Abstract
To estimate the impact of profit taxation on the financial leverage of corporations, this study uses a pseudopanel constructed from comprehensive corporate tax return microdata for the period 1998–2001, which saw the introduction of major corporate tax reform in Germany. Financial leverage refers to the ratio of long-term debt to total capital. The endogeneity of the firm-specific marginal after-financing corporate income tax rate is controlled for by an instrumental variable approach. The instrument for the observed marginal tax rate is the counterfactual tax rate that a corporation would have faced in a particular period had there been no endogenous change, triggered by the tax reform, of its financial leverage and tax base. This counterfactual tax rate is derived from a detailed microsimulation model of the corporate sector, based on tax return microdata. The marginal tax rate has a statistically significant and relatively large positive effect on corporate leverage; for firms reporting positive profits, an increase of the marginal tax rate of 1 % would increase the financial leverage by approximately 0.7 %, on average. The debt ratio is less responsive to tax incentives for small corporations and firms facing high economic risks.
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Notes
The United States, the United Kingdom, and Japan tax corporate income in higher income brackets at a higher rate; some European countries (e.g., Belgium, the Netherlands) provide a basic allowance for corporate income. Overall, there seems to be a tendency to reduce the “progressivity” of the corporate income tax (e.g., OECD 2007).
Dammon and Senbet (1986) note that an increase in investment-related tax shields does not necessarily lead to a decrease in debt; therefore, in addition to the substitution effect, an income effect must be considered. Higher investment may lead to higher output and earnings, which makes interest deductions more valuable as tax shields. Graham and Tucker (2006) investigate the repercussions of tax sheltering on firms’ financial structure during 1975–2000 and find that firms which use tax shelters rely less on debt than do nonshelter firms on average. Ceteris paribus, tax sheltering firms’ debt-to-asset ratios are more than 5 percentage points lower than leverage for nonshelter firms.
Bernasconi et al. (2005) do not find support of the tax exhaustion hypothesis but a substitution effect for the whole sample. As they conjecture, this might be due to the fact that the investment tax credit studied was introduced for a short time and sizable.
Gordon and Lee (2001) report a semielasticity of 3.5 % if the corporate tax rate is reduced by 10 percentage points (from 46 % to 36 %). Converting this semielasticity into an elasticity estimate yields a value of 0.16: The reduction in the corporate tax rate of 10 percentage points corresponds with a (46 %–36 %)/46 %=21.74 % reduction in tax rates, which increases corporate leverage by 3.5 %. A reduction in the corporate tax rate by 1 % thus increases corporate leverage by 1 %∗(3.5 %/21.74 %)=0.16 %.
All individual data is anonymous. Researchers have access to the data through the research centers of the Statistical Offices (www.forschungsdatenzentren.de). Some information in English about this data is available at https://www.destatis.de/EN/FactsFigures/SocietyState/PublicFinanceTaxes/Taxes/CorporationTax/CorporationTax.html and https://www.destatis.de/EN/FactsFigures/SocietyState/PublicFinanceTaxes/Taxes/TradeTax/TradeTax.html. Similar to the CIT statistics, the local business tax statistics are constructed from all tax returns filed for local business taxation. The local business tax statistics also include nonincorporated firms, which we dropped from the data set.
We imposed a minimum group size of 50 to reduce the measurement error in both dependent and explanatory variables, which arises because the composition of the group might slightly change over time through the entry/exit of firms. Only if the measurement error is small, due to the large number of observations per group, can it be ignored. Then the time fixed effects can be eliminated by first-differencing all variables. We argue that this assumption holds because we use a data set that includes the universe of corporations subject to German CIT and impose a relatively large group size for the construction of our pseudopanel. As a robustness check, we constructed a pseudopanel with minimum group sizes of 40 and 45. Although the number of groups increases slightly with a lower minimum group size (plus 28 and 2 groups, respectively), the results remain unchanged.
We took the changes in the classification of industries between 1998 and 2001 into account by matching the old industry identifier with new ones. This match was not always possible though, so we rearranged a few groups to ensure the data sets for the two years were comparable. We excluded observations for which the industry was unknown or obviously erroneous. Revealing the industry is compulsory but leaves taxes for a given corporation unchanged; it is unlikely that there is any systematic concealment of the industry, so discarding those observations should not bias our results. We also dropped all private households from the data set, because they were only partly included in the 1998 data set and are not the focus of our study.
We do not have information on initial deposits. If they exceed the legal minimum deposit, we underestimate total capital. Because initial deposits remain unchanged over time, this should be purged from our regression by group fixed effects.
Half of the interest payments on long-term debt are liable to local business taxes. The definition of long-term debt is quite broad and includes debt that is either not paid back within 12 months or that is taken out to improve or expand business operations. This definition covers, for instance, bonds issued as well as bank loans. Using average interest rates for firm credits of the Deutsche Bundesbank (series SU0506 and SU0509), we can infer long-term debt from interest payments. This might introduce unsystematic measurement error in the dependent variable, which could affect the efficiency of our estimates, in particular the standard error of the estimated leverage elasticity. We account for this by calculating robust standard errors of estimated coefficients in the regressions below.
To this effect, our measure of MTR is static. It is not based on forward-looking simulated profit paths as in Graham (1996, 2000) but it is based on a firm’s current profit situation. One might argue that tax incentives to use debt are based not just on current profits, but also on expected future profitability. Simulating profit paths is one way to approximate unobserved expectations, but would require not-testable assumptions about the dynamics of profits and losses.
In Germany, a net operating loss does not lead to an immediate tax rebate but is deductible against positive profits from other years. Companies that have paid corporate income tax in previous year(s) may carry back the loss and receive a tax refund. If the loss in the following year exceeds profits or a legally defined maximum carry-back, the remaining loss must be carried forward in time; the resulting tax loss carry-forward, which is valid for an unlimited period of time, is deductible against future positive profits.
In the estimations, we use unweighted averages for several reasons. First, the tax statistics, on which our analysis is based, represent the full population of German firms liable for the corporate income tax. Because we are interested in an average effect of the corporate income tax on financial leverage across all different types of firms, we are reluctant to give priority (or more weight) to specific groups of corporations. Second, with regard to potential selection effects resulting from our focus on firms with nonnegative profits, we prefer a more general selection correction to a weighting scheme that tries to correct for different probabilities of firms incurring a loss. Such a weighting scheme by, say, firm size inevitably remains somewhat arbitrary and probably ineffective as the determinants for making a loss do not only depend on firm size. Third, there is support in the literature for using unweighted data. For instance, Dickens (1990) shows that with grouping by geographic location or industry, error terms are likely to be correlated due to group-specific error components. If the variance of this component is not too small relative to the individual specific error component, and the average group size is relatively large, there is little bias of estimated standard errors of coefficients in OLS regressions on unweighted data. By contrast, weighting by, say, the root of group size would introduce a severe heteroskedasticity problem.
Unlike in the United States, where the “check-the-box” rules imply that companies can choose to be taxed as either corporate or pass-through entities, German companies cannot easily shift income between corporate and individual tax bases, but must change their legal form to do so. Corporations are also liable for the local business tax, which is levied on an adjusted profit measure at a rate that varies across municipalities (for details, see Fossen and Bach 2008). Until 2008, only 50 % of interest payments, but the entire returns on equity, were subject to the local business tax. The local business tax hence set incentives for debt financing, which varied across communities because of municipality-specific rates. Gropp (2002) used cross-sectional differences in these municipality-specific rates to identify tax effects on financial leverage. Identification in our analysis stems from variation in the change in MTR over time. Because there was no change in the local business tax between 1998 and 2001 and the municipality-specific rates hardly changed (German Federal Statistical Office Realsteuervergleich 1998, 2001), we have not taken it into account in our MTR simulation.
In 1998, interest payments were not taxed at the corporate level but were subject to the personal income tax of 53 % (top personal income tax rate) at the shareholder level, resulting in a total tax burden on the return on debt of 53 %. CIT on distributed earnings were fully allowable against personal income taxes, also leading to a combined tax burden on the return on equity equal to the personal income tax rate of 53 %.
As before, interest payments were not taxed at the corporate level in 2001 but were fully taxed at the shareholder level (top personal income tax rate of 48.5 % in 2001), resulting in a combined tax burden of 48.5 % in 2001. The return on equity r was subject to the CIT (25 %) at the corporate level; in 2001, half of after-tax distributed earnings were additionally subject to the personal income tax yielding a combined tax burden of 43.2 % (=r∗0.25+(r∗(1−0.25)∗0.5∗0.485)).
We neither know their participation quota nor have knowledge about other sources of income or their personal income tax. Personal income taxation in Germany is highly progressive, and taxation partly depends on the participation quota. Therefore, without this information, we cannot satisfactorily include personal income taxation in our analysis.
Until 1998, profits could be carried back two years up to a value of 5.1 million euros. In 1999, loss carry-back was restricted to one year. At the same time, it was reduced in volume; in 1999 and 2000, it was limited to 1 million euros and since 2001, it has been capped at 511,500 euros (Dwenger 2010).
Some corporations even saw their tax rate rise: operators of merchant ships in international bodies of water were liable for a reduced rate of 22.5 % in 1998, but in 2001, the universal tax rate of 25 % applied.
Of course, this situation cannot happen in general equilibrium. The example only serves for illustration purposes of reverse causality.
If fixed assets may be used as collateral for debt, depreciation allowances and the debt ratio likely correlate positively, because the amount of depreciation allowances and the value of fixed assets correlate positively.
In Sect. 5.1 we run several robustness checks wherein we additionally include MTR in 1998, initial debt level, and tax loss carryforwards and carrybacks in 1998 to control for any correlation between the change in financial leverage and tax variables in 1998. Our estimation results prove to be very robust to the inclusion of these variables.
There are 13 inflation parameters for the different sources of income (profits and losses, dividends and income from interest, differentiated by financial and nonfinancial corporations). These parameters were computed such that inflated profits and interest reflected changes in the corresponding aggregates in the national accounts and the Deutsche Bundesbank corporate balance sheet statistics.
BizTax is a microsimulation model for business taxation in Germany, based on official tax return data developed at DIW Berlin in cooperation with the Federal Ministry of Finance. In addition to a detailed local business tax module, it contains a CIT simulation module that replicates the corporate income tax assessed by tax authorities for more than 99 % of all corporations; these corporations account for more than 99 % of the overall CIT revenue. BizTax can calculate the CIT liability of each corporation under past regulations, under current law, and under different tax reform scenarios. Currently, the model does not predict the behavioral responses of companies that could be triggered by tax reforms, such as changes in financing and investment decisions or entries and exits.
We note the concern that this counterfactual MTR might not be completely exogenous for corporations that offset part (or all) of their profits in 1998 against losses from the past or from 1999 (loss carry-back), because the amount of profits that can be offset against losses from other periods is a function of the tax rules. The Tax Relief Act broadened the tax base and increased profits liable to taxation, such that a larger volume of losses from other periods is needed to offset higher profits. The ability to offset higher profits resulting from the tax reform could relate to unobserved factors that also may influence the debt ratio. To address this potential endogeneity, we inflated the amount of profits offset against losses from other periods in 1998 and used this amount as an upper limit for the profits that could be offset against losses in our simulation of a corporation’s MTR for 2001. In a similar vein, we used the inflated amount of allowable deductions effectively used in 1998 when we constructed the corporation’s MTR for 2001.
We use the coefficient of variation rather than the variance of sales to account for differences in the volume of sales across industries. To more intuitively interpret our estimation results, we normalize the coefficient of variation by its standard error in the estimation.
We approximated log(debt ratio g,2001/debt ratio g,1998) and log(MTR g,2001/MTR g,1998) by, respectively, [(debt ratio g,2001−debt ratio g,1998)/0.5(debt ratio g,2001+debt ratio g,1998)] and [(MTR g,2001−MTR g,1998)/0.5(MTR g,2001+MTR g,1998)]. A sensitivity check shows that estimating the log–log specification does not significantly change the estimation results.
Alternatively, we could use the pre-reform MTR from 1998 as an IV for the change in the marginal tax rate due to the Tax Relief Act. However, the MTR from 1998 turns out to be a weaker instrument. On the one hand, the correlation between the relative change in the MTR observed and MTR 1998 is significant at the 1 % level (t-value −8.38). In the first-stage regression including all control variables, however, the t-value reduces markedly to −5.35, with a partial R 2 of 0.11. Point estimates in the second stage of the regression are similar to what we find with the counterfactual MTR as an instrument but come with large standard errors. The point estimate of the tax elasticity of debt turns statistically insignificant. These results are available from the authors upon request.
As a sensitivity check, we included the square of the tax variable to identify any nonlinear effects of tax changes on changes of financial leverage. The estimated coefficients of the linear and quadratic terms of the tax variable remained jointly statistically significant at the 10 percent level (\(\mbox{$F$-value}= 2.87\)) and the estimated elasticities, evaluated at the sample means, were virtually identical in the two specifications.
In all baseline specifications, we implicitly assume the elasticity of debt with respect to firm size to be constant. To test whether the size elasticity of debt is nonlinear, we re-estimated the specifications from Table 2 including the square of the natural logarithm of equity in 1998. Probably because of the relatively small size of our pseudopanel, both the log of firm size and its square are individually insignificant; F-tests on the joint significance of the two size variables cannot reject the null hypothesis of no influence of size on financial leverage (\(\mbox{$p$-value}=0.87\) in specification analogous to column (5) in Table 2). However, the estimates of the tax elasticity of debt are very similar to the ones presented in Table 2. We thus consider the robustness check as evidence that should alleviate potential concerns about the influence of firm size on the tax elasticity of financial leverage. The results are available from the authors.
There are two qualifications to this result: First, because the coefficient of variation of sales is derived from the 1998–2005 VAT statistics, it excludes exports not liable to VAT. The VAT statistics is the only data source available at a level of aggregation required to match the coefficient of variation to our pseudopanel, so we cannot adjust the coefficient of variation for export shares. This data limitation should not matter, if export shares have not changed between 1998 and 2005. Second, sales in post-reform years are used to calculate our risk measure, which may induce correlation with the error term in the regression equation. To account for measurement error or potential endogeneity bias, we also estimated the regression without the coefficient of variation of sales. The estimated tax elasticity remained unaffected. The estimation results for this specification are available on request. In a robustness check, we use two separate volatility measures for the periods 1998–2000 and 2001–2005, as volatility might have changed over time. The tax elasticity of financial leverage again remains unaffected. The estimation results are available from the authors.
These results are available from the authors on request.
Mutual insurance companies, private foundations, and public corporations might follow a different rationale than ordinary profit-maximizing companies. In a further robustness check, we excluded those few industries in which these corporations cluster. The results are very similar to the ones shown in Table 2 and are available from the authors upon request.
In 2001, total interest payments I were 33.46 bn. euros and total CIT assessed, TA, 25.43 bn. euros. Each euro of interest payment saves corporate income taxes of τ=0.25 euros. That is, the elasticity of CIT assessed to interest payments is 0.25×(33.46 bn. euros/25.43 bn. euros)=0.329. If we are willing to assume that firms can take out new loans to established conditions, an increase in debt of 1 % leads to an equal increase in interest payments, i.e., η I,F =1.
The t-test applies to a regression that includes an interaction term between the MTR and a dummy variable equal to one for groups with large firms, where the interaction term also is instrumented by the interaction between the counterfactual MTR and the dummy.
To gauge the importance of our selection variable for the results along subgroups, we also estimated the regressions in Table 3 without correcting for selection. The results remained unchanged and are available from the authors upon request.
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Acknowledgements
We would like to thank Dhammika Dharmapala and two anonymous referees for very helpful comments on a previous version of this paper. We also thank Martin Simmler, Florian Walch, and seminar participants at the Free University Berlin, the Max-Planck Institute for Tax Law and Public Finance Munich, and the European Meeting of the Econometric Society for helpful comments on an early draft of this article. We are grateful to David Houser for copyediting the paper. This paper is part of a research project supported by the Federal Ministry of Finance. Results and opinions expressed in this paper are those of the authors and do not necessarily reflect views of the Federal Ministry of Finance or DIW Berlin.
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Appendices
Appendix A: Sequential procedure for the construction of the pseudopanel
Appendix B: Sensitivity check of imputation: imputed and firm-specific interest payments
Percentiles | Distribution of interest payments (group average) | |
|---|---|---|
With imputed interest payments | With firm-specific interest payments | |
1 % | 421.79 | 316.13 |
5 % | 2 125.31 | 1 924.93 |
10 % | 3 102.79 | 2 951.70 |
25 % | 5 013.36 | 4 947.01 |
50 % | 12 295.58 | 12 224.69 |
75 % | 29 537.75 | 30 281.15 |
90 % | 73 220.83 | 75 819.60 |
95 % | 146 246.70 | 158 565.80 |
99 % | 933 509.20 | 1 197 997.00 |
Mean | 58 509.42 | 65 041.42 |
Standard Deviation | 346 843.50 | 375 607.70 |
Appendix C: First stage of the IV regression
Dependent variable: log(MTR g,2001/MTR g,1998) | ||
|---|---|---|
(1) | (2) | |
Counterfactual log(MTR g,2001/MTR g,1998) | 0.652 | 0.807 |
(0.054) | (0.054) | |
Share of corporations under the tax credit method | – | 0.026 |
(0.068) | ||
Change in the number of corporations in the group | – | 0.110 |
(0.016) | ||
Dummy indicating groups which only consist of firms located in Western Germany | – | −0.031 |
(0.009) | ||
Coefficient of variation of sales/standard deviation of the coefficient of variation | – | −0.001 |
(0.004) | ||
log(equity g,1998) | – | −0.013 |
(0.003) | ||
Change in the share of firms reporting a positive NPBL | – | 0.787 |
(0.066) | ||
Constant | −0.200 | 0.097 |
(0.036) | (0.059) | |
R 2 | 0.125 | 0.277 |
Number of observations | 1,029 | 1,029 |
F-Statistic | 146.75 | 55.82 |
Partial R 2 | – | 0.182 |
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Dwenger, N., Steiner, V. Financial leverage and corporate taxation: evidence from German corporate tax return data. Int Tax Public Finance 21, 1–28 (2014). https://doi.org/10.1007/s10797-012-9259-3
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DOI: https://doi.org/10.1007/s10797-012-9259-3