Balance sheet effects in Colombian non-financial firms

Barajas, Adolfo; Restrepo Ochoa, Sergio Iván; Steiner, Roberto; Medellín, Juan Camilo; Pabón, César Working Paper Balance sheet effects in Colombian non-financial firms IDB Working Paper Series, No. IDB-WP-740 Provided in Cooperation with: Inter-American Development Bank (IDB), Washington, DC Suggested Citation: Barajas, Adolfo; Restrepo Ochoa, Sergio Iván; Steiner, Roberto; Medellín, Juan Camilo; Pabón, César (2016) : Balance sheet effects in Colombian non-financial firms, IDB Working Paper Series, No. IDB-WP-740, Inter-American Development Bank (IDB), Washington, DC, https://hdl.handle.net/11319/7936 This Version is available at: https://hdl.handle.net/10419/173824 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. http://creativecommons.org/licenses/by-nc-nd/3.0/igo/legalcode Balance Sheet Effects in Colombian Non-Financial Firms A dolfo Barajas Sergio Restrepo Roberto Steiner Juan Camilo Medellín César Pabón IDB WORKING PAPER SERIES Nº IDB-WP-740 October 2016 Department of Research and Chief Economist Inter-American Development Bank October 2016 Balance Sheet Effects in Colombian Non-Financial Firms A dolfo Barajas* Sergio Restrepo** Roberto Steiner*** Juan Camilo Medellín*** César Pabón*** * International Monetary Fund ** Banco de la República *** Fedesarrollo Cataloging-in-Publication data provided by the Inter-American Development Bank Felipe Herrera Library Balance sheet effects in Colombian non-financial firms / Adolfo Barajas, Sergio Restrepo, Roberto Steiner, Juan Camilo Medellín, César Pabón. p. cm. — (IDB Working Paper Series ; 740) Includes bibliographic references. 1. Devaluation of currency-Colombia. 2. Debts, External-Colombia. 3. Foreign exchange futures-Colombia. I. Barajas, Adolfo. II. Restrepo, Sergio. III. Steiner, Roberto. IV. Medellín, Juan Camilo. V. Pabón, César. VI. Inter-American Development Bank. Department of Research and Chief Economist. VII. Series. IDB-WP-740 Copyright © Inter-American Development Bank. This work is licensed under a Creative Commons IGO 3.0 AttributionNonCommercial-NoDerivatives (CC-IGO BY-NC-ND 3.0 IGO) license (http://creativecommons.org/licenses/by-nc-nd/3.0/igo/ legalcode) and may be reproduced with attribution to the IDB and for any non-commercial purpose, as provided below. No derivative work is allowed. Any dispute related to the use of the works of the IDB that cannot be settled amicably shall be submitted to arbitration pursuant to the UNCITRAL rules. 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The opinions expressed in this publication are those of the authors and do not necessarily reflect the views of the Inter-American Development Bank, its Board of Directors, or the countries they represent. http://www.iadb.org 2016 1 Abstract* After building up foreign currency-denominated (FC) liabilities over several years, the balance sheets of Colombian firms might be particularly vulnerable to a shift in external conditions. This paper undertakes four exercises in order to get a better understanding of these vulnerabilities. First, probit/logit estimations are used to identify the firm-level and macroeconomic determinants of FC borrowing by non-financial corporations. Second, the implications of the balance sheet vulnerability for real activity are investigated. Evidence is found of an FC balance sheet effect that transmits exchange rate fluctuations to firm-level investment, and show that that this effect is asymmetric, much greater for depreciations than for appreciations. Third, using logit/probit estimations, it is shown that not all firms use forward exchange derivatives solely to hedge their FC liabilities. This might be a consequence of exchange rate intervention by the monetary authority, protecting against extreme exchange rate misalignments. Finally, results are reported of a survey-based qualitative analysis on the hedging policies and activities of 12 large non-financial firms. JEL classifications: E22, F31 Keywords: Colombia, Depreciation, Dollar debt, Balance sheet effects * Adolfo Barajas is an Economist at the IMF Institute for Capacity Development ([email protected]); Roberto Steiner ([email protected]g.co), Juan Camilo Medellín ([email protected]) and César Pabón ([email protected]) are at Fedesarrollo; Sergio Restrepo works in the Programming and Inflation Department at Banco de la República ([email protected]). Authors would like to thank participants for comments at seminars held at IDB and Fedesarrollo, in particular Julián Caballero, Ugo Panizza, Andrew Powell, Leonardo Villar and César Tamayo. We are also grateful to Alberto Boada, Enrique Montes and Hernando Vargas from Banco de la República for support with the database. This paper was prepared as part of the IDB’s Latin American and Caribbean Research Network project “Structure and Composition of Firms’ Balance Sheets.” 2 1. Introduction In recent years many emerging markets have benefitted from benign global conditions, including ample liquidity and, until mid-2014, very favorable terms of trade. Enactment of expansionary and often unconventional monetary policies, coupled with very low yields in mature markets, has facilitated access to foreign debt markets by emerging firms and the sovereign. In Latin American countries bond issuance was facilitated by strong macroeconomic fundamentals and/or the upswing in commodity prices. According to Rodríguez, Kamil and Sutton (2015), gross bond issuance by non-financial corporates in LA-5 countries (Brazil, Chile, Colombia, Mexico and Peru) increased from US$15 billion in 2003 to US$77 billion in 2013 (totaling US$435 billion over the entire period). Issuance in these countries has increased rapidly since 2009, specifically in export sectors linked to commodities (such as mining, oil and gas), in response to abundant liquidity and strong investor appetite. Colombia was no stranger to this phenomenon. Private sector debt increased from around 30 percent of GDP in 2000 to 45 percent of GDP in 2015 (Figure 1). Although more than half of total corporate debt is with domestic financial institutions and two thirds is peso-denominated, since 2009 the share of foreign-currency (FC) debt has increased markedly, from 20.2 percent to 33.3 percent (Panel A), and the share of credit with foreign financial institutions has gone from 14.1 percent to 21.1 percent (Panel B). Figure 1. Private Corporate Debt by Instrument and Currency Denomination Panel A. By currency 0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0 40.0 45.0 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 % of GDP Debt in U.S. Dollars Debt in Colombian Pesos 3 Panel B. By instrument Source: Central Bank based on Superintendencia de Sociedades. Note: Because of data limitations, 2015 suppliers’ debt is assumed equal to that observed in 2014. In spite of its well-known benefits, the sustained increase in foreign borrowing by nonfinancial firms is also a matter of concern, particularly in the post-2014 global scenario in which monetary conditions have tightened and are expected to tighten even further, and in which the collapse in the price of oil has provided Colombia with the sharpest decline ever in its terms of trade.1 The current account deficit went from 3.2 percent of GDP in 2012 to 6.5 percent in 2015 and the peso depreciated by almost 53 percent against the US dollar during that period. The buildup of foreign currency debt in the context of a volatile and weakening currency is a potential vulnerability, particularly if firms do not match the currency composition of their liabilities with that of their assets, do not use financial derivatives to hedge their exposure to exchange rate risk, or do not benefit from a natural hedge in the form of FC revenues. It is important to highlight that, like many other emerging economies, Colombia has experienced large swings in international capital flows since the early 1990s. These swings have generally been associated with similar swings in economic activity, working through two channels: exchange rate changes and bank credit. Regarding exchange rates, the resulting real appreciation during the upswing has contributed to the expansion in activity being biased toward 1 Between June 2014 and December 2015 the price of Brent crude oil declined by more than 60 percent and the terms of trade by more than 40 percent. Even though oil represents only 7 percent of GDP, the macro-economy is highly sensitive to variations in the price of hydrocarbons. In 2014 oil exports accounted for 53 percent of total exports, while FDI in the hydrocarbons sector represented 30 percent of total FDI. Not to mention the fiscal dependence on oil: in 2014 taxes from oil companies and profits from Ecopetrol amounted to 20 percent of central government current revenue. 0 5 10 15 20 25 30 35 40 45 50 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 (% of GDP Foreign suppliers National suppliers a/ Bonds issued abroad Bonds issued in the local market Crédit with foreign financial institutions c/ Credit with domestic financial institutions b/ 4 the non-tradable sector (Cano, 2010), while slowdowns in activity have been cushioned by a tradable sector benefitting from real depreciation. Regarding bank credit, pronounced cycles have largely coincided with swings in international capital flows (Barajas and Steiner, 2002). During the upswing, banks have found it easier to access foreign capital and also have encountered more rapid deposit growth, an indirect consequence of the surge in capital inflows. During the downswing, the opposite occurs. To the extent that certain businesses in the economy are “bank-dependent” in their financing possibilities, these bank credit cycles can serve to transmit and even amplify the effects of international capital cycles on the real economy. What has received less attention in the empirical literature in Colombia has been the possible impact of these swings in capital flows on economic activity and on financial vulnerability, working through a balance sheet channel. Since the work of, among others, Krugman (1999) and Céspedes, Chang and Velasco (2002), it has been recognized that if currency mismatches are large enough, the traditional beneficial impact of depreciations might be overturned as firms with large currency mismatches experience distress as a result of a weaker local currency. One strand of empirical work has focused on factors that contribute to the buildup of liabilities in foreign currency and on the firm-level balance sheet effects that ensue in the event of sizable depreciations. For 32 developed and developing countries, Calvo, Izquierdo and Mejía (2004) found that the interaction of large current account deficits and high dollarization may be a dangerous cocktail, as potential balance sheet effects become highly relevant in determining the probability of a Sudden Stop. For six Latin American countries, Kamil (2012) showed that fixed exchange rates may play a role in building up these vulnerabilities; after countries switch from pegged to floating exchange rates, firms reduced their foreign currency exposures. In the case of Colombia, Echeverry et al. (2003) found that during 1995-2001 vulnerabilities were relatively limited, mainly because the buildup of FC liabilities was modest and mostly limited to “naturally hedged” firms, those with a sizable portion of revenues in foreign currency. It also showed that, amidst a real exchange rate depreciation, firms with foreign currency debt generally had lower profitability but no different investment than that of other firms. Recently, Restrepo, Cuervo and Montes (2014) found that firms in Colombia do not match the currency composition of their liabilities with those of their assets and income. Following Cowan, Hansen and Herrera (2005), they also found that, following a 10 percent 5 depreciation of the real exchange rate, investment fell by 3 percentage points more in firms with half of their debt denominated in dollars compared with firms that held no dollar debt. As the recent upswing phase of the international capital cycle reaches its conclusion, it is relevant to assess to what extent vulnerabilities may have been built up over the past few years. The purpose of this paper is to identify econometrically the determinants of foreign borrowing by non-financial corporations and the effect of such borrowing on firm performance (i.e., profits and investment) in the presence of exchange rate fluctuations. We also want to understand what drives firms’ decision to use forward exchange derivatives. The paper is divided into five sections, including this introduction. In the second section we describe the database. In the third section we motivate and undertake the econometric exercises. In the fourth section we report the results of a qualitative analysis of the hedging policies and activities of 12 large non-financial firms, while the last section draws conclusions. 2. Firm-Level Database We use balance sheet and income statements for non-financial firms for the 2005-2013 period, as provided by Superintendencia de Sociedades (SS) and Superintendencia Financiera (SF).2 These standardized data sets cover firms with assets or annual revenue in excess of 30,000 times the monthly minimum wage.3 The total number of observations is 215,016 with a yearly average of 23,890 firms, ranging between 19,744 and 27,091. As reported in Table 1, retail and manufacturing comprise the largest number of firms in the data base. For the currency composition of assets and liabilities, firm-level FDI and use of financial derivatives, we use data from Banco de la República (BdR). Import (CIF) and export data (FOB) come from DANEDIAN. The definition and sources of all variables are reported in Annex A.4 Larger firms tend to have more exposure to exchange rate movements and are more likely to hold either a natural or a financial hedge. Therefore, they are of particular interest in this paper. We will also consider large firms which do not have foreign currency denominated debt, 2 Even though SS information is available as of 1995, we consider only information since 2005 in order to ensure that we work with high-quality data. Other variables, including financial derivatives, are only available for this shorter period of time. 3 In 2015 the monthly minimum wage was US$235. Hence, only firms with assets above US$7 million were subject to mandatory reporting. 4 Two important differences between the data sources in this paper and those used in other recent papers on the same topic (including Restrepo, Cuervo and Montes 2014) are: i) we use information not only from SS, but also from SF, which includes firms listed on the stock exchange; and ii) we have considered one additional year (2013). 12 Table 5. Descriptive Statistics for Firms Holding Financial Derivatives Panel A. Firm-level variables, descriptive statistics (average 2005–2013) Firms descriptive statistics by industry % of firms with Fwds % of firms with Fwds and international trade % of firms with Fwds and FC debt Agriculture 11.5 10.0 6.7 Retail 9.6 8.1 6.7 Construction 3.1 2.2 2.5 Electricity, gas and water 7.7 7.7 3.9 Manufacturing 16.9 16.7 13.7 Oil & Mining 3.9 3.5 3.0 Transport and communications 5.9 5.1 4.1 Panel B. Long, short and net forwards positions (as a ratio of total assets; average 2005-2013) Firms descriptive statistics by industry Net Fwds/Total assets Long Fwds/Total assets Short Fwds/Total assets Mean Median Mean Median Mean Median Agriculture 2.0 0.0 2.2 0.0 0.2 0.0 Retail -0.7 0.0 0.3 0.0 1.0 0.0 Construction -0.1 0.0 0.0 0.0 0.1 0.0 Electricity, gas and water -0.2 0.0 0.0 0.0 0.2 0.0 Manufacturing -0.5 0.0 0.6 0.0 1.1 0.0 Oil & Mining 0.0 0.0 0.2 0.0 0.2 0.0 Transport and communications 0.4 0.0 0.6 0.0 0.2 0.0 Source: Authors calculations based on SS, DIAN-DANE, SF and BdR. 3. The Drivers and Consequences of FC Debt In this section we address three questions: i) which factors drive non-financial firms’ decision to issue FC debt, and how much do they issue? ii) What is the impact on firm performance (i.e., profits and investment) of holding FC debt in the presence of exchange rate fluctuations? and iii) what are the determinants of the use of exchange rate derivatives (forwards) by firms? 3.1 Determinants of Issuance of FC Debt In order to assess the importance of the different factors that might affect a firm’s decision to issue foreign currency denominated debt, we follow Echeverry et al. (2003), as follows: 𝐹𝐹𝑖,𝑡=∝𝑖+𝛽1𝐴𝑖,𝑡+𝛽2𝐿𝑖,𝑡+𝛽3𝑍𝑖,𝑡+𝛽4𝐹𝐹𝑖,𝑡+𝛽5𝐺𝑖,𝑡+𝛽6𝐼𝑖,𝑡+𝛽7𝑆𝑖,𝑡+𝛽8𝐼𝐼𝑖,𝑡+𝜌1𝑠𝑡+ 𝜌2𝑐𝑡+ 𝑒𝑖,𝑡 (1) 13 FC is a dummy variable equal to 1 for the year in which the firm acquired (any amount of) FC debt, 0 otherwise. The firm-level explanatory variables are: A, the logarithm of total assets; L, leverage, the ratio of total liabilities to total assets; Z, exports in relation to total sales; FO, a dummy variable equal to 1 if 50 percent or more of the firm’s equity is owned by foreigners; G, the rate of growth of sales; I, FC assets held abroad;13 S, the ratio of short-term debt to total debt; and IP, imports as a ratio of total operational expenses. We have included two macroeconomic variables: s, the difference between the domestic interbank rate of interest, the overnight Libor, and the premium on exchange rate forward contracts; and c, domestic credit to the private sector as a percentage of GDP. We also estimate a slightly different specification in which macroeconomic variables are replaced with time effects. We run three different versions of (1), all of them as a logit regression.14 In the first, the dependent variable includes all three types of FC liabilities (bonds, bank loans, and trade credit). In the second, it only includes financial debt (bonds and bank loans). The third only includes trade credit. Results are very similar for the three exercises. As can be seen in Table 6, most variables have the expected sign and are significant at the 10 percent confidence level. The probability of issuing any kind of FC debt increases with size, leverage, and the ratio of exports to total sales. The significance of exports to total sales provides evidence of “natural hedging.” The probability of issuing any kind of FC debt declines with the rate of growth of sales, an indication that the ability of the firm to self-finance its investment needs increases with sales, as in Rajan and Zingales (1998). Firms that rely more on short-term debt, that are importers, or that are foreign-owned, are more likely to issue FC debt. With regard to macroeconomic variables, the probability of issuing any kind of FC debt increases when the domestic interest rate increases relative to the foreign interest rate or when the forward premium goes up, the latter presumably because a weaker exchange rate is to be expected. Total and financial FC debt does not appear to substitute for domestic bank credit; issuance is actually more likely when aggregate credit to the private sector is on the upswing, thus issuance is procyclical with respect to domestic credit conditions. Trade credit behaves somewhat differently, as it decreases when domestic credit conditions tighten. 13 FC-denominated assets held within the country is not known. 14 When a probit model is used for the Total FC Debt and Financial FC debt estimations, results are robust to the econometric methodology. However, there are no firm-level determinants when the FC trade credit version takes the form of a probit model. See Annex B. 14 Table 6. Determinants of the Decision to Issue FC Debt (Marginal effects after logit) (1) Logit (2) Logit (3) Logit (4) Logit (5) Logit (6) Logit VARIABLES Total FC Debt Financial FC Debt FC Trade Credit Total FC Debt Financial FC Debt FC Trade Credit A, Assets 0.0512*** 0.0338*** 0.000120*** 0.0518*** 0.0341*** 0.000101*** (0.00452) (0.00296) (2.66e-05) (0.00453) (0.00297) (2.35e-05) I, Assets held abroad -0.00959 -0.0123 0.000399 -0.0128 -0.0133 0.000387 (0.0537) (0.0318) (0.000574) (0.0571) (0.0342) (0.000511) L, Leverage 0.289*** 0.175*** 0.000763*** 0.291*** 0.177*** 0.000649*** (0.0389) (0.0241) (0.000210) (0.0389) (0.0242) (0.000186) S, Short term debt 0.0585*** 0.0393*** 0.000132* 0.0564*** 0.0389*** 7.76e-05 (0.0119) (0.00757) (7.87e-05) (0.0119) (0.00758) (6.79e-05) Z, Exports 0.293*** 0.173*** 0.000597*** 0.288*** 0.170*** 0.000459*** (0.0318) (0.0192) (0.000146) (0.0314) (0.0191) (0.000122) IP, Imports 0.102** 0.0356* 0.000181 0.0974** 0.0343* 0.000128 (0.0479) (0.0200) (0.000172) (0.0458) (0.0196) (0.000129) G, Sales growth -0.00535*** -0.00250** -4.39e-05** -0.00488** -0.00207* -4.75e-05** (0.00206) (0.00127) (2.02e-05) (0.00202) (0.00124) (1.98e-05) FO, Foreign ownership 0.0416*** 0.00516 0.000524*** 0.0482*** 0.00854** 0.000460*** (0.00915) (0.00411) (0.000132) (0.00942) (0.00425) (0.000119) s, Spread 0.0353** 0.0152* 0.000468*** (0.0143) (0.00857) (0.000122) c, Credit to private sector 0.0998*** 0.119*** -0.00117*** (0.0244) (0.0163) (0.000271) RE YES YES YES YES YES YES Time Effects YES YES YES NO NO NO Number of Observations 34,064 34,064 34,064 34,064 34,064 34,064 Number of firms 5,012 5,012 5,012 5,012 5,012 5,012 Source: Authors’ calculations based on SS, DIAN-DANE, SF and BdR. Note: Robust standard error in parenthesis.***p<0.01, **p<0.05, *p<0.1 Having specified regression (1) to determine the decision of whether or not to issue FC debt, we now limit the sample to issuing firms in order to understand the determinants of the firms’ share of FC debt issued. 𝐹𝐹𝑆𝑖,𝑡=𝑋𝑖,𝑡𝐵+𝑎𝑖+𝜌1𝑠𝑡+ 𝜌2𝑐𝑡 +𝑠𝑡�𝛽1𝐴𝑖,𝑡+𝛽2𝑍𝑖,𝑡+𝛽3𝐹𝐹𝑖,𝑡+𝛽4𝐼𝐼𝑖,𝑡� +𝑐𝑡�𝛿1𝐴𝑖,𝑡+𝛿2𝑍𝑖,𝑡+𝛿3𝐹𝐹𝑖,𝑡+𝛿4𝐼𝐼𝑖,𝑡�+ 𝑒𝑖,𝑡 (2) where X=[A, L, Z, FO, G, I, S, IP] In this specification, FCS is FC debt denominated as a ratio of total assets. The firm-level and macroeconomic explanatory variables are as in (1) above. In addition, the macro variables 15 are interacted with assets, with exports (as a proportion of sales), with the foreign ownership dummy variable and with imports (as a percentage of operational expenses). As in the previous exercise, we run different versions of (2), using three definitions of FC debt as the dependent variable. We run equation (2) as a fixed effects panel regression. We also include a specification using time effects in place of the macroeconomic variables. As Table 7 shows, all coefficients of firm-level, non-interacted and interacted macroeconomic variables are significant at the 10 percent confidence level. In general terms, all types of FC debt behave as expected. The shares of total and financial FC debt increase with size, leverage, short-term debt and exports to total sales—yet again evidence of natural hedging—and decreases with the ratio of imports to total expenses. They are both pro-cyclical with respect to domestic bank credit. In contrast, the share of trade credit exhibits a negative sign for size and exports to total sales ratio, a positive sign for imports to total expenses ratio, is counter-cyclical with respect to domestic bank credit, and is not significant with respect to other explanatory variables. Some effects are common across all three types of FC debt: shares of FC debt all decrease with sales growth—giving support to the idea of investment self-financing by the firm—and increase if the firm is owned by foreigners. With respect to the interaction with macroeconomic variables, the larger the exports to total revenue ratio, the more sensitive the use of trade credit to the interest rate differential and forward premium. Firms with higher export ratios are also less likely to show countercyclical use of trade credit with respect to domestic bank credit. Also, for larger or foreign-owned firms, use of financial FC debt is less pro-cyclical, presumably because swings in domestic bank credit have a smaller effect on their access to financing. Firms with larger imports to total expenses ratio tend to be more sensitive to interest rate and forward premium movements, their use of FC financial debt is more pro-cyclical and their use of trade credit is less pro-cyclical with respect to swings in domestic bank credit. 16 Table 7. Determinants of the Share of FC Debt Issued (1) (2) (3) (4) (5) (6) (7) (8) (9) VARIABLES Total FC debt Financial FC debt Trade Credit Total FC debt Financial FC debt Trade Credit Total FC debt Financial FC debt Trade credit A, Assets 0.0120*** 0.0145*** -0.00126 0.0120*** 0.0142*** -0.000991 0.0150** 0.0229*** -0.00628*** (0.00352) (0.00375) (0.00131) (0.00337) (0.00395) (0.00102) (0.00605) (0.00541) (0.00172) I, Assets held abroad 0.0762 0.0718 0.000827 0.0755 0.0722 -0.000214 0.0769 0.0723 0.00114 (0.0568) (0.0577) (0.00247) (0.0582) (0.0488) (0.00189) (0.0555) (0.0580) (0.00217) L, Leverage 0.153*** 0.151*** -9.63e-06 0.153*** 0.151*** -9.87e-05 0.152*** 0.152*** -0.00221 (0.0203) (0.0185) (0.00414) (0.0181) (0.0175) (0.00446) (0.0203) (0.0217) (0.00428) S, Short term debt 0.0215*** 0.0215*** -0.00118 0.0214*** 0.0222*** -0.00190 0.0213*** 0.0226*** -0.00245 (0.00642) (0.00505) (0.00287) (0.00600) (0.00518) (0.00288) (0.00662) (0.00675) (0.00290) Z, Exports 0.0357*** 0.0389*** -0.00390* 0.0365*** 0.0411*** -0.00517* 0.0577 0.0826* -0.0269*** (0.0115) (0.0110) (0.00219) (0.0109) (0.0101) (0.00266) (0.0468) (0.0445) (0.00772) IP, Imports -0.0103 -0.00890* -0.00137 -0.00977 -0.00797 -0.00177 0.0951*** 0.0110 0.0827*** (0.00628) (0.00469) (0.00241) (0.00858) (0.00594) (0.00378) (0.0246) (0.0234) (0.0175) G, Sales growth -0.00266** -0.00198* -0.000717 -0.00256** -0.00163 -0.000951* -0.00256** -0.00167 -0.000912** (0.00126) (0.00115) (0.000581) (0.00118) (0.00113) (0.000538) (0.00128) (0.00123) (0.000431) FO, Foreign ownership 0.00545* 0.00594** -0.00106 0.00491 0.00611* -0.00174 0.00694 -0.0174 0.0241*** (0.00315) (0.00288) (0.00111) (0.00340) (0.00321) (0.00134) (0.0174) (0.0155) (0.00682) s, Spread 0.00733 -0.00260 0.00987*** -0.0192 -0.0289 0.0105 (0.00607) (0.00615) (0.00284) (0.0346) (0.0330) (0.00832) c, Credit to the private sector 0.0254*** 0.0419*** -0.0190*** 0.0775*** 0.108*** -0.0270*** (0.00965) (0.0109) (0.00339) (0.0294) (0.0316) (0.00883) Spread*assets 0.00144 0.00316 -0.00189 (0.00579) (0.00522) (0.00148) Spread*exports 0.0309 0.0487 -0.0174** (0.0480) (0.0515) (0.00861) Spread*imports 0.0976*** 0.0447* 0.0523*** (0.0272) (0.0252) (0.0188) Spread*foreign ownership -0.0209 -0.0254 0.00496 (0.0171) (0.0177) (0.00868) Credit to the private sector*assets -0.00609 -0.0132** 0.00597*** (0.00458) (0.00514) (0.00159) Credit to the private sector*exports 0.0173 0.00413 0.0174* (0.0519) (0.0609) (0.00936) Credit to the private sector*imports -0.0150 0.0626** -0.0758*** (0.0318) (0.0257) (0.0163) Credit to the private sector*foreign ownership -0.0455** -0.000572 -0.0445*** (0.0210) (0.0177) (0.00879) Constant -0.0464** -0.0656*** 0.0127 -0.0535*** -0.0927*** 0.0342*** -0.0968*** -0.147*** 0.0431*** (0.0223) (0.0207) (0.00852) (0.0171) (0.0207) (0.00642) (0.0361) (0.0336) (0.0103) FE YES YES YES YES YES YES YES YES YES Time effects YES YES YES NO NO NO NO NO NO Number of Observations 16,450 16,450 16,450 16,450 16,450 16,450 16,450 16,450 16,450 Number of firms 2,325 2,325 2,325 2,325 2,325 2,325 2,325 2,325 2,325 R-squared 0.039 0.052 0.016 0.038 0.051 0.015 0.042 0.054 0.042 Source: Authors’ calculations based on SS, DIAN-DANE, SF and BdR. Note: Standard errors estimated by bootstrapping in parentheses.*** p<0.01, ** p<0.05, * p<0.1 17 3.2 Profits and the Currency Composition of Debt We estimate the effect of FC exposure on firm profits in the presence of changes in the real exchange rate. In particular, we estimate a fixed-effects model where the dependent variable is net profits as a proportion of total assets at 𝑡 − 1. Recall that our critical value FC exposure is defined as the difference between FC debt, FC assets and net forward position. We estimated two versions of equation (3); results are reported in Table 8. 𝐼𝑃𝑖𝑡 =𝛽1𝑥𝑖𝑡 +𝜗𝑖+𝜀𝑖𝑡 𝑖= 1, … . , 𝑁 𝑡= 1, … . ,9 (3) 𝐼𝑃𝑖𝑡: Firm net profits as a share of total assets. 𝜗𝑖: Fixed effects 𝜀𝑖𝑡: i.i.d error term with variance 𝜎𝜖 2 Specification 1 𝑥𝑖𝑡: our two key variables of interest are the interaction between FC debt in t-1 and the log of the real exchange rate and the interaction between FC assets in t-1 and the log of the real exchange rate. We also include as controls lagged total financial liabilities; net exports and net forwards interacted with the real exchange rate; the foreign-owned dummy FO; I, investments owned abroad; and G, the rate of growth of sales. Specification 2 𝑥𝑖𝑡: here we interact FC balance-sheet exposure with the log of the real exchange rate. We include the same control variables as in Specification 1. In specification 1, where we disaggregate the components of FC exposure, we see that only net exports transmit exchange rate fluctuations to firm profits; as expected, profits increase following a depreciation. In specification 2 we see that FC balance-sheet exposure does not transmit exchange rate fluctuations to profits, but net exports do. To some extent, this is to be expected, as most of the impact of exchange rate fluctuations for firms with sizable balance sheet exposures would tend to be on capital gains or losses rather than on revenue or expenses. Also of note is the fact that net profits increase with a smaller FC balance sheet exposure (and FC debt in particular), with higher net exports, with FC assets or ownership of investments abroad, and with more rapid sales growth. 18 Table 8. Net Profits and the Currency Composition of Debt (1) (2) VARIABLES FC debt*loge -0.0508 (0.0723) FC assets*loge 0.547 (0.409) Balance-sheet exposure*loge -0.0831 (0.0584) Balance-sheet exposure -0.0267*** (0.00951) Net exports*loge 0.0351*** 0.0321*** (0.0108) (0.0102) Net Forwards*loge -0.0144 (0.0970) FC debt -0.0424*** (0.0132) Total liabilities 0.00141 0.00127 (0.00516) (0.00502) FC assets 0.209** (0.0818) Net exports 0.00276 0.00227 (0.00191) (0.00203) Net Forwards -0.00109 (0.0143) Sales growth 0.0172*** 0.0174*** (0.00133) (0.00133) Ownership abroad 0.118*** 0.114*** (0.0336) (0.0332) Foreign -0.00160 -0.00131 (0.00283) (0.00283) Constant 0.0315*** 0.0324*** (0.00286) (0.00290) Observations 33,321 33,321 R-squared 0.522 0.521 Source: Authors’ calculations based on SS, DIAN-DANE, SF and BdR. Note: The dependent variable is profits in millions of COP adjusted by CPI. All variables (except dummies) are scaled by firms’ previous period total assets. The real exchange rate is the nominal COP/USD exchange rate divided by the domestic CPI. Net forward position corresponds to nominal values of long and short positions with local banks. Accounting information was obtained from SS and SF. Macroeconomic variables were obtained from various sources. Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. 19 3.3 Investment and the Currency Composition of Debt Here we explore whether FC indebtedness increases the sensitivity of firm-level investment to exchange rate fluctuations. Taking advantage of the similarity of the data, we begin this section by replicating the estimation of Restrepo, Cuervo and Montes (2014),15 the results of which are reported in Annex C. We ran 11 static panel data models in order to elucidate the firm´s investment behavior as a result of holding FC debt and facing exchange rate fluctuations. We begin with a very basic specification in which only FC-denominated debt is interacted with the real exchange rate. In specifications (2) – (4) we progressively introduce new controls such as net exports, a dummy variable that indicates if the firm belongs to a tradable sector, cash flow, net forward position and lagged capital stock. Then, in specifications (5) – (11) we introduce the interaction between the real exchange rate and FC assets, a dummy variable for whether the firm is engaged in the production of a tradable good, its balance sheet exposure, and net exports. Our results are similar to those reported in Restrepo, Cuervo and Montes (2014). Regardless of the controls introduced, the results indicate that, following a depreciation (appreciation), firms holding FC debt will reduce (increase) investment more rapidly than firms that do not hold FC debt. On the other hand, neither holding FC assets, net forwards, nor being a net exporter seems to have an offsetting effect; the interaction with the real exchange rate is not statistically significant in any of the specifications. Similarly, investment by firms in tradable sectors is no more sensitive to exchange rate fluctuations. These findings suggest that the balance sheet exposure matters for investment: the greater the exposure, the stronger the cutback in investment following a real depreciation. In particular, ceteris paribus, a 10 percent depreciation of the real exchange rate would imply a 2 percent reduction in the rate of investment in fixed assets of those firms with half of their debt denominated in foreign currency when compared with those that do not have any foreign currency debt. This result compares with the 3 percent estimated in Restrepo, Cuervo and Montes (2014). The main shortcoming of Restrepo, Cuervo and Montes (2014) is that investment is estimated using a static panel regression. In what follows we adopt a more appropriate dynamic panel estimation method (Arellano and Bond, 1991; Arellano and Bover, 1995; Blundell and 15 A very important difference between their work and ours is that their sample was composed of only FC indebted firms and not the full sample as we do. For comparison reasons, in the estimations presented in Annex C we did exactly the same procedures and data cleaning (which means we only left FC indebted firms also). 20 Bond, 1998) for which our database is well suited since: i) we have few periods and many individual firms; ii) a linear functional relationship is reasonable; iii) the dependent variable is dynamic, a function of its past realizations; iv) independent variables are not strictly exogenous; v) we need to account for firm-level fixed effects; and vi) there is likely to be heteroskedasticity and autocorrelation within individuals but not across them.. The model we estimate is the following: 𝐼𝑁𝐼𝑖𝑡 =∑𝛼𝑗𝐼𝑁𝐼𝑖,𝑡−𝑗 1 𝑗=1 +𝑥′𝑖𝑡𝛽1+𝑤𝑖𝑡𝛽2+𝜗𝑖+𝜀𝑖𝑡 𝑖= 1, … . , 𝑁 𝑡= 1, … . ,9 (4) We include yearly dummy variables (𝑦𝑡) and firm-specific fixed effects (𝜗𝑖). Yearly dummies capture aggregate shocks common to all firms, including changes in the exchange rate. Firm-specific fixed effects capture differences among firms in their optimal capital stock, which we presume does not change over our sample period. 𝐼𝑁𝐼𝑖𝑡: Fixed investment by firm i in period t 𝑥′𝑡: Year dummies (exogenous) 𝜗𝑖: Panel effects (which might be correlated with the co-variables) 𝜀𝑖𝑡: i.i.d error term with variance 𝜎𝜖 2. We estimate five specifications of (4) for our sample of firms during 2005-2013. All five specifications use 2-stage GMM, reporting GMM standard errors (Table 9). Endogenous and predetermined variables are optional, with restrictions regarding the number of instruments used. The dependent variable appears with one lag and this establishes the limit to the lags for the instruments.16 Annex D reports the Arellano-Bond test of no autocorrelation of first differences in the error term and the Sargan test for over-identification of restrictions. Both tests confirm the validity of our specification—i.e., that there is zero autocorrelation of the first difference of the error term and over-identification of restrictions are valid. We begin with a very basic specification and progressively include additional relevant predetermined and endogenous variables. In specifications (1) – (4) we include interactions of the change in the real exchange rate with one or more individual components of a firm’s balance sheets exposure—FC debt, FC assets, and net forwards—and in specifications (3) and (4) we also include the interaction with net exports. Both FC debt and FC assets transmit exchange rate fluctuations to investment as expected; having greater FC debt causes investment to decline 16 Option endogenous is used to indicate that the variables appear as contemporary regressors. 21 (increase) following a depreciation (appreciation), while the opposite is true for FC assets. Thus, for a given level of FC debt, having FC assets can reduce the sensitivity of firm-level investment to exchange rate fluctuations. Similarly, having larger net exports—a natural hedge— is associated with a higher level of investment when a depreciation occurs, also dampening the curtailment in investment for a firm holding FC debt. These effects are summarized in specification (5), where we include the FC balance-sheet exposure variable as defined earlier (section 3.2) as well as net exports. As expected, the coefficient of the interaction between the change in the real exchange rate and FC balance sheet exposure is negative and significant, indicating that a real exchange depreciation (appreciation) would have a stronger contractionary (expansionary) effect on investment for firms exhibiting larger FC balance sheet exposure. Table 9. Fixed Capital Investment and Foreign Currency Exposure (1) (2) (3) (4) (5) VARIABLES Lagged investment 0.00650*** 0.00699*** 0.00489*** 0.00472*** 0.00480*** (0.00222) (0.00220) (0.000940) (0.000911) (0.000975) Lagged FC debt*loge -0.0367* -0.0345* -0.0179 -0.0195 (0.0212) (0.0204) (0.0204) (0.0188) Lagged FC assets*loge 0.105*** 0.100*** 0.100*** (0.0386) (0.0379) (0.0389) Lagged Net exports*loge 0.00575 0.00641 0.00813* (0.00497) (0.00477) (0.00486) Lagged Net Fwds*loge 0.000842 (0.0184) Lagged balance-sheet exposure*loge -0.0282** (0.0133) FC debt -0.0235 -0.0221 -0.0248*** -0.0263*** (0.0155) (0.0138) (0.00908) (0.00819) FC assets -0.0328** -0.0326* -0.0344** (0.0166) (0.0171) (0.0170) Balance-sheet exposure -0.0132* (0.00705) 28 To sum up, we have found qualitative and quantitative support for the fact evidence that Colombian non-financial firms engage in the use of FC derivatives with both hedging and with speculative purposes. In addition, there is evidence that the monetary authority has been active in the FC market trying to smooth exchange fluctuations to some extent. Therefore, firms, have not had the urgency of using the FC derivatives market for hedging reasons, and instead they opted for hedging in a very limited manner or engaging in FC speculation. Table 12. Determinants for the Use of Forward Exchange Derivatives by Firms (Marginal Effects after logit) (1) Logit (2) Logit (3) Logit (4) Logit (5) Logit (6) Logit VARIABLES Long or short positions Long or short positions Long position Long position Short position Short position A, Assets 0.00780*** 0.00797*** 0.000465*** 0.000489*** 0.00544*** 0.00558*** (0.000760) (0.000765) (9.38e-05) (9.67e-05) (0.000552) (0.000558) I, Assets owned abroad -0.0217 -0.0222 -0.00110 -0.00119 -0.0163 -0.0167 (0.0166) (0.0171) (0.000889) (0.000940) (0.0112) (0.0115) L, Leverage 0.0197*** 0.0201*** 4.91e-05 5.94e-05 0.0192*** 0.0195*** (0.00536) (0.00542) (0.000390) (0.000411) (0.00403) (0.00411) FCS, FC debt 0.0621*** 0.0633*** 0.00139*** 0.00150*** 0.0474*** 0.0484*** (0.00589) (0.00595) (0.000388) (0.000408) (0.00464) (0.00471) S, Short term debt 0.0196*** 0.0193*** 9.82e-05 7.23e-05 0.0179*** 0.0178*** (0.00287) (0.00289) (0.000182) (0.000191) (0.00234) (0.00236) Z, Exports 0.0262*** 0.0253*** 0.00355*** 0.00373*** -0.00512** -0.00624** (0.00372) (0.00373) (0.000739) (0.000761) (0.00260) (0.00267) IP, Imports 0.0152*** 0.0145*** -4.79e-05 -9.32e-05 0.0137*** 0.0132*** (0.00505) (0.00494) (0.000155) (0.000166) (0.00423) (0.00416) G, Sales growth -0.00194*** -0.00226*** -3.12e-05 -4.95e-05 -0.00138*** -0.00161*** (0.000603) (0.000639) (4.88e-05) (5.58e-05) (0.000454) (0.000484) FO, Foreign ownership -0.00211* -0.00196 0.000258* 0.000250* -0.00307*** -0.00296*** (0.00120) (0.00120) (0.000135) (0.000137) (0.000850) (0.000863) s, Interest rate differential -0.128*** -0.00179 -0.0849*** (0.0229) (0.00204) (0.0170) f, Forward Premium -0.0242*** -0.00182*** -0.0110*** (0.00453) (0.000550) (0.00324) NER, Exchange rate -2.12e-05*** -1.36e-06*** -1.05e-05*** (2.97e-06) (3.46e-07) (2.09e-06) FE YES YES YES YES YES YES Time effects YES NO YES NO YES NO Observations 34,064 34,064 34,064 34,064 34,064 34,064 Number of nit 5,012 5,012 5,012 5,012 5,012 5,012 Source: Authors calculations based on SS, DIAN-DANE, SF and BdR. Note: Standard error in parenthesis. ***p<0.01, **p<0.05, *p<0.1 29 4. Questionnaire to CFOs on Hedging Activities We undertook a two-stage survey on hedging policies and activities of 12 non-financial firms, all of which were large, spanned a variety of sectors and included some firms known to have no foreign currency liabilities. In particular, we used the following criteria in selecting the 12 firms (Table 13):  Firms listed in the Superintendencia de Sociedades (SS) or Superintendencia Financiera (SF) and that are among the 30 largest firms in these databases (measured by assets)  Firms listed in the Dealogic database, which covers the universe of bond issuers  If not issuers, firms that play a central role in their respective industry and satisfy the first condition mentioned above Table 13. Selected Non-financial Firms COMPANY NAME INDUSTRY SECTOR ISSUER HEADQUARTERS Almacenes Éxito SA Retail Traded Yes Envigado Avianca Holdings SA Transport NonTraded Yes Bogotá Colombia Telecomunicaciones SA Telecom NonTraded No Bogotá Danone Alquería SA Food_bev NonTraded No Cajica Ecopetrol SA Mining Commodities Yes Bogotá Empresa de Energia de Bogota SA Utilities NonTraded Yes Bogota ISAGEN Energy NonTraded Yes Medellín Grupo Argos SA Utilities Traded Yes Medellín Grupo Nutresa SA Food_bev Traded Yes Medelllín Interconexion Electrica SA ESP Utilities NonTraded Yes Medellín Organización Terpel SA Utilities Traded Yes Bogotá Promigas SA ESP Utilities NonTraded Yes Barranquilla The questionnaire is divided into two parts: one was sent in advance of the interview, with questions that require more time and may be answered by a less senior person. The second part of the questionnaire—focused on policy and strategy—was used for the interview with the 30 CFO. All 12 firms completed the first part of the questionnaire. We then interviewed nine of the twelve CFOs. Results of the in-advance questions suggest that foreign currency hedging is only common for “transactional exposure,”25 and is generally made in small amounts. Although nine of 12 companies stated that they typically hedge to manage transactional exposure, seven answered that the proportion typically hedged for this type of exposure was under 25 percent. With regard to “translation exposure”26 which is close to our definition of “balance sheet exposure,” only five firms hedge to manage this type of risk, while only 1 firm hedging for economic/competitive exposure.27 Interest rate risk is very common among these 12 firms. In fact, only one company ample indicated that it does not hold any debt at floating interest rates. Furthermore, eight of 12 companies reported that floating interest rate debt represented more than 50 percent of total debt. Regarding capital structure and debt policy, in 10 of 12 firms total debt represents more than 50 percent of total assets. In terms of liquidity policy, with only two exceptions, firms’ currency holdings in cash and marketable securities represent less than 25 percent of assets. Concerning the interview with the CFO, there exist heterogeneous results among the strategy and policy revealed by each of them. Results confirm that FC exposure is common for all these companies; all have operations (revenues, expenditures, assets or debt) denominated in FC. Three-fourths of CFOs acknowledged that during the last two years the firm had acquired bank loans or issued bonded debt in foreign currency abroad, mainly as a result of lower interest rates in foreign currency, more abundant funding and longer maturity terms. Acquiring bank loans or issuing bonded debt in foreign currency with domestic institutions is much less common—only four of nine indicating that they have considered this type of operation. Additionally, companies that have preferred to issue bonds in FC rather than acquiring bank loans in FC acknowledged that the main reasons for this decision were lower funding costs, longer maturity and more funding availability. 25 Refers to the risks faced by the firm stemming from changes in exchange rates after the firm has contracted financial obligations. This exposure thus refers to risks to the company’s future cash flows. 26 Refers to the risk that the firm’s assets, equity, liabilities or income change in value due to fluctuations in exchange rates. This exposure thus refers to the risk that the financial figures reflected in the accounting statements will change their value as a result of the translation of foreign accounts into the domestic currency. 27 For example, a weakening foreign currency benefiting foreign competitors. 31 Six of nine CFOs acknowledged that their companies engaged in FC Risk Management activities, essentially concentrated in short terms currency derivatives, forwards contracts and debt in foreign currency. The CFOs that did not engage in these activities mentioned that this was due to insufficient exposure to FC risk or to accounting complexity. Moreover, with only 3 exceptions, CFOs consider that there is a relatively high or complete match among the currencies in which the company’s different operations are denominated. Regarding interest rate exposure and hedging, five of 9 CFOs reported that their company engaged in interest rate risk management activities, particularly using derivatives as forwards or options. In addition, only three of 9 firms stated that it was very usual (either frequently or always) that their markets view of interest rates forced them to actively take positions in interest rate derivatives or to alter the timing or size of hedging. In terms of control and reporting, seven of 9 CFOs disclosed that they consider risk management during their strategic planning, and basically all of them assert that their planning process explicitly considers risks and measure them. Additionally, six of 9 CFOs note that they frequently (quarterly or monthly) report risk management activities to the Board of Directors, and that this practice is decided by the Board itself. Along this same line, six CFOs reckon that they calculate “value-at-risk” for some or its entire derivatives portfolio. Furthermore, six of 9 CFOs stated that their firm had a target capital structure, which responded to leverage and EBITDA, and was guided by concerns regarding ratings by credit rating agencies. In terms of liquidity, the sample observed that the currency of their cash holdings was largely determined by depreciation expectations and by the currency denomination of expenses. Additionally, they stated that if FC debt were less available locally, most of them would react by increasing debt in foreign jurisdictions. On the other hand, if funds were less available in international markets, the common answer would be to increase debt in domestic currency. Finally, seven of 9 CFOs estimated that a further peso depreciation could have a slightly negative effect on the financial results of the company. To sum up, in spite of significant heterogeneity among companies, results lead us to conclude that most of them have considerable FC and interest rate exposure. However, with only a few exceptions, financial hedging policies have not been undertaken. Most CFOs consider that their firms are “naturally hedged”—because they hold assets in FC, export significant amounts, or sell domestically with prices indexed to the exchange rate. 32 5. Conclusion As in many emerging economies, Colombia has in recent years experienced a period of surging capital inflows, in which international bond issuance and other borrowing by domestic nonfinancial corporations has become increasingly important. With global monetary conditions now beginning to tighten and expected to tighten further, compounded with the collapse Colombia’s terms of trade, the accumulated foreign borrowing by non-financial firms is a matter of concern. In this paper we have first identified the determinants of foreign borrowing, then estimated to what extent the resulting FC balance sheet mismatches have affected firm performance (i.e., investment and profits), in the context of exchange rate fluctuations. We provide evidence that larger, more leveraged, foreign-owned firms are more likely to acquire FC debt, as well those engaging in international trade—either imports or exports—and those who have a higher share of short-term debt. In addition, firms tend to borrow more in FC when the interest rate differential or the forward premium is higher. Finally, we find that overall FC borrowing behaves procyclically with respect to domestic bank credit, they do not appear to be substitutes. We found evidence of a balance sheet effect transmitting exchange rate fluctuations to real activity. Firms with a larger FC balance-sheet mismatch reduce (increase) their investment by more following a depreciation (appreciation). On the other hand, net exports serve as a natural hedge, dampening the above effects of balance sheet exposure on investment. Although statistically significant, the magnitude of the balance sheet effect estimated over the entire 2005-13 sample period was relatively small, thus suggesting that there might be asymmetry in the sensitivity to depreciations vs appreciations. This was confirmed by our event study, in which we isolated the 2009 episode, the one year in which a substantial real depreciation had occurred. This exercise showed an estimated effect that was several times greater than that for the full period, which had been characterized by an almost continuous appreciation. We also investigated the factors behind non-financial firms’ decision to participate in the FC forward market. Certainly those with FC debt were more likely to do so, but in both short and long FC positions, thus suggesting that these instruments are not being used solely for hedging purposes. Furthermore, while exporting firms tended to substitute their natural hedge with the financial hedge provided by FC forwards, importers were shown to hold FC forward positions 33 that were consistent with hedging. This behavior could be related to exchange rate intervention by the Colombian monetary authority, even under an inflation targeting regime rate targeting behavior. There is evidence that intervention is particularly acute during periods of prolonged appreciation. Under these circumstances, firms might feel protected against extreme exchange rate misalignments and therefore engage in FED markets mainly for speculative purposes. Finally, the survey-based qualitative analysis regarding hedging policies and activities allow us to conclude that, in spite of significant heterogeneity among the different large companies surveyed, most of them have considerable foreign currency activities and positions and do participate in FC derivatives markets. However, with few exceptions and in a limited manner, these companies have not been using these instruments to hedge their FC balance sheet. 34 References Arellano, M., and S. Bond. 1991. “Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations.” Review of Economic Studies 58(2): 277-297. Arellano, M., and O. Bover. 1995. “Another Look at the Instrumental Variable Estimation of Error-Components Models.” Journal of Econometrics 68: 29-51. Blundell, R., and S. Bond. 1998. “Initial Conditions and Moment Restrictions in Dynamic Panel Data Models.” Journal of Econometrics 87: 115-143. Barajas, A. and R. Steiner. 2002. “Why Don’t They Lend? Credit Stagnation in Latin America.” IMF Staff Papers 49(Special Issue: IMF Annual Research Conference): 156-184. Barajas, A. et al. 2014. “Singular Focus or Multiple Objectives? 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Sutton. 2015. “Corporate Financing Trends and Balance Sheet Risks in Latin America: Taking Stock of ‘The Bon(d)anza.’” IMF Working Paper 15/10. Washington, DC, United States: International Monetary Fund. 36 Annex A. Data Availability and Variables Definition Variable Description Firm level variables Total Assets Logarithm of real value of assets. Source: Superintendencia de Sociedades and Super. Financiera. Imports CIF value of goods imported plus imports of services. We convert the dollar value of exports into pesos using the average exchange rate of the corresponding year. Source: DANE-DIAN. Exports FOB value of exports of goods & services. For estimation it is normalized by total sales. We convert the dollar value into pesos using the average exchange rate for the year. Source: DANEDIAN. Sales Source: SS and SF. Foreign participation in Ownership Share of company owned by foreign investors. Source: SS and SF. Cash flow A revenue or expense stream that changes a cash account over a given period. Source: SS and SF. Total Profit/losses Total income (operational+non-operational) net of total expenses and taxes. Source: SS and SF. Total debt or leverage Total liabilities (excluding net worth) as reported in balance sheets. Source: SS and SF. ShortTerm Debt Debt that has to be repaid within 1 year. Source: SS and SF. Total “dollar” debt Debt (including bonds) acquired by firms with foreign and domestic banks or corporations. Source: BdR. Tradable Takes the value 1 if the firm belongs to any of the following sectors: agriculture, mining or industry. Zero otherwise. Financial debt Source: SS and SF. Long forward COP/USD Value of the active long cop/usd forwards at December 31 of the corresponding year at firm level. Source: BdR. Short forward COP/USD Value of the active short cop/usd forwards at December 31 of the corresponding year at firm level. Source: BdR. Investment in fixed capital Capital in t minus capital in t-1. Capital is the addition of physical properties as equipment, edification, ongoing constructions, and other assets. Source: SS and SF. Foreign Direct Investment Annual net flow of FDI at firm level. Source: BdR. Portfolio Investment Annual net flow of portfolio investment. Source: BdR. Colombian Direct Investment Abroad Annual net flow of direct investment abroad at firm level. Source: BdR. Macroeconomic variables Real GDP growth Annual percentage change of real GDP. Source: DANE Inflation Annual percentage change in Consumer Price index. Source: DANE Average Exchange Rate Average of the exchange rate for the respective year. Source: BdR. Exchange Rate End of Period Exchange rate as of December 31 of each year. Source: BdR. Exchange Rate Forward28 Average forward rate of the COP / USD traded forwards during the period t + 1 which are due in December of each year. Source: BdR. Exchange rate forward premium Forward Exchange Rate over Exchange Rate End of period. Source: BdR. Private Credit Total credit granted to the private sector as a percentage of GDP. Source: BdR and DANE. 28 Information regarding the position of currency derivatives firms is harder to build. Only recently regulators and investors have begun to demand more systematic information on these financial transactions. As in Restrepo et al (2014), information was used from operations of foreign currency derivatives of banks established in Colombia. We took only the forwards COP/USD, which represent about 85% of the total notional amount of derivatives traded in Colombia. 37 Annex B. Determinants of Firm Issuance of Debt in Foreign Currency (Marginal effects after probit) Probit Probit Probit Probit Probit Probit VARIABLES Total FC Debt Financial FC Debt FC Trade Credit Total FC Debt Financial FC Debt FC Trade Credit Assets 0.0590*** 0.0378*** 3.82e-07 0.0596*** 0.0381*** 1.64e-06* (0.00650) (0.00429) (2.55e-07) (0.00649) (0.00429) (9.48e-07) Assets owned abroad -0.00908 -0.0126 1.37e-06 -0.0120 -0.0134 6.06e-06 (0.0602) (0.0365) (2.10e-06) (0.0634) (0.0387) (8.60e-06) Leverage 0.301*** 0.184*** 2.52e-06 0.305*** 0.188*** 1.03e-05* (0.0472) (0.0299) (1.73e-06) (0.0471) (0.0301) (6.25e-06) Short term debt 0.0644*** 0.0419*** 4.45e-07 0.0626*** 0.0417*** 1.33e-06 (0.0141) (0.00894) (3.75e-07) (0.0140) (0.00896) (1.22e-06) Exports 0.312*** 0.187*** 1.94e-06 0.308*** 0.185*** 7.48e-06* (0.0407) (0.0256) (1.33e-06) (0.0401) (0.0254) (4.49e-06) Imports 0.0354 0.0178 3.71e-07 0.0350 0.0177 1.49e-06 (0.0278) (0.0136) (3.84e-07) (0.0274) (0.0136) (1.44e-06) Sales growth -0.00563** -0.00269* -1.36e-07 -0.00509** -0.00220 -7.29e-07 (0.00227) (0.00139) (1.08e-07) (0.00223) (0.00136) (4.87e-07) Foreign ownership 0.0488*** 0.00646 4.01e-06 0.0557*** 0.0101** 1.62e-05* (0.0111) (0.00468) (2.76e-06) (0.0114) (0.00488) (9.74e-06) Spread 0.0429*** 0.0168* 7.47e-06* (0.0158) (0.00952) (4.32e-06) Credit to private sector 0.101*** 0.124*** -1.96e-05* (0.0270) (0.0198) (1.12e-05) RE YES YES YES YES YES YES Time Effects YES YES YES NO NO NO Number of Observations 34,064 34,064 34,064 34,064 34,064 34,064 Number of firms 5,012 5,012 5,012 5,012 5,012 5,012 Source: Authors calculations based on SS, DIAN-DANE, SF and Banco de la República. Note: Standard Error in parenthesis ***p<0.01, **p<0.05, *p<0.1