency of large companies to hold more dollar debt, whereas small firms have no access to bank loans or the ability to issue corporate bonds. The findings of their study suggest the utility of size as collateral to seek credit in foreign currency. According to Kirch and Terra (2012), large firms enjoy better collateral and consequent lower bankruptcy; these firms face lower transaction and monitoring costs and a lower level of the asymmetric information. These factors, along with better credit quality, enable larger firms to include foreign currency loans in their debt structure. Accordingly, different proxies for firm’s size, such as total assets (Gelos, 2003), total employees (Kesriyeli et al., 2005) and total sales (Aguiar, 2000) are employed in empirical research.

Firms with export revenue hold a relatively higher proportion of dollar debt because export sales act as a collateral or natural hedge against such debt. Gelos (2003), Allayannis, Brown, and Klapper (2003), Aguiar (2005), Bleakley and Cowan (2008) and Brown, Ongena, S., and Yeşin (2011) find that firms match their liabilities to their revenues in foreign currencies due to the sensitivity of exports to currency depreciation. Firms possibly face a trade-off between domestic stability provided by revenues in foreign currency and relatively cheaper access to dollar debt. Studies (Benavente et al., 2003; Carranza, Cayo, & Galdón-Sánchez, 2003; Echeverry, Leopoldo, Roberto, & Camila, 2003; Kesriyeli et al., 2005) document a positive and significant relationship between FCBs and international tradability for Peru, Columbia, Mexico, Chile and Turkey, respectively. In contrast, Bonomo, Martins, and Pinto (2003) and Galiani, Yeyati, and Schargrodsky (2003) find no such evidence for Brazil and Argentina, respectively.

Ex-post profits also influence the decision of firms to denominate debt in foreign currency. The proposition that higher ex-post profit induces firms to prefer FCBs is in line with the signalling theory² (Aguiar, 2000; Chamon, 2001; Jeanne, 1999), and static trade-off models.³ The contrary proposition, based on the pecking-order hypothesis, is that less-profitable firms resort to FCBs.⁴ Allayannis et al. (2003) empirically corroborates the positive relationship between ex-post profit and the decision of firms to incur FCBs. In contrast, Mora, Neaime, and Aintablian (2013) find a negative and statistically significant association between ex-post profits and dollar debt, and thus reject the signalling hypothesis. With a survey of 588 representative Lebanese firms from 2005 to 2006, they apply Probit models to understand the preferences and perceptions of firms regarding FCBs. Similarly, Gelos (2003) finds that firms with lower ex-post proﬁts prefer more foreign currency loans.

Asset tangibility, the ratio of net tangible fixed assets to total assets, is another applicant among the determinants discussed in the literature. A higher degree of asset tangibility reduces the problem of asymmetric information and facilitates firm access to the debt market. Further, tangible assets accompanied by lower bankruptcy and agency costs act as collateral against debt (Chauhan, 2017). The theory postulates that greater asset tangibility raises the share of domestic currency debt, as foreign creditors possibly find it difficult to liquidate tangible assets to recover a non-paid foreign currency debt. Nevertheless, a highly dollarized financial system in emerging markets blurs the distinction between domestic and foreign creditors. In such a dollarized system, tangible assets enable firms to raise debt, which provides a positive association between assets and debt. The impact of asset tangibility on debt composition, therefore, is ambiguous for a financially dollarized country (Kesriyeli et al., 2005). Kirch and Terra (2012) opine that firms with higher tangibility imply more considerable real assets and better collateral (lower bankruptcy) compared to firms with lower tangible assets.

The preference of a firm for FCBs also depends upon its leverage. Theoretically, higher domestic leverage ratios severely constrain the capacity of a firm’s borrowings. Hence, a positive (negative) coefficient of the leverage ratio is represented as a firm’s inability to borrow in domestic (foreign) currencies (Kesriyeli et al., 2005). The authors argue that an insignificant coefficient of the leverage ratio indicates that the debt level itself equivalently constrains borrowing conditions (in domestic and foreign currencies). Benavente et al. (2003) probe the determinants of the dollar composition of corporate debt in Chile and finds a positive relation between leverage and the maturity of dollar-denominated debt.

2.1.2 Macroeconomic Factors

Besides firm-specific factors, the pertinent literature shows the importance of macroeconomic determinants of FCBs. The costs and benefits of borrowing influence firms’ choice for dollar debt. Firms hold dollar debt primarily because of the failure of uncovered interest rate parity. Empirically, Allayannis et al. (2003) document a positive nexus between the interest rate differentials between domestic and foreign credit markets and FCBs in East Asia. The real interest rate differential (RIRD) reflects the relative price of FCBs—higher RIRDs induce greater borrowings in foreign currency. This proposition finds empirical support in Latin America (Barajas & Morales, 2003) Croatia and Central and Eastern European countries (Cuaresma, Fidrmuc, & Hake, 2014; Rosenberg & Tirpak, 2009). However, Cuaresma et al. (2014) document the significance of supply factors compared to demand factors such as RIRD in determining foreign currency loans in Europe. Singh (2007) suggest a positive relationship between interest rates and FCBs.

Exchange rate depreciation is another macroeconomic factor that influences firms’ preferences for FCBs. Currency depreciation inflates the domestic currency, raises the cost of FCBs, reduces the value of the collateral and ultimately increases the likelihood of default by the borrower. The relationship between exchange rate depreciation and FCBs is expected to be negative. Kesriyeli et al. (2005) document a negative and significant nexus between real exchange rate depreciation and corporate dollarization, as depreciation increases the cost of foreign currency debt in terms of domestic currency. They also show that such effects are stronger for firms that have lower export earnings.

The business confidence index (BCI) is a yardstick that assesses the degree of optimistic or pessimistic perceptions of entrepreneurs about the prospects of their enterprises. This index also measures opinions related to the current state of the economy. A higher value of the BCI index implies greater confidence, which eventually strengthens the trust of economic agents in the domestic economy. Kesriyeli et al. (2005) find a significant and negative coefficient of the BCI on a firm’s dollarization.

The review of the literature shows that studies on the determinants of FCBs differ from one another in terms of factors analysed and methodologies followed. The research on determinants is confined to East Asia and Latin America, which have experienced debt crises in the past. Interestingly, there is no study on factors inducing firms in East Asia to borrow in foreign currencies. A comprehensive and holistic approach to the issue is a major gap in the literature, and the present research fills this gap and contributes to the literature.

3. Data and Methodology

We collect firm-level and macroeconomic data from the Centre for the Monitoring of the Indian Economy (CMIE) Prowess, the Reserve Bank of India (RBI), Federal Reserve Bank of Saint Louis, OECD, World Bank and annual reports of the companies. The study spans the year 2004 to 2015. The study period is because of the issuance of masala bonds on September 29, 2015 (Business Today, 2018). The CMIE’s comprehensive database on Indian companies is the primary source of our firm-level data. As per the CMIE Prowess database, 4,160 firms borrowed in foreign currency in 2015. To maintain heterogeneity among the firms, we include firms from diverse sectors, such as manufacturing, mining, construction, electricity and services. We choose firms that regularly borrow in foreign currency, and our final data comprises 705 non-financial and 107 financial firms for which time series data on explanatory variables are available. We had to drop several firms because of either one time borrowing or lack of data on various balance-sheet variables. The analysis is carried out using STATA software.

To quantify the determinants of ECBs, we employ the Generalized Method of Moments (GMM) of the Arellano and Bond (1991) approach to estimate a dynamic panel regression. The dynamic panel regression includes the cross-sectional effects and a lagged dependent variable. When the lagged component of the dependent variable is included as an explanatory variable, the within estimator turns biased. In such cases, the GMM approach is preferred, as it alleviates the bias, given that an appropriate set of instrumental variables are selected. Hence, we estimate the following dynamic panel regression equation to quantify the determinants of ECBs:

E C B_{i t} = α_{i} + β_{1} (E C B_{i t - 1}) + β_{2} F_{i t} + β_{3} M_{i t} + μ_{t} + ν_{i} + φ_{i t}

(1)

Where, ECB_it is the dependent variable and represented as the ratio of ECB to total borrowings, and ECB_it_-1 represents lagged ECB. F_it denotes the matrix of firm-specific variables, namely return on assets (ROA), ratio of export earnings to net sales (EXPS), log of total assets (LNTA), ratio of net fixed assets to total assets (NFATA) and debt to total assets (DEBTTA). M_it is a vector of macroeconomic variables such as change in the real effective exchange rate (ΔREER), difference in India’s interest rate and the LIBOR (RIRD) and the BCI. The subscripts i represent the ith firm that borrows in foreign currency and t denotes the time component. The term µ_t is an unobserved time-specific effect, whereas v_i represents the unobserved firm-specific effect and φ_it is a zero mean random disturbance with variance σ_v². The selection of the explanatory variables for the present analysis is based on theoretical relevance, the feasibility of the variables in the model and data availability. Besides, we ensured the degrees of freedom and precision of the results.

4. Results and Discussions

The summary statistics in Table 1 indicate positive mean values for all the variables. The skewness of the variables such as the RIRD, ΔREER, RIRD and BCI is negative indicating a distribution that is flatter to the left than the normal distribution; rest of the variables are positively skewed. The kurtosis values of all the variables display a leptokurtic distribution, with a higher peak and a fatter tail than a normal distribution. The significant Jarque–Bera statistic in our model suggests that all the variables are non-normally distributed.

Table 1
Summary Statistics

Variables Mean S.D. Skewness Kurtosis Jarque–Bera

ECB 0.365 0.291 0.783 2.521 308.487
(0.000)

ROA 0.089 0.078 0.604 15.811 19029.52
(0.000)

EXPS 0.271 0.286 1.016 2.862 476.844
(0.000)

LNTA 6.875 1.758 0.214 3.080 21.765
(0.000)

NFATA 0.329 0.174 0.343 2.761 60.511
(0.000)

DEBTTA 0.368 0.171 0.097 2.495 33.614
(0.000)

ΔREER 0.014 0.061 –0.089 2.225 72.087
(0.000)

RIRD –0.304 2.631 –0.169 1.596 239.555
(0.000)

BCI 99.531 1.443 –0.621 2.730 185.625
(0.000)

Variables	Mean	S.D.	Skewness	Kurtosis	Jarque–Bera
ECB	0.365	0.291	0.783	2.521	308.487 (0.000)
ROA	0.089	0.078	0.604	15.811	19029.52 (0.000)
EXPS	0.271	0.286	1.016	2.862	476.844 (0.000)
LNTA	6.875	1.758	0.214	3.080	21.765 (0.000)
NFATA	0.329	0.174	0.343	2.761	60.511 (0.000)
DEBTTA	0.368	0.171	0.097	2.495	33.614 (0.000)
ΔREER	0.014	0.061	–0.089	2.225	72.087 (0.000)
RIRD	–0.304	2.631	–0.169	1.596	239.555 (0.000)
BCI	99.531	1.443	–0.621	2.730	185.625 (0.000)

Source: The authors.

Notes: ECB, External commercial borrowing to total borrowing; ROA, Return on assets; EXPS, Exports earnings to net sales; NFATA, Net fixed assets to total assets; LNTA, Log of total assets; DEBTTA, Debt to total assets; ΔREER, Change in real effective exchange rate; RIRD, Difference in India’s interest rate and LIBOR; BCI, Business Confidence Index. Values in the parenthesis are P values.

The correlation matrix shows insignificant and low correlations indicating no multicollinearity among the variables (Table 2). Our panel is unbalanced, therefore we employed panel unit root tests of the Fisher type (Choi, 2001), and the statistics presented in Table 3 confirms that all the variables are stationary.

The estimates of Equation (1) for the full sample period (2004–2015) (Table 4) suggest that both the firm-specific and macroeconomic variables are significant in explaining the Indian corporate sector’s dollar liabilities during the study period. In 2006, the RBI enhanced the cap on ECBs from US $500 million to US$ 750 million. To capture the effect, we carry out a sub-sample analysis from 2007 to 2015, denoting as the post-ECB liberalization period. We also divide the sample into the post-GFC period from 2008 to 2015, to measure the impact of the crisis on factors influencing firms to go in for ECBs. The estimates for both sub-samples are presented in Table 5.⁵

Table 2

Correlation Matrix

Variables	ECB	ROA	EXPS	LNTA	NFATA	DEBTTA	ΔREER	RIRD	BCI
ECB	1
ROA	–0.001	1
EXPS	0.106	0.117	1
LNTA	–0.134	0.029	–0.127	1
NFATA	–0.074	–0.157	–0.125	–0.122	1
DEBTTA	–0.317	–0.298	0.026	0.025	0.233	1
ΔREER	0.001	–0.007	–0.014	0.023	–0.011	–0.048	1
RIRD	0.026	–0.122	0.005	0.057	0.045	–0.117	0.225	1
BCI	0.015	0.076	–0.021	–0.048	–0.038	–0.006	0.568	–0.026	1

Source: The authors.

Note: Variables as defined in Table 1.

Table 3

Panel Unit Root Test Statistics

Variables	ADF		PP
Variables	No Trend	Trend	No Trend	Trend
ECB	2343.029*	1787.044*	2343.029*	1787.044*
ROA	2237.355*	1321.916*	2237.355*	1321.916*
EXPS	2147.285*	1428.836*	2147.285*	1428.836*
LNTA	2595.486*	1390.855*	2595.486*	1390.855*
NFATA	2065.202*	1582.690*	2065.202*	1582.690*
DEBTTA	1922.605*	1941.537*	1922.605*	1941.537*
ΔREER	7422.369*	2963.848*	7422.369*	2963.848*
RIRD	1066.804*	523.692	1066.804*	523.692
BCI	4269.159*	1558.602*	4269.159*	1558.602*

Source: The authors.

Notes: Variables as defined in Table 1. The values in the table are the inverse chi-squared (P) statistic, which requires the number of panels to be finite. *Denotes rejection of the null hypothesis suggesting at least one panel is stationary at 1% level of significance.

Table 4

Determinants of ECBs

Variables	Baseline Model
Variables	Coefficients	P values
C	0.133	(0.000)
ECB_i,t–1	0.776	(0.000)
ROA_i,t	–0.047	(0.384)
EXPS_i,t	0.033	(0.005)
LNTA_i,t	–0.003	(0.142)
NFATA_i,t	0.051	(0.007)
DEBTTA_i,t	–0.203	(0.000)
ΔREER_i,t	–0.074	(0.022)
RIRD_i,t	0.002	(0.027)
Autocorrelation
(1) Order	–6.625 (0.000)
(2) Order	–1.02 (0.308)
Sargan test
x ²	69.732 (0.291)

Source: The authors.

Notes: ECB_i,t–1= lagged value of the external commercial borrowing to total borrowing. Rest of the variables are the same as defined in Table 1.

We find that the coefficients of all the variables are consistent with the literature. The firm-specific variables, namely the ROA, EXPS, LNTA, NFATA and DEBTTA, are possibly endogenous to the dependent variable, ECB, in some instances. To address this, we estimated Equation (1) by employing GMM procedures. We assume that macroeconomic variables, such as the ΔREER, RIRD and BCI, are strictly exogenous to ECBs. The dynamic panel results show a positive and significant coefficient for the lagged ECB, implying that past FCBs necessitate current borrowings in foreign currency (Table 4). The current result also indicates that present corporate FCBs are incurred to service past debts and interest payments. The positive sign and significance of the coefficient of the lagged component of the ECB variable indicates that current borrowings in foreign currency are basically to finance the current and future projects or to expand or modernize production units and meet other requirements. The coefficient of the lagged ECB term is also significant for the post-ECB liberalization period and the post-GFC sub-sample. Notably, the value of the coefficient of ECBs after the relaxation of the cap on ECBs is greater for the post-GFC period, or the full sample period, indicating that the relaxation helped firms to repay their debt by borrowing further. This suggests a positive impact from the decision to raise the cap on borrowings. However, the global meltdown following the sub-prime crisis discouraged firms from borrowing in foreign currency, as is evident from Table 5.

Table 5

Determinants of ECBs

Variables	Panel A		Panel B
	Post ECBs Liberalization Period		Post-Global Financial Crisis (GFC)
	Coefficients	P values	Coefficients	P values
C	0.71	(0.000)	0.181	(0.001)
ECB_i,t–1	0.783	(0.000)	0.699	(0.000)
ROA_i,t	–0.084	(0.182)	–0.136	(0.117)
EXPS_i,t	0.035	(0.009)	0.001	(0.009)
LNTA_i,t	–0.004	(0.068)	–0.003	(0.258)
NFATA_i,t	0.056	(0.007)	0.059	(0.031)
DEBTTA_i,t	–0.241	(0.000)	–0.257	(0.000)
ΔREER_i,t	–0.025	(0.617)	–0.002	(0.976)
RIRD_i,t	0.001	(0.437)	0.003	(0.071)
BCI_i,t	–0.005	(0.017)
Autocorrelation
(1) Order	–6.355 (0.000)		–4.968 (0.000)
(2) Order	–1.277 (0.202)		–0.771 (0.441)
Sargan Test
x ²	46.625 (0.326)		20.795 (0.348)

Source: The authors.

Notes: ECB_i,t–1= lagged value of the external commercial borrowing to total borrowing. Rest of the variables are the same as defined in Table 1.

The ROA is an important measure of accounting performance. According to the signalling and static trade-off models, the coefficient of the ROA should be positive, and its value should be relatively higher for firms that borrow in foreign currency. However, we find a negative and insignificant coefficient of ROA consistent with the pecking order hypothesis. The negative relation between ECBs and the ROA is possible because of the persistence of asymmetric information between firms and their investors, which constrains a profitable firm from internal finance. Rather less profitable firms resort to external finance.

Export earnings are represented as collateral against foreign currency loans. We find a positive and significant sign for the variable EXPS for non-financial firms (Table 4). The result implies that firms that export can match their liabilities against their export revenues and are protected from currency depreciation. The coefficient of EXPS is low during the post-global financial crisis period indicating that the sluggish growth in exports discouraged firms from borrowing in foreign currency. Our results on exports are consistent with the studies of Benavente et al. (2003) and Kesriyeli et al. (2005) for Chile and Turkey, respectively, but contradicts the findings on Brazil (Bonomo et al., 2003) and Argentina (Galiani et al., 2003).

We use the log of total assets as an indicator of the size of the firm (LNTA). Large companies have higher reputation and hence possess easy access to international capital markets, compared to that of the smaller firms (Keloharju & Niskanen, 2001; Nandy, 2010). Therefore, larger firms are capable of holding higher FCBs relative to smaller firms. However, Titman and Wessels (1988) find a negative correlation between size and debt structure. In a similar vein, we document a negative but insignificant relationship between the variable LNTA and ECBs (Tables 4 and 5). However, the coefficient is negative and significant for non-financial firms after the relaxation of the caps on ECBs in 2006 (Table 5). The statistics imply that borrowing in foreign currency does not depend on a firm’s size. Sahoo (2015) argues that the current ECB framework is vulnerable to problems of political economy and lobbying by interested parties. Potential borrowers (irrespective of their firm’s size) lobby for inclusion under the automatic or approval route. Therefore, the ECB framework favours certain types of borrowers (sectors) and discriminates against others. Further, the information asymmetry between insiders and outsiders are lower for larger firms (Rajan & Zingales, 1995), which reduces the chances of undervaluation of the new equity issue, and thus large firms go for equity financing.

The relationship between asset tangibility—the ratio of net fixed assets to total assets (NFATA)—and FCBs is theoretically ambiguous. We obtain a positive and significant sign for the coefficient of NFATA. We find no change in the results including the sub-sample analysis (Table 5). The finding suggests that a more substantial degree of asset tangibility lessens the problem of asymmetric information and improves credit quality and hence enables firms to borrow in foreign currency (Kedia & Mozumdar, 2003). The tangible assets are suitable collateral for lending and thus reduce bankruptcy and agency costs. We use debt-to-total assets (DEBTTA) to gauge financial leverage, and the result suggests a negative relationship with ECBs. Nandy (2010) points out that a highly leveraged firm is riskier and possesses poor creditworthiness and, hence, unable to access the foreign currency debt markets. Therefore, the relationship between financial leverage and FCBs is expected to be negative. Firms with greater leverage are likely to have a higher probability of financial distress and usually operate with under-investment problems. Our finding of a negative inverse relation for all the models is consistent with the theory.

The macroeconomic environment in which firms operate influences the debt structure and currency composition. Hence, we examine the critical macroeconomic factors influencing ECBs. We use RBI’s change in the real effective exchange rate (ΔREER) to measure exchange rate changes. Real exchange rate depreciation increases the real cost of debt in foreign currency and thus discourages dollarization especially among non-export-oriented sectors. We find a statistically significant negative relationship between ECBs and ΔREER (Panel A), a finding consistent with Kesriyeli et al. (2005) for Turkey.

We define the interest rate differential (RIRD) as the difference between India’s interest rate and the LIBOR—the more significant the difference, the higher are ECBs. The sign of the variable RIRD is positive and significant for the sample firms, and thus consistent with theoretical expectation. However, the coefficient of RIRD becomes positive but insignificant in the post-2006 (liberalization) period. Our results indicate that the relatively higher interest rates in India encourage firms to increasingly borrow in foreign currency. We include the BCI to evaluate the positive and negative responses of business managers in the macroeconomic environment. Such an assessment is made based on the current position and future expectations related to production, orders and stocks. We find a negative and significant coefficient of the BCI on firms’ ECBs (Tables 4 and 5). Our results suggest that firms’ reliance on ECBs decline when business managers’ expectations about domestic economic activity improve and boosts their confidence in the local currency.

The significance of the AR (1) test statistic in all the cases indicate that the disturbance term in the first difference is serially correlated. The AR (2) detects no autocorrelation in level. Therefore, the models are free from serial correlation. The insignificant values of the Sargan test statistics further suggest that the instruments are exogenous and valid (Tables 4 and 5).

Additionally, we analyse the determinants of ECBs of financial firms (commercial banks, mutual funds, securities firms and insurance companies) which are highly leveraged and have a distinct capital structure in comparison to non-financial firms. Financial firms are prone to shocks which destabilize the financial system (Batten, Loncarski, & Szilagyi, 2013; Kollmann, 2013). Investors’ insurance schemes and liabilities strongly influence the capital structure of financial firms, and hence they are not comparable to the debt issued by non-financial firms. Even though financial firms are highly vulnerable to financial shocks, research in the corporate finance literature that decipher the reasons for financial firms’ borrowings from global credit market is scarce. Hence, our analysis assumes importance and extend the research on these lines.

Similar to our analysis on non-financial firms, we segregate the data on financial firms into subsamples for robustness. In our sample, financial firms constituted 48.06 per cent of total ECBs in 2003, which increased to 70.98 per cent in 2015. The sign and significance of the coefficient of all the independent variables for financial firms are identical to the estimates for the non-financial firms, except for the variable NFATA (Table 6). The baseline results as well as the sub-sample analysis show that the sign of the lagged term of ECB is positive and significant, indicating that increased borrowing was to repay past loans due to increased costs. However, the lagged term of the variable ECB turns insignificant during the post-GFC period (Table 7). The ROA is negative and significant for the full sample analysis, but the variable is insignificant when we carried out a sub-sample analysis.

Table 6

Determinants of ECBs

Variables	Financial Firms
Variables	Coefficients	P values
C	0.454	(0.000)
ECB_i,t–1	0.195	(0.000)
ROA_i,t	–0.225	(0.328)
NFATA_i,t	–0.165	(0.115)
LNTA_i,t	–0.019	(0.000)
RIRD_i,t	0.008	(0.000)
ΔREER_i,t	–0.051	(0.000)
Autocorrelation
(1) Order	–3.139 (0.003)
(2) Order	0.848 (0.397)
Sargan test
x ²	35.956 (0.992)

Source: The authors.

Notes: ECB_i,t–1= lagged value of the external commercial borrowing to total borrowing. Rest of the variables are same as defined in Table 1.

Table 7

Determinants of ECBs

Variables	Panel A		Panel B
	Financial Firms
	Post ECBs Liberalization Period		Post-Global Financial Crisis
	Coefficients	P values	Coefficients	P values
C	0.499	(0.000)	0.451	(0.001)
ECB_i,t–1	0.165	(0.000)	0.091	(0.254)
ROAΔ_i,t	–0.299	(0.063)	1.141	(0.170)
NFATA_i,t	–0.155	(0.160)	–0.057	(0.164)
LNTA_i,t	–0.022	(0.000)	–0.024	(0.076)
RIRD_i,t	0.007	(0.000)	0.004	(0.088)
ΔREER_i,t	–0.032	(0.000)
Autocorrelation
(1) Order	–3.152 (0.002)		–2.868 (0.004)
(2) Order	0.774 (0.439)		0.516 (0.606)
Sargan test
x ²	36.223 (0.758)		18.780 (0.471)

Source: The authors.

Notes: ECB_i,t–1= lagged value of the external commercial borrowing to total borrowing. Rest of the variables are same as defined in Table 1.

We employ the ratio of net fixed assets-to-total assets in measuring assets tangibility for financial firms. W document that commercial banks in developed countries have an average of 26.6 per cent of their assets as collateral, lower than that of developing countries (43.5 per cent). We find a negative and insignificant relationship between ECBs and NFATA. The negative and significant values of the LNTA indicates that the larger the firm, the lower is their preference for ECBs. These findings are in line with the pecking order theory and contradict the static trade-off theory and agency cost theory. This inference implies the preference of larger firms for internal financing rather than external debt. Because ECBs are long-term debt, and a negative relationship between size and ECBs indicates that smaller banks have limited access to the equity capital market, this makes them dependent on long-term debt for their financial requirements. In other words, larger size financial firms can issue equity at a lower cost and with short notice. Our results are consistent across the sub-samples.

We find a positive and significant relationship between the RIRD and ECBs. Persisting higher interest rates in developing markets increase borrowing costs and, therefore, firms choose ECBs. The sign of the variable RIRD is positive and significant for financial firms, and thus consistent with theoretical expectation. The result corroborates our findings on non-financial firms, and the sub-sample analysis further supports these inferences. A depreciation in domestic currency discourages firms from FCBs especially in the non-tradable sectors, because firms find it challenging to match their liabilities with their income stream in the absence of a natural hedge in the sector. Therefore, we obtain a statistically significant and negative relationship between ECBs and the real exchange rate. The models pass the diagnostic tests suggesting that our results are free from the problem of serial correlation. The insignificant values of the Sargan test statistics further indicate that the instruments are exogenous and valid.

The empirical results suggest the significance of both firm-specific and macroeconomic factors in influencing Indian firms to go in for ECBs. The lagged ECB component is an equally important determinant of ECBs. The evidence indicates that firms resorted to ECBs to roll over their past debt or to repay interest and principal amounts. Export sales, asset tangibility and real interest differentials are important factors encouraging borrowing in foreign currency. On the other hand, domestic financial leverage, depreciation in the real exchange rate and better business and regulations in the economy act as disincentives to denominate debt in foreign currency. Financial firms borrow in foreign currency primarily because of the lower cost of foreign debt and to repay their debt. The exchange rate depreciation discourages financial firms from borrowing externally because of the higher risk of shocks, as they are sensitive to the interest rate and exchange rate.

The performance of India’s exports has been unsatisfactory because of its low competitiveness, which seeks further investigation. In a study, Bhatt (2008) finds that in the event of an appreciation in the real and nominal exchange rates, the price of exports become more competitive, but the competitiveness of profits worsens. Furthermore, the lack of a well-developed credit market, and higher transaction costs and interest rates act as a disincentive for firms to borrow from domestic credit markets, so the lower interest in global markets makes the ECB channel attractive. Hence, developing the local credit market to match a varied maturity structure and lower domestic lending rates will help firms reduce their dependency on ECBs. The overdependence of firms on ECBs could have a devastating effect on their balance sheets in the event of a global shock.

5. Conclusion

In this article, we investigated the factors determining ECBs among Indian firms. Our results suggest that both firm-specific and macroeconomic factors are significant determinants of ECBs. The non-financial firms borrow because of cheaper access to external debt and these firms possess tangible assets which ease the borrowing in foreign currency. We find that financial firms denominate debt in foreign currency mainly because of lower interest rates in global markets. The exchange rate depreciation discourages both non-financial and financial firms to go for ECBs. The findings show that borrowing in foreign currency increased after the relaxation of norms to raise the debt through the ECB channel, but the GFC adversely affected the balance sheets of the indebted firms. This finding suggests the need for macro prudential norms before further relaxation of caps on ECBs. An important policy implication is also the development of a local debt market, especially the tradable bonds market, to help firms reduce their dependency on external sources of finance, as clearly the firms that have access to domestic debt hardly incur ECBs. Domestic monetary policy plays a vital role in encouraging domestic credit through policy instruments, so that the financial firms reduce their reliance on low-cost borrowing from abroad.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

India’s Policy on ECBs

Indian firms can borrow from overseas capital markets using the ECBs channel either through freely convertible foreign currency ECBs or rupee-denominated ECBs. Borrowing and lending in foreign currencies are governed by the Foreign Exchange Management Act, 1999. The government announces changes in the ECB policy from time to time based on recommendations from the High-Level Committee on External Commercial Borrowings and the Reserve Bank of India.

The ECBs are accessed through the automatic and approval routes except FCEBs which can be issued only through the approval route (Pradhan & Hiremath, 2019). The ECBs used for different purposes can be raised from any foreign equity holder for a minimum average maturity period of 5 years. Similarly, ECBs up to US $ 50 million availed by special dispensation and raised by manufacturing units are eligible for a one-year minimum average maturity period (Reserve Bank of India, 2019).

Entities engaged in activities such as manufacturing and infrastructure, software development, microfinance and other remaining activities can borrow up to US $750 million, US$ 200 million and US$ 100 million, respectively. Eligible entities have to borrow through the approval route if their borrowings exceed the sectoral borrowing limits. They can raise ECBs for investment in capital goods imports (classified by The Directorate General of Foreign Trade) for new projects, expansion and modernization of units in the real sector—industrial sector including small and medium enterprises, listed service sectors and other infrastructure sectors, such as software, hospitals, hotels and so on. Eligible borrowers of ECBs include all entities which receive foreign direct investment. Other eligible entities include SIDBI, port trusts, EXIM banks, SEZ units and other registered micro-finance institutions, that is, registered not-for-profit companies, registered trusts/cooperatives/societies and non-government organizations.

The ECB cap was raised in 2006 from US $500 million to US $750 million. The regulator revised the ECBs approval route framework by permitting the corporate to borrow up to an additional US $ 250 million for an average maturity period of 10 years or more. Moreover, ECBs was further permitted to the eligible multi-State co-operative societies, non-banking financial companies, and non-Government organizations who were not earlier allowed to borrow in foreign currencies (Reserve Bank of India, 2008). Now firms in India are also permitted to have access to short-term credit for importing capital goods. Additionally, Indian firms pertaining to the infrastructure sector were now permitted to use 25 per cent of the ECBs to service the past debt of the domestic banks which are made in Indian rupee (Reserve Bank of India, 2011).

Appendix B

Variable	Coefficient	T-Stat
S{1}	–1.6835	–6.5305
Constant	–0.6301	–0.2869
D(6)	–4.2275	–0.7626
DT(6)	19.8776	4.9392
D(9)	–27.2011	–5.0239
DT(9)	16.2136	4.2612

Source: The authors.

Notes: The critical values of the Lee-Strazicich Unit Root Test (Trend Break Model with two breaks) at 1%, 5% and 10% are –7.196, –6.312 and –5.893, respectively.

Notes

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