Financial Reporting Quality, Turnover Risk, and Wage Differentials:

Evidence from Worker-level Data

Jung Ho Choi

Brandon Gipper

Sara Malik

Stanford University Graduate School of Business

Current draft: October 2019

-Preliminary-

Please do not cite or circulate.

Abstract

We examine whether and how financial reporting quality influences employee turnover and wages

using employer-employee matched data in the U.S. We hypothesize and find that low financial

reporting quality is associated with high employee turnover risk due to over- and under-

employment, so workers demand wage premiums to bear this risk. High corporate governance

firms exhibit a weaker association between financial reporting quality and turnover rates,

suggesting that corporate governance mitigates turnover risk related to low financial reporting

quality. We further find that more educated and higher paid workers receive higher wage premiums

associated with financial reporting risk although turnover rates are similar across these different

groups of workers, consistent with sophisticated workers identifying financial reporting risk. With

Sarbanes-Oxley-mandated reports of internal controls weaknesses as a research setting, we show

that as a firm’s internal control system weakens, workers start to require wage premiums. Overall,

these analyses indicate that low financial reporting quality firms compensate for higher turnover

risk by paying higher wages to workers.

Keywords: Financial Reporting Quality, Wage, Turnover

JEL classifications: D83, J31, J63, M41, M51, M52

____________________________

Contact: jungho@stanford.edu, gipperbr@stanford.edu, and maliks@stanford.edu; We thank Matthew Bloomfield, Jessica Kim-Gina, Sheffield E Lesure, Hyungil Oh, and Sorabh Tomar for helpful comments. Any opinions and

conclusions expressed herein are those of the author(s) and do not necessarily represent the views of the U.S. Census

Bureau. This research was performed at a Federal Statistical Research Data Center under FSRDC Project Number

FSRDC1668. All results have been reviewed to ensure that no confidential information is disclosed. This research

uses data from the Census Bureau's Longitudinal Employer Household Dynamics Program, which was partially

supported by the following National Science Foundation Grants SES-9978093, SES-0339191 and ITR-0427889;

National Institute on Aging Grant AG018854; and grants from the Alfred P. Sloan Foundation. We gratefully

acknowledge financial support from Stanford University Graduate School of Business.

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1. Introduction

A large literature in labor economics has studied the substantial income dispersion across

workers. Recent papers emphasize that firm heterogeneity and employer-employee matching, in

addition to worker heterogeneity, mostly account for wage variation in recent decades (e.g., Card

et al., 2013; Song et al., 2019). These papers reinforce the importance of firm heterogeneity in

workers’ wage dispersion (such as firm’s industry, Krueger and Summers, 1988; or firm size,

Brown and Medoff, 1989). In other words, similar workers are paid differently because they work

for different firms. We conjecture that financial reporting quality could be among the firm’s

characteristics that shape the rank-and-file worker labor market and wage structure. In this paper,

we examine whether workers are compensated for poor quality financial reporting because

workers of those firms may experience higher turnover rates. To answer this question, we use

employer-employee matched data because the dataset allows us to estimate the wage premiums

while controlling for both firm and worker characteristics, which are a key challenge in the

literature (e.g., Abowd et al., 1999; Levetti and Shumutte, 2018).

We predict that workers will demand wage premiums for the assumption of turnover risk.

We also predict that employee turnover (measured in separations from and joins with the firm) is

associated with low financial reporting quality; this turnover is due to low financial reporting

quality’s association with both overemployment and underemployment (Biddle et al., 2009; Jung

et al., 2014) and investment inefficiency’s disruptive nature. 1 When firms over-employ and

workers join aggressively, subsequent corrections will be disruptive to the firm, including its

workers (Kedia and Philippon, 2008; Choi and Gipper, 2019). For example, if capital markets fail

to effectively monitor a firm, and the firm overinvests, then subsequently some of those

investments will fail disproportionately and associated workers will need to be reallocated or

separated from the firm.2 The turnover can be costly for the worker due to unemployment spells

1 Evidence from a series of papers suggests that higher financial reporting quality will cause an improvement in both

physical and human capital investment efficiency (e.g., Biddle and Hilary, 2006; Biddle et al., 2009; Chen et al.,

2011; Jung et al., 2014; Lara et al., 2016). Agency theory motivates these papers; higher financial reporting quality

causes improved allocation and monitoring of capital. 2 Because agency theory motivates this prediction, firm governance should moderate the relation between financial

reporting quality and employee turnover because improved monitoring can substitute for low financial reporting

quality.

2

and earnings losses (Jacobson et al., 1993; Couch and Placzek, 2010), and its prospect represents

a risk for which the firm may need to compensate (e.g., Baily, 1974; Rosen, 1986).3

Estimating this compensating wage differential is challenging because the relation between

financial reporting risk and wage premiums is conceptually identified for workers with the same

abilities and firms with the same characteristics except for financial reporting risk (Lavetti and

Schmutte, 2018).4 For example, consider two firms and two workers. Firm A has higher financial

reporting risk than Firm B. Worker A receives a high wage from Firm A. Worker B receives a low

wage from Firm B. These observations may not support a compensating wage differential for

financial reporting risk because worker and firm heterogeneity biases the estimate. This challenge

becomes further complicated because workers can move to different firms. If Firm B experiences

a shock to its financial reporting risk, Worker A can move to Firm B if he or she prefers high

financial reporting risk firms. Thus, we are unable to identify a compensating wage differential

unless we are also able to observe worker movements.

To tackle this empirical challenge, we use confidential employer-employee matched data

from the U.S. Census Bureau. This panel dataset contains detailed information on worker

characteristics, which measure various attributes of human capital.5 It allows us to control for

observable and time-invariant unobservable employee characteristics as well as employer

characteristics. Specifically, we prevent wage effects of employees’ inherent abilities and skills

and of employers’ average wage premiums from biasing our estimates. Abowd et al. (1999)

proposes an econometric model utilizing this employer-employee matched and demonstrates

quantitatively how the data set and model jointly tackle the empirical challenge. The ultimate

model which we estimate is from Abowd et al. (1999, herein referred to as “AKM”) and includes

both worker and firm fixed effects. Unobserved employee ability and how employees match to

employers (e.g., assortative matching) are sources of variation that are important to understand

3 Because we have predicted turnover, we believe that some portion of aggregate wage premiums will be a result of

worker composition and, therefore, be cross-sectionally related to worker characteristics. For example, turnover

risk arising from financial reporting quality can be hard to understand, so we expect more sophisticated workers

to receive compensating wage differentials, such as (already) high-wage or college-educated workers. 4 Financial reporting risk is the opposite of financial reporting quality. 5 Worker characteristics are important determinants of job choices and wages (e.g., Becker, 1993). Our baseline

turnover and wage regressions are comparable, showing the association between turnover and wage premiums and

(abnormal) accruals. With the wage premiums tests, we then more carefully expand the fixed effects to measure

the impact of employer-employee match composition.

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when making inferences about labor outcomes.6 AKM-type regressions enable us to decompose

the influence of this variation when estimating compensating wage differentials. 7 Another

advantage of this data set is the ability to examine worker flows as opposed to only net changes in

total workers (Burgess, Lane, and Steven, 2000). In our regression analyses, we examine the

association between dependent variables such as establishment-level employee turnover,

separations, and joins with independent variables (i) lagged, absolute abnormal accruals (i.e.,

Dechow and Dichev, 2002; McNichols, 2002) and (ii) lagged, absolute total accruals, along with

comprehensive time-varying controls and fixed effects.8

We find that low financial reporting quality firms have higher employee turnover; when

examining worker wages, we find that high financial reporting quality firms pay lower wage

premiums to worker (i.e., high financial reporting quality firms have lower compensating wage

differentials (Rosen, 1986)). First, the turnover effect is observable in both separations and joins.

These turnover effects are also highly durable over at least a period of four years. This effect is

also present when examining within-firm financial reporting quality variation.9 We also find that

the turnover effect is attenuated for high governance firms. Next on the wage effect, the result is

robust even after controlling for worker characteristics, worker fixed effects (a measure for

unobserved worker ability), and firm fixed effects (a measure for unobserved average wage

premiums). Despite the persistence of the financial reporting quality-wage association with these

controls, it is clear that much of the wage premium comes from changes in worker composition.

6 Bertrand and Schoar (2003) highlight (in part) these concerns with respect to executive labor markets, and their

model has been adopted in the accounting literature, e.g., Dyreng et al. (2010) or Ge et al. (2011). In labor

economics, Abowd et al. (1999) has been the workhorse model for many papers with a much wider cross-section

of firms’ workers, such as Card et al. (2013), Lavetti and Schmutte (2018), and Song et al. (2019). This model has

two main identification assumptions, (i) the orthogonal matching condition—or the model controls for all attributes

that are correlated with our variable of interest and contribute to the employer-employee match, and (ii) our variable

of interest is a good measure of the theoretical construct (i.e., financial reporting quality in this case) after

controlling for other variables. We discuss the research design more in Section 4. 7 We also can speak to whether workers demand premiums conditional on worker ability or whether workers simply

reallocate themselves across firms due to the firm’s (time-varying) financial reporting quality risk profile. These

effects are particularly important to understand all costs of low financial reporting quality to firms and workers,

i.e., not just higher wages but also costs associated with worker reallocation, separations, and hiring, such as

severance pay or training costs. 8 We also examine the durability of disruption effects by estimating the association of financial reporting quality on

turnover, separations, and joins up to four years ahead. We additionally show subsample flows by workers’

characteristics, such as education and gender. 9 Note that one association is only partially consistent with our expectations, joins are negatively associated with

absolute total accruals. Moreover, if financial reporting quality is negatively associated with employment

disruption, we might expect that joins would be positively associated with absolute total accruals. However, it is

also possible that firms have more trouble attracting workers when financial reporting quality is low.

4

With worker fixed effects (the fully-specified AKM model), coefficient estimates equal between

one-fourth to one-eighth (about one-tenth) of the coefficient magnitude excluding these controls.

That is, workers appear to demand wage premiums but also reshuffle themselves among firms to

bear this turnover risk. We further document that wage premiums are larger for higher income

workers and more educated workers, indicating worker sophistication affects wage setting.

Turnover ratios are (weakly) consistent with the argument. Descriptively, female workers also

have slightly higher turnover and wage premiums; though the cause is not obvious, possible

explanations include discrimination or differential preferences.

As is common in accounting and finance papers, we are also subject to other sources of

endogeneity besides unobserved worker ability and worker-to-firm matching, such as

unobservable variation in the riskiness of a firm’s projects and the impact of that risk on proxies

for financial reporting quality and returns to labor (e.g., Roberts and Whited, 2013). In certain

specifications, we use firm fixed effects; however, there could also be endogenous variation within

firms. To reduce the effect of such concerns, we examine the wage premiums in the post-SOX era

when firms announce an internal control weakness over financial reporting (ICW). We limit the

firms to those that remediate ICWs to reduce the influence of financial distress on wage premiums

(e.g., Agrawal and Matsa, 2013; Graham et al., 2019). We find that workers respond to this time

series variation in financial reporting quality which is plausibly unrelated to the riskiness of a

firm’s projects. Tabulated in the Internet Appendix, we do not find that these ICWs are

significantly associated with time-series variation in employee turnover. On average, we find that

workers receive compensating wage differentials after ICWs are announced. Additionally, we find

that college-educated workers tend to demand some wage premiums before the announcement and

incrementally more after, while non-college workers only demand wage premiums after the ICW

announcements. This is consistent with college-educated workers being more sophisticated or

occupying jobs that are closer to / use financial reporting systems and identifying low financial

reporting quality ex ante while non-college workers only finding out about low financial reporting

quality ex post.

We make three main contributions. First, we contribute to a prominent set of papers that

associates financial reporting quality and cost of capital or capital investment efficiency (e.g.,

Bushman and Smith, 2001; Francis et al., 2004; Biddle and Hilary, 2006; Lambert et al., 2007; and

Biddle et al., 2009). Instead of focusing on costs in capital markets or physical capital, we examine

5

costs in labor markets. As Whited (2019) writes, “[Costs in labor markets are] of growing interest

because over the last several decades, many advanced economies have shifted employment away

from capital-intensive manufacturing industries and toward labor-intensive service industries.”

This paper documents costly consequences of low financial reporting quality for an important

production input. These costs arise because firms share risks with workers or offer them

compensating wage differentials when they cannot share risks, such as turnover (e.g., Baily, 1974;

Guiso et al., 2005; Guvenen et al., 2017). When the firm’s financial reporting quality does not

indicate that the firm can successfully share risks, costs of labor rise. Because workers cannot

easily diversify this risk, it is plausibly more costly to firms than capital costs, which can be

diversified by investors.

Second, we contribute to an emerging and well-populated group of papers in both finance

and accounting that associate labor market outcomes, such as wages, turnover, and job searches

with characteristics of the firm. For example, those published in finance journals and some

working papers show that employee turnover, demand wage premiums, or apply elsewhere when

the firm is distressed (e.g., John et al, 1992; Berk et al., 2010; Chodorow-Reich, 2014; Brown and

Matsa, 2016; Baghai et al., 2018). Working papers that examine firms’ accounting attributes, such

as real earnings management, earnings persistence, or accounting fraud, show associations with

worker mobility or wages (e.g., Bai et al., 2018; Hass et al., 2018; Baik et al., 2019; Gipper and

Choi, 2019, Makridis and Zhou, 2019). 10 Our paper documents worker-level responses and

heterogeneity to accounting characteristics of the firm. Using this micro data, we show that

workers are mobile and behave strategically (consistent with Matsa, 2018), so they may leave or

negotiate higher wages if there is concern about the firm’s ability to avoid employee turnover due

to inefficient investments that arise out of low financial reporting quality.

Third, besides scattered use of Bertrand and Schoar (2003) for executives, we apply and

discuss the implications for the use of the research design and the employer-employee matched

data from wage regressions in AKM (Abowd et al., 1999) in accounting research. Papers in

accounting widely recognize endogeneity concerns with respect to firm-level variation. However,

studying employment or wage (whether with aggregated data or with worker-level data) introduces

10 Other papers use non-wage, non-turnover worker attributes (e.g., unionization, unemployment insurance coverage)

to test that workers are stakeholders to whom managers cater when preparing financial reports; for example, see

Ng et al. (2015), Chung et al. (2016), and Hamm et al. (2018).

6

another set of endogeneity concerns that are carefully treated by labor economists (e.g., Card et

al., 2013; Song et al., 2019). We discuss the importance of using models like AKM and employee-

employer matched data for understanding the correlations in the analysis. This differentiates our

paper from two concurrent working papers that examine related questions (Bai et al., 2018, and

Baik et al., 2019); moreover, these papers regress aggregated firm (establishment)–level wages on

a firm’s financial reporting attributes. We find that workers reallocate themselves among firms

with different financial reporting quality, so worker-firm matching and worker characteristics are

important determinants of compensating wage differentials for financial reporting quality.

2. Conceptual Framework

2.1. Wage Differentials

A long strand of literature studies wage setting to compensate for varying job characteristics

(e.g., Smith, 1979).11 For example, Thaler and Rosen (1976) find that jobs with higher injury

incidence pay higher wages to workers to compensate for the physical risk. Abowd and Ashenfelter

(1981) document that workers are compensated for unemployment possibilities. Graham et al.

(2019) demonstrate a positive relation between firm leverage and worker wages, attributing a

compensating wage differential to bankruptcy risk. These papers usually model a wage differential

by using a hedonic pricing model with implicit contracts (Rosen, 1974). The estimate in the

hedonic pricing model reflects workers’ revealed preferences over job characteristics and

willingness to be compensated for job specific risks.12

11 One reason for wage differentials across firms is to compensate for firm characteristics. These wage differentials

can have important consequences, such as contributing to income inequality. For example, Song et al. (2019)

document that the 90th percentile worker earns 10.6 times more than the 10th percentile worker in 2013. Although

the phenomenon is well documented in the literature, more recently the role of firm in the income dispersion has

drawn the attention of the literature. Card et al. (2013) find that not only intra-firm income inequality but also inter-

firm income inequality are important to explain cross worker income dispersion in Germany. Song et al. (2019)

demonstrate that across firm income dispersion in the U.S. account for 42% of income dispersion of workers in

2013. Their analysis additionally indicates that another important factor to explain an increase in income inequality

in the U.S. is assortative matching, high paid workers match with high paying firms. Overall, these papers point

out that understanding wage differentials across firms are important to understand income dispersion across

worker. 12 One limitation of the hedonic pricing model to estimate this wage differential is unobserved worker and firm

characteristics (Hwang et al, 1992). In other words, it is difficult to observe an offer curve from the hedonic pricing

model for employees with the same ability from employers with the same characteristics except for financial

reporting risk. In Section 4, we build on Abowd et al. (1999) to improve our estimate of compensating wage

differentials for accounting characteristics to deal with these two side heterogeneities. In addition, an endogenous

movement of workers can influence the estimate in the hedonic pricing model when a compensating wage

differential is estimated even with both firm and worker fixed effects (Lavetti and Schmutte, 2018).

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2.2. Financial Reporting Quality, Turnover, and Wage Differentials

Financial reporting risk can generate compensating wage differentials through turnover risks.

Prior studies demonstrate negative consequences of high turnover rates (e.g., Jacobson et al., 1993;

Couch and Placzek, 2010). The immediate costs of high involuntary turnover rates are

unemployment spells and earnings losses. Jacobson et al. (1993) find that displaced workers due

to factory closing suffer wage losses averaging 25% per year for the next six years. These displaced

workers take multiple months to find a new job. These findings indicate that a potential link

between financial reporting quality and turnover rates can explain why workers may require wage

premiums for financial reporting risk.

Low financial reporting quality can increase turnover rates though inefficient employment.

Financial reporting risk may lead to over- or under-employment and, thus, excessive turnover

rates. Prior studies document the effect of financial reporting quality on investment inefficiency

both in physical and human capital. 13 Inefficient employment increases the volatility of

employment unless it is negatively correlated with productivity shocks in general. Overemployed

workers (due to over-investment) are likely to be separated when firms receive negative

productivity shocks and can no longer afford to maintain the overemployed workers. Poor financial

reporting quality also limits firms’ abilities to choose employment decisions efficiently when firms

are under financial constraints. Falato and Liang (2016) find that firms contract their worker

headcount substantially when they violate loan covenants probably due to negative productivity

shocks. This effect can be larger for low financial reporting quality firms than for high financial

reporting quality firms. Moreover, all of these outcomes can be amplified, because poor financial

reporting quality limits shareholders ability to deter managers’ empire building behaviors (Jensen,

1986; Richardson, 2006). In this sense, we conjecture that low financial reporting quality also

increases employment volatility and, thus, employee turnover ratios. Nonetheless, this relation is

an empirical question due to the relation between inefficient employment and productivity shocks.

13 E.g., Biddle et al., 2009; Jung et al., 2014; Roychowdhury et al., 2019. Jung et al. (2014) find that low financial

reporting quality induces labor investment inefficiency via overemployment and financial constraints. They find

that the absolute value of abnormal net hires is larger as financial reporting quality is worse. McNichols and

Stubben (2008, page 1,599) document that discretionary revenues lead to excessive physical investments although

subsequent investment drops are not explicitly tested.

8

Collectively, these arguments suggest that workers employed by high financial reporting risk

firms are likely to face higher turnover risks and to be compensated by higher wages for those

risks.14 In this sense, our first hypotheses are as follows.15

Hypothesis 1-1: Workers of low financial reporting quality firms experience higher turnover

ratios due to inefficient labor investments.

Hypothesis 1-2: Workers of low financial reporting quality firms receive higher wages as

compensation for potential risks.

A major focus of this paper is the mechanism driving our findings, specifically the efficiency

of labor investments, but we acknowledge there are other potential mechanisms consistent with

our predictions about worker wage and employee turnover. Here, we discuss two of those

mechanisms. First, low financial reporting quality can engender high turnover rates through

increased risk of financial misreporting (as opposed to over- or under-investment). Serious

financial misreporting may also lead to negative effects on labor market outcomes of former

workers because former workers are forced into crowded labor markets and experience stigma

effects (Choi and Gipper, 2019). Second, low financial reporting quality can also increase workers’

voluntary movement and, thus, employee turnover rates. Jovanovic (1979) shows that in a world

where employers and employee can be mismatched, the mismatch generates employee turnover as

both parties learn about the quality of their matches. Conceivably, initial uncertainty about match

quality may be higher for low financial reporting quality firms than high financial reporting quality

firms. This would lead to low financial reporting firms having higher employee turnover. While

Jovanovic (1979) is silent on human capital accumulation while on the job, it is straightforward to

claim that frequent job switching is costly because workers lose investments and opportunities in

firm-specific human capital (Light and McGarry, 1997). Munasinghe and Sigman (2004) show

that considering search costs and human capital, frequent movers are worse than stayers. Thus, if

financial reporting risk increases even voluntary turnover rates, workers working for high financial

14 Peters and Wagner (2014) explores whether CEO turnover risk is priced in executive compensation by using a

two-stage regression. They estimate a probability of forced dismissals and regress CEO compensation on the

predicted probability of forced dismissals. They find the positive relation between turnover risk and CEO

compensation. 15 Financial reporting risk influences the cost of labor differently than the cost of capital because capital investment

is diversifiable and, thus, an idiosyncratic risk might not be priced. On the other hand, human capital investment

is difficult to diversify unless it is not specific (Topel, 1991), e.g., the investment increases the worker’s

productivity across many firms. So, the analysis between financial reporting risk and wage differentials is related

but distinct from the analysis between financial reporting risk and the cost of capital (e.g., Lambert et al., 2007).

9

reporting risk firms are more likely to suffer wage losses in the future and to demand wage

premiums ex ante.

2.3. Financial Reporting Quality and Worker Characteristics

Workers’ human capital can influence their reactions to the firm characteristics (Brown and

Matsa, 2014). First, sophisticated workers may be better able to assess job characteristics and

therefore demand a wage adjustment. Workers without sophistication may either not fully

understand the implication of a firm’s information environment or it is likely too costly for them

to seek to understand those firm features. Thus, relative to sophisticated workers, the wages of

unsophisticated workers might not react to differences in financial reporting quality.16 In addition,

all else equal, more educated workers and higher-paid workers stand to lose more wages facing

similar turnover risks than less educated workers and low-paid workers, respectively, because

forgone wages during unemployment spells are typically larger. Following this logic, our next two

hypotheses are as follows.

Hypothesis 2-1: More educated workers request larger wage premiums in response to low

financial reporting quality than less educated workers.

Hypothesis 2-2: High paid workers request larger wage premiums in response to low financial

reporting quality than low paid workers.

2.4. Competing Hypotheses

In addition to competing mechanisms, there exist competing hypotheses to explain how

financial reporting risk influences wage differentials. We discuss these alternative hypotheses in

this section. First, low financial reporting quality can decrease, rather than increase, turnover rates.

It is intuitive that fundamental uncertainty increases turnover rates due to more volatile

employment policies (Bailey, 1974). However, informational uncertainty can have the opposite

effect. In the extreme case of total informational uncertainty, a firm’s optimal employment policy

is to remain unchanged over time precisely because firms do not have any information about future

productivity. Financial reporting quality measures capture both fundamental and informational

uncertainty. Thus, whether financial reporting quality is associated with lower or higher turnover

rates depends on which type of uncertainty dominates.

16 In another setting, Egan et al. (2019) demonstrate that misbehaving financial advisors move to an area in which

unsophisticated financial consumers reside such as elderly and less-educated customers.

10

Second, financial reporting risk can reflect uncertainty about upholding implicit contracts

(such as fringe benefits) which might affect initial wage setting. A firm’s reputation to uphold

implicit contracts in labor markets can be correlated with financial reporting quality and related to

turnover and wages. Benson et al. (2019) develop a reputational model to explain an equilibrium

in which firms establish a reputation of good payment history and those firms attract more workers

at lower wages than firms without any history of payment. Benson et al. (2019) find the results

supporting this model in a setting with temporary workers. For example, low financial reporting

quality makes it more challenging for a firm to hire a high-skill worker if these workers are less

willing to work for unreliable firms. For example, prior studies show that public corporations pay

reputational penalties for federal crimes (Alexander, 1999).17 Chakravarthy et al. (2014) suggest

that workers might worry that implicit contracts between employees and employers might be

violated. Finally, survey results also indicate that workers prefer to work for esteemed employers.

Low financial reporting quality is plausibly correlated with a firm’s reputation in labor markets.

So to attract these workers, firms with low financial reporting quality might have to pay higher

wages.

3. Data

We are interested in the relation between firm characteristics, worker characteristics, worker

movements, and wages. We measure firm characteristics using Compustat and CRSP. Compustat

provides information about firm financials. We use CRSP to calculate operational volatility as

measured by the standard deviation of monthly stock returns. The U.S. Census Bureau employer-

employee matched dataset we refer to above is the Longitudinal Employer-Household Dynamics

(LEHD) data set; we use it to measure major dependent variables, such as wages and turnover, as

well as worker characteristics.

3.1. Financial Reporting Quality

We measure financial reporting quality with measures that follow Dechow and Dichev (2002)

and McNichols (2002). Dechow and Dichev (2002) measure financial reporting quality by

regressing accruals on lagged, current, and future cash flows within industry-year cross-sections.

Estimated residuals capture both intentional and unintentional errors in accounting earnings,

indicating low financial reporting quality. McNichols (2002) augments the accrual model by

17 One form of the reputational costs is high job turnover.

11

introducing Property, Plant, and Equipment and changes in revenues as additional regressors,

which Jones (1991) uses in her accrual model. Biddle et al. (2009) document that this financial

reporting quality measure is highly correlated with investment efficiency.18 To supplement this

measure, we also document the same regression with absolute total accruals. To mitigate the

concern of unsigned earnings quality measures, we use stock returns volatility as a control variable

(Hribar and Nichols, 2007).

3.2. Employer-Employee Matched Data

The LEHD dataset includes information from 23 states that have opted to participate. In these

states, the LEHD dataset provides comprehensive coverage of workers, on average covering 96%

of all private-sector jobs across years (e.g., Abowd et al., 2005). These data include wage data

when the earnings are covered by a state’s unemployment insurance program and generally include

salaries, bonuses, equity, tips, and other perquisites (e.g., meals, housing, and retirement

contributions, among others) (BLS, 2016). We observe these earnings at both the quarterly and

annual level. Self-employed, unemployed, and workers who move to non-participating states are

not observable in the LEHD data. We require that workers are between 20 and 60 years old; this

requirement generally limits the sample to workers who are (or desire to be) full-time participants

in the workforce. We also require that the worker’s annual real wages are higher than $10,000 to

exclude temporary workers. We calculate turnover ratios for each establishment. We observe full-

time workers’ joins and separations, as well as their employment levels at the beginning and end

of each year. Turnover rate is calculated as the sum of total joins and total separations in year t

divided by two and divided again by average employment in year t. Separation ratio is calculated

as the total separations in year t divided by average employment in year t. Joins ratio is calculated

as the total joins in year t divided by average employment in year t.

3.3. Sample Selection

We focus on public firms listed in the U.S. stock exchanges to understand the effect of financial

reporting quality on the wage setting. We require sample firms to have all the relevant information

to calculate firm-level control variables such as leverage and Tobin’s Q. Sample employers have

18 Biddle et al. (2009) used Francis et al.’s (2005) specific execution of measuring financial reporting quality. Francis

et al. (2005) use a separate definition of industry (Fama and French 48 rather than SIC 2-digit) and requirement

for number of firms (20 per industry-year). We generally follow McNichols (2002), though require six firms in an

industry-year.

12

at least one employee in the sample period. The LEHD contains worker-level data from 1985

(though not for all states) until 2014. We randomly sample 10% of employees in the LEHD dataset,

who worked for public firms from 1985 to 2014. To control for outliers, all the continuous

variables are winsorized at the top and bottom one percent. We use log wages and turnover rates

as dependent variables. Log wages are also winsorized at the top and bottom 1% (Lavetti, and

Schmutte, 2018). Turnover rates are normalized to one if turnover rates are larger than one.

Observations with negative turnover rates are removed.

In Table 1, we give descriptive data for the sample firms, establishments, and workers. We

describe the construction of our variables in Appendix 1 Panel A. The number of firm,

establishment, and worker observations is 58,000, 350,000, and 11,700,000, respectively. There

are some descriptive points worth noting. In Table 1 Panel A, the mean and standard deviation of

two financial reporting quality measures is similar each other. The mean of size and tangibility is

comparable to those of Biddle et al.’s (2009) Compustat/CRSP sample. The M&A Indicator of

0.154 means that 15.4% of public firms on average engage in substantial mergers and acquisitions

activities every year. The mean of employment is 3,039. In Table 1 Panel B, the mean of turnover

rates is 0.378, meaning that joins and separations are frequent every year. In Table 1 Panel C, a

representative sample worker earns $58,700 annually. The average worker is 40 years old, has 19

years of experience, and has either taken some college coursework or has an associate degree.

4. Research Design

4.1. Turnover Regressions

We estimate the association between turnover and lagged financial reporting quality using

the uniform, base model from our wage regressions (discussed below) but excluding individual

characteristics. We estimate the following ordinary least squares (OLS) regressions:

Turnoverj,t = βFRQ × Financial Reporting Qualityf(j,t-1),j,t-1

+ βf × Xf(j,t-1),t-1 + Fixed Effectsf(j,t-1),j,t-1 + εj,t (1)

In this specification, j represents establishments, f represents firms (and the function is the

correspondent firm which owns establishment j), and t represents year. Specific variables of

interest are defined above and in Appendix Table A. X is a vector of control variables and includes

firm size (natural logarithm of firm assets), Tobin’s Q, leverage, return on assets, asset tangibility,

a mergers and acquisitions indicator, standard deviation of market share price returns, and

13

establishment size (natural logarithm of establishment employees). Financial Reporting Quality

and X are lagged by one-year relative to the dependent variable. We use lags because

contemporaneous financial reporting quality is plausibly not observable by most rank-and-file

workers of the firm; lagged Financial Reporting Quality is observable in subsequent years.

Depending on the tabulated specification, we variously include fixed effects that include (i) firm

industry and year, (ii) establishment industry-by-establishment county-by year, and (iii) the fixed

effects from (ii) plus firm fixed effects. These fixed effects absorb the effects of economic shocks

on turnover rates. We also estimate this specification with Separationsj,t and Joinsj,t as dependent

variables.

We include additional forward measures of the dependent variables, i.e., Turnoverj,t+1,

Turnoverj,t+2, and Turnoverj,t+3, to examine the durability of the turnover effect of financial

reporting quality. We also include the forward measures of Separations and Joins. When using

any of these forward measures, we use a sample where forward measures are non-missing. In doing

so, we lose (at least) 62 thousand establishment-years. In doing subsample analyses, the LEHD

data contains these worker flow measures within an establishment. For example, we are able to

observe Turnoverj,t for an establishment’s college educated workforce separately from its

Turnoverj,t for the same establishment’s non-college workforce. We do not lose any data estimating

these subsamples as long as establishments have both college and non-college workforce.

4.2. Wage Regressions

Our baseline wage regressions are comparable to the turnover regressions, excluding firm

fixed effects, showing the association between wage premiums and absolute (abnormal) accruals.

Our first model is relatively simple and the independent variables are similar to those independent

variables used in prior studies examining the relation between firm characteristics and firm wages

(e.g., Chemmanur et al., 2013; Baik et al., 2019). The wage regression can be interpreted as a

hedonic pricing model:

Ln(Wagesi,t) = βFRQ × Financial Reporting Qualityf(i,t-1), t-1 + βf × Xf(i,t-1),t-1 + εi,t (2)

Similar to the turnover regressions, i represents workers, f represents firms (and the function is the

correspondent firm where i works), and t represents year. Variables of interest are defined above

and in Appendix. We include wages transformed with the natural logarithm function to deal with

skewed wage distributions and extreme observations. Xf(i,t-1) includes firm size (natural logarithm

of firm assets), Tobin’s Q, leverage, return on assets, asset tangibility, a mergers and acquisitions

14

indicator, and standard deviation of market share price returns. Financial Reporting Quality and X

are lagged by one-year relative to Ln(Wages).

This regression framework has multiple identification challenges to study the relation

between firm characteristics and worker wages; however, this empirical model is widely used in

the accounting and finance literature to study the effect of financial reporting quality on various

firm behaviors (e.g., Hwang et al., 1992). To illustrate the points concretely, we specify the wage

regression introduced by Abowd et al. (1999), which is widely used in the labor economics

literature (e.g., Card et al., 2013; Sorkin, 2018).

Ln(Wagesi,t) = βFRQ × Financial Reporting Qualityf(i,t-1), t-1

+ βf × Xf(i,t-1),t-1 + βi × Xi,t-1 + θi + ψf(i,t-1) + εi,t (3)

Xi,t-1 is a vector of employee characteristics. βi can be interpreted as a combination of worker life-

and job-cycle and other attributes that influence worker i’s productivity and so cause observable

variation in how firms compensate workers. θi measure the worker effect which is interpreted as a

combination of unobserved (i.e., not part of Xi,t-1), fixed skills and other factors that firms

compensate equally. ψf(i,t-1) measure the firm effect which is interpreted as a fixed premium that

firm f pays to all matched employees. The identification assumption of this econometric model is

E(εi,t|FRQf(i,t-1), t-1,Xf(i,t-1),t-1,Xi,t-1,i,f(i,t-1),t) = 0, which we will visit later. Under this econometric

model, Equation (2) has three different sources of biases if the correlation between Financial

Reporting Quality and Xi,t-1, θi, and ψf(i,t-1) are not zero because the error term in Equation (2)

contains all three terms.

We explain the economic origin of these biases: individual and firm heterogeneity.

Unobserved worker abilities and firm characteristics have been an important, empirical concern in

labor economics. For example, when describing the value of life as evidenced by compensating

wage differentials for physically risky job, Thaler and Rosen (1976, p. 267) write, “individuals

have different attitudes toward risk bearing and/or different physical capabilities to cope with risky

situations.” A worker with preferences (abilities) to bear (cope with) more risk will likely have a

job characterized by combinations of wages and risk that have less and more, respectively, wages

and risk compared to another worker with preferences for less risk. However, these workers might

have different abilities and skills. In this case, βFRQ in Equation (3) (also Equation (2)) captures

cross-sectional variation in abilities and skills across these two workers in addition to the

compensating wage differential for financial reporting risk, which we prefer to measure. Moreover,

15

with unobserved worker and firm heterogeneities, variation in wage-financial reporting risk has

two sources: (1) variation from firms that compensate for financial reporting risk, associated with

differences in workers’ preferences and (2) variation from worker type, associated with differences

in ability, and firm type, associated with average wage premiums.

Figure 1: Graphical Model of Financial Reporting Risk and Wages

Figure 1 shows a stylized graphical model of the relation between financial reporting risk and wages. Financial

reporting risk is increasing along the x-axis; worker wage is increasing along the y-axis. U1 and U2 represent

indifference curves of workers.

We represent these two sources of variation in Figure 1. The former, variation from firms’

compensation for financial reporting risk, is the slope along an offer curve, e.g., the slope of u1 at

point (FRR1, w1). The latter, variation from worker type and/or firm type, is the slope along an

expansion path, i.e., moving between points (FRR1, w1) to (FRR2, w2). For example, if high-ability

workers show greater indifference to planned job stability than low-ability workers, then high-

ability workers are likely to accept higher earnings potential in exchange for financial reporting

risk, causing a measurement of the relation between financial reporting risk and wages to be

positively biased without controlling for worker ability. That is, variation along the offer curve

16

(i.e., more wages for more financial reporting risk) is needed to identify the compensating wage

differential, but variation along the expansion path (i.e., accepted wages from firms by different

workers) that comes from differences in workers’ abilities (or different firm characteristics)

contaminates the observed correlation between wages and financial reporting risk (Lavetti and

Schmutte, 2018).

In addition, job switches further complicate these biases. Especially, βFRQ in Equation (2)

can be a biased estimate for a compensating wage differential even if we have a perfect shock to

Financial Reporting Quality. When workers search for jobs, they may search for or take a job

based on amenities or risks associated with the job. When that search is associated with some

frictions or workers and/or firms learn about each other over time 19 (i.e., the rank-and-file

employee labor market is not perfectly competitive), a job change can be between firms offering

different levels of total pay and different levels of financial reporting risk. Again using Figure 1,

the first job could offer (FRR1, w1) while the second job could offer (FRR2, w2), and some labor

market frictions prevent these two points from being on the same offer curve. If a worker moves

from (FRR1, w1) to (FRR2, w2) as the second job gets a positive shock to its financial reporting

risk, βFRQ in Equation (2) is still biased because the coefficient partly captures the worker’s ability.

Introducing individual effects in the model—to control for unobserved and observed abilities—

can isolate some bias coming from job changes relative to a pooled OLS model. However, some

bias, even when examining within-worker variation, can occur because we do not have a perfect

shock to firms’ financial reporting risk in general. The sign of the bias is ambiguous; it will depend

on how firm-wage levels will relate to the worker sorting among firms. For example, high-wage

firms may have a relative advantage at shielding workers from financial reporting risk or,

alternatively, high-wage firms may have additional financial reporting risks that result from riskier

operations that naturally offer higher wages. Prior evidence suggests that wage changes and worker

sorting are correlated in other contexts (e.g., Woodcock, 2008; Abowd et al., 2017). If a job switch

results in both a wage increase and increased financial reporting risk, we would be likely to

overestimate the compensating wage differential for financial reporting risk without controlling

for worker as well as firm heterogeneity.

To mitigate these identification challenges of a firm-level cross-sectional regression, we

estimate the compensating wage differentials with our employer-employee matched data by

19 See Gibbons and Katz, 1992; Hwang et al., 1998; Lang and Majumdar, 2004; Gibbons et al., 2005.

17

including financial reporting quality proxies in the Abowd et al. (1999; “AKM”) wage model.20

With these tests, we carefully expand the fixed effects and individual characteristics to measure

the impact of worker and firm heterogeneity. At first, rank-and-file employees will have wages set

by their personal characteristics. We use standard control variables in a human capital wage

regression such as education and experience (Mincer, 1974; Willis, 1986). We also use gender as

a control variable following Graham et al. (2019). Finally, we expand our fixed effects to account

for local labor markets; we use interacted effects of the firm’s establishment’s (i.e., j’s) industry,

county, and year.

Ln(Wagesi,t) = βFRQ × Financial Reporting Qualityf(i,t-1), t-1 + βf × Xf(i,t-1),t-1

+ βi × Xi,t-1 + εi,t (3)

The final model which we estimate is from AKM, including both worker and firm fixed

effects.

Ln(Wagesi,t) = βFRQ × Financial Reporting Qualityf(i,t-1), t-1 + βf × Xf(i,t-1),t-1

+ βi × Xi,t-1 + ψf(i,t-1) + εi,t (4a)

Ln(Wagesi,t) = βFRQ × Financial Reporting Qualityf(i,t-1), t-1 + βf × Xf(i,t-1),t-1

+ βi × Xi,t-1 + θi + εi,t (4b)

Ln(Wagesi,t) = βFRQ × Financial Reporting Qualityf(i,t-1), t-1 + βf × Xf(i,t-1),t-1

+ βi × Xi,t-1 + θi + ψf(i,t-1) + εi,t (4c)

While we describe the changes in βFRQ above as “bias”, these changes also enable us to

better describe how average wages differ as a result of unobserved ability, average firm wage

premiums, and job changes that are related to financial reporting quality. Finally, βFRQ from

Equation (4c) from an AKM specification offers an estimate of compensating wage differential

for accounting-quality related turnover risk while mitigating the impact of these two important

sources of bias.

20 We follow Abowd et al.’s (1999) model, but we do not use the “connected set” of establishments that are linked by

worker mobility. The connected set are establishments with only switching workers and is necessary to uniquely

estimate both employee and employer fixed effects separately. Thus, we do not analyze the correlation of financial

reporting risk with workers’ inherent abilities and firms’ average wage premiums separately.

18

4.3. Sarbanes-Oxley Act and Internal Control Weaknesses

The identification assumption of Abowd et al. (1999) can be violated in our paper (Card et

al., 2013; Lavetti and Shumutte, 2018).21 Especially, omitted time-varying firm characteristics can

be correlated with financial reporting quality. We use the Sarbanes-Oxley Act as a research setting

to mitigate this concern. Accounting scandals in 2002 lead the U.S. Congress to pass the Sarbanes-

Oxley Act, which overhauled many security regulations. The act requires that executives report

and external auditors opine on the quality of internal controls over financial reporting. If internal

controls are not sufficient to prevent material misstatements in financial reporting, then the

executives need to report that their firm has an internal control weakness (henceforth, “ICW”) in

its financial reporting controls. The regulation was designed to provide regular updates to

shareholders about the reliability of financial reporting systems; unreliable financial reporting may

be correlated with misreporting and mistakes in quarterly or annual reports. Auditor opinions only

apply to annual reports. We use these ICWs as another source of intertemporal variation in

financial reporting quality.

ICWs serve as a measure of financial reporting quality (e.g., Doyle et al., 2007; Costello

and Wittenberg-Moerman, 2011). ICWs are also negatively associated with investment efficiency

(e.g., Cheng et al., 2013; Feng et al., 2015; Harp and Barnes, 2018). In particular, Cheng et al.

(2013) show a time series pattern that is consistent with a causal relation of financial reporting

quality on investment efficiency. Firms experience reduced investment efficiency when there are

material control weaknesses over financial reporting and then improved investment efficiency after

remediation. Cheng et al. (2013) suggest that this could be a governance effect, as described by

other papers that associate financial reporting quality and investment efficiency (e.g., Biddle and

Hilary, 2006). However, Roychowdhury et al. (2019) describe in their review paper that the causal

chain is one of enhanced internal information that leads to improved decision-making. For ICWs,

we are largely agnostic to the specific channel that leads from higher (lower) financial reporting

quality to better (worse) investment efficiency.

With ICWs, we exploit time series variation, the announcement and subsequent

remediation, as an approach to mitigate this final endogeneity challenge in our setting. The omitted

21 Card et al. (2013) point out that one plausible violation of the identification assumption of Abowd et al. (1999) is

the match effect between employees and employers, such as complementarities between the two that enhance

productivity. However, they also point out that the magnitude is relatively small. Though the match effect may be

important, we focus on omitted time-varying firm characteristics as a source of identification violation.

19

correlated variables in an association between financial reporting quality and other characteristics

of the firm (such as rank-and-file employee pay) are widely acknowledged in the accounting

literature (e.g., Larcker and Rusticus, 2010).

For these tests of ICWs, we estimate the following OLS regression:

Ln(Wagesi,t) = βT-2 × ICW Indicator, T-2 f(i),t + βT-1 × ICW Indicator, T-1 f(i),t

+ βT × ICW Indicator, T f(i),t + βT+1 × ICW Indicator, T+1 f(i),t

+ βT+2 × ICW Indicator, T+2 f(i),t + βT+3 × ICW Indicator, T+3 f(i),t

+ βf × Xf(i,t-1),t-1 + βi × Xi,t-1 + θi + ψf(i,t-1) + εi,t (5)

We limit the sample to years 2004 through 2014 to ensure that all potential firm-years from our

worker-year panel are plausibly subject to ICW reporting. We include firms that either (i) do not

have any ICW during this time window or (ii) have an ICW for (at least) the annual report for a

fiscal year (i.e., have an auditor report with an ICW) and remediate after one year. The baseline

specification is AKM. We additionally include indicator variables for ICW firms in the years

surrounding the ICW event, denoted in the variable names and coefficients as T. Specifically, we

include the indicators for the two years prior to the ICW event (T-2 and T-1), the year of (T), and

the three years after (T+1, T+2, and T+3). We predict that a time series pattern of worker wage

effects will generally be consistent with compensating wage differentials that incorporate the

changes in investment efficiency from Cheng et al. (2013). Moreover, we expect that workers

(firms) will respond to the ICW, after its disclosure in early T+1, to receive (pay out) wage

premiums.

5. Empirical Results

Table 2 presents univariate correlations. Columns (1) – (3) present establishment-level

correlations of Turnover, Separations, and Joins. Because these variables are constructed from the

same worker flow data, we expect and find that they are all strongly positively correlated with

each other. In fact, Turnover has a mechanical relation with the other two ratios. In addition, both

total accruals and abnormal accruals are positively correlated with Turnover, Separations, and

Joins. These univariate correlations are consistent with our expectations discussed in Section 2.

Other correlations also appear reasonable. For example, size is negatively correlated with worker

flows (Lane, Isaac, and Stevens, 1996); profitability (i.e., return on assets) and investment

opportunities / growth options (i.e., Tobin’s Q) are positively related to joins; economic volatility

(i.e., standard deviation of daily returns) is positively related to Turnover, Separations, and Joins.

20

Overall, these turnover correlations suggest that changes in workers (and/or worker composition)

are associated with financial reporting quality.

Column (4) presents univariate correlations of the worker-level data. Real wages have a

slight negative correlation with total accruals, which is inconsistent with our predictions from

Section 2; however, abnormal accruals strongly positively correlate with real wages. As with

turnover, many other firm-level variable correlations with wages are generally intuitive. Size and

investment opportunities / growth options are positively correlated with worker wages (Troske,

1999). Leverage and tangibility (i.e., physical capital intensive firms) are negatively correlated

with wages; this finding could be surprising given the positive predicted relation between leverage

and worker wages (e.g., Berk et al., 2010). However, even in samples of bankrupt (and matched

control) firms, the observed correlation flips for larger firms (e.g., see Graham et al., 2019, Table

7). Also perhaps surprisingly, lagged profitability and lagged economic volatility are weakly,

negatively correlated with worker wages. All worker characteristics are correlated with wages as

predicted by widely understood notions of human capital (Becker, 1993) and the gender gap;

specifically, age, experience, education, and male gender are strongly, positively associated with

wages.22

5.1. Turnover Results

Table 3 presents our first main result. We find that abnormal accruals are positively

associated with turnover. Unobserved, local labor market and unobserved firm heterogeneity can

contribute to this relation. Incrementally including establishment-industry, year, and county fixed

effects and, separately, firm fixed effects, cause the effect to attenuate from 0.103 to 0.058 to 0.028

in columns (1) through (3), respectively. The magnitude (e.g., from column (1)) indicates that a

one standard deviation change in Abnormal Accruals is associated with a 3.2% change in Turnover

relative to the sample mean or about 37 (2.16 million) additional separating or joining workers per

firm-year (across the entire sample).23 This is consistent with our discussion in Section 2, financial

22 Untabulated results indicate that total accruals and abnormal accruals are correlated but different constructs. For

example, these variables have different correlations with leverage, tangibility, and the mergers and acquisitions

indicator. Also, total (abnormal) accruals are negatively (positively) related to worker age and education. Both

accruals measures are negatively related to worker experience and female gender. 23 Standard deviation of Abnormal Accruals (0.119) multiplied by the column (1) coefficient (0.103) divided by

Turnover mean (0.378) equals 3.2%. Standard deviation of Abnormal Accruals (0.119) multiplied by the column

(1) coefficient (0.103) multiplied by mean Firm Employee Size (3,039) equal to 37. This quantity multiplied by 58

thousand firm-year observations.

21

reporting quality is negatively associated with employee turnover, so plausibly workers are having

voluntary or involuntary separations or sorting themselves (e.g., increasing joins) for reasons

related to financial reporting quality. In columns (4) through (6), we find positive but insignificant

correlations between Total Accruals and turnover. While this insignificant correlation is not

consistent with our predictions, we are able to dig further into this result when individually

examining separations and joins in Panel B. Overall, Abnormal Accruals are associated with lower

investment efficiency (e.g., Biddle et al., 2009) and with employee turnover. These findings are

consistent with our predictions that workers experience financial reporting quality-related turnover

risk. Although we do not empirically distinguish voluntary turnover from involuntary turnover,

plausibly some turnover is workers voluntarily reallocating themselves to firms that constitute

better matches for their own (risk) preferences or “type,” and some turnover is workers experience

involuntary separations due to the consequences of low investment efficiency.

We also discuss the coefficient estimates on the control variables. Turnover and size

(measured by assets) are negatively correlated, except in the specification with firm fixed effects.

Moreover, larger firms have more worker stability; however, as firms change in size, they have

less worker stability, i.e., more turnover. Within firm investment opportunities (i.e., Tobin’s Q)

and leverage are also associated with more employee turnover; these variables tend not to be

significantly (or are only weakly) correlated with turnover without firm fixed effects. Profitability

(return on assets) are positively correlated with turnover across firms and negatively associated

within firm. Establishment size (measured by worker count) is negatively correlated with turnover

across and within firms. Finally, returns volatility is positively correlated with turnover without

firm fixed effects. This control variable is plausibly related to other types of operational or financial

risks that could be correlated to financial reporting quality. Interestingly, when firm fixed effects

are included in the turnover specification, the association between turnover and returns volatility

is no longer significant at conventional levels (and attenuates substantially in magnitude) while

the correlation between Abnormal Accruals and turnover persists.

In Table 3 Panel B, we decompose turnover into Separations and Joins. In columns (1) and

(2), Abnormal Accruals are positively associated with both separations and joins. The separations

(joins) magnitude can be interpreted as follows: a one standard deviation change in Abnormal

Accruals is associated with about 18 (21) additional separating (joining) workers per firm-year (or

about 0.5% - 0.7% of the average firm’s workforce). Total Accruals have a split correlation

22

between separations and joins; moreover, Total Accruals is positively (negatively) associated with

separations (joins). This suggests that high (low) financial reporting quality firms, using this

measure, tend to have more joins (separations). These directional differences in separations and

joins are likely driving the statistically weak correlation between Total Accruals and turnover. We

find that other regressors generally have similar correlations as with Turnover, except that Tobin’s

Q splits between negative (positive) associations with separations (joins) and vice versa for

establishment-level worker size. In general, these results are consistent with the inferences that we

draw in Panel A. Financial reporting quality is negatively correlated with separations; workers

plausibly face higher voluntary and involuntary turnover risk when working for these low financial

reporting quality firms. The split correlation for joins is more challenging to interpret. Abnormal

Accruals seems also to be correlated with, at least, volatility of worker employment, even if

(somewhat) beneficial for workers who do join these firms. Alternatively, our measures also

capture between establishment worker transitions, so low financial reporting quality could be

associated with job location instability. If investment efficiency decreases, abandoning some

investment projects for others could result in workers being moved around, a disruption for the

worker. The negative association between joins and Total Accruals could indicate that workers

identify and avoid joining firms with low financial reporting quality. Overall, the evidence from

Table 3 suggests that workers face turnover risk on average; this risk can manifest as voluntary or

involuntary separations or disruptive / avoided joins for workers.

Figure 2 shows coefficient estimates along with 95% confidence interval bars for future

turnover, separations, and joins regressed on Abnormal Accruals.24 We use the specifications with

interacted establishment industry, year, and county effects (but excluding firm effects). We extend

the dependent variables up to four years after the independent variables, showing the estimates,

which range between 0.032 and 0.058 across the three panels; the coefficients are generally

significant at conventional levels, except year t+3 for turnover and separations. These results

indicate that the financial reporting quality-related turnover risk is highly durable, lasting at least

4 years. This can be consistent with continued voluntary and involuntary turnover arising out of

lower investment efficiency as a result of low financial reporting quality.

24 As with Table 3, using Total Accruals as a proxy for financial reporting quality or with firm fixed effects in the

specification estimating future years of turnover, separations, and joins has attenuated coefficient estimates.

Indeed, few coefficients are significant at conventional levels using these alternative designs as in Panel A of Table

3.

23

5.2. Wage Differential Results

Table 4 contains our results showing associations between financial reporting quality and

wages. Panel A contains baseline wage regressions that are comparable to the turnover regressions

from Table 3. We include specifications that follow Equations (2) and (3), so the estimates include

firm industry and year in columns (1) and (3) and establishment industry, year, and county effects

interacted plus worker characteristics. We again use Abnormal Accruals and Total Accruals as

proxies for low financial reporting quality. We find significant positive correlations between

proxies for low financial reporting quality and wages across all tests, consistent with workers

receiving positive compensating wage differentials for financial reporting risk, which from

evidence in Table 3, could be from a channel such as workers experiencing turnover risk.

Coefficient estimates for financial reporting quality proxies vary between 0.140 and 0.242; to

understand the magnitude, a one standard deviation increase in Abnormal Accruals would net the

average worker approximately a 1.7% increase in wages or $978 per year.25 Even in Panel A, we

note the importance of local labor markets and worker characteristics, coefficient estimates

decrease by 19% and 32% for Abnormal Accruals and Total Accruals, respectively, when

including these variables as controls.

In this table, some control variables have expected correlations. For example, we see the

strong, positive firm size wage effect (Brown and Medoff, 1989). Tobin’s Q is also positively

related to wages, so firms with growth options or higher-valued investment opportunities pay

workers more. Returns volatility is also positively associated with wages, so workers bearing

fundamental risk is also compensated (e.g., Baily, 1974). Worker characteristics have correlations

with wages consistent with accumulating human capital or the gender wage gap; i.e., more

experienced (but with diminishing returns), more educated, and male workers have higher wages.

As with the univariate correlations, surprisingly (e.g., Berk et al., 2010) wages are negatively

correlated with leverage. Perhaps these firms are not at risk of bankruptcy or have more physical

capital intensity, which is also negatively correlated with wages in these multivariate regressions.

Finally, and also surprisingly, cross-sectional profitability is negatively related to wage levels.

However, this coefficient and coefficients for asset tangibility and returns volatility, meaningfully

attenuate when including controls for local labor markets and worker characteristics. So,

25 For this calculation, we use the column (2) coefficient estimate (0.140) multiplied by standard deviation in

Abnormal Accruals (0.119) equals about 0.017. This approximate percentage increase multiplied by the average

wage ($58.7 thousand) equals $978.

24

profitable, tangible asset intensive, risky firms have a geographic and labor pool mix that drive

some of these associations. Control variable coefficient estimates in Panel B shows additional

evidence of this. The signs of coefficients flip for Return on Assets and Returns Volatility and

attenuate and are no longer significant for Tobin’s Q and Tangible Assets.

Table 4 Panel B includes the incremental fixed effects, progressing through to the AKM

model (Abowd et al., 1999). First, we include firm fixed effects in columns (1) and (4) to control

for average wage premiums of firms. A substantial positive bias in the cross-sectional results are

the result of firm heterogeneity (and unobserved worker effects matched to these firms that cannot

be disentangled empirically). We see attenuation and reduced statistical significance for these

specifications (even the sign flips, though the coefficient is very close to zero, in column (4) for

Total Accruals). Within firm wage differentials for financial reporting risk appears to be small;

however, this is before we account for unobserved worker heterogeneity and job matching. In

columns (2) and (5), we include worker fixed effects; again we see the coefficient attenuate (though

is still highly significant in both specifications). Worker effects alone account for worker

heterogeneity and some unobserved firm heterogeneity because employer-employee matches can

be sticky. As one would expect, worker effects substantially increases the explanatory power of

the model; r-squared increases from about 40% (Panel A) to about 93%. Finally, in columns (3)

and (6), we include both firm and worker fixed effects (i.e., AKM). With these specifications, we

get coefficient estimates with similar magnitudes, though the statistical significance is higher for

Abnormal Accruals. Also, for Abnormal Accruals, we find some incremental attenuation compared

with both the firm and, separately, worker fixed effect specification, suggesting that job-matching

causes incremental positive bias when estimating wage differentials for financial reporting risk.

To understand the magnitude, a one standard deviation increase in Abnormal Accruals would net

the average worker approximately a 0.2% increase in wages or $112 per year. Referencing back

to Figure 1, job switches appear to move workers along a positively-slopped expansion path. This

bias indicates that workers prefer to “consume” more of these risks as their income increases. This

could be consistent with financial reporting risk being associated with outcomes that could benefit

workers somehow, such as higher wages in the future.26

26 Alternatively, with Total Accruals, the bias between column (4) and (6) indicates that workers treat financial

reporting risk like a normal “bad” (i.e., opposite of normal good), where workers prefer less financial reporting

risk as their income increases. These different types of bias again indicate that Abnormal Accruals and Total

Accruals likely reflect different measures of financial reporting quality and can be consistent with Abnormal

25

5.3. Cross-sectional Results

We examine cross-sectional variation in turnover and wage associations with financial

reporting quality. We begin by examining variation in worker sophistication. Financial reporting

quality-related turnover risk is a complex causal chain; first, a (potential) worker has to identify

that a firm has low financial reporting or information quality and that this can cause managers to

make poor investments. Second, the worker needs to understand that poor investments will

increase the proportion of workers turning over in the future, i.e., she will have some financial

reporting quality-related turnover risk. Third and finally, this worker should reallocate herself to a

firm that matches her preferences or receive wage premiums to bear this type of risk. We use

worker education, non-college and college, and worker wage levels, below median and above

median, as proxies for sophistication. College workers and above median wage workers are

sophisticated and are likely to respond more to this complex causal chain. For turnover and

separations, we are limited to within establishment flow measures available in the LEHD data so

can (cannot) measure turnover and separations based on education (wage levels).

Table 5 Panel A presents associations from turnover and separation regressions for these

worker (though not establishment) subsamples. Columns (1) and (2) show results for non-college

workers; the magnitudes are slightly smaller than for college workers in columns (3) and (4).

Plausibly these turnovers that occur shortly after the revelation of financial reporting quality (i.e.,

in t+1 relative to the Abnormal Accruals) more likely measure voluntary (rather than involuntary)

realized turnover or separations. Instead, college workers could face very little (incremental)

turnover risk, resorting to wage premiums. In Panel B, we present these wage premium results.

For columns (2) and (4)—college workers—versus columns (1) and (3)—non-college workers, we

do measure larger increases in pay for college workers at 0.172 versus 0.117 and 0.188 versus

0.134 for Abnormal Accruals and Total Accruals, respectively. When examining worker splits by

wage levels in Panel C, we observe similar coefficient magnitude differences. The above median

wage worker subsample has coefficients of 0.140 and 0.118 while below median wage worker

subsample has coefficients of 0.031 and 0.018, for Abnormal Accruals and Total Accruals,

respectively. Sophisticated workers receive higher wage differentials from low financial reporting

quality firms relative to less sophisticated workers who receive lower wage differentials. It is

Accruals having a positive association with Joins—this type of low financial reporting quality has some desirable

feature across workers—while Total Accruals has a negative association with Joins, this type of low financial

reporting quality is generally “bad” across workers.

26

unclear that these wage differences are driven by turnover risk differences, which are much closer

for non-college and college workers. Instead, education and wage level is plausibly correlated with

worker skills or productivity and so these workers have more capacity to bargain for wage

differentials that compensate for (similar) financial reporting quality-related turnover risk.

In Table 6, we examine variation in firm governance. We use institutional investment as

our measure of firm governance. While Larcker et al. (2007) do not find much evidence that

activism and blockholders do not correlate with (absolute) abnormal accruals, they find some weak

evidence that, at least, activism is associated with future performance. Also, Chen et al. (2011)

find that closely held firms, i.e., firms with concentrated ownership, similar in some ways to

institutional investment, with higher financial reporting quality have improved investment

efficiency. Other papers have also examined the beneficial role of governance in the association

between financial reporting quality and investment efficiency (e.g., see Roychowdhury et al.,

2019, for a review). We expect that improvements in governance can moderate the turnover risk

of low financial reporting quality because governance can enhance investment efficiency through

other channels besides financial reporting quality. The effect on wages is not as obvious. On the

one hand, lower turnover risk should suggest that workers receive lower wage differentials to

compensate for turnover risk. On the other hand, better governed firms could offer wage

differentials to avoid the disruption to the firm from having a workforce with high turnover;

moreover, recruiting, training, and managing at firms with excessive turnover is likely costlier than

offering workers wage differentials to retain them, reducing turnover.

Panel A shows the turnover result split between terciles of firm governance; here, we

correlate with Abnormal Accruals. In general, we see reasonably high turnover in the low and mid

tercile subsamples. High governance firms (those in the top tercile) have relatively low turnover.

In Panels B and C, we use the most sensitive subsamples from Table 5, specifically, college

workers and above median wage workers, respectively. We believe that using these workers with

increased sensitivity to financial reporting quality (while limiting the generalizability) will help us

differentiate the stories about the ambiguous effects of governance on wage differentials. Across

both groups, college workers in Panel B and above median wage workers in Panel C, we generally

find an increasing association between financial reporting quality and wages. So, high governance

firms have low turnover consistent with reduced impact coming from financial reporting quality

on investment efficiency. However, workers do not receive lower wage differentials at these high

27

governance firms; in fact, the opposite, associations between low financial reporting quality and

wage are most positive at these firms, at least for groups of workers who are sensitive to financial

reporting quality (i.e., college and above median wage workers).

In the Internet Appendix, we also examine worker’s gender, worker’s tenure (i.e., new hire

versus non-new) and establishment’s labor market thickness, measured by number of

establishments in the same industry-year-county. One might expect gender to matter for variation

in risk tolerance, tenure to matter for bargaining power, and labor market conditions to matter for

outside options (again bargaining power). We do not find consistent differences across these

subsamples. Except gender (where the risk tolerance channel might not apply to financial reporting

quality), confounding measurement concerns could drive a lack of confirming cross-sectional

associations as we predict. We are working to better understand our data and seek feedback on

these results.

5.4. ICW Results

We examine remediated internal control weaknesses (ICWs). As described in Section 4,

we are largely motivated by Cheng et al. (2013) to use the disclosure and remediation of ICWs.

Table 7 reports results from estimating Equation (5). Moreover, the time series indicators around

the timing of the ICW period (announcement) show the incremental worker wages at firms in

period denoted by T (T+1). Other controls are those covariates that we use generally, e.g., size,

Tobin’s Q, etc., experience, squared experience, and fixed effects from the AKM model, interacted

establishment industry, year, and county and worker and firm fixed effects. We find that workers

receive wage premiums in the year leading up to and the year of the ICW (years T-1 and T); the

magnitudes are about 1%. Then after the ICW is disclosed, the wage differential jumps up to about

1.7%, then up to 2.8% which persists into the next year, i.e., years T+1, T+2, and T+3,

respectively.

We then split the workers by education into non-college and college and separately

examine wage differentials in the time series around ICWs for these subsamples. The cross-

sectional predictions are not obvious. Sophisticated workers may have more wage sensitivity to

low financial reporting quality, as we see in Table 5. However, sophisticated workers could also

anticipate the low financial reporting quality from an ICW by observing internal financial reporting

dysfunction, which gives rise to ICWs. If the sequence of events includes that latter, sophisticated

workers may demand price premiums before the ICW is disclosed and receive wage differentials

28

for low quality financial reporting that can be correlated with ICWs (e.g., Doyle et al., 2007;

Ashbaugh-Skaife et al., 2008). In examining these two subsamples, we find that non-college

workers exhibit most of the rise (and durability) in wage premiums in the post-ICW period. College

workers, on the other hand have some positive wage premiums in the pre-ICW period, then these

premiums rise in the post-ICW period before reverting. In results in the Internet Appendix, these

wage premiums do not seem to be associated with realized turnover risk, as workers are likely to

turnover (nor separate nor join) at different rates than workers in the control sample on average.

These results generally confirm our main results that workers respond to low financial reporting

quality by receiving wage premiums. Plausibly these wage differentials are due to the investment

inefficiencies that accompany ICWs (e.g., Cheng et al., 2013).

6. Conclusion

This paper explores whether financial reporting risk create compensating wage differentials

due to high turnover rates. The paper first documents that low financial reporting quality firms

experience high turnover rates and both separation and accession rates are high for these firms.

Then, the paper finds that low financial reporting quality firms pay higher wages for workers. This

cross-sectional relation is robust as we add worker and firms fixed effects to control for worker

and firm heterogeneity although the relation becomes weaker, meaning that taking care of

employer and employee heterogeneity is important to understand the relation. According to these

results, we conclude that financial reporting risk generates compensating wage differentials

through high turnover risk.

We note several important caveats. First, the identification assumption of the AKM model is

strict (Card et al, 2013). Although we purge worker and firm heterogeneity bias from estimates for

wage differentials, workers’ endogenous movements and wage changes hinder us from perfectly

identifying compensating wage differentials for financial reporting risk. Second, our financial

reporting quality measure can be correlated with omitted variables although we attempt to mitigate

this concern by using multiple control variables, including return volatility, and by examining a

dynamic pattern of wages for workers of firms disclosing internal control weaknesses. Third, we

show evidence that is consistent with our hypotheses; however, we are unable to completely rule

out any competing hypothesis consistent with the positive effect of financial reporting risk on wage

premiums and turnover rates. Overall, these concerns suggest interpreting our findings with

29

caution; however, our results are useful for understanding an important role of financial reporting

risk in sharping worker wages and turnover rates.

30

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35

Appendix Table A: Variable Definitions

Variable Definition Data

Source

Dependent Variables

Annual Real Wages Annual earnings from a primary employer divided by the

Consumer Price Index (2010)

LEHD

Log Wages Log of Annual Real Wages LEHD

Firm Controls

Total accrual

The absolute value of total accruals which are calculated by

subtracting operating cash flows (data308) from income before

extraordinary items (data18)

Compustat

Abnormal accrual

The absolute value of a residual from a regression of accruals on

lagged, current, and future cash flows and sales changes and

tangible assets following Dechow and Dichev (2002) and

McNichols (2002)

Compustat

Sizet-1 Natural log of total assets (data6) Compustat

Tobin’s Qt-1 Market value of assets divided by book value of assets, where

market value of assets is calculated by multiplying the number of

shares outstanding (data25) by the stock price (data199) and

adding the difference between total assets and common equity

Compustat

Leveraget-1 The ratio of total debt (data9+data34) to market value of assets,

which is calculated by multiplying the number of shares

outstanding (data25) by the stock price (data199) and by adding

total debt (data9+data34) to it

Compustat

ROAt-1 Operating income after depreciation (data178) divided by total

assets (data6)

Compustat

Tangible Assetst-1 The ratio of tangible assets to total assets (data6) Compustat

M&A Indicatort-1 In indicator equal to 1 when the absolute value of cash for

acquisitions divided by total assets is greater than 5%

Compustat

StdDev of Stock

Returnst-1

The standard deviation of monthly returns in a year CRSP

Firm Governance Measured by the number of institutional investors. Firms are

grouped into terciles after ranking based on the count of

institutional investor

Thomson

Reuter

Establishment Controls

Log of

Establishment

Employee Countt-1

Natural log of establishment employment, where establishment

employment is the mean of employment in each quarter over the

given year.

LEHD

Turnoverj,t

The sum of separations and accessions of full-time employees

divided by 2 and then divded again by the average of

employments at the beginning and end of a year. Turnover rates

larger than 1 are normalized to 1. Turnover rates less than 0 are

excluded.

LEHD

Separations j,t

The sum of separations of full-time employees divided by the

average of full-time employments at the beginning and end of a

year. Turnover rates larger than 1 are normalized to 1. Turnover

rates less than 0 are excluded.

LEHD

36

Variable Definition Data

Source

Joins j,t

The sum of accessions of full-time employees divided by the

average of full-time employments at the beginning and end of a

year. Turnover rates larger than 1 are normalized to 1. Turnover

rates less than 0 are excluded.

LEHD

Thin / Thick Labor

Markets

Establishments are ranked into terciles by the number of industry-

specific employers in the same county-year

LEHD

Employee Controls

Age Age of an employee LEHD

Education Four levels of education are transformed into numerical values by

using the highest number of years in each category: less than high

school, high school or equivalent, no college, some college or

associate degree, and bachelor’s degree or advanced degree

LEHD

Experience Age of a worker in year t minus education minus 6. Experience

less than zero is normalized to zero.

LEHD

Gender 1 if a person is female; 0 otherwise LEHD

Employee Indicators

College Educated 1 if a worker is college educated LEHD ≥ Median Wage

Workers 1 if a worker’s wage is higher than the median wage of all

employees in the sample

LEHD

New employees New Employee Worker newly hired in the year and didn’t work

for the same employer in the last year

LEHD

37

Figure 2. Worker Movements through Time

This figure reports estimates from OLS regression analyses estimating Equation (1): specifications which measure the

correlation between accounting quality and worker turnover, separations, or joins at the establishment-year level along

with firm-level controls and various fixed effects. We use lagged Abnormal Accruals as the proxy for accounting

quality. Coefficient estimates and 95% confidence intervals are presented for one, two, three, and four year ahead

dependent variables. Regression models are estimated with all control variables from Table 3 and interacted fixed

effects for establishment industry, year, and establishment county. In Panel A, we use dependent variable Turnover.

In Panel B, we use dependent variable Separations. In Panel C, we use dependent variable Joins. Appendix Table A

defines the variables. Standard errors for confidence intervals are calculated with clustering by firm. Statistics are

rounded to comply with disclosure requirements of the U.S. Census Bureau.

Panel A: Subsequent Turnover Coefficient Estimates

(continued)

-0.02

0

0.02

0.04

0.06

0.08

0.1

t+1 t+2 t+3 t+4

38

Figure 2—continued

Panel B: Subsequent Separations Coefficient Estimates

Panel C Subsequent Joins Coefficient Estimates

-0.02

0

0.02

0.04

0.06

0.08

0.1

t+1 t+2 t+3 t+4

-0.02

0

0.02

0.04

0.06

0.08

0.1

t+1 t+2 t+3 t+4

39

Table 1. Descriptive Statistics

This table shows averages and standard deviations of firm characteristics (Panel A), establishment characteristics

(Panel B), and worker characteristics (Panel C). The full sample comprises about 58, 350, and 11,700 thousand firm,

establishment, and worker-year observations, respectively, with available U.S. Census data and available (main)

control variables used in the regression analyses. These data are from years 1985 through 2014. Firm characteristic

data is from Compustat; establishment worker flow and worker wage and characteristics data are from U.S. Census

LEHD datasets. Appendix Table A defines the variables. Statistics are rounded to comply with disclosure requirements

of the U.S. Census Bureau.

Panel A: Firm Characteristics

(1) (2)

Sample Mean Standard Deviation

Total Accrualst-1 0.085 0.112

Abnormal Accrualst-1 0.093 0.119

Sizet-1 6.246 2.037

Tobin’s Qt-1 1.843 1.366

Leveraget-1 0.250 0.245

Return on Assetst-1 0.047 0.171

Tangible Assetst-1 0.248 0.221

M&A Indicatort-1 0.154 0.361

Returns Volatilityt-1 0.134 0.085

Firm Employee Size 3,039 13,130 N=58,000

Panel B: Establishment Characteristics

(1) (2) Mean Standard Deviation

Total Accrualst-1 0.067 0.080

Abnormal Accrualst-1 0.075 0.090

Turnovert 0.378 0.275

Separationst 0.362 0.274

Joinst 0.365 0.290 N=350,000

Panel C: Worker Characteristics

(1) (2)

Sample Mean Standard Deviation

Real Waget 58,700 47,100

Aget 40.1 10.7

Experiencet 19.2 10.9

Education 15.0 3.01

Female 0.431 0.495

N=11,700,000

40

Table 2. Univariate Correlations

This table shows Pearson correlations between variables of interest, i.e., turnover and wage variables and lagged firm

characteristics, lagged establishment-level employee count, and contemporaneous worker characteristics. Column (1)

shows correlations with establishment-level employee turnover. Column (2) shows correlations with establishment-

level employee separations. Column (3) shows correlations with establishment-level employee joins. Column (4)

shows correlations with worker-level wages. ^ indicates that the correlation is not statistical significant at 5% level;

all other correlations statistically significant at least at the 5% level. Appendix Table A defines the variables. Statistics

are rounded to comply with disclosure requirements of the U.S. Census Bureau.

(1) (2) (3) (4) Turnovert Separationst Joinst Waget

Total Accrualst-1 0.065 0.087 0.037 -0.004

Abnormal Accrualst-1 0.035 0.030 0.032 0.107

Sizet-1 -0.196 -0.191 -0.195 0.125

Tobin’s Qt-1 0.045 -0.001^ 0.087 0.096

Leveraget-1 -0.051 -0.019 -0.083 -0.064

Return on Assetst-1 0.025 -0.004^ 0.060 -0.018

Tangible Assetst-1 0.079 0.076 0.078 -0.214

M&A Indicatort-1 -0.001^ -0.008 0.001^ 0.003

Returns Volatilityt-1 0.102 0.113 0.079 -0.001

Ln(Employee Countt-1) -0.015 0.057 -0.037 -

Aget - - - 0.239

Experiencet - - - 0.131

Education - - - 0.368

Female - - - -0.235

41

Table 3. Accounting Quality, Turnover, Separations, and Joins

This table reports estimates from OLS regression analyses estimating Equation (1): specifications which measure the

correlation between accounting quality and worker turnover, separations, or joins at the establishment-year level along

with firm-level controls and various fixed effects. In Panel A, we use dependent variable Turnover; columns (1)

through (3) use lagged Abnormal Accruals as the proxy for accounting quality; columns (4) through (6) use lagged

Total Accruals as the proxy for accounting quality. Also for Panel A, columns (1) and (4) include fixed effects for

firm industry and year; columns (2) and (5) include fixed effects for establishment industry, year, and establishment

county; columns (3) and (6) include interacted fixed effects from the prior columns. In Panel B, we use dependent

variables Separations (columns (1) and (3)) and Joins (columns (2) and (4)); columns (1) and (2) use lagged Abnormal

Accruals as the proxy for accounting quality; columns (3) and (4) use lagged Total Accruals as the proxy for

accounting quality. Also for Panel B, all columns include interacted fixed effects for establishment industry, year, and

establishment county. Appendix Table A defines the variables. Standard errors are in parentheses and calculated with

clustering by firm. Statistical significance at the 10%, 5%, and 1% levels is indicated by *, **, and ***, respectively.

Statistics are rounded to comply with disclosure requirements of the U.S. Census Bureau.

Panel A: Turnover Regressions (1) (2) (3) (4) (5) (6)

Dependent Variable: Turnovert Turnovert Turnovert Turnovert Turnovert Turnovert

Abnormal Accrualst-1 0.103*** 0.058*** 0.028*** - - - (0.019) (0.018) (0.008)

Total Accrualst-1 - - - 0.028 0.018 0.008

(0.018) (0.017) (0.009)

Sizet-1 -0.006*** -0.011*** 0.010*** -0.006*** -0.011*** 0.010*** (0.002) (0.001) (0.003) (0.002) (0.001) (0.003)

Tobin’s Qt-1 -0.004 -0.00368 0.007*** -0.003 -0.003 0.007*** (0.003) (0.003) (0.001) (0.003) (0.003) (0.001)

Leveraget-1 -0.019* -0.017 0.018** -0.020* -0.017 0.018** (0.011) (0.0118) (0.008) (0.011) (0.012) (0.008)

Return on Assetst-1 0.045** 0.049*** -0.045*** 0.039* 0.044** -0.044*** (0.020) (0.018) (0.013) (0.020) (0.020) (0.014)

Tangible Assetst-1 0.004 -0.003 -0.028 0.001 -0.005 -0.030 (0.020) (0.015) (0.024) (0.020) (0.015) (0.024)

M&A Indicatort-1 -0.004 -0.006 0.004** -0.003 -0.005 0.005*** (0.004) (0.004) (0.002) (0.004) (0.004) (0.002)

Returns Volatilityt-1 0.170*** 0.180*** 0.020 0.179*** 0.185*** 0.021 (0.027) (0.027) (0.015) (0.027) (0.027) (0.015)

Ln(Employee Countt-1) -0.014*** -0.009*** -0.020*** -0.019*** -0.009*** -0.020*** (0.002) (0.002) (0.001) (0.002) (0.002) (0.001)

Fixed Effects

Firm Ind.

& Year

Est. Ind.

× Year ×

County

Est. Ind.

× Year ×

County &

Firm

Firm Ind.

& Year

Est. Ind.

× Year ×

County

Est. Ind.

× Year ×

County &

Firm

Observations 350,000 350,000 350,000 350,000 350,000 350,000

R-squared 0.241 0.415 0.555 0.241 0.415 0.555

(continued)

42

Table 3—continued

Panel B: Separations and Joins Regressions (1) (2) (3) (4)

Dependent Variable: Separationst Joinst Separationst Joinst

Abnormal Accrualst-1 0.049*** 0.057*** - - (0.017) (0.018)

Total Accrualst-1 - - 0.071*** -0.050***

(0.017) (0.017)

Sizet-1 -0.013*** -0.012*** -0.012*** -0.012*** (0.001) (0.001) (0.001) (0.001)

Tobin’s Qt-1 -0.012*** 0.005 -0.012*** 0.006** (0.003) (0.003) (0.003) (0.003)

Leveraget-1 -0.002 -0.033*** -0.003 -0.032*** (0.012) (0.012) (0.012) (0.012)

Return on Assetst-1 0.009 0.111*** 0.018 0.091*** (0.019) (0.019) (0.020) (0.020)

Tangible Assetst-1 -0.019 0.002 -0.024 0.004 (0.015) (0.016) (0.015) (0.016)

M&A Indicatort-1 -0.007* -0.006 -0.006 -0.006 (0.004) (0.004) (0.004) (0.004)

Returns Volatilityt-1 0.179*** 0.159*** 0.173*** 0.174*** (0.026) (0.028) (0.026) (0.028)

Ln(Employee Countt-1) 0.006*** -0.013*** 0.006*** -0.013*** (0.002) (0.002) (0.002) (0.002)

Fixed Effects

Est. Ind.

× Year ×

County

Est. Ind.

× Year ×

County

Est. Ind.

× Year ×

County

Est. Ind.

× Year ×

County

Observations 350,000 350,000 350,000 350,000

R-squared 0.394 0.409 0.394 0.409

43

Table 4. Accounting Quality and Wage Differentials

This table reports estimates from OLS regression analyses estimating Equations (2) through (4): specifications which

measure the correlation between accounting quality and wages at the worker-year level along with firm-level controls

and various fixed effects. All columns use the natural logarithm of inflation adjusted wages as the dependent variable.

In Panel A, columns (1) and (2) use lagged Abnormal Accruals as the proxy for accounting quality; columns (3) and

(4) use lagged Total Accruals as the proxy for accounting quality. Also for Panel A, columns (1) and (3) include fixed

effects for firm industry and year, i.e., Equation (2); columns (2) and (4) include interacted fixed effects for

establishment industry, year, and establishment county, i.e., Equation (3). In Panel B, columns (1) through (3) use

lagged Abnormal Accruals as the proxy for accounting quality; columns (4) and (6) use lagged Total Accruals as the

proxy for accounting quality. We progressively add fixed effects to these models, corresponding to Equations (4a),

(4b), and (4c), respectively. Appendix Table A defines variables. Standard errors are in parentheses and calculated

with clustering by firm. Statistical significance at the 10%, 5%, and 1% levels is indicated by *, **, and ***,

respectively. Statistics are rounded to comply with disclosure requirements of the U.S. Census Bureau.

Panel A: Baseline Wage Regressions (1) (2) (3) (4)

Dependent Variable: Ln(Wagest) Ln(Wagest) Ln(Wagest) Ln(Wagest)

Abnormal Accrualst-1 0.173** 0.140*** - - (0.068) (0.0321)

Total Accrualst-1 - - 0.242*** 0.164***

(0.066) (0.038)

Sizet-1 0.065*** 0.040*** 0.065*** 0.040*** (0.008) (0.004) (0.008) (0.004)

Tobin’s Qt-1 0.057*** 0.048*** 0.058*** 0.048*** (0.010) (0.006) (0.009) (0.006)

Leveraget-1 -0.060 -0.063*** -0.064* -0.065*** (0.038) (0.022) (0.038) (0.022)

Return on Assetst-1 -0.349*** -0.253*** -0.320*** -0.239*** (0.114) (0.046) (0.116) (0.049)

Tangible Assetst-1 -0.124** -0.052** -0.142*** -0.066*** (0.054) (0.025) (0.054) (0.025)

M&A Indicatort-1 -0.013 -0.001 -0.009 0.001 (0.012) (0.006) (0.011) (0.006)

Returns Volatilityt-1 0.414*** 0.298*** 0.409*** 0.295*** (0.100) (0.062) (0.102) (0.061)

Experiencet - 0.049*** - 0.049*** (0.001) (0.001)

Experiencet2 / 1,000 - -0.900*** - -0.900***

(0.021) (0.021)

Education - 0.067*** - 0.067***

(0.001) (0.001)

Female - -0.272*** - -0.272***

(0.005) (0.005)

Fixed Effects

Firm Ind.

& Year

Est. Ind.

× Year ×

County

Firm Ind.

& Year

Est. Ind.

× Year ×

County

Observations 11,700,000 11,700,000 11,700,000 11,700,000

R-squared 0.394 0.409 0.394 0.409

(continued)

44

Table 4—continued

Panel B: Wage Regressions with Individual and Firm Fixed Effects, AKM Models (1) (2) (3) (4) (5) (6)

Dependent Variable: Ln(Wagest) Ln(Wagest) Ln(Wagest) Ln(Wagest) Ln(Wagest) Ln(Wagest)

Abnormal Accrualst-1 0.024* 0.041*** 0.016*** - - -

(0.014) (0.006) (0.006)

Total Accrualst-1 - - - -0.003 0.024*** 0.012* (0.010) (0.007) (0.006)

Sizet-1 0.004 0.014*** 0.027*** 0.004 0.014*** 0.027***

(0.005) (0.001) (0.003) (0.005) (0.001) (0.003)

Tobin’s Qt-1 -0.000 0.006*** 0.002 -0.001 0.006*** 0.001

(0.002) (0.001) (0.001) (0.002) (0.001) (0.001)

Leveraget-1 -0.045*** -0.020*** -0.048*** -0.046*** -0.020*** -0.048***

(0.009) (0.007) (0.007) (0.009) (0.007) (0.007)

Return on Assetst-1 0.041** 0.017 0.082*** 0.044** 0.015 0.079***

(0.019) (0.011) (0.012) (0.019) (0.011) (0.012)

Tangible Assetst-1 -0.069** -0.015 -0.018 -0.068** -0.012 -0.016

(0.027) (0.010) (0.012) (0.027) (0.010) (0.012)

M&A Indicatort-1 -0.001 0.004*** 0.000 -0.001 0.003** 0.000

(0.002) (0.001) (0.001) (0.002) (0.001) (0.001)

Returns Volatilityt-1 -0.020 0.012 -0.022** -0.021 0.010 -0.022**

(0.018) (0.014) (0.011) (0.018) (0.014) (0.011)

Ind. Characteristic Controls Yes Yes Yes Yes Yes Yes

Fixed Effects

Est. Ind.

× Year ×

County &

Firm

Est. Ind.

× Year ×

County &

Individual

Est. Ind.

× Year ×

County &

Firm &

Individual

Est. Ind.

× Year ×

County &

Firm

Est. Ind.

× Year ×

County &

Individual

Est. Ind.

× Year ×

County &

Firm &

Individual

Observations 11,700,000 11,700,000 11,700,000 11,700,000 11,700,000 11,700,000

R-squared 0.555 0.934 0.935 0.555 0.933 0.935

45

Table 5. Cross-sectional Variation in Turnover and Wage Differentials

This table reports estimates from OLS regression analyses estimating turnover and wage regressions split by cross-

sectional characteristics of workers, including education and wage levels. The specifications follow from Tables 3 and

4 that include interacted fixed effects for establishment industry, year, and establishment county, i.e., Equations (1)

and (3). Panel A presents turnover regressions split by worker education level within establishments, using lagged

Abnormal Accruals as the proxy for accounting quality. Panel B presents wage regressions split by worker education

level, using both lagged Abnormal Accruals and Total Accruals as proxies for accounting quality. Panel C presents

wage regressions split by worker wage level, using both lagged Abnormal Accruals and Total Accruals as proxies for

accounting quality. Appendix Table A defines variables. Standard errors are in parentheses and calculated with

clustering by firm. Statistical significance at the 10%, 5%, and 1% levels is indicated by *, **, and ***, respectively.

Statistics are rounded to comply with disclosure requirements of the U.S. Census Bureau.

Panel A: Turnover Regressions Split by Education (1) (2) (3) (4)

Worker Education Levels: Non-college Non-college College College

Dependent Variable: Turnovert Separationst Turnovert Separationst

Abnormal Accrualst-1 0.058*** 0.049*** 0.061*** 0.054*** (0.018) (0.017) (0.017) (0.016)

Controls Yes Yes Yes Yes

Fixed Effects EI × Y × C EI × Y × C EI × Y × C EI × Y × C

Observations 350,000 350,000 350,000 350,000

R-squared 0.407 0.384 0.356 0.334

Panel B: Wage Regressions Split by Education

Worker Education Levels: Non-college College Non-college College

Dependent Variable: Ln(Wagest) Ln(Wagest) Ln(Wagest) Ln(Wagest)

Abnormal Accrualst-1 0.117*** 0.172*** - - (0.033) (0.031)

Total Accrualst-1 - - 0.134*** 0.188***

(0.040) (0.034)

Controls Yes Yes Yes Yes

Fixed Effects EI × Y × C EI × Y × C EI × Y × C EI × Y × C

Observations 8,122,000 3,576,000 8,122,000 3,576,000

R-squared 0.461 0.442 0.461 0.442

Panel C: Wage Regressions Split by Income Level

Worker Wage Levels: Below Median Above Median Below Median Above Median

Dependent Variable: Ln(Wagest) Ln(Wagest) Ln(Wagest) Ln(Wagest)

Abnormal Accrualst-1 0.031* 0.140*** - - (0.018) (0.021)

Total Accrualst-1 - - 0.018 0.118***

(0.027) (0.020)

Controls Yes Yes Yes Yes

Fixed Effects EI × Y × C EI × Y × C EI × Y × C EI × Y × C

Observations 5,849,000 5,849,000 5,849,000 5,849,000

R-squared 0.291 0.302 0.291 0.302

46

Table 6. Governance

This table reports estimates from OLS regression analyses estimating turnover and wage regressions split by cross-

sectional characteristics of firms, particularly terciles of governance measured by institutional ownership. The

specifications follow from Tables 3 and 4 that include interacted fixed effects for establishment industry, year, and

establishment county, i.e., Equations (1) and (3). Across all specifications, we use lagged Abnormal Accruals as the

proxy for accounting quality. Panel A presents turnover regressions. Panel B presents wage regressions conditional

on workers having college education levels. Panel C presents wage regressions conditional on workers having above

median wage levels. Appendix Table A defines variables. Standard errors are in parentheses and calculated with

clustering by firm. Statistical significance at the 10%, 5%, and 1% levels is indicated by *, **, and ***, respectively.

Statistics are rounded to comply with disclosure requirements of the U.S. Census Bureau.

Panel A: Turnover Regressions Split by Firm Governance (1) (2) (3)

Firm Governance Level: Low Tercile Mid Tercile High Tercile

Dependent Variable: Turnovert Turnovert Turnovert

Abnormal Accrualst-1 0.058** 0.067** 0.015 (0.023) (0.029) (0.040)

Controls Yes Yes Yes

Fixed Effects EI × Y × C EI × Y × C EI × Y × C

Observations 117,000 117,000 117,000

R-squared 0.488 0.480 0.488

Panel B: College Educated Worker Wage Regressions Split by Firm Governance

Firm Governance Level: Low Tercile Mid Tercile High Tercile

Worker Education Levels: College College College

Dependent Variable: Ln(Wagest) Ln(Wagest) Ln(Wagest)

Abnormal Accrualst-1 0.058* 0.194** 0.269*** (0.031) (0.077) (0.056)

Controls Yes Yes Yes

Fixed Effects EI × Y × C EI × Y × C EI × Y × C

Observations 1,175,000 1,289,000 1,111,000

R-squared 0.455 0.473 0.466

Panel C: Above Median Wage Worker Wage Regressions Split by Firm Governance

Firm Governance Level: Low Tercile Mid Tercile High Tercile

Worker Wage Levels: Above Median Above Median Above Median

Dependent Variable: Ln(Wagest) Ln(Wagest) Ln(Wagest)

Abnormal Accrualst-1 0.043** 0.207*** 0.201*** (0.021) (0.039) (0.035)

Controls Yes Yes Yes

Fixed Effects EI × Y × C EI × Y × C EI × Y × C

Observations 1,812,000 2,184,000 1,853,000

R-squared 0.310 0.352 0.305

47

Table 7. Internal Control Weaknesses

This table reports estimates from OLS regression analyses estimating Equation (5): the specification which measures

the correlation between time-series indicators for firms reporting internal control weaknesses (“ICWs” in year T that

are later remediated) and wages at the worker-year level along with firm-level controls and various fixed effects. The

specification is similar to Table 4 Panel B, an AKM regression model including both firm and worker fixed effects.

All columns use the natural logarithm of inflation adjusted wages as the dependent variable. Column (1) reports the

results for all workers in the sample, excluding workers at firms that do not remediate ICWs or ICW firms that do not

contain at least seven years around the ICW year T. Column (2) [(3)] splits this sample by education level with the

non-college [college] subsample of workers. Appendix Table A defines variables. Standard errors are in parentheses

and calculated with clustering by firm. Statistical significance at the 10%, 5%, and 1% levels is indicated by *, **,

and ***, respectively. Statistics are rounded to comply with disclosure requirements of the U.S. Census Bureau.

(1) (2) (3)

Worker Education Levels: All Non-College College

Dependent Variable: Ln(Wagest) Ln(Wagest) Ln(Wagest)

ICW Year IndicatorT-2 0.007 0.006 0.008* (0.004) (0.005) (0.005)

ICW Year IndicatorT-1 0.011* 0.012 0.007

(0.007) (0.008) (0.006)

ICW Year IndicatorT 0.010* 0.011* 0.005

(0.0058) (0.007) (0.007)

ICW Year IndicatorT+1 0.017** 0.018* 0.012

(0.008) (0.009) (0.008)

ICW Year IndicatorT+2 0.028** 0.031** 0.021**

(0.011) (0.013) (0.010)

ICW Year IndicatorT+3 0.028*** 0.034*** 0.012

(0.010) (0.011) (0.010)

Controls Yes Yes Yes

Fixed Effects

EI × Y × C &

Firm &

Individual

EI × Y × C &

Firm &

Individual

EI × Y × C &

Firm &

Individual

Observations 6,653,000 4,641,000 2,011,000

R-squared 0.953 0.942 0.953

48

Financial Reporting Quality, Turnover Risk, and Wage Differentials

Online Appendix

IA Table 1. Cross-sectional Variation in Turnover and Wage Differentials, Gender and

Tenure Status

This Internet Appendix table reports estimates from OLS regression analyses estimating turnover and wage

regressions split by cross-sectional characteristics of workers, including gender and tenure. The table structure follows

Table 5 from the manuscript. Panel A presents turnover regressions split by gender within establishments, using lagged

Abnormal Accruals as the proxy for accounting quality. Panel B presents wage regressions split by gender, using both

lagged Abnormal Accruals and Total Accruals as proxies for accounting quality. Panel C presents wage regressions

split by worker tenure level, using both lagged Abnormal Accruals and Total Accruals as proxies for accounting

quality. Appendix Table A defines variables. Standard errors are in parentheses and calculated with clustering by firm.

Statistical significance at the 10%, 5%, and 1% levels is indicated by *, **, and ***, respectively. Statistics are rounded

to comply with disclosure requirements of the U.S. Census Bureau.

Panel A: Turnover Regressions Split by Gender (1) (2) (3) (4)

Worker Gender: Male Female Male Female

Dependent Variable: Turnovert Separationst Turnovert Separationst

Abnormal Accrualst-1 0.060*** 0.050*** 0.050*** 0.045*** (0.018) (0.017) (0.018) (0.017)

Controls Yes Yes Yes Yes

Fixed Effects EI × Y × C EI × Y × C EI × Y × C EI × Y × C

Observations 350,000 350,000 350,000 350,000

R-squared 0.397 0.370 0.350 0.334

Panel B: Wage Regressions Split by Gender

Worker Gender: Male Female Male Female

Dependent Variable: Ln(Wagest) Ln(Wagest) Ln(Wagest) Ln(Wagest)

Abnormal Accrualst-1 0.131*** 0.151*** - - (0.033) (0.034)

Total Accrualst-1 - - 0.155*** 0.171***

(0.038) (0.044)

Controls Yes Yes Yes Yes

Fixed Effects EI × Y × C EI × Y × C EI × Y × C EI × Y × C

Observations 6,659,000 5,039,000 6,659,000 5,039,000

R-squared 0.497 0.464 0.497 0.464

Panel C: Wage Regressions Split by Worker Tenure

Worker Tenure Status: Non-new

Hires

New

Hires

Non-new

Hires

New

Hires

Dependent Variable: Ln(Wagest) Ln(Wagest) Ln(Wagest) Ln(Wagest)

Abnormal Accrualst-1 0.171*** 0.154*** - - (0.033) (0.034)

Total Accrualst-1 - - 0.149*** 0.162***

(0.038) (0.044)

Controls Yes Yes Yes Yes

Fixed Effects EI × Y × C EI × Y × C EI × Y × C EI × Y × C

Observations 8,839,000 2,859,000 8,839,000 2,859,000

R-squared 0.503 0.535 0.503 0.534

49

IA Table 2. Local Labor Market Conditions

This table reports estimates from OLS regression analyses estimating turnover and wage regressions split by cross-

sectional characteristics of establishment county, particularly terciles of labor market thickness measured by number

of establishments in the same establishment-industry and year. The table structure follows Table 6 from the manuscript

though without using worker subsamples. Across all specifications, we use lagged Abnormal Accruals as the proxy

for accounting quality. Panel A presents turnover regressions. Panel B presents wage regressions. Appendix Table A

defines variables. Standard errors are in parentheses and calculated with clustering by firm. Statistical significance at

the 10%, 5%, and 1% levels is indicated by *, **, and ***, respectively. Statistics are rounded to comply with

disclosure requirements of the U.S. Census Bureau.

Panel A: Turnover Regressions Split by Local Labor Market Conditions (1) (2) (3)

Local Labor Market Conditions--

I.e., Establishment Count: Low Tercile Mid Tercile High Tercile

Dependent Variable: Turnovert Turnovert Turnovert

Abnormal Accrualst-1 0.036** 0.046** 0.057** (0.017) (0.019) (0.023)

Controls Yes Yes Yes

Fixed Effects EI × Y × C EI × Y × C EI × Y × C

Observations 117,000 117,000 117,000

R-squared 0.555 0.383 0.244

Panel B: Wage Regressions Split by Local Labor Market Conditions

Local Labor Market Conditions--

I.e., Establishment Count: Low Tercile Mid Tercile High Tercile

Dependent Variable: Ln(Wagest) Ln(Wagest) Ln(Wagest)

Abnormal Accrualst-1 0.137*** 0.159*** 0.131*** (0.034) (0.036) (0.048)

Controls Yes Yes Yes

Fixed Effects EI × Y × C EI × Y × C EI × Y × C

Observations 3,897,000 3,902,000 3,899,000

R-squared 0.525 0.520 0.489

50

IA Table 3. Internal Control Weaknesses, Turnover Regressions

This table reports estimates from OLS regression analyses estimating a variation of Equation (5); specifically, this

specification measures the correlation between time-series indicators for firms reporting internal control weaknesses

(“ICWs” in year T that are later remediated) and Turnover, Separations, and Joins at the establishment-year level

along with firm-level controls and various fixed effects. The specification is similar to Table 3 Panel B. Column (1),

(2), and (3) reports the results for dependent variables Turnover, Separations, and Joins, respectively. Appendix Table

A defines variables. Standard errors are in parentheses and calculated with clustering by firm. Statistical significance

at the 10%, 5%, and 1% levels is indicated by *, **, and ***, respectively. Statistics are rounded to comply with

disclosure requirements of the U.S. Census Bureau.

(1) (2) (3)

Dependent Variable: Turnovert Separationst Joinst

ICW Year IndicatorT-2 -0.001 0.001 0.000 (0.008) (0.010) (0.009)

ICW Year IndicatorT-1 -0.003 0.005 -0.008

(0.011) (0.014) (0.011)

ICW Year IndicatorT 0.003 0.014 -0.005

(0.013) (0.017) (0.012)

ICW Year IndicatorT+1 0.005 0.017 -0.007

(0.011) (0.013) (0.011)

ICW Year IndicatorT+2 0.004 0.011 -0.004

(0.012) (0.016) (0.011)

ICW Year IndicatorT+3 0.010 0.017 0.000

(0.013) (0.016) (0.013)

Controls Yes Yes Yes

Fixed Effects EI × Y × C &

Firm

EI × Y × C &

Firm

EI × Y × C &

Firm

Observations 198,000 198,000 198,000

R-squared 0.557 0.533 0.543