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Söhnke M. Bartram*, Gregory W. Brown†, and Frank R. Fehle‡
Abstract
This paper presents international evidence on the use of financial derivatives for a sample of 7,292 non-financial firms from 48 countries including the U.S. Across all countries, 59.8% of the firms use derivatives in general, while 43.6% use currency derivatives, 32.5% interest rate derivatives, and only 10.0% commodity price derivatives. Firm-specific factors associated with derivatives use are very similar across different countries. Some factors are associated only with specific types of derivatives. With the exception of the size of the lo-cal derivatives market, country-specific factors are not consistently good predictors of de-rivative use. Together these results show that a wide range of factors likely determine the use of derivatives by non-financial firms thus explaining the mixed results from studies examining primarily U.S. firms. However, some of the results are unambiguously counter to theoretical predictions. Finally, we examine whether derivatives use is associated with higher firm value. Surprisingly, we find positive valuation effects primarily for firms using interest rate derivatives.
Keywords: Derivatives, corporate finance, risk management, hedging, international finance JEL Classification: G3, F4, F3 This version: May 19, 2003. Lancaster University, Graduate School of Management, Lancaster LA1 4YX, United Kingdom, Phone: +44 (1524) 592 083, Fax: +1 (425) 952 10 70, Email: Corresponding author. Kenan-Flagler Business School, University of North Carolina at Chapel Hill, CB 3490 McColl Building, Chapel Hill, NC 27599-3490, USA, Phone: +1 (919) 962-9250, Fax: +1 (919) 962-2068, Email: ‡ †* International Evidence on Financial Derivatives Usage Barclays Global Investors and Moore School of Business, University of South Carolina, Columbia, SC 29208, USA, Phone: +1 (803) 777-6980, Fax: +1 (803) 777-6876, Email: The authors gratefully acknowledge research funding by the Maastricht Research School of Economics of Technol-ogy and Organizations (METEOR), as well as support by Mike Pacey, Global Reports, Standard & Poor’s Global Rating Service and Thomson Financial in establishing the dataset. We also thank Kevin Aretz and Yaw-heui Wang for providing excellent research assistance and brown-bag workshop participants at the University of North Carolina and Duke University. International Evidence on Financial Derivatives Usage Abstract This paper presents international evidence on the use of financial derivatives for a sample of 7,292 non-financial firms from 48 countries including the U.S. Across all countries, 59.8% of the firms use derivatives in general, while 43.6% use currency derivatives, 32.5% interest rate derivatives, and only 10.0% commodity price derivatives. Firm-specific factors associated with derivatives use are very similar across different countries. Some factors are associated only with specific types of derivatives. With the exception of the size of the lo-cal derivatives market, country-specific factors are not consistently good predictors of de-rivative use. Together these results show that a wide range of factors likely determine the use of derivatives by non-financial firms thus explaining the mixed results from studies examining primarily U.S. firms. However, some of the results are unambiguously counter to theoretical predictions. Finally, we examine whether derivatives use is associated with higher firm value. Surprisingly, we find this to be the case primarily for firms using interest rate derivatives. 1 Introduction The use of financial derivative contracts by non-financial corporations has grown rapidly over the last two decades, yet to date there is little consensus regarding both how and why firms use derivatives. Especially lacking are comprehensive data on the use of derivatives by non-financial firms outside of the United States even though these firms represent the majority of users.1 This paper takes a step toward filling the gap by examining the use of financial derivatives by 7,292 companies in 48 countries including the U.S—the largest and broadest sample of firms studied to date. This research has four main objectives. First, we seek to document the usage of foreign ex-change (FX), interest rate (IR), and commodity price (CP) derivatives and compare characteris-tics of users across countries and firm type. Our results show that in many countries outside the United States firms commonly use derivatives. Across all countries, more than half of the sample firms (59.8%) use some type of financial derivative. More precisely, 43.6% of the firms use FX derivatives, 32.5% interest rate derivatives, and 10.0% use commodity price derivatives. For the 2,242 U.S. firms in the sample, the rates are similar: 64.0% of firms use some type of derivative with 36.9% using FX derivatives, 40.3% using interest rate derivatives and 16.3% using com-modity derivatives. We find that the type of derivatives used varies across the different classes of financial risk. For example, 35.3% of firms use forwards to hedge FX risk and 11.0% use swaps. Usage rates are reversed for interest rate derivatives where swaps are the most popular risk man-agement instrument (used by 28.8% of firms) and forwards are used by only 0.6% of firms. The use of non-linear derivatives varies less across types of risk: 9.6% of firms use FX options, 7.3% of firms use some type of non-linear interest rate derivative (e.g., option, cap, floor, and/or swap-tion). In contrast to FX and IR derivatives, commodity price hedgers do not appear to have a pre-ferred type of contract with forwards, futures, swaps and options all used in about the same pro-portion by hedgers. Our second objective is to use a large and diverse sample of international firms to increase the power of tests examining the motivations for derivative use. Since prior research and anecdo-tal evidence suggests that corporations use derivatives primarily for financial risk management Sections 2 and 5.1 provide summary statistics on derivatives users. Existing research on derivative usage by non-U.S. firms are summarized in Section 5.1. 1 1 purposes2, the extant empirical research (mostly using samples of U.S. firms) has sought to test theories of financial risk management. For example, financial theory suggests that corporate risk management is apt to increase firm value in the presence of capital market imperfections such as bankruptcy costs, a convex tax schedule (Smith and Stulz, 1985), or underinvestment problems (Bessembinder, 1991; Froot, Scharfstein, and Stein, 1993). While recent empirical studies pro-vide some evidence in support of these theories (Nance, Smith and Smithson, 1993; Mian, 1996; Géczy, Minton, and Schrand, 1997; Allayannis and Ofek, 2001, among others), some findings suggest that risk management may arise from principal-agent conflicts between managers and shareholders or additional factors not well motivated by existing risk management theory such as earning management and speculation (Tufano, 1996; Brown, 2001; Core, Guay and Kothari, 2002). We find that derivatives use appears consistent with some predictions of theories of share-holder value maximization. At a basic level, we find strong evidence that the use of derivatives is, in fact, risk management rather than simply speculation. For example, firms that use FX deriva-tives have higher proportions of foreign assets, sales, and income and firms that use interest rate derivatives have higher leverage. In line with the financial distress hypotheses, tests indicate that derivatives users have significantly higher leverage and income tax credits as well as lower li-quidity (as measured by quick ratios and coverage ratios). However, we also find some evidence clearly counter to theory. For example, that more profitable firms and firms with fewer growth opportunities (market-to-book ratios) tend to hedge more. Our third objective is to examine the use of derivatives at the country level and determine what country-specific factors, if any, are important for explaining cross-sectional variation. Over-all, these factors seem to be of less importance than firm-specific factors. One factor that does appear to be consistently relevant is the size of the local derivative market (as measured by daily turnover in FX and IR derivatives). This suggests that supply side constraints are an important determinant of derivatives use. These results are particularly relevant given recent policy debates surrounding financial risk (discussed subsequently). Our fourth objective is to determine if derivative use is associated with higher firm value. A limited amount of recent research has started to examine this important issue. Our comprehensive 2 See Hentschel and Kothari (2001), among others. 2 sample allows for more powerful tests regarding the relation between risk management and firm value. Consistent with the findings of Graham and Rogers (2002) and Allayannis and Weston (2001) we find some evidence that derivative use is associated with higher firm value. However, the results for U.S. firms using FX derivatives which are analogous to those in Allayannis and Weston are fairly weak in our (later) sample period and are not present in broad sample of inter-national firms. Nonetheless, we do find strong results indicating that interest rate risk manage-ment is associated with higher firm value for both U.S. and international firms. Overall, the re-sults of this paper show that studying companies headquartered in the U.S. is not sufficient for understanding the risk management practices of non-financial firms. The remainder of the paper is organized as follows: General motivation as well as a sum-mary of the evidence from related studies is presented in Section 2. Section 3 details the theory and hypotheses that are tested in this study. The data are described in Section 4. Empirical results are presented in Section 5 while Section 6 discusses some alternative specifications and robust-ness checks. Section 7 concludes. 2 Motivation and Related Literature Studying the use of derivatives is important. While a few commodity-based (e.g., agricultural) industries have a long history of hedging with exchange-traded derivatives, the use of derivatives has grown rapidly since the introduction of foreign exchange and interest rate products in the 1970s. For example, Panel A of Table 1 reports data from the Bank for International Settlements (BIS) triennial surveys showing that the FX derivatives market continued to grow rapidly in the 1990s. Specifically, daily turnover of FX forwards and swaps grew from an average of 187 bil-lion U.S. dollars (USD) in 1989 to a peak of 1,206 billion USD in 1998.3 While companies in the U.S. are an important part of the global derivatives market they ac-count for a minority of derivatives turnover. Panel B of Table 1 shows the daily turnover for all FX derivatives and (single currency) interest rate derivatives as well as the U.S. share of those markets in 2001. U.S. firms are responsible for only about 13.7% of total FX and 18.1% of total interest rate derivatives turnover. Examining only non-financial firms reveals that the U.S. share is higher but still only about a quarter of turnover. In our subsequent firm-level analysis, the 3 The introduction of the Euro in 1999 is the primary reason for the decline from 1998 to 2001. 3 number of non-U.S. derivatives users in our sample is substantially greater than the number of U.S. derivatives users (though the fraction of U.S. firms using some type of derivatives is mar-ginally higher). As a consequence, it seems important to examine firms outside the U.S. to get a complete picture of the use of derivatives. While the primary users of derivatives are financial institutions such as banks, insurance companies, and money managers, the use of derivatives by non-financial firms is considerable. Panel C of Table 1 shows the notional value (and percentage) of outstanding FX and IR deriva-tive contracts held by non-financial firms in 2001. The notional value of all types of FX deriva-tives held by non-financial firms was more than 4 trillion USD in 2001. Examining values from 1995, 1998, and 2001 shows that non-financial firms have consistently held a little more than 20% of FX derivatives outstanding. In aggregate, non-financial firms are even bigger users of interest rate derivatives—holding more than 7 trillion USD in notional value in 2001. From 1995 to 2001 usage grew rapidly (by about 75%), but because the notional value of IR derivatives held by other users grew even more rapidly, non-financial firms’ share of the IR derivative market has declined from 16.1% in 1995 to 9.9% in 2001. However, by any measure the combined notional values of FX and IR derivatives held by non-financial firms in 2001 is large—exceeding the GDP of the U.S. or the European Union. Studying non-financial firms is also important because their motivations and strategies for using derivatives are the least well understood. In addition, non-financial firms make up the ma-jority of firms using derivatives since, by definition, the use of derivatives among financial firms is concentrated in a few industries. As accounting disclosure requirements changed in the early 1990s, numerous academic studies have examined derivative usage by non-financial firms. The majority of these studies use samples of U.S. firms primarily because of data availability: disclo-sure is relatively good and there are many companies to study. Nonetheless, the U.S. is probably not the best laboratory for examining derivative usage by non-financial firms. For instance, the U.S. is among the most financially stable countries in the world so financial risk management with derivatives may be less critical for U.S. firms. Likewise, international trade (imports plus exports) as a percent of GDP is not particularly high for the U.S. suggesting that FX hedging (the most studied type of risk management) may also be relatively less important for U.S. firms. Perhaps the biggest shortcoming to the existing studies using U.S. firms is that the results, taken as a whole, are rather inconclusive. Different studies find support for different rationales of 4 derivative usage. The general approach of empirical studies is to consider the neoclassical frame-work of Modigliani and Miller (1958) where financial risk management at the firm level can create shareholder value when capital market imperfections give rise to deadweight costs born by shareholders. (In the next section we provide a brief review of risk management theory.) Early studies test the hedging motives of firms on the basis of survey data. For example, Nance, Smith and Smithson (1993) study the use of derivatives by 159 large U.S. non-financial corporations based on their responses to a questionnaire. They find that firms using derivatives have more growth options, are larger, employ fewer hedging substitutes, have less coverage of fixed claims, and face more convex tax functions. Mian (1996) studies a sample of about 3,000 U.S. non-financial firms after the FASB introduced new reporting requirements for derivatives. The results support the hypothesis that hedging activities exhibit economies of scale while the evidence is weak with respect to taxes and inconsistent with regard to hedging based on financial distress costs. Géczy, Minton, and Schrand (1997) analyze a sample of 372 Fortune 500 non-financial firms. They find that firms with greater growth options, tighter financial constraints, extensive foreign exchange rate exposure and economies of scale in hedging activities are more likely to use currency derivatives. Graham and Smith (1999) and Graham and Rogers (2002) in-vestigate the tax incentive to hedge and provide evidence that firms hedge to increase debt capac-ity (but probably not in response to tax schedule convexity). Other studies that examine specific industries or individual firms benefit from the availabil-ity of detailed data on exposure and corporate hedging activities. Typically, these data allow for calculating more precise measures of the extent of hedging. In a study of the gold mining indus-try, Tufano (1996) finds evidence for theories of managerial risk-aversion as the use of commod-ity derivatives is positively (negatively) related to the stock (option) holdings of managers. Tu-fano finds little evidence that managers maximize shareholder value. Brown, Crabb, and Haushalter (2002) also examine the gold mining industry and find evidence consistent with man-agers changing hedge ratios as the result of speculative motives. In a study of the oil and gas in-dustry, Haushalter (2000) finds support for the relationship between hedging and financial dis-tress costs. On the other hand, Brown (2001) undertakes a clinical study of a U.S.-based manu-facturer’s use of FX derivatives and finds little support for the financial distress (or other pri-mary) theories of risk management and instead proposes that hedging is motivated by earnings management, competitive factors in the product market, or contracting efficiency gains. 5 Some studies provide evidence indicating that derivatives use reduces firms’ exchange rate exposures (for example, Allayannis and Ofek, 2001), which presumably increases firm value. Allayannis and Weston (2001) undertake a more direct test and find that firm value (as measured by Tobin’s Q) is higher for U.S. firms with foreign exchange exposure that hedge it with deriva-tives. However, Guay and Kothari (2002) estimate the cash flow implications from hedging pro-grams for 234 large U.S. non-financial firms and find that the economic significance is small. Other studies have examined the use of derivatives in countries besides the U.S. Most ex-amine just one or two countries and many rely on studies similar to the Wharton survey of U.S. firms (see Bodnar, Hayt, and Marston, 1998). For example, Bodnar and Gebhardt (1999) and Alkeback and Hagelin (1999) compare results from the Wharton survey of U.S. firms to similar surveys of 126 German firms and 163 Swedish firms, respectively. These authors find that both German and Swedish firms are more likely to use derivatives though the motivations for deriva-tive use vary somewhat across countries. In both cases larger firms are more likely to use deriva-tives. Berkman, Bradbury, and Magan (1997) survey 79 companies based in New Zealand and also find a greater use of derivatives than for U.S. firms, but the motivations for risk management in New Zealand appear very similar to those cited by U.S. firms in the Wharton survey. Grant and Marshall (1997) survey firms in the United Kingdom; Bodnar, Jong, and Macrae (2002) sur-vey firms in the Netherlands; Downie, McMillan, and Nosal (1996) survey Canadian firms; De-Ceuster et al. (2000) survey Belgian Coordination Centres and firms; Loderer and Pichler (2000) survey Swiss firms; Sheedy (2002) surveys firms in Hong Kong and Singapore. In general, these studies also find higher derivative usage rates than in the U.S. especially for FX derivatives. Of particular interest are the results of Bodnar, Jong, and Macrae (2002) who find that institutional differences between the Netherlands and the U.S. seem to explain differences in risk management behavior thus suggesting the possibility of important country-level effects. To the best of our knowledge, only a couple studies have looked at derivative usage by firms in many countries. Allayannis, Brown, and Klapper (2003) examine the use of foreign cur-rency debt, including FX hedging, by firms in eight East Asian countries. They generally find support for value-maximizing risk management theories. A recent paper by Lel (2002) provides complementary evidence to this paper, as it investigates hedging theories on the basis of 124 American Depository Receipts (ADRs) from 28 countries. Results suggest that corporate hedging has an important country-specific component, for example, that the legal environment and credi- 6 tor rights in different countries matters. Lel also finds that firm-specific factors have limited power in explaining the probability of hedging. Studying ADRs is advantageous in terms of common reporting requirements and quality of data. However, it comes at the cost of a relatively small sample and a selection bias that favors the biggest and best international firms. Our sample includes 630 ADRs, so where appropriate, we compare results. In contrast to Lel, we find that many firm-specific factors are important, and that country-specific factors seem relatively less important, in our larger sample of firms. 3 Theories of Corporate Derivatives Use and Hypotheses One of the primary objectives of this paper is to investigate the rationales for corporate hedging on an international scale. As noted previously, hypotheses tested in prior research are derived mostly from existing theories describing the incentives for derivatives use based on such factors as bankruptcy (financial distress) costs, taxes, the underinvestment problem, and managerial in-centives. Below in Section 3.1 we only briefly describe these theories and predictions since many existing papers provide excellent detailed discussions (see, among others, Géczy, Minton, and Schrand, 1997; and Graham and Rogers, 2002). To facilitate comparison we carefully follow the existing literature wherever possible. In Section 3.2 we develop additional hypotheses regarding differences in derivatives use across countries. These propositions are based on measures of de-rivative market access, country-level risk, and the protection of shareholder and creditor rights. 3.1 Incentives for Derivatives Use at the Firm Level 3.1.1 Financial Distress Costs and Taxes Cash flow volatility can lead to situations where a firm’s available liquidity is insufficient to fully meet fixed payment obligations, such as wages and interest payments, on time. Financial risk management can reduce the probability of encountering such states of nature and thus lower the expected value of costs associated with financial distress (Smith and Stulz, 1985; Shapiro and Titman, 1986; Stulz, 1996). Likewise, lowering the chance of financial distress can increase the optimal debt-equity ratio and therefore the associated tax shield of debt (Myers, 1993; Myers, 1984; Leland, 1998). In addition, if firms face a convex tax schedule reducing the volatility of taxable income will reduce the expected value of tax liabilities (Smith and Stulz, 1985). These theories have led previous researchers to predict that firms with higher leverage, shorter debt maturity, lower interest coverage, and less liquidity (e.g., lower quick ratio) are more 7 likely to use derivatives to hedge financial risk. Preferred stock is often viewed as a type of addi-tional leverage. Similarly, firms with higher dividend yield are likely to be more financially con-strained since it is widely believed that cutting dividends is a negative signal of firm quality. Firms with higher profitability and firms with a larger fraction of tangible assets are expected to have lower financial distress costs and are thus less likely to hedge with derivatives. Since bank-ruptcy costs are less than proportional to firm size (Warner, 1977), smaller firms should be more likely to hedge. However, other researchers have posited (and documented) a positive relation as large firms may realize economies of scale in implementing a risk management program. Tax motivations for risk management have been tested empirically by employing the tax rate and in-come tax credits as explanatory variables (Graham and Smith, 1999; Graham and Rogers, 2002). 3.1.2 Underinvestment Risk management can also increase shareholder value by harmonizing financing and investment policies (Froot, Scharfstein, and Stein, 1993). When raising external capital is costly (e.g., be-cause of transaction costs), firms may underinvest. Derivatives can be used to increase share-holder value by coordinating the need for and availability of internal funds. Conflicts of interest between the shareholders and debtholders can also lead to underinvestment. An underinvestment problem can occur when leverage is high and shareholders only have a small residual claim on a firm’s assets, thus the benefits of safe but profitable investment projects accrue primarily to bondholders and may be rejected (Myers, 1977; Bessembinder, 1991). A credible risk manage-ment plan can mitigate underinvestment costs by reducing the volatility of firm value. As the underinvestment problem is likely to be more severe for firms with significant growth and in-vestment opportunities, various measures such as the market-to-book ratio, research and devel-opment (R&D) to sales ratio, capital expenditure to sales, net assets from acquisitions to size are used for testing the underinvestment hypothesis. Theory suggests that convertible debt can be used to mitigate bondholder-shareholder agency conflicts, so its use may signal the existence of such problems. Other researchers (Géczy, Minton, and Schrand, 1997) suggest that underinvest-ment is likely to be most severe for highly levered firms with significant growth opportunities and thus interact the market-to-book ratio (among others) with leverage to quantify this effect. 8 3.1.3 Management Incentives Conflicting interests in the agency relationship between managers and shareholders may also motivate the use of derivatives. Most senior managers have a highly undiversified financial posi-tion because they derive substantial (monetary and non-monetary) income from their employment by the firm. Consequently, risk aversion may cause managers to deviate from acting purely in the best interest of shareholders (Stulz, 1984; Stulz, 1990; Mayers and Smith, 1982) by expending resources to hedge diversifiable risk. The time horizon of managers and shareholders may also differ because management compensation is tied to short-term accounting measures. These con-flicts of interest can be mitigated by corporate risk management if compensation schemes appro-priately link managers’ pay to the stock price of the firm (Han, 1996; Campbell and Kracaw, 1987; Smith and Stulz, 1985; Stulz, 1984). This suggests that the use of stock option plans in a corporation can be a determinant of corporate hedging. Executive stock options can effectively reduce a manager’s risk aversion and thus lower the propensity for using derivatives to decrease idiosyncratic risk.4 Firms with multiple classes of shares often have a controlling group with su-perior voting rights. Often the controlling group has representatives of management, thus the de-gree of risk-aversion is more likely to affect corporate actions. Thus, the existence of multiple share classes is expected to be positively related to the use of derivatives. 3.2 Country-Specific Determinants of Derivative Use Recently, influential policy makers have suggested that access to derivatives can enhance macro-economic development. For example, in a recent speech U.S. Federal Reserve Board Chairman Alan Greenspan5 remarked, “The further development of derivatives markets, particularly in smaller economies where idiosyncratic risk may be more difficult to hedge, will likely facilitate greater cross-border flows and a more productive distribution of global savings.” Thus, it is im-portant to determine what country-specific factors, if any, promote or inhibit the use of deriva-tives especially if these factors can be influenced by policy. With this goal in mind, we propose a On the other hand, if these options are very in-the-money, the effect is likely to be more like that of straight equity ownership. This should be positively related to corporate derivatives use as this further worsens the problem of an undiversified position. From comments at the Banque de France International Symposium on Monetary Policy, Economic Cycle, and Financial Dynamics, Paris, France, March 7, 2003. 54 9 set of four hypotheses relating the use of derivatives to aspects of countries’ economic and legal environments. Casual inspection of the triennial BIS survey indicates a positive relation between the eco-nomic size (GDP) of a country and the amount of total derivatives turnover. It is less obvious if this relation is proportional to the size of non-financial businesses’ financial exposures or applies to derivative usage rates. Because larger economies are likely to have larger and more liquid financial markets we hypothesize that usage rates will be positively related to firms’ access to derivatives, ceteris paribus. To measure derivative market access, we construct a variable quanti-fying the size of a derivatives markets relative to the size of the economy. Specifically, we sum average daily turnover net of inter-dealer double-counting in the FX and IR derivatives market (excluding turnover with non-financial firms to avoid a mechanical relation) and divide by nomi-nal gross domestic product (GDP).6 We also consider alternative measures characterizing overall economic development such as GDP per capita, and Organization for Economic Co-operation and Development (OECD) membership. On the other hand, economies with more developed markets tend to be more stable and therefore firms based in these countries may have less of a need for risk management. Thus, our second hypothesis is that measures of economic, financial, and political risk are directly related to derivatives usage, ceteris paribus. As measures of country risk we employ the aggregate meas-ures of economic, financial, and political risk reported in the International Country Risk (ICR) Guide for 2000 as well as the composite measure.7 The index values are inverse measures of country risk since higher scores indicate lower risk. As alternatives to these metrics we consider (1) the natural log of GDP since larger countries should be more economically diversified and therefore provide a less risky operating environment for non-financial business8, and (2) imports plus exports as a percent of GDP (henceforth, trade magnitude) since, the more open an econ-omy, the more likely it is that firms are exposed to FX or other financial risk. 6 Derivatives market data are from the 2001 BIS Triennial Survey. GDP estimates are from the World Bank. Both estimates are in US Dollars and GDP is calculated using market exchange rates. Since the values are very positively skewed due to a small number of countries that are currency trading centers (e.g., the United Kingdom and Switzer-land) we take the inverse rank of this statistic and assign countries without BIS data a rank of one. The ICR Guide is published by The PRS Group, 6320 Fly Road, Suite 102, East Syracuse, NY 13057-0248, USA. Note that this is an inverse measure of county risk. 78 10 Additional hypotheses about the use of derivatives use by nonfinancial firms across coun-tries can be derived from differences in the legal environments. We expect that firms located in countries where the legal system is efficient and contracts are enforced should be more likely to use derivatives since contracting costs are lower. On the other hand, in countries with deficient legal environments financial risk management may be more valuable because the direct costs of bankruptcy are higher, thus creditors may lend only on the condition of risk management or man-agers may find it beneficial to develop a reputation for high quality financial risk management. We examine several measures of legal environment. Thus the theoretical effect of legal environ-ment on derivative use is ambiguous. Our primary variable for examining these issues is the le-gality index constructed by Berkowitz, Pistor, and Richard (2001) which effectively measures both the legal environment and enforcement of contracts. Low values of the index reflect poor legal quality. According to La Porta et al. (1998), creditor protection is strongest in common-law countries and weakest in French civil-law countries so we create a dummy variable for civil law countries. We also examine the La Porta et al. (1998) aggregate index of creditor rights and the rule-of-law index created by Kaufman, Kray, and Zoido-Lobaton (1999). Our forth hypothesis centers on the agency relationship between shareholders and manag-ers. Likewise, in countries that afford shareholders significant rights managers may wish to un-dertake risk management with derivatives to avoid being replaced because of poor firm perform-ance attributable to financial risks (see Breeden and Viswanathan, 1996, for related arguments), Consequently, we predict a positive relation between shareholder rights and derivative use. As our proxy we utilize the index of shareholder rights described in La Porta et al. (1998). An alter-native hypothesis suggests weak shareholder protection may also encourage managers to use de-rivatives but for their own benefit (e.g., insuring their personal wealth). A similar argument sug-gests that high ownership concentration implies more effective monitoring by, and at the same time lower diversification of, shareholders each of which suggests a desire for more hedging with derivatives. Consequently, we predict a positive relation between the percentage of market capi-talization of closely held shares as reported by Dahlquist et al. (2003) and the use of derivatives. 4 Data Until the last few years, data on derivative usage by firms outside of the U.S. was disclosed on a largely voluntary basis. A move toward common international accounting standards and new 11 standards in many countries that specifically address derivatives means that it is now practical to systematically study international derivatives use at the firm level.9 Our sample was constructed by matching firms with accounting data on the Thomson Analytics database with firms that have annual reports in English for the year 2000 or 2001 on the Global Reports database.10 Firms appear in our sample only once, either in 2000 or 2001. This initial screen resulted in 9,173 companies. We exclude corporations in the financial services in-dustry leaving 7,467 firms. We dropped an additional 158 companies for assorted reasons, such as an unreadable annual report, resulting in a final sample size of 7,292 companies in 48 coun-tries. The 48 countries in our sample represent 99.3% of global market capitalization. The firms in our sample represent 60.3% of overall global market capitalization and 79.1% of global market capitalization of non-financial firms.11 We searched annual reports for information about derivatives use. Firms are classified as derivatives users if their annual report specifically mentions the use of derivatives. To search the reports we undertook a combination of electronic and manual searches. Initially, a list of search terms was established by manually analyzing a subsample of about 200 annual reports across all countries to identify expressions that indicate the use of particular types of derivatives. Derivative users are classified by the underlying asset (i.e., foreign exchange, interest rates, and/or commod-ity price) as well as by type of derivative (i.e., forward, future, swap, option, and/or any type). Next, we implemented an automated search for 37,537 expressions created by a corcordancer search.12 From this initial dataset 200 firms (100 derivative users and 100 non-users) were ran-domly sampled to identify errors. Average reliability across exposure categories was 94.6%. To improve the search we identified omitted terms by creating an index based on search hits of terms 9 For example, the following are recent standards (and effective dates) adopted by so-called G4+1 countries and the International Accounting Standards Board (IASB) as part of the movement toward common reporting standards: United States, FAS 133 (effective June 15, 1999); United Kingdom, FRS 13 (effective March 23, 1999); Australia, AAS 33 (effective January 1, 2000); Canada, AcSB Handbook Section 3860 (Financial Instruments - Disclosure and Presentation, effective January 1, 1996); New Zealand, FRS-31 (effective December 31, 1993); IASB, IAS 32 (March 1995, modified March 1998 to reflect issuance of IAS 39 effective January 1, 2001) 10 Global reports (www.global-reports.com) is an online information provider of public company documents in full-color, portable document format (PDF). While our sample represents a broad selection of international companies, casual inspection indicates that there is a bias toward larger companies. 11 Since our data span two years, these values are calculated by calculating each firm’s percent of global market capitalization for the year it appears and summing across all firms. 12 A full list of the search terms is available on request from the authors. 12 too general to be included in the concordancer search but likely to be related to derivative use.13 We then manually checked firms with high scores that were initially classified as non-users. We also manually checked firms initially identified as users but with few primary search hits. When possible we added or deleted terms to the primary search. After rerunning the improved primary search, a random sample of 200 additional firms yielded an average reliability rate of 96.0%. Additional adjustments to the search did not improve reliability, however we continued to manu-ally check firms with few hits and high index scores since these firms have higher error rates. Overall, we are confident that the error rate in the dataset is around 3-4%. Nonetheless, we can only make an estimate because in some cases it is not clear, even after a careful reading of the annual report, if a firm is actually using derivatives. For example, some firms state that they “may” use derivatives or include a non-specific “boiler-plate” statement about accounting for the use of derivatives without specifically stating that they are, in fact, users. Our iterative search procedure appears best at identifying commodity price derivatives users and worst at identifying foreign exchange derivatives users. The advantages of our approach are that a large dataset can be created and the classification is fairly systematic. It comes at the cost of potentially adding noise to the process if the search result leads to a wrong decision with regard to the classification of a firm. Misclassifying an oc-casional firm will simply add some noise to our analysis and lower the power of our tests. Given the large size of our sample and that we appear to misclassify users about as frequently as non-users, misclassification should be a minor problem. Since data on the corporate use of stock op-tions and foreign currency denominated debt are not readily available, we also search the annual reports information on these and create two dummy variables with value one (and zero otherwise) if the annual report contains information on stock options or foreign debt, respectively. All accounting data from Thomson Analytics is in millions of U.S. dollars to be comparable across countries. In many cases the data we analyze are ratios, so these are also largely compara- 13 The terms include futures, swap or swaps, swaption.*, collar.*, derivat.*, call option.* or put option.*, hedg.*, cash flow hedg.*, fair value hedg., risk management, effective portion.* or ineffective portion.*, notional amount.*, op-tion.* contract., option.* where “.*” signifies any additional characters. The index sums the number of these terms found in the annual report (regardless of the number of times) for a maximum score of 14. 13 ble across years.14 In order to eliminate outliers, the top and bottom one percent of the observa-tions are dropped from the dataset. We also apply “logical limits” to few of our proxies to retain the economic intuition. For example, we require the market-to-book ratio to be non-negative (but also include a dummy for negative book value in the multivariate analysis). Appendix A provides details on each of the explanatory variables construction, predicted signs for the tests in Section 5, mean values by country for all of the variables used in the primary analysis (and robustness checks), and a correlation table. To control for systematic (e.g., reporting) differences across countries and for industry ef-fects, we adjust variables constructed from the accounting data. We estimate regressions with each of the accounting measures as the dependent variables and include as the independent vari-ables country, 44 industry, and fiscal year dummy variables. We use the residuals from these re-gressions as our explanatory variables.15 Not all variables are available for all firms so we exam-ine alternative specifications in a separate robustness section. 5 Results 5.1 International Derivatives Usage Rates Table 2 reports the percentages of firms using derivatives of different types by country, geo-graphic region, and major industry grouping. Across the whole sample of 7,292 non-financial firms, more than half (59.8%) use some kind of derivative. Most common is the use of foreign exchange rate derivatives (43.6%), followed closely by interest rate derivatives (32.5%) with commodity price derivatives a distant third (10.0%). There is substantial variation in derivatives use across countries. To illustrate, if we consider countries with at least 30 observations, only 20.9% of Malaysian firms in the sample use derivatives while 95.6% of firms in New Zealand report derivatives use. In contrast, usage rates across major geographic regions are not very dif-ferent, ranging from a low of 51.0% for firms in the Asia-Pacific region to 77.3% for firms in Africa and the Middle East. Usage rates in the U.S. and Canada (63.2%) and Europe (60.8%), 14 However, we also include a dummy variable for the year (2000 or 2001) in our multivariate analysis and have undertaken robustness checks for all the analysis to make sure that our results are not driven by which year we exam-ine. We do this type of adjustment because it is sometimes not possible to include many industry dummies when we examine countries separately. Alternatives such as median adjusting by country and using industry dummy variables (when possible) lead to very similar results. 15 14 which comprise the majority of our sample, are very similar. Usage rates are significantly higher for firms located in more developed (OECD) countries, 63.6%, versus 39.9% in non-OECD countries. Interestingly, the usage rate for U.S. firms is significantly higher than for all non-U.S. firms but this is almost entirely due to differences with non-OECD countries. Examining deriva-tives use by major industry reveals that usage rates are highest in the utility and chemicals indus-tries and lowest in the consumer goods and miscellaneous (mostly service) industries.16 While these general derivative usage rates are interesting, they mask differences when de-rivatives are categorized by type of underlying risk. U.S. and Canadian firms are the most com-mon users of interest rate and commodity price derivatives whereas they are the least likely to use foreign exchange derivatives. Derivatives use among non-OECD countries is lower for all types of risk, but disparities are extreme for interest rate and commodity price derivatives where the rates differ by nearly a factor of four. Examining derivative usage by type of exposure and indus-try also reveals distinct patterns. As one would expect, the use of commodity price derivatives is concentrated in a few industries such as utilities, oil, mining, steel, and chemicals. However, the use of interest rate derivatives also varies substantially across industries with utilities having the highest usage rates (63.0%) and mining the lowest (21.3%). FX derivatives usage is somewhat more uniform with rates in all but two industries between 35% and 60%. Table 2 also breaks down derivative usage by type of instrument. Specifically, we consider forwards, futures, swaps, and options separately. For interest rate derivatives, options include swaptions, caps, collars, and floors.17 For currency risk, forwards (used by 35.3% of firms) are the most commonly used instrument while swaps (11.0%) and options (9.6%) are much less com-mon. Almost no firms (1.1%) use foreign exchange futures contracts. For managing interest rate risk, swaps are the most common instrument (28.8%) followed by options (7.3%). Less than 1% of firms use interest rate forwards or futures. In contrast to these results, firms use different types of commodity price derivatives at roughly the same rate, from a high of 3.1% for futures to a low of 2.3% for options. These patterns are surprisingly robust across geographic region and OECD 16 These industries correspond to the 17 industry classification of Kenneth French available at: http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html In our sample, about two-thirds of the firms in the miscellaneous category are in three sub-industries (by 2 digit SIC code): various business services (73), communication services (48), professional services (87). 17 For completeness we allow for foreign exchange and commodity price swaptions, caps, collars and floors but these never yield usage rates greater than 0.2%. 15 OECD membership. The same is true across industries for foreign exchange and interest rate de-rivatives, but for commodity price derivatives there are apparent differences. For example, for-wards are the most common instrument in the mining industry whereas swaps are most common in the oil and utilities industries.18 Overall, the general pattern of usage is consistent with that found in prior studies of U.S. firms. For example, Nance Smith and Smithson (1993) find 61.5% of the 169 non-financial firms they survey use derivatives. Géczy, Minton, and Shrand (1997) report usage by 59.1% (for any derivative) and 41.4% (for currency derivatives) for their sample of 372 Fortune 500 firms. Note, that the percentage of firms that use derivatives in these studies is likely to be higher because the studies focused on large firms, which numerous studies have found to be more likely to use de-rivatives. Bodnar, Hayt, and Marston (1998) report that 50% of the 399 non-financial firms use some form of derivatives: 42% use foreign exchange, 38% use interest rate, and 28% use com-modity price derivatives. A study of 451 firms by Howton and Perfect (1998) results in percent-ages of derivatives use of 61.7% for all derivatives contracts, 45.0% for FX derivatives, and 45.4% for IR derivatives. Mian (1996) studies a larger sample of 3,022 firms and finds percent-ages of derivatives’ users of 25.5% for all derivatives, 14.6% for FX, 14.5% for IR and 5.2% for CP derivatives. 5.2 Univariate Analysis Table 3 reports the country and industry adjusted means, medians, and standard deviations of our explanatory variables and control variables for hedgers and non-hedgers (i.e., derivative users and non-users). Also reported are results from nonparametric Wilcoxon tests for differences in sam-ples. Since about 30% of the firms in our sample are in the U.S., we break the results into two groups, U.S. firms and non-U.S. firms. This helps us be sure that results are not driven simply by U.S. firms and facilitates comparison with prior studies. We also examine results separately for general, FX, IR, and CP derivatives users. We do not table these results to conserve space but make note of differences in the text. Since we subsequently examine the same variables in a mul-tivariate setting, we discuss these results only briefly. 18 Since it is not a primary focus of this paper, we leave an analysis of the choice of instrument(s) to future research. 16 The first part of each panel in Table 3 presents results for firm-specific variables. Most of the evidence supports the financial distress and tax hypotheses. General derivatives users, both in the U.S. and internationally, have higher leverage, dividend yields, and income tax credits as well as lower quick and current ratios and less tangible assets. With the exception of the income tax credits, the results are similar across types of risk. However, some results are counter to the fi-nancial distress hypothesis. In particular, hedgers are larger, are more profitable, have longer debt maturity, and have higher interest coverage ratios. The univariate results do not generally support the underinvestment hypothesis. Hedgers have lower market-to-book ratios and capital expendi-tures and tend to be less R&D intensive. Nonetheless, as predicted by the underinvestment hy-pothesis hedgers have higher values of the variable interacting the market-to-book ratios and lev-erage. Most of these results hold for U.S. as well as international firms and across types of risk. Almost never is a result significant in the U.S. and significant with the opposite sign for non-U.S. firms. One notable exception is that FX derivatives users in the U.S. have higher market-to-book ratios--a finding similar to Géczy, Minton, and Schrand (1997). Table 3 provides mixed support for the managerial incentives hypothesis. In the full sample, firms with multiple share classes and firms using stock options are more likely to hedge (but the difference for stock options is not sig-nificant for non-U.S. firms). Results are similar for FX and IR derivatives users but there are no significant differences for CP derivatives users. We also examine some variables that we use as control variables or in later parts of our analysis. Some previous research has suggested that convertible debt and preferred stock may be alternatives to derivatives as risk management tools. Our data suggests that if this is true, these securities act as compliments rather than substitutes since hedgers have significantly higher levels of both. Allayannis and Weston (2001) find that firms with foreign exchange exposure that use derivatives to hedge have higher values of Tobin’s Q. While we examine this in greater detail subsequently, the opposite result is found when considering the firms in our sample together, that is, regardless of exposure or exposure type. While it would be preferable to only include firms in our sample that are known to have fi-nancial exposures, it is not simple to distinguish between firms with and without exposures of different types. For example, a firm without any foreign sales or assets can have a significant exchange rate exposure if its primary competitors are foreign firms. As a consequence, we con-sider all firms in our primary analysis. However, we do make an attempt to categorize firms as 17 having exposures of various types. Specifically, for FX exposure we consider firms’ foreign as-sets, sales and income. Table 3 reports values for these variables individually. We also create an FX exposure dummy variable that is equal to one for firms that have non-zero values of any of the three measures. Similarly, we define dummy variables identifying (1) interest rate exposure for firms with leverage above the 50th percentile and (2) commodity price exposure for firms in the utilities, oil, mining, steel, and chemicals industries. Finally we create a general exposure dummy variable that is equal to one if any of the FX, IR, or CP exposure variables is equal to 1. While these measures are not perfect, they should on average separate firms with high exposure from those with low exposure. The results in Table 3 show that in all cases, hedgers are more likely to be identified as having an exposure. In the subsequent analysis we also consider results based only on firms identified as having exposures.19 We also note that hedgers tend to have more industry segments and are more likely to have a foreign equity listing. Hedgers are no more likely to have a negative book value than non-hedgers. Examining the country-specific variables in Table 3 also reveals mixed evidence for our hypotheses. First, the evidence supports our market access hypothesis. Hedgers tend to be in countries with larger derivatives markets, higher GDP per capita, and OECD countries. The uni-variate statistics do not generally support our country risk hypothesis. Country risk as measured by the ICR composite index, the logarithm of GDP, and trade magnitude all indicate that hedgers tend to be in safer countries. However, when we examine the ICR financial, economic and po-litical risk measures separately, the results depend on type of risk. Hedgers are in countries with lower political and economic risk, but (slightly) higher financial risk. This highlights one of the main shortcomings of the univariate analysis. Since politically and economically safer countries are likely to have more developed capital markets it is difficult to disentangle the roles of market access and country risk. The evidence regarding our legal structure hypothesis is ambiguous. Hedgers tend to be in countries with better overall legal environments as measured by the legality and rule-of-law indi- 19 When we limit the sample, we exclude 664 hedgers in the general derivatives classification, 1036 hedgers in the FX derivatives classification, 647 hedgers in the IR derivatives classification, and 382 hedgers in the CP derivatives classification. Thus, between 15.2% and 52.4% of hedgers are excluded from the sample when we restrict it to firms identified as having high exposures. 18 ces, yet they also are more likely to be in countries with a civil law legal origin and weak creditor rights. The results related to our agency hypothesis indicate that agency costs related to deriva-tive use are probably low. Hedgers tend to be in countries with better shareholder rights and less concentrated equity ownership. With the exception of the result for shareholder rights, the results are robust to the exclusion of U.S. firms. We once again caution that these relationships should be interpreted with skepticism because of similar fundamental factors that are likely to influence many of these country-specific variables. In addition, the magnitudes of differences are quite small in many cases though the statistical significances are considerable because of our large sample size. In summary, there exist many differences in firm-specific characteristics between hedgers and non-hedgers. Many, but certainly not all, of these differences are consistent with theoretical motivations for financial risk management. Examining country-level variables reveals that there are frequently statistically significant differences between hedgers and non-hedgers but the re-sults do not consistently support our hypotheses and the economic significance of the differences is often minimal. This suggests that firm-specific characteristics may be more important than country-specific characteristics in determining risk management policy. 5.3 Multivariate Analysis by Country and Risk Type In order to look at the simultaneous effects of the different factors on the likelihood of derivatives use, we estimate a LOGIT model. We estimate the model for a variety of samples: all countries, all countries other than the U.S., the six individual countries with the most observations in our sample (the U.S., United Kingdom, Japan, Germany, Canada, and Australia20) and all other coun-tries. The explanatory variables we examine here are a subset of those discussed in Section 3. We use two criteria for including variables in this analysis. First, we exclude variables that are close substitutes for other variables we include. Second, we exclude some variables (e.g., R&D) that have a significant negative impact on the sample size. A more complete selection of variables is examined in the robustness section. We estimate similar models for all countries, but since not all variables are available for all firms (or relevant for all countries), we chose the specification 20 Although there are more firms in our sample from Hong Kong than Australia, more Australian firms have com-plete data for the specification we examine here. 19 with the maximum number of explanatory variables that allows the estimation algorithm to con-verge. Models are also estimated separately for general, FX, IR, and CP derivatives’ use. Table 4 shows results for general derivative users. For the full sample (first column), suffi-cient data are available for 6,723 firms. The results are very similar to those suggested by the univariate statistics. The financial distress and tax hypotheses are supported by the positive coef-ficients for leverage, the dividend dummy variable, and the income tax credit dummy variable, as well as the negative coefficient for the quick ratio. However, contrary to this theory are the posi-tive coefficients found for gross profit margin and size. The full sample provides mixed support for the underinvestment theory. Contrary to the prediction, the coefficient for the market-to-book ratio is negative, yet consistent with predictions the coefficient for the interaction between mar-ket-to-book and leverage is positive. There is also mixed support for the managerial incentives hypothesis--both the presence of stock options and multiple share classes are positively related to derivatives use. It is possible that these results might both support the managerial incentives hy-pothesis if managers on average hold in-the-money options and are thus using derivatives to pre-serve the value of their equity-like positions. We have also included several variables in the analysis as control variables. For example, we would like to condition our analysis on the unobserved levels of exposure. For IR exposure, leverage is our proxy (and is already included in the analysis). To identify firms more likely to use derivatives because of significant FX exposure we include our FX exposure dummy variable which is, as predicted, positively related to derivative use. We include the foreign debt dummy variable separately because it may be a source of exposure (e.g., for firms in developing coun-tries) or a FX hedging tool that substitutes for derivatives (e.g., for U.S. firms). The former sug-gest a positive relation between foreign debt and derivatives use, and the latter suggests a nega-tive relation. Thus, the estimated positive coefficient is consistent with foreign debt creating an FX exposure on average. The foreign listing dummy identifies firms with an ADR. This allows us to see how comparable our firms are to those examined in Lel (2002). Unsurprisingly, firms with ADRs are significantly more likely to hedge. Another control variable is the percent of each countries’ market capitalization included in our sample. Our concern is that we are more likely to get larger, and therefore more globally oriented companies, in countries where our sample in-cludes a smaller fraction of firms. If these types of firms are more likely to use derivatives this could create a sample bias. A negative coefficient on the “percentage market cap” variable 20 would signal a potential problem, however, we obtain a positive coefficient. Consequently, it appears unlikely that this potential bias is important. We include a dummy variable that is equal to one for U.S. firms in case they are uniquely different from the rest of firms. Finally, a dummy variable equal to one identifies firms when the observation-year in our sample is 2000 (as op-posed to 2001.) Because of changing macroeconomic or financial conditions, or simply because of the trend over time towards more derivatives use, there may exist a systematic difference be-tween the two years. The negative coefficient suggests that more firms hedged in 2001. Overall, these findings are quite similar to the composite of results from previous studies. A major contribution of this study is to compare derivatives use across many countries us-ing comparable data. The remaining columns in Table 4 show that the results discussed above differ surprisingly little across countries. In fact, for all cases where a variable is significant for the full sample, the sign of the significant coefficients for individual countries is the same. The two coefficients that are not significantly different from zero in the full sample appear to be im-portant for some individual countries. Specifically, the coverage ratio is positively (negatively) related to derivative use in the U.S. (U.K.) and U.S. firms with negative book values are margin-ally more likely to hedge. Overall, these results are very interesting since they indicate that fac-tors determining derivative usage are common across different countries. We also note the appar-ent increase in statistical power gained from examining firms across many countries. Even coef-ficients that are the same sign in most sub-samples but only statistically significant in a few coun-tries, are statistically significant in the full sample. Comparing the results for different types of risks also yields some noteworthy insights. Specifically, some factors are important for one type of risk but not another. For FX derivatives (Panel B of Table 4), the coefficients for income tax credits, multiple share classes, and stock options are no longer significantly greater than zero. The coefficient on leverage (which for gen-eral derivative use was significant in many cases and always positive) is only statistically signifi-cant in the full sample and the non-U.S. sample. Nonetheless, the coefficient for the coverage ratio is negative and significant. In contrast, for the interest rate derivatives (Panel C of Table 4) the coefficients for the leverage variable increase significantly in magnitude in all countries ex-cept Japan. As was the case for FX derivatives, the coefficients for income tax credits, multiple share classes, and stock options are statistically zero. A still different set of factors is significant in the regression for commodity price derivatives’ use (panel D of Table 4). Results are similar to 21 the general derivatives specification for leverage, the dividend dummy variable, size, income tax credits, market-to-book, and the market-to-book-leverage interaction term. However, the coeffi-cients for stock options and the coverage ratio are negative and significant. Interestingly, these results are consistent with our predictions. As noted, the results in Table 4 are from estimations using all firms with sufficient data. We repeat the estimations (results not tabled) using only firms defined as having exposures. For example, we do the estimation for FX derivatives users only with firms that have foreign assets, sales, or income. For general derivative users (N=5,234) and FX derivative users (N=3,625) the results are nearly identical for the full sample of firms. The only consistent difference is that lev-erage is no longer statistically different from zero for most countries or the full sample. For IR derivative users with above median leverage (N=3,354), leverage is again no longer significant in most sub-samples. In addition, the quick ratio is not significant in the full sample, but sometimes negative and sometimes positive for individual countries. Gross profit margin and stock options are positive and marginally significant in the full sample. For CP derivatives the differences are greatest probably because the sub-sample (defined by industry) is much smaller than the full sample, 877 firms compared to 6,273. Stock options retain the positive coefficient and becomes significant at the 3% level, but leverage, the coverage ratio, and the multiple share class dummy all turn out to be insignificant. Overall, examining only the firms most likely to have a signifi-cant exposure appears to strengthen the results on balance, at least for the multi-country samples. The exception appears to be leverage which is consistently not significant in the sub-samples. Results for individual countries also seem generally robust in that the signs and approximate magnitudes of most coefficients are very stable. The results from the analysis in this section, especially those by exposure type (Panels B through D), yield another intriguing finding. Although, different factors are important for differ-ent types of exposure, the factors are surprisingly robust across countries and sub-samples of countries. In only one case (dividends for CP derivatives) is a significant coefficient for a single country different from the significant coefficient for the full sample. Since it is extremely unlikely that these results would be obtained by chance, it suggests that: (1) Firm-specific factors are very important in determining risk management policy across countries. 22 (2) Contrary to risk management theory that treats all financial risks similarly, there are somewhat different factors that determine whether or not firms hedge different types of exposures. (3) The findings are not supportive of any one theory entirely. Some of the results are con-sistent with theoretical predictions and some are clearly counter to predictions. This third point may be the most important. While taken together, other studies have found each of the relations we document, most document a subset of our results that leads them to con-clude one or more theories are best supported by the data. Because our results are so strong and consistent, we feel that a logical conclusion from our analysis is that none of the theories are clearly supported by the data. An alternative, but somewhat farfetched, interpretation would be that our econometric specification is incomplete or incorrect and that the correct specification would resolve all of the inconsistencies. If our interpretation is correct, this suggests a need for additional insights into the motivations for derivative use. While not a proper theory, it appears that the findings are broadly consist with the naive hypothesis that more financially advanced companies are more likely to use derivatives. Revisit-ing the first column in Table 4, stock options, multiple share classes, more leverage, foreign busi-ness ventures and financing, large size, low quick ratio, dividend payouts, etc. are all suggestive of what one might (somewhat ambiguously) consider urbane financial policy. Consequently, it may simply be that derivatives are financial tools that firms integrate into their financial opera-tions once they obtain a certain level of sophistication. At a minimum, our results suggest that we should demand that theory provide additional insights or testable predictions beyond this sim-plistic proposition. 5.4 Multivariate Analysis of Country Factors In order to test the relation between derivatives use and country-specific factors, we estimate two types of models. The first is a by-country regression with usage rates as the dependent variables. The second is a LOGIT model using the full sample of firms that includes the firm-specific fac-tors discussed above as well as country-specific variables. In the by-country OLS regressions we limit the analysis to countries where we have 10 or more observations resulting in a maximum of 40 countries. We include average levels of some unadjusted firm variables that were consistently significant in the firm-level analysis in table 4. 23 Since the number of observations is limited we keep these to a minimum by focusing only on size, leverage, the quick ratio, and the dividend yield.21 As discussed in Section 3.2 we examine variables measuring derivative market access, country risk, rule of law, and manager-shareholder conflict. Since we have several proxies for each of these characteristics we consider a variety of alternative specifications using one proxy for each characteristic at a time. This allows us to judge robustness and manage the trade-off between variable inclusion and sample size. We esti-mate models for general, FX, IR, and CP derivatives. The results from this analysis are limited so we do not bother tabling them. None of the factors, either firm-specific or country-specific are consistently different from zero at the 5% level. In many specifications, some variables are significant. For example, rule-of-law is fre-quently positively related to general, FX and IR derivative use and shareholder rights is positively related to CP derivative use. The signs of the coefficients are fairly stable across specifications so the lack of significance may be due to low statistical power resulting from the necessarily lim-ited number of observations.22 In addition, important differences across countries are either roughly controlled for (e.g., firm size) or ignored altogether (e.g., differences in industry mix). For these reasons we turn to an alternative analysis that augments the specifications presented in Table 4 by including country specific variables. We also include estimates of the marginal effects in this analysis so we can compare the relative importance of different factors. Since companies based in the U.S. constitute about a third of the sample, we also include a dummy variable for U.S. firms.23 The results are presented in Table 5. We only table results using what we think are the best proxies for each hypothesis but we discuss the alternative proxies. We first point out that adding country variables has little effect on the coefficients of the firm-specific variables. For example, in the general derivatives specification, the coefficient on stock options is the only one to materially change significance level (it is no longer significant). Moreover, the magnitude of the coefficients in Table 5 is remarkably similar to those in Table 4. 21 In a regression with just averages of the firm-specific variables as explanatory variables, only size was signifi-cantly different from zero at the 10% level. 22 We tried various methods for improving power such as using the percentage of firms with exposure that use de-rivatives, weighting observations by the square root of the number of firms in the sample or the percent market capi-talization covered by our sample, and only including the (up to) 100 largest firms from each country. None of these adjustments significantly affected the negative quality of the results. We also estimate the models excluding the U.S. firms (results not tabled) and find that the results discussed subse-quently are robust to this sub-sample. 23 24 The positive coefficients on derivative market size support the market access hypothesis. The insignificant coefficient for CP derivatives may be because our measure of derivative market size does not include commodity derivatives. (To the best of our knowledge, country-level data for commodity derivatives trading excluding non-financial firms are not available). There is little evidence for the country risk hypothesis. The coefficient on the ICR composite index is never negative and statistically significant and it is significantly positive for FX derivatives. Other measures of country risk (ICR components, log of GDP, and trade magnitude) also fail to consis-tently support the predicted relation. There is mixed support for the rule-of law hypotheses. A strong legal environment is negatively related to FX derivatives use but positively related to IR and CP derivatives use. It is hard to interpret these results unless there are some systematic dif-ferences (that we are unaware of) between the contracts employed by users of different types of derivatives. For example, perhaps FX derivative contracts (or the firms that use them) are more likely to fall under the jurisdiction of (better) external legal systems than IR or CP derivative con-tracts.24 Alternative measures of rule-of-law are similarly inconclusive. The results do not sup-port our hypothesis regarding the agency relationship between managers and shareholders except for CP derivatives. Again, it is not clear why our hypothesis would apply only to these firms, unless by chance cross-country variation in the Dahlquist et al. (2003) measure of ownership concentration depended primarily on firms that also happened to use CP derivatives (e.g., mines, utilities, chemical producers, etc.). The alternative variable, shareholder rights, provides simi-larly ambiguous results. Overall, it appears that only the market access hypothesis has consistent support from the data. The economic importance of these factors (i.e., marginal effects) are relatively small com-pared to the most important firm-specific factors (generally, size, dividend payout, market-to-book, and leverage). For example, the biggest firm-specific effects are larger than the biggest country-specific effects for all but CP derivatives. Similarly, the average magnitude of the four most important firm-specific effects is always greater than the average magnitude of the four country-specific effects. Still, the results regarding market access support the conjectures of pol-icy makers that firms in some countries encounter costly constraints in the derivatives market. It 24 This might be the case if FX derivatives counter-parties are more likely to be global banks or dealers, and IR and CP derivatives counter-parties are more likely to be local banks or dealers. 25 is also worth noting that two of the firm-specific factors with the largest marginal effects, size and market-to-book, are counter to the predictions of theory. 5.5 Analysis of Sub-Samples One advantage of our large sample is that we can use it to identify sub-samples of firms that should be very likely (as well as very unlikely) to be motivated by particular theories of risk management. Specifically, in this section we present tests based on the applying a set of screens to our sample firms so as to identify sub-samples most likely (and unlikely) to be affected by expected financial distress costs, underinvestment costs, managerial incentives, etc. For example, to identify firms that are most likely to have high expected financial distress costs we create a sub-sample of firms which have leverage above the median, coverage ratios below the median, and tangible assets below the median.25 We also limit the analysis to firms that are identified as having an exposure. These screens identify a sub-sample of 1,048 firms. We compare derivative use in this sub-sample to the sub-sample identified by reversing the screen (i.e., leverage below the median, coverage ratios above the median, and tangible assets above the median). The results are shown in the first row of Table 6. Among firms identified as having high expected financial distress costs 68.0% use derivatives as compared to 69.2% of firms identified as having low ex-pected costs. The difference in usage across the two sub-samples is not statistically significant. Using the same methodology, we examine additional screens for financial distress and other hypotheses. We describe this here. Financial Distress 2 applies the same screens as Finan-cial Distress 1 but also restricts high cost firms to those in countries with weak creditor rights. In this case, high cost firms are much more likely to use derivatives. Similarly, Financial Distress 3 applies the same screens as Financial Distress 1 but also restricts high cost firms to those in coun-tries with high country risk. As for the first test, the difference in usage rates is insignificant. Financial Distress 4 and 5 highlight the importance of firm size in derivatives use. In particular, Financial Distress 4, screens on high leverage, low coverage, and small size. These firms hedge much less often than the firms with low leverage, high coverage, and large size. Financial Dis-tress 5 includes large firms instead of small firms in the high cost sub-sample. In this case the 25 All variables are country and industry adjusted. 26 results flip, and high cost firms hedge much more often than low cost firms. In sum, these results do not appear to provide a great deal of support for the financial distress theory. We also create a variety of screens to identify firms most likely to suffer from underin-vestment costs. To identify the sub-samples we rely on a variety of variables, some that we have already used such as market-to-book, the coverage ratio, and leverage, but especially some addi-tional variables such as capital expenditures, R&D, and average interest rates for 1999-2001 (to proxy for the cost of external capital). These additional variables should allow us to construct some tests that are independent of those in the prior analysis. In total we create 11 different screens designed to identify firms with high and low underinvestment costs. Without exception the tests based on these screens show that for firms with high underinvestment, hedging is more common. On average about 75% of high costs firms use derivatives versus about 55% of low cost firms. Screens based on managerial incentives attempt to identify firms where free cash flow may be a problem and there is likely to be less effective monitoring of managers. The first measure identifies high incentive firms as having low leverage, not paying a dividend, and having multiple share classes. The second measure adds a screen based on weak shareholder rights. However, these firms do not use derivative significantly more often than the firms with the opposite charac-teristics which should have low managerial incentives. Finally, we generate a sub-sample of firms based on the probable availability of substitutes for derivatives. In particular, we conjecture that firms with low levels of convertible debt, pre-ferred stock, and liquid assets (quick ratio) will be more likely to use derivatives than firms with high levels of the same factors. The sub-samples labeled Substitutes 1 show that in fact these firms are significantly more likely to use derivatives though the difference is usage rates is fairly small (69.2% versus 61.5%). Overall, these results are similar when considering only non-U.S. firms. Estimates for only U.S. firms (when possible) are somewhat weaker. 5.6 Derivative use and Firm Value Recently, several studies have examined the valuation effects of derivatives use. For example, Graham and Rogers (2002) find that derivatives increase the value of the debt tax-shield by al-lowing for higher leverage. We estimate simultaneous equation models similar to theirs (dis-cussed in the robustness section) and calculate valuation effects in line with their reported values. 27 More specifically, we also find that hedging is an important factor determining leverage and use the estimated coefficients to generate estimates for the increase in firm value ranging from about 1% to 5% depending on the specification and type of derivatives examined. The largest esti-mated values are for commodity price derivatives and the smallest are for foreign exchange de-rivatives. Our next test considers the relation between derivatives use and firm value more directly. Allayannis and Weston (2001) find that U.S. firms with foreign exchange exposure using FX derivatives have higher firm value. We undertake an analysis similar to theirs but examine firms from many countries as well as different types of derivatives. Our tests are substantially more powerful because we have many more firms and we do not suffer from the problem that value is likely to be related to exchange rate realizations. Specifically, Allayannis and Weston find that their results are strongest in years when the U.S. dollar appreciates against other currencies. These effects should wash out in our large international sample since one countries appreciating currency is another countries depreciation currency. Table 7 shows univariate tests of the relation between Tobin’s Q and derivatives use. We use the natural log of the ratio of the market value of the firm to the book value of total assets as our primary measure of Tobin’s Q. Mean, median and nonparametric Wilcoxon tests are em-ployed to assess differences between hedgers and non-hedgers. We examine firms with and with-out exposure separately since Allayannis and Weston hypothesize and document that the effect should only be pronounced for firms with exposures. The relation between firm value and general derivatives’ use is rather mixed, both for U.S. and Non-U.S. firms (Panel A). In particular, there are several significant results but they are not all consistent with the valuation hypothesis. For example, there appears to be a valuation effect for the 473 U.S. firms identified as not having an exposure. In addition, there is a negative valuation effect for general derivative users with FX exposure in the U.S. and both with and without FX exposure internationally. Similar results are found for general derivatives users partitioned by commodity price exposure. The only signifi-cant positive valuation effect for general derivative users both in the U.S. and internationally is for firms with interest rate exposure (i.e., firms with high leverage). Results in Panel B show a positive association between FX derivatives use and Tobin’s Q only for U.S. firms identified as not having an exposure. Interestingly, this finding is reversed for firms outside the U.S. This is consistent with the hypothesis that these tests are sensitive to con- 28 temporaneous exchange rate movements. Recall that the 2000-2001 period was characterized by a general strengthening of the U.S. dollar against other world currencies. Nevertheless, we can not explain why this result would be present only for firms we identify as not having an FX expo-sure. Firms that use interest rate derivatives (Panel C) have systematically higher firm value for all categories except international firms with low leverage. In contrast, Panel D shows that U.S. firms tend to have a higher Tobin’s Q if they do not use commodity price derivatives. Because these results are often counter to those documented by Allayannis and Weston and vary by type of derivative and location, we investigate further by undertaking a multivariate analysis. OLS regressions are used to control for other variables that may have an impact on Tobin’s Q. In particular, we use the log of total assets to control for firm size, as large firms may be more likely to use derivatives given fixed setup cost of hedging and potential economies of scale. Similarly, using the ratio of total debt to size controls for effects of financial leverage on firm value. If firms are restricted with regard to their access to financing, they are subject to valu-able discipline which capital markets impose on managers’ investment decisions. As a result, capital constrained firms may have high Qs as they are forced to undertake only positive NPV projects (Lang and Stulz, 1994; Servaes, 1996). A dummy variable with value 1 if the company has paid a dividend (and 0 otherwise) is used to proxy for the availability of internal funds for investment projects. Firm profitability is expected to have a positive impact on the valuation of a company and is controlled for by the return on assets. Since firms with large growth opportunities are more likely to use derivatives (Froot, Scharfstein, and Stein, 1993) and since growth opportunities are related to firm value (Myers, 1977), we control for investment growth with the ratios R&D to sales and capital expenditures to sales. Industrial and geographic diversification has been shown to impact firm value as well. In particular, Berger and Ofek (1995), Lang and Stulz (1994) and Servaes (1996) present empirical for a discount of conglomerate diversification. We control for industrial diversification with the number of business segments (SIC codes) that make up the company’s revenue and a dummy variable with value 1 if the firm operates in more than one segment (and 0 otherwise), respectively. Similarly, there may be a discount or premium for oper-ating in several countries (see e.g. Coase, 1937; Dunning, 1973; Morck and Yeung, 1991; Bod-nar, Tang and Weintrop, 1997). Including the ratio of foreign sales to total sales in the regression controls for the impact of geographic diversification, since it indicates operations in more than 29 one country. All regressions also include a year dummy and a dummy variable for negative book values. Results of these multivariate tests of are presented in Table 8. We table three different specifications since the inclusion of the R&D and Capex ratios limit our sample size considera-bly. The coefficient for the hedging variable is the one of interest. Panel A reveals some support for a positive value effect of general derivatives use but only for firms without exposure. Similar results are found for FX derivatives (Panel B). Interest rate derivatives (Panel C) are generally associated with positive valuation effects regardless of exposure while there are no significant effects for CP derivative users. Statistically, the findings are strongest for firms with interest rate exposure. These firms have significantly higher Tobin’s Q when using interest rate derivatives, for both U.S. as well as international firms. Investors reward users of interest rate derivatives with a hedging premium from 2.4% to 4.7% of firm value. As an aside we note that several of the control variables are significant and often have the expected sign. Size, leverage and divi-dends have significant negative coefficients, while geographic diversification, R&D and capital expenditures have a positive impact on value. For robustness we also (1) examine the ratio of the market value of the firm to total sales as an alternative estimate of Tobin’s Q, and (2) conduct the analysis without taking the natural log of Q. We also examine other sub-samples. For example, we consider only U.S. firms with more than $500 million in assets as do Allayannis and Weston. None of these alternatives lead to materially different outcomes. Assuming that the results are not due to chance or model misspecification, it is difficult to interpret the findings. In particular, it is hard to rationalize the results for interest rate derivatives. If there exist opportunities to increase value only with interest rate derivatives, then there must be some property of their market or aspect of their use that is different from FX or CP derivatives. For example, interest rate derivatives tend to have longer maturities than FX or CP derivatives. Payoffs from IR derivative are also less likely to be directly related to operating cash flows since their primary use is for adjusting the duration of liabilities. We leave the examination of these issues to future research. 6 Alternative Specifications and Robustness Checks We undertake a set of additional robustness checks to gauge the reliability of our results. One primary concern is that for some firms in our sample, disclosure of derivative use is not mandated by local accounting standards. This may bias the results, especially for country vari- 30 ables where values are for some reason correlated with disclosure. To investigate this possible bias, we create a sub-sample of firms for which we know disclosure is mandatory. These are firms in the so-called G4+1 group (U.S., U.K., Canada, Australia, Canada) and New Zealand as well as firms conforming to international accounting standards. This leads to a sub-sample of 4,620 firms. Table 9 repeats the analysis originally reported in Table 5 using only these firms. Overall, the results are quite similar.26 As noted in the previous section, we also estimate a system of simultaneous equations simi-lar to those in Géczy, Minton, and Shrand (1997) and Graham and Rogers (2002). Specifically, we include separate equations for leverage and derivatives use. Results are presented in Table 10. In the leverage equation we include additional variables that reduce the sample size to 4,927 firms. The primary differences in estimated coefficients in the hedging equations are that the quick ratio and the market-to-book ratio are no longer significantly negative. The marginal ef-fects for leverage increase in all cases. For the interaction term between the market-to-book ratio and leverage the results are sometimes stronger (IR and CP derivatives) and sometimes weaker (General and FX derivatives). In the leverage equation, the appropriate hedging variable is al-ways positive and significant. Taken together, these results do not alter our general conclusions. To compare our results with those presented in Lel (2002) we also estimate our model us-ing only the firms with American Depository Receipts. Results are presented in Table 11. Quali-tatively, the results are similar in so far as the signs of coefficients to those in the full sample es-timation, but an obvious decrease in power is revealed by the limited number of statistically sig-nificant coefficients. In this sub-sample almost all of the explanatory power comes from only two variables: firm size and the dividend yield. This result highlights the importance of examin-ing a large sample of international firms. 7 Summary and Conclusion This study examines the use of derivatives by 7,292 firms in 48 countries that together comprise about 80% of the global market capitalization of non-financial companies. Our study is the first comprehensive examination of hedging practices and, in contrast to most prior studies, examines 26 For general derivatives users, the closely held variable is no longer significant. For FX derivatives, leverage, gross profit margin, the ICR composite index, and legality are no longer significant. For IR derivatives, coverage becomes significant. For CP derivatives, coverage and market-to-book become insignificant. 31 the use of several types of derivatives (foreign exchange, interest rate, and commodity price). Overall, we find that derivative use by international firms is widespread. More than half of the sample firms (59.8%) use some type of derivative. Roughly a third of firms use FX and IR de-rivatives and 10% use CP derivatives. Beyond its descriptive content, there are several other valuable aspects of our sample. First, the large size increases power of statistical tests examining the determinants of hedging and thus helps sheds light on the ambiguity of studies investigating mostly U.S. firms. Second, the wide array of countries represented allows us to examine if similar factors are associated with deriva-tive use in different countries. Third, we can similarly compare the importance of country-specific factors and firm-specific factors. Forth, we can use the broad scope of the sample to identify reasonably sized sub-samples of firms that may be of particular interest (e.g., have high expected financial distress costs). Fifth, the international sample means we are less likely to have our tests (especially valuation tests) biased by exchange rate movements since these should wash out in the full sample. Overall, the results appear to raise as many questions as they answer. For example, we are able to resolve the often conflicting conclusions from prior studies regarding the determinants of hedging. In short, almost all of the factors studied by prior researchers appear to be associated with derivative use. This suggests that the mixed results from prior studies is due to a lack of power. This is the good news. The bad news is that the strong results from our tests are fre-quently consistent with theoretical predictions but sometimes unambiguously inconsistent. This suggest a need for both further theoretical and empirical analysis. On the theoretical side, it im-plies a need for rethinking the factors that are associated with derivatives use. On the empirical side, we propose that research should examine more general (or complex) models of firm risk that may be able to resolve the apparent conflicts with theory that we document. For example, it could be that credit risk is an important factor for determining derivatives use. New theory could suggest a framework which could in turn help specify an appropriate empirical test for this rela-tion. This might, for instance, help explain why profitability is in many cases positively related to derivative use (contrary to the prediction from the simple financial distress theory). It also appears that new insights are needed to explain the results regarding firm valuation and derivative use. In particular, additional analysis needs to be undertaken to understand the precise mechanisms by which derivative use affects firm value. Is it through the tax shield as 32 suggested by the positive relation between hedging and leverage? Is the effect stronger for users of interest rate derivatives, because of risk-bearing by firms? Perhaps corporate governance plays a role. Alternatively, is it possible that some firms are able to profitably take advantages of predictable changes in the yield curve or credit spreads with derivatives as suggested by Titman (1992) and implied by Baker, Greenwood, and Wurgler (2002)? We leave these questions to subsequent research. An important (and robust) conclusion is that firms, typically in less developed countries, with less liquid derivatives markets are less likely to hedge. This supports the assertions of finan-cial policy makers that derivatives have been important in limiting the severity of economic downturns in developed economies. Thus, there appears to be the potential for an increase in so-cial welfare if relatively inexpensive ways to improve access to derivative markets can be identi-fied. Overall, the results of this paper show that studying companies headquartered in the U.S. is not sufficient for understanding the risk management practices of non-financial firms. 33 References Alkeback, P., and N. 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The tables in the 2001 BIS survey with the corresponding data are as follows: Panel A, Table E-36; Panel B, Tables E-8, E-9, E-10, and E-11; Panel C, Tables E-49 and E-50. Panel A: Daily Turnover in Foreign Exchange Forwards and Swaps Forward ContractsFX SwapsTotal 198922165187 199270457527 1995115777892 19981541,0521,206 20011649331,097 Panel B: Daily Derivatives Turnover Attributed to U.S. Firms in 2001 Number ofCountries Foreign Exchange Derivatives All Countries U.S. Only U.S. Percent of TotalInterest Rate Derivatives All Countries U.S. Only U.S. Percent of Total 47 All Types ofCounterparties 1,34218413.7%81214618.1% Non-FinancialFirms Only 4279823.0%25728.3% 37 Panel C: Notional Values of Derivatives Outstanding with Non-financial Firms Foreign Exchange199519982001 Forwards* Swaps Option SoldOptions Bought Total: Non-financial FirmsTotal - All Types, All FirmsNon-financial Percent of Total *Includes FX swaps for FX derivatives Interest Rate1995199820013213,1215053514,298 5644,1138626286,167 8435,0591,0525767,531 1,7897982922573,1362,6736888927204,9732,5241,2153403784,457 13,09522,05520,43523.9%22.5%21.8%26,64548,12475,81316.1%12.8%9.9% 37 Table 2: Summary Statistics of Derivatives Use The table shows summary statistics of derivatives use by country, region, industry, and for all firms. In particular, it presents the number of firms and the percentage of firms using derivatives. Separately for foreign exchange rate derivatives, interest rate derivatives and commodity price derivatives, the percentage of firms using derivatives in general and a particular instrument (forward, future, swap, option) is shown. _____Foreign Exchange Derivatives______ _______Interest Rate Derivatives_______ ______Commodity Price Derivatives______ Firms User General Forward Future Swap Option General Forward Future Swap Option General Forward Future Swap Option ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ Argentina 11 63.6 63.6 9.1 27.3 18.2 0.0 36.4 0.0 0.0 36.4 27.3 18.2 0.0 0.0 18.2 18.2 Australia 301 65.4 51.5 48.5 0.7 8.6 17.9 41.9 2.0 1.7 38.9 15.0 14.0 8.3 2.3 3.7 4.3 Austria 44 59.1 56.8 36.4 9.1 18.2 22.7 25.0 0.0 0.0 20.5 9.1 6.8 0.0 2.3 4.5 2.3 Bahamas 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Belgium 65 46.2 26.2 13.8 1.5 6.2 6.2 21.5 0.0 0.0 20.0 1.5 4.6 1.5 0.0 3.1 0.0 Bermuda 4 75.0 50.0 50.0 0.0 0.0 25.0 50.0 0.0 0.0 50.0 0.0 0.0 0.0 0.0 0.0 0.0 Brazil 19 73.7 47.4 15.8 0.0 21.1 10.5 10.5 0.0 0.0 5.3 5.3 15.8 0.0 0.0 0.0 0.0 Canada 599 60.3 45.2 32.6 0.5 8.2 7.7 26.7 0.7 0.0 23.9 3.2 18.2 6.5 2.7 5.0 6.2 Cayman Islands 1 100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Chile 13 100.0 84.6 53.8 7.7 23.1 7.7 53.8 0.0 0.0 38.5 7.7 15.4 7.7 0.0 7.7 7.7 China 36 16.7 5.6 5.6 0.0 2.8 0.0 5.6 0.0 0.0 5.6 0.0 5.6 0.0 5.6 0.0 0.0 Czech Republic 23 30.4 8.7 8.7 0.0 4.3 4.3 17.4 0.0 0.0 13.0 0.0 0.0 0.0 0.0 0.0 0.0 Denmark 88 85.2 67.0 59.1 1.1 11.4 13.6 22.7 0.0 1.1 21.6 5.7 4.5 0.0 1.1 2.3 1.1 Egypt 1 100.0 100.0 0.0 0.0 100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Finland 105 63.8 56.2 40.0 5.7 19.0 26.7 38.1 4.8 5.7 30.5 18.1 7.6 1.0 2.9 1.0 2.9 France 162 64.8 49.4 31.5 4.3 22.8 24.7 42.6 0.6 1.9 37.0 13.6 3.7 0.0 1.2 1.2 0.6 Germany 410 44.9 36.8 21.2 5.4 10.7 12.4 23.7 0.2 1.2 17.8 9.5 4.6 1.0 1.7 0.5 0.5 Greece 19 15.8 15.8 10.5 0.0 5.3 5.3 10.5 0.0 0.0 10.5 0.0 5.3 0.0 5.3 0.0 0.0 Hong Kong 337 22.8 16.6 11.0 0.3 3.6 1.2 6.5 0.0 0.0 5.0 1.5 0.3 0.0 0.0 0.0 0.0 Hungary 14 42.9 35.7 28.6 0.0 0.0 14.3 14.3 0.0 0.0 7.1 0.0 7.1 0.0 0.0 7.1 0.0 India 44 70.5 52.3 50.0 0.0 6.8 0.0 11.4 0.0 0.0 11.4 0.0 4.5 0.0 2.3 0.0 0.0 Indonesia 9 44.4 44.4 44.4 0.0 11.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Ireland 51 88.2 72.5 62.7 0.0 25.5 7.8 51.0 2.0 2.0 45.1 7.8 13.7 7.8 2.0 5.9 3.9 Israel 68 67.6 61.8 32.4 0.0 1.5 16.2 10.3 0.0 0.0 7.4 2.9 1.5 0.0 1.5 0.0 0.0 Italy 99 58.6 30.3 22.2 2.0 14.1 4.0 30.3 3.0 0.0 24.2 4.0 3.0 0.0 1.0 2.0 0.0 Japan 362 80.9 75.1 70.7 0.3 32.9 18.2 60.2 0.0 0.6 59.1 14.4 9.9 3.0 3.9 1.9 1.7 Jordan 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Korea, Republic of 25 68.0 52.0 36.0 8.0 16.0 8.0 24.0 0.0 0.0 24.0 0.0 8.0 4.0 0.0 0.0 4.0 Luxembourg 11 81.8 54.5 54.5 0.0 9.1 27.3 27.3 0.0 0.0 18.2 9.1 9.1 0.0 9.1 0.0 0.0 Malaysia 296 20.9 14.9 10.8 0.0 1.4 1.0 4.1 0.0 0.0 3.7 1.0 1.7 0.0 1.7 0.0 0.0 Mexico 39 61.5 35.9 23.1 5.1 7.7 10.3 38.5 2.6 0.0 35.9 0.0 12.8 2.6 7.7 2.6 2.6 Netherlands 134 56.7 47.0 37.3 1.5 18.7 13.4 34.3 2.2 0.0 28.4 9.7 4.5 0.0 0.7 0.7 0.7 New Zealand 45 95.6 80.0 75.6 0.0 22.2 40.0 77.8 4.4 0.0 75.6 31.1 20.0 2.2 0.0 13.3 11.1 Norway 86 67.4 48.8 40.7 3.5 18.6 16.3 27.9 0.0 1.2 23.3 5.8 7.0 2.3 2.3 1.2 2.3 (continued) 38 Table 2: Summary Statistics of Derivatives Use (continued) _____Foreign Exchange Derivatives______ _______Interest Rate Derivatives_______ ______Commodity Price Derivatives______ Firms User General Forward Future Swap Option General Forward Future Swap Option General Forward Future Swap Option ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ Peru 2 100.0 50.0 50.0 0.0 50.0 0.0 0.0 0.0 0.0 0.0 0.0 100.0 50.0 0.0 50.0 100.0 Philippines 14 57.1 50.0 42.9 0.0 21.4 0.0 21.4 0.0 0.0 21.4 0.0 7.1 0.0 0.0 7.1 0.0 Poland 13 46.2 38.5 23.1 0.0 23.1 30.8 15.4 7.7 0.0 7.7 7.7 7.7 0.0 0.0 0.0 0.0 Portugal 6 66.7 50.0 16.7 0.0 33.3 16.7 33.3 0.0 0.0 33.3 0.0 0.0 0.0 0.0 0.0 0.0 Singapore 226 54.0 49.1 41.2 0.0 7.1 3.5 11.1 0.4 0.0 9.7 1.8 2.2 0.0 0.0 1.8 0.0 South Africa 58 89.7 87.9 84.5 0.0 8.6 13.8 36.2 0.0 0.0 31.0 5.2 15.5 8.6 5.2 0.0 3.4 Spain 29 58.6 27.6 17.2 3.4 10.3 10.3 34.5 3.4 0.0 34.5 13.8 24.1 3.4 6.9 6.9 6.9 Sweden 143 58.0 32.2 23.8 2.1 8.4 9.1 15.4 2.1 1.4 12.6 2.8 5.6 0.7 1.4 1.4 1.4 Switzerland 123 76.4 68.3 58.5 4.1 15.4 23.6 41.5 3.3 0.0 34.1 6.5 6.5 2.4 0.8 0.8 0.8 Thailand 26 69.2 61.5 50.0 0.0 34.6 0.0 19.2 0.0 0.0 19.2 0.0 0.0 0.0 0.0 0.0 0.0 Turkey 3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 United Kingdom 882 64.9 54.4 48.5 0.0 17.2 7.9 35.9 0.3 0.1 31.6 10.7 4.0 1.5 1.5 1.4 0.7 United States 2242 64.0 36.9 30.2 0.2 6.2 7.1 40.3 0.3 0.3 35.8 6.8 16.3 3.8 6.2 5.1 3.2 Venezuela 2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 US & Canada 2841 63.2 38.6 30.7 0.3 6.6 7.2 37.4 0.4 0.2 33.3 6.1 16.7 4.4 5.4 5.1 3.8 Europe 2510 60.8 48.0 38.0 2.3 15.3 12.4 31.6 1.0 0.8 26.7 9.1 5.1 1.2 1.6 1.4 1.0 Asia & Pacific 1721 51.0 42.9 38.0 0.3 12.1 9.0 26.7 0.5 0.4 25.3 7.1 6.1 2.2 1.7 1.7 1.5 Africa/Middle East 128 77.3 73.4 55.5 0.0 5.5 14.8 21.9 0.0 0.0 18.0 3.9 7.8 3.9 3.1 0.0 1.6 Latin Amer./Carib. 92 69.6 47.8 25.0 6.5 14.1 8.7 32.6 1.1 0.0 28.3 5.4 15.2 3.3 3.3 5.4 6.5 OECD 6123 63.6 45.6 37.2 1.2 12.0 10.8 36.8 0.7 0.5 32.7 8.3 11.4 3.2 3.5 3.4 2.6 Non-OECD 1169 39.9 33.1 25.1 0.4 5.6 3.3 10.0 0.1 0.0 8.6 1.9 3.0 0.6 1.0 0.8 0.6 United States 2242 64.0 36.9 30.2 0.2 6.2 7.1 40.3 0.3 0.3 35.8 6.8 16.3 3.8 6.2 5.1 3.2 Non-US 5050 58.0 46.6 37.5 1.4 13.1 10.7 29.1 0.8 0.5 25.7 7.5 7.2 2.3 1.8 2.0 1.9 Automobiles 165 71.5 57.6 41.8 1.8 17.0 10.3 43.0 0.6 0.6 40.0 8.5 6.1 2.4 1.2 1.2 0.0 Chemicals 175 78.9 64.0 58.9 1.7 16.6 16.0 49.7 0.6 0.6 46.9 8.6 17.7 4.6 5.1 3.4 4.0 Clothing 127 66.9 49.6 37.8 1.6 8.7 13.4 32.3 0.0 0.0 26.8 4.7 6.3 0.0 3.9 2.4 0.8 Construction 441 59.0 40.1 30.2 0.7 14.5 8.2 36.3 0.9 0.5 31.7 7.7 6.6 0.7 1.8 3.2 0.9 Consumer Goods 279 54.1 44.8 36.6 0.7 14.0 15.4 31.5 1.8 1.4 27.2 7.5 2.5 0.4 1.4 0.0 1.1 Durables 214 58.4 50.5 44.4 1.4 8.4 12.6 29.4 0.9 0.5 25.7 4.7 5.6 0.5 1.4 1.9 0.0 Fabr. Products 48 70.8 62.5 54.2 2.1 25.0 10.4 43.8 0.0 0.0 37.5 12.5 10.4 6.3 2.1 0.0 2.1 Food 353 69.1 50.7 42.2 1.4 18.1 12.2 45.6 1.7 1.4 42.5 10.2 16.7 4.0 12.5 2.3 2.8 Machinery 911 67.1 58.0 50.6 1.5 8.9 12.6 29.2 0.7 0.1 25.6 6.3 3.6 1.0 0.9 0.5 0.2 Mines 240 61.3 44.6 36.7 0.0 6.3 12.1 21.3 0.4 0.0 17.5 6.3 36.7 25.0 4.2 2.9 17.1 Miscellaneous 2901 49.9 35.4 27.7 0.8 7.5 7.0 25.2 0.2 0.4 21.7 6.4 2.8 0.4 0.7 0.6 0.3 Oil 280 72.5 37.5 28.9 1.4 11.1 8.9 37.9 1.4 0.4 33.9 5.7 48.9 7.1 14.6 24.6 11.8 Retail 407 58.2 35.9 29.0 0.5 9.1 6.9 35.9 0.2 0.5 30.5 7.4 3.7 0.7 1.0 0.5 0.0 Steel 164 71.3 59.1 51.8 1.8 16.5 11.0 40.2 1.2 0.0 37.8 8.5 29.9 14.6 12.2 4.3 3.7 Transportation 352 69.9 51.7 42.0 1.1 17.0 12.2 47.4 0.9 0.3 44.3 11.6 16.8 3.4 2.6 8.0 3.7 Utilities 235 84.3 42.6 27.2 2.1 28.5 8.9 63.0 1.7 0.9 59.1 13.6 45.5 11.5 17.4 18.7 14.9 All firms 7292 59.8 43.6 35.3 1.1 11.0 9.6 32.5 0.6 0.5 28.8 7.3 10.0 2.8 3.1 2.9 2.3 39 Table 3: Univariate Tests of Derivatives Use The table shows the number of observations (N), the mean, median and standard deviation of different variables for hedgers and non-hedgers. The last column presents p-values of Wilcoxon rank sum tests. Panel A refers to variables for incentives of hedging. Panel B lists proxies of exposure. Panel C presents country-level characteristics of the legal origin. Panel D specifies variables of shareholder right protection. Panel E reports variables of creditor right protection. Panel F lists proxies of law enforcement. Panel G reports meas-ures of ownership concentration, and Panel H presents control variables. Panels A and B distinguish between U.S. firms and interna-tional firms, whiles Panels C to H report results including and excluding U.S. firms. Panel A: All firms ____________Hedger_____________ _____________Nonhedger______________ _Tests__ Variable N Mean Median Std.Dev. N Mean Median Std.Dev. Wilcoxon ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ Firm-specific variables Leverage 4222 0.028 -0.011 0.23 2826 -0.042 -0.101 0.23 0.000 Coverage_3y 4362 0.743 0.888 5.22 2930 -1.106 -1.095 6.30 0.000 Quick_Ratio 4126 -0.248 -0.436 1.44 2690 0.381 -0.267 2.33 0.000 Tangible_Assets 3854 -0.004 0.024 0.14 2567 0.006 0.029 0.14 0.000 Logsize 4159 0.430 0.354 1.61 2822 -0.633 -0.624 1.46 0.000 Logassets 4181 0.458 0.358 1.53 2818 -0.680 -0.679 1.36 0.000 Dividend 4362 0.577 1.000 0.49 2930 0.385 0.000 0.49 0.000 GrossProfitMargin_3y 4362 0.054 0.096 0.36 2930 -0.081 0.013 0.44 0.000 D_Income_Tax_Credit 4362 0.033 0.000 0.18 2930 0.013 0.000 0.11 0.000 Market_to_Book 4362 -0.029 -0.583 2.76 2930 0.044 -0.649 3.17 0.027 MB_Leverage 4222 0.483 0.363 0.73 2826 0.279 0.110 0.56 0.000 R_D_to_Sales 2085 -0.035 -0.032 0.27 1077 0.067 -0.027 0.53 0.024 CapEx 4132 -0.006 -0.030 0.19 2632 0.009 -0.040 0.27 0.000 LogTobinQ1 4205 -0.035 -0.068 0.63 2794 0.052 -0.023 0.76 0.000 LogTobinQ2 4168 -0.060 -0.077 0.94 2680 0.094 -0.024 1.22 0.000 MultShareClass 4362 0.153 0.000 0.36 2930 0.083 0.000 0.28 0.000 Stock_Options 4362 0.824 1.000 0.38 2930 0.793 1.000 0.41 0.000 ConvDebt 3811 0.001 -0.006 0.03 2385 -0.002 -0.008 0.03 0.000 PrefStock 4153 0.000 -0.004 0.02 2763 -0.001 -0.004 0.02 0.000 Foreign_Assets 2318 0.017 -0.029 0.18 1244 -0.033 -0.062 0.17 0.000 Foreign_Income_3y 1380 0.029 -0.023 0.32 723 -0.056 -0.100 0.25 0.000 Foreign_Sales 3178 0.025 -0.005 0.24 1762 -0.046 -0.092 0.27 0.000 FX_Exposure 4362 0.595 1.000 0.49 2930 0.397 0.000 0.49 0.000 Foreign_Debt 4362 0.245 0.000 0.43 2930 0.152 0.000 0.36 0.000 IR_Exposure 4222 0.598 1.000 0.49 2826 0.353 0.000 0.48 0.000 CP_Exposure 4362 0.162 0.000 0.37 2930 0.081 0.000 0.27 0.000 Exposure 4362 0.848 1.000 0.36 2930 0.618 1.000 0.49 0.000 NumIndSeg 4325 3.696 3.000 1.97 2907 3.284 3.000 1.86 0.000 Foreign_Listing 4362 0.125 0.000 0.33 2930 0.050 0.000 0.22 0.000 NegBookValue 4362 0.025 0.000 0.16 2930 0.025 0.000 0.16 0.468 Country-specific variables DerMktRank 4362 38.115 43.000 9.29 2930 36.084 41.000 11.40 0.000 OECD 4362 0.893 1.000 0.31 2930 0.760 1.000 0.43 0.000 GDP_Capita 4361 26.991 23.679 8.93 2930 24.275 23.679 9.82 0.000 ICR_Composite 4358 83.251 84.250 4.05 2929 82.258 84.250 4.40 0.000 LogGDP 4361 28.006 27.978 1.69 2930 27.633 27.889 1.74 0.000 LogEXIM_GDP 4358 3.803 4.024 0.78 2928 4.136 4.130 0.94 0.000 ICR_Financial 4358 38.414 37.000 3.70 2929 38.851 37.000 3.42 0.000 ICR_Economic 4358 42.169 42.000 2.14 2929 42.138 42.000 2.04 0.000 ICR_Political 4358 85.919 90.000 7.44 2929 83.526 88.000 8.96 0.000 KKZ_RuleofLaw 4358 1.374 1.422 0.40 2929 1.320 1.333 0.40 0.000 Civil_Law 4362 0.296 0.000 0.46 2930 0.268 0.000 0.44 0.004 Creditor_Rights 4358 1.950 1.000 1.26 2925 2.296 2.000 1.38 0.000 Legality 4324 20.212 20.850 1.80 2865 19.898 20.440 1.87 0.000 Shareholder_Rights 4358 4.142 5.000 1.27 2928 4.033 5.000 1.39 0.003 Closely_Held 4358 0.260 0.235 0.19 2928 0.306 0.375 0.20 0.000 40 (continued) Table 3: Univariate Tests of Derivatives Use (continued) Panel B: Non-U.S. firms ____________Hedger_____________ _____________Nonhedger______________ _Tests__ Variable N Mean Median Std.Dev. N Mean Median Std.Dev. Wilcoxon ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ Firm-specific variables Leverage 2814 0.027 -0.003 0.23 2033 -0.037 -0.089 0.25 0.000 Coverage_3y 2927 0.624 0.888 5.20 2123 -0.860 -0.950 6.15 0.000 Quick_Ratio 2743 -0.216 -0.367 1.40 1938 0.306 -0.290 2.28 0.000 Tangible_Assets 2696 -0.001 0.022 0.13 1981 0.001 0.023 0.14 0.149 Logsize 2787 0.466 0.377 1.65 2031 -0.639 -0.650 1.43 0.000 Logassets 2800 0.485 0.384 1.56 2023 -0.671 -0.651 1.35 0.000 Dividend 2927 0.653 1.000 0.48 2123 0.454 0.000 0.50 0.000 GrossProfitMargin_3y 2927 0.072 0.130 0.38 2123 -0.100 -0.003 0.44 0.000 D_Income_Tax_Credit 2927 0.027 0.000 0.16 2123 0.011 0.000 0.10 0.000 Market_to_Book 2927 -0.044 -0.584 2.66 2123 0.060 -0.574 2.98 0.377 MB_Leverage 2814 0.484 0.358 0.79 2033 0.282 0.134 0.54 0.000 R_D_to_Sales 1200 -0.036 -0.039 0.27 551 0.078 -0.040 0.58 0.203 CapEx 2736 -0.004 -0.024 0.18 1860 0.006 -0.039 0.26 0.000 LogTobinQ1 2803 -0.038 -0.074 0.62 2014 0.053 -0.032 0.75 0.000 LogTobinQ2 2773 -0.054 -0.091 0.95 1921 0.079 -0.032 1.22 0.005 Acqu_Assets 2365 0.003 -0.012 0.06 1547 -0.004 -0.012 0.04 0.241 Debt_Maturity 2678 0.019 0.062 0.29 1727 -0.029 -0.014 0.33 0.000 MultShareClass 2927 0.164 0.000 0.37 2123 0.085 0.000 0.28 0.000 Stock_Options 2927 0.746 1.000 0.44 2123 0.734 1.000 0.44 0.163 ConvDebt 2454 0.002 -0.005 0.03 1616 -0.003 -0.005 0.03 0.000 PrefStock 2767 0.001 -0.002 0.02 1986 -0.001 -0.001 0.02 0.001 Foreign_Assets 1223 0.019 -0.025 0.21 695 -0.034 -0.093 0.21 0.000 Foreign_Income_3y 741 0.021 -0.033 0.34 344 -0.045 -0.092 0.32 0.000 Foreign_Sales 1981 0.024 0.004 0.27 1134 -0.043 -0.099 0.29 0.000 FX_Exposure 2927 0.585 1.000 0.49 2123 0.405 0.000 0.49 0.000 Foreign_Debt 2927 0.324 0.000 0.47 2123 0.200 0.000 0.40 0.000 IR_Exposure 2814 0.591 1.000 0.49 2033 0.373 0.000 0.48 0.000 CP_Exposure 2927 0.157 0.000 0.36 2123 0.090 0.000 0.29 0.000 Exposure 2927 0.833 1.000 0.37 2123 0.635 1.000 0.48 0.000 NumIndSeg 2897 4.034 4.000 2.03 2103 3.589 3.000 1.92 0.000 Foreign_Listing 2927 0.186 0.000 0.39 2123 0.069 0.000 0.25 0.000 NegBookValue 2927 0.017 0.000 0.13 2123 0.025 0.000 0.16 0.024 Country-specific variables DerMktRank 2927 35.230 37.000 10.16 2123 33.075 37.000 12.11 0.000 OECD 2927 0.840 1.000 0.37 2123 0.669 1.000 0.47 0.000 GDP_Capita 2926 23.092 22.960 8.52 2123 20.221 22.800 8.57 0.000 ICR_Composite 2923 82.761 85.000 4.88 2122 81.500 83.250 4.97 0.000 LogGDP 2926 27.068 27.257 1.26 2123 26.765 26.690 1.21 0.000 LogEXIM_GDP 2923 4.181 4.195 0.69 2121 4.556 4.328 0.76 0.000 ICR_Financial 2923 39.354 38.500 4.20 2122 39.746 38.750 3.64 0.000 ICR_Economic 2923 42.251 42.000 2.61 2122 42.191 42.000 2.39 0.001 ICR_Political 2923 83.916 87.000 8.39 2122 81.065 85.000 9.43 0.000 KKZ_RuleofLaw 2923 1.433 1.549 0.48 2122 1.345 1.483 0.47 0.000 Civil_Law 2927 0.441 0.000 0.50 2123 0.369 0.000 0.48 0.000 Creditor_Rights 2923 2.416 2.000 1.31 2118 2.790 3.000 1.32 0.000 Legality 2889 19.895 20.410 2.13 2058 19.525 20.410 2.09 0.000 Shareholder_Rights 2923 3.721 4.000 1.36 2121 3.665 4.000 1.47 0.360 Closely_Held 2923 0.349 0.384 0.17 2121 0.392 0.427 0.16 0.000 41 (continued) Table 3: Univariate Tests of Derivatives Use (continued) Panel C: U.S. firms ____________Hedger_____________ _____________Nonhedger______________ _Tests__ Variable N Mean Median Std.Dev. N Mean Median Std.Dev. Wilcoxon ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ Leverage 1408 0.030 -0.029 0.22 793 -0.054 -0.118 0.20 0.000 Coverage_3y 1435 0.987 0.869 5.26 807 -1.755 -1.884 6.66 0.000 Quick_Ratio 1383 -0.312 -0.598 1.52 752 0.573 -0.215 2.46 0.000 Tangible_Assets 1158 -0.010 0.032 0.15 586 0.020 0.064 0.15 0.000 Logsize 1372 0.356 0.297 1.55 791 -0.618 -0.573 1.55 0.000 Logassets 1381 0.405 0.326 1.46 795 -0.704 -0.732 1.38 0.000 Dividend 1435 0.421 0.000 0.49 807 0.206 0.000 0.40 0.000 GrossProfitMargin_3y 1435 0.018 0.028 0.32 807 -0.032 0.053 0.44 0.333 ROA_3y 1435 0.027 0.046 0.21 807 -0.047 0.031 0.28 0.000 D_Income_Tax_Credit 1435 0.044 0.000 0.20 807 0.019 0.000 0.14 0.001 Income_Tax_Credit 1287 0.013 -0.027 0.55 718 -0.023 -0.039 0.25 0.005 Market_to_Book 1435 -0.000 -0.571 2.96 807 0.000 -0.944 3.63 0.001 MB_Leverage 1408 0.482 0.387 0.59 793 0.272 0.054 0.63 0.000 R_D_to_Sales 885 -0.033 -0.028 0.27 526 0.056 -0.026 0.46 0.028 CapEx 1396 -0.010 -0.038 0.21 772 0.018 -0.042 0.27 0.367 LogTobinQ1 1402 -0.028 -0.056 0.64 780 0.050 0.014 0.77 0.011 LogTobinQ2 1395 -0.071 -0.064 0.92 759 0.131 -0.003 1.24 0.005 Acqu_Assets 1376 0.003 -0.018 0.06 774 -0.006 -0.018 0.04 0.047 Debt_Maturity 1297 0.032 0.102 0.25 634 -0.065 0.051 0.34 0.000 MultShareClass 1435 0.130 0.000 0.34 807 0.077 0.000 0.27 0.000 Stock_Options 1435 0.983 1.000 0.13 807 0.948 1.000 0.22 0.000 ConvDebt 1357 0.001 -0.010 0.04 769 -0.002 -0.010 0.04 0.000 PrefStock 1386 0.000 -0.006 0.03 777 -0.001 -0.006 0.03 0.000 Foreign_Assets 1095 0.015 -0.032 0.15 549 -0.031 -0.046 0.11 0.000 Foreign_Income_3y 639 0.038 -0.023 0.28 379 -0.065 -0.100 0.17 0.000 Foreign_Sales 1197 0.027 -0.022 0.20 628 -0.052 -0.072 0.21 0.000 FX_Exposure 1435 0.616 1.000 0.49 807 0.375 0.000 0.48 0.000 Foreign_Debt 1435 0.084 0.000 0.28 807 0.026 0.000 0.16 0.000 IR_Exposure 1408 0.611 1.000 0.49 793 0.300 0.000 0.46 0.000 CP_Exposure 1435 0.172 0.000 0.38 807 0.058 0.000 0.23 0.000 Exposure 1435 0.878 1.000 0.33 807 0.574 1.000 0.49 0.000 NumIndSeg 1428 3.011 3.000 1.62 804 2.486 2.000 1.40 0.000 NegBookValue 1435 0.041 0.000 0.20 807 0.024 0.000 0.15 0.015 42 Table 4: Determinants of Derivative Use The table reports regression coefficients and their p-values (in brackets) from LOGIT regressions of the relation between the likelihood of derivatives use, proxies of incentives for hedging, proxies for foreign exchange rate exposure, and control variables. Below the coefficients, information about the goodness of fit and the number of observations is reported. The specifications cover the following subsamples: (1) All countries, (2) All countries other than United States, (3) United States, (4) United Kingdom, (5) Japan (6) Germany, (7) Canada, (8) Australia, (9) All other countries. Panel A refers to general derivatives use, Panel B to FX derivatives use, Panel C to IR derivatives use, and Panel D to CP derivatives use. Panel A: General Derivatives Use All Countries All ex. U.S. U.S. U.K. Japan Germany Canada Australia All Other Variable Coef pvalue Coef pvalue Coef pvalue Coef pvalue Coef pvalue Coef pvalue Coef pvalue Coef pvalue Coef pvalue ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ Intercept -1.73 [0.00] -1.63 [0.00] -1.45 [0.00] -1.71 [0.04] 0.96 [0.18] -0.89 [0.02] 0.54 [0.31] 0.82 [0.20] -1.29 [0.00] Leverage 0.68 [0.00] 0.46 [0.01] 1.53 [0.00] -0.30 [0.62] 3.22 [0.00] 1.49 [0.02] 0.45 [0.45] 1.83 [0.04] 0.35 [0.18] Coverage_3y 0.00 [0.73] -0.01 [0.08] 0.02 [0.01] -0.07 [0.00] 0.05 [0.10] -0.01 [0.61] -0.02 [0.39] 0.03 [0.44] -0.00 [0.95] Quick_Ratio -0.06 [0.00] -0.05 [0.02] -0.09 [0.00] -0.05 [0.26] 0.21 [0.32] -0.19 [0.00] -0.08 [0.13] 0.13 [0.12] -0.01 [0.75] Logsize 0.35 [0.00] 0.36 [0.00] 0.34 [0.00] 0.42 [0.00] 0.17 [0.29] 0.52 [0.00] 0.46 [0.00] 0.49 [0.00] 0.32 [0.00] Dividend 0.48 [0.00] 0.46 [0.00] 0.55 [0.00] 1.02 [0.00] 0.48 [0.47] 0.70 [0.03] 0.18 [0.51] 0.10 [0.80] 0.17 [0.15] GrossProfitMargin_3y 0.19 [0.01] 0.35 [0.00] -0.07 [0.64] 0.19 [0.41] 0.15 [0.83] -0.11 [0.76] 0.82 [0.00] 0.58 [0.28] 0.22 [0.18] D_Income_Tax_Credit 0.75 [0.00] 0.77 [0.00] 0.95 [0.00] 0.41 [0.51] -1.05 [0.56] 0.75 [0.18] 0.77 [0.09] Income_Tax_Credit -2.63 [0.34] Market_to_Book -0.08 [0.00] -0.09 [0.00] -0.04 [0.02] -0.11 [0.00] -0.00 [0.95] -0.08 [0.18] -0.26 [0.00] -0.14 [0.00] -0.06 [0.00] MB_Leverage 0.47 [0.00] 0.47 [0.00] 0.38 [0.00] 1.45 [0.00] -0.21 [0.59] -0.23 [0.42] 0.63 [0.02] 1.69 [0.00] 0.31 [0.00] MultShareClass 0.37 [0.00] 0.34 [0.00] 0.31 [0.06] 0.60 [0.28] 0.22 [0.61] 0.30 [0.32] 0.56 [0.00] Stock_Options 0.15 [0.04] 0.07 [0.40] 1.56 [0.00] 0.91 [0.25] -0.29 [0.43] 0.46 [0.12] -0.34 [0.49] -0.18 [0.73] 0.36 [0.00] FX_Exposure 0.39 [0.00] 0.27 [0.00] 0.70 [0.00] 0.72 [0.00] 0.77 [0.01] 0.65 [0.04] 0.01 [0.96] 0.05 [0.90] 0.29 [0.00] Foreign_Debt 0.37 [0.00] 0.36 [0.00] 0.20 [0.44] 0.73 [0.00] 0.80 [0.31] -0.27 [0.29] 0.10 [0.83] 1.05 [0.08] 0.42 [0.00] Foreign_Listing 0.55 [0.00] 0.54 [0.00] 0.17 [0.62] 0.81 [0.11] 0.36 [0.62] -0.55 [0.28] 0.70 [0.00] NegBookValue 0.16 [0.40] -0.17 [0.50] 0.62 [0.05] -0.14 [0.83] 12.12 [0.98] -14.46 [0.98] -0.31 [0.63] -0.40 [0.73] -0.15 [0.70] PctMktCap 2.13 [0.00] 2.16 [0.00] 1.14 [0.00] USROW 0.21 [0.00] D_year -0.21 [0.00] -0.14 [0.04] -0.36 [0.00] -0.34 [0.06] -0.61 [0.08] -0.36 [0.15] -0.02 [0.91] -0.68 [0.08] -0.01 [0.90] AIC 7679.2 5352.2 2284.0 840.54 305.85 440.11 548.91 269.19 2412.3 SC 7808.7 5468.1 2374.4 920.83 366.88 507.58 616.25 322.83 2513.0 -2 Log L 7641.2 5316.2 2252.0 806.54 273.85 406.11 516.91 239.19 2376.3 R-Square 0.19 0.19 0.21 0.28 0.12 0.28 0.24 0.29 0.16 Observations 6723 4626 2097 831 335 391 497 264 1991 (continued) 43 Table 4: Determinants of Derivative Use (continued) All Countries All ex. U.S. U.S. U.K. Japan Germany Canada Australia All Other Variable Coef pvalue Coef pvalue Coef pvalue Coef pvalue Coef pvalue Coef pvalue Coef pvalue Coef pvalue Coef pvalue ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ Panel B: FX Derivatives Use 44 (continued) Intercept -2.20 [0.00] -2.02 [0.00] -3.12 [0.00] -2.20 [0.01] 0.14 [0.82] -1.07 [0.00] -0.54 [0.27] -0.27 [0.61] -1.97 [0.00] Leverage 0.29 [0.05] 0.35 [0.04] 0.32 [0.31] -0.83 [0.16] 2.75 [0.01] 0.92 [0.15] 0.10 [0.86] 0.71 [0.35] 0.36 [0.15] Coverage_3y -0.01 [0.01] -0.02 [0.02] -0.01 [0.34] -0.05 [0.00] 0.05 [0.13] 0.02 [0.50] -0.03 [0.14] 0.02 [0.58] -0.01 [0.43] Quick_Ratio -0.07 [0.00] -0.06 [0.00] -0.07 [0.02] -0.07 [0.16] 0.28 [0.15] -0.18 [0.01] -0.04 [0.45] -0.02 [0.81] -0.05 [0.14] Logsize 0.32 [0.00] 0.33 [0.00] 0.28 [0.00] 0.37 [0.00] 0.37 [0.01] 0.55 [0.00] 0.49 [0.00] 0.34 [0.00] 0.23 [0.00] Dividend 0.33 [0.00] 0.40 [0.00] 0.28 [0.01] 0.65 [0.00] 0.69 [0.24] 0.49 [0.14] -0.03 [0.91] 0.26 [0.45] 0.14 [0.22] GrossProfitMargin_3y 0.21 [0.01] 0.30 [0.00] 0.05 [0.77] 0.10 [0.67] -0.66 [0.32] -0.28 [0.48] 0.67 [0.01] 0.15 [0.75] 0.40 [0.01] D_Income_Tax_Credit 0.14 [0.43] 0.60 [0.01] -0.08 [0.81] 0.80 [0.16] -0.70 [0.71] 0.06 [0.89] 0.68 [0.09] Income_Tax_Credit -2.74 [0.25] Market_to_Book -0.05 [0.00] -0.07 [0.00] -0.01 [0.75] -0.11 [0.00] -0.04 [0.55] -0.12 [0.06] -0.25 [0.00] -0.07 [0.16] -0.04 [0.06] MB_Leverage 0.15 [0.00] 0.21 [0.00] 0.02 [0.89] 0.81 [0.00] -0.03 [0.92] -0.27 [0.38] 0.61 [0.02] -0.13 [0.75] 0.08 [0.22] MultShareClass 0.04 [0.61] 0.09 [0.36] -0.13 [0.44] 0.69 [0.18] 0.02 [0.96] 0.27 [0.31] 0.34 [0.00] Stock_Options 0.06 [0.45] 0.06 [0.43] 1.26 [0.00] 0.98 [0.27] -0.01 [0.96] 0.28 [0.34] -0.03 [0.94] 0.32 [0.46] 0.51 [0.00] FX_Exposure 0.87 [0.00] 0.43 [0.00] 1.98 [0.00] 1.00 [0.00] 0.79 [0.00] 0.66 [0.05] 0.22 [0.33] 0.08 [0.79] 0.30 [0.00] Foreign_Debt 0.48 [0.00] 0.37 [0.00] 0.85 [0.00] 0.85 [0.00] 1.15 [0.14] -0.39 [0.14] 0.43 [0.30] 1.37 [0.00] 0.33 [0.00] Foreign_Listing 0.62 [0.00] 0.63 [0.00] 0.28 [0.37] 0.73 [0.11] 0.49 [0.46] -0.43 [0.32] 0.82 [0.00] NegBookValue -0.25 [0.19] -0.16 [0.52] -0.42 [0.17] 0.01 [0.98] 12.50 [0.98] -13.96 [0.98] 0.01 [0.99] -1.25 [0.31] -0.32 [0.42] PctMktCap 2.00 [0.00] 2.01 [0.00] 1.37 [0.00] USROW -0.51 [0.00] D_year -0.14 [0.01] -0.14 [0.03] -0.18 [0.10] -0.46 [0.00] -0.32 [0.34] -0.31 [0.23] -0.14 [0.50] -0.12 [0.70] 0.04 [0.68] AIC 7875.2 5555.9 2167.8 904.53 345.57 428.99 592.88 339.28 2470.0 SC 8004.7 5671.8 2258.2 984.82 406.59 496.45 660.22 392.92 2570.7 -2 Log L 7837.2 5519.9 2135.8 870.53 313.57 394.99 560.88 309.28 2434.0 R-Square 0.19 0.17 0.26 0.28 0.16 0.26 0.22 0.19 0.14 Observations 6723 4626 2097 831 335 391 497 264 1991 Table 4: Determinants of Derivative Use (continued) Panel C: Interest Rate Derivatives Use All Countries All ex. U.S. U.S. U.K. Japan Germany Canada Australia All Other Variable Coef pvalue Coef pvalue Coef pvalue Coef pvalue Coef pvalue Coef pvalue Coef pvalue Coef pvalue Coef pvalue ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ 45 (continued) Intercept -2.99 [0.00] -3.00 [0.00] -2.53 [0.00] -16.34 [0.98] -0.05 [0.93] -2.95 [0.00] -2.10 [0.00] -2.14 [0.00] -3.26 [0.00] Leverage 1.43 [0.00] 1.22 [0.00] 1.87 [0.00] 1.73 [0.01] 2.93 [0.00] 2.02 [0.01] 3.06 [0.00] 0.32 [0.73] 0.65 [0.04] Coverage_3y 0.01 [0.28] -0.03 [0.00] 0.05 [0.00] -0.05 [0.02] 0.02 [0.44] 0.01 [0.73] -0.00 [0.89] -0.02 [0.55] -0.03 [0.02] Quick_Ratio -0.07 [0.00] -0.06 [0.05] -0.14 [0.00] 0.02 [0.80] 0.02 [0.91] -0.48 [0.00] -0.13 [0.19] 0.11 [0.32] 0.01 [0.84] Logsize 0.41 [0.00] 0.48 [0.00] 0.28 [0.00] 0.63 [0.00] 0.26 [0.05] 0.63 [0.00] 0.56 [0.00] 0.40 [0.00] 0.43 [0.00] Dividend 0.86 [0.00] 1.07 [0.00] 0.57 [0.00] 1.71 [0.00] 0.45 [0.41] 1.11 [0.01] 1.09 [0.00] 1.78 [0.00] 0.62 [0.00] GrossProfitMargin_3y 0.02 [0.87] 0.26 [0.04] -0.49 [0.00] -0.26 [0.38] -0.66 [0.29] 0.81 [0.16] 0.30 [0.38] 0.06 [0.91] 0.49 [0.02] D_Income_Tax_Credit 0.18 [0.31] 0.38 [0.13] 0.17 [0.52] 0.06 [0.90] 0.02 [0.99] 0.85 [0.11] 0.44 [0.30] Income_Tax_Credit -1.32 [0.45] Market_to_Book -0.12 [0.00] -0.16 [0.00] -0.05 [0.01] -0.16 [0.00] -0.06 [0.42] -0.20 [0.03] -0.13 [0.16] -0.32 [0.00] -0.16 [0.00] MB_Leverage 0.59 [0.00] 0.59 [0.00] 0.54 [0.00] 1.14 [0.00] 0.42 [0.27] -0.08 [0.83] 0.08 [0.65] 3.12 [0.00] 0.46 [0.00] MultShareClass 0.09 [0.26] -0.15 [0.15] 0.46 [0.00] 0.41 [0.44] -0.12 [0.78] 0.08 [0.79] 0.23 [0.09] Stock_Options 0.08 [0.34] -0.05 [0.56] 1.61 [0.00] 13.89 [0.98] -0.16 [0.61] 0.23 [0.54] 0.29 [0.57] -0.39 [0.43] 0.41 [0.00] FX_Exposure 0.05 [0.46] 0.09 [0.25] -0.04 [0.68] -0.32 [0.13] 0.30 [0.28] 0.22 [0.63] -0.11 [0.67] 0.09 [0.80] 0.21 [0.10] Foreign_Debt 0.21 [0.00] 0.21 [0.00] 0.19 [0.34] 0.76 [0.00] 0.26 [0.61] 0.18 [0.59] 0.53 [0.21] 0.59 [0.23] 0.32 [0.00] Foreign_Listing 0.58 [0.00] 0.47 [0.00] 0.47 [0.14] 0.36 [0.31] 1.14 [0.11] -0.80 [0.13] 0.59 [0.00] NegBookValue 0.41 [0.03] 0.21 [0.43] 0.62 [0.02] 0.68 [0.31] -0.54 [0.70] -13.38 [0.98] -0.09 [0.88] 1.86 [0.11] -0.08 [0.87] PctMktCap 1.61 [0.00] 1.60 [0.00] 0.99 [0.00] USROW 0.73 [0.00] D_year -0.16 [0.00] -0.20 [0.00] -0.12 [0.25] -0.24 [0.21] -1.13 [0.00] -0.08 [0.80] -0.13 [0.60] 0.04 [0.90] 0.08 [0.51] AIC 6872.4 4416.1 2368.6 737.18 401.19 287.13 450.66 264.45 1854.3 SC 7001.9 4532.0 2459.0 817.46 462.22 354.60 518.00 318.09 1955.0 -2 Log L 6834.4 4380.1 2336.6 703.18 369.19 253.13 418.66 234.45 1818.3 R-Square 0.22 0.23 0.21 0.37 0.21 0.35 0.29 0.39 0.18 Observations 6723 4626 2097 831 335 391 497 264 1991 Table 4: Determinants of Derivative Use (continued) Panel D: Commodity Price Derivatives Use All Countries All ex. U.S. U.S. U.K. Japan Germany Canada Australia All Other Variable Coef pvalue Coef pvalue Coef pvalue Coef pvalue Coef pvalue Coef pvalue Coef pvalue Coef pvalue Coef pvalue ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ 46 Intercept -3.77 [0.00] -3.12 [0.00] -2.44 [0.00] -4.83 [0.00] -3.37 [0.00] -3.62 [0.00] -0.61 [0.27] -1.94 [0.01] -3.65 [0.00] Leverage 1.11 [0.00] 0.59 [0.06] 1.36 [0.00] -1.70 [0.30] 2.38 [0.14] 1.09 [0.39] 0.87 [0.20] 1.41 [0.20] 0.37 [0.47] Coverage_3y -0.02 [0.03] -0.01 [0.41] -0.03 [0.02] -0.04 [0.46] -0.02 [0.70] 0.00 [0.99] 0.01 [0.83] 0.04 [0.44] -0.05 [0.02] Quick_Ratio -0.01 [0.62] 0.02 [0.67] -0.04 [0.34] 0.01 [0.90] 0.58 [0.03] 0.18 [0.16] -0.12 [0.13] 0.19 [0.11] 0.01 [0.92] Logsize 0.28 [0.00] 0.46 [0.00] 0.10 [0.02] 0.34 [0.02] 0.54 [0.01] 0.61 [0.00] 0.43 [0.00] 0.56 [0.00] 0.40 [0.00] Dividend 0.68 [0.00] -0.30 [0.03] 1.40 [0.00] 0.40 [0.53] 0.02 [0.97] -0.11 [0.71] -1.01 [0.05] 0.38 [0.13] GrossProfitMargin_3y 0.13 [0.35] 0.65 [0.00] -0.61 [0.00] -0.46 [0.47] -0.59 [0.65] -0.46 [0.62] 0.96 [0.00] 0.25 [0.74] 0.24 [0.51] D_Income_Tax_Credit 1.04 [0.00] -0.05 [0.90] 1.46 [0.00] 0.15 [0.86] -0.54 [0.43] -0.65 [0.53] Income_Tax_Credit -1.97 [0.27] Market_to_Book -0.04 [0.02] -0.09 [0.00] -0.02 [0.50] -0.11 [0.32] -0.06 [0.74] -0.21 [0.28] -0.10 [0.22] -0.16 [0.18] -0.04 [0.47] MB_Leverage 0.19 [0.00] 0.11 [0.21] 0.35 [0.00] 0.58 [0.25] 0.17 [0.70] 0.21 [0.73] 0.03 [0.87] 0.27 [0.66] -0.15 [0.55] MultShareClass -0.28 [0.03] -0.23 [0.17] -0.42 [0.05] -0.08 [0.94] 0.20 [0.76] -1.23 [0.00] -0.01 [0.97] Stock_Options -0.08 [0.50] -0.25 [0.07] 0.13 [0.71] 0.02 [0.97] -0.35 [0.57] -0.45 [0.38] 0.41 [0.52] -0.49 [0.02] FX_Exposure -0.38 [0.00] -0.57 [0.00] -0.08 [0.58] 0.37 [0.51] 0.89 [0.10] 0.55 [0.53] -0.89 [0.00] -0.86 [0.07] -0.51 [0.01] Foreign_Debt -0.17 [0.13] -0.13 [0.32] -0.37 [0.15] 0.11 [0.81] 1.28 [0.02] 0.53 [0.33] 0.21 [0.62] 0.15 [0.76] 0.23 [0.26] Foreign_Listing 0.67 [0.00] 0.50 [0.00] 1.65 [0.00] 0.06 [0.89] 0.40 [0.63] 1.02 [0.04] 0.81 [0.00] NegBookValue 0.22 [0.39] -0.70 [0.14] 0.84 [0.00] -13.83 [0.99] -11.30 [0.99] -1.08 [0.20] 0.48 [0.72] 0.01 [0.99] PctMktCap 1.34 [0.00] 1.55 [0.00] 0.82 [0.10] USROW 1.11 [0.00] D_year -0.23 [0.00] -0.08 [0.53] -0.47 [0.00] -0.38 [0.39] -0.99 [0.08] -1.07 [0.07] -0.12 [0.64] -0.42 [0.35] 0.23 [0.25] AIC 3924.2 2176.1 1622.0 234.53 205.15 145.85 454.07 205.22 833.98 SC 4053.7 2292.0 1712.4 305.37 266.17 205.38 521.41 258.86 937.28 -2 Log L 3886.2 2140.1 1590.0 204.53 173.15 115.85 422.07 175.22 797.98 R-Square 0.07 0.05 0.13 0.06 0.12 0.07 0.13 0.17 0.04 Observations 6723 4626 2097 831 335 391 497 264 2295 Table 5: Examination of Country-Specific Determinants in Firm Level Analysis The table reports regression coefficients, marginal effects, and the p-values (in brackets) from LOGIT regressions of the relation between the likelihood of de-rivatives use and firm-specific as well as country-specific explanatory variables. Below the coefficients, information about the goodness of fit and the number of observations is reported. Marginal effects are calculated as the approximate increase in probability of using derivatives for a change in the explanatory variable from the mean value to the mean value plus one standard deviation. General FX_Derivatives IR_Derivatives CP_Derivatives ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ Variable Coef MarEff pvalue Coef MarEff pvalue Coef MarEff pvalue Coef MarEff pvalue ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ Intercept -2.97 [0.00] -4.69 [0.00] -4.01 [0.00] -7.76 [0.00] Leverage 0.67 0.15 [0.00] 0.26 0.06 [0.08] 1.48 0.34 [0.00] 1.11 0.25 [0.00] Coverage_3y 0.00 0.01 [0.74] -0.01 -0.07 [0.02] 0.00 0.03 [0.45] -0.02 -0.12 [0.03] Quick_Ratio -0.06 -0.12 [0.00] -0.07 -0.13 [0.00] -0.08 -0.15 [0.00] -0.02 -0.03 [0.58] Logsize 0.36 0.59 [0.00] 0.33 0.53 [0.00] 0.42 0.68 [0.00] 0.28 0.45 [0.00] Dividend 0.46 0.23 [0.00] 0.31 0.16 [0.00] 0.85 0.43 [0.00] 0.72 0.36 [0.00] GrossProfitMargin_3y 0.21 0.08 [0.01] 0.22 0.08 [0.01] 0.01 0.00 [0.93] 0.11 0.04 [0.45] D_Income_Tax_Credit 0.72 0.11 [0.00] 0.11 0.02 [0.53] 0.15 0.02 [0.41] 1.08 0.17 [0.00] Market_to_Book -0.08 -0.23 [0.00] -0.05 -0.16 [0.00] -0.12 -0.34 [0.00] -0.04 -0.13 [0.03] MB_Leverage 0.47 0.31 [0.00] 0.15 0.10 [0.00] 0.57 0.38 [0.00] 0.18 0.12 [0.00] MultShareClass 0.48 0.16 [0.00] 0.12 0.04 [0.17] 0.19 0.06 [0.03] -0.29 -0.10 [0.03] Stock_Options -0.00 -0.00 [0.98] -0.08 -0.03 [0.33] -0.06 -0.02 [0.45] 0.03 0.01 [0.81] FX_Exposure 0.32 0.16 [0.00] 0.81 0.40 [0.00] -0.09 -0.04 [0.19] -0.43 -0.21 [0.00] Foreign_Debt 0.41 0.17 [0.00] 0.50 0.20 [0.00] 0.30 0.12 [0.00] -0.05 -0.02 [0.67] Foreign_Listing 0.60 0.18 [0.00] 0.67 0.20 [0.00] 0.75 0.22 [0.00] 0.85 0.25 [0.00] NegBookValue 0.19 0.03 [0.34] -0.23 -0.04 [0.22] 0.43 0.07 [0.02] 0.23 0.03 [0.36] DerMktRank 0.01 0.11 [0.02] 0.01 0.11 [0.02] 0.03 0.30 [0.00] -0.01 -0.07 [0.44] ICR_Composite 0.02 0.07 [0.25] 0.05 0.19 [0.00] -0.03 -0.11 [0.13] 0.01 0.03 [0.78] Legality 0.01 0.02 [0.68] -0.06 -0.12 [0.04] 0.13 0.24 [0.00] 0.15 0.27 [0.01] Closely_Held -0.75 -0.14 [0.00] -0.16 -0.03 [0.53] -0.46 -0.09 [0.09] 2.25 0.42 [0.00] PctMktCap 1.38 0.18 [0.00] 1.43 0.18 [0.00] 1.45 0.19 [0.00] 0.82 0.11 [0.13] USROW -0.03 -0.01 [0.79] -0.58 -0.27 [0.00] 0.34 0.16 [0.00] 1.78 0.83 [0.00] D_year -0.23 -0.11 [0.00] -0.14 -0.07 [0.01] -0.17 -0.08 [0.00] -0.22 -0.11 [0.01] AIC 7524.8 7765.7 6680.2 3852.8 SC 7681.2 7922.1 6836.6 4009.2 -2 Log L 7478.8 7719.7 6634.2 3806.8 R-Square 0.19 0.19 0.23 0.08 Observations 6629 6629 6629 6629 47 Table 6: Sub-Sample Analysis The table shows the number of observations/firms (N), the mean and standard deviation of general derivatives’ use for firms with high/low cost or incentives to use derivatives. Firms are classified into groups with high/low cost/incentives based on a combination of firm and country characteristics corresponding to various hypotheses of derivatives use tabled below. All firms are required to have Exposure=1. The last column presents p-values of Wil-coxon rank sum tests. Panel A refers to results for all firms. Panel B lists results for U.S. firms. Panel C presents re-sults for Non-U.S. firms. Financial Distress 1: High (Low) Cost = Leverage above (below) median, Coverage_3y below (above) median, Tangible Assets below (above) median. Financial Distress 2: Same as Financial Distress 1 plus Creditor Rights below (above) 3. Financial Distress 3: Same as Financial Distress 1 plus ICR_Composite above (below) median. Financial Distress 4: High (Low) Cost = Leverage above (below) median, Coverage_3y below (above) median, Log(Size) below (above) median. Financial Distress 5: High (Low) Cost = High (Low) Cost = Leverage above (below) median, Coverage_3y be-low (above) median, Log(Size) above (below) median. Underinvestment 1: High (Low) Cost = CapEx above (below) median, R&D/Sales above (less or equal to) zero Underinvestment 3: High (Low) Cost = Same as Underinvestment 1 plus IR_Ave above (below) 6% Underinvestment 5: High (Low) Cost = Leverage above (below) median, Market/Book above (below) median Underinvestment 6: High (Low) Cost = Same as Underinvestment 1 plus Civil_Law = 0 (1) Underinvestment 8: High (Low) Cost = Same as Underinvestment 1 plus IR_Ave above (below) 6%, Civil_Law = 0 (1) Underinvestment 10: High (Low) Cost = Leverage above (below) median, Coverage_3y below (above) median, R&D/Sales above (less or equal to) zero, Tangible Assets below (above) median, CapEx above (below) median, Market/Book above (below) median Incentives 1: High (Low) Cost = Dividend = 0 (1), Multiple Share Class = 1 (0), Leverage below (above) me-dian Incentives 2: High (Low) Cost = Same as Incentives 1 plus Shareholder Rights below (above) 4 Substitutes 1: High (Low) Cost = Convertible Debt below (above) median, Preferred Stock below (above) me-dian, Quick Ratio below (above) median ___High Cost/Incent. ___ ___Low Cost/Incent.__ _Tests__ N Mean Std.Dev. N Mean Std.Dev. Wilcoxon ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ Panel A: All firms Financial Distress 1 1048 0.680 0.47 607 0.692 0.46 0.313 Financial Distress 2 676 0.797 0.40 139 0.597 0.49 0.000 Financial Distress 3 947 0.687 0.46 30 0.667 0.48 0.405 Financial Distress 4 915 0.553 0.50 772 0.776 0.42 0.000 Financial Distress 5 792 0.817 0.39 513 0.554 0.50 0.000 Underinvestment 1 1033 0.786 0.41 1806 0.602 0.49 0.000 Underinvestment 2 542 0.745 0.44 850 0.579 0.49 0.000 Underinvestment 3 1291 0.720 0.45 723 0.556 0.50 0.000 Underinvestment 4 618 0.728 0.45 417 0.588 0.49 0.000 Underinvestment 5 532 0.742 0.44 337 0.582 0.49 0.000 Underinvestment 6 38 0.895 0.31 42 0.452 0.50 0.000 Incentives 1 68 0.721 0.45 1562 0.770 0.42 0.175 Incentives 2 17 0.647 0.49 867 0.804 0.40 0.055 Substitutes 1 497 0.692 0.46 330 0.615 0.49 0.011 (continued) 48 Table 6: Sub-Sample Analysis (continued) ___High Cost/Incent. ___ ___Low Cost/Incent.__ _Tests__ N Mean Std.Dev. N Mean Std.Dev. Wilcoxon ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ Panel B: U.S. firms Financial Distress 1 294 0.786 0.41 198 0.747 0.44 0.162 Financial Distress 4 251 0.653 0.48 253 0.838 0.37 0.000 Financial Distress 5 239 0.854 0.35 187 0.663 0.47 0.000 Underinvestment 1 358 0.740 0.44 467 0.726 0.45 0.323 Underinvestment 3 344 0.823 0.38 261 0.552 0.50 0.000 Underinvestment 6 20 0.900 0.31 15 0.667 0.49 0.049 Incentives 1 37 0.730 0.45 381 0.848 0.36 0.032 Substitutes 1 388 0.698 0.46 15 0.800 0.41 0.200 Panel C: Non-U.S. firms Financial Distress 1 754 0.639 0.48 409 0.665 0.47 0.190 Financial Distress 2 382 0.806 0.40 139 0.597 0.49 0.000 Financial Distress 3 653 0.643 0.48 30 0.667 0.48 0.397 Financial Distress 4 664 0.515 0.50 519 0.746 0.44 0.000 Financial Distress 5 553 0.801 0.40 326 0.491 0.50 0.000 Underinvestment 1 675 0.810 0.39 1339 0.559 0.50 0.000 Underinvestment 2 184 0.755 0.43 850 0.579 0.49 0.000 Underinvestment 3 947 0.683 0.47 462 0.558 0.50 0.000 Underinvestment 4 260 0.712 0.45 417 0.588 0.49 0.001 Underinvestment 5 174 0.747 0.44 337 0.582 0.49 0.000 Underinvestment 6 18 0.889 0.32 27 0.333 0.48 0.000 Incentives 1 31 0.710 0.46 1181 0.744 0.44 0.332 Incentives 2 17 0.647 0.49 486 0.770 0.42 0.121 Substitutes 1 109 0.670 0.47 315 0.606 0.49 0.120 49 Table 7: Univariate Analysis with Tobin’s Q The table reports results of univariate tests with (the natural log of) Tobin’s Q. In particular, it shows the mean, median and standard devia-tion of Tobin’s Q for hedgers and non-hedgers. Moreover, the difference in the means and medians as well p-values of mean tests, median tests as well as Wilcoxon rank sum tests are shown. N is the number of observations (firms). Tests are conducted separately for U.S. firms as well as Non-U.S. firms, and for firms with and without different types of exposure. Panel A refers to general derivatives’ use, while Panel B documents results for FX derivatives’ use. Results for IR and CP derivatives are reported in Panels C and D, respectively. U.S. firms Non-U.S. firms Hedger Nonhedger Diff. pval Hedger Nonhedger Diff. pval ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ Panel A: General Derivatives Use Exposure = 1 Mean -0.08 -0.07 -0.01 0.84 -0.09 -0.03 -0.06 0.02 Median -0.09 -0.12 0.03 0.24 -0.11 -0.10 -0.01 0.36 Stdev 0.62 0.75 0.57 0.72 N 1254 455 2403 1326 Wilcoxon 0.34 0.06 Exposure = 0 Mean 0.40 0.22 0.18 0.01 0.25 0.21 0.04 0.42 Median 0.43 0.26 0.17 0.00 0.27 0.16 0.11 0.04 Stdev 0.64 0.77 0.80 0.79 N 148 325 400 688 Wilcoxon 0.01 0.08 FX_Exposure = 1 Mean -0.03 0.07 -0.10 0.04 -0.04 0.05 -0.08 0.00 Median -0.06 0.02 -0.08 0.05 -0.08 -0.04 -0.03 0.10 Stdev 0.66 0.77 0.59 0.73 N 883 296 1700 856 Wilcoxon 0.03 0.01 FX_Exposure = 0 Mean -0.02 0.04 -0.06 0.19 -0.04 0.06 -0.10 0.00 Median -0.06 0.01 -0.07 0.18 -0.07 -0.03 -0.04 0.04 Stdev 0.60 0.78 0.68 0.77 N 519 484 1103 1158 Wilcoxon 0.08 0.00 IR_Exposure = 1 Mean -0.25 -0.38 0.13 0.00 -0.24 -0.27 0.03 0.17 Median -0.22 -0.30 0.08 0.00 -0.21 -0.24 0.03 0.06 Stdev 0.50 0.59 0.45 0.58 N 860 234 1658 751 Wilcoxon 0.00 0.03 IR_Exposure = 0 Mean 0.33 0.23 0.09 0.03 0.25 0.25 0.01 0.80 Median 0.34 0.22 0.12 0.01 0.25 0.19 0.05 0.08 Stdev 0.67 0.77 0.71 0.77 N 542 546 1145 1263 Wilcoxon 0.01 0.16 CP_Exposure = 1 Mean -0.09 0.07 -0.16 0.08 -0.01 0.13 -0.14 0.02 Median -0.08 0.14 -0.23 0.01 -0.00 0.06 -0.06 0.32 Stdev 0.44 0.55 0.46 0.70 N 241 46 440 177 Wilcoxon 0.01 0.04 CP_Exposure = 0 Mean -0.02 0.05 -0.06 0.07 -0.04 0.05 -0.09 0.00 Median -0.04 -0.00 -0.04 0.13 -0.09 -0.04 -0.06 0.01 Stdev 0.67 0.79 0.65 0.76 N 1161 734 2363 1837 Wilcoxon 0.05 0.00 50 (continued) Table 7: Univariate Analysis with Tobin’s Q (continued) U.S. firms Non-U.S. firms Hedger Nonhedger Diff. pval Hedger Nonhedger Diff. pval ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ Panel B: FX Derivatives Use FX_Exposure = 1 Mean -0.02 0.01 -0.03 0.42 -0.03 0.02 -0.05 0.05 Median -0.05 -0.03 -0.03 0.20 -0.07 -0.06 -0.01 0.19 Stdev 0.68 0.70 0.59 0.70 N 685 494 1450 1106 Wilcoxon 0.35 0.08 FX_Exposure = 0 Mean 0.11 -0.01 0.12 0.08 -0.06 0.04 -0.10 0.00 Median 0.12 -0.06 0.17 0.00 -0.08 -0.03 -0.05 0.03 Stdev 0.65 0.70 0.68 0.75 N 125 878 809 1452 Wilcoxon 0.02 0.00 Panel C: IR Derivatives Use IR_Exposure = 1 Mean -0.24 -0.33 0.08 0.01 -0.21 -0.28 0.07 0.00 Median -0.22 -0.27 0.05 0.01 -0.19 -0.24 0.05 0.00 Stdev 0.49 0.56 0.43 0.54 N 646 448 1011 1398 Wilcoxon 0.00 0.00 IR_Exposure = 0 Mean 0.37 0.26 0.12 0.03 0.27 0.25 0.03 0.48 Median 0.45 0.25 0.20 0.00 0.27 0.20 0.07 0.05 Stdev 0.69 0.73 0.64 0.77 N 235 853 409 1999 Wilcoxon 0.00 0.09 Panel D: CP Derivatives Use CP_Exposure = 1 Mean -0.11 0.01 -0.12 0.04 0.01 0.04 -0.03 0.45 Median -0.09 0.05 -0.14 0.01 -0.01 0.02 -0.03 0.33 Stdev 0.37 0.56 0.41 0.59 N 166 121 169 448 Wilcoxon 0.00 0.32 CP_Exposure = 0 Mean -0.08 0.02 -0.10 0.07 -0.05 -0.00 -0.05 0.27 Median -0.15 -0.03 -0.13 0.01 -0.12 -0.07 -0.05 0.24 Stdev 0.68 0.72 0.58 0.70 N 191 1704 175 4025 Wilcoxon 0.02 0.14 51 Table 8: Multivariate Analysis with Tobin’s Q The table reports the coefficients and p-values of multivariate tests with Tobin’s Q. OLS regressions are estimated separately for firms with and without a particular exposure as well as for the whole sample, U.S. firms and Non-U.S. firms. Panel A refers to gen-eral derivatives’ use (Hedger), while Panel B documents results for FX derivatives’ use (FX_Derivatives). Results for IR derivatives (IR_Derivatives) are reported in Panel C, and results for CP derivatives (CP_Derivatives) are reported in Panel D. Panel A: General Derivatives Use Exposure = 0 Exposure = 1 ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ (01) (02) (03) (01) (02) (03) Variable Coef pvalue Coef pvalue Coef pvalue Coef pvalue Coef pvalue Coef pvalue ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ Intercept -0.17 [0.14] -0.09 [0.41] -0.05 [0.79] 0.01 [0.77] 0.04 [0.41] -0.02 [0.80] Hedger 0.16 [0.00] 0.16 [0.00] 0.18 [0.00] 0.01 [0.70] 0.01 [0.69] 0.01 [0.76] Leverage -1.20 [0.00] -1.26 [0.00] -1.67 [0.00] -1.43 [0.00] -1.43 [0.00] -1.71 [0.00] Logassets -0.02 [0.15] -0.02 [0.31] 0.01 [0.57] -0.00 [0.47] -0.00 [0.53] -0.00 [0.93] Dividend -0.07 [0.12] -0.05 [0.29] -0.21 [0.00] -0.04 [0.01] -0.04 [0.02] -0.05 [0.11] ROA_3y 0.01 [0.93] 0.01 [0.91] 0.12 [0.25] 0.10 [0.00] 0.10 [0.00] 0.17 [0.00] R_D_to_Sales 0.19 [0.00] 0.10 [0.07] CapEx 0.12 [0.11] 0.12 [0.12] -0.20 [0.09] 0.20 [0.00] 0.20 [0.00] 0.13 [0.10] Foreign_Sales 0.16 [0.00] 0.16 [0.00] 0.18 [0.00] Foreign_Debt 0.19 [0.05] 0.05 [0.13] NumIndSeg -0.03 [0.02] -0.02 [0.26] -0.00 [0.87] 0.00 [0.94] MultIndSeg -0.04 [0.47] 0.03 [0.34] PctMktCap 0.22 [0.13] 0.17 [0.24] 0.06 [0.82] -0.03 [0.62] -0.04 [0.56] 0.05 [0.67] D_year 0.11 [0.00] 0.10 [0.01] 0.07 [0.26] -0.01 [0.52] -0.01 [0.50] -0.01 [0.60] AdjRSq 0.04 0.04 0.07 0.27 0.27 0.30 Observations 1410 1410 580 4211 4211 2159 Panel B: Foreign Exchange Rate Derivatives Use FX_Exposure = 0 FX_Exposure = 1 ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ (01) (02) (03) (01) (02) (03) Variable Coef pvalue Coef pvalue Coef pvalue Coef pvalue Coef pvalue Coef pvalue ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ Intercept 0.03 [0.66] 0.03 [0.60] -0.01 [0.92] -0.01 [0.84] 0.03 [0.59] -0.00 [0.95] FX_Derivatives 0.06 [0.02] 0.06 [0.02] 0.10 [0.03] -0.00 [0.96] -0.00 [0.97] -0.02 [0.55] Leverage -1.30 [0.00] -1.30 [0.00] -1.55 [0.00] -1.47 [0.00] -1.46 [0.00] -1.72 [0.00] Logassets -0.00 [0.77] -0.00 [0.82] 0.02 [0.20] -0.01 [0.33] -0.01 [0.43] 0.00 [0.94] Dividend -0.09 [0.00] -0.08 [0.00] -0.17 [0.00] -0.03 [0.15] -0.02 [0.27] -0.02 [0.48] ROA_3y -0.01 [0.78] -0.01 [0.78] 0.05 [0.48] 0.11 [0.00] 0.11 [0.00] 0.18 [0.00] R_D_to_Sales 0.17 [0.00] 0.08 [0.18] CapEx 0.11 [0.00] 0.11 [0.00] -0.17 [0.04] 0.23 [0.00] 0.23 [0.00] 0.21 [0.01] Foreign_Sales 0.17 [0.00] 0.17 [0.00] 0.19 [0.00] Foreign_Debt 0.10 [0.08] 0.05 [0.15] NumIndSeg -0.00 [0.68] 0.00 [0.90] -0.00 [0.38] -0.01 [0.44] MultIndSeg -0.01 [0.81] 0.02 [0.50] PctMktCap -0.05 [0.52] -0.05 [0.50] -0.00 [0.99] 0.01 [0.93] -0.01 [0.92] 0.07 [0.59] D_year 0.02 [0.35] 0.02 [0.36] 0.01 [0.84] -0.01 [0.64] -0.01 [0.61] -0.01 [0.68] AdjRSq 0.21 0.21 0.24 0.26 0.26 0.28 Observations 2991 2991 1040 3551 3551 1951 (continued) 52 Table 8: Multivariate Analysis of Tobin’s Q (continued) Panel C: Interest Rate Derivatives Use IR_Exposure = 0 IR_Exposure = 1 ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ (01) (02) (03) (01) (02) (03) Variable Coef pvalue Coef pvalue Coef pvalue Coef pvalue Coef pvalue Coef pvalue ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ Intercept -0.06 [0.49] 0.00 [0.97] -0.13 [0.39] -0.02 [0.73] -0.03 [0.56] -0.08 [0.40] IR_Derivatives 0.09 [0.02] 0.10 [0.01] 0.05 [0.35] 0.07 [0.00] 0.07 [0.00] 0.08 [0.00] Leverage -1.68 [0.00] -1.74 [0.00] -2.20 [0.00] -1.13 [0.00] -1.14 [0.00] -1.30 [0.00] Logassets 0.00 [0.86] 0.01 [0.45] 0.03 [0.02] -0.02 [0.00] -0.02 [0.00] -0.02 [0.02] Dividend -0.08 [0.02] -0.05 [0.13] -0.06 [0.19] -0.01 [0.63] -0.01 [0.46] -0.02 [0.59] ROA_3y 0.07 [0.24] 0.06 [0.27] 0.13 [0.11] 0.06 [0.19] 0.06 [0.18] 0.29 [0.00] R_D_to_Sales 0.14 [0.00] 0.68 [0.00] CapEx 0.16 [0.01] 0.15 [0.01] 0.04 [0.72] 0.26 [0.00] 0.26 [0.00] 0.01 [0.93] Foreign_Sales 0.18 [0.00] 0.17 [0.00] 0.17 [0.02] 0.10 [0.00] 0.10 [0.00] 0.14 [0.01] Foreign_Debt 0.11 [0.05] 0.05 [0.08] NumIndSeg -0.03 [0.00] -0.02 [0.05] 0.01 [0.01] 0.01 [0.23] MultIndSeg -0.05 [0.28] 0.05 [0.08] PctMktCap 0.11 [0.38] 0.06 [0.62] 0.16 [0.42] -0.14 [0.04] -0.12 [0.10] -0.01 [0.95] D_year 0.11 [0.00] 0.11 [0.00] 0.11 [0.00] -0.10 [0.00] -0.10 [0.00] -0.13 [0.00] AdjRSq 0.06 0.06 0.08 0.24 0.24 0.34 Observations 2265 2265 1321 2473 2473 1092 Panel D: Commodity Price Derivatives Use CP_Exposure = 0 CP_Exposure = 1 ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ (01) (02) (03) (01) (02) (03) Variable Coef pvalue Coef pvalue Coef pvalue Coef pvalue Coef pvalue Coef pvalue ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ Intercept 0.01 [0.90] 0.05 [0.38] -0.04 [0.70] 0.19 [0.02] 0.10 [0.25] 0.14 [0.43] CP_Derivatives 0.05 [0.24] 0.04 [0.25] -0.00 [0.99] -0.00 [0.96] 0.00 [0.88] 0.01 [0.88] Leverage -1.48 [0.00] -1.47 [0.00] -1.79 [0.00] -0.98 [0.00] -0.98 [0.00] -0.92 [0.00] Logassets -0.01 [0.33] -0.01 [0.47] 0.01 [0.23] 0.00 [0.72] -0.00 [0.95] -0.01 [0.51] Dividend -0.06 [0.00] -0.05 [0.01] -0.05 [0.09] -0.02 [0.64] -0.03 [0.38] -0.02 [0.71] ROA_3y 0.08 [0.04] 0.08 [0.05] 0.18 [0.00] 0.07 [0.46] 0.06 [0.53] -0.14 [0.44] R_D_to_Sales 0.18 [0.00] 0.39 [0.34] CapEx 0.23 [0.00] 0.23 [0.00] 0.00 [0.96] 0.09 [0.20] 0.11 [0.12] 0.19 [0.18] Foreign_Sales 0.17 [0.00] 0.17 [0.00] 0.18 [0.00] -0.04 [0.51] -0.05 [0.48] -0.02 [0.85] Foreign_Debt 0.07 [0.03] 0.05 [0.42] NumIndSeg -0.01 [0.23] -0.01 [0.47] 0.01 [0.30] 0.01 [0.29] MultIndSeg 0.01 [0.61] -0.07 [0.12] PctMktCap -0.00 [0.97] -0.02 [0.78] 0.09 [0.53] -0.16 [0.17] -0.14 [0.23] -0.21 [0.37] D_year 0.02 [0.41] 0.01 [0.44] 0.02 [0.57] -0.10 [0.00] -0.10 [0.00] -0.11 [0.04] AdjRSq 0.24 0.24 0.28 0.24 0.24 0.18 Observations 4140 4140 2145 598 598 268 53 Table 9: Examination of Country-Specific Determinants in Firm Level Analysis for G4+1 and IAS Compliant Firms The table reports regression coefficients, their marginal effects and p-values (in brackets) from LOGIT regressions of the relation between the likelihood of derivatives use, firm-specific and country-specific proxies of incentives for hedging, proxies of exposure, and control variables. Below the coefficients, information about the goodness of fit and the number of observations are reported. The sample is limited to G4+1 firms and firms in New Zealand. General FX_Derivatives IR_Derivatives CP_Derivatives ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ Variable Coef MarEff pvalue Coef MarEff pvalue Coef MarEff pvalue Coef MarEff pvalue ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ Intercept -1.93 [0.29] -3.28 [0.07] -1.57 [0.40] -13.85 [0.00] Leverage 0.90 0.20 [0.00] 0.24 0.05 [0.21] 1.79 0.39 [0.00] 1.30 0.29 [0.00] Coverage_3y 0.00 0.03 [0.49] -0.01 -0.08 [0.04] 0.02 0.10 [0.02] -0.02 -0.10 [0.08] Quick_Ratio -0.07 -0.14 [0.00] -0.07 -0.14 [0.00] -0.08 -0.15 [0.00] -0.03 -0.05 [0.41] Logsize 0.36 0.61 [0.00] 0.32 0.54 [0.00] 0.39 0.65 [0.00] 0.23 0.39 [0.00] Dividend 0.55 0.27 [0.00] 0.37 0.18 [0.00] 0.86 0.43 [0.00] 0.84 0.42 [0.00] GrossProfitMargin_3y 0.23 0.08 [0.02] 0.19 0.07 [0.07] -0.11 -0.04 [0.34] 0.04 0.01 [0.81] D_Income_Tax_Credit 0.74 0.13 [0.00] -0.01 -0.00 [0.95] 0.14 0.02 [0.46] 1.05 0.18 [0.00] Market_to_Book -0.08 -0.24 [0.00] -0.05 -0.15 [0.00] -0.10 -0.30 [0.00] -0.04 -0.11 [0.11] MB_Leverage 0.54 0.34 [0.00] 0.19 0.12 [0.00] 0.58 0.37 [0.00] 0.21 0.13 [0.00] MultShareClass 0.48 0.15 [0.00] 0.08 0.03 [0.44] 0.29 0.09 [0.00] -0.44 -0.14 [0.00] Stock_Options -0.03 -0.01 [0.78] -0.16 -0.05 [0.18] -0.14 -0.04 [0.25] 0.20 0.06 [0.23] FX_Exposure 0.53 0.26 [0.00] 1.17 0.58 [0.00] -0.03 -0.02 [0.69] -0.35 -0.18 [0.00] Foreign_Debt 0.26 0.10 [0.01] 0.53 0.20 [0.00] 0.28 0.11 [0.00] -0.32 -0.12 [0.02] Foreign_Listing 0.63 0.16 [0.00] 0.69 0.17 [0.00] 0.67 0.17 [0.00] 1.01 0.25 [0.00] NegBookValue 0.30 0.05 [0.21] -0.30 -0.05 [0.18] 0.56 0.09 [0.01] 0.28 0.05 [0.28] DerMktRank 0.03 0.24 [0.00] 0.03 0.24 [0.00] 0.05 0.34 [0.00] -0.01 -0.07 [0.50] ICR_Composite -0.03 -0.09 [0.35] -0.02 -0.06 [0.55] -0.09 -0.24 [0.01] 0.09 0.23 [0.06] Legality 0.10 0.12 [0.16] 0.08 0.09 [0.29] 0.22 0.26 [0.00] 0.13 0.15 [0.20] Closely_Held 0.31 0.06 [0.49] 0.67 0.12 [0.13] 0.39 0.07 [0.39] 3.57 0.63 [0.00] PctMktCap 1.88 0.17 [0.00] 2.03 0.19 [0.00] 2.25 0.21 [0.00] 0.21 0.02 [0.79] USROW -0.19 -0.09 [0.17] -0.82 -0.41 [0.00] 0.16 0.08 [0.25] 1.85 0.92 [0.00] D_year -0.33 -0.16 [0.00] -0.23 -0.11 [0.00] -0.23 -0.11 [0.00] -0.29 -0.14 [0.00] AIC 4982.8 5194.1 4899.0 3111.6 SC 5130.8 5342.2 5047.1 3259.7 -2 Log L 4936.8 5148.1 4853.0 3065.6 R-Square 0.21 0.23 0.24 0.09 Observations 4620 4620 4620 4620 54 Table 10: Examination of Country-Specific Determinants in Firm Level Analysis in Simultaneous Equations The table reports in Panel A regression coefficients, their marginal effects and p-values (in brackets) from LOGIT regressions of the relation between the likelihood of derivatives use, firm-specific and country-specific proxies of incentives for hedging, proxies of exposure, and control variables. In Panel B, coefficients and correspond-ing p-values (in brackets) of OLS regression of leverage on derivatives usage and other firm characteristics are show. Below the coefficients, information about the goodness of fit and the number of observations are reported. The estimation is based on a simultaneous equation approach. Panel A: Logit Results General FX_Derivatives IR_Derivatives CP_Derivatives Variable Coef MarEff pvalue Coef MarEff pvalue Coef MarEff pvalue Coef MarEff pvalue ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ Intercept -2.52 [0.00] -5.08 [0.00] -3.92 [0.00] -8.12 [0.00] Leverage* 6.35 0.69 [0.00] 2.92 0.31 [0.00] 10.86 1.20 [0.00] 5.83 0.62 [0.00] Coverage_3y 0.01 0.04 [0.28] -0.01 -0.04 [0.25] 0.02 0.11 [0.01] -0.03 -0.15 [0.02] Quick_Ratio -0.00 -0.00 [0.98] -0.03 -0.05 [0.17] -0.00 -0.00 [0.98] 0.02 0.03 [0.58] Logsize 0.41 0.66 [0.00] 0.34 0.54 [0.00] 0.46 0.73 [0.00] 0.32 0.51 [0.00] Dividend 0.53 0.26 [0.00] 0.44 0.22 [0.00] 0.85 0.42 [0.00] 0.92 0.45 [0.00] GrossProfitMargin_3y 0.34 0.10 [0.00] 0.29 0.09 [0.01] 0.11 0.03 [0.44] 0.34 0.10 [0.10] D_Income_Tax_Credit 0.94 0.14 [0.00] 0.24 0.04 [0.25] 0.23 0.04 [0.30] 1.00 0.15 [0.00] Market_to_Book 0.01 0.03 [0.49] -0.02 -0.05 [0.16] 0.03 0.09 [0.05] 0.01 0.03 [0.60] MB_Leverage* 0.32 0.09 [0.02] -0.11 -0.03 [0.38] 1.45 0.42 [0.00] 0.63 0.18 [0.00] MultShareClass 0.64 0.22 [0.00] 0.20 0.07 [0.03] 0.20 0.07 [0.05] -0.29 -0.10 [0.06] Stock_Options -0.04 -0.02 [0.68] -0.07 -0.03 [0.40] 0.00 0.00 [0.97] -0.05 -0.02 [0.74] FX_Exposure 0.29 0.14 [0.00] 0.80 0.39 [0.00] -0.14 -0.07 [0.07] -0.38 -0.19 [0.00] Foreign_Debt 0.42 0.18 [0.00] 0.49 0.21 [0.00] 0.25 0.11 [0.00] -0.03 -0.01 [0.83] Foreign_Listing 0.54 0.17 [0.00] 0.69 0.21 [0.00] 0.66 0.21 [0.00] 0.82 0.26 [0.00] NegBookValue -0.01 -0.00 [0.97] -0.32 -0.05 [0.16] 0.38 0.06 [0.12] -0.16 -0.02 [0.66] DerMktRank 0.02 0.17 [0.00] 0.02 0.17 [0.00] 0.03 0.27 [0.00] -0.01 -0.11 [0.26] ICR_Composite 0.01 0.03 [0.66] 0.05 0.21 [0.00] -0.03 -0.13 [0.14] 0.02 0.09 [0.49] Legality 0.03 0.05 [0.44] -0.08 -0.14 [0.03] 0.16 0.30 [0.00] 0.13 0.23 [0.06] Closely_Held -0.92 -0.17 [0.00] -0.17 -0.03 [0.55] -0.73 -0.14 [0.02] 1.62 0.30 [0.00] PctMktCap 1.51 0.20 [0.00] 1.74 0.23 [0.00] 1.32 0.17 [0.00] 0.36 0.05 [0.57] USROW -0.09 -0.04 [0.45] -0.59 -0.27 [0.00] 0.31 0.14 [0.01] 1.85 0.84 [0.00] D_year -0.22 -0.10 [0.00] -0.13 -0.06 [0.05] -0.08 -0.04 [0.30] -0.22 -0.11 [0.04] R-Square 0.21 0.20 0.30 0.10 Observations 4927 4927 4927 4927 Panel B: OLS Results: Derivatives Coefficients Only Variable Coef pvalue Coef pvalue Coef pvalue Coef pvalue ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ Hedger* 0.07 [0.00] FX_Derivatives* 0.04 [0.00] IR_Derivatives* 0.08 [0.00] CP_Derivatives* 0.09 [0.00] 55 Table 12: Examination of Country-Specific Determinants in Firm Level Analysis for ADRs The table reports regression coefficients, their marginal effects and p-values (in brackets) from LOGIT regressions of the relation between the likelihood of derivatives’ use, firm-specific and country-specific proxies of incentives for hedging, proxies of exposure, and control variables. Below the coefficients, information about the goodness of fit and the number of observations are reported. The sample is limited to firms with a foreign listing (ADR, GDR, etc.). General FX_Derivatives IR_Derivatives CP_Derivatives Variable Coef MarEff pvalue Coef MarEff pvalue Coef MarEff pvalue Coef MarEff pvalue ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ Intercept -5.87 [0.03] -9.70 [0.00] -7.30 [0.00] -1.42 [0.62] Leverage -0.22 -0.05 [0.71] -0.58 -0.13 [0.30] 0.80 0.18 [0.18] 1.23 0.28 [0.08] Coverage_3y -0.04 -0.21 [0.10] -0.05 -0.25 [0.04] -0.03 -0.14 [0.27] -0.01 -0.05 [0.75] Quick_Ratio -0.17 -0.27 [0.02] -0.26 -0.43 [0.00] -0.21 -0.34 [0.01] 0.04 0.06 [0.67] Logsize 0.43 0.73 [0.00] 0.39 0.65 [0.00] 0.54 0.90 [0.00] 0.48 0.80 [0.00] Dividend 0.89 0.41 [0.00] 0.50 0.23 [0.04] 1.08 0.50 [0.00] 0.01 0.00 [0.98] GrossProfitMargin_3y -0.00 -0.00 [0.99] -0.25 -0.08 [0.51] -0.85 -0.27 [0.03] 0.01 0.00 [0.97] D_Income_Tax_Credit 0.62 0.09 [0.48] 1.23 0.18 [0.17] 0.86 0.13 [0.28] -0.89 -0.13 [0.41] Market_to_Book -0.07 -0.19 [0.14] -0.06 -0.18 [0.14] -0.18 -0.50 [0.00] -0.13 -0.36 [0.08] MB_Leverage 0.41 0.21 [0.19] 0.52 0.27 [0.07] 1.03 0.54 [0.00] 0.36 0.19 [0.24] MultShareClass 0.45 0.16 [0.22] 0.23 0.08 [0.46] -0.30 -0.11 [0.33] -0.63 -0.23 [0.09] Stock_Options 0.20 0.08 [0.47] 0.21 0.09 [0.41] -0.32 -0.13 [0.21] -0.14 -0.06 [0.66] FX_Exposure 0.43 0.20 [0.11] 0.41 0.19 [0.09] 0.12 0.05 [0.64] -0.67 -0.31 [0.02] Foreign_Debt 0.88 0.43 [0.00] 0.65 0.32 [0.00] 0.51 0.25 [0.02] 0.00 0.00 [0.99] NegBookValue 1.57 0.24 [0.17] 1.97 0.30 [0.08] -0.02 -0.00 [0.97] -0.71 -0.11 [0.39] DerMktRank -0.00 -0.00 [0.98] 0.01 0.09 [0.60] 0.00 0.03 [0.85] -0.02 -0.23 [0.22] ICR_Composite 0.07 0.41 [0.16] 0.14 0.87 [0.00] 0.05 0.34 [0.19] -0.02 -0.13 [0.69] Legality -0.02 -0.05 [0.86] -0.17 -0.50 [0.08] 0.04 0.12 [0.64] 0.07 0.20 [0.53] Closely_Held 0.32 0.05 [0.77] 1.46 0.24 [0.16] -0.01 -0.00 [0.99] -0.12 -0.02 [0.91] PctMktCap 0.56 0.09 [0.48] 0.40 0.06 [0.58] 0.51 0.08 [0.48] 0.80 0.13 [0.35] D_year 0.01 0.00 [0.97] 0.02 0.01 [0.92] 0.11 0.05 [0.62] -0.10 -0.05 [0.69] AIC 537.16 634.87 647.35 521.86 SC 630.12 727.82 740.30 614.82 -2 Log L 495.16 592.87 605.35 479.86 R-Square 0.20 0.21 0.33 0.08 Observations 618 618 618 618 56 Appendix Table A-1: Variable Definitions and Predicted Relations The table reports the independent variables of the study and their definition. The second column indicates the expected sign of the relationship with derivatives use (+: positive, -: negative, ?: indeterminate). Panel A describes firm-specific variables. Panel B country-specific variables. A suffix of “_3y” to a variable indicates a three-year average. Variable Prediction Definition Panel A: Firm-specific variables Leverage Coverage_3y Quick_Ratio Current_Ratio Tangible_Assets SGA_Expense Logsize Logassets Dividend DivYield DivPayout GrossProfitMargin_3y ROA_3y Cash_Flow D_Income_Tax_Credit Income_Tax_Credit Tax_Rate Market_to_Book MB_Leverage R_D_to_Sales R_D_to_Size CapEx PPE_to_Size PPE_to_Sales LogTobinQ1 LogTobinQ2 Earningsyield_3y Debt_Maturity MultShareClass Stock_Options + - - - - + ? ? ? ? ? - - - + + - + + + + + + + + + - + + ? Total Debt / Size EBIT / Interest Expense on Debt (Cash & Equivalents + Receivables (Net)) / Current Liabilities-Total Current Assets-Total / Current Liabilities-Total (Total Assets - Intangibles) / Total Assets (Selling, General and Administrative Expenses - Research and Development Expense) / Net Sales or Revenues Natural logarithm of the sum of market capitalization, total debt and preferred stock Natural logarithm of Total Assets Dummy variable with value 1 if dividend yield, dividend payout or dividend per share is positive; 0 otherwise Dividends Per Share / Market Price-Year End Common Dividends (Cash) / (Net Income before Preferred Dividends - Preferred Dividend Requirement) Gross Income / Net Sales or Revenues (Net Income before Preferred Dividends + ((Interest Expense on Debt-Interest Capitalized) * (1-Tax Rate))) / Last Year's Total Assets Funds from Operations / Net Sales or Revenues Dummy variable with value 1 if income tax credits exist; 0 otherwise Includes: (1) Tax losses carryforward/carrybackward (2) Royalty tax credits (3) Research and development tax credits Income Taxes / Pretax Income Market Price-Year End / Book Value Per Share Market_to_Book * Leverage Research and Development Expense / Net Sales or Revenues Research and Development Expense / Size Capital Expenditures / Net Sales or Revenues Property, Plant, and Equipment - Total (Gross) / Size Property, Plant, and Equipment - Total (Gross) / Net Sales or Revenues Natural logarithm of (Size / Total Assets) Natural logarithm of (Size / Net Sales or Revenues) Earnings Per Share / Market Price-Year End Total Long-Term Debt / Total Debt. Long-term debt represents debt obligations due more than one year from the company's balance sheet date or due after the current operating cycle Dummy variable with value 1 if currently multiple share classes exist; 0 otherwise Dummy variable with value 1 if stock options are repoted in the annual report; 0 otherwise (continued) 57 Table A-1: Variable Definitions and Predicted Relations (continued) Variable Prediction Definition Panel A: Firm-specific variables ConvDebt PrefStock Foreign_Assets Foreign_Income_3y Foreign_Sales FX_Exposure Foreign_Debt IR_Exposure CP_Exposure Exposure NumIndSeg MultIndSeg Foreign_Listing NegBookValue PctMktCap DerMktRank ? ? + + + + ? + + + ? ? ? ? - + Convertible Long-Term Debt / Size Preferred Stock / Size International Assets / Total Assets International Operating Income / Operating Income International Sales / Net Sales or Revenues Dummy variable with value 1 if any foreign assets, foreign income or foreign sales are reported; 0 otherwise Dummy variable with value 1 if any foreign debt is reported; 0 otherwise Dummy variable with value 1 if the firm has leverage higher then the median leverage in its coun-try; 0 otherwise Dummy variable with value 1 if the firm is in one of the industries chemicals, mines, oil, steel, or utilities; 0 otherwise Dummy variable with value 1 if any of the dummy variables for foreign exchange rate exposure, interest rate exposure or commodity price exposure are 1; 0 otherwise Number of business segments (SIC codes) that make up the company's revenue (between 1 and 8) Dummy variable with value 1 if the firm has several business segments; 0 otherwise Dummy variable with value 1 if the firm has a foreign listing (ADR, GDR, NYS); 0 otherwise Dummy variable with value 1 if the firm has negative book value of equity; 0 otherwise Percentage of market capitalization covered by the sample firms in a particular country Inverse ranking of the size of the derivatives’ market relative to the market of the other countries in the sample. Size is calculated by summing daily turnover in the FX and IR markets in 2001 for non-financial firms and standardizing by nominal GDP. Dummy variable with value 1 if the country is a member of the Organization for Economic Coop-eration and Development; 0 otherwise Nominal GDP per capita in 2001 International Country Risk composite index Natural logarithm of GDP Natural logarithm of ((Exports + Imports) / GDP) International Country Risk index of financial risk (from PRS Group) International Country Risk index of economic risk (from PRS Group) International Country Risk index of political risk (from PRS Group) Index of rule of law (from Kaufmann, Kraay and Zoido-Lobaton, 2003) Dummy variable with value 1 if the legal origin of the country is civil law; 0 otherwise (from La Porta et al., 1998) Aggregate index of creditor right protection with values from 0 (low) to 4 (high) (from La Porta et al., 1998) Index of effective legal institutions (from Berkowitz, Pistor and Richard, 2003) Aggregate index of shareholder right protection with values from 0 (low) to 6 (high) (from La Porta et al., 1998) Dahlquist et al (2003) measure of ownership concentration Panel B: Country-specific Variables OECD GDP_Capita ICR_Composite LogGDP LogEXIM_GDP ICR_Financial ICR_Economic ICR_Political KKZ_RuleofLaw Civil_Law Creditor_Rights Legality Shareholder_Rights Closely_Held + + - - + - - - ? ? ? ? + + IR_Ave Average of short-term interest rates USROW Dummy variable with value 1 if the firm is incorporated in the United States; 0 otherwise D_year Dummy variable with value 1 if the annual report is from year 2000; 0 otherwise 58 Table A-2: Descriptive Statistics for Explanatory Variables The table shows mean values of various company characteristics by country, region, and for all firms. All accounting data are in millions of U.S. dollars. Definitions of all variables are reported in Table A-1. Other countries are Bahamas, Ber-muda, Cayman Islands, Egypt, Indonesia, Jordan, Peru, Portugal, Turkey, and Venezuela. Coverage Tang. SGA Log Log Div. Div. Margin Leverage 3yr Quick Current Asset Exp. Size Assets Div. Yield Payout 3yr ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ Argentina 0.491 1.723 0.801 1.807 0.983 0.141 7.161 7.381 0.636 0.017 0.317 0.212 Australia 0.244 -1.295 1.651 2.074 0.876 0.326 5.026 4.885 0.558 0.025 0.254 -0.223 Austria 0.329 2.177 1.221 1.678 0.885 0.254 5.543 5.722 0.659 0.025 0.245 -0.096 Belgium 0.349 2.786 1.085 1.539 0.858 0.290 5.696 5.651 0.646 0.016 0.257 -0.196 Brazil 0.498 0.180 0.890 1.203 0.966 0.165 7.312 7.451 0.632 0.020 0.235 0.097 Canada 0.274 -1.384 1.949 2.517 0.892 0.311 5.343 5.175 0.277 0.007 0.077 -0.181 Chile 0.411 3.481 1.100 1.613 0.944 0.198 7.242 7.203 0.923 0.026 0.364 0.320 China 0.438 0.428 1.315 1.668 0.981 0.165 6.170 6.348 0.639 0.034 0.237 0.124 Czech Republic 0.330 2.434 1.051 1.523 0.984 0.080 4.758 5.671 0.478 0.017 0.112 -0.177 Denmark 0.318 2.196 1.633 2.120 0.920 0.278 5.454 5.312 0.614 0.014 0.168 -0.082 Finland 0.302 4.031 1.306 1.860 0.908 0.211 5.268 5.345 0.800 0.032 0.294 -0.081 France 0.247 2.987 1.254 1.592 0.794 0.281 6.631 6.288 0.667 0.011 0.184 -0.001 Germany 0.232 0.037 2.179 2.807 0.848 0.284 5.135 5.089 0.415 0.012 0.142 -0.248 Greece 0.207 5.246 1.191 1.591 0.957 0.148 6.570 6.131 0.842 0.017 0.268 0.201 Hong Kong 0.303 -0.829 1.813 2.258 0.970 0.362 4.620 4.864 0.475 0.020 0.160 -0.057 Hungary 0.242 4.011 1.518 2.197 0.980 0.124 5.033 5.275 0.500 0.008 0.120 0.244 India 0.301 3.582 1.701 2.261 0.987 0.153 6.444 6.208 0.864 0.024 0.190 0.141 Ireland 0.345 -0.563 1.302 1.659 0.849 0.341 5.319 5.543 0.471 0.016 0.130 -0.086 Israel 0.214 -5.034 3.360 3.838 0.917 0.370 5.423 4.851 0.132 0.005 0.111 -0.397 Italy 0.289 3.412 1.411 1.792 0.868 0.315 6.310 6.067 0.707 0.017 0.232 0.071 Japan 0.340 3.780 1.206 1.659 0.981 0.184 8.048 7.934 0.878 0.010 0.248 0.205 Korea, Republic of 0.585 0.904 1.038 1.453 0.965 0.135 7.741 8.089 0.680 0.032 0.236 0.100 Luxembourg 0.366 -0.157 2.275 2.392 0.827 0.222 6.698 6.798 0.364 0.008 0.107 -0.164 Malaysia 0.378 1.310 1.538 2.014 0.969 0.174 4.799 5.082 0.672 0.017 0.185 0.079 Mexico 0.378 2.734 1.447 2.059 0.916 0.205 7.247 7.492 0.564 0.013 0.170 0.310 Netherlands 0.281 2.115 1.444 1.878 0.888 0.283 6.078 5.943 0.582 0.022 0.147 -0.008 New Zealand 0.337 1.694 1.003 1.636 0.897 0.177 5.462 5.419 0.778 0.036 0.393 -0.056 Norway 0.352 0.192 1.711 2.068 0.909 0.306 5.444 5.345 0.395 0.013 0.130 -0.214 Other countries 0.413 -1.148 1.342 2.209 0.926 0.145 6.551 6.310 0.433 0.014 0.188 -0.146 Philippines 0.533 0.722 0.854 1.270 0.975 0.367 6.209 6.408 0.429 0.007 0.094 0.026 Poland 0.203 4.956 1.071 1.345 0.944 0.120 5.706 5.955 0.385 0.007 0.123 0.040 Singapore 0.303 1.451 1.497 1.937 0.985 0.172 4.654 4.846 0.655 0.020 0.238 -0.008 South Africa 0.247 4.009 1.399 1.791 0.889 0.165 6.289 6.302 0.724 0.025 0.212 0.003 Spain 0.336 5.503 0.800 1.127 0.907 0.057 7.427 7.163 0.931 0.025 0.295 0.160 Sweden 0.203 0.527 1.879 2.423 0.845 0.331 5.383 5.154 0.476 0.014 0.168 -0.256 Switzerland 0.250 3.968 1.655 2.290 0.884 0.274 6.241 6.078 0.724 0.014 0.228 0.103 Thailand 0.505 1.866 1.556 2.643 0.943 0.159 5.638 5.731 0.500 0.026 0.142 0.315 United Kingdom 0.188 1.020 1.778 2.192 0.834 0.471 5.333 5.139 0.619 0.022 0.223 0.040 United States 0.235 0.492 1.992 2.673 0.824 0.336 6.820 6.480 0.343 0.008 0.088 0.220 US & Canada 0.243 0.097 1.984 2.642 0.838 0.333 6.522 6.214 0.329 0.008 0.085 0.136 Europe 0.241 1.561 1.683 2.151 0.857 0.370 5.569 5.440 0.587 0.018 0.196 -0.053 Asia & Pacific 0.329 1.019 1.505 1.972 0.957 0.232 5.634 5.707 0.656 0.019 0.216 0.018 Africa/Middle East 0.232 -0.835 2.269 2.910 0.901 0.280 5.916 5.520 0.406 0.016 0.177 -0.210 Latin Amer./Carib. 0.424 1.168 1.167 1.829 0.937 0.184 7.174 7.223 0.587 0.017 0.216 0.142 OECD 0.251 0.878 1.780 2.337 0.859 0.331 6.152 5.946 0.485 0.013 0.149 0.045 Non-OECD 0.336 0.488 1.647 2.163 0.966 0.245 5.063 5.225 0.577 0.019 0.188 -0.002 United States 0.235 0.492 1.992 2.673 0.824 0.336 6.820 6.480 0.343 0.008 0.088 0.220 Non-US 0.278 0.959 1.654 2.145 0.897 0.305 5.601 5.535 0.569 0.017 0.188 -0.044 All firms 0.264 0.816 1.760 2.310 0.877 0.318 5.979 5.829 0.500 0.014 0.154 0.037 59 (continued) Table A-2: Descriptive Statistics of Variables (continued) Tax ROA Cash Credit Tax Tax Market\\ R&D\\ R&D\\ Cap. PPE\\ PPE\\ Log 3yr Flow Dummy Credit Rate Book Sales Size Exp. Size Sales TobQ1 ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ Argentina -0.033 0.233 0.000 0.000 0.259 0.866 0.000 0.000 0.177 1.226 2.706 -0.220 Australia -0.386 -0.172 0.020 0.108 0.327 2.559 0.093 0.012 0.138 0.550 1.132 0.224 Austria -0.098 0.013 0.023 0.059 0.369 1.631 0.044 0.023 0.097 1.023 0.932 -0.179 Belgium -0.026 0.042 0.015 0.006 0.360 2.096 0.091 0.040 0.119 0.751 0.771 -0.031 Brazil -0.075 -0.009 0.105 0.113 0.322 1.109 0.005 0.004 0.117 0.990 1.541 -0.139 Canada -0.187 -0.172 0.043 0.079 0.337 2.206 0.287 0.040 0.206 0.755 1.495 0.125 Chile 0.042 0.155 0.000 0.000 0.198 1.566 0.009 0.004 0.095 0.869 2.291 0.039 China -0.189 0.109 0.000 0.000 0.178 1.005 0.013 0.006 0.232 1.140 2.105 -0.424 Czech Republic -0.080 0.021 0.000 0.000 0.310 0.709 0.000 0.000 0.225 2.547 1.909 -0.913 Denmark -0.079 -0.080 0.068 0.200 0.293 2.751 0.437 0.058 0.143 0.725 0.909 0.109 Finland -0.022 0.052 0.057 0.127 0.318 2.425 0.046 0.027 0.086 0.766 0.645 0.036 France -0.020 0.008 0.019 0.112 0.364 4.302 0.123 0.057 0.094 0.440 0.460 0.251 Germany -0.157 -0.064 0.007 0.118 0.406 2.399 0.146 0.069 0.125 0.679 0.565 0.002 Greece 0.010 0.000 0.000 0.000 0.359 3.721 0.007 0.004 0.469 0.483 1.451 0.439 Hong Kong -0.365 -0.058 0.012 0.006 0.146 1.616 0.070 0.013 0.123 0.828 1.254 -0.208 Hungary 0.023 0.061 0.071 1.575 0.106 1.097 0.008 0.003 0.180 0.998 0.917 -0.241 India -0.042 0.180 0.000 0.000 0.142 2.489 0.007 0.004 0.108 0.740 0.965 -0.007 Ireland -0.151 -0.140 0.000 0.000 0.214 2.096 0.109 0.028 0.159 0.718 1.213 0.070 Israel -0.559 -0.409 0.000 0.000 0.201 1.375 0.215 0.065 0.107 0.370 0.693 0.204 Italy -0.102 0.052 0.040 0.415 0.399 2.892 0.062 0.036 0.097 0.544 0.747 0.123 Japan 0.003 0.076 0.003 0.001 0.458 2.371 0.032 0.027 0.057 0.840 0.991 0.032 Korea, Republic of -0.094 0.115 0.000 0.000 0.379 0.767 0.010 0.008 0.133 1.139 1.556 -0.458 Luxembourg -0.292 -0.064 0.000 0.000 0.294 1.072 0.057 0.033 0.202 0.400 0.882 -0.045 Malaysia -0.088 0.067 0.007 0.001 0.271 1.334 0.005 0.003 0.088 0.938 1.847 -0.233 Mexico 0.031 0.156 0.051 0.115 0.327 1.375 0.001 0.002 0.085 1.252 1.303 -0.245 Netherlands -0.037 -0.028 0.000 0.000 0.309 2.892 0.197 0.060 0.098 0.666 0.650 0.103 New Zealand -0.122 0.103 0.000 0.000 0.303 2.120 0.002 0.004 0.096 0.870 1.721 0.043 Norway -0.117 -0.102 0.000 0.000 0.362 2.235 0.331 0.026 0.239 0.669 1.296 0.106 Other countries -0.236 -0.251 0.000 0.000 0.238 1.352 0.035 0.008 0.127 0.796 1.813 -0.066 Philippines 0.005 0.069 0.000 0.000 0.366 0.917 0.000 0.000 0.281 1.178 2.067 -0.333 Poland -0.027 0.089 0.000 0.000 0.340 1.761 0.014 0.005 0.131 0.813 1.012 -0.249 Singapore -0.221 0.054 0.009 0.010 0.255 1.465 0.031 0.014 0.109 0.870 1.367 -0.208 South Africa -0.032 0.126 0.017 0.004 0.284 2.535 0.005 0.005 0.109 0.572 0.704 -0.013 Spain 0.061 0.171 0.000 0.000 0.213 2.721 0.015 0.007 0.176 0.921 1.714 0.016 Sweden -0.190 -0.140 0.000 0.000 0.319 3.028 0.255 0.058 0.113 0.440 0.689 0.320 Switzerland -0.055 0.046 0.024 0.038 0.263 2.790 0.131 0.049 0.083 0.627 0.727 0.176 Thailand 0.028 0.207 0.000 0.000 0.212 1.381 0.000 0.000 0.104 0.885 1.793 -0.093 United Kingdom -0.137 -0.082 0.033 0.034 0.332 2.955 0.222 0.042 0.130 0.514 0.849 0.253 United States -0.056 -0.013 0.035 0.055 0.370 2.997 0.156 0.040 0.132 0.473 0.826 0.352 US & Canada -0.084 -0.047 0.037 0.059 0.364 2.830 0.169 0.040 0.145 0.520 0.936 0.307 Europe -0.109 -0.047 0.023 0.053 0.335 2.780 0.183 0.047 0.126 0.605 0.800 0.142 Asia & Pacific -0.191 0.008 0.009 0.012 0.301 1.874 0.043 0.017 0.105 0.822 1.340 -0.085 Africa/Middle East -0.316 -0.165 0.008 0.003 0.257 1.887 0.124 0.038 0.110 0.506 0.726 0.079 Latin Amer./Carib. -0.073 0.044 0.043 0.087 0.288 1.166 0.012 0.004 0.105 1.140 1.696 -0.197 OECD -0.104 -0.043 0.028 0.053 0.357 2.746 0.154 0.039 0.131 0.586 0.907 0.209 Non-OECD -0.218 0.006 0.009 0.005 0.220 1.520 0.051 0.014 0.114 0.859 1.447 -0.184 United States -0.056 -0.013 0.035 0.055 0.370 2.997 0.156 0.040 0.132 0.473 0.826 0.352 Non-US -0.151 -0.044 0.020 0.037 0.320 2.351 0.135 0.035 0.126 0.709 1.084 0.055 All firms -0.122 -0.035 0.025 0.045 0.336 2.550 0.145 0.037 0.128 0.631 0.998 0.147 60 (continued) Table A-2: Descriptive Statistics of Variables (continued) Earnings Mult. Forgn Log Acqu. yield Debt Share Stock Conv. Pref. Forgn Inc Forgn FX TobQ2 Asset 3yr Mat Class Options Debt Stock Asset 3yr Sales Exp. ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ Argentina 0.761 0.000 -0.075 0.613 0.818 0.364 0.000 0.000 0.000 0.000 0.000 Australia 0.636 0.026 -0.279 0.649 0.013 0.867 0.004 0.005 0.224 0.202 0.258 0.299 Austria 0.041 0.015 -0.107 0.441 0.136 0.432 0.000 0.000 0.490 0.541 0.626 0.659 Belgium 0.033 0.015 -0.090 0.482 0.292 0.708 0.002 0.000 0.200 0.000 0.606 0.492 Brazil 0.223 0.004 -0.158 0.609 1.000 0.368 0.002 0.000 0.000 0.000 0.219 0.211 Canada 0.578 0.026 -0.225 0.663 0.184 0.938 0.009 0.008 0.193 0.112 0.422 0.419 Chile 0.692 0.000 -0.011 0.688 0.231 0.231 0.000 0.000 0.811 0.077 China 0.605 0.001 -0.170 0.367 0.750 0.167 0.002 0.000 0.004 0.047 0.088 0.139 Czech Republic -0.671 0.000 -0.207 0.571 0.000 0.000 0.000 0.000 0.000 0.000 0.044 0.087 Denmark 0.355 0.026 -0.146 0.571 0.477 0.511 0.001 0.000 0.412 0.941 0.670 0.466 Finland -0.110 0.022 -0.059 0.665 0.400 0.514 0.007 0.000 0.000 0.000 0.578 0.629 France 0.488 0.021 -0.084 0.561 0.086 0.864 0.013 0.000 0.378 0.928 0.549 0.753 Germany 0.238 0.017 -0.219 0.444 0.122 0.671 0.005 0.002 0.282 0.340 0.458 0.520 Greece 0.985 0.001 0.006 0.408 0.263 0.105 0.000 0.000 0.000 0.000 0.013 0.053 Hong Kong 0.350 0.011 -0.352 0.354 0.003 0.929 0.009 0.002 0.332 0.402 0.547 0.656 Hungary -0.250 0.011 0.026 0.556 0.071 0.214 0.000 0.000 0.041 0.000 0.269 0.500 India 0.593 0.010 -0.085 0.642 0.000 0.727 0.000 0.004 0.000 0.000 0.054 0.045 Ireland 0.316 0.026 -0.214 0.616 0.039 0.961 0.004 0.011 0.322 0.598 0.651 0.569 Israel 1.004 0.024 -0.571 0.431 0.000 0.912 0.003 0.000 0.107 -0.011 0.763 0.309 Italy 0.529 0.009 -0.182 0.450 0.323 0.677 0.004 0.000 0.110 0.123 0.484 0.505 Japan 0.166 0.001 -0.023 0.489 0.000 0.215 0.023 0.000 0.159 0.193 0.203 0.597 Korea, Republic of -0.068 0.000 -0.187 0.457 0.000 0.480 0.007 0.003 0.028 0.181 0.040 Luxembourg 0.680 0.020 -0.506 0.679 0.182 0.818 0.019 0.000 0.412 0.803 0.983 0.455 Malaysia 0.566 0.010 -0.121 0.370 0.007 0.642 0.008 0.000 0.110 0.108 0.164 0.351 Mexico 0.138 0.016 -0.016 0.752 0.718 0.410 0.001 0.000 0.245 0.154 0.327 0.256 Netherlands 0.011 0.027 -0.117 0.553 0.015 0.799 0.020 0.006 0.501 0.676 0.599 0.672 New Zealand 0.393 0.025 -0.135 0.763 0.022 0.533 0.016 0.000 0.311 0.336 0.319 0.267 Norway 0.425 0.020 -0.183 0.752 0.151 0.581 0.011 0.001 0.322 0.231 0.508 0.349 Other countries 0.649 0.001 -0.361 0.655 0.400 0.433 0.002 0.000 0.087 -0.017 0.175 0.167 Philippines 0.653 0.000 -0.162 0.626 0.286 0.643 0.016 0.005 0.000 0.000 0.000 0.000 Poland -0.172 0.022 0.004 0.592 0.077 0.154 0.019 0.000 0.000 0.000 0.189 0.154 Singapore 0.230 0.012 -0.176 0.404 0.027 0.898 0.003 0.001 0.326 0.417 0.444 0.788 South Africa 0.099 0.043 -0.029 0.525 0.086 0.793 0.005 0.000 0.245 0.204 0.314 0.603 Spain 0.547 0.006 0.039 0.661 0.069 0.448 0.002 0.002 0.000 0.000 0.292 0.379 Sweden 0.592 0.030 -0.216 0.737 0.545 0.441 0.009 0.000 0.705 0.606 0.420 Switzerland 0.340 0.033 -0.093 0.578 0.553 0.659 0.008 0.000 0.338 0.573 0.577 0.805 Thailand 0.763 0.000 -0.171 0.652 0.962 0.154 0.014 0.003 0.005 0.004 0.110 0.192 United Kingdom 0.495 0.042 -0.116 0.558 0.029 0.989 0.005 0.007 0.157 0.278 0.319 0.590 United States 0.594 0.021 -0.095 0.755 0.111 0.970 0.010 0.008 0.089 0.120 0.203 0.529 US & Canada 0.591 0.022 -0.122 0.737 0.126 0.963 0.010 0.008 0.103 0.119 0.238 0.506 Europe 0.356 0.031 -0.138 0.554 0.163 0.757 0.006 0.003 0.205 0.300 0.444 0.563 Asia & Pacific 0.390 0.011 -0.183 0.469 0.045 0.660 0.010 0.002 0.198 0.198 0.311 0.485 Africa/Middle East 0.488 0.035 -0.318 0.492 0.039 0.852 0.004 0.000 0.221 0.154 0.478 0.438 Latin Amer./Carib. 0.361 0.009 -0.143 0.683 0.663 0.402 0.001 0.000 0.123 0.096 0.275 0.163 OECD 0.461 0.024 -0.130 0.639 0.131 0.821 0.009 0.005 0.139 0.173 0.316 0.519 Non-OECD 0.434 0.012 -0.227 0.417 0.094 0.761 0.006 0.001 0.205 0.192 0.371 0.494 United States 0.594 0.021 -0.095 0.755 0.111 0.970 0.010 0.008 0.089 0.120 0.203 0.529 Non-US 0.394 0.022 -0.169 0.537 0.131 0.741 0.008 0.003 0.200 0.228 0.395 0.509 All firms 0.457 0.022 -0.146 0.603 0.125 0.811 0.009 0.005 0.148 0.176 0.324 0.515 61 (continued) Table A-2: Descriptive Statistics of Variables (continued) Neg Forgn IR CP Num Mult Forgn Book Pct DerMkt GDP IR Debt Exp. Exp. Exp. IndSeg IndSeg List. Value. MktCap Rank Capita Ave ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ Argentina 0.545 0.455 0.545 0.818 4.818 1.000 0.818 0.000 0.070 1 7.68 9.73 Australia 0.153 0.536 0.179 0.674 3.591 0.924 0.163 0.013 0.611 36 20.34 5.87 Austria 0.295 0.524 0.114 0.864 4.545 0.955 0.273 0.000 0.616 26 23.31 4.12 Belgium 0.231 0.516 0.108 0.754 3.554 0.923 0.046 0.015 0.262 31 22.11 4.12 Brazil 0.632 0.588 0.316 0.632 4.263 0.947 0.474 0.053 0.199 16 3.48 0.76 Canada 0.072 0.495 0.250 0.736 2.783 0.774 0.000 0.030 0.625 35 22.37 5.62 Chile 0.692 0.538 0.308 0.538 3.846 0.923 0.846 0.077 0.251 9 4.64 0.52 China 0.417 0.472 0.278 0.722 3.917 0.917 0.278 0.000 0.105 1 0.86 2.44 Czech Republic 0.391 0.500 0.565 0.783 5.174 0.957 0.087 0.000 0.550 17 4.94 6.22 Denmark 0.307 0.482 0.057 0.761 3.227 0.875 0.011 0.000 0.727 32 30.42 4.72 Finland 0.324 0.476 0.057 0.819 3.581 0.914 0.095 0.010 0.965 15 23.46 4.12 France 0.167 0.484 0.080 0.864 4.420 0.957 0.204 0.012 0.543 41 21.98 4.12 Germany 0.320 0.505 0.056 0.729 3.902 0.941 0.059 0.005 0.491 43 22.80 4.12 Greece 0.211 0.526 0.158 0.737 4.737 0.947 0.158 0.000 0.205 19 10.67 7.37 Hong Kong 0.104 0.492 0.059 0.792 4.795 0.973 0.169 0.045 0.429 38 23.93 6.78 Hungary 0.214 0.500 0.214 0.571 4.786 0.929 0.643 0.000 0.386 6 4.47 12.86 India 0.773 0.455 0.227 0.591 4.659 0.955 0.477 0.000 0.599 18 0.45 10.20 Ireland 0.431 0.489 0.176 0.784 2.686 0.706 0.353 0.039 0.643 27 24.74 3.69 Israel 0.074 0.489 0.044 0.529 2.822 0.733 0.059 0.015 0.437 5 17.71 10.00 Italy 0.152 0.500 0.101 0.697 4.434 0.980 0.162 0.000 0.322 34 18.62 4.12 Japan 0.077 0.494 0.166 0.776 5.161 0.983 0.243 0.008 0.553 42 38.16 0.35 Korea, Republic of 0.840 0.480 0.280 0.640 3.720 1.000 0.360 0.000 0.360 22 9.76 7.01 Luxembourg 0.091 0.500 0.091 0.818 3.000 0.800 0.182 0.000 0.598 29 43.09 4.48 Malaysia 0.328 0.492 0.139 0.699 5.111 0.956 0.041 0.064 0.632 11 3.85 3.78 Mexico 0.359 0.513 0.128 0.641 4.231 0.949 0.641 0.000 0.772 24 5.92 19.06 Netherlands 0.224 0.512 0.067 0.776 3.679 0.893 0.224 0.037 0.270 37 22.91 4.12 New Zealand 0.333 0.558 0.222 0.689 3.886 0.932 0.111 0.000 0.888 21 13.03 6.30 Norway 0.512 0.549 0.140 0.674 2.895 0.802 0.116 0.000 0.666 28 36.02 6.71 Other countries 0.267 0.476 0.200 0.567 3.875 0.958 0.167 0.033 0.183 6 8.36 11.31 Philippines 0.500 0.462 0.214 0.571 4.643 1.000 0.214 0.000 0.176 8 0.99 11.61 Poland 0.308 0.615 0.308 0.692 5.385 0.923 0.462 0.000 0.391 23 4.08 15.86 Singapore 0.257 0.495 0.035 0.881 4.681 0.956 0.058 0.018 0.676 40 22.96 2.69 South Africa 0.483 0.491 0.190 0.793 4.190 0.914 0.414 0.017 0.520 25 2.99 11.52 Spain 0.379 0.448 0.345 0.586 4.172 0.931 0.138 0.000 0.262 33 14.15 4.12 Sweden 0.825 0.485 0.070 0.636 3.154 0.846 0.105 0.000 0.638 30 25.63 4.37 Switzerland 0.203 0.521 0.122 0.902 4.683 0.984 0.106 0.008 0.735 39 33.39 2.80 Thailand 0.808 0.423 0.154 0.577 3.000 0.808 0.115 0.038 0.304 13 2.01 5.99 United Kingdom 0.382 0.499 0.086 0.780 3.033 0.810 0.138 0.025 0.605 45 23.68 6.17 United States 0.063 0.499 0.131 0.769 2.822 0.805 0.000 0.035 0.697 44 34.94 6.31 US & Canada 0.065 0.498 0.156 0.762 2.814 0.798 0.000 0.034 0.682 42 32.29 6.17 Europe 0.347 0.501 0.093 0.765 3.569 0.877 0.134 0.014 0.564 38 23.84 5.05 Asia & Pacific 0.221 0.500 0.132 0.747 4.609 0.957 0.157 0.027 0.557 31 20.50 4.28 Africa/Middle East 0.266 0.485 0.117 0.648 3.590 0.838 0.227 0.016 0.472 14 10.79 10.70 Latin Amer./Carib. 0.457 0.524 0.272 0.620 4.233 0.953 0.598 0.022 0.443 15 6.65 10.63 OECD 0.193 0.501 0.133 0.758 3.323 0.851 0.084 0.023 0.623 40 28.18 5.44 Non-OECD 0.285 0.491 0.113 0.744 4.641 0.946 0.152 0.038 0.511 25 13.93 5.61 United States 0.063 0.499 0.131 0.769 2.822 0.805 0.000 0.035 0.697 44 34.94 6.31 Non-US 0.272 0.500 0.129 0.750 3.847 0.893 0.137 0.021 0.565 34 21.88 5.09 All firms 0.208 0.500 0.130 0.756 3.531 0.866 0.095 0.025 0.605 37 25.90 5.46 62 (continued) Table A-2: Descriptive Statistics of Variables (continued) Log KKZ Share ICR Log EXIM\\ ICR ICR ICR Rule Civil Credit Lega holder Closely Comp GDP GDP Fin Econ Polit of Law Law Rights lity Rights Held ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ Argentina 71.3 26.4 3.103 29.5 39.0 74.0 0.319 1 1 12.3 4 0.527 Australia 83.3 26.7 3.547 35.0 43.5 88.0 1.596 0 1 20.4 4 0.249 Austria 84.0 26.0 4.306 41.0 40.0 87.0 1.812 1 3 20.8 2 0.549 Belgium 79.3 26.1 5.152 37.0 43.5 78.0 0.797 1 2 20.8 0 0.471 Brazil 63.8 27.1 3.145 31.0 33.5 63.0 -0.222 1 1 14.1 3 0.671 Canada 85.0 27.3 4.328 39.0 43.0 88.0 1.549 0 1 21.1 5 0.488 Chile 71.8 25.0 4.137 36.5 38.0 69.0 1.086 1 2 14.7 5 0.649 China 73.8 27.7 3.894 44.0 39.5 64.0 -0.040 0 4 4 0.687 Czech Republic 75.0 24.7 4.988 37.5 35.5 77.0 0.543 1 3 3 0.781 Denmark 85.5 25.8 4.375 37.0 42.0 92.0 1.691 1 3 21.5 2 0.251 Finland 88.0 25.5 4.315 35.5 47.5 93.0 1.736 1 1 21.5 3 0.235 France 79.5 27.9 4.024 37.5 43.5 78.0 1.077 1 0 19.7 3 0.380 Germany 82.5 28.3 4.195 38.5 41.5 85.0 1.483 1 3 20.4 1 0.447 Greece 74.5 25.4 4.146 32.5 39.5 77.0 0.496 1 1 14.9 2 0.752 Hong Kong 78.5 25.8 5.688 44.0 42.0 71.0 1.333 0 4 19.1 5 0.427 Hungary 74.8 24.5 4.861 35.0 33.5 81.0 0.706 1 4 3 0.495 India 66.5 26.8 3.418 39.5 33.5 60.0 0.160 0 4 12.8 5 0.403 Ireland 86.3 25.3 4.985 39.0 45.5 88.0 1.395 0 1 18.9 4 0.131 Israel 70.3 25.4 4.465 39.0 38.5 63.0 0.966 0 4 16.5 3 0.580 Italy 75.5 27.7 4.018 38.5 41.5 71.0 0.861 1 2 17.2 1 0.375 Japan 83.8 29.2 2.876 48.0 40.5 79.0 1.422 1 2 20.4 4 0.384 Korea, Republic of 80.0 26.9 4.460 40.5 44.5 75.0 0.943 1 3 14.2 2 0.392 Luxembourg 89.0 23.7 5.386 40.5 47.5 90.0 1.621 1 3 1 0.667 Malaysia 75.8 25.2 5.443 42.0 41.5 68.0 0.834 0 4 16.7 4 0.522 Mexico 69.8 27.1 4.160 36.5 35.0 68.0 -0.474 1 0 12.8 1 0.261 Netherlands 86.0 26.6 4.818 36.0 43.0 93.0 1.584 1 2 21.7 2 0.337 New Zealand 79.5 24.6 3.997 29.5 41.5 88.0 1.824 0 3 21.5 4 0.775 Norway 86.3 25.8 4.345 46.5 48.0 78.0 1.833 1 2 21.8 4 0.411 Other countries 65.4 24.8 4.163 33.1 34.1 63.6 -0.111 1 2 12.2 2 0.587 Philippines 71.5 25.0 4.668 37.0 37.0 69.0 -0.078 1 0 8.5 3 0.511 Poland 74.8 25.8 4.126 38.0 35.5 76.0 0.538 1 2 3 0.643 Singapore 88.0 25.2 5.833 45.5 46.5 84.0 1.939 0 4 19.5 4 0.571 South Africa 70.5 25.6 3.994 38.5 35.5 67.0 -0.351 0 3 14.5 5 0.529 Spain 75.0 27.0 4.130 37.0 40.0 73.0 1.032 1 2 17.1 4 0.421 Sweden 84.0 26.1 4.494 36.0 45.0 87.0 1.623 1 2 21.6 3 0.210 Switzerland 87.0 26.2 4.232 44.0 44.0 86.0 1.996 1 1 21.9 2 0.257 Thailand 74.0 25.5 4.835 38.5 38.5 71.0 0.413 0 3 12.9 2 0.578 United Kingdom 85.5 28.0 4.031 37.0 42.0 92.0 1.689 0 4 20.4 5 0.099 United States 84.3 29.9 3.033 36.5 42.0 90.0 1.254 0 1 20.8 5 0.079 US & Canada 84.4 29.4 3.306 37.0 42.2 89.6 1.316 0 1 20.9 5 0.166 Europe 83.7 27.2 4.246 37.9 42.6 87.0 1.527 1 3 20.5 3 0.275 Asia & Pacific 80.6 26.5 4.513 42.4 42.1 76.8 1.303 0 3 18.8 4 0.440 Africa/Middle East 70.4 25.5 4.247 38.8 37.1 64.9 0.360 0 4 15.5 4 0.556 Latin Amer./Carib. 68.7 26.3 3.771 34.4 35.5 67.6 -0.079 1 1 13.3 2 0.463 OECD 83.9 28.3 3.693 37.9 42.3 87.6 1.413 0 2 20.6 4 0.234 Non-OECD 77.4 25.6 5.221 42.3 41.4 71.1 1.030 0 4 17.3 4 0.515 United States 84.3 29.9 3.033 36.5 42.0 90.0 1.254 0 1 20.8 5 0.079 Non-US 82.2 26.9 4.339 39.5 42.2 82.7 1.396 0 3 19.7 4 0.367 All firms 82.9 27.9 3.937 38.6 42.2 85.0 1.352 0 2 20.1 4 0.279 63 Table A-3: Pearson Correlation Coefficients The table shows Pearson correlation coefficients (in percent) between the variables used in the paper. Panel A refers to firm-specific variables, while Panel B re-fers to country-specific variables; a (b, c) indicate significance at the 1% (5%, 10%) significance level. Panel A: Firm-specific variables Cover Dummy Lever age Quick Curr. Tang. SGA Log Log Div Div Margi ROA Cash Tax Variable age 3y Ratio Ratio Asset Exp. size asset Div. Yield Pay. n 3y 3y Flow Cred. ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ Coverage_3y -3.2 a Quick_Ratio -30.0 a -28.9 a Current_Ratio -30.9 a -25.1 a 96.3 a Tangible_Assets -7.4 a -1.6 16.0 a 17.7 a SGA_Expense -10.7 a -34.7 a 31.4 a 29.1 a 2.6 c Logsize 3.2 a 28.3 a -13.0 a -14.7 a -12.9 a -17.1 a Logassets 22.5 a 30.0 a -23.4 a -24.5 a -13.9 a -23.1 a 89.5 a Dividend -8.3 a 38.1 a -13.5 a -12.3 a 0.3 -17.5 a 26.4 a 27.7 a DivYield 2.9 b 24.9 a -9.5 a -9.1 a 7.7 a -13.8 a 10.7 a 17.5 a 52.7 a DivPayout -4.6 a 30.2 a -12.3 a -11.8 a 1.0 -13.6 a 24.0 a 26.1 a 56.3 a 62.7 a GrossProfitMargin_3y -1.4 41.8 a -17.2 a -15.0 a -2.1 c -22.4 a 33.8 a 33.6 a 22.4 a 15.5 a 20.5 a ROA_3y 4.7 a 61.5 a -23.3 a -19.8 a 0.1 -39.8 a 29.3 a 32.2 a 29.7 a 21.9 a 25.7 a 56.7 a Cash_Flow 4.3 a 50.6 a -28.6 a -25.3 a -7.3 a -58.5 a 25.9 a 28.0 a 28.6 a 18.5 a 20.1 a 49.4 a 53.8 a D_Income_Tax_Credit 1.3 -0.1 -1.6 -2.1 c 0.8 -0.1 2.5 b 3.6 a 6.6 a 5.3 a 6.3 a 1.4 1.8 1.6 Income_Tax_Credit 1.0 -2.0 -1.0 -1.3 0.5 0.9 3.1 b 4.3 a 2.4 c 3.6 b 4.3 a -0.8 -2.2 -1.6 59.5 a Tax_Rate 8.0 a -0.7 -8.3 a -8.5 a -17.2 a -1.4 2.7 c 6.3 a 0.0 -2.4 5.9 a 0.7 2.3 -4.4 a 2.6 c Market_to_Book -28.9 a 7.5 a 3.7 a 2.1 c 3.7 a 7.7 a 23.5 a -2.4 b 3.7 a -10.9 a 2.4 c 6.9 a 5.6 a 6.3 a 0.3 MB_Leverage 28.5 a 4.8 a -18.2 a -19.7 a -9.6 a -3.5 b 16.1 a 16.6 a 6.8 a -0.6 2.8 b 2.5 b 6.0 a 3.6 a 3.4 a R_D_to_Sales -11.1 a -32.1 a 38.9 a 34.8 a 12.2 a 45.5 a -9.7 a -16.9 a -15.3 a -9.9 a -11.4 a -25.8 a -32.6 a -58.8 a 1.1 R_D_to_Size 5.9 a -16.7 a 1.7 1.5 7.1 a 12.8 a -27.8 a -14.6 a -10.0 a -4.7 a -7.9 a -2.7 -16.2 a -20.7 a 3.2 c CapEx -2.7 b -15.6 a 21.3 a 18.5 a 8.0 a 33.3 a -1.1 -3.8 a -11.2 a -8.6 a -8.2 a -13.5 a -17.4 a -30.7 a -1.5 PPE_to_Size 35.1 a 0.7 -18.4 a -18.7 a 19.1 a -10.3 a -13.5 a 8.7 a 3.2 a 17.3 a 5.8 a 5.2 a 6.4 a 7.3 a 0.5 PPE_to_Sales 9.0 a -12.7 a 10.9 a 8.3 a 13.9 a 21.7 a 4.6 a 4.7 a -8.0 a -5.8 a -3.8 a -6.8 a -7.1 a -20.5 a -2.2 c LogTobinQ1 -46.9 a 0.4 19.9 a 18.8 a 2.3 c 12.6 a 26.7 a -12.4 a -1.0 -17.5 a -2.4 c 2.5 b -5.1 a -3.3 a -1.1 LogTobinQ2 -30.8 a -22.5 a 42.5 a 38.3 a -3.5 a 40.2 a 22.0 a -4.3 a -13.7 a -23.5 a -12.0 a -6.4 a -20.1 a -25.8 a -0.8 a (b,c) : 1% (5%, 10%) significance level (continued) 64 Table A-3: Pearson Correlation Coefficients (continued) Cover Dummy Lever age Quick Curr. Tang. SGA Log Log Div Div Margi ROA Cash Tax Variable age 3y Ratio Ratio Asset Exp. size asset Div. Yield Pay. n 3y 3y Flow Cred. ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ Acqu_Assets 11.5 a 7.0 a -11.8 a -11.7 a -25.7 a -5.6 a 5.9 a 12.2 a 4.9 a 3.9 a 2.6 c 3.2 b 4.7 a 5.5 a -0.0 Earningsyield_3y -22.5 a 51.7 a -9.9 a -6.7 a 1.0 -23.9 a 27.5 a 22.1 a 34.2 a 29.7 a 31.0 a 45.2 a 68.8 a 42.5 a 2.5 b Debt_Maturity 12.5 a 7.7 a 4.1 a 6.7 a -9.2 a -6.3 a 20.2 a 20.3 a 5.7 a 2.6 b 5.5 a 6.1 a 9.6 a 7.5 a -0.8 MultShareClass 5.3 a 7.5 a -5.1 a -4.8 a -1.1 -2.1 10.0 a 12.3 a 9.9 a 6.0 a 7.0 a 9.5 a 8.6 a 4.2 a 1.2 Stock_Options -2.0 c -1.0 -0.7 -0.8 -4.8 a -0.5 6.7 a 4.3 a -18.3 a -5.3 a -3.1 b 2.9 b -1.0 -0.2 1.4 ConvDebt 14.9 a -1.2 -0.6 -1.3 -3.3 b -1.2 6.8 a 9.6 a -5.8 a -4.5 a -5.0 a 0.5 0.8 0.4 0.3 PrefStock 7.7 a -3.2 a -4.3 a -4.8 a -1.0 -0.7 2.3 c 4.8 a -0.8 1.5 1.4 -1.8 -1.6 -2.1 c 0.3 Foreign_Assets 13.0 a 9.1 a -15.8 a -16.0 a -10.4 a -9.0 a 21.4 a 24.5 a 4.0 b -2.1 3.8 b 9.9 a 10.4 a 13.9 a -1.7 Foreign_Income_3y 7.2 a 13.0 a -9.3 a -10.2 a -5.1 b -6.9 a 20.1 a 22.3 a 6.3 a 1.8 6.3 a 9.2 a 8.9 a 10.8 a 1.3 Foreign_Sales 6.5 a 2.1 -4.5 a -4.9 a -6.4 a -2.1 20.5 a 21.0 a 0.1 -5.1 a 1.8 9.4 a 2.4 c 6.5 a 1.3 FX_Exposure 5.0 a 16.9 a -12.0 a -11.5 a -8.2 a -9.2 a 28.5 a 30.0 a 13.9 a 4.5 a 11.2 a 30.2 a 19.4 a 19.8 a -1.6 Foreign_Debt 7.4 a 5.9 a -6.4 a -6.5 a -3.2 b -4.1 a 15.3 a 16.4 a 13.4 a 3.9 a 3.3 b 6.7 a 7.4 a 4.8 a -1.6 IR_Exposure 70.9 a 2.4 b -28.8 a -28.8 a -8.4 a -11.7 a 7.0 a 22.7 a 13.4 a 8.3 a 3.8 a 3.0 b 8.2 a 7.5 a 5.1 a CP_Exposure 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 9.9 a 0.0 0.0 0.0 0.0 0.0 9.6 a Exposure 39.3 a 17.4 a -25.7 a -24.3 a -8.5 a -13.9 a 19.0 a 27.9 a 16.5 a 4.3 a 6.4 a 24.6 a 22.8 a 22.9 a 2.9 b NumIndSeg 10.0 a 12.4 a -12.2 a -12.8 a -3.2 b -5.6 a 25.6 a 29.2 a 31.7 a 12.3 a 15.2 a 14.6 a 13.6 a 6.4 a -0.4 MultIndSeg 2.6 b 8.4 a -8.3 a -8.3 a -2.0 -4.3 a 10.9 a 12.3 a 17.8 a 7.5 a 7.7 a 9.0 a 7.7 a 5.4 a -0.2 Foreign_Listing 1.6 4.5 a -1.6 -1.5 -1.1 -2.6 c 27.6 a 26.7 a 12.8 a 0.8 3.8 a 13.3 a 9.4 a 5.7 a -0.0 NegBookValue 18.6 a -5.9 a -7.4 a -8.1 a 2.1 1.6 -1.5 -0.9 -11.8 a -6.6 a -8.3 a -2.6 b -4.7 a -4.9 a -1.4 a (b,c) : 1% (5%, 10%) significance level 65 (continued) Table A-3: Pearson Correlation Coefficients of Variables (continued) MB Earn Mult Tax Tax Mkt Lever R&D\\ R&D\\ PPE\\ PPE\\ Log Log Acqu yield Debt Share Variable Cred. Rate \\Book age Sales Size CapEx Size Sales TobQ1 TobQ2 Asset 3y Mat Class ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ Tax_Rate 1.0 Market_to_Book -1.6 -5.1 a MB_Leverage 0.0 5.0 a 34.0 a R_D_to_Sales 3.2 -5.2 b 2.8 -8.7 a R_D_to_Size 0.4 -3.8 c -19.8 a -8.0 a 22.3 a CapEx -0.8 -6.3 a 0.9 -0.2 41.5 a -0.2 PPE_to_Size 2.1 1.1 -29.8 a -3.2 a -9.0 a 14.9 a 0.9 PPE_to_Sales -0.7 -4.4 a -2.7 b 2.6 b 28.3 a 0.7 49.4 a 25.0 a LogTobinQ1 -1.7 -9.7 a 66.6 a -1.2 12.2 a -32.3 a 6.9 a -55.7 a -0.6 LogTobinQ2 0.2 -9.7 a 37.8 a -2.8 b 45.2 a -20.7 a 37.7 a -40.2 a 39.3 a 66.1 a Acqu_Assets 4.1 a 4.9 a -9.8 a 6.1 a -8.6 a -0.9 -7.0 a -1.5 -5.0 a -14.2 a -9.0 a Earningsyield_3y -0.9 2.6 c 14.3 a 2.4 b -16.5 a -25.0 a -11.8 a -6.5 a -6.4 a 12.6 a -4.4 a 3.3 a Debt_Maturity -1.5 1.3 0.4 10.1 a -6.9 a -8.3 a 6.3 a 3.1 b 10.3 a 1.8 7.8 a 5.2 a 10.3 a MultShareClass 0.5 1.5 -3.1 a 3.7 a -4.8 a -0.8 -1.8 4.4 a -0.6 -4.6 a -4.7 a 2.0 7.5 a 3.3 a Stock_Options 1.0 3.0 b 2.7 b -2.2 c -0.6 -1.6 0.0 -8.7 a -3.1 b 3.7 a 3.4 a 2.8 b 0.0 0.5 -13.2 a ConvDebt -1.3 2.3 -3.9 a 8.5 a -1.4 -0.4 1.4 3.5 a 5.3 a -6.6 a 0.3 0.5 -0.7 18.4 a 1.0 PrefStock 1.7 -0.3 -2.6 b 3.4 a -1.6 2.2 0.7 3.2 b 3.8 a -6.6 a -3.4 a -0.0 -5.9 a 1.1 2.0 c Foreign_Assets -1.8 2.4 -2.2 5.5 a -13.8 a -3.9 c -6.3 a -0.6 -2.0 -5.3 a -4.9 a 6.9 a 3.6 b 5.4 a 3.1 c Foreign_Income_3y 1.5 2.7 -0.7 3.3 -11.9 a -5.3 c -6.5 a -3.3 -5.1 b -2.8 -2.7 3.5 3.8 c 1.4 2.2 Foreign_Sales 0.8 0.6 3.2 b 4.0 a -7.1 a 3.7 c -4.0 a -6.7 a -3.2 b 1.1 2.9 b 2.1 -0.3 1.3 2.9 b FX_Exposure -1.1 2.6 c 2.7 b 3.3 a -12.8 a 1.5 -8.0 a -1.0 -4.6 a -1.1 -4.1 a 5.3 a 14.8 a 3.4 a 1.8 Foreign_Debt -1.1 3.4 b 1.0 4.0 a -6.1 a -4.8 a -2.0 0.7 -1.0 -1.6 -1.2 2.9 b 3.9 a 4.8 a 8.2 a IR_Exposure 1.7 6.5 a -23.8 a 34.7 a -11.4 a 1.2 -2.3 c 26.8 a 6.3 a -38.4 a -27.5 a 13.5 a -4.8 a 15.6 a 7.7 a CP_Exposure -0.0 0.0 0.0 4.1 a 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -1.0 Exposure -1.4 4.3 a -6.8 a 20.7 a -18.4 a 1.1 -4.3 a 11.7 a -0.2 -19.1 a -17.2 a 8.5 a 11.7 a 9.3 a 5.8 a NumIndSeg 1.7 3.0 b -1.8 9.1 a -6.8 a -3.0 c -7.2 a 5.2 a -3.6 a -7.6 a -8.6 a 6.6 a 11.0 a 4.0 a 7.7 a MultIndSeg 1.7 1.2 1.3 3.8 a -2.6 -2.1 -5.3 a 1.6 -4.5 a -2.9 b -4.0 a 4.0 a 6.2 a 0.8 4.2 a Foreign_Listing -0.1 0.8 2.0 c 4.0 a -0.1 1.6 0.8 -0.7 4.1 a 1.9 4.9 a 0.0 7.8 a 5.8 a 4.0 a NegBookValue -0.9 -1.7 -13.8 a -9.7 a -1.5 2.0 -0.7 -1.0 3.8 a 4.8 a 0.8 -3.6 a -15.2 a -0.5 0.8 a (b,c) : 1% (5%, 10%) significance level 66 (continued) Table A-3: Pearson Correlation Coefficients of Variables (continued) Stock Forgn Num Mult Optio Conv Pref Forgn Inc. Forgn FX Forgn IR CP Ind Ind Forgn Variable n Debt Stock Asset 3y Sales Exp. Debt Exp. Exp. Exp. Seg Seg List. ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ ConvDebt 0.9 PrefStock 1.0 0.7 Foreign_Assets 4.9 a 7.3 a -0.3 Foreign_Income_3y 1.2 0.8 0.5 51.8 a Foreign_Sales 5.3 a 7.7 a 1.4 63.5 a 56.8 a FX_Exposure 8.9 a 4.8 a 1.0 41.7 a 33.3 a 43.9 a Foreign_Debt -8.6 a 3.0 b -0.4 12.5 a 14.8 a 8.3 a 7.0 a IR_Exposure -3.9 a 15.1 a 6.7 a 11.6 a 7.1 a 4.2 a 6.1 a 9.5 a CP_Exposure -6.8 a 0.0 0.0 0.0 0.0 0.0 -4.4 a 2.0 c 14.1 a Exposure 1.8 9.0 a 3.0 b 25.0 a 20.5 a 30.2 a 58.6 a 7.4 a 53.8 a 22.0 a NumIndSeg -16.5 a 1.9 2.8 b 8.9 a 7.2 a 3.5 b 16.5 a 10.7 a 16.7 a 1.7 15.5 a MultIndSeg -7.7 a -0.5 2.6 b 2.9 c 7.2 a 1.8 8.6 a 6.6 a 5.6 a -2.0 c 7.6 a 51.5 a Foreign_Listing -3.9 a 3.6 a 0.2 13.9 a 10.7 a 12.4 a 9.3 a 16.5 a 4.0 a 4.7 a 7.3 a 17.3 a 5.7 a NegBookValue 2.6 b 3.4 a 8.5 a 2.5 3.5 4.2 a 1.4 0.2 9.0 a -1.5 4.0 a -0.2 -1.2 -0.4 a (b,c) : 1% (5%, 10%) significance level 67 (continued) Table A-3: Pearson Correlation Coefficients of Variables (continued) Panel B: Country Variables Log KKZ Share PctMkt DerMkt GDP IR ICR Log EXIM\\ ICR ICR ICR Rule Civil Credit Legal holder Variable Cap Rank OECD Capita Ave Comp GDP GDP Fin Econ Polit of Law Law Rights ity Rights ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ DerMktRank 56.4 a OECD 49.1 a 63.9 a GDP_Capita 57.8 a 66.0 a 50.3 a IR_Ave -9.8 -32.2 b -11.8 -44.9 a ICR_Composite 67.6 a 67.9 a 56.0 a 80.9 a -51.4 a LogGDP 14.7 61.7 a 37.7 a 16.7 -17.3 18.8 LogEXIM_GDP 13.7 8.4 0.9 11.3 -1.7 28.5 c -53.4 a ICR_Financial 29.0 c 37.2 b -3.9 40.0 a -25.6 46.9 a 22.8 26.2 c ICR_Economic 57.6 a 62.0 a 44.1 a 76.3 a -53.8 a 88.5 a 20.9 32.7 b 47.0 a ICR_Political 64.4 a 58.9 a 65.1 a 71.0 a -39.9 b 91.0 a 9.9 18.4 10.0 69.7 a KKZ_RuleofLaw 65.9 a 65.3 a 55.7 a 79.4 a -58.1 a 89.3 a 12.5 28.6 c 33.4 b 80.8 a 83.8 a Civil_Law -31.1 b -2.3 34.6 b -6.7 2.3 -13.2 5.2 -12.0 -15.7 -12.6 -8.0 -13.6 Creditor_Rights 4.0 -9.8 -30.9 b -10.7 3.4 -5.0 -16.6 34.4 b 30.6 b -7.0 -16.5 5.7 -36.3 b Legality 69.9 a 77.1 a 67.1 a 88.6 a -59.4 a 85.9 a 36.4 b 16.7 32.1 b 77.9 a 83.4 a 89.1 a -19.4 2.8 Shareholder_Rights 26.6 c 15.2 -18.4 3.4 -9.1 13.8 22.4 -18.7 16.0 12.1 9.2 18.7 -61.9 a 10.0 18.0 Closely_Held -51.9 a -56.1 a -41.5 a -52.8 a 10.1 -55.4 a -50.2 a 10.7 -20.6 -48.2 a -53.4 a -44.4 a 13.4 21.8 -51.8 a -17.0 a (b,c) : 1% (5%, 10%) significance level 68 因篇幅问题不能全部显示，请点此查看更多更全内容