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International Tax and Public Finance

Erschienen in:

13.05.2020

Pouring oil on fire: interest deductibility and corporate debt

verfasst von: Pietro Dallari, Nicolas End, Fedor Miryugin, Alexander F. Tieman, Seyed Reza Yousefi

Erschienen in: International Tax and Public Finance | Ausgabe 6/2020

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Abstract

This paper investigates the role of tax incentives towards debt finance in the buildup of leverage in the nonfinancial corporate (NFC) sector, using a large firm-level dataset. We find that so-called debt bias is a significant driver of leverage, for both small and medium-sized enterprises and larger firms, with its effect accounting for up to a quarter of total leverage. These findings support the tradeoff theory of capital structure. We furthermore explore what firm traits affect these results, documenting how the strength of this effect differs with firm size, the availability of collateral, income and income volatility, cash flow, and capital intensity. We conclude that leveling the playing field between debt and equity finance through tax policy reform would decrease NFC leverage.

Vorheriger Artikel How effective is an incremental ACE in addressing the debt bias? Evidence from corporate tax returns

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De Mooij (2011) provides an overview of recent research and methods employed. Heckemeyer and de Mooij (2017), De Mooij and Keen (2016), and Luca and Tieman (2019) look at debt bias in the financial sector.

Defined as firms with fewer than 250 employees.

Our finding that the impact of debt bias on leverage is nonlinear with firm size supports this notion.

A theoretical model illustrating this tradeoff is developed in Appendix A.

See, for example, Forte et al. (2013) for Brazil, Requejo (2002) for Spain, as well as Rajan and Zingales (1995) for advanced economies, and Booth et al. (2001) for a group of developing economies.

Gal and Hijzen (2016) further reduce the sample to those countries that have a reasonable number of observations with non-missing employment and revenues, as well as information on product market regulation (PMR). Nonfinancial sectors are defined by industry codes 5–82 in NACE Rev. 2 or 10–74 in NACE Rev. 1.1.

The leverage can still be influenced by debt-shifting strategies, that is by optimization behaviors for firms active in various tax jurisdictions. Yet, this phenomenon is likely to be dominant mostly for large companies.

A robustness check using debt-to-equity instead yields similar results.

We explore the stylized facts in our entire sample of 14 million observations. Patterns are similar among the 3½ million observations in our main regressions.

Our approach differs from Rajan and Zingales (1998) in that we use continuous firm characteristics variables. Discretizing firm characteristics into binary dummies yields similar results (available from the authors upon request).

We include country-time (C*T), sector-time (S*T), and country-sector (C*S) fixed effects.

In addition, the unbalanced nature of the data subtracts from the reliability of the GMM estimates. Although we cover a long temporal dimension, each firm in our data has on average only a few observations, which makes instrumenting infeasible—but also confines concerns regarding serial correlation between error terms.

In order to see the variation of the leverage across firms with different sizes and in an average CIT environment, we multiply the long-term effect β₁ \(/\left( {1 - \alpha } \right)\) with the average CIT rate (0.28) and the difference between the 1st and 99th percentile of asset size (12.52)—i.e., \(\frac{{\beta_{1} }}{1 - \alpha } \cdot \bar{\tau } \cdot \Delta_{{1{\text{st}}}}^{{99{\text{th}}}} = \frac{0.907}{1 - 0.842} \times 0.28 \times 12.52 = 20.13\).

The long-term effect is β₁ \(/\left( {1 - \alpha } \right)\), which we multiply by the average CIT rate (0.28) in the sample and the median asset size (15.10). That is: \(\frac{{\beta_{1} }}{1 - \alpha } \cdot \bar{\tau } \cdot \bar{x} = \frac{0.907}{1 - 0.842} \times 0.28 \times 15.10 = 24.27\).

This results in a smaller sample of just under 1 million firm-year observations. With the unbalanced sample, quantile regressions proved computationally too complex and unstable.

Table available upon request.

The up-to-date version of Orbis covers data from 1990 to 2016, but the sample is strongly unbalanced. It starts with just several thousand observations per year in the 1990 s. The coverage increases to several million observations per year only after 2005.

Tables with these regression results are not included, but available upon request.

The corresponding threshold is USD 9 million, which is the order of magnitude of the thresholds used by standard SME definitions.

We use the live Orbis database for this robustness check, as our dataset borrowed from Gal and Hijzen (2016) does not include firm ownership. Therefore, the number of observations in this robustness check is larger than in our baseline regressions.

Table available upon request.

The support of the density function is kept as general as possible; in particular, it includes the possibility for negative profitability.

This is equivalent to assuming there is no issue of asymmetric information between investors and managers, or that their utility functions are perfectly aligned.

Assuming risk-averse lenders would yield a similar result but complicate the model.

Indeed, the second-order condition \(\int u^{\prime\prime}\left[ {\left( {1 - \beta } \right)\left( {1 - \tau } \right)\left( {\left( {\theta - r_{t} } \right)D + \theta E_{t - 1} } \right)} \right]\left( {\theta - r_{t} } \right)^{2} \left( {1 - \beta } \right)^{2} \left( {1 - \tau } \right)^{2} dF\left( \theta \right) < 0\) is true \(\forall D\).

With a constant absolute risk aversion utility function, \(u\left( \delta \right) = 1 - e^{ - \alpha \delta }\), we get an explicit equation in terms of the moments of \(\varTheta - r_{t}\):

\(\mathop \sum \limits_{n = 0}^{\infty } \frac{{\left( { - \alpha } \right)^{n} }}{n!}\left( {1 - \beta } \right)^{n} \left( {1 - \tau } \right)^{n} \left( {D^{*} + E} \right)^{n} {\mathbb{E}}\left[ {\left( {\varTheta - r} \right)^{n + 1} } \right] = 0\)

In the general case, any \(\delta f\) can be decomposed as a series of such functions with limited support.

This is the intuition behind the Markovitz’ mean–variance analysis.

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Titel: Pouring oil on fire: interest deductibility and corporate debt
verfasst von: Pietro Dallari
Nicolas End
Fedor Miryugin
Alexander F. Tieman
Seyed Reza Yousefi
Publikationsdatum: 13.05.2020
Verlag: Springer US
Erschienen in: International Tax and Public Finance / Ausgabe 6/2020
Print ISSN: 0927-5940
Elektronische ISSN: 1573-6970
DOI: https://doi.org/10.1007/s10797-020-09604-7

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