Why do private firms hold less cash than public firms? International evidence on cash holdings and borrowing costs

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Why do private firms hold less cash than public firms? International evidence on cash holdings and borrowing costs Sandra Mortal , Vikram Nanda , Natalia Reisel PII: DOI: Reference:

S0378-4266(19)30295-X https://doi.org/10.1016/j.jbankfin.2019.105722 JBF 105722

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Journal of Banking and Finance

Received date: Accepted date:

9 May 2018 12 December 2019

Please cite this article as: Sandra Mortal , Vikram Nanda , Natalia Reisel , Why do private firms hold less cash than public firms? International evidence on cash holdings and borrowing costs, Journal of Banking and Finance (2019), doi: https://doi.org/10.1016/j.jbankfin.2019.105722

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Why do private firms hold less cash than public firms? International evidence on cash holdings and borrowing costs

Sandra Mortal University of Alabama Tuscaloosa, AL 35487 [email protected]

Vikram Nanda University of Texas at Dallas Richardson, TX 75083 [email protected]

Natalia Reisel Fordham University 45 Columbus Ave. New York, NY 10019

[email protected]

This draft: November 2019

We would like to thank Alex Butler, Mark Flannery, Pankaj Jain, Marc Lipson, and participants at the 2014 FMA, the 2015 EFMA meetings, and seminar participants at University of Mississippi for valuable comments. This paper was previously circulated under the title “Do Firms Hold too Much Cash? Evidence from Private and Public Firms”.

Abstract

We contend that high borrowing costs can overwhelm precautionary motives and induce low cash holdings in private firms. Supportive of our hypothesis, we find European private firms hold less cash than public firms and this differential relates to borrowing costs. Results are robust to endogeneity concerns and reveal private firms use cash flow to pay-down existing debt instead of building cash reserves. Further, stronger creditor rights and debt market development lead to convergence in cash policies of private and public firms.

JEL classification: G32, G38 Keywords: Cash Holdings, Creditor Rights, European Firms, Borrowing Costs, Precautionary Motive, Private Firms

I.

Introduction

This paper investigates how fundamental differences between public and private firms affect their cash policies. Keynes (1936) predicts that cash holdings may be beneficial to firms with limited access to external capital markets because they can be used to finance future valuable

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projects or activities. 1 Along these lines, Harford, Klasa, and Maxwell (2014) emphasize that cash reserves are important in reducing refinancing risk for firms with short-term debt. Additionally, Pinkowitz and Williamson (2001) show that public firms that borrow from banks rather than public debt markets tend to hold more cash. This line of research suggests that private firms should hold higher precautionary cash reserves than public firms given that private firms have limited access to external capital markets, have more short-term debt and are subject to monitoring by banks. However, contrary to these predictions, empirical evidence using U.S. data documents that private firms tend to hold less cash than public firms (e.g., Gao et al, 2013). In this paper, we propose a borrowing cost explanation for the difference in cash policies of public and private firms and test our hypothesis with cross-country data. Gao et al. (2013) use a sample of public and private firms in the U.S. to argue that public firms hold more cash than private firms because public firms are subject to higher levels of agency costs. We propose an alternative non-mutually exclusive reason for this phenomenon. We hypothesize that higher opportunity costs of holding cash for private firms can lead to a cash holdings differential between public and private firms. The rationale is that firms face a trade-off between retaining cash for future investment opportunities and paying down debt. By reducing debt, the firm lowers its borrowing costs – the downside is that the firm may have less investment capital in future states when it cannot raise sufficient external borrowing. Hence, when the current costs of debt financing are significantly high, the costs of holding cash may outweigh the precautionary benefits of holding cash, and a firm may use funds to reduce debt rather than hold cash. We refer to this as the cost of cash hypothesis. Reducing debt today may

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This benefit is described as a precautionary motive for holding cash. Opler et al. (1999) and Fresard (2010), among others, provide evidence consistent with this precautionary motive.

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also increase future funding capacity and lower restrictive covenants, providing an additional rationale for using cash to pay-off debt. The existing literature documents that the cost of debt financing is higher for private firms than for public firms (Pagano, Panetta, and Zingales, 1998; Saunders and Steffen, 2011). Saunders and Steffen, for example, find that private firms face higher borrowing costs than public firms because of the higher costs of information production, lower bargaining power and the higher likelihood of shareholder-debtholder conflicts. Thus, private firms may not only face considerable precautionary benefits of holding cash but also substantial costs of holding cash because of high borrowing costs. This could lead to lower cash balances for private firms than public firms. To test our cost of cash hypothesis, we analyze a sample of private and public firms across European countries. Unlike in the U.S., both public and private firms in Europe are required to publish financial statements, and thus we are able to analyze a comprehensive sample of public and private firms. 2 Furthermore, the cross-country nature of our sample allows us to take advantage of variation in the development of legal and financial institutions. Thus, we are able to perform an in-depth analysis of the role of financing frictions in explaining cash holdings. We start by documenting that, similar to U.S. evidence, European unlisted firms hold significantly less cash than listed firms. 3 These results are robust to alternative specifications, including a fixed effects model that exploits within firm variation in the listing status, an

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In contrast, Gao, Harford and Li (2015) examine cash holdings in a limited subset of large U.S. private firms that opt to disclose financial information. For example, some large U.S. firms with unlisted equity choose to issue bonds in public markets. These hybrid firms with private equity and public debt must follow SEC rules and disclose in a similar fashion to U.S. public equity firms. 3 We use the terms “listed” and “public” (and “unlisted” and “private”) interchangeably.

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instrumental variable model as well as a treatment effects model, and hold in both matched and unmatched samples. Next, we show that listed and unlisted firms differ in their propensity to save cash out of cash flow. Following Acharya et al. (2007), we jointly estimate responses of firms‟ cash and debt policies to cash flow innovations. Listed firms display a higher propensity to save cash from cash flow (i.e., their cash flow sensitivity of cash is significantly higher than that of unlisted firms). In contrast, unlisted firms are more likely to use cash flow to pay off their debt (i.e., their cash flow sensitivity of debt is significantly higher than that of listed firms). These results hold in both a matched sample and a sub-sample of firms that change status from listed to unlisted and vice versa. These findings are consistent with the hypothesis that holding cash is relatively costlier for private firms. We then explore the determinants of the differential in cash holdings across listed and unlisted firms to provide additional insight into the economic forces that drive corporate liquidity. Our cross-sectional analysis shows that the differential in cash holdings across listed and unlisted firms is related to the degree of debt market development. Consistent with our hypothesis, in countries with less developed debt markets and high cost of debt financing, unlisted firms retain significantly less cash than their counterparts in countries with more developed debt markets. Furthermore, our time-series analysis shows that variation in the cost of loans within a country is negatively related to cash holdings of unlisted firms, and this relation is stronger in countries with less developed debt markets. As debt markets develop and the cost of debt financing is reduced, unlisted firms increase their cash holdings and the differential in cash holdings between unlisted and listed firms shrinks significantly. We note that using these country-level proxies for the cost of debt financing helps mitigate endogeneity concerns in firm-

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level regressions. Finally, we also provide evidence that private firms are less sensitive to the precautionary motives for holding cash suggesting that costly debt financing may override the benefits to private firms from accumulating cash for precautionary reasons. Overall, our findings support the hypothesis that private firms retain relatively little cash because of the high opportunity costs implicit in the steep borrowing costs they face. While the traditional (perfect market) approach suggests that cash should be viewed as negative debt, this is not necessarily the case in the presence of market imperfections. 4 As our findings illustrate, firms‟ preference for higher cash versus lower debt depends on borrowing costs as indicated, for instance, by whether the firms are listed or unlisted. Our results are robust to controlling for agency costs (Gao et al., 2013) as measured by anti-self-dealing index from Djankov et al. (2008). They are also unlikely to be due to the difference in selective disclosure between public and private firms (Farre-Mensa, 2017).5 We also show that the cash holdings differential between private and public firms is unlikely to be due to the differences in firms‟ hedging needs (Acharya et al., 2007).6 Our results highlight the role of financing frictions in affecting firms‟ liquidity management decisions. Most importantly, they suggest that the relation between financing frictions and cash holdings is non-monotonic. On the one hand, firms with the greatest access to the capital markets, such as large U.S. public firms with high credit ratings, hold relatively little cash because the benefits are low (Opler et al., 1999). On the other hand, firms that lack access to public equity markets and face high cost of

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Acharya et al. (2007), offers a model in which cash flow is stochastic to show that cash is not equivalent to negative debt. Our model provides another context in which cash is not equivalent to negative debt. 5 In our sample, both private and public firms are required to publish their financial statements, and the difference in disclosing requirements does not vary systematically across countries and through time, thus, our results cannot be attributed to differences in disclosure across private and public firms. 6 The results (Appendix B) show that firms‟ hedging needs do not significantly explain the variation in cash holdings between private and public firms.

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debt financing, such as private firms, hold relatively little cash because the opportunity costs of holding cash are high. Our paper contributes to the literature studying the determinants of cash holdings. To the best of our knowledge, our paper is the first to examine the effects of high borrowing costs on cash holdings. Lins, Servaes, and Tufano (2010) in a survey of chief financial officers find that factors related to the opportunity cost of cash such as borrowing costs are the second most important determinant of cash holdings. Our paper provides empirical evidence that is consistent with the results from this survey. Our paper is closest in spirit to papers that contrast public and private firms to study the determinants of cash holdings. Gao et al. (2013) exploits variation in agency costs; Farre-Mensa (2017) exploits variation in disclosure while our paper exploits variation in the opportunity cost of cash such as borrowing costs to explain the cash holding differential. Hall, Mateus, and Mateus (2014) focus on a sample of firms from Central and Eastern Europe to study the determinants of cash holdings for private and public firms and find that the determinants are similar.7 Unlike this study, we provide evidence consistent with the borrowing cost explanation for the gap in cash holdings between private and public firms. Further, our study is related to the literature examining the determinants of cash holdings in private firms. Bigelli and SanchezVidal (2014) and Gogineni, Linn, and Yadav (2012), for example, find evidence consistent with the pecking order and trade-off explanations for cash holdings in private firms.

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Interestingly, in their sample they show that privately held firms tend to hold more cash than publicly traded firms. This is surprising, given other studies that show that public firms hold more cash (see Gao, 2013; and Farre-Mensa, 2017).

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Our paper is also related to studies that investigate the impact of legal institutions on cash holdings. 8 Closest to our study, Ferreira and Vilela (2004) use a sample of public firms to study the impact of creditor rights on cash holdings, and find a positive relation. Unlike their study, we focus on the differences between public and private firms in their cash holdings‟ policies and show for private firms, creditor rights are inversely related to cash holdings, consistent with our cost of cash hypothesis. More broadly, our paper furthers understanding of the trade-offs of public vs. private listing. 9 Importantly, it highlights an additional cost private firms must bear: because of high borrowing costs, private firms hold less cash than public firms, and as a result are more vulnerable when there is an unexpected need for cash. The remainder of the paper is organized as follows. Section II develops our hypothesis and corresponding predictions. Section III describes the data and sample selection procedure. Section IV examines the differences in cash holdings between listed and unlisted firms. Section V investigates the cash flow sensitivity of debt and cash for listed and unlisted firms. Section VI examines the determinants of the differences in cash holdings between listed and unlisted firms. Section VII investigates the precautionary motives for holding cash by listed and unlisted firms. Section VIII concludes the paper. We present a simple stylized model to support our hypothesis and predictions in Appendix A.

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See, e.g., Dittmar et al. (2003), Ferreira and Vilela (2004), Kalcheva and Lins (2007), Pinkowitz et al. (2006), Caprio, Faccio and McConnell (2011), Kusnadi and Wei (2011) and Chen, Li, Xiao and Zou (2014). 9 See, e.g., Bharath and Dittmar, 2010; Celikyurt, Sevilirb, and Shivdasani, 2010; Bodnaruk et al., 2008; Bernstein, 2015; Mortal and Reisel, 2013; and Asker, Farre-Mensa, and Ljungqvist, 2015.

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II.

Costly External Financing and Cash Holdings Costly external financing affects both the costs and benefits of holding cash (Almeida et

al., 2004). The extant research emphasizes the benefits of holding cash when the cost of external capital is high due to asymmetric information and agency problems. Holding cash is beneficial because firms can invest in valuable future projects and activities without the substantial costs and uncertainty associated with raising new external capital. The literature refers to this as the precautionary motive for holding cash. Existing empirical literature provides evidence that factors related to the precautionary benefits of cash such as growth opportunities and cash flow volatility positively impact cash holdings (e.g., Opler et al., 1999). The opportunity cost of cash for financially constrained firms has received less attention in the literature than have the benefits of holding cash. Firms‟ executives, however, emphasize costs of holding cash when the cost of external capital is high (Lins, Servaes, and Tufano, 2010). Lins et al, in a survey of chief financial officers from 29 countries, find that factors related to the cost of cash are the second most important determinant of cash holdings. As we would expect, managers recognize that holding cash entails an incremental cost that is equal to the cost of debt financing (or interest paid on debt) minus the rate of return that the firm earns on its cash holdings. 10 Thus, a firm with a high cost of debt could find it optimal to use funds to pay down debt. We refer to this as the cost of cash hypothesis. Also, paying down debt can enhance the firm‟s future debt capacity, especially if the retired debt included restrictive covenants. The downside of not retaining cash, however, is that in some future states of the world the firm may

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The rate of return that a firm earns on its cash holdings is the risk free rate minus a carry cost (Bolton et al (2011)). A carry cost may arise from a difference between the corporate and personal income tax rates: Corporations generally face higher tax rates on interest earned on its cash holdings than individuals (Graham, 2000; Faulkender and Wang, 2006). Also, a carry cost may arise when a firm retains some portion of its cash holdings in non-interest bearing accounts (Azar et al, 2016) These carry costs are not related directly to the degree of financial constraints. We investigate the opportunity cost of holding cash that is directly related to the degree of financial constraints

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find it costly or difficult to raise capital, e.g., if it lacks sufficient collateral, and may find itself largely dependent on its cash holdings to undertake investment opportunities. We illustrate this trade-off using a simple stylized model presented in Appendix A. Given the information production and monitoring functions of the stock market (Dow and Gorton, 1997; Subrahmanyam and Titman, 1999; and Edmans, 2009), private firms are likely to have greater „opaqueness‟ than public firms and are thus likely to face higher borrowing costs. Existing empirical literature, indeed, suggests that the cost of debt financing is higher for private firms than for public firms (Pagano, Panetta, and Zingales (1998) and Saunders and Steffen (2011)). Saunders and Steffen (2011), for example, find that private firms face higher borrowing costs than public firms because of the higher costs of information production, lower bargaining power and the higher likelihood of shareholder-debtholder conflicts. Thus, our model generates a few testable predictions with respect to private firms. First, from our arguments leading to Prediction 1, we expect private firms to have lower cash holdings relative to public firms and to be more likely to apply cash flow to pay down existing debt. Second, we expect a convergence in the cash polices of public and private firms when creditor rights are stronger and the debt market is more developed. There is literature that indicates that lower levels of creditor protection and debt market development are associated with higher cost of debt (Qian and Strahan (2007) and Bae and Goyal (2009)). This is captured in our arguments leading to Prediction 2 in the model, and we expect that creditor protection and debt market development is positively related to cash holdings of private firms. However, for public firms, the impact of debt market development on cash holdings could be less positive and may even be negative (Prediction 3). The reason is that as the likelihood of such firms being able to finance their projects increases, they may tend to hold less cash since the need for

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precautionary cash-holding decreases.11 Similarly, we expect that variation in the cost of debt in a country is related to cash holdings of private firms. In the empirical analysis that follows, we investigate cash policies of private and public firms and test our predictions using cross-country data. We also take a closer look at the ability of private firms to accumulate cash for precautionary reasons. As we have noted, an important benefit of cash holdings is that they allow firms to better cope with adverse shocks and finance valuable projects or activities in the future. High costs of cash may, however, impair the ability of private firms to respond to this precautionary motive for holding cash.

III.

Data Description In this section, we describe our data and sample selection procedure.

A.

The Sample Selection Our primary data source is the 2011 version of Amadeus, by Bureau van Dijk. This

database provides balance sheet and income statement items for a set of European firms from 1996 to 2011. An important advantage of Amadeus is that it includes data for a comprehensive set of public and private firms. This advantage is made possible because European law requires both public and private firms to report financial statements. The data are collected from each nation‟s official public body in charge of collecting the annual financial statements in its country, and always come from the officially filed and audited accounts. Amadeus then standardizes the data to make it comparable across countries.

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A caveat to the convergence argument is that there can be factors other than debt market development and creditor rights that preclude convergence in cash policies between public and private firms. For instance, the US “worldwide” taxation system (as opposed to “territorial” system) induces multinational (typically public) firms to retain relatively high levels of cash abroad rather than pay taxes upon repatriation (Foley et al. (2007)).

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The Amadeus dataset is divided into three parts. The first contains the largest firms in the database, the second contains the next largest, and the third contains the remaining firms. Our sample comes from the first part of the dataset – the largest firms. However, even this part of Amadeus includes very small firms, with total assets less than one million dollars. The dataset includes a flag for whether the company is listed on a major stock exchange. All other firms are classified as unlisted. The dataset reports only contemporaneous information rather than historical information for this variable. We use historical Amadeus DVDs to track changes in listing status over time. We use the variable “Legal Form” to exclude unlimited partnerships, sole proprietorships, cooperatives, foreign companies, foundations, and government enterprises. As in Giannetti (2003), we exclude Eastern European economies since the quality of the accounting data provided for these economies is poor. We exclude firm-years with total assets less than 10 million U.S. dollars, and exclude financial and miscellaneous firms (US SICequivalent codes 60-69 and 89). With a few exceptions, we set a variable to missing if its observations are within its top or bottom percentile, to avoid the effect of outliers. The exceptions are standard deviation of cash flows and balance sheet items, such as cash scaled by total assets. In these cases, we set a variable to missing if its values are non-positive or its observations are within the top percentile. These filters result in 1,004,674 firm-year observations. We complement firm-level data with country indexes of financial and legal development. We measure debt market development using private credit to GDP from Djankov, McLeish and Shleifer (2007) and the index of creditor rights from La Porta, Lopez-de-Silanes, Shleifer, and Vishny (1998). To gauge the degree of shareholder rights, we use the anti-self-dealing index from Djankov et al. (2008), which measures how difficult it is for minority shareholders to

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thwart the consumption of private benefits by controlling parties. Djankov et al. (2008), argue that self-dealing is the central problem of corporate governance in most countries. Following Dermiguc-Kunt and Levine (1996) and Love (2003), we compute an index of stock market development that equals the sum of standardized market capitalization to GDP, total value traded to GDP, and turnover (total value traded to market capitalization). We obtain each of the elements of this index from the World Bank. B.

The Matching Procedure Only 2.5% of the firm-year observations in our sample are listed firms. All other firms

are unlisted. To make the samples of listed and unlisted firms more comparable in size, we match listed firms to unlisted firms based on country, industry code, and total assets. We keep our matching criteria simple to allow for comparisons between public and private firms across multiple characteristics. In order to match each listed firm to an unlisted firm, we first consider all listed firms in 2008, choosing this year because it contains the largest number of firms for any given year in our sample. We then exclude the largest listed companies (total assets of the company exceeds total assets of the largest unlisted company in the country by 20 million U.S. dollars or more) as these companies are likely to have easy access to international financial markets and are less likely to be subject to the constraints imposed by domestic markets (see Giannetti, 2003; and Mortal and Reisel, 2013). Next, we require exact matches on country and industry code and the closest possible match on total assets, measured as of 2008. The matching is done without replacement. Median total assets in the matched sample is 127 million USD, and the bottom decile of total assets is 19.3 million USD. For robustness, we also perform some of the tests using the full (unmatched) sample. Our unmatched sample includes firms as small as 10 million USD in total 12

assets, and the bottom decile and median total assets are 12.6 and 33.9 million USD, respectively. C.

Descriptive Statistics We present descriptive statistics in Table 1 for the matched sample of listed and unlisted

firms. The table contains total assets in millions of USD and various balance sheet items as percentage of total assets. As indicated, unlisted firms hold significantly less cash and cash equivalents than listed firms: unlisted firms hold 9% in cash as a proportion of total assets, while listed firms hold 14%. This difference of 5% is statistically significant, and is consistent with Prediction 1, that private firms hold less cash because their borrowing costs are higher. We confirm this univariate result in various tests below. Interestingly, unlisted firms also hold higher short-term debt, a variable typically associated with higher levels of cash (see Falato, Kadyrzhanova and Sim, 2013; Harford, Klasa, and Maxwell, 2014). 12 We also note unlisted firms are somewhat smaller than their listed counterparts, even in our matched sample. In table 2, we present cash holdings across countries. Unlisted firms hold less cash in most countries, and this difference is statistically significant in 11 out of 16 countries. This pattern is also very consistent over time. In Figure 1 we plot average cash holdings over time for listed and unlisted firms, and find that unlisted firms hold less cash than listed firms for every year in the sample period.

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Giannetti (2003) and Brav (2009) also document that unlisted firms hold more short term debt. This result is consistent with long term debt being significantly more costly and inaccessible to unlisted firms than short term debt.

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IV.

Differences in Cash Holdings between Public and Private Firms

A.

Baseline Results In this section, we test whether public firms hold less cash than private firms using

regression analysis. We consider the following model: Cash Holdingsit = β*Listedit + δ*Xit + εit ,

(1)

where Cash Holdings is cash and cash equivalents divided by total assets. Listed is an indicator variable for the firm being listed on a major stock exchange in the country. We include a set of firm-level control variables, as well as country, industry, and year dummy variables (X). We include country fixed effects to ensure we are measuring within-country differences between listed and unlisted firms as well as controlling for unobserved time-invariant country effects. We also include industry and year fixed effects to control for industry wide factors and time trends that may affect cash holdings. Standard errors are clustered at the firm level. The set of firm-level controls includes variables that have been found to determine cash holdings in earlier studies (e.g., Opler et al., 1999, and Bates et al., 2009). Leverage is measured as total debt divided by the sum of total debt and shareholder funds. Total debt is the sum of long-term debt plus short-term loans. Size is measured as the log of total assets, where assets are in USD. Decreasing marginal benefits to holding cash would predict a negative relation between size and cash. We use sales growth to proxy for growth opportunities. Later in the paper, we consider alternative proxies for growth opportunities. Firms with growth opportunities may prefer to hold more cash for precautionary reasons to avoid foregoing growth opportunities due to financing difficulties. Cash flow to assets is operating cash flow divided by lagged assets. Firms with high cash flow may be able to accumulate more cash.

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We compute the standard deviation of cash flows from the current and the past four values of annual cash flow to assets ratio. If two or more cash flow to assets ratios are missing, then the variable is set to missing. We expect firms with high cash flow risk to hold more cash for precautionary reasons. Investment in tangible assets is the change in tangible fixed assets divided by lagged assets. To the extent that investment in tangible assets increases debt capacity, it may reduce the demand for cash. Investment in intangible assets is the change in intangible fixed assets divided by lagged assets. Investment in intangible assets tends to be associated with higher levels of information asymmetry, and greater difficulty accessing external capital markets. As a result, it may increase demand for cash retention. Net working capital is current assets minus current liabilities minus cash divided by lagged assets. Net working capital consists of assets that substitute for cash, and thus we expect a negative relation between cash and net working capital. Finally, firm age is observation year minus year of incorporation. More mature firms typically have more stable cash flows and lower growth opportunities and require less cash. Descriptive statistics for the control variables are presented in Panel B of Table 1. Results of the regression analysis are presented in Panel A of Table 3. We continue to find that unlisted firms hold less cash than listed firms when controlling for other determinants of cash holdings. For the matched sample (model 1), the coefficient on listed is 0.033 and is statistically significant at the 1% level, indicating that unlisted firms hold 3% less of their assets in cash than listed firms. This is economically significant since it indicates that cash holdings of listed firms are more than one third larger than those of unlisted firms that hold, on average, about 9% of their assets in cash. The coefficients on the firm-level control variables are as expected and consistent with previous studies. Results are robust to various specifications. In model 2, we include country-year fixed effects to account for any cross-country variation in a

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given year. In model 3, we use an alternative control sample that matches public and private firms not only on total assets but also on sales growth to mitigate concerns that differences in growth opportunities drive our results. Specifically, we match listed to unlisted firms in 2008, and require exact matches on country and industry and the closest possible match on the Euclidean distance between listed and unlisted firms total assets and sales growth. To ensure a close match on size, we drop listed/unlisted firm duos, if the difference in total assets is greater than 30%. 13 Finally, in model 4 we present results for the whole (unmatched) sample. The coefficients on listed are positive and statistically significant in all specifications, indicating that listed firms hold more cash than unlisted firms. We should note that the analysis in Panel A of Table 3 does not control for the dividend policies of listed and unlisted firms. This is because Amadeus does not contain dividend variables. Previous research, such as Opler et al. (1999), however, shows that firms that pay dividends hold less cash. To investigate whether differences in dividend policies explain the difference in cash holdings between listed and unlisted firms, we obtain data on dividend payouts for listed firm using Orbis, another dataset distributed by Bureau van Dijk with extended coverage of financial data for listed firms. Panel B of Table 3, compares cash holdings of listed firms that pay dividends to cash holding of unlisted firms. If higher propensity of unlisted firms to pay dividends explains our results in Panel A, then we should find no difference in cash holdings between listed firms that pay dividends and unlisted firms. This is not the case however: both listed firms that pay dividends and listed firms that do not pay dividends hold more cash than unlisted firms. In Table 4 we use a sub-sample of firms that change listing status from public to private and vice-versa, and include firm fixed effects to exploit within firm variation in listing status 13

Total assets and asset growth are standardized to have mean zero and unit standard deviation.

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while controlling for time-invariant firm characteristics. The advantage of this approach is that it mitigates concerns of an omitted variable bias. Model (1) does not include year dummies, but model (2) does. The coefficient on listed are 0.0137 and 0.013 for models (1) and (2) respectively, suggesting an increase in cash holdings for listed firms of at least 1.3%. This increase is economically significant. Given that the unconditional average of cash holdings is 9%, 1.3% represents a 14% increase in cash holdings. We further explore robustness of our results in Models (3) and (4). These models are analogous to the first two models, with all control variables lagged. Results remain unchanged. Overall our results indicate that unlisted firms hold less cash and are consistent with our hypothesis that high borrowing costs induce low cash holdings in private firms. We note that our results are unlikely due to the inability of the private firms to generate enough cash from operations because we control for cash flow. This is different from Denis and Sibilkov (2010), who show that some public firms with limited access to external markets exhibit low cash holdings because of persistently low cash flows. One difference between public and private firms is that owners/managers of private firms may be more risk averse (e.g., on account of a concentrated stake in the firm). We expect that bankruptcy risk, coupled with higher borrowing costs, could spur the managers of unlisted firms to prioritize reducing debt rather than holding cash. 14 Since the cost of borrowing is, in part, determined by potential costs of bankruptcy, these costs can be appropriately regarded as part of

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Risk-aversion does not necessarily yield clear predictions and could conceivably induce managers/owners of private firms to hoard cash for precautionary reasons instead of paying down debt. For example, cash holdings may allow private firms to pay interest on debt in case of sudden cash-flow shortfalls and avoid bankruptcy.

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our costs of borrowing hypothesis. Hence, we do not regard the risk-aversion argument as being distinct from our borrowing cost hypothesis. 15 B.

Additional Estimation Approaches Our matched sample and analysis of firms that change listing status alleviates concerns

that our results are driven by the endogeneity of the listing status. However, matching can only be done on observable characteristics, and private and public firms may differ along unobservable characteristics. To further address this concern, we employ a two-stage least squares regression (2SLS) and a treatment effects model. Our instrumental variable for listing status, in the 2SLS regression, is the number of private investment funds in a country-year. The notion is that within country variation in the number of private investment funds (such as VC or PE funds) affects the availability and, possibly, the cost of private equity capital (through competitive effects) for unlisted firms. A greater availability of private equity funds in a country-year could reduce the marginal benefit of becoming listed and, thereby, induce fewer firms to be listed (the extensive margin). Our country-year data on private investment funds is from the SDC VentureXpert database. As we show in Table 5, Panel A, the number of private investment funds in a countryyear is very significantly and negatively associated with the likelihood of a firm being listed. The instrument has an F-statistic of 16.85 in the first stage, indicative of instrument strength. The coefficient on the instrumented listed variable is 0.19 and is statistically significant at the 5% level. The coefficient suggests that a listed firm holds on average 19% more cash as a percentage of assets than an unlisted firm.

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Empirical results, provided in the paper, suggest that risk-aversion alone is unlikely to explain all our findings. For instance, cash holdings of private firms are generally less sensitive to firm risk, as measured by cash flow volatility (Table 10).

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Regarding the exclusion requirement, one potential concern is that the number of private investment funds may be directly related to cash holdings of public firms as VC-backed firms may continue to raise money from VCs even after they go public (Iliev and Lowry, 2019). To minimize this concern, we first run a regression of cash holdings on the number of private investment funds. The results show that cash holdings of listed firms are unrelated to the number of private funds in the country. We should also note that Iliev and Lowry (2019) document that only 15% of the VC backed IPO firms continue to raise money from VCs during the first few years after they go public. In turn, Ritter (2018) documents that only 33% of IPOs are VCbacked. These combined statistics suggest that only a small percentage of the firms in our sample is likely to raise capital from VCs during the first few years after the IPO and thus this type of capital rising is unlikely to be a big concern in our analysis. 16 Finally, given the difficulty with finding the “perfect” instrument, we also present results for a treatment effects model with no instrument, which exploits non-linearities in the model as a source of exogenous variation. The results of the analysis are presented in Table 5, Panel B. We continue to find that unlisted firms hold less cash than listed firms when controlling for other determinants of cash holdings. As we have discussed, the positive cash holdings differential between listed and unlisted firms would not be expected within Keynes‟s (1936) framework that predicts that cash holdings serve a precautionary function for firms with limited access to external capital markets (such as unlisted firms) because cash holdings can help those firms seize valuable projects or activities in the future. Opler et al. (1999), among others, provide evidence consistent with the precautionary

16

In untabulated analysis, we use two alternative instruments. First, in spirit of Leary and Roberts (2014), we use the percentage of firms in a country-region that are listed during the first year of the sample period. Second, following Saunders and Steffen (2011), we use the firm‟s distance to the country‟s main stock exchange. Results are robust to these instruments.

19

motive for holding cash among listed firms. The results in this section, however, are consistent with our cost of cash hypothesis suggesting that the high costs of debt for unlisted firms dominate the precautionary motive inducing unlisted firms to hold relatively little cash. In the sections below, we provide more direct evidence consistent with the cost of cash hypothesis.

V.

The Cash Flow Sensitivity of Debt and Cash for Public and Private

Firms In this section we provide further support for our cost of cash hypothesis. Our hypothesis implies that private firms will use cash flow to pay off debt, in lieu of accumulating cash, because private firms bear a higher cost of debt than public firms. To test this hypothesis directly, we investigate the cash flow sensitivity of debt and cash for listed and unlisted firms. We expect the cash flow sensitivity of debt to be higher for unlisted firms than for listed firms, and the cash flow sensitivity of cash to be lower for unlisted firms. In our empirical analysis, we follow Acharya, Almeida, and Campello (2007), and endogenize debt and cash policies. Specifically, we estimate the following system of equations using three-stage least squares:

ΔDebtit = α1Listedit + α2CashFlowit + α3CashFlowxListedit + α4ΔCashHoldingsit + a5Debtit-1 + δ*Xit + εit ,

(2)

ΔCashHoldingsit = α1Listedit + α2CashFlowit + α3CashFlowxListedit + α4ΔDebtit + a5CashHoldingsit-1 + δ'*Xit + ε'it ,

(3)

20

where Listed is an indicator variable for the firm being listed. ΔDebt is change in total debt scaled by beginning of the period assets. ΔCashHoldings is change in cash scaled by initial of period assets. CashFlow is cash flow scaled by lagged assets. We also include a set of firm-level control variables such as firm size, growth opportunities as measured by sales growth, and country, industry and year fixed effects (X). Standard errors are clustered at firm-level. Results are presented in Table 6, Panel A. They are consistent with our predictions. In the regressions of changes in debt we find that higher levels of cash flow are associated with debt reductions for unlisted firms. This effect is muted for listed firms, as evidenced by the positive coefficient on the interaction variable that is of similar magnitude to the negative coefficient on cash flow. For unlisted firms, debt drops by 1.28% for a one standard deviation increase in cash flow. For listed firms, debt increases by a meager 0.01% for a one standard deviation increase in cash flow. 17 The difference in debt changes for listed and unlisted firms is captured by the interaction term and is statistically significant at the 1% level. In the regressions of changes in cash, we find that high cash flows are associated with increases in the cash account for both listed and unlisted firms, and this effect is more pronounced for listed firms, as evidenced by the positive coefficient on the interaction variable. Cash holdings of unlisted firms increase by 1.35% for a one standard deviation increase in cash flows, while the cash holdings of listed firms increase by an additional 0.58% for a total of 1.93%. The difference in cash holdings between these two groups of firms is statistically significant at the 1% level.

17

Cash flow scaled by total assets has a mean of 0.081 and a standard deviation of 0.105. The economic effect for listed firms is computed as the sum of the coefficients on the cash flow relative to total assets, -0.122 and its interaction with the indicator variable for listed, 0.123 multiplied by 1 standard deviation of cash flow scaled by assets.

21

In Table 6, Panel B we examine a sample of firms that change listing status to investigate how a firm‟s propensity to use cash flow to pay off debt varies for a firm as it changes its own listing status. The sample contains firms that switch status from unlisted to listed or vice-versa. We re-estimate equations 1 and 2, but include firm and year fixed effects. Standard errors are clustered at the firm level. This analysis allows us to examine the effects of firms‟ listing status while keeping fixed its unobserved, time-invariant characteristics. Results are consistent with our previous findings. We find that firms that switch their status from unlisted to listed or vice-versa, are more likely to use cash flow to pay off debt when they are unlisted, and are more likely to use cash flow to increase their cash holdings when they are listed. These results suggest that propensity to use cash flow to pay off debt changes with the change in listing status. In sum, we find that public firms are more likely to use cash flow to accumulate cash, while private firms are more likely to pay down debt. These results are consistent with our cost of cash hypothesis.

VI.

Cross-Country and Time-Series Variation in Cash Held by Public and

Private Firms In this section, we explore cross-country differences in the development of legal and financial institutions and their effect on cash holdings of public and private firms to test our hypothesis. In addition, we explore time variation in the cost of loan financing within countries to provide further support for our hypothesis. We also discuss alternative explanations for the cash holdings differential.

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A.

Creditor Protection, Loan Market Development and the Differences in Cash Holdings We first investigate the effect of creditor protection and loan market development on the

difference in cash holdings between public and private firms. Our cost of cash hypothesis predicts that as the cost of private debt financing declines and the cost of holding cash is reduced, private firms will increase cash holdings and the difference in cash holdings between public and private firms is likely to shrink (Predictions 2 and 3 in the model). The latter condition is because private firms with their greater opaqueness and limited monitoring by, say, institutional investors are more impacted by weaker creditor rights and debt market development than are public firms. Results of the analysis are tabulated in Table 7. We proxy for loan market development and the cost of loan financing at the country level with the amount of private credit as a percentage of GDP from Djankov et al. (2007), and the creditor rights index from La Porta et al. (1998).18 In panel A, we partition the sample in countries with private credit to GDP above the median and countries with private credit to GDP below the median. In panel B, we partition the sample in firms with the creditor rights index above the median and below the median. For the sake of brevity, we do not tabulate the results for firm-level control variables. Control variables are the same as in Table 3. Results in Panel A show that listed firms hold more cash in both subsamples; however, this differential is significantly larger when private credit to GDP is low and the cost of loan financing is high. Listed firms hold 1.3% more cash than unlisted firms when private credit to GDP is high; and 5.2% more cash when private credit to GDP is low. The difference 3.9% is statistically and economically significant considering unlisted firms‟ average cash holdings is 9%. Results in Panel B are similar. Listed firms hold more cash in both sub-samples, but listed 18

The size of the credit markets, as measured by private credit to GDP, is a broad measure of credit market development and should reflect the quality of a country‟s financial and legal institutions, including contract enforceability (Bae and Goyal, 2009; Djankov et al, 2007).

23

firms hold relatively more cash in countries with low creditor rights. Listed firms hold 1.3% more cash in countries where creditor rights are above the median, and 4.5% more cash where creditor rights are below the median. As with private credit to GDP, the difference between these coefficients is statistically and economically significant.19 Overall, these findings suggest that cost of cash is an important economic factor that explains corporate liquidity. 20 To that end, they are consistent with Azar et al (2016) who find that the carry cost of cash, calculated as a product of rate of return on Treasury bills and a share of firm‟s cash holding kept in zero interest rate accounts explains the dynamics of corporate cash holdings over time. B.

Country-level Variables and Cash Holdings of Public and Private Firms In this sub-section, we study the determinants of cash holdings of private and public

firms separately to investigate whether debt market development has a positive effect on the cash holdings of private firms, and an insignificant or even negative effect on the cash holdings of public firms (Predictions 2 and 3). These tests allow us to further understand what drives the cross-country difference-in-differences in cash holdings that we document in the previous section and provide additional support for the cost of cash hypothesis. Results are presented in Panels A and B in Table 8. Columns 1 and 2 report results for the sub-sample that includes unlisted firms only, while columns 3 and 4 report results for the subsample that includes listed firms. In addition to proxies for the cost of debt financing, in regressions 2 and 4 we include a proxy for managerial agency problems at the country-level and a proxy for stock market development in our regressions. We proxy for managerial agency 19

For robustness we run regressions similar to those in Table 3, model 1, but include in one regression an indicator variable for private credit to GDP above median, and its interaction with Listed, and in another regression we include an indicator for creditor rights above median and its interaction with Listed (results are untabulated). In both regressions the coefficient on the interaction term is negative and statistically significant, implying results qualitatively similar to those presented in Table 7. 20 To mitigate concerns of generalizability, in untabulated analysis we confirm that results documented in Table 7 also hold for the unmatched sample.

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problems with anti-self-dealing index from Djankov et al. (2008), which measures how difficult it is to prevent the consumption of private benefits by controlling parties. We measure stock market development following Dermiguc-Kunt and Levine (1996) and Love (2003). This measure is based on trading activity and size of the country‟s stock market (see section III for details). Variables proxying for cost of debt financing, managerial agency problems and market development are indicators for their values being above median. Each regression also includes the firm-level determinants of cash holdings reported in Table 3, year and industry fixed effects, and the log of gross domestic product as an additional control variable for cross-country differences. Standard errors are clustered at the firm level. In Panel A we proxy for cost of debt using private credit to GDP, while in Panel B we use creditor rights. We find that debt market development is associated with a higher level of cash holdings for unlisted firms and lower levels of cash holdings for listed firms. In Panel A, the coefficient on Private-Credit-to-GDP for unlisted firms is 0.022 (statistically significant at the 1% level), while for listed firms, the coefficient is -0.014 (also statistically significant at the 1% level). The results are consistent with private firms finding it expensive to accumulate cash in countries where private credit availability is scarce and consequently the cost of debt is high, while public firms will accumulate more cash when debt market development is low for precautionary reasons. Our results are robust when we use creditor rights index to proxy for the cost of debt. In Panel B, the coefficient on Creditor Rights is 0.028 for unlisted firms (statistically significant at the 1% level), and -0.012 for listed firms (also statistically significant). In models 2 and 4 we include two additional control variables: a proxy for managerial agency problems (anti-self-dealing index), and a proxy for stock market development. The use of the anti-self-dealing index is motivated by previous research that links managerial agency

25

problems and cash holdings, though the direction of the relationship is ambiguous. Selfinterested managers may hold excess cash rather than paying it out to shareholders because cash reduces firm risk and increases managerial discretion (Gao et al., 2013). However, findings in Dittmar and Mahrt-Smith (2007) and Harford, Mansi and Maxwell (2008), suggest that firms with high degree of managerial agency problems actually have smaller cash reserves because managers dissipate cash quickly. The use of the stock market development proxy is motivated by the literature that emphasizes the precautionary benefits of holding cash. Specifically, the precautionary motive suggests that firms hold more cash to better cope with adverse shocks when external capital markets are difficult to tap. As the access to external capital markets, such as the stock market improves, firms should reduce cash holdings due to a decrease in the benefits of holding cash. Each regression also includes firm-level determinants of cash holdings reported in Table 3, year and industry fixed effects, and the log of gross domestic product as an additional control variable for cross-country differences. We continue to find that debt market development is associated with a higher level of cash holdings for unlisted firms. In model 2 of Panel A, the coefficient on Private-Credit-to-GDP is 0.025 and statistically significant at the 1% level. For listed firms, the coefficient on PrivateCredit-to-GDP is 0.001 and is no longer statistically significant (model 4). The difference in the coefficients between listed and unlisted firms is statistically significant at the 1% level (untabulated). Our results are robust when we use creditor rights index to proxy for the cost of debt (see Table 8, Panel B). The anti-self-dealing index and stock market development have an effect on the cash holdings of listed firms only. This is expected, since managerial agency problems should be a

26

bigger concern for public firms due to their more dispersed ownership, and only public firms are directly affected by the stock market development. Similar to Dittmar et al. (2003), we find that the anti-self-dealing index is negatively related to the cash holdings of listed firms. Additionally, we find that stock market development is negatively related to the cash holdings of listed firms. This result is consistent with the precautionary motive for holding cash. C.

Time-variation in the Cost of Debt Financing and Cash Holdings In this sub-section, we investigate how time-variation in the cost of debt affects cash

holdings for listed and unlisted firms. We use country-level interest rates reported by the European Central Bank. 21 These interest rates are calculated from all outstanding loans with a maturity of 1 year or less made by monetary financial institutions in a given country to nonfinancial corporations. We recognize that a country-level measure of the cost of debt does not differentiate between the cost of debt for public and private firms. We expect, however, that this cost of loan financing is likely to have greater impact on private firms. This is because private firms typically hold more debt, and have more limited financing alternatives, and are thus more exposed to variations in the cost of debt. We believe these tests are important, because they provide a source of exogenous variation for the cost of loans at the firm level and thus help mitigate concerns of endogeneity. Results are reported in Table 9. In Panel A, we regress cash holdings on loan interest rates, the set of firm-level control variables included in Table 3, model (1), and firm- and yearfixed effects. We find that for unlisted firms, cash holdings drop as the cost of debt increases. Specifically, for unlisted firms, a percentage point increase in interest rates is associated with a

21

The link to the data is: http://sdw.ecb.europa.eu/browse.do?node=9484266

27

decrease in cash holdings of 8.8% relative to its mean. 22 Cost of loans have an insignificant effect on cash holdings of listed firms. Our results are consistent with our hypotheses, in that we find that variation in the cost of debt has a stronger effect on private firms. In Panel B we include an interaction variable, to capture the effect of the cost of loans when creditor rights are low vs. high. We interact the cost of loans with an indicator variable for the country‟s creditor rights being above median. Stiglitz and Weiss (1981) argue that as interest rates rise, many low risk borrowers may decide not to borrow, and the risk of the pool of borrowers seeking bank financing increases, amplifying the frictions private firms face when raising capital. This effect should be strongest in countries with poor creditor rights. Thus, an increase in interest rates should have a stronger negative effect on cash holdings for firms from countries with low creditor rights. We find that the coefficient on the interaction variable is positive and significant for unlisted firms, suggesting that the cost of loans has a stronger negative effect on cash holdings when creditor rights are low. For listed firms, the interaction is insignificant. Overall, results are consistent with our cost of cash hypothesis. D.

Alternative Explanations In this sub-section, we first investigate whether differences in the hedging needs between

public and private firms explain the difference in cash holdings we document. Acharya et al (2007) show that firms with high hedging needs may benefit by hoarding cash, while firms with low hedging needs may benefit by building debt capacity. High/low hedging needs arise when investment opportunities tend to arrive in low/high cash flow states. We follow Acharya et al (2007) to measure firms‟ hedging needs. For each firm-year in the sample we compute the correlation between investment opportunities and the firm‟s cash

22

Computed as the coefficient on cost of loans -0.008 divided by private firms‟ sample mean for cash/total assets.

28

flow. To proxy for investment opportunities, we compute the median three-year-ahead sales growth rate in the firm‟s three-digit SIC industry. This approach relies on the premise that firms‟ perceptions of investment opportunities are related to estimates of future sales growth in their industries and that those estimates, on average, coincide with the ex-post observed data. We also create two dummy variables: Low hedging needs, which takes the value of one when the correlation between investment opportunities and the firm‟s cash flow is above 0.20, and High hedging needs, which takes the value of one when the correlation between investment opportunities and the firm‟s cash flow is below -0.20. Appendix B presents the results of the analysis. We run a specification similar to the ones presented in Table 3 additionally controlling for the hedging needs. We continue to find that listed firms hold more cash than unlisted firms. In a paper contemporaneous with ours, Farre-Mensa (2017) argues that in the US private firms hold less cash than public firms because of differences in disclosure requirements. In the US, private firms can selectively disclose confidential information to private investors, while public firms cannot. Farre-Mensa contends that these differences in disclosure requirements make public firms susceptible to mispricing, which results in public firms hoarding more cash to optimize the timing of equity issues. Private firms are not susceptible to the same mis-valuation and do not need to hoard as much cash. We highlight that in Europe (1) both private and public firms have an obligation to report financial statements, resulting in a narrower gap in disclosure between private and public firms, and (2) the difference in disclosure requirements for private and public firms does not vary across the countries in our study. As a result, our cross-country evidence that private firms hold less cash in countries with less developed private debt markets is unlikely to be explained by differences in disclosure requirements. Nor are disclosure requirements likely to explain the

29

finding that the difference in cash holdings between private and public firms is wider for countries with less developed private debt markets. Overall, the results in this section highlight the fact that financing frictions play an important role in explaining firms‟ cash policies. The previous literature demonstrates that public firms that are financially constrained accumulate more cash than financially unconstrained public firms because the precautionary benefits of holding cash are lower for unconstrained firms (e.g., Opler et al., 1999). Our results, however, suggest that in extreme cases, when external financing is significantly costly, firms accumulate relatively little cash because the opportunity cost of holding cash is high. Thus, the relation between cash holdings and financing frictions is nonlinear. Private firms with limited access to external markets and with a high cost of external financing behave similarly to financially unconstrained public firms with low financing costs, and both accumulate relatively little cash, albeit for different reasons, while constrained public firms accumulate relatively high cash reserves. The identification of this non-monotonic relation highlights the benefits of an expanded sample such as ours.

VII. Precautionary Motive for Holding Cash In this section, we take a closer look at the ability of private firms to accumulate cash for precautionary reasons. As we have noted, an important benefit of cash holdings is that they allow firms to better cope with adverse shocks and finance valuable projects or activities in the future. High costs of cash may, however, impair the ability of private firms to respond to this precautionary motive for holding cash. The precautionary motive would suggest that high levels of cash flow volatility should be associated with higher levels of cash since these types of firms are more likely to suffer from

30

cash shortfalls. We run specifications similar to those in Table 3 separately for listed and unlisted firms and compare the coefficients on cash flow volatility between these two types of firms. We present results in Table 10. We document a notable difference in the coefficients for cash flow volatility for listed and unlisted firms. Consistent with the precautionary motive, we find that the coefficient on cash flow volatility is positive and highly statistically significant for listed firms. The coefficient on cash flow volatility is mostly insignificant for unlisted firms suggesting that the cost of debt may be high enough to dominate the effect of the volatility. Thus, the incremental borrowing costs may outweigh the benefits of cash holdings for precautionary reasons. We then take a closer look at the relation between cash holdings and growth opportunities. Previous research suggests that high growth firms would benefit from accumulating cash for precautionary reasons (Opler et al., 1999, and Bates et al., 2009). Cash holdings help these firms avoid missing out on valuable growth opportunities due to difficulties with external financing. Results using sales growth suggest that growth opportunities are positively related to cash holdings in both listed and unlisted firms in specifications 1 and 2, and the relation is insignificant in specifications 3 and 4. Sales growth, however, is likely to capture not only growth opportunities but also cash flow from assets in place. To alleviate this problem, we also consider industry market-to-book.23 Results are presented in specifications 5-8. We find that growth opportunities are positively related to cash holdings only for listed firms.

23

In untabulated analysis, we also consider global PE ratios (computed following Bekaert et al., 2007). We obtain the annual global PE ratios for each industry from DataStream and manually match the DataStream industry codes to 3-digit SIC codes available in Amadeus. Results are similar to those obtained when industry market-to-book is used.

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Overall, our results provide some evidence that private firms are less sensitive to the precautionary benefits of holding cash than public firms, consistent with the cost of cash hypothesis.

VIII. Summary and Conclusions In this paper, we emphasize that financing frictions lead not only to considerable precautionary benefits of holding cash but also to substantial costs of holding cash. We hypothesize that private firms, with higher opportunity costs of holding cash, will hold significantly less cash than public firms. Using a comprehensive sample of private and public firms, we provide evidence consistent with this cost of cash hypothesis. The results are shown to be robust to endogeneity concerns. We further show that private firms tend to use cash flow to redeem existing debt rather than to accumulate cash. Consistent with our hypothesis, we show that in countries with less developed debt markets and higher costs of borrowing, private firms retain significantly less cash than their counterparts from countries with more developed loan markets. We also show that time-series variation in the country-level cost of loans is negatively related to cash holdings of unlisted firms, and this relation is stronger when the country‟s debt market is less developed. Overall, stronger creditor-rights and debt-market development is associated with a convergence in the cash holdings by unlisted and listed firms. By analyzing private firms, we are able to show that the relation between financing frictions and cash holdings is non-linear. The prior literature demonstrates that financially constrained firms accumulate more cash than unconstrained firms. Our results, however, suggest

32

that in extreme cases, when external financing is significantly costly, firms accumulate relatively little cash due to the high cost of holding cash.

Author Contributions All authors contributed equally

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Stiglitz, Joseph E. and Andrew Weiss, 1981, “Credit Rationing in Markets with Imperfect Information,” American Economic Review 71 (3), 393-410 Subrahmanyam, A. and Titman, S., 1999, “The Going-Public Decision and the Development of Financial Markets,” The Journal of Finance, 54, 1045–1082

37

Table 1. Descriptive Statistics The table presents balance sheet data and additional control variables for the firms in the matched sample in 2008. Details of the matching procedure are provided in the text. The data are from the 2011 version of Amadeus. The sample includes non-financial firms from Western European countries. Accounting items are scaled by total assets. ***, **, * denote statistical significance at the 1%, 5% and 10% levels, respectively. Panel A. Balance Sheet Data Listed

Assets Fixed assets (of which) Intangible Tangible Current assets Cash and cash equiv. Total assets ($ mill.) Liabilities Shareholders‟ funds Non-current liabilities (of which) Long-term debt Current liabilities (of which) Loans

Unlisted

Mean

N

Mean

N

Diff. in means

0.50

2,897

0.44

2,886

0.05***

0.19 0.23 0.50 0.14 1,745

2,893 2,897 2,897 2,734 2,898

0.06 0.25 0.56 0.09 1,267

2,782 2,810 2,887 2,428 2,898

0.14*** -0.02** -0.05*** 0.05*** 478**

0.47 0.21

2,833 2,873

0.38 0.24

2,685 2,846

0.09*** -0.04***

0.14 0.34

2,766 2,898

0.16 0.43

2,641 2,888

-0.02*** -0.09***

0.08

2,789

0.13

2,756

-0.06***

Panel B. Additional Control Variables Listed

Leverage Sales growth Cash flow/total assets Std. deviation of cash flows Inv. in tangible assets Inv. in intangible assets Working capital (net of cash) Firm age

Unlisted

Mean

N

Mean

N

Diff. in means

0.312 0.261 0.070 0.078 0.092 0.025 0.038 30.905

2,832 2,624 2,540 1,911 2,595 2,482 2,747 2,838

0.381 0.157 0.079 0.067 0.072 0.003 0.058 24.711

2,808 2,234 2,126 1,598 2,166 2,496 2,387 2,885

-0.068*** 0.104*** -0.008** 0.011*** 0.020*** 0.023*** -0.020* 6.194***

38

Table 2. Descriptive Statistics: Cash Holdings across Countries The table presents mean cash holdings of listed and unlisted firms across countries for the matched sample over the 1996-2011 period. Details of the matching procedure are provided in the text. We present pooled sample means and sample size for each country. The data are from the 2011 version of Amadeus. The sample includes non-financial firms from Western European countries. ***, **, * denote statistical significance at the 1%, 5% and 10% levels, respectively.

Austria Belgium Denmark Finland France Germany Greece Ireland Italy Netherlands Norway Portugal Spain Sweden Switzerland United Kingdom

Listed Mean 0.09 0.14 0.11 0.11 0.18 0.15 0.07 0.21 0.10 0.12 0.14 0.04 0.09 0.17 0.12 0.14

N

Cash/Total Assets Unlisted Mean

N

151 785 344 732 3,095 3,178 1,807 233 1,486 966 907 212 869 945 249 7,302

0.08 0.13 0.08 0.07 0.10 0.11 0.07 0.09 0.06 0.10 0.11 0.04 0.10 0.09 0.08 0.10

152 771 356 610 2,943 2,107 1,636 148 1,266 757 960 169 802 966 211 7,005

39

Diff. in means 0.01 0.01 0.03*** 0.04*** 0.08*** 0.04*** 0.00 0.13*** 0.04*** 0.02*** 0.03*** 0.00 0.00 0.08*** 0.04*** 0.04***

Table 3. Differences in Cash Holdings across Listed and Unlisted Firms: Regression Analysis The table presents results of OLS regressions for the matched and unmatched samples (Panel A), and for listed and unlisted firms where listed firms are partitioned into firms that have paid a dividend in that year and firms that have not paid a dividend in that year (Panel B). Alternative matching in specification (3) is described in the text. The dependent variable is cash and cash equivalents divided by total assets. Listed is an indicator variable for the firm being listed on a major stock exchange. Leverage is measured as total debt divided by the sum of total debt and shareholder funds. Total debt is the sum of long term debt plus short term loans. Sales growth is computed as the one-year change in sales divided by beginning-of-period sales. Standard deviation of cash flows is the standard deviation of current and the past four cash flows to assets. If two or more cash flows to assets are missing, then the variable is set to missing. Investment in tangible assets is the one-year change in the value of tangible fixed assets divided by lagged assets. Investment in intangible assets is the one-year change in the value of intangible fixed assets divided by lagged assets. Net working capital is current assets minus current liabilities minus cash divided by lagged assets. Firm age is years since incorporation. The estimation procedures correct standard errors for clustering at the firm level. ***, **, * denote statistical significance at the 1%, 5% and 10% levels, respectively. Panel A. Regressions for Matched and Unmatched Samples.

Listed Leverage Log(total assets) Sales growth Cash flow/total assets Std. deviation of cash flows Investments in tangible assets Investments in intangible assets Working capital (net of cash) Firm age Country FE Industry FE Year FE Country FE x Year FE N Adjusted R2

Matched (1) 0.033*** -0.132*** -0.009*** 0.016*** 0.261*** 0.147***

Matched (2) 0.0327*** -0.1340*** -0.0093*** 0.0160*** 0.2608*** 0.1473***

Alternative Matching (3) 0.0333*** -0.1313*** -0.0104*** 0.0115*** 0.2781*** 0.1549***

Unmatched (4) 0.032*** -0.128*** -0.011*** 0.011*** 0.286*** 0.055***

-0.161*** 0.053* -0.138*** -0.000*** Yes Yes Yes No 16,377 0.218

-0.1616*** 0.0458 -0.1389*** -0.0003*** No Yes No Yes 16,363 0.225

-0.1845*** 0.0537* -0.1300*** -0.0002*** Yes Yes Yes No 13,664 0.225

-0.181*** 0.009 -0.165*** -0.000*** Yes Yes Yes No 379,065 0.218

40

Table 3. (continued)

Panel B. Regressions for Matched Sample Partitioned on whether Listed Firms Pay Dividends

Listed Firm-level controls Country FE Year FE Industry FE N Adjusted R2

Listed Firms that pay dividends (1) 0.036*** Yes Yes Yes Yes 12,991 0.2112

Listed firms that do not pay dividends (2) 0.031*** Yes Yes Yes Yes 10,408 0.1927

41

Diff.

0.005

Table 4. Differences in Cash Holdings across Listed and Unlisted Firms: Regression Analysis for Firms that Changed Listing Status The table presents results of OLS regressions for the subsample of firms that changed listing status. The dependent variable is cash and cash equivalents divided by total assets. Listed is an indicator variable for the firm being listed on a major stock exchange. Leverage is measured as total debt divided by the sum of total debt and shareholder funds. Total debt is the sum of long term debt plus short term loans. Sales growth is computed as the one-year change in sales divided by beginning-of-period sales. Standard deviation of cash flows is the standard deviation of current and the past four cash flows to assets. If two or more cash flows to assets are missing, then the variable is set to missing. Investment in tangible assets is the one-year change in the value of tangible fixed assets divided by lagged assets. Investment in intangible assets is the oneyear change in the value of intangible fixed assets divided by lagged assets. Net working capital is current assets minus current liabilities minus cash divided by lagged assets. Firm age is years since incorporation. The estimation procedures correct standard errors for clustering at the firm level. ***, **, * denote statistical significance at the 1%, 5% and 10% levels, respectively.

Listedt Leveraget Leveraget-1 Log(total assets)t Log(total assets)t-1 Sales growtht Sales growtht-1 Cash flow/total assetst Cash flow/total assetst-1 Standard deviation of cash flowst Standard deviation of cash flowst-1 Investments in tangible assetst Investments in tangible assetst-1 Investments in intangible assetst Investments in intangible assetst-1 Working capital (net of cash)t Working capital (net of cash)t-1 Firm aget Firm aget-1 Firm FE Year FE N Adjusted R2

(1) 0.014*** -0.064***

(2) 0.013*** -0.063***

-0.006

0.005

0.301***

0.302***

-0.060

*

0.0002

0.0005

-0.0660***

-0.0662***

-0.0049

-0.0038

0.1205***

0.1226***

0.0633

0.0593

-0.0041

-0.0052

0.0099

0.0135

0.064

***

-0.061

0.135*** -0.175

(4) 0.0114*

-0.004

0.006

0.064

(3) 0.0114*

***

0.132**

***

-0.174

-0.003***

***

-0.0410***

-0.0410

***

Yes Yes

-0.0012 Yes No

-0.0045 Yes Yes

3,718 0.776

2,881 0.773

2,881 0.774

-0.015

Yes No 3,718 0.696

42

Table 5. Differences in Cash Holdings across Listed and Unlisted Firms: Additional Estimation Approaches The table presents results of a two-stage least squares estimation for the matched sample (Panel A), and the treatment effects model for the unmatched sample (Panel B). The dependent variable is cash and cash equivalents divided by total assets. Listed is an indicator variable for the firm being listed on a major stock exchange. Leverage is measured as total debt divided by the sum of total debt and shareholder funds. Total debt is the sum of long-term debt plus short term loans. Sales growth is computed as the one-year change in sales divided by beginning-of-period sales. Standard deviation of cash flows is the standard deviation of current and the past four cash flows to assets. If two or more cash flows to assets are missing, then the variable is set to missing. Investment in tangible assets is the one-year change in the value of tangible fixed assets divided by lagged assets. Investment in intangible assets is the one-year change in the value of intangible fixed assets divided by lagged assets. Net working capital is current assets minus current liabilities minus cash divided by lagged assets. Firm age is years since incorporation. The number of private equity funds in a country is used as an instrument in Panel A. The estimation procedures correct standard errors for clustering at the firm level. ***, **, * denote statistical significance at the 1%, 5% and 10% levels, respectively. Panel A. Two-Stage Least Squares. Instrument: No. of Country-Level Private Equity Funds

Listed Leverage Log(total assets) Sales growth Cash flow/total assets Standard deviation of cash flows Investments in tangible assets Investments in intangible assets Working capital (net of cash) Firm age Log(fund count) Country FE Year FE Industry FE N Adjusted R2 F-Statistics

Dependent variable Listed Cash/total assets (1) (2) 0.190** *** -0.160 -0.103*** 0.027*** -0.014*** ** 0.024 0.014*** -0.216*** 0.301*** 0.253*** 0.102*** *** 0.205 -0.204*** 1.881*** -0.246 -0.009 -0.138*** *** 0.001 -0.001*** -0.037*** Yes Yes Yes Yes Yes Yes 14,547 14,547 0.1264 0.0657 16.851

43

Panel B. Treatment Effects Model

Listed Leverage Log(total assets) Sales growth Cash flow/total assets Standard deviation of cash flows Investments in tangible assets Investments in intangible assets Working capital (net of cash) Firm age Country FE Year FE Industry FE Selectivity variable N

Dependent variable Listed Cash/total assets (1) (2) 0.016*** -0.361*** -0.129*** *** 0.346 -0.011*** 0.028*** 0.011*** -0.329*** 0.286*** *** 1.093 0.056*** -0.182*** 0.009 -0.165*** 0.004*** -0.000*** Yes Yes Yes 0.0076*** 379,065 379,065

44

Table 6. Cash Flow, and Changes in Cash Holdings and Debt The table presents results of the 3SLS regressions. Panel A presents results for the matched sample. Panel B presents results for the sub-sample of firms that changed listing status. Dependent variables are indicated in column headings. Change in Debt is change in total debt scaled by beginning of period assets. Total debt is the sum of long-term debt plus short term loans. Change in cash is change in cash scaled by beginning of period assets. Listed is an indicator variable for the firm being listed on a major stock exchange. Sales growth is computed as the one-year change in sales divided by beginning-of-period sales. We include country, industry and year dummies. The estimation procedures correct standard errors for clustering at the firm level. ***, **, * denote statistical significance at the 1%, 5% and 10% levels, respectively. Panel A. 3SLS Regressions for Matched Sample

Listed Cash flow/total assets Listed x cash flow/total assets Sales growth Log(total assets) Change in cash Change in debt Lagged debt Lagged cash Country FE Year FE Industry FE N

Change in Debt (1) -0.012*** -0.122*** 0.123*** 0.041*** 0.005*** 0.159

Change in Cash (2) 0.001 0.129*** 0.055*** 0.006* -0.002*** 0.161**

-0.013*** -0.060*** Yes Yes Yes 24,771

Yes Yes Yes 24,771

45

Table 6. (continued) Panel B. 3SLS Regressions for the Sub-sample of Firms that Changed Listing Status

Listed Cash Flow/total Assets Listed x cash flow/total assets Sales growth Log(total assets) Change in cash Change in debt Lagged debt Lagged cash Year FE Firm FE N

Change in Debt (1) -0.035*** -0.067* 0.146*** 0.056*** 0.089*** -0.164***

Change in Cash (2) -0.007** 0.149*** 0.082*** 0.019*** 0.014*** -0.063**

-0.157*** -0.350*** Yes Yes 5,583

Yes Yes 5,583

46

Table 7. Differences in Cash Holdings of Listed and Unlisted Firms and Private Debt Market Development The table presents results of OLS regressions for the matched sample. We run regressions on two subsamples of firms: firms in countries with above median debt market development and firms in countries with below median debt market development. In Panel A, we measure debt market development with Private Credit/GDP, which we get from Djankov et al. (2007). In Panel B, we use an index on creditor rights from La Porta, et al. (1998). The dependent variable is cash and cash equivalents divided by total assets. Listed is an indicator variable for the firm being listed on a major exchange. Each regression includes the same set of control variables that is included in Table 3. Each regression includes country, year and industry dummies. The estimation procedures correct standard errors for clustering at the firm level. We test for the null hypothesis that the coefficients are equal across the two models using seemingly unrelated estimation. ***, **, * denote statistical significance at the 1%, 5% and 10% levels, respectively. Panel A. Sample is Sorted on Countries‟ Private Credit/GDP

Listed Firm-level controls Country Year FE Industry FE N Adjusted R2

Private Credit/GDP Above median Below median (1) (2) ** 0.013 0.052*** Yes Yes Yes Yes Yes Yes Yes Yes 8,439 7,938 0.2338 0.2143

Diff. -0.039***

Panel B. Sample is Sorted on Countries‟ Creditor Rights

Listed Firm-level controls Country FE Year FE Industry FE N Adjusted R2

Creditor Rights Above median Below median (1) (2) ** 0.013 0.045*** Yes Yes Yes Yes Yes Yes Yes Yes 7,245 9,132 0.2384 0.2148

47

Diff. -0.032***

Table 8. Cash Holdings of Listed and Unlisted Firms, and Legal and Financial Development The table presents results of OLS regressions for listed and unlisted firms for the matched sample. In Panel A we control for debt market development with Private Credit/GDP from Djankov, McLeish, and Shleifer (2007), and in Panel B with Creditor rights index from La Porta, et al. (1998). The dependent variable is cash and cash equivalents divided by total assets. The anti-self-dealing index is from Djankov et al. (2008). The stock market development index is constructed from World Bank data following Dermiguc-Kunt and Levine (1996). GDP is from the World Bank. Each regression includes the same set of control variables that is included in Table 3. The estimation procedures correct standard errors for clustering at the firm level. ***, **, * denote statistical significance at the 1%, 5% and 10% levels, respectively. Panel A. Private Credit/GDP as Proxy for Debt Market Development Unlisted Private credit/GDP Anti-self-dealing Stock market development Log(GDP) Firm-level controls Year FE Industry FE N R2

(1) 0.022***

Yes Yes Yes 7,022 0.1622

Listed (2) 0.025*** 0.001 -0.006 -0.005 Yes Yes Yes 7,022 0.1624

(3) -0.014***

(4) 0.001 -0.030*** -0.024** 0.049*** Yes Yes Yes 9,355 0.2682

Yes Yes Yes 9,355 0.2619

Panel B. Creditor Rights as Proxy for Debt Market Development Unlisted Creditor rights index Anti-self-dealing Stock market development Log(GDP) Firm-level controls Year FE Industry FE N R2

(1) 0.028***

Yes Yes Yes 7,022 0.1641

Listed

(2) 0.031*** -0.003 -0.006 -0.012 Yes Yes Yes 7,022 0.1642

48

(3) -0.012**

Yes Yes Yes 9,355 0.2682

(4) 0.001 -0.031*** -0.026** 0.049*** Yes Yes Yes 9,355 0.2682

Table 9. Cash Holdings of Listed and Unlisted, and Cost of Loans

This table presents results of OLS regressions for listed and unlisted firms for the matched sample. The dependent variable is cash and cash equivalents divided by total assets. Cost of loans is country-level (time-varying) interest rates from the European Central Bank calculated using all outstanding loans with a maturity of 1 year or less made by monetary financial institutions in a given country to non-financial corporations. In Panel A, the main variable of interest is cost of loans, while in Panel B the main variable of interest is the interaction of Cost of Loans with an indicator variable for creditor rights being above median. Each regression includes the same set of firm-level control variables that is included in Table 3. The estimation procedures correct standard errors for clustering at the firm level. ***, **, * denote statistical significance at the 1%, 5% and 10% levels, respectively. Panel A. The Effect of Cost of Loans on Cash Holdings

Cost of loans Firm-level controls Year FE Firm FE N R2

Unlisted (1) -0.008*** Yes Yes Yes 6,465 0.7897

Listed (2) 0.002 Yes Yes Yes 9,051 0.7620

Panel B. The Effect of the Interaction of Cost of Loans and Creditor Rights

Cost of loans Cost x (D=1 if high cr. rights) Firm-level controls Year FE Firm FE N R2

Unlisted (1) -0.014*** 0.005** Yes Yes Yes 6,465 0.7899

Listed (2) 0.004 -0.001 Yes Yes Yes 9,051 0.7620

49

Table 10. Precautionary Benefits and Cash Holdings The table presents results of OLS regressions for the matched sample. The dependent variable is cash and cash equivalents divided by total assets. Listed is an indicator variable for the firm being listed on a major stock exchange. Leverage is measured as total debt divided by the sum of total debt and shareholder funds. Total debt is the sum of long term debt plus short term loans. Sales growth is computed as the one-year change in sales divided by beginning-of-period sales. Standard deviation of cash flows is the standard deviation of current and the past four cash flows to assets. If two or more cash flows to assets are missing, then the variable is set to missing. Investment in tangible assets is the one-year change in the value of tangible fixed assets divided by lagged assets. Investment in intangible assets is the one-year change in the value of intangible fixed assets divided by lagged assets. Net working capital is current assets minus current liabilities minus cash divided by lagged assets. Firm age is years since incorporation. The estimation procedures correct standard errors for clustering at the firm level. ***, **, * denote statistical significance at the 1%, 5% and 10% levels, respectively.

50

Table 10. (continued) Listed (1)

Unlisted (2)

Listed

Unlisted

(3)

(4)

Leverage

-0.192***

-0.097***

-0.0833***

Log(total assets) Sales growth Cash flow/total assets Std. dev. of cash flows

-0.005***

-0.012***

-0.0009

Inv. in tangible assets Inv. in intangible assets Working capital Firm age Market-to-book ind. Firm FE Country FE Year FE Industry FE N Adjusted R2

0.010

*

0.291*** 0.290*** -0.158

***

0.072** -0.130

***

-0.000

***

0.016

***

0.207*** -0.008 -0.154

***

-0.104* -0.150*** 0.000

Listed

Unlisted

(5)

(6)

Listed (7)

-0.0356***

-0.187***

-0.105***

-0.1163***

0.0173***

-0.006***

-0.012***

0.0273**

*

0.0021

0.0063

0.004

0.016

0.3417***

0.2132***

0.291***

0.221***

0.3205***

0.1319***

0.0561*

0.263***

0.023

0.2472***

*

***

-0.0328

*

0.0275 -0.2142***

-0.0328 -0.0262

0.056

-0.1634***

-0.0049

0.0031

-0.121

*

-0.145***

-0.138

(8) -0.0287 0.0170 -0.0025 0.2381*** 0.1033

-0.0083

-0.0415

-0.044

-0.0018

-0.0807

-0.148***

-0.2154***

-0.1776***

***

0.000

0.016***

-0.009

0.000

***

-0.0042

Unlisted

**

-0.0016 0.0137**

0.0075** -0.0093

No

No

Yes

Yes

No

No

Yes

Yes

Yes

Yes

No

No

Yes

Yes

No

No

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

No

No

Yes

Yes

No

No

9,355

7,022

9,355

7,022

3,807

2,808

3,807

2,808

0.2835

0.1666

0.837

0.2927

0.1733

0.838

0.853

0.819

51

0.16

Cash holdings / Total assets

0.14 0.12 0.1 0.08

Unlisted

0.06

Listed

0.04 0.02 0 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Year

Figure 1. Cash Holdings for Listed and Unlisted Firms across Time The figure contains average cash holdings to total assets ratio for listed and unlisted firms from 1997 through 2010.

52

Appendix A A Simple Model of Cash Holdings, Debt Market Development and Cost of Debt We propose a simple stylized model to illustrate the tradeoff firms can face between cash retention and debt pay-down when borrowing is costly and there is the possibility of being constrained in future periods. The model applies most directly to the case of firms that are largely dependent on external debt financing. The model has three relevant dates

A firm decides on its cash retention and

debt pay down decisions on date 0, prior to learning about its investment opportunities. On date 1, the firm learns of its opportunities and makes its investment decision using its retained cash and additional borrowing to finance investment. Date 2 is the terminal date on which project cash flows are realized and debt is repaid. The firm begins with cash holdings of

on date 0. The

cash holdings are the result of cash inflow from past investments and the firm‟s prior cash holdings. On this date, the firm has pre-existing debt with a face value of

The debt is due on

date 2. All market participants are taken to be risk-neutral. There are two sources of friction in the model that constrain borrowing and affect the firm‟s choice of cash that is retained from date 0 to date 1. The first friction is the cost of borrowing . This is the borrowing cost associated with asset verification and monitoring of loans and is incremental to the time-value-of-money, which we normalize to zero for simplicity. The borrowing costs are taken to be proportional to the loan amount i.e., the cost of borrowing is if

is the loan amount. Since our analysis is centered on the cash retention/pay-down

decision on date 0, we simplify exposition by assuming that the cost held between date 0 and 1. Further, it is assumed that

applies only to debt that is

, so that the firm is unable to fully

payoff its outstanding debt on date 2 in the absence of new and profitable investment. As a result, if existing debt cannot be junior to new debt issued on date 1, the firm will be unable to raise additional capital unless lenders are persuaded that the firm has a sufficiently profitable investment opportunity. The second friction we consider is the possibility that, even when the firm has a positive NPV investment, it may be unable to raise additional capital because potential lenders/banks are unable to verify the existence of a profitable opportunity. The likelihood that a firm with good investments is unable to raise external financing is denoted by 53

and provides a measure of

banking and debt market development (or, more specifically, its absence) in the environment in which the firm operates.24 We assume that the cost of borrowing and of a firm specific „opaqueness‟ measure denoted by

is an increasing function of

which is intended to capture the cost

of firm information acquisition and monitoring by lenders Hence, the cost of borrowing can be expressed as a function (

). The advantage of separate parameters,

and , to represent

debt-market development and firm specific transparency is that it allows us to develop predictions on the interaction between firm specific transparency and debt-market development on cash policies e.g., the possibly differential effect of debt market development on public versus private firms. To develop our predictions, we assume a plausible functional form for borrowing costs: (

)

*

+ where

and where

borrowing that all firms face. For now, we will assume that we will explicitly take note of the minimum cost

denotes some minimum cost of and that (

)

in the context of firms with rather low

monitoring and information costs e.g., firms that are listed and well established. The advantage of a Cobb-Douglas-type functional form cross-partial

is that it implies that

and the

The positive cross-partial captures the notion that, when the debt

market/banking sector is less developed (i.e., α is larger), there is greater divergence in the overall borrowing costs between more and less transparent firms. In other words, public firms, with their greater transparency and greater monitoring by, say, institutional investors are impacted less by weaker creditor rights and debt market development than private firms. An assumption of this nature is necessary to obtain specific predictions – that we subject to empirical tests – on the interaction between firm-specific transparency and debt market development. In making its cash retention decision on date 0, the firm takes account of future capital needs to fund investment on date 1. It is assumed that there are no productive investments on date

and, further, that the firm does not distribute cash in the form of dividends/repurchases

since it faces the risk of being cash constrained on date 1. Hence, on date 0, the firm limits its use of cash

to paying down its outstanding debt, while retaining the rest. The amount of cash used

24

For simplicity, we assume that is reflective of financial market development and unaffected by firm characteristics Assuming to be a function of leads to more involved expressions but does not affect our predictions, as long as the effect of on is sufficiently small.

54

to pay down debt is denoted by , with

representing the cash retained. The benefit of

paying down debt is the reduction in the borrowing costs by

between dates 0 and 1.

We next turn to the investment opportunity available to the firm on date 1. We assume that there is a probability probability

that the firm has a profitable investment opportunity, while with

it has no positive NPV investment. While the firm knows whether it has such

an opportunity, this is not necessarily apparent to outside providers of capital. As indicated above, a source of friction is that even if the firm has a profitable investment opportunity, there is still a probability

that it will be unable to raise additional financing; while with probability

outside investors will be able to verify the opportunity and provide sufficient funds to invest optimally. The profitable opportunity is taken to be such that an investment of produces a cash payoff of ( ) on date 2, where opportunity, 25 the optimal investment level is

, where

and

With this investment

( )

We analyze the firm‟s cash retention decision on date 0 by examining the payoff implications in the three possible states on date 1: (1) With probability ( project and can invest optimally (2) with probability

) the firm has a

, the firm has a project but is unable to

raise additional financing: this is the state in which cash retention proves valuable; and (3) the firm has no project and cannot raise additional financing. In state (1), the firm is able to raise sufficient funds to invest optimally. Hence, its payoff on date 2, assumed to be positive, can be expressed as: ( )

( ( )

(

)(

)

(A1)

)(

)

(A1‟)

In expression (A1) above, the debt pay-down on date 0 is (

)(

is the retained cash, while

) is the portion of the date 0 debt that will be repaid on date 2. To invest

firm raises external financing of

the

As noted earlier, for expositional ease we have

assumed that there is no incremental borrowing cost between dates 1 and 2. As expression (A1‟) makes evident, the advantage of paying down

is that it results in a saving of

in terms of

payoff on date 2. In state (2), lenders are unable to verify that the firm has a profitable project. Given our assumption that

, lenders will not make new loans without being able to verify that the

25

Note that the optimal investment level is unaffected by the borrowing cost on account of our simplifying assumption that the borrowing costs apply only to existing debt between dates 0 and 1.

55

firm has access to a profitable project (since they would not expect to be repaid otherwise). The firm is, therefore, able to invest only its available resources

in the investment

opportunity. We will assume that the firm‟s payoff on date 2 is positive after investing in the project, since it would otherwise have no incentive to invest. The firm‟s payoff can be expressed as: (

)

(

)(

)

(A2)

Finally, in state (3), the firm has no investment opportunity and its payoff on date 2 is 0 since its existing cash holdings are below its date 2 obligations. We can now determine the firm‟s optimal choice of

on date 0, assuming that the firm

(or manager) seeks to maximize its expected value as of date 2. The date 0 objective function can be expressed as: (

), ( )

, (

)

(

(

)

)(

)-

(A3)

In expression (A3) above, the two terms represent the probabilities

[expected payoffs] in state

1 and state 2 respectively. The payoffs in the two states are as indicated in equations (A1‟) and (A2). To determine the optimal value of

that maximizes the objective function (A3), we

obtain the first-order-condition: (

), -

,

(

)

(

)-

(A4)

which gives, 0

(

1

)

(

)

(A5)

or, .

/

(

)

Substituting for the function ( (

(

0 1

)

)

we can express (A5‟) as:

)

(A6)

56

(A5‟)

Equation (A6) represents the trade-off between cash retention and debt pay-down: A unit of cash holdings imposes an expected marginal cost of (

value of

and yields an expected positive

)

To obtain the effect of the firm opaqueness and difficulty of monitoring ( ) on the firm‟s cash retention choice we take the derivative of equation (A6) with respect to (

(keeping

fixed):

). /

(A7) Since

(

)

it follows that . /

This leads to our first prediction:

Prediction 1: An increase in firm opaqueness and cost of monitoring, will be associated with a greater pay-down of debt

and lower cash retention

.

We next consider the effect of debt market and banking development on the cash retention decision by firms. In particular, we are interested in the differential effect that debt market and banking sector development can have on firms that are more or less transparent. We do this by showing that

0

1

under our assumptions. The positive sign implies that

the lack of debt market development will have a greater marginal impact on the reduction in the cash holdings of more opaque firms. Or, conversely, stronger debt market development will have a greater marginal effect on the cash holdings of less transparent firms. We proceed by taking the derivative of equation (A7) with respect to (

)

(

).

(

/

)

(A8) Since the project is assumed to be such that:

and

it follows that .

/

We can state:

Prediction 2: An increase in debt market and banking sector development will result in a relatively greater increase in the cash holdings by more opaque and harder-to-monitor firms, relative to cash holdings by more transparent and easier-to-monitor firms.

57

We turn next to the case of firms that have low monitoring costs and their borrowing costs are such that equation (

(i.e., where borrowing costs are such that

) in this case becomes: ( (

)

)

Taking derivatives with respect to ( Since,

) It follows that

we obtain:

). /

(A9)

it follows from equation (9) that for firms with low monitoring and borrowing

costs, we can have . /

In other words, for these firms an improvement in the debt market

and banking-sector development (i.e., lower

) can lead to less cash retention. This is the

opposite of the effect for firms that are costly to monitor and have relatively large borrowing costs. The reason is that as the likelihood of the firm being able to finance its project increases (i.e.,

increases), it tends to hold less cash since its need for precautionary cash-holding

decreases; while its borrowing costs are relatively insensitive to debt market development. We can state:

Prediction 3: For firms that are more transparent and have relatively low monitoring and borrowing costs, an increase in debt market and banking sector development can lead to a decrease in the firms’ cash holdings.

58

Appendix B. Cash holdings and Hedging Needs The table presents results of OLS regressions for the matched sample. The dependent variable is cash and cash equivalents divided by total assets. Listed is an indicator variable for the firm being listed on a major stock exchange. Hedging needs is the correlation between investment opportunities and the firm‟s cash flow, where investment opportunities is the median three-yearahead sales growth rate in the firm‟s three-digit SIC industry. Low hedging needs is a dummy variable that takes the value of one when the correlation between investment opportunities and the firm‟s cash flow is above 0.20, and zero otherwise. High hedging needs is a dummy variable that takes the value of one when the correlation between investment opportunities and the firm‟s cash flow is below -0.20, and zero otherwise. Each regression includes the same set of firm-level control variables that is included in Table 3. The estimation procedures correct standard errors for clustering at the firm level. ***, **, * denote statistical significance at the 1%, 5% and 10% levels, respectively.

Listed Hedging needs Low hedging needs High hedging needs Firm-level controls Country FE Year FE Industry FE N Adjusted R2

(1) 0.032*** 0.003

(2) 0.032*** 0.001 -0.001 Yes Yes Yes Yes 14,910 0.2175

Yes Yes Yes Yes 14,910 0.2116

59