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Do Charitable Auditors Deliver Better Audit Quality? -- Evidence from Chinese CPAsBy: Jiaxin Liu, Yakun Wang and Yu Zhou

August, 2020

Abstract: This paper investigates the relationship between auditor’s participation in charity and their audit outcomes. Using audit-partner level charitable activity records of Chinese CPAs, we find that auditors who engage in charitable activities are associated with significantly better audit quality. Specifically, we examine engagement auditors and review auditors separately and find that engagement auditors with records of charitable activities demonstrate lower discretionary accruals, and are more conservative in issuing modified audit opinion. While firms with review auditors who engage in charitable activities are associated with lower frequency of meeting or beating analyst forecast and occurrence of restatements. Our results hold after controlling for other individual-level auditor characteristics such as gender, age, and education background as well as audit firm fixed effects.Additional test suggests that the results for engagement auditors are driven by female auditors rather than male auditors. Taken together, our results suggest that individual auditor’s charity activity is associated with audit quality as it reflects auditor’s intrinsic motivation of ethical conduct.

Keywords: Intrinsic Motivation, Moral Reasoning, Codes of Ethics/Professional Conduct, Audit Quality JEL Classification Numbers: M42’

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1. Introduction

Auditor plays an indispensable role in the capital market because to make informed investment

decisions, the users of the financial statements rely heavily on the information verified by the

auditors to fairly understand a company’s operations. Since the series of corporate accounting

scandals in the early 2000’s, unethical misconducts of the accounting professions have drawn the

attention of numerous stakeholders, including the SEC, professional accounting organizations such

as American Institute of Certified Public Accountant (AICPA) and Public Company Accounting

Oversight Board (PCAOB). The AICPA’s Codes of Professional Conduct stresses the importance

of ethical and professional behaviors of accountants in ensuring the audit quality and public trust

in the profession.

The determinants of audit quality are at the heart of audit research and have attracted

considerable attention from scholars. Earlier literature investigated the impact of audit firm-level

characteristics, such as size, tenure and industry specialization on audit quality assuming

homogeneity in audit quality across individual engagement auditors (e.g. Simunic and Stein (1987),

Francis and Wilson (1988), Becker et al. (1998) and Francis and Krishnan (1999)). Recently

studies have examined the external disciplinary mechanisms, such as legal infrastructure,

reputation effect and regulatory punishment, in affecting audit quality (Knechel et al. (2007),

DeFond et al.(2014)). In the wake of recent accounting scandals, there has been a growing

recognition of the importance of intrinsic motivations on the behaviors of individuals, especially

auditors (Benabou and Tirole 2006; Hart 2010). For example, behavioral accounting research has

studied the impact of moral reasoning on auditors’ behavior. (e.g., Rest (1979); Ponemon 1992;

Ponemon 1993; Ponemon and Gabhart 1990; Windsor and Ashkanasy 1995; Falk, Lynn,

Mestelman, and Shehata 1999). Other behavioral auditing studies advocate for more research on

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the influence of intrinsic motivation, especially moral reasoning, on auditor behaviors (e.g.,

Calegari, Schatzberg, and Sevcik 1998; Lampe and Finn 1992; Schatzberg, Sevcik, and Shapiro

1996; Schatzberg et al. 2005). However, to the best of our knowledge, there has been little

empirical evidence on whether the intrinsic ethical motivation of individual auditors affects the

quality of work performed1.

Prosocial behaviors lie at the centre of social ethics and occur when people engage in

activities that benefit others rather than themselves. Charitable giving, a form of prosocial behavior,

has been studied extensively in social sciences. Prior studies examined the intrinsic and extrinsic

motivations of charitable giving and suggest that both pure altruism—from emotional arousal and

self-expectations-- and social pressure are possible motivations (Andreoni and Payne (2013) and

Ellingsen and Johannesson (2009), Cappelari et al. (2011); Schwartz (1977)). Incorporating the

ethics perspective of individual auditors, we extend prior literature on the determinants of audit

quality by investigating the association between individual auditors’ charitable giving and audit

quality.

On one hand, altruistic motivation enables auditors to adhere to the codes of professional

conducts and make the “right” judgment call when facing ethical dilemmas instead of acquiescing

to clients’ pressure. We conjecture that charitable auditors are potentially associated with higher

audit quality. On the other hand, charitable giving could also be motivated not by pure altruism

but by social pressure (DellaVigna, List and Malmendier (2012)) that forces the individual to give

in order to “look good” , or in exchange of personal interests such as career advancement (Amos

(1982), Frisch and Gerrard (1981)). If this is the case, the charitable behaviors of auditors may not

be an indication of their intrinsic ethical standards, and we may not find an association between

1 Ponemon (1992), Penemon (1993) and Ponemon and Gabhart (1990) have examined auditors’ moral reasoning and ethical decision-making using surveys/behavioral research methods.

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auditors’ charity activities and audit quality. Therefore, it is an empirical question whether or not

auditors’ charitable behaviors are associated with audit quality.

Utilizing the unique setting in China where the disclosures of signing audit partners on clients’

audit report are required, we examine whether the engagement and review partners’ charitable

activities are associated with audit quality, measured using discretionary accruals, frequency of

modified audit opinions, frequency of meeting or beating analysts forecasts, frequency of reporting

small profits and restatements2.Using a sample of 14,561 firm-years of Chinese companies listed

on the country’s A-share stock market with 1,640 engagement auditors from 2010 to 2014, we find

that engagement auditor partners with charitable activity records are associated with lower

discretionary accruals and a higher frequency of issuing modified audit opinions than those without

charitable records. Also, for a sample of 15,541 firm-years with 3,683 review auditors, we find

that review audit partners who have charitable activity records are associated with lower frequency

of meeting or beating analysts’ forecasts and fewer restatements than those without charitable

records. Our results are robust after controlling for audit firm fixed effects and are found to be

primarily driven by female rather than male auditors. Taken together, our results suggest that

auditors’ intrinsic ethical perception, manifesting into their charitable giving, influences the work

quality provided by the auditors (i.e., audit quality).

This paper makes a number of contributions. First, existing literature on audit quality focuses

almost exclusively on the determinants external to individual audtiors, such as audit firm size,

auditor tenure, audit firm independence, industry expertise, regulatory intervention (e.g., the

Sarbanes-Oxley Acts) and reputation concerns (Knechel et al. 2007; DeFond et al. 2014). However,

2 With weaker legal system and enforcement, one would expect that the influence of individual auditors to be more pronounced on audit quality than its U.S. counterpart, making China an ideal setting with less confounding factors for our research question.

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the impact of auditors’ intrinsic motivation on audit quality has largely been ignored. Our study

contributes to the literature by providing empirical evidence on the connection between a prosical

behavior – charity participation– of auditors and the audit quality.

Secondly, prior studies on audit quality examine firm-level characteristics and the impact on

audit quality, assuming homogeneity in audit quality across different engagement and review

auditors. More recent studies (e.g., Reynolds and Francis 2000; DeFond and Francis 2005; Chen

et al. 2009; Gul et al. 2013 ) highlighted the importance of audit-partner level characteristics.

Analysis at the individual partner level enables us to better understand audit quality that is

associated with invidiual auditors’ characteristics (Chen et al. 2010) as individual audit partner has

direct influence by exercising discretions over various audit tasks, such as assessing client risk,

reviewing critical assessments, and communicating with clients (e.g., O’Keefe et al. 1994;

Hackenbrack and Knechel 1997). Our study adds to this stream of literature by providing evidence

that the ethical characteristics of individual auditors matter in determining audit quality.

Thirdly, this paper addresses the calls for the disclosure of the identity of engagement audit

partners on companies’ audit reports by the regulatory bodies around the world. A new regulation

has been in effect in the U.S. in January, 2017 mandating the disclosure of audit partners’ identity

on the audit reports of all public companies in the U.S.. A survey by the International Accounting

and Auditing Standards Board (IAASB) reveals that more than one hundred CPA associations in

both developed and emerging markets (e.g., Malaysia) are considering similar disclosures. 3

Potential benefits of the disclosure include enhancing audit partner’s sense of accountability to the

financial statement users, and providing useful information for investors regarding audit quality.

The findings of our papers suggest that individual auditors’ charitable behaviors are associated

3 See http://pcaobus.org/News/Events/documnets/10132010_SAGMeeting/OCA_standards-setting_agenda.pdf.

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with audit quality, and investors and other stakeholders may be interested in employing this

characteristic in their assessment of the credibility of the audited financial statements.

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

background and literature review. Section 3 develops the hypothesis. Section 4 discusses the

sample, methodology and summary statistics. Section 5 discusses the empirical results and main

findings. Section 6 discusses the robustness test. Section 7 performs and reports findings of an

additional test. Section 8 concludes the paper.

2. Literature Review

2.1 Auditor independence and audit quality

The auditor’s independence and audit quality are two concepts that are closely related. The

nature of the audit is to attest that the financial statement is of fair representation of client’s

financial position and that the financial statements are free from material misstatements. Empirical

studies suggest that the economic bonding and close relationship between incumbent auditor and

the firm and/or firm management could erode auditors’ independence and integrity, resulting in

auditors acquiescing to clients’ pressure, a deviation from ethical judgements in auditors and lower

audit quality (Blay and Geiger (2013); Krishnan and Krishnan (1996), Deis and Giroux (1992),

Carey and Simnett (2006),Ye et al.(2011), etc.). In the AICPA Codes of Ethics and Professional

Conducts, it stipulates that auditor independence and integrity should be the ethical cornerstone of

the practices in the public accounting profession. The Securities Act of 1933 requires all registrants

of SEC having their financial statements audited by independent auditors while the Sarbanes-

Oxley Act of 2002 restricts the non-audit services and mandates the five-year audit partners

rotation to ensure auditor independence and audit quality.

2.2 Individual auditor and audit quality

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Prior studies have examined and find evidence on the effect of audit firm characteristics,

such as audit firm size, tenure, and industry specialization on audit quality. (e.g., Simunic and Stein

1987; Francis and Wilson 1988; Becker, DeFond, Jiambalvo, and Subramanyam 1998; Francis

and Krishnan 1999). An implicit assumption is that there is homogeneity in audit quality across

individual auditors within the audit firm as audit firms may enforce uniform firm-wide policies

on key decision-making process, such as new clients acceptance, retention of existing clients, etc.

through employing a uniform set of training materials, industry-specific databases, internal

benchmarks for best practices, automatic IT systems, and internal consultative practices.

However, many key procedures in an audit engagement demand considerable amount of

discretions in judgment and decision-making by individual auditors – such as evaluation of a

client’s internal control, audit planning, substantive evidence collection and interpretation of audit

evidence. Studies on characteristics of individual auditors’ and audit quality is limited largely due

to data unavailability on the disclosure of engagement and review auditors A few studies, using

the Chinese, Taiwanese, Swedish, Australian and proprietary data from the US, find that

characteristics of individual auditors, such as education background, experience in the audit firm,

ranks in audit firm, political affiliations, auditor’s tenure, auditor’s client portfolio composition are

associated with a number of audit quality measure including discretionary accruals, propensity to

issue going-concern opinions, restatements, etc. (Gul et al. (2013), Zerni (2012), Chen et al. (2008),

Bedard and Johnstone (2010), Carey and Simnett (2006), Sundgren and Svanstrom (2014) and

Karjalainen (2011), Bedard and Johnstone (2010), etc.)

2.3 Motivation of charity, ethics and auditor’s decision-making

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The motivation of the pro-social behaviors – people engaging in activities that are

beneficial to others rather than to themselves – has been question of interest for economists,

psychologists and sociologists for hundreds of years.

One strand of economic literature argues that individuals gain utility – in a warm glow effect-

- simply to feel good from “giving” or doing charity (Andreoni (1989,1990), etc.). Studies have

provided evidence on this form of motivation, i.e. pure altruism or moral reasoning, by conducting

survey experiment, field experiment and natural science experiment such as neuro-imaging

(Crumpler and Grossman (2008), Tonin and Vlassopoulos (2010), Harbaugh,Mayr, and Burghart

(2007), etc.). Another strand of studies provide similar but slightly alternative moral reasoning

driver of charitable behaviors – self expectation (Schwartz 1977, Amos 1982, Johnson 1973). In

other words, it is suggested that individuals “give” or do charity in order to meet the internalized

social standards that were formed during the social interactions throughout the upbringing and/or

at their professional career experience. Such motivation arises out of their psychological needs to

blend-in and/or to avoid social costs associated with failing social standards/expectation, including

ethical standards which entails charitable/philanthropic goals (Schwartz 1977).

Contrary to such “selfless” motivation, motivation to do charity could be entirely self-interests

oriented, and charity could be used as a marketing and reputation management/manipulation tool

by the individual. This reasoning is based on the agency-cost theory (Jensen and Meckling 1976)

and suggests that firm managers use socially responsible behaviors as a legitimacy strategy to serve

personal interests, such as career advancement, increasing social power, supporting personal

causes, or even political agendas etc.(Atkinson and Galaskiewicz, 1988; Navarro, 1988, Werbel

and Carter 2002, Friedman 1970.), to divert stakeholder’s attention and to cover up unethical

corporate misbehavior (Hemingway and Maclagan 2004). For example, Chen et al. (2007) suggest

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that firms use philanthropic activities as remediating measures for their less than ethical conducts

in certain areas, finding that firms’ corporate philanthropic activities are associated with worse

social performance in the environmental and product safety area. This is consistent with Amos

(1982) and Johnson (1973)’s conclusion that drivers of charitable behaviors of individuals include

“employment conditioned motive” and “income motive.

An extensive body of behavioral research examines auditors’ moral reasoning and its effect

on the auditor’s decision-making process in audit engagements. The evidence have been mixed

(e.g., Ponemon 1992;Ponemon 1993; Ponemon and Gabhart 1990; Windsor and Ashkanasy 1995;

Schatzberg et al. 2005). Penemon (1993) find no evidence that ethics education is associated with

the moral development of accounting students. Schatzberg et al. (2005) find that misreporting and

audit fee premium are higher for high level than for low level moral reasoning when economics

incentives is high. Windsor and Ashkanasy (1995) find that moral reasoning development and the

belief in a just world enable auditors resist client pressure in an ethical dilemma. Fleming et al.

(2010) find that there is significant difference in the level of moral reasoning between Chinese and

U.S. accounting students and between Chinese accounting students and Chinese experienced

auditors.

3. Hypothesis Development

Based on the findings that there could be an association between individual auditor’s

characteristics and audit quality, we construct our hypothesis by the following reasoning. If

auditor’s charitable behavior is driven by either pure altruism [Andreoni (1989,1990), etc.] or by

the need to adhere to social expectations of moral conducts [Schwartz 1977, etc.], we should

observe a positive association between charitable auditor and the audit quality. This is because that

morally superior auditors, measured by their propensity of engaging in charitable activities, are

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more likely than not to adhere to the codes of ethics and professional conducts, and would choose

to maintain auditor’s independence and integrity in the face of an ethical dilemma and be less

willing to compromise integrity by acquiescing to client pressure on aggressive reporting.

However, if the auditor is primarily driven by self-interests including seeking revenue or

promotion based on client retainment, the auditors would be more likely than not to give in to the

client’s accounting manipulation given an audit disagreement. And we should observe a negative

relationship between charitable auditors and audit quality. And we also recognize that it is possible

that auditors do charity because of self-interests which involves non-monetary prospect, such as

political agenda, power seeking and personal values [Lin, Tan, Zhao and Karim 2015; Werbel and

Carter 2002 ]. In this case, we may not observe any association between charitable auditors and

audit quality based on compromised independence due to economics reasons.

Therefore, we posit our hypothesis as follows:

H1: Charitable auditors are not associated with higher audit quality than non-charitable auditors.

4. Sample, Methodology and Summary Statistics

4.1. Sample Selection

The sample selection is presented in Table 1. According to the Chinese auditing standards, the

engagement and review auditors in China are required to include their signatures on the audit report

(Ministry of Finance, 1995). The China Securities Markets & Accounting Research (CSMAR)

provides the names of these signatory auditors. We begin our sample selection process by

including all firms trading on China Securities Markets & Accounting Research (CSMAR) for the

sample period of 2003-2015, resulting in 26,574 observations. We exclude financial firms and

firms with b-shares, firm-years with missing auditors’ signatures, and firm-years that are

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associated with undistinguishable review v.s. engagement auditors4 , resulting in a sample of

20,773 observations. We obtain financial and stock information of the sample firms from CSMAR.

We obtain demographic characteristics of auditors including charity participation from the enquiry

system compiled by the Chinese Institute of Certified Pubic Accountants(CICPA) (available at

http://cmis.cicpa.org.cn, in Chinese)5. We merge CSMAR and the CICPA database, and we delete

observations with missing firm-specific, audit firm-specific control variables and demographic

information on individual auditors. The resulting merged sample consists of 14,561 firm-year

observations for the engagement auditor and 15,541 firm-year observations for the review auditor.

A number of observations are lost during the construction of the five audit quality variables (e.g.

abnormal accrual, Small Profit, Modified Audit Opinions (MAO), Beat Analysts and

Restatements6). Table 1 presents the number of observations in each regression sample for the

engagement and review auditors respectively. We finally come to five final sub-samples in the

period of year 2010-2014 for the engagement (review) auditors consisting 14,220 (15,179) firm-

auditor-years for the abnormal accrual, 14,319 (15,269) firm-auditor-years for MAO, 14,538

(14,538) for firm-auditor-years SP and 13,469 (14,924) firm-auditor-years for BeatAnalyst and

10,715 (11,576) firm-auditor-years for Restatements.

4 Following prior research by Gul et al. (2012) ,we distinguish review auditors from engagement auditors based on the size of client portfolios of each signatory auditor. Usually, review auditors are associated larger client portfolios in terms of client firms’ assets. In CSMAR, there are firm-years with two or three auditors with the same client portfolio sizes. We exclude these observations from our sample because we are not able to separate them into review v.s. engagement auditors based on client portfolio sizes. 5 This sample consists of all CPAs with active license since year 2010. There are in total 588,678 CPA-years in our sample from 2010-2014. 6 We use variables in the CSMAR to calculate the five audit quality measures except for restatements (for the discussion of audit quality measures, please see section 4.2.1). For restatements, we manually restatements from the corporate disclosures regarding restatements in the website the China Information website (http://www.cninfo.com.cn). In addition, in annual financial reports where firms disclose financial restatement information in the section “The causes for and effects of significant accounting errors,” a publicly listed firm also disclose financial restatement information in the section “The causes for and effects of significant accounting errors”. So we download annul financial statements and corporate disclosure regarding restatements from China, and manually collect the causes for restatements, the restated RMB amounts and the periods for restatements. The sample period for restatements we collected is from 2003 to 2013.

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4.2 Methodology:

4.2.1. Measuring audit quality:

The important audit outcomes are audit reports and audited financial statements. Accordingly,

we measure audit quality by focusing on the effect of auditors’ reporting decisions on the financial

statement quality of client firms (Francis, 2004). Following prior studies (Lennox (2005)), we use

five proxies for audit quality: Abnormal Accruals(AbAccrual), auditor propensity to issue

modified audit opinions (MAO), the presence of small profits(SP), analyst forecasts beating

(BeatAnalyst) and restatements (Restatement).

For the first audit quality measure, Abnormal Accruals(AbAccrual), we use performance-

adjusted abnormal accruals estimated from the following model (Kothari, et al. 2005):

!"##!" !"!"⁄ = &# + &$ (1 !"!") + &% (∆,"-.,!" − ∆"0!") !"!" + && 11.!" !"!"⁄⁄⁄ +

&'02"!" + 3!" (1)

where TACCt is a firm’s total accruals in year t, calculated as the net income before

extraordinary items less operating cash flow; TAt is the total assets at the end of year t; �SALESt

is the growth in sales from year t-1 to year t; �ARt is the growth in net total receivables from year

t-1 to year t; PPEt is the net property, plant, and equipment (PPE) at the end of year t; and ROAt is

the net income in year t divided by lagged total assets. The model in equation (1) is estimated

separately by industry-year. At least 10 observations in an industry-year group are required to run

the regression. We use two-digit SICs for the manufacturing sector which is the largest one and

one-digit SICs for other industries. The residuals from the regression model are used to measure

the abnormal accruals. We examine both the absolute value of abnormal accruals (AbAccrual) and

the signed abnormal accruals i.e. AbAccrualU and AbAccrualD, which is the income increasesing

and decreasing abnormal accruals. For the signed abnormal accrual we use the tobit model because

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y is censored at above or below zero. The higher the absolute value of abnormal accruals, the lower

the audit quality is.

For the second audit quality measure, Modified Audit Opinions (MAO), we use indicator

variable equals to one if the client receives an unqualified opinion with explanatory notes, a

qualified opinion, a disclaimer opinion, or an adverse opinion (e.g., DeFond et al. (2000), Gul et

al. (2013)). The indicator variable equals to zero if otherwise, i.e. unqualified opinions without

explanatory notes. Chinese auditing standards (Ministry of Finance, 1995) require auditors to issue

qualified (disclaimed or adverse) opinions for: (1) GAAP violations; (2) scope limitations; (3)

inconsistencies in applying accounting standards. Moreover, auditing standards also allow auditors

to use explanatory notes in an unqualified opinion to indicate significant events, such as pending

lawsuits that may materially affect future performance.

For the third audit quality measure, we define the variable Small Profit (SP) equals one if the

client’s return on assets is between zero and one percent that year. Prior studies (Burgstahler and

Dichev (1997); Francis and Wang (2008); Francis and Yu (2009); Jorgensen et al. (2012))

generally regard the presence of a small profit as evidence of income-increasing earnings

management. In China, a firm has to be profitable for three consecutive years to qualify for the

issuance of a seasoned equity offering according to the securities law. Thus Chinese companies

have particularly strong incentives to inflate earnings to report a small profit. Moreover, if a

company incurs losses for two consecutive years, it will be subject to special treatment -- e.g.,

imposing a cap of five percentage to its daily price change -- and to the risk of delisting if the

company cannot generate a profit in the third year. Jiang and Wang (2008) show that this

regulatory requirement induces Chinese companies to inflate earnings to report small profits. Chen

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et al. (2001) demonstrate that Chinese companies with small profits are more likely to receive

MAOs, suggesting that small profits are likely to result from earnings management.

For the fourth audit quality measure, client’s propensity to meet or beat analysts’ earnings

forecasts (BeatAnalyst ) is an indicator variable equals to one if the realized earning per share is

above the median consensus forecasts by one percent, zero otherwise. Prior studies suggest

managers have the incentive to manipulate earnings to meet analyst forecast or beat the forecast

by a small amount to maintain firm valuation on the short-term, thus this measure could be used

to measure audit quality (Minutti-Meza (2017), Reichelt&Wang (2010)). The higher frequency of

meet or beat analysts’ earnings forecasts, the lower audit quality.

The fifth proxy for audit quality is the occurrence of restatement (Restatement). As in Francis

and Michas (2013), we restricted the restatement to over-restatement, i.e. an instance where the

audited income of a client firm for a given year is subsequently restated downwards, with the

magnitude of downward restatement be at least 10% of the originally reported net income (Li et

al. (2016)). Downward restatements suggest that the original income was inflated. Since the

overstatements of net income has more negative impact on auditor than understatement due to

auditors’ potential legal liability sought by investors (Basu 1997; Skinner 1994), we focus on over-

statement of income in our study. The higher frequency of restatements, the lower audit quality.

4.2.2 Measuring auditors’ charitable behaviors

We use the indicator variable, Charity, equals to one if a signatory auditor has a record of

doing charitable activities in the past, zero otherwise, to measure auditors’ propensity to engage in

charitable activities. As discussed previously, the enquiry system of the CICPA database consists

of information on individual auditors’ charity participation in a given sample year.

4.2.3. The regression model and control variables

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We run the following regression model to test the hypothesis.

Audit Qualityi,t= b0 + b1 Charityi,t + b2 Controls i,t + b3 Year Fixed Effects i,t

+ b4 Industry Fixed Effects i,t + e i,t (2)

Audit Qualityi,t are the five audit quality measures defined above. For the continuous audit quality

variables (first and second measure), we use the Ordinary Least Squares (OLS) regression. For the

dichotomous audit quality variables (second to fifth measure), we use logit regression. Charityi,t is

defined above and is the variable of interests capturing auditors’ charitable behaviors. We have

controlled for year and industry fixed effects to delineate the effects of auditors’ charitable

behaviors on audit quality from any year and industry factors that correlated with characteristics

of auditors and audit quality.

In addition, we have included a number of control variables in the regression that are known

determinants of audit quality, including client firm characteristics, audit firm characteristics and

individual auditor (Certified Public Accounts, CPA) characteristics.

For client firm’s financial characteristics, I have included the following control variables:

return on assets (ROA); the presence of loss (Loss) in current year; financial statement account’s

complexity, defined as the ratio of inventory and receivables to total assets (Complexity); client

firm size (Size), defined as the log value of total assets (Size); leverage ratio (Leverage); sales

growth (Growth), defined as the difference between current and last year sales divided by last year

sales; cash flow volatility in the past three years (CFO_VOL), defined as the standard deviation of

a firm’s cash flows from operations from year t-2 through year t; firm’s tobin’s Q value (Tobin’s

Q), defined as firm’s market value of equity scaled by the book value of equity at the end of year

t; stock return volatility(RetVOl), defined as the standard deviation of a firms monthly stock

returns during year t ; new share issuance (ShareIssue), defined as indicator variable equals 1 if

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firm issues additional shares in year t, 0 otherwise; age of firm (FirmAge), the number of years a

company has been listed; and the quick ratio (Quick). Moreover, previous studies find that in China

earnings management is affected by local government ownership in the company (Wang et al.

2008; Chan et al. 2006). Therefore, we include a variable to indicate whether a client is controlled

by a local government or the central government or not (State). We winsorize Quick, Complexity,

Growth, ROA, Tobin’s Q at 1% on both sides.

Dechow et al. (2010) suggest that earnings quality is affected by time-varying audit firm

characteristics. Since earnings quality is often used as a measure of audit quality, we control for

audit firm industry specialization (expertiseAF)), audit firm size ( the log value of total assets of

all the client portfolio by an audit firm (sizeAF), the client importance for an audit firm

(influenceAF), and audit firm tenure (tenureAF).

In addition, prior studies suggest that audit quality is associated with individual auditors’

characteristics (Gul et al. (2016)). Therefore, we repeat for the above audit-firm characteristics at

individual auditor level 7 using the following variables: sizeAP, influenceAP, tenureAP and

expertiseAP. Furthermore, we control for demographic characteristics of the signing auditors that

may affect audit quality, including gender (GENDER), accounting major (ACCOUNTING),

whether auditors hold a master degree(MASTER) or not, and the total years since the signing

auditors obtained his/her CPA license (EXPERIENCE).

Definition of all variables are presented in Appendix A.

4.2.4. Summary statistics of the sample

Table 2 presents the descriptive statistics for all the dependent and independent variables.

Panel A shows the descriptive of all CPAs recorded in the CICPA enquiry system. It shows that

7 Since there are two or more signing auditors for each audit engagement, we average the characteristics associated with each signing auditors: sizeAP, influenceAP, tenureAP and expertiseAP.

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about 2.7% of CPAs have a record of doing charitable activity in the past. About 47% of CPAs are

female. The average working experience, which is defined as the total number of working years

since an auditor obtained the CPA license, is 8.69 years, indicating that the CPAs in China are

relatively young compared to some western countries such as the U.S.. In the statistics, about 45.7%

of CPAs have an accounting degree while only 5% of them have master’s degrees. Panel B

presents the statistics for engagement and review auditors separately. In Panel B and C, it shows

that about 5.9% engagement auditors and 7.3% review auditors have past charitable activities.

Table 3 presents the difference in demographic characteristics, client firms’ characteristics

and audit quality between the charitable auditors (Charity=1)and non-charitable auditors

(Charity=0) group. Panel A reports the difference in demographic characteristics of auditors’. On

average, 51.5 % of charitable auditors are female, whereas 49.6% of non-charitable auditors are

female. Similarly, 53.5% of charitable CPAs has an accounting major while only 45.5% of non-

charitable CPAs has accounting as the major. Charitable CPAs have been on average working in

the auditing industry for 9.185 years since they obtained their CPA license, compared with non-

charitable CPAs who have been working for 8.683 years. The difference in these three aspects of

auditors’ demographic characteristics between the two groups is statistically significant at <0.01

level. While the difference in the mean ratio of master’s degree between the two groups (5.2% v.s.

5.0%) is not statistically significant at <0.01 level.

Table 3 Panel B shows the difference in control variables between the charitable and non-

charitable sample engagement auditors. Among all the client-related characteristics, companies

with charitable engagement auditors have significantly lower quick ratio(Quick), have higher

leverage, lower Tobin’Q and are more likely to be stated-owned company. Similarly, Panel D

shows the difference in control variables between the two groups for review auditors. Firms

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audited by charitable review auditors have significantly lower quick ratio, are larger in size, have

higher leverage, are less profitable in ROA, have lower Tobin’s q, higher cash flow volatility and

are more likely to be state-owned firms.The results above, except for quick ratio and Tobin’s Q,

generally suggest that firms with charitable auditors tend to have higher inherent risk and/or audit

risk than the ones with non-charitable auditors.

Panel C and E in table 3 present the univariate student’s t test of difference in means for the

five audit quality measures between the charitable and non-charitable group for the sample of

engagement auditors and review auditors respectively. For the engagement auditor sample (Panel

C), we find that the difference in absolute value of abnormal accrual(AbAccrual) between firms

with charitable engagement auditors and firms without are statistically significant positive, but we

do not find statistical evidence for the other four audit quality measures. However, the sign of the

difference in MAO and BeatAnalyst is consistent with the expectation though not as statistically

significant. Specifically, 5.9% of firm-years employing charitable engagement auditors receive

modified opinion while 4.7% of firm-years without charitable engagement auditors receive

modified opinion. As for meeting or beating analyst forecasts, only 0.8% of firm-years with

charitable engagement auditors have occurrence of meeting or beating analyst forecast, while 1.4%

of firm-years without charitable auditors have earnings meeting or beating analyst forecast. Both

the t-statistics for the difference in MAO and BeatAnalyst is close to 1.5, suggesting the difference

is not trivial.

Panel E display results for the sample of review auditors. There are no statistical differences

in all the five audit quality measures in the charitable v.s. non-charitable auditors. Nevertheless,

the signs of the difference in AbAccrual , AbAccrualU, SP and BeatAnalyst are consistent with

the conjecture that charitable auditors are associated with higher audit quality. Moreover, the

19

economic magnitude of the difference in SP and BeatAnalyst in the sample of review auditors are

much larger than that in the sample of engagement auditors. While the economic magnitude of

difference in AbAccrual, AbAccrualU and MAO for the sample of review auditors are smaller

than those in the engagement auditors sample. Since SP and BeatAnalysts are associated with

management level earnings reporting decisions and the negotiation between review auditors and

management, this is preliminary evidences that the effect of charity by review auditors is

concentrated on higher-level earnings reporting strategy. Whereas the charity effect on the

engagement auditors is leaning towards the application of accounting principles to specific

accounts and transactions that determines the amount in abnormal accruals, which ultimately finds

their way into the audit opinions. The latter is consistent with the nature of work of the

engagement auditors: they conduct substantive audit work and go through a company’s accounts

in details.

5. Empirical results

Table 4 and 5 shows results of our main test of H1-- the effect of employing a charitable

engagement and review auditor on audit quality.

Table 4 presents the regression results for the sample of engagement auditors. Column (1)-(3)

relate to the first audit quality measure (AbAccrual). The estimated coefficient of Charity is -

0.00424 and is significant at p<0.01, suggesting that firms with charitable engagement auditors

report significantly lower discretionary accruals. We separate the income-increasing and income-

decreasing discretionary accruals in our test in column (2) and (3) (Ittonen et al. (2013)). Previous

studies indicate that auditors are more likely to disagree with their clients about income-increasing

than income-decreasing financial reporting practices(see e.g., DeFond and Jiambalvo 1993;

Kinney and Martin 1994;Nelson et al. 2002). As discussed by Caramanis and Lennox (2008),

20

auditors face higher litigation and reputational risks when financial statements are overstated than

when financial statements are understated. We find that the effect of employing charitable

engagement auditors only concentrates in the income-increasing earnings management as shown

in column (2). The coefficient of AbAccrual is -0.00424, and the mean ROA in the sample of the

engagement auditors is 0.036. So the ROA audited by charitable engagement auditors would be

11.78% (0.00424/0.036) lower than that audited by non-charitable engagement auditors in our

sample.

Column (4) in Table 4 shows the regression results on the third audit quality measure, the

issuance of modified audit opinion (MAO). The coefficient on MAO is 0.450 and statistically

significant at p<0.05 level, suggesting that firms with a charitable auditor are more likely to

receive a modified audit opinion conditional on a firm’s financial position. The marginal effects

suggests that a firm audited by a charitable engagement auditor has 1.39% greater chance of

receiving a qualified audit opinion than the one by non-charitable auditor, holding all other

variables at their means.

Column (5) and (7) of Table 4 show that for the engagement auditors sample, the coefficients

on third and fifth audit quality measure, small profit (SP) and restatement (Restatements), are not

statistically significant. In Column (6), however, the coefficient on Charity is marginally negative

at a significance level of p<0.1, providing some evidence that charitable auditors are associated

with lower probability of beating or meeting analyst earnings than non-charitable auditors.

Marginal effects suggest that the probability is reduced by 1.06% when charitable engagement

auditors is in charge of the audit compared to non-charitable engagement auditors, holding all other

variables at mean.

21

Table 5 presents the regression results for the sample of review auditors. Column (1)-(3)

shows that the coefficients on Charity are negative for AbAccrual and income-increasing accruals

(AbAccrualU) but not significant statistically. This is consistent with the univariate results and

explanation that compared with engagement auditors, review auditors are not as much involved in

lower-level audit procedures which results in a variation in firm-level accruals. Similarly, column

(4) and (6) shows that employing charitable review auditors do not affect the probability of MAO

and of firm meeting or beating analyst forecast in that year. However, the coefficient of Charity

in the SP (column (5)) and Restatement (Column (7)) is negative with marginal significance

(p<0.1), providing some evidence that charitable review auditors are associated with lower small

profits and probability of restating previous years’ earnings. The marginal analysis demonstrates

that a firm audited by a charitable review auditor has 1.53% less chances of restating the profits

and 1.92% less chances of having small profits than a firm audited by a non-charitable review

auditor.

In sum, the results discussed above provide evidences that both engagement auditors and

review auditors with charitable activities demonstrate better audit quality than their non-charitable

counterparts. In other words, the results support the argument that charitable auditors are

associated with inherent ethical motivations rather than selfish motives that encourage them to

adhere strictly to the codes of professional conducts in an ethical dilemma and curb unethical

behaviors of management such as earnings management. More specifically, firms with charitable

engagement auditors have significantly lower absolute and income-increasing discretionary

accruals and are more likely to receive modified audit opinions than firms with non-charitable

engagement auditors. Firms with charitable review auditors have marginally lower frequencies of

reporting small profits and restatements. This is consistent with the fact that engagement auditors

22

are more involved than review auditors with accounting-related earnings management affecting

specific earnings accounts and audit opinions. And the results on the review auditors suggest that

review auditors are mostly involved in examining audit results on upper level as well as negotiating

with client management regarding the overall earnings reporting strategy. It is not surprising that

review auditors are associated with more salient earnings management behavior such as small

profits and restatements.

6. Robustness Test

Endogeneity because of auditor’s selection to do charity

Our sample is highly unbalanced with regards to the number charitable and non-charitable

auditors. Only 5.9% (7.3%) of firm-years in the engagement (review) auditor sample are

charitable auditors. An unbalanced sample may result in model misspecification because of

nonlinearity and self-selection problem. We address this concern by using propensity score

matching (PSM) methodology8. We use the control variables in model (2) as determinants of

participation (i.e. auditors’ choice to do charity) in estimating the propensity scores. Then we

match the treatment and control group using the nearest neighboring matching based on the

propensity scores.

Panel A in table 6 and 7 presents the descriptive statistics of the variables in the main regression

of the two matched groups for the engagement and review auditor sample respectively. Charity=1

8 Ho et al. (2007) recommends that before parametric estimation, matching could be used as a preprocessing technique to eliminate model misspecification. Both the linear and nonlinear relation between the treatment variable and the control variables in the population is eliminated in the matched samples, and this mitigates model misspecification problem. For example, after matching clients audited by charitable auditors and eliminated in the matched samples, mitigating the model misspecification problem. For example, after matching clients audited by charitable auditors and by non-charitable auditors on client size, the linear and non-linear correlation (e.g. polynomial relationship) between the Charity and Size variable disappear, and the nonlinear effect of size is less likely to be captured by the Charity variable. A key advantage of matching is that it does not require identifying exogenous variables or exclusion restrictions (e.g., variables uncorrelated with the main outcome variable) in predicting the treatment choice.

23

is the treatment group and Charity=0 is the control group after the matching. It shows in Panel A

that the differences in all variables between the two groups, except TenureAF and Accounting, are

not statistically significant at p<0.1 level, suggesting that the PSM has attenuated the self-

selection and non-linearity problem. Panel B presents the coefficients of the variables in the

selection model. Panel C presents the treatment effects, i.e. auditor’s charity behaviors, on audit

quality post-PSM. The results are largely and qualitatively similar to the previous results in Table

4 and 5 and are consistent with the conjecture that charitable auditors are associated with high

audit quality. Note that for the review auditors sample, it seems the treatment effect has become

significant for the BeatAnalyst measure and dissipates for the SP measure. In addition, the average

treated effect of the engagement auditors on the BeatAnalyst measures disappear after the PSM as

well.

Biased sample because of the auditor’s choice to disclose the charitable activities

We also recognize that the underlying motivation of charity engagement could be correlated with

the choice to disclose the charitable activities as charitable activities disclosure is voluntary for

firms in China. This could create survival bias in our sample. We respond to this issue using the

following reasoning. Based on the three suggested motivatins of charitable behaviors according

to the previous sutides, truly altruistic auditors should be least likely to disclose while the self-

interests seeking auditors are more likely to disclose. Assuming such sample bias, we should

observe a negative relationship between charitable auditors and audit quality. In other words, this

potential sample bias, if exists, should work against our current findings. Therefore, the

conclusion of our paper should not be affected by this possibility.

24

Audit firm fixed effects

To rule out the possibility that time-invariant characteristics of an audit firm, such as the company

culture on corporate social responsibility correlates with audit quality and individual auditor’s

tendency of doing charity, we incorporate the audit firm dummy variables into our regression. As

suggested in table 8, all the results hold except for the effect of Charity on the SP for review auditor,

which turns insignificant and the effect of Charity on AbACCRUALU by review auditors becomes

statistically significant.

Cross-sectional tests based on auditor’s demographic characteristics

We also conduct several cross-sectional analysis based on the demographic characteristics of both

engagement and review auditors, including gender, experience, masterDegree, and accounting.

Specifically, we add the interaction term between the demographic characteristics and charity into

the regression. But we only find some statistically significant results for the interaction term on

gender and charity of the engagement auditors. The results in table 9 indicates that firms audited

by female engagement auditors experience less absolute abnormal accruals, upward abnormal

accruals, and more likely to receive modified opinions than ones audited by male engagement

auditors. The difference between the female and male engagement auditors may arise from the fact

that female auditors are doing charitable activity out of altruism while male auditors use charitable

activity to promote their self-image or reputation, thus leading to different outcomes in terms of

audit quality.

Audit firm’s charitable activity

Audit firms conduct charitable actitives on the firm-level. To rule out the possibility that

charitable auditors are more likely to be selected by audit firms with charitable activities which

also affect audit quality, we control for audit firm’s charity acitvity. Results are untabulated. Firstly,

25

we find that the correlation between audit firm charity and auditor charity for the engagement

auditors and review auditor sample are only 0.0071 and 0.031. Secondly, the untabulated results

shows that our results are the same after controlling for audit firm charitable activities.

7. Conclusion

In this study, we examine whether auditors who engage in charitable activity are associated with

higher audit quality. Motivated by the studies that examine the motivations of people’s pro-social

behaviors and by a strand of behavioral auditing literatures that examine auditors’ moral reasoning,

we conjecture that auditors with charitable activities in the past possess high inherent ethical

standards due to pure altruism (Andreoni (1989,1990), Schwartz (1977), etc.) and/or to the needs

to meet the social ethical expectation, and thus they are associated with higher audit quality. They

tend to more likely to adhere to the professional codes of conducts regarding maintaining auditor

independence and integrity and are able to resist client pressure in an ethical dilemma. However,

some studies also suggest that prosocial behaviors may be driven by self-interests or social

pressure, such as and maintaining good social image (Funk et al. 2005 and Chen et al. 2007). Thus

it is also possible that charity is only a form of self-promoting/marketing strategy by charitable

auditors and that these auditors are not necessarily associated with high audit quality. Therefore,

it remains an empirical question whether charitable auditors are associated with higher audit

quality than non-charitable auditors.

Utilizing the auditor database in China where individual auditors’ information is disclosed

publicly, we obtain detailed demographic information on individual auditors including the records

of charity records in the past from the Enquiry System of the Chinese Institute of Certified Public

Accountant (CICPA) database. We find that auditors with a record of charitable activities are

associated higher audit quality, supporting that the motivation of auditors’ charitable activities

26

derives from an intrinsic ethical beliefs and/or social expectations rather than self-interests.

Specifically, our empirical evidences suggest that client firms audited by charitable engagement

auditors have lower abnormal accruals are more likely to issue an modified audit opinion. And

client firms audited by charitable review auditors experience less frequent restatements and

meeting or beating analyst forecasts. This finding is qualitatively robust to both ordinary least

squares (OLS) regression and propensity score matching (PSM) and to controlling for audit firm

fixed effects and audit firm charitable activity . In addition, we also find that certain demographic

characteristics of auditors are associated with their choice to engage in charitable activity.

Auditors in our sample who are female, have longer work experience and have accounting degrees

or master’s degrees are more likely to do charity. Last of all, an cross-sectional analysis based on

gender shows that our results are primarily driven by female instead of male auditors.

Our study underscores the importance of individual auditors for audit quality and

demonstrate that the intrinsic motivation of individual auditors affect audit quality. Our studies

have important implication for financial statement users, regulators and policymakers in

understanding how audit quality is affected by engagement and review auditors’ characteristics.

Specifically, our studies suggest that it is important to emphasize ethics in accounting education

and enhance auditors’ inherent ethics standards work. This should be of particular interests in

developing countries such as China since the current audit market is undergoing rapid development,

and the legal and institutional infrastructure is less well developed than its western counterpart

such as the U.S.. Therefore, intrinsic ethical motivations of individual auditors could complement

the weak monitoring and disciplining mechanism in this country in guaranteeing audit quality.

Lastly, our paper is subject to the caveat of mutual selection between ethical auditors and

socially responsible firms. It is possible that the charitable auditors tend to select clients that

27

commit to high ethical standards and that tend to report more responsibly with better accounting

quality and thus low inherent risk, resulting in high audit quality. We do not intend to differentiate

this possibility from the causality relationship since our paper is only interested in documenting

an association

28

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Singhapakdi, Anusorn, and Scott J. Vitell. 1990. Marketing ethics: Factors influencing perceptions of ethical problems and alternatives. Journal of Macromarketing 10, (1): 4-18.

Skinner, Douglas J. 1994. Why firms voluntarily disclose bad news. Journal of Accounting Research 32, (1): 38-60.

Sundgren, S., and T. Svanström. 2014. Auditor-in-charge characteristics and going concern reporting. Contemporary Accounting Research Forthcoming, .

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Trevino, Linda K., and Stuart A. Youngblood. 1990. Bad apples in bad barrels: A causal analysis of ethical decision-making behavior. Journal of Applied Psychology 75, (4): 378.

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36

AbAccrual the absoluate value of abnormal accruals estimated from the equation (1)AbAccrualU the upward abnormal accruals AbAccrualD the downward abnormal accrualsMAO equals to one for disclaimers or adverse opinions, qualified opinions, unqualified opinions with explanatory notes, and zero for clean opinions.SP equals to one if the client's return on assets is between zero and one percent in that yearBeatAnalyst equals to one if the realized earnings per share is above the mdian consensus forecasts by one percent, zero otherwise.Restatement

Charity equals to one if a signatory auditor has a record of doing charitable activities in the past,ROA return on assetsLoss Indicator for bottom-line lossesComplexity the ratio of inventory and receivables to total assetsSize the log value of total assets of a firmLeverage the ratio of total liabilities to total assetsGrwoth Sales growthCFO_VOL the standard deviation of a firm’s cash flows from operations from year t-2 through year t;Tobin's Q firm’s market value of equity scaled by the book value of equity at the end of year tRetVOl the standard deviation of a firms monthly stock returns during year t ShareIssue indicator variable equals 1 if firm issues additional shares in year t,0 otherwiseFirmAge the number of years a company has been listedQuickState equals one if a firm is owned by the central government or a local governmentexpertiseAF audit firm industry specialization and equals one for audit firms that have the largest market share in a given industry in terms of audit feessizeAF the log value of total assets of all the client portfolio by an audit firminfluenceAF the client importance for an audit firm defined as the ratio of total assets of the client firm to sizeAFtenureAF number of consecutive years that the audit firm has audited the clientsizeAP client portfolio size of an individual auditor, measured as sum of total sizes of a sinatory auditor influenceAP the ratio of total assets of the client firm to sizeAPtenureAP the number of consecutive years that the signing auditors have signed the client’s annual audit reportexpertiseAP equals to one for a signatory that have the largest market share in a given industry in terms of audit feesGENDER equal to one if a signatory auditor is femaleACCOUNTING equals to one if the signatory auditor majored in accountancy during their college educationMASTER equals to one if the signatory auditor has obtained a master’s degree or above, and 0 otherwise;EXPERIENCE the total years since the signing auditors obtained his/her CPA license

(current assets – inventory)/total assets

equals to one if the audited income of a client firm for a given year is subsequently restated downwards, with the magnitude of downward restatement be at least 10% of the originally reported net income.

Appendix A Variables Definitions

37

# Firm-yearsCompanies in the CSMAR for the period2003-2015

26,574

Delete: Number of observations in the financial sector and with B shares

(1,800)

Delete: The number of observationsmissing the names of signatory auditorsin CSMAR dataset

(734)

Delete: Number of observations with threeCPAs or two CPAs with same magnitudeof sizeAP (3,267)Number of observations for further analysis

20,773

Number of auditors, total 5,323

For Engagemenent Auditors SampleMissing data to calculate firm-specific andaudit-firm-specific control variables

(2,006)

Missing observations with missingdemographic information on signatoryauditors from the Equiry system compliedby CICPA

(4,206)

Remaining for audit quality regression 14,561

Number of auditorsEngagement auditors 1,640

For each audit quality measure:Abnormal Accruals

Modified Audit

Opinions Small Profit

(SP) BeatAnalyst RestatementMissing observations from calculating audit quality measure

(341) (242) (23) (1,092) (3,846)

Final sample 14,220 14,319 14,538 13,469 10,715 Continued on next page

Table 1 Sample Selection -- Audit Partners and Audit Quality

38

For Reivew Auditors Sample # Firm-yearsMissing observations with missing data oncalculating firm-specific and audit-firm-specificcontrol variables

(2,004)

Missing observations with missing demographicinformation on signatory auditors from the Equirysystem complied by CICPA (3,227)Remaining for audit quality regression 15,541

Number of auditorsReview auditors 3,683

For each audit quality measure:Abnormal Accruals

Modified Audit

Opinions Small Profit

(SP) BeatAnalyst RestatementMissing observations from calculating audit quality measure (362) (272) (1,003) (617) (3,965)

Final sample 15,179 15,269 14,538 14,924 11,576

Table 1 Sample Selection continued

39

N Mean Median Min Max Std. Dev.Charity 486,607 0.027 0.000 0.000 1.000 0.163Gender 486,607 0.470 0.000 0.000 1.000 0.499

Accounting 486,607 0.457 0.000 0.000 1.000 0.498Experience 486,607 8.697 9.000 1.000 17.000 5.123

Master 486,607 0.050 0.000 0.000 1.000 0.218Panel B: Descriptive Statistics for all firm-years of Engagement Auditors

N Mean Median Min Max Std. Dev.

AbAccrual 14,220 0.046 0.033 0.000 0.466 0.045AbAccrualU 14,220 0.023 0.000 0.000 0.340 0.039

AbAccrualD 14,220 0.023 0.000 0.000 0.466 0.039MAO 14,319 0.048 0.000 0.000 1.000 0.214

SP 14,538 0.124 0.000 0.000 1.000 0.329BeatAnalyst 13,469 0.014 0.000 0.000 1.000 0.117Restatement 10,715 0.053 0.000 0.000 1.000 0.224

Charity 14,561 0.059 0.000 0.000 1.000 0.236

Quick 14,561 1.242 0.791 0.180 4.893 1.237Complexity 14,561 0.270 0.249 0.027 0.625 0.166

Size 14,561 21.791 21.660 11.348 28.136 1.276Growth 14,561 0.063 0.050 -0.197 0.378 0.135

Lev 14,561 0.462 0.466 0.116 0.836 0.208ROA 14,561 0.036 0.033 -0.076 0.125 0.045

Tobin's Q 14,561 1.963 1.488 0.370 5.802 1.500State 14,561 0.189 0.000 0.000 1.000 0.391LOSS 14,561 0.106 0.000 0.000 1.000 0.308

CFOVol 14,561 2.331 0.560 0.000 526.624 9.125RetVol 14,561 0.145 0.124 0.011 8.332 0.146

ShareIssue 14,561 0.112 0.000 0.000 1.000 0.316FirmAge 14,561 9.179 9.000 0.000 25.000 5.875

SizeAF 14,561 27.567 27.267 20.768 32.780 2.500

InfluenceAF 14,561 0.020 0.005 0.000 0.960 0.050TenureAF 14,561 1.940 1.000 1.000 13.000 1.540

ExpertiseAF 14,561 0.234 0.000 0.000 1.000 0.423Big4 14,561 0.046 0.000 0.000 1.000 0.209

Table 2 Descriptive StatisticsPanel A: Descriptive Statistics for all CPA-years in China

B4. Audit-Firm-related characteristics

To be continued on next page.

B1. Dependent variables

B2. Independent Variables

B3. Client-related Characteristics

40

Gender 14,561 0.337 0.000 0.000 1.000 0.473

Accounting 14,561 0.536 1.000 0.000 1.000 0.499

Master 14,561 0.088 0.000 0.000 1.000 0.283

SizeAP 14,561 22.419 22.414 13.763 28.136 1.250

InfluenceAP 14,561 0.690 0.864 0.000 1.000 0.348

TenureAP 14,561 1.140 1.000 1.000 6.000 0.406

ExpertiseAP 14,561 0.018 0.000 0.000 1.000 0.132

Experience 14,561 8.802 8.000 2.000 19.000 4.646

Panel C: Descriptive Statistics for all firm-years of Review Auditors

N Mean Median Min Max Std. Dev.

AbAccrual 9,679 0.045 0.033 0.000 0.426 0.044

MAO 9,854 0.039 0.000 0.000 1.000 0.194

SP 9,854 0.116 0.000 0.000 1.000 0.320

BeatAnalyst 9,854 0.014 0.000 0.000 1.000 0.117

Restatement 9,854 0.016 0.000 0.000 1.000 0.126

Charity 15,541 0.073 0.000 0.000 1.000 0.260

Quick 15,541 1.220 0.778 0.180 4.893 1.218

Complexity 15,541 0.270 0.249 0.027 0.625 0.166

Size 15,541 21.770 21.641 11.348 28.136 1.268

Growth 15,541 0.064 0.051 -0.197 0.378 0.135

Lev 15,541 0.465 0.471 0.116 0.836 0.207

ROA 15,541 0.036 0.033 -0.076 0.125 0.045

Tobin's Q 15,541 1.954 1.483 0.370 5.802 1.495

State 15,541 0.191 0.000 0.000 1.000 0.393

LOSS 15,541 0.107 0.000 0.000 1.000 0.309

CFOVol 15,541 2.275 0.551 0.000 526.624 9.031

RetVol 15,541 0.145 0.125 0.011 8.332 0.142

ShareIssue 15,541 0.111 0.000 0.000 1.000 0.314

FirmAge 15,541 9.131 9.000 0.000 25.000 5.795

SizeAF 15,541 27.487 27.087 21.876 32.780 2.505

InfluenceAF 15,541 0.020 0.005 0.000 0.960 0.049

TenureAF 15,541 1.941 1.000 1.000 13.000 1.545

ExpertiseAF 15,541 0.226 0.000 0.000 1.000 0.418

Big4 15,541 0.049 0.000 0.000 1.000 0.216

Gender 15,541 0.250 0.000 0.000 1.000 0.433

Accounting 15,541 0.586 1.000 0.000 1.000 0.493

Master 15,541 0.160 0.000 0.000 1.000 0.366

SizeAP 15,541 23.650 23.620 19.728 29.915 1.091

InfluenceAP 15,541 0.264 0.172 0.000 1.000 0.248

TenureAP 15,541 1.165 1.000 1.000 7.000 0.453

ExpertiseAP 15,541 0.029 0.000 0.000 1.000 0.167

Experience 15,541 12.775 13.000 2.000 19.000 4.216

This table presents descriptive statistics of the dependent variables (audit quality measure), experimental variable

(Charity ) and control variables. The variables definitions are provided in Appendix A.

C2. Independent Variables

C3. Client-related Characteristics

C4. Audit-Firm-related characteristics

C5. CPA-related characteristics

B5. CPA-related characteristics

C1. Dependent variables

Table 2 Continued

41

Variables

Mean Median Mean Median Difference T-statisticsGender 0.469 0.000 0.515 1.000 -0.0458*** (10.410)Accounting 0.455 0.000 0.535 1.000 -0.0800*** (18.210)Experience 8.683 9.000 9.185 9.000 -0.501*** (11.090)Master 0.050 0.000 0.052 0.000 (0.002) (0.950)

Mean Mean Median Difference T-statistics

1.250 1.115 0.721 0.135*** 3.110 0.269 0.278 0.258 (0.009) (1.540)

21.788 21.847 21.691 (0.060) (1.330)0.063 0.070 0.057 (0.007) (1.470)0.460 0.488 0.509 -0.0281*** (3.860)0.036 0.035 0.033 0.000 0.310 1.976 1.753 1.292 0.223*** 4.230 0.190 0.162 0.000 0.0280** 2.040 0.107 0.095 0.000 0.012 1.120 2.311 2.654 0.644 (0.343) (1.070)0.145 0.145 0.123 (0.001) (0.100)0.113 0.103 0.000 0.010 0.900 9.192 8.971 9.000 0.221 1.070

27.604 26.976 26.341 0.628*** 7.160 0.020 0.031 0.010 -0.0112*** (6.380)1.923 2.211 1.000 -0.288*** (5.330)0.238 0.169 0.000 0.0685*** 4.620 0.046 0.046 0.000 (0.001) (0.100)

Table 3 Univariate Student-t’s test on the difference in auditors’ demographic characteristics, control variables and audit quality measures between charitable and non-charitable auditors

Charity=0(N=473,398) Charity=1(N=13,209)

Panel A: The difference in demographic characteristics between charitable CPAs and non-Charitable CPAs.

Panel B: the difference in Control Variables between firms audited by Engagement Auditors with charitable activities and firms audited by Engagement Auditors without charitable activities.

Big4 0.000

TenureAF 1.000 ExpertiseAF 0.000

SizeAF 27.273 InfluenceAF 0.004

FirmAge 9.000 B2. Audit-Firm-related characteristics

0.000

LOSS 0.000 CFOVol 0.557

Tobin's Q 1.501 State 0.000

MedianB1. Client-related Characteristics

Charity=0(N=13,698) Charity=1(N=863) Difference

To be continued on next page.

Lev 0.463 ROA 0.033

Size 21.657 Growth 0.050

Quick 0.794 Complexity 0.248

RetVol 0.124 ShareIssue

42

0.340 0.294 0.000 0.0459*** 2.760

0.527 0.674 1.000 -0.147*** (8.440)

0.089 0.071 0.000 0.0182* 1.840 22.418 22.432 22.359 (0.014) (0.330)0.689 0.697 0.848 (0.008) (0.660)1.138 1.158 1.000 (0.019) (1.350)0.018 0.012 0.000 0.007 1.420

8.742 9.766 9.000 -1.024*** (6.290)

Mean Mean Difference T-statistics

0.046 0.043 0.00316** 1.980

0.023 0.022 0.002 1.340 0.023 0.022 0.001 0.920

Mean Mean Difference T-statistics0.047 0.059 (0.012) (1.540)

Mean Mean Difference T-statistics0.123 0.132 (0.009) (0.770)

Mean Mean Difference T-statistics0.014 0.008 0.007 1.550

Mean Mean Difference T-statistics0.053 0.055 (0.002) (0.280)

Master 0.000 SizeAP 22.415

Gender 0.000

Accounting 1.000

B3. CPA-related characteristicsTable 3 Continued

Difference

ExpertiseAP 0.000 Experience 8.000

InfluenceAP 0.865 TenureAP 1.000

Median Median

AbAccrual 0.033 0.031

Charity=0(N=13,382) Charity=1(N=838)

AbAccrualU 0.000 0.000 AbAccrualD 0.000 0.001

Charity=0(N=13,472) Charity=1(N=847) Difference

Charity=0(N=13,675) Charity=1(N=863) Difference

Median Median

MAO 0.000 0.000

Charity=0(N=12,678) Charity=1(N=791) Difference

Median Median

SP 0.000 0.000

Charity=0(N=10,028) Charity=1(N=687) Difference

Median Median

BeatAnalyst 0.000 0.000

Median Median

Restatement 0.000 0.000 Table to be continued on next page.

Panel C: the difference in Audit Quality Measures between firms audited by Engagement Auditors with charitable activities and firms audited by Engagement Auditors without charitable activities.

43

Mean Mean Difference T-statistics

0.049 0.054 (0.005) (0.690)

Mean Mean Difference T-statistics

0.124 0.114 0.010 1.020

Mean Mean Difference T-statistics

0.014 0.008 0.005 1.520

Mean Mean Difference T-statistics

0.058 0.053 0.004 0.530

All continuous variables are winsorized at the 5th and 95th percentiles The superscripts ***, **, and * indicate two-tailed statistical significance atthe1%,5%,and10% level, respectively.

Median Median

SP 0.000 0.000

Charity=0 (N=14,387) Charity=1 (N=1,130) Difference

Median Median

Median Median

Restatement 0.000 0.000

Charity=0 (N=10,749) Charity=1 (N=827) Difference

Median Median

BeatAnalyst 0.000 0.000

Charity=0 (N=13,816) Charity=1 (N=1,108) Difference

MAO 0.000 0.000

44

Mean Mean Difference T-statistics1.226 1.150 0.0753** 2.000 0.269 0.276 (0.006) (1.230)

21.764 21.841 -0.0771** (1.970)0.064 0.066 (0.002) (0.420)0.464 0.488 -0.0243*** (3.800)0.036 0.033 0.00277** 1.980 1.969 1.767 0.202*** 4.370 0.193 0.171 0.0222* 1.830 0.107 0.114 (0.008) (0.790)2.225 2.906 -0.681** (2.440)0.145 0.145 0.001 0.120 0.111 0.107 0.004 0.400 9.133 9.103 0.030 0.170

Mean Mean Difference T-statistics27.510 27.207 0.303*** 3.920 0.020 0.026 0.00645*** (4.240)1.928 2.107 -0.179*** (3.750)0.230 0.178 0.0517*** 4.000 0.049 0.047 0.003 0.380

Mean Mean Difference T-statistics0.252 0.220 0.0317** 2.370 0.580 0.660 -0.0804*** (5.290)0.162 0.133 0.0290** 2.560

23.645 23.719 -0.0743** (2.200)0.264 0.254 0.011 1.370 1.165 1.167 (0.003) (0.190)0.029 0.019 0.0108** 2.090

12.761 12.954 (0.193) (1.480)

Difference

Panel D: the difference in Control Variables between firms audited by Review Auditors with charitable activities and firms audited by Review Auditors without charitable activities.

Quick 0.784 0.727 Complexity 0.249 0.259

Median Median

Charity=0(N=14,411) Charity=1(N=1,130)

Lev 0.468 0.508 ROA 0.033 0.030

Size 21.632 21.742 Growth 0.051 0.053

LOSS 0.000 0.000 CFOVol 0.546 0.628

Tobin's Q 1.494 1.294 State 0.000 0.000

FirmAge 9.000 9.000

RetVol 0.125 0.122 ShareIssue 0.000 0.000

TenureAF 1.000 1.000

SizeAF 27.187 26.469 InfluenceAF 0.005 0.009

MedianGender 0.000 0.000

ExpertiseAF 0.000 0.000 Big4 0.000 0.000

SizeAP 23.609 23.765 InfluenceAP 0.173 0.169

Accounting 1.000 1.000 Master 0.000 0.000

Experience 13.000 13.000

TenureAP 1.000 1.000 ExpertiseAP 0.000 0.000

Median

Median

D1. Client-related Characteristics

D2. Audit-Firm-related characteristics

D3. CPA-related characteristics

Median

45

Mean Mean Difference T-statistics0.046 0.046 0.000 0.080 0.023 0.023 0.001 0.590 0.023 0.024 (0.001) (0.500)

Mean Mean Difference T-statistics0.049 0.054 (0.005) (0.690)

Mean Mean Difference T-statistics0.124 0.114 0.010 1.020

Mean Mean Difference T-statistics0.014 0.008 0.005 1.520

Mean Mean Difference T-statistics0.058 0.053 0.004 0.530

This table presents student t-test of difference in means of dependent and independent variables in the expetiment group(Charity=1) and control group (Charity=0). P-values of two-tailed student t-test are presented. All continuous variables are winsorized at the 5th and 95th percentiles The superscripts ***, **, and * indicate two-tailed statistical significance atthe1%,5%,and10% level, respectively.

Median MedianSP 0.000 0.000

Charity=0(N=14,387) Charity=1(N=1,130) Difference

Median Median

Median MedianRestatement 0.000 0.000

Charity=0(N=10,749) Charity=1(N=827) Difference

Median MedianBeatAnalyst 0.000 0.000

Charity=0(N=13,816) Charity=1(N=1,108) Difference

MAO 0.000 0.000

Charity=0(N=14,153) Charity=1(N=1,116) Difference

AbAccrualU 0.000 0.000 AbAccrualD 0.000 0.001

Difference

Panel E: the difference in Audit Quality Measures between firms audited by Review Auditors with charitable activities and firms audited by Review Auditors without

Table 3 Continued

Median MedianAbAccrual 0.033 0.034

Charity=0(N=14,072) Charity=1(N=1,107)

46

Table 4 Multivariate analysis of engagement auditors' charity activity and audit quality. D.V. (1) (2) (3) (4) (5) (6) (7) AbAccrual AbAccrualU AbAccrualD MAO SP BeatAnalyst Restatement ID.V. Charity -0.00424*** -0.00451* -0.000447 0.450** 0.0463 -0.799* -0.122 p-value 0.004 0.078 0.853 0.013 0.679 0.067 0.507 Controlled for

Firm characteristics yes yes yes yes yes yes yes Audit firm characteristics yes yes yes yes yes yes yes Individual auditor characteristics

yes yes yes yes yes yes yes

Industry Fixed Effect No No No Yes Yes Yes Yes Yearly Fixed Effect No No No Yes Yes Yes Yes

Observations 14,220 14,220 14,220 14,319 14,538 13,469 10,715

Pseudo-/Adjusted R2 0.064 -0.057 -0.052 0.3851 0.1242 0.1101 0.1638

This table presents the OLS regression analysis of audit quality and engagement auditors' charity activity. We include control variables described in Section 4.2.3.. Specifically, in the control variables, we include firm characteristics such as Quick, Complexity, Size, Growth, Lev, ROA, Tobin's Q, LOSS, CFOVol, RetVol, State, ShareIssue, FirmAge. We include audit firm characteristics such as SizeAF, InfluenceAF, TenureAF, ExpertiseAF,Big4. We include auditor individual characteristics such as Gender, Accounting, Master, SizeAP, SizeAP, InfluenceAP, TenureAP, ExpertiseAP, Experience. Variables definitions are in Appendix A. All continuous variables are winsorized at the 5th and 95th percentiles. The superscripts ***, **, and * indicate two-tailed student t-test of the coefficients at statistical significance level of p<0.01, p<0.05 and p<0.1.

47

Table 5 Multivariate analysis of review auditors' charity activity and audit quality. (1) (2) (3) (4) (5) (6) (7) D.V. AbAccrual AbAccrualU AbAccrualD MAO SP BeatAnalyst Restatement ID.V, Charity -0.0008 -0.0018 0.0010 0.1480 -0.197* -0.5330 -0.309* p-value 0.569 0.423 0.637 0.357 0.059 0.131 0.071 Controlled for

Firm characteristics yes yes yes yes yes yes yes Audit firm characteristics

yes yes yes yes yes yes yes

Individual auditor characteristics

yes yes yes yes yes yes yes

Industry-Fixed Effect No No No Yes Yes Yes Yes Yearly-Fixed Effect No No No Yes Yes Yes Yes Observations 15,179 15,179 15,179 15,269 15,517 14,924 11,576

Pseudo/Adjusted R2 0.065 -0.0567 -0.0546 0.3879 0.1229 0.1066 0.1578

This table presents the OLS regression analysis of audit quality and review auditors' charity activity. We include control variables described in Section 4.2.3.. Specifically, in the control variables, we include firm characteristics such as Quick, Complexity, Size, Growth, Lev, ROA, Tobin's Q, LOSS, CFOVol, RetVol, State, ShareIssue, FirmAge. We include audit firm characteristics such as SizeAF, InfluenceAF, TenureAF, ExpertiseAF,Big4. We include auditor individual characteristics such as Gender, Accounting, Master, SizeAP, SizeAP, InfluenceAP, TenureAP, ExpertiseAP, Experience. Variables definitions are in Appendix A. All continuous variables are winsorized at the 5th and 95th percentiles. The superscripts ***, **, and * indicate two-tailed student t-test of the coefficients at statistical significance level of p<0.01, p<0.05 and p<0.1.

48

Mean Median Mean Difference T-statistics

Quick 1.107 0.713 1.115 -0.007 -0.140Complexity 0.287 0.268 0.278 0.008 1.120Size 21.840 21.704 21.847 -0.008 -0.120Growth 0.068 0.054 0.070 -0.002 -0.280Lev 0.498 0.526 0.488 0.010 0.990ROA 0.036 0.034 0.035 0.001 0.440Tobin's Q 1.810 1.305 1.753 0.057 0.810State 0.159 0.000 0.162 -0.003 -0.200LOSS 0.087 0.000 0.095 -0.008 -0.590CFOVol 2.407 0.583 2.654 -0.247 -0.680RetVol 0.142 0.122 0.145 -0.003 -0.510ShareIssue 0.108 0.000 0.103 0.005 0.310FirmAge 9.334 9.000 8.971 0.363 1.340

SizeAF 27.046 26.341 26.976 0.070 0.560InfluenceAF 0.035 0.009 0.031 0.004 1.310TenureAF 2.158 1.000 2.211 -0.053 -0.620ExpertiseAF 0.192 0.000 0.169 0.023 1.250Big4 0.051 0.000 0.046 0.005 0.450

Gender 0.321 0.000 0.294 0.027 1.200Accounting 0.688 1.000 0.674 0.014 0.620Master 0.058 0.000 0.071 -0.013 -1.080SizeAP 22.431 22.407 22.432 -0.001 -0.010

InfluenceAP 0.695 0.843 0.697 -0.002 -0.140

TenureAP 1.126 1.000 1.158 -0.031 -1.560

ExpertiseAP 0.016 0.000 0.012 0.005 0.820

Experience 9.687 9.000 9.766 -0.079 -0.360

Table 6 PSM Estimation Results for Engagement Auditors

This panel presents the mean and median of variables in the selection model. The results of the student t-test of the difference in mean characteristic variables between the treatment and control samples are presented. Table to be continued on next page.

22.359

0.848

1.000

0.000

9.000

0.000A3. CPA-related characteristics

0.0001.0000.000

9.000A2. Audit-Firm-related characteristics

26.3410.0101.0000.000

0.6440.1230.000

0.7210.25821.6910.0570.5090.033

Panel A: Descriptive Statistics for PSM Sample of Engagement Auditors

Charity=0(N=863) Charity=1(N=863)Difference in Mean characteritics variables between the treatement

Median

A1. Client-related Characteristics

1.2920.0000.000

49

p-value

Big4 0.840***p-value 0.00

ExpertiseAF -0.181p-value (0.24)

Table and panel to be continued on next page.

TenureAF 0.0765***p-value 0.00

InfluenceAF 0.15p-value (0.84)

SizeAF -0.217***p-value 0.00

FirmAge -0.00181p-value (0.82)

ShareIssue -0.0485p-value (0.69)

RetVol 0.0637p-value (0.77)

CFOVol 0.00312p-value (0.36)

State -0.188*p-value (0.07)

Tobin's Q 0.0338p-value (0.42)

ROA 0.529p-value (0.63)

Lev 0.678**p-value (0.03)

Growth -0.166p-value (0.59)

Size 0.269**(0.04)

Complexity -0.0495p-value (0.86)

Quick 0.0374p-value (0.45)

(1)Charity

Panel B: Selection Model

Table 6 PSM Estimation Results for Engagement Auditors (Cont'd)

50

(1) (2) (3) (5)

AbAccrual AbAccrualU AbAccrualD MAO

Difference -0.00546*** -0.00564*** 0.000181 0.0214**

P-value (0.00) 0.00 (0.92) (0.01)(6) (7) (8)SP BeatAnalyst Restatement

Difference -0.00187 -0.00263 -0.00819

P-value (0.90) (0.62) (0.30)

Table 6 Panel C Average Treatment Effect on population

This table presents the results of multivariate analysis of treatement and control group that are matched using the nearest neighbring methods. All continuous variables are winsorized at the 5th and 95th percentiles The superscripts ***, **, and * indicate two-tailed statistical significance atthe1%,5%,and10% levels, respectively

51

Mean Median Mean Difference T-statistics

Quick 1.161 0.749 1.150 0.011 0.210Complexity 0.267 0.250 0.276 -0.009 -1.370Size 21.825 21.676 21.841 -0.016 -0.290Growth 0.061 0.050 0.066 -0.005 -0.800Lev 0.488 0.504 0.488 0.000 -0.020ROA 0.033 0.031 0.033 0.000 -0.110Tobin's Q 1.767 1.337 1.767 0.000 0.000State 0.171 0.000 0.171 0.000 0.000LOSS 0.127 0.000 0.114 0.013 0.970CFOVol 4.126 0.633 2.906 1.220 1.270RetVol 0.144 0.123 0.145 -0.001 -0.090ShareIssue 0.088 0.000 0.107 -0.020 -1.560FirmAge 9.130 9.000 9.103 0.027 0.110

SizeAF 27.228 26.565 27.207 0.022 0.190InfluenceAF 0.026 0.007 0.026 0.000 0.000TenureAF 2.239 1.000 2.107 0.132* 1.670ExpertiseAF 0.180 0.000 0.178 0.002 0.110Big4 0.057 0.000 0.047 0.010 1.040

Gender 0.214 0.000 0.220 -0.006 -0.360Accounting 0.617 1.000 0.660 -0.0434** -2.150Master 0.139 0.000 0.133 0.006 0.430SizeAP 23.708 23.621 23.719 -0.012 -0.260InfluenceAP 0.258 0.166 0.254 0.004 0.380TenureAP 1.179 1.000 1.167 0.012 0.580ExpertiseAP 0.027 0.000 0.019 0.009 1.400Experience 13.090 13.500 12.954 0.136 0.770

Charity=0(N=1,130)

This panel presents the mean and median of variables in the selection model. The results of the student t-test of the difference in mean characteristic variables between the treatment and control samples are presented. Table to be continued on next page.

Table 7 PSM Estimation Results for Review Auditors

23.7650.1691.0000.00013.000

0.0001.0000.000

9.000A2. Audit-Firm-related characteristics

26.4690.0091.0000.000

A3. CPA-related characteristics

0.1220.000

0.7270.25921.7420.0530.5080.030

0.000

Panel A: Descriptive Statistics for PSM Sample of Review Auditors

Charity=1(N=1,130) Difference

MedianA1. Client-related Characteristics

1.2940.0000.0000.628

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Panel B: Selection Model

Levp-value

Growthp-value

Sizep-value

Complexityp-value

Quickp-value

RetVolp-value

CFOVolp-value

Statep-value

Tobin's Qp-value

ROAp-value

TenureAFp-value

InfluenceAFp-value

SizeAFp-value

FirmAgep-value

ShareIssuep-value

Big4p-value

ExpertiseAFp-value

(0.003)0.0570***(0.766)0.219

(0.002)0.583***0.000 -0.618***

(0.097)-0.148*

(0.468)-0.0774(0.866)0.0378

0.000 -0.251***(0.775)-0.00191

Table and panel to be continued next page

(0.019)0.0997**Charity(1)

Table 7 PSM Estimation Results for Review Auditors (Cont'd)

(0.020)0.162**(0.083)-0.434*

(0.001)0.894***(0.544)0.164

(0.263)-0.0414(0.864)-0.162

(0.007)0.00748***

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Observations

p-value

Accountingp-value

Genderp-value

0.0584

15,440-0.745YesYes

Pseudo R2

Industry Fixed EffectConstant

Experiencep-valueYearly Fixed Effect

ExpertiseAPp-value

TenureAPp-value

InfluenceAPp-value

SizeAPp-value

Master

(0.405)0.0484(0.007)-0.249***

(0.937)0.00549(0.014)-0.681**

(0.637)0.00411(0.160)-0.347

0.000 0.308***(0.019)-0.179**

Panel 7B presents the regression results of the selection model. Significance-levels 0.1, 0.05 and 0.01 are denoted by *, ** and *** .

Table 7 Panel B Continued

54

(1) (2) (3) (5)

AbAccrual AbAccrualU AbAccrualD MAO

Difference -0.000611 0.000221 -0.000833 0.00324

P-value (0.702) (0.874) (0.552) (0.668)

(6) (7) (8)

SP BeatAnalyst Restatement

Difference -0.0217 -0.00719*** -0.0194***

P-value (0.110) (0.002) (0.001)

Table 7 Panel C the Average Treatment Effect on population

This table presents the results of multivariate analysis of treatement and control group that are matched using the nearest neighbring methods. All continuous

variables are winsorized at the 5th and 95th percentiles The superscripts ***, **, and * indicate two-tailed statistical significance atthe1%,5%,and10% levels, respectively

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Table 8 Panel A Multivariate analysis of engagement auditors' charity activity and audit quality after controlling for audit firm fixed effects. (1) (2) (3) (4) (5) (6) (7) D.V. AbAccrual AbAccrualU AbAccrualD MAO SP BeatAnalyst Restatement ID.V, Charity -0.00619*** -0.00255*** -0.0048 0.514** 0.112 -0.994* -0.0542 p-value 0.002 0 0.114 0.03 0.446 0.088 0.83 Controlled for

Firm characteristics yes yes yes yes yes yes yes Audit firm characteristics yes yes yes yes yes yes yes

Individual auditor characteristics yes yes yes yes yes yes yes

Industry-Fixed Effect No No No Yes Yes Yes Yes Yearly-Fixed Effect No No No Yes Yes Yes Yes

Audit-Firm Fixed-Effects Yes Yes Yes Yes Yes Yes Yes Observations 12,370 12,370 12,370 11,984 12,557 10,723 12,416 Pseudo/Adjusted R2 0.051 -0.081 -0.0702

This table presents multivariate analysis of engagement auditors' charity activity and audit quality after controlling for audit firm fixed effects.. We include control variables described in Section 4.2.3.. Specifically, in the control variables, we include firm characteristics such as Quick, Complexity, Size, Growth, Lev, ROA, Tobin's Q, LOSS, CFOVol, RetVol, State, ShareIssue, FirmAge. We include audit firm characteristics such as SizeAF, InfluenceAF, TenureAF, ExpertiseAF,Big4. We include auditor individual characteristics such as Gender, Accounting, Master, SizeAP, SizeAP, InfluenceAP, TenureAP, ExpertiseAP, Experience. Variables definitions are in Appendix A. All continuous variables are winsorized at the 5th and 95th percentiles. The superscripts ***, **, and * indicate two-tailed student t-test of the coefficients at statistical significance level of p<0.01, p<0.05 and p<0.1.

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Table 8 Panel B Multivariate analysis of review auditors' charity activity and audit quality after controlling for audit firm fixed effects. (1) (2) (3) (4) (5) (6) (7) D.V. AbAccrual AbAccrualU AbAccrualD MAO SP BeatAnalyst Restatement ID.V, Charity -0.00124 -0.00167*** 0.000267 0.108 -0.219 -0.749 -0.596** p-value 0.496 0.001 0.927 0.647 0.107 0.113 0.016 Controlled for

Firm characteristics yes yes yes yes yes yes yes Audit firm characteristics yes yes yes yes yes yes yes

Individual auditor characteristics yes yes yes yes yes yes yes

Industry-Fixed Effect No No No Yes Yes Yes Yes Yearly-Fixed Effect No No No Yes Yes Yes Yes

Audit-Firm Fixed-Effects Yes Yes Yes Yes Yes Yes Yes Observations 13,276 13,276 13,276 11,984 12,557 10,723 12,416 Pseudo/Adjusted R2 0.051 -0.0805 -0.0732

This table presents multivariate analysis of review auditors' charity activity and audit quality after controlling for audit firm fixed effects. We include control variables described in Section 4.2.3.. Specifically, in the control variables, we include firm characteristics such as Quick, Complexity, Size, Growth, Lev, ROA, Tobin's Q, LOSS, CFOVol, RetVol, State, ShareIssue, FirmAge. We include audit firm characteristics such as SizeAF, InfluenceAF, TenureAF, ExpertiseAF,Big4. We include auditor individual characteristics such as Gender, Accounting, Master, SizeAP, SizeAP, InfluenceAP, TenureAP, ExpertiseAP, Experience. Variables definitions are in Appendix A. All continuous variables are winsorized at the 5th and 95th percentiles. The superscripts ***, **, and * indicate two-tailed student t-test of the coefficients at statistical significance level of p<0.01, p<0.05 and p<0.1.

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Table 9 Cross-sectional Analysis based on Gender of the engagement auditor (1) (2) (3) (4) (5) (6) (7) D.V. AbAccrual AbAccrualU AbAccrualD MAO SP BeatAnalyst Restatement ID.V, Charity -0.00252 -0.00142 -0.00129 0.186 0.0478 -0.658 -0.0268 p-value 0.165 0.646 0.664 0.427 0.718 0.169 0.903 Charity*Gender -0.00578* -0.0108** 0.00278 0.755** -0.00492 -0.637 -0.322 p-value 0.051 0.049 0.584 0.041 0.984 0.576 0.438 Controlled for

Firm characteristics yes yes yes yes yes yes yes Audit firm characteristics yes yes yes yes yes yes yes

Individual auditor characteristics yes yes yes yes yes yes yes

Industry-Fixed Effect No No No Yes Yes Yes Yes Yearly-Fixed Effect No No No Yes Yes Yes Yes

Observations 14,220 14,220 14,220 14,319 14,538 13,469 10,715 Pseudo/Adjusted R2 0.065 -0.0577 -0.0518 0.3858 0.1242 0.1103 0.164

This table presents the OLS cross-sectional analysis based on gender of the engagement auditor by including the interaction term between Gender and Charity. We include control variables described in Section 4.2.3.. Specifically, in the control variables, we include firm characteristics such as Quick, Complexity, Size, Growth, Lev, ROA, Tobin's Q, LOSS, CFOVol, RetVol, State, ShareIssue, FirmAge. We include audit firm characteristics such as SizeAF, InfluenceAF, TenureAF, ExpertiseAF,Big4. We include auditor individual characteristics such as Gender, Accounting, Master, SizeAP, SizeAP, InfluenceAP, TenureAP, ExpertiseAP, Experience. Variables definitions are in Appendix A. All continuous variables are winsorized at the 5th and 95th percentiles. The superscripts ***, **, and * indicate two-tailed student t-test of the coefficients at statistical significance level of p<0.01, p<0.05 and p<0.1.