Corporate diversification, debt maturity structures and firm value: The role of geographic segment data

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Corporate diversification, debt maturity structures and firm value: The role of geographic segment data Kingsley O. Olibe a,∗ , Zabihollah Rezaee b , James Flagg c , Richard Ott d a

College of Business Administration, Kansas State University, Manhattan, KS, 66506-0502, United States Fogelman College of Business and Economic, The University of Memphis, Memphis, TN, 38152-3520, United States Mays College of Business Administration, Department of Accounting, Texas A & m University College Station, TX, United States d Department of Accounting, College of Business Administration, Department of Accounting, Kansas State University, Manhattan, KS, 66506=0502, United States b c

a r t i c l e

i n f o

Article history: Received 30 March 2018 Received in revised form 2 November 2018 Accepted 26 January 2019 Available online xxx Keywords: Long-term debt Short-term debt Corporate international diversification Firm value

a b s t r a c t In this paper, we investigate whether foreign and domestic assets of US firms are financed with borrowed funds (e.g., with short-and long-term debt maturity structures). Our regression analysis documents a positive association between foreign assets and long-term debt, and a negative association between foreign assets and short-term debt. Estimation results show that 1% increase in FAS leads to, on average, a 39 percent increase in leverage, an economically important effect. We also document the opposite relations between long-term (negative relation) and short-term (positive relation) and domestic fixed assets. Further analysis suggests that a one percent increase in domestic assets corresponds to -20.13% decrease in long-term debt, while a 1% increase in domestic assets raises short-term debt by 16.66 percent, on average. We further find that foreign assets are incrementally, positively associated with Tobin’s q, indicating that foreign investment is a successful path to higher equity value, a result inconsistent with Denis et al (2002). In the partition sample, we find that variation in debt affects the pricing of foreign assets. We document that foreign assets of high debt-to-asset ratios are positively related to Tobin’s q, whereas the relation is less positive for medium debt-to-asset ratio firms and insignificant for low debt-to-asset ratios, implying that near-all equity firms do not trade at a discount. © 2019 Board of Trustees of the University of Illinois. Published by Elsevier Inc. All rights reserved.

1. Introduction In the aftermath of the global 2007–2009 financial crises, unexpected exogenous events heighten the need to further understand firms’ external financing choices. There have been substantial increases in foreign operations and debt by US firms in the past decade.1 Prior research examines the link between a firm’s disclosure and the cost of debt financing (e.g., Sengupta, 1998; Francis, Nadia, & Olsson, 2008; Shivakumar et al., 2001)2 and whether the

∗ Corresponding author E-mail addresses: [email protected] (K.O. Olibe), [email protected] (Z. Rezaee). 1 The Wall Street Journal (January 22-23, 2011) highlights the increase in the issuance of long-term debt with an article titled “Flood of Corporate Debt Hits (Burne, 2011)” Corporate issuers generated nearly $10 billion of debt to the market led by a $2.5 billion sale of debt by Goldman Sachs Group Inc., Bank of America and, Merrill Lynch projects $230 billion in new loans in 2011, while Highland Capital projects $200 billion. 2 Sengupta (1998) reports that bond ratings are higher and bond yield are lower for firms with high quality disclosure scores based on analysts’ ratings. Shivakimar,

quality of segment disclosures affect the firms’ cost of debt (e.g., Franco, Urcan, & Vasvari, 2016). However, the results of these studies are often mixed as explained in detail in Section 2. For example, Chen et al. (1987), Burgman (1996) and Lee and Kwok (1986) provide evidence suggesting that US multinational companies (MNCs) use less debt in their capital structures compared to their domestic counterparts. Mansi and Reeb (2002) report a 30 percent increase in long-term debt for MNCs, which contradicts the findings of Chen et al. (1987), Burgman (1996) and Olibe, Strawser, and Strawser, (2011). Motivated by prior studies and their mixed findings, we investigate whether MNCs foreign and domestic assets are funded with short-and long-term debt maturity structures by examining the association between foreign and domestic asset intensity and longterm debt. We are also inspired to undertake this study because of the recent growth in debt and increase in foreign operations

Urcan, Vasvari, and Zhang, (2011) find that high-quality management earnings forecasts induce stronger response in the credit markets.

https://doi.org/10.1016/j.qref.2019.01.011 1062-9769/© 2019 Board of Trustees of the University of Illinois. Published by Elsevier Inc. All rights reserved.

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by US firms in recent years. Both of which are important to regulators, investors, and corporations. For instance, US holdings of foreign securities jumped 7.6-fold, increased from $1.2 trillion in 2006 to $9.1 trillion by 2013, and US holdings of foreign long-term debt jumped from $1294 billion in 2006 to $2305 billion in 2013 (Department of the Treasury and Federal Reserve Bank of New York 2013). Incidental to the primary objective, we also examine the link between firm value as measured by Tobin’s Q and foreign assets. The theoretical foundation for our study is built based on asymmetric information in the sense that firms with a superior information environment raise more equity, while firms operating in an inferior information environment issue more debt when they seek external financing (Chang, Dasgupta, and Hillary 2006, 2009). Following this premise we argue that foreign assets are funded with long-term debt because short-term debt is more expensive to the firm (Wall Street Journal, 2016).3 Further, because short-term debt comes up for renewal more frequently than does long-term debt and the fact that short-term debt faces frequent monitoring by lenders (Myers, 1977), firms likely finance foreign operations with long-term debt to avoid frequent lender monitoring. We argue that a greater level of foreign fixed assets can contribute to larger amounts of long-term debt because of the interest deductibility benefit on the worldwide income of US-based MNCs (Collins & Shackelford, 1992). We further contend that corporate diversification can affect short-and long-term debt via its effect on a firm’s expected cash flow and its effect on the variance of the firm’s own cash flows. On the other hand, corporate diversification can result in adjustments to tax savings through interest deductions on debt which likely increases expected future cash flows. These possibilities provide tension in our research question of whether, and to what extent, the foreign and domestic assets of US firms are financed with borrowed funds (e.g., short-and long-term debt maturity structures). We analyze US MNCs with significant amounts of foreign assets during the year period 2000 through 2014 for evidence of a linkage between foreign assets and debt.4 Consistent with our expectation, we document that foreign assets are incrementally, positively linked to long-term debt, on average. Estimation results show that a 1 percent increase in foreign assets (FAS) leads to, on average, a 39% increase in long-term debt, an economically important effect. Further analysis reveals that a 1 percent increase in foreign assets leads to a -0.60 percent decrease in short-term debt, suggesting that US firms operating abroad have less short-term debt. Having provided evidence that foreign operations increase firm demand for long-term debt, it seems prudent to examine whether domestic assets are financed with long-term debt or short-term debt. We find that domestic assets exhibit an incremental, positive effect on short-term debt, whereas there is a significant, inverse relationship between long-term debt and domestic assets. Further analysis reveals that a 1 percent increase in domestic assets raises short-term debt by 16.66 percent, on average. We also report that a 1 percent increase in domestic assets corresponds to -20.13 percent decrease in long-term debt. These findings suggest that as firms

3 The Wall Street Journal (2016), B18) reports that “the three-month dollar London Interbank offered rate, a widely watched benchmark for the cost of corporate debt was at 0.96 percent, the highest since 2009.” 4 We focus on US firms because US Generally Accepted Accounting Principles (GAAP) do not permit fixed assets to be recognized in the financial statements at revalued amounts. Under US GAAP, managers decrease the carrying value of assets when asset values change. Upward revaluations are not permitted under US GAAP. Thus, US firms’ foreign and domestic assets provide an opportunity to test whether fixed assets of US firms are financed with borrowed funds. UK GAAPs permit fixed assets to be recognized in financial statements at revalued amounts and UK managers can increase or decrease the carrying value of assets when asset values change.

become more geographically diversified debt financing becomes vital as a source of external capital for multinational corporations. We also test whether foreign involvement is a wealth-increasing corporate decision. To the extent that such an expansion of the firm’s operations accomplishes the investors’ international diversification objectives while improving the firm’s ability to profit from the systematic advantages inherent in a multinational network, foreign expansion may result in higher Tobin’s Q. Tobin’s Q summarizes not only investors’ assessments of firms’ asset values and expectations about future operating performance, but also the effects of firms’ investing and financing decisions. Our analysis reveals that there is a positive association between the level of foreign assets and Tobin’s Q. We interpret these results as providing evidence against international diversification discount documented in Denis, Denis, and Yost, (2002). Contrary to the basic cost of capital prediction that, holding other factors constant, long-term debt should be associated with higher equity value, we document that long-term debt is negatively related to Tobin’s Q.5 Our paper is related to the literature emphasizing the importance of supply effects of long-term debt (e.g., Leary, 2009), and how corporate managerial decisions affect their capital structure (Berger, Ofek, & Yermack, 1997). Our evidence that foreign operations are funded with borrowed funds implies that lenders are an important source of external capital for MNCs foreign expansion. Second, foreign expansion funded with debt leads to wealth transfer from shareholders to debtholders (Campa and Kedia (2002); Doukas and Kan (2006); Errunza & Senbet, 1984; Villalonga (2004), Dastider (2009). Our results suggest that firms finance investments with borrowed funds to take full advantage of the tax and incentive benefits of long-term debt, trading these benefits against the costs of financial distress. An implication of our findings is that characteristics related to the debt capacity of a given firm, such as tangibility of assets and market conditions should, on average explain why firms’ chose debt financing rather than equity issuance. Our study should be of interest to a wide range of capital providers, including lenders in deciding whether to provide financing to firms’ foreign expansion. Understanding the financing choice of these firms is important to practitioners and equity investors because high debt is believed to hamper the value of the firm at the micro level, as well as induce financial instability at the macro level (Olibe et al., 2011). Also, high debt repayments affect firms negatively by inhibiting profitable investment opportunities available to the firm. Our results support the argument made by Yadav (2013) that lenders exercise their debt governance powers in influencing corporate governance of borrowers and protecting their investments through debt contracts which constrains firms from maintaining financial flexibility.6 In addition, large debt constrains managers from maintaining financial flexibility and causes higher distress costs. This paper advances the argument that foreign and domestic operations can have significant impact on short-and longterm debt and thus firm value. The remainder of the paper proceeds as follows. Section 2 presents theoretical framework and reviews prior related studies, while section 3 presents the empirical predictions. Section 4 describes the research method. Data collection procedure and empirical results are delineated in section 5, and Section 6 contains the conclusions and implications.

5 The debt and valuation issues are addressed together because Stevens and Lipsey (1992) show that foreign and domestic investments of multinationals are limited by the firm’s debt-to asset ratio and are inter-dependent with each other. 6 A diversified firm can benefit from a co-insurance effect that decreases the variability of its overall earnings and helps the firm to mitigate countercyclical deadweight costs (Hann et al., 2013).

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2. Theoretical framework and literature review 2.1. Theoretical framework Three theories are relevant to our research questions. The tradeoff theory of capital structure presents balancing the tax advantages of debt financing against the cost of financial distress that arise from bankruptcy risks (Myers, 1977) and agency costs (Jensen & Meckling, 1976). The pecking order theory suggests that equity financing is preferable when information disparity between insiders and outside investors is high because mispricing of equity is more likely when external investors are less informed about the value of the firm and the expected payoffs to equity are more sensitive to firm value (Myers & Majuf, 1984). The market timing view is that the debt market should respond more when debt financing is “cheap” than to the firm characteristics suggested by the tradeoff theory. These theories provide the foundation for our empirical research question of determining the financing choice of US multinationals regarding foreign and domestic assets. The earlier theoretical work of Lewellen (1971), and Higgins and Schall (1975) highlight that diversification can provide a coinsurance effect through the aggregation of different business segments with imperfectly correlated earnings streams. The coinsurance effect can reduce the volatility of a diversified firm’s overall earnings and, hence, the firm’s risk of default, compared to a portfolio of comparable undiversified firms.7 This line of research also documents that debt contracts often require the use of conservative accounting methods (e.g. Leftwich, 1983; Leuz et al., 1998). For example, Leftwich (1983) finds that modifications to GAAP in debt contracts are typically conservative. Hann, Ogneva, and Ozbas, (2013) show that, due to the coinsurance effect, multisegment firms have a lower weighted average cost of capital relative to the portfolios of single segment firms. By aggregating different geographic segments with imperfectly correlated earnings, a diversified firm can benefit from a co-insurance effect that decreases multinational overall earnings variability (e.g., Lewellen 1971; Shapiro, 1982). 2.2. Prior research Several streams of prior research address the link between corporate diversification and debt financing by multinational firms. For example, Burgman (1996) and Lee and Kwok (1986) provide evidence suggesting that US based MNCs use less debt in their capital structures relative to their domestic counterparts, whereas Mansi and Reeb (2002) report a 30 percent increase in firm long-term debt for multinational firms. These conflicting results suggest that MNC debt financing has not reached a decisive conclusion. Prior research suggests that firms finance investments with borrowed funds to take full advantage of the tax and incentive benefits of long-term debt, trading these benefits against the costs of financial distress (Mayers and Majluf 1984). Since most international tax planning involves the use of subsidiaries in low tax jurisdictions like tax havens, we are uncertain whether the same tax and incentive benefits are the same for non-US firms. Further, prior literature indicates that firms have optimal debt levels depending on the tax savings of debt relative to the cost of debt (Graham, 1996) and that firms with high agency cost increase debt financing as a bonding mechanism (Jensen & Meckling, 1976). This stream of research provides minimal evidence on whether debt market participants fund domestic

7 Moody’s Investor Service rating methodology (Moody 2006) ranks the diversification of a borrower’s operations as one of the key factors in its credit ratings of firms.

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and foreign assets of US firms differently. The question of whether foreign and domestic assets are funded with short-term or longterm debt has not been considered. The second stream investigates whether the quality of segment disclosures is reflected in the firms’ cost of debt financing. For example, Franco et al. (2016) examine whether disclosure of business segment information helps lenders assess the coinsurance effect of diversification and report that industrially diversified firms assigned to the high-quality segment disclosure group have significantly lower bond offering yields, relative to both undiversified and diversified firms with less-quality segment disclosure.8 Franco et al. (2016) focus on whether quality disclosure of segment information is associated with lower cost of debt whereas we focus on whether geographic segment data, as measured by foreign and domestic fixed assets, are funded with debt. Franco, Urcan, and Vaasvari (2016) report that debts issued by industrially diversified firms with high quality segment disclosure have significantly lower yield relative to debts issued by diversified firms with low-quality segment information. The final stream of research examines the performance of multinational firms (MNCs) relative to pure domestic firms. (e.g., Bodnar, Tang, & Weintrop, 1999; Campa & Kedia, 2002; Villalonga, 2004; Dastider 2009 ; Errunza & Senbet, 1984; Doukas & Kan, 2006). Lang and Ofek (1995) report a positive and significant relationship between-two-day excess returns and firms’ Tobin’s Q ratio of US investments abroad. In contrast, Jacquillat and Solnik (1978) conclude that investing in MNCs is a poor substitute to international portfolio diversification. Similarly, Denis et al. (2002), using the Berger and Ofek (1995) excess value framework report the international diversification reduces shareholder value by 18 percent, while industrial diversification results in 20 percent shareholder value loss. Olibe et al. (2011) document that the level of debt in a firms’ capital structure is positively (negatively) associated with foreign assets (foreign sales). Our study differs from, and thus contributes to, the study by Olibe et al. (2011) in two respects. First, we examine whether foreign and domestic fixed assets are financed with borrowed funds. Second, we control for endogeneity of the foreign operations decisions of managers, whereas as Olibe et al. (2011) do not consider endogeneity in their analysis. As Chen, Lim, and Lobo (2016) note, “a firm’s capital structure decision may be a function of unobservable omitted variables (an endogeneity issue).” Taken together, the results of prior studies are often mixed as to whether foreign involvement is a wealth-increasing corporate decision. Neither of these streams of research examine whether the foreign and domestic fixed assets of US firms are funded with debt, which is the focus of our paper. 3. Empirical predictions 3.1. Debt-maturity choice hypothesis The effect of corporate diversification on debt is an empirical issue because theory alone cannot determine the sign and the magnitude of the relation between foreign and domestic assets and debt. On one hand, as corporate decisions are made by managers, the decision to fund foreign assets with debt could be viewed as a good example of agency relationship between managers and debt-holders. On the other hand, foreign expansion by US firms could be driven by management’s incentives (Jensen, 1986) and tax factors (Scholes & Wolfson, 1992). Desai and Dharmapala (2006) argue the existence of complementarities between tax sheltering

8 They measure the extent to which a firm is industrially diversified by using the number of distinct industrial segments disclosed in the firm’s financial statements and the corresponding sales-based Herfindahl index

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and rent extraction activities by presenting the example of Dynegy Inc., in which tax-shielding activities claim to facilitate managerial misrepresentation and destroy shareholder value.9 However, fundamental market-based forces are essential in curtailing managerial self-maximizing interest. These market forces can arise from country’s financial structure, legal environment and regulatory pressures. In addition, the finance literature has identified the monitoring role of debt. For example, Myers (1977) argues that short-term debt can be an effective mechanism to monitor management to alleviate the underinvestment problem. There is support for our short-term debt hypothesis (domestic assets being funded with short-term debt) in anecdotes documenting situations in which US MNCs have found ways to get foreign cash back to the US without incurring repatriation taxes. For example, the Hewlett-Packard Company used a series of short-term loans to circumvent the rule that treats a loan from a foreign subsidiary to a US parent as dividend (and, thus, a repatriation) to get some of its foreign cash back to the US (Novack, 2012). The Wall Street Journal reports that GE, Sonoco Products Company, and other companies also use this strategy. In the article, Sonoco’s head of investor relations, Roger Schrum, was quoted as saying “Many, if not most, companies with similar opportunities do the same thing, although they are probably less diligent in disclosing it” (Linebaugh 2013). To the extent that such strategies are widely practiced and sustainable, we may find support for our short-term debt hypothesis. We argue that firms finance foreign fixed assets with long-term debt and domestic assets with short-term debt because of three reasons. First, short-term debt comes up for renewal more frequently than long-term debt. Second, because of greater complexity of international operations which stems from greater earnings volatility relative to that of domestic earnings, managers likely prefer to finance foreign operations with long-term debt. Third, financing foreign assets with long-term debt provides managers an opportunity for entrenchment as they become more valuable to a more complex firm. Uncertainty surrounding some foreign operations make it a less than optimal business decision to fund such operations with short-term debt. Long-term debt reduces the possibility of non-favorable renewal terms reflected by the current economic conditions. In essence, because of the complexity and risk inherent in foreign operations, it is less likely that firms will finance foreign operations with short-term debt as this increases the probability of default risk.10 Lenders typically have more knowledge about their home countries than they do of foreign countries, suggesting that they are likely to have less information about a firm’s foreign operations than about its domestic operations. Each country’s corporate ethics, legal environment, and institutional structures play a vital role in each country’s lending practices. The influence of institutional environment on capital structure and debt maturity choices has been studied in several studies (e.g., Claessens & Klapper, 2005; Giannetti, 2003; De Jong, Rezaul Kabir, & Nguyen, 2008; Djankov, Hart, McLiesh, & Shleifer, 2008). In general, these studies suggest that firms in countries that provide better protection for investors and are less susceptible to corruption, have capital structures with more equity and relatively more long-term debt. The issue of legal enforcement systems, debt market access conditions, accounting and tax issues, competition rules, and the effectiveness of legal systems imply that lenders have less-

9 Shevlin, Urcan, and Vasvari, (2013) document that firms with greater tax avoidance incur higher bond yield spreads whereas Hasan, Hoi, Wu, and Zhang, (2014) report that firms with greater tax avoidance incur higher bank loan spreads. 10 Corporate diversification increases firm exposure to economic factors unavailable in domestic markets (e.g., currency risk, political risk, regulatory intervention and different accounting and tax rules).

specialized knowledge of foreign credit markets that they can use to analyze and assess the credit risks of each country. Alternatively, more information could be available to lenders because of delegated monitoring of lenders. Because of the existence of delegation monitoring, lenders are likely to finance foreign operations with long-term debt as monitoring increases higher–quality corporate governance mechanisms (Gul & Goodwin, 2010). However, we argue that lenders’ unfamiliarity with foreign operating environments may incentivize lenders to provide debt financing to these firms because of their familiarity of U.S. markets. Research by Myers (1977) and Data, Iskandar-Data, and Patel, (1999) suggests that debt maturity can play a significant role in reducing agency costs as debt increases the frequency of lender monitoring of managerial activities in addition to tax-saving advantages of debt that could increase consolidated net income. Beaver, Ketler, and Scholes, (1970) suggest that debt can proxy for investment prospects; and as discussed further below, Myers (1977) argues that debt can be an effective tool to monitor management to alleviate the overinvestment problem.11 This agency theory-based reasoning suggests a direct beneficial effect of debt as lender monitoring constrains managers from “empire building.” We focus on whether foreign and domestic fixed assets are funded with borrowed funds since the extant literature provides minimal evidence on whether the foreign and domestic fixed assets of US firms are financed and the mechanism through which debt levels affect the valuation of foreign assets. We argue that foreign assets are funded with long-term debt to lessen frequent renewal and monitoring characterized of shortterm debt. Because of the frequent monitoring and liquidity risk associated with short-term debt, managers may not have the incentive to fund foreign assets with short-term borrowing. However, if the cost of additional debt financing (e.g., an increase in default risk and monitoring by lenders) is a concern to managers, then we will not find results consistent with the predictions. The premise of our hypothesis is the linkage between foreign assets and debt maturity structures. We expect foreign assets to be incrementally useful in explaining long-term debt in a firm’s capital structure and thus state our debt structure hypotheses as follows: H1. Ceteris paribus, there is a positive (negative) relationship between foreign assets (domestic assets) and long-term debt H2. Ceteris paribus, there is a negative (positive) relationship between foreign assets (domestic assets) and short-term debt 3.2. Firm value hypothesis A number of papers show that, on average, diversification is not a successful path to higher equity value. Lang and Stulz (1994) provide evidence that during the 1980s the Tobin’s Q of diversified firms was significantly smaller than the Q of matching portfolios of specialized firms. Berger and Ofek (1995) conclude that on average, diversified firms are valued less than matching portfolios of specialized firms by 13–15 percent. Denis et al. (2002), using the Berger and Ofek (1995) excess value measure report that international diversification reduces shareholder value by 18%, while industrial diversification results in 20% shareholder value loss. This, however, raises the question of why shareholders go along with the geographic diversification decision that potentially harms share-

11 Agency theory describes the natural conflict between managers and shareholders. As Jensen & Meckling, 1976) note, the conflict arises because individuals choose actions to maximize their own utility suggesting that managers do not always act in the best interest of firm owners. One means to lessen this conflict is through monitoring, and one obvious monitoring system is debt through lenders and debt covenants as well as financial disclosures.

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holder value. Bodnar and Weintrop (1999), using a similar excess valuation measure, report shareholder value to increase with international diversification, contradicting the evidence of Denis et al. (2002). Negative market value may result because managers do not always act in the best interest of firm owners (Jensen & Meckling, 1976). This should be the case when managers indulge their preferences for nonvalue-maximizing behavior. Hope and Thomas (2008), 592), note that “managers are able to behave this way because investors are less capable of linking managerial decisions to firm performance.” We expect reputational concerns to be an input into mangers’ decision making in order not to indulge in empire building. Supporting our argument, Becker and Milbourn (2011) point out that S&P claims that “Reputation is more important than revenues.” Thus, we state our second hypothesis as follows: H3. Ceteris paribus, there is a positive link between foreign operations and Tobin’s Q. 4. Sample selection and research method 4.1. Sample selection Our sample consists of US –incorporated multinational firms having foreign and domestic fixed assets available in the Compustat geographic segment files from 1998 through 2012, since we expect debtholders to mainly rely on public financial reports when assessing a firm’s degree of international diversification. Further, we hand collected data on foreign long-lived assets to augment Compustat data. Our sample starts with the year 1998 because important changes occurred in 1997: the rules for segment disclosures changes (Financial Accounting Standards (SFAS 131, FASB 1997).12 The sample ends in 2012 because that was the most recent year of available data on Compustat when we began the study. Financial firms (SIC 6000–6999) and utilities (SIC codes between 4900 and 4999) are excluded because they face different regulations from that of industrial firms. Long-term debt and short-term debt also are from the Compustat data base. We use CRSP data to estimate systematic risk (BETA) via the market model regression.13 As shown in Table 1, this results in a final sample of 1477 firm year observations, with foreign fixed assets. Attention was restricted to US MNCs to avoid the influence of differences in accounting and incentives induced by varying tax systems and accounting standards. To ensure that our estimates are not driven by a very few influential observations, we delete any observation that has a Cook’s Distance outlier statistic in the top 2 percent of the sample in each model we estimate. We use 2 percent because this is roughly equivalent to truncating observations at the 1st and 99th percentiles as

12 Disclosure of segment information has become an increasingly important part of financial reporting since the FASB altered the reporting requirements under SFAS131 [Reporting Disaggregated Information about a Business Enterprise], which became effective January 1, 1998. Under SFAS 131, firms must report segment assets of 10 percent or more of total assets. 13 The sample begins in 1998 because that is when SFAS 131 was implemented (the Financial Accounting Standards Board’s (FASB 1997). Under SFAS 131, segment information is reported in accord with the way management organizes the firm internally for making operating decisions and assessing performance (e.g., lines of products and services, geographic area, major customer, long-lived assets. Prior to SFAS 131, firms reported total assets. After the implementation of SFAS 131, firms began to report identifiable long-lived assets. In addition, Compustat codes geographic asset data as missing if the firm does not report total assets, even though this firm is likely to report long-lived assets. This procedure resulted in many missing observations for longlived assets.

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Table 1 Sample Selection and Distribution of Sample. Panel A; Sample Selection: Number of firm-year observations (1998- 2012) 13,080 (11,933) Less: observations without identifiable foreign assets Observations with identifiable foreign assets 1444 13,080 Number of firm-year observations (1998- 2012) (4912) Less: observation missing market value of equity 8168 Observations reporting market value of equity 13,080 Number of Observations (1998- 2012) (4896) Less: observations without long-term debt Observations with long-term debt 8184 Number of Observations (1998- 2012) 13,080 (4887) Less: observations without total assets 8193 Sample of investigating market-to-book ratio to foreign assets and foreign sales Number of Observations (1998- 2012) 13,080 Less: observations without cash dividends (4932) 8148 Observations reporting cash dividends 13,080 Number of Observations (1998- 2012) (4894) Less: observations missing operating income The selection process yielded 1444 firm year-observations for foreign assets. Subsequent to SFAS 131, Observations reporting with operating income 8186 firms are thinly disclosing their foreign assets. These exogenous regulation altered materially the disclosure of segment data (e.g., foreign assets).

the results are not sensitive to the exact percentage. We use this procedure to correct for outliers rather than winsorization or truncation of the top and bottom percentiles of our variables because prior research (e.g., Leone, Minutti-Meza, and Wasley, (2013) show that these techniques can lead to biased results. As a result of our outlier corrections, the number of observations varies from model to model. Our empirical models include industry and year fixed effects. 4.2. Research method The research design consists of four stages. The first stage examines the relationship between foreign assets and debt. The second stage examines the relationship between US domestic assets and debt. The third stage examines the link between foreign assets and Tobin’s Q market value. The fourth stage, which is conditional on the presence of a significant relation between foreign assets and debt, examines whether investors place different weights on a firm with more long-term debt relative to a firm with less (i.e., stage 1).14 4.2.1. Long-term debt research method If US firms are financing foreign fixed assets with long-term debt, then firms with higher levels of these assets should, ceteris paribus, have higher long-term debt. Conversely, if foreign fixed assets are not funded with long-term debt, one would expect to observe a weak association between long-term debt and foreign assets. To address this hypothesis, the following regression equations are estimated: LTDit = ␥0 + ␥1FASit + ␥2AEARNit + ␥3MTBit + ␥4BETAit +␥5SIZEit +



YDit +



INDit + ␧it

(1a)

14 Because the types of data used in this analysis tend to require control for endogeneity, our analyses are conducted using both OLS and Heckman (1979) 2-stage regression while controlling for other factors (e.g., profitability, size, firm growth, risk, the inverse Mills ratio in a Probit model framework). The benefit of such a statistical approach is that it provides more efficient parameter estimates.

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LTDit = ␥0 + ␥1FASit + ␥2AEARNit + ␥3MTBit + ␥4BETAit +␥5SIZEit + ␥6INVERSEMILLSRATIOit +



YDit +



INDit + it

(1b)

4.2.2. Short-term debt research design If US firms are funding foreign assets with short-term debt, then firms with high levels of foreign assets should, ceteris paribus, have higher short-term debt in their capital structure. Conversely, if US firms are not financing foreign assets with short-debt, one would expect to see an insignificant relation between foreign assets and short-term debt. To address this issue, we estimate the following regression similar to (1), but using short-term debt as the dependent variable are estimated. STDit = ␥0 +␥1FASit + ␥2AEARNit + ␥3MTBit +␥4BETAit␥5SIZEit +



YDit +



INDit + ␧it

(2a)

STDit = ␥0 + ␥1FASit + ␥2AEARNit + ␥3MTBit + ␥4BETAit +␥5SIZEit + ␥6INVERSEMILLRATIOit + +





INDit + ␧it

YDit (2b)

We control for other determinants of debt that could influence the results. However, a firm’s long-term debt decision may be a function of unobservable omitted variables (an endogeneity issue). We use the Heckman (1979) two-stage procedure to address sources of endogeneity.

sured as the market-to-book ratio (MTB). The relation between growth and debt is ambiguous (Frank & Goyal, 2009). We include market-to-book ratio to control; for a variety of effects, including a firm’s investment and growth opportunities and its relative valuation level. Firms with high growth opportunities tend not to need the disciplinary role of debt to curtail problem related to excess free cash flows and thus have less long-term debt. However, as noted by Myers and Majuf (1984), high-growth firm may suffer more from information asymmetry problems and hence are more likely to use debt than equity. Because MTB can be positive or negative, we have no prior expectations for the sign of the MTB coefficient on LTD. We control for firm size, determined as the natural logarithm of total sales (SIZE), because prior studies (e.g., Fama & French, 2002)) document that large firms are more likely to use debt than equity. Barth, Beaver, and Landsman, (1998), among others, find that firm size affects a variety of economic phenomena, including accounting practices and financial health (risk). We expect the coefficient on SIZE to be positive. We also control for systematic risk (BETA). BETA is the equally-weighted market model beta, obtained from a market model estimated with a minimum of 60 monthly returns. It is a risk measure that has been widely used in the literature (e.g., Goh, Lee, Lim, & Shevlin, 2016). Prior research indicates that higher values of beta (systematic risk) imply higher equity risk. We expect a positive relation between systematic risk (BETA) and long-term debt. Finally, we control for industry membership (IND) because industry characteristics can relate to long-term debt parameters and characteristics. For example, if a loan is issued to a borrower that operates in an industry whose performance is more sensitive to that of the economy, then that loan is likely to have a higher systematic risk. All regressions are estimated with a vector of year dummies (YD) and industry (IND) effects to capture any macro effects in a given year. IND is a set of firm dummy variables equal to 1 for firm i and 0 for all other firms. The subscripts i, t, and k refer to company, year, and industry.

4.3. Measurement and description of variables 4.3.1. Dependent variable Long-term debt (LTD) is the ratio of long-term debt to total assets. Long-term debt provides a measure of the debt holders’ fixed claim on firm assets. Because the relation between international diversification and long-term debt can be positive or negative, we make no sign prediction as to the direction of the relationship (see Appendix A for alternative measure of long-term debt). Short-term debt (STD) is a measure of a firm’s debt obligations maturing in one year, deflated by total assets. 4.3.2. Control variables Following prior literature, we control for a set of borrowerspecific variables that are known to affect debt. We control for operating performance (EARN) because prior studies show that operating performance is associated with the use of long-term debt. However, the direction of association between operating performance and long-term debt is ambiguous. Theories on the trade-off between debt and bankruptcy risks and the presence of non-debt tax shields suggest that long-term debt increases with profitability (Barclay, Morellec, & Smith, 2006; Fama & French, 2002). Conversely, dynamic trade-off theories predict a negative relation between operating performance and long-term debt (Strebulaev, 2007; Hennessy and whited 2005). We use abnormal earnings to measure operating performance.15 We control for growth, mea-

15 The variable AEARN refers to abnormal earnings, measured as net income before extraordinary items and discontinued operation, minus 0.12 x book value of equity (lagged one year) scaled by total assets

4.4. US domestic assets and debt research design This section addresses the issue of whether US domestic fixed assets are financed with short-and/or long-term debt. Such investigation is important because foreign assets reflect a firm’s dependence on overseas production, whereas domestic assets reflect firm’s reliance on domestic production. We regress both short-term and long-term debt maturity structures on US domestic assets (ASSETD) a) in the following regressions to assess the sign and magnitude of the relation between domestic assets on short-term debt versus long-term debt. STDit = ␥0 + ␥1ASSETDit + ␥2AEARNit + ␥3MTBit +␥4BETAit + ␥5SIZEit +



YDit +



INDit + ␧it

(3a)

LTDit = ␥0 + ␥1ASSETDit + ␥2AEARNit + ␥3MTBit +␥4BETAit + ␥5SIZEit + ␥6INVERSEMILLSRATIOit +



YDit +



INDit + ␧it

(3b)

The dependent variables are short-term debt (STD) and longterm debt (LTD). ASSETD is US domestic fixed assets scaled by total assets. The key explanatory variable in both equations is domestic assets (ASSETD). We control for other determinants of debt, including size, market-to-book, profitability, and systematic risk. All variables are as previously defined and scaled.

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Table 2 Descriptive Statistics. Variable

N

Mean

Std.Dev.

Median.

Maximum

75th

50th

25th

Minimum

Q MTB LTD FAS AEARN BETA DIV SIZE STD R&D BV-ADJ ASSETD

7824 7780 7440 1477 8186 8271 6113 8199 2626 7724 1447 1477

41.6695 4.3427 .2168 .4162 .0479 1.1300 .0266 8.7307 .1739 .0716 −.4078 .1874

23.4326 3.8803 .1103 .2700 .1209 .6150 .0205 1.4357 .1198 .0833 .5821 .5200

38.2800 3.1232 .2161 .3619 .0569 1.0800 .0200 8.6736 .1513 .0371 −.196 .1929

84.3700 70.6300 .6600 .9234 .2800 3.0000 .1200 12.2400 .6400 .9300 .8100 1.000

55.5400 5.0321 .2846 .5508 .0936 1.4400 .0333 9.6628 .2310 .0957 .0445 .6381

38.2800 3.1232 .2161 .3619 .0569 1.0800 .0199 8.6738 .1513 .0371 −.4196 .1929

23.9900 2.0937 .1459 .2100 .0206 .7100 .0130 7.8235 .0873 .0205 −.8375 −.2160

1.5570 −27.82 .0017 .0013 −.2500 .0689 .0130 .0400 .0021 .0010 −2.2800 −1.4400

Qit = market value of common equity for firm I at time t scaled by total assets. MTBit = market-to-book ratio(#60)for firm i at time t. LTDit = firmi’s long-term debt(#9) at time t scaled by average total assets. FASit = firm i’s identifiable foreign assets (GDATA5) file at time t scaled by average total assets. AEARNit =abnormal earnings measured as net income before extraordinary items and discontinued operations,minus0.12xBVE(lagged one year) deflated by total assets. BETAit = firmi’s systematic risk at time t. DIVit = dividend for firm i at time t scaled by total assets. SIZEit = natural log of global sales for firm i at time (a control for size). STDit =firmi’s short-term debt deflated by total assets at time t. R&Dit =firm i’s research and development expenditure deflated by sales at time t.

4.5. Tobin’s q market value test This study uses Tobin’s q market value similar to those developed by Lang and Stulz (1994), and Hope and Thomas (2008) to analyze the market’s implications of foreign operations. As Lang and Stulz (1994) point out, the advantage of Tobin’s q is that it incorporates the capitalized value of the benefits from diversification. Another advantage of q is that it captures investors’ expectations of the future stream of cash flows. Specifically, since q is the “present value of future cash flows divided by the replacement cost of tangible assets, no risk adjustment or normalization is required to compute q across firms in comparisons to other accounting performance measures.” A firm’s q value is the ratio of its market value to the current replacement costs of its assets (Tobin 1979). Q ratio measures changes in firm value. Thus, q provides a market-based measure of shareholder’s expectation about a firm’s future cash flows. All variables that affect firm value affect q (Lang & Stulz, 1994).16 We control for other determinants of firm value that could influence the results. If US firms are using foreign fixed assets to create value for the investing shareholders, then firms with high levels of foreign fixed assets should, ceteris paribus, have higher firm value. Conversely, if foreign fixed assets are not value additive, one would expect firms with these assets to have lower firm value. To examine this hypothesis, the following regressions are estimated: Tobin’sQit = ␥0 + ␥1FASit + ␥2AEARNit + ␥4DIVit +␥5LEVit + ␥6BETAit + ␥8SIZEit +



YDit +



INDit + ␧it

(4a)

Tobin’sQit = ␥0 + ␥1FASit + ␥2AEARNit + ␥4DIVit +␥5LEVit + ␥6BETAit + ␥8SIZEit + ␥7INVERSEMILLSRATIOit +



YDit +



INDit + ␧it

(4b)

16 Several studies have used q to measure the market’ s response to diversification. From a theoretical point, there are also various precedents for using q as a measure of value. In this Untabulated results of long-term debt to market value of common equity are qualitative the same as the tabulated results.

Several approaches have been used in calculating the q ratio, but different methods tend to yield similar results (Chung and Pruitt 1994). In this study, we use both Hope and Thomas (2008) and Chung and Pruittt’s (1994) methods. The dependent variable in equation (1), Tobin’s Qit , is calculated as follows: Tobin’sQ it = (MVEit +PSit +LTDit +STDit )/TAit , Where MVEit is the year-end market value of firm i’s equity, defined as the product of the year-end price and the year-end number of common stock shares outstanding; PSit is the year-end book value of the firm’s preferred stock; LTDit is the year-end book value of the firm’s long-term debt with maturity greater than one year; STDit is the firm’s year-end book value of the firm’s short-term debt and TAit is the firm’s year-end total assets. In Equation (3), our primary independent variable of interest is the firm’s identifiable foreign assets (FASit ). We expect a positive sign on FAS coefficient because greater growth opportunities abroad are associated with firm value (Bodnar and Weintrop 1999). In Eq. (3), we use several control variables that are motivated from the existing literature (e.g., Ohlson (1995); Doukas and Kan (2006) and Olibe et al. (2012) and are described as follows: AEARN represents abnormal profitability of the firm measured as net income before extraordinary items and discontinued operations minus 0.12 x BVE (lagged one year) deflated by total assets. LEV is long-term debt deflated by total assets; BETA is equally-weighted market model beta (a control for systematic risk), obtained from market model estimated over the ten-year period with a minimum of 60 monthly returns. DIV is dividends (other than stock dividends) declared on common stock divided total assets. It is expected that dividend paying firms should have higher equity value. Defining AEARN based on net income before extraordinary items and discontinued operations is consistent with prior empirical research (e.g., Barth et al., 1998).17 Ohlson (1999, p.160) concludes that this approach is justified in empirical work because one-time items have no predictive value. We do not rely on the Ohlson (1995) model as basis for interpreting our predictions,

17 Ohlson (2000) points out that even bottom line net income will not satisfy the clean surplus equation under the pooling-of-interest method accounting for business combinations. The problem arises from recording equity transactions using nonmarket prices.

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Table 3 PearsonandSpearmanCorrelationMatrix(PearsonabovethediagonalandSpearmanbelowthediagonal). Q Q LTD STD FAS BETA DIV AEARN SIZE MTB R&D

−.522** .547** .126** −.160** .447** .654** .119** .747** .454**

LTD

STD

FAS

BETA

DIV

AEARN

SIZE

MTB

R&D

−.513**

.532** −.927**

.095** .328** −.247**

−.136** −.142** .125** −.297**

.497** −.170** .158** .117** −.490**

.489** −.180** .105** .118** −.298** .555**

.107** −.061** .081** .223** −.193** .161** .232**

.604** −.056** .166** −.008 −.237** .505** .439** .095**

.369** −.269** .255** −.130** .257** .103** −.213** −.218** .130**

−.926** .338** −.121** −.113** −.227** −.126** −.124** −.331**

−.261** .126** .081** .221** .123** .209** .371**

−.317** .177** .148** .162** .183** −.097**

−.546** −.383** −.188** −.311** .179**

.576** .198** .493** .033**

.087** .627** .061**

.194** −.034**

.246**

Qit = market value of common equity for firm i at time t scaled by total assets. MTBit = market-to-book ratio (#60)for firm I at time t. LTDit = firmi’s long-term debt (#9) at time t scaled by average total assets. FASit =firm i’s identifiable foreign assets (GDATA5)file at time t scaled by average total assets. AEARNit =abnormal earnings measured as net income before extraordinary items and discontinued operations, minus0.12xBVE (lagged one year) scaled by total assets. BETAit =firmi’ssystematic risk at time t. DIVit = dividend for firm I at time t scaled by total assets. SIZEit =natural log of global sales for firm i at time (a control for size). STDit =firmi’s short-term debt deflated by total assets at time t. R&Dit =firm i’s research and development expenditure deflated by sales at time t. * Correlation is significant at the 0.05level (2-tailed).

because it relies on several restrictive assumptions, such as clean surplus and a particular linear information model. We also control for firms’ preferred method of financing by including a measure of the consolidated entity’s long-term debt levels. The variable Lev is the ratio of long-term debt to total assets. Prior literature indicates that firms have “optimal” debt levels depending on the tax savings of debt relative to cost of debt (Dhaliwal, Trezevant, & Wang, 1992). Firms with higher longterm debt can experience tax shield because of interest deductions, which results in high operating cash flows and thus a higher Tobin’s Q. On the other hand, firms with higher levels of long-term debt tend to be riskier. We have no prior expectations for the sign of the LEV coefficient on Q. Prior research indicates that higher values of beta (systematic risk) imply higher equity risk. We expect a negative relation between beta and Tobin’s Q. Finally, all regressions are estimated with a vector of year dummies (YD) and industry (IND) effects to capture any macro effects in a given year or an average effect in an industry, not reported in the tables. IND is a set of firm dummy variables equal to 1 for firm i in industry k and 0 for all other firms.

5. Empirical Results 5.1. Descriptive statistics Table 2 presents summary statistics for the sample described above. The following descriptive statistics of the sample firms: mean, median, standard deviation, minimum and maximum values as well as the 25th, 50th and 75th percentiles of the sample distribution are tabulated. Several observations can be made from examining Table 2. The mean (median) values of the three dependent variables are: .2127 (.2125) for LEV, 1739 (.1513) for short-term debts and 41.67 (.38.28) for firm value. These statistics suggest that LEV, short-term debt, and firm value have balanced distributions as the mean and median are very close. Table 2 also reveals that the mean (median) foreign assets scaled by total assets is 41.62 percent (36.19 percent), indicating that foreign assets are economically material to the firm. Earnings (AEARN) has a mean (median) values of 4.8 percent (5.7 percent) with a maximum (minimum) values of .2800 (-.2500), indicating that some firms incurred losses, consistent with economic trends during the sample period. Market value of equity has a minimum of $1.557 to a maximum of

$84.37 per share, with a standard deviation of $23.433, implying high variability in firm value. 5.2. Long-term debt results The first hypothesis cannot be rejected. The results of estimating equation (2) are reported in Table 3. The data suggest that firms with sizable foreign assets have higher long-term debt. We report one version of the model without an adjustment for endogeneity and a second version with the inverse Mills ratio. We begin the discussion with the model excluding the Inverse Mills ratio. The reported statistics reflect elimination of influential observations. The most important finding is that foreign assets (FAS) is positive and significantly related to long-term debt (LEV) at the 0.001 level with a coefficient of 0.043 (t-statistic = 7.862). These results suggest that foreign fixed assets of US firms are financed with borrowed funds. Further, this finding is consistent with Krull (2004) argument that growth abroad is likely financed with debt. Estimation results show that a 1 percent increase in FAS leads to, on average, a 39 percent increase in long-term debt (LEV).18 Regarding the control variables, Table 3 reveals that AEARN is significantly negative (t-statistic = -4.20), indicating that profitability reduces a firm’s need for long-term debt. Market-to-book ratio, a growth proxy, is negative and significantly related to LEV (t-statistic = -3.868), suggesting that growth reduces a firm’s need for debt (e.g., Smith & Watts, 1992). Systematic risk (BETA) and SIZE coefficients are negative and significant (t-statistic = -.0661 for BETA and -.0155 for firm SIZE). The SIZE result is surprising in view of large firms’ capacity to carry more debt relative to small firms. Table 3 shows the results for the model with inverse Mills ratio to control for potential endogeneity. The coefficient on inverse Mills ratio is, on average, statistically significant at the conventional confidence interval (95 percent). The significance of the inverse Mills ratio confirms that it is important to control for self-selection bias and endogeneity. The most important feature of this model is that, after controlling for endogeneity, FAS is still significantly and positively associated with LEV at the 1 percent level or better (t-statistic = 7.710). The coefficient, however, increases by about

18 Mean LEV and FAS for the sample firms are 0.2168 and 0.4472, respectively. I estimate the average firm-level increase in long-term debt as [(mean FAS x FAS coefficient) x mean LEV].

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K.O. Olibe et al. / The Quarterly Review of Economics and Finance xxx (2019) xxx–xxx Table 4 MultivariateAnalysisofLEVandForeignAssets.

9



LTDit =␥0 +␥1 FASit + ␥2 AEARNit +␥3 MTBit +␥4BETAit +␥5 SIZEit +

␥6 YDit +␥7 INDit +␧it



LTDit =␥0 +␥1 FASit+ ␥2 AEARNit +␥3 MTBit +␥4 BETAit +␥5 SIZEit +␥6 INVERSEMILLSRATIOit +



YDit +

INDit +␧it

Model with Inverse OLS Model Mills Ratio Variable

Pred.

Coef.Estimate

t-test

Coef.Estimate

t-test

Intercept FAS AEARN MTB BETA SIZE INVERSEMills N Adj.R2

? (+) (+) (-) (?) (?) (?)

.3658 .0436 −.1702 −.0012 −.0661 −.0155

21.255*** 7.862*** −4.199*** −3.868*** −15.039*** −7.727***

0.3397 0.0446 −0.2283 −.0008 −0.0662 −0.0117 7.4078 1360 28.44%

19.00*** 7.71*** −5.30*** −3.73*** −13.99*** −5.88*** 2.47**

1291 27.10%

** ,and***indicates statistical significance at the 0.10,0.05and0.01level. N is not equal in each regression because of reduction in the sample due to missing observations or outlier deletions. To control for potential endogeneity between long-term debt and foreign assets, we run a first-stage ordered probit model predicting foreign assets (FAS) and generate an inverse Mills ratio. The ratio is included in the regression above. Variable definitions: LTDit = long-term debt (#9) deflated by average total assets. FASit = a proxy the degree of international diversification of the firm defined as the ratio of foreign assets to total assets. AEARNit = abnormal earnings measured as net income before extraordinary items and discontinued operations, minus 0.12xBVE (lagged one year) deflated by total assets. MTBit = market-to-book ratio for firm i at time t (a control for risk and growth). BETAit = equally-weightedmarketmodelbeta,obtained from market model estimated over theten year period with a minimum of 60 monthly returns(a control for systematic risk). SIZEit = log of global sales for firm i at time (a control for size).

Table 5 Multivariate Analysis of STD and Foreign Assets.



STDit =␥0 +␥1 FASit +␥2 AEARNit +␥3 MTBit +␥4 BETAit ␥5 SIZEit +



YDit +

INDit +␧it



STDit =␥0 +␥1 FASit +␥2 AEARNit +␥3 MTBit +␥4 BETAit +␥5 SIZEit +␥6 INVERSEMILLSRATIOit +

YDit +



IND it + ␧it

Model with Inverse OLS Model Mills Ratioa Variable

Pred.

Coef.Estimate

t-test

Coef.Estimate

t-test

Intercept FAS AEARN MTB BETA SIZE INVERSEMills N Adj.R2

? (-) (+) (+) (?) (?) (?)

−.563 −.084 .509 .003 .113 .032

−12.381*** −5.607*** 4.965*** 1.916* 10.064*** 6.412***

−.599 −.295 .463 .904 .078 .026 −.155 1240 27.10%

−15.700*** −2.400** 8.140*** 2.840*** 10.900*** 9.160*** −2.310**

1240 25.00%

** , and***indicates statistical significance at the 0.10, 0.05 and 0.01level. N is not equal in each regression because of reduction in the sample due to missing observations or outlier deletions. To control for potential endogeneity between leverage and foreign assets, we run a first-stage ordered probit model predicting foreign assets (FAS)and generate an inverse Mills ratio. The ratio is included in the regression above. Variable definitions: STDit = short-term debt (#5) deflated by average total assets. FASit = a proxy for the degree of international diversification of the firm defined as the ratio of foreign assets to total assets. AEARNit = abnormal earnings measured as net income before extraordinary items and discontinued operations, minus 0.12xBVE (lagged one year) deflated by total assets. MTBit =market-to-book ratio for firm i at time t (a control for risk and growth). BETAit = equally-weighted market model beta, obtained from market model estimated over theten year period with a minimum of 60 monthly returns (a control for systematic risk). SIZEit = log of global sales for firm i at time t (a control for size).

0.16 percent from 0.0430 to 0.0446 in the model containing Inverse Mills ratio, suggesting the need to control for endogeneity in foreign operations decision of the firm. In the model with Inverse Mills ratio, the coefficient of AEARN is significantly negative and smaller than the version of the model that does not control for endogeneity.19 The market-to-book ratio (MTB) coefficient is also highly significant and negative, indicating that firms with growth opportunities have about 0.12 percent lower long-term debt. Examining the remaining variables, BETA and SIZE

19 We follow Files, Swanson, and Tse, (2009) and report only the second stage of Heckman (1979) two-stage regression.

coefficients are both negative and significant at the 1 percent levels, respectively. The conclusion from Table 5 is that foreign fixed assets of US MNCs exhibits an incremental effect on the use of long-term debt. Collectively, the results indicate the likelihood of long-termdebt increasing with the extent of foreign assets. 5.3. Short-term debt results The second hypothesis cannot be rejected. Table 4 presents the findings from estimating Eq. (2), which relates foreign assets to short-term debt (STD). We also report one version of the model without an adjustment for endogeneity and a second version with Inverse Mills ratio. The reported statistics reflect elimination of

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influential observations. Foreign assets ratio (FAS) is, on average, negative and significantly related to short-term debt at the 0.001 level with a coefficient of -0.084. The analysis suggests that a 1 percent increase in FAS leads to, on average, a -0.65percent decrease in short-term debt.20 The results of using the Heckman two-stage procedure are presented on columns 5 and 6 of Table 4. The Inverse Mills ratio included in the second stage estimation is statistically significant at conventional confidence level. The coefficient on FAS is negative and significantly related to short-term debt (t statistic = 2.40), indicating that foreign assets, are on average, not financed with short-term debt. With respect to the control variables, the coefficient estimates are similar in direction and magnitude for both versions of the model. Overall, the regression results in Tables 3 and 4 confirm the robustness of the results (without control for endogeneity) that foreign involvement is both persistent and positively (negatively) related to long-term (short-term) debt. 5.4. US domestic assets and debt results Table 5 presents the results of regressing short-term debt (STD) and long-term debt (LTD) on US domestic fixed assets. We find that the coefficient on US domestic assets (ASSETD) is positive and significant with a t-ratio of 3.446. Estimation results show that a 1% increase in ASSETD raises short-term debt by 16.66 percent on average.21 These findings suggest that the net effect of US domestic assets is the augmentation of short-term debt. The coefficient on MTB, the proxy for growth, is positive and significant (t = 2.805), indicating that firm growth is financed with short-term debt. The coefficient on BETA, the proxy for systematic risk is positive and significant at the 1 percent level or better (t = 10.685), suggesting that short-term debt increases a firm’s systematic risk. The coefficients on abnormal earnings (AEARN) and SIZE (LnSALE) are both positive and significant at the 1 percent levels, respectively (t = 5.385 for abnormal profitability; and 9.467 for firm SIZE). The SIZE results suggest that large firms have the capacity to carry more short-term debt. 5.5. Tobin’s Q results The third hypothesis cannot be rejected. Table 6 presents summary statistics from estimating Eqs. (4a and 4b), which relate foreign assets to Tobin’s Q. We also report one version of the model without an adjustment for endogeneity and a second version with Inverse Mills ratio. We begin the discussion with the model excluding Inverse Mills ratio. The reported statistics reflect the elimination of observations for the top and bottom 1 percent. We find that foreign assets, FAS, is significant at the 5 percent level, with a coefficient of .088 and a t-ratio of 2.402, indicating that foreign assets of U.S. firms have a market benefit. The results are consistent with prior research that demonstrates that segment disclosures enhance security valuation Erunza and Senbet 1984, Doukas & Kan, 2006; Dastidar, 2009, among others). Regarding the control variables, as expected, AEARN and DIV are significantly positive (t-statistic = 27.143 and 3.999).22 The coefficient on LEV is significantly negative (t-statistic = -2.624), implying that debt has an attenuating effect on firm value. The coefficient on

20 Mean STD and FAS for the sample firms are 0.1739 and 0.4472, respectively. We estimate the average firm-level increase in long-term debt as [(mean FAS x FAS coefficient) x mean STD]. 21 Mean STD and ASSETD for the sample firms are .0716 and .1874, respectively. We estimate the average firm-level increase in short-term debt as [(mean ASSETD x ASSETD coefficient) x mean STD]. 22 Supplementing abnormal earnings with contemporaneous earnings yield similar results.

BETA is significantly positive with a t-ratio of 5.185. Research and development (R&D) is negative and significantly associated with firm value (t-statistic = 7.185), and the SIZE coefficient is positive and significant (t-statistic = 8.645), suggesting that large firms have higher equity value. Columns 5 and 6 of Table 6 show the results with Inverse Mills ratio. After controlling for endogeneity of international diversification decision, we find that foreign fixed assets exhibit an incremental positive and significant effect on firm value (tstatistic = 3.240). The FAS coefficient is .759 in the model that controls for endogeneity, whereas its .088 in the model without control for endogeneity, implying the existence of an international diversification premium, consistent with Dastidar (2009). The coefficient of AEARN is again highly significant and larger than the versions of the model that do not control for endogeneity. The LEV coefficient is also significant and negative (t-statistic = -8.400), indicating that the net effect of long-term debt is a reduction in firm value. Examining the remaining variables, BETA retains its positive coefficient, but is statistically insignificant at the conventional levels. The SIZE coefficient, however, is positive and significant at 0.001 percent or better (t-statistic = 5.540). Taken together, the results indicate that the net effect of foreign expansion is an augmentation of firm value. As shown in regressions 3a and 3b, the coefficient estimates of the foreign operation variable, measured as the ratio of foreign assets to total assets, are positive and significant. This indicates that there is no shareholder value loss to international diversification, even after controlling for long-term debt. 5.6. Supplemental analysis: debt partition sample Further, we test how the association between foreign assets and firm value shifts around varying levels of debt: high debt-to-asset ratio, medium debt to-asset ratio, and low debt-to-asset ratio firms, and estimate a separate equation for each group. Incentives facing management and shareholders might determine the amount of debt required to fund a given level of foreign assets. If investors do not distinguish between relatively high, medium, and low levels of debt, the coefficients for the three subgroups will not differ significantly. It is conceivable that different debt levels can have different market implications in the pricing of foreign assets. Such evidence is likely to extend our understanding of how other participants in the financial market, in this case, equity investors react to different levels of long-term debt in firm valuation. This evidence is not available in the extant accounting or finance literature. This effect, however, works on the opposite direction for foreign assets of medium debt-to-asset ratios. Investors might increase their assessment of firm value because of the anticipated benefits associated with international diversification, and lenders bearing a higher proportion of foreign investment risk. In contrast, investors might decrease their assessment of firm value when high debt-toasset ratio firms fund foreign assets with debt because doing so increases the probability of debt default. Investors likely discount foreign assets of medium and low debt-to-asset ratios, perceiving such assets as less valuable to lenders because of limited coinsurance (e.g., Doukas & Kan, 2006). Table 7 reveals that the incremental coefficient on FAS of high debt-to-asset ratios, is significantly positive (t-statistic = 2.527), indicating FAS of firms with high debt-to-asset ratios have strong association with Q, supporting a prediction of a positive incremental coefficient on FAS. Such a finding is analogous to Aboody, Barth, and Kasznik, 1999, 170) who find that the sum of the coefficients on fixed assets revaluation balance and revaluation balance interacted with debt is significantly positively related to price,” even for firms with high debt-to equity ratios.” A possible explanation for the positive relation between FAS and high debt-to asset ratio firms and Q is that

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K.O. Olibe et al. / The Quarterly Review of Economics and Finance xxx (2019) xxx–xxx Table 6 Multivariate Analysis of Short-term/Long-term Debt and Foreign Assets.









STDit = ␥0 +␥1 ASSETDit +␥2 AEARNit +␥3 MTBit +␥4 BETAit +␥5 SIZEit +

YDit +

LTDit = ␥0 +␥1 ASSETDit +␥2 AEARNit +␥3 MTBit +␥4 BETAit +␥5 SIZEit +

YDit +

11

INDit+␧it

INDit +␧it

STD Model and LTD Model Variable

Pred.

Coef.Estimate

t-test

Coef.Estimate

t-test

Intercept ASSETD AEARN MTB BETA SIZE N Adj.R2

? (+) (+) (-) (?) (?)

.-.623 .051 .527 .003 .123 −.045 1247 .202

−13.606*** 3.446*** 5.385*** 2.805*** 10.685*** 9.467***

−.379 −.043 −.290 .001 −.061 −.010 1245 .230

21.034*** −7.873*** −7.060*** 3.519*** −13.232*** −6.121***

** , and*** indicates statistical significance at the 0.10, 0.05 and 0.01 level. N is not equal in each regression because of reduction in the sample due to missing observations or outlier deletions. To control for potential endogeneity between long-term debt) and foreign assets, we run a first-stage ordered probit model predicting foreign assets (FAS) and generate an inverse Mills ratio. The ratio is included in the regression above. Variable definitions: STDit = short-term debt for firm i at time t deflated by average total assets. LTDit = long-term debt for firm i at time t deflated by average total assets. ASSETDit = a proxy for assets located in the US at time t for firm i scaled total assets. AEARNit = abnormal earnings measured as net income before extraordinary items and discontinued operations, minus 0.12xBVE (lagged one year) deflated by total assets. MTBit = market-to-book ratio for firm i at time t (a control for risk and growth). BETAit = equally-weighted market model beta, obtained from market model estimated over the. ten year period with a minimum of 60 monthly returns (a control for systematic risk). SIZEit = log of global sales for firm I at time (a control for size).

Table 7 Multivariate Analysis of Tobin’s Q and Foreign Assets.



Tobin’sQit = ␥0+␥1FASit+␥2AEARNit+␥4DIVit+␥5LEVit+␥6BETAit+␥8SIZEit+



YDit+

INDit+␧it



Tobin’sQit = ␥0+␥1FASit+␥2AEARNit+␥4DIVit+␥5LTDit+␥6BETAit+␥8SIZEit+␥7INVERSEMILLSRATIOit+



YDit+

INDit+␧it

Model with Inverse Mills Ratioa Variable

Pred.

Coef.Estimate

t-test

Coef.Estimate

t-test

Intercept FAS AEARN DIV LTD BETA SIZE INVERSEMill N Adj.R2

? (+) (+) (+) ? (-) ? (+)

−1.1730 .1984 5.8141 11.8487 −2.3762 .0941 .1328

−9.053*** 6.661*** 20.409*** 7.584*** −14.551*** 3.429*** 11.936***

−1.7012 0.1062 13.369 13.4518 −3.3933 0.3209 0.2339 4.6079 986 76.89%

−7.63*** 1.91** 25.85*** 10.27*** −11.96*** 6.36*** 12.73*** 1.73*

1026 70.5%

* ,**and***indicates statistical significance at the 0.10,0.05 and 0.01 level. N is not equal in each regression because of asymmetric reduction in the sample due to missing observations or outlier deletions. To control for potential endogeneity between firm value (proxied byTobin’s q) and international diversification, as measured by foreign assets, we run a first-stage ordered probit model predicting foreign assets(FAS)and generate an inverse Mills ratio. The ratio is included in the regression above. Variable definitions: Tobin’s Qit is the capital contributed by equity holders plus total liabilities scaled by total assets. FASit is a proxy for the degree of international diversification of the firm defined as the ratio of foreign assets tototal assets. AEARNit is abnormal earnings measured as net income before extraordinary items and discontinued operations, minus 0.12 x BVE (lagged one year) deflated by total assets. BETAit is equally-weighted market model beta, obtained from market model estimated over theten-year period with a minimum of 60monthly returns (a control for systematic risk). DIVit = dividends (other than stock dividends) declared on common stock (#21) divided by total assets. LTDit = long-term debt for firm i at time t deflated by average total assets. SIZEit = log of global sales for firm I at time (a control for size).

equity investors perceive lenders as bearing a higher proportion of the foreign investment risk, thus increasing their assessment of foreign fixed assets. We find also that foreign assets of medium debt-to-asset ratio firms is significantly negative (t-statistic = -2.609). We also find that the incremental coefficient on low debt-to-asset ratios, is positive but insignificant (t-statistic = .999), implying that near-all equity firms do not trade at a discount. We have no explanation for the negative relation between FAS of medium debt-to-asset ratios and Q, except that equity investors may be concerned with bearing higher

foreign investment risk relative to lenders. Alternatively, it is consistent with investors perceiving foreign fixed assets of medium debt-to-asset ratio firms as less valuable than foreign fixed assets of high debt-to-assets ratios. The positive relation between foreign assets of high debt-toasset ratio firms and Q comes from the Modigliani-Miller theorem that predicts debt should increase expected returns. This effect, however, works in the opposite direction for foreign assets of medium debt-to-asset ratio firms. Modigliani-Miller logic implies that debt should increase average equity returns, while the results

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12 Table 8 Sample Partition Based on Long-Term Debt.



Tobin’s Qit = ␥0 +␥1 FASit +␥2 AEARNit +␥3 DIVit +␥4 BETAit +␥5 STD(ROA)it +



YDit+

+

INDit + ␧it

Higher Medium Low Variable

Pred.

Coeff. Estimate

T-statistic

Coeff.Estimate

T-statistic

T-statistic

Constant FAS NI BETA Std(ROA) DIV N Adj.R2

? ? + – – +

.818 5.723*** .374 2.527** 3.740 3.026*** .337 4.046*** −2.537 -1.011 6.038** 220 .258

−1.347 −.241 12.523 .854 10.296 5.634 386 .709

−6.870*** −2.609*** 9.643*** 7.196*** 4.044*** 4.401***

−1.018 .101 15.576 .294 9.733 32.822 345 .833

−5.811*** .991 11.841*** 2.688*** 8.179*** 3.617***

Note: N is not equal in each regression because of reduction in the sample due to missing observations or outlier deletions. Coefficients and t-statistics for industry and year dummy variables are not reported. *** ,**,*indicates that the coefficient estimate is significant at 0.01,0.05 and 0.10 in two-tailed. Tobin’s Qit is the capital contributed by equity holders plus total liabilities scaled by total assets. FASit is aproxy for the degree of international diversification of the firm defined as the ratio of foreign assets to total assets. AEARNit is abnormal earnings measured as net income before extraordinary items and discontinued operations, minus0.12xBVE (lagged one year) deflated by total assets. BETAit is equally-weighted market model beta, obtained from market model estimated over theten year period with a minimum of 60monthly returns (a control for systematic risk). DIVit = dividends (other than stock dividends) declared on common stock (#21) divided by total assets. Std(ROA)it = firm i standard deviation on return on assets.

presented in Table 8 are consistent (inconsistent) with high (medium) debt-toasset ratios in the pricing of foreign assets. In brief, our analysis indicate that foreign assets of high debt-to-asset ratios are significantly, positively related to Q, whereas the relation is negative for medium debt-to-asset ratio firms and insignificant for low debt-to-asset ratio firms, implying that near-all equity firms do not trade at a discount. 6. Summary and concluding remarks This study provides evidence suggesting that the foreign and domestic assets of US firms are financed with borrowed funds. We find that the ratio of fixed assets domiciled abroad is positively related to long-term debt, but not with short-term debt. This evidence is consistent with Krull (2004) conjecture that growth abroad is likely to be financed with borrowed funds. We also document that assets domiciled in the US are funded with short-term debt but not with long-term debt. Further, we find that US firms’ that diversify overseas have high equity value. That is, foreign involvement of US firms is a wealthincreasing corporate decision. We find that variation in debt-sizes affect the pricing of foreign assets. We find that foreign assets of high debt-to-asset ratios are positively related to Tobin’s Q, whereas the relation is less positive for medium debt-to-asset ratio firms, and insignificant for low debt-to-asset ratios, implying that near-all equity firms do not trade at a discount. Our findings contribute to the literature on the external financing choice of US MNCs (e.g., Chen et al., 1997; Burgman, 1996). In particular, our analysis provides evidence that foreign assets are funded with long-term debt whereas domestic assets are financed with short-term debt. The literature provides inconclusive evidence of the role of long-term debt in economy. Some evidence (e.g., Beaver et al., 1970) suggests that debt can proxy for investment prospects whereas Myers (1977) argues that debt can be an effective tool to monitor management to alleviate the overinvestment problem. However, high debt in a firm’s capital structure is believed to hamper the value of the firm at the micro level and induce financial instability at the macro level (Olibe et al., 2011). The results show how foreign fixed assets impact long-term debt and firm value. This issue affects a cross section of disciplines including accounting, corporate finance, and economics. Although we report that long-term debt and foreign fixed assets are positively correlated, this correlation does not necessarily imply that

long-term debt has a causal impact on foreign assets, since both are likely to be functions of common, unobservable factors. External financing documented in our study, implies that financial managers, equity investors and lenders have a growing interest to understand the role debt plays in financing foreign and domestic operations. By providing evidence on how corporate diversification impacts the relation between foreign and domestic assets and debt maturity structures, we add to the literature on the role of these assets in debt markets. 23 Moreover, we provide additional insights into the role of long-term debt as a determinant of the diversification discount in the partition sub-sample tests. This paper offers implications for the business community because fundamental knowledge of foreign operations and the financing choices of firms along with their essential characteristics are at the core of business disciplines. To our knowledge, this study is the first empirical study to investigate how different debt levels affect the pricing of foreign assets, and whether foreign and domestic assets are funded with debt. Identification of an additional important determinant of long-term debt can be valuable to users of accounting and finance information. For example, our findings indicate that researchers should control for international diversification in studies examining the financing choices of firms. Our evidence is also relevant to policy makers as the increasing relevance of foreign operations has spurred the accounting and investment communities to improve the transparency and consistency of geographic segment information by introducing new reporting standards, including the Statement of Financial Accounting Standards SFAS No. 131 (FASB 1997). Our inferences are subject to several limitations. We focus exclusively on the role of lenders in financing foreign and domestic fixed assets. Furthermore, there are other sources of capital for the firm (e.g., internal capital). In addition, we do not examine whether these funds are from overseas or US domestic lenders. We provide evidence on this issue but in the context of firm debt financing choices. Given the level of financing required to implement a firm’s domes-

23 The debt measure may not capture all the debt of the sample firms. Recently, the Wall Street Journal (July 10-11, 2010) reported that the Bank of America “Admits Hiding Debt” in billions of dollars in an attempt to reduce the size of a unit’s balance sheet to meet internal financial targets. Finally, whether the disposition of MNC managers to take on leverage is good or bad for shareholders depends on how leverage affects the wealth of shareholders. Thus, firms must weigh the expected benefits of debt and foreign operations against its cost and risks.

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tic and foreign investment opportunities, we assume that managers wish to finance their foreign investments with borrowed funds because of the incentive benefits of debt. Our focus in this study is on debt financing by US multinational firms. We do not examine debt financing that may occur among foreign subsidiaries, nor do we examine whether the financing is from the US or foreign source. While such debt financing is potentially important and interesting, our main objective is a more modest one: to investigate whether the foreign and domestic fixed assets of US firms are financed with borrowed funds. We leave to future research the investigation of internal and foreign subsidiaries debt financing.

Acknowledgements We thank the following for their helpful comments: Professors Anwer Ahmed, John Doukas, Mike Kenney and Senyo Tse for detailed comments since this project’s inception. We also thank Seminar and Conference participants at Kansas State University Faculty Research Seminar; the University of Memphis the International Accounting Section of the American Accounting Association (IASAAA), Midyear Meeting participants, Southwest AAA Midyear Meeting. Alan Montgomery supplied the data.

Appendix A. (Alternative measure of long-term debt) The following formula is used to estimate LTDebt.24

LTDebtt = SBondt



n−2 

A ft,1−j {(Rt−j /RtA )[I − (1 + PtA )

−(n−j)

]

j=0

A−(n−j)

+ 1 + Rt



}

Where LTDebt is the long-term debt, S Bond is the year-end book value of the firm’s long-term debt in year t;

n−2

ft,t−1 = Nt−i /

k=0

Nt−k ;

i = 0,. . ., n - 2; Nt is the sum of all new debt issued in year t,RtA is the yield to maturity of a firm’s debt at time t under the simplifying assumption that all debt issued in year t is priced to yield the average interest rate in A rated debt for that year. The replacement value of the firm’s assets, RC, is defined as RCt = TAt +RNPt –HNPt +RINVt -HINVt where TA  t is the book value of total assets in year t; RNP t = RNP t−1

1+ ˚T 1+ ıT

+ It ; t > 0 is the estimated value of net plant

replacement cost in year t; RNPt=0 = HNPt = 0 ; ˚t is the growth of capital goods prices in year t estimated by the Gross National Product deflator for nonresidential fixed investment; ıt is the real depreciation rate in year t estimated by DEPt /HNPt –1 , where DEPt represents book depreciation in year t; It is the investment in new plant in year t; HNPt is the historical book value of net plant in year t; RINVt is the firm-reported replacement value of inventories in year t; and HINVt is the historical book value of inventories in year t. Finally, RNP is substituted into the replacement cost equation to yield Perfect and Wiles’ (1994) RC estimate, which is a modification of the Lindenberg and Ross’ (1981) RC estimate.

24

13

References Aboody, D., Barth, M. E., & Kasznik, R. (1999). Revaluations of fixed assets and future firm performance: Evidence from UK. Journal of Accounting and Economics, 26(September), 149–178. Barclay, M. J., Morellec, E., & Smith, C. W. (2006). On debt capacity of growth options. Journal of Business, 79, 37–59. Barth, M. E., Beaver, W. H., & Landsman, W. R. (1998). Relative valuation roles of equity book value and net income as a function of financial health. Journal of Accounting and Economics, (January), 1–34. Beaver, W., Ketler, P., & Scholes, M. (1970). The association between market determined and accounting determined risk measures. The Accounting Review, (October), 654–681. Ketller and Scholes. Becker, B., & Milbourn, T. T. (2011). How did increased competition affect credit rating? Journal of Financial Economics, 101, 493–514. Berger, P. G., & Ofek, E. (1995). Diversification’s effect on firm value. Journal of Financial Economics, 52(1), 39–65. Berger, P., Ofek, E., & Yermack, D. (1997). Managerial entrenchment and capital structure decisions. The Journal of Finance, 52, 1411–1438. Bodnar, G. M., Tang, l. C., & Weintrop, J. (1999). Both sides of corporate diversification: The value impacts of geographic and industrial diversification NBER Working paper no. 6224. Burgman, T. (1996). An empirical examination of multinational capita structure. Journal of International Business Studies, 27, 553–570. Campa, J. M., & Kedia, S. (2002). Explaining the diversification discount. The Journal of Finance, 57(October), 1731–1762. Claessens, S., & Klapper, L. (2005). Bankruptcy around the World: Explanations of Its Relative Use. American Law and Economics Review, 7, 253–283. Collins, J. H., & Shackelford, D. A. (1992). Foreign tax credit limitations and preferred stock issuances. Journal of Accounting Research, 30(Supplement), 13124. Dastidar, P. (2009). International corporate diversification and performance: Does self-selection matter? Journal of International Business Studies, 40, 71–85. Data, S., Iskandar-Data, M., & Patel, A. (1999). Banking monitoring and the pricing of corporate public and private debt. Journal of Financial Economics, 51(3), 435–449. De Jong, A., Rezaul Kabir, R., & Nguyen, T. (2008). Capital structure around the world: The roles of firm- and country-specific determinants. Journal of Banking & Finance, 32, 1954–1969. Denis, D. J., Denis, D. K., & Yost, K. (2002). Global diversification, industrial diversification and firm value. The Journal of Finance, 57(5), 1959–1979. Dhaliwal, D., Trezevant, R., & Wang, S. (1992). Taxes, investment-related tax shields and optimal capital structure. Journal of the American Taxation Association, 1–21 (Spring). Djankov, S., Hart, O., McLiesh, C., & Shleifer, A. (2008). Debt Enforcement around the World. The Journal of Political Economy, 116, 1105–1149. Doukas, J. (1995). Overinvestment, Tobin’s q and gains from foreign acquisitions. Journal of Banking & Finance, 19, 1285–1303. Doukas, J., & Kan, O. B. (2006). Does global diversification destroy firm value? Journal of International Business Studies, 37, 352–371. Errunza, V. R., & Senbet, L. W. (1984). International corporate diversification, market valuation, and size adjusted evidence. The Journal of Finance, 39(3), 727–745. Fama, E., & French, K. (2002). Testing trade-oof and pecking order predictions about dividends and debt. The Review of Financial Studies, 15, 1–33. Files, R., Swanson, E. P., & Tse, S. (2009). Stealth disclosure of accounting restatements. The Accounting Review, 84(5), 1495–1520. Francis, J. D., Nadia, & Olsson, P. (2008). Voluntary disclosure, earnings quality, and cost of capital. Journal of Accounting Research, 46, 53–99. Franco, F., Urcan, O., & Vasvari, F. P. (2016). Corporate diversification and the cost of public debt: The role of segment disclosures. The Accounting Review, 91(July), 1139–1163. Frank, M., & Goyal, V. (2009). Capital structure decisions: Which factors are reliably important? Financial Management, 38, 1–37. Giannetti, M. (2003). Do Better Institutions Mitigate Agency Problems? Evidence from Corporate Finance Choices. The Journal of Financial and Quantitative Analysis, 38, 185–212. Goh, B. W., Lee, J., Lim, C. Y., & Shevlin, T. (2016). The effect of corporate tax avoidance on the cost of equity. The Accounting Review, 9(November), 16471670. Graham, J. (1996). Debt and the marginal tax rate. Journal of Financial Economics, 41(1), 41–73. Gul, F. A., & Goodwin, J. (2010). Short-term debt maturity structures, credit ratings, and the pricing of audit services. The Accounting Review, 85(3), 877–909. Hann, R., Ogneva, M., & Ozbas, O. (2013). Corporate diversification and the cost of capital. The Journal of Finance, 68, 1961–1999. Hasan, L., Hoi, C.-K., Wu, Q., & Zhang, H. (2014). Beauty in the eye of the beholder: The effect of corporate tax avoidance on the cost of bank loans. Journal of Financial Economics, 113, 109–130. Heckman, J. J. (1979). Sample selection bias as a specification error. Econometrica, 47(1), 153–161. Higgins, R. C., & Schall, L. D. (1975). Corporate bankruptcy and conglomerate merger. The Journal of Finance, 30, 93–113. Hope, O., & Thomas, W. B. (2008). Managerial empire building and firm disclosure. Journal of Accounting Research, 46(3), 591–625.

Appendix A is adopted from Doukas (1995).

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ARTICLE IN PRESS K.O. Olibe et al. / The Quarterly Review of Economics and Finance xxx (2019) xxx–xxx

Jacquillat, B., & Solnik, B. H. (1978). Multinationals are poor tools for diversification. The Journal of Portfolio Management, 4, 8–12, winter. Jensen, M. C. (1986). Agency costs of free cash flow, corporate finance and takeovers. The American Economic Review, 76, 323–329. Jensen, M. C., & Meckling, W. H. (1976). Theory of the firm: Managerial behavior, agency costs and ownership structure. Journal of Financial Economics, 3(4), 305–360. Krull, L. K. (2004). Permanently reinvested foreign earnings, Taxes, and earnings management. The Accounting Review, 79(July), 745–767. Lang, I. H. P., & Ofek, E. (1995). Why do firms invest in Eastern Europe? European Financial Management, 1, 147–171. Lang, L. H. P., & Stulz, R. M. (1994). Tobin’s Q, corporate diversification, and firm performance. The Journal of Political Economy, 102, 1248–1281. Leary, M. T. (2009). Bank loan supply, lender choice, and corporate capital structure. The Journal of Finance, 64, 1143–1185. Lee, K., & Kwok, C. (1986). Multinational corporation vs. Domestic corporation. International environmental factors and determinants of capital structure. Journal of International Business Studies, 19, 195–217. Leftwich, R. (1983). Accounting information in private markets: Evidence from private lending agreements. The Accounting Review, 58(1), 23–42. Leone, A. J., Minutti-Meza, M., & Wasley, C. (2013). Influential observations and inference in accounting research Working paper. University of Miami. Mansi, S., & Reeb, D. M. (2002). Corporate diversification, what gets discounted? The Journal of Finance, 57(5), 2167–2183. Myers, S. C. (1977). Determinants of corporate borrowing. Journal of Financial Economics, 5, 147–175. Myers, S. C., & Majuf, N. S. (1984). Corporate financing and investment decisions when firms have information that investors do not have. Journal of Financial Economics, 13, 187–221. Novack, J. (2012). Senate report details HP and Microsoft offshore tax ploys Forbes (September 20). Accessed October 20, 2018. Available at:. http://www.forbes. com/site/janetnovack/2012/09/20/senate-report0hits-hpmicrosoft-foroffshore-ploys-saving-billions-in-tax/

Ohlson, J. A. (1995). Earnings, book values and dividends in security valuation. Contemporary accounting research. pp. 661–687. Olibe, K. O., Strawser, R. H., & Strawser, W. R. (2011). Why shareholders and debtholders value internationally diversified firms: Evidence from the United States. Journal of Accounting and Finance, 11(2), 26–52. Scholes, M., & Wolfson, M. (1992). Taxes and business strategy: A planning approach. Englewood, Cliffs, N.J: Prentice-Hall. Sengupta, P. (1998). Corporate disclosure quality and the cost of debt. The Accounting Review, 73, 459–474. Shapiro, A. (1982). Multinational financial management. Boston: Allyn and Bacon. Shevlin, T., Urcan, O., & Vasvari, F. (2013). Corporate tax avoidance and debt yields Working paper. University of California, Irvine and London Business School. Shivakimar, L., Urcan, O., Vasvari, F. P., & Zhang, L. (2011). The debt market relevance of management earnings forecast: Evidence from before and during the credit crises. Review of Accounting Studies, 16, 464–486. Smith, C. W., Jr, & Watts, R. L. (1992). The investment opportunity set and corporate financing, dividend, and compensation policies. Journal of Financial Economics, 32, 263–292. Stevens, G. V. G., & Lipsey, R. E. (1992). Interactions between domestic and foreign investment. Journal of Money & Finance, 11, 40–62. Strebulaev, I. A. (2007). Do tests of capital structure theory mean what they say? The Journal of Finance, 62, 1747–1787. Villalonga, B. (2004). Does diversification cause the “Diversification Discount”? Financial Management, 33, 5–27. Wall Street Journal. 2010. Bank of America “Admits Hiding Debt”, July7, 10-11. Wall Street Journal. (2016). Short-term Loans get more expensive December 14. pp. B18. Yadav, Y. (2013). The new market in debt governance, working papers [on line]. Vanderbilt University. http://ssrn.com/abstract=2225524

Please cite this article in press as: Olibe, K. O., et al. Corporate diversification, debt maturity structures and firm value: The role of geographic segment data. The Quarterly Review of Economics and Finance (2019), https://doi.org/10.1016/j.qref.2019.01.011