Managing the balance sheet with operating leases

Transcription

1 Managing the balance sheet with operating leases Kimberly J. Cornaggia, Laurel A. Franzen, and Timothy T. Simin * August 2012 ABSTRACT We test whether firms use the off balance sheet (OBS) treatment of operating leases in order to strengthen their balance sheets. We find that firms lease versus buy decision has changed over time. Time series evidence suggests that firms and industries not expected to have traditional economic benefits of leasing are increasingly financing with operating leases. We infer that such firms use operating leases to expand OBS debt capacity and we explore their incentives to report conservative balance sheets. We find that (1) OBS leasing allows firms to better manage debt covenants limiting debt or capital expenditures (2) unexplained OBS leasing is diminished by scrutiny of institutional investors and (3) firms investigated by the SEC or DOJ for financial misrepresentation exhibit high levels of unexplained operating leases. JEL classification: G32, M41, M48 Keywords: Financial risk, Manipulation, Lease financing, Off-Balance-Sheet * Franzen is the corresponding author and can be reached via (lfranzen@lmu.edu) at Loyola Marymount University. Cornaggia (kcornagg@indiana.edu) is at Indiana University and Simin (tsimin@psu.edu) is at the Pennsylvania State University. The authors are grateful to John Graham for estimated marginal tax rates and to Jonathan Karpoff, Scott Lee and Jerry Martin for Securities and Exchange Commission (SEC) and Department of Justice (DOJ) enforcement actions data. For comments and suggestions we thank Jess Cornaggia, Guangzhong Li, Craig Lewis, Irina Stefanescu, Mark Walker, participants at the 2010 FMA and 2010 Purdue Alumni conferences, and seminar participants at Indiana University, Laval University, Loyola Marymount University, University of Texas, Texas A&M University, and Texas Christian University. Portions of this draft were previously circulated under the title Manipulating the balance sheet? Implications of off-balance-sheet lease financing.

2 Managing the balance sheet with operating leases August 2012 ABSTRACT We test whether firms use the off balance sheet (OBS) treatment of operating leases in order to strengthen their balance sheets. We find that firms lease versus buy decision has changed over time. Time series evidence suggests that firms and industries not expected to have traditional economic benefits of leasing are increasingly financing with operating leases. We infer that such firms use operating leases to expand OBS debt capacity and we explore their incentives to report conservative balance sheets. We find that (1) OBS leasing allows firms to better manage debt covenants limiting debt or capital expenditures (2) unexplained OBS leasing is diminished by scrutiny of institutional investors and (3) firms investigated by the SEC or DOJ for financial misrepresentation exhibit high levels of unexplained operating leases. JEL classification: G32, M41, M48 Keywords: Capital structure, Financial risk, Manipulation, Lease financing, Off-Balance- Sheet

3 I. Introduction Presently, the International Accounting Standards Board (IASB) and the Financial Accounting Standards Board (FASB) are working on a joint project to update lease accounting. The proposed change in the accounting for leases is based on a right-of-use model that would require balance sheet recognition of the leased asset and corresponding lease liability. This would effectively curtail the off-balance sheet accounting treatment of operating leases longer than one year. 1 In a comment letter received by the IASB and the FASB on their proposed update to lease accounting, the executive vice president and controller of Wells Fargo writes: We acknowledge the concern of the FASB and some users that leases are sometimes structured to achieve off balance sheet accounting treatment Given the valid economic benefits and business flexibility that leasing offers, we do not think standard setting should be used to moderate perceived accounting abuses that may be present in a small percentage of transactions. 2 Our first objective is to provide empirical evidence to inform this ongoing debate. We investigate (1) whether trends in operating lease activity are explained by the theoretical, economic determinants of the lease versus buy decision and (2) whether unexplained operating lease activity is positively associated with incentives to structure leases to keep debt off the balance sheet. 1 Current Generally Accepted Accounting Principles (Accounting Standards Codification Topic 840 Leases) requires that a lease be recognized as a capital lease if it meets any of the four criteria: (1) the agreement specifies that ownership of the asset transfers to the lessee (2) the agreement contains a bargain purchase option (3) the lease term is equal to 75% or more of the expected economic life of the asset (4) the present value of the minimum lease payments is equal to or greater than 90% of the fair value of the asset. If any of these criteria are met, the arrangement is recognized as a capital lease with a leased asset and the corresponding lease liability recognized on the balance sheet. Otherwise, the lease qualifies as an operating lease which requires no balance sheet recognition. 2 This is excerpted from Comment Letter No. 242 received on December 31, The FASB and IASB received 760 comment letters to their proposed update to lease accounting. Comment letters and periodic updates on this joint project are available at 1

4 The finance and economics literature identifies the theoretical determinants of leasing activity as financial distress, marginal tax rates, asset specificity, growth options and firm size. 3 We investigate the relationship between operating lease activity and these theoretical determinants of leasing activity for a broad sample of firms over the period We measure operating lease activity as the present value of non-cancelable minimum lease payments provided in note disclosures. 4 Traditional leasing theory predicts that the propensity to lease assets would be highest for financially distressed firms with low marginal tax rates leasing fixed assets of general usage. Given that fixed assets of general usage are more prevalent in certain industries such as retail and air transport (Finucane 1988, Krishnan and Moyer 1994), these industries should benefit from leasing rather than purchasing assets. We find, however, that the observed trend toward OBS lease financing (and away from capital leasing) is pervasive across industries. The divergent direction of trends in leasing activity across lease classifications is important as the economic benefits identified in traditional leasing theory should hold across either of these lease classifications. Our industry analysis suggests that the offbalance sheet treatment of operating leases, a benefit not recognized in traditional leasing theory, may influence firms propensity to lease assets. 5 We next investigate the relation between firm-level characteristics and operating lease activity across time. We document that the trend toward operating leases is greatest 3 Efficient contracting theories of leasing and prior empirical evidence suggest that leasing activity is increasing (cross-sectionally) in financial constraints, expected costs of bankruptcy, costs of contracting, and asset generality, but decreasing in marginal tax rates and market-to-book; e.g. McConnell and Schallheim (1983), Smith and Wakeman (1985), Krishnan and Moyer (1994), Sharpe and Nguyen (1995), Graham, Lemmon and Schallheim (1998), and Eisfeldt and Rampini (2009). 4 Accounting Standards Codification Topic 840 Leases requires disclosure for operating leases of future minimum payments in the aggregate and for each of five subsequent years. 5 Beatty, Liao and Weber (2010) find for a sample of manufacturing firms that operating lease activity unexplained by traditional leasing theory is negatively related to accounting quality. 2

5 for firms not traditionally predicted to have a high propensity to lease assets. We plot the average ratio of OBS leases to conventional debt for portfolios sorted by indicators of financial distress, marginal tax rates, R&D intensity and market to book ratios. 6 We find that the trend toward OBS leasing is greatest among the least financially distressed firms, firms with high growth options, and firms with the highest levels of R&D intensity. We also find increases in operating lease activity across marginal tax rate quintiles. We conclude that the trend toward OBS leasing is greatest among firms without the traditional economic benefits of lease financing. In multivariate analysis, we find that a traditional leasing model explains a significant portion of the cross-sectional variation in OBS lease levels (R 2 = 26%). Graham, Lemmon and Schallheim (1998) report similar explanatory power for the model in earlier time periods. This is consistent with arguments from opponents of the proposed standard for leasing that operating lease activity reflects the economics of the lease versus buy decision. However, the estimated magnitude of operating lease activity has increased significantly over the last two decades (Cornaggia, et al. 2012). The U.S. Securities and Exchange Commission (SEC) estimates undiscounted obligations under operating leases are $1.25 trillion (SEC 2005). This change in scale warrants additional investigation as to whether leases are intentionally structured as operating leases. We control for leasing activity explained by theoretical economic determinants using the standard leasing model of Graham, et al. (1998). We expect firms with stronger incentives to keep debt off the balance sheet are more likely to have unexplained operating lease activity. We first consider incentives to manage balance sheet debt that arise if contracts limit debt or capital expenditures. We use the Dealscan database to directly identify loan 6 We are interested specifically in the proportion of fixed-cost financing that is off balance sheet, not the choice of debt-versus-equity or optimal capital structure. 3

6 covenants that limit debt or capital expenditures. Although firms with contractual restrictions on debt have the option to expand through internal (equity) funding, leasing may be cheaper. Firms with contractual restrictions on capital expenditures face leasing as a last resort for asset acquisition. Finally, we consider whether unexplained operating leasing activity is positively associated with the likelihood of intentional financial misrepresentation. We note that intentional balance sheet management intended to manage investors perceptions should be curtailed by market monitors. If the unexplained OBS lease activity reflects this intent, it should diminish in the presence of market scrutiny. We also note that if unexplained OBS leasing is associated with financial misrepresentation, we should observe higher levels of unexplained leasing among firms investigated by the SEC or Department of Justice (DOJ) for misrepresentation or fraud. Our empirical analysis supports our expectations. We find that unexplained operating lease activity increases with the existence of debt covenants limiting debt and/or capital expenditures. We find that unexplained operating lease activity decreases with market monitoring by institutional investors. 7 Firms that are investigated for financial misreporting or fraud by DOJ or SEC exhibit high levels of unexplained operating lease activity. Our study contributes to the literature on accounting choices and incentives to manage the balance sheet. Prior research provides evidence that firms use costly and complex off-balance sheet financing arrangements set up through special purpose entities (SPEs) to obscure debt levels (Mills and Newberry 2005, Feng, Gramlich, and Gupta 7 Because most of our sample firms are not subject to scrutiny from these monitors, we cannot assume their existence is a general deterrent. Our median firm has zero stock held by Qualified Institutional Investors and zero analyst coverage. Only 15.5% of our sample has a credit rating from S&P. 4

7 2009, Zechman 2010, Altamuro, 2006). However, the role of the operating lease in balance sheet management is less obvious for two reasons. First, operating lease activity is more transparent than synthetic leases or other off-balance sheet financing arrangements and thus should be less useful for obfuscation. 8 Required disclosures for operating leases allow market participants to adjust financial statement information (Beatty and Weber 2003, Beatty, Liao and Weber 2010, De Franco, Wong and Zhou 2011 and Altamuro et al. 2012). 9 Therefore, firms with the greatest incentives to obscure debt may prefer less transparent financing vehicles. 10 Second, the trends observed in OBS financing may reflect a change in the supply of capital rather than a change in demand for a particular form of financing. 11 In this case, we should find no relation between our measure of unexplained operating lease activity and firms incentives to obscure debt. Therefore, it is an open empirical question as to whether operating lease activity is positively associated with incentives to manage or obscure debt levels. Our study also contributes to the current debate between regulators and constituents as to whether firms financial misreporting practices include structuring leases as operating leases. We employ a hand-collected sample of SEC and DOJ enforcement actions for financial misrepresentation from 1978 through September 30, These data are provided by Karpoff, Lee, and Martin (2008a,b). Although 8 Synthetic leases qualify as operating leases for financial reporting purposes and as capital leases for tax reporting purposes. See Zechman (2010) and Altamuro (2006) for discussion of the motivations for these SPEs. 9 The disclosure requirements are the same for synthetic leases and operating leases. However, Zechman (2010) notes that it is common for synthetic leases to include large residual value guarantees that are not included in disclosed future minimum lease payments. Therefore, without additional voluntary disclosure, off-balance sheet financing estimated for synthetic leases would suffer severe underestimation. 10 Fin 46 improved recognition and disclosure of variable interest entities. 11 See our discussion in section II. 5

8 accounting errors related to leases were fairly common in recent years, the sample we employ includes only the firms with accounting errors that trigger an SEC or DOJ enforcement action. 12 Our results suggest that unexplained leasing reflects intentional financial misreporting practices rather than unintentional accounting errors. This result is important given that proposed new lease accounting standards contain loopholes allowing firms to avoid balance sheet recognition of leases if they have incentives to do so (Hales, Venkataraman, and Wilks, 2012). The paper proceeds as follows. We review related literature and develop hypotheses in Section II. We describe our research design in Section III. We discuss empirical results in Section IV and Section V concludes. II. Literature Review and Hypothesis Development Early evidence on debt contracting suggests that managers can mitigate debt covenant constraints by leasing assets (El-Gazzar 1993). However, more recent evidence suggests that lenders adjust for operating leases in assessing credit risk (Beatty and Weber 2003 and Altamuro, et al. 2008). Mills and Newberry (2005) report that firms closer to violating debt covenants are more likely to use off-balance sheet financing structured through special purpose entities. Their evidence suggests that lenders do not properly adjust for off-balance sheet financing. Similarly, Imhoff, Lipe and Wright (1993) find that compensation committees do not properly adjust for operating leases in setting executive compensation. The evidence on stock market participants is mixed. Imhoff, et al. (1993) conclude that stock market participants do not appropriately impound the effects of 12 Acito, Burks and Johnson (2009) report that between 2004 and 2006 more than 250 U.S. firms had to correct accounting errors related to methods of accounting for operating leases. These accounting errors involved improper recognition or classification of rent expense during rent holidays, amortization of leasehold improvements, and incentives from landlords. 6

9 operating leases into stock price and Ge (2008) suggests that investors do not adequately estimate the effect of operating leases on future earnings. To the contrary, others conclude that prices do reflect adjustments for disclosed items such as operating lease activity; see Ely (1995) and De Franco, Wong and Zhou (2011). Explanations for why lenders and other stakeholders may not adequately adjust for OBS leasing include (1) limited attention and other potential systematic biases, (2) costly information processing and (3) differences in the perceived reliability of information which is recognized versus disclosed. 13 To the extent that counterparties do not adequately incorporate OBS assets and obligations, firms can use operating leases to manage the balance sheet to gain more favorable contracting terms. We expect unexplained operating lease activity (levels beyond the leasing predicted by theoretical economic determinants) will be increasing for firms with incentives to keep debt off of the balance sheet. Managers can choose from a host of earnings management techniques including real accounting choices and accrual-based methods in order to obscure financial statement information. Given significant disclosure of operating leases, firms may prefer less obvious methods for managing or obscuring balance sheet debt. However, Dechow, Ge, Larson and Sloan (2011) find that firms with overstatements to earnings have more operating lease activity in the year of the overstatement. This is consistent with firms using operating leases as one tool to obscure financial statements. We model the demand for leased assets on the traditional economic benefits of the lease versus buy decision and benefits that accrue as a result of the off-balance sheet treatment of operating leases. However, if leasing activity is driven by the supply side, 13 See Aboody (1996) Davis-Friday, et al. (1999), Hirshleifer and Teoh (2003), Barth et al. (2003), Libby et al. (2006), and Ahmed, et al. (2009). 7

10 we may not find support for our hypothesis. Wall Street was seeking new sources of marginal financing over our sample period. The explosion in structured finance markets maps well to our latter sample period marked by the greatest increase in OBS leasing. Feng, Gramlich, and Gupta (2009) document a 250% increase between in the percentage of firms using special purpose vehicles (SPVs). Our trend in lease financing may be a supply-side innovation rather than concern by borrowers about their balance sheets. The following quote from Lewis (1989, pages ) explains OBS innovation in response to regulatory constraint faced by a particular sector. The Mortgage trading desk evolved from corner shop to supermarket. By increasing the number of products, they increased the number of shoppers. The biggest shoppers, the thrifts, often had a very particular need. They wanted to grow beyond the limits imposed by the Federal Home Loan Bank Board in Washington. It was a constant struggle to stay one step ahead of thrift regulators in Washington. Many new products invented by Salomon Brothers were outside the rules of the regulatory game; they were not required to be listed on thrift balance sheets and therefore offered a way for thrifts to grow. In some cases, the sole virtue of a new product was in its classification as off-balance sheet. We cannot cleanly extrapolate this motivation to industrials, but it demonstrates a supply side push in order to circumvent regulatory restrictions. Lease financing has long been offered by commercial banks and equipment manufacturers interested in increasing revenues from marginal customers who may otherwise lack the requisite capital for a traditional (debt- or equity-financed) purchase. We provide evidence on lessor growth over our sample period in Table 1. Firms like AerCap Holding N.V. (AER) and Aircastle Ltd (AYR) both of which lease (but do not manufacture) aircraft to U.S airlines have seen remarkable growth over the latter part of 8

11 our sample period. 14,15 Indeed, the largest aircraft lessor, International Lease Finance Corp (ILFC), is a third-party lessor rather than a manufacturer. We report lessors by SIC along with their total assets at three points in time: fiscal years 1980, 1995, Tejon Ranch Company (NYSE: TRC), which leases farmland and oil fields, appears in SIC 6519 in all three years reported. Total assets for Tejon are $29,826,000 in 1980, $45,203,000 in 1995 and $175,503,000 in Cumulative (average) total assets in SIC 7359, which contains AerCap Holding (AER) and Aircastle Ltd (AYR) mentioned above, increased from $2,369,101,000 ($139,358,900) in 1980 to $81,381,100,000 ($3,538,309,000) in Growth in SIC 7377 appears to have peaked in the 1990s and waned since. However, negative growth in SIC 7377 (computer rentals and leasing) may be misleading as this SIC does not include the largest computer lessors, such as IBM. [Insert Table 1 here.] Because the largest lessors are either subsidiaries of larger firms in broader industries (including GE Capital Aviation Services, Textron, Deere, IBM, etc.) or are not publicly traded (International Lease Finance Corp) the data reported in Table 1 greatly underestimate the magnitude of the leasing industry. Because some firms represented in these data are likely leasing on a smaller scale to consumers rather than corporations, we cannot make strong claims based on these data. 17 But the growth seen here is consistent 14 AerCap Holding fleet includes models from Airbus, Boeing, and McDonnell-Douglas. Aircastle's aircraft portfolio consists of 130 passenger and freighter aircraft leased to 58 lessees located in 32 countries. 15 Market capitalization (from CRSP) for AER increased from $15,415,000 to $255,961,000 over the period January 1980 through December AYR was not listed on CRSP until late 2006 with December 2008 market cap $375,813, Additional SIC codes pertaining to leasing are found at OSHA.gov. Those tabulated are those containing firms covered by Compustat. We discarded SIC 6794 Patent Owners and Lessors which are firms leasing franchises such as Choice Hotels and Dunkin Donuts. 17 For example, SIC code 7359 contains Rent-A-Center as well as AT&T Capital, AER and AYR. 9

12 with growth in third-party leasing companies such as AER, AYR and ILFC. In any event, lessor growth may reflect growing demand for off-balance sheet leasing from lessees or increased supply by lessors facing pressures similar to those described above for the mortgage industry. III. Research Design Sample Selection Our initial sample contains all firms in the merged CRSP-Compustat database from 1980 through We include only firms with common stock and exclude regulated financial firms (SIC codes ) and utilities (SIC codes ). Before matching Compustat and CRSP data we adjust for the Compustat convention that companies with fiscal years ending between June of year t and May of year t+1 are coded as year t. We assign firms with fiscal years ending in the first quarter of year t+1 to year t+1 and assume a 6-month lag between the end of a firm's fiscal year end and when financial statement information is publicly available. We remove firms with less than $1 million in total assets, negative sales, and no debt. In addition to accounting and returns data collected from Compustat and CRSP, we employ annual analyst recommendations obtained from I/B/E/S, institutional ownership data from Spectrum, and loan covenant information from Dealscan. We thank John Graham for providing us with simulated marginal tax rates and Michael Roberts for providing us the file to link Dealscan and Compustat. We acknowledge use of the 38 industry designations obtained from Ken French s web page and information on firm age from Jay Ritter s web page. Finally, we employ the exhaustive record of SEC and DOJ enforcement actions for financial misrepresentation from 1978 through September 30, These hand-collected data are provided by Karpoff, Lee, and Martin (2008a,b). 10

13 All variables are defined in Appendix A. We winsorize each variable, except the marginal tax rate, at the upper and lower 0.05 percentiles. Variable distributions look similar to samples in previous studies: most are fat tailed with Ecost and negative owners equity (Oeneg) having a considerable amount of positive skew. Measurement of Operating Lease Activity We estimate the debt equivalent value of OBS lease liabilities (Oplease) as the present value of non-cancellable minimum lease payments following Graham, Lemmon and Schallheim (1998), hereafter GLS (1998). 18 We consider employing a firm-specific discount rate to estimate the lease commitments, rather than the constant 10%. However, firm-specific discount rates lower the estimated value of OBS debt for firms with high conventional debt ratios. This approach systematically understates the extent of OBS leasing for firms with higher conventional debt relative to firms with lower conventional debt (see Appendix C for detail). Thus, this approach disproportionately under-penalizes firms with both on- and off-balance-sheet debt (or over-penalizes firms with only OBS debt). Ignoring commitments beyond five years, which are disclosed in financial statement footnotes as a sum of commitments thereafter, underestimates the noncancellable liability (Lim et. al 2003). However, the required thereafter portion is only available on Compustat from the year Thus, we rely on the more conservative GLS (1998) measure for the full sample analyses. Our conservative estimate thus underestimates the impact on the risk and performance metrics examined below. We employ the more complete estimate in the Tekelec company example in Appendix B. Empirical Model of the Leasing Decision 18 Our estimate of Oplease ignores synthetic leases and thus underestimates the risk associated with offbalance-sheet financing. See appendix A for computation of Oplease. 11

14 We estimate the leasing model of Graham, Lemmon and Schallheim (GLS, 1998), in equation (1) over our sample period ( ) using panel regressions. We first report results from a model without firm fixed effects for comparison to the GLS (1998) results. We then replace the constant with firm fixed effects, allowing an intercept for each firm. 19 Oplease/TV it = α 1 + β 1 MTR + β 2 Ecost + β 3 Zmod + β 4 Oeneg + β 5 Mtb + β 6 Coll (1) + β 7 Size + β 8 d β 9 d β 10 d β 11 d86 + β 12 d ε it The dependent variable is the estimate of operating lease activity scaled by total firm value. Independent variables in equation (1) are before-interest marginal tax rate (MTR), expected cost of distress (Ecost), modified Z-score (Zmod), a dummy variable for whether owners equity is negative (Oeneg), market-to-book (MTB), collateral (Coll), and firm size (Size). All variables are defined in Appendix A. We include industry dummy variables and time period indicators as in GLS (1998). Leasing theory predicts that leasing activity should be negatively related to the potential borrower s (lessee s) marginal tax rate (MTR) because leasing can transfer tax shields from the lessee to the lessor. A profitable lessor can take advantage of the tax benefits of interest and depreciation and thus offer lower-cost financing to a lessee unable to fully utilize the tax benefits of ownership. 20 Lewis and Schallheim (1992) and GLS (1998) provide empirical evidence that leasing activity is negatively related to the marginal tax rate. 19 Flannery and Rajan (2006) and Lemmon, Roberts and Zender (2008) find that the majority of variation in capital structure is driven by time invariant fixed firm effects. 20 Potential tax benefits depend upon true lease classification by the IRS; not upon GAAP financial reporting. Unfortunately, the IRS classification is not publicly available. Graham et al. (1998) provide a detailed comparison of tax and financial accounting rules and conclude that operating leases are more likely than capital leases to be classified by the IRS as true. 12

15 Ecost, Zmod and Oeneg are proxies for financial constraints. Leasing theory predicts that leasing activity should increase with financial constraints. Financially distressed firms that cannot raise conventional debt or equity capital in order to purchase equipment may find lease financing an available alternative. Consistent with this prediction, the propensity to lease assets is empirically related to financing constraints (Eisfeldt and Rampini, 2009) and higher costs of external capital (see Sharpe and Nguyen, 1995, and Barclay and Smith, 1995). Similarly, Krishnan and Moyer (1994) and Graham, et al. (1998) find that leasing activity is positively related to expected costs of bankruptcy in a large cross-section of firms. MTB is included as a proxy for growth options. Theory suggests a negative relation between market to book ratios and lease financing. Firms with more growth options in their investment opportunity sets should have a lower proportion of fixed claims including leases in their capital structure. 21 Coll is a proxy for asset specificity. Theory suggests that the specificity of the asset should be negatively related to the propensity to lease assets. Fixed assets of general usage are readily transferable and thus more likely to be purchased than leased. Size is included because small firms are more likely to lease assets due to capital constraints. One digit SIC industry dummies are included to control for industry effects (d2000, d3000 and d4000) and time dummies (d86 and d8707) are included to control for tax policy changes related to the 1986 Tax Reform Act. The first time dummy (d86) is equal to one in 1986 and zero otherwise. The second time dummy (d8707) is 1 for the years 21 Yan (2006) suggests a more complicated effect of growth options on the propensity to lease whereby the cost of debt financing increases in lease financing to a larger degree for high-growth firms than for lowgrowth firms, but his results are consistent with a substitution effect suggesting lower propensity to lease among high-growth firms. 13

16 1987 to 2007 and zero otherwise. This time dummy is adjusted from the GLS specification to reflect our sample period. To test our predictions, we control for the theoretical determinants of leasing (Equation 1) and add proxies for incentives to manage off-balance sheet debt. We estimate Equation 2 in panel regressions including firm fixed effects. Oplease/TV it = β 1 MTR + β 2 Ecost + β 3 Zmod + β 4 Oeneg + β 5 Mtb + β 6 Coll (2) + β 7 Size + β 8 d β 9 d β 10 d β 11 d86 + β 12 d β 13 Incentive +ε it We first define Incentive as a dummy variable of 1 if the firm has a debt covenant limiting maximum debt (Max debt) or alternately, a debt covenant limiting capital expenditures (Cap Ex) and zero otherwise. Maximum debt covenants may be scaled by equity, assets, tangible net worth or EBITDA. Next, we define Incentive based on the existence of market monitors. We measure potential scrutiny of market monitors using the ownership held by Qualified Institutional Buyers (QIBs) as a percentage of total shares outstanding, a dummy variable for the existence of a credit rating from Standard and Poor s (S&P), and a measure of analyst coverage. 22 IV. Empirical Results Descriptive Statistics We estimate OBS lease trend regressions for equally-weighted industry portfolios. Leasing theory suggests that liquid fixed assets of general usage are more appropriate for lease financing than more specialized assets. Therefore, we expect to find lease financing primarily in industries such as retail (real estate) and transportation (airplanes, rail cars, and trucking fleets). However, Panel A of Table 2 indicates that 14 of the 29 industries 22 SEC Rule 144a establishes QIBs as sufficiently savvy to be exempt from certain SEC protection. For instance, in the 144a market QIBs are exempt from the two-year holding requirement for privately-placed debt faced by the investing public. Entities investing $100 million or more in unaffiliated issuers qualify. 14

17 exhibit significant positive trends in operating leases as a percentage of total debt. There is some evidence that Oil and Gas Extraction and Tobacco exhibited a decrease over this period. [Insert Table 2 here.] Panel B indicates a significant contemporaneous reduction in capital leases; all but two of the industries portfolios had a significant negative trend in capital leases as a percentage of total debt with 14 of the trends falling within the 90% Vogelsang confidence intervals. We conclude from Table 2 that the significant trend toward OBS leasing is pervasive across industries and is not driven by retail and transportation firms. 23 Table 3 reports summary statistics for variables used in time series plots and regression analyses. Our main sample includes 23,962 firm-year observations over the period All variables are defined in Appendix A. [Insert Table 3 here.] In Figure 1, we classify firms by year into five portfolios based on the likelihood of bankruptcy, using Ohlson s (1980) O-score as adjusted in Franzen et al. (2007) in Panel A and Altman s (1968) Z-score in Panel B. For each portfolio, we plot the mean level of Oplease/TD. We scale total leases by total balance sheet debt (rather than total firm value) in our time series plots because we are interested in the mix (on and off the balance sheet) of fixed-cost financing. For the sake of consistency in Figure 1, we reverse the parameter estimates of Z-Score so that in both panels, Portfolio 1 (p1) represents the least distressed firms; Portfolio 5 (p5) the most distressed. These plots indicate that average OBS leases to on-balance sheet debt is highest for firms with lowest bankruptcy 23 Ruah and Sufi (2012) document the impact of OBS leasing on leverage ratios across industries, but they do not explore the time trend. 15

18 risk (p1) and increasingly so. In untabulated analysis, we confirm that the upward trend in the mean level of Oplease/TD in the top and bottom O- and Z-score portfolios is statistically significant using the Vogelsang confidence intervals. [Insert Figure 1 here.] These results are at least partly due to the fact that the O-Score and Z-Score do not incorporate OBS obligations. We consider both scores because the O-Score relies exclusively on accounting data and the Z-score contains a market data component. The latter should reflect OBS obligations to the extent that market participants account for them properly. Imperfect distress proxy notwithstanding, we cannot conclude that the increase in OBS lease financing is attributable to those most likely to go bankrupt. Figure 1 suggests that leasing is not merely financing of last resort and is becoming even less so over time. A related consideration is the differential tax status of the lessee vis-à-vis the financier. If the potential lessee (borrower) faces a lower marginal tax rate, this firm may attempt to transfer the tax benefit of ownership to the lessor. However, because tax consequences are determined by the IRS rather than GAAP reporting requirements, intentionally manipulating terms to qualify for OBS treatment under GAAP does not necessarily result in a tax-advantaged lease. We investigate the relation between tax status and OBS leasing activity over time in Figure 2. We sort firms into quintiles based on the estimated before interest marginal tax rate (MTR) and plot the mean Oplease/TD over time for each. The upward trend is evident in each quintile; untabulated trend regressions confirm significant increases in the lowest and highest MTR portfolios. While the lowest MTR portfolio has higher lease financing on average, there are periods (including ) where highest MTR portfolio overtakes the lowest. There is a 16

19 wealth of evidence that MTR is a factor in the lease-versus-buy decision, but it cannot explain the trends. 24 [Insert Figure 2 here.] In Figure 3, we investigate the relation between OBS leasing and asset specificity. We use R&D expense (scaled by total sales) as a proxy since R&D intensive firms are more likely to have specialized assets. We sort firms by year into three R&D portfolios. Because the median level of R&D is zero, the bottom portfolio (pl) contains only firms without R&D expenses. The highest portfolio (p3) contains firms with highest R&D and the middle portfolio contains both zero and low, but non-zero R&D firms. We divide firms in this fashion in order to demonstrate the highest increase among the high R&D firms in a more stark contrast than observed in a bifurcation. Untabulated trend coefficients are significantly positive for all portfolios. But from 1982 forward, OBS lease activity is highest among the high R&D portfolio. Finding the greatest increase in OBS leasing among industrial firms - particularly those with high R&D - is inconsistent with leasing of only liquid assets of general usage such as real estate (retail) and aircraft. For firms without debt capacity, specialized assets should theoretically be financed with capital leases rather than operating leases - unless the motivation is keeping debt off the balance sheet. [Insert Figure 3 here.] It is well established that high-growth firms should have a lower proportion of fixed claims in their capital structure; e.g., Myers (1977). But of fixed claims, how do 24 During our sample period, there were two major changes in tax law that affected the tax depreciation of assets. The Economic Recovery Act of 1981 introduced the Accelerated Cost Recovery System (ACRS) for assets placed in service after 1980 and The Tax Reform Act of 1986 introduced the Modified Accelerated Cost Recovery System (MACRS) for assets placed in service after Tax considerations are unlikely to be an increasingly important determinant of OBS lease activity since. 17

20 growth options influence the amount found off the balance sheet? We employ market-tobook as our proxy for growth options. In Figure 4, we sort firms into quintiles based on market-to-book and then plot Oplease/TD across time for each. Firms with the highest market-to-book ratios employ OBS leases to a greater extent than firms with lower market-to-book ratios, and increasingly so. The evidence does not suggest that OBS lease financing has increased because firms have fewer growth options. Rather, the trend in OBS lease financing appears to be greatest among firms with the highest anticipated growth. This result is consistent with lease financing as a risk management tool; real investment options are more valuable when the assets have a walk-away put option. [Insert Figure 4 here.] Thus far, we have documented that the trend toward greater OBS lease financing is not confined to retail or transportation firms with fungible assets and that the trend is not attributable to financially distressed firms with lower marginal tax rates. Rather, we document that the greatest increases in OBS leasing are among the least financially distressed firms including industrial firms with high growth options and intangible assets such as R&D. We revisit these theoretical determinants in multivariate settings in Tables 4 and 5 below. Equation (1) results are reported in Table 4. We report traditional t-statistics and t- statistics based on an extension of cluster-robust standard errors which allows for clustering along more than one dimension proposed by Cameron et al. (2011) and Thompson (2011). 25 With the exception of collateral, all explanatory variables remain significant in the same direction as reported by GLS (1998) and the explanatory power of 25 Two-way clustering corrects for both time-series and cross-sectional dependence. We cluster by firm and year to capture time series dependence within firms and cross-sectional dependence within year. 18

21 the model has not diminished; the R squared for our sample period ( ) is 26% compared to 25% in their period. [Insert Table 4 here.] In Column II of Table 4, we add fixed firm effects to the GLS model to control for time invariant effects identified in prior literature as significant determinants of corporate capital structure (Flannery and Rangan 2006 and Lemmon, Roberts and Zender 2008). The explanatory power of the model improves (R-squared = 98%.) The inclusion of firm fixed effects changes coefficients significantly. The coefficients on marginal tax rate and collateral become significantly positive. This relation between marginal tax rate and operating lease activity is no longer as predicted, however, the positive coefficient on collateral is now consistent with leasing theory. The coefficient on Z-score becomes significantly negative which is consistent with theory that leasing increases in financial distress. Industry dummies were originally included to control for industry leasing policies but the inclusion of fixed firm effects reverses the observed relation. Given the higher R-squared, the predicted results on Collateral value and Z-Score, and the arguments of Lemmon, et al. (2008) we employ this specification (Column II) in tests of Equation (2) including incentives for off-balance sheet financing. Debt Covenants Structuring lease contracts to qualify as operating (rather than capital) leases may help firms manage existing debt covenants restricting additional debt or capital expenditures. 26 We explore this motivation for OBS leasing with an example firm in Appendix B. Tekelec (TKLC) manufactures, markets, and supports network systems and 26 Nini, Smith, and Sufi (2009) examine a large sample of private credit agreements between banks and public firms and find that 32% of the agreements contain an explicit restriction on the firm s capital expenditures. 19

22 service applications for telecommunications carriers (SIC 3663) and is traded on the NASDAQ. Tekelec reported $116 million in long-term debt (and a conventional leverage ratio of 29.4%) in the year However, we estimate an additional $30 million in longterm non-cancellable lease obligations which, if capitalized, would increase the leverage ratio to 34%. We estimate that bringing these fixed-cost obligations onto the balance sheet increases the leverage ratio by as much as 35% in year The Maximum Debt / EBITDA covenant established at Tekelec in 1999 limits this ratio to 3.0. In the year 2000, this ratio was We estimate that capitalizing the leased assets would drive this ratio to The OBS accounting treatment of the lease obligations makes this covenant easier to manage. Similarly, the Maximum Capital Expenditure covenant (Max Capex) established for this firm in the year 2000 limits the firm to $25 million in new fixed assets. If Tekelec s leased assets were capitalized, we estimate their capital expenditures would have been closer to $38 million in 2001 than the $16 million reported (see Appendix B). We employ standard estimation procedures to value leased assets, but we recognize that our estimates are imprecise. Thus, we cannot claim that Tekelec structured long-term financing in order to circumvent existing debt covenants. However, firms like Tekelec may face restructuring or technical default following changes to lease accounting standards that recognize these obligations. Chava and Roberts (2008) show that capital investment declines sharply following financial covenant violations, so this anecdotal evidence supports contentions that the proposed new standard is especially costly for firms with these types of debt covenants in place A summary of potential costs of the proposed new standard on leases is provided in Lease Comment Letter Summary (January 2011), a staff paper of the Financial Accounting Board. 20

23 In Table 5, we investigate whether operating lease activity is positively related to the existence of a Maximum Debt (scaled by equity, assets, tangible net worth, or EBITDA) or Maximum Capex covenant. Specifically, we include these indicators as proxies for Incentives to keep debt off the balance sheet in Equation (2). We control for theoretical determinants, industry indicators, time indicators, and include firm fixed effects. In Table 5 Column I we find that operating leasing activity is significantly positively related to Max Debt after controlling for leasing explained by traditional leasing models (OLS t-statistic = 11.89; Robust t-statistic = 1.79) Similarly, OBS leasing is significantly positively related to Max Capex in Column II (OLS t-statistic = 29.87; Robust t-statistic = 2.31). These results support our contention that the OBS accounting treatment of leased assets allows firms to manage, or even circumvent, existing debt covenants. [Insert Table 5 here.] Market scrutiny If the increase in OBS lease financing reflects efforts to manage or obscure balance sheet information, we expect market monitors should curtail the phenomenon. We consider first scrutiny of credit rating agencies (CRAs). Moody s Investors Service (Moody s) and Standard & Poor s (S&P) capitalize leased assets when assigning credit ratings and we discuss their respective methodologies in Appendix C. We also consider scrutiny by equity analysts and potential mitigating effects of Qualified Institutional Investors (QIBs). We note that only 15.5% of our sample has a credit rating from S&P. And while the average firm has 15.8% of its stock held by QIBs, the median firm has none. Similarly, while the average firm has 1.9 annual analyst recommendations, the median 21

24 firm has none. Thus, although we expect these monitors to inhibit balance sheet distortion, low representation in our sample suggests that analysts, credit rating agencies, and QIBs cannot deter excessive OBS leasing in general. In order to test the influence of these market monitors on lease financing, we add institutional holdings, an indicator for credit rating (or, separately, an investment grade rating) and a measure of analyst coverage to the leasing model. The results reported in Table 5 suggest that firms with more concentrated institutional ownership exhibit significantly lower levels of OBS lease financing, controlling for theoretical determinants, time indicators, and firm fixed effects. However, the negative coefficients observed on average analyst coverage are insignificant. Finally, Table 5 results indicate that lease financing is positively related to the presence of a credit rating with marginal significance II (OLS t-statistic = 3.118; Robust t-statistic = 1.094). This result is interesting given that credit ratings are commonly interpreted as indication that firms have access to public debt markets. Investment grade credit ratings indicate better (cheaper) access to public debt markets and are insignificantly correlated to lease financing (controlling for firm size and other determinants). We consider next the extent to which unexplained OBS leasing (orthogonal to leasing explained by theoretical determinants) is correlated with financial misrepresentation investigated by the SEC and DOJ. Scrutiny by regulators We employ the comprehensive record of SEC and DOJ enforcement actions (N=884) for financial misrepresentation from 1978 through September 30, We 28 These data are provided by Karpoff, Lee, and Martin (2008a,b). These 884 actions include 858 books and records violations, 775 internal controls violations, 421 circumvention violations, 658 include fraud violations, 146 include insider trading violations, and 103 include Sarbanes-Oxley violations. We are 22

25 obtain relevant Compustat data available for 600 observations. However, we have estimated marginal tax rates (MTR) from John Graham for only 106 of these. To estimate abnormal (unexplained) leasing, we sum the intercept and error term from the leasing model employed in Table 4 (Equation 1 including firm fixed effects). We find that of the 106 SEC or DOJ enforcement actions, 53 have high levels of unexplained OBS leasing. That is, in the year that an enforcement action was initiated, the level of unexplained operating leasing for these 53 observations falls in the fourth or fifth quintile of unexplained operating leasing for the 23,962 firm-years in our sample. Although half of the enforcement actions exhibit large unexplained OBS leasing, the sample size is small. In order to compensate for the size of the enforcement sample, we perform two bootstrap exercises. In the first exercise, we draw 10,000 random samples of 106 unexplained lease levels from our full sample. From this exercise we calculate that the probability of finding a sample where half of the 106 firm-years have unexplained leasing above the fourth quintile cutoff value for the entire sample is ; an expected 2.32%. In the second exercise we randomly draw four unexplained leasing levels from our sample and one from the sample of firms under enforcement actions that exhibits unexplained leasing activity. Using 1000 of these samples we calculate the probability that a firm from the enforcement action data set falls in the fourth or fifth quintile as 0.73%. Taken together, these exercises indicate a positive relation between unexplained OBS leasing and enforcement action. This result is consistent with prior evidence that lease financing is negatively related to reporting quality; Beatty, Liao and Weber (2010). This evidence does not suggest that high levels of OBS leasing necessarily indicates testing the extent to which unexplained leasing is associated with any financial misrepresentation (with or without fraud) and we thus include all actions for which we have data. 23