Does Tax Aggressiveness Reduce Transparency?

Does Tax Aggressiveness Reduce Transparency? Karthik Balakrishnan Email: kbalakri@wharton.upenn.edu Phone: (215) 898-2610 Jennifer Blouin* Email: blouin@wharton.upenn.edu Phone: (215) 898-1266 Wayne Guay Email: guay@wharton.upenn.edu Phone: (215) 898-7775 All authors are at the Wharton School, University of Pennsylvania 1300 Steinberg Hall-Dietrich Hall First draft: October 24, 2010 Very preliminary draft: Please do not quote without authors permission Abstract This paper investigates whether aggressive tax planning firms have less transparent information environments. Although tax planning provides expected tax savings, it can simultaneously increase the complexity of the organization. And, to the extent that this greater complexity cannot be adequately communicated to outside parties, such as equity investors, creditors, and analysts, transparency problems can arise. Our investigation of the association between a newly developed measure of tax aggressiveness and measures of information uncertainty, information asymmetry and earnings quality suggests that aggressive tax planning increases the opacity of a firm s information environment. We find some evidence, however, that managers increase the volume of disclosure in an attempt to mitigate these transparency problems. Overall, our results suggest firms face a trade-off between financial transparency and aggressive tax planning thereby potentially explaining why some firms appear to engage in more conservative tax planning than would otherwise be optimal. JEL Classification: H20; M41 Keywords: Tax aggressiveness; tax planning; information content; earnings quality * Corresponding author. We appreciate comments from seminar participants at the University of Melbourne.

1. Introduction Corporations engage in various forms of tax planning to reduce expected tax liabilities. These expected benefits, however, do not come without costs. Such costs include direct labor and information systems necessary to carry out the tax planning, as well as expected costs of negotiation and penalties stemming from interactions with taxing authorities. In this paper, we examine a previously unexamined cost of tax planning related to financial transparency. Specifically, we argue that aggressive tax planning can increase organizational complexity, which can, in turn, reduce financial transparency. As an illustration of this point, consider the following Double Irish tax planning technique used by pharmaceutical firm Forest Laboratories. Forest Laboratories Irish subsidiary, Forest Laboratories Holdings Ltd. reorganized in 2005 by creating a new Irish subsidiary, Forest Laboratories Ireland, and relocating itself to Bermuda. Although the new Irish subsidiary handled manufacturing, its new Bermudian entity was responsible for the licensing of patents. Forest Laboratories Ireland paid the Bermudian firm a royalty fee for the use of the patents and, since Bermuda does not have an income tax, this organization structure reduced Forest Laboratories Ireland s tax rate to 2.4% from 10.3%. To further reduce Forest s worldwide tax liabilities, the royalty payment made to the Bermudian entity is paid to Forest Finance BV, a Dutch affiliate. By routing the royalty payment through the Netherlands, Forest Laboratories Ireland avoided paying a 20% withholding tax that would be necessary if the royalty was paid to an entity outside of the EU (the Netherlands has no such withholding requirement). In the year of the reorganization, Forest s effective tax rate dropped by 21.8% (see Drucker 2010 for a detailed description of the transaction). 1

Although the tax planning described above appears to provide expected tax savings to the parent company, it simultaneously increases the complexity of the organization. And, to the extent that this greater complexity cannot be adequately communicated to outside parties, such as equity investors, creditors, and analysts, transparency problems can arise. Bushman, Chen, Engel, and Smith (2004, p. 175) describe this transparency potential problem as follows: Operational complexities can arise as firms act to arbitrage institutional restrictions such as tax codes and financial restrictions (Bodnar et al., 1998). For example, firms may employ complex transfer pricing schemes to shift profits to low tax jurisdictions that can complicate efforts by shareholders and board members to understand firms foreign operations. Of course, managers may respond to this greater financial complexity by augmenting financial reporting and disclosures in an attempt to maintain a transparent information environment. Managers incentives, however, can conflict with outside demands for transparency. In particular, although US corporations are required to disclose details about material operations in other tax jurisdictions, managers may be hesitant to transparently disclose the details of these subsidiaries if doing so would provide a roadmap for an audit by the tax authorities. Hence, when aggressive tax planning increases organizational complexity, managers financial reporting and disclosure choices may not serve to mitigate the increased opacity. Thus, we view the extent to which aggressive tax planning reduces transparency to be an empirical issue. Although our Forest Laboratories example above describes a specific tax planning technique that most would agree is aggressive, we are aware of no universally accepted definition of aggressive tax planning. Frank, Lynch, and Rego (2009) define aggressive tax reporting as downward manipulation of taxable income through tax planning that may or may 2

not be considered fraudulent tax evasion. However, Slemrod (2004) argues that tax aggressiveness is a more specific activity, being encompassed by transactions where the primary purpose is to lower the firm s tax liability. For our study, we define a tax aggressive firm as one that pays an unusually low amount of tax given the firm s industry and size. Although our measure of tax aggressiveness does not rely/capture any specific tax planning technique, it does incorporate the notion that, all else equal, similar firms should have similar tax planning opportunities. And, among firms with similar planning opportunities, firms with lower tax liabilities can be considered more tax aggressive. As an alternative measure of tax aggressiveness, we use the number of tax haven countries in which a firm has operations. By introducing additional foreign subsidiaries through which intercompany transactions flow, organizational complexity is increased. We document that tax aggressive firms, measured as having unusually low tax liabilities or operating in relatively more tax haven countries, are characterized by lower transparency. Specifically, we find that firms with unusually low tax liabilities or more tax haven operations have higher bid-ask spreads and greater absolute analyst forecast errors. Tax aggressive firms also exhibit lower accruals quality, measured using several approaches advanced in the earnings quality literature. Overall, these results suggest that the benefits of tax aggressiveness come at a cost of lower financial transparency. We also investigate whether tax aggressive firms recognize that tax planning can give rise to transparency problems, and respond by augmenting their disclosures. Consistent with this conjecture, we find that the management discussion and analysis (MD&A) section of the 10-K report is, on average, lengthier for tax aggressive firms. Further, tax aggressive firms that provide additional disclosure in the MD&A, exhibit significantly lower spreads than tax aggressive firms 3

that do not provide additional disclosure (we do not find significant differences in analyst forecast errors between the high and low disclosure groups). Our results advance the literature on the relation between financial transparency and taxrelated decisions. It is well-documented that differences between book and tax income provide information to market participants (see Hanlon and Heitzman 2010). For example, Lev and Nissim (2004) and Weber (2009) find that the ratio of taxable income to book income is useful in predicting earnings growth, and Hanlon (2005) documents that extreme book-tax differences provide a signal on the persistence of accruals. Further, when earnings management increases the spread between book and tax income, the ability of accruals to provide information about future cash flows can be constrained (e.g., Dhaliwal et al. 2008 and Comprix et al. 2010). These papers, however, do not investigate how aggressive tax planning, irrespective of book-tax differences, alters the firm s information environment. We provide evidence suggesting that aggressive tax planning can increase organization complexity in a manner that increases investor uncertainty about future profitability as well as increases information asymmetry between investors. Overall, our findings highlight lower financial transparency as a potentially important cost of aggressive tax planning. These results may help explain why firms appear to engage in more conservative tax planning than would otherwise be optimal. In addition, we develop a new measure of tax aggressiveness that incorporates the notion that tax planning opportunities are expected to vary cross-sectionally, and over time. Our measures benchmarks firms estimated tax burdens by size and industry to obtain a measure of the aggressiveness of a firm s tax planning relative to other firms in their operating environment. The measure may be useful to future researchers investigating the relation between tax aggressiveness and firm behaviour. 4

2. Prior Research and Hypothesis Development A. Trade-offs between tax and non-tax costs In their seminal textbook, Scholes and Wolfson explain that managers face conflicts between financial reporting and tax planning. While managers often desire to report high levels of income to investors, they simultaneously desire to report low levels of income to the tax authorities. In the U.S., as in many other countries, tax reporting rules differ from financial reporting rules, allowing firms to report disparate levels of income to tax authorities and to investors. However, as many economic transactions are reported similarly for book and tax reporting, firms often face a trade-off between cash tax savings and lower reported earnings. 1 Of course, costs of lower reported earnings is only one of many potential direct and indirect costs of tax planning. Direct costs of tax planning include labor, information systems, coordination among business units, expected audit costs, and expected penalties in the event that tax planning strategies are found to be inappropriate. Indirect costs include potential agency conflicts between managers and shareholders, as well as reputational costs of being an overly aggressive tax planning firm. A further potential indirect cost, and the focus of our study, is the effect of tax planning on organizational transparency. As illustrated in our Forest Laboratories example above, tax planning strategies can substantially increase organizational complexity. Other examples of how tax planning strategies can increase complexity include the creation of entities for multistate tax planning (e.g., captive REITs, intangible holding companies); net operating loss monetization; and capital loss utilization. 1 For example, see Scholes, Wilson and Wolfson (1990), Guenther, Maydew, and Nutter (1997); Maydew (1997); Matsunaga, Shevlin, and Shores (1992). 5

As noted by Bushman, Chen, Engel, and Smith (2004), organizational complexity can, in turn, hinder efforts by investors to understand the firm s operations. Complexity can influence transparency through multiple channels. One possibility is the influence of tax planning-related complexity on the quality of financial accounting, say through the influence of tax planning on the accrual process. For example, consider that many planning opportunities require the bifurcation of business activities into separate legal structures (e.g., income that qualifies for treaty based withholding taxes, activity qualifying for the domestic manufacturers deduction). If the separation of the business activities increases the complexity of the accounting because either there is more accounting to be done or there is less understanding of how to record activity for separate activities, then the quality of the accruals process may decline. Even in the absence of financial accounting issues, however, the influence of tax planning on operational and financial strategies can render the firm more opaque (as the Forest Laboratories example illustrates). Thus, even the most well-intentioned management may have difficulty providing investors with disclosures that reduce information uncertainty and/or information asymmetries between investor groups. In summary, we predict that aggressive tax planning will increase information uncertainty and information asymmetry, and will reduce the quality of financial reporting. Our research question is related to the call by Shackelford and Shevlin (2001) for further research on the drivers of cross-sectional variation in tax planning. If reduced corporate transparency is a cost of aggressive tax planning, and this cost varies cross-sectionally, then one would expect to observe variation in tax planning across firms. Our study may also shed light on the observation by Armstrong, Blouin and Larcker (2010) that aggressive tax planning appears to be underutilized by firms given large potential benefits and relatively small potential costs 6

stemming from audits, interest and penalties. Transparency-related costs may explain some of this apparent underutilization of aggressive tax planning. Before moving on to our analysis, we note that our research question differs from existing work on book-tax differences. Differences between book and tax reporting have been used extensively to study the earnings quality of firms (e.g., Lev and Nissim 2004; Hanlon 2005) and whether taxable income contains information incremental to pre-tax income (e.g., Hanlon, LaPlante, and Shevlin, 2005; Hanlon, Maydew and Shevlin, 2008). 2 Hanlon (2005) and Comprix et al. (2010) document that large book-tax differences are associated with less persistent earnings and lower quality of accruals. In a similar vein, Dhaliwal et al. (2008) examines whether large book-tax differences are associated with a higher cost of equity capital. The extant book-tax difference literature implies that book-tax differences are informative because they capture earnings management behaviour; not because they include information regarding tax planning. Our interest is determining whether tax planning itself can leave its imprint on firms accounting quality. We conjecture that firms entering into complex tax transactions potentially lessen the transparency (or increase the opacity) of their financial statements. Consequently, firms are effectively trading off earnings quality for cash tax benefits. 2 These papers have led to what is known as the book-tax conformity. Many have argued that allowing firms to report separate incomes for book and tax is precisely what leads to aggressive tax and GAAP reporting (e.g., Desai, 2005). If firms were forced to conform their book and tax reporting, then firms would be relatively less incentivized to undertake earnings management or extreme tax planning. Economists propose that firms should report their GAAP income on their tax returns (so, taxable income should confirm to book). Hanlon, LaPlante, and Shevlin (2005) and Hanlon, Maydew, and Shevlin (2008) argue that conformity would result is the loss of information to the capital markets. To date, the book-tax conformity debate continues with little consensus. Although many in the academic community agree that conforming tax reporting and book reporting is not a good idea (see Shackelford and Slemrod 2004), there is less agreement as to whether conformity ultimately leads to a loss of information to the capital market s (e.g. Raedy, Seidman, and Shackelford, 2010; Atwood, Drake, and Myers, 2010). 7

B. Measuring Aggressive Tax Planning There is no well-accepted measure of tax aggressive. Lisowsky et al. (2010) provide a continuum of the ability of specific measures of firms tax attributes to capture tax aggressiveness but they do not empirically test whether their conjectures are true. Although several recent papers have attempted to develop measures intended to specifically capture tax aggressiveness, each suffers from several weaknesses. Wilson (2009) and Lisowsky (2010) create measures of the estimated probability that a firm has entered into tax shelter. Both measures are derived by estimating a probability from the coefficients of a logit model of attributes of firms discovered engaging in shelter activity. The trouble with both of these measures is two-fold. First, the measures rely on a very small sample of firms whose shelter behavior was a) detected by the tax authorities and b) litigated. 3 Wilson (2009) and Lisowsky (2010) rely on 59 and 211 of shelter-engaging firms, respectively, to generate the parameters for estimating sheltering probabilities. Frank, Lynch, and Rego (2009) develop a measure of discretionary permanent differences, DTAX, which relies on the premise that permanent differences are more aggressive than timing differences. Although anecdotal evidence suggests that the optimal tax planning opportunity is one that creates permanent differences because of their financial statement benefits, there is little evidence to support this conjecture (see Hanlon and Heitzman, 2010). Much of the work on tax planning is focused on understanding cross-sectional variation in tax aggressiveness. Papers have found evidence that extreme tax planning is associated with executive compensation and risk taking (e.g., Rego and Wilson 2010; Brown and Martin 2010; 3 Wilson (2009) relies on court records discussed in the popular press to identify his shelter firms. Lisowsky (2010), which is an extension of Wilson (2009), identifies shelters using proprietary IRS data. His sample of shelter transactions come from the Office of Tax Shelter Analysis, which identifies shelters through the audit process. 8

Li et al. 2010). However, these papers do not reach a consensus on how to measure tax aggressiveness. Furthermore, we are unaware of any measures that attempt to capture firms aggregate level of tax aggressiveness. Although the shelter probabilities and DTAX are likely correlated with aggressive tax planning, neither satisfactorily captures all potential tax planning. The shelter probabilities rely on detecting specific law breaking transactions entered into by firms. Though we would agree that these transactions intended to be captured by the shelter probability are aggressive, the measure does not capture the type of legal planning described Forest Industries example in the introduction. We would like to have a measure of tax aggressiveness that includes legal strategies that lack any business purpose beyond the potential tax benefits as well as any specific tax shelter activity. Finally, we would like to study variation in extreme tax planning that stems from both timing and permanent differences. DTAX only includes aggressive permanent differences. In summary, although some proposed measures of aggressive tax planning exist, none seems to encompass the aggregate level of tax aggressiveness of a particular firm. Existing measures also fail to measure aggressiveness relative to some benchmark of a normal level of tax planning. For example, some industries have far more extensive foreign operations than others (computer manufacturing as compared to food distributers). These industries may well have greater ability to take advantage of various tax planning strategies, but presumably investors or analysts that follow these industries will be aware of such strategies. When investors and analysts are aware of common industry practices, it seems plausible that such strategies do not create substantial transparency problems. Thus, for our purposes, the ideal measure would capture a) variation in firms total tax planning (timing and permanent) and b) some notion of how this firm compares to other firm s 9

with a similar underlying production function. We construct our measure in two steps. First, we use the GAAP and Cash effective tax rates (ETR) as proxies for each firm s aggregate tax burden. GAAP ETR (Cash ETR) is the total tax expense (total cash tax paid for income taxes) over the pre-tax income. These measures are bottom-line measures of tax burden, and therefore will reflect the firm s total tax planning efforts. To avoid year-to-year noise in our measures of tax planning, we estimate the effective tax rates by aggregating three years of data (see Dyreng, Hanlon, and Maydew 2008). So, GAAP ETR (Cash ETR) is the sum of the past three years (t to t-2) of total tax expense (total tax paid for income taxes) over the sum of the past three years of pre-tax income. To obtain a measure of whether a firm engages in an unusual (i.e., aggressive) amount of tax planning, we then adjust each firm s average three-year ETR based on industry (48 Fama and French 1997) and size (quintile of total assets). We adjust the ETRs for size and industry by sorting independently on industry and size, and then subtracting the mean ETR over the last three year for the size-industry matched bin. In order to estimate a measure of tax aggressiveness, we require that each size industry bin has at least 25 firm-year observations. Our measures, TA_GAAP and TA_CASH, are the industry-size matched GAAP ETR and Cash ETR less the firm s GAAP ETR and Cash ETR, respectively. Thus, positive values of TA_GAAP and TA_CASH suggest that the firm pays less tax than its size-industry peers, and greater values for this measure imply greater tax aggressiveness. Although TA_GAAP and TA_CASH are bottom line measures that reflect the results of aggressive tax planning, they do not speak to any specific tax planning strategies. As noted above, strategic choices related to geographic operating and financing activities are one common tax planning strategy for large corporations. As a proxy for these geographic tax planning activities, we examine the number of tax haven countries in which a firm has operations. Tax 10

havens are countries that are well known to offer firms various tax advantages for locating certain operating and financing activities within the countries borders. Hines and Rice (1994) and Dyreng and Lindsay (2009) both provide evidence consistent with firms using tax havens to reduce their tax obligations. We discuss our measurement of our tax havens variable in more detail in Section 6. C. Measuring Transparency We predict that aggressive tax planning will increase information uncertainty and information asymmetry, and will reduce the quality of financial reporting. Therefore, we construct measures of each of these three facets of transparency. As a proxy for information uncertainty, we use absolute analyst forecast errors and the dispersion of analysts forecasts. If aggressive tax planning increases organizational complexity and reduces transparency, we expect that analysts will forecast earnings with greater absolute errors and dispersion. We measure analyst forecast errors as the average absolute analysts forecast errors over the three years corresponding to the measurement of our tax aggressiveness measures. Each year, the forecast errors are the absolute value of the difference between median analyst estimate and the actual earnings for that fiscal year scaled by the price at the end of previous year. Forecast dispersion is estimated as the standard deviation of the analysts forecasts scaled by lagged price over the same three years as tax aggressiveness. We use bid-ask spreads as a proxy for information asymmetry. We expect that if aggressive tax planning decreases organizational transparency and, in doing so, provides greater opportunity for some investors to gain private information about the firm s activities, then bidask spreads will be greater. We compute bid-ask spreads as the average transaction weighted 11

effective bid-ask spread over the three years corresponding to the measurement period our tax aggressiveness measures. As a proxy for the quality of financial reporting, we use a measure of accruals quality (AQ) that follows Francis et al. (2005). We construct the AQ measure by first estimating annual cross-sectional regressions for each of the 48 Fama and French (1997) industries (at least 20 observations are required for each industry-year regression): (1) where, for year t and firm i, TCA is total current accruals and is calculated as the difference between income less the cash flow from operations, ATA is the average total assets, CFO is the cash flow from operations, is the change in sales less the change in accounts receivables, PPE refers to the property, plant and equipment. The accruals quality measure is estimated for each firm i and each year t as the standard deviation of residuals from the above cross-sectional regression over the period t-5 to t-1. We expect that if aggressive tax planning confounds the ability of accrual accounting to resolve timing and matching problems with cash flows, then accruals quality will be lower. Because the Dechow-Dichev measure is decreasing with accruals quality, we expect that aggressive tax planning will be positively related to the accruals quality measure. Further, recognizing the lack of a well-accepted measure of accruals quality in the literature, we construct and examine three alternative measures: 1) Alt_AQ1 is calculated as AQ scaled by the mean absolute value of TCA over the period t-5 to t-1; 2) Alt_AQ2 is a modified 12

version of the accruals quality measure proposed by Wysocki (2008), who argues that the accruals-based measure derived in Dechow and Dichev(2002) does not reliably capture highquality accruals because of a confounding relation with opportunistic earnings management. He proposes a modification to the AQ measure that aims to extract the contemporaneous association between accruals and cash flows, and better capture the incremental association between current accruals and past and future cash flows over the association between current accruals and current cash flows. This measure is estimated in two steps. First, we estimate two variations of the Dechow and Dichev (2002) model. The first model is a regression of working capital accruals on current cash flows. The second model is the original Dechow and Dichev model that regresses working capital accruals on lagged, current, and future cash flows. We then compute the standard deviation of the residuals of each model during the years t-5 to t-1 and measure Alt_AQ2 as the ratio of the standard deviation of the residuals from the first model to the second model; and, 3) Asset_AQ modifies AQ by first estimating the cross-sectional regression indicated above for each year and each of the 48 Fama and French (1997) industries by quintiles of asset size. This measurement technique is adopted so as to be consistent with the measurement procedure adopted for the tax aggressiveness measures. D. Control Variables We include controls for factors that are expected to influence the quality of a firm s information environment. To control for the size of the firm, we include Size, the log of market value of equity. We expect a negative coefficient on this variable in tests of information uncertainty, information asymmetry, and accruals quality because larger firms typically have a higher quality information environments. We include Leverage, the ratio of long-term debt to 13

total assets, to control for firms debt service needs and capital structure. Age is the natural logarithm of the number of years the firm has been listed on Compustat. Typically, older firms tend to have better information environment. Accordingly, we predict a negative coefficient on this variable. We also control for the geographical complexity of the organization. Complexity is Bushman et al. s (2004) revenue based Hirfindahl-Hirschman index calculated as the sum of the squares of each geographic segment s sales as a percentage of the total firm sales. We anticipate that more geographically complex firms are relatively more opaque. To control for firms growth opportunities, we include Mkt to Book which is the ratio of the market value of assets to the book value of assets. We also include an indicator variable, Loss, which is equal to one if the firm s income before extraordinary items is less than zero in the current year and zero otherwise. Loss firms typically have lower earnings quality. In addition, loss firms will typically appear to be very aggressive tax planners, when, in actuality, they merely have very low income. Finally, we include industry as well year fixed effects in all specifications. To ensure that our inferences regarding aggressive tax planning are not confounded by the influence of book-tax differences, we include the mean of the past three-year s Book-Tax Gap, measured as the difference between pre-tax income less taxable income (defined as current federal tax expense grossed up by the maximum federal statutory tax rate (i.e., 35%) plus pre-tax foreign income less the annual change in NOLs) scaled by total assets, because it has been shown to include information regarding firm s earnings management activity. 4 Comprix et al (2010) and Dhaliwal et al. (2008) both provide evidence that larger book-tax gaps adversely affect firms earnings quality and cost of equity capital, respectively. 4 Note that all of our inferences are identical if we use book-tax differences (i.e., deferred tax expense grossed up by 35%) in place of the book-tax gap. 14

In our Spread analyses, we include several measures that contribute to asymmetric information on the true value of the stock and are known to explain variation in the bid-ask spread. The Std Deviation of Returns and the Std Deviation of Volatility are included to capture expected volatility. Log Volume is included to capture the liquidity of the security because less frequently traded stocks can have more information problems. Finally, with respect to our accruals quality tests, we follow Liu and Wysocki (2008) and include additional control variables for general uncertainty in the firm s operating environment when investigating the association between accruals quality and tax aggressiveness: The standard deviation of operating cash flows (Std Dev of Cash Flows) and the standard deviation of sales (Std Dev of Sales). 3. Sample We obtain our main sample from Compustat s annual database. The sample period spans from fiscal year 1990 through 2007. We retain firms for which we are able to compute the tax aggressiveness measures as well as all of the control variables used in the regression specifications. This results in a sample of 28,355 firm-year observations. Analyst forecast estimates are obtained from I/B/E/S. NYSE TAQ database is used to compute the transaction weighted bid-ask spread. The additional data requirement reduces the sample size to 18,604 firm-year observations in regressions involving analyst data and to 20,812 firm-year observations in regressions involving bid-ask spreads. The tests relating to length of MD&A and tax haven information from Exhibit 21 involves extracting the MD&A section as well as Exhibit 21 from a firm s annual 10-K report. We obtain these data from SEC EDGAR. We were able to extract and match MD&A data for 15,701 firm-year observations and Exhibit 21 data for 8,538 15

observations. 5 The descriptive statistics for the sample used in this study are presented in Table 1. 4. Results We begin our analysis by examining the relation between aggressive tax planning and our three sets of financial transparency measures. Table 2 reports regressions of absolute analyst forecast errors and forecast dispersion on our ETR-based proxies for tax aggressiveness. The regressions include controls for firm and earnings characteristics discussed above. Consistent with our conjecture that aggressive tax planning increases uncertainty faced by investors with respect to forecasting future profitability, we find that absolute earnings forecast errors are significantly larger for firms with relatively low effective tax rates. In addition, we find that the dispersion of forecast errors is also higher for tax aggressive firms. Interestingly, the association between the Book-Tax Gap and analysts forecasts suggests that firms with larger spreads between book income and taxable income have lower analysts forecast errors/dispersion. In Table 3, we examine whether aggressive tax planning is related to information asymmetry between investors, as measured by bid-ask spreads. Our regressions indicate that bidask spreads are positively to related to both the GAAP and Cash-based ETR measures of tax aggressive measures. Taken together with the results in Table 2, this finding suggests that aggressive tax planning not only increases investor uncertainty about future profitability, but also increases the information gap between informed and uninformed investors. Thus, it appears that some investors have a relative advantage in processing information about corporate tax planning. 5 MD&A data is available from 1995. However, for Exhibit 21 we focused on the post-2001 10-K forms for ease of extraction. Post-2001 companies filed 10-K using the XHTML format that enables easier identification of data. 16

Table 4 explores whether accrual quality is adversely affected by aggressive tax planning. Panel A reports results for regressions of AQ on the ETR-based tax aggressiveness proxies. The findings indicate that GAAP_ETR and CASH_ETR are positively related to AQ (recall that because AQ is the standard deviation of residuals from the accruals regression model, greater values of AQ imply lower quality accruals). Panel B of Table 4 shows that the results in Panel A are robust to using our three alternative proxies for accruals quality. The results in Table 4 indicate that, across an array of accruals quality measures, more aggressive tax planning distorts the relation between accruals and cash flows. We next explore whether our results are robust to an alternative measure of tax planning aggressiveness: the number of haven countries with material subsidiary operations. Firms use of haven countries for financial and operating activities has been linked with evidence of extensive transfer pricing activity (Hines and Rice 1994) and lower effective tax rates (Dyreng and Lindsay 2009). We collect data on the number of haven countries reported in firms Exhibit 21 in their 10-K. Exhibit 21 is a required element of a firm s 10-K and includes a listing of all of the firms subsidiaries with material operations. Tax havens are jurisdictions that structure a tax regime to take advantage of firms desire to reduce their tax burdens. Generally, tax havens have low or no tax rates and have very little information sharing of tax information with other jurisdictions thereby making it more difficult for one jurisdiction to determine whether a firm artificially is stripping its earnings into the haven. However, a haven could also include countries that have modified their tax laws in order to attract foreign capital (e.g., Ireland). Either type of haven could be utilized for tax avoidance purposes. Because there is no consensus on which countries are considered havens, we rely on two sources to determine whether a country is considered a tax haven (1) Table 1 in Dyreng and 17

Lindsay (2009) and (2) the seven havens identified in Hines and Rice (1994). TAX_HAVENS (TAX_HAVENS_BIG7) is the number of reported subsidiaries in Dyreng and Lindsay s (Hines and Rice s) list of haven countries. Because havens are used for tax avoidance purposes, we predict that greater haven usage is consistent with more aggressive tax planning. The mean number of material operations in havens is 2.3 and 1.2 (see Table 1) for TAX_HAVEN and TAX_HAVEN_BIG7, respectively. However, the median is zero for both measures suggesting that havens are not used by the majority of firms. 6 In Table 5, we replicate the results in Tables 2, 3 and Panel A of Table 4 using the tax haven measures of tax aggressiveness in place of the ETR-based tax aggressiveness variables. The results are similar to those in the earlier tables. Specifically, firms with operations in a greater number of tax haven countries have larger absolute analyst forecast errors, larger bid-ask spreads, and lower accruals quality. 5. Management disclosure decisions in the presence of aggressive tax planning Presumably, management recognizes the transparency problems that arise when aggressive tax planning is undertaken. In anticipation or response to these problems, management may respond by increasing disclosure to at least partially mitigate the difficulties that investors have in understanding the tax-induced organizational complexity. In Table 6, we explore whether managers who make aggressive tax planning choices increase the volume of disclosure in the Management Discussion & Analysis (MD&A) section of the annual report, as 6 The correlation between our haven measures and TA_GAAP (TA_CASH) is positive and significant (insignificant). However, the correlations between the haven measures and TA_GAAP are only 7%. In later drafts of the paper, we plan to investigate the correlation between our tax aggressiveness measures and a scaled version of havens (i.e., scaling the haven subsidiaries by the total number of subsidiaries). 18

measured by the number of words. 7 We find that the MD&A is significantly longer for firms with more aggressive tax planning. We also explore whether the firms that provide additional disclosure in the MD&A are successful in reducing some of the transparency problems that are generated by aggressive tax planning. We find that greater volume of disclosure in the MD&A reduces the relation between aggressive tax planning and spreads, but does not appear to significantly alter the relation between tax planning and analysts forecasts. 6. Conclusion Corporations engage in various forms of tax planning to reduce expected tax liabilities. These expected benefits, however, do not come without costs. Such costs include direct labor and information systems necessary to carry out the tax planning, as well as expected costs of negotiation and penalties stemming from interactions with taxing authorities. In this paper, we find evidence that a previously unexamined cost of tax planning, financial transparency, is associated with the extent of a firms aggressive tax planning. We specifically investigate whether aggressive tax planning can increase organizational complexity, which can, in turn, reduce financial transparency. We develop a new measure of tax planning aggressiveness that benchmarks firms tax burdens against firms of similar size and industry that are expected to have similar tax planning opportunities. We also examine tax planning aggressiveness as a function of the presence of subsidiaries in tax haven countries. Overall, our results suggest that as firms become more tax aggressive, absolute analyst forecast errors and bid-ask spreads increase, while their accruals quality decreases. However, managers 7 Admittedly, the number of words is a noisy proxy for tax planning disclosure, and we expect to develop richer disclosure measures in future revisions of the paper. 19

appear to be aware of this potential cost, and aggressive tax planning firms increase the volume of disclosure in the MD&A sections of their financial statements. And, this increased disclosure appears to help reduce the affect of aggressive tax planning on spreads. Overall, our findings highlight lower financial transparency as a potentially important cost of aggressive tax planning. These results may help explain why firms appear to engage in more conservative tax planning that would otherwise be optimal. 20

Appendix 1: Variable Definitions Tax Aggressiveness Measures (NOTE - Higher values imply more aggressive tax planning): TA_CASH = The firm s industry size GAAP ETR less the firm s GAAP ETR, where GAAP_ETR is the sum of current tax expense over periods t, t-1 and t-2 dividend by the sum of pre-tax income over periods t, t-1 and t-2. TA_GAAP = The firm s industry size CASH ETR less the firm s CASH ETR, where CASH_ETR is the sum of cash paid for taxes over periods t, t-1 and t-2 dividend by the sum of pre-tax income over periods t, t-1 and t-2. Tax Haven measures based on Exhibit 21 data from 10-Ks: TAX_HAVENS: The number of times one of the following tax haven locations (as described in Dyreng and Lindsay 2009) are mentioned in Exhibit 21: ANDORRA, ANGUILLA, ANTIQUA AND BARBUDA, ARUBA, BAHAMAS, BAHRAIN, BARBADOS, BELIZE, BERMUDA, MOTSWANA, BRITISH VIRGIN ISLANDS, BRUNEI, CAPE VERDE, CAYMAN ISLANDS, COOK ISLANDS, COSTA RICA, CYPRUS, DOMINICA, GIBRALTAR, GRENADA, GUERNSEY AND ALDERNEY, HONG KONG, IRELAND, ISLE OF MAN, JERSEY, KITTS AND NEVIS, LATVIA, LEBANON, LIBERIA, LIECHTENSTEIN, LUXEMBOURG, MACAO. MACAU, MALDIVES, MALTA, MARSHALL ISLANDS, MAURITIUS, MONACO, MONTSERRAT, NAURU, NETHERLANDS ANTILLES (or DUTCH ANTILLES), NIUE, PALAU, PANAMA, SAMOA, SAN MARINO, SEYCHELLES, SINGAPORE, ST. LUCIA, ST. VINCENT AND THE GRENADINES, SWITZERLAND, U.S. VIRGIN ISLANDS, URUGUAY, VANUATU TAX_HAVEN_BIG7: The number of times one of the following tax haven locations (as described in Hines and Rice 1994) are mentioned in Exhibit 21: HONG KONG, IRELAND, LEBANON, LIBERIA, PANAMA, SINGAPORE, SWITZERLAND Information Environment Variables: AFError = Absolute Analyst Forecast Errors measured as the average absolute analyst forecast errors over the 3 years in which tax aggressiveness measures are calculated. Each year, the forecast errors are the absolute value of the difference between median analyst estimate and the actual earnings for that fiscal year scaled by the price at the end of previous year. AFDisp = Average Dispersion of Analyst Earnings Forecasts measured as the average dispersion of analysts annual earnings forecasts over the 3 years in which tax aggressiveness measures are calculated. Each year, the dispersion in forecasts is scaled by the price at the end of previous year. 21

Spread = A measure of information asymmetry and is computed as the average transaction weighted effective bid-ask spread over the three years during which the tax aggressiveness measure is computed. AQ = Accruals Quality measured as the standard deviation of residuals over 5-year rolling window from an industry-year level Dechow-Dichev model augmented with fundamental variables from Jones model (Francis et al. 2004). The variable is coded such that a lower value of AQ means that the firm is more transparent. Alternative accrual quality measures: Alt_AQ1 The AQ measure described above scaled by mean absolute value of accruals over the 5 years over which the model is estimated. Alt_AQ2 is a modified version of the accruals quality measure proposed by Wysocki (2008) to account for the innate component of accruals quality. Asset_AQ AQ measure obtained by running regressions at an industry asset bin level to closely match the process used to obtain the tax aggressiveness measures. Corporate Disclosure Variables: MDA = The number of words in the MD&A section of the annual 10-K report. Control Variables: Book-Tax Gap is the average of period t, t-1 and t-2 book tax gap (defined as pre-tax income less taxable income which is current federal tax expense grossed up by the maximum federal statutory tax rate (i.e., 35%) plus pre-tax foreign income less the annual change in NOLs /average of the last three years of total assets Size is measured as the log of market value of equity. Leverage is the ratio of long term debt to total assets. Age is the natural logarithm of the difference between the first year when the firm appears in COMPUSTAT and the current year. Complexity is a measure of multinational complexity of a firm. Following Bushman et al (2004), we calculated this measure as a revenue-based Hirfindahl-Hirschman index, calculated as the sum of the squares of each geographic segment s sales as a percentage of the total firm sales. Mkt to Book is the market-to-book ratio measured as the ratio of the market value to the book value of total assets. Loss is a dummy variable that takes a value 1 if the firm has incurred a loss in the year and zero otherwise. Loss variable is included because losses have a differential impact on taxes as well as on accruals quality. Std Dev of Cash Flows is the standard deviation of cash flow from operations. This is used as a control variable only in regressions using Accruals Quality variable. The objective is to control for the innate or business environment related portion of the standard deviation of the residuals from the Dechow-Dichev model. 22

Std Dev of Sales is the standard deviation of the sales. Similar to S_CFO, is used as a control variable only in regressions using Accruals Quality variable. The objective is to control for the innate or business environment related portion of the standard deviation of the residuals from the Dechow-Dichev model. Log Volume is the natural logarithm of the average monthly volume of stock traded over the three year period over which the tax aggressiveness measures are estimated. Std Dev of Volume is the natural logarithm of standard deviation of monthly volume of stock traded over the three year period over which the tax aggressiveness measures are estimated. Std Dev of Returns is the natural logarithm standard deviation of monthly stock returns over the three year period over which the tax aggressiveness measures are estimated. 23

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Table 1: Statistics This table presents the descriptive statistics for the variables used in this study. All variables are defined in Appendix 1. Variable N Mean Std Dev Minimum Median Maximum TA_GAAP 28,355 0.032 0.190-0.333 0.030 0.757 TA_CASH 28,355 0.014 0.207-0.306-0.018 0.789 AFError 18,604 0.011 0.028 0.000 0.003 0.203 AFDisp 12,505 0.003 0.006 0.000 0.001 0.045 Spread 20,812 0.016 0.014 0.002 0.011 0.066 AQ 28,355 0.058 0.052 0.005 0.042 0.288 MDA 15,701 6710 5059 22 5620 25242 Book-Tax Gap 28,355-0.008 0.134-0.792 0.007 0.461 Size 28,355 5.601 2.259 0.873 5.550 10.654 Leverage 28,355 0.176 0.183 0.000 0.135 0.880 Mkt to Book 28,355 1.733 1.015 0.772 1.355 4.662 Age 28,355 2.997 0.547 2.197 2.944 4.043 Std Dev of Cash Flows 28,355 0.097 0.103 0.007 0.065 0.637 Std Dev of Sales 28,355 0.195 0.188 0.011 0.137 1.053 Complexity 28,355 0.979 0.100 0.210 1.000 1.000 Loss 28,355 0.264 0.441 0.000 0.000 1.000 Log Volume 25,691 9.594 2.039 4.934 9.610 14.304 Std Dev of Returns 25,691 0.130 0.069 0.036 0.116 0.388 Std Dev of Volume 25,691 9.085 1.844 4.788 9.143 13.408 Alt_AQ1 28,355 0.937 0.418 0.288 0.860 2.791 Alt_AQ2 28,355 1.082 0.616 0.223 0.964 3.892 Asset_AQ 28,355 0.052 0.049 0.004 0.036 0.269 TAX_HAVENS 8,538 2.281 6.458 0.000 0.000 101.000 TAX_HAVEN_BIG7 8,538 1.257 3.789 0.000 0.000 63.000 29

Table 2: Tax Aggressiveness and Absolute Analyst Forecast Errors and Dispersion This table presents pooled time-series cross-sectional regression coefficients of Analyst Forecast Errors (AFError) and Dispersion of Analyst Forecasts (AFDisp) on tax aggressiveness measures and control variables. All variables are defined in Appendix 1. Industry and time effects are included in all specifications. t-statistics, reported in parentheses, are calculated based on standard errors obtained by clustering at the firm as well as time level. Statistical significance (two-sided) at the 10%, 5% and 1% level is denoted by *, **, and ***, respectively. Predicted (1) (2) (3) (4) VARIABLES Sign AFError AFError AFDisp AFDisp TA_GAAP + 0.014*** 0.003*** (5.316) (4.495) TA_CASH + 0.012*** 0.002*** (5.471) (4.326) Book-Tax Gap? -0.032*** -0.034*** -0.011*** -0.012*** (-4.056) (-4.250) (-3.411) (-3.540) Size - -0.003*** -0.003*** -0.000*** -0.000*** (-11.44) (-11.15) (-3.610) (-3.521) Leverage? 0.018*** 0.017*** 0.006*** 0.006*** (4.882) (4.640) (6.351) (6.094) Age - 0.001 0.001-0.000-0.000 (0.987) (1.300) (-1.409) (-1.179) Complexity - 0.001 0.000-0.000-0.000 (0.415) (0.0993) (-0.155) (-0.488) Mkt to Book? -0.002*** -0.003*** -0.001*** -0.001*** (-5.733) (-5.855) (-5.376) (-5.561) Loss + 0.015*** 0.016*** 0.003*** 0.003*** (7.851) (7.999) (6.736) (6.793) Observations 18,604 18,604 12,505 12,505 R-squared 0.162 0.162 0.158 0.158 30

Table 3: Tax Aggressiveness and Information Asymmetry This table presents pooled time-series cross-sectional regression coefficients of Information Asymmetry (Spread) on tax aggressiveness measures and control variables. All variables are defined in Appendix 1. Industry and time effects are included in all specifications. t-statistics, reported in parentheses, are calculated based on standard errors obtained by clustering at the firm as well as time level. Statistical significance (two-sided) at the 10%, 5% and 1% level is denoted by *, **, and ***, respectively. Predicted (1) (2) VARIABLES Sign Spread Spread TA_GAAP + 0.003*** (4.582) TA_CASH + 0.003*** (5.847) Book-Tax Gap? -0.004*** -0.004*** (-3.025) (-3.200) Size - -0.002*** -0.002*** (-12.76) (-12.62) Leverage? 0.005*** 0.005*** (4.040) (3.876) Age - 0.002*** 0.002*** (5.494) (5.522) Complexity - 0.002** 0.002* (1.967) (1.918) Mkt to Book? -0.001*** -0.001*** (-8.554) (-8.567) Loss + 0.002*** 0.002*** (5.774) (6.077) Log Volume - -0.004*** -0.004*** (-5.402) (-5.315) Std Dev of Returns + 0.065*** 0.065*** (9.126) (9.247) Std Dev of Volume + 0.002*** 0.002*** (4.077) (3.975) Observations 20,629 20,629 R-squared 0.618 0.619 31

Table 4: Tax Aggressiveness and Financial Reporting Quality This table presents pooled time-series cross-sectional regression coefficients of Financial Reporting Quality on tax aggressiveness measures and control variables. Panel A uses the accruals quality (AQ) measure based on Francis et al. (2004) as the measure of financial reporting quality. Panel B employs alternate measures of financial reporting quality. All variables are defined in Appendix 1. Industry and time effects are included in all specifications. t- statistics, reported in parentheses, are calculated based on standard errors obtained by clustering at the firm as well as time level. Statistical significance (two-sided) at the 10%, 5% and 1% level is denoted by *, **, and ***, respectively. Panel A: Effect on Accruals Quality as measured in Francis et al (2004) Predicted (1) (2) VARIABLES Sign AQ AQ TA_GAAP + 0.007*** (2.962) TA_CASH + 0.007*** (4.016) Book-Tax Gap + -0.005-0.005 (-0.999) (-1.063) Std Dev of Cash Flows? 0.254*** 0.255*** (33.02) (33.26) Std Dev of Sales? 0.049*** 0.049*** (13.24) (13.20) Size - -0.005*** -0.005*** (-12.88) (-12.69) Leverage? -0.003-0.003 (-1.273) (-1.402) Age - 0.001 0.001 (1.302) (1.428) Complexity - -0.006-0.006 (-1.400) (-1.458) Mkt to Book + 0.003*** 0.003*** (6.076) (5.967) Loss + 0.006*** 0.006*** (6.045) (6.136) Observations 28,355 28,355 R-squared 0.595 0.595 32

Panel B: Alternate measures of financial reporting quality Predicted (1) (2) (3) (4) (5) (6) VARIABLES Sign Alt_AQ1 Alt_AQ1 Alt_AQ2 Alt_AQ2 Asset_AQ Asset_AQ TA_GAAP + 0.089** 0.047** 0.005*** (2.478) (2.313) (2.677) TA_CASH + 0.127*** 0.052*** 0.006*** (4.937) (3.253) (4.363) Book-Tax Gap + -0.112** -0.115** -0.081*** -0.084*** -0.004-0.004 (-2.399) (-2.493) (-3.117) (-3.216) (-0.832) (-0.879) Std Dev of Cash Flows? 0.327*** 0.324*** -0.085-0.084 0.199*** 0.199*** (3.260) (3.191) (-1.273) (-1.241) (25.21) (25.34) Std Dev of Sales? 0.006 0.005 0.133*** 0.133*** 0.039*** 0.039*** (0.0865) (0.0812) (2.865) (2.865) (12.29) (12.24) Size - 0.027*** 0.027*** 0.016*** 0.016*** -0.007*** -0.007*** (3.972) (4.030) (5.375) (5.494) (-15.90) (-15.68) Leverage? 0.224*** 0.215*** 0.126*** 0.123*** -0.001-0.002 (5.357) (5.123) (5.310) (5.154) (-0.483) (-0.622) Age - 0.045** 0.047** 0.008 0.009 0.002* 0.002** (2.314) (2.419) (0.747) (0.824) (1.949) (2.072) Complexity - 0.004 0.001-0.005-0.006-0.002-0.002 (0.0657) (0.0163) (-0.142) (-0.192) (-0.643) (-0.693) Mkt to Book + 0.014** 0.013* 0.009** 0.009** 0.004*** 0.004*** (2.011) (1.808) (2.506) (2.351) (10.37) (10.23) Loss + 0.057*** 0.056*** 0.022*** 0.022*** 0.007*** 0.007*** (3.979) (3.995) (2.634) (2.592) (7.780) (7.783) Observations 28,355 28,355 28,355 28,355 28,355 28,355 R-squared 0.082 0.083 0.030 0.030 0.591 0.591 33

Table 5: Tax Havens Measure of Tax Planning Aggressiveness This table re-estimates the regressions reported in Tables 2, 3 and 4, but using TAX_HAVENS and TAX_HAVENS_BIG7 as the proxies for aggressiveness tax planning. TAX_HAVENS and TAX_HAVENS_BIG7 are computed from 10-K Exhibit 21 reporting on the country breakdown of firm s operations. All variables are defined in Appendix 1. Industry and time effects are included in all specifications. t-statistics, reported in parentheses, are calculated based on standard errors obtained by clustering at the firm as well as time level. Statistical significance (two-sided) at the 10%, 5% and 1% level is denoted by *, **, and ***, respectively. (1) (2) (3) (4) (5) (6) (7) (8) VARIABLES AFError AFError AFDisp AFDisp Spread Spread AQ AQ TAX_HAVENS 0.165*** 0.008 0.147*** 0.131** (2.825) (0.837) (5.199) (2.038) TAX_HAVEN_BIG7 0.307*** 0.019 0.225*** 0.170 (2.581) (0.958) (5.192) (1.596) Book-Tax Gap -0.039*** -0.039*** -0.010** -0.010** -0.005** -0.005** 0.002 0.002 (-2.898) (-2.907) (-2.109) (-2.110) (-2.440) (-2.470) (0.188) (0.181) Size -0.003*** -0.003*** -0.000** -0.000** -0.001*** -0.001*** -0.006*** -0.006*** (-9.180) (-9.361) (-2.467) (-2.475) (-4.820) (-4.759) (-8.855) (-8.817) Leverage 0.014** 0.014** 0.005*** 0.005*** 0.001 0.001 0.003 0.003 (2.269) (2.271) (3.271) (3.274) (0.637) (0.585) (0.712) (0.688) Age 0.001* 0.001* 0.000 0.000 0.002*** 0.002*** 0.004*** 0.004*** (1.828) (1.799) (0.977) (0.967) (3.956) (4.029) (3.078) (3.113) Complexity 0.003** 0.003** 0.001 0.001 0.002** 0.002*** -0.002-0.002 (2.121) (2.216) (1.222) (1.244) (2.536) (2.645) (-0.416) (-0.409) Mkt to Book -0.001-0.001-0.000-0.000-0.001*** -0.001*** 0.001* 0.001* (-1.337) (-1.348) (-0.826) (-0.833) (-3.112) (-3.224) (1.699) (1.694) Loss 0.009*** 0.009*** 0.002*** 0.002*** 0.001*** 0.001*** 0.004** 0.004** (7.877) (7.812) (4.768) (4.721) (2.597) (2.696) (2.502) (2.514) Std Dev of Cash Flows 0.246*** 0.246*** (18.52) (18.52) Std Dev of Sales 0.047*** 0.047*** (8.781) (8.752) Log Volume -0.007*** -0.007*** (-10.81) (-10.89) Std Dev of Returns 0.053*** 0.053*** (10.74) (10.78) Std Dev of Volume 0.004*** 0.004*** (5.351) (5.459) Observations 6474 6474 4764 4764 5290 5290 8538 8538 R-squared 0.170 0.170 0.213 0.214 0.665 0.664 0.587 0.587 34

Table 6: Tax aggressiveness and Management s Disclosure Panel A of this table presents pooled time-series cross-sectional regression coefficients of the length of the Management, Discussion & Analysis section of firms 10-K reports on tax aggressiveness measures and control variables. Panel B contrasts the relation between tax aggressiveness and transparency in firms with high level of MD&A disclosure with that in firms with low level of MD&A disclosure. The analysis in Panel B focuses only on the subsample of firms that have above-median tax aggressiveness. TA_GAAP_HI_MDA (TA_CASH_HI_MDA) is a variable that takes the value TA_GAAP (TA_CASH) if the length of the MD&A is above median and zero otherwise. TA_GAAP_LO_MDA (TA_CASH_LO_MDA) is a variable that takes the value TA_GAAP (TA_CASH) if the length of the MD&A is below median and zero otherwise. All other variables are defined in Appendix 1. Industry and time effects are included in all specifications. t-statistics, reported in parentheses, are calculated based on standard errors obtained by clustering at the firm as well as time level. Statistical significance (two-sided) at the 10%, 5% and 1% level is denoted by *, **, and ***, respectively. Panel A: Effect of Tax Aggressiveness on Length of MD&A Predicted (1) (2) VARIABLES Sign MDA MDA TA_GAAP + 1.537*** (4.855) TA_CASH + 1.049*** (4.432) Book-Tax Gap? -1.479*** -1.607*** (-3.034) (-3.184) Size + 0.832*** 0.841*** (10.53) (10.47) Leverage? 0.943** 0.912** (2.423) (2.382) Age? -0.593*** -0.574*** (-3.903) (-3.802) Complexity - -0.604-0.662 (-1.082) (-1.176) Mkt to Book? -0.113* -0.122** (-1.822) (-1.961) Loss + 1.111*** 1.173*** (7.963) (7.761) Observations 15701 15701 R-squared 0.304 0.303 35

Panel B: Effect of Increased Disclosure on Transparency Predicted (1) (2) (3) (4) (5) (6) VARIABLES Sign AFError AFError AFDisp AFDisp Spread Spread TA_GAAP_HI_MDA + 0.025*** 0.004** 0.001 (3.183) (2.078) (0.297) TA_GAAP_LO_MDA + 0.029** 0.007* 0.013*** (2.401) (1.712) (4.987) TA_CASH_HI_MDA + 0.013** 0.002* 0.001 (2.577) (1.834) (1.397) TA_CASH_LO_MDA + 0.009* 0.002 0.005*** (1.801) (1.527) (2.806) Book-Tax Gap? -0.028*** -0.033*** -0.011*** -0.011*** -0.003** -0.002* (-2.715) (-3.444) (-2.900) (-3.098) (-2.225) (-1.668) Size + -0.004*** -0.004*** -0.001*** -0.001*** -0.002*** -0.003*** (-8.978) (-8.800) (-3.862) (-4.219) (-7.751) (-8.696) Leverage? 0.017** 0.018*** 0.008*** 0.008*** 0.004* 0.004** (2.468) (2.712) (5.467) (5.749) (1.838) (2.198) Age - 0.003*** 0.003** 0.000 0.000 0.002*** 0.002*** (2.968) (2.339) (0.849) (0.0408) (4.435) (4.398) Complexity - 0.002 0.000-0.000-0.000 0.002 0.000 (0.534) (0.0288) (-0.170) (-0.0401) (1.151) (0.146) Mkt to Book? -0.002** -0.003*** -0.000-0.000** -0.001*** -0.001*** (-2.349) (-2.827) (-1.275) (-1.973) (-4.417) (-5.886) Loss + 0.011*** 0.012*** 0.002*** 0.002*** 0.000 0.001*** (5.144) (6.186) (4.762) (6.555) (0.451) (2.832) Log Volume - -0.005*** -0.005*** (-5.990) (-5.927) Std Dev of Returns + 0.051*** 0.045*** (9.591) (8.830) Std Dev of Volume + 0.003*** 0.003*** (4.362) (4.136) Observations 5068 5347 3565 3723 5111 5336 R-squared 0.167 0.159 0.191 0.189 0.648 0.643 Test for Differences: TA_GAAP_HI_MDA - TA_GAAP_LO_MDA Difference -0.004 0.004-0.003-0.000-0.013*** -0.004*** t-stat -0.540 0.811-0.998-0.145 (-5.202) (-2.259) 36