tax aggressiveness and political corruption

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Tax Aggressiveness and Political Corruption

Abstract

In this study, we examine the relation between political corruption and corporate tax aggressiveness among firms headquartered in the U.S. Our measure of political corruption is based on yearly conviction rates of public officials included in the Department of Justice’s Public Integrity Section annual reports. We find that firms headquartered in politically corrupt districts engage in more tax aggressiveness, as captured by FIN 48 tax reserve balances. This finding is consistent with political corruption impacting the political environment surrounding firms’ headquarter locations, with tax aggressiveness being viewed as more acceptable in districts with a greater number of convictions of public officials. We find corroborating evidence when tax aggressiveness is alternatively measured based on the existence of subsidiaries in identified tax haven jurisdictions and firms’ predicted tax shelter score. The results of our study extend the literature examining the determinants of tax aggressiveness by documenting how political corruption impacts the reputational effects associated with tax aggressiveness. Our findings also expand our understanding of how political corruption impacts firms’ accounting choices. Keywords: Political Corruption; Tax Aggressiveness; FIN 48; Tax Havens. We thank workshop participants at the University of Alabama for their helpful comments and suggestions on the paper.


1 Introduction

This study examines the relation between political corruption and tax aggressiveness for

U.S firms. While the U.S. is not widely considered a corrupt nation, the U.S. Department of

Justice reports approximately a thousand convictions per year of corrupt public officials

nationwide.1 These cases generally include crimes related to bribery, conflicts of interest,

election crimes, and extortion. In some instances, however, public corruption is directly related

to the taxation of corporations. For example, a former revenue agent for the Internal Revenue

Service (IRS) was convicted in 2006 of bribery and conspiring to assist firms in the avoidance of

filing required tax disclosures.2 The former agent was responsible for educating firms on filing

currency transaction reports, but instead assisted them with hiding cash transactions in exchange

for bribes. Although this case provides anecdotal evidence of a connection between political

corruption and tax aggressiveness, it remains unknown whether political corruption impacts the

extent to which U.S. firms take aggressive tax positions on a broad scale, given that large sample

evidence of this relation has not been examined previously.3 Accordingly, we seek to provide

evidence with respect to the following empirical question: what is the association, if any,

between corporate tax aggressiveness and political corruption?

On one hand, prior research documents that the presence of political corruption impacts

corporate decisions, consistent with firms seeking to shield assets from expropriation by corrupt

public officials (Smith 2016; Durnev and Fauver 2011; Stulz 2005). For instance firms at a

greater risk of expropriation have been found to prepare less transparent disclosures. An

1 See the U.S. Department of Justice Public Integrity Section Report to Congress on the Activities and Operations of the Public Integrity Section. 2 United States v. Walker, United States District Court, Northern District of California (November 28, 2006). 3 We note that the majority of cases cited in the Public Integrity Section’s yearly reports are not specifically related to tax issues. The reports also do not provide enough granular information for us to isolate cases associated with both corruption and corporate tax aggressiveness.


alternative accounting choice that firms located in more corrupt political districts can make to

shield their assets is to take less aggressive tax positions. This behavior is consistent with

evidence from prior studies suggesting that firms pay higher levels of taxes to safeguard

themselves in other settings, such as to protect federal contract revenues (Mills, Nutter, and

Schwab 2013) and defend against rent seeking from labor unions (Chyz, Leung, Li, and Rui

2013). If firms take more conservative tax positions to shield their assets from expropriation

from public officials, then corporations headquartered in more corrupt political districts may

engage in less tax aggressiveness than other firms.

On the other hand, prior literature also demonstrates that reputational effects influence

firms’ tax policies (Hasan, Hoi, Wu, and Zhang 2017; Hoi, Wu, and Zhang 2016). Specifically,

these studies document that strong social environments and corporate cultures are associated

with less tax aggressiveness, consistent with such attributes increasing the perceived reputational

costs associated with corporate tax aggressiveness. Additionally, other research on reputational

effects documents a stock price response, at least temporarily, to disclosures that firms are

engaging in tax shelters (Gallemore, Maydew, and Thornock 2014; Hanlon and Slemrod 2009).

Another factor that may influence the perceived reputational effects associated with

engaging in tax aggressiveness is the political environment surrounding firms. A substantial

majority of respondents to an IRS survey indicated that paying a fair share of taxes is a civic duty

(IRS 2014), suggesting that corporate tax aggressiveness is not be a costless exercise. Consistent

with political environments impacting tax decisions, Dyreng, Hoopes, and Wilde (2016) find that

public pressure to disclose the location of all subsidiaries, including tax havens, results in

decreased operations in tax havens and increased effective tax rates among affected firms.

Managers of firms located in more compliant political environments may assign greater


reputational costs to taking aggressive tax positions than managers of firms located in more

corrupt political environments since the perceived appropriateness of such behavior may likely

be based on the information conveyed in their respective environments. Accordingly, firms

headquartered in more corrupt political districts may engage in more tax aggressiveness than

other firms. Given the competing theories provided in prior research, it is unclear how political

corruption impacts the willingness of U.S. firms to take aggressive tax positions.

To empirically examine the association between political corruption and corporate tax

aggressiveness, we follow prior research and measure corruption using U.S. Department of

Justice data on the number of convictions of public officials in each federal judicial district

(Smith 2016; Butler, Fauver, and Mortal 2009). The Department of Justice identifies and

prosecutes crimes in a similar manner across districts. Therefore, the number of convictions

scaled on a per capita basis indicates the level of corruption in the judicial district where a firm’s

headquarters is located. As our primary measure of tax aggressiveness, we use the ending

balance of firms’ tax reserve for unrecognized tax benefits (UTB) accrued for tax positions

managers deem uncertain as required under Financial Accounting Standards Board Interpretation

No. 48 (FIN 48) consistent with prior research (Kim and Zhang 2016; Lisowsky, Robinson, and

Schmidt 2013; Hoi et al. 2013).

Using a sample of 16,125 firm-year observations over the sample period from 2007 to

2015, we find a positive and significant relation between the level of political corruption and tax

aggressiveness. This result suggests that firms take more aggressive tax positions when their

headquarters are located in more politically corrupt districts. This evidence is consistent with the

claim that firms’ surrounding political environments influence their tax policy choices. In

particular, managers of firms headquartered in locations with more political corruption assign


less reputational costs to engaging in tax aggressiveness, given that such behavior is less likely to

be viewed as inappropriate based on the information conveyed in their political environments.

Our primary results take the reserve for uncertain tax positions at face value, although

prior research indicates that managers enjoy wide latitude in determining additions to the reserve

(De Simone, Robinson and Stomberg, 2014). In order to address concerns over manager

discretion in applying the standards for uncertain tax positions and corroborate our findings, we

perform a series of supplemental tests. First, we repeat our main empirical analysis using

alternative proxies for corporate tax aggressiveness identified in prior literature, including the

number of firm subsidiaries located in identified tax haven locations (Lisowsky 2010; Dyreng

and Lindsey 2009) as well as firms’ tax shelter prediction score (Wilson 2009). Consistent with

our main results, we find that firms headquartered in politically corrupt districts are more likely

to report subsidiaries in tax haven jurisdictions and have a higher predicted probability of

engaging in tax sheltering. Second, we perform additional tests to ensure that financial reporting

incentives, rather than tax aggressiveness, are not driving our documented association between

firms’ FIN 48 tax reserve and political corruption. We find that our results still hold after

controlling for firms’ financial reporting aggressiveness, captured by performance-matched

discretionary accruals (Kothari, Leone, and Wasley 2005). We also perform a falsification test,

where we examine the association between political corruption and firms’ performance-matched

discretionary accruals. The results of this analysis are not statistically significant, suggesting that

firms located in corrupt political districts are not engaging in more aggressive financial reporting

than other firms. These supplemental tests provide additional support for our main empirical

finding, that there is a positive association between political corruption and corporate tax

aggressiveness.


Our study contributes to the tax literature that identifies determinants of tax

aggressiveness. Prior research documents that corporate tax aggressiveness is related to a variety

of firm characteristics associated with political and reputational costs, including firms’ political

involvement (Kim and Zhang 2016; Brown, Drake, and Wellman 2015), procurement of federal

contracts (Mills et al. 2013), propensity for engaging in corporate social responsibility (CSR)

(Davis, Guenther, Krull, and Williams 2016; Watson 2015; Hoi et al. 2013), as well as the

existence of valuable consumer brands (Austin and Wilson 2017). However, Wilde and Wilson

(2017) assert that the “nature and magnitude of reputational costs associated with aggressive tax

planning remain unclear (pg. 12).” The authors also posit these reputational costs may be

associated with the political process. Our study extends our understanding of how the political

process impacts the reputational effect associated with tax aggressiveness. Namely, our results

are consistent with political corruption impacting the political environment surrounding firms,

which influences the perceived reputational costs that managers assign to engaging in tax

aggressiveness. Stated simply, managers of firms located in politically corrupt districts view

aggressive tax behavior as being more acceptable based on the information conveyed in their

political environment, and as such, firms headquartered in politically corrupt districts engage in

more tax aggressiveness compared to other firms.

Our study also contributes to the accounting literature by expanding the use of political

corruption in accounting research. To date, prior studies have only examined the effect that rent

seeking by public officials has on the transparency of firms’ disclosures (Durnev and Fauver

2011; Stulz 2005). We extend this stream of literature by investigating the influence of political

corruption on a different accounting choice, aggressive tax positions. Our findings are consistent


with political corruption impacting a broader range of corporate accounting decisions than has

previously been identified in the literature, which may be informative to regulators.

The remainder of the paper is organized as follows. Section 2 discusses prior literature

examining tax aggressiveness and develops our hypotheses. Section 3 describes our research

design. Section 4 outlines our sample selection process and presents our empirical results.

Section 5 includes our supplemental analyses, and Section 6 concludes.

2 Related research and hypothesis development

2.1 Tax aggressiveness literature

In Shackelford and Shevlin’s (2001) review of empirical tax research, the authors note

that few cross-sectional differences in firms’ willingness to engage in tax avoidance had been

identified in the accounting literature, and they specifically called for research into the

determinants of firms’ tax aggressiveness. Hanlon and Heitzman (2010) broadly define tax

avoidance as a continuum of tax planning strategies that reduce taxes, where legal strategies that

decrease explicit taxes, such as investing in municipal bonds, are located on one end of the

continuum, while actions associated with tax aggressiveness, sheltering, and evasion are on the

other end of the continuum. Several studies have answered Shackelford and Shevlin’s (2001) call

for research and have identified a variety of determinants of corporate tax avoidance, including

various firm attributes, constraints, operating environments, and incentives (Wilde and Wilson

2017). Wilde and Wilson (2017), Hanlon and Heitzman (2010), and Shevlin (2007) provide more

recent reviews of the tax avoidance literature and summarize the identified determinants of

firms’ tax avoidance behavior.


Of particular interest to our study, prior literature documents an association between

corporate tax avoidance and characteristics connected to political and reputational costs. For

instance, previous studies (Kim and Zhang 2016; Brown et al. 2015) find that firms’ political

involvement, including political connections and lobbying activities, as well as the procurement

of federal contracts (Mills et al. 2013) influence their tax avoidance behavior. Evidence from

prior literature is also consistent with CSR activities impacting firms’ willingness to avoid taxes,

consistent with reputational effects impacting these firms to a greater extent than firms that do

not engage in CSR activities (Davis et al. 2016; Watson 2015; Hoi et al. 2013). Finally, Austin

and Wilson (2017) observe that firms’ vulnerability to reputational costs, captured by the

existence of valuable consumer brands, also influences corporate tax avoidance. Although prior

literature identifies various political and reputational cost determinants associated with firm’s

willingness to avoid taxes, Wilde and Wilson (2017) assert that the nature of reputational costs

associated with tax aggressiveness is still uncertain and that they may manifest themselves

through the political process. In this study, we examine one such reputational effect, namely,

whether the level of corruption within a firm’s surrounding political environment influences its

willingness to engage in tax aggressiveness.

2.2 Hypothesis development

Although prior literature has identified various political and reputational cost

determinants of corporate tax aggressiveness, it is still unknown whether the level of political

corruption in the location of a firm’s headquarters is associated with more or less tax aggressive

behavior. Economic theory of regulation proposes that public officials can solicit bribes and

extort firms through the threat of unfavorable regulation and taxation (McChesney 1987).

Consequently, firms located in corrupt districts have the incentive to make policy decisions that


shield their assets from this risk of expropriation. Previous studies have documented that firms

make financing as well as accounting choices indicative of this shielding behavior in the

presence of more political corruption. Smith (2016) finds that firms headquartered in more

corrupt political districts have more debt and less cash, consistent with firms reducing their

liquidity so that less cash is available to pay bribes to public officials. Prior studies also provide

evidence consistent with firms at a greater risk of expropriation preparing less transparent

disclosures in an attempt to protect their assets (Durnev and Fauver 2011; Stulz 2005).

Another accounting choice that firms headquartered in more corrupt political districts can

make to shield assets from expropriation is to take less aggressive tax positions. Evidence from

prior literature suggests that firms make tax decisions in order to protect themselves in other

settings. Chyz et al. (2013) find that firms are less tax aggressive when their cash flows are more

vulnerable to rent seeking from labor unions. Additionally, Mills et al. (2013) observe that

politically sensitive federal contractors, particularly those with less bargaining power, pay higher

amounts of federal taxes in order to protect their contract revenues. If firms similarly make tax

choices to shield their assets from expropriation from corrupt public officials, then those

corporations headquartered in more corrupt political districts may engage in less tax

aggressiveness than other firms. This prediction is formally stated in the following hypothesis:

Hypothesis 1a (H1a): Firms headquartered in more corrupt political districts are associated with less tax aggressiveness than other firms.

However, prior literature also demonstrates that reputational effects influence corporate

decisions, including firms’ tax policy choices. Hasan et al. (2017) find that firms headquartered

in counties with higher social capital, captured by strong social norms and dense social networks,

are less tax aggressive than other firms. Additionally, Hoi et al. (2013) document a positive

association between aggressive corporate tax avoidance and socially irresponsible activities,


which they attribute to the impact of corporate culture on firms’ willingness to take aggressive

tax positions. Social environments and corporate culture influence corporate decisions through

the effect that they have on defining appropriate corporate behavior, including tax policies. As a

result, managers assign greater reputational costs to actions that are viewed as inconsistent with

these established norms, such as taking aggressive tax positions. Consistent with this claim,

Graham, Hanlon, Shevlin, and Shroff (2014) surveyed 594 tax executives and document that

potential harm to firm reputation is the second most important reason cited for not engaging in

corporate tax planning. In particular, 69.5 percent of respondents indicated that it was important

or very important to their tax planning decisions. Hence, prior studies document that social

environments and corporate culture influence these perceived reputational effects and managers

consider the reputational costs associated with taking aggressive tax positions when making tax

policy decisions.

Another factor that may influence the perceived reputational effects associated with

engaging in tax aggressiveness is the political environment surrounding firms. Kuklinski, Quirk,

Jerit, and Rich (2001) define a political environment as the entirety of political information that

is communicated to citizens. It includes all communications from such relevant parties as public

officials, the media, and interest groups.4 It is a widely held belief in the U.S. that taxpayers,

including individuals as well as corporations, have a civic duty to pay taxes. Specifically, in the

IRS Oversight Board’s most recently released Taxpayer Attitude Survey (2014), 94 percent of

4 Kim and Zhang (2016) consider corporate tax aggressiveness in the political connections setting and conjecture that politically connected firms are more tax aggressive because of lower expected costs of tax enforcement, access to better tax-related information, lower capital market pressure for transparency, and greater risk-taking tendencies. The political environment in which a firm operates differs from its political connections, with the latter reflecting an individual firm’s political involvement. For instance, two firms can operate in the same political environment, while one chooses to establish political connections and the other does not. Accordingly, the underlying motivations for the politically connected firm’s tax aggressiveness likely do not exist for the non-politically connected firm, even though they operate in the same political environment.


the responding general public assert that paying a fair share of taxes is every American’s civic

duty, while 86 percent believe that it is not acceptable to cheat on paying income taxes.

Additionally, corporate tax aggressiveness is viewed as costly to society, given the tax revenue

lost as a result of implementing aggressive tax policies (Weisbach 2002). Dyreng et al. (2016)

also find that public pressure to disclose the location of all subsidiaries influenced the

reputational costs and benefits of tax avoidance and tax haven operations, resulting in a

decreased use of tax havens and increased effective tax rates among affected firms. Consistent

with the reputational effect evidence documented in prior literature, managers of firms located in

more compliant political environments may assign greater reputational costs to taking aggressive

tax positions, given that this action is inconsistent with the political information being conveyed

in their political environment. In contrast, managers of firms located in more corrupt political

environments perceive lesser reputational costs from engaging in tax aggressiveness, since such

behavior is less likely to be viewed as inappropriate. Accordingly, firms headquartered in more

corrupt political districts may engage in more tax aggressiveness than other firms. This

prediction is formally stated in the following hypothesis:

Hypothesis 1b (H1b): Firms headquartered in more corrupt political districts are associated with more tax aggressiveness than other firms.

3 Research methodology

3.1 Measurement of political corruption

Following the methodology applied by Smith (2016), we measure political corruption

based on the number of corruption convictions of every federal judicial district disclosed in the

federal Department of Justice’s annual Public Integrity Section (PIN) report issued to Congress.

PIN reports contain aggregated conviction data from the Department of Justice’s own cases,


cases from other judicial districts that the Department of Justice assists with, as well as U.S.

attorneys’ offices cases. The rationale for using convictions as a proxy for underlying political

corruption is that the Department of Justice identifies and prosecutes crimes based on similar

criteria across federal districts. Thus, areas with more underlying corruption should have higher

conviction rates on a per capita basis. Accordingly, we follow prior literature (Smith 2016; Ellis,

Smith, and White 2016; Glaser and Saks 2006) and assume that a larger number of convictions

per capita corresponds to a more politically corrupt operating environment.

In particular, our variable Corruption is measured as the yearly number of corruption

convictions per federal judicial district, scaled by the population of that federal judicial district,

as reported by the U.S. Census Bureau.5 This procedure yields an annual measure that proxies for

the overall level of political corruption facing a firm based on the district of its headquarter

location. However, Glaser and Saks (2006) note that the association between the number of

convictions and underlying political corruption is more reasonable over longer time intervals due

to the timing disconnect between when a public official actually engages in political corruption

and when the official is ultimately convicted. PIN reports do not provide adequate detail to

precisely identify the actual timing of the corrupt behavior in most cases, and as a result, year-

over-year fluctuations in conviction rates may represent measurement error rather than actual

changes in corruption.6 Accordingly, we smooth out the volatility of our yearly Corruption

variable by measuring the three-year trailing sum of convictions. A three-year window is

5 Refer to Appendix A for all variable definitions. 6 For example, Ellis et al. (2016) identify a corruption case from Rhode Island where the underlying political corruption began in 1992 but was not reported in a PIN report until its successful conviction in 2000. While this extreme example is outside of our sample window, it illustrates the possible severity of the timing disconnect.


selected for consistency with the three-year statute of limitation associated with firms’ FIN 48

tax reserve, our primary measure of tax aggressiveness.7

3.2 Research design

We examine the relationship between political corruption and corporate tax

aggressiveness by estimating an empirical model that regresses the ending balance of firms’ FIN

48 tax reserve on our measure of corruption based on convictions. The ending tax reserve

balance for uncertain tax benefits is commonly used in prior research to empirically measure

corporate tax aggressiveness (e.g. Kim and Zhang 2016; Lisowsky et al. 2013; Hoi et al. 2013).

Rego and Wilson (2012) posit that the FIN 48 tax reserve is most theoretically similar to the

construct of tax aggressiveness, compared to other publicly available data. Additionally,

Lisowsky et al. (2013) validate the use of tax reserve data as a proxy for aggressive tax positions.

Accordingly, we use the following OLS regression model for firm i at time t:

Log UTBi,t = β1Corruptionj,t + β2Equity Incomei,t + β3Foreign Incomei,t +

β4Intangiblesi,t + β5Leveragei,t + β6MTBi,t + β7NOLi,t + β8 ΔNOLi,t +

Β9PP&Ei,t + β10R&Di,t + β11Pretax ROAi,t + β12Sizei,t +

β13Cash Holdingsi,t + β14CAPEXi,t + β15Log GDPk,t + !"#! + !"!! +

εi,j,k,t (1)

Where, Log UTB is our proxy for tax aggressiveness and is defined as the natural logarithm of (1

+ TXTUBEND), where TXTUBEND is the ending balance in the FIN 48 tax reserve as reported

in the COMPUSTAT database for firm i in period t.

The independent variable of interest in our regression model is Corruption. As noted in

the previous section, Corruption captures the amount of corruption convictions per federal 7 We note that our empirical results are not sensitive to the number of trailing years used to measure corruption convictions. In untabulated analyses, our findings are qualitatively similar when we use one-year or five-year trailing conviction rates.


judicial district. Accordingly, it is measured at the federal judicial district level and is represented

with subscript j. A negative coefficient on β1 implies that greater political corruption is

associated with less corporate tax aggressiveness, consistent with H1a. In contrast, a positive

coefficient on β1 suggests that greater political corruption is associated with more tax

aggressiveness, as predicted in H1b.

In equation (1), we also control for firm characteristics that have been identified in prior

research as determinants of corporate tax aggressiveness (e.g., Hasan et al. 2017; Klassen,

Lisowsky, and Mescall 2016). Our first set of control variables account for firms’ operating

performance (Pretax ROA, NOL, and ΔNOL), foreign operations (Foreign Income), and leverage

(Leverage). Our second set of variables control for differences in book and tax reporting and

include property, plant, and equipment (PP&E), intangibles (Intangibles), research and

development (R&D), and capital expenditures (CAPEX). We also incorporate other common tax

aggressiveness control variables into our model, including firm size (Size), growth (MTB), and

cash needs (Cash Holdings). The log of gross domestic product (GDP) per capita of the state

where each firm’s headquarters is located (Log GDP) is additionally used as a control variable in

order to orthogonalize the corruption measure to the underlying economic forces that the firm is

exposed to at its headquarter location.8 We also control for firm, !, and year, !, fixed effects.

Following Lisowsky et al. 2013, all control variables are computed using three-year measures,

given that Log UTB and Corruption contain information regarding the prior three years.9 Finally,

8 State-level GDP data is obtained from the U.S. Department of Commerce's Bureau of Economic Analysis. As GDP is measured at the state, rather than the federal judicial district, level, it is represented with subscript k. It is important to note that many states include more than one attorney district and that the economic activity of the state is not evenly distributed. Therefore, we acknowledge that Log GDP is a coarse control. Nevertheless, its inclusion provides some assurance that the Corruption variable is not simply capturing time-varying economic conditions in each state. 9 We used the following procedures to compute the three-year measures used in our regression model. Continuous variables were computed as the three-year trailing average of that variable. Indicator variables were set equal to one if the one-year measure is equal to one during the current or two prior years and set equal to zero otherwise.


all continuous variables are winsorized at the 1 and 99 percentiles, and we cluster standard errors

by firm (Petersen 2009).

4 Empirical results

4.1 Sample selection and descriptive statistics

The sample consists of firm-year observations with the necessary financial data from

COMPUSTAT and political corruption data from annual PIN reports over the period of 2007 to

2015. The sample begins in 2007 because FIN 48 became effective for fiscal years beginning

after December 15, 2006 (FASB 2006). Additionally, the sample ends in 2015 because our hand

collection of PIN corruption convictions extends through that year. For each firm-year

observation, data from COMPUSTAT and the PIN reports are merged together by matching the

zip code of a given firm’s headquarters location with the corresponding federal judicial district.10

We then match state-level GDP data to each firm’s headquarters location by year. We restrict our

sample to firms with a recorded FIN 48 tax reserve balance in COMPUSTAT. The final sample

consists of 16,125 firm-year observations. Figure 1 presents the sample composition using the

Fama-French 12 industry classification and figure 2 presents the sample composition by year.

In Table 1, we report descriptive statistics of the variables used in our regression model

(equation (1)) for our sample. The mean value of our variable of interest, Corruption, is equal to

0.306. This amount indicates an average of approximately 1.2 PIN convictions for every 400,000

people per district, per year. Additionally, the mean value of Log UTB, our primary measure of

10 Because COMPUSTAT only provides a firm’s current headquarters location (Heider and Ljungqvist 2015), we manually collect each firm’s historical business address from its 10-K filings to identify its historical headquarters location.


tax aggressiveness, is 1.719, which corresponds to an average balance of $50.7 million in the tax

reserve account.

Table 2 presents a univariate correlation matrix for our regression variables. Pearson

(Spearman) correlations are provided above (below) the diagonal. We note that our measure of

political corruption is significantly correlated with 10 out of 14 control variables. The

documented correlations further justify the inclusion of these variables as controls in our

multivariate tests, as it ensures that Corruption is not simply capturing variation in firm-level

characteristics that are common across all firms located in the same judicial district. We do not,

however, observe a clear association between political corruption and tax aggressiveness at the

univariate level. The reported Pearson correlation between these two variables is negative and

statistically significant, while the Spearman correlation is insignificant. Accordingly, we next

examine this relationship in a multivariate setting.

4.2 Main results

We present the results of our regression model (equation (1)) in Table 3. Column 1

contains the results of our model without the inclusion of the full set of control variables. We

find that β1, our coefficient of interest, is positive and statistically significant (t-statistic = 3.99)

at the one-percent level. In column 2, we include the full set of control variables in the model and

find that β1 remains positive and statistically significant (t-statistic = 4.05) at the one-percent

level. In terms of economic significance, the coefficient estimate of β1 in column 2 indicates that

a one standard deviation increase in Corruption is associated with a 4.05 percent increase in Log

UTB. The findings presented in Table 3 suggest that firms take more aggressive tax positions, as

measured by FIN 48 tax reserve balances, when their headquarters are located in more politically

corrupt districts. This evidence supports H1b, as opposed to H1a, and is consistent with the claim


that firms’ surrounding political environments influence their tax policy choices. Managers of

firms headquartered in federal judicial districts with more political corruption assign less

reputational costs to engaging in tax aggressiveness because this behavior is less likely to be

viewed as inappropriate based on the information conveyed in their political environments.

The estimated coefficients of the control variables are largely consistent with prior

research (Klassen et al. 2016; Lisowsky et al. 2013; Rego and Wilson 2012). We find that

Foreign Income, Intangibles, ΔNOL, Size, and Log GDP are positively and significantly

associated with the tax reserve balance On the other hand, Pretax ROA is negatively associated

with Log UTB. We note that the remaining control variables are statistically insignificant.

However, if a firm has a relatively time-invariant policy regarding one of these control variables,

then it will be included in the firm fixed effect. We also note that the adjusted R2 of our model is

92 percent. We attribute this high level of explanatory power to the inclusion of firm-level fixed

effects rather than industry-level fixed effects, although results are similar using industry and

district level fixed effects rather than firm-level fixed effects. Overall, the evidence provided in

Table 3 is consistent with firms being more tax aggressive when their headquarters are located in

more politically corrupt federal judicial districts.

5 Supplemental analysis

5.1 Alternative tax aggressiveness proxies

In our main analysis, we empirically measure corporate tax aggressiveness as the ending

balance of firms’ FIN 48 tax reserve. However, prior research indicates that managers enjoy

wide latitude in applying the standard for determining additions to the reserve (De Simone et al

2014). Additionally, there are other meaningful proxies of tax aggressiveness that have been


employed in prior research. We seek to validate our main findings presented in Table 3, that

firms headquartered in politically corrupt judicial districts take more aggressive tax positions, by

repeating our empirical analysis using different proxies for tax aggressiveness. Our first

alternative measure of tax aggressiveness is based on the number of firm subsidiaries located in

identified tax havens (Lisowsky 2010; Dyreng and Lindsey 2009). Hasan et al. (2017) discuss

the widespread concern among policy makers and government agencies that U.S. companies use

offshore tax haven subsidiaries to avoid paying taxes in the U.S. For our second alternative tax

aggressiveness proxy, we use firms’ tax shelter prediction score (Wilson 2009). The U.S.

Department of the Treasury (1999) indicates that tax shelters represent the most aggressive type

of tax planning because they serve little or no legitimate business purpose. For this analysis, we

estimate the following linear probability model for firm i at time t:

Tax Aggi,t = β1Corruptionj,t + β2Equity Incomei,t + β3Foreign Incomei,t +

β4Intangiblesi,t + β5Leveragei,t + β6MTBi,t + β7NOLi,t + β8 ΔNOLi,t +

Β9PP&Ei,t + β10R&Di,t + β11Pretax ROAi,t + β12Sizei,t +

β13Cash Holdingsi,t + β14CAPEXi,t + β15Log GDPk,t + !"#! + !"!! +

εi,j,k,t (2)

The dependent variable, Tax Agg, is alternatively measured as Tax Haven and Shelter in

equation (2). Tax Haven is an indicator variable set equal to one if a firm has a subsidiary located

in a known tax haven jurisdiction and is set equal to zero otherwise (Dyreng and Lindsey 2009).

Tax haven information is publicly available in Exhibit 21 of firms’ 10-k reports. For our

analysis, we base our tax haven measure on data provided on Dr. Scott Dyreng’s website.11

Shelter is an indicator variable set equal to one if a firm’s estimated sheltering probability ranks 11 Refer to https://sites.google.com/site/scottdyreng/Home/data-and-code.


in the top quintile for a given year and is set equal to zero otherwise. Each firms’ tax shelter

prediction score is computed based on the Wilson (2009) model. All other variables are as

previously defined and are computed using three-year trailing averages, consistent with our main

empirical analysis.

The results of this supplemental analysis are presented in Table 4. In column 1, we report

the results using Tax Haven as our measure of corporate tax aggressiveness. We find that our

coefficient of interest, β1, is positive and statistically significant (t-statistic = 2.17) at the five

percent level, suggesting that firms headquartered in politically corrupt judicial districts are more

likely to report subsidiaries in tax haven jurisdictions. The results using Shelter as our alternative

tax aggressiveness proxy are reported in column 2. We similarly find that β1 is positive and

statistically significant (t-statistic = 1.99) at the five percent level, indicating that firms

headquartered in politically corrupt judicial districts have a higher predicted probability of

engaging in tax sheltering. The findings documented in Table 4 are consistent with the results

presented in our main analysis and provide corroborating evidence that firms headquartered in

politically corrupt judicial districts engage in more tax aggressiveness than other firms.

5.2 The effect of financial reporting incentives on firms’ FIN 48 tax reserve

As is discussed in Hanlon and Heitzman (2010), firms’ FIN 48 tax reserve balance can be

driven by two underlying factors. The first factor relates to taxes. Namely, a higher tax reserve

balance represents more uncertainty in a firm’s tax positions, which is indicative of tax

aggressiveness. However, the FIN 48 tax reserve can also be driven by firms’ financial reporting

incentives. The tax reserve is an accounting accrual that is based on management’s judgment,

and as result, it is vulnerable to earnings management. Accordingly, we conduct a series of

supplemental tests to confirm that our main finding, that there is a positive association between


political corruption and firms’ FIN 48 tax reserve, is not due to financial reporting

aggressiveness. First, we repeat our primary empirical analysis with an additional control for

firms’ financial reporting aggressiveness. In particular, we add firms’ performance-matched

discretionary accruals, Discretionary Accruals, (Kothari et al. 2005) to equation (1). The results

of our revised regression model are presented in Column 1 of Table 5. We find that the

coefficient estimate of β1 remains positive and statistically significant (t-statistic = 3.64) at the

one-percent level after controlling for firms’ financial reporting aggressiveness.

As a second test, we follow Klassen et al. (2016) and perform a falsification test, utilizing

firms’ performance-matched discretionary accruals as the dependent variable in equation (1)

instead of the ending balance of their FIN 48 tax reserve. If we observe a positive association

between Discretionary Accruals and Corruption, then it would be consistent firm financial

reporting incentives, as opposed to tax policy incentives, driving our findings. The results of the

falsification test are presented in column 2 of Table 5. The coefficient estimate of β1 is not

statistically significant (t-statistic = 0.17). This test fails to provide evidence that political

corruption is associated with firms’ financial reporting aggressiveness. Overall, the results of

these supplement tests provide additional support to our findings that political corruption

influences firms’ willingness to take aggressive tax positions.

6 Conclusion

In this study, we examine the association between political corruption and corporate tax

aggressiveness. Prior research documents that firms make disclosure and financing decisions in

order to shield their assets from expropriation by corrupt public officials (Smith 2016; Durnev

and Fauver 2011; Stulz 2005). Corporations may tend to adopt less aggressive tax positions in an


effort to protect themselves from political extortion and bribes, suggesting that firms

headquartered in corrupt political districts may engage in less tax aggressiveness than other

firms. On the other hand, evidence from another stream of literature (Hasan et al. 2017; Hoi et al.

2016) indicates that social environments and corporate culture influence firms’ tax policy

decisions through the perceived reputational costs associated with tax aggressiveness. If firms

headquartered in corrupt political districts view tax aggressiveness as being more acceptable

based on the information conveyed in their surrounding political environment, then they may

engage in more tax aggressiveness than other firms. Our results are consistent with the second

explanation. Namely, firms headquartered in more politically corrupt judicial districts report

larger FIN 48 tax reserve balances.

We corroborate our findings through a series of supplemental analyses. First we perform

our main empirical test with alternative proxies for tax aggressiveness, based on the reporting of

subsidiaries in identified tax haven jurisdictions and firms’ tax shelter probability score, and we

find similar results. Second, we examine whether the documented association between political

corruption and FIN 48 tax reserves is due to financial reporting incentives as opposed to tax

aggressiveness. We find that our results still hold after controlling for firms’ financial reporting

aggressiveness. We also perform a falsification test and do not observe a significant association

between political corruption and financial reporting aggressiveness, providing additional support

to our findings that political corruption influences firms’ willingness to take aggressive tax

positions.

Our study extends the tax literature that examines the determinants of firms’ tax

aggressiveness by documenting political corruption as a previously unidentified determinant. We

also provide incremental evidence on the impact of reputational effects on firms’ tax policy


decisions. In particular, our results suggest that political corruption influences the political

environment surrounding firms, which impacts the perceived reputational costs associated with

taking aggressiveness tax positions. Our study also expands our understanding of how political

corruption influences firms’ accounting decisions. We look forward to future research that

examines the impact that political corruption has on other corporate accounting choices.


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Appendix A: Variable definitions

Variable Definition Dependent Variables Log UTB Natural log of (1 + the ending balance of FIN 48 unrecognized tax benefit

(TXTUBEND)). Source: Compustat Tax Haven An indicator variable set equal to one for firms that have a subsidiary located

in a known tax haven jurisdiction and is set equal to zero otherwise. Known tax havens are provided from Dyreng and Lindsey 2009.

Shelter An indicator variable set equal to one if the firm’s estimated sheltering probability based on the Wilson [2009] model ranks in the top quintile of the corresponding distribution in a given year and is set equal to zero otherwise.

Corruption Measure Corruption(t-2, t-1, t) Three year trailing average of the number of corruption convictions from the

Department of Justice’s Public Integrity Section scaled by population as reported by the US Census Bureau.

Control Variables Equity Income Ratio of Equity Income in Earnings to Lagged Total Assets (ESUB/AT(t-1)).

Source: Compustat Foreign Income Ratio of the absolute value of Foreign Pretax Income to Total Assets

(abs(PIFO)/AT). If a firm is missing PIFO, it is set to 0. Source: Compustat Intangibles Ratio of Total Intangible Assets to Total Assets (INTAN/AT). If the firm has a

missing value for INTAN, it is set to 0. Source: Compustat Leverage Ratio of the sum of Total Long Term to Lagged Total Assets. ((DLTT /AT(t-

1))). If the firm has a missing value for DLTT, it is set to 0. Source: Compustat MTB Ratio of the market value of equity to book value of equity

(CSHO*PRCC_F/CEQ). Source: Compustat NOL An indicator variable that is equal to 1 if TLCF is not missing and not equal to

0. Source: Compustat ΔNOL Change in loss carry forward for a firm in given year, scaled by Lagged Total

Assets (TLCF/AT(t-1)) Source: Compustat PP&E Ratio of Total Property, Plant, and Equipment to Total Assets (PPEGT/AT).

Source: Compustat R&D Ratio of R&D Expense to Assets (XRD/AT). If the firm has a missing value

for XRD, it is set to 0. Source: Compustat Pretax ROA Ratio of Pretax Income to Total Assets (PI/AT). Source: Compustat Size The natural logarithm of Total Assets (log(AT)). Source: Compustat Cash Holdings Ratio of Cash and Short-Term Investments to Total Assets (CHE/AT). Source:

Compustat CAPEX Ratio of Capital Expenditures to Total Property, Plant, and Equipment

(CAPX/PPEGT). If the firm has a missing value for CAPX, it is set to 0. Source: Compustat

Log GDP Natural logarithm of the state level per capita GDP. Source: Bureau of Economic Analysis.

Discretionary Accruals Performance-adjusted modified cross-sectional Jones model discretionary accruals (Kothari, Leone, and Wasley 2005)


Figure 1 – Sample Composition by Industry

This figure shows our sample composition using the Fama-French 12 industry classification.


Figure 2 – Sample Composition by Year

This figure displays our sample composition by year.


Table 1 - Descriptive Statistics

Variable N Mean Std Dev p25 p50 p75 Corruption(t-2, t-1, t) 16,125 0.306 0.275 0.141 0.245 0.391 Log UTB 16,125 1.719 1.713 0.141 1.271 2.815 Tax Haven 16,125 0.531 0.499 0.000 1.000 1.000 Shelter 13,635 0.228 0.420 0.000 0.000 0.000 Discretionary Accruals 15,063 0.124 0.131 0.051 0.084 0.141 Equity Income 16,125 0.001 0.004 0.000 0.000 0.000 Foreign Income 16,125 0.020 0.033 0.000 0.003 0.028 Intangibles 16,125 0.181 0.194 0.017 0.114 0.293 Leverage 16,125 0.201 0.243 0.004 0.129 0.305 MTB 16,125 2.866 5.385 1.190 1.957 3.397 NOL 16,125 0.635 0.481 0.000 1.000 1.000 ΔNOL 16,125 0.779 2.800 0.000 0.030 0.257 PP&E 16,125 0.481 0.395 0.173 0.364 0.710 R&D 16,125 0.061 0.125 0.000 0.004 0.069 Pretax ROA 16,125 -0.039 0.387 -0.044 0.042 0.098 Size 16,125 6.272 2.135 4.940 6.334 7.684 Cash Holdings 16,125 0.214 0.208 0.055 0.143 0.309 CAPEX 16,125 0.113 0.082 0.057 0.091 0.146 Log GDP 16,125 10.825 0.141 10.728 10.854 10.911 This table provides descriptive statistics for the sample of firms with a reported FIN 48 tax reserve balance from 2007-2015. All variables are defined in Appendix A. Continuous variables are winsorized at the 1st and 99th percentile by year.


Table 2 – Univariate Correlations

Variable (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17) (18) (19) Corruption

-0.038* -0.000 0.002 -0.037* 0.059* -0.032* 0.047* 0.129* -0.021* -0.030* -0.057* 0.060* -0.198* 0.071* 0.089* -0.171* -0.019 -0.069* Log UTB

-0.002

0.480* 0.501* -0.241* 0.205* 0.436* 0.258* 0.223* 0.156* 0.048* -0.105* -0.023* -0.014 0.268* 0.710* -0.070* 0.024* 0.002 Tax Haven

0.005 0.452*

0.342* -0.135* 0.125* 0.552* 0.194* 0.066* 0.079* 0.107* -0.012 -0.073* 0.087* 0.171* 0.408* 0.015 0.023* 0.027* Shelter

0.004 0.570* 0.342*

-0.122* 0.183* 0.402* 0.141* 0.116* 0.148* 0.072* -0.034* -0.018 0.017 0.223* 0.505* -0.050* 0.008 0.012 Discr. Accruals

-0.034* -0.219* -0.171* -0.074*

-0.128* -0.105* -0.177* -0.155* 0.017 0.058* 0.225* -0.144* 0.278* -0.294* -0.391* 0.271* 0.044* 0.091* Equity Income

0.036* 0.166* 0.085* 0.118* -0.070*

0.102* 0.067* 0.146* 0.016 -0.048* -0.112* 0.079* -0.127* 0.164* 0.276* -0.182* -0.041* -0.049* Foreign Income

-0.014 0.332* 0.383* 0.309* -0.036* 0.071*

0.208* -0.022* 0.122* 0.124* 0.030* -0.048* 0.204* 0.195* 0.287* 0.091* 0.045* 0.095* Intangibles

0.031* 0.182* 0.121* 0.109* -0.122* 0.003 0.011

0.253* 0.075* 0.105* -0.006 -0.293* -0.020 0.120* 0.266* -0.249* 0.095* 0.028* Leverage

0.068* 0.128* 0.005 0.034* -0.005 0.041* -0.076* 0.215*

-0.041* 0.054* -0.019 0.239* -0.298* -0.018 0.432* -0.505* -0.074* -0.144* MTB

0.007 0.034* 0.001 0.042* 0.056* 0.003 0.049* -0.001 -0.026*

0.006 0.003 -0.099* 0.185* 0.309* 0.096* 0.210* 0.234* 0.070* NOL

-0.030* 0.031* 0.107* 0.072* 0.072* -0.061* 0.070* 0.092* 0.055* 0.041*

0.808* -0.045* 0.136* -0.197* -0.005 0.056* 0.004 0.044* ΔNOL -0.034* -0.170* -0.141* -0.057* 0.527* -0.072* -0.069* -0.092* 0.024* 0.072* 0.204*

-0.060* 0.299* -0.413* -0.259* 0.210* -0.021* 0.099*

PP&E 0.016 -0.047* -0.116* -0.041* -0.065* 0.052* -0.049* -0.332* 0.188* -0.066* -0.042* 0.017

-0.228* 0.016 0.065* -0.349* -0.324* -0.220*

R&D -0.111* -0.124* -0.083* -0.048* 0.458* -0.092* 0.006 -0.148* -0.092* 0.069* 0.116* 0.537* -0.114*

-0.261* -0.343* 0.531* 0.030* 0.220*

Pretax ROA 0.043* 0.207* 0.178* 0.108* -0.593* 0.091* 0.090* 0.078* -0.056* 0.040* -0.111* -0.649* -0.041* -0.549*

0.366* -0.121* 0.151* -0.100*

Size 0.072* 0.726* 0.411* 0.490* -0.449* 0.182* 0.194* 0.211* 0.234* -0.001 -0.020 -0.412* 0.032* -0.408* 0.437*

-0.358* 0.060* -0.107*

Cash Holdings -0.111* -0.134* -0.058* -0.078* 0.275* -0.130* 0.037* -0.300* -0.297* 0.136* 0.057* 0.242* -0.342* 0.545* -0.195* -0.361* 0.161* 0.248*

CAPEX 0.001 -0.046* -0.028* -0.039* 0.057* -0.045* 0.026* 0.022* -0.041* 0.115* 0.013 -0.047* -0.266* 0.011 0.049* -0.002 0.192* 0.080*

Log GDP -0.027* 0.021* 0.028* 0.015 0.062* -0.024* 0.067* 0.034* -0.088* 0.051* 0.037* 0.051* -0.192* 0.152* -0.041* -0.073* 0.217* 0.081*

This table provides correlations for the sample of firms with a reported FIN 48 tax reserve balance from 2007-2015. Pearson (Spearman) Correlations are presented below (above) the diagonal. Correlations marked with * are significant at the 1% level. All variables are defined in Appendix A. Continuous variables are winsorized at the 1st and 99th percentile by year.


Table 3 – Political Corruption and Tax Aggressiveness

Dependent Variable: Log UTB

(1) (2)

Corruption(t-2, t-1, t) 0.267808*** 0.253450***

(3.99) (4.05)

Equity Income 0.941803 (0.23) Foreign Income 1.499838*** (2.62) Intangibles 0.286998* (1.80) Leverage 0.016573 (0.21) MTB -0.002574 (-1.51) NOL -0.007545 (-0.32) ΔNOL 0.027229*** (5.02) PP&E 0.049607 (0.62) R&D 0.002742 (0.02) Pretax ROA -0.100700** (-1.96) Size 0.373059*** (10.90) Cash Holdings -0.007145 (-0.05) CAPEX -0.228938 (-0.86) Log GDP 0.253450*** (4.05) Fixed Effects Firm & Year Firm & Year Cluster SE Firm Firm Adj R2 0.918 0.922 N 16,125 16,125 This table presents results on whether political corruption has a significant effect on the ending balance of firms’ FIN 48 tax reserve account. The sample is restricted to firm-year observations where the firm reports a FIN 48 tax reserve balance. Corruption is matched on the firm’s headquarter zip code and is calculated as the 3 year moving average of per capita convictions reported by the Department of Justice. We cluster standard errors by firm. *, **, and *** represent significance at 10%, 5%, and 1%, respectively (two-tailed). T-values are presented beneath the coefficient estimates in parentheses.


Table 4 – Political Corruption and the Use of Tax Havens and Shelters

Dependent Variables:

Tax Haven Shelter

(1) (2) Corruption(t-2, t-1, t) 0.029932** 0.049676**

(2.17) (1.99)

Equity Income 0.691913 -0.725038 (1.29) (-0.54) Foreign Income 0.090749 0.589214** (0.50) (2.05) Intangibles -0.009379 -0.136479** (-0.19) (-1.96) Leverage -0.013738 -0.056483* (-0.81) (-1.86) MTB 0.000197 -0.000108 (0.57) (-0.12) NOL 0.009276 0.082012*** (1.28) (7.55) ΔNOL 0.000529 0.005118* (0.46) (1.66) PP&E -0.020038 -0.033569 (-1.11) (-0.90) R&D 0.005328 0.253350*** (0.11) (2.68) Pretax ROA -0.021068** 0.091408*** (-2.15) (3.42) Size 0.039867*** 0.040045*** (3.75) (3.03) Cash Holdings -0.045030 -0.019678 (-1.05) (-0.31) CAPEX -0.075242 -0.194921** (-1.03) (-1.98) Log GDP 0.029932** 0.049676** (2.17) (1.99)

Fixed Effects Firm & Year Firm & Year Cluster SE Firm Firm Adj R2 0.929 0.561 N 16,111 13,635 This table presents results on whether political corruption has a significant effect on other forms of tax aggressiveness. Column (1) examines the likelihood of firms establishing subsidiaries in tax haven jurisdictions as provided in Dyreng and Lindsey (2009). Corruption is matched on the firm’s headquarter zip code and is calculated as the 3 year moving average of per capita convictions reported by the Department of Justice. All controls variables are also 3 year moving averages. We cluster standard errors by firm. *, **, and *** represent significance at 10%, 5%, and 1%, respectively (two-tailed). T-values are presented beneath the coefficient estimates in parentheses.


Table 5 –The Effect of Financial Reporting Incentives on FIN 48 Tax Reserves

Dependent Variables

Log UTB Discretionary Accruals

(1) (2)

Corruption(t-2, t-1, t) 0.233094*** 0.001281

(3.64) (0.17)

Discretionary Accruals 0.018547 (0.24) Equity Income 1.316060 0.392198 (0.29) (0.69) Foreign Income 1.502301*** 0.354192*** (2.66) (3.05) Intangibles 0.160699 -0.023961 (0.98) (-0.73) Leverage -0.001365 0.026541* (-0.02) (1.68) MTB -0.002294 -0.000046 (-1.31) (-0.15) NOL -0.009692 -0.002048 (-0.40) (-0.56) ΔNOL 0.031743*** 0.009618*** (5.38) (4.12) PP&E 0.030232 -0.019605 (0.40) (-1.22) R&D 0.031397 0.002370 (0.17) (0.03) Pretax ROA -0.088181* -0.084087*** (-1.87) (-5.71) Size 0.394665*** -0.022673*** (11.18) (-2.93) Cash Holdings -0.144216 0.010784 (-0.95) (0.38) CAPEX -0.047994 0.028336 (-0.18) (0.84) Log GDP 0.233094*** 0.001281 (3.64) (0.17) Fixed Effects Firm & Year Firm & Year Cluster SE Firm Firm Adj R2 0.924 0.677 N 15,063 15,063 Corruption is matched on the firm’s headquarter zip code and is calculated as the 3 year moving average of per capita convictions reported by the Department of Justice. We cluster standard errors by firm. *, **, and *** represent significance at 10%, 5%, and 1%, respectively (two-tailed). T-values are presented beneath the coefficient estimates in parentheses.