Abstract
We examine whether tax-motivated income shifting by U.S. multinational corporations affects information asymmetry. Using a new firm-year measure of income shifting and a two-stage least squares approach, we find income shifting is positively associated with four measures of information asymmetry. Cross-sectional tests reveal that this effect is more pronounced for firms with large differences between foreign and domestic earnings growth. Using SFAS 131 to improve identification and establish evidence consistent with a causal relation between income shifting and information asymmetry, we demonstrate that the adverse impact of income shifting on information asymmetry is concentrated in firms that discontinue geographic earnings disclosures. Overall, our study provides evidence that significant consequences of information asymmetry are associated with tax-motivated income shifting.
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Notes
The jurisdiction where firms report income for tax purposes is not necessarily the jurisdiction where the income is earned. We view the true location of income as being based on where the economic earnings activities took place. Income shifting, then, is the extent to which income is reported elsewhere, due to lower tax rates.
Collins et al. (1998) provide evidence that investors, on average, at least partially recognize the effect of income shifting on firm value. Even if investors understand the amount of shifting that occurs on average, however, it is unclear whether they will understand the amount of shifting that occurs at any given firm. Insofar as investors can ascertain the extent of shifting at a sufficiently low cost, we will fail to find support for a relation between tax-motivated income shifting and a firm’s external information asymmetry.
Similar to Rego and Wilson (2012), we use two-stage least squares to estimate our simultaneous system of equations, where proxies for tax-motivated income shifting and external information asymmetry are the endogenous, dependent variables. See Section 3 for more details.
Assuming managers make decisions to maximize firm value, an alternative interpretation of this result is that our estimates provide a lower bound of the benefits of tax-motivated income shifting.
Since 2013, the United States has the highest corporate tax rate among OECD countries. (See Part C, Table II.1, of the OECD Tax Database, available at http://www.oecd.org/tax/tax-policy/tax-database.htm .)
Our study differs from the work of Gallemore and Labro (2015) and McGuire et al. (2017) in that they focus on internal information quality as a determinant of tax avoidance and tax-motivated income shifting, respectively, whereas we consider the consequences of tax-motivated income shifting on the firm’s external information asymmetry.
Our treatment of loss firms is comparable to research by Collins et al. (1998) and Klassen and Laplante (2012). Collins et al. (1998, p. 216) “exclude observations with negative pretax domestic or foreign income,” and Klassen and Laplante (2012, p. 1262) “exclude firm years with negative five-year summed pretax domestic or foreign income (loss firms) because their income shifting incentives are more difficult to reliably estimate.”
We use four-digit GICS (or GICS groups) as our industry definition. The classifications have the advantage of being revenue stream-based rather than product-based (e.g., SIC and NAICS schemes). Historical GICS are broadly available from 1985 through the present, which covers our entire sample period. Results are robust to using two-digit SIC, three-digit NAICS, and Fama-French 48 industries to estimate industry membership fixed effects (untabulated).
For each three-year period, there are 36 trading months and approximately 753 trading days. Hence 32 trading months and 670 trading days represent roughly equivalent fractions of the respective totals.
Our results are qualitatively unchanged throughout when we (i) limit the analyses to the final quarter of each firm-year, (ii) include fiscal quarter fixed effects, or (iii) include calendar quarter fixed effects.
Our sample extends from 1995 to 2012, and we thus use 35 percent as the U.S. statutory rate. Beginning in 1993, the U.S. statutory rate for corporations with taxable income of at least $18,333,333 became 35 percent (https://www.irs.gov/pub/irs-soi/02corate.pdf). Although statutory rates other than 35 percent exist for corporations with taxable income below $18,333,333, the statutory rates for lower levels of income rarely apply to the MNCs in our sample. The MNCs in our sample are profitable (only 5.2 percent of firm-years report a loss) and have a mean market capitalization of $2.6 billion.
We exclude the FTR_AVE main effect, because it is a linear combination of the firm-specific FTR_AVE interactions.
As in the work of Collins et al. (1998), a negative (positive) coefficient suggests the effects of income shifting dominate (are dominated by) implicit taxes.
We conduct sensitivity analyses requiring that each α3i is statistically significantly less than zero at the 5 percent and 10 percent levels to be treated as evidence of income shifting (i.e., no evidence of shifting if insignificant or significantly positive). Each of these sensitivity checks (untabulated) produces qualitatively similar results to our reported results.
As do Klassen and Laplante (2012), we originally include a fourth lagged value of FTR and the ratio of foreign sales divided by worldwide sales as instruments, but based on t-statistics and an over-identifying restrictions test, we eliminate these two instruments and retain the more parsimonious equation (4).
We thank Terrence Blackburne for providing us with monthly LAMBDA estimates.
Bid-ask spread is affected by the stock’s market microstructure, such as exchange rules, trading activity, execution costs, and dealer borrowing costs needed to support inventory positions (Lee and Masulis 2009). Bid-ask spread is also used as a measure of market liquidity in the literature (e.g., Daske et al. 2008). Therefore we use the adverse-selection component of the bid-ask spread (LAMBDA) to better capture information asymmetry. However, we acknowledge that LAMBDA may be difficult to estimate accurately in the presence of algorithmic trading.
We also examine the absolute value of analysts’ forecast errors. The results (untabulated) remain both statistically and economically significant when we use this alternative measure.
As a robustness check, we replace TA_GAAP with TA_CASH (industry-size-adjusted cash ETR), and the results are qualitatively unaffected.
Consistent with the approach of Hope et al. (2013), we verify that the firms in our sample disclose geographic earnings in the pre-SFAS 131 period.
We also conduct our main information asymmetry tests using outbound and inbound shifting measures separately. Results (untabulated) are consistent with H1 that both forms of income shifting increasing firms’ information asymmetry. The results are relatively stronger both in terms of significance level and economic significance for outbound shifting, consistent with outbound shifting being more prevalent than inbound shifting over our sample. Additionally, within inbound shifting only, our evidence for income shifting affecting information asymmetry is stronger for the first half of our sample, consistent with inbound shifting being more common in the early years of our sample.
As a robustness check, we repeat our main analyses using the income shifting model of Dyreng and Markle (2016). The results (untabulated) are qualitatively consistent with our main findings.
We estimate the average cost to traders by taking α1 as estimated from equation (6) times the standard deviation of predicted SHIFT_AVE and predicted SHIFT_IV, respectively. We divide by 100 to convert percentage to decimals. We then multiply that by the average monthly volume and average price per share for our sample. We multiply by 12 to annualize our estimate of the average cost to traders.
In contrast to Daske et al. (2008), we examine the adverse selection component of the bid-ask spreads, rather than percentage bid-ask spreads. When using percentage bid-ask spreads as our information asymmetry proxy and a similar set of control variables as Daske et al. (2008), we find statistically significant results. The economic magnitude of these results are slightly smaller than the results we find using the adverse selection component bid-ask spread, which may be attributable to percentage bid-ask spreads being a noisier measure of adverse selection.
We use different measurement windows in our income shifting and information asymmetry proxies. The income shifting measures require five years of data, while we use three years of data to calculate LAMBDA and IDVOL. As a robustness check, we measure income shifting, information asymmetry, and control variables all using four and five years of data. The results (untabulated) are qualitatively similar under these alternative measurement windows.
Results are qualitatively similar when we allow the sample period to span years t–5 through t+5.
To have a measure of income shifting for all firms, including those that discontinue disclosure, we take the average of SHIFT_AVE (SHIFT_IV) over the pre-adoption period. Results are qualitatively unaffected when we instead take the value at t–1, t–3, or the largest value over the period t–3 through t–1.
We check whether the firm continues to list at least one significant tax haven subsidiary in years t+1 through t+3. If the firm does not, we remove that observation from this analysis. Some firms remove tax havens outside of this window. A removal could represent a divestiture or a liquidation of the tax haven subsidiary. Alternatively, it could represent a strategic reporting decision. We include a control variable MULTI that is an indicator if a firm (i) reports a tax haven entry, (ii) continues to report the subsidiary throughout the five-year window from entry year t to year t+3, (iii) at some subsequent year no longer reports a tax haven subsidiary, and (iv) then reports a new tax haven subsidiary entry. MULTI takes a value of one for 17 percent of the firms in the haven entry sample. Our results are robust to restricting the tests to firms where MULTI is zero. In additional sensitivity analysis, we find our inferences are unchanged when considering various windows (i.e., one year, two years, four years, and five years) around the initiation year.
In addition to the tax haven initiation tests, we also use tax haven counts as an alternative proxy of income shifting in our levels analyses. The results (untabulated) remain both statistically and economically significant when we use this alternative income shifting measure.
Initiating a tax haven requires beginning operations in a new country, which may increase organizational complexity and consequently information asymmetry. To examine this alternative explanation, we conduct a counterfactual analysis. In this analysis, we set the POST variable in Table 7 equal to one for a firm’s initial entry into a nonhaven country. Inconsistent with the alternative explanation, the POST variable is not statistically different from zero in any of the four regressions.
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Acknowledgements
We appreciate helpful comments from Chelsea Rae Austin, Terrence Blackburne, Brad Blaylock, Jennifer Blouin (discussant), Travis Chow, Asher Curtis, Lisa De Simone (discussant), Alex Edwards, Cristi Gleason, Jeffrey Gramlich, John Hand, Paul Hribar, Jing Huang, Mehmet Kara, Ken Klassen, Bruce Johnson, Sean McGuire, Michelle Nessa, Tom Omer, Stephen Penman (editor), Scott Rane, John Robinson, Steven Savoy, Terry Shevlin, Katie Spangenberg, Bridget Stomberg (discussant), Jake Thornock, Chris Yust, an anonymous referee, and workshop participants at the Review of Accounting Studies 2017 Conference, the 2014 AAA Annual Meeting, the 2014 ATA Midyear Meeting, Texas A&M University, the University of Iowa, and Washington State University. We also thank Scott Dyreng for providing Exhibit 21 subsidiary data. The authors gratefully acknowledge generous financial support from the Gies College of Business, the Mays Business School, the Foster School of Business, and the Lundquist College of Business.
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Appendix. Variable Descriptions
Appendix. Variable Descriptions
1.1 Dependent Variables
LAMBDA i,t | The mean of monthly estimates of the adverse selection component of the bid-ask spread (following Madhavan et al. 1997) for firm i over years t to t-2, multiplied by 100 for ease of interpretation. |
INS_PROFIT i,t | Insider trading profit for firm i over years t to t-2, measured as the abnormal return on the stock over the six months following the trade multiplied by the value of the trade (in millions of dollars). For sales transactions, this value is multiplied by -1 so that losses avoided on sales have the same sign as gains on purchases. Following Huddart and Ke (2007), we estimate abnormal returns using the Fama-French (1993) three factors plus the momentum factor (Carhart 1997). Each firm must have at least 120 trading day observations over the estimation window, which is 250 to 50 trading days prior to the trade date. |
IDVOL i,t | Idiosyncratic return volatility, measured as the standard deviation of residuals from firm-specific regressions of daily returns on daily values of the Fama-French (1993) three factors plus the momentum factor (Carhart 1997), over years t to t-2, multiplied by 100 for ease of interpretation. |
AF_DISP i,t | Dispersion of analyst earnings forecasts, measured as the standard deviation of analysts’ quarterly earnings forecasts. |
1.2 Income Shifting and Tax Rate Variables
SHIFT_AVE i,t | Tax-motivated income shifting measure (adapted from Collins et al. 1998). This semi-continuous measure takes the absolute value of the firm-year specific coefficient on FTR_AVE if the following two conditions are met: (i) the firm-year specific FTR_AVE coefficient from equation (3) is negative, indicating that the foreign tax rate is inversely related to unexpected foreign profitability, and (ii) FTR_AVEi,t is not equal to zero, indicating a tax rate-based incentive to shift income. If either condition is not met, the measure takes a value of 0. |
FTR_AVE i,t | Incentive to shift income measured over five-year rolling windows (following Klassen and Laplante 2012), calculated as \( {\Sigma}_{m=0}^4T{E}_{i,t-m}/{\Sigma}_{m=0}^4 PT{I}_{i,t-m}-1/5\ast {\Sigma}_{m=0}^4 ST{R}_{US,t-m} \), where TEi,t is the tax expense reported by firm i for foreign jurisdictions for year t (TXFO+TXDFO), PTIi,t is the pretax income reported for firm i the foreign jurisdictions for year t (PIFO), and STRUS,t is the top U.S. federal tax rate facing corporations for year t. |
SHIFT_IV i,t | Tax-motivated income shifting measure (adapted from Collins et al. 1998). This semi-continuous measure takes the absolute value of the firm-year-specific coefficient on FTR_IV if the following two conditions are met: (i) the firm-year specific FTR_IV coefficient from equation (3) is negative, indicating that the foreign tax rate is inversely related to unexpected foreign profitability, and (ii) FTR_IVi,t is not equal to zero, indicating a tax rate-based incentive to shift income. If either condition is not met, the measure takes a value of 0. |
FTR_IV i,t | Instrumental variables approach to income shifting incentive (following Klassen and Laplante 2012), measured as the fitted-values from a regression of FTR on three lagged values of FTR, with controls for return on sales (PI/SALE), industry fixed effects, and year fixed effects. FTRi,t is defined as (TEi,t /PTIi,t ) - STRUS,t. |
CASH_ETR1 i,t | One-year cash effective tax rate (ETR), measured as cash taxes paid (TXPD) scaled by pretax income adjusted for special items (PI - SPI). |
CASH_ETR3 i,t | Three-year cash ETR, measured as the sum of cash taxes paid (TXPD) over years t to t–2 divided by the sum of adjusted pretax income (PI - SPI) over years t to t–2. |
1.3 Control and Cross-Sectional Variables
GROWTHGAP i,t | Indicator variable equal to 1 if either (i) three-year average of annual growth in pretax domestic income is in the top (bottom) quintile, relative to firms in the same fiscal year, and three-year average of annual growth in pretax foreign income is in the bottom (top) quintile, relative to firms in the same fiscal year, or (ii) three-year average of absolute annual growth in pretax domestic income is in the top (bottom) quintile, relative to firms in the same fiscal year, and three-year average of absolute annual growth in pretax foreign income is in the bottom (top) quintile, relative to firms in the same fiscal year. Indicator variable equals 0 otherwise. Averages are over years t through t–2. Domestic (foreign) earnings growth is the annual change in PIDOM (PIFO), scaled by lagged PIDOM (PIFO). |
PTROA i,t | Pretax income (PI) scaled by average assets (AT). |
LN_ASSETS i,t | Natural logarithm of lagged total assets (AT). |
LEV i,t | Lagged long-term debt (DLTT) scaled by lagged assets (AT). |
NOL i,t | Indicator variable equal to 1 if the firm has positive tax-loss carryforwards (TLCF) and 0 otherwise. |
ΔNOL i,t | Ending balance of tax-loss carryforwards (TLCF) less the beginning balance, scaled by lagged total assets (AT). |
MTB i,t | Market-to-book ratio at the beginning of the year, measured as market value of equity (CSHO*PRCC_F) scaled by book value of equity (CEQ). |
PP&E i,t | Net PP&E (PPENT) scaled by lagged assets (AT). |
DEP i,t | Depreciation expense (XDP) scaled by lagged assets (AT). |
EQINC i,t | Equity in earnings of unconsolidated subsidiaries (ESUB) scaled by lagged assets (AT). |
FOR_INC i,t | Pretax foreign income (PIFO) scaled by lagged assets (AT). |
GEO_CONC i,t | Revenue-based Herfindahl-Hirschman indices, calculated as the sum of the squares of each geographic segment’s sales as a percentage of total firm sales, following Bushman et al. (2004). |
R&D i,t | Research and development expense (XRD) scaled by lagged assets (AT). |
CASH i,t | Cash and cash equivalents (CHE) scaled by lagged assets (AT). |
FOLLOW i,t | Natural logarithm of one plus the number of analysts following the firm. |
AGE i,t | Natural logarithm of the difference between the first year when the firm appears in Compustat and the current year. |
VOLUME i,t | Natural logarithm of the average monthly trading volume (in hundreds) over years t to t–2. |
σ i,t (VOLUME) | Natural logarithm of the standard deviation of monthly trading volume over years t to t–2. |
σ i,t (RET) | Standard deviation of monthly stock returns over years t to t-2. |
M&A i,t | Indicator variable equal to 1 if a firm engages in a merger or acquisition of at least one foreign or U.S. multinational target firm in the year and 0 otherwise. |
NGEOSEGS i,t | Total number of geographic segments, with nonmissing sales data, reported in Compustat Historical Segments. |
SIZE i,t | Natural logarithm of market value of equity (CSHO*PRCC_F) at the beginning of the year. |
σ i,t (REV) | Standard deviation of annual revenues (SALE) over years t to t–4, each scaled by the respective period’s total assets (AT). |
LOSS i,t | Indicator variable equal to 1 if the firm has a pretax loss in the current year and 0 otherwise. |
TA_GAAP i,t | Industry-size adjusted GAAP ETR (following Balakrishnan, Blouin, and Guay 2012), measured as the mean GAAP ETR of the same industry-size portfolio firms less the firm i’s GAAP ETR, where GAAP ETR is the sum of total tax expense (TXT) over years t to t–2 divided by the sum of pretax income (PI) over years t to t–2. Higher values indicate greater amounts of relative tax avoidance. |
BTD i,t | Absolute value of average book-tax differences, measured over years t to t–2. Book-tax differences are defined as pretax income less taxable income: (PIi,t - (TXFEDi,t + TXFOi,t) / STRt )/ATi,t-1, where STRt is the top U.S. federal statutory tax rate faced by corporations in year t. |
INTANG i,t | Intangible assets (INTAN) scaled by lagged assets (AT). |
NODISC i,t | Indicator variable equal to 1 if a firm omits disclosure of geographic subsidiaries after adoption of SFAS 131 and 0 otherwise. We follow the approach of Hope et al. (2013) to define omission as not reporting earnings for at least two foreign segments in the first two years after SFAS 131. |
POST i,t | Indicator variable equal to 1 for the years after a firm first discloses a tax haven subsidiary during our sample period and 0 for the years prior first listing (i.e., begins the sample period with zero tax haven subsidiaries). Tax haven jurisdictions are determined using the criteria of Dyreng and Lindsey (2009), and subsidiary location data are drawn from Ex. 21 of form 10-K filings. |
FOR_DISC i,t | Indicator variable equal to 1 if a firm provides disclosure of geographic subsidiaries and 0 otherwise. |
MULTI i,t | Indicator variable equal to 1 if the firm has more than one tax haven entry during our sample period and 0 otherwise. |
EGUIDE i,t | Indicator variable equal to 1 if management issues earnings-based forecasts for the reporting period and 0 otherwise. |
SGUIDE i,t | Indicator variable equal to 1 if management issues sales-based forecasts for the reporting period and 0 otherwise. |
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Chen, CW., Hepfer, B.F., Quinn, P.J. et al. The effect of tax-motivated income shifting on information asymmetry. Rev Account Stud 23, 958–1004 (2018). https://doi.org/10.1007/s11142-018-9439-1
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DOI: https://doi.org/10.1007/s11142-018-9439-1