Tax Shelters and Corporate Debt Policy

John R. Graham

Alan L.

Abstract:

We gather a unique sample of 44 tax shelter cases to investigate the magnitude of tax shelter activity and whether participating in a shelter is related to corporate debt policy. The average deduction produced by the shelters in our sample is very large, equaling approximately nine percent of asset value. These deductions are more than three times as large as interest deductions for comparable companies. The firms in our sample use less debt when they engage in tax sheltering. Compared to companies with similar pre-shelter debt ratios, the debt ratios of firms engaged in tax shelters fall by about 8%. The tax shelter firms in our sample appear underlevered if shelters are ignored but do not appear underlevered once shelters are considered.

Keywords: Taxes, tax shelters, debt, capital structure

JEL Classifications: G32, K34

Corporate tax shelters are our number one problem (in enforcing the tax laws), not just because they cost money but because they breed disrespect for the tax system

– Then Treasury Secretary Lawrence Summers quoted in “Corporations’ Taxes are Falling Even as

Individuals’ Burden Rises,” The New York Times, February 20, 2000.

The tax department (of a corporation) is viewed … as a profit center and a place that has

… an obligation … to aggressively reduce the tax burden.

– Larry Langdon, commissioner of the IRS large and midsize business division, and former tax director at Hewlett-Packard, quoted in the same New York Times article.

1. Introduction

During both 1991 and 1992 Compaq Computer Corporation reported taxable income that averaged more than $170 million. According to public financial statements, during these same years Compaq’s debt averaged 1.3 percent of total assets at a time when comparable firm debt ratios averaged 25 percent. Based on its apparently low debt ratio, Compaq appeared to leave money on the table in terms of paying more taxes than necessary. If the company had levered up to a 25 percent debt-to-assets ratio, the incremental debt would have produced interest deductions worth approximately $65 million annually (assuming a nine percent coupon rate, which was the average for newly issued investment grade corporate debt in the early 1990s). If Compaq’s federal tax rate was the maximum 35%, the firm could have saved $23 million annually in federal taxes (not to mention state and local taxes). Apparent bypassing of such large tax benefits has led some researchers to argue that many firms appear to be under-levered (e.g., Miller 1977, Graham 2000).

One potential problem with the above argument is that apparently under-levered firms may have “off balance sheet” tax deductions that are not easily observable, and which are therefore often ignored in empirical analyses. For example, Graham, Lang, and Shackleford (2004) document that debt policy at S&P 100 and Nasdaq 100 firms appears to be conservative if stock option deductions are ignored but is notably less conservative once option effects are considered. Stefanescu (2005) finds a similar effect for firms that use defined benefit pension plans. In this paper, we investigate the magnitude of corporate tax shelters, and also whether the use of tax shelters is related to corporate debt policy.

Compaq is one of the tax shelter firms in our sample. In the case of Compaq, the government alleges that the firm used cross-border dividend capture and transfer pricing (described in Section 2) to produce at least $115 million in annual tax deductions in 1991 and 1992. That is, Compaq used tax shelters to produce tax deductions that are nearly twice as large as the interest deductions that the firm appeared to forgo by maintaining a debt ratio lower than at comparable firms (i.e., the $65 million in interest deductions mentioned above). Said differently, if one were to factor in all deductions, including both legal deductions that appear on financial statements and tax shelter deductions, Compaq does not appear to be underlevered.

Given the sharp decline in corporate tax payments in the past decade, the possibility exists that tax sheltering is a widespread and growing problem. For example, S&P 500 firms paid federal taxes of 29 cents per dollar of reported profits in 1994, but this fell to just 18 cents per dollar one decade later (see Fig. 1). A similar declining effective tax rate pattern also holds for all publicly traded firms. (The main exceptions to the declining tax rate are the increase that occurred in 1993 when the maximum statutory corporate tax rate increased from 34% to 35%, and at the end of the sample period). Corporate tax sheltering activity may play an important role in this reduction in tax collections.

Current estimates indicate that sheltering allows U.S. firms to avoid over $10 billion in federal income taxes annually.[1] Recently the IRS claimed that one firm alone, GlaxoSmithKline P.L.C., owes $5.2 billion in back taxes and penalties related to a transfer pricing strategy dating back to 1989.[2] Tax sheltering activity has also allegedly led to a significant reduction in state tax collections. The Multistate Tax Commission reports that state corporate income tax revenue, which totaled $35.4 billion in 2001, would have been one-third larger had tax sheltering not occurred.[3]

Information about tax shelters is notoriously hard to find. Companies do not publicize their use of shelters and the Internal Revenue Service (IRS) treats tax investigations confidentially. Based on an exhaustive search of Tax Court records and financial news stories, we identify 44 tax-sheltering cases (involving 43 firms) between 1975 and 2000. To the best of our knowledge, our sample of tax sheltering cases is the largest collected to date. Because the firms in our sample were caught and/or filed suit regarding their shelter activity, there are sample selection issues related to our sample (discussed more fully in Section 3.2). One issue is that our sample selection procedure might identify primarily large tax shelter cases. Another issue is that the firms in our sample might have taken specific actions, such as reducing debt ratios or filing suit against the government, that led to their shelter being detected by the government and by our sample selection process. To the extent that this is true, it hinders our ability to conclude that the characteristics of firms in our sample are representative of other shelter firms. These issues potentially make our results difficult to directly generalize to the population of firms, and we interpret the results accordingly.

The tax shelters in our sample are large. The median shelter produces, on an annual basis, a deduction sufficient to shield income equal to approximately nine percent of asset value. In comparison, a debt-to-assets ratio of 30 percent produces interest deductions equal to only three percent of assets if coupon rates are 10 percent.

We also investigate whether companies appear to substitute between the tax shields provided by shelters and debt interest. As predicted by DeAngelo and Masulis (1980), we document that firms use less debt when their non-debt tax shields (in this case, deductions from tax shelters) are large. In particular, in the year(s) that the tax shelters are in use, sheltering firm debt ratios are more than 800 basis points lower than the debt ratios of similar-sized same-industry firms. This is true even though several years before the shelter is implemented, shelter firm debt ratios are indistinguishable from matched- firm debt ratios.

We also investigate whether the existence of tax shelters affects incremental financing decisions. We find that 60 percent of the shelter firms in our sample issue debt sometime during the years preceding the inception of tax shelter activity. In contrast, 70 percent of matched firms issue debt. Holding all else constant in a logistic regression, shelter firms are significantly less likely to issue debt than are non-shelter firms.

Tax shelters potentially affect more than just debt ratios. Stock prices can also be affected. McGill and Outslay (2004, p. 751) summarize Tyco International Ltd’s belief that by reducing its tax rate by 700 basis points via offshore activity, it increased its EPS, which in turn increased its market capitalization by nearly $5 billion. Moreover, if tax shelters substitute for debt-induced tax deductions, sheltering may also increase financial slack, reduce expected bankruptcy costs, enhance credit quality, reduce the risk of covenant violation, and reduce the cost of debt. For example, in our sample in the years leading up to the inception of the tax shelter, we find that shelter firm credit ratings improve one notch relative to matched firms, most likely due to falling debt ratios. Of course, all of this must be weighed against the risk, potential penalties, and other negative effects of engaging in tax shelters.

Our paper is related to other research that investigates tax shelters. Desai (2003) investigates whether the growing wedge between taxable income reported on financial statements and tax return income can be explained by accelerated depreciation, stock options, or earnings management. He concludes that these traditional vehicles explain only a portion of the wedge. Desai argues (p. 1) that new “enhanced opportunities for avoiding and evading taxes through cheaper, more sophisticated, and less transparent mechanisms” (i.e., tax shelters) explain at least one-third of the book-tax income gap as of 1998. Schallheim and Wells (2004) measure nondebt tax shields based on the difference between taxes paid and financial statement tax expense in an attempt to capture the effect of “off financial statement” deductions such as accelerated depreciation, stock option deductions, tax shelters, and the like. In contrast to often-found results that are based on traditional measures of nondebt tax shields (NDTS), Schallheim and Wells find that tax spread is negatively related to debt usage.

One advantage our approach has over Desai’s (2003) and Schallheim and Wells’ (2004) is that we know actual sheltering activity (as alleged or proven by the government) and hence do not need to indirectly infer the propensity of sheltering. The accompanying disadvantage to our approach is that our sample is relatively small due to the difficulty of identifying firm-specific instances of sheltering.

Our analysis is also related to papers that study whether observed debt ratios are too low (e.g., Miller (1977), Parrino and Weisbach (1999), and Graham (2000)).[4] For example, because the ratio of interest deductions to expected income is quite small at many firms, Graham (2000) argues that the average magnitude of debt usage appears to be small relative to the tax benefits of debt. Underleverage appears to be severe at some firms that on the surface appear to have a low marginal cost of debt (e.g., Compaq, AHP, Microsoft).

Our analysis complements these papers, and for the firms in our sample offers a partial solution to both the magnitude and cross-sectional “under-leverage” issues raised by Graham (2000). In terms of magnitude, the shelters in our sample are very large, at least three times what would be expected to be generated by debt interest deductions. Cross- sectionally, as shown in Table 1, many of the firms in our sample appear to be “low cost of debt” firms (that appear to use too little debt when shelters are ignored). Therefore, debt policy at these firms is not as conservative as appears on the surface, due to plentiful tax shelter deductions that reduce the need for debt. We do not claim, however, that incorporating the effects of tax shelters eliminates the possibility that some firms are underlevered. For one thing, we investigate a select sample of primarily large firms, so our results do not directly address debt policy at small firms. For another, our analysis does not address whether firms are underlevered in the years before or after our sample period.

Finally, our paper is related to other works that investigate tax shelters, but at the macro level. Clausing (2003) and Bartelsman and Beetsma (2003) find substantial evidence of tax-motivated transfer pricing, which is one of the most popular tax shelters in our sample. Hines (1997) reviews literature that finds indirect evidence of transfer pricing. Finally, Desai, Foley, and Hines (2004) find evidence that multinationals relocate income to tax haven countries, in part to delay repatriation to high tax rate parents.

Bankman (1999) provocatively argues that, as of the end of the 1990s, the tax shelter industry was “growing at breakneck speed” and was valued at least in the tens of billions of dollars. He reasons that even though many shelters will be struck down by the courts if litigated, the dollar benefit of participating in a shelter far outweighs the cost, once one incorporates the low probability of detection. Bankman cites at least two factors that have contributed to increased sheltering activity: 1) the relative ease with which shelter promoters can obtain favorable opinion letters from attorneys, which generally protect corporations from penalties if the shelter is ruled illegal, and 2) business norms that have weakened in the face of the attractive economics of sheltering income. While our sample represents only the tip of the shelter iceberg described by Bankman, we believe that our analysis sheds light on tax shelter participation and its effect on corporate policies.

The rest of the paper proceeds as follows. Section 2 provides details about tax shelters including the legal doctrines the government uses to combat tax evasion. It also lays out the debt substitution hypothesis. Section 3 describes our sample and presents summary statistics. Section 4 investigates how tax shelters affect the corporate use of debt and Section 5 concludes.

2. Classifying and combating corporate tax shelters[5]

The U.S. Congress (Joint Committee on Taxation, 1999) defines a tax shelter as an endeavor principally designed to avoid taxation without exposure to economic risk or loss. Bankman (2003) argues that tax shelters are tax-motivated vehicles that use a literal interpretation of government statute or regulation to misstate economic income in a manner that is inconsistent with the spirit or intent of the statute or regulation. While Bankman focuses on vehicles that are promoted by outside parties such as investment banks, we also investigate activity that is more likely to be self-motivated, such as non- arms length transfer pricing. (More details on sample selection are provided in Section 3.)

Section 2.1 provides the context for our debt analysis by describing the hypothesis that deductions from tax shelters can substitute for debt interest deductions. Section 2.2 sketches background information about the tax shelters used by firms in our sample. Numerous other tax shelter vehicles exist but are not discussed due to space constraints. Section 2.3 describes the key legal doctrines that the government uses as it attempts to detect and eliminate tax shelters.

2.1. Tax shelters and the use of corporate debt

The tax management department at most modern corporations has many tools at its disposal to reduce tax obligations (Scholes et al. 2002). One common feature in most of the tax shelters described in Section 2.2 is that they effectively produce deductions that can be used to offset income or gains. Therefore, under the right circumstances, each tax shelter can be thought of as a separate lever that a corporate tax planner can pull to reduce tax obligations in a given year.

In the spirit of DeAngelo and Masulis (1980), deductions produced by tax shelters are non-debt tax shields (NDTS). DeAngelo and Masulis show that NDTS substitute for interest tax deductions. Each firm has an optimal amount of total deductions, and if a firm uses more NDTS it will use fewer debt tax deductions. One focus of our paper is to investigate empirically how tax shelter deductions fit into the corporate debt decision- making process. Our hypothesis is that tax shelters substitute for the use of debt.

We do not argue that firms engage in (potentially illegal) tax shelters for the sole purpose of reducing their use of debt. Rather, our perspective is that if a firm engages in tax sheltering for any reason, to the extent that the shelters reduce taxable income, the firm will use less debt financing. We also acknowledge that, as in most corporate finance research, it is difficult to unambiguously prove direction of causality or order of sequential choice. That is, a firm might use less debt after having first established tax shelters, or it might resort to sheltering after discovering that it is unable to issue much debt (for whatever reason). Therefore, our results should be interpreted as documenting correlations, not causality.

In the cleanest test of the substitution hypothesis, tax shelters would lead to deductions that enter the tax return in the exact same manner as interest deductions, that is, by leading to a deduction that reduces taxable income (or equivalently by reducing revenues, which also leads to less taxable income). Many of the shelters described below lead to interest-like deductions; however, some lead to capital losses. Even in the case of capital losses, shelter deductions can be transformed into interest-like deductions (e.g., see Bankman (1999) for a discussion of a section 988 transaction or Cetta (2002) for other techniques to convert capital losses into ordinary losses). Nonetheless, to study the cleanest possible sample at one point, in some of our empirical tests we delete shelters that produce paper capital losses.

2.2. Tax shelters used by firms in our sample

This section provides thumbnail sketches of the tax shelters used by the firms in our sample. We note whether a shelter leads to interest-like deductions or capital losses, and whether the shelter implementation has a direct effect on the debt ratio. For interested readers, the references listed in footnote 5 and in the text below provide more detail about the mechanics of tax shelters.

2.2.1. Lease-in, lease out (LILO)

Lease-in, lease-out transactions were popular from 1995 to 1999. In a typical deal, a U.S. corporation leases long-lived property (e.g., a power plant) from a tax-indifferent party (often a foreign municipality) and immediately subleases the property back to the same party. The initial lease calls for the U.S. corporation to prepay the bulk of its rental obligations. The U.S. company enjoys accelerated deductions on its tax return, while amortizing the cost over a much longer horizon in financial statements. The accelerated deductions produce a “time value” benefit vis-à-vis the (often large) net income the U.S. firm realizes in the final year of the deal, when the transaction is reversed. (In some cases, taxpayers attempt to generate paper losses through other shelters to offset the final year income.) LILO transactions shelter ordinary income and are therefore a substitute for interest expense. Revenue Ruling 99-14 and related 1999 regulations eliminate LILO tax benefits.[6]

American Banker reports that the IRS has identified 56 corporations that engaged in LILO transactions, including a few of the largest banks in the U.S.: AmSouth Bankcorp, FleetBoston Financial Corp., and BB&T Corp.[7 ] These three banks also disclose in their SEC filings that the IRS sent them a Notice of Deficiency, and we include them in our sample. (A Notice of Deficiency denies the tax treatment sought in these deals, effectively stating the amount of additional taxes the IRS believes that the firm must pay. After receiving the Notice, the taxpayer must sue the government if it wants to try to preserve the tax treatment that it sought in its initial tax filing.) As described in the sample selection section below, we do not include firms that are reputed to have participated in a tax shelter if we can not find evidence of a Notice of Deficiency.

2.2.2. Transfer pricing (TP)

In a typical transfer pricing deal, a U.S. corporation produces an asset at a low-tax foreign subsidiary and said asset is sold to the parent company at an above market price. The transfer is not arms-length because the price does not reflect the asset’s value. Tax sheltering transfer pricing has the effect of subjecting most of the profit from the ultimate sale of the asset to the relatively low subsidiary tax rate. Because the parent company’s taxable income is lower than it would be with arms-length transfer pricing, the firm has less need for deductions from debt income. Hence, TP deals are indirect substitutes for interest expense because they serve to reduce ordinary income. We hypothesize that, all else equal, a firm engaged in income shifting via transfer pricing will use less debt. For analysis of optimal transfer pricing, see Baldenius, Melumad, and Reichelstein (2004).

2.2.3. Corporate-owned life insurance (COLI)

In a typical COLI deal, the corporation purchases cash value life insurance on its employees and borrows to pay some or all of the premiums. As time passes and a given policy approaches payout, the value of the policy increases, as does the amount that can be borrowed on that value. The eventual payout from the policy is not taxable, while the interest on the borrowings is fully deductible. Due to interest and fees, a COLI deal yields little if any before-tax value but produces positive net present value once tax benefits are considered. See Bankman (2003) for details.

COLIs shelter ordinary income by producing interest deductions; therefore, they substitute for non-shelter-related interest deductions. We give special attention to COLI shelters in our empirical analysis. COLIs could increase a firm’s debt usage if they effectively lead to additional borrowing (i.e., above “normal” borrowing) to finance the insurance premiums, and therefore could be positively correlated with a company’s debt ratio. In contrast, if companies that use a given tax shelter (e.g., a COLI) are also likely to use other shelters and non-debt tax shields, COLI use could be negatively correlated with the use of debt.[8] Or these two effects could offset each other. Due to this ambiguity, at one point in our empirical analysis, we exclude COLI firms.

In 1993, Winn-Dixie entered a COLI program to insure approximately 36,000 employees. Winn-Dixie purchased whole-life policies and was the sole beneficiary. The company regularly borrowed against the policy values. The all-in borrowing costs exceeded the net cash surrender value and benefits paid on the policies. Winn-Dixie lost money on a pretax basis each year. However, the tax deductions on the interest and fees produced several billion dollars of tax benefits. Winn-Dixie terminated its program in 1997, after 1996 tax law changes limited the deductibility of interest on COLI policy loans.[9]

2.2.4. Cross-border dividend capture (CBDC)

In this strategy, a taxable entity purchases American Depository Receipts (ADRs) cum dividend, capturing the dividend and the foreign withholding tax credit. Through a cross trade, the entity then quickly resells the ADRs at the ex-dividend and withholding tax adjusted price. The loss on the cross trade offsets the dividend to be collected by the taxable entity, ignoring trading costs and sponsor fees. The tax credit is used to reduce tax obligations. The sale and repurchase of the ADRs are done through an intermediary. But ultimately the real seller is a tax-exempt institution such as an international mutual or pension fund that can not make use of foreign tax credits. They are compensated by short interest rebates on their ADR lending activity. In short, this strategy creates a secondary marketplace wherein tax exempt institutions sell their unusable tax credits in return for interest payments. (See Tucker (2002) for more details.) Graham (2003) demonstrates that, all else equal, the tax incentive to finance with debt decreases with foreign tax credits such as those described in this deal.

The appendix contains a case study of a CBDC involving Compaq. In deposition testimony in the Compaq case, a single sponsor of this strategy indicated that it had conducted hundreds of these deals during the early and mid 1990s (Tucker, 2002). Changes to the Internal Revenue Code (IRC) made in 1997 were intended to preclude this type of transaction from being conducted.

2.2.5. Contingent-payment installment sales (CPIS)

In a typical deal, a U.S. corporation having a sizable capital gain establishes an offshore partnership with a tax-exempt foreign entity. The foreign entity is the majority partner. The partnership engages in a series of securities transactions involving the sale of existing assets in exchange for contingent-payment financial securities. Under (then existing) IRC regulations pertaining to the partnership tax treatment of contingent payment assets, this action had the claimed effect of producing a sizeable paper capital gain for the partnership. The majority of said gain went to the tax-exempt majority partner. Then the U.S. partner would buy out most of the foreign partner, leaving the U.S. partner with the vast majority of the partnership. Later, when the contingent payment securities were sold, a back-end paper capital loss was created, which was subsumed into the U.S. parent corporation’s financials for tax calculation purposes. Thus the preexisting real capital gain was offset by a paper capital loss.

Often these deals were managed such that the credit and market risks occasioned by the securities trades were hedged, so that any losses suffered by the partnership were shared primarily by the domestic partner/US taxpayer, leaving the accommodating foreign entity with little down-side risk. The temporary regulation upon which this strategy was based has been changed so this type of deal is now precluded. See Bankman (1999) for more on CPIS deals, which he refers to as “high-basis low-value” tax shelters.

CPIS offset capital gains and therefore are not a direct substitute for interest expense (i.e., they do not directly offset ordinary income). However, Bankman (1999) and Cetta (2002) explain how a capital loss can be transformed into an ordinary loss. The appendix contains a case study of a CPIS involving Colgate Palmolive.

2.2.6. Liquidation, recontribution (LR)

In a LR, a foreign partnership is established involving tax-exempt foreign entities. This partnership engages in a series of securities trades that involve an asset or liability whose future value is uncertain, for example, an open short position in a Treasury bill. Soon thereafter, a U.S. corporation buys the vast majority of the partnership, serving to liquidate the partnership, and then immediately reconstitutes a new partnership. By attempting to make use of partnership regulations that speak to the computation and allocation of a partnership’s bases in its assets and liabilities, this liquidation and re- contribution strategy can produce a targeted capital gain or loss for the U.S. firm. Like CPIS deals, LR deals address capital income, not ordinary income, and are therefore not a direct substitute for interest expense. Again, though, a converting transaction can transform the capital loss into an ordinary loss. The appendix contains an example of a LR deal involving Florida Power and Light. For more on LR deals, see Bird and Tucker (2002).

2.2.7. Offshore intellectual property havens (OIPH)

U.S. multinationals have incentive to house intellectual property abroad so as to shelter income from overseas sales.[10] For example, a multinational may transfer a patent to a newly formed Bermuda subsidiary so that royalties from sales of products made outside the U.S. flow to the subsidiary, where they accumulate tax-free. The subsidiary pays for part of the patent but the price is allegedly quite subjective. While royalties collected by the subsidiary need to be reported to the IRS, the payments transferred back to the parent, and subject to U.S. taxation, are often artificially low.[11] OIPH deals therefore are akin to transfer pricing in that they reduce revenue, which in turn reduces the marginal value of interest deductions.

The American Jobs Creation Act, signed into law October 2, 2004, reduces for about one year the tax rate on repatriated foreign profits (for profits accumulated prior to June 30,

2003). The tax rate on repatriated profits is temporarily reduced to 5.25% from its standard level of 35%. Such a cut permits profits that have accumulated in the foreign subsidiaries via OIPHs to be repatriated at favorable tax rates.

2.2.8. Contested liability acceleration strategy (CLAS)

In this strategy a firm establishes a “contested liability trust” with itself as beneficiary. The firm transfers non-cash assets (e.g., an intracompany note or IOU) into the trust equal to approximately what the firm expects to pay to resolve claims it is still contesting. These claims may be related to liabilities including medical malpractice, shareholder lawsuits, personal injury, or environmental actions. The firm receives deductions equal to the amount of assets placed in the trust, thereby reducing taxable income. (The IRS contends that CLAS deductions are invalid because the firm maintains control of the trust assets.) The firm benefits because it accelerates the deductions on claims that could take years if not decades to resolve. (Normally deductions can not be taken until the claim is actually paid.) Due to the negative correlation predicted in Section 2.1, in years in which CLAS deductions are large, we expect interest write-offs to be small, all else equal.

The Wall Street Journal reports that KPMG marketed this transaction to 29 companies during 1999-2001, saving these firms an estimated $1.7 billion in federal taxes.[12] We explicitly confirm that five U.S. firms have engaged in this strategy. Delta Airlines, Whirlpool Corp., Clear Channel Communications Inc., WorldCom Inc. (now MCI Inc.), and Tenet Healthcare Corp disclose in their financial statements that they have received a Notice of Deficiency from the IRS. These five firms are in our sample.

2.3. Key judicial doctrines

The government has developed and invoked five non-mutually exclusive judicial doctrines to curb corporate tax shelters. Each is described in turn.

2.3.1. Sham transaction doctrine

Courts disallow two types of sham transactions. Shams in fact are transactions that never occur. Shams in substance occur but, absent tax considerations, lack economic substance and/or business purpose. (The economic substance and business purpose “prongs” of the sham transaction doctrine are described in Sections 2.3.2 and 2.3.3, respectively.) Beginning with Winn-Dixie Stores, Inc. v. Commissioner, the government has used the sham transaction doctrine to battle company-owned life insurance (COLI) programs.[13] In this case, the Court essentially held that the program was a sham in substance (and disallowed the deductions sought) because absent tax considerations, the company lost money.

The government is not always successful in applying the sham transaction doctrine. For instance, in United Parcel Service of America, Inc. (“UPS”) v. Commissioner, UPS engaged in a program with a related Bermuda corporation.[14] UPS insured its customers’ packages up to $100 at no extra cost, and also offered customers the ability to purchase additional coverage. Before the program was established, UPS self-insured the risk. With the advent of the related Bermuda corporation, UPS continued to administer all aspects of the insurance, but through a ceding arrangement with an unrelated insurance company, UPS paid all premiums to the Bermuda firm that reinsured the risk. The Court ruled that this assignment of income was a sham in substance, and shifted all of the premium income back to UPS for tax purposes. However, the Eleventh Circuit reversed the Tax Court, finding that the arrangement had economic substance, in part because UPS could no longer use the income stream it had access to when it self-insured. Still, the Eleventh Circuit remanded the case for determination under more specific statutory income reallocation provisions of the Internal Revenue Code.

2.3.2. Economic substance doctrine

Tax law requires that transactions have economic substance (a “profit motive”) separate and distinct from an economic benefit achieved solely from tax reduction. In short, a transaction must change a taxpayer’s economic position in a meaningful nontax way for the IRS to recognize the transaction’s tax treatment. This doctrine is two-pronged, having both objective and subjective elements, with the latter being virtually identical to the business purpose doctrine (discussed below).

Modern application of this doctrine can be found in a series of related cases beginning with ACM Partnership v. Commissioner. Here Colgate-Palmolive Company (“Colgate”) entered an offshore partnership with affiliates of Merrill Lynch (the strategy sponsor) and ABN-AMRO (a Dutch bank). The partnership engaged in a series of securities trades that had the effect of producing for Colgate a substantial paper capital loss. The transactions conducted were complicated contingent payment installment sales (CPIS) that exploited the application of special ratable basis recovery rules under a temporary Treasury regulation that was then part of IRC Section 453. The Court found these CPIS payments to be meaningless economically. In particular, the purchase and resale of private placement notes in return for cash and interest-only securities by the partnership placed it in the same economic position as if it had elected simply to buy the interest-only securities directly. And without the purchase and resale of the notes, the ratable basis recovery rules would not apply, thus eliminating Colgate’s claimed capital loss. Merrill Lynch and its competitors marketed a similar strategy to other firms, including AlliedSignal, Inc., Borden Inc., Brunswick Corporation, and American Home Products Corp., collectively producing well over $1 billion in losses.

As with its application of the sham transaction doctrine, the government is not always successful in applying the economic substance doctrine. Two examples are Compaq Computer Corp. v. Commissioner and IES Industries, Inc. v. United States. These cases deal with cross-border dividend capture. In essence, both companies engaged in the nearly simultaneous purchase and resale of millions of dollars of ADRs, allowing each company to capture the foreign tax credit occasioned by the withholding tax on the dividends paid by the foreign companies underlying the ADRs. The government argued that the trading strategy lacked economic substance because it merely presented a means of creating a secondary marketplace wherein tax-exempt owners of ADRs could sell their unusable foreign tax credits to firms that valued them more highly. However, the tax benefits claimed by the taxpayers ultimately were sustained in litigation.[15]

2.3.3. Business purpose doctrine

The business purpose doctrine speaks to the motivation of the taxpayer when entering the transaction. This doctrine tests whether, when entering the transaction, the taxpayer was motivated by a business purpose other than obtaining tax benefits. In short, a transaction’s tax treatment is not valid in the eyes of the IRS if the transaction does not have a non-tax business purpose. Relative to the economic substance doctrine, the business purpose doctrine is a more subjective inquiry into whether the taxpayer intends the transaction to serve a truly useful non-tax purpose. Often a corporation will attempt to imbue a transaction with an alleged business purpose, to satisfy the doctrine and preserve the strategy’s tax treatment. For example, in ACM v. Commissioner, Colgate unsuccessfully attempted to argue that the offshore partnership and its transactions served to smooth the company’s debt maturity profile.

2.3.4. Substance over form doctrine

The substance over form doctrine is closely related to the aforementioned judicial doctrines. Substance over form holds that two or more transactions (the “devious path”) that achieve the same underlying economic result that could have been achieved with fewer transactions (the “straight path”) should not be taxed differently than had the straight path been followed. The substance over form doctrine therefore permits the government to distinguish between economic form and formalistic, legal tax form.

This doctrine allows the government to deny tax benefits occasioned by a taxpayer undertaking economically ancillary or circular steps, even if those steps are themselves properly treated for tax purposes. For example, in ASA Investerings Partnership v. Commissioner the government argued that certain transactions, key to producing AlliedSignal’s claimed capital loss, were circular and therefore should not be recognized for tax purposes. And in Zeelandia Investerings Partnership v. Commissioner the government contended that the transactions conducted by the offshore partnership were simply reversed-engineered by Borden Inc. and its other partners through their trading of off-balance sheet swaps. As such, the transactions conducted by the partnership had no substance and should not be recognized in form for tax purposes.[16]

2.3.5. Step transaction doctrine

The step transaction doctrine is closely related to the substance over form doctrine. Basically, the former holds that each separate transaction in a series of related transactions (“steps”) must have independent economic purpose, else the transactions can be “stepped together” for tax purposes.

3. Data and summary statistics

3.1. Tax shelter and matched firm sample formation

We form a sample of companies that have been involved in tax sheltering cases against the government, and/or have been served a Notice of Deficiency related to an alleged tax shelter. Effectively, we include a firm in our sample if we can confirm that the government has accused it of sheltering. A majority of the firms that litigated ultimately lost their cases. Just two have definitively won, and one case has been remanded.

We use Lexis-Nexis to conduct exhaustive electronic searches for sheltering firms, using two primary sources. First, we search the dockets of the various Tax Courts and other courts for litigation involving public corporations and the use of an alleged tax shelter. Second, we search the popular press for firms identified as having received a Notice of Deficiency from the IRS stemming from an alleged tax shelter. In particular, we search for word strings such as “tax shelter,” “transfer pricing,” “sham transaction doctrine,” “Notice of Deficiency,” etc. When a firm is identified through the latter search process, its SEC filings are checked to confirm that it indeed received a Notice. In addition, each firm’s SEC filings are searched to identify the tax years at issue. If the relevant tax years can not be ascertained, the firm is not included in the sample.

This process identifies 43 publicly traded corporations (involving 44 total instances of sheltering) that have been alleged or proven by the government to have illegally sheltered taxable income between 1975 and 2000. In 29 of these 44 cases the taxpayer brought litigation against either the Commissioner or the United States in an attempt to preserve the tax treatment sought. While a few court cases involved tax years dating back two decades or more, except the five CLAS deals, all the cases were docketed in the 1990s.

Table 1 contains information about 44 separate tax shelter instances, 16 of which involve transfer pricing, 11 COLI transactions, five contingent payment installment sales, five contested liability accelerations, three LILO deals, two cross border dividend capture cases, one liquidation/re-contribution, and one intellectual property haven. One firm, Compaq, was accused of both cross-border dividend capture and transfer pricing for the same tax years. The average shelter was active approximately five years. (By “active” we mean the years that the government alleges that the firm used this particular tax shelter.) The 44 cases involve tax shelters that were active a total of 152 firm-years during which the firms were accused of sheltering.

Many firms in our sample allegedly participated in a given tax shelter for several consecutive tax years. In part of our empirical analysis we study corporate activity a given number of years before (or after) a shelter was conducted. Therefore, we collapse the years that a firm allegedly sheltered down into a single “event year” in much of the analysis that follows. We do this by averaging the corporate data for a given firm over all the years the shelter occurred into a single “year t” observation. By doing this we can easily compare across firms because t-1 represents the year before the shelter began and t+1 is the year after it ended, regardless of the length of the sheltering activity. A statistical advantage of collapsing the observations into one per shelter is that it helps address concerns about lack of independence of multiple observations for the same firm.

Without a doubt, many other firms shelter in varying degrees but are not in our sample. For example, trial testimony in ACM v. Commissioner indicates that Merrill Lynch marketed (and likely facilitated) its contingent payment installment sale strategy to firms not in our sample. However, due to private settlements and the like we can not identify these firms. In other situations, once discovered by the government, a tax shelter is terminated via legislation or regulation without ever denying the tax treatment sought or occasioning litigation. At other times the government denies the tax treatment sought and the taxpayer either acquiesces or settles out of court. In these cases, no public record of sheltering exists.

We mention these caveats about identifying sheltering firms because we form a matched sample in part of our empirical analysis. (The matching procedure is described in the next paragraph.) It is likely that some of the match firms in fact use tax shelters, which should work against our ability to identify differences in debt policy between sheltering and non- sheltering firms.

To form the matched sample, we examine the universe of Compustat firms that are not in our tax shelter sample. We identify firms that are in the same 2-digit SIC industry as a given sheltering firm in year t-1 (the year before the sheltering activity began). Among same industry firms, matched firms are those with assets (return on assets) within +/- 25 percent (+/- 50 percent) of the sheltering firm’s assets (return on assets) in year t-1.[17] This procedure produces many matches in some cases and very few in others. Therefore, for much of our analysis, for a given shelter firm we collapse all the matched firms down into a single match; that is, one match per shelter observation. By doing this, our empirical analysis is not dominated by cases for which our matching procedure happens to produce a large number of matches. As described next, this procedure produces a matched sample that is very similar in most dimensions to the shelter sample.

Table 2 provides summary statistics for the sheltering and matched firms. All financial statement data are from Compustat. By construction the sheltering and matched firms are close in asset value, with both samples averaging per firm total assets of more than ten billion dollars. The average sales revenue at both sets of firms is about $7 billion annually. Likewise, the firms have similar asset collateralization, with inventory plus plant, property, and equipment divided by assets averaging about 50 percent in both samples. Sheltering firms on average pay slightly less in federal income taxes (as a percentage of taxable income), even though they are equally as profitable as the matched firms. Sheltering firms have higher market-to-book ratios than do the matched firms. Out of all these characteristics, only the difference in market/book is statistically significant based on a comparison of means.

3.2. How big are tax shelters?

We are able to identify the dollar value of the tax deficiency for 24 of the firms in our sample. For these firms, the median tax deficiency is more than $350 million per year, or a median tax deficiency ratio of 3.1 percent of asset value. That is, the IRS claims that due to tax sheltering, the firm underpaid taxes equal to about three percent of asset value. Grossed up by a 35% corporate tax rate, this implies that the typical deduction associated with these tax shelters is more than $1 billion per firm per year, or about nine percent of total assets. If coupon rates were 10 percent, the typical firm would need to maintain a debt ratio of 90 percent of asset value to produce tax deductions this large. The use of tax shelters by the firms in our sample, therefore, is economically very important.

We do not claim that every company that shelters uses a tax shelter that produces deductions of this magnitude. It is plausible that the government detects and pursues large tax shelter instances, and therefore our sample contains tax shelters that are larger than those that go undetected. Nonetheless, our sample indicates that tax shelters are sometimes huge and research cited in the introduction indicates that the economy-wide magnitude of sheltering is very large. It is also noteworthy that some of the firms in our sample (e.g., Microsoft) are frequently cited as examples of companies that are underlevered. The fact that we identify tax shelter activity at these companies indicates that it is not a simple task to identify firms that are underlevered.

It is interesting that in the year of the alleged sheltering activity, the average debt ratio of sheltering firms is 19 percent versus 27.4 percent for the matched firms (see Table 2). This difference is statistically different at a 1% level. Observing smaller debt ratios among sheltering firms is consistent with these firms using less debt because they obtain their tax deductions from non-debt sources, namely from tax shelter vehicles. However, it is possible that sheltering firms are fundamentally different from the matched firms, and perhaps they have lower debt ratios for other reasons.

To further investigate the differences in debt ratios, we graph the mean debt ratio for both sets of firms in Fig. 2. Debt is total debt (i.e, short-term plus long-term) divided by total assets. The top panel in the figure indicates that shelter and matched firms have approximately the same debt ratios seven or eight years before the sheltering activity took place. (The shelter and matched firm debt ratios are not statistically different through year t-4.) By the year of the alleged shelter, however, the debt-to-asset ratio of sheltering firms is more than 800 basis points lower than for the matched firms, and the difference is near its maximum in the year of the shelter. The yea after the shelter ends, the difference narrows between the debt ratios of the two sets of firms.[18] This same basic pattern occurs in the lower panel, which plots debt-to-market value (where market value is measured as market equity plus book debt).

While we have no direct evidence explaining why debt ratios fall off gradually leading up to the shelter year and then rebound, we note that this time-series pattern is consistent with 1) the group of shelter firms sheltering in increasing amounts in the years leading up to t=0, and 2) the government identifying only the years with the largest sheltering. Or, a firm may play down its debt usage in anticipation of upcoming tax shelter activity. In either scenario, it is not surprising to observe shelter firms having relatively low debt ratios on either side of the shelter year. One implication of these scenarios is that one can not interpret a sheltering firm’s debt ratio in the years just before or just after an alleged shelter period as being “normal.”

In the next section we perform multivariate regressions to investigate whether firms that use tax shelters have low debt ratios, controlling for other characteristics. That analysis, however, cannot eliminate the possibility that the negative relation between debt and sheltering is at least partially caused by sample selection. For example, if the government were to focus on firms with low and recently declining debt ratios as possible tax shelter cases, then firms with these debt ratio patterns would end up in our sample. In this case, the pattern in Fig. 2 might occur for reasons other than shelters causing low debt ratios.

While the possibility of such a spurious relation suggests caution in generalizing our results to the full population of firms, there are three reasons why we do not think that this issue is a major problem for our central hypothesis. First, the government often does not detect tax shelter cases until several years after the shelter activity has ended; therefore, the spurious relation story would need to also explain the recovery in debt ratios that occurs after the shelter ends (but before detection). While this is possible, it puts additional demands on the story. Second, we talked to several IRS agents who have expertise in the shelters undertaken by our sample firms. These agents say that having a debt ratio that declined for many years and then recovered would not make a firm more likely to end up under IRS scrutiny for possible tax sheltering, especially during our sample period. Historically, a firm was investigated as a possible sheltering case when an IRS field agent detected an unusually large (ordinary or capital) gain or loss. The IRS traditionally has not employed statistical detection models for corporate sheltering activity – models that might include factors such as debt ratios.[19] Finally, even if the government were to focus on companies with debt ratios that decline and then recover (like the shelter firms in Fig. 2), this would not preclude the possibility that these firms were in fact substituting between tax sheltering and the use of debt. The government might just happen to choose to focus on firms that optimize in the manner hypothesized by DeAngelo and Masulis (1980).

4. Do tax shelter deductions substitute for interest deductions?

In this section, we perform multivariate regressions to determine whether sheltering activity is correlated with debt ratios, all else equal. We control for other factors that are known to affect corporate debt policy by including right-hand side variables that have been used in previous studies to explain debt policy. We start with the right-hand side variables identified by Frank and Goyal (2004) as the most significant regressors in empirical studies of debt policy. These variables are also used by Graham, Lemmon, and Schallheim (1998) in their study of capital structure.

4.1 Regression analysis of the debt policy of tax shelter firms

The main regressions are purely cross-sectional, with two observations for each tax shelter (one for the shelter firm and one for the matched firm). If the matching procedure identifies more than one match firm, a single observation is created by averaging across all the matches. We use this approach so that the relative number of matches for any particular shelter case does not unduly affect our results. In a robustness check discussed in Section 4.2, we alternatively use all the matched observations for all firms, thereby greatly increasing sample size. The dependent variable in the main regressions is short- term plus long-term debt, the quantity divided by total assets. Ordinary least squares regressions are used (rather than tobit) because every observation in the main regressions has nonzero debt.

The key right-hand side variable is a dummy variable equal to one if the firm has been identified as having an active tax shelter and equal to zero otherwise. The other regressors are intended to control for factors known to affect debt policy. Firm size (sales revenue) is included to capture any economies of scale that exist in the issuance or use of debt. The market-to-book ratio is intended to control for differences in investment opportunities. A dividend dummy variable is included to control for differences in informational asymmetry. Dividend-paying firms are hypothesized to be subject to less information asymmetry and hence have more debt capacity. One of the most pervasive facts about debt policy is that profitable firms use less debt (Myers, 1993), which could reflect pecking order behavior in which internal funds (if available) are used before external funding sources such as debt. Therefore, we include ROA as a control variable. Another pervasive fact about debt policy is that firms with highly collateralizable assets (e.g., inventory, property, plant, and equipment) use more debt, so we include the proportion of assets that are collateralizable.

Finally, because recent research (e.g., Leary and Roberts, 2005) shows that companies have a tendency to work towards target debt ratios, we include a lagged debt ratio as a control variable. We choose a five-year lag to strike a balance between a lag of seven years, at which time we observe nearly equal debt ratios between sheltering and matched firms (in Fig. 2), and the more traditional one-year lag. A one-year lag seems inappropriate in our setting because we include lagged debt to capture some element of “normal” debt policy, and it seems likely that the shelters were already affecting the firms in our sample by t-1. (Note also that the mean 5-year lagged debt ratios for matched and shelter firms are not significantly different from each other.) All of the independent variables (except the shelter dummy and 5-year lagged debt ratio) are lagged one period because their year-t values are potentially determined jointly with debt policy.

The estimated coefficients for many of the right-hand side variables have the expected signs (see Table 3). Dividend-paying firms use more debt than non-dividend firms. The estimated coefficient indicates that dividend-paying firms have debt ratios that are 650 basis points higher than for non-dividend firms. The coefficients also indicate that profitable firms use less debt, and that firms with collateralizable assets use more debt, both as predicted. Firm size is not significant in this regression (and profitability only at a 10% level), perhaps because of the matching procedure that we used to form the sample. The market-to-book ratio is positive, surprisingly, indicating that all else equal high market-to-book firms in our sample use more debt.

The most important variable for the purposes of this paper is the tax shelter dummy. The estimated coefficient indicates that firms that use tax shelters have debt ratios that are 550 basis points lower than debt ratios for the matched firms. Therefore, controlling for other factors, tax shelter firms use less debt than do non-shelter firms, which is consistent with shelters producing non-debt tax shields that substitute for the use of corporate debt. In this main specification the adjusted R-squared is 54.2%, indicating a good fit. There are

76 observations used in the main specification, consisting of 38 shelter firms and 38 matched firms that have sufficient non-missing data to perform the regression.

Recent anecdotal evidence (e.g., Enron) suggests that firms that use tax shelters might also use other forms of off-balance sheet financing. If this is true, then the negative relation between debt ratios and tax shelter activity might not indicate that shelters are the sole factor correlated with reduced use of debt; instead, participating in a shelter might be correlated with the use of other NDTS that also lead to substitution away from debt. Mills and Newberry (2004), however, show that firms with high debt ratios are more likely to use off-balance sheet and hybrid financing instruments. Thus, the low debt ratios for our tax shelter firms imply less non-shelter off-balance sheet financing. Therefore, contrary to what the Enron anecdote might suggest, it seems unlikely that non-shelter off-balance sheet NDTS are behind the negative relation that we document between debt ratios and tax shelter activity.

4.2. Robustness of debt policy analysis

We perform several robustness checks to the main specification. Column 2 of Table 3 adds a variable equal to the calendar year that the shelter was allegedly being used. This should control for time-trends in debt ratios that are not captured by the other variables. While the trend variable is significant, the tax sheltering dummy variable remains negative and significant, so our central finding holds. Column 3 repeats the main regression (i.e., the specification summarized in Column 1) but drops the two variables for which the estimated coefficients have unexpected signs in the main specification: size and market-to-book. The estimated coefficients for the remaining factors are qualitatively unchanged. Finally, Column 4 drops the five year lagged debt ratio from the specification, which leads to the inclusion of eight additional observations in the analysis (four shelters and four matches). In this case, the negative coefficient on the tax shelter dummy variable is even larger and again indicates that sheltering firms have lower debt ratios.

Although we use lagged explanatory variables in an attempt to circumvent possible endogeneity, we recognize that this might be an imperfect fix. To further address this issue, we perform 2-stage analyses in which we use “predicted sheltering” as an explanatory variable (in place of the tax shelter dummy). Predicted sheltering is created using the estimated coefficients from a regression with 0/1 sheltering as the dependent variable. In addition to the tax shelter observations, the dataset for the first stage regression includes 68 randomly chosen Compustat observations for firms that, as far as we know, do not use tax shelters. The first-stage regression indicates that firms that are large, profitable, and engage in intense R&D are relatively likely to shelter.[20] The second- stage regression indicates that predicted tax sheltering leads to lower debt ratios, corroborating our main result.

In an alternative 2-stage approach, we follow Lee (1978), Heckman (1979), and Fang (2005) and use an endogenous switching model, which recognizes that the tax shelter dummy might be endogenous to debt policy. The results of this approach indicate that if tax shelter firms were to hypothetically not use shelters, their debt ratio would nearly double, increasing from the observed mean of 19% to 36.6%. If nonshelter firms were to hypothetically begin using tax shelters, their debt ratios would fall from the observed mean of 27.4% to 15.3%. Both of these differences are significant (t-scores greater than 10). These analyses quantify that, controlling for endogeneity, the effect of tax sheltering activity on debt usage is economically very important in our sample.

Additional robustness checks are presented in Table 4. The specification in Column 1 of Table 4 mimics the specification in Column 1 of Table 3, except the dependent variable is debt-to-value. The estimated coefficient indicates that debt-to-value is 530 basis points lower for tax sheltering firms, relative to matched firms, all else equal. In Column 2 and the remaining columns, we return to using debt-to-assets as the dependent variable. In Column 2 of Table 4, we delete the CPIS and LR observations because these shelters address capital losses, which are not directly substitutable for interest deductions. We also delete COLI observations because their effect on the debt ratio is ambiguous (see Section 2.2.) These deletions produce a “clean sample” but do not affect the overall inference.[21]

In Columns 4 and 5, we use a panel of data from t-8 to t+6. In this panel, rather than collapsing all the matches down into one observation per shelter firm, we include all the match firms in the regression. Also, each firm-year in which a tax shelter is active receives its own observation (i.e., we do not collapse all active shelter years down into a single observation like we do in Table 3 and the other columns of Table 4). Like the main specification in Column 1 of Table 3, there are 38 sheltering firms in this specification; however, there are now 1140 observations.

The main result holds in the panel regressions shown in Column 4 of Table 4: Firms that use tax shelters have lower debt ratios. This specification is performed using OLS, to enhance comparability with the other regressions. However, a couple dozen of the 1,140 observations are censored, so a tobit regression is also preformed. The estimated tobit coefficients are nearly identical to those reported in Table 4.

In column 5, we add a new variable, shelter firm dummy, to address whether the lower shelter-firm debt ratio is a time-series or a cross-sectional effect. That is, we investigate whether the lower debt ratio for sheltering firms emanates from a debt ratio that falls through time for shelter firms and/or results from cross-sectionally lower debt ratios for shelter firms relative to matched firms. The new shelter firm dummy equals one for a shelter firm in any year and equals zero for matched firms. This new variable captures cross-sectional differences between shelter and matched firms. The shelter active dummy captures differences in debt ratios for shelter firms in years for which a tax shelter is being used (relative to debt ratios of shelter firms when the shelter is not being used). The negative coefficients on both variables indicate that shelter firms use less debt than matched firms in the years the shelter is not active, and also that shelter firms redefine their debt ratios when the shelter is active. In this sense, therefore, the tax shelter effect is both time-series and cross-sectional.

In two regressions that do not appear in the table, we more closely control for market-to- book and for sample composition. In the first, we require matching firms to have market- to-book that is at least half as large as the sheltering firm’s market-to-book, and no larger than twice as big. Regressions based on this extra matching requirement produce coefficients the magnitude and significance of which are similar to what is reported for the main specification. In the other untabulated regression, we only examine sheltering and matched firms that exist at least three years prior to and three years after the year of the alleged shelter, reducing the sample to 36 shelters and 36 matches. The qualitative results are similar to those previously reported, with sheltering firms using less debt than matched firms.

Next, we address the possibility that asset value or profits increased more for shelter firms (relative to match firms) during our sample period. This could explain why debt-to- assets falls for shelter firms. First, we note that actual asset growth for shelter firms is nearly identical to growth for match firms in the years leading up to the inception of the shelter activity (not shown in a table). Similarly, cumulative profit from t-5 to t-1 for shelter firms is statistically indistinguishable from cumulative profit for matched firms. Second, we include a variable in the main specification that equals cumulative ROA from year t-5 to t-1. We do this to better control for the effect of cumulative profitability on debt ratios than can be done using the usual control of one-period lagged ROA. While the cumulative ROA variable is significant at a 5% level, the shelter dummy still indicates that shelter firms have lower debt ratios than do matched firms, all else equal (Column 3 of Table 4). These results taken together indicate that the lower debt-to-assets ratio for the shelter firms is not a denominator effect.

Finally, we examine incremental debt issuance decisions from t-8 to t=0. By focusing on the numerator of the debt ratio, we avoid the possibility that denominator effects drive our results. If tax shelters create deductions that substitute for interest deductions, we expect to see sheltering firms issue less debt. Unconditionally, 60 percent of sheltering firms issue debt in the years leading up to tax shelter activity, in comparison to 70 percent of non-sheltering firms. In a logistic regression that holds all else equal, sheltering firms are significantly less likely to issue debt than are non-sheltering matched firms (see Table

5). The estimated coefficient for the tax shelter dummy indicates that tax shelter firms are

13.7% less likely to issue debt, all else equal.

5. Summary and conclusions

We investigate the use of 44 corporate tax shelters at 43 firms from 1975 to 2000. The shelters in our sample are very large economically, producing annual deductions that average about nine percent of asset value. The tax savings produced by these shelters are much larger than interest tax deductions for comparable firms that we do not identify as using tax shelters.

Not only are tax shelters large economically, they appear to interact with corporate debt policy in interesting ways. Firms that use tax shelters use less debt on average than do non-shelter firms. Regression coefficients indicate that, everything else equal, tax sheltering firms’ debt-to-asset ratios are more than five percentage points lower than leverage for non-shelter firms. These results are consistent with tax shelters being a non- debt tax shield that substitutes for the use of interest tax deductions (DeAngelo and Masulis, 1980).

Overall, our results are consistent with the belief that tax sheltering activity is important economically (Bankman (2003) and Desai (2003)). Given the recent dramatic reduction in corporate tax receipts, learning more about the magnitude of tax shelters, and their effect on other corporate policies, is an intriguing area for future research. Currently, the confidentiality accorded to the identities of tax shelter firms hinders such research. Future investigations that creatively obviate this lack of information have the potential to make important contributions to our understanding of tax shelter activity. Some help might come from the expanded disclosure requirements of the new tax form Schedule 10-3 (see Boynton and Mills (2004) for details).

Appendix – Abridged Case Studies

Case study of Compaq’s cross-border dividend capture shelter (CBDC) As an illustration of a cross-border dividend capture shelter, consider the transactions conducted by Compaq Computer Corporation. On September 16, 1992, Compaq bought from and then immediately sold to the same party 10 million American Depository Receipts (ADRs) of the Royal Dutch Petroleum Company (RDP) of the Netherlands. The transactions were sponsored and structured by Twenty First Securities Corporation (TFSC).[22] Because TFSC was a small brokerage firm and not a member of the New York Stock Exchange (NYSE), TFSC engaged Bear Stearns, a major brokerage firm and member of the Exchange, to clear the ADR transactions. Specifically, Bear, Stearns Securities Corporation acted as TFSC’s clearing broker, searched the market, located and borrowed the ADRs from institutional investors (such as pension funds or other tax-exempt institutions[23]) and banks and other brokerage firms (who held ADRs for unidentified investors believed to be tax-exempt) for short selling to Compaq. Arthur J. Gallagher

& Co., an insurance broker and client of TFSC, was engaged by TFSC as the short-seller.

In accordance with prior arrangements by TFSC, in a series of 23 cross-trades completed within a

1-hour time span, Gallagher sold short the ADRs to Compaq at “prevailing” market prices plus the expected net dividend. The net dividend is defined as the gross dividend adjusted for the both the 15% foreign withholding tax and the guilder-dollar exchange rate. In each case, Compaq then immediately sold the securities back to Gallagher at the same price less the net dividend.

Each of the 23 purchases resulted in an average ADR price that equaled the “prevailing” (at time of trade) ex-dividend price of the ADRs plus the net dividend of $1.9165. (This $1.9165 net figure equals the gross dividend declared by RDP, adjusted for the 15% dividend withholding tax, and also adjusted for the spot exchange rate (dollar-guilder) at which the declared dividend net of the foreign withholding tax was repatriated into dollars.) This unique average ADR price (which is a four-decimal-place price at a time when NYSE stocks were quoted in eighths) was accomplished by trading different blocks of ADRs at the same time but at different prices.

The resale of the ADRs from Compaq to Gallagher occurred at the prevailing ex-dividend price, implying a loss to Compaq equivalent to the dollar value of the net dividend, ignoring trading costs. Compaq’s purchases totaled $887,577,130 (before commissions and margin interest) while the sales amounted to $868,412,130 (before commissions and fees). The $19,165,000 difference is equivalent to the $1.9165 per share net dividend.

The specific time line follows. As of the trading date (September 16, 1992), Royal Dutch had already declared a dividend on its ADRs, to be paid on October 2. The ADR went ex-dividend on September 14, and the dividend record date was September 18. To enable Compaq to capture the declared dividend, the company purchased the ADRs with a special settlement date of September 17, 1992. Since this settlement date preceded the record date, Compaq was entitled to the declared dividend. The ADRs, however, were already selling in the market at the ex-dividend price. Hence, as structured by TFSC with a special settlement date, Compaq paid the prevailing market price plus the net dividend that would be due the holder of the ADR.[24] Compaq then sold back the ADRs at the same price less the net dividend (which in this case was the “prevailing” ex- dividend price) with a regular settlement date, i.e., settlement in five days after the trading date or September 21, 1992. Compaq agreed to pay commission to TFSC of $0.05 per ADR traded, i.e., $1,000,000 for the 20 million ADRs traded (purchases and sales). Compaq also paid margin interest (at 4.75%) totaling $457,845.73 for the ADR purchase, and SEC fees of $28,947 on its sale of the ADRs.

On October 2, 1992, Compaq’s reported gross dividend from RDP’s ADRs was $22,545,800. Of this amount, $3,381,870, i.e., the 15% Netherlands withholding tax on the dividends, was withheld by RDP. Thus, Compaq received a net dividend payment of $19,163,930. Compaq claimed a foreign tax credit of $3,382,050 for 1992 in connection with the taxes withheld. In general terms, Compaq paid $1.5 million to obtain a $3.3 million tax credit (note: a tax credit, not a deduction). Graham (2003) demonstrates that foreign tax credits decrease the incentive to issue domestic debt, as long as a firm has a positive probability of operating in “excess foreign tax credit” status. This occurs because interest allocation rules can reduce allowable domestic interest deductions for firms with foreign operations when the firm is excess credit.

A summary of Compaq’s transactions is provided below. This schedule shows that the ADR strategy designed by TFSC had the economic effect of enabling Compaq to purchase a 34-cent per share tax credit at a net cost of 21.5 cents per share.

clip_image002

clip_image004

Case study of Colgate’s contingent-payment installment sale (CPIS)

Colgate Palmolive Company (Colgate) undertook a CPIS with the assistance of Algemene Bank Nederland N.V. (ABN). Colgate reported a $105 million gain for 1988 (occasioned by a division sale), and Merrill Lynch & Co. (Merrill) sponsored a plan to shelter said gain. Through newly

formed entities known as Southampton (for Colgate), Merrill Lynch MLCS Inc. (for Merrill), and

Kannex (for ABN), the three firms created, in October of 1989, a Curacao-based, Delaware- incorporated partnership called ACM. ACM served as a special purpose vehicle to create a large capital loss for Colgate, with corresponding gain allocated to Kannex, which was not subject to U.S. tax. ACM was initially capitalized with $205 million, and the initial partnership interests were Colgate/Southampton 17.1%, Kannex/ABN 82.6%, and MLCS/Merrill 0.3%. Upon its formation, ACM purchased $205 million face value of private placement notes issued by Citicorp. Three weeks later, in November 1989, ACM sold $175MM face value of these notes to two buyers – BFCE (a French bank) and BOT (a Japanese bank) – for approximately $140 million in cash and approximately $35 million worth of floating-rate, interest-only securities (issued by said buyers). ACM reported the transaction under the contingent-payment installment sale provisions of temporary IRC section 453 (known as the “ratable basis recovery rule”). ACM reported a $110.7 million capital gain in the year of the sale (1989), mostly allocated to Kannex. In a later tax year, after ACM had redeemed Kannex’s interest, ACM sold the interest-only securities and reported an $85 million capital loss that was allocated to Colgate. Colgate carried the loss back to offset part of its 1988 gain.

More specifically, because ACM was to receive part of the consideration for the sale of the Citicorp notes “after the close of the taxable year in which the disposition occurs” pursuant to IRC section 453(b)(1), on its partnership return for the tax year ending November 30, 1989 ACM treated the sale of the Citicorp notes as an installment sale under Temporary Treasury Regulation Section 15a.453-1(c), whose ratable basis recovery rule provides that the taxpayer’s basis “shall be allocated to the taxable years in which payment may be received under the agreement in equal annual amounts.” So, ACM divided its $175,504,564 basis in the Citicorp notes ($175 million in principal and $504,564 in accrued interest) equally among the six years over which payments were to be received in exchange for those notes (reflecting the maturity of the interest-only

securities or “IOs”), and thus recovered one-sixth of that basis, or $29,250,761, during 1989. Subtracting this basis from the $140 million in cash proceeds, ACM reported a 1989 gain of

$110,749,239, which it allocated among its partners according to their partnership shares. This resulted in a gain of about $91.5MM to Kannex (which was not subject to U.S. income tax),

$18.9MM to Southampton, and $324k to MLCS.

The tax basis to be recovered over the remaining five years became $146,253,803. Of this amount, $41,786,801 was attributable to the BFCE-issued IOs (whose actual cost was

$10,144,161). The remaining $104,467,002 in unrecovered tax basis was attributable to the BOT-issued IOs (whose actual cost was $25,630,403). ACM distributed the BFCE IOs to Southampton in early December 1989 (as a return of part of Southampton’s contributed capital), and Colgate/Southampton sold the BFCE IOs in late December 1989 for $9,406,180.

On its 1989 return, Southampton reported its $18,908,407 share of the capital gain from the

$140MM in cash received for the sale of the Citicorp notes, and reported a $32,429,839 capital loss from its sale of the BFCE IOs. Because these capital losses completely offset the capital gains, Southampton reported a net 1989 capital loss of $13,521,432 and did not report any net tax liability on its share of ACM’s gain from the disposition of the Citicorp notes.

In June 1991 Colgate/Southampton acquired a 38.31% share in ACM from ABN/Kannex for

$85,897,203, and Colgate/Southampton acquired an additional 6.69% share from Kannex for

$15,000,000, giving Colgate/Southampton a majority interest in ACM. This permitted Colgate to consolidate ACM’s holdings with its own on its books. ACM exercised a put option embedded in the remaining $30MM face value of Citicorp notes, thus selling the remaining notes back to Citicorp. In November of 1991, ACM redeemed the rest of ABN/Kannex’s interest for

$100,775,915, leaving Southampton/Colgate with 99.7% of ACM. Then ACM sold the BOT IOs (to BFCE) for $10,961,581. So, for the tax year 1991 ACM reported a capital loss of $84,997,111 from its sale of the BOT IOs (reflecting the selling price and the remaining $95,958,692 basis in these IOs). Of this approximately $85MM loss, $5.8MM reflected a decline in value due to lowered interest rates while $79.2MM resulted from the application of the ratable basis recovery

rule (which effectively added to the tax basis of the IOs five-sixths of the $140MM value of the

Citicorp notes which had been sold for cash in 1989).

Colgate claimed 99.7% of this $85MM capital loss on its 1991 return. The company then filed an amended 1988 return reporting this loss as a carryback pursuant to IRC section 1212 to offset a portion of its 1988 capital gain. So, the carryback of about $85MM plus the aforementioned reported net loss in 1989 of about $13.5MM together provided a shelter of nearly $100MM for Colgate. Given its marginal federal income tax rate, this shelter provided Colgate with a real economic benefit of nearly $40MM, before transaction costs. The costs totaled only about

$15MM for this shelter.

Case study of FPL’s liquidation/re-contribution deal

In 1992, Florida Power and Light (FPL, currently the largest utility in the U.S.) allegedly wanted to refresh an expiring loss carry forward of about $337 million. Goldman Sachs devised a plan in

which a newly created offshore partnership, called Salina, engaged in a series of securities transactions. Originally Salina consisted of two partners that were also newly created and, in a complex way, related to and managed by a large foreign bank – ABN AMRO Holdings NV. After its formation in mid-December 1992, Salina immediately engaged in a series of securities

transactions, most notably the short sale of $344.4 million worth of 6-month Treasury bills and the purchase of $140.3 million worth of 2-year Treasury notes. Salina also engaged in a reverse repurchase agreement with ABN’s New York office, that is, it loaned ABN NY $343.9 million. Salina borrowed, via a repurchase agreement, from Goldman Sachs $70.1 million, and the two original partners collectively posted $75.4 million in capital.

Following these transactions that became effective December 18, 1992, Salina’s balance sheet was as follows:

clip_image006

Just ten days later, on December 28, 1992, FPL purchased a 98% partnership interest in Salina. Under then existing partnership accounting rules,[25] this act occasioned a liquidation of the partnership whose assets and liabilities were then immediately re-contributed to an allegedly new partnership. This new partnership kept the same name and same tax identification number as the liquidated partnership. The liquidation/re-contribution occasioned the desired gain of $337 million as follows: The short bill position was valued by FPL at zero, leaving approximately $145 million (the “outside basis”) in liabilities and equity for the new partnership (approximately $70 million on the Goldman repo and $75 million of equity).[26],[27] The approximately $5 million of time deposits (cash) was then subtracted from the outside basis to give an “inside basis” of $140 million. This amount was then proportionally allocated to the remaining assets of approximately $483 million: the approximately $343 million loan to ABN on the reverse repo plus the approximately $140 million worth of Treasury notes. The loan represented about 71% of the remaining assets, the notes the other 29%. Thus the inside basis of $140 million was allocated as follows: $99.4 million to the loan (71% of $140 million) and $40.6 million to the notes (29% of

$140 million). Under existing partnership accounting rules previously cited, this allocation would therefore lead to a paper gain on the loan, to be triggered whenever the loan receivable is collected, of $243.6 million ($343MM – $99.4MM). This allocation would also lead to a paper gain on the notes, to be triggered whenever the notes are sold, of $99.4MM ($140MM –

$40.6MM). Adding these two amounts results in a gain of $343 million.

By the end of December 1992, and therefore after FPL became a 98% partner in Salina, the loan receivable was collected and the notes were sold. Under existing partnership accounting rules, FPL thereby obtained 98% of the $343 million gain, which matches its desired gain of $337 million.[28] December of 1992 also marked the end of FPL’s fiscal tax year as well as the expiration of its loss carry forward.

Thus, FPL attempted to refresh its expiring capital loss carry forward by the engineering of a paper gain in this LR transaction. It is the contention of the IRS that FPL hoped to generate real gains beyond 1992 that could be offset by the refreshed capital loss carry forward. And that FPL intended to engineer a new paper capital loss (via another tax shelter) that would serve to offset the paper gain required to refresh the original but about-to-expire capital loss carry forward. If this or similar strategies were applied repeatedly, the firm could conceivably delay the payment of gains taxes indefinitely.

References

Baldenius, Tim, Melumad, Nahum, and Stefan Reichelstein, 2004, Integrating managerial and tax objectives in transfer pricing, Accounting Review 79, 591-615.

Bankman, Joseph, 1999, The new market in corporate tax shelters, Tax Notes 83, 1775.

Bankman, Joseph, 2003, The tax shelter battle, in Crises and Tax Administration, ed. H. Aaron and J. Slemrod, Brookings Institution.

Bankman, Joseph, and Daniel Simmons, 2003, Terminating tax shelters: Has California broken the legislative logjam?, Tax Notes.

Bartelsman, Eric J., and Roel M. W. J. Beetsma, 2003, Why pay more? Corporate tax avoidance through transfer pricing in OECD countries, Journal of Public Economics 87, 2225-2252.

Bird, Robert, and Alan Tucker, 2002, Tax sham or prudent investment?: A case study of Salina Partnership v. Commissioner of Internal Revenue, Virginia Tax Review 22, 231-272.

Bonyton, Charles, and Lillian Mills, 2004, The evolving schedule M-3: A new era of corporate show and tell? National Tax Journal LVII, 757-769.

Cetta, Anthony J., 2002, Tax efficiency in corporate cash management, Derivatives Report.

Clausing, Kimberly A., 2003, Tax-motivated transfer pricing and US intrafirm trade prices, Journal of Public Economics 87, 2207-2223.

DeAngelo, Harry and Ronald W. Masulis, 1980, Optimal capital structure under corporate and personal taxation, Journal of Financial Economics 8, 3-29.

Desai, Mihir A., 2003, The divergence between book and tax income, Tax Policy and the Economy 17, ed. J. Poterba, MIT Press, 169-206.

Desai, Mihir A., C. Fritz Foley, and James R. Hines, Jr., 2004, Economic effects of regional tax havens, Working Paper, Harvard University.

Desai, Mihir A., C. Fritz Foley, and James R. Hines, Jr., 2005, The demand for tax haven operations, Working Paper, Harvard University.

Fang, Lily, 2005, Investment bank reputation and the price and quality of underwriting services, Journal of Finance forthcoming.

Frank, Murray, and Vidhan Goyal, 2004, Capital structure decisions: Which factors are reliably important? Working Paper, University of British Columbia.

Graham, John R., 2000, How big are the tax benefits of debt? Journal of Finance, 55, 1901-1941. Graham, John R., 2003, Taxes and corporate finance: A review, Review of Financial Studies, 16, 1074- 1128.

Graham, John R., Mark Lang, and Doug Shackelford, 2004, Employee stock options, corporate taxes and debt policy, Journal of Finance 59, 1585-1618.

Graham, John R., Michael Lemmon, and James Schallheim, 1998, “Debt, leases, taxes, and the endogeneity of corporate tax status,” Journal of Finance 53, 131-162.

Green, Richard C. and Burton Hollifield, 2003, The personal-tax advantages of equity, Journal of Financial Economics, 67, 175-216.

Heckman, J. James, 1979, Sample selection bias as a specification error, Econometrica 47, 153-161. Hennessy, Christopher A., and Toni M. Whited, 2004, Debt dynamics, Journal of Finance forthcoming. Hines, James, 1997, Tax Policy and the activities of multinational corporations. Ed.

Alan Auerbach, Fiscal Policy: Lessons from Economic Research. MIT Press, Cambridge, 401-445.

Joint Committee on Taxation, 1999, Study of present-law penalty and interest provisions as required by Section 3801 of the Internal Revenue Service Restructuring and Reform Act of 1998 (Including Provisions Relating to Corporate Tax Shleters). JCS 3-99, Washington, D.C.: U.S. Government Printing Office.

Ju, Nengjiu, Robert Parrino, Allen M. Poteshman, and Michael S. Weisbach, 2004, Horses and rabbits? Optimal dynamic capital structure from shareholder and manager perspectives, Journal of Financial and Quantitative Analysis forthcoming.

Leary, Mark, and Michael Roberts, 2005, Do firms rebalance their capital structures?, Journal of Finance forthcoming.

Lee, Lung-Fei, 1978, Unionism and wage rates: A simultaneous equations model with qualitative and limited dependent variables, International Economic Review 19, 415-433.

Luitjens, Marlyce, 2004, Sale in-lease out (SILO) transactions, FSLG Newsletter (June).

McGill, Gary A., and Edmund Outslay, 2004, Lost in translation: Detecting tax shelter activity in financial statements, National Tax Journal 57, 739-756.

Miller, Merton H., 1977, Debt and taxes, Journal of Finance, 32, 261-275.

Mills, Lillian, and Kaye Newberry, 2004, Firms’ off-balance sheet and hybrid debt financing: Evidence from their book-tax reporting differences, Journal of Accounting Research forthcoming.

Myers, Stewart C., 1993, Still searching for the optimal capital structure, Journal of Applied Corporate Finance, 6, 4-14.

Parrino, Robert, and Michael Weisbach, 1999, Measuring investment distortions arising from stockholder- bondholder conflicts, Journal of Financial Economics 53, 3-42.

Plesko, George, 2004, Corporate tax avoidance and the properties of corporate earnings, National Tax Journal 57, 727-737,

Schallheim, James, and Kyle Wells, 2004, Are firms really under-levered?, Working Paper, University of Utah.

Scholes, Myron., Mark Wolfson, Merle Erickson, Edward Maydew, and Terry Shevlin, 2002, Taxes and business strategy, 2nd edition, Prentice-Hall: Englewood Cliffs, NJ.

Stefanescu, Irina, 2005, Capital structure decisions and corporate pension plans, Working paper, University of North Carolina

Tucker, Alan, 2002, Trafficking in foreign tax credits, Global Finance Journal 13, No. 2, 1-15.

Figure 1. Mean effective tax rate for U.S. corporations.

The effective tax rate is defined as taxes paid from the statement of cash flows divided by pretax net income. The tax rate is averaged over all firms in the S&P 500 or all firms on NYSE/AMEX/Nasdaq (excluding REITs, ADRs, closed-end mutual funds, preferred stocks, foreign stocks, unit investment trusts, and Americus Trust). The statutory marginal tax rate for firms in the highest income tax bracket was 34% until 1992 and

35% thereafter.

clip_image008

Figure 2. Time Series of debt ratios for tax shelter firms versus matched firms Panel A plots debt-to-assets ratios for shelter firms and matched firms. Matched firms are in the same industry as the shelter firm and have book assets, profitability, and market/book ratios that are similar to shelter firm ratios. Panel B is similar but is based on debt-to-market value (where market value is market equity plus book debt). The shelter firms allegedly participate in tax shelters in year 0. For multi-year shelters, the debt ratio is averaged for all years the shelter is active and presented as year 0. Year –1 is the year before the shelter begins and +1 is the year after the shelter ends.

Panel A: Debt/Assets

clip_image009

Table 1. Sample of tax shelter firms

This table identifies the 43 publicly traded corporations, involving 44 total shelters, that are identified as tax shelter users in our sample. One firm, Compaq Computer Corporation, was alleged to use two separate shelters (transfer pricing and cross border dividend capture). The total number of shelter years is 152. Case citations are provided when the corporation has litigated against the government. Each firm received a Notice of Deficiency from the government.

clip_image010Firm Shelter type Years Case Citation (if applicable)

Colgate-Palmolive CPIS (a) 1988 73 T.C.M. (CCH) 2189, 2215
AlliedSignal CPIS 1990-1992 76 T.C.M. (CCH) 325
American Home Products CPIS 1990-1993 167 F. Supp. 2d 298
Brunswick CPIS 1990-1991 78 T.C.M. (CCH) 684
Borden Inc. CPIS 1995 12927-95 (b)
Compaq Computer CBDC/TP (c) 1991-1992 277 F.3d 778
IES Industries CBDC 1991-1992 253 F.3d 350
Florida Power and Light LR (d) 1992-1994 80 T.C.M. (CCH) 686
Seagate Tech Inc. TP 1981-1987 102 T.C. No. 9
St. Jude Medical TP 1981-1983 34 F.3d 1394
Microsoft Corp. TP 1987-1989, 1991 75 T.C.M. (CCH) 1747
Bausch & Lomb TP 1983-1987 T.C. Memo 1996-57
National Semiconductor TP 1976-1981 67 T.C.M. (CCH) 2849
Exxon Corp. TP 1980-1982 752 F.2d 650
Intel Corp. TP 1978-1980 76 F.3d 976
Boeing Co. TP 1979-1987 258 F.3d 958
Archer-Daniels-Midland Co. TP 1975-1978 37 F.3d 321
Phillips Petroleum TP 1979-1982 104 T.C. 256
Perkin -Elmer Corp. TP 1975-1981 66 T.C.M. (CCH) 634
Computervision Corp. TP 1981 96 T.C. 652
Sunstrand Corp. TP 1977-1978 96 T.C. 226
Brown-Forman Corp. TP 1981, 1983 94 T.C. 919
Chevron Corp. TP 1977-1978 104 T.C. No. 35
American Electric Power COLI 1990-1996 136 F. Supp. 2d 762
Winn-Dixie COLI 1993 254 F.3d 1313
Dow Chemical Co. COLI 1989-1991 250 F. Supp. 2d 748
CM Holdings Inc. COLI 1991-1994 254 B.R. 578
Bmc Software Inc. OIPH (f) 1993 73 F. Supp. 2d 751
W.R. Grace & Co. COLI 1989-1998
Hershey Foods Corp. COLI 1989-1998
Western Resources Co. COLI 1992-1993
Hillenbrand Industries Inc. COLI 1996-1998
Donnelly RR and Son Inc. COLI 1990-1998
Ruddick Corp. COLI 1993-1998
National City Corp. COLI 1990-1995
AmSouth Bancorp LILO (g) 1998-1999
FleetBoston Financial Corp. LILO 1995-1997
BB&T Corp. LILO 1996-1998
Delta Air Lines CLAS (h) 2000
Whirlpool Corp. CLAS 2000
Clear Channel Com. Inc. CLAS 2000
WorldCom Inc. CLAS 2000
Tenet Healthcare Corp. CLAS 2000

—————————————————————————————————————————————— (a) CPIS: Contingent payment installment sale. (e) COLI: Company owned life insurance.

(b) Tax Court Doc. No. only as the Borden case never went to trial. (f) OIPH: Offshore intellectual property haven. (c) CBDC: Cross border dividend capture. TP: Transfer pricing.(g) LILO: Lease in/lease out.(d) LR: Liquidation/re-contribution. (h) CLAS: Contested liability acceleration strat.

Table 2. Summary statistics

Summary statistics are presented for the years that the shelter is active. If the shelter lasted multiple years, the statistics are averaged across all of the years that the shelter was allegedly active. Matched firms are in the same industry as the shelter firm and have book assets within +/- 25 percent and profitability within +/- 50% of the shelter firm’s ratios in the same year. All numbers are means except for tax deficiency/assets. The market/book ratio is market equity plus book debt, the sum divided by book assets. Collateralization is measured as plant, property, and equipment divided by book assets. ROA is the return on assets, measured by net income divided by assets. Debt/Assets is total debt divided by total assets. Deficiency is the dollar amount of taxes (divided by asset value) that the Internal Revenue Service claims that the taxpayer is deficient, due to the tax shelter, in its actual tax payments. Therefore, a $1 Deficiency would be produced by a tax shelter deduction equal to $1/(corporate tax rate).

S helter firms M atch firms D ifference
Assets (millio ns $) 12,440 10,728 1712
Sales (millio ns $) 7,687 6,581 1106
M kt/Bo o k 2.99 2.26 0.73 *
(PP E +invento ry)/Assets 0.5 0.518 -0.018
RO A 0.068 0.068 0
FedT axP aid/Pre-tax inc. 0.234 0.272 -0.038
D ebt/Assets 0.19 0.274 -0.084 ***
D ebt/M kt value 0.138 0.23 -0.092 ***
D eficiency/Assets (median) 0.031 n.a.

*** difference is statistically significantly different from zero (based on a differences in means test) at a 1% confidence level, ** at 5% confidence level, * at 10% confidence level.

Table 3. Regression analysis investigating whether shelter firms use less debt

OLS regressions are performed to identify the factors that affect a given company’s debt- to-assets ratio. There are two observations for each tax shelter firm in the sample: one for the tax shelter firm, and one for the matched firm, where the matching identifies same- industry firms in the same years with assets within +/- 25% of shelter firm t=-1 assets and +/- 50% of shelter firm profits. If there is more than one match that meets these criteria, the mean of the values for a given variable is used. If the shelter is active more than one year, the mean value for all years that the shelter is active is used in the regression. The dependent variable is debt-to-assets, which is book debt divided by book assets. Shelter dummy equals one if the firm has an active tax shelter and zero if the firm is a match firm. A negative shelter coefficient indicates that tax shelter firms have lower debt ratios, all else equal. Lag5(debt) is the debt ratio from five years before the shelter became active. Mkt/book is the market value of the firm (market equity plus book debt) divided by book assets. Div-pay dummy equals one if the firm pays dividends. ROA is net income divided by book assets. Collateral/assets equals inventory, plant, property, and equipment divided by book assets. Time trend equals the calendar year. The right-hand side variables are all lagged one year, except for Lag5(debt) and the time trend. Robust t statistics adjusted for year clustering and heteroskedasticity are in parentheses.

Main

specification

(1)

(2) (3) (4)
Intercept 0.082**

(2.313)

-9.353***

(-4.872)

0.102***

(2.805)

0.171***

(3.260)

Shelter active

dummy

-0.055***

(-3.523)

-0.052***

(-3.791)

-0.055***

(-3.423)

-0.075***

(-4.203)

Lag5(debt) 0.357***

(4.794)

0.327***

(4.504)

0.387***

(4.724)

Sales

(x100,000)

-0.034

(-0.351)

-0.046

(-1.031)

-0.057

(-0.482)

Mkt/book

(x10)

0.059***

(2.697)

0.041*

(1.805)

0.047

(1.045 )

Div-pay

dummy

0.065**

(2.340)

0.073**

(2.511)

0.052**

(1.964)

0.050

(1.246)

ROA -0.385*

(-1.879)

-0.150

(-0.749)

-0.293*

(-1.765)

-0.594***

(-2.656)

Collateral

/assets

0.101***

(2.958)

0.147***

(4.687)

0.087**

(2.397)

0.165***

(4.775)

Time Trend 0.005***

(4.936)

Adj-R2 54.2% 63.4% 53.2% 38.7%
N 76 76 78 84

*** means statistically significantly different from zero at a 1% confidence level, ** at 5% confidence level, * at 10% confidence level

Table 4. Robustness analysis of the effect of tax shelters on corporate debt policy

OLS regressions are performed to determine which factors affect a given company’s debt ratio. The caption to Table 3 applies with the following exceptions. The dependent variable is debt/assets except in column (1), where debt/market value is used. Market value is measured as book debt plus market equity. In column (2), the shelters that lead to capital losses (CPIS and LR) and the shelter that might lead to an increased use of debt ratio (COLI) are deleted, leaving only the shelters that most likely lead to interest-like deductions. In column (3), one year-lagged ROA is replaced with the cumulative ROA from t=-5 to t=-1. In columns (4) and (5), rather than averaging all years in which a shelter is active into a single observation (as is done in column (1), (2), and (3), and in Table 3), a full panel of data is used, with each firm-year being represented by its own observation. Also, in columns (4) and (5), rather than averaging all matched firms into a single observation (as is done in column (1), (2), and (3), and in Table 3), all match observations are used, so each matched firm-year is represented by its own observation. In column (5), the Shelter firm dummy equals one for shelter firms in all years (even when the shelter is not active). Robust t statistics adjusted for year clustering and heteroskedasticity are in parentheses.

Dep. Var=

debt/mktval

(1)

No CPIS,

LR, COLI

shelters (2)

(3) Multiple

match obs. (4)

Multiple

match obs. (5)

Intercept 0.041

(1.225)

0.155***

(3.816)

0.091**

(2.341)

0.090***

(6.342)

0.104***

(6.753)

Shelter active

dummy

-0.053***

(-2.840)

-0.074***

(-3.648)

-0.058***

(-3.691)

-0.031***

(-3.900)

-0.020**

(-2.418)

Shelter firm

Dummy

-0.021***

(-3.009)

Lag5(debt) 0.351***

(5.144)

0.308***

(2.884)

0.341***

(4.290)

0.525***

(18.114)

0.525***

(18.129)

Sales

(x100,000)

0.032

(0.385)

-0.074

(-1.347)

-0.034

(-0.355)

-0.059***

(-3.158)

-0.056***

(-3.134)

Mkt/book

(x10)

0.018

(0.432)

0.087***

(4.908)

0.062***

(2.752)

0.019

(0.939)

0.021

(1.015)

Div-pay

dummy

0.074***

(3.232)

0.038

(1.224)

0.059*

(1.881)

-0.006

(-0.470)

-0.010

(-0.757)

ROA -0.248

(-1.142)

-0.721***

(-5.128)

-0.283***

(-5.092)

-0.308***

(-5.579)

ROA from

t=-5 to t=-1

-0.051**

(-2.217)

Collateral

/Assets

0.073*

(1.704)

0.076

(1.534)

0.096***

(3.058)

0.093***

(6.768)

0.094***

(6.771)

Adj-R2 42.8% 63.3% 53.0% 40.3% 40.7%
N 76 50 76 1140 1140

*** means statistically significantly different from zero at a 1% confidence level, ** at 5% confidence level, * at 10% confidence level

Table 5. Debt issuance decision

A logistic regression is performed to determine the factors that affect debt issuance. The observations used in this analysis are from both shelter firms and matched firms. The dependent variable equals one if a firm issues debt and equals zero otherwise. Data are included from eight years before the shelter is active until the last year the shelter is active (i.e., from t=-8 to t=0). Shelter dummy equals one for shelter firms and equals zero if the firm is a match firm, so a negative coefficient means that shelter firms are less likely to issue debt. Lag5(debt) is the debt ratio from five years before the shelter became active. Mkt/book is the market value of the firm (market equity plus book debt) divided by book assets. Div-pay dummy equals one if the firm pays a dividend. ROA is net income divided by book assets. Collateral/assets equals inventory, plant, property, and equipment divided by book assets. Marginal effects (slopes) for independent variables (except Intercept) are reported. Robust t statistics adjusted for year clustering and heteroskedasticity are in parentheses.

Issue Debt

in t=0

Intercept -5.763***

(-8.331)

Shelter dummy -0.137***

(-4.122)

Lag5(debt) 1.017***

(7.467)

Log(Sales) 0.063***

(6.284)

Mkt/book 0.029**

(2.046)

Div-pay dummy -0.026

(-0.436)

ROA 0.284

(0.923)

Collateral/Assets 0.578***

(8.430)

N 778
Model AIC

p-value

0.0001
Correct

predictions

78%

*** statistically significantly different from zero at a 1% confidence level, ** at 5% confidence level, * at 10% confidence level

We thank an anonymous referee, Joseph Bankman, Alon Brav, Stefano Della Vigna, Mihir Desai, Lily Fang, Wayne Ferson, Vidhan Goyal, Tom Keller, Mark Leary, Bob McDonald, Guy McDonough, Avri Ravid, Michael Roberts, Chip Ryan, Bill Schwert, and participants in seminars at Boston College, the Chicago Fed, DePaul, Harvard Business School, Louisiana State, the 2005 NBER Corporate Finance Summer Institute, the Securities and Financial Markets conference, the UNC tax conference, York University, and the 2005 WFA annual conference for helpful comments. Brian An, Si Li, Bin Wei, and Julia Wu provided excellent research assistance. All errors are our own. Corresponding author contact information: john.graham@duke.edu, phone (919) 660-7857, fax (919) 660-8038, or John R. Graham, Fuqua School of Business, Duke University, Durham NC 27708-1020.

[1] See Bankman (1999). Moreover, recently California offered a one-time tax amnesty that led to corporations reporting and extinguishing $30 billion in shelters, suggesting that aggregate sheltering at the national level is much larger still. See also Bankman and Simmons (2003).

[2] See “IRS says Glaxo owes $5.2 billion in taxes, interest,” Philadelphia Inquirer, January 8, 2004.

[3] See “Study: Corporate Tax Sheltering Linked to as Much as $12.4 Billion in Lost State Tax Revenues,” PRNewswire, Washington, July 15, 2003.

[4] Another group of papers argues that observed debt ratios are in fact not too low (e.g., Green and Hollifield (2003), Hennesey and Whited (2004), Ju, Parrino, Poteshman, and Weisbach (2004)).

[5]Information for this section is principally obtained from: (1) Joint Committee on Taxation, “Background and Present Law Relating to Tax Shelters (JCX-19-02)”, March 19, 2002; (2) “The Hustling of X-Rated Tax Shelters,” Forbes, December 14, 1998; (3) “The Problem of Corporate Tax Shelters: Discussion, Analysis and Legislative Proposals,” Department of the Treasury, July 1999; and (4) expert witness involvement by one of the authors.

[6] For more on the maturation of LILO deals, and closely related sale-in, lease-out (SILO) deals, see

Luitjens (2004). Many of the hard assets of the nation’s transit system have been ostensibly purchased by a taxpayer and leased back to the transit system under SILO arrangements.

[7] See “A New Tax Battle for Big Banks: LILO Deals,” American Banker, Vol. 168, Issue 165, August 27,2003.

[8] Some anecdotal evidence is consistent with this possibility. Internal KPMG documents advise partners to market aggressive new tax shelters to firms that have engaged in risky tax shelters in the past (The Wall Street Journal, June 16, 2004, “KPMG Shelter Shaved $1.7 Billion Off Taxes of 29 Large Companies”).

[9] See “Health Insurance Portability and Accountability Act of 1996,” Pub. L. 104-191, sec. 510, 110 Stat. 2090 (1996).

[10] “A new twist in tax avoidance: Firms send best ideas abroad,” The Wall Street Journal, June 24, 2002.

[11]Appleby, Spurling, and Kempe, one of Bermuda’s biggest law firms, promotes its expertise in such transactions in a brochure titled “Holding Intellectual Property Offshore.”

[12] The Wall Street Journal, June 16, 2004, “KPMG Shelter Shaved $1.7 Billion Off Taxes of 29 Large Companies.”

[13] Winn-Dixie, 113 T.C. 254 (1999), aff’d 254 F.3d 1313 (11th Cir. 2001).

[14] 254 F.3d 1014 (11th Cir. 2001), rev’g 78 T.C.M. (CCH) 262 (1999).

[15] Section 901(k) of the Internal Revenue Code, enacted in 1997 after the Compaq and IES transactions, disallows the foreign tax credits claimed in any similar transactions conducted after 1997.

[16] The Borden case never went to trial because the company withdrew the litigation.

[17] We alter the match criteria in three instances. For Exxon and Tenet, we require an asset match of +/-50%, rather than 25%. For another firm, we match on year t rather than t-1 because Compusat data are not available for this firm in year t-1. Excluding these three shelter firms does not affect our results. Also, note that our results do not change if we define return on assets based on pre-tax income rather than net income.

[18] It is worth noting that the IRS typically does not contact a company about tax sheltering until t+2 or t+3.

[19 ]The IRS could move toward a statistical detection model in the future because corporations now file electronic tax returns, and the new M-3 filing contains information that would be helpful in developing a prediction model (see Boynton and Mills, 2004).

[20] Desai, Foley, and Hines (2005) find that a similar set of variables explains tax haven activity.

[21] In untabulated regressions, we find that the main results hold for just the firms that used transfer pricing shelters, as well as the firms that did not use transfer pricing.

[22] According to deposition testimony of TFSC officers, the firm had also arranged hundreds of similar transactions for other clients during the 1990s.

[23] As discussed shortly, the whole transaction was occasioned by the fact that ADRs could be borrowed from a tax-exempt institution that had no use for the foreign tax credit but would still receive dividends that were net of the foreign tax. TFSC’s marketing documents for the transactions noted that the strategy was facilitated by the fact that pension funds and other tax exempt entities, that had significant holdings of ADRs as a result of increased global investing, could not utilize the foreign tax credit. These tax-exempt institutions participated – lending their ADRs and accommodating TFSC’s clients – in return for standard short interest rebates earned by the lending of securities.

[24] NYSE regulations allowed transactions to be undertaken in this manner.

[25]IRC section 708(b)(1)(B) states that a sale of 50% or more of a partnership within a twelve-month period constitutes a termination of said partnership.

[26]FPL contended that the Treasury bill short sale obligation was not a “liability” as governed by IRC section 752, but instead was governed by IRC section 1233 and regulation 1.1233-(1)(a), which states that a short sale is treated as an “open transaction” for income tax purposes. Here a short seller can defer recognition of income until closing the transactions by replacing the borrowed securities. Therefore, any adjustments to FPL’s basis must be deferred until the short sale transaction is complete. Remarkably, neither subchapter K partnership provisions of the IRC nor relevant regulations interpreting IRC section 752 clearly define “liability.”

[27]IRC section 732(b) discusses the pertinent basis allocations resulting from the termination of a partnership.

[28]If a corporation/parent (FPL) owns more than 50% of a partnership, the partnership’s financial statements are integrated into those of the corporation/parent. In other words, the $337 million gain was reflected in FPL’s financials for 1992.

Previously published by the Duke University- Fuqua School of Business, August 2005

  • Was this helpful ?
  • Yes   No