Measuring the Costs of High Leverage

To measure the costs of high leverage, we build on the framework of Campello (2006; Model 1 of Table 2.2), who models the effect of high leverage on relative-to-rival

their panel sample studies (e.g., Djankov et al., 2007; Brockman and Unlu, 2009; Cho et al., 2014). In unreported analysis, we find that our main findings are qualitatively unchanged if we instead use a cross- sectional creditor rights index.

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sales growth, adapting it to a global setting. Specifically, we employ the following specification: 𝑆𝐴𝐿𝐸𝑆_𝐺𝑖,𝑡= 𝑎 + 𝜆1𝐻𝐿𝐸𝑉_{𝑖,𝑡−2} + 𝜆2𝑆𝐼𝑍𝐸𝑖,𝑡+ ∑ 2 𝑘=1 𝜆3𝑘𝑃𝑅𝑂𝐹𝐼𝑇𝑖,𝑡−𝑘 + ∑ 2 𝑘=1 𝜆4𝑘𝐼𝑁𝑉𝐸𝑆𝑇𝑀𝐸𝑁𝑇𝑖,𝑡−𝑘+ ∑ 2 𝑘=1 𝜆5𝑘𝑆𝐸𝐿𝐿𝐸𝑋𝑃𝑖,𝑡−𝑘+ + 𝜆6𝐺𝐷𝑃𝐺𝑐,𝑡 + 𝜆7𝐼𝑁𝐹𝐿𝐴𝑇𝐼𝑂𝑁𝑐,𝑡+ 𝜀𝑖,𝑡, (1)

where i, t, and c index firms, years, and countries, respectively. SALES_G captures customer- and competitor-driven product market performance. To proxy for high leverage, we employ the dummy variable HLEV (high leverage), where a firm is classified as in high leverage in a given year if the firm’s long-term debt ratio is in the top three deciles of the country in which the firm is headquartered. A more negative coefficient on HLEV (𝜆1) thus indicates that customers and competitors have more adverse responses to high leverage.27 Note that we use a firm’s long-term debt ratio as a basis for capturing high leverage to

27 _{One may wonder why a firm would choose to have high leverage if it is associated with costly consequences.}

With this question in mind, we follow prior research (e.g., Opler and Titman, 1994) and assume that otherwise-identical firms choose different leverage ratios. This assumption is justified by Maksimovic and Zechner (1991), who argue that firms in the same industry are indifferent between a high-leverage/high-risk strategy and a low-leverage/low-risk strategy, or by Opler and Titman (1994), who argue that otherwise- identical firms may simultaneously choose a high-leverage/tax advantage strategy and a low-leverage/cheap assets acquisition strategy. This assumption is also supported empirically by our propensity score matching analysis, in which we match each high-leverage firm with a low-leverage firm with similar characteristics. We find that HLEV continues to load significantly negatively on SALES_G, with an impact of similar magnitude.

mitigate reverse causality between SALES_G and HLEV, as long-term debt is less subject to adjustment following firm forecasts of future performance than short-term debt (Campello, 2006). To isolate the impact of high leverage on sales growth, we follow Campello (2006) and control for SIZE (firm size), measured as the natural logarithm of total assets; PROFIT (profitability), measured as operating earnings plus depreciation divided by total assets; INVESTMENT (investment), measured as capital expenditures over total assets; and SELLEXP (sell expenses), measured as the ratio of advertising and selling expenses to total sales. To account for country-level macroeconomic influences, we further control for GDPG (GDP growth) and INFLATION (inflation). More detailed variable definitions are provided in Appendix D.

Following common practice (Opler and Titman, 1994; Campello, 2003, 2006), we adopt the relative measurement method when calculating the firm-level variables in Equation (1). In particular, a firm’s HLEV is defined relative to its country peers,28 and the other firm-level variables are subtracted from their country-industry-year means. This method helps strengthen the exogeneity of the firm-level regression variables because a firm has little control over the performance or strategies of its peers. To control for the influence of outliers, we winsorize PROFIT, INVESTMENT, and SELLEXP at the 1% and

28_{Defining a firm’s HLEV relative to its country peers mitigates the concern of a high correlation between}

creditor rights and leverage (two components of the interaction term discussed in next subsection). As shown in Cho et al. (2014), creditor rights negatively affect a firm’s use of debt. However, our model suggests that around 30% of firm-year observations are highly levered, which means that country-level variables such as creditor rights are not likely to significantly influence HLEV. Consistent with this view, Table 2 find that

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99% levels. In addition, all reported t-statistics are based on standard errors that are heteroskedasticity-consistent and allow for clustering at the firm level.

2.3.4. Empirical Design

To test our main hypothesis, which posits that strong creditor rights increase the costs of high leverage by intensifying the adverse responses of customers and competitors, we run the following model:

𝑆𝐴𝐿𝐸𝑆_𝐺𝑖,𝑡= 𝑏 + 𝛽1𝐶𝑅𝐼𝐺𝐻𝑇𝑆𝑐,𝑡−2×𝐻𝐿𝐸𝑉𝑖,𝑡−2+ 𝛽2𝐶𝑅𝐼𝐺𝐻𝑇𝑆𝑐,𝑡−2+ 𝛽3𝐻𝐿𝐸𝑉𝑖,𝑡−2 + 𝛽4𝑆𝐼𝑍𝐸𝑖,𝑡+ ∑ 2 𝑘=1 𝛽5𝑘𝑃𝑅𝑂𝐹𝐼𝑇𝑖,𝑡−𝑘+ ∑ 2 𝑘=1 𝛽6𝑘𝐼𝑁𝑉𝐸𝑆𝑇𝑀𝐸𝑁𝑇𝑖,𝑡−𝑘 + ∑ 2 𝑘=1 𝛽7𝑘𝑆𝐸𝐿𝐿𝐸𝑋𝑃𝑖,𝑡−𝑘+ 𝛽8𝐺𝐷𝑃𝐺𝑐,𝑡+ 𝛽9𝐼𝑁𝐹𝐿𝐴𝑇𝐼𝑂𝑁𝑐,𝑡+ 𝜀𝑐,𝑖,𝑡, (2)

where 𝛽1captures the effect of creditor rights on the costs of high leverage. Our hypothesis that the dark side of creditor rights intensifies the costs of high leverage suggests a negative coefficient on CRIGHTS×HLEV (i.e., 𝛽1<0).

Table 2.1 presents descriptive statistics for the key variables (before country- industry-year adjustment) in Equation (2). Similar to prior studies (e.g., Cho et al., 2014), we find that the U.S. and Japan account for the largest percentage of firm-year observations (39% and 12%, respectively). In robustness tests, we show that these countries do not drive our results. Further, around 30% of firm-years in our sample are highly leveraged, consistent with our definition of HLEV. The results also reveal a large degree of variation in average sales growth, firm operating conditions, the macroeconomic indicators, as well as the strength of creditor protection. Table 2.2 reports pairwise correlation coefficients

between the key variables. We find a relatively low correlation between the control variables, reducing concerns that multicollinearity is affecting our results.29

Since one cannot directly observe the interaction between CRIGHTS and HLEV for highly leveraged firms, we provide suggestive evidence on this effect by running Equation (1) separately for five groups of countries as classified by their creditor rights scores and plotting the costs of high leverage (𝜆1) for each group in an unreported figure. Consistent with our prediction, the figure shows that the costs of high leverage tend to increase the strength of creditor rights. In particular, the coefficient on HLEV is approximately zero for countries with a creditor rights score of zero, suggesting that weak creditor rights do not impact high leverage costs, while a medium degree of creditor protection (CRIGHTS=1 or 2) leads to a 0.5% decline in country-industry-adjusted sales growth and strong creditor protection (CRIGHTS=3 or 4) leads to a 1.3% decline in sales growth on average.

2.4.RESULTS

In this section, we first provide evidence on the dark-side effects of creditor rights on the costs of high leverage, and show that this finding passes a battery of endogeneity tests and robustness checks. We then run subsample tests in which we explore which

29 _{An exception is the high correlation between the control variables and their own one-year lags. For example,}

the correlation between SELLEXPt-1 and SELLEXPt-2 is 0.84. However, we find that the multicollinearity problem is not severe. First, we examine the variance inflation factors (VIF) in the regression of Equation (2). We find a mean value of 2.17, which is substantially lower than the threshold value (10) of multicollinearity problems. Second, we replace the control variables with their six principal components and re-run the main regressions. The results are qualitatively similar.

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groups of firms are likely to suffer more intense costs of high leverage under strong creditor rights. Next, we examine whether strong creditor protection leads to more adverse responses from customers and competitors as suggested by our theoretical arguments, as well as other stakeholder groups, in particular, employees and suppliers. Finally, we show that the main finding does not support the alternative explanation.