Economic Policy Uncertainty and Firm Tax Avoidance

Transcription

1 Economic Policy Uncertainty and Firm Tax Avoidance Huu Nhan Duong Phone: Department of Banking and Finance, Monash University, Clayton, VIC 3800, Australia Ferdinand Gul Ferdinand.Gul@deakin.edu.au Deakin Business School, Deakin University, Burwood, VIC 3125, Australia Justin Hung Nguyen Jhnguyen6868@gmail.com School of Accounting and Commercial Law, Victoria University of Wellington, Wellington 6140, New Zealand My Nguyen My.Nguyen@rmit.edu.au ; Phone: School of Economics, Finance and Marketing, RMIT University, Melbourne, VIC 3001 Australia This version: 10 Aug

2 Economic Policy Uncertainty and Firm Tax Avoidance Abstract We investigate whether and how economic policy uncertainty is related to firm tax avoidance. We predict that an increase in policy uncertainty results in greater financial constraints, which in turn, lead firms to increase tax avoidance activities. We find a strong positive association between economic policy uncertainty and firm tax avoidance. This relation is robust to alternative measures of tax avoidance and several tests to address endogeneity concerns. Firms use several strategies to avoid tax including tax deferrals and shelters. Further analysis shows that the effect of policy uncertainty on tax avoidance is less pronounced for firms with higher level of cash holdings. Overall, our findings highlight the importance of uncertainty around government policy in determining firm tax avoidance activities. Key words: Policy uncertainty, tax avoidance, financial constraints JEL Classification: G18, G31, G32, G34 2

3 1. Introduction In this paper, we empirically investigate whether and how economic policy uncertainty is systematically related to corporate tax avoidance. Our investigation is motivated by the central role of politics on corporate taxation and that a country s corporate tax regimes are products of a political process. In the United States, many businesses are now facing with substantial uncertainty from a host of policy changes, especially on corporate tax policy such as the expiration of the Bush tax-cuts in 2010, the alternative minimum tax policy (AMT), the expiration of a two-percentage-point federal payroll tax cut and over 55 other tax breaks that are set to expire in Whether corporate tax rates are scheduled to go up is far from certain for firms, which make it difficult for firms to forecast their future earnings and financing sources. These policy uncertainties may also cause firms existing tax planning strategies become ineffective and encourage them to increase their tax avoidance activities while their current tax planning are still effective under the current regime. Despite this potential interaction between policy uncertainty and corporate taxation, the effect of policy uncertainty on corporate tax avoidance remains unexplored from the academic literature. We hypothesize that economic policy uncertainty may impact on corporate tax avoidance through a financial constraint channel. The literature on economic policy uncertainty and financial constraints states that policy uncertainty will lead to capital supply frictions and increase firm financing costs (Pástor and Veronesi, 2012, 2013; Gungoraydinoglu et al., 2017). When traditional debt and equity financing sources become costlier and more difficult to obtain, firms may acquire alternative source of funds through their tax planning by reducing their current reported taxable income or increase tax credits, thereby decreasing cash taxes paid (Edwards et al., 2015). Similarly, Law and Mills (2015) state that when the frictions in external finance increase, financially constrained firms will pursue more aggressive tax planning on the margin as a substitute for a more expensive source of external financing from 3

4 lenders or capital markets. Edwards et al. (2015) suggest several reasons why constrained firms will employ tax saving as a source of fund. First, tax savings, unlike many other cost-cutting techniques (i.e. reducing advertising, research and development, capital expenditure and staffing), are less likely to adversely affect the firm s operations. Second, firms are also highly likely to have additional opportunities to generate current cash tax savings via deferral-based tax planning strategies. Third, constrained firms are also receptive to employ tax avoidance activities as a source of generate more cash (Hanlon et al., 2017). Collectively, the financial constraints channel suggests that firms heighten their tax avoidance activities when policy uncertainty increases. We examine the relation between policy uncertainty and corporate tax avoidance from 1988 to 2014 using the Baker et al. (2016) s (hence BBD thereafter) policy uncertainty index. This index is a weighted average measure of the frequency of articles containing key terms related to policy uncertainty in 10 leading U.S. newspapers, uncertainty about future changes in the federal tax code measured by the dollar impact of tax provisions set to expire in the near future, and the forecast disagreement concerning government spending and consumer price index which are used as proxy for uncertainty about future fiscal and monetary policy. While election years are also used in the literature as a measure of policy uncertainty, we employ Baker et al. (2016) s index instead as this index also captures policy uncertainty unrelated to elections or outside of election years (Gulen and Ion, 2016). It also accounts for the effect of elections as well as the extent to which the election outcomes are uncertain (Gulen and Ion, 2016). We find a positive association between policy uncertainty and firm tax avoidance. Our estimation suggests that when policy uncertainty doubles, firms on average lower their cash tax effective rates by 1.97%, implying a reduction of annual tax expense and annual tax payment of $1.81 million and $4.06 million on average, respectively. Further, we find that the 4

5 positive impact of policy uncertainty on corporate tax avoidance persists for the following two years and is economically stronger when 100% jump in policy uncertainty results in an 2.97% surge in tax avoidance (which equivalent to a reduction of income tax payment of $6.13 million on average per firm-year). This positive impact, however, disappears after three years. The BBD index may capture the effects of general economic uncertainty that potentially confounds our findings of a positive relationship between policy uncertainty and tax avoidance. We address this concern by controlling for several proxies for economic uncertainty suggested by Gulen and Ion (2016). These include the election year (Julio and Yook, 2012), the Livingstone survey of uncertainty about future economic growth, cross-sectional standard deviation of firm-level profit growth, the VXO index of implied volatility, cross-sectional dispersion in stock returns and the Jurado et al. (2015) s index. Our findings are qualitatively unchanged when we augment our baseline regression model with these six general economic uncertainty proxies. Another potential issue with using the BBD index is that it may capture the effects of other non-policy related factors such as currency uncertainty or labour market variation which raises an error-in measurement concern that could potential bias the association between policy uncertainty and tax avoidance (Nguyen and Phan, 2017). To address the error-in-measurement, we regress the measure of the news-based component of the US BBD index on the Canada index to obtain the regression residuals. Given that many of the shocks that affect economic uncertainty in the United States will also affect general economic uncertainty in Canada, the residuals should be free from potential confounding effects of macroeconomic forces common to both countries. The results further corroborate our findings that firms engage in more tax avoidance activities when policy uncertainty increases. To further alleviate endogeneity concerns, we follow Gulen and Ion (2016) and use an instrumental variable specification in which a measure of political polarisation in the United 5

6 States is used as an instrument for policy uncertainty. The results of these tests confirm our main findings that policy uncertainty is positively associated with corporate tax avoidance. In addition, we use alternative measures to capture aggressive and deliberate corporate tax avoidance practices. We find that policy uncertainty is positively associated with the probability that a firm undertakes a tax-sheltering transaction in given year, the likelihood that a firm is tax dodger in a given year in that it reports a positive pre-tax income but pays no tax. Moreover, the baseline results are also robust when we use long-run tax rates as alternative proxy for corporate tax avoidance. We next examine a specific mechanism by which policy uncertainty increases firm tax avoidance, specifically through its impact on firm financial constraints. We perform two separate analyses. First, we provide evidence that the aggregate bank credit conditions, as proxied by the spread of commercial and industrial loan rates (on loans greater than USD 1 million) over the federal funds rates, tighten when policy uncertainty increases. Second, prior studies show that firms have a precautionary motive to hold more cash when financial constraints increase (Opler et al., 1999; Bates et al., 2009). Thus, if policy uncertainty affects corporate tax avoidance through its effect in increasing financial constraints, the effects of policy uncertainty on firm tax avoidance should be less severe for firms with higher cash holdings. We find support for this empirical prediction. We further show that the moderating effect of cash holdings on the relation between policy uncertainty and tax avoidance is stronger for financially constrained firms at which we characterize as being smaller in size, higher dividend payout ratio, younger age and not having debt and paper rated, than for unconstrained firms. Taken in their entirety, our these results support the financial constraints hypothesis that firm tax avoidance increases when financial constraints heighten in the period of high policy uncertainty. 6

7 Our study contributes to the literature in several ways. First, we expand the literature on the determinants of corporate tax behaviour. Much of the prior research focuses on crosssectional variation in tax avoidance and identifies firm-level factors associated with firm tax avoidance, such as financial leverage (Lisowsky, 2010), ownership (Chen et al., 2010; Cheng et al., 2012; Badertscher et al., 2013), executives (Dyreng et al., 2010) and corporate governance (Armstrong et al., 2015). We contribute to this literature by uncovering the association between economic policy uncertainty and tax avoidance. Our study also contributes to the literature on political costs and taxes, which is dated back to Zimmerman (1983) who envisions taxes as one component of political costs. Most of the researchers focus on reputational costs and scrutiny effects on valuation of tax benefits. They suggest that public pressure from disclosure of firms' tax haven subsidiaries causes firms to curtail their tax avoidance (Dyreng et al., 2016). Likewise, Hanlon and Slemrod (2009), Graham et al. (2014), Lisowsky et al. (2013) and Gallemore et al. (2014) examine whether reputational costs result in aggressive tax behaviour, where reputation costs are defined to include political costs. While political uncertainty is broadly captured by our economic uncertainty measures, our study fundamentally differs from the prior works. Specifically, rather than focusing on political scrutiny, which mitigates tax avoidance, we study economic policy uncertainty, which we hypothesize exacerbates tax avoidance. The remaining of this article proceeds as follows. Section 2 provides details on data and variable description. Section 3 discusses the main findings and implications while Section 4 concludes the paper. 7

8 2. Data and sample selection 2.1 Sample and data sources Our data comes from several sources. The financial information for all publicly traded firms with headquarters located in the USA between 1988 and 2014 from Standard and Poor s Compustat database. The monthly economic policy uncertainty index of Baker et al. (2016) is sourced from Following the prior literature (Chen et al., 2010; Dyreng et al., 2016; Cen et al., 2017), we remove firm-year observations with negative pre-tax income or book value with non-positive sales or with total assets of less than $1 million. Firms from the financial services and utilities industries are also excluded. We also drop firmyear observations with unavailable information from Compustat to calculate the key tax avoidance variables and other control variables. These screening criteria yield a final full sample of 65,822 firm-year observations. 2.2 Measures of firm tax avoidance Following Hasan et al. (2017), we use the firm s cash effective tax rate (CETR) which equals to cash taxes paid divided by pre-tax book income before special items. This measure reflects both temporary (i.e. tax deferral differences) and permanent differences and is also unaffected by tax accruals. For an ease of our interpretation, CETR is multiplied by -1 and denoted as TA_CETR as measures of firm tax avoidance. By definition, higher TA_CETR implies greater tax avoidance. The definition and detailed calculation of this variable are provided in Appendix A. 2.3 Measures of economic policy uncertainty The economic policy uncertainty index (PU) is developed by Baker et al. (2016) which is a weighted average of the three components. The first component quantifies the volume of news- 8

9 based policy uncertainty every month starting from January This is done by searching on the 10 leading newspapers: USA Today, Miami Herald, Chicago Tribune, Washington Post, Los Angeles Times, Boston Globe, San Francisco Chronicle, Dallas Morning News, New York Times and Wall Street Journal that contain the following key words: uncertainty or uncertain ; economic or economy ; and one of the following policy terms: congress, deficit, Federal Reserve, legislation, regulation or White House. To control for changing in the volume of articles across newspapers and time, total numbers of word counts are scaled by the total numbers of articles in the same newspaper and month, which yields a monthly policy uncertainty series for each newspaper. These monthly newspaper-level uncertainty series are then standardized by unit standard deviation from 1985 to 2010 and then averaged across the ten papers per month. Finally, the series are then normalized to a mean of 100 from 1985 to The second component of the PU index measures the level of uncertainty related to future changes in the tax code. It is a transitory measure constructed by the number of temporary federal tax code provisions set to expire in the contemporaneous calendar year and future ten years and reported by the Joint Committee on Taxation. The third and final component is the CPI disagreement and expenditure dispersion. It is measured by the forecasters disagreement (the interquartile range of forecast) over future outcomes about inflation rates and federal government purchases, respectively. The overall measure of policy uncertainty is calculated by normalising each of the three components above and then weighted average of the resulting series, using a weight of onehalf for the news-based component, one-sixth of the tax component and one-third for the forecaster disagreement component. Baker at al. (2016) use several robustness tests. These include comparing the index with the Chicago Board Options Exchange Market Volatility Index (VIX); controlling for the potential for political slant to skew newspaper coverage 9

10 of policy uncertainty; and using uncertainty indicators based on the Beige Book releases before each regularly scheduled meeting of the Federal Open Market Committee (FOMC). All the tests confirm that policy uncertainty index remains a consistent measure of economic policy uncertainty, 2.4 Control variables We identify several control variables following the literature including firm size (SIZE), financial leverage (LEVERAGE), cash holdings (CASH), profitability (ROA), loss carryforwards (NOL), equity income (EQUITY INCOME), capital investment (PPE) asset intangibility (INTANGIBLE) and foreign income (FOREIGN_INCOME). We include a size proxy which is the natural logarithm of the firm s market capitalisation (SIZE). Prior studies provide conflicting evidence of the association between tax avoidance and firm size. Consistent with the political cost hypothesis, larger firms have greater incentive to engage in tax avoidance activities (Zimmerman, 1983). Large firms are often more sophisticated and better equipped to structure complex tax-reduction transactions (Hanlon et al., 2005). However, some other studies (Jacob, 1996; Gupta and Newberry, 1997) do not find a statistically significant relationship. Financial leverage (LEVERAGE) is included to capture the effect of the tax shield on debt, which higher corporate tax shields can reduce marginal tax rates the incentives for incremental tax planning (Graham, 1996a; 1996b, 2000). Newberry and Dhaliwal (2001) also argue that multinationals can place debt in high-tax locations to reduce their effective tax rates. They can also structure off-balance sheet financing to maximize interest deductions without decreasing book income (Mills and Newberry, 2004) or can structure debt to use foreign tax credits (Newberry, 1998). Collectively, these studies suggest that increased levels of debt are positively associated with firm tax avoidance. 10

11 We also control for cash holdings (CASH) to capture the firm tax planning incentives. The association between cash holdings on firm tax avoidance is not determined the priori. On the one hand, firms with more cash have less incentive to defer taxes (Cen et al., 2017). On the other hand, tax aggressive firms may also hold more cash as a precautionary motive for future settlement with the Internal Revenue Service (IRS) (Hanlon et al., 2017). ROA is a firm s operating income scaled by lagged total assets. It is used to control for the effect of firm profitability on taxes and we expect a positive association between profitability on both ETR and CETR following Edwards et al. (2015). Firm loss carry-forwards (NOL) is also included as loss carry-forward may also cause a firm s tax rate to differ from the statutory rate (Auerbach and Poterba, 1987). EQUITY_INCOME is also included because prior research suggests that economies of scale and firm complexity resulting in greater equity income are positive associated with tax avoidance (Rego, 2003; Chen et al., 2010). We also control for the existence of foreign jurisdictions (FOREIGN_INCOME) and asset intangibility (INTANGIBLE) because these are likely to affect both firms likelihood of using debt and firms possibility of engaging in taxavoiding behaviour. Specifically, firms taking advantage of foreign tax rate differentials should avoid more tax on average and so we expect FOREIGN_INCOME to be positively associated with tax avoidance. 2.5 Descriptive statistics Table 1 reports descriptive statistics of our tax avoidance measures (Panel A), economic policy uncertainty index (Panel B) and control variables (Panel C) used in our baseline regression in Equation 1 below. The mean values of (inverse) cash effective tax rates (TA_CETR) and effective tax rates (TA_ETR) are % and % respectively. The mean of value of DTAX is

12 3. Research methodology, results and discussions 3.1 Policy uncertainty and corporate tax avoidance To investigate the relationship between policy uncertainty and corporate tax avoidance, we use the following augmentations of panel regressions common to the tax avoidance literature: _, = +, +,, + +, (1) Here, i indexes firms, t indexes calendar quarters and l {1,2,3) stands for the year lead between the dependent and independent variables. Our standard errors are clustered at the firm level to correct for potential cross-sectional correlation in the error term, (Petersen, 2009). TA_CETR is an inverse measure of firm cash effective tax rates or greater tax avoidance for a firm i from year t to t+l. For each firm i, the policy uncertainty variable (PU) is measured as the natural logarithm of the arithmetic average of the PU index in the twelve months of the firm s fiscal year t. Since the majority of the explanatory power of the overall policy uncertainty comes from its news-based component, for brevity, we follow Gulen and Ion (2016) to present the regression results from the news-based index. This also serves to eliminate any possible confusion as to which of the components of the PU index is driving our results. Nevertheless, our results are qualitatively the same if we use the overall index instead. Similar to Gulen and Ion (2016), we do not include the time fixed effects in our specification since doing so will automatically absorb all explanatory power of the policy uncertainty variables. Even though the baseline model includes firm attribute control variables, it might still omit some unknown firm characteristics that affect uncertainty and corporate tax avoidance. To ease this concern, we use firm fixed-effect regressions and industry fixed effects to control for the influences of unknown time-invariant firm level and industry level factors, respectively. To reduce the impact of extreme outliers, all variables have been winsorised at the 1% and 99% level. 12

13 <Insert Table 2 here> Table 2 displays the results of the association between tax avoidance and policy uncertainty. The first model (Column (1)) contains no controls aside from policy uncertainty index while in the second to fifth models (in Columns (2) to (5) include the full set of controls and industry fixed effects. Also, cash tax effective rates in current year (t) is replaced by one-year (t+1), two-year (t+2) and three-year (t+3) leads as dependent variables in Columns (2) to (5), respectively. Results in Columns (1) to (5) suggest that an increase in policy uncertainty leads to lower cash tax effective rates or higher firm tax avoidance firm cash holdings both in the current year and the following years. In particular, the coefficient of PU of (Column (2)) indicates that when policy uncertainty doubles, firms on average lower their cash tax effective rates by 1.97% implying a reduction of annual tax expense and annual tax payment of $1.81 million and $4.06 million on average, respectively. Further, the coefficient of PU remains significant in Columns (3) and (4), suggests that the positive impact of policy uncertainty on tax avoidance persists after two years and the effect is economically stronger when 100% jump in policy uncertainty results in an 2.97% surge in the tax avoidance in two years later. This positive impact, however, disappears after three years as suggested by the insignificant explanatory power of PU on _ in Column (5). The adjusted R 2 increases considerably from in the first model to suggesting the addition of a powerful set of controls. 3.2 Control for confounding effect of economic uncertainty The BBD index may be highly likely correlated with other sources of general economic uncertainty such as recessions, wars, financial crisis that potentially confound our findings of a positive relationship between policy uncertainty and firm tax avoidance. To control for this possible contamination, we follow Gulen and Ion (2016) to include several proxies for 13

14 economic uncertainty and separately run time-series regressions of PU on a list of macro uncertainty variables. First, we include the GDP forecast from Livingston survey published by the Philadelphia Federal Reserve, we calculate the coefficient of variation of GDP forecast as a proxy for expected economic growth uncertainty (GDPDIS). 1 Second, we compute the annual cross-sectional standard deviation of firm profit growth as a proxy for future profitability variation, where firm profit growth is defined as the ratio of the change in net income to average sales (SDPROFIT). Third and fourth, to control for the equity market uncertainty, we calculate the monthly standard deviation of stock returns (SDRETURN) and the Chicago Board Options Exchange s VXO index of implied volatility (VXO). Fifth, we use another comprehensive measure of aggregate uncertainty (JLN), developed by Jurado et al. (2015) which is based on the co-movement in the unpredictable component of a big number of economic indicators. Finally, we follow Julio and Yook (2012) to construct an election year dummy (ELECYEAR) which is equal one on the years of presidential elections. Through our sample period , there were seven U.S. presidential elections happening every four years in 1988, 1992, 1996, 2000, 2004, 2008, and We take natural logarithms of all of these economic uncertainty measures (except for the election year dummy) and gradually add each of them and then all of them to Equation (1). The results of this regression analysis are presented in Table 3 below. <Insert Table 3 here> Except minor discrepancy in the result when GDP forecast is added to Equation (1) in Column 2, the regression results provided in Table 3 show that the positive association between PU and firm tax avoidance remains highly statistically significant in the presence of these five other macro-economic uncertainty variables. After all the economic uncertainty controls are introduced as in Column (7), the coefficient of PU_LOG is suggesting that a 100% 1 Biannual GDP forecasts from the Livingstone survey of the Philadelphia Federal Reserve Bank. 14

15 increase in policy uncertainty, the firm cash tax effective rates is reduced or tax avoidance increases by 1.02%. Additionally, in relation to the results of the control variables for firm tax avoidance, both Tables 2 and 3 indicate the significant explanatory powers of these proxies on firm tax avoidance and the signs of their coefficient estimates are generally consistent with predictions. The statistically significant evidence also reveals that the explanatory power of policy uncertainty on is not fully absorbed by any of these six proxies, that highlights the robustness of our baseline results. This also supports the argument of Gulen and Ion (2016) that BBD index comprises macroeconomic uncertainty information that is not captured by any of the other well-known measures adopted in the existing literature. Another potential issue with using the BBD index as a proxy for policy uncertainty is that it may capture the effects of other non-policy related factors, such as currency uncertainty, which may cause an error-in-measurement concerns that could potentially bias our estimation. To address this error-in-measurement issue, we follow Gulen and Ion (2016) to extract the economic uncertainty components from the original PU measure. We do so by using the twostep regression approach. First, we regress the measure of the news-based component of the U.S. BBD policy uncertainty index on the Canadian news-based uncertainty measure together with other six macro-economic variables described above. We then obtain the regression residuals (RPU) which are the difference between the actual and the predicted U.S news-based uncertainty measure. Canadian news-based uncertainty index is chosen due to the close relationship between the US and Canadian economies and, thus, any aggregate shock to Canada would affect the U.S. as well. Hence, if the BBD index partially captures policy-unrelated economic uncertainty, the inclusion of the Canadian index helps to remove the economic uncertainty in U.S. that is derived from economic and policy uncertainty in Canada. This technique presents an econometric advantage compared to the previous one which just includes the six macroeconomic variables. This is because this approach helps mitigate the concern of 15

16 multi-collinearity resulted from the inclusion of too many correlated variables such as PU and macroeconomic variables into one regression. In particular, we follow Gulen and Ion (2016) to propose an augmented monthly timeseries model: USPUt = α0 + β1canput + βkmacro_variablesk,t + ϵt (2) Here, USPUt and CANPUt are the logarithm transformation of news-based policy uncertainty measures developed by BBD for the United States (U.S.) and Canada. The term MACRO_VARIABLESt represents a vector of six direct measures of macroeconomic uncertainty for U.S. as defined above. The residuals obtained from running Equation (2) should represent a cleaner policy uncertainty index as it is exempt from the direct and indirect sources of general economic uncertainty. We then aggregate those monthly residuals in Equation (3) to yearly level using arithmetic average, and denote the new and cleaner measure of policy uncertainty for US as RPU. We then repeat the baseline analysis in Equation (1) with PU being replaced by RPU to be the main variable of interest. Specifically, we run the following model: _, = +, +,, + +, (3) The regression result using Equation (3) is presented in Column (8) of Table 3. This result confirms our main findings that policy uncertainty is positively associated with firm cash holdings. The relation remains statistically and economically significant when an economicfree policy uncertainty measure is adopted. In particular, the result indicates that a doubling in the residual policy uncertainty leads to a surge by 2.49% in the corporate cash-to-assets ratio. The larger positive coefficient on policy uncertainty suggests that the cleaner measure, i.e. exempt from aggregate economic shocks, even possesses stronger explanatory power over cash holdings. This evidence strengthens our argument that policy-related uncertainty indeed positively drives corporate cash holdings. 16

17 3.3 Addressing endogeneity concern: Instrumental variable analysis Another concern with our regression analysis described above is that despite the inclusion of both firm and industry fixed effects, our policy uncertainty and corporate tax avoidance may be jointly correlated with the unobservable factors which raise an endogeneity concern in our models. We address this by conducting an instrument variable analysis. Following McCarty et al. (1997) and Gulen and Ion (2016), we use the partisan polarization measure (POLAR) as an instrument for policy uncertainty. This measure is based on the DW-NOMINATE scores developed by McCarty et al. (1997) to track legislators ideological positions over time. In particular, the measure is calculated as the difference in the first dimension of the DW- NOMINATE scores between the Republican (code: 200) and Democratic (code: 100) 2 parties. We measure the polarizations for the members in both the Senate and House of Representatives as alternative instruments. It is argued that partisan polarisation makes it more difficult to build legislation, resulting in policy gridlock and greater variation in policy (McCarty, 2004). Thus, partisan polarisation is used as an instrument for policy uncertainty because it is directly related to policy uncertainty and is unlikely to have a direct impact on firm tax avoidance. In particular, we execute a two-stage regression strategy as follows: PUt = α0 + β1polart + βkmacro_variablek,t + ϵt (4) TA_CETRi,t+l = α0 + β1fpui,t + βjcontrolj,i,t + ifirmi + ϵi,t (5) Similar to Equation (2), Equation (4) is a monthly time-series regression where a measure of political polarization (POLAR) is further added to the model. The fitted values of PU estimated from Equation (4) are aggregated to yearly level to be the key variable of interest, FPUi,t, in Equation (5). The specification of Equation (5) is the same with Equation (1), except that the 2 Data are obtained from for the period , that is the maximum availability period. 17

18 original news-based PU is replaced by the fitted PU. Firm-level controls, firm fixed and cluster effects are included in Equation (5) as in Equation (1). <Insert Table 4 here> The regression results using Equation (6) are documented in Table 4. In Column (1) to (4), we add one more year lead in each model to examine the impact of policy uncertainty on firm tax avoidance over time. The significantly positive coefficients of the fitted PU from Columns (1) to (4) confirm our baseline result of the positive association between policy uncertainty and firm tax avoidance. This impact, however, reverses in year 4, suggesting that firms decrease their tax avoidance activities when policy uncertainty is resolved in the future. The coefficient estimates of PU also reveal that firms increase their tax avoidance initially when PU increases and reduce through time when the uncertainty becomes less severe. Economically, after controlling for potential endogeneity issue between policy uncertainty and tax avoidance, the impact of policy uncertainty on corporate tax avoidance becomes much stronger. In particular, the coefficients of the fitted PU in Column (2) reports that a doubling in the level of policy uncertainty leads to a reduction by 5.97% in the cash tax effective rates (i.e. equivalent to an average reduction of tax payment of $12.33 million per firm) in the following year. As a robustness check, in Columns (6) and (8) we report results when CANPU is included from Equation (5). In Columns (7) and (8) we replace the Senate DW-NOMINATE with House DW-NOMINATE scores as the instrumental variable. The results on the coefficients of the fitted PU consistently further corroborate our findings of a negative relationship between policy uncertainty and tax avoidance. 3.4 Alternative measures of tax avoidance Several alternative measures of tax avoidance are also used to ensure the robustness of our results. These include TA_ETR, DTAX, DEFERAL, TA_CETR5, CASH_RATIO, Low_CETR, 18

19 CETR_dodger, CURRENT_ETR, SHELTER_LEVEL and SHELTER_DUMMY. The firm effective tax rate (ETR) which is defined under the Generally Accepted Accounting Principles (GAAP). ETR is defined as total tax expenses divided by pre-tax book income before special items. This measure captures firm practices that reduce tax expenses for financial reporting purposes. One drawback of this measure is that it only reflects tax avoidance strategies that generate permanent differences and does not capture the effects of temporary book-tax differences (i.e. deferral strategies). It is also subject to GAAP tax accruals such as the valuation allowance and unrecognised tax benefits. DTAX is used to capture the tax practices that drive a permanent difference between book income and tax income (Frank et al., 2009). This measure is more likely to reflect a deliberate attempt to avoid tax. Following Frank et al. (2009), we estimate DTAX as the residual of a regression of permanent book-tax differences on various non-tax planning determinants. Conceptually, DTAX captures tax avoidance activities that are more aggressive and directly affect net income through a reduction in total tax expense. DEFERAL is included following Edwards et al. (2016) which equals to -1 times the ratio of deferred tax expenses to pre-tax income adjusted for special items as an alternative measure of firm tax avoidance. Dyreng et al. (2008) argue that the numerator in the annual TA_ETR formula may include taxes paid on earnings in a different fiscal year period which may cause a mismatch problem. To alleviate this problem, we use long run tax rates measured over a five-year period as an alternative measure of firm tax avoidance following Dyreng et al. (2008). A firm that is successful in avoiding paying tax over a long period of time (i.e. 5 years) is considered as an aggressive tax avoider. As a result, TA_CETR5 is -1 times the sum of total tax paid over five years (t to t+4) scaled by pre-tax income net of total special items over the same accumulation period. 19

20 Hassan et al. (2017) also argue that more tax aggressive firms are those that pay significantly lower taxes when compared to their peers belonging to the same industry. As a result, based on measures of tax avoidance using CETR, they use dummy variables to capture firms paying significantly lower taxes than their counterparts in a given year. Specifically, Low_CETR is constructed analogously to capture firms paying lower CETR when compared to their industrial counterparts in a given year. Low_CETR captures firms that pay fewer taxes than their counterparts but they do not necessarily reflect the most extreme case of tax avoidance practices, especially those that are labelled under the Center of Tax Justice as tax dodgers. Tax dodgers companies are those that are profitable but pay no corporate tax rates. As a result, following Hassan et al. (2017) we also include ETR_Dodger which equals to one if a firm has a positive pre-tax profit and a zero ETR in a given year and zero otherwise. CETR_Dodger equals to one if a firm has positive pre-tax profit and a zero CETR in a given year and zero otherwise. We also employ a tax-shelter prediction score (SHELTER_LEVEL) as computed in Wilson (2009) and SHELTER_DUMMY following Hassan et al. (2017). Tax shelters refer to those complex transactions used by corporations to obtain significant tax benefits probably never intended by the tax code (Hanlon and Slemrod, 2009). To capture conforming tax avoidance which occurs when a firm lowers its taxes by reducing both taxable income and pretax accounting income, we follow Cen et al. (2017) to use CASH RATIO which uses operating cash flows as the denominator. Specifically, the CASH RATIO measure is defined as cash taxes paid divided by pre-tax operating cash flows adjusted for extraordinary items and discontinued operations. To further test the robustness of our results, we use a cash tax differential (CTD) measure developed by Henry and Sansing (2014) which is calculated as the difference between cash taxes paid and the product of statutory tax rate and pre-tax income, scaled by lagged total 20

21 assets. Finally, we also estimate effective tax rates using current tax expenses (CURRENT_ETR) to capture current taxes owed to the tax authorities. Table 5 present the results associated with these variables. For dummy variables, such as LOW_CETR, CETR_DODGER and SHELTER_DUMMY, we use logistic regressions. Across all our models, we find that the coefficients on policy uncertainty is positive and significant suggesting that in the period of higher policy uncertainty, firms are likely to pay significant higher tax rates, dodge as well as shelter more their taxes. It also indicates a positive association between long run corporate tax avoidance and policy uncertainty in the US. <Insert Table 5 here> 3.5 Financial constraints The literature suggests a negative association between policy uncertainty and bank credit growth at both firm and aggregate levels (Bordo et al., 2016). If policy uncertainty exacerbates financial constraints, we expect that firms will increase their tax avoidance for their precautionary incentives. To test the financial constraints mechanism, we first examine if aggregate bank credit is tightened due to heightened policy uncertainty, and as the results, firms will increase tax avoidance Policy uncertainty and credit market conditions To investigate the effect of policy uncertainty on general credit market conditions, the following regression model is used: CISPREADt = α0 + β1put + βkmacro_variablesk,t + δtquartert + ϵt (6) Equation (6) is quarterly time-series regression of a proxy for credit market conditions with CISPREAD is run on news-based measure of policy uncertainty, PU, together with six 21

22 macroeconomic variables described above. Following Harford (2005), Officer (2007), and Harford et al. (2014), we measure credit market conditions by CISPREAD which is the spread of commercial and industrial loan rates (on loans greater than USD 1 million) over the federal funds rate. 3 The authors argue that larger CISPREAD indicates that credit conditions are more tightening. We also include four quarter dummies to account for the possible seasonality as well as time trend effects on credit supply. The results for this test are displayed in Table 6. <Insert Table 6 here> The result shows that commercial and industrial loans become costlier when policy uncertainty is more heighten, manifested by the positive coefficient for PU. This makes it harder for firms to access these main sources of external finance. In sum, the results provide evidence that policy uncertainty exacerbates the credit market conditions at aggregate level that is consistent with findings of Bordo et al. (2016) Policy uncertainty, tax avoidance and cash holdings Our findings so far suggest that firm tax avoidance increases when financial constraints heighten in the period of high uncertainty. In this section, we examine whether cash holdings serve as a moderating channel to alleviate the positive impact of policy uncertainty on firm tax avoidance. Specifically we argue if firms have a precautionary motive to hold more cash when financial constraints increase (Opler et al., 1999; Bates et al., 2009), the effects of policy uncertainty on firm tax avoidance should be less severe for cash-rich firms. Hence, the moderating effect of cash holdings on the relation between policy uncertainty and tax avoidance is expected to be stronger for more financially constrained firms. 3 Following Harford et al. (2014), the spread of commercial and industrial loan rates (on loans greater than USD 1 million) over the federal funds rate are collected from the Federal Reserve Senior Loan Office (SLO) survey published in January,

23 This analysis will help explain why many U.S (especially multinational) firms hold more cash in the period of higher uncertainty instead of engaging more in tax avoidance. This is because the latter activities are usually challenged by the foreign and state jurisdictions. For example, the recent Wall Street Journal highlights that France has challenged Google for its tax avoidance activities and demanded 1.7 billion in back taxes and penalties. Likewise, Apple has been challenged by tax authorities in Australia and Amazon.com has been challenged by the France and various U.S states (Pfanner, 2012). We argue that if those firms have not saved enough cash, then paying the tax, including both the penalties and interests, could force them to forgo capital spending or raise external funds (which are very costly, especially in the period of high economic policy uncertainty). As a result, when faced with greater uncertainty, firms may have precautionary motives to have sufficient cash on hand to avoid paying more tax penalties resulting from their tax avoidance activities. To test this hypothesis, we estimate the following model: TA_CETRi,t = α0 + β1cashi,t + β2pui,t*cashi,t + βjcontrol VARIABLESj,i,t + ifirmi + δtyeart + ϵi,t (7) In this Equation (7), all the variables are the same as in Equation 1 and the variables of interest is the interaction term, PU*CASH, that capture the impact of cash holdings on the association between policy uncertainty and tax avoidance. If cash holdings weaken the positive impact of policy uncertainty on capital investment, the coefficient of the interaction term should be negative. We further divide the sample into financial constrained firms (FC) and unconstrained firms (UC) following Almeida et al. (2004) and Denis and Sibilkov (2010) to test if the moderating role of cash holding is more pronounced for more financially constrained firms. Since there is no agreement in the literature regarding the classification of constrained versus unconstrained firms, we rely on the following five well documented categorization schemes, 23

24 including firm size, dividend payout ratio, age, debt and paper ratings. According to Almeida et al. (2004) and Hadlock and Pierce (2010), financially constrained firms are those that are small, young, low both short-term and long-term credit quality and hence are more vulnerable to capital market frictions. The four classification schemes are classified as below: Scheme #1: We rank firms based on their asset size per year and assign to the financially constrained (unconstrained) group those firms in the bottom (top) three deciles of the annual size distribution. Scheme #2: Similarly, firms are ranked based on their payout ratio for every year over the and allocate those firms in the bottom (top) three decides to the financially constrained (unconstrained) categories. The payout ratio is computed by taking the common dividend paid divided by operating income. Note that firms who do not pay dividend for a particular year are assigned zero value for their payout ratio. Scheme #3: We classify financially unconstrained firms are those that have their debt rated by Standard & Poor s (S&P Long-term Senior Debt rating) and their debt not in default (rating of D ). Firms that do not have their debt rated but report positive longterm debt are defined as financially constrained. Scheme #4: Firms are classified as financially unconstrained if they have their shortterm rated by S&P s and their debt is not in default. Firms are defined as financially constrained if they have positive short-term debt but are not rated by S&P s. Scheme #5: We calculate firm age by taking the difference between the year of interest and IPO year. For every year in the sample period, we again rank firms by their ages and assign those firms in bottom (top) three deciles into financially constrained (unconstrained) groups. We then rerun Equation (7) separately on the two groups for each classification scheme and their results are reported in Table 7 below. 24

25 <Insert Table 7 here> The absence of all macro-level independent variables described in Equation (2) above allows us to include both year and industry fixed effects in the regression models. In relation to the full sample result, Column (1) shows that the coefficient on the interaction term, PU*CASH, is negative and statistically significant as expected suggesting the mitigating role of cash holdings on the impact of policy uncertainty on tax avoidance. Columns (2) through (11) of Table 7 present regression results on subgroups of constrained (FC) and unconstrained (UC) firms using five aforementioned classification schemes. We find that the coefficients of the interaction term, PU*CASH, are negative and statistically significant for the FC subsample while obtaining statistically insignificant coefficient for the interaction term PU*CASH for financially unconstrained firms. In other words, the results indicate that the increase in cash reserves is likely to discourage financially constrained firms to engage in tax avoidance activities induced by higher policy uncertainty. The results strongly support our hypothesis that cash holdings serve as a mechanism to mitigate the positive association between policy uncertainty and tax avoidance, and the moderating impact is more pronounced for more financially constrained firms. 4. Conclusions In this paper we empirically investigate the impact of economic policy uncertainty on firm tax avoidance. We find a strong and economically meaningful positive association between economic policy uncertainty and firm tax avoidance. This relation is robust to alternative measures of tax avoidance and several tests to address endogeneity concerns that arise from the possibility that the measure of policy uncertainty may inadvertently capture economic uncertainty. In addition, firms use several strategies to avoid tax including tax deferrals and shelters. Further analysis shows that the effect of policy uncertainty on tax avoidance is less 25

26 pronounced for firms with higher level of cash holdings. Overall, our findings shed more lights on the importance of uncertainty around government policy in determining firm tax avoidance activities, thereby contributing to the emerging literature on the economic effect of policy uncertainty. 26

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