How Quickly Do Firms Adjust to Target Levels of Tax Avoidance? Jaewoo Kim Simon School of Business University of Rochester

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1 How Quickly Do Firms Adjust to Target Levels of Tax Avoidance? Jaewoo Kim Simon School of Business University of Rochester Sean McGuire Mays Business School Texas A&M University Steven Savoy Tippie College of Business University of Iowa Ryan Wilson Lundquist College of Business University of Oregon September 2015 Abstract: Prior research that examines firms tax avoidance activities focuses on the determinants of tax avoidance. Because a firm s tax environment is constantly evolving, studying how quickly firms adjust their tax avoidance activities is necessary to develop a better understanding of the tradeoffs managers face to achieve a given level of tax avoidance. We test for evidence of targeting behavior by managers. Our results suggest that firms do have target levels of tax avoidance and that the typical firm converges towards its target at a rate of almost 69 percent over a three-year period. We also find that the speed of adjustment varies with a firm s growth potential, presence of foreign operations, and governance. We appreciate helpful comments from John Campbell, Katherine Drake, Nancy Du, Nathan Goldman, Jeff Gramlich, Dave Guenther, Russ Hamilton, Shane Heitzman, Paul Hribar, John Jiang, Ed Outslay, Santhosh Ramalingegowda, Casey Schwab, James Stekelberg, Bridget Stomberg, Erin Towery, Brian Williams, Julie Xiao, and workshop participants at the UBCOW conference, Michigan State University, the University of Arizona, the University of Georgia, the University of Oregon, and Washington State University.

2 I. Introduction Dyreng, Hanlon, and Maydew (2008) document substantial variation in the level of firms tax avoidance. In an effort to explain the variation in tax avoidance, recent research examines the determinants of the level of firms tax avoidance. While determinant studies provide valuable insights, the full implications of their findings are not clear because they do not consider firms tax planning objective. The tradeoff literature (e.g., Scholes, Wilson, and Wolfson 1990) suggests that firms trade off tax benefits against nontax costs, which implies that firms target a level of tax avoidance based on the costs and benefits. Despite the clarity of the tradeoff literature s prediction, there is limited evidence on whether firms target a specific level of tax avoidance. The purpose of this study is to examine whether firms adjust to a target level of tax avoidance and, if so, how quickly firms converge toward their predicted target. In prior tax avoidance studies, researchers often regress a measure of tax avoidance on potential determinants in the cross-section of firms, but the implications of a significant coefficient in such a regression are unclear. A significant coefficient implies that the determinant explains variation in the target level of tax avoidance across firms but less about deviations from that target. Furthermore, a determinant may have a different effect on the level of tax avoidance across the tax avoidance distribution. For example, Armstrong, Blouin, Jagolinzer, and Larcker (2015) find that the impact of corporate governance on tax avoidance is most pronounced at the extremes of the tax avoidance distribution. 1 For these reasons, examining the determinants of the level of a firm s tax avoidance does not provide a complete understanding of managers tax planning activities. Studying whether firms adjust to a certain target level of tax avoidance and 1 Armstrong et al. (2015) provide evidence of the direction of tax avoidance in the cross-section of firms. Our study complements and extends Armstrong et al. (2015) by examining the speed with which firms adjust their tax avoidance activities in a dynamic setting. 1

3 the speed with which a firm converges to its target offers new insight into firms tax planning activities. Further, examining how the convergence speed varies with firm characteristics provides evidence on the tradeoffs that firms face between tax savings and the costs of convergence. The tradeoff literature argues that firms weigh tax benefits against nontax costs in making investment and financing decisions. The nontax costs of tax avoidance include financial reporting considerations, agency costs, and implementation costs (Shackelford and Shevlin 2001). For example, Scholes, Wilson, and Wolfson (1990) examine tradeoffs in the context of banks security transactions. They find evidence that banks consider nontax costs related to asset turnover, the level of income reported to shareholders, and regulatory capital concerns when making the decision to sell investments in order to minimize taxes. Other studies document tradeoffs related to LIFO adoptions (Cushing and LeClere 1992; Dopuch and Pincus 1988), deferred compensation (Matsunaga 1992), and income shifting (Guenther 1994). In summarizing the tradeoff literature, Shackelford and Shevlin (2001) identify some common themes from the papers. Among their takeaways from this area of research they note that taxes are not a cost that taxpayers inevitably avoid. An important implication of the tradeoff literature is that the costs and benefits of tax avoidance are different for each firm depending on its characteristics and the parties involved in its transactions. As a result, each firm has a unique target level of tax avoidance. In a world where firms are able to change an infinitesimal level of tax avoidance without logistic frictions, tradeoff theory predicts a firm will always maintain its target level of tax avoidance and will never deviate from the target. However, a firm s tax environment is dynamic suggesting that a 2

4 firm s target level of tax avoidance is constantly changing. 2 Further, implementing and unwinding tax avoidance activities is likely associated with nontax costs. In such an environment, the tradeoff literature implies that firms will adjust their level of tax avoidance because either the firm has temporarily deviated from its target level of tax avoidance or the firm identified a new target level of tax avoidance. Because firms tax environments are dynamic in nature, it is important that our empirical model allows firms target level of tax avoidance to vary over time and also recognizes that firms potentially cannot converge to a target in a single period. Accordingly, we follow the capital structure literature and estimate a partial adjustment model that allows incomplete adjustment toward a target level of tax avoidance (e.g., Flannery and Rangan 2006). 3 Specifically, we estimate a firm s target level of avoidance as a function of operating, financing, and investing characteristics that prior research finds are associated with the level of a firm s tax avoidance (e.g., Chen, Chen, Cheng, and Shevlin 2010). We use a three year cash effective tax rate (ETR) to proxy for firms level of tax avoidance because it is a visible tax avoidance metric that is not influenced by tax accrual management (Dyreng, Hanlon, and Maydew 2008). Our results suggest that the typical firm converges toward its target level of tax avoidance at a rate of almost 69 percent per year. This rapid adjustment speed is important for two reasons. First, it suggests firms are able to adjust rapidly to the target level of tax avoidance despite the costs and logistical difficulties of implementing changes in tax planning strategies. Second, prior 2 Changes to the target level of tax avoidance could stem from changes in tax law. Alternatively, changes in firm characteristics such as profitability, capital structure, firm size, product mix, or geographic markets will cause a firm s target level of tax avoidance to change. 3 Although partial adjustment models are used in the finance literature to examine the speed with which firms adjust to their optimal capital structure (e.g., Flannery and Rangan 2006) or optimal level of cash holdings (Dittmar and Duchin 2010; Venkiteshwaran 2011), we acknowledge that it is not possible to measure a firm s optimal level of tax avoidance. Rather, it is our intent to measure a firm s target level of tax avoidance before considering adjustment costs. 3

5 capital structure research argues that a slow annual adjustment speed does not support the presence of targeting behavior. 4 Thus, consistent with tradeoff theory, our estimated adjustment speed suggests that firms actively adjust toward a specific tax avoidance target. To provide additional insight into firms targeting behavior, we examine whether there is cross-sectional variation in how quickly firms adjust to their target level of tax avoidance. First, we examine if the speed of adjustment varies depending on whether a firm s actual level of tax avoidance is above or below the firm s tax avoidance target. We expect to observe asymmetric adjustment speeds to the extent that the cost of implementing a new tax strategy for firms below their target level of tax avoidance differs from the cost of unwinding a current tax position for firms above their target level of tax avoidance. Consistent with our expectations, we find that firms that are above their target level of tax avoidance exhibit an adjustment speed of approximately 89 percent while firms that are below their target have an adjustment speed of approximately 47 percent. These results are consistent with the tradeoff theory s prediction that managers actively attempt to achieve a target level of tax avoidance regardless of whether their initial level of tax avoidance is above or below their target. Further, our results suggest that firms engaged in too little tax avoidance increase their tax avoidance more quickly than the rate at which firms engaged in too much tax avoidance scale back their tax avoidance. One possible explanation for this result is that the costs of failing to operate at the target level of tax avoidance are higher when the firm is engaged in too little tax avoidance which motivates these firms to adjust more quickly. 4 The capital structure literature proposes several theories to explain the variation in firms leverage ratios (e.g., pecking order, market timing, and tradeoff theories). Prior to Flannery and Rangan (2006), estimates of firm s annual adjustment speed ranged from eight percent to 15 percent. Fama and French (2002) note that such slow adjustment speeds do not provide support for firms pursuing a target level of capital structure. Although, the speed of adjustment towards a tax avoidance target is much faster than the speed of adjustment toward a capital structure target, we argue that Fama and French s (2002) logic applies in the tax avoidance context as well. 4

6 Second, we examine whether the adjustment speed varies based on a firm s growth potential. It is costly for firms to acquire highly implicitly or explicitly taxed assets if they are not in the correct tax clientele. Extending this line of thinking, we posit that firms more prone to shifting between tax clienteles across time will find it more costly to commit to long-term tax planning strategies for fear of experiencing a shift in profitability or operations and ending up in the wrong clientele. Thus, we expect that firms with greater growth potential will exhibit slower adjustment speeds relative to firms with less growth potential. We find firms with above the sample median levels of growth potential exhibit significantly slower adjustment speeds to the target level of tax avoidance relative to firms below the sample median. Consistent with the tradeoff view, this result suggests that firms whose target level of tax avoidance is more likely to change exhibit slower adjustment speeds because the costs of adjustment likely exceed the benefits. Third, we examine whether the adjustment speed varies based on the presence of foreign operations. We expect that firms with foreign operations (MNCs) will exhibit faster adjustment speeds relative to domestic-only firms because the presence of foreign operations provides MNCs with more tax planning opportunities and, thus, lower costs of adjustment. Consistent with our expectations, we find that MNCs exhibit significantly faster adjustment speeds relative to domestic corporations. Finally, we examine whether the speed of adjustment varies with corporate governance. Recent research finds that more independent boards have greater ability and incentives to monitor managers tax avoidance decisions (Armstrong et al. 2015). We expect that the adjustment speed is increasing in board independence because independent directors are more likely to pressure managers to operate at their target level of tax avoidance. Consistent with 5

7 our expectations, we find that firms with greater board independence exhibit faster adjustment speeds relative to firms with less independent boards. This study contributes to the tax avoidance literature by providing a new method to examine a firm s tax avoidance decisions. Prior research investigates a wide variety of determinants to help gain a better understanding of the substantial variation in the level of firms tax avoidance (Hanlon and Heitzman 2010). Because firms constantly adjust to their unique target, our results suggest that the speed of adjustment to target levels of tax avoidance has been an overlooked characteristic in the literature. If the construct of interest in a tax study is the effectiveness of a firm s tax function, the speed of adjustment may better capture the construct relative to the level of tax avoidance. Furthermore, our findings suggest that the speed of adjustment varies in the cross-section, providing indirect insight into the costs to firms of adjusting their tax planning strategies. Finally, our study should be of interest to corporate stakeholders. For tax managers, executives, investors, analysts, and members of the compensation committee it can be more useful to evaluate the effectiveness of a firm s tax function by thinking in terms of the speed at which the firm adjusts to its target tax rate rather than focusing on the level of tax avoidance. The rest of the paper is organized as follows: Section II discusses prior literature. Section III describes our research design. Section IV describes data and presents our results. Section V presents our sensitivity analysis and Section VI concludes. II. Background and Prior Literature Target Level of Tax Avoidance The tradeoff literature in tax accounting suggests that effective tax planning considers all of the potential taxes (both implicit and explicit) to a transaction, all of the parties in the 6

8 transaction, and all of the costs (both tax and non-tax) associated with a given transaction (Scholes et al. 2014). Because each firm faces an idiosyncratic combination of potential taxes, contracting parties, and non-tax costs, each firm has its own unique target level of tax avoidance. One implication of the tradeoff view is that firms target level of tax avoidance is not going to be equivalent to the maximum amount of tax that firms can avoid. Armstrong et al. (2015) argue once firms exceed their optimal level of tax avoidance, the marginal costs of tax avoidance exceed the marginal benefits. 5 Consistent with this notion, prior research provides evidence that suggests high levels of tax avoidance is potentially costly. For example, Cook, Moser, and Omer (2015) find that the cost of capital increases for firms with high levels of tax avoidance. Likewise, Armstrong et al. (2015) find that better governance reduces tax avoidance, but only among firms with extremely high levels of tax avoidance. Another implication of the tradeoff literature is that a firm s target level of tax avoidance changes over time as the firm s mix of potential taxes, parties, and costs changes. Changes to the target level of tax avoidance could stem from changes in tax law, profitability, capital structure, firm size, product mix, or geographic markets. For example, Gupta and Mills (2002) provide evidence on how a firm s tax planning opportunities change as they expand into new states. Specifically, Gupta and Mills (2002) examine how firms exploit difference in state tax regimes to lower their tax burden. Because each state has slightly different reporting requirements, firms have the ability to locate their operations in states with favorable tax reporting requirements. However, as firms expand to more states, they eventually must expand into states with less favorable tax reporting requirements, which begins to eliminate their tax planning advantages. 5 Armstrong et al. (2015) note that the costs of tax avoidance include implementation costs, the inability to repatriate and invest foreign earnings as well as potential political, regulatory, and reputational costs. Further, tax avoidance is associated with reduced transparency (Balakrishnan, Blouin, and Guay 2012) as well as greater implicit taxes. 7

9 Gupta and Mills (2002) argue that a firm s state tax planning opportunities increases when a firm operates in more than one state, but fewer than all states. Consistent with their expectations, they find that operating in 24 states yields the minimum state effective tax rate. Because firms target level of tax avoidance changes over time due to such factors as expanding into new jurisdictions, we expect that firms are constantly adjusting to their target level of tax avoidance. It is possible firms will pursue the maximum amount of tax avoidance possible if the marginal costs of excessive tax avoidance are not significant. Specifically, Jennings, Weaver, and Mayew (2012) find that after the Tax Reform Act of 1986 (TRA86) implicit taxes are slow to erode the after-tax benefits of new tax preferences. 6 They attribute the decline in implicit taxes to an increased use of tax shelters and aggressive tax planning. They argue the proprietary nature of these transactions makes it more difficult for competitive forces to equalize after-tax rates of return. The lack of significant non-tax costs associated with aggressive tax planning strategies has led to questions about why all firms do not use tax shelters (Weisbach 2002). Providing further evidence that certain firms have the ability to avoid taxes without bearing meaningful nontax costs, DeSimone et al. (2014) identify a group of U.S. MNCs whose operations allow them numerous opportunities for low risk tax avoidance. DeSimone et al. (2014) note the difficulty of taxing mobile capital, and that U.S. MNCs can locate valuable capital in low-tax jurisdictions to avoid taxes with relatively low risk. Thus, to the extent that non-tax costs are not significant, firms may not target a specific level of tax avoidance and instead attempt to maximize their level of tax avoidance. Tradeoffs of Adjusting to the Target Level of Tax Avoidance 8

10 The idea that managers will take steps to minimize the distance between their firm s level of tax avoidance and the target level of tax avoidance is similar to the tradeoff theory of capital structure. The speed at which firms adjust to the target level of tax avoidance is a function of the tradeoff between the costs of adjustment and the costs of failing to achieve their tax avoidance target. If the costs of adjustment are zero then firms would never deviate from their target level of tax avoidance. In contrast, if the costs of adjustment are infinite we would expect firms to maintain a constant level of tax avoidance. The reality is between these two extreme cases, suggesting that at any given point in time firms are moving toward their target level of avoidance. Prior research on corporate tax avoidance fails to empirically investigate how quickly firms converge to a target level of tax avoidance. 7 We fill this void. As discussed above, the speed of adjustment is likely a function of the costs of adjustment. Therefore, firms will likely adjust their tax avoidance activities more slowly as the costs of adjustment, including logistic frictions, increase. To shed light on the costs of adjustment and tradeoffs that firms make, we compare the speed of adjustment for firms with actual levels of tax avoidance above and below their target. We expect to observe asymmetric adjustment speeds to the extent that the cost of implementing a new tax strategy for firms below their target level of tax avoidance differs from the cost of unwinding a current tax position for firms above their target level of tax avoidance. Because each firm faces a unique tradeoff when adjusting to its target tax avoidance level, the tradeoff theory predicts that the speed of adjustment varies in the cross-section. Accordingly, we examine two cases where we expect the costs of adjustment to be salient. First, 7 One exception is Hoopes, Mescall, and Pittman (2012). The authors conduct a survey of 25 tax executives and find that 69.2 percent of all tax plans or positions can be changed within one year while approximately 90 percent of all tax positions can be changed within two to three years. Our finding that the typical firm converges towards its target at a rate of approximately 69 percent per year is consistent with this survey evidence. 9

11 we examine whether the speed of adjustment varies as a function of a firm s growth potential. Firms with high growth potential are more likely to enter new markets and introduce new product lines relative to firms with lower growth potential. As firms with high growth potential expand, they likely encounter shifts in the tax clientele that they belong to and, consequently, significant changes in their target level of tax avoidance. Scholes et al. (2014) note that it is costly for investors to acquire highly implicitly or explicitly taxed assets if they are not in the correct tax clientele. We argue firms that are more likely to shift between tax clienteles across time will find it more costly to commit to long-term tax planning strategies. The 1981 Research and Development (R&D) tax credit provides an example of the costs of operating in the wrong tax clientele. Berger (1993) examines the stock price reaction to the R&D tax credit s enactment for firms that compete for R&D factor inputs and customers, but that are unable to receive tax credits because of low marginal tax rates. He notes that because the pretax returns to R&D investments are bid down by the competition for the explicit tax benefits offered by the tax credit that the tax subsidy created by the R&D tax credit only benefits firms that can use the credit. Berger finds a significant negative market reaction to the credit s enactment for the low marginal tax rate firms and concludes the implicit tax costs for the firms unable to use the credit are substantial. Thus, the costs of adjustment for high growth firms includes the cost of engaging in additional tax planning as well as the present value of the expected future costs associated with being in the wrong tax clientele. Because high growth firms are likely to change tax clienteles frequently, their costs of adjustment likely exceed the cost of failing to operate at their target level of tax avoidance. In contrast, because low growth firms are better able to predict their tax clientele, the costs of not achieving their tax avoidance 10

12 target likely exceed the costs of adjustment. For this reason, we expect that high growth firms will adjust to their target level of tax avoidance at a slower rate relative to low growth firms. Second, we examine whether the speed of adjustment varies based on whether a firm has foreign operations. Relative to a domestic-only corporation, multinational corporations have substantial opportunities to avoid income tax. For example, MNCs have the opportunity to locate operations in low-tax jurisdictions, shift income from high-tax to low-tax jurisdictions, exploit differences between tax rules in different countries, and take advantage of tax subsidy agreements with certain countries (Rego 2003). Consistent with MNCs having more opportunities to avoid taxes, Rego (2003) finds that MNCs have lower worldwide effective tax rates relative to domestic-only corporations. Rego s (2003) finding suggest that MNCs enjoy economies of scale with respect to tax planning and that the marginal cost of implementing additional tax planning or reducing current tax avoidance is likely lower for MNCs relative to domestic-only firms. Consequently, we predict that MNCs will adjust to their target level of tax avoidance more quickly than domestic-only firms. Finally, we examine whether the speed of adjustment varies with the quality of a firm s corporate governance. Although corporate governance has multiple dimensions, we focus on the independence of the board of directors because prior research suggests that it is an effective monitor of firm operations. For example, Klein (2002) finds that firms with more independent boards engage in less earnings management. Further, Armstrong et al. (2015) find that board independence constrains additional tax avoidance for firms that are already engaging in excessive tax avoidance and encourages additional tax avoidance for firms that are not engaging in large amounts of tax avoidance. As discussed earlier, it is likely that the marginal costs exceed the marginal benefits when firms do not operate at their target level of tax avoidance. To the 11

13 extent that managers and shareholders have different preferences for tax avoidance, corporate governance mechanisms likely discipline managers to operate at the firm s target level of tax avoidance. Thus, we expect that firms with greater board independence will adjust more quickly to their target level of tax avoidance. III. Research Design To examine the tradeoff between the tax benefits and the nontax costs of operating at a target level of tax avoidance, we employ a regression specification that allows each firm s target level of tax avoidance to vary over time. Our primary measure of tax avoidance is a firm s cash effective tax rate (CASHETR), CCCCCCCCCCCCCC tt = tt tt 2 TTTTTTTTTT PPPPPPPP (1) We use cash ETR as opposed to GAAP ETR for two reasons. First, GAAP ETRs do not reflect tax avoidance activities that create temporary book-tax differences by deferring payment of taxes to future periods (Hanlon and Heitzman 2010). Given that deferring taxes is a strategy used by many firms (Dyreng et al. 2008), GAAP ETRs understate firms actual tax avoidance. Second, financial accounting accruals such as the valuation allowance and tax contingency reserves (or unrecognized tax benefits) also affect GAAP ETRs (Dyreng et al. 2008). Consequently, changes to a firm s GAAP ETR could be due to the reversal of accruals as opposed to firms adjusting to their target level of tax avoidance. We calculate a firm s cash ETR over a three-year period to avoid measurement error when it is calculated over a short time period (Dyreng et al. 2008). Specifically, the timing of tax payments can occur in a different year than the corresponding pre-tax income and cause cash ETR to be a misleading measure of tax avoidance when calculated over a single year. 12

14 Calculating cash ETR over a three-year period reduces measurement error related to the mismatch between tax payments and pre-tax income. Furthermore, using a tax avoidance measure calculated over a three-year period also reduces the possibility that our results are simply due to mean reversion as opposed to firms strategically converging to a target level of tax avoidance. To estimate the average speed of adjustment to a target level of tax avoidance, we modify Flannery and Rangan s (2006) model that estimates how quickly firms adjust to their target capital structure. Specifically, we first model a firm s target level of tax avoidance as a function of the firm s operating, financing, and investment characteristics such that: CCCCCCCCCCCCCC ii,tt+3 = ββxx ii,tt (2) where CCCCCCCCEEEEEE ii,tt+3 is firm i s desired level of tax avoidance over the period t + 1 to t + 3, XX ii,tt is a vector of firm characteristics that have been shown to be determinants of tax avoidance, and ββis a coefficient vector. Prior research suggests that economies of scale and firm complexity are associated with additional tax planning opportunities (e.g., Mills, Erickson, and Maydew 1998; Rego 2003). Accordingly, the vector of determinants includes firm size (SIZE), income from foreign operations (FORINC), leverage (LEV), capital intensity (CAPINT), and research and development activities (R&D). In addition, the need to avoid income taxes varies with firm profitability (Chen, Chen, Cheng, and Shevlin 2010; Rego 2003). Consequently, we include firm profitability (ROA), net operating loss carryforwards (NOL and CNOL), and the number of pre-tax losses that a firm experienced over the previous four years (LOSSINT). We include income related to the equity method of accounting (EQINC) to control for differences in financial and tax accounting treatment that influence our measures of tax avoidance (Chen et al. 2010; Frank, Lynch, and 13

15 Rego 2009). The vector also includes firms growth opportunities (MTB) because Chen et al. (2010) note that growing firms potentially invest in more tax-favored assets. Please see Appendix A for a detailed definition of the above variables. To the extent that the tradeoff literature is descriptive, we expect that ββ 0 and that CCCCCCCCCCCCCC ii,tt+3 will vary across firms and over time. Base Partial Adjustment Model In a frictionless world, a firm will always maintain its target level of tax avoidance and will never deviate from the target. However, the costs of adjusting a firm s level of tax avoidance are expected to be non-zero, and firms must trade off adjustment costs against the costs of failing to operate at their target level of tax avoidance. Furthermore, given the dynamic components of firms tax environments, it is possible that firms will constantly make adjustments to their level of tax avoidance either because the firm has temporarily moved away from its target level of tax avoidance or because the firm identified a new level of target tax avoidance. Therefore, we estimate a model that allows partial adjustment of the firm s level of tax avoidance toward its target each fiscal year, which allows us to estimate the average adjustment speed and examine whether firms exhibit targeting behavior. The partial adjustment model is as follows: CCCCCCCCCCCCCC ii,tt+3 CCCCCCCCCCCCCC ii,tt = θθ CCCCCCCCEEEEEE ii,tt+3 CCCCCCCCCCCCCC ii,tt + εε ii,tt+3 (3) Each fiscal year, the typical firm closes a proportion, θθ, of the gap between its actual and its target level of tax avoidance. After substituting Eq. (2) into Eq. (3), rearranging gives the estimable model: CCCCCCCCCCCCCC ii,tt+3 = (θθθθ)xx ii,tt + (1 θθ)cccccccccccccc ii,tt + εε ii,tt+3 (4) 14

16 Equation (4) implies that firms actual level of tax avoidance eventually converges to its target at a given adjustment speed. Note that this specification implies all firms have the same adjustment speed (θθ). We interpret θθ as the average adjustment speed for a typical firm, but we later examine whether the adjustment speed varies in the cross-section. The partial adjustment model is subject to two different criticisms. First, in a capital structure context, Chang and Dasgupta (2009) examine the partial adjustment model on simulated data that is designed not to exhibit targeting behavior. The authors find that the partial adjustment model yields reasonable estimates of firms speed of adjustment on the simulated data, calling into question the power of the model. Second, prior research argues that the estimates produced by the partial adjustment model are due to mean reversion (Chen and Zhao, 2007; Chang and Dasgupta, 2009; Shyam-Sunder and Myers, 1999). Specifically, this stream of research argues that because debt ratios are bounded at zero and one, firms with extreme debt ratios are forced to revert to the mean because the debt ratio cannot become negative or exceed one. However, Öztekin and Flannery (2012) note that if the speed of adjustment varies predictably in the cross-section, it provides evidence that the partial adjustment model is properly specified and not due to mean reversion. Accordingly, our tests that examine the cross-sectional variation in the speed of adjustment provide additional insight into firms tax planning function as well as validating our partial adjustment model. Further, as discussed below, we conduct additional analysis to provide evidence that our results are not primarily attributable to mean reversion. 8 8 We perform an analysis on the middle fifty percent of the CASHETR distribution which represents effective tax rates ranging between 20.0% and 36.6%. We find similar adjustment speeds for this sample which helps rule out a mean reversion explanation. We also note that the proportion of firm-year observations with a CASHETR of zero or one is quite small. Specifically, we find that one percent of our firm-year observations have an CASHETR equal to zero while none of our firm-year observations have an ETR equal to one. Furthermore, the fifth and ninety-fifth percentile of the CASHETR distribution are 5.2% and 50.2% respectively indicating that extreme observations are unlikely to be causing mean reversion in our sample. 15

17 Asymmetric Adjustment Speed To examine the possibility of an asymmetric adjustment speed for firms above and below their target level of tax avoidance, we first perform profile analyses. Specifically, we calculate the proportion of the gap between the firm s target and actual ETR that is closed within the next year: CCCCCCCCCCCCCC ii,tt+3 CCCCCCCCCCCCCC ii,tt CCCCCCCCCCCCCC ii,tt+3 CCCCCCCCCCCCCC ii,tt. For example, consider a firm with a target ETR of 33% and an actual ETR of 35%. If the firm adjusts its ETR to 34% in the following year, it closed half of the gap between its target and actual ETR. We plot the median gap that is closed for each quartiles based on the distance between actual and target ETRs. Next, we conduct regression analyses to investigate whether the adjustment speed is significantly different for firms above and below their target level of tax avoidance. We classify firms based on whether the distance between their actual and target ETRs is positive or negative. If CCCCCCCCCCCCCC ii,tt+3 CCCCCCCCCCCCCC ii,tt is positive (negative), the firm has a level of tax avoidance that is greater (less) than its target level. 9 We re-estimate Eq. (4) separately for firms above and below their target level of tax avoidance. We then compare the adjustment speed coefficient, θθ, across the two regressions and test for a significant difference. Cross-sectional Variation in Adjustment Speed We next examine whether a firm s adjustment speed varies as a function of its growth options, governance, and whether it is a multinational corporation. We use sales growth (SALESGROWTH) to proxy for a firm s growth options. SALESGROWTH is calculated by taking the revenue growth over the prior three years. Firms are classified as multinational if they have any foreign income over the three-year period. Following Armstrong et al. (2015), we use the 9 Byoun (2008) investigates whether firms above and below target debt ratios have different speeds of adjustment toward their target debt ratios. The methodology we use to classify firms as above or below target is similar to the methodology in Byoun (2008). 16

18 ratio of independent directors to proxy for governance (IND_RATIO). Firm-years are sorted by SALESGROWTH and IND_RATIO within each fiscal year. We estimate equation (4) separately for firms with above and below median SALESGROWTH and IND_RATIO as well as for domestic and multinational firms. Finally, we compare the adjustment speed coefficient, θθ, across the various regressions and test for significant differences. IV. Results Sample Selection Our sample includes all firms with available data in the COMPUSTAT industrial annual files from 1990 through The sample begins in 1990 because three years of Statement of Cash Flows data is required to calculate CASHETR. The sample ends in 2011 to ensure that data is available to calculate three-year ahead CASHETR. We exclude financial institutions (SIC codes ), utilities (SIC codes ) because they operate in regulated industries and face different tax planning incentives. Firms incorporated outside the United States are also eliminated as they face a different set of tax and/or financial reporting rules. We delete any observations without the available data to calculate the variables described in Appendix A, including observations with non-positive values of book equity. We also delete firm-year observations with a negative denominator in the calculation of CASHETR as it is unclear if loss firms have a target level of tax avoidance. Observations with beginning total assets below $10 million are also deleted to avoid scaling issues with extremely small firms. Finally, we delete observations with an CASHETR outside of the [0,1] range to avoid the influence of outliers. To further avoid influence of outliers, we winsorize all continuous variables other than CASHETR at the 1 st and 99 th percentile. The sample selection criteria produces a final sample of 24,762 firmyears. 17

19 Descriptive Statistics Table 1 provides the descriptive statistics for our sample of firm-year observations. The mean (median) CASHETR is (0.294) which is comparable to three-year CASHETR measures in prior studies. Note that the control variables are measured over the same three-year window as CASHETR. Income statement variables are accumulated over the three years while balance sheet variables are averaged. For example, ROA is calculated by summing three years of income statement data and dividing by average total assets over the period. [INSERT TABLE 1 HERE] Partial Adjustment to Target Level of Tax Avoidance Table 2 presents the estimation of the partial adjustment model. In addition to the common determinants of tax avoidance discussed previously, the regressions in Columns 1-2 include industry and year fixed-effects. The coefficient on CASHETR is our primary interest because it is the estimation of (1 θθ), with θθ representing the average adjustment speed for a typical firm. The base model in Column 1 shows a coefficient on CASHETR of implying an adjustment speed of 69 percent. Stated differently, a firm closes 69 percent of the gap between its actual and target levels of tax avoidance in the following three-year period. This adjustment speed suggests that firms are able to rapidly adjust to their target level of tax avoidance despite the adjustment costs associated with altering tax planning strategies. In addition, the adjustment speed for tax avoidance is significantly higher than the adjustment speed documented in the capital structure literature, which suggests that the costs of adjusting towards a tax avoidance target are significantly less than the costs of adjusting towards a capital structure target. 10 The magnitude of the estimated adjustment speed is also significant because it indicates that firms 10 For example, Flannery and Rangan (2006) estimate that the firms adjust toward their target capital structure at a rate of 34 percent per year. 18

20 actively attempt to adjust their level of tax avoidance to achieve a specific target. In the context of capital structure adjustments, Fama and French (2002) note that slow adjustment speeds do not provide support for firms pursuing a target level of capital structure. Although, the speed of adjustment towards a tax avoidance target is significantly different than the speed of adjustment towards a capital structure target, Fama and French s assertion applies in the tax avoidance context. It is possible that the estimated speed of adjustment is driven by mean reversion. Specifically, observations that are farther from their target are more likely to have relatively low or high levels of tax avoidance, and firms with a relatively low or high level of tax avoidance may have a tendency to move back toward the mean level of tax avoidance. To investigate whether targeting behavior is apparent in the firms with less extreme levels of tax avoidance, we follow Flannery and Rangan (2006) and rank observations by CASHETR for each fiscal year. In Column 2, we present the results of estimating equation (4) on only the middle 50% of the CASHETR distribution, quartiles 2 and 3. The coefficient on CASHETR is which translates into an adjustment speed of approximately 62%. Though a tendency for extreme observations to mean revert may partially explain the rapid adjustment speed in Column 1, the fact that the adjustment speed is 62% for the middle quartiles provides strong evidence of targeting behavior. [INSERT TABLE 2 HERE] The partial adjustment model implies that a firm s actual level of tax avoidance eventually converges to its target level of tax avoidance. If our model estimates meaningful targets, we should find that firms begin to adjust to these targets the following year. To investigate whether the sample firms adjust their level of tax avoidance towards our estimated targets, we first split firm-years into two groups based on whether the distance between their 19

21 actual and target levels of tax avoidance, CCCCCCCCCCCCCC ii,tt CCCCCCCCCCCCCC ii,tt+3 is positive or negative. For each group, we rank firm-years as above or below the median based on the distance between their actual and target levels of tax avoidance. We next plot the mean and median year-over-year change in CASHETR for the following four groups: Far Below Target, Below Target, Above Target, Far Above Target. Figure 1 provides further evidence of targeting behavior. Firms with below target CASHETR increase their CASHETR in the following year while the opposite is true for firms with above target CASHETR. [INSERT FIGURE 1 HERE] Figure 1 also shows that the mean change in CASHETR is substantially greater than the median change. Also of note, firms that are farther away from their target (far left and far right bars) appear to close a greater proportion of the gap between their actual and target CASHETR in the next year then firms that begin closer to their target (middle two bars). A faster adjustment speed for the extreme observations suggests the costs of a firm failing to achieve its target CASHETR are more likely to exceed the adjustment costs for firms that are further away from their target. When a firm is already near its target, adjustment costs could be more likely to exceed the costs of not achieving its target CASHETR because it is difficult to identify costeffective tax strategies that create small changes in the CASHETR. Asymmetric Adjustment Speed Analyses Figure 2 shows the proportion of the gap between a firm s target and actual CASHETR that is closed within the next period, CCCCCCCCEEEEEE ii,tt+3 CCCCCCCCCCCCCC ii,tt, for the median firm in each of the CCCCCCCCCCCCCC ii,tt CCCCCCCCCCCCCC ii,tt+3 groups from Figure 1. Firms that are above their target CASHETR appear to close a larger proportion of the gap between their target and actual level of tax avoidance in the next period. The Far Above Target group closes 75.6% of the gap in the following year while the Far Below 20

22 Target group closes 45.7%. Overall, Figure 2 provides initial visual evidence that the adjustment speeds may be asymmetric for firms with above and below target CASHETRs. [INSERT FIGURE 2 HERE] Table 3 presents the results of estimating equation (4) separately for firms with below target CASHETRs and firms with above target CASHETRs. Recall, the tradeoff literature predicts that the adjustment speed will be between zero and one for firms whose actual CASHETR is below the target. Column 1 shows that firms with below target CASHETRs have an adjustment speed of approximately 47% ( ) whereas Column 2 shows that firms with above target CASHETRs have an adjustment speed of approximately 89% ( ). In combination, these results are consistent with the tradeoff theory s prediction that managers actively attempt to achieve a target level of tax avoidance regardless of whether their initial level of tax avoidance is above or below their target. In addition, the difference in adjustment speeds between the two columns is significant at the 1% level. A faster adjustment speed for firms with above target CASHETRs suggests that firms engaged in too little tax avoidance increase their tax avoidance more quickly than the rate at which firms engaged in too much tax avoidance scale back their tax avoidance. [INSERT TABLE 3 HERE] Cross-sectional Variation in Adjustment Speed Analyses Table 4 presents the results of estimating equation (4) for subsamples partitioned on sales growth. Consistent with our expectations, firms with high sales growth have a slower adjustment speed than firms with low sales growth. High sales growth firms have an adjustment speed of 63%. In contrast, low sales growth firms have an adjustment speed of 74%. The difference in adjustment speeds between Columns 1 and 2 is significant at the 1% level. A slower adjustment 21

23 speed suggests that the costs of failing to meet the tax avoidance target are less likely to outweigh the adjustment costs for high sales growth firms. This result is consistent with firms with high sales growth being more likely to change tax clienteles in the future, which reduces the net present value of the benefits to operating at its target level of tax avoidance. In other words, a high sales growth firm does not see the benefits in quickly adjusting its target level of tax avoidance because its target is likely to change in the near future as a result of its growth. [INSERT TABLE 4 HERE] Table 5 presents the results of estimating equation (4) separately for domestic and multinational corporations (MNCs). Consistent with our expectations, MNCs have a faster adjustment speed than domestic firms. MNCs have an adjustment speed of 73% compared to 69% for domestic firms. The difference in adjustment speeds is significant at the 10% level. This result is consistent with MNCs enjoying economies of scale with respect to tax planning and having lower costs of adjusting to a target level of tax avoidance. [INSERT TABLE 5 HERE] Table 6 presents the results of estimating equation (4) for subsamples partitioned on board independence. Consistent with our expectations, firms with a higher ratio of independent directors have a faster adjustment speed than firms with less independent boards. Firms with an above median ratio of independent directors have an adjustment speed of 73%. In contrast, firms with a below median ratio of independent directors have an adjustment speed of 69%. The difference in adjustment speeds between the subsamples is significant at the 10% level. This result is consistent with the finding in Armstrong et al. (2015) that more independent boards have greater ability and incentives to monitor managers tax avoidance decisions. 22

24 [INSERT TABLE 6 HERE] V. Sensitivity Analysis Alternative Measure of Tax Avoidance As discussed above, we proxy for tax avoidance using the cash effective tax rate measured over a three-year period (CASHETR). We chose CASHETR as our proxy to capture temporary tax avoidance strategies and to avoid the influence of tax accruals such as reserves for uncertain tax positions and valuation allowances. However, prior research finds that GAAP ETR is the tax avoidance measure that is most important to public firms (Graham et al. 2014). To confirm our results are robust to using GAAP ETR, we replicate our analysis in Table 2 using GAAP ETR (GAAPETR) as our proxy for tax avoidance. We still measure GAAPETR over a three-year period in an effort to mitigate the effect of extreme observations which could cause mechanical mean reversion. Specifically, we modify equation (4) to estimate the following: GGGGGGGGGGGGGG ii,tt+3 = (θθθθ)xx ii,tt + (1 θθ)ggggggggeeeeee ii,tt + εε ii,tt+3 (5) where GAAPETRt+3 is measured over years t+1 to t+3 and GAAPETRt is measured over years t-2 to t. All other control variables are measured as defined in Appendix A. Table 7 presents the results of this sensitivity analysis. We find that the estimated adjustment speed is similar when we use the GAAPETR as our measure of tax avoidance. The convergence speed is approximately 73 percent when GAAPETR serves as our proxy for tax avoidance. For comparison purposes, we find that the convergence speed is approximately 69 percent when CASHETR serves as our proxy for tax avoidance. This result suggests that the convergence speeds we document are robust to using GAAP ETR as the measure of tax avoidance. 23

25 VI. Conclusion In an effort to explain the variation in tax avoidance, recent research examines the determinants of the level of firms tax avoidance (e.g., Chen et al. 2010; Cheng et al. 2012; Dyreng et al. 2010; McGuire et al. 2014). While determinant studies provide valuable insight, the full implications of their findings are not clear because they do not consider firms tax planning objectives. The tradeoff literature (e.g., Scholes, Wilson, and Wolfson 1990) implies that firms select target levels of tax avoidance based on the costs and benefits. Despite the clarity of the tradeoff literature s prediction, there is limited evidence on whether firms target a specific level of tax avoidance. The purpose of this study is to examine whether firms adjust to a target level of tax avoidance and, if so, how quickly firms converge toward their predicted target. Our results suggest firms do target a specific level of tax avoidance and that the typical firm converges toward its target level of tax avoidance at a rate of almost 69 percent per year. In addition, we find that firms whose actual cash ETR is below their target exhibit an adjustment speed of approximately 47 percent per year while the average speed of convergence for firms whose actual cash ETR is above their target is approximately 89 percent per year. In combination, these results are consistent with the tradeoff theory s prediction that managers actively attempt to achieve a target level of tax avoidance regardless of whether their initial level of tax avoidance is above or below their target. Further, our results suggest firms that are engaged in too little tax avoidance increase their tax avoidance more quickly than the rate at which firms that are engaged in too much tax avoidance scale back their tax avoidance. Finally, we investigate whether there is cross-sectional variation in the speed of adjustment. We examine whether the adjustment speed varies based on a firm s growth options and find that firms with low sales growth exhibit significantly faster adjustment speeds to their 24

26 target level of tax avoidance. We next examine whether the speed of adjustment varies with the presence of foreign operations and find that MNCs experience faster adjustment speeds than domestic firms. We also find the speed of adjustment varies with board independence such that firms with more independent boards have faster adjustment speeds. Together, these results suggest firms that are less likely to shift tax clienteles, firms with more tax planning opportunities, and firms with better governance adjust to their target level of tax avoidance more quickly. In summary, our results imply that managers attempt to minimize deviations between actual and target levels of tax avoidance rather than minimize taxes. The finding that firms move toward target levels of tax avoidance supports the tradeoff view of tax planning. Given that each firm faces a unique mix of costs associated with any tax strategy, the optimal level of tax avoidance differs for each firm. As such, it is not possible to simply examine the level of firms effective tax rates to evaluate the effectiveness of the firm s tax function. Because firms constantly adjust to their unique target, our results suggest that the speed of adjustment to target levels of tax avoidance has been an overlooked characteristic in the literature. If the construct of interest in a tax study is the effectiveness of a firm s tax function, the speed of adjustment may better capture the construct relative to the level of tax avoidance. For example, if a researcher is interested in how executive characteristics impact the effectiveness of a firm s tax function, examining adjustment speeds may be more appropriate than examining levels of tax avoidance. Furthermore, our findings suggest that the speed of adjustment varies in the cross-section, providing indirect insight into the costs to firms of adjusting their tax planning strategies. Finally, our study should be of interest to corporate stakeholders. For tax managers, executives, investors, analysts, and members of the compensation committee it can be more useful to evaluate the 25

27 effectiveness of a firm s tax planning by thinking in terms of the speed at which the firm adjusts to its target tax rate rather than focusing on the level of tax avoidance. 26

28 References Armstrong, C., Blouin, J., Jagolinzer, A., and Larcker, D. (2015). Corporate governance, incentives, and tax avoidance. Journal of Accounting and Economics, 60 (1) (2015): Balakrishnan, K., J. Blouin, and W. Guay. Does Tax Aggressiveness Reduce Corporate Transparency? Working paper (2012), London Business School and University of Pennsylvania. Berger, P. Explicit and implicit tax effects of the R & D tax credit. Journal of Accounting Research (1993): Byoun, S. How and when do firms adjust their capital structures toward targets?. The Journal of Finance 63.6 (2008): Chang, X., and S. Dasgupta. Target behavior and financing: How conclusive is the evidence? Journal of Finance 64.4 (2009): Chen, L., and X. Zhao. Mechanical mean reversion of leverage ratios. Economics Letters 95 (2007): Chen, S., X. Chen, Q. Cheng, and T. Shevlin. Are family firms more tax aggressive than nonfamily firms?. Journal of Financial Economics 95.1 (2010): Cook, K., W. Moser, and T. Omer. Towards an optimal level of tax avoidance. Working paper (2015), Texas Tech University. Dittmar, A., and R. Duchin. The dynamics of cash. Working paper (2010), University of Michigan. Dyreng, S., M. Hanlon, and E. Maydew. Long-run corporate tax avoidance. The Accounting Review 83.1 (2008): Fama, E., and K. French. Testing trade-off and pecking order predictions about dividends and debt. Review of Financial Studies 15.1 (2002): Flannery, M., and K. Rangan. Partial adjustment toward target capital structures. Journal of Financial Economics 79.3 (2006): Frank, M., L. Lynch, and S. Rego. Tax reporting aggressiveness and its relation to aggressive financial reporting. The Accounting Review 84.2 (2009): Graham, J., M. Hanlon, and T. Shevlin. Real effects of accounting rules: Evidence from multinational firms investment location and profit repatriation decisions. Journal of Accounting Research 49.1 (2011):

29 Graham, J., M. Hanlon, T. Shevlin, and N. Shroff. Incentives for tax planning and avoidance: Evidence from the field. The Accounting Review 89.3 (2014): Gupta, S., and L. Mills. "Corporate multistate tax planning: benefits of multiple jurisdictions." Journal of Accounting and Economics 33.1 (2002): Hanlon, M., and S. Heitzman. A review of tax research. Journal of Accounting and Economics 50.2 (2010): Hoopes, J., D. Mescall, and J. Pittman. Do IRS audits deter corporate tax avoidance? The Accounting Review 87.5 (2012): Mills, L., M. Erickson, and E. Maydew. Investments in tax planning. Journal of the American Taxation Association 20.1 (1998): Öztekin, Ö., and M. Flannery. Institutional determinants of capital structure adjustment speeds. Journal of Financial Economics (2012): Rego, S. Tax Avoidance Activities of US Multinational Corporations. Contemporary Accounting Research 20.4 (2003): Scholes, M., M. Wolfson, M. Erickson, M. Hanlon, E. Maydew, and T. Shevlin. Taxes and Business Strategy: A Planning Approach. (2014). Shackelford, D., and T. Shevlin. Empirical tax research in accounting. Journal of Accounting and Economics 31.1 (2001): Shyam-Sunder, L., and S. Myers. Testing static tradeoff against pecking order models of capital structure. Journal of Financial Economics 51 (1999): Tax Executives Institute, Inc. Corporate tax department survey. Tax Executives Institute, Inc. ( ). Washington, D.C. Venkiteshwaran, V. Partial adjustment toward optimal cash holding levels. Review of Financial Economics 20.3 (2011):

30 Appendix A: Variable Definitions Variable Defintion Dependent Variable 1 CASHETR GAAPETR Tax Avoidance Determinants 2 ROA LEV NOL CNOL FORINC CAPINT EQINC RD MTB SIZE LOSSINT Cross-Sectional Variables The sum of cash taxes paid from year t-2 to year t divided by the sum of pre-tax book income less special items over the same period: tt tt 2CCXGGD tt GGI CCGGI The sum of cash taxes paid from year t-2 to year t divided by the sum of pre-tax book income less special items over the same period: tt tt 2CCXCC tt GGI CCGGI Sum of pre-tax income (COMPUSTAT PI) less extraordinary items (COMPUSTAT XI) over the three-year period divided by average total assets (COMPUSTAT AT). Average long-term debt over the three-year period (COMPUSTAT DLTT) scaled by average total assets (COMPUSTAT AT). Indicator variable equal to one if the firm has a tax loss carryforward (COMPUSTAT TLCF is positive) at anytime during years t-2 to t ; zero otherwise. Change in tax-loss carryforward (COMPUSTAT TLCF) from beginning of year t-2 to end of year t scaled by average total assets. Sum of pre-tax foreign income over the three-year period (COMPUSTAT PIFO) scaled by average total assets (COMPUSTAT AT). Average net PPE over the three-year period (COMPUSTAT PPENT) scaled by average total assets (COMPUSTAT AT). Sum of equity income over the three-year period (COMPUSTAT ESUB) scaled by average total assets (COMPUSTAT AT). Sum of research and development expense over the three-year period (COMPUSTAT XRD) scaled by average total assets (COMPUSTAT AT). The average of the market-to-book ratio at the beginning of year t-2 and at the end of year t. The market-to-book ratio is measured as market value of equity (COMPUSTAT PRCC_F x CSHO) divided by book value of equity (COMPUSTAT CEQ). Natural log of average total assets over the three-year period (COMPUSTAT AT). Loss intensity over the previous four year period defined as the number of years a firm has negative pre-tax book income (COMPUSTAT PI) from year t-4 to year t-1 scaled to range from [0,1]. SALESGROWTH Sales growth from year t-3 to year t (COMPUSTAT REVT). IND_RATIO The ratio of independent directors as of the meeting that occurred in the fiscal year t+3. 1 Observations with a negative denominator in the calculation of CASHETR are deleted. Observations with an CASHETR outside of the [0,1] range are also deleted. 2 The determinants, with the exception of NOL and LOSSINT, are winsorized at the 1st and 99th percentile. Missing values of DLTT, ESUB, PIFO, PPENT, SPI, TLCF, XI, and XRD are set to zero. Balance sheet variables are the average of the data item at the beginning of year t-2 and at the end of year t. Income statement variables are summed over years t-2 through t. tt 2 tt 2 29

31 Table 1 Descriptive Statistics Variable N Mean Std Dev Q1 Median Q3 CASHETR 24, ROA 24, LEV 24, NOL 24, CNOL 24, FORINC 24, CAPINT 24, EQINC 24, RD 24, MTB 24, SIZE 24, LOSSINT 24, SALESGROWTH 24, IND_RATIO 12, *We winsorize all continuous variables other than CASHETR at the 1 st and 99 th percentile. CASHETR is truncated at [0,1]. Please see Appendix A for full variable definitions. 30

32 Table 2 Partial Adjustment Model to Target Level of Tax Avoidance CCCCCCCCEEEEEE ii,tt+3 = (θθθθ)xx ii,tt + (1 θθ)cccccccceeeeee ii,tt + εε ii,tt+3 (4) Where XX includes an intercept term, ROA, LEV, NOL, CNOL, FORINC, CAPINT, EQINC, RD, MTB, SIZE, and LOSSINT. CASHETR t+3 CASHETR t+3 (1) Base Model (2) Middle 50% Coefficient Coefficient Variable (Std Error) (Std Error) CASHETR t 0.306*** 0.377*** (0.011) (0.027) ROA 0.057*** 0.033*** (0.007) (0.008) LEV *** (0.011) (0.013) NOL ** (0.003) (0.003) CNOL * (0.023) (0.038) FORINC (0.017) (0.018) CAPINT *** *** (0.009) (0.010) EQINC * (0.129) (0.136) RD *** *** (0.013) (0.018) MTB *** *** (0.001) (0.001) SIZE *** *** (0.001) (0.001) LOSSINT (0.009) (0.014) Adjustment Speed 69.4% 62.3% Number of Observations 24,762 12,393 R Industry and Year Fixed Effects Yes Yes Standard Errors Clustered by Firm Yes Yes *, **, and *** denote significance at the p < 0.10, 0.05, and 0.01 levels, respectively. The adjustment speed is calculated as one minus the coefficient on CASHETR. Please see Appendix A for full variable definitions. 31

33 Table 3 Asymmetric Adjustment to Target Level of Tax Avoidance CASHETR t+3 CASHETR t+3 (1) Below Target (2) Above Target Coefficient Coefficient Variable (Std Error) (Std Error) CASHETR t 0.532*** 0.107*** (0.023) (0.019) ROA *** (0.009) (0.009) LEV *** (0.013) (0.015) NOL *** (0.003) (0.004) CNOL *** (0.023) (0.043) FORINC *** (0.019) (0.025) CAPINT *** *** (0.011) (0.012) EQINC (0.181) (0.164) RD *** *** (0.017) (0.019) MTB ** *** (0.001) (0.001) SIZE *** *** (0.001) (0.001) LOSSINT 0.022** (0.011) (0.013) Adjustment Speed 46.8% 89.3% Test of difference in CASHETR coefficient chi2(1) = Prob > chi2 = *** Number of Observations 12,518 12,244 R Industry and Year Fixed Effects Yes Yes Standard Errors Clustered by Firm Yes Yes *, **, and *** denote significance at the p < 0.10, 0.05, and 0.01 levels, respectively. The adjustment speed is calculated as one minus the coefficient on CASHETR. Please see Appendix A for full variable definitions. 32

34 Table 4 Sales Growth and Cross-Sectional Variation in Adjustment Speeds CASHETR t+3 CASHETR t+3 (1) Below Median SALESGROWTH (2) Above Median SALESGROWTH Coefficient Coefficient Variable (Std Error) (Std Error) CASHETR t 0.257*** 0.374*** (0.014) (0.017) ROA 0.064*** 0.045*** (0.009) (0.009) LEV ** ** (0.014) (0.014) NOL *** (0.004) (0.003) CNOL ** (0.036) (0.027) FORINC 0.050** (0.024) (0.020) CAPINT *** *** (0.012) (0.012) EQINC ** (0.167) (0.171) RD *** *** (0.019) (0.016) MTB *** *** (0.001) (0.001) SIZE *** *** (0.001) (0.001) LOSSINT ** (0.012) (0.012) Adjustment Speed 74.3% 62.6% Test of difference in CASHETR coefficient chi2(1) = Prob > chi2 = *** Number of Observations 12,376 12,386 R Industry and Year Fixed Effects Yes Yes Standard Errors Clustered by Firm Yes Yes *, **, and *** denote significance at the p < 0.10, 0.05, and 0.01 levels, respectively. The adjustment speed is calculated as one minus the coefficient on CASHETR. Please see Appendix A for full variable definitions.

35 Table 5 Foreign Operations and Cross-Sectional Variation in Adjustment Speeds CASHETR t+3 CASHETR t+3 (1) Domestic (2) Multinational Coefficient Coefficient Variable (Std Error) (Std Error) CASHETR t 0.313*** 0.273*** (0.008) (0.009) ROA 0.052*** 0.062*** (0.006) (0.007) LEV *** (0.009) (0.011) NOL *** ** (0.003) (0.002) CNOL *** (0.024) (0.021) FORINC (0.012) CAPINT *** *** (0.007) (0.009) EQINC (0.116) (0.108) RD *** *** (0.014) (0.010) MTB *** *** (0.001) (0.001) SIZE *** *** (0.001) (0.001) LOSSINT (0.008) (0.008) Adjustment Speed 68.7% 72.7% Test of difference in CASHETR coefficient chi2(1) = 3.23 Prob > chi2 = * Number of Observations 12,789 11,973 R Industry and Year Fixed Effects Yes Yes Standard Errors Clustered by Firm Yes Yes *, **, and *** denote significance at the p < 0.10, 0.05, and 0.01 levels, respectively. The adjustment speed is calculated as one minus the coefficient on CASHETR. Please see Appendix A for full variable definitions. 34

36 Table 6 Board Independence and Cross-Sectional Variation in Adjustment Speeds CASHETR t+3 CASHETR t+3 (1) Below Median IND_RATIO (2) Above Median IND_RATIO Coefficient Coefficient Variable (Std Error) (Std Error) CASHETR t 0.315*** 0.266*** (0.012) (0.012) ROA 0.062*** 0.070*** (0.009) (0.009) LEV *** (0.014) (0.014) NOL (0.003) (0.003) CNOL *** (0.032) (0.030) FORINC (0.018) (0.017) CAPINT *** *** (0.011) (0.011) EQINC 0.318** 0.532*** (0.160) (0.128) RD *** *** (0.015) (0.014) MTB *** *** (0.001) (0.001) SIZE *** *** (0.001) (0.001) LOSSINT *** (0.012) (0.011) Adjustment Speed 68.5% 73.4% Test of difference in CASHETR coefficient chi2(1) = 2.82 Prob > chi2 = * Number of Observations 5,997 6,059 R Industry and Year Fixed Effects Yes Yes Standard Errors Clustered by Firm Yes Yes *, **, and *** denote significance at the p < 0.10, 0.05, and 0.01 levels, respectively. The adjustment speed is calculated as one minus the coefficient on CASHETR. Please see Appendix A for full variable definitions. 35

37 Table 7 Partial Adjustment Model Using an Alternative Measure of Tax Avoidance GGGGGGGGEEEEEE tt+1,tt+3 = (θθθθ)xx ii,tt + (1 θθ)ggggggggeeeeee tt 2,tt + εε ii (5) Where XX includes an intercept term, ROA, LEV, NOL, CNOL, FORINC, CAPINT, EQINC, RD, MTB, SIZE, and LOSSINT. Variable GAAPETR t+3 Coefficient (Std Error) GAAPETR t 0.270*** (0.016) ROA 0.020*** (0.006) LEV (0.009) NOL (0.002) CNOL (0.020) FORINC *** (0.015) CAPINT ** (0.007) EQINC (0.115) RD *** (0.011) MTB (0.000) SIZE *** (0.001) LOSSINT (0.007) Adjustment Speed 73.0% Number of Observations 24,762 Adj R Industry and Year Fixed Effects Yes Standard Errors Clustered by Firm Yes *, **, and *** denote significance at the p < 0.10, 0.05, and 0.01 levels, respectively. The adjustment speed is calculated as one minus the coefficient on GAAPETR. Please see Appendix A for full variable definitions. 36

38 Figure 1 Cash ETR Changes in Following Period

39 Figure 2 Percentage of Distance from Target Closed in Following Year 38