Long-term persistence in corporate capital structure: Evidence from India

Accepted Manuscript Title: Long-term persistence in corporate capital structure: Evidence from India Author: Prateek Sharma PII: DOI: Reference:

S0275-5319(17)30009-0 http://dx.doi.org/doi:10.1016/j.ribaf.2017.07.094 RIBAF 784

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Research in International Business and Finance

Received date: Accepted date:

2-1-2017 3-7-2017

Please cite this article as: Sharma, Prateek, Long-term persistence in corporate capital structure: Evidence from India.Research in International Business and Finance http://dx.doi.org/10.1016/j.ribaf.2017.07.094 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Title: Long-term persistence in corporate capital structure: Evidence from India

Details for the author Name: Dr. Prateek Sharma Designation: Assistant professor Department: Finance and Accounting Affiliation: Indian Institute of Management Sirmaur Contact: +91-9794305142 Email: [email protected] Address: Indian Institute of Management Sirmaur, Rampur Ghat - Engineering College Rd, Kunja Matralion, Paonta Sahib, Himachal Pradesh 173025

ABSTRACT This study examines the stability of corporate capital structure in a sample of listed Indian firms for the period 1988 to 2015. In general, the firms do not maintain a stable level of leverage over long durations. The firm specific temporal variations in leverage are large and significant. We find that capital structure models that incorporate time varying firm effects perform better in explaining the variation in leverage than those that employ time invariant firm effects. The cross-sectional distribution of leverage also exhibits considerable variations over time. The quartile decompositions of leverage cross-sections reveal that migrations across leverage quartiles are pervasive. Only the firms with low leverage ratios ratio exhibit some persistence in their leverage ratios. High leverage ratios are not rare but are invariably transient.

Keywords: Capital structure, panel regression, book leverage, persistence, firm fixed effects 1

JEL Classification: G3; G32

1. Introduction The capital structure choice is an important strategic decision as it affects the cost of capital, profitability, valuation, and solvency of a firm. A large number of theoretical and empirical studies attempt to identify the factors influencing the capital structure choice (Bradley et al. 1984; Myers 1984; Titman and Wessels 1988; Harris and Raviv 1991; Rajan and Zingales 1995; Fan et al. 2012; Graham et al. 2015). A stylized fact in the capital structure literature is that the corporate leverages are largely persistent over time, and therefore, most of the extant studies focus on the determinants of cross-sectional variations in the distribution of firm leverage. Lemmon et al. (2008) show that the leverage ratios of nonfinancial U.S. firms were remarkably stable over the period 1965-2003. The firms with relatively high (low) leverage continued to have relatively high (low) leverage for over two decades. Within-firm variations in the leverage were generally temporary as the firms reverted back to firm-specific target leverage ratios. The persistence of leverage would imply that the capital structure choice is largely based on time-invariant factors instead of time-varying factors. The authors found that the adjusted 𝑅 2 values from regressions of leverage on firm fixed effects were almost three times those from regressions on the more traditional time-varying factors such as size, profitability and market-to-book ratios. In view of the empirical findings of Lemmon et al. (2008), Frank and Goyal (2008) note that a satisfactory theory of capital structure must explain why firms tend to maintain stable leverage levels over long periods of time. Parson and Titman (2008) and Graham and Leary (2011) also found significant firm-fixed effects, and recommend that the capital structure research should focus on time invariant firmspecific factors such as managerial preferences, governance structure, geography, and corporate culture. Rauh and Sufi (2012) claim that empirical evidence suggests that capital structure research should only focus on the determinants of cross-sectional variations in 2

leverage. They find that firm-specific time invariant factors such as the type of the product and the type of the assets used in production are the most significant determinants of the cross-variation in leverage. The consensus view that the capital structure are stable over long durations of time was challenged by DeAngelo and Roll (2015) who point out that capital structure stability is an exception rather than a norm. With a dataset of U.S. firms, they found large temporal variations in leverage ratios, and note that the firm fixed effects can differ considerably over time. They reject stability of firm-mean leverage, in the sense that the firms do not tend to revert to a time-invariant target level of leverage. They also reject the stability of the crosssectional distribution of leverage in the sense that the firms with relatively high (low) leverage do not maintain high (low) leverage over time. They found that, over long durations, cross-sectional migrations across different leverage quartiles are quite common. This study is an exploratory analysis of the distribution of corporate leverage for Indian firms. Specifically, we attempt to answer questions like: Do firms maintain stable capital structures over long periods? Are there any stable leverage regimes where the firm leverages vary in a narrow band, and what is the typical duration of such regimes? Are the firms having low (high) leverage more likely to persist with their capital structure as compared to the firms having high (low) leverage? Does having a relatively high (low) leverage in a given year lead to relatively high (low) leverage in the future? We model the cross-sectional and temporal variations in distribution of firm leverage using panel regressions with time invariant effects such as firm fixed effects and time-varying effects such as year effects, decade effects, and firm-decade interaction effects. To test the stability of the cross-section, we contrast the cross-sectional distribution of leverage in a given year to future cross-sections of leverage.

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This study contributes to the existing literature in a number of ways. First, to the best of our knowledge, this is the first study that examines the persistence of corporate capital structure for the Indian firms. Since the early nineties till present, many market-oriented reforms have been introduced in the Indian corporate debt markets. For instance, the Securities and Exchange Board of India (SEBI) allowed the Indian stock exchanges to provide trading, clearing and settlement services for corporate bonds. SEBI also introduced comprehensive disclosure norms for all companies issuing debt securities, and made it mandatory for mutual funds to report inter-scheme transfers of corporate bonds. In addition, during this period, the Indian financial markets have grown at a considerable rate. From 2005 to 2010 the Indian corporate bond market grew more than six times, from $3.81 billion to $24.99 billion. The National Stock Exchange of India (NSE) has become of the most liquid stock exchanges in the world. In 2015, NSE had more electronic order book equity trades than NYSE, NASDAQ and EURONEXT, according to the data published by the World Federation of Exchanges. The introduction of a series of capital market reforms and the deepening and widening of the various financial markets have provided the Indian firms more flexibility in deciding their capital structure. Therefore, India presents an interesting case for a longitudinal analysis of the distribution of corporate capital structure. Second, the existing works on the capital structure of Indian firms generally concentrate on the identification of the determinants of cross-sectional variations in leverage, and they do not explicitly model the long run temporal variations in firm leverage (Bhaduri 2002; Guha-Khasnobis and Bhaduri 2002; Chakraborty 2010; Sharma and Paul 2015; Bandyopadhyay and Barua 2016; Jadiyappa et al. 2016). A key finding of this study is that there are significant temporal variations in firm-mean leverage and the cross-sectional distribution of leverage, which implies that a credible model of capital structure should also incorporate relevant timevarying determinants such as market-timing opportunities, investments, profitability and

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margins, among others. As the capital structure of Indian firms are not persistent over time, the models that ignore time-varying determinants of leverage may lead to misleading results. Third, as compared to most of the existing capital structure studies in India, we use a more comprehensive dataset comprising all the Indian firms that were listed at any time on the NSE. By including all the firms that were listed at any point of time, we ensure that our dataset is free from survivorship bias. More importantly, the long sample period of our dataset allows us to examine long term temporal variations in the distribution of firm leverage using decade effects and firm-decade interaction effects, which were hitherto unexplored for the Indian firms. Overall, our sample comprises 2864 firms for a period of 28 years (1988– 2015). In comparison, Bandyopadhyay and Barua (2016) used a sample of 1594 firms for a period of 14 years (1998–2011); Bhaduri (2002) used a sample of 363 firms for a period of 7 years (1989–1995); Chakraborty (2010) used a sample of 1169 firms for a period of 14 years (1995–2008); Guha-Khasnobis and Bhaduri (2002) used a sample of 697 firms for a period of 9 years (1990–1998); and Sharma and Paul (2015) used a sample of 279 firms for a period of 9 years (2003–2011). The remainder of this article is organized as follows. Section 2 describes the dataset for this study. Section 3 reports descriptive statistics about the distribution of firm leverage, and provides a preliminary analysis of the stability of leverage ratios. Section 4 examines the temporal variations in firm leverage and the cross-sectional distribution of leverage. Section 5 provides some concluding remarks.

2. Data We identify all the firms that were listed on the NSE at any point of time. Following the norm in the capital structure literature, we exclude financial firms and utility firms as their capital structure choice are influenced by regulations (Kolodny and Suhler 1985; Bancel and

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Mittoo 2004; Jiraporn and Gleason 2007; Lipson and Mortal 2009). After excluding the financial and utility firms, we are left with 2864 listed firms that constitute our sample. The dataset for this analysis comprises firm-year data for the sample firms, for the period 1988– 2015. All data were sourced from the Thompson Reuters Datastream.

3. Leverage Stability: Descriptive statistics The sample firms are divided in five subsamples: firms listed for 1 to 4 years, 5 to 9 years, 10 to 14 years, 15 to 19 years and 20 years or more. In addition, we define a constant composition sample that includes all firms that were continuously listed for the twenty year period—1996 to 2015. We define firm leverage in three ways: book leverage is the ratio of book debt to total assets; market leverage is the ratio of book debt to the sum of book debt and the market value of common stocks; and net-debt ratio is the ratio of book debt net of cash and total assets. For each firm, we calculate the time-series range and the time-series standard deviation for all three leverage variables. The time-series range is simply the difference between the highest and the lowest leverage ratios of a firm. The time-series standard deviation is the standard deviation of the annual leverage ratios for that firm. These two measures show the temporal variability in firm leverage. The median of these measures are reported in columns 2 to 7 of Table 1, and columns 8 to 10 report the cross-sectional correlation between these two measures of leverage variability. Both range and standard deviation are highly correlated for all leverage measures and across all subsamples. This is expected as the range measures the cumulative effect of year-by-year variation in firm leverage. In other words, firms exhibiting large variations in leverage are also likely to have a wider range of leverage, and vice versa. We find that there is significant time-series variation in leverage as seen from both the range and standard deviation, for the firms listed for at least 20 years. The median range for

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book leverage is 0.404; for market leverage it is 0.607 and for net-debt ratio, it is 0.526. The corresponding values for the median standard deviation are 0.115, 0.177, and 0.147, which implies ±2 standard deviation bands of 0.484, 0.708, and 0.588. Therefore, large variations in leverage are the norm. For the firms listed for smaller periods, the median range and median standard deviations are expectedly smaller, but are nevertheless significant. Columns 11 to 13 report the median leverage ratios. We find no distinct relationship between the median leverage ratio and the number of years for which the firms have been listed. For instance, the firms listed for 1 to 4 years have lower median market leverage than the firms listed for 20 years or more. However, the firms listed for 5 to 9 years have higher median market leverage than the firms listed for 15 to 19 years. Similarly, the median book debt and net debt ratios are fairly homogenous across the various subsamples, and therefore, imply no relationship with the number of years for which the firms have been listed.

{Insert Table 1 here} A comparison of the median range and median standard deviation measures indicates that market leverage tends to vary more than book leverage. Figure 1 shows the changes in book and market leverage for three randomly selected sample firms. For all three firms, there is significant variation in leverage over time, and the market leverage is more volatile than the book leverage. These observations hold for a majority of our sample firms. Since book leverage tends to be more stable than the market leverage, for the remainder of this article we examine the persistence of book leverage which provides a lower bound on the stability of leverage. Thus, hereafter, firm leverage refers to book leverage or the 𝐷𝑒𝑏𝑡/𝑇𝑜𝑡𝑎𝑙 𝐴𝑠𝑠𝑒𝑡𝑠 ratio.

{Insert Figure 1 here} Following DeAngelo and Roll (2015), we define firms with no leverage as conservatively levered firms and the firms with a leverage greater than 0.4 as highly levered firms. Figures 2 7

and 3 illustrate how the proportion of conservatively levered and highly levered firms has changed over time. A comparison of leverage trends of the firms in the overall sample (Figure 2) and those belonging to the constant composition sample (Figure 3) reveals some interesting insights. Note that the full sample of 2864 firms is dominated by the newly listed firms, as it includes only 323 firms that have been listed for at least 15 years. In comparison, the constant composition sample comprises 191 firms that have been continuously listed for at least 20 years. In the full sample (Figure 2), the leverage levels have have generally declined. In 1991, 52% of the sample firms had high leverage, whereas in 2015 only 31% firms were highly levered. From 1991 to 2004, the incidence of high leverage dropped from around 50% to 30% and has remained stable at that value since then. The proportion of conservatively levered firms has increased marginally from 1991 to 2015, although it is always below 5% throughout this period. This indicates that very few listed Indian firms operate without any debt in their capital structure. For the constant composition sample (Figure 3), the firms were generally characterized by moderate leverage 0 < 𝐷𝑒𝑏𝑡/ 𝑇𝑜𝑡𝑎𝑙 𝐴𝑠𝑠𝑒𝑡𝑠 < 0.4 in 1991. However, the incidence of both high leverage and conservatively leverage has steadily increased over time. In 2015, around 16% of the firms had no debt, whereas 19% of the firms had high leverage. Admittedly, the definitions of conservative, moderate and high leverage are arbitrary and hence the conclusions drawn from figures 2 and 3 are at best indicative. Nonetheless, these results imply that cross-sectional distribution of firm leverage does not remain stable over long periods.

4. Empirical results The cross-sectional stability of leverage is the extent to which the current year leverage cross-section is related to the future leverage cross-sections. Using ordinary least squares regression, we test whether the relative high (or low) positions of firms in the current

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leverage cross-section can predict the relative positions in the future cross-sections. As a measure of cross-sectional stability, we calculate average 𝑅 2 for k-year difference in crosssections. For example, to generate average 𝑅 2 for one-year difference, we first identify all the firms having leverage data for 1988 and 1989. Next, we regress the firm leverage values in 1989 on the corresponding leverage values in 1988. Similar regressions are performed for all one year apart cross-sections, that is, for the years 1989 & 1990, 1990 & 1991, and so on. The average 𝑅 2 for these regressions estimates the degree to which the leverage cross-section in a given year predicts the leverage cross-section in the subsequent year. This procedure is repeated for k-year apart leverage cross-sections, where k varies from 1 to 15. Figure 4 report the average 𝑅 2 for k-year difference for the full sample. High values for average 𝑅 2 indicate that the relatively high (or low) positions in a given leverage cross-section are preserved in future leverage cross-sections. Conversely, low values for average 𝑅 2 indicate the current leverage cross-section has no significant relationship with the future leverage cross-sections, that is, there is no evidence for cross-sectional stability in firm leverage.

{Insert Figures 4 & 5 here} The average 𝑅 2 is around 90% for cross-sections having a one-year difference. However, the average 𝑅 2 declines rapidly as the years between leverage cross-sections increases. The average 𝑅 2 for 5-year difference is around 60% and for a 15-year difference it is around 30%. Similar observations were made for the constant composition sample. The results suggest that whereas the leverage cross-section may appear to be stable over short durations, they vary significantly over long periods. Small but positive average 𝑅 2 values for leverage crosssections separated by 15 years may suggest that, at least for some firms, the leverage may be stable over long durations. To identify the incidences of long term persistence in firm leverage, we define stable leverage regimes as periods where firm leverage varies within a narrow range. Table 2 9

identifies stable leverage regimes using three ranges of widths 0.05, 0.1 and 0.2. The narrowest range of 0.05 implies a highly stable leverage regime, and the wider ranges of 0.1 and 0.2 provide slightly weaker definitions for a stable leverage regime. The minimum periods wherein the firm leverage varies within the specified range are fixed at the values of 5, 10, 15 and 20 years. For example, the first value in column 2 implies that, in the full sample, Debt/Total Assets ratio of 25.29% firms varies within a band of 0.05 for at least 5 years. The last column shows the median number of years for the longest stable regime.

{Insert Table 2 here} The results show that a significant proportion of firms display short term persistence, however, very few firms are able to maintain stability of leverage over long time intervals. For example, in the full sample period, 25.29% firms maintain a stable Debt/Total Assets ratio within a range of 0.05 for at least 5 years, whereas only 4.33% firms maintain it for 10 years or more and a miniscule 0.14% firms maintain it for 20 years or more. Similar results were obtained for the various subsets of the full sample: firms listed for at least 5 years, firms listed for at least 10 years, firms listed for at least 15 years and the constant composition sample. As expected, with a weaker definition of the stable leverage regime, the proportion of firms falling under stable leverage regime increases. However, even under the weakest definition of stable leverage regime (Debt/Total Assets range ≤ 0.200); the firms rarely maintain stable leverage over long durations. For example, in the constant composition sample, 91.10% firms maintain a stable Debt/Total Assets ratio within a range of 0.2 for at least 5 years, whereas only 4.71% firms maintain it for 20 years or more. A comparison of various subsets of the sample reveals that mature firms are more likely to have stable leverage than recently listed firms. Regardless of the definition of the stable leverage regime, as we increase the years for which the firms have been listed, the proportion of firms falling under the stable leverage regime also increases. The firms in the constant composition sample maintain the most stable 10

leverage among all subsamples. In the full sample, 25.29%, 50.53%, and 71.73% firms maintain a stable Debt/Total Assets ratio within a range of 0.05, 0.1, and 0.2, respectively, for 5 years or more. The corresponding proportions for the constant composition sample are 39.27%, 72.77%, and 91.10%. The median numbers of years of the longest stable regime also confirm that whereas short term persistence in leverage is not unusual, firms rarely maintain stable leverage over extended periods of 10 years or more. Depending on the definition of the stable leverage regime (Debt/Total Assets range ≤ 0.050, 0.100 or 0.200), the median number of years of the longest stable regime varies from around 3 to 8 years. Table 3 examines the level of firm leverage during such stable leverage regimes. We identify four subsets of firms whose Debt/Total Assets ratio varies within a specified range (0.05 or 0.1) for a specific interval (at least 10 years or at least 20 years). For each such firm, we calculate the median Debt/Total Assets ratio during the stable leverage regime, and report the cross-sectional frequency distribution of this measure in Table 3. The evidence suggests that stable leverage regimes are generally observed at low levels of leverage. Out of all the firms for which the Debt/Total Assets ratio varied within a range of 0.05 for 20 years, only 7.67% firms had a Debt/Total Assets ratio of 0.6 or higher. Irrespective of the definition of a stable leverage regime, the median Debt/Total Assets ratio during stable regime was 0.4 or less for around 70% of the firms, and it was 0.6 or less for around 90% of the firms.

{Insert Table 3 here} Table 4 shows the proportion of firms with a specified combination of maximum and minimum leverage ratios. Panel A uses a sample of firms that have been listed for at least 15 years. In Panel A, the row total column shows that 23.5% firms have had zero leverage at some point (row 1), and 87.6% firms have had a leverage lower than 0.3 at some point (row 1, 2 and 3). Therefore, the incidence of conservative leverage is quite common among the Indian firms. Conversely, aggressive leverage is rare with only 17.30% firms ever having a 11

leverage of 0.7 or more, and only 3.7% firms consistently maintained a leverage of 0.4 or higher. We obtain qualitatively similar results for the firms listed for at least 20 years, and they are reported in Panel B of Table 4.

{Insert Table 4 here} We model the cross-sectional and temporal variations in firm leverage using a variety of fixed effect panel specifications as recommended by DeAngelo and Roll (2015). Specifically, we attempt measure the firm fixed effects and the time effects, as well as the firm-time interaction effects. Long term temporal variations in leverage are explained using decade and firm-decade interaction effects. Since approximately half of the sample firms have been listed for less than nine years, and therefore, do not provide any meaningful firm-decade interactions; we also include year effects and firm-year interaction effects in our model specifications. In addition, we use a set of ancillary control variables that are conventionally used a determinants of leverage in the capital structure literature. These include asset growth rate, earnings before interest tax depreciation and amortization (EBITDA), financing deficit, capital expenditures, change in debt, logarithm of sales, and asset tangibility ratio. Asset growth rate is the difference between total assets in the current year and the total assets in previous year, all divided by the total assets in the previous year. Financing deficit is the sum of net debt and net equity issues in the year. Current year’s capital expenditures, EBITDA, change in debt and financing deficit are standardized by dividing them with by the total assets in the previous year. Asset tangibility ratio is the ratio of fixed assets to total assets. We estimate five linear panel models. Model 1 includes the firm-decade effects. Model 2 includes the firm effects. Model 3 includes the year-effects. Model 4 includes the firm effects and the year effects. Model 5 includes the firm-decade and the year effects. The model specifications are defined as follows: 𝑀𝑜𝑑𝑒𝑙 1: 𝐿𝑒𝑣𝑖𝑡 = 𝛼1 + 𝑋𝑖𝑡 𝛽1 + 𝑓1𝑖 ∗ 𝑑1𝑡 + 𝜀1,𝑖𝑡

(1) 12

𝑀𝑜𝑑𝑒𝑙 2: 𝐿𝑒𝑣𝑖,𝑡 = 𝛼2 + 𝑋𝑖,𝑡 𝛽2 + 𝑓2𝑖 + 𝜀2,𝑖𝑡

(2)

𝑀𝑜𝑑𝑒𝑙 3: 𝐿𝑒𝑣𝑖,𝑡 = 𝛼3 + 𝑋𝑖,𝑡 𝛽3 + 𝑦3𝑡 + 𝜀3,𝑖𝑡

(3)

𝑀𝑜𝑑𝑒𝑙 4: 𝐿𝑒𝑣𝑖,𝑡 = 𝛼4 + 𝑋𝑖,𝑡 𝛽4 + 𝑓4𝑖 + 𝑦4𝑡 + 𝜀4,𝑖𝑡

(4)

𝑀𝑜𝑑𝑒𝑙 5: 𝐿𝑒𝑣𝑖,𝑡 = 𝛼5 + 𝑋𝑖,𝑡 𝛽5 + 𝑓5𝑖 ∗ 𝑑5𝑡 + 𝑦𝑡 + 𝜀5,𝑖𝑡

(5)

where, 𝐿𝑒𝑣𝑖𝑡 is the Debt/Total Assets ratio of firm i in the year t. 𝑋𝑖,𝑡 is a 1 × 𝐾 vector of control variables and 𝛽1 to 𝛽5 are 𝐾 × 1 vectors of parameters. The 𝛼, 𝑓, 𝑑, 𝑦, and 𝑓 ∗ 𝑑 terms are parameters that represent intercepts, firm effects, decade effects, year effects, and firm decade interaction effects, respectively, in the various model specifications. All five models were estimated for the full sample, for the firms listed for at least 5 years, for the firms listed for at least 10 years, for the firms listed for at least 15 years and for the constant composition sample. Table 5 reports the adjusted 𝑅 2 values for these regression models. Panel A shows the results for basic regression models without including the ancillary control variables (𝑋𝑖,𝑡 ), and Panel B shows the results for regressions with ancillary control variables. Note that the models (2) and (4) can be expressed as restricted versions of models (1) and (5), respectively. We carry out F-tests to test the equivalence of these nested model specifications. Models (1) and (5) allow the firm effects to vary across decades, whereas models (2) and (4) assume that firm effects are time-invariant. The F-tests reject the null hypothesis of model equivalence for all comparisons between models (1) and (2), as well as all comparisons between models (4) and (5), at the 1% confidence level. Therefore, there is evidence that the firm effects can vary significantly over decades. This observations also reflected by the adjusted 𝑅 2 values. For all ten comparisons shown in Panels A and B, the adjusted 𝑅 2 of model (1) is always higher than that of model (2), and the adjusted 𝑅 2 of model (5) is always higher than that of model (4). This indicates that the variations in leverage can be better explained by the models that allow firm fixed effects to vary over the 13

decades than the models that employ time-invariant firm fixed effects. The adjusted 𝑅 2 values for model (2) and (4) indicate that a model with both firm fixed effects and year fixed effects performs only marginally better than a model with only firm fixed effects. The estimates of the year effects can be misleading due to the changing composition of the sample in different yeas. In addition, the adjusted 𝑅 2 of the models with firm fixed effects can be biased upwards by including large number of firms with only few years of data. As the constant composition sample obviates both these problems, the comparisons based on the constant composition are the most useful. The results are consistent with the observations made earlier. In Panel A (B), the F-statistic of 7.40 (7.16) strongly rejects the equivalence of models (1) and (2). Similarly, the F-statistic of 6.82 (6.67) strongly rejects the equivalence of models (4) and (5). Therefore, models with time-varying firm effects perform better than those with time-invariant firm effects. The highest adjusted 𝑅 2 is achieved by model (5), followed in order by models (1), (4), (2) and (3). Again, this shows that the model with firm-decade effects have the highest explanatory power for the variation in leverage.

{Insert Table 5 here} Next, we decompose the firm-decade effects using a two-way analysis of variance. Table 6 reports a two-way analysis of variance with a purely additive model that includes firm fixed effects and decade fixed effects, and an interaction inclusive model that includes firm and decade fixed effects as well as the firm-decade interaction effects. The percentage values reported in the table denote the proportion of the variance (measured as type III sum of squares) explained by a particular effect, relative to the total variance explained by the model. In the purely additive specifications, firm fixed effects dominate the decade fixed effects, and they explain 99.48% of the observed variance in the full sample and 99.65% of the observed variance in the constant composition sample. This is expected as variations in more than half of the firms in our sample have less than 10 years of data, and therefore, do not register any 14

decade effect. Despite this limitation, in the interaction inclusive models, the firm-decade interaction effects are significant, and they account for 11.72% (13.49%) of the explained variance in the full (constant composition) sample. In other words, the large explanatory power attributed to the firm fixed effects in purely additive specification is due to the omission of significant firm-decade effects. As we include the firm-decade effects in the interaction inclusive specification, the variance attributed to firm fixed effects declines from 99.48% to 87.82% for the full sample, and from 98.65% to 85.35% for the constant composition sample.

{Insert Table 6 here} To assess the stability of leverage cross-sections, we divide the firms, which were listed for 20-plus years, into leverage quartiles based on their Debt/Total Assets ratio for each year. We then track the migration of these firms between the different leverage quartiles over the sample period. In each year of our sample period (1988 to 2015), the firms were sorted in to four leverage quartiles: lowest leverage, low-medium leverage, medium-high leverage and highest leverage. We treat 1988 as the base year (𝑡 = 0) and calculate the proportion of firms that continue to be in same quartile (as their original quartile in the base year) for the next 19 years, i.e., 𝑡 = 1 to 𝑡 = 19. Then, we treat the next year, 1989, as the base year and repeat this process. Overall, this process is repeated nine times in sample period 1988 to 2015, with the base year varying from 1988 to 1996. Table 7 reports the average proportions for these nine sample runs.

{Insert Table 7 here} Columns 2 to 6 of Table 7 show the fraction of firms that have always been in their original quartile. Columns 7 to 11 show the fraction of firms that are currently in their original quartile. We find that firms frequently migrate among the leverage quartiles. For instance, only 6.2% of the firms in the full sample remain in their initial group through all the 15

20 years. For the low-medium, medium-high, and highest leverage firms, this proportion is 0%, 0%, and 2.7%, respectively. Remarkably, among the lowest leverage firms, 21.6% firms always remain in the lowest leverage group, and therefore, these firms exhibit some persistence in their leverage ratios relative to those of other firms. The migration across the leverage quartiles is pervasive. On an average, during the 20-year period, 69.2% firms are placed in three different leverage quartiles, whereas 34.9% firms are placed in all four leverage quartiles. There is some evidence of short term persistence in cross-sectional leverage, for example, 41.8% (35.6%) firms continue remain in their original leverage quartile for 2 (3) years. However, over the longer periods of 10-plus years, there is marked instability in leverage cross-sections.

5. Conclusion This study examines long term persistence in the capital structure choice of Indian firms. We find the Indian firms show significant temporal variations in their debt ratios. Whereas, there is some evidence of stability in leverage over small periods (5 years or less), persistence in leverage is completely absent over longer durations (10 years of more). The firm specific temporal variations, modeled using firm-decade interaction effects, are large and significant. As a result, the models that incorporate time varying firm effects perform better in explaining the variation in leverage than those that employ time invariant firm effects. Similar results were obtained for the tests for cross-sectional stability, i.e. whether the relative high (or low) positions of firms in the current leverage cross-section are maintained in the future crosssections. We find that the leverage cross-section may appear to be stable over short durations; however, they vary significantly over long periods. The quartile decompositions of leverage cross-sections reveal that migration across leverage quartiles is pervasive, and almost 70% firms are placed in three different leverage quartiles over a period of 20 years. Only the firms

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with the lowest leverage ratios tend to maintain the same leverage ratios over long periods. High leverage ratios are not rare but are invariably transient. A vast majority of capital structure studies concentrate on identifying the determinants of the cross-section variability in leverage, relying on the implicit assumption that assumption that firm leverage are stable over time. The key recommendation of this study is that there are significant temporal variations in firm-mean leverage and the cross-sectional distribution of leverage, which implies that a credible model of capital structure should also incorporate relevant time-varying determinants such as market-timing opportunities, investments, profitability and margins, among others.

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Table 1: Descriptive Statistics Median Range Median Standard Deviation Correlation (range, std dev) Median Leverage Ratio Book Market Net Debt Book Market Net Debt Book Market Net Debt Book Market Net Debt Firms listed for: Leverage Leverage Ratio Leverage Leverage Ratio Leverage Leverage Ratio Leverage Leverage Ratio 20-plus years 0.404 0.607 0.526 0.115 0.177 0.147 0.921 0.953 0.949 0.314 0.368 0.272 15 to 19 years 0.337 0.477 0.484 0.101 0.151 0.147 0.954 0.975 0.978 0.247 0.267 0.200 10 to 14 years 0.280 0.460 0.358 0.090 0.154 0.116 0.967 0.966 0.982 0.311 0.423 0.268 5 to 9 years 0.254 0.375 0.340 0.090 0.139 0.117 0.962 0.967 1.000 0.269 0.433 0.243 1 to 4 years 0.088 0.051 0.159 0.068 0.081 0.118 0.979 0.986 0.991 0.290 0.214 0.207 Constant Comp. 0.402 0.605 0.519 0.114 0.177 0.145 0.919 0.957 0.944 0.312 0.361 0.269 Notes: Book Leverage is the ratio of book debt to total assets; Market Leverage is the ratio of book debt to the sum of book debt and the market value of common stocks; and Net Debt Ratio is the ratio of book debt net of cash and total assets. For each firm, we calculate the time-series range and the time-series standard deviation for all three leverage variables. The time-series range is the difference between the highest and the lowest leverage ratios of a firm. The time-series standard deviation is the standard deviation of the annual leverage ratios for that firm. We report median range (columns 2 to 4), median standard deviation (columns 5 to 7), the cross-sectional correlation between range and standard deviation (columns 8 to 10), and median leverage ratios (columns 11 to 13). # Firms is the number of firms in the particular subsample, specified in the first column.

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# Firms 200 123 1246 474 821 191

Table 2: Stable Leverage Regimes % of firms with Debt/TA in specified range for at least:

5 years

10 years

15 years

20 years

Median # of years of longest stable regime

Full Sample Debt/TA range ≤ 0.050 Debt/TA range ≤ 0.100 Debt/TA range ≤ 0.200

25.29% 50.53% 71.73%

4.33% 7.69% 25.34%

0.72% 1.44% 4.18%

0.14% 0.24% 1.25%

3 5 7

Firms listed for at least 5 years Debt/TA range ≤ 0.050 Debt/TA range ≤ 0.100 Debt/TA range ≤ 0.200

25.26% 50.66% 71.66%

4.31% 7.68% 25.31%

0.73% 1.47% 4.21%

0.15% 0.24% 1.27%

3 5 7

Firms listed for at least 10 years Debt/TA range ≤ 0.050 Debt/TA range ≤ 0.100 Debt/TA range ≤ 0.200

27.41% 53.47% 75.33%

4.84% 8.35% 27.09%

0.89% 1.85% 5.10%

0.19% 0.32% 1.53%

3 5 7

Firms listed for at least 15 years Debt/TA range ≤ 0.050 Debt/TA range ≤ 0.100 Debt/TA range ≤ 0.200

39.01% 69.97% 87.93%

8.36% 11.76% 37.15%

1.86% 4.64% 10.53%

0.31% 0.62% 3.10%

4 6 8

Constant Composition Sample Debt/TA range ≤ 0.050 Debt/TA range ≤ 0.100 Debt/TA range ≤ 0.200

39.27% 72.77% 91.10%

9.42% 12.57% 37.70%

1.57% 5.76% 11.52%

0.52% 1.05% 4.71%

4 6 8

Notes: This table identifies stable leverage regimes—periods where firm leverage varies within a narrow range. We use three ranges of widths 0.05, 0.1 and 0.2. The narrowest range of 0.05 implies a highly stable leverage regime, and the wider ranges of 0.1 and 0.2 provide slightly weaker definitions for a stable leverage regime. The minimum periods wherein the firm leverage varies within the specified range are fixed at the values of 5, 10, 15 and 20 years. For example, the first value in column 2 implies that, in the full sample, Debt/Total Assets ratio of 25.29% firms varies within a band of 0.05 for at least 5 years. The last column shows the median number of years for the longest stable regime.

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Table 3: Leverage levels during stable leverage regimes % of firms with median Debt/TA during stable regime that falls in interval: Debt/TA stays in the specified 0.200 or 0.200 to 0.400 to 0.600 or bandwidth less 0.400 0.600 higher ≤ 0.050 for 20 years 34.50% 36.42% 21.41% 7.67% ≤ 0.050 for 10 years 39.59% 30.96% 21.73% 7.72% ≤ 0.100 for 20 years ≤ 0.100 for 10 years

35.20% 38.41%

33.20% 32.11%

21.20% 20.93%

10.40% 8.54%

Notes: This table reports the level of firm leverage during stable leverage regimes—periods where firm leverage varies within a narrow range. We identify four subsets of firms whose Debt/Total Assets ratio varies within a specified range (0.05 or 0.1) for a specific interval (at least 10 years or at least 20 years). For each such firm, we calculate the median Debt/Total Assets ratio during the stable leverage regime, and report the cross-sectional frequency distribution of this measure. For example, the last value in row 1 implies that among the firms for which the Debt/Total Assets ratio varied within a range of 0.05 for 20 years, 7.67% firms had a Debt/Total Assets ratio of 0.6 or higher.

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Table 4: Proportion of firms with specified combination of maximum and minimum book leverage Maximum Debt/Total Assets Minimum Debt/Total Assets:

0

0.000 to 0.100

0.100 to 0.200

0.200 to 0.300

0.300 to 0.400

0.400 to 0.500

0.500 to 0.600

0.600 to 0.700

0.700 or higher

Row Total

Panel A: Firms listed for at least 15 years 0

0.00%

0.000 to 0.100

3.10%

2.80%

4.60%

2.50%

3.10%

2.80%

3.40%

1.20%

23.50%

0.60%

1.50%

2.20%

5.90%

10.20%

4.60%

3.70%

2.20%

31.00%

0.00%

0.30%

1.50%

4.30%

6.80%

2.20%

3.70%

18.90%

0.00%

0.00%

2.80%

4.30%

3.70%

3.40%

14.20%

0.00%

0.30%

1.90%

2.20%

4.30%

8.70%

0.00%

0.00%

1.20%

1.90%

3.10%

0.00%

0.00%

0.30%

0.30%

0.00%

0.30%

0.30%

0.00%

0.00%

16.40%

17.30%

100.00%

0.100 to 0.200 0.200 to 0.300 0.300 to 0.400 0.400 to 0.500 0.500 to 0.600 0.600 to 0.700 0.700 or higher Column Total

0.00%

3.70%

4.30%

7.10%

9.90%

20.70%

20.40%

Panel B: Firms listed for at least 20 years 0

0.00%

0.000 to 0.100

1.50%

2.00%

3.50%

2.00%

3.00%

3.00%

5.00%

1.50%

21.50%

0.00%

2.00%

1.50%

3.00%

10.50%

6.50%

4.00%

3.00%

30.50%

0.00%

0.00%

1.50%

6.00%

8.50%

2.00%

3.00%

21.00%

0.00%

0.00%

3.50%

4.50%

4.50%

5.00%

17.50%

0.00%

0.00%

1.50%

2.00%

4.50%

8.00%

0.00%

0.00%

0.50%

1.00%

1.50%

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

18.00%

100.00%

0.100 to 0.200 0.200 to 0.300 0.300 to 0.400 0.400 to 0.500 0.500 to 0.600 0.600 to 0.700 0.700 or higher Column Total

0.00%

1.50%

4.00%

5.00%

6.50%

23.00%

24.00%

18.00%

Notes: This table shows the proportion of firms with a specified combination of maximum and minimum Debt/Total Assets ratio. Panel A (B) uses a sample of firms that have been listed for at least 15 (20) years. The last column provides the sum of all the proportion in a particular row. For example, in Panel A, the last value (Row Total) in row 1 implies that 23.5% firms have had zero leverage at some point. In each panel, the last row provides the sum of all the proportion in a particular column. For example, in Panel A, the second value of the last row (Column Total) implies that for 3.70% firms, the maximum Debt/Total Assets ratio varied between 0.000 and 0.100 over the sample period.

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Table 5: Explanatory power of firm, year and firm-decade effects Adjusted-R2 for models with:

Panel A: Basic Regressions Full Sample Firms listed for at least 5 years Firms listed for at least 10 years Firms listed for at least 15 years Constant Composition Sample

F-Statistic to compare (1) versus (2)

Firm-decade effects (1)

5.47 5.62 5.70 7.32 7.40

0.766 0.771 0.772 0.763 0.771

0.652 0.657 0.658 0.610 0.623

0.006 0.006 0.006 0.008 0.008

0.791 0.798 0.799 0.790 0.801

0.694 0.700 0.701 0.653 0.671

0.170 0.172 0.176 0.215 0.247

Panel B: Regressions with ancillary control variables 5.05 Full Sample 5.29 Firms listed for at least 5 years 5.41 Firms listed for at least 10 years 7.09 Firms listed for at least 15 years 7.16 Constant Composition Sample

Firm effects (2)

Year effects (3)

Firm and year effects (4)

Firm-decade and year effects (5)

F-Statistic to compare (4) versus (5)

0.660 0.666 0.668 0.630 0.646

0.769 0.774 0.775 0.768 0.777

5.31 5.45 5.49 6.80 6.82

0.701 0.708 0.711 0.673 0.694

0.794 0.801 0.803 0.795 0.809

4.89 5.12 5.20 6.57 6.67

Notes: This table reports the adjusted 𝑅2 values for the regression models specified by equations (1) to (5). Panel A shows the results for basic regression models without including the ancillary control variables, and Panel B shows the results for regressions with ancillary control variables. Following ancillary control variables are included: asset growth rate, earnings before interest tax depreciation and amortization (EBITDA), financing deficit, capital expenditures, change in debt, logarithm of sales, and asset tangibility ratio. Asset growth rate is the difference between total assets in the current year and the total assets in previous year, all divided by the total assets in the previous year. Financing deficit is the sum of net debt and net equity issues in the year. Asset tangibility ratio is the ratio of fixed assets to total assets. For any year t, capital expenditures, EBITDA, change in debt and financing deficit are standardized by dividing them with by the total assets in the year t-1. F-tests are used to test the equivalence of models (1) and (2), and models (4) and (5). Columns 1 and 7 report the F-Statistic for these tests.

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Table 6: Relative Explanatory Power of Firm Fixed Effects, Decade Fixed Effects, and Firm-Decade Interaction Effects % of explained variation accounted for by: Firm-Decade Decade fixed Firm fixed effects interaction effects effects Full Sample 1. Interaction-inclusive model 11.72% 87.82% 0.46% 2. Purely additive model ---99.48% 0.52% Constant Composition Sample 1. Interaction-inclusive model 2. Purely additive model

13.49% ----

85.35% 98.65%

1.17% 1.35%

Notes: This reports the results for a two-way analysis of variance with a purely additive model that includes firm fixed effects and decade fixed effects, and an interaction inclusive model that includes firm and decade fixed effects as well as the firm-decade interaction effects. The percentage values reported in the table denote the proportion of the variance (measured as type III sum of squares) explained by a particular effect, relative to the total variance explained by the model.

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Table 7: Migration across different leverage quartiles over time Fraction of firms always in initial leverage Fraction of firms currently in initial leverage quartile quartile LowMedLowMedFull Lowest Highest Full Lowest Highest Med High Med High Years elapsed sample leverage leverage sample leverage leverage leverage leverage leverage leverage (1) (2) (5) (1) (2) (5) (3) (4) (3) (4) 0 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1 0.685 0.784 0.611 0.556 0.784 0.685 0.784 0.611 0.556 0.784 2 0.418 0.649 0.306 0.222 0.486 0.466 0.676 0.389 0.278 0.514 3 0.356 0.595 0.222 0.167 0.432 0.493 0.649 0.389 0.361 0.568 4 0.295 0.486 0.139 0.139 0.405 0.438 0.595 0.278 0.361 0.514 5 0.267 0.459 0.111 0.111 0.378 0.411 0.541 0.333 0.306 0.459 6 0.233 0.405 0.083 0.111 0.324 0.390 0.514 0.306 0.278 0.459 7 0.212 0.378 0.056 0.111 0.297 0.370 0.486 0.250 0.333 0.405 8 0.192 0.378 0.056 0.083 0.243 0.363 0.514 0.250 0.278 0.405 9 0.164 0.351 0.056 0.056 0.189 0.342 0.459 0.222 0.333 0.351 10 0.123 0.324 0.056 0.028 0.081 0.315 0.405 0.278 0.250 0.324 11 0.096 0.297 0.056 0.000 0.027 0.370 0.432 0.361 0.333 0.351 12 0.089 0.297 0.028 0.000 0.027 0.329 0.432 0.278 0.278 0.324 13 0.082 0.270 0.028 0.000 0.027 0.322 0.405 0.278 0.333 0.270 14 0.075 0.270 0.000 0.000 0.027 0.308 0.405 0.278 0.278 0.270 15 0.075 0.270 0.000 0.000 0.027 0.295 0.378 0.222 0.250 0.324 16 0.075 0.270 0.000 0.000 0.027 0.342 0.378 0.389 0.278 0.324 17 0.075 0.270 0.000 0.000 0.027 0.349 0.378 0.333 0.306 0.378 18 0.062 0.216 0.000 0.000 0.027 0.342 0.324 0.306 0.361 0.378 19 0.062 0.216 0.000 0.000 0.027 0.322 0.378 0.222 0.278 0.405 Fraction of firms with leverage in: All 4 quartiles 0.349 0.459 0.333 0.278 0.324 ----------At least 3 0.692 0.622 0.611 0.750 0.784 ----------quartiles At least 2 0.938 0.784 1.000 1.000 0.973 ----------quartiles Notes: We divide the firms, which were listed for 20-plus years, into leverage quartiles based on their Debt/Total Assets ratio for each year. In each year of our sample period (1988 to 2015), the firms were sorted in to four leverage quartiles: lowest leverage, low-medium leverage, medium-high leverage and highest leverage. We treat 1988 as the base year (𝑡 = 0) and calculate the proportion of firms that continue to be in same quartile (as their original quartile in the base year) for the next 19 years, i.e., 𝑡 = 1 to 𝑡 = 19. Then, we treat the next year, 1989, as the base year and repeat this process. Overall, this process is repeated nine times over the sample period 1988 to 2015, with the base year varying from 1988 to 1996. This table reports the average proportions for these nine sample runs. Columns 2 to 6 of Table 7 show the fraction of firms that have always been in their original quartile. Columns 7 to 11 show the fraction of firms that are currently in their original quartile.

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Figure 1: Book and Market leverages

Notes: This figure shows the changes in book and market leverage from for three randomly selected sample firms.

25

Figure 2: Variation in incidence of high and low leverage in the Full Sample

Notes: This figure illustrate how the proportion of conservatively levered (Debt/Total Assets ratio = 0) and highly levered firms (Debt/Total Assets ratio > 0.4) has changed over time in the full sample.

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Figure 3: Variation in incidence of high and low leverage in the Constant Composition Sample

Notes: This figure illustrate how the proportion of conservatively levered (Debt/Total Assets ratio = 0) and highly levered firms (Debt/Total Assets ratio > 0.4) has changed over time in the constant composition sample.

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Figure 4: Correspondence between current and future leverage cross sections in the Full Sample

Notes: This figure reports average 𝑅2 values that indicate whether the relative high (or low) positions of firms in the current leverage cross-section can predict the relative positions in the future cross-sections. T is the difference in years between leverage cross-sections, which takes a minimum value of 1 and maximum value of 15, and it is represented on the X-axis. For a particular value of T, we regress leverage cross-section of year m+T on leverage cross-section of year m, where m varies from 1988 to 2000. For example, to generate average 𝑅2 for one-year difference (T=1), we first identify all the firms having leverage data for 1988 and 1989. Next, we regress the firm leverage values in 1989 on the corresponding leverage values in 1988. Similar regressions are performed for all one year apart cross-sections, that is, for the years 1989 & 1990, 1990 & 1991, and so on. The figure reports the average 𝑅2 of these regressions. This procedure is repeated for all values of T, from 1 to 15, and each time the average 𝑅2 is plotted. Also, for each value of T, we obtain two standard deviation confidence intervals for the average 𝑅2 by resampling with replacement the individual 𝑅2 values, using 1000 resamples. The two standard deviation confidence intervals are represented by dashed lines.

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Figure 5: Correspondence between current and future leverage cross sections in the Constant composition sample

Notes: This figure reports average 𝑅 2 values that indicate whether the relative high (or low) positions of firms in the current leverage cross-section can predict the relative positions in the future cross-sections. T is the difference in years between leverage cross-sections, which takes a minimum value of 1 and maximum value of 15, and it is represented on the X-axis. For a particular value of T, we regress leverage cross-section of year m+T on leverage crosssection of year m, where m varies from 1988 to 2000. For example, to generate average 𝑅 2 for one-year difference (T=1), we first identify all the firms having leverage data for 1988 and 1989. Next, we regress the firm leverage values in 1989 on the corresponding leverage values in 1988. Similar regressions are performed for all one year apart cross-sections, that is, for the years 1989 & 1990, 1990 & 1991, and so on. The figure reports the average 𝑅 2 of these regressions. This procedure is repeated for all values of T, from 1 to 15, and each time the average 𝑅 2 is plotted. Also, for each value of T, we obtain two standard deviation confidence intervals for the average 𝑅 2 by resampling with replacement the individual 𝑅 2 values, using 1000 resamples. The two standard deviation confidence intervals are represented by dashed lines.

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