Abstract
This study investigates whether firm-level political risk increases managers’ propensity to avoid taxes. Using a measure of firm-level political risk developed by Hassan et al. (Quart J Econom 134(4):2135–2202, 2019) and several commonly used measures of tax avoidance, we find that corporate tax avoidance increases with firm-level political risk and that the main results hold in various supplemental and endogeneity tests. In cross-sectional tests, we document that the positive relationship between firm-level political risk and tax avoidance is stronger for lower levels of tax avoidance, and is weaker when firms have superior cash flow performance and higher cash holdings. In addition, we show that the positive relationship between firm-level political risk and tax avoidance is stronger during the Trump administration’s trade policy changes as well as during the global financial crisis.
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Notes
Recent initiatives undertaken by various domestic and international bodies (e.g. European Commissions’ corporate social responsibility (CSR) policy, the Tax Justice Network (TJN), Global Reporting Initiative (GRI) and the United Nations (UN) Global Compact) underscore the importance of corporate tax payments to properly run various government welfare payments (Lanis and Richardson 2018).
For instance, tax avoidance practice of large reputable firms such as Apple, Amazon, Boeing, Citigroup, Google, Microsoft ConocoPhilips, Exon-Mobile and Carnival Cruise Lines, and revelations of tax scandals such as the “Panama Papers” and the “Paradise Papers” raise major public concerns about the efficacy of existing tax rules and laws in constraining corporate tax avoidance activities (Internal Revenue Service 2020); https://www.irs.gov/compliance/criminal-investigation/tax-fraud-alerts.
For instance, on his first day of office President Trump withdrew USA from the Trans-Pacific Partnership (TPP), which involves 11 other countries around the Asia-Pacific Region (Politico 2017). (https://www.politico.com/story/2019/01/23/trans-pacific-trade-pact-2017-1116638). The Trump administration imposed tariffs of about 10-50 percent on $326 billion Chinese products such as washing machines, solar panels, aluminum and steel. In response to this, China imposed high tariffs on US exports to counter the US move (Bloomberg 2020) (https://www.bloomberg.com/news/articles/2020-01-15/the-u-s-china-war-over-trade-and-tariffs-explained-quicktake). A big portion of the US business community got panicked due to these policies; as Economist (2020) argues: “The tariffs shuffled resources around: towards American producers of products shielded by the tariffs, away from the businesses and people having to pay for more expensive imports, as well as producers affected by foreign retaliation.”.
In a recent study on tax policy uncertainty (TPU), Gallemore et al. (2019) find evidence that in response to TPU, firms take certain actions such as building connections with congressional members on tax-related committees; however, they do not reduce their corporate tax avoidance.
HHLT find that variation of aggregate political risk over time and across sectors respectively account for 0.81% and 7.50% of the variation in their measure. The remaining 91.69% of the variation relates to firm level political risk.
HHLT further contend that much of the economic impact is not described well by conventional models where individual firms have relatively stable exposure to aggregate political risk. A large portion of variation of political risk is driven by changes over time in the identity of firms most affected by political risk within a given sector.
Using HHLT’s measure of political risk, Gad et al. (2021) find that changes in borrower’s level of political risk leads to changes in interest rates set by lenders. Furthermore, El-Ghoul et al. (2021) demonstrate that during periods of high economic policy uncertainty, investors are more likely to scrutinize the financial information of firms having high firm-level political risk (in terms of HHLT’s measure), thus motivating managers to improve reported accounting quality.
The United States has one of the highest statutory corporate tax rates among developed countries. Even after deductions and other exclusions are taken into consideration, the effective tax rate is still among the highest among countries. Therefore, firms faced with pressure to maximize shareholders’ value, have necessary incentives to reduce their tax liability to report higher income after-tax.
For a detailed description of how the HHLT index is developed, please refer to Hassan et al. (2019). We provide a brief description of the process in Appendix B.
We sort size and industry independently. Industry is based on the two-digit SIC industry classification. We censor GAAP_ETR and Cash_ETR to be between 0 and 1.
POLRISK has a standard deviation of 1 as this variable is demeaned.
TA_GAAP: [(0.0593*(1.102–0.272))/0.201]; TA_CASH: [(0.0212*(1.102–0.272))/0.219]; DTAX: [(0.014*(1.102–0.272))/0.372]. For SHELTER, we use the mean values of all independent variables to estimate the increase of tax shelter when POLRISK moves from the first quartile to the third quartile.
When we include these additional variables in the analysis, we lose substantial observations due to lack of data. Therefore, we conduct a full sample analysis without the four variables and then, a reduced sample analysis by including those variables.
Non-availability of proper data to calculate the change value of several variables reduces our full sample into a reduced sample to conduct the analysis. However, we are able to use a sizable sample of 28,416 firm years in the analysis that provides credibility to the results.
For instance, in March 2, 2018, Mr. Trump tweets, “When a country (USA) is losing many billions of dollars on trade with virtually every country it does business with, trade wars are good, and easy to win.” Such statements are likely to increase widespread panics in business community (CNBC, Mar 2, 2018, https://www.cnbc.com/2018/03/02/trump-trade-wars-are-good-and-easy-to-win.html). In a similar vein Tax Foundation’s Tariff Tracker states that in response to Trump administration’s increased tariffs “other countries have announced intentions on impose tariffs on US exports.” (September 18, 2020, https://taxfoundation.org/tariffs-trump-trade-war/).
Though Trump administration’s trade policy has created an exogenous shock to business, it is likely to impact U.S. corporations in varying degrees especially those who rely on Chinese imports and involve in international trades with Canadian and Mexican business. However, according to HHLT (2019), major variation of political risk resides at the firm-level and has the real economic content. Following this observation, we examine whether political risk created at the firm level during the Trump administration incrementally affects corporate tax avoidance.
These results are interesting, as they imply that even after Trump administration’s substantial corporate tax cuts, companies seem to increase their tax avoidance practice.
Based on the analogy that a major portion of cross-sectional variation of political risk plays out at the firm- level, it is worthwhile to examine how such cross-sectional variation of such risk incrementally affects corporate tax avoidance in the period of global financial crisis.
Capital market-based accounting research commonly use industry average of regressors as instrumental variables to address endogeneity. To address endogeneity in the relationship between R&D expenditure and future earnings, Lev and Sougiannis (1996) use the industry average R&D expenditure as an instrument. Chan et al. (2012) use industry mean as instrument to assess the link between audit and non-audit fees. In a similar vein, Xue (2007) apply industry mean R&D expenditure and purchased intangibles to address endogeneity problem. Additionally, while examining the type of relationship between firm-level innovation and default probability, Hsu (2015) use industry average as an instrument. Collectively, we contend that it is logical in our study to use industry average political risk as an instrumental variable. We use two-digit SIC codes to define industry.
For example, increase in tariff on aluminum is likely to affect political risks of companies using aluminum in their manufacturing process, but not the other manufacturing companies with similar ownership and capital structure.
For the sake of brevity, we briefly present some relevant information on firm-level political risk measurement scores developed by HHLT (2019). For a more information, please refer HHLT (2019, pp. 7–12 and 45–47).
Synonym of “risk” or “uncertainty” is written in caps and surrounded by dashes.
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Appendices
Appendix A
1.1 Variable definitions
Variable | Definition |
|---|---|
TA_GAAP | The firm’s mean industry-size GAAP ETR minus the firm’s GAAP ETR, where GAAP ETR is the sum of current income tax expense over the years t, t − 1, and t − 2, divided by the sum of pre-tax financial income over the years t, t − 1, and t − 2. Higher value indicates greater tax avoidance |
TA_CASH | The firm’s mean industry-size CASH ETR minus the firm’s CASH ETR, where CASH ETR is the sum of cash paid for income taxes over the years t, t − 1, and t − 2, divided by the sum of pre-tax financial income over the years t, t − 1, and t − 2. Higher value indicates greater tax avoidance |
DTAX | The discretionary permanent book–tax difference of Frank et al. (2009), which is the residual from the following regression, estimated by year and two-digit SIC code: PERMDIFFit = β0 + β1INTANit + β2 UNCONit + β3MIit + β4CSTEit + β5NOLit + β6LAGPERMit + eit, where PERMDIFF = total book–tax difference – temporary book–tax difference = [{PI – [(TXFED + TXFO) / STR]} – (TXDI / STR)], scaled by lagged assets (AT); INTAN = goodwill and other intangible assets (INTAN), scaled by lagged assets; UNCON = income (loss) reported under the equity method (ESUB), scaled by lagged assets; MI = income (loss) attributable to minority interest (MII), scaled by lagged assets; CSTE = current state tax expense (TXS), scaled by lagged assets; NOL = change in net operating loss carryforwards (TLCF), scaled by lagged assets; LAGPERM = PERMDIFF in year t − 1; and STR is the statutory tax rate |
SHELTER | An indicator variable that takes the value of one for firms in the top quintile of the predicted probability that the firm is engaged in tax sheltering, based on Wilson’s (2009) model: SHELTER = − 4.86 + 5.20 × BTD + 4.08 × DA—1.41 × LEV + 0.76 × LAT + 3.51× ROA + 1.72 × FI + 2.43 × R&D, where BTD is the total book–tax difference, scaled by lagged total assets (AT), DA is the absolute value of discretionary accruals from the performance-adjusted modified cross-sectional Jones model, LEV is long-term debt (DLTT) divided by total assets (AT), LAT is the logarithm of total assets (AT), ROA is pre-tax earnings (PI) divided by lagged total assets, FI is an indicator variable equal to one for firm observations reporting foreign income (PIFO) and zero otherwise, and R&D is R&D expenses (XRD) divided by lagged total assets |
POLRISK | Firm-specific political risk scores developed by Hassan et al. (2019) and obtained from www.policyuncertainty.com. Following Hassan et al. (2019), we use a standardized measure of the political risk scores by dividing the raw measure by its standard deviation so that the standard deviation of PRisk is one. Please see Appendix B for more discussion |
ROA | Return on assets, calculated as pre-tax income (PI) divided by lagged total assets (AT) |
SD(ROA) | Standard deviation of ROA over the past five years |
NOL | An indicator variable that equals one for net operating loss carryforwards (Compustat: TLCF) 0 otherwise |
∆NOL | Change in net operating loss carryforwards (Compustat TLCF) scaled by lagged total assets (AT) |
FOR_INCOME | Foreign income (PIFO), scaled by lagged total assets (AT) |
∆GOODWILL | Change in goodwill (GDWL) scaled by lagged total assets (AT). If the value is negative, then it is set to zero |
NEWINVST | New investment, calculated as Compustat (XRD + CAPX + AQC − SPPE − DPC), scaled by lagged total assets (AT) |
PPE | Net property, plant, and equipment at the end the year, calculated as Compustat PPENT scaled by lagged total assets (AT) |
INTAN | Intangible assets at the end of the year, calculated as Compustat INTAN scaled by lagged total assets (AT). If INTAN is missing, then INTAN = GDWL |
EQINC | Equity income in earnings, calculated as Compustat ESUB scaled by lagged total assets (AT) |
DACC | The absolute value of performance-adjusted discretionary accruals, estimated using modified cross-sectional Jones model |
CASH | Cash holdings at the end of the year, calculated as Compustat CHE scaled by lagged total assets (AT) |
SIZE | Log of market value of equity at the end of the year, calculated as Compustat PRCC_F × CSHO |
LEV | Financial leverage at the end of the year, calculated as long-term debt (DLTT) scaled by total assets (AT) |
MTB | Market-to-book ratio at the end of the year, calculated as the market value of equity (Compustat PRCC_F × CSHO) divided by the book value of equity (Compustat CEQ) |
BUSSEG | Log of the number of business segments |
GEOSEG | Log of the number of geographic segments |
CSR | Composite CSR score, calculated using the following six CSR scores: community (CSR_COM), diversity (CSR_DIV), employee relations (CSR_EMP), human rights (CSR_HUM), and product characteristics (CSR_PRO) and applying the approach used by Deng et al. (2013) |
LOBBY | Log of money spent on lobbying; we obtain these data from the Center for Responsive Politics’ OpenSecrets database |
TRUMP EINDEX | An indicator variable that equals 1 if year is greater than 2016, 0 otherwise Managerial entrenchment index, developed by Bebchuk et al. (2009). It considers 6 antitakeover provisions. This index ranges from a feasible low of 0 to a high of 6; a high score is associated with weak shareholder rights (higher managerial entrenchment) and a low score is associated with high shareholder rights (lower managerial entrenchment) |
INST IND_POLRISK | Percentage of Institutional stock ownership The instrumental variables used in 2SLS using mean political risk of all firms belonging to an industry; here we us 2 digit SIC code to define industry |
Pred-POLRISK | Estimated value of firm-specific political risks estimated in the first stage of the 2SLS approach, using the coefficients of variables estimated using model 6 |
Appendix B
2.1 HHLT’s (2019) estimation of firm-level political risk Footnote 26
Most US listed companies hold regular earnings conference calls with their analysts and other interested parties. In these calls, managers give their view on their firms’ past and future performance and respond to questions from call participants. HHLT (2019) conduct textual analysis of quarterly earnings conference-call transcripts to develop firm-level political risk score, which is supposed to measure the extent of political risk faced by each firm and how this risk varies over time. The authors first quantify political risk faced by an individual company at a given point of time based on the share of conversations on conference calls devoted to politics in general, and with specific political topics.
They apply a ‘simple pattern-based sequence-classification method’ developed by experts in computational linguistics to distinguish between political and non-political language. To construct overall political risk score, they use a training library of political texts, which they develop using an undergraduate textbook on American politics and articles from the political section of US newspapers. Similarly, they construct a training library of non-political text using different sources such as an accounting textbook, articles from the non-politics section of US newspapers, and transcripts of speeches on non-political issues. The authors then identify two-word combinations (bigrams) that are frequently used in political texts. They then count the number of times these bigrams are used in conjunction with synonyms for ‘risk’ and ‘uncertainty’ and divide by the total length of calls to develop a measure of the proportion of the conversation concerned with political risk.
In the following table, we provide some examples of common bigrams identified by HHLT and text surrounding these bigrams for some firms. For more comprehensive examples, please see HHLT (2019).
Bigram | Firm name (date) | Text surrounding the bigram (from conference call transcript)Footnote 27 |
|---|---|---|
the constitution | Nevada Gold Casinos (10 September 2008) | gaming industry is currently supporting a ballot initiative to amend the constitution to authorize an increase in the—BET—limits allow additional |
the states | World Acceptance Corporation (25 July 2006) | management analyst i wanted to followup on the regulatory front the states that you had mentioned the—POSSIBILITY—of some positive legislation |
of government | Applied Energetics, Inc. (11 May 2009) | of products and the—UNCERTAINTY—of the timing and magnitude of government funding and customer orders dependence on sales to government customers |
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Hossain, M., Lobo, G.J. & Mitra, S. Firm-level political risk and corporate tax avoidance. Rev Quant Finan Acc 60, 295–327 (2023). https://doi.org/10.1007/s11156-022-01095-3
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DOI: https://doi.org/10.1007/s11156-022-01095-3
Keywords
- Firm-level political risk
- Corporate tax avoidance
- Change analysis
- Cash flow performance
- Corporate cash holding