CEO political ideology and financial reporting quality 1 24 ......CEO Political Ideology and Financial Reporting Quality Avishek Bhandari [email protected] Joanna Golden [email protected]

CEO Political Ideology and Financial Reporting Quality

Avishek Bhandari [email protected]

Joanna Golden [email protected]

Maya Thevenot [email protected]

Very preliminary. Please do not cite without permission.

January 2018

ABSTRACT: The study investigates whether CEO political ideology is associated with financial reporting quality. Our evidence indicates that Republican CEOs exhibit lower discretionary accruals and lower likelihoods of beating earnings benchmarks, earnings misstatements and internal control weaknesses, which suggests that Republican CEOs promote higher financial reporting quality, relative to their Democratic and independent counterparts. Additional analyses reveal that firms with Republican CEOs pay lower audit fees, which is consistent with such firms’ perceived lower risk. In sum, our results support Francis et al.’s (2016) risk acceptance hypothesis and imply that the political ideology of CEOs, as captured by their political donations, may have significant influence on their firms’ financial reporting quality. KEYWORDS: CEO political ideology; financial reporting quality; earnings benchmark beating; discretionary accruals; restatements; internal controls; audit fees

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CEO Political Ideology and Financial Reporting Quality

1. INTRODUCTION

The set of values a person holds is often reflected in their personal political ideology, which

in term could influence an individual’s economic behavior (Hutton et al. 2014). Recent studies in

psychology, political science, and finance show that CEOs’ political ideologies influence the level

of financial conservatism (Hutton et al. 2014), investment decisions (Elnahas and Kim 2017), tax

avoidance (e.g., Francis et al. 2016), and corporate political donations (Unsal et al. 2016). These

studies find that Republican CEOs tend to adopt financially conservative policies, choose safer

investments, and shy away from ambiguity, uncertainty, and complexity. Motivated by this

literature, we seek to examine whether Republican CEOs are more likely to maintain a higher

quality of financial reporting.

Individuals with conservative ideology fear the possibility of a loss and value financial

security (Hutton et al. 2014). According to the risk acceptance hypothesis, an individual’s political

preferences can serve as a proxy for his or her risk preferences and Republican CEOs have been

found to be more risk averse than other executives. For example, Kam and Simas (2010) find that

Republicans prefer less risk than Democrats, while Hutton et al. (2016) and Elnahas and Kim

(2017) show that Republican CEOs exhibit more conservative corporate policies and are less likely

to take on risky projects than Democratic CEOs. Following this reasoning, we argue that politically

conservative CEOs may also be more cautious and less aggressive in their financial accounting

reporting. Therefore, we predict that Republican CEOs would maintain higher quality financial

reporting, relative to their Democratic or nonpartisan counterparts.

We consider four important indicators of financial reporting quality: earnings management,

the likelihood of meeting or beating analyst earnings forecasts, the probability of a financial

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statement misstatement, and the likelihood of an internal control weakness. Prior research widely

uses earnings management, proxied by discretionary accruals, to determine the quality of financial

reporting (e.g., Dechow and Dichev 2002). We expect that Republican CEOs are less likely to

engage in the manipulation of earnings. Further, Garrett et al. (2014) and He (2015), among others,

argue that meeting or just beating analyst forecasts, the likelihood of a misstatement and an internal

control deficiency can also capture financial reporting quality. We analyze whether Republican

CEOs’ increased concern with higher quality financial reporting is related to these three factors.

Finally, we explore whether higher financial reporting quality translates into lower perceived risk,

proxied by audit fees.

To determine CEOs’ political affiliation, we follow Francis et al. (2016) and Elnahas and

Kim (2017) and consider CEO political contributions to Republican and Democratic Senate and

House representatives, presidential candidates and party committees in political campaigns. We

acquire CEOs’ political contributions data from the Federal Election Commission (FEC).1 The

FEC website provides information regarding contributions made by specific individuals starting

in 1979. However, since the financial reporting quality data on Audit Analytics became available

in year 2002 and there are very few firm-year observations available before 2003 on FEC, we

search for donations starting with the election cycle of 2003-2004.2 We match the FEC data with

ExecuComp using donor occupations and CEO names. If a given CEO donates to both parties, we

follow prior research (e.g., Francis et al. 2016) and determine the CEO’s party preference based

on the amount.

1 The Federal Election Commission (FEC) data could be found at www.fec.gov. 2 Election cycles start with an odd year and end with an even year. In our study, we have seven election cycles: 2003-

2004, 2005-2006, 2007-2008, 2009-2010, 2011-2012, 2013-2014, and 2015-2016.

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Consistent with our expectations, we find that Republican CEOs engage in less earnings

management and exhibit lower likelihoods of meeting or just beating analyst earnings forecasts,

of financial statement misstatements and internal control weaknesses. This is consistent with

Republican CEOs promoting higher financial reporting quality, relative to their Democratic and

nonpartisan counterparts. Further analysis shows that Republican CEOs exhibit lower risk,

reflected in lower audit fees.

This study makes several significant contributions to the literature. First, we contribute to

the accounting literature by proposing a new factor that may influence firm financial reporting

quality. To the best of our knowledge, this is the first paper that directly examines and provides

interesting insights on the relationship between political preferences of CEOs and financial

reporting quality. Furthermore, this paper shows that Republican CEOs’ demand for higher

financial reporting quality is reflected in lower audit fees, which adds to Frankel et al.’s (2002)

evidence that audit fees are lower for firms with higher financial reporting quality. Similarly,

Abbott et al. (2006) show that audit fees increase with the clients’ risk of upward earnings

management, proxied by positive discretionary accruals. Our results suggest that political

affiliation of firm executives may be another factor auditors consider in setting the price of the

audit.

Second, our study complements a growing stream of research on the effect of political

ideology on corporate decisions (e.g., Francis et al. 2016; Elnahas and Kim 2017). Prior studies

find that CEOs’ political preferences influence corporate investment and financing decisions.

Hutton et al. (2014) show that Republican managers maintain more conservative corporate

policies. DeFond et al. (2016) find that client conservatism is associated with lower audit fees,

fewer going concern opinions, and less auditor resignations, as client conservatism is a critical

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factor in the assessment of engagement risk. Our paper extends this research by investigating

whether CEO conservatism influences financial reporting quality and hence, auditors’ contracting

decisions, an interesting, yet underexplored issue. Our findings indicate that CEOs’ political

preferences may capture various individual attributes of the executive that may affect their

financial reporting decisions. Furthermore, understanding the extent to which CEO traits and

financial reporting quality are related is important to the SEC, investors, and auditors.

Finally, our study also contributes to a growing literature on behavioral consistency. The

behavioral consistency theory suggests that individuals display stable behavioral characters across

various fields, contexts, and circumstances (Hutton et al. 2014). We examine whether the personal

ideologies of CEOs, as proxied by their political orientation, influence financial reporting and audit

quality.

The rest of this paper is organized as follows. Second II discusses the prior literature and

develops the hypotheses. Section III presents the development of our research design. Section IV

provides the sample selection and main empirical results, while Section VI concludes.

2. LITERATURE REVIEW AND HYPOTHESIS DEVELOPMENT

Literature Review

Ideology refers to an individual’s beliefs and attitudes regarding various aspects of society,

institutions, and customers, ranging from ideas about politics and economics to religion (Jost

2006). Research in psychology and political science show that conservative individuals are

sensitive to losses and emphasize financial security (Jost et al. 2003), prefer familiar versus

unfamiliar situations (Glasgow and Cartier 1985), prefer job security rather than task variety

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(Atiech et al. 1987), and are more sensitive to the possibility of a loss (Wilson 1974). Therefore,

political conservatism and attitudes may translate into predictable actions.

A growing strand of literature shows that a CEO’s political ideology significantly affects

numerous corporate policies (e.g. Hutton et al. 2014). An executive’s political ideology has

significant economic consequences at both the individual and firm level. At the personal level,

Chin and Semadeni (2017) document that more liberal CEOs are more likely than their

conservative peers to decrease pay differentials for other top executives. This behavior reflects the

liberal CEOs’ endorsement of equitable pay treatment for other top executives. In addition,

Fremeth et al. (2013) show that individuals change their political behaviors significantly once they

enter the ranks of top management. Specifically, they show that once individuals become CEO of

an S&P 500 firm, they donate more money to political companies. Briscoe et al. (2014) find that

CEOs who are significantly more liberal, as proxied by their publicly observable political donation

patterns, are more open and welcome to social activist groups. This result is consistent with the

view that liberal CEOs are relatively open to changes rather than committed to the preservation of

traditional values.

At the corporate level, prior research shows that managers who lean toward the Republican

Party prefer more conservative corporate policies. For example, Hutton et al. (2014) investigate

whether CEOs’ political ideology influences a firm’s financial conservatism. They find that firms

with Republican CEOs have less corporate debt, spend less on capital and research and

development (R&D), and invest in projects that are less risky, but enjoy higher profitability. Chin

et al. (2013) find that a CEO’s political ideology affects his or her firm’s corporate social

responsibility (CSR) practices. The researchers show that Republican CEOs are less likely to

emphasize CSR than Democratic CEOs, and this association is amplified by a CEO’s relative

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power and financial performance. Republican CEOs are also more conservative related to

investment decisions. For instance, Elnahas and Kim (2017) investigate the association between a

CEOs’ political ideologies and their firms’ investment decisions. They find that Republican CEOs

are less likely to be associated with M&A activities. When there are acquisitions, these CEOs tend

to use cash as the primary method of business acquisition. They tend to acquire publicly traded

companies in the same industry. Finally, CEOs play a significant role in corporate policies, and as

a result, their political orientation influences corporate lobbying activities. Unsal et al. (2016) show

that when firms have Republican CEOs, they are more likely to spend large amounts of money on

lobbying by making more corporate political donations and hiring more lobbyists.

When examining the association between CEOs’ political ideology and firms’ tax policies,

prior research provides conflicting results. Christensen et al. (2015) show that when firms have

Republican top executives, they engage in less tax avoidance than firms with Democratic

executives. This evidence suggests that executives who follow the values of the Republican Party

are more conservative and thus sacrifice their preference for lower taxes. However, Francis et al.

(2016) suggest that Republican CEOs prefer more corporate tax sheltering than Democratic CEOs.

This evidence suggests that a CEO’s political ideology influences tax sheltering decisions. The

researchers also find that when Democratic CEOs’ stock-based incentives are high, they are more

likely to practice corporate tax sheltering. Therefore, the relation between political ideology and

financial reporting quality is an empirical question worthy of exploration.

Hypothesis Development

Gallup Poll, as well as research in psychology and political science, provide strong

evidence that conservative individuals tend to favor the core values of the Republican Party,

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whereas the Democratic Party is self-identified as more liberal (e.g., Levitt 1996; Goren 1997).

Furthermore, a conservative person has a conservative psychological preference (e.g., Carney et

al. 2008). For example, conservative individuals tend to prefer the status quo, while liberals are

more willing to seek changes and new experiences (Jost et al. 2003). The evidence from

psychology shows that individuals with a conservative ideology value familiar over unfamiliar

stimuli (Glasgow and Cartier 1985), prefer job security over task variety (Kish et al. 1973), and

strongly dislike uncertainty and complication (Wilson 1973). In addition, conservative individuals

are less open to adopting unconventional views or experiences (Jost and Thompson 2000),

engaging in sensation-seeking behavior (Kish 1973), and making major changes in their personal

lives (Feather 1970). According to the behavioral consistency theory, individuals maintain their

behavioral characters across different fields, contexts, and circumstances. Therefore, political

conservatism may imply conservative psychological inclination that prompts certain predictable

economic decisions (Hutton et al. 2014).

Consistently, prior research shows that Republicans CEOs apply their political

conservatism into conservative attitudes in their corporate policies (e.g., Elnahas and Kim 2017).

Additionally, as individuals become CEOs, their political behaviors strengthens (Fremeth et al.

2013). The risk acceptance hypothesis suggests that Republican CEOs are more conservative in

their financial decisions due to their conservative personality traits (Francis et al. 2016). For

example, Hutton et al. (2014) show that firms with Republican CEOs tend to have more

conservative corporate policies, as reflected by lower leverage ratios, lower capital and R&D

expenditures, less risky investments, and greater profitability. As such, management style could

be reasonably predicted by financial conservatism, as revealed though political preferences

(Hutton et al. 2014). Individuals that lean toward the Republican Party are likely to display

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conservative personality traits and adopt financially conservative policies. Republican-leaning

managers tend to be more risk-averse and conservative in financial reporting decisions, and less

likely to engage in opportunistic behavior. As a result, we predict that Republican CEOs who have

conservative attributes are more cautious and less aggressive in their financial reporting and hence,

are more likely to produce higher quality financial reporting.

We study the impact of CEO political ideology on high quality financial reporting using

the following four indicators: accrual quality and the likelihoods of meeting or just beating analyst

earnings forecasts, having a misstatement, or an internal control weakness. We adopt these

constructs to measure financial reporting quality because they are widely used in prior literature

(Cao et al. 2012; Bruynseels and Cardinaels 2014; Garrett et al. 2014). We discuss these constructs

and how they may be related to political ideology in the following paragraphs.

Accounting standards allow managers to use their own discretion to a degree; however,

managers may opportunistically engage in accrual earnings management to meet earnings targets

or avoid negative earnings. By doing this, managers increase their wealth because compensation

is often directly tied to earnings-related factors. Prior research shows that managers increase their

personal wealth by engaging in accrual-based earnings management (e.g., Collins and Hribar

2000). However, such practices have been found to lower financial reporting quality (Dechow and

Dichev 2002). We predict that Republican CEOs are more likely to demand higher accrual quality;

hence, they are less likely to engage in earnings management because they are more risk-averse

and cautious with respect to financial reporting quality. Thus, we expect a negative association

between Republican CEOs and earnings management.

H1: The magnitude of earnings management is lower with Republican CEOs than with

Democratic CEOs.

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We use earnings target beating as our additional indicator of financial reporting quality. A

stream of prior literature suggests that managers are likely to manage earnings when firms are just

below analyst earnings expectations, which then leads to meeting or just beating analyst forecasts

(e.g., Dhaliwal et al. 2004; McVay et al. 2006). In addition, based on a large body of literature,

Dechow et al. (2011) argue that comparing to the other target beating, such as small profits and

small loss avoidance, meeting or beating analyst earnings forecasts provides a better indication of

financial reporting quality. As discussed in the previous paragraphs, Republican CEOs are more

conservative in their financial decisions; therefore, they are more committed to higher financial

reporting quality and are less likely to resort to earnings management. Therefore, we predict that

Republican CEOs are less likely to meet or just beat analyst forecasts.

H2: The likelihood of meeting or just beating analyst forecasts is lower with Republican

CEOs than with Democratic CEOs.

We use the probability of a financial statement misstatement as our third indicator of

financial reporting quality. A financial misstatement occurs when there is a misapplication of the

Generally Accepted Accounting Principles (GAAP), in which case the financial statements were

not correct when issued (Hennes et al. 2008). Prior studies show that financial misstatements that

result in a restatement are associated with higher CEO/CFO turnover (e.g. Desai et al. 2006).

Managers of restatement firms are more likely to have lower compensation, reduced credibility,

and loss of employment, among other consequences (e.g., Hennes et al. 2008). Republican CEOs

are conservative individuals who value job security and are sensitive to the possibility of a loss.

Therefore, conservative behaviors make Republican CEOs more cautious about financial

reporting, resulting in a lower probability of financial misstatements. We conjecture that

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Republican CEOs are less likely to misstate, as this type of financial reporting problem is against

their personality traits.

H3: The probability of a misstatement is lower with Republican CEOs than with

Democratic CEOs.

We use the probability of an internal control deficiency as our final indicator of financial

reporting quality. An internal control deficiency indicates that there is a reasonable possibility that

a firm’s internal control will not prevent or detect a material misstatement in financial reporting.

In other words, ineffective internal controls reduce the reliability of a company’s financial

reporting, thus lowering financial reporting quality (Costello and Whittenberg-Moerman 2011).

Similarly, Doyle et al. (2007) indicate that effective internal controls indicate a higher quality of

financial reporting. Sections 302 and 404 of the Sarbanes-Oxley Act of 2002 require executives to

certify the adequacy of internal controls over financial reporting. Since conservative individuals

value job security (Atich et al. 1987), Republican CEOs are unlikely to jeopardize their job by

allowing internal control weaknesses to exist. In addition, Eilifsen and Messer (2000) show that

the presence of an internal control weakness increases the risk of financial restatement. Therefore,

we predict that Republican CEOs are less likely to have internal control problems.

H4: The probability of an internal control weakness is lower with Republican CEOs than

with Democratic CEOs.

The above arguments suggest that Republican CEOs have incentives to provide high

quality financial reporting. However, prior research shows that a risk-averse manager may be

motivated to smooth his or her firm’s reported income (e.g., Dye 1988) and hence, potentially lead

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to more earnings management. Republican-leaning managers may also be more likely to avoid

earnings decreases and the uncertainty created by missing an earnings forecast. Further, Francis et

al. (2016) show that Republican CEOs engage in more tax avoidance. Thus, the relation between

CEO political ideology and financial reporting quality is an empirical question that we attempt to

answer in this study.

3. RESEARCH DESIGN

CEO Political Ideology Measure

We follow Hong and Kostovetsky (2012), Hutton et al. (2014), Unsale et al. (2016), and

Elnahas and Kim (2017) in determining CEO political affiliation by considering CEOs’

contributions to Republican and Democratic senate, house, presidential candidates and party

committees in political campaigns. We acquire CEO political contributions data from the FEC’s

website.3 The FEC provides information about donor and donee identities, contribution amounts,

and statistics for aggregated contributions data. An individual can make contributions directly

through personal contributions to candidates or party committees. Alternatively, he/she can

indirectly contribute through his/her own company’s political action committees (PACs). PACs

simultaneously make contributions to multiple parties (Cooper et al. 2010). Therefore, we consider

the first form of contributions because it helps us to identify the political orientations of a CEO.

To construct the CEO political contribution data, we first obtain detailed CEO information from

ExecuComp and then match it to the FEC data using donors’ occupations and names, following

prior literature (e.g., Hutton et al. 2014; Francis et al. 2016).

3 The Federal Election Commission (FEC) data could be found at www.fec.gov.

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We construct two measures of CEO political ideology. First, we follow Hong and

Kostovetsky (2012) and use indictor variables for Republican CEO and Democratic CEOs. The

Republican indicator variable (REP_DUM) equals one if a CEO made more donations to a

Republican than a Democrat during his or her tenure as a CEO, and zero otherwise. The

Democratic CEOs indicator variable (DEM_DUM) is defined analogously to take the value of one

if the CEO made more donations to a Democrat than a Republican, and zero otherwise. Second,

we also calculate a continuous measure of political affiliation. Specifically, we take CEO donations

to the Republican party minus donations to the Democratic party, divided by the total donation

during an election cycle (RELREPIND). This measure captures the relative strength of CEO

support of Republican relative to their support of Democratic causes in a given election cycle.

Unlike REP_DUM and DEM_DUM, RELREPIND varies over time as the amount of CEO

donations changes across election cycles.

Empirical Models

Our first metric for financial reporting quality is accrual quality as proposed by Dechow

and Dichev (2002) and modified by McNichols (2002). This proxy measures how well accruals

map into cash flows of the firm (Dechow and Dichev 2002). The model, which is discussed in

detail in Appendix B, regresses the change in working capital accruals on prior, current and future

cash flows, property, plant and equipment and the change in revenues. Then, we take the standard

deviation of the residuals from this model estimated over the past five years to obtain yearly firm

measures of accrual quality, DD.

The second measure of financial reporting quality is the probability of meeting or beating

analyst expectations. Following He (2015), we define an indicator variable, ANASUR, equal to one

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if the firm meets or beats by up to one percent the median consensus forecast as reported in I/B/E/S,

and zero otherwise. The third proxy for financial reporting quality is the probability of a

misstatement, RESTATE. We use two proxies for RESTATE – an ex ante measure following

Dechow et al. (2011) and an ex post metric, defined using an indicator variable equal to one if the

current financial statements were restated, and zero otherwise. The calculation of the ex ante

measure, referred to as an F-score, entails obtaining a continuous probability of a misstatement

using certain firm characteristics and the detailed procedure is provided in Appendix B.

The final proxy for financial reporting quality, IC, is equal to one if a general (systemic)

material weakness in internal controls is reported, and zero otherwise. Similar to Garrett et al.

(2014), we consider a material weakness in internal controls when reported either under SOX 302

or SOX 404.

Following prior research, we define various control variables that have been found to affect

firm financial reporting quality. These variables include firm size, SIZE, leverage, LEVERAGE,

return on assets, ROA, the proportion of loss years over the past 10 years, LOSS_PROP, foreign

operations, FOROPS, market-to-book ratio, MB, sales growth, SLAEGR, cash flow volatility,

STDCASH, sales volatility, STDSALE, and stock return volatility, STDRET (e.g., Hribar and

Nichols 2007; Zhao and Chen 2008; Ayer et al. 2011; Garrett, Hoitash and Prawitt 2014; He 2015).

In addition, we also control for corporate governance characteristics because Klein (2002) shows

an association between corporate governance and earnings management, although Larcker,

Richardson, and Tuna (2007) provide mixed results on this topic. We control for Big 4 auditors,

BIG4, shareholder rights, EINDEX, the proportion of nonpartisan directors, PCTINDEP, CEO

duality, CEOCHAIR, CEO age, CEOAGE, CEO gender, CEOGENDER, CEO tenure,

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LNTENURE, and CEO compensation, LNSALARY (e.g., Ayer et al. 2011). All variables are

defined in Appendix A.

To test H1 through H3, we estimate the following regression model:

FQi,t = β0 + β1 CEO_PIDEOLOGYi,t + β2 SIZEi,t + β3 LEVERAGEi,t + β4 ROAi,t +

β5 LOSS_PROPi,t + β6 FOROPSi,t + β7 MBi,t + β8 SALEGRi,t + β9 STDCASHi,t +

β10 STDSALEi,t + β11 STDRETi,t + β12 BIG4i,t + β13 EINDEXi,t + β14 PCTIDEPi,t + (1)

β15 CEOCHAIRi,t + β16 CEOAGEi,t + β17 CEOGENDERi,t +β18 LNTENUREi,t+

β19 LNSALARYi,t +Year fixed effects + Industry fixed effects + ɛi,t ,

where FQ is DD, ANASUR, or RESTATE and the main variable of interest, CEO_PIDEOLOGY,

is either REP_DUM, DEM_DUM or RELREPIND. To minimize the impact of invariant year and

industry characteristics, we include year- and industry-fixed effects. Consistent with our

hypotheses, we expect a negative coefficient on CEO_REPUBLICAN and RELREPIND, which

would suggest that Republican CEOs promote higher quality financial reporting, relative to their

Democratic or nonpartisan counterparts.

To test H4, we follow Bruynseels and Cardinaels (2014), Garrett et al. (2014) and He

(2015) and use the following pooled probit regression model:

ICit = δ0 + δ1 CEO_PIDEOLOGYi,t + δ2 SIZEi,t + δ3 LEVERAGEi,t + δ4 ROAi,t +

δ5 LOSS_PROPi,t + δ6 FOROPSi,t + δ7 MBi,t + δ8 SALEGRi,t + δ9 STDCASHi,t +

δ10STDSALEi,t + δ11STDRETi,t + δ12BIG4i,t + δ13EINDEXi,t + δ14PCTIDEPi,t + (2)

δ15CEOCHAIRi,t + δ16CEOAGEi,t + δ 17 CEOGENDERi,t + δ18LNTENUREi,t +

δ19LNSALARYi,t + δ20RESTRUCTi,t + δ21SEGSi,t + δ22FIRMAGEi,t +

Year fixed effects + ɛi,t ,

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where RESTRUCT is an indicator variable, equal to one if the firm undergoes a major restructuring

or a merger, zero otherwise, SEGS is the number of business segments, FIRMAGE is the natural

logarithm transformation of the number of years a firm is in the CRSP database, and all other

variables are as defined previously and in Appendix A. Similar to Garrett et al. (2014), we consider

a material weakness in internal controls, IC, as reported under SOX 302 and SOX 404.

4. SAMPLE SELECTION AND EMPIRICAL RESUTLS

Sample Selection

Table 1 summarizes our sample selection process. We match the FEC data, which is the

source of CEO political ideology, with ExecuComp using donor occupations to obtain our initial

sample of CEOs. Over the period between years 2003 and 2016, we manually match CEO names

from the ExecuComp database with CEO names from the FEC database.4 After matching the

ExecuComp sample with the FEC database, we form our initial sample of 25,968 firm-year

observations during seven election cycles. We remove firm-years with missing data on

ExecuComp (587), Compustat (5,142), CRSP (1,144), Audit Analytics (623), and ISS–Directors

and Governance database (4,814). Our final sample consists of 13,658 observations.

< Insert Table 1 around here >

Empirical Results

Table 2, Panels A and B present the sample distribution by year and industry, respectively.

The statistics suggest that overall CEO contributions and those by nonpartisan CEOs have

increased slightly over the sample period, while both Republican and Democratic CEO

4 We start our sample with year 2003 because the financial reporting quality data on Audit Analytics became available beginning in year 2002 and there are very few firm-year observations available before 2003 on FEC.

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contributions peaked around year 2011 and then decreased slightly in the following years. Almost

twice as many CEOs contribute to Republican causes than Democratic. Panel B suggests that most

observations come from the Consumer Durables industry, while in the Finance industry, we

observe the closest numbers between Republican and Democratic CEOs at 286 and 244,

respectively. Panel C of Table 2 presents the descriptive statistics pertaining to the variables used

in our empirical analyses. The mean of Republican CEOs for the sample firms is 29.2%, while the

mean of Democratic CEOs for the sample firms is 15.9%, which confirms that more CEOs

contribute to Republican than Democratic causes. In addition, 14.9% of our sample firms have

restatements. The mean (median) log of total assets is 7.820 (7.635), suggesting that our sample

firms are generally large, which is supported by the fact that ExecuComp usually covers larger

firms. On average, the firms report a debt leverage of 51.9% of their total assets and 12.4% of our

sample firms report a loss, while the mean of ROA is 13.8%. Foreign operations are held by 35.5%

of our sample firms and the mean market-to-book ratio is 3.092, suggesting that our sample firms

trade at a premium above book value. The average sales growth of our sample firms is 8.1%. The

mean of Big4 is 0.94, suggesting that a large portion of our sample firms are audited by Big 4

auditors. The descriptive statistics for the corporate governance variables are consistent with those

reported in Bruynseels and Cardinaels (2014). Around 77% of the board members are nonpartisan

directors. On average, 56.11% of the CEOs in our sample also hold the position of the chairman

of the board.


Table 3 presents the estimation results for the earnings management model and the test of

H1. Columns 1 and 2 provide the results that include only either the Republican CEO indicator or

the Democratic CEO indictor variable, respectively. The result suggests that Republican CEOs are

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associated with significantly lower accrual quality, relative to their Democratic or nonpartisan

counterparts. The coefficient on REP_DUM is -0.0322 with a t-statistic of -3.11, which is

statistically significant at the one percent level. Results in Column 2 provide weak evidence, at the

ten percent level, that Democratic CEOs have lower accrual quality, relative to other CEO.

However, when both indicator variables are included in the same model in Column 3, DEM_DUM

becomes statistically insignificant but the coefficient on REP_DUM continues to be negative and

statistically significant at the five percent level. In column 4, we measure CEO political ideology

using RELREPIND, which is based on the difference in contributions to Republican and

Democratic causes, and the coefficient is -0.0581 with a t-statistic of -4.08, which is statistically

significant at the one percent level. Taken together, the results in Table 3 support hypothesis H1

and suggest that Republican CEOs are less likely to engage in accrual earnings management, which

results in higher accruals quality. In contrast, there is some evidence that Democrat CEOs may

actually reduce accrual quality.

The coefficients on the control variables yield some interesting insights, and are generally

consistent with prior studies. The coefficient estimates on ROA are significantly positive,

suggesting that abnormal accruals and earnings performance are positively correlated, which is

consistent with prior research (e.g., Dechow et al. 1995). Companies with Big 4 auditors are less

likely to engage in earnings management. Out of the corporate governance controls, only CEOAGE

is statistically significant and negative implying that older CEOs have better accrual quality.


To provide further insight into whether CEO political ideology impacts financial reporting

quality, Table 4 presents the regression results for the impact of CEO ideology on the incident of

meeting or beating analyst earnings forecasts. The number of available observations decreases for

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this analysis due to unavailable data on analyst forecasts in I/B/E/S. In column 1, the coefficient

estimate on REP_DUM is negative and marginally statistically significant at the ten percent level

providing some evidence that firms with Republican CEOs are less likely to just meet or beat

analyst expectations. When both REP_DUM and DEM_DUM are included in the same model, the

results are statistically insignificant, although the coefficients on the Republican CEO index

variable in column 4 is negative and statistically significant at the five percent level (t-statistics -

2.17), implying that conservative ideology reduces the likelihood of meeting or beating analyst

earnings forecasts, another dimension of financial reporting quality. These results further support

our argument that CEO political ideology is an important factor that drives earnings management

behavior. Consistent with H2, we find that firms with Republican CEOs are less likely to manage

earnings to meet or beat analyst earnings forecasts, which is likely due to their more conservative

and risk averse attitudes.


Next, we turn to testing H3 and examine the extent to which CEO political preference

affects financial misstatements. We measure the probability of an earnings misstatement using

both an ex ante measure – Dechow et al.’s (2011) F-score, and an ex post metric that captures

whether a was restated. The F-score developed by Dechow et al. (2011) attempts to predict the

probability of having a material accounting misstatement given certain firm characteristics.5 Prior

accounting literature widely uses this model to measure the quality of financial reporting (Hobson

et al. 2012). Higher F-score values indicate a higher probability of misstatement. Alternatively, we

use an indicator variable for whether the current yearly report was restated due to a violation of

GAAP.

5 Appendix B provides detail explanation on the calculation of the F-score.

19

Table 5, columns 1 and 2 report the regression results for the impact of CEO ideology on

the firm’s ex ante probability of committing financial misstatement. The coefficient on the

Republican CEO indicator variable in column 1 is -0.0404 and statistically significant at the one

percent level with a t-statistics of -3.14, while the coefficient on the Democratic CEO indicator

variable is significantly positive at the ten percent level (coefficient estimate 0.0231, t-statistic

1.68). Untabulated results show that including only either REP_DUM or DEM_DUM separately

in the model provides consistent results; the coefficient on REP_DUM is negative and statistically

significant at the one percent level (coefficient estimate -0.0462, t-statistic -3.67) and the

coefficient on DEM_DUM is positive and also statistically significant at the one percent level

(coefficient estimate 0.0383, t-statistic 2.82). These results support the notion that firms with

Republican CEOs (Democratic CEOs) have a lower (higher) ex ante probability of committing

financial misstatement. However, using RELREPIND provides consistent but statistically

insignificant result.

Table 5, Columns 3 and 4 report the regression results for the impact of CEO ideology on

the firm’s ex post probability of committing a financial misstatement. Only the coefficient on

Democratic CEOs is significantly positive at the conventional five percent level (coefficient

estimate 0.0908, t-statistics 2.38). The coefficient estimates on Republican CEOs and the index

are negative but statistically insignificant. Untabulated results show that the inclusion of

REP_DUM or DEM_DUM separately in the model provides consistent results.

Overall, the results suggest that Democratic CEOs increase the probability of a financial

statement misstatement, while there is evidence that Republican CEOs decrease the ex ante

probability of a misstatement. The results in Table 5 add additional insights to the previously

reported results that Republican CEOs have higher financial reporting quality, relative to their

20

Democratic and nonpartisan counterparts. These results are consistent with our prediction that

conservative personality attributes make Republican CEOs more cautious regarding financial

reporting, which reduces the probability of financial misstatements. In addition, results on the

control variables in the model are generally consistent with prior research.


Our last hypothesis, H4, predicts that Republican CEOs are less likely to report a material

internal control weakness than Democratic or nonpartisan CEOs. We consider reported material

internal control weaknesses under either SOX Section 302 (columns 1 and 2) or 404 (columns 3

and 4). Taken together, the results in Table 6 are consistent across columns and demonstrate that

Republican CEOs are negatively and significantly associated with the probability of a material

internal control weakness. The coefficient estimates on REP_DUM in columns 1 and 3 are both

negative and statistically significant at the five percent level. Furthermore, the coefficient estimate

on RELREPIND in column 2 (4) is -0.1145 (-0.1637) with t-statistic of -2.20 (-2.66), which is

statistically significant at the five (one) percent level. The coefficient estimates on DEM_DUM are

not statistically significant and control variables are consistent with prior studies. One way to

ensure higher financial reporting quality is to implement adequate and effective internal control

measures and Republican CEOs are motivated to improve internal controls to prevent financial

misreporting. Our findings reported in Table 6 support H4 and provide further evidence that

Republican CEOs are associated with higher financial reporting quality.


21

5. ADDITIONAL ANALYSES

Audit Fee Analysis

Next, we examine whether the higher quality financial reporting provided by firms with

Republican CEOs results in a benefit to such firms in the form of lower audit fees. Audit fees has

also been used as another proxy for financial reporting quality. Since high financial reporting

quality is more likely to be associated with assessments of lower inherent risk by auditors, then it

can be expected that Republican CEOs would pay lower audit fees relative to their Democratic

and nonpartisan counterparts. For example, Frankel et al. (2002) provide evidence that audit fees

are lower for firms with higher financial reporting quality and Abbott et al. (2006) provide

evidence that audit fees increase with the clients’ risk of upward earnings management, as

estimated by positive discretionary accruals. In addition, DeFond et al. (2016) show that auditors

consider client conservatism as an important determinant of engagement risk and charge lower

fees. Consistent with this research, we predict and test whether Republican CEOs’ higher financial

reporting quality results into lower audit fees.

For our audit fee model, we follow Ashbaugh et al. (2003), Bruynseels and Cardinaels

(2014), Krishnan and Wang (2015), and Bhandari et al. (2017). In addition to our variable of

interest, CEO_PIDEOLOGY, we control for client- and auditor-specific characteristics, which

have been found to impact audit fees. We estimate the following regression model:

LAUDITFEEi,t = β0+ β1 CEO_PIDEOLOGYi,t + β2 SIZEi,t + β3 LEVERAGEi,t +

β4 ROAi,t + β5 LOSSi,t + β6 XDOPSi,t + β7 FOROPSi,t + β8 MBi,t + β9 SALEGRi,t +

β10 INITIALi,t + β11 AUDLAGi,t + β 12 SEGSi,t + β13 EMP_PLANi,t + β14 FYEi,t + (3)

β15 FIRMAGEi,t + β16 GCi,t + β17RESTATEi,t + β18 RESTRUCTi,t + β19 NEWFINi,t + β20

BIG4i,t + β21 INDSPi,t + β22 FEERATIOi,t + β23 ICW302i,t + β24 ICW404i,t +

22

Year fixed effects + Industry fixed effects + ei,t,

where LAUDITFEE is the natural logarithm transformation of audit fees, XDOPS is an indicator

variable equal to one if the firm has extraordinary items, zero otherwise, INITIAL is an indicator

variable equal to one if the firm has been with the current auditor for one year or less, zero

otherwise, AUDLAG is the number of days between the audit opinion signature date and the fiscal

year-end, EMP_PLAN is equal to one if the firm has an employee retirement plan, zero otherwise,

FYE is an indicator variable equal to one if the firm has a December 31 fiscal year end, zero

otherwise, GC is equal to one if the auditor issued a going concern opinion, zero otherwise,

RESTATE is equal to one if the firm restated its earnings, zero otherwise, INDSP is equal to one if

the auditor is an industry leader, zero otherwise, FEERATIO is the ratio of non-audit fees to total

fees, ICW302 (ICW404) is equal to one if the firm reported a material internal control weakness

under SOX 302 (404). The model also includes year and industry fixed effects to control for time

and industry-variant factors. The results of this analysis are provided in Table 7.

Interestingly, all coefficient estimates of interest are highly statistically significant at the

one percent level and in the predicted directions. For example, the coefficient estimate on the

Republican CEO indicator variable provided in columns 1 (3) is -0.1041 (-0.0812) with t-statistic

of -9.97 (-7.47), which indicates that firms with Republican CEOs pay lower audit fees than other

firms. In addition, the coefficients on the Democratic CEO indicator variables provided in columns

2 and 3 are significantly positive at the one percent level (coefficient estimates 0.1225, 0.0927, t-

statistics 9.53 6.92, respectively) suggesting that Democratic CEOs pay higher audit fees than their

counterparts. Further, the coefficient on the Republican CEO index variable in column 4 is also

negative and highly statistically significant at the one percent level (coefficient estimate -0.0898,

t-statistics -7.19). These results suggest that there is a negative association between Republican

23

CEOs and audit fee, which is consistent with the observation that auditors assess lower inherent

risk in response to higher financial reporting quality supplied by Republican CEOs.


6. CONCLUSION

A growing literature documents that CEO political preferences affect various corporate

decisions (e.g., Hutton et al. 2014; Elnahas and Kim 2017). We attempt to add to this stream of

literature by examining the effects of CEO political preference on financial reporting quality. We

find that Republican CEOs demand higher quality financial reporting than Democratic CEOs.

Specifically, we show that Republican CEOs are significantly less likely to engage in earnings

management, less likely to meet or beat analyst earnings forecasts, and have lower likelihoods of

reporting an earnings misstatement or an internal control weakness. Additional analysis provides

evidence that auditors’ assess lower inherent risk in response to Republican CEOs’ high financial

reporting and internal control quality, which translates into lower audit fees.

Overall, our results imply that CEO political ideology has a significant impact on financial

reporting quality via CEOs’ conservative or liberal personality traits. Political ideology drives

Republican CEOs’ to commit to higher financial reporting quality, which results in their payment

of lower audit fees, while Democratic CEOs’ propensity to engage in more earnings management

that results in an misstatement, prompts auditors to assess higher fee, likely to compensate for an

increased risk and higher effort necessary to complete the audit. Therefore, we add to Bhandari et

al. (2017) who find that socially connected CEO promote higher financial reporting quality. Future

research may identify other personal traits of CEOs that influence financial reporting. These

attributes will help us further analyze and understand CEO behavior.

24

Although our paper provides strong evidence that CEO political ideology drives CEO

commitment to financial reporting quality, our paper is subject to two limitations. First, we are not

able to examine a CEO-fixed effect or identification test because we assume that CEO political

ideology is developed and established in adolescence or early adulthood and remains stable during

the entire adult life (Hutton et al. 2014; Green et al. 2004). Second, we may not capture the CEO

political ideology if the contributions are subject to opportunistic behavior (with the intention to

benefit from the political party) rather than driven by ideology. However, we believe that our

findings are consistent with other prior research (e.g., Hutton et al. 2014; Jiang et al. 2016; Elnahas

and Kim 2017), which suggests that the majority of the donations are not opportunistic; rather,

they reflect CEO political attributes.

25

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29

APPENDIX A Variable Definitions DD = the standard deviation of residuals from the modified Dechow and Dichev

(2002) model (see Appendix B for detailed estimation procedure);

REP_DUM = indicator variable equal to one if a CEO contributes more to Republican than Democratic causes during his/her tenure, and zero otherwise;

DEM_DUM = indicator variable equal to one if a CEO contributes more to Democratic than Republican causes during his/her tenure, and zero otherwise;

RELREPIND = the difference in contributions to Republican and Democratic causes, divided by the sum of contributions to both parties in a given election cycle;

ANASUR= = indicator variable equal to one if a firm meets or beats by one percent the median consensus analyst earnings forecast as reported in I/B/E/S database over a fiscal year, and zero otherwise;

RESTATE = indicator variable equal to one if a firm restated its financial statements, zero otherwise;

SIZE = the natural logarithm transformation of total assets; LEVERAGE = total liabilities divided by total assets; ROA = income before extraordinary items divided by total assets; LOSS_PROP = the proportion of loss years over the past ten years; FOROPS = indicator variable equal to one if a firm has foreign operations as indicated

by the foreign currency adjustments to income, and zero otherwise; MB = book value of equity divided by market value of equity; SALEGR = growth rate in sales over the previous fiscal year; STDCASH = the standard deviation of cash flow from operations over the past ten

years; STDSALE = the standard deviation of sales over the past ten years; STDRET = the market adjusted annual stock returns volatility; BIG4 = indicator variable equal to one if the auditor is a Big4 auditor, and zero

otherwise; EINDEX = entrenchment score based on Bebchuk, Cohen, and Ferrell (2009) as the

sum of six provisions: staggered boards, limits to shareholder bylaw amendments, poison pills, golden parachutes, and supermajority requirements for mergers and charter amendments. A higher value of EINDEX indicates weaker investor protection, i.e., weaker corporate governance.

PCTINDEP = number of independent outside directors divided by the total number of directors on the board;

CEOCHAIR = indicator variable equal to one if a company's CEO also serves as board chair, and zero otherwise;

CEOAGE = the current age of a CEO; CEOGENDER = indicator variable equal to one if a company's CEO is a male, and zero

otherwise; LNTENURE = the natural logarithm of the number of years a CEO has held the post; LNSALARY = the natural logarithm of a CEO's total compensation including bonus;

30

ICW302 = indicator variable equal to one if a firm discloses a material weakness under Section 302, and zero otherwise;

ICW404 = indicator variable equal to one if a firm discloses a material weakness under Section 404, and zero otherwise;

RESTRUCT = indicator variable equal to one if a firm undergoes a major restructuring or merger (i.e., Compustat footnote data item “sale_fn”= AB"), zero otherwise. Compustat footnote data item “sale_fn”= "AB" indicates that sales have been "restated for/reflects a major merger or reorganization resulting in the formation of a new company;

SEGS = the number of business segments; FIRMAGE = the natural logarithm transformation of the number of fiscal years since a

firm was included in CRSP; LOSS = indicator variable equal to one if income before extraordinary item is

negative, and zero otherwise; LAUDITFEE = The natural logarithm of audit fees; XDOPS = indicator variable equal to one if a firm has extraordinary items, and zero

otherwise; INITIAL = indicator variable equal to one if a firm’s auditor has been with the client

for one year or less, zero otherwise; AUDLAG = the number of days between audit opinion signature date and fiscal year-

end; EMP_PLAN = indicator variable equal to one if a firm has an employee retirement plan,

and zero otherwise; FYE = indicator variable equal to one if a firm’s fiscal year-end is December 31,

and zero otherwise; GC = indicator variable equal to one if a firm received a qualified going concern

opinion issued by the auditor expressing substantial uncertainty about its ability to continue, and zero otherwise;

NEWFIN = indicator variable equal to one if a firm received any financing, and zero otherwise;

INDSP = indicator variable equal to one if the auditor is the industry leader, and zero otherwise. We measure industry expertise based on national level industry market share. We use client sales as the basis to calculate market share captured by an auditor; and

FEERATIO = the ratio of non audit fees to total fees.

31

APPENDIX B Calculation of DD:

We follow Dechow and Dichev (2002) and McNichols (2002) to measure accruals quality.

This proxy captures how well accruals map into cash flows of the firm (Dechow and Dichev 2002).

We use the modified version of the model proposed by McNichols (2002), which includes

property, plant, and equipment and changes in revenue in addition to cash flows. She argues that

these modifications can help provide better expectations of current accruals. Therefore, we

estimate the following model by year and industry, requiring at least twenty observations:

∆WCi,t = α1 + α2 CFOi,t-1 + α3 CFOi,t + α4 CFOi, t+1 + α5 ∆REVi,t + α6 PPEi,t + ɛi,t,

where ∆WC is the change in working capital accruals from year t-1 to t, computed as ∆AR +

∆Inventory - ∆AP - ∆TP + ∆Other Assets (net), where AR is accounts receivable, AP is accounts

payable, and TP is taxes payable; CFO is cash flow from operations; ∆REV is the change in revenue

from time period t-1 to t; and PPE is the gross value of plant, property, and equipment. All

variables are scaled by beginning total assets.

We estimate the model by year and each of the 48 Fama and French’s industries and use

the standard deviation of the residual from the model over the past five years for each firm, as our

measure of accrual quality (DD). High DD represents lower earnings quality.

Calculation of F-score:

Following Dechow et al. (2011), we first calculate the predicted values, p, using the

following model:

pi,t = (-7.893) + 0.790 × RSST_ACCi,t + 2.518 × ΔRECi,t + 1.191 × Δ INVi,t + 1.979 ×

SOFTi,t + 0.171 × ΔCSalei,t + (-0.932) × Δ ROAi,t + 1.029 × ISSUEi,t,

32

where RSST_ACC is Richardson et al.’s (2005) measure of accruals for firm i in fiscal year t,

which is the sum of the change in non-cash working capital, WC ((#4-#1)-(#5-#34)), the change in

net non-current operating assets, NCO ((#6-#4-#32)-(#181-#5-#9)), and the change in net financial

assets, FIN ((#193+#32)-(#9+#34+#130)), scaled by average total assets; 6 ΔREC is the change in

receivables (#2) scaled by average total assets; ΔINV is the change in inventory (#3), scaled by

average total assets; SOFT is the percentage of soft assets, which is equal to total assets minus

property, plant, and equipment and cash (#6-#8-#1), scaled by average total assets (#6); ΔCSsale

is the percentage change in cash sales (#12-Δ#2); ΔROA is the change in return on assets

(#18/averaged#6); and ISSUE is an indicator variable that equals one if the firm has issued new

debt or equity (#108>0 or #111>0) during fiscal year t and zero otherwise.

The probability of a misstatement is then calculated as Pri,t = e pi,t /(1+e pi,t) and F-score is

equal to Pri,t /0.0037. A higher value of F-score indicates higher ex ante probability of a financial

misstatement.

6 For expositional convenience, we refer to some variables with their Compustat item number (#).

33

Table 1 – Sample Selection

CEO political contribution data matched to ExecuComp (2003-2016) 25,968

Less: Missing ExecuComp data 587 Missing Compustat data 5,142 Missing CRSP data 1,144 Missing Audit Analytics data 623 Missing ISS Directors and Governance data 4,814

12,310Final Sample 13,658

Notes: Table 1 presents the sample selection. The Federal Election Commission’s (FEC) CEO political contributions for years 2003-2016 are matched to ExecuComp executives data using donor occupation and names. Directors and Governance data is provided by the Institutional Shareholder Service (ISS).

34

Table2: Number of Firms by Year and Industry and Descriptive Statistics

Panel A: Frequency Distribution by Year Fyear Freq. Percent REP_DUM=1 DEM_DUM=1 NEUTRAL 2003 918 6.72 252 117 549 2004 873 6.39 258 115 500 2005 856 6.27 260 119 477 2006 824 6.03 260 120 444 2007 817 5.98 246 135 436 2008 965 7.07 294 169 502 2009 998 7.31 304 177 517 2010 1,018 7.45 323 179 516 2011 1,044 7.64 328 189 527 2012 1,046 7.66 313 181 552 2013 1,073 7.86 309 176 588 2014 1,075 7.87 295 169 611 2015 1,071 7.84 284 164 623 2016 1,080 7.91 268 156 656 Total 13,658 100 3,994 2,166 7,498

Panel B: Frequency Distribution by Industry

Fyear N Percent REP_DUM=1 DEM_DUM=1 NEUTRALConsumer Non-durables 794 5.81 237 130 427 Consumer Durables 6,119 44.8 1,640 1,099 3,380 Manufacturing 1,810 13.25 612 192 1,006 Utilities 640 4.69 330 106 204 Finance 981 7.18 286 244 451 Other 3,314 24.26 889 395 2,030 Total 13,658 100 3,994 2,166 7,498

35

Panel C: Descriptive Statistics Sample (N=13,658) VARIABLE MEAN SD Q1 MED Q3 DD 0.331 0.604 0.054 0.127 0.373 REP_DUM 0.292 0.455 0 0 1 DEM_DUM 0.159 0.365 0 0 0 RELREPIND 0.052 0.379 0 0 0 FSCORE 1.057 0.621 0.634 0.978 1.379 RESTATE 0.149 0.356 0 0 0 SIZE 7.820 1.604 6.632 7.638 8.82 LEVERAGE 0.518 0.216 0.369 0.521 0.661 ROA 0.138 0.086 0.088 0.13 0.181 LOSS_PROP 0.124 0.175 0 0 0.2 FOROPS 0.355 0.479 0 0 1 MB 3.092 3.425 1.543 2.322 3.676 SALEGR 0.081 0.193 -0.009 0.065 0.15 STDCASH 0.045 0.033 0.025 0.038 0.056 STDSALE 0.233 0.199 0.109 0.179 0.288 STDRET 0.023 0.009 0.016 0.021 0.027 BIG4 0.940 0.238 1 1 1 E_INDEX 3.070 1.125 2 3 4 PCTINDEP 77.150 12.482 70 80 87.5 CEOCHAIR 0.516 0.500 0 1 1 CEOAGE 56.112 7.094 51 56 61 CEOGENDER 0.970 0.170 1 1 1 LNTENURE 1.691 0.978 1.096 1.792 2.391 LNSALARY 6.777 1.141 6.477 6.802 7.106

Notes: Table 2 presents sample descriptive statistics. Panel A presents number of firms by fiscal year. Panel B presents number of firms by Fama-French 12 industry classification. Panel C presents descriptive statistics of the variables in the main regression analyses.

36

TABLE 3

The effect of CEO political ideology on accruals quality, DD Sample (N=13,658)

Variable Coeff. t-stat Coeff. t-stat Coeff. t-stat Coeff. t-stat

Intercept 0.4012*** 4.31 0.4133*** 4.43 0.4008*** 4.30 0.4008*** 4.32

REP_DUM -0.0322*** -3.11 -0.0269** -2.50

DEM_DUM 0.0312* 1.73 0.0211 1.12

RELREPIND -0.0581*** -4.08

SIZE -0.0174*** -2.74 -0.0194*** -3.13 -0.0182*** -2.93 -0.0188*** -2.98

LEVERAGE 0.0034 0.09 0.0018 0.05 0.0036 0.10 0.0050 0.13

ROA 0.4020*** 4.92 0.3958*** 4.84 0.4034*** 4.92 0.4004*** 4.92

LOSS_PROP 0.2808*** 5.82 0.2828*** 5.85 0.2800*** 5.81 0.2793*** 5.74

FOROPS 0.0735*** 6.41 0.0751*** 6.50 0.0738*** 6.39 0.0737*** 6.45

MB 0.0029 1.28 0.0028 1.23 0.0028 1.23 0.0029 1.29

SALEGR 0.1068*** 2.98 0.1073*** 3.02 0.1058*** 2.97 0.1058*** 2.97

STDCASH 0.0001*** 3.82 0.0001*** 3.86 0.0001*** 3.85 0.0001*** 3.87

STDSALE 0.0000 1.42 0.0000 1.33 0.0000 1.37 0.0000 1.38

STDRET 1.3563 1.58 1.2820 1.49 1.3261 1.55 1.3340 1.55

BIG4 -0.0744*** -3.28 -0.0743*** -3.28 -0.0735*** -3.24 -0.0749*** -3.31

E_INDEX 0.0040 0.85 0.0032 0.68 0.0038 0.80 0.0038 0.80

PCTINDEP 0.0002 0.27 0.0002 0.28 0.0002 0.31 0.0002 0.33

CEOCHAIR -0.0077 -0.68 -0.0085 -0.75 -0.0080 -0.71 -0.0074 -0.66

CEOAGE -0.0028*** -3.75 -0.0027*** -3.65 -0.0027*** -3.65 -0.0027*** -3.54

CEOGENDER 0.0256 0.93 0.0237 0.87 0.0264 0.97 0.0252 0.92

LNTENURE -0.0008 -0.13 -0.0033 -0.57 -0.0017 -0.30 -0.0022 -0.39

LNSALARY -0.0045 -0.99 -0.0047 -1.03 -0.0046 -1.02 -0.0045 -1.00

Adj. R2 6.15% 6.13% 6.15% 6.22%

Notes: The regression models include industry and year fixed effects and reported significance is based on robust standard errors of two-tailed tests, adjusted for heteroscedasticity.

***, **, and * represent significance at 1%, 5%, and 10% levels, respectively.

37

TABLE 4 The effect of CEO political ideology on the likelihood of meeting or beating analyst earnings forecasts (ANASUR) Sample (N=9,935)


Intercept -1.4375*** -2.75 -1.3916*** -2.68 -1.4278*** -2.74 -1.4250*** -2.73

REP_DUM -0.1110* -1.72 -0.0896 -1.31

DEM_DUM 0.1159 1.58 0.0814 1.05

RELREPIND -0.1543** -2.17

SIZE -0.0090 -0.25 -0.0173 -0.49 -0.0125 -0.34 -0.0154 -0.43

LEVERAGE 0.1773 1.18 0.1764 1.17 0.1708 1.14 0.1878 1.24

ROA 2.1555*** 5.99 2.1466*** 5.96 2.1618*** 6.03 2.1634*** 5.99

LOSS_PROP -1.2857*** -4.15 -1.2869*** -4.13 -1.2928*** -4.16 -1.2906*** -4.16

FOROPS -0.1650*** -2.64 -0.1562** -2.51 -0.1623*** -2.60 -0.1619*** -2.59

MB -0.0045 -0.59 -0.0056 -0.72 -0.0052 -0.67 -0.0049 -0.64

SALEGR 0.2776* 1.66 0.2775* 1.65 0.2748 1.64 0.2676 1.60

STDCASH -0.0001* -1.80 -0.0001* -1.75 -0.0001* -1.79 -0.0001* -1.75

STDSALE 0.0000** 2.10 0.0000** 2.05 0.0000** 2.07 0.0000** 2.11

STDRET -16.4937*** -3.15 -16.6369*** -3.16 -16.7153*** -3.17 -16.4945*** -3.14

BIG4 -0.1761 -1.47 -0.1701 -1.42 -0.1678 -1.40 -0.1740 -1.45

E_INDEX 0.0166 0.64 0.0143 0.55 0.0151 0.58 0.0168 0.64

PCTINDEP -0.0018 -0.75 -0.0017 -0.72 -0.0016 -0.69 -0.0015 -0.64

CEOCHAIR 0.0426 0.66 0.0416 0.65 0.0421 0.66 0.0413 0.64

CEOAGE -0.0054 -1.15 -0.0052 -1.12 -0.0052 -1.11 -0.0050 -1.08

CEOGENDER 0.2208 1.16 0.2119 1.12 0.2217 1.16 0.2161 1.15

LNTENURE 0.0053 0.16 -0.0043 -0.13 0.0010 0.03 -0.0003 -0.01

LNSALARY 0.0329 0.79 0.0313 0.79 0.0316 0.78 0.0313 0.77

Pseudo R2 7.16% 7.13% 7.20% 7.21%

Notes: The probit regression models follow He (2015) and year fixed effects and reported significance is based on robust standard errors of two-tailed tests, adjusted for heteroscedasticity.


38

TABLE 5

The effect of CEO political ideology on the probability of a misstatement

Sample (N=13,658)

Ex ante probability of Ex post probability of

financial misstatements (F-score) financial misstatements (restatement)


Intercept 1.1600*** 12.07 1.1842*** 12.53 -0.4548** -2.03 -0.4316* -1.92

REP_DUM -0.0404*** -3.14 -0.0044 -0.13

DEM_DUM 0.0231* 1.68 0.0908** 2.38

RELREPIND -0.0064 -0.50 -0.0101 -0.28

SIZE -0.0030 -0.58 -0.0039 -0.74 -0.0289* -1.91 -0.0264* -1.76

LEVERAGE 0.2518*** 8.58 0.2485*** 8.40 0.3050*** 4.14 0.3051*** 4.14

ROA -1.3064*** -16.44 -1.3215*** -16.77 -1.2377*** -6.66 -1.2498*** -6.72

LOSS_PROP -0.4703*** -12.19 -0.4649*** -11.98 0.0849 0.90 0.0886 0.94

FOROPS 0.0734*** 7.79 0.0749*** 7.91 0.0862*** 3.03 0.0848*** 2.98

MB -0.0014 -1.03 -0.0012 -0.90 -0.0061 -1.37 -0.0058 -1.29

SALEGR 1.0175*** 23.20 1.0223*** 23.34 0.0293 0.39 0.0347 0.46

STDCASH -0.0001*** -4.63 -0.0001*** -4.67 -0.0001*** -2.81 -0.0001*** -2.85

STDSALE 0.0000** 2.30 0.0000** 2.31 0.0000* 1.83 0.0000* 1.91

STDRET -2.7692*** -3.55 -2.7534*** -3.52 5.8732*** 2.78 5.9655*** 2.82

BIG4 -0.0422** -2.00 -0.0454** -2.15 0.2598*** 4.02 0.2530*** 3.91

E_INDEX 0.0002 0.04 -0.0004 -0.09 0.0082 0.62 0.0085 0.65

PCTINDEP -0.0009** -2.00 -0.0010** -2.20 -0.0054*** -4.70 -0.0056*** -4.83

CEOCHAIR -0.0247** -2.22 -0.0249** -2.24 -0.1020*** -3.39 -0.1002*** -3.33

CEOAGE -0.0037*** -3.67 -0.0038*** -3.78 0.0019 0.93 0.0016 0.78

CEOGENDER 0.0566** 2.54 0.0502** 2.24 -0.0729 -0.92 -0.0804 -1.02

LNTENURE 0.0129** 2.39 0.0121** 2.26 0.0320** 1.98 0.0351** 2.18

LNSALARY 0.0326*** 7.02 0.0328*** 7.02 -0.0207* -1.88 -0.0205* -1.84

Adj. R2 12.31% 12.19% Pseudo R2 4.78% 4.73% Notes: The probit regression models include year fixed effects and reported significance is based on robust standard errors of two-tailed tests, adjusted for heteroscedasticity.


39

TABLE 6 The effect of CEO political ideology on the report of a material internal control weakness

Material internal control weakness SOX 302 Material internal control weakness SOX 404

Sample (N=12,686) Sample (N=11,478)


Intercept -0.8671*** -2.65 -0.8658*** -2.64 0.0367 0.09 0.0470 0.11

REP_DUM -0.1016** -2.12 -0.1317** -2.24

DEM_DUM -0.0347 -0.63 -0.0475 -0.73

RELREPIND -0.1145** -2.20 -0.1637*** -2.66

SIZE -0.1493*** -6.68 -0.1540*** -6.94 -0.1681*** -6.20 -0.1744*** -6.48

LEVERAGE 0.5332*** 5.56 0.5315*** 5.55 0.7449*** 6.77 0.7434*** 6.76

ROA -1.2826*** -5.20 -1.2858*** -5.22 -1.3743*** -4.73 -1.3821*** -4.75

LOSS_PROP 0.4168*** 3.35 0.4204*** 3.38 0.1239 0.79 0.1271 0.81

FOROPS 0.1856*** 4.71 0.1892*** 4.81 0.1633*** 3.42 0.1669*** 3.51

MB -0.0167*** -2.96 -0.0169*** -3.01 -0.0084 -1.41 -0.0086 -1.46

SALEGR -0.0218 -0.21 -0.0223 -0.21 -0.0320 -0.24 -0.0306 -0.23

STDCASH 0.0000 0.59 0.0000 0.60 0.0001 0.79 0.0001 0.84

STDSALE -0.0000 -0.01 -0.0000 -0.04 0.0000 0.47 0.0000 0.41

STDRET 6.2767** 2.04 6.3702** 2.07 8.1796** 2.18 8.2105** 2.19

BIG4 -0.1467** -2.00 -0.1490** -2.04 -0.0436 -0.48 -0.0464 -0.51

E_INDEX -0.0241 -1.30 -0.0254 -1.37 -0.0176 -0.79 -0.0187 -0.84

PCTINDEP -0.0011 -0.69 -0.0011 -0.69 -0.0042** -2.08 -0.0041** -2.07

CEOCHAIR 0.0006 0.01 0.0006 0.01 -0.0291 -0.57 -0.0283 -0.55

CEOAGE -0.0013 -0.43 -0.0011 -0.34 -0.0041 -1.09 -0.0037 -0.97

CEOGENDER -0.0278 -0.25 -0.0271 -0.24 -0.1399 -1.10 -0.1356 -1.07

LNTENURE -0.0463* -1.94 -0.0519** -2.21 -0.0032 -0.11 -0.0115 -0.41

LNSALARY -0.0240 -1.58 -0.0241 -1.60 -0.0150 -0.84 -0.0156 -0.90

RESTRUCT -0.1017** -1.98 -0.1002* -1.95 -0.0373 -0.61 -0.0366 -0.60

SEGS 0.0092** 2.43 0.0092** 2.41 0.0077* 1.68 0.0077* 1.67

FIRMAGE -0.0036 -0.14 -0.0015 -0.06 0.0169 0.53 0.0200 0.63

Pseudo R2 10.44% 10.44% 11.04% 11.08%

Notes: The probit regression models include year fixed effects and reported significance is based on robust standard errors of two-tailed tests, adjusted for heteroscedasticity.


40

TABLE 7

The effect of CEO political ideology on audit fees

Sample (N=11,479)


Intercept 10.3676*** 260.83 10.3476*** 259.60 10.3678*** 259.76 10.3340*** 261.65

REP_DUM -0.1041*** -9.97 -0.0812*** -7.47

DEM_DUM 0.1225*** 9.53 0.0927*** 6.92

RELREPIND -0.0898*** -7.19

SIZE 0.4849*** 123.95 0.4777*** 121.00 0.4815*** 121.67 0.4809*** 122.85

MB 0.0127*** 8.52 0.0124*** 8.34 0.0123*** 8.33 0.0128*** 8.64

LEVERAGE -0.0398 -1.47 -0.0417 -1.54 -0.0393 -1.46 -0.0396 -1.46

ROA -0.4132*** -6.19 -0.4229*** -6.34 -0.3986*** -5.99 -0.4362*** -6.52

LOSS 0.0871*** 5.49 0.0902*** 5.67 0.0884*** 5.58 0.0886*** 5.57

XDOPS -0.0457 -0.86 -0.0463 -0.86 -0.0453 -0.85 -0.0485 -0.91

FOROPS 0.2892*** 30.32 0.2950*** 30.85 0.2912*** 30.56 0.2919*** 30.48

SALEGR -0.1613*** -5.97 -0.1655*** -6.07 -0.1671*** -6.16 -0.1606*** -5.91

INITIAL -0.1414*** -4.93 -0.1385*** -4.81 -0.1408*** -4.91 -0.1377*** -4.79

AUDLAG 0.0002*** 5.18 0.0002*** 5.07 0.0002*** 4.90 0.0003*** 5.31

SEGS 0.0147*** 16.41 0.0145*** 16.01 0.0146*** 16.24 0.0146*** 16.18

EMP_PLAN 0.2286*** 20.11 0.2300*** 20.21 0.2316*** 20.40 0.2278*** 20.00

FYE 0.0095 0.77 0.0050 0.40 0.0102 0.82 0.0057 0.46

FIRM_AGE 0.0181*** 2.61 0.0193*** 2.77 0.0200*** 2.90 0.0183*** 2.63

GC -0.1687 -0.88 -0.1776 -0.89 -0.1721 -0.87 -0.1617 -0.85

RESTATE 0.0153 1.12 0.0115 0.84 0.0127 0.92 0.0152 1.11

RESTRUCT 0.0723*** 6.10 0.0754*** 6.37 0.0740*** 6.26 0.0722*** 6.10

NEWFIN 0.0118 0.96 0.0138 1.13 0.0126 1.03 0.0142 1.16

BIG4 0.1157*** 6.05 0.1186*** 6.16 0.1219*** 6.36 0.1115*** 5.84

INDSP 0.0258** 2.48 0.0263** 2.54 0.0230** 2.23 0.0285*** 2.74

FEERATIO -0.2951*** -8.12 -0.3066*** -8.45 -0.2995*** -8.27 -0.3024*** -8.30

ICW302 0.2547*** 9.46 0.2593*** 9.60 0.2564*** 9.53 0.2566*** 9.42

ICW404 0.0612* 1.70 0.0639* 1.78 0.0623* 1.74 0.0597* 1.65

Adj. R2 75.27% 75.26% 75.37% 75.17%

Notes: The regression models include industry and year fixed effects and reported significance is based on robust standard errors of two-tailed tests, adjusted for heteroscedasticity.


Documents

CEO political ideology and financial reporting quality 1 24 ......CEO Political Ideology and Financial Reporting Quality Avishek Bhandari [email protected] Joanna Golden [email protected]