Is Accounting Information Quality Priced in Managerial Labor Market?

Xinghua GaoCarson College of BusinessWashington State University

(509) 335-2222xinghua.gao@wsu.edu

Yonghong JiaIvy College of Business Iowa State University

(515) 294-6032yonghong@iastate.edu

Xiumin MartinOlin Business School

Washington University in St. Louis(314) 935-6331

xmartin@wustl.edu

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Is Accounting Information Quality Priced in Managerial Labor Market?

ABSTRACT

We study whether accounting information quality (AIQ) is priced in the executive labor market. Focusing on externally hired CEO compensation at their initial appointment, we find a 7.38% pay premium for a one-standard deviation decline in AIQ measured in the years prior to the appointment. This result is robust to the inclusion of a comprehensive set of variables that control for compensation contract, and other factors at the CEO, firm, industry, and state levels. Thus our findings are unlikely due to correlated omitted variables. Additional evidence suggests job security concern and accounting failure risk might be the channels to explain the pricing premium for poor AIQ. We also find firms with less effective boards have lower sensitivity of compensation to AIQ, suggesting that private benefits might offset pay premium required by a CEO for low AIQ firms. Finally, we show the pay premium-AIQ relation is stronger for young CEOs and for CEOs with outside employment restriction, lending further support to the job security mechanism.

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Is Accounting Information Quality Priced in Managerial Labor Market?

1. Introduction

This study examines whether accounting information quality (AIQ) is priced in the

managerial labor markets. Financial reports are an important source of information to aid various

stakeholders in making decisions. Graham, Harvey, and Rajgopal (2005)’s survey results

indicate managers perceive earnings as a critical metric evaluated by the capital market. Ample

empirical evidence suggests financial information affects decision quality, and is thus priced by

various market participants. For example, in the context of market for corporate control,

Marquardt and Zur (2014) find that when target firms have high quality financial information,

proposed deals are more likely to be completed, an indication of better decision quality at the

acquirer ex ante; and Amel-Zadeh and Zhang (2012) show acquirers pay lower price for targets

with poor reporting quality. Focusing on product markets, Raman and Shahruer (2008) show

poor accounting quality negatively affects the duration of customer-supplier relationships, an

indication of decision quality. Studies also find AIQ affects investors’ uncertainty about payoffs

and cost of capital (e.g., Akins (2018) and Bharath, Sunder, and Sunder (2008) in the context of

credit market, and Behn, Choi, and Kang (2008), Bhattacharya, Desai and Venkataraman (2012),

and Francis, LaFond, Olsson, and Schipper (2004) in the context of equity market). However,

little research has examined the implication of financial reporting quality for labor markets,

particularly in the segment of high profile corporate CEOs. Our study attempts to fill in this gap

by focusing on externally hired new CEOs.1

By AIQ, we take the view of Francis et al. (2004) ‒ the degree of uncertainty or imprecision

of financial information in predicting future cash flows. The higher the uncertainty or

imprecision, the lower the AIQ. From a risk-return tradeoff perspective, we argue that CEOs 1 There is, to our knowledge, only one contemporaneous work by Choi, Gipper, and Malik (2019) that examines the relation between financial reporting quality and wage differentials for rank-and-file employees.

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may require a pay premium to compensate for the uncertainty in job security and the risk of

accounting failure due to poor reporting quality. There are at least three non-mutually exclusive

reasons for poor AIQ to raise external CEOs' job security concern. First, because low AIQ makes

it difficult to assess the firm's future cash flows, a new CEO is concerned about the possibility of

joining a “lemon” firm, which might result in a higher likelihood of dismissal, compensation

reduction, and damage to human capital (Gilson 1989; Gilson and Vetsuypens 1993). This

concern is exacerbated by the market’s learning process about the new CEO’s ability (Pan, Wang

and Weisbach 2015) and the CEO’s risk aversion.

Second, low AIQ may limit the ability of a CEO candidate to assess her matching quality

with the firm. Financial economists have long argued that efficient matching of executives with

different styles, skills, and experiences to firms with different attributes is crucial to productivity

(Rosen 1981, 1982). As a critical source of information, financial reports can help an external

CEO to adequately evaluate the new employer's characteristics such as financial condition, status

in life cycle, and position in industry, which is pivotal for an efficient matching. For example, a

CEO with expertise in cost-cutting may work well for a firm in poor financial conditions that

needs formulating and implementing a turnaround strategy, whereas an acquisition style CEO

fits nicely to a firm with strong financial position (McKinsey Quarterly 2009). Low AIQ likely

causes low quality match, raising the new CEO's concern about future job termination, which can

be initiated by either party.

Third, low AIQ may adversely affect the effectiveness of implementing corporate strategies,

thus raising the new CEO’s concern about achieving expected performance goals. In the process

of implementing firm strategies, managers need frequent feedbacks emanated from accounting

systems for adjustment and optimization, and low accounting quality is prone to error, bias,

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and/or untimeliness, thus hindering managers’ ability to dynamically adjust and improve their

strategies (Cheng, Goh, and Kim 2018).

With regard to accounting failure risk, sufficiently low quality of earnings can be related to

fraud and/or lead to shareholder lawsuits and accounting restatements (e.g., Ettredge, Scholz,

Smith, and Sun 2010; Palmrose and Scholz 2004), tarnishing a CEO’s reputation and

jeopardizing her career prospects. For example, Desai et al. (2006) find that managers in firms

announcing restatements are twice more likely to be displaced, and that ousted managers face

reduced employment opportunities. Groysberg, Lin, Serafeim (2017) report that even executives

who are not obviously involved in financial misreporting are stigmatized by a mere association

with the misreporting firms and suffer reputational damage in the managerial labor market

evidenced by being hired less often or getting paid less. In sum, this line of argument suggests

poor AIQ increases the risk assessment of an incoming CEO. Therefore, we expect a

compensation premium for her to bear higher risk associated with poor AIQ.

However, from an agency perspective, poor AIQ may enable CEOs to capture a bigger

portion of firm cash flows (Jin and Myers 2006; Hutton, Marcus, and Tehranian 2009), and enjoy

private benefits (Bushman and Smith 2003). Thus, a new CEO may choose to trade her pay

premium at initial appointment for expected private benefits that she can extract after

appointment to maximize her total utility. If this is the case, we may expect zero compensation

premium or even a compensation discount for CEOs joining firms with low AIQ. Bebchuk,

Fried, and Walker (2002) emphasize the importance of taking managerial private benefits into

account in any examination of executive compensation arrangements.

To examine whether CEOs price AIQ, we focus on newly, externally hired CEOs to sidestep

the potential reverse causality issue. That is because an incumbent CEO has both the incentive

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and ability to directly influence the firm's financial reporting. For example, prior studies show

that CEOs' compensation contracts incentivize them to manage accounting earnings (e.g., Cheng

and Warfield 2005; Burns and Kedia 2006), which suggests that it is the high CEO compensation

that leads to low AIQ. By studying the relation between an external CEO's first-year

compensation in the new firm and the firm's AIQ in the years before her appointment, we avoid

the reverse causality issue.

Relying on several databases as well as manual search, we compile a comprehensive sample

of 940 external CEO hires from 1994 to 2018. We measure a firm's accounting quality by its

accruals property following Hutton et al. (2009). More specifically, we use modified Dechow

and Dichev (2002)’s accruals as our primary AIQ measure. As a robustness check, we also

consider performance-adjusted abnormal accruals estimated from the model of Kothari, Leone,

and Wasley (2005). After controlling for firm and CEO characteristics, and industry and year

fixed effects, we find that low AIQ of a firm measured in the years before a CEO’s appointment

is associated with a higher level of total compensation at initial appointment. To put in economic

terms, a one-standard deviation decrease in AIQ is associated a 7.38% increase in total

compensation, which translates into roughly one-third of a million dollars per year.

Although we include a comprehensive set of control variables in our main tests, one might

still be concerned that our finding – higher CEO compensation premium for lower AIQ – is due

to correlated omitted variables. To address this issue, we search for potential correlated omitted

variables that reflect the characteristics of compensation contracts, executives, firms, industries,

and states. Specifically, with respect to compensation contract characteristics, we control for the

presence of severance agreements, cash vs. equity pay mix, and equity incentives (delta and

vega); concerning executive characteristics, we control for CEO education, CEO generalist

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index, and CEO pay from her prior employer; As to firm characteristics, we control for

governance mechanism, strategic shift, and predecessor CEO pay; and finally, we include time-

varying industry and state effects to control for industry and state level factors. Overall, our

results remain robust to these additional controls.

To explore the underlying mechanisms of CEO pay sensitivity to firm AIQ, we conduct three

tests. The first test examines whether a firm's AIQ measured before the CEO appointment is

associated with the CEO’s subsequent separation risk. We find CEOs joining a low AIQ firm are

23.6% more likely to leave the firm than those joining a high AIQ firm. Our second test

examines whether a firm’s AIQ is related to subsequent revelation of accounting irregularities

committed before the new CEO appointment. Results show the likelihood of misreporting

revelation is 3.6 % higher for low AIQ firms than for high AIQ firms, accounting for 33% of the

sample mean. In the third test, we examine whether the CEO pay-AIQ relation varies with levels

of corporate governance. If a new CEO can take advantage of the slack governance effected by a

weak board to extract private benefits (Jensen and Meckling 1976; Yermack 2006; Andrews,

Linn and Yi 2009), and poor reporting quality further shields her from external parties’ discipline

(Bushman, Chen, Engel, and Smith 2004), we expect a tradeoff between private benefits and

initial pay premium, which weakens the pay-AIQ relation in firms with less effective boards. We

find evidence consistent with this prediction. Taken together, our findings suggest that job

security and misreporting concerns as well as private benefits may explain the pricing of

accounting quality in managerial labor markets.

To further substantiate the evidence of job security concern channel for the pay-AIQ relation,

we conduct two cross-sectional analyses. In particular, we consider costs to a CEO upon her

separation from the firm and expect the pay-AIQ relation to increase with such costs. First, we

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test this prediction in the context of CEO career horizon. As young CEOs have longer career

horizons (Gibbons and Murphy 1992), the costs of turnovers (e.g., job search cost, and

reputational loss, among others) are likely to be more relevant and critical for them than for

CEOs who are close to retirement. Second, we consider a CEO's external employment

opportunity. Because an adoption of the Inevitable Disclosure Doctrine (IDD) by the state in

which a firm is headquartered limits the CEO external employment opportunity and mobility (Li,

Shevlin, Zhang 2018), the turnover costs are expected to be higher. Using CEO age and state

IDD adoption as proxies for turnover costs, we find that the positive relation between AIQ and

CEO pay is stronger for young CEOs and CEOs of firms headquartered in the states that adopted

the IDD. These cross-sectional results provide further support for our argument that the CEO

pay-AIQ relation filters through job security concern. Given the cross-sectional evidence, any

correlated omitted variable explanation would not only have to explain why AIQ affects CEO

compensation, but would also have to explain why the relation varies with CEO age and state

adoption of IDD. In addition, the state courts' decisions on the IDD generates exogenous

variation in job security concerns, which is not affected by factors that are related to CEO

compensation contracts. Thus, these cross-sectional results helps further mitigate concerns about

correlated omitted variables.

Our study contributes to three strands of literature. First, it relates to the literature examining

the role of AIQ in financial markets. Prior studies provide evidence suggesting that poor AIQ

increases market participants’ uncertainty about firms’ future cash flows. For example, Akins

(2018) finds credit rating agencies tend to disagree on the level of credit rating when a firms’

financial reporting quality is low; and Behn et al. (2008) shows higher dispersion in financial

analysts’ earnings forecasts for firms with low reporting quality. More relevant to our study,

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prior research documents risk premium for poor AIQ in credit markets, equity markets, the

market for corporate control, and product markets. We complement these studies by uncovering

the pricing effect of AIQ in the managerial labor market. Perhaps more importantly, our evidence

suggests that the underlying mechanisms operate through the effect of AIQ on separation risk,

accounting failure risk, and the extraction of private benefits. Distinct from a concurrent study by

Choi et al. (2019) that shows a firm’s reporting quality is associated with the labor costs of rank-

and-file employees, our paper focuses on the managerial labor market that exhibits greater

ability, potency, latitude, and bargaining power on the supply side. As these two labor markets

operate differently in many dimensions, we believe these two studies are complementary to each

other, and as a whole enrich our understanding of financial reporting implications in labor

markets.

Second, our paper relates to the literature on managerial compensation. There has been a

long-standing debate on whether the high level of executive pay represents managerial rent

extraction through entrenchment (e.g., Bebchuk et al. 2002) or optimal contracting for risk

(Kaplan 2008). Our findings suggest pay premiums associated with poor AIQ are likely a

compensation for risk, be it from job security or accounting failures. In spirit, our results are

consistent with a recent paper by Carter, Franco and Tuna (2020) that shows managers are paid

higher for the uncertainty of their matching with a new firm. On the other hand, we find evidence

suggesting that low AIQ may facilitate managerial extraction of private benefits, resulting in a

lower sensitivity of pay to AIQ. This novel result highlights the importance to consider private

benefits when studying managerial compensation, a call from Bebchuk et al. (2002) and

Bebchuk and Fried (2004).

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Lastly, our study links the accounting literature with the labor economics literature that

examines the relation between employment risk and compensating differentials. While the latter

theorizes that employees should receive compensating differentials for bearing employment risk

(Abowd and Ashenfelter 1981), empirical evidence on the specific source that affects employee

perception of employment risk is limited, and mostly focuses on job types, industry variations in

turnover risk, and financial distress risk (e.g., Hamermesh and Wolfe 1990; Peters and Wagner

2014; Chemmanur, Cheng, and Zhang 2013). Our findings feature another source – accounting

information quality – that should be and is priced in the labor market.

3. Data, Sample, and Research Design

3.1. Measure of accounting information quality

We estimate the normal level of accruals for a firm using coefficients derived from an

industry-year cross-sectional model of accruals and use unsigned abnormal accruals to measure

accounting quality. The cross-sectional estimate imposes less data restrictions relative to the

firm-specific time-series estimation and thus can avoid survivorship bias in the sample.

Specifically, we rely on the modified Dechow and Dichev (2002) model used by McNichols

(2002), Ball and Shivakumar (2006), and Dou, Khan, and Zou (2016). We estimate the following

specification by industry-year (two-digit SIC code) and use the absolute value of the residual as

our measure of accruals quality:

TAt = a0 + a1CFOt-1 + a2CFOt + a3CFOt+1 + a4△REVt + a6PPEt + εt , (1)

where TAt is total accruals, computed as the difference between income before extraordinary

items and operating cash flow (CFO); △REVt is change in sales; PPEt is gross property, plant,

and equipment. All variables are scaled by total assets (ATt-1). To make our accruals measure

representative of a firm's accounting quality and capture the underlying characteristics of a firm's

information uncertainty, we use the three-year average of the accruals measure as proxy for the

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firm's AIQ. Because a higher level of accruals indicates lower AIQ, we multiply the accrual

measure by -1 to create our AIQ variable AIQ_DD so that a higher value of AIQ_DD denotes

higher AIQ. As a robustness check, we use an alternative accruals measure based on Kothari et al

(2005) to measure AIQ and label it as AIQ_KLW.

3.2. Measure of CEO compensation

We use the ExecuComp variable tdc1 as the measure of total CEO compensation. Total

compensation includes salary, bonus, other annual compensations, the value of restricted stock

granted in the year, the Black-Scholes value of stock options granted, long-term incentive

payouts, and all other compensations. We convert it to 1992 dollars using the Consumer Price

Index from the Bureau of Labor Statistics.

3.3. Data and sample

We collect and merge data from several databases (ExecuComp, AuditAnalytics, BoardEx,

and Capital IQ), and complement that with manual search of firms' press releases, SEC filings,

Bloomberg, Linkedin and other online resources to identify externally hired new CEOs.2 From

ExecuComp, we identify 5,281 new CEOs from 1994 to 2018.3 After requiring data availability

on firm characteristics (firm size, leverage, market-to-book, and return on assets, stock return,

and return volatility), we are left with 3,405 new CEOs. We classify them into three exclusive

groups: (1) 2,201 internal hires, (2) 1,009 external hires, and (3) 195 interim CEOs or CEOs

2 Specifically, we take five steps. First, in ExecuComp we locate a CEO based on the ceoann field, and then a new CEO on the becameceo field and on comparing the CEOs of the same firm in two consecutive years. Second, we identify a new CEO as an inside hire if (1) she joined the company ( joined_co from ExecuComp) one year before her CEO appointment, (2) she appears in ExecuComp as an executive of the same firm the previous year, or (3) the reason for the new CEO appointment in AuditAnalytics is "Position Change within Company". Third, we identify a new CEO as an external hire if (1) she joined the firm less than one year before her CEO appointment, or (2) she appears in ExecuComp as an executive of another company in the previous year. Fourth, we further use BoardEx and Capital IQ to obtain CEO's employment history to determine the new CEO status. Lastly, for those new CEOs whose inside/outside status cannot be determined from the above procedures, we manually search firms' press releases, SEC filings, Bloomberg, LinkedIn, and other online resources.

3 Our sample period starts at 1994 because data collection of ExecuComp on the S&P 1500 firms began in 1994. Before that, the data are mostly for the S&P 500 firms.

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recruited from the board of directors. An internally hired CEO, or a CEO with prior directorship

with the firm, can have significant influence on the firm's past financial reporting quality and the

determination of her compensation package. An inclusion of these new CEOs might raise

endogeneity concern. Thus, we focus on newly appointed CEOs who are hired from outside the

firms. After requiring sample firms to have data on AIQ_DD, we end up with 940 external

CEOs. Panel A of Table 1 details the sample selection process.

In Panel B, we present the sample distribution by year. While more external CEO hires are

observed during economic recessions (i.e., 2000-2003 and 2007-2009), there exist no obvious

yearly clusters. In Panel C, we report the sample distribution by industry. Firms in different

industries exhibit substantial variations in external CEO hires with the top three industries being

business equipment, manufacturing, and wholesale & retail, and the bottom three industries

being telephone & television transmission, chemicals & allied products, and oil & gas & coal

extraction. This is generally in line with Cadman, Carrizosa, and Peng (2020) despite that their

sample is smaller than ours.

3.4. Model specification

To study whether external CEOs price their new employers’ accounting quality when

negotiating compensation packages, we estimate the following regression model:

Log(CEOTotPayt) = β0 + β1*AIQ_DDt-1 + β2*Log(ATt-1) + β3*Leveraget-1

+ β4*MTBt-1 + β5*ROAt-1 + β6*Returnt-1 + β7*StdReturnt-1

+ β8*Chairmant + β9*CEOMalet + Industry fixed effects + Year fixed effects + ϵt. (2)

The model relates the first-year compensation of newly appointed external CEOs to firms'

AIQ measured as the average over the most recent three years (t-3 to t-1) prior to their

appointment (AIQ_DD). Due to the right skewness of CEO compensation, we use the logarithm

transformation [Log(CEOTotPayt)] to ease the undue effect of extreme observations. β1 is the

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coefficient of interest, which reflects the sensitivity of new external CEO pay to the employer’s

pre-existing condition of AIQ.

We include in the regressions several firm-level control variables that are generally used in

the executive compensation literature. To control for firm size, we use the logarithm of total

assets [Log(AT)], because large firms are known to pay their executives more (Murphy 1999). To

control for the effect of firm financial structure, we use book leverage ratio (Leverage). On the

one hand, CEOs may demand a pay premium for firms with higher leverage to compensate for

financial distress risk. On the other hand, higher leverage may signal better firm quality and

higher debt capacity, which may suggest a lower firm risk. We use market-to-book ratio (MTB)

to control for growth options, as firms with ample growth options are expected to have a higher

level of accruals (low AIQ) and offer higher pay to executives to induce greater efforts. To

control for firm performance, we include return on assets (ROA) and stock return (Return). CEOs

are generally rewarded for good firm performance and therefore their pay is positively associated

with firm performance. In our setting, new external CEOs obviously do not contribute to firm

performance before their appointment, so it is unclear whether their pay will be affected.

Operational risk can bear on both accounting quality and CEO pay, so we control for stock return

volatility. All these firm-level control variables are measured in the year prior to the CEO

appointment. We control for CEO characteristics by including CEO-chairman duality

(Chairman) and CEO gender (CEOMale).

Given the variations in external CEO compensation across years and industries, we control

for industry and year fixed effects, where industry membership is classified based on the two-

digit SIC code. To avoid the influence of outliers, we winsorize all continuous variables at the

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1st and 99th percentiles. All standard errors are adjusted for clustering at the industry level.

Detailed variable definitions are in Appendix A.

3.5. Summary statistics

Panel A of Table 2 reports summary statistics for the CEO sample. The mean (median) total

compensation, cash compensation (salary and bonus), and equity-based pay (options and

restricted stocks) for external CEOs are $3.892 (2.116), 0.673 (0.481), 2.948 (1.351) million,

respectively. As shown in Panel B of Table 1 and Figure 1, there exhibit substantial variations in

total CEO pay across years, which is mostly driven by equity-based pay. The CEO pay reaches

an all-time high right before the passage of the Sarbanes-Oxley Act of 2002 and drops to a new

low during the 2007 financial crisis. Panel C of Table 1, together with Figure 2, presents the

external CEO pay by industry. Industry variations in CEO pay are substantial with the total pay

in the top industry (telephone & television) tripling that in the bottom industry (customer

durables).

The mean (median) value of AIQ_DD is -0.095 (-0.073). Panel A of Table 1 shows variations

in AIQ across years, characterized by lower levels of AIQ during economic recessions (i.e.,

2001-2003 and 2007-2009). We also find variations in the level of AIQ across industries:

manufacturing and wholesale & retail are the lowest and telephone & television and healthcare

highest, with the level of AIQ of the former being 70% higher than that of the latter.

The average firm size in terms of total assets is $2,734 million. About 23% of total assets are

in the form of debt. The average market-to-book ratio is 1.854 and stock return volatility is

0.136. Consistent with prior studies (e.g., Cadman et al. 2020, Parrino 1997), we find that

external CEO hires are preceded by poor firm performance with negative return on assets and

stock return. In terms of CEO characteristics, about 29% of external hires are also appointed as

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board chairman. This number is lower than the average from the ExecuComp universe (57%)

over the same time period, suggesting that quite some external CEOs become board chairman

after joining the companies.4 Only 3.3% external CEOs are female. We report pairwise

correlations of independent variables used in the main regression for CEO pay in Panel B of

Table 2. The correlations between control variables are low, suggesting a minimal concern about

multicollinearity.

4. Accounting Information Quality and CEO Pay

In this section, we first present the main empirical results on the relation between AIQ and

external CEO pay, and then address a main endogeneity issue – omitted variable bias – that may

complicate the interpretation of our main results.

4.1. Main Empirical Results

Table 3 reports the estimation of Equation (2) that relates the first-year total pay for a new

external CEO to the AIQ that is measured before the new CEO joining the firm. The coefficient

on AIQ_DD is negative and significant, which is consistent with our expectation that external

CEOs demand a pay premium to compensate for the risk associated with poor AIQ. Specifically,

its magnitude of -0.901 (t = -2.55) implies that a one-standard deviation decrease in AIQ_DD

leads to a 7.38% [exp(0.901*0.079)-1] increase in total CEO pay, equivalent to $288,008

($3,892,000 * 0.074) in annual pay premium. This amount represents roughly 27% of the

average contemporaneous non-CEO executive pay in the ExcuComp universe ($1.056 million).

By comparison, we estimate the economic impact of stock return volatility on CEO pay and find

that a one-standard deviation increase in StdReturn is associated with a 7.08%

4 In the ExecuComp universe, more than 66% of CEOs serve as board chairman from 1994 to 2004 and the fraction goes down to 50% between 2005 and 2017, suggesting a trend in U.S. firms from a preference of duality towards a separation of the two responsibilities.

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[exp(0.888*0.077)-1] increase in total CEO pay. Thus, the economic significance of the AIQ

effect on CEO pay is comparable to that of stock return volatility.

Because external CEOs have nothing to do with their new employers’ financial reporting pre-

existing condition, the coefficient on the AIQ measure likely captures the effect of AIQ on CEO

pay, that is, external CEOs ask for a pay premium commensurate with firm risk associated with

poor AIQ while negotiating their compensation. Also, by including year fixed effects and

industry fixed effects, our results highlight the cross-sectional variation in CEO compensation. In

spirit, our finding is consistent with Peters and Wagner (2009) that the effect on pay of dismissal

risk is eight times as high along the cross-section as over the time series. Overall, our result

suggests that external CEOs perceive employment risk associated with poor AIQ and price this

risk in compensation at their initial appointment.

As to coefficient estimates for the control variables, we find that large firms and firms with

more growth options pay their new CEOs higher. Firms' past performance is not positively

related to the level of compensation of incoming CEOs. We find that firms with higher leverage

pay their new CEOs less.5 Consistent with CEOs pricing operational risk, we find a positive

coefficient on return volatility. We fail to find significant compensation difference between male

and female external CEOs. Neither is there evidence that external CEOs with concurrent

chairmanship appointment get paid more.

4.2. Addressing omitted variables bias

Because external CEOs have no influence on the pre-existing condition of AIQ of their

employers, the documented relation between AIQ and CEO pay is unlikely to be plagued by

reverse causality. However, omitted variables that affect both AIQ and CEO pay can still drive

5 The extant literature presents mixed evidence on the relation between leverage and CEO pay. While Chemmanur et al. (2013) find a positive association, Dai, Rau, Stouraitis, and Tan (2020) document a negative relation and Otto (2014) reports both negative and positive relations in different specifications.

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the results, making the documented relation spurious. In this sub-section, we explicitly search for

and address omitted variables at the compensation contract, firm, executive, industry, and state

levels to validate the interpretation of our findings.

4.2.1. Omitted variables at the compensation contract level

We conduct three tests to address potential omitted variables at the compensation contract

level and report the results in Panel A of Table 4. Severance agreements, as separation

settlement, are widely used in CEO pay package especially in uncertain business environments

and high employment risk situations (Chen, Cheng, Lo, and Wang 2015; Rau and Xu 2013).

Thus, if severance agreements are negotiated alternatively to provide new external CEOs

protection against employment risk associated with poor AIQ, this may weaken the power of

annual flow compensation. To deal with this issue, we first examine whether the presence of

severance agreements is related to AIQ. The data on severance agreements are available from

1995 to 2009 and we use a dummy variable, Severance, to represent the presence of severance

agreements.6 About 87% of our sample firms offer severance packages to external CEOs, which

is higher than the 60% for average CEOs documented by Rau and Xu (2013).

We estimate a variant of Equation (2) by replacing Log(CEOTotPay) with Severance as the

dependent variable. Untabulated results show that Severance is not statistically related to

AIQ_DD. Next, we control for the presence of severance agreements in Equation (2) and find

that the effect of AIQ on CEO pay remains robust. The overall evidence suggests that severance

packages are not used as an alternative mechanism to compensate for employment risk in

relation to poor AIQ, possibly because separation pay is settled on a discretionary basis

(Yermark 2006) and can have negative reputational effect (Dash 2011). We obtain similar results

if we use an ordinal variable that measures the strength of monetary protection of the CEO

6 We thank the authors of Chen et al. (2015) for sharing the data on severance agreements.

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severance package based on the ratio of the severance pay to the starting salary (0 for no

coverage, 1 for the ratio below 2, 2 for the ratio between 2 and 3, and 3 for the ratio above 3).

Relative to the average CEOs in ExecuComp, external CEOs receive a much higher

proportion of equity-based pay (0.558 vs. 0.432). To test whether the documented pay premium

is driven by risky equity-based pay, we first examine the relation between individual pay

components and AIQ by replacing Log(CEOTotPay) with cash pay and equity-based pay one at

a time in Equation (2). Untabulated results show that the coefficient on AIQ_DD is negative but

not significant in both specifications, suggesting that the pay premium to compensate for poor

AIQ is not driven by risky equity-based pay. To further control for the riskiness of CEO pay

package, we include the proportion of equity-based pay (PayMix) in Equation (2) and find that

the effect of AIQ on total CEO pay is reduced but still significant.

To minimize the likelihood that our results are due to compensation for effort cost, we

control for the incentive alignment (delta) and risk-taking incentive (vega) induced by granted

stocks and options and find that the pay premium remains significant. Specifically, we include in

the regression delta which measures the sensitivity of CEO portfolio of current grants of stocks

and options to changes in stock prices, and vega which measures its sensitivity to changes in

stock return volatility.7

4.2.2 Omitted variables at the firm level

We conduct three tests to address potential omitted variables at the firm level and report the

results in the last three columns of Panel A. First, given that board of directors plays a critical

role in monitoring a firm's financial reporting and compensating its executives (Klein 2002;

Chhaochharia and Grinstein 2009), the positive relation between AIQ and CEO pay may simply

7 The calculation of Delta and Vega is based on Core and Guay (2002) and we thank the authors of Coles, Daniel, and Naveen (2006) for making the data available.

16

reflect governance failure – lax monitoring leads to both poor AIQ and undue CEO pay. To

address this concern, we control for board monitoring in the regression. We collect data on three

board characteristics (board size, board independence, and director busyness) from Institutional

Shareholder Services (ISS). We measure board size as the total number of directors and board

independence as the percentage of outside directors on the board. We use multiple board

appointments of independent directors to measure board busyness, which captures director

efforts and diligence. Specifically, an indicator variable, NonBusyBoard, equals one if the board

does not have busy independent directors (with more than three board appointments). Since ISS

data are available only from 1996 and board characteristics are generally sticky especially in the

early 1990s, we use the 1996 status for observations in 1994 and 1995. Among the three board

characteristics, only board busyness is significantly related to CEO total pay. Reassuringly, the

coefficient on AIQ_DD remains unchanged after controlling for board monitoring.

Second, leadership transitions may be surrounded by a strategic shift or accounting failures.

The strategic shift can increase operational risk, leading to a higher level of accruals and CEO

pay. As well, accounting failures are related to poor accruals quality and can prompt a firm to

hire a high quality CEO (i.e., high pay) to rectify the situation. To address the omitted variable

concerns associated with these scenarios, we control for the involuntary departure of predecessor

CEOs (ForcedTurnover) and the incidence of CFO turnover before the new CEO appointment

(CFOTurnover) as forced CEO turnover and CFO changes are likely indicative of a strategic

shift and accounting problems. Empirical results show that the relation between AIQ and CEO

pay remains robust to the controls of forced CEO turnover and CFO change.

Third, to account for unobservable factors that may affect a firm's practice to pay its top

executive, we control for the total pay of predecessor CEO (DepartCEOPay) in the year before

17

the external CEO appointment. While the pays for the predecessor CEO and the external CEO

are positively related, we find that the coefficient on AIQ_DD is largely unchanged, suggesting

that the effect of AIQ on CEO pay is not an artifact of the firm's compensation practices.8

4.2.3. Omitted variables at the executive level

We address omitted variables at the executive level and report the results in Panel B of

Table 7. Although external CEOs have no relation with new employers' pre-existing condition of

accounting quality, firms with certain characteristics (such as low AIQ) may prefer certain type

of CEOs with superior ability to negotiate a high pay. This self-section can lead to a spurious

relation between AIQ and CEO pay. We first include CEO education and the holding of an MBA

degree to account for the possibility that CEO's education and financial expertise may affect her

bargaining power in negotiating pay package. We obtain data on CEOs' education from BoardEx

as well as through manual search. While we find that CEOs with MBA degrees are paid better,

the relation between AIQ and CEO pay remains unchanged.

Second, a CEO may accumulate broad skills over time along her career path and these skills

can qualify her for pay premium (Custodio, Ferreira, and Matos 2013). We use CEO generalist

index (Generalist) constructed by Custodio et al. (2013) to capture a CEO's general ability that is

transferable to other firms or industries. Consistent with Custodio et al. (2013), we find that

CEOs with greater general skills get higher pay. More importantly, the effect of AIQ on CEO

pay is not subsumed by managers' general ability.

Finally, we use the total pay an external CEO received from, and characteristics of, her

previous employer to capture her overall ability and skills as both can be indicative of the CEO

quality. Specifically, we control for the total compensation received from the previous employer

8 A firm may change its compensation practice when hiring an external CEO. To address this unobservable changing pay policy, we control for contemporaneous non-CEO executive total pay and find that the coefficient on AIQ_DD remains positive and significant.

18

(OutFirm_TotPay), its firm size (OutFirm_SIZE), firm performance (OutFirm_ROA),

operational volatility (OutFirm_StdCFO), whether the external CEO was the CEO of previous

employer (OutFirm_CEO), and whether the previous and current employers are in the same

industry (SameIndustry). Not surprisingly, we find that the compensation from, and the

accounting performance of, her previous employer are positively related to her compensation in

the new firm. More conspicuously, our main inference remains unchanged after controlling for

overall CEO ability.

4.2.4. Omitted variables at the industry and state levels

We address omitted correlated variables at the industry and state levels and report the

estimations in the last three columns of Panel B, Table 7. To address time-varying industry

effects, we re-estimate Equation (2) by including the interaction of industry and year fixed

effects. The coefficient on AIQ_DD remains positive and significant and its magnitude is not

reduced. State-level factors such as quality of life can also affect executive compensation (Deng

and Gao 2013). We control for time-varying state effects by including the interaction of state and

year fixed effects and find that the effect of AIQ remains robust. Lastly, we use an alternative

way to control for time-varying industry- and state-level factors by including annual average pay

for CEOs in the same industry [Log(IndAvgTotPay)] and in the same state

[Log(StateAvgTotPay)]. Our results remains largely the same.

5. Channel Analyses

We propose three channels that may explain the pricing of AIQ at initial CEO appointment.

The first channel is through job security concerns about higher likelihood of separation in

relation to poor AIQ. The second channel concerns reputational risk arising from the revelation

of misreporting, that is, poor AIQ may foretell potential accounting failures. The last channel

incorporates agency perspective by positing that poor AIQ may induce new CEOs to trade her

19

pay premium at initial appointment for expected private benefits that she can extract after

appointment to maximize her total utility. In this section, we assess the relevance of these three

channels.

5.1. Job security concern channel

If poor AIQ increases job security concerns, this should manifest itself in a higher likelihood

of subsequent departures of external CEOs. To test the second channel, we first report the

distribution of subsequent CEO departures in Panel A of Table 5. Roughly 3.7% external CEOs

depart within one year (t), 12.1% in one year after (t+1), 15.3% in two years after (t+2), 12.7% in

three years after (t+3), 9.5% in four years after (t+4), 7.7% in five years after (t+5), 21.6% in

more than five years after (>=t+6), and 17.3% are still in office. Panel B reports the comparisons

of subsequent CEO departures between high and low AIQ firms, which is defined based on

annual median values of AIQ_DD. We find that low AIQ firms have higher departure rates than

high AIQ firms in the first four years following external CEO appointment, suggesting that

external CEOs are more likely to leave their employers with poor accounting quality.

To control for the effect of other covariates on the likelihood of external CEO departure, we

further examine the issue in a multivariate setting. Because our data are right-censored (i.e., our

sample period ends as of December 2018 and thus we could not observe what happens after

that), we conduct a duration analysis by estimating the following Cox proportional hazards

model to examine the effect of AIQ on the occurrence and the speed of CEO departures:

Depart (t,t+1,…) = β0 + β1*Low_AIQ_DDt-1 + β2*Log(ATt-1) + β3*Returnt-1

+ β4*StdReturnt-1 + β5*StdCFOt-1 + β6*ForcedTurnovert-1 + β7*CFOTurnover (t-2,t) + Industry fixed effects + Year fixed effects + ϵt, (3)

where the dependent variable, Depart, is set to 1 if an observation has external CEO departure

(uncensored), and 0 otherwise (no departure, right-censored). We measure time to departure as

20

the length between the date an external CEO joining the company and the date the CEO

departing the company or the end of 2018 for right-censored observations. To ensure the external

CEO departures are related to AIQ, we exclude CEO departures due to retirements, health

reasons, and deaths. We include both voluntary and involuntary departures because poor quality

match can cause either side to terminate the employment. To ease the interpretation of estimated

results (i.e., hazard ratio), we use an indicator variable Low_AIQ_DD, which is defined based on

the annual median values of AIQ_DD.

In addition to controlling for firm size, stock return, stock return volatility, and cash flow

volatility which are measured in the year before external CEO appointment, we control for

involuntary departure of the predecessor CEO and incidence of CFO turnover as these may be

signaling a higher future CEO turnover. We set ForcedTurnover to 1 if the predecessor CEO is

fired and 0 otherwise,9 and set CFOTurnover to 1 if there is a CFO change in the most recent

three year (from t-2 to t), and 0 otherwise. About 30.2% of CEO turnovers are involuntary and

44.5% of the firms experience CFO change before CEO succession.

Panel C of Table 5 reports the estimation results of Equation (3). The coefficient on

Low_AIQ_DD is positive and significant, suggesting that external CEOs who join firms with

poor AIQ are more likely to depart than those who join firms with high AIQ. The hazard ratio

reported along with the coefficient shows that the CEOs in the former situation are 23.6% more

likely to leave their employers than those in the latter situation. The evidence is consistent with

poor AIQ giving rise to higher separation risk, which is priced in external CEO pay premium.

5.2. Revelation of misreporting channel

9 We follow Peter and Wagner (2014) and Jenter and Kanaan (2015) to define forced departure and obtain data on forced turnover from https://www.uva.nl/en/profile/p/e/f.s.peters/f.s.peters.html?cb.

21

To test the second channel, we run the following regression to relate the likelihood of

subsequent revelation of misreporting to AIQ before new CEO appointment:

Restatement(t,t+1,…) = β0 + β1*Low_AIQ_DDt-1 + β2*Log(ATt-1) + β3*Returnt-1

+ β4*StdReturnt-1 + β5*StdCFOt-1 + β6*ForcedTurnovert-1 + β7*CFOTurnover(t-2,t) + Industry fixed effects + Year fixed effects + ϵt, (4)

where t is the year when an external CEO joins the firm. Because we include industry and year

fixed effects, we follow Kim, Shroff, Vyas, and Wittenberg-Moerman (2018) and estimate the

regression as a linear probability model to avoid incidental parameter problem.

To ensure that misreporting results from firms’ past reporting quality on which the new

external CEO has no influence, we require that the beginning date of misreporting is before the

new CEO appointment and restatement announcement occurs during her tenure with the

company.10 That is, the misreporting is committed before the new CEO appointment but

uncovered and made public during her tenure. We rely on AuditAnalytics to define Restatement

because it has the beginning date of misreporting and announcement date of restatement. Among

668 observations with the available data, 71 (10.63%) have a value of 1 for Restatement and the

remaining have a value of 0. This is generally consistent with the restatement rate of 12% around

CEO turnover documented by Huang, Parker, Yan, and Li (2014) although our criteria are more

restrictive. Given that in general sufficiently low quality of earnings is more likely to be related

to misreporting, we use an indicator measure of AIQ, Low_AIQ_DD, which is defined based on

the annual median values of AIQ_DD. Other variables are as defined in Equation (3).

We report the estimates of Equation (4) in Table 6. As expected, the coefficient on

Low_AIQ_DD is positive and significant at the traditional levels, suggesting that potential

misreporting in relation to poor AIQ is another risk factor priced by external CEOs. The

10 For example, if an external CEO joins the firm on 3/15/2005 and departs on 5/20/2009, we require that misreporting begin before 3/15/2005 and restatement be announced between 3/15/2005 and 5/20/2009.

22

magnitude of 0.036 implies that low AIQ firms are 3.6% more likely to have accounting

misreporting than high AIQ firms. Given the sample mean of 10.63% for Restatement, this effect

is substantial.

5.3. Extraction of private benefits

On the one hand, poor AIQ increases employment risk for new CEOs; on the other hand, the

opaqueness may help shelter CEOs from external monitoring after appointment. The latter may

induce external CEOs to trade after-appointment private benefits for pay premium at initial

appointment to maximize their total utility. In a principal-agency framework, this is more likely

to occur in situations with poor board monitoring. Thus, we expect that the commensurability

between CEO pay and the risk she bears at initial appointment is less pronounced in poorly-

governed firms.

Prior literature finds that directors׳ busyness is detrimental to board monitoring quality and

shareholder value (e.g, Hauser 2018; Falato, Kadyrzhanova and Lel 2014). Indeed, we find that

among three board characteristics, only board busyness matters to CEO pay (Table 4). Thus, we

gauge the extent of board monitoring by multiple board appointments of independent directors.

We augment Equation (2) by adding the indicator variable, NonBusyBoard, which captures the

board's diligence and effort, and its interaction with AIQ_DD. The coefficient on AIQ_DD marks

the effect of AIQ on CEO pay in weakly-governed firms and that on the interaction term shows

the incremental effect in well-governed firms. As reported in Table 7, the effect of AIQ on CEO

compensation is not significant in poorly-governed firms but is much stronger in well-governed

firms. This evidence seems to suggest that external CEOs refrain from compeling a pay premium

when factoring in future private benefits associated with poor accounting quality.

6. Supplementary Analyses

23

Two supplementary analyses are performed in this section. We first substantiate the evidence

of job security concern channel by exploring the variation in the CEO pay-AIQ relationship.

Then we assess the robustness of our results to an alternative measure of AIQ.

6.1. Cross-sectional Analyses

We conduct two cross-sectional analyses to inquire whether external CEOs are more

sensitive to poor AIQ when they have greater job security concerns. Employment risk involves

the likelihood of separation and the cost of separation. The higher the separation costs, the

greater the pay premium is required for a given level of AIQ, which increases the sensitivity of

pay premium to AIQ. We explore two measures of the cost of separation: (1) CEO’s career

horizon and (2) restriction on CEOs' outside employment opportunity as a result of the adoption

of the IDD by the states in which their new employers are headquartered.

As argued by Gibbons and Murphy (1992), career concerns are stronger when a CEO is

further from retirement because of a longer prospective career. Specifically, these CEOs bear

higher turnover costs for being associated with poor AIQ firms as this association will negatively

affect market assessments of their ability. In contrast, a CEO who is close to retirement would

have less career concerns and thus be less sensitive to employment risk. Therefore, we expect

that young CEOs demand a higher pay premium than old CEOs to compensate for perceived

higher employment risk. We define young CEOs as those who are 56 years old or younger (56 is

75 percentile of CEO age of our sample) and old CEOs as those who are older than 56. We

create an indicator variable, Young, that takes a value of 1 for young CEOs and 0 for old CEOs.

Interacting Young with AIQ_DD, we expect a negative coefficient on the interaction term. As

shown in the first column of Table 8, the coefficient on AIQ_DD*Young is negative and

24

significant, consistent with our expectation that the relation between AIQ and CEO pay is more

pronounced when CEOs have greater job security concerns.

The IDD is a legal principle to preempt a formal employee’s attempt to work for a

competitor. The employer can obtain a judicial injunction if a district court concludes that the

employee who knows the employer’s trade secrets would likely use the confidential information

(i.e., inevitable disclosure) to the employer’s detriment. Trade secrets include a wide, abstract

area of subject matters such as business plans, manufacturing process, customer lists, financial

information, logistics, etc. Because a manager has access to her firms’ confidential information,

an adoption of the IDD by a court in the firm’s headquarter state can restrict her external

employment opportunities and thus increase the costs of job loss. Li, Shevlin, and Zhang (2018)

find that IDD can effectively reduce top executives' mobility.

Many U.S. states adopt or reject the IDD by precedent-setting cases at different points in

time. According to Klasa, Ortiz-Molina, Serfling, and Srinivasan (2018) and Flammer and

Kacperczyk (2019), by 2013, four states rejected the IDD; 21 states adopted the IDD, 10 of

which later rejected it.11 Because in general court decision was little influenced by the lobbying

of affected parties, the staggered adoption/rejection of the IDD is an exogenous shock that

generates variation in employment mobility and risk (Klasa et al. 2018). We use this setting to

examine whether external CEOs demand a higher pay when their mobility is limited and their

employment risk increased.

11 AR adopted in 1997 and rejected in 2009, CT adopted in 1996, DE adopted in 1964, FL adopted in 1960 and rejected in 2001, GA adopted in 1998 and rejected in 2013, IL adopted in 1989, IN adopted in 1995, IA adopted in 1996, KS adopted in 2006, MA adopted in 1994 and rejected in 2012, MI adopted in 1966 and rejected in 2002, MN adopted in 1986, MO adopted in 2000, NJ adopted in 1987 and rejected in 2012; NY adopted in 1919 and rejected in 2009, NC adopted in 1976, OH adopted in 2000 and rejected in 2008, PA adopted in 1982, TX adopted in 1993 and rejected in 2003, UT adopted in 1998, WA adopted in 1997 and rejected in 2012, CA rejected in 2002, MD rejected in 2004, NH rejected in 2010, and VA rejected in 1999.

25

We thus focus on these 25 states with precedent-setting cases on the IDD by the state courts.

We create an indicator variable, IDD, that equals 1 for the years that an IDD adoption was in

place in the state where a firm is headquartered and 0 for the years that an IDD rejection is in

place. We interact IDD with AIQ_DD and expect a negative coefficient on the interaction term if

the restricted employment opportunity moderates the relation between AIQ and CEO pay

premium. Consistent with our expectation, column 2 of Table 8 shows a negative and significant

coefficient on AIQ_DD*IDD. As the adoption/rejection of the IDD generates exogenous

variation in employment risk, which is not affected by factors that determine CEO compensation

contracts, this result can alleviate the concern that unobserved omitted variables may drive the

documented relation between AIQ and CEO pay.

6.2. Alternative measure of accounting quality

We test the robustness of our results to an alternative AIQ measure, AIQ_KLW, which is

estimated based on Kothari et al. (2005). We replicate the main regression and the three channel

analyses with AIQ_KLW as the variable of interest. In Panel A of Table 9, we find that

AIQ_KLW is negative and significantly related to external CEO total pay. The magnitude of the

coefficient implies that a one-standard-deviation decrease in AIQ_KLW leads to an 8.01%

increase in total CEO pay. In Panel B, we find that external CEOs joining firms with a lower

level of AIQ_KLW is 21.5% more likely to leave than those joining firms with higher AIQ_KLW.

In Panel C, we find that a lower level of AIQ_KLW portends a greater likelihood of subsequent

revelation of misreporting perpetrated before CEO appointment. In Panel D, we find a similar

pricing pattern for AIQ_KLW as for AIQ_DD with varied internal governance quality.

7. Conclusion

Accounting statements are a critical source of information for various stakeholders in making

decisions. Prior studies find ample evidence that accounting information affects decision quality

26

and is priced in credit markets, equity markets, the market for corporate control, and product

markets. We complement these studies by examining the role of accounting information in the

managerial labor markets.

We focus on newly, externally hired CEOs to avoid the potential reverse causality issue. We

find that low accounting quality is significantly associated with higher CEO total compensation,

with a 7.38% pay premium for a one-standard deviation decrease in accounting quality. The

result is unlikely to be driven by correlated omitted variables as we include an exhaustive set of

additional controls at the compensation contract, CEO, firm, industry, and state levels. We

uncover job security concern and subsequent revelation of accounting failures as two primary

mechanisms that explain the pay premium for low accounting quality. We also find firms with

less effective boards have lower sensitivity of CEO pay to accounting quality, suggesting that

expected future private benefits might offset pay premium required by a CEO for the firm’s low

accounting quality at initial appointment. The overall evidence suggests that while in general

AIQ is priced to be commensurate with CEO pay premium in the managerial labor, CEO’s

potential private benefit extraction can also be a factor in the equation in certain circumstances.

Finally, we show the result is more pronounced for young CEOs and for CEOs with restricted

outside employment opportunity, lending further support for the job security concern

mechanism.

27

REFERENCES

Abowd, J. M., and O. C. Ashenfelter. 1981. Anticipated unemployment, temporary layoffs, and compensating wage differentials. In Studies in labor markets: University of Chicago Press, 141-170.

Akins, B. 2018. Financial reporting quality and uncertainty about credit risk among ratings agencies. The Accounting Review 93 (4):1-22.

Amel-Zadeh, A., and Y. Zhang. 2015. The economic consequences of financial restatements: Evidence from the market for corporate control. The Accounting Review 90 (1).

Andrews, A., S. Linn, and H. Yi. 2017. Corporate governance and executive perquisites. Review of Accounting and Finance 16 (3):385-402.

Ball, R., and L. Shivakumar. 2006. The role of accruals in asymmetrically timely gain and loss recognition. Journal of Accounting research 44 (2):207-242.

Bebchuk, L. A., and J. M. Fried. 2004. Stealth compensation via retirement benefits: National Bureau of Economic Research. Working paper. Accessed at https://www.nber.org/papers/w10742.

Bebchuk, L. A., J. M. Fried, and D. I. Walker. 2002. Managerial power and rent extraction in the design of executive compensation: National bureau of economic research. Working paper. Accessed at https://www.nber.org/papers/w9068.

Behn, B. K., J.-H. Choi, and T. Kang. 2008. Audit quality and properties of analyst earnings forecasts. The Accounting Review 83 (2):327-349.

Bhattacharya, N., H. Desai, and K. Venkataraman. 2013. Does earnings quality affect information asymmetry? Evidence from trading costs. Contemporary Accounting Research 30 (2):482-516.

Bharath, S. T., J. Sunder, and S. V. Sunder. 2008. Accounting quality and debt contracting. The Accounting Review 83 (1):1-28.

Burns, N., and S. Kedia. 2006. The impact of performance-based compensation on misreporting. Journal of Financial Economics 79 (1):35-67.

Bushman, R., Q. Chen, E. Engel, and A. Smith. 2004. Financial accounting information, organizational complexity and corporate governance systems. Journal of Accounting and Economics 37 (2):167-201.

Cadman, B., R. Carrizosa, and X. Peng. 2019. Inducement grants, hiring announcements, and adverse selection for new CEOs. Review of accounting studies:1-34.

Carter, M. E., F. Franco, and A. I. Tuna. 2020. Matching Premiums in the Executive Labor Market. The Accounting Review Forthcoming.

Chemmanur, T. J., Y. Cheng, and T. Zhang. 2013. Human capital, capital structure, and employee pay: An empirical analysis. Journal of Financial Economics 110 (2):478-502.

Chen, X., Q. Cheng, A. K. Lo, and X. Wang. 2015. CEO contractual protection and managerial short-termism. Available at SSRN 2142230.

Cheng, Q., B. W. Goh, and J. B. Kim. 2018. Internal control and operational efficiency. Contemporary Accounting Research 35 (2):1102-1139.

Cheng, Q., and T. D. Warfield. 2005. Equity incentives and earnings management. The Accounting Review 80 (2):441-476.

Chhaochharia, V., and Y. Grinstein. 2009. CEO compensation and board structure. The Journal of Finance 64 (1):231-261.

Choi, J. H., B. Gipper, and S. Malik. 2019. Financial reporting quality, turnover risk, and wage differentials: Evidence from worker-level data. Turnover Risk, and Wage Differentials: Evidence from Worker-level Data (November 30, 2019).

Coles, J. L., N. D. Daniel, and L. Naveen. 2006. Managerial incentives and risk-taking. Journal of Financial Economics 79 (2):431-468.

28

Core, J., and W. Guay. 2002. Estimating the value of employee stock option portfolios and their sensitivities to price and volatility. Journal of Accounting Research 40 (3):613-630.

Custódio, C., M. A. Ferreira, and P. Matos. 2013. Generalists versus specialists: Lifetime work experience and chief executive officer pay. Journal of Financial Economics 108 (2):471-492.

Dai, Y., P. R. Rau, A. Stouraitis, and W. Tan. 2020. An ill wind? Terrorist attacks and CEO compensation. Journal of Financial Economics 135 (2):379-398.

Dash, E. 2011. The Lucrative Fall from Grace. The New York Time. September 30, 2011Dechow, P. M., and I. D. Dichev. 2002. The quality of accruals and earnings: The role of accrual

estimation errors. The Accounting Review 77 (s-1):35-59.Deng, X., and H. Gao. 2013. Nonmonetary benefits, quality of life, and executive compensation. Journal

of Financial and Quantitative Analysis 48 (1):197-218.Desai, H., C. E. Hogan, and M. S. Wilkins. 2006. The reputational penalty for aggressive accounting:

Earnings restatements and management turnover. The Accounting Review 81 (1):83-112.Dou, Y., M. Khan, and Y. Zou. 2016. Labor unemployment insurance and earnings management. Journal

of Accounting and Economics 61 (1):166-184.Ettredge, M., S. Scholz, K. R. Smith, and L. Sun. 2010. How do restatements begin? Evidence of earnings

management preceding restated financial reports. Journal of Business Finance & Accounting 37 (3‐4):332-355.

Falato, A., D. Kadyrzhanova, and U. Lel. 2014. Distracted directors: Does board busyness hurt shareholder value? Journal of Financial Economics 113 (3):404-426.

Flammer, C., and A. Kacperczyk. 2019. Corporate social responsibility as a defense against knowledge spillovers: Evidence from the inevitable disclosure doctrine. Strategic Management Journal 40 (8):1243-1267.

Francis, J., R. LaFond, P. M. Olsson, and K. Schipper. 2004. Costs of equity and earnings attributes. The Accounting Review 79 (4):967-1010.

Gibbons, R., and K. J. Murphy. 1992. Optimal incentive contracts in the presence of career concerns: Theory and evidence. Journal of Political Economy 100 (3):468-505.

Gilson, S. C. 1989. Management turnover and financial distress. Journal of Financial Economics 25 (2):241-262.

Gilson, S. C., and M. R. Vetsuypens. 1993. CEO compensation in financially distressed firms: An empirical analysis. The Journal of Finance 48 (2):425-458.

Graham, J. R., C. R. Harvey, and S. Rajgopal. 2005. The economic implications of corporate financial reporting. Journal of Accounting and Economics 40 (1-3):3-73.

Groysberg, B., E. Lin, and G. Serafeim. 2017. Does financial misconduct affect the future compensation of alumni managers? Harvard Business School Working Paper, accessed at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3069937

Hamermesh, D. S., and J. R. Wolfe. 1990. Compensating wage differentials and the duration of wage loss. Journal of Labor Economics 8 (1, Part 2):S175-S197.

Hauser, R. 2018. Busy directors and firm performance: Evidence from mergers. Journal of Financial Economics 128 (1):16-37.

Huang, H.-W., R. J. Parker, Y.-C. A. Yan, and Y.-H. Lin. 2014. CEO turnover and audit pricing. Accounting Horizons 28 (2):297-312.

Hutton, A. P., A. J. Marcus, and H. Tehranian. 2009. Opaque financial reports, R2, and crash risk. Journal of Financial Economics 94 (1):67-86.

Jensen, M. C., and W. H. Meckling. 1976. Agency Costs and the Theory of the Firm. Journal of Financial Economics 3 (4):305-360.

Jenter, D., and F. Kanaan. 2015. CEO turnover and relative performance evaluation. The Journal of Finance 70 (5):2155-2184.

Jin, L., and S. C. Myers. 2006. R2 around the world: New theory and new tests. Journal of Financial Economics 79 (2):257-292.

Kaplan, S. N. 2008. Are US CEOs overpaid? Academy of Management Perspectives 22 (2):5-20.

29

Kim, J. B., P. Shroff, D. Vyas, and R. Wittenberg‐Moerman. 2018. Credit default swaps and managers’ voluntary disclosure. Journal of Accounting Research 56 (3):953-988.

Klasa, S., H. Ortiz-Molina, M. Serfling, and S. Srinivasan. 2018. Protection of trade secrets and capital structure decisions. Journal of Financial Economics 128 (2):266-286.

Klein, A. 2002. Audit committee, board of director characteristics, and earnings management. Journal of Accounting and Economics 33 (3):375-400.

Kothari, S. P., A. J. Leone, and C. E. Wasley. 2005. Performance matched discretionary accrual measures. Journal of Accounting and Economics 39 (1):163-197.

Li, N., T. J. Shevlin, and W. Zhang. 2018. Managerial career concerns and corporate tax avoidance: Evidence from the inevitable disclosure doctrine. Available at SSRN 3273200.

Marquardt, C., and E. Zur. 2015. The role of accounting quality in the M&A market. Management Science 61 (3):604-623.

McNichols, M. F. 2002. Discussion of the quality of accruals and earnings: The role of accrual estimation errors. The Accounting Review 77 (s-1):61-69.

Murphy, K. J. 1999. Executive compensation. Handbook of Labor Economics 3:2485-2563.Otto, C. A. 2014. CEO optimism and incentive compensation. Journal of Financial Economics 114

(2):366-404.Palmrose, Z. V., and S. Scholz. 2004. The circumstances and legal consequences of non-GAAP reporting:

Evidence from restatements. Contemporary Accounting Research 21 (1):139-180.Pan, Y., T. Y. Wang, and M. S. Weisbach. 2015. Learning about CEO ability and stock return volatility.

Review of Financial Studies 28 (6):1623-1666.Parrino, R. 1997. CEO turnover and outside succession a cross-sectional analysis. Journal of Financial

Economics 46 (2):165-197.Peters, F. S., and A. F. Wagner. 2014. The executive turnover risk premium. The Journal of Finance 69

(4):1529-1563.Rau, P. R., and J. Xu. 2013. How do ex ante severance pay contracts fit into optimal executive incentive

schemes? Journal of Accounting Research 51 (3):631-671.Rosen, S. 1981. The economics of superstars. The American Economic Review 71 (5):845-858.Rosen, S. 1982. Authority, control, and the distribution of earnings. The Bell Journal of Economics:311-

323.Yermack, D. 1996. Higher market valuation of companies with a small board of directors. Journal of

Financial Economics 40 (2):185-211.

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Appendix AVariable definitions

Employee pay variables. Sources: ExecuComp and Compustat. CEOTotPay = total annual compensation for external CEO (TDC1 from ExecuComp).CEOCashPay = annual cash-based compensation for external CEO estimated as the sum

of salary and bonus (SALARY and BONUS from ExecuComp).CEOEquityPay = annual equity-based compensation for external CEO estimated as the

sum of restricted stocks granted and stock options granted (OPTION_AWARDS_BLK_VALUE and RSTKGRNT before 2006 and OPTION_AWARDS_FV and STOCK_AWARDS_FV since 2006 from ExecuComp).

Accounting information quality variables. Source: Compustat.AIQ_DD = -1 times abnormal accruals based on the modified Dechow and Dichev

(2002) model, which is the absolute value of the residual of the following regression estimated using industry-year (2-digit SIC) panel data (at least 20 observations): TAt = a0 + a1CFOt-1 + a2CFOt + a3CFOt+1 + a4△REVt + a6PPEt + εt, where TA is the difference between earnings and operating cash flow (IBC - OANCF) scaled by lagged total assets; CFO is operating cash flow (OANCF) scaled by lagged total assets; △REV is change in revenue (SALE) scaled by lagged total assets; PPE is property, plant and equipment (PPGET) scaled by lagged total assets.

AIQ_KLW = -1 times abnormal accruals based on the Kothari, Leone, Wasley (2005) model, which is the absolute value of the residual of the following regression estimated using industry-year (2-digit SIC) panel data (at least 20 observations): TAt = a0 + a1(1/ATt-1) + a2(△REVt -△ARt) + a3PPEt + a4ROAt-1 + εt, where TA is the difference between earnings and operating cash flow (IBC - OANCF) scaled by lagged total assets; △REV -△AR is change in revenue (SALE) minus change in accounts receivable (RECCH) scaled by lagged total assets; PPE is property, plant and equipment (PPGET) scaled by lagged total assets; and ROA is earnings before extraordinary items (IB) divided by lagged total assets.

Variables in Main Analyses of CEO Pay. Source: Compustat, CRSP, and ExecuComp.TA = total assets (AT) in millions of dollars.Leverage = leverage ratio calculated as long-term debt (DLTT) plus short-term debt

(DLC) divided by total assets (AT). MTB = total assets (AT) minus book value of equity (CEQ) plus market value of

equity (PRCC × CSHO) divided by total assets (AT). ROA = operating income before extraordinary items (IB) divided by total assets

(AT).Return = annual buy-and-hold return of a stock minus annual buy-and-hold return

of value-weighted market index.StdReturn = stock return volatility estimated as the standard deviation of firm monthly

stock returns in a yearChairman = 1 if a CEO is also chairman of board of directors and 0 otherwise

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CEOMale = 1 if a CEO is male and 0 otherwise

Variables in channel and supplementary analyses. Source: Compustat, ExecuComp, and ISS. ForcedTurnover = 1 if the predecessor CEO is forced out before the new CEO joining the

company and 0 otherwise.CFOTurnover = 1 if there is a CFO turnover in the three years from t-2 to t, where t is the

year that the external CEO joins the company, and 0 otherwise.Young = 1 if an external CEO is 56 or younger, and 0 otherwise (56 is p75 of CEO

age of the sample).StdCFO = standard deviation of cash flow over the last five years, where cash flow

is estimated as (EBITDA-XINT-TXPD-DVC)/AT.IDD = 1 if the state court of a firm’s headquarters adopts the Inevitable

Disclosure Doctrine by year t, and 0 if the state court rejects the doctrine.BoardSize = number of directors on board.OutsideBoard = number of independent directors. NoBusyBoard = 1 if there is no busy independent directors (the number of directorship is

less than 4), and 0 otherwise.DepartCEOPay = total annual pay for predecessor CEO in the year before the externally-

hired CEO joining the company.Severance = 1 if firm provides the externally-hired CEO severance pay and 0

otherwise.Education = 0 for without college degree, 1 for bachelor degree, 2 for master degree,

and 3 for Ph.D. degree.MBA = 1 for MBA degree and 0 otherwiseGeneralist = an index of general skills for CEO constructed by Custodio et al. (2013).OutFirm_TotPay = total annual pay to the externally-hired CEO in previous employment.OutFirm_CEO = 1 if the externally-hired CEO served as CEO in previous employment

and 0 otherwise.SameIndustry = 1 if the previous employer and current employer are in the same industry

defined as two-digit SIC Code, and 0 otherwise.OutFirm_SIZE = natural logarithm of total assets (AT) of the previous employer.OutFirm_ROA = return on assets (IB/AT) of the previous employer.OutFirm_StdCFO = standard deviation of cash flow [(EBITDA-XINT-TXPD-DVC)/AT] of

the previous employer.IndAvgTotPay = average CEO total pay in each industry-year.StateAvgTotPay = average CEO total pay in each state-year.Paymix = ratio of equity-based pay to total CEO pay.Delta = dollar value of change in CEO's equity-based compensation with a 1%

change in firm stock price.Vega = dollar value of change in CEO's equity-based compensation with a 1%

change in firm stock return volatility.

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Figure 1Distribution of external CEO compensation by year

1994

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Table 1Sample selection and distribution of external CEO

Panel A: Sample selection Attrition Remaining Obs.Number of CEO appointments from 1994 to 2018 (ExecuComp) 5,281 Exclude observations with missing information on total assets, leverage, MTB, ROA, stock return, and return volatility (1,876) 3,405 Exclude internally hired CEOs (2,201) 1,204 Exclude interim CEOs (61) 1,143 Exclude CEOs that are recruited from the board of directors (134) 1,009 Exclude CEOs in firms with missing information on accrual measure (69) 940Final sample   940Panel B: CEO sample distribution by yearYear External CEO AQ_DD CEOTotPay CEOCashPay CEOEquityPay1994 32 -0.067 2,537 682 1,7511995 42 -0.084 2,444 668 1,6631996 41 -0.064 4,723 730 3,7331997 38 -0.075 4,311 609 3,3821998 42 -0.089 3,692 748 2,7811999 45 -0.107 4,699 818 3,5342000 55 -0.091 4,926 904 3,9772001 51 -0.130 5,885 832 4,9872002 43 -0.110 3,280 586 2,5712003 39 -0.123 4,399 722 3,6032004 38 -0.079 5,853 968 5,1792005 39 -0.109 3,752 879 2,9212006 37 -0.087 2,219 546 1,2752007 51 -0.098 4,174 629 3,0342008 49 -0.127 3,123 527 2,2352009 39 -0.105 2,553 522 1,5742010 19 -0.086 2,556 515 1,433

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2011 27 -0.095 2,438 521 1,6582012 19 -0.086 4,163 521 3,0182013 33 -0.082 3,070 583 2,1082014 37 -0.080 4,516 587 3,3392015 43 -0.094 3,159 518 2,1992016 29 -0.078 3,888 569 2,8122017 23 -0.102 4,592 686 3,5512018 29 -0.080 4,653 586 3,451Total 940        Panel C: CEO sample distribution by industryIndustry External CEO AIQ_DD CEOTotPay CEOCashPay CEOEquityPayCustomer Nondurables 52 -0.075 5,261 984 3,890Customer Durables 41 -0.079 2,876 784 1,672Manufacturing 147 -0.072 3,156 689 2,280Oil, Gas, and Coal Extraction 34 -0.118 3,113 513 2,308Chemicals and Allied Products 25 -0.081 4,312 857 3,123Business Equipment 265 -0.108 4,212 561 3,414Telephone and Television Transmission 24 -0.131 8,246 1,199 6,019Wholesale, Retail, and Some Services 143 -0.072 3,464 736 2,483Healthcare, medical equipment, and drugs 108 -0.121 3,657 546 2,912Other 101 -0.103 3,816 666 2,869Total 940

The table presents the sample selection process for external CEO pay analyses (Panel A), the external CEO sample distribution by year (Panel B), and the external CEO sample distribution by industry (Panel C).

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Table 2Summary statistics and correlation table for CEO sample

Panel A: Summary statistics

Variable N Mean Std. Dev. 25th Median 75th

CEO Pay MeasuresCEOTotPay(in $'000) 940 3,892 4,783 1,054 2,116 4,661CEOCashPay(in $'000) 940 673 554 324 481 825CEOEquityPay(in $'000) 940 2,948 4,517 385 1,351 3,426

Accounting Quality MeasuresAIQ_DD 940 -0.095 0.079 -0.122 -0.073 -0.040

Control Variables in Primary AnalysesTA (millions) 940 2,734 6,016 311 786 2,362Leverage 940 0.231 0.214 0.028 0.209 0.349MTB 940 1.854 1.192 1.134 1.479 2.082ROA 940 -0.045 0.256 -0.058 0.021 0.065Return 940 -0.138 0.470 -0.425 -0.184 0.070StdReturn 940 0.136 0.077 0.082 0.114 0.168Chairman 940 0.286 0.452 0 0 1CEOMale 940 0.967 0.179 1 1 1

Variables in Channel and Supplementary AnalysesSeverance 383 0.872 0.334 1 1 1Paymix 940 0.558 0.312 0.369 0.633 0.812Delta 696 149.473 751.618 21.850 57.242 122.680Vega 733 58.276 129.575 9.395 25.724 64.989BoardSize 582 8.646 2.190 7.000 9.000 10.000OutsideBoard 582 0.716 0.167 0.625 0.750 0.857NonBusyBoard 469 0.797 0.402 1 1 1DepartCEOPay 889 2,249 2,890 579 1,199 2,699Education 890 1.712 0.678 1.000 2.000 2.000MBA 795 0.465 0.499 0 0 1Generalist 505 0.371 1.006 -0.266 0.292 0.977OutFirm_TotPay 189 2,341 2,731 866 1,535 2,772OutFirm_CEO 189 0.243 0.430 0 0 0SameIndustry 189 0.439 0.498 0 0 1OutFirm_SIZE 189 7.940 1.283 7.195 8.003 8.709OutFirm_ROA 189 0.029 0.095 0.007 0.050 0.078OutFirm_StdCFO 189 0.051 0.033 0.029 0.042 0.064IndAvgTotPay 940 3,615 1,479 2,646 3,368 4,189StateAvgTotPay 940 3,624 1,359 2,842 3,411 4,048

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ForcedTurnover 940 0.302 0.459 0 0 1CFOTurnover 940 0.445 0.497 0 0 1StdCFO 822 0.067 0.051 0.033 0.051 0.083Young 940 0.806 0.395 1 1 1IDD 533 0.737 0.440 0 1 1AIQ_KLW 918 -0.085 0.068 -0.105 -0.065 -0.040

Panel B: Pairwise correlation   1 2 3 4 5 6 7 81. AIQ_DD 12. Log(AT) 0.214 13. Leverage -0.003 0.290 14. MTB -0.276 -0.222 -0.119 15. ROA 0.293 0.283 -0.148 -0.050 16. Return 0.021 0.068 -0.079 0.274 0.252 17. StdReturn -0.284 -0.238 0.034 -0.003 -0.338 -0.083 18. Chairman 0.081 0.183 0.110 -0.038 0.033 -0.033 0.024 19. CEOMale 0.021 -0.030 0.055 -0.031 -0.005 -0.073 0.029 0.038

This table presents summary statistics for all the variables used in CEO pay analyses and the Pearson correlations of the independent variables used in the primary regression of CEO pay. The correlations that are statistically significant at the 5% level or better are in boldface. Continuous variables are winsorized at the 1st and 99th percentiles to reduce the influence of outliers. See Appendix A for variable definitions.

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Table 3Accounting information Quality and external CEO pay

Log(CEOTotPayt)Variable Coef. t-Stat.Intercept 3.601*** 15.95AIQ_DDt-1 -0.901** -2.55Log(ATt-1) 0.511*** 28.92Leveraget-1 -0.449** -2.20MTBt-1 0.167*** 6.59ROAt-1 0.061 0.46Returnt-1 -0.128* -1.75StdReturnt-1 0.888** 2.11Chairmant 0.056 0.96CEOMalet 0.164 1.27Industry FE Yes YesYear FE Yes YesN 940R2 50.29%

This table presents the OLS estimation of the relation between accounting information quality and external CEO pay. Time subscript t refers to the year that the external CEO joins the company. Industry fixed effects are based on the two-digit SIC code. Variables are as defined in Appendix A. Standard errors are clustered by industry and t-statistics are reported in parentheses. ***, **, and * denote significance at the 1%, 5%, and 10% levels based on two-tailed tests, respectively.

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Table 4Accounting information quality and external CEO pay: omitted variables

Panel A: Contract- and firm-level omitted variables    Dependent variable = Log(CEOTotPayt)Variable Model 1 Model 2 Model 3 Model 4 Model 5 Model 6Intercept 3.089*** 4.118*** 4.269*** 3.853*** 3.499*** 2.940***

(4.15) (24.27) (12.14) (8.90) (15.10) (9.38)AIQ_DDt-1 -2.307*** -0.510* -1.248*** -2.333*** -0.889** -0.783**

(-4.61) (-1.85) (-3.04) (-2.73) (-2.64) (-2.51)Severancet 0.146

(1.28)Paymixt 0.962***

(18.90)Deltat 0.157***

(3.58)Vegat 0.148***

(4.21)BoardSizet-1 0.024

(1.09)OutsideBoardt-1 -0.299

(-1.29)NonBusyBoardt-1 -0.228***

(-3.31)ForcedTurnovert-1 0.104*

(1.68)CFOTurnover(t-2,t) 0.010

(0.16)DepartCEOPayt-1 0.133***

(4.50)Control variables Yes Yes Yes Yes Yes YesIndustry FE Yes Yes Yes Yes Yes YesYear FE Yes Yes Yes Yes Yes YesN 940 383 696 469 940 889

R2 68.5% 52.4% 58.8% 54.4% 50.45% 52.1%

Panel B: Executive-, industry-, and state-level omitted variables  Dependent variable = Log(CEOTotPayt)  

Variable Model 1 Model 2 Model 3 Model 4 Model 5 Model 6Intercept 3.542*** 3.669*** 2.540** 3.669*** 3.289*** -1.484

(19.51) (8.85) (2.33) (8.33) (6.58) (-1.03)

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AIQ_DDt-1 -0.863** -1.653*** -1.782** -0.903* -1.694** -0.972**(-2.64) (-6.16) (-2.04) (-1.73) (-2.07) (-2.45)

Educationt -0.006(-0.12)

MBAt 0.132***(2.74)

Generalistt 0.081**(2.34)

OutFirm_TotPayt-1 0.185**(2.20)

OutFirm_CEOt-1 0.071(0.35)

SameIndustryt-1 0.222(1.12)

OutFirm_SIZEt-1 0.054(0.74)

OutFirm_ROAt-1 1.842**(2.21)

OutFirm_StdCFOt-1 2.207(1.21)

Log(IndAvgTotPayt) 0.340*** (2.95)

Log(StateAvgTotPayt) 0.289** (2.57)

Control variables Yes Yes Yes Yes Yes YesIndustry FE Yes Yes Yes Yes YesYear FE Yes Yes Yes YesState FE YesIndustry*Year FE YesState*Year FE YesN 793 505 189 940 932 932

R2 51.87% 50.85% 64.8% 70.1% 73.71% 53.1%This table presents estimations that examine the robustness of the relation between accounting quality and CEO pay to omitted variables. Time subscript t refers to the year that the external CEO joins the company. Industry fixed effects are based on the two-digit SIC code. Variables are as defined in Appendix A. Standard errors are clustered by industry and t-statistics are reported in parentheses. ***, **, and * denote significance at the 1%, 5%, and 10% levels based on two-tailed tests, respectively.

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Table 5Accounting information quality and likelihood of external CEO departure

Panel A: Distribution of external CEO departure  Year Departure PercentT 28 3.73t+1 91 12.13t+2 115 15.33t+3 95 12.67t+4 71 9.47t+5 58 7.73>=t+6 162 21.60Still in office 130 17.33Total 750 100.00

Panel B: Comparison of departures between high and low accounting quality firmsYear Low AIQ (%) High AIQ (%)T 19 (5.16) 9 (2.36)t+1 48 (13.04) 43 (11.26)t+2 59 (16.03) 56 (14.66)t+3 53 (14.40) 42 (10.99)t+4 29 (7.88) 42 (10.00)t+5 28 (7.61) 30 (7.85)>=t+6 73 (19.84) 89 (23.30)Still in office 59 (16.03) 71 (18.59)Total 368 (100.00) 382 (100.00)

Panel C: Likelihood of departure using COX proportional hazards modelDependent variable = Departt,t+1,...

Variable Coef. Hazard RatioLow_AIQ_DDt-1 0.212** 1.236

(5.64)SIZEt-1 0.008

(0.04)Returnt-1 0.035

(0.10)StdReturnt-1 2.629***

(13.58)StdCFOt-1 -0.829

(0.63)ForcedTurnovert-1 -0.005

(0.00)CFOTurnover(t-2,t) 0.182**

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(3.88)Chairmant 0.127

(1.45)CEOMalet -0.011

(0.00)Industry FE YesYear FE YesN 750Chi-square 115.236  

This table presents the distribution of external CEO departure (Panel A), the comparison of external CEO departures between high AIQ and low AIQ firms (Panel B), and an estimation of the Cox proportional hazards model that examines the likelihood of external CEO departure (Panel C). Time subscript t refers to the year that the external CEO joins the company. Industry fixed effects are based on the two-digit SIC code. Low_AIQ_DD is set to 1 if AIQ_DD is below the annual median value and 0 otherwise. Variables are as defined in Appendix A for other variables. Standard errors are clustered by industry and t-statistics are reported in parentheses. ***, **, and * denote significance at the 1%, 5%, and 10% levels based on two-tailed tests, respectively.

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Table 6Accounting information quality and subsequent revelation of misreporting

  Dependent variable = Restatementt,t+1,...

Variable Coef. t-Stat.Intercept 0.146 1.66Low_AIQ_DDt-1 0.036* 1.68SIZEt-1 0.007 0.61Returnt-1 -0.036 -1.38StdReturnt-1 -0.215 -1.67StdCFOt-1 -0.143 -0.48ForcedTurnovert-1 -0.048** -2.14CFOTurnover(t-2,t) 0.013 0.65Industry FE Yes YesYear FE Yes YesN 668R2 11.09%

This table presents the OLS estimation that examines whether external CEOs price poor accounting quality as indicative of higher probability of subsequent revelation of misreporting. Time subscript t refers to the year that the external CEO joins the company. Industry fixed effects are based on the two-digit SIC code. The dependent variable, Restatement, is 1 if misreporting starts before the external CEO joins the company and restatement occurs during the CEO tenure, and 0 otherwise. Low_AIQ_DD is set to 1 if AIQ_DD is below the annual median value and 0 otherwise. Other variables are as defined in Appendix A. Standard errors are clustered by industry and t-statistics are reported in parentheses. ***, **, and * denote significance at the 1%, 5%, and 10% levels based on two-tailed tests, respectively.

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Table 7Accounting information quality and extraction of private benefit

Dependent variable = Log(CEOTotPayt)Variable Coef. t-Stat.Intercept 4.197*** 11.32AIQ_DDt-1 0.269 0.24NonBusyBoardt-1 -0.499*** -4.37AIQ_DDt-1*NonBusyBoardt-1 -3.134*** -3.71Control variables Yes YesIndustry FE Yes YesYear FE Yes YesN 469R2 54.7%

This table presents estimations examining the pricing pattern of poor accounting quality with varying internal governance monitoring. Time subscript t refers to the year that the external CEO joins the company. Industry fixed effects are based on the two-digit SIC code. See Appendix A for variable definitions. Standard errors are clustered by industry and t-statistics are reported in parentheses. ***, **, and * denote significance at the 1%, 5%, and 10% levels based on two-tailed tests, respectively.

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Table 8Accounting information quality and external CEO pay: cross-sectional analyses

Dependent variable = Log(CEOTotPayt)Variable Model 1 Model 2Intercept 3.622***

(15.90)AIQ_DDt-1 0.358 0.049

(0.63) (0.05)Youngt -0.038

(-0.38)AIQ_DDt-1*Youngt -1.669***

(-2.97)IDD -0.257

(-1.34)AIQ_DDt-1*IDD -2.037*

(-1.88)Control variables Yes YesIndustry FE Yes YesYear FE Yes YesState FE YesN 940 533R2 50.7% 56.0%

This table presents estimations that examine cross-sectional variations in the relation between accounting quality and CEO pay. Time subscript t refers to the year that the external CEO joins the company. Industry fixed effects are based on the two-digit SIC code. See Appendix A for variable definitions. Standard errors are clustered by industry and t-statistics are reported in parentheses. ***, **, and * denote significance at the 1%, 5%, and 10% levels based on two-tailed tests, respectively.

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Table 9Robustness checks: alternative measure of accounting information quality

Panel A: CEO pay    Log(CEOTotPayt)

Variable Coef. t-Stat.Intercept 3.647*** 14.49AIQ_KLWt-1 -1.191*** -3.06Control variables Yes YesIndustry FE Yes YesYear FE Yes YesN 918R2 49.88%

Panel B: The likelihood of departure Using COX proportional hazards modelDependent variable = Departuret,t+1…

Variable Coef. Hazard RatioLow_AIQ_KLWt-1 0.195** 1.215

(4.16)Control variables YesIndustry FE YesYear FE YesN 734Chi-square 116.660  

Panel C: Subsequent restatements  Dependent variable = Restatementt,t+1…

Variable Coef. t-Stat.Intercept 0.169* 1.92Low_AIQ_KLWt-1 0.073*** 2.71Control variables Yes YesIndustry FE Yes YesYear FE Yes YesN 652R2 11.51%

Panel D: Extraction of private benefit  Dependent variable = Log(CEOTotPayt)Variable Coef. t-Stat.Intercept 4.397*** 11.66

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AIQ_KLWt-1 1.607 1.12NonBusyBoardt-1 -0.628*** -4.97AQ_KLWt-1*NonBusyBoardt-1 -5.203*** -4.06Control variables YesIndustry FE YesYear FE YesN 456R2 54.9%

This table presents estimations that examine the robustness of our results to an alternative measure of accounting quality. Time subscript t refers to the year that the external CEO joins the company. Industry fixed effects are based on the two-digit SIC code. Low_AIQ_KLW is set to 1 if AIQ_KLW is below the annual median value and 0 otherwise. Other variables are as defined in Appendix A. Standard errors are clustered by industry and t-statistics are reported in parentheses. ***, **, and * denote significance at the 1%, 5%, and 10% levels based on two-tailed tests, respectively.

47