A New Wave of Audit Partners: Evidence from the Chinese Localization Rule

Pietro A. Bianchi pbianchi@fiu.edu

Florida International University

Lin Liao liaolin@swufe.edu.cn

Southwestern University of Finance & Economics

Miguel Minutti-Meza* mminutti@bus.miami.edu

University of Miami

Yini Wang y.wang33@umiami.edu

University of Miami

August 2021

THIS IS WORK IN PROGRESS, PLEASE DO NOT CITE OR CIRCULATE

Abstract

We examine a unique regulation that accelerated the localization of the Big 4 operations in China, requiring these firms to have at least 80 percent locally licensed audit partners. This shift required a reorganization of the firms’ human capital, which could have direct and indirect consequences for their partnerships’ structure and audit quality. We examine fourteen years around the implementation of the regulation and rely on a difference-in-differences research design, comparing several outcomes between Big 4 and other top-10 local audit firms. We demonstrate that the Big 4 met the requirements by promoting local talent, increasing the number of incoming partners occupying junior roles, and diluting each partner’s share of the total firm’s clients. However, we do not find evidence that the regulation had an incremental effect on audit quality. Our findings suggest that the regulation achieved its intended objectives, primarily developing local human capital, without impairing audit quality. *Corresponding author: mminutti@bus.miami.edu .

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1. Introduction

China is the world’s second largest economy, the largest exporter of manufactured goods,

one of the largest internal markets for consumer goods, and the second largest jurisdiction of origin

of foreign issuers listed in U.S. markets (Jones 2016; Macve 2020; PCAOB 2021).1 In relatively

recent times, Chinese market institutions and financial reporting have evolved to keep pace with

the country’s rapid economic development. This environment offers a unique setting to examine

the institutional features of an emerging audit profession, presently aiming to compete with leading

global accounting firms (DeFond et al., 2020). For instance, in 2009, the Chinese Ministry of

Finance (MoF) released ambitious targets for the local audit profession, aimed to create a tiered

structure of 10-200-7000 firms, where the top 10 firms would compete with the Big 4 in China

and abroad (State Council, GBF [2009] No. 56). In this study, we examine the consequences of a

unique regulation that forced the accelerated localization of the Big 4 firms’ operations in

Mainland China since 2012.

In 1992, the Big 5 (now Big 4) accounting firms were granted special 20-year permission

to operate in China under joint venture (JV) agreements with Chinese firms. The JV provided a

legal structure that allowed partners with foreign licenses (i.e., certified public accountant or

equivalent) to participate in audit engagements for Chinese companies. In 2012, upon the

expiration of the JV agreements, the MoF issued a “localization rule” allowing the Big 4 to

continue practicing without a JV conditional on the reorganization of their partnerships. 2

1 As of March 2021, among 1,166 U.S. issuers with foreign auditors registered with the PCAOB, 213 had auditors from Mainland China or Hong Kong (PCAOB 2021, p. 35). 2 Simultaneously, in the past 20 years, Chinese accounting firms started a wave of consolidation and consistent internal growth encouraged by government institutions (DeFond et al., 2020; Macve 2020). In this period the licenses to audit publicly listed clients also decreased from 105 in 2002 to 40 in 2014.

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Following a five-year transition period between 2012 and 2017, the Big 4 were expected to have

at least 80 percent of their partners to be locally licensed by the Chinese Institute of Certified

Public Accountants (CICPA), and hence subject to the CICPA’s regulation and discipline. The

other 20 percent of the partners could practice with foreign licenses but must have more than 10

years of experience and 5 or more years practicing in China. 3 Moreover, the Big 4 firms’

management committee could not have more than 20 percent of partners with foreign licenses by

2012 and after 2015 the top managing partners must be a Chinese national with CICPA

qualifications. The primary objective of this regulation was to develop local audit partners

(Accounting Department of MoF, 2014). The localization rule is unique because it only targeted

the Big 4 firms–previously operating under JV agreements–and required a shift in their human

capital, which can have direct consequences for the structure of their partnerships as well as

indirect consequences for audit quality.

Chinese auditing standards require at least two individuals to sign the audit report for public

companies and in practice the first signer is the most senior individual. Following prior literature

(e.g., Pittman et al., 2021), we refer to these two signers as “partners” or “engagement partners”,

and we separately examine the effects of the localization rule for both the “first” and the “second”

signer. In our first set of analyses, we examine changes in the structure of the Big 4 partnerships

and their human capital, focusing on (a) the total number of partners in Big 4 firms and the size

3 A number of Big 4 partners signing audit reports, including the Big 4 managing partners hold only professional licenses issued by jurisdictions outside of Mainland China. For example: Daniel (Kim Tak) Chan, Head of Telecommunications in KPMG Huazhen LLP, engagement partner for China Unicom (Hong Kong) Ltd. (CIK: 0001113866), holds professional licenses issued by The Hong Kong Institute of Certified Public Accountant (HKICPA) and CPA Australia; Bong Chan, Southern Region Leader of China TMT Industry in Deloitte Touche Tohmatsu LLP China, engagement partner for China Education Group Holdings Ltd. (0839.HK), IBO Technology Company Ltd. (2708.HK) etc., is a member of HKICPA and CPA Australia; Jessie Qian, engagement partner of YUM China Holdings, Inc. (CIK: 0001673358), GDS Holdings Limited (CIK: 0001526125), is a member of American institute of Certified Public Accountant (AICPA).

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of each partners’ share from the total firm’s clients, (b) the characteristics of incoming partners,

and (c) the allocation of existing and new clients (i.e., with the firm before and after 2012) to

incumbent and incoming partners. Before the localization rule (in 2011) 58% of the Big 4 audit

partners had foreign licenses, and at the end of the rule’s transition period (in 2017) all Big 4 firms

had 20% or less audit partners with foreign licenses (Accounting Department of MoF, 2014). This

change implied a noticeable shift in the firms’ partnerships. We aim to provide evidence about

how Big 4 met the regulatory requirements.

In our second set of analyses, we examine whether the restructuring of Big 4 partnerships

indirectly affected audit quality. Some observers were concerned that over-hasty changes in the

Big 4 partnerships could put inexperienced individuals in charge of audit engagements and

adversely impact audit quality (Pierson 2012; McMahon and Hong, 2012). However, partners with

local experience could have tacit knowledge of the client environment and work comparatively

more effectively.

Our sample spans from 2005 to 2018, i.e., seven years before and after the localization rule

was passed in 2012. In our empirical analyses, we implement a difference-in-differences research

design, where we compare several outcomes between Big 4 and other top-10 local audit firms

operating in China (hereafter other top-10 firms), not subject to the localization rule. In China, the

Big 4 and other top-10 firms are comparable in the type and size of clients that they audit. Another

interesting feature of the Chinese setting is that we have access to detailed information about

partners’ backgrounds and qualifications.

Regarding some general changes in the structure of Big 4 and other top-10 firms’

partnerships, between 2012 and 2018, all large Chinese firms experienced a rapid client growth in

our sample, averaging 219% and 155% percent for Big 4 and other top-10 firms, (increasing from

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72 to 158, and 997 to 1,547 total clients), respectively. However, the ratio of a partner’s

engagements to the total firm’s engagements (i.e., a partner’s share of the firm’s clients) decreased

for Big 4 firms from 11.9% to 5.2%, compared to a decrease from 5.0% to 1.3% for other top-10

firms (i.e., a net dilution of 3% for Big 4 partners between 2012 and 2018). This pattern also

manifests itself in a reduction in the typical sizes of Big 4 partner’s client portfolios, especially in

the top quintile of the distribution of portfolio sizes of the senior signers, going from a median of

4 to 3 clients per partner.4

Moving to the characteristics of incoming Big 4 versus other top-10 firms’ partners, we

find that after the localization rule, incoming first signers at Big 4 firms are comparatively less

likely to have a degree from a non-Chinese university, more likely to have a degree from a Chinese

top university, and more likely to have a master’s degree. Next, incoming second signers at Big 4

firms are comparatively less likely to have a degree from a non-Chinese university, more likely to

have a master’s degree, more likely to have an accounting major, and more likely to have other

qualifications (e.g., Chinese Certified Public Valuer, Certified Tax Agents, Real Estate Valuers

and Agents, International Certified Internal Auditor, etc.).

Examining how Big 4 partners were assigned to clients after the localization rule, we find

that incumbent first signers (i.e., those partners who signed at least one audit report before 2012)

were assigned 67% of the new clients and kept 86% of the existing clients. In contrast, incumbent

second signers were assigned only 9% of the new clients and kept 47% of existing clients. This

pattern indicates that incoming partners were primarily assigned to new clients as second signers

(i.e., junior roles). However, there was also some substitution effect within the existing client group,

4 However, the median portfolio for first signers remained two engagements per partner before and after the localization rule. The rule affected primarily partners with a relatively large number of clients.

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primarily among second signers (i.e., 14% and 53% of the incoming partners took first and second

signer roles for existing clients). Hence, the Big 4 met the requirements of the localization rule

primarily through client growth and not by a substitution of incumbent by incoming partners.

Finally, we also investigate whether the localization rule resulted in changes in audit

quality, using various discretionary accruals proxies, as well as the incidence of modified opinions,

restatements, below the line items, and small profits. We do not find evidence of an incremental

change in audit quality for Big 4 firms in the post-rule period (2012 to 2018).

Our study is among the first examining the outcomes of the localization rule imposed by

the Chinese MoF. This regulation offers a unique opportunity to examine how large audit firms

reorganize to mitigate large shocks to their human capital. Our findings suggest that the regulation

achieved its intended objectives, primarily developing local partners, without impairing audit

quality. We extend a stream of recent studies of the Chinese audit environment (DeFond et al.,

2020), including the consequences of individual characteristics of Chinese partners (Lennox et al.,

2018). To the best of our knowledge, recent studies on Chinese partners do not explicitly consider

the change in Big 4 human capital that occurred since 2012.

2. Institutional Background

2.1 Evolution of the Chinese Audit Profession and Big 4’s Footprints

Following the centrally planned economy of the Mao era, started as early as 1978 “reform

and opening” China introduced a new vertical alignment of economic incentives from top to

bottom. A contemporaneous Chinese audit profession emerged following China opening its gate

to foreign investment in the 1980s and the establishment of the Shanghai and Shenzhen stock

exchanges in 1990. Broadly the last thirty years can be traced to three ten-year stages of

development in terms of ideology and Big 4’s role: (1) standardization and initial development

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(1992-2002), (2) rapid development and competition (2002-2012), and (3) accelerating

localization (2012-now).

At the beginning of Stage 1, the Chinese contemporaneous audit profession was in its

infancy, featuring a significant shortage of qualified local auditing professionals, lack of

standardized regulations and standards, as well as substantial government control. 5 It needed

international support, including provision of advice and overseas experience, to train selected

young recruits. Responding to such demand, in 1992 the Big 6 (now Big 4) made their first

appearance in China. They were expected to play a key role in facilitating capital market reforms

and accounting standards convergence, as well as providing local talents with training and

education.6 These accounting firms were granted a 20-year special permission to operate in China

under joint venture (JV) agreements with Chinese firms. 7 Notably, the JV provided a legal

structure that allowed partners with foreign certified public accountant licenses (CPA or equivalent)

to sign audit reports for Chinese companies. During this decade, with the help of the “imported

expertise”, the Chinese auditing profession underwent a period of standardization in terms of

5 Due to the Cultural Revolution, university operations and admissions in China have been suspended for ten years, and the revitalized universities begun to offer accounting degrees in 1980s, thus Stage 1 begun with a generation of young people who had been deprived of normal higher education opportunities. In 1991, there were 459 accounting firms nationwide, and 6722 registered CPAs. However, the first national examination for CPAs were held in December 1991, and the CPA Law was not established until October 1993. Based on an interview with a retired official of Shanghai Audit Bureau, individuals who have obtained their CPA license before 1991 went through an evaluation process rather than unified examination. Lack of regulations and standards is attributable to the 30 years of planned economy from 1949 to 1978, when there had been no need for CPAs and accounting/auditing firms. Until the “Disaffiliation” program initiated by the MoF in 1998, most Chinese CPA firms were established by the government and sponsored by the state (Macve 2020, Jui and Wong 2018). 6 The Big 6 achieved their dominant position through three hegemonic projects: foreign direct investment, the reform of State-owned enterprises through international capital markets, and the enabling of private enterprises to access international capital markets (Gillis, 2011). 7 KPMG, Arthur Andersen, and Ernst & Young obtained permission to form joint ventures with the MoF in the summer of 1992. The remaining Big Six firms formed joint ventures in the months that followed, selecting different joint partners. All the joint venture partners of the Big Six were State-controlled entities. Deloitte Touche Tohmatsu selected Shanghai Certified Public Accountants, a unit of the Shanghai Finance Bureau, as its partner. Price Waterhouse affiliated with Da Hua, a firm associated with the Shanghai University of Finance and Economics (SUFE). Copper & Lybrand jointed with CIEC, a branch of the powerful China International Trade and Investment Corporation (CITIC). The largest local Hong Kong firm, politically connected BDO member firm KWTF (who would merge with Deloitte Touche Tohmatsu in 1997), was also allowed to establish a joint venture in Shenzhen (Macve 2020).

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internal talent selection, implementation of laws and regulations, and disaffiliation from

governmental control.8

As the twenty-first century began, the Chinese audit profession transitioned into a period

of rapid growth and competition. Stage 2 began with China’s fast-growing economy and financial

market. As a result of joining WTO in 2001, the profession had more access overseas,

accompanying the internationalization of Chinese firms. The corresponding rapid development of

initial public offerings (IPOs), seasoned equity offerings (SEOs), and publicly listed firms boosted

the demand for audit services, driving significant changes in the audit market. In such landscape,

the Big 4 were in a better position of achieving dominance relative to domestic audit firms, since

they had accumulated considerable experiences in serving large complex companies and

companies seeking listing on the US and other overseas capital markets. To capture these growth

opportunities, the Big 4 had been expanding their investments in China.9 In response, domestic

audit firms resorted to various methods to protect their market shares, including merging with

peers. The Chinese government explicitly and implicitly supported such move by introducing

series of policies, which resulted in a bottom-up merger tide within the domestic audit firms

8 MoF held the first national examination for CPAs in December 1991. The examination system laid a solid foundation of talent for the professionalization and standardization of the CPA profession. From 1991 to 1993, the CICPA issued various implementation standards and regulations. In 1994, the MoF approved the establishment of the Independent Auditing Standards Group of Chinese CPAs to develop China’s auditing standards. The Group formulated and issued a total of 48 standards. Meanwhile, the profession also made significant progress on the legislative front. In October 1993, the Fourth Plenum of the Eighth National People’s Congress passed the CPA Law, which regulated the obtaining of CPA qualifications, the establishment of accounting firms, their business scope, practice standards, and legal liabilities, and the responsibilities of CPA institutes. Under the “Disaffiliation” program all accounting firms were uncoupled from their governmental institutions and became self-governing, thus responsible for their own development, operations, and risks (Jui and Wong 2018).

9 For example, on June 1st, 2004, the CEO of Deloitte announced a $150 million new investment in China as a part of Deloitte’s five-year plan, aiming at increasing its staff and revenue by four to five times (China Securities News 2004). KPMG, E&Y, and PwC have also announced their ambitious expansion plans. For example, in 2005, PwC announced plans to recruit more than 1,000 workers every year during the following five years (China Financial Times 2005).

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(DeFond, Zhang, and Zhang 2020).10 In 2009, the MoF and CICPA released ambitious targets,

aiming to create a tiered structure of 10-200-7000 firms, where the top 10 firms would compete

with the Big 4 in China and abroad. In 2010, the MoF and the SAIC issued the regulations on

transformation of the legal form of large accounting firms, including the Big 4.11 These initiatives

taken towards the end of Stage 2 reflect the Chinese regulators’ intention of closing the gap

between the Big 4 and domestic audit firms.

In 2012, upon the beginning of Stage 3, the initial 20-year joint venture (JV) agreements

between the Big 4 and the Chinese audit firms expired. Ever since the Big 4’s first presence in

China, they made a commitment of building a base of qualified and experienced local talents,

ultimately “localizing” their operations.12 This “automatic” localization trend has been working

out continuously within the Big 4 throughout the past twenty years: Big 4’s pool of partners at the

beginning of Stage 1 mostly consisted of partners with foreign licenses, who usually started their

careers at a global office and held CPA licenses from jurisdictions outside of Mainland China (e.g.,

member of ICAEW, HKICPA, AICPA, or CPA Australia).13 While the level of foreign credentials

stayed stable over time, the Big 4’s pool of partners had been constantly rising throughout Stage 1

10 At first, these mergers were partially compulsorily mandated by CICPA but after 2005 “soft” persuasion was driven by exhortation from CICPA to embrace market opportunities. Examples of mergers/acquisitions that happened during this period are: Zhongruihua Hengxin and Yuehua (2007); Tianjian Guanghua acquired Chongqing Tianjian (2008); Xinyong Zhonghe acquired Sichuan Junhe (2009). 11 October 3rd, 2009, the General Office of the State Council passed MoF’s Opinions on Accelerating the Development of China’s CPA profession (State Council, GBF [2009] No. 56). This document further explained the key objectives of CPA professional development, actively promoting the development of medium-sized accounting firms and guiding the standardization of small accounting firms. In July 2010, the MoF and the SAIC issued the Interim Regulations on Promoting Special General Partnerships for Medium and Large Accounting Firms (CH [2010] No.12). 12 Big 4’s strategy worldwide has generally been to establish a presence that is initially managed by expatriates who are expensive to maintain and compensate, but with the longer-term aim of “naturalizing”/ “localizing” the firm to be run by locals, a process which would normally requires some 20-30 years or more of building up sufficient numbers of experienced local partners who, as far as possible, have acquired sufficient “tacit” knowledge and internalized firm’s culture (Macve 2020). 13 Appendix A2 provides specific examples of partners with foreign licenses: Mr. John Hung, started his audit career with Deloitte’s London office in 1987, trained as member of ICAEW, and then joined the partnership of Deloitte China in 1996 as an experienced auditor. Mr. Norbert Meyring, started his career in the KPMG Frankfurt office, member of AICPA, and he joined KPMG Shanghai in 2001.

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and Stage 2, driven by the growing number of new partners with local licenses. These partners

usually achieved bachelor’s degree in China, started their audit career directly with the Big 4’s

Chinese arm as entry-level accountants, obtained CICPA licenses, and gradually became

experienced through working with senior partners.14 In 2011, the Accounting Department of the

MoF conducted a survey to find out the status of the “automatic” localization process, while

recognizing the Big 4’s contribution in training local talents, the regulator wished to accelerate the

localization process, so that all partners who sign off Chinese audit engagements were subject to

the CICPA’s regulation and discipline.15

2.2 Implementation of the Localization Rule

To accelerate the promotion of partners with local licenses and further “leveling the playing

field” between the Big 4 and domestic audit firms, the Chinese regulators passed the localization

rule (ACC [2012] No.8) in May 2012.16 Compliance with the new regulation was mandatory by

December 2017, with a five-year transition period. The localization rule applies exclusively to the

Big 4 and requires them to have up to 20% of “nonqualified partners”, i.e., partners with only

foreign CPA licenses, with at least ten years of experience of which five years in China.17

The localization rule also provides the Big 4 with a gradual compliance schedule to follow

over the five-year transition period. The percentage of “nonqualified partners” should be no more

than 35% and 25% by December 2014 and 2016 respectively. Partners who at the time the

14 Appendix A2 also provides specific examples of partners with local licenses: Mr. Jacky Zhou, started his career with KPMG Beijing in 1993 after obtaining a bachelor’s degree from a Chinese top university, and obtained a CICPA license. 15 As of December 31st, 2010, the overall percentage of partners with foreign license was 50%, 70%, 61%, and 55% for Deloitte, KPMG, PwC and E&Y, respectively (MoF, 2014). 16 Chinese regulators here refer to: the Chinese Ministry of Finance (MoF), State Administration for Industry and Commerce (SAIC), Ministry of Commerce (MoC), State Administration of Foreign Exchange (SAFE) and China Securities Regulatory Commission (CSRC). 17 The localization rule specifies: the Big 4 refers to the four audit firms currently operating under the 20-year joint venture (JV) agreements approved by the MoF, including: Ernst & Young Hua Ming LLP, KPMG Huazhen LLP, Deloitte Touche Tohmatsu Certified Public Accountants LLP, and PricewaterhouseCoopers Zhong Tian LLP.

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localization rule was passes had a foreign CPA license could remain in their position if they

obtained a CICPA license. However, the CICPA exam is notoriously difficult and is only offered

in Mandarin, therefore penalizing foreign partners.18 The MoF explicitly set the expectation of Big

4 to achieve compliance with the localization rule through “addition rather than subtraction” (i.e.,

fast promotion of partners with CICPA licenses rather than firing experienced partners with foreign

licenses only). Finally, the Chinese regulator believed that the localization rule would not have

impaired audit quality (MoF, 2014).

After the localization rule enactment, the MoF has been closely monitoring Big 4’s

compliance progress. By December 2013, the total number of partners in the Big 4 increased by

25% (from 101 in 2012 to 506 in 2012), and the percentage of Big 4 partners with a CICPA license

reached 62%. In Appendix B, we show that by the end of 2018 (i.e., one year after the five-year

transition period) 79.42% of Big 4 partners held a CICPA license while 20.58% held a foreign

license.19

3. Literature Review and Research Questions

3.1 Auditing in China

The Chinese market is much more dispersed relative to the oligopolistic market structure

in the U.S., as the 10 largest audit firms (including the Big 4) audit less than 30% of publicly listed

18 Many such partners, including the managing partners, came from Hong Kong, and the current exemption policy between HKICPA and CICPA, since December 2010, has made it much easier for both sides’ members to get the other’s qualification. But the examination requirements remain a barrier to the older generation who are familiar with English and (mainly spoken) Cantonese and not with writing in the simplified characteristics used on the mainland for writing in Mandarin (Macve 2020).

19 As Appendix B1 shows: In 2018, the number of partners in KPMG, EY, PwC and Deloitte were 135, 163, 174 and 150, respectively, with percentage of partners holding a CICPA license being 85.99%, 80.98%, 74.13% and 80%, respectively. While, for the other Top 10 audit firms (excluding the Big 4), the total number of partners was 1,081 in aggregation, with 1,076 partners holding a CICPA license (i.e., 99.54%).

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companies in China. Therefore, the audit market in China is more competitive that in the U.S. and

auditors face great pressure to retain and attract clients (Guan et al., 2016).

The Chinese CPA profession is subject to scrutiny by a battery of governmental and

regulatory bodies (e.g., Ministry of Finance, the CICPA, and the CSRC) who have the power to

oversee auditing practices and might have the authority to examine auditor’s working papers and

to impose sanctions upon discovery of wrongdoings (Li et al., 2017).

In China, two auditors must sign the audit report to clarify who was responsible for the

audits performed (Chen et al., 2010). The signing auditors can be partners or senior managers, with

the more senior signing auditor mainly performing the review work at the end and the relatively

junior signing auditor mainly administering the fieldwork. The role of signing auditors in China is

like that of engagement partners in other markets as the signing auditors lead the audit team and

are responsible for decision-making in the audit process (Gul et al., 2013). The two signing auditors

share the same legal liability and are accountable for audit reports in their names as sanctions for

detected audit failures are imposed on both audit firms and individual signers (Lennox et al., 2014).

The names of the two signing auditors for each engagement are publicly disclosed. Furthermore,

the CICPA also publicly discloses auditors’ personal profiles (e.g., gender, date of birth,

educational level, university of graduation, major, and CCP membership status) (Li et al., 2017).

3.2 Human Capital and Labor Market

Human capital is a key resource in professional service firms such as auditing firms, as it

plays a key role in explaining the success and failure in the audit market (Morris and Empson

1998). Bröcheler et al. (2004) apply human capital theory to investigate the role of human capital

of individual auditors on audit firm performance and find a positive association with survivorship

rates. Prior research has also found that education positively affects auditors’ knowledge, values,

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and risk preferences as well as their ability to implement more complex auditing procedures (e.g.,

Hunton and Wier 1996; Gul et al., 2013; Li et al., 2017).

Recent studies examine the effect of human capital on different audit outcomes. Beck,

Francis, and Gunn (2018) examine whether the level of human capital in a city is associated with

audit office’s ability to conduct high-quality audits. They proxy human capital with the workforce

education level of the metropolitan statistical area (MSA) in which the lead engagement office is

located and find a positive association with audit quality. Relatedly, Call et al. (2017) examine the

association between employee quality and financial reporting outcomes. They also use the average

workforce education level in MSAs as a proxy for employees’ quality and find a positive

association with financial reporting quality. These papers use aggregate measures of human capital

at the MSA level, therefore making difficult to infer the role of individual auditors’ human capital.

A few studies have used more direct measures of human capital to examine the relation

between individuals and audit quality. For example, Li et al. (2020) use firm-level education data

for employees of Chinese listed companies to examine the association between employee quality

and audit fees and find a positive association with audit quality. They interpret their results as

suggestive that high employee quality can reduce audit effort and perceived audit risk, thereby

leading to higher quality accounting information and more effective internal control systems. In a

similar vein, Gul et al. (2020) use accounting personnel’s education and salary of a sample of

Chinese listed companies to examine the relation between employee human capital and audit risk.

They find that auditors are significantly more likely to make audit adjustments to companies that

have a lower percentage of accounting personnel with bachelor’s degrees. These papers examine

the association between human capital at the individual level of clients’ employees and financial

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reporting outcomes, thereby overlooking the role that auditor human capital might play on audit

related outcomes.

More recently, Barrios (2021) uses CPA’s individual resumes and labor market measures

of individual career outcomes to examine the role of audit personnel on audit quality around the

staggered adoption of the 150-hour rule in the U.S. but fails to find any significance difference

between rule and non-rule CPAs. Finally, Du et al. (2018) examine whether the education level of

the signing auditors, as a proxy of human capital, affects audit quality. Using a Chinese sample,

they find that the education level of signing auditors is negatively associated with the risk of

financial misstatements, which they interpret as suggestive that higher education can enhance the

ethics and the independence of auditors, thereby mitigating the risk of financial misstatements.

3.3 Hypothesis Development

Recent studies (e.g., Barrios 2021; Du et al., 2018) represent a first attempt to advance our

understanding of auditor human capital. However, these studies are silent about the strategic

decisions (e.g., hiring and retaining) that audit firms undertake about their people who are

fundamental inputs to the audit process (Francis 2011). Audit firms are usually organized in

partnerships as human capital is the main factor affecting service quality and customers cannot

perfectly monitor the quality of services they receive (Lennox and Wu 2018). Partnerships

overcome potential information asymmetries between auditors and clients by hiring and

maintaining high-quality human capital (Lennox and Wu 2018). Prior studies that examine audit

partner characteristics (e.g., Gul et al., 2013; Li et al., 2017) can only observe the existing matching

between audit partners and clients as data about the hiring process of partners are difficult to obtain.

The localization rule required Big 4 to change the structure of their partnerships to have at least

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80% of their partners with a CICPA license. Therefore, the localization rule provides a unique

setting to examine changes in the partnership structure of Big 4 firms before and after 2012 and

whether these changes affected Big 4 human capital and the audit quality of their clients.

Our first research question examines changes in portfolio size. On the one hand, Big 4

could hire new local partners (i.e., certified with CICPA) to replace incumber partners and assign

them to existing and new clients without any consequences on the average portfolio size. On the

other hand, Big 4 could hire new local partners and flank them with incumbent partners, hence

reducing the average portfolio size. Therefore, we present our first hypothesis in the null form as

follows:

H1: There are no changes in portfolio size around the localization rule.

After exploring the changes in portfolio size, we examine the characteristics of the new

partners. Recent studies have found that auditor characteristics such as educational background,

gender, prior reporting history, level of busyness, risk preferences, and political affiliation have a

significant impact on audit quality (Gul et al., 2013; Li et al., 2017). The underlying assumption

in these studies is that the personal characteristics of individuals are related to attributes relevant

for the auditing decision making process (Gul et al., 2013). Given, that in China the names of the

two signing auditors are publicly disclosed and that the CICPA discloses auditors’ personal

profiles (e.g., gender, date of birth, educational level, university of graduation, major, and CCP

membership status), in our second prediction we examine whether individual characteristics of the

engagement partners change after the enactment of the localization rule. If the incoming partners

are perfect substitutes of the incumbent ones, we should expect no changes in human capital;

otherwise, ex ante it is difficult to predict the direction of these changes on human capital as the

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new partners can be potentially either more or less experienced that the incumbents. For these

reasons, we present our second hypothesis in the null form as follows:

H2:There are no changes in engagement partner human capital around the localization

rule.

Next, we examine whether the enactment of the localization rule had any consequences for

audit quality. Prior literature on audit quality has generally demonstrated that reputation, litigation,

and regulatory concerns shape the incentives that drive audit quality (Gutierrez et al., 2018). The

localization rule may possibly influence audit quality by placing Big 4 and their clients under

relatively more scrutiny, therefore improving audit quality. However, China is a relationship-based

economy where social ties (i.e., guanxi) are crucial for business success (Lennox and Wu 2018).

Prior studies that exploit Chinese data (Guan et al., 2016; He et al., 2017) examine the impact of

social ties between partners and clients and find that these connections impair audit quality. New

local partners might have comparatively stronger connections with their clients’ management,

which can potentially impair audit quality. Given the tension in the above arguments, we present

our third hypothesis in the null form as follows:

H3: There are no changes in audit quality around the localization rule.

4. Research Design and Sample Selection

4.1 Difference-in-differences Models

Our research questions investigate the impact of the stipulation of the localization rule on

audit partnership structure, audit firms’ human capital and audit quality. Because the localization

rule is applicable to Big 4 in China since 2012, we use a difference-in-difference (D-i-D) research

design to compare changes in dependent variables among Big 4 (i.e., treatment group) against

17

other top-10 audit firms excluding Big 4 (i.e., control group).20 We collect data from audit reports

for a 14-year period from 2005 to 2018, spanning seven years before and seven years after the

stipulation of the localization rule. Since the localization rule constitutes a source of variations in

the composition of audit partners within Big 4 and is not applicable to other top-10 audit firms, we

estimate the following D-i-D model:

DEPVARi,t = β0 + β1BIG4i,t+ β2POSTi,t + β3BIG4 * POSTi,t +βi∑CLIENTCONTROLSi,t

+βi∑PARTNERCONTROLSi,t+ IndustryFE + YearFE+ εi,t (1)

where, for client company i in year t, DEPVARi,t is one of our proxies for (1) audit firms’

partnership structure, (2) audit firms’ human capital, and (3) audit quality. BIG4 is an indicator

variable that equals one for audit reports signed by the Chinese Big 4 audit firms and zero for those

signed by other top-10 audit firms. POST is a period indicator variable that equals one for audit

reports with fiscal year in and after 2012, and zero otherwise. Our variable of interest, BIG4*POST

is an interaction of the treatment and time indicator variables that captures the difference-in-

differences effect.21

Consistent with prior studies (e.g., Ashbaugh et al., 2003; Myers et al., 2003; Menon and

Williams 2004; Chen et al., 2010; DeFond and Zhang 2014; Huang et al., 2015; Lennox et al.,

2016; Li et al., 2017; Chang et al., 2019), we include the following control variables to control for

client characteristics and individual signer’s characteristics. Specifically, we control for client size

(SIZE), liquidity and leverage (LIQ and LEV), cash flow (CFO), client complexity (REC and INV),

profitability (ROA and LOSS), sales growth (GROWTH), and market to book ratio (MTB). We also

20 We identified the top 10 audit firms based on the annual ranking by Chinese Institute of Certified Public Accountants (CICPA) from 2005 to 2018. 21 To capture the impacts of the localization rule on Big 4 alone, we estimate the pre-post 2012 models for partnership structure, human capital and audit quality by using observations audited by Big 4 only. The untabulated results show that our findings in Tables 3-5 remain qualitatively similar.

18

control for ownership effect by including State-Owned-Enterprises (SOE) and the percentage of

shares held by institutional investors (INSTOWN). FIRMAGE is the natural logarithm of the

number of years the company has been listed on the stock exchange. Furthermore, we control for

individual signers’ characteristics, including gender (Male), overseas and domestic universities

(OverseasUniv and ChinaTopUniv), education degree (Master), and accounting major

(ACCmajor). Finally, we control for partner’s local CPA experience (localCPAexp), which is

equal to number of years that an individual signer has been signing audit reports, and partner’s

professional qualification (num_otherQ) that is the number of professional qualifications other

than CPA held by signer.22 Consistent with Lennox et al. (2020), we consider all audit report

signers in our sample as audit partners.23 Finally, we include client industry fixed effects and year

fixed effects to control for the impact of time-invariant industry and year characteristics.24 To

address autocorrelation concerns regarding observations associated with a given client company,

we cluster standard errors by company in all models. All continuous variables are winsorized at

the top and bottom one percent. Definitions of the variables are presented in Appendix A.

4.2 Analyses of the Localization Rule and Audit Firm Partnership Structure

Our first research question examines the impact of the localization rule on audit firms’

partnership structure. The audit firms’ partnership structure captures the importance of individual

audit report signers to audit firms. To construct this variable for two partners (S1PortSize and

S2PortSize, respectively), we first count the total number of clients for each partner in a given year,

and then divide it by the total number of clients of each audit firm for the same year. In addition,

22 We include the signer characteristic variables for both the first and second signers of the audit reports in the regression models with a prefix of “S1” and “S2”, respectively. 23 Since our main interest is in the personal characteristics of audit report signers, we do not make a distinction between engagement quality reviewers and audit engagement partners as in Lennox et al., 2020. 24 Our results remain similar if we control for client fixed effects and year fixed effects. To alleviate the concerns on the multicollinearity between POST and year fixed effects, we also replicate all main analyses without year fixed effects. All of our results hold.

19

we also construct AvgPortSize, which is the average values of S1PortSize and S2PortSize. The

control variables for client and audit partners’ characteristics remain the same as Equation (1).

4.3 Analyses of the Localization Rule and Human Capital

Our second research question investigates the impact of the localization rule on audit firm’s

human capital. Following Gul et al. (2013), Li et al. (2017) and Barrios (2021), we measure human

capital based on individual signers’ gender (Male), education background (OverseasUniv,

ChinaTopUniv, Master, ACCmajor), local working experience (localCPAexp) and other

professional qualifications (num_otherQ). We perform these analyses for the first (S1) and the

second signer (S2) separately. The control variables for client and audit partners’ characteristics

remain the same as Equation (1).

4.4 Analyses of the Localization Rule and Audit Quality

Finally, we analyze the impact of the localization rule on audit quality. Following prior

studies (e.g. Lawrence et al., 2011; Huang et al., 2015 ; Ke et al.,2015; Fung et al., 2017, and

Gutierrez et al., 2018), we use several proxies for audit quality. The first measure is absolute value

of discretionary accruals (Abs_DA) as measured by Kothari et al. (2005). 25 Discretionary accruals

capture audit quality with respect to financial reporting, with higher audit quality being defined as

greater assurance that the financial statements faithfully reflect a company’s underlying economics

(DeFond and Zhang 2014). Our second measure of audit quality is signed value of discretionary

accruals (DA), because the absolute DA is arguably less informative than its signed value (Hribar

and Nichols 2007; Lennox et al., 2016). Furthermore, Ashbaugh et al. (2003) show that companies

25 Following Kothari et al. (2005), DA is the residual of the following annual cross-sectional model for each industry: TACCRi,t= α+ β1ROAi,t-1+β2∆Salesi,t+ β3PPEi,t + εi,t , where, for client company i and year t, ROAi,t-1 is net profits/total assets in year t-1. We require at least 10 observations in each industry-year combination to estimate these two models. The untabulated sensitivity tests show that our results still hold when (1) we require at least 20 observations in each industry-year combination to estimate DA; and (2) we use total discretionary accruals (TACCR) as a dependent variable.

20

with positive discretionary accruals are different from those with negative discretionary accruals.

We, therefore, split the full sample into observations with positive and negative discretionary

accruals (POS_DA and NEG_DA), and then repeat our analyses on each subsample, respectively.26

The final proxy for audit quality is signed abnormal working capital accruals (AWCA), as defined

by DeFond and Park (2001) and Ke et al. (2015).27 AWCA is the difference between realized

working capital and an expected level of working capital needed to support a current sales, where

an historic relation of working capital to sales captures expected working capital. DeFond and Park

(2001) and Ashbaugh et al. (2003) argue that AWCA is a more powerful measure than accruals,

and management has the most discretion over working capital accruals. The control variables for

client and audit partners’ characteristics remain the same as Equation (1). We also use a battery of

alternative measures for audit quality, including incidence of modified audit opinions (MAO),

incidence of subsequent restatements of financial reports (RESTATE), below-the-line items (BL)

and the presence of small profits (SP).

4.5 Sample Selection

Table 1 shows our sample selection procedure. We start with 15,543 client-year

observations available in China Security Market and Accounting Research (CSMAR) and Chinese

Research Data Services Platform (CNRDS) over the period 2005-2018. 28 We delete 1,573

observations without information on individual signer’s characteristic. We further exclude 504

26 For ease of interpretation, we use absolute value of positive and negative discretionary accruals. Further, in untabulated sensitivity tests, we truncate POS_DA and NEG_DA and estimate a tobit regression, respectively. We find consistent results. 27 AWCAi,t = WCi,t – WCi,t-1*SALESi,t/SALESi,t-1, scaled by the average of the beginning and ending total assets. WCi,t = (current assets – cash – trading financial assets – short-term investment)- (current liabilities – short-term debts – long-term liabilities matured in one year), scaled by the average total assets. 28 China Security Market and Accounting Research (CSMAR) and Chinese Research Data Services Platform (CNRDS) are two primary databases that offer data on the financial statements and audit partner information of all companies listed in China. These databases are widely used in extant research using the Chines setting (e.g. Lennox et al., 2016; Chan et al., 2021; Kuang et al., 2021; Liu et al., 2021).

21

observations that pertain to financial institutions.29 Finally, 2,247 observations are removed due to

insufficient data to compute discretionary accruals, abnormal working capital and other control

variables. These procedures yield a final sample of 11,219 client-year observations for our analyses.

<Insert Table 1 here>

5 Results

5.1 Descriptive Statistics

Figure 1 illustrates the time-series evolution in partnership structure of the top 10 audit

firms in China. Panel A presents the separate trends of number of engagements and number of

signers, and Panel B presents the trend of engagement-to-signer ratio. Both graphs separate Big 4

and other top-10 audit firms by year, and the red dashed vertical line is Year 2012 when the

localization rule became effective. In Panel A, the solid green and navy lines represent the average

number of engagements by Big 4 and other top-10 audit firms, respectively. The dashed yellow

and red line stand for the average number of audit report signers in Big 4 and other top-10 audit

firms, respectively. The figure demonstrates that, the audit engagements and audit signers of both

groups increase across the entire sample period, but the other top-10 group experiences a much

more rapid growth. 30 Interestingly, we find that the trend of signers (the dashed red line)

approximates the trend of client portfolio size for other top-10 firms (the solid navy line), but the

growth of signers (the dashed yellow line) surpasses the growth of engagements in Big 4 (the solid

green line), especially in the post-localization rule period.31 Panel B of Figure 1 confirms these

trends by showing a relatively stable engagement-to-signer ratio for other top-10 (the solid navy

29 Financial institutions are deleted because their financial statements are significantly different from those of companies in other industries, and many of our variables thus are not meaningful (Ke et al., 2015). 30 The mean client portfolio size of other Top 10 in the pre- and post-localization rule periods is 71 and 258, respectively; the mean client portfolio size of Big 4 in the pre- and post-localization periods is 24 and 44, respectively. 31 The mean number of signers for other Top 10 in the pre- and post-localization rule periods is 68 and 258, respectively; the mean number of signers for Big 4 in the pre- and post-localization rule periods is 29 and 64, respectively.

22

line), but a much steeper decline in the engagement-to-signer ratio for Big 4 (the solid red line)

over our sample period.32.

Table 2 reports univariate difference-in-differences for variables used in our analyses.

Panel A shows a test of differences in means of audit firms’ partnership structure variables between

Big 4 and other top-10 before and after the stipulation of the localization Rule. The difference

between the pre- and post-localization rule periods for the Big 4 group is reported in column (5)

and the difference between the pre- and post-localization rule periods for the other top-10 group is

reported in column (6). The difference-in-differences is reported in column (7). In our sample,

1,079 (9.61%) observations are audited by Big 4, and 10,140 (90.38%) observations are audited

by other top-10 audit firms. Consistent with Figure 1, we find that client portfolio sizes of both

signers significantly decrease in Big 4 and other top-10 firms. However, the reduced size for Big

4 is greater than that for other top-10 firms.

Pane B shows the descriptive statistics of human capital for Big 4 and other top-10 audit

firms before and after the adoption of the localization rule. We present the results of the first signer

and the second signer separately. Interestingly, the localization rule seems to have a greater impact

on the second signers than on the first signers. Specifically, compared to other top-10 audit firms,

Big 4 are less likely to hire the first signers graduated from overseas universities, but prefer them

from top Chinese universities. With respect to local CPA experience, Big 4 are more likely to hire

partners with local audit experience, and this phenomenon is more prevalent for the first signers.

Panel C of Table 2 presents the descriptive statistics of audit quality. The summary

statistics are generally comparable to the prior Chinese literature (Gul et al., 2013; Ke et al., 2015;

32 The mean engagement-to-signer ratio for other Top 10 in the pre- and post-localization rule periods is 1.03 and 0.99, respectively; The mean engagement-to-signer ratio for Big 4 in the pre- and post-localization rule periods is 0.87 and 0.68, respectively.

23

Huang et al., 2016; Chen et al., 2016). In general, we find that the audit quality of Big 4 and other

top-10 improves by a similar extent in the post-localization rule period. Panel D shows the

descriptive statistics of client characteristics. We also find that changes in most client

characteristics between the pre- and post-localization rule periods for Big 4 and other top-10 audit

firms are statistically significant.33

<Insert Table 2 here>

5.2 The Localization Rule and Partnership Structure

Table 3 shows the regression results for our analyses of partnership structure. All models

are estimated using OLS regression models, with S1PortSize, S2PortSize and AvgPortSize as

dependent variables, and inclusion of the list of partner and client-company control variables

specified in Equation (1). Consistent with the univariate tests in Table 2, our regression results

show that the coefficients on the interaction between BIG 4 and POST are significantly negative

at the 1% level across all three columns (all p<0.01). The results suggest that after the

implementation of the localization rule, the number of local partners increases dramatically at a

faster rate than the growth in clients, resulting in the decline of the client portfolio size in Big 4,

compared to that in other top-10 audit firms.

<Insert Table 3 here>

5.3 The Localization Rule and Human Capital Characteristics

Table 4 shows the regression results for our analyses of human capital. Panel A presents

analyses specific to the first signers of audit reports and Panel B presents analyses specific to the

second signers. With respect to the first signers, Panel A shows that the localization rule seems to

have a significant impact on their education background. The interaction between POST and BIG4

33 The average values of these variables are similar to those in prior studies (e.g., Ke et al., 2015, Chang et al., 2019).

24

is significantly negative in column (2) (p=0.052), but significantly positive in column (3)

(p=0.064). The results suggest that after the implementation of the localization rule, Big 4 are more

likely to hire first signers graduated from Chinese top universities, instead of overseas universities.

In terms of education degree, column (4) demonstrates that the coefficients on POST and BIG4 are

significantly positive at the 10% level (p=0.093), meaning that first signers in Big 4 after the

localization rule are more likely to have master’s degree. However, we do not find empirical

evidence showing that the localization rule has substantial impact on first signers’ gender,

accounting major, local experience, and other professional qualifications.

Panel B of Table 4 presents different, but intriguing results for the second signers. First,

the localization rule discourages Big 4 to hire overseas returnees as second signers (p=0.019) but

has no significant impact on second signers graduated from Chinese top universities. Second,

compared to other top-10 audit firms, Big 4 are keen on hiring second signers with a master’s

degree and accounting major (p=0.000 and 0.022, respectively). Third, different from the first

partners, the second signers in Big 4 have diversified professional background since they have

more qualifications other than the CPA license (p=0.003).

To sum up, the above results show that the localization rule brings significant impact on

the human capital in Big 4. Specifically, the audit partners under the localization rule regime are

more likely to be graduated from a Chinese domestic top university, hold higher educational degree,

and have more professional qualifications other than the CPA license. More importantly, the

localization rule has a stronger effect on these characteristics of the second partners than these of

the first partners. Our findings are consistent with the MoF’s intention to promote local partners,

and they alleviate concerns from international regulators and media that the localization rule would

impose a big challenge for the Big 4 to find high-quality local partners.

25

<Insert Table 4 here>

5.4 Client Assignment to Partners within the Big 4

Table 5 shows how the Big 4 assign partners to their clients. As Panel A shows, incumbent

first signers (i.e., those who signed at least one audit reports before 2012) were assigned 67% of

the new clients and kept 86% of existing clients. In contrast, Panel B shows that incumbent second

signers were assigned only 9% of the new clients and kept 47% of existing clients. With the joining

new partners, the rate of existing clients audited by incumbent first and second signers both

decreased. The rate of existing clients audited by incumbent second signers experienced a more

drastic decrease (from 63% in 2012 to 10% in 2018), compared to the rate of existing clients

audited by incumbent first signers (from 96% in 2012 to 74% in 2018). This pattern indicates that

incoming partners were primarily assigned to new clients as second signers (i.e., junior roles).

However, there was also some substitution effect within the existing client group, primarily

among second signers. As Panel A and B shows, 14% and 53% of the incoming partners took first

and second signer roles for existing clients. The rate of existing clients audited by new second

signers experienced a more drastic increase (from 38% in 2012 to 90% in 2018), compared to the

rate of existing client audited by new second singers (from 4% in 2012 to 26% in 2018). Overall,

the Big 4 met the requirements of the localization rule through client growth and not by a

substitution of incumbent by incoming partners.

<Insert Table 5 here>

5.5 The Localization Rule and Audit Quality

Our third research question examines the impact of the localization rule on audit quality.

Table 6 Panel A presents the main regression results and Panel B presents the results using

alternative proxies for audit quality. Consistent with descriptive statistics reported in Table 2, we

26

find insignificant interaction effects between Big 4 and POST across all five columns. These results

suggest that the changes in audit quality between the pre- and post-localization rule periods for

Big 4 and other top-10 audit firms are statistically insignificant. Our findings are consistent with

the MoF’s intention of “leveling the play field” between Big 4 and Chinese local firms, and its

expectation of ensuring the compliance of the localization rule without impairing audit quality.

We use a battery of alternative proxies for audit quality in Panel B. The first measure is

MAO, an indicator variable that equals to one if the client company receives a modified audit

opinion, and zero otherwise.34 High-quality auditors are more likely to maintain lower thresholds

for issuing MAO and constrain aggressive earnings management (DeFond et al. 2000; Gong et al.

2016; Huang et al. 2016). Second, we use RESTATE, an indicator that equals to one if the financial

report of a client-year observation is subsequently restated, and zero otherwise. According to

DeFond and Zhang (2014), restatements are usually strong evidence of poor audit quality, and a

higher level of audit quality should be associated with a lower probability of restatements. The

third proxy is below-the-line items (BL), which is the sum of investment net income, profits from

other operations, and non-operating net income, scaled by the average of the beginning and ending

total assets. The previous studies (e.g., Chen and Yuan 2005; Haw et al. 2005; Kao et al. 2009)

find that Chinese companies tend to inflate earnings by timing the executive of transactions

pertaining to below-the-line items, and these transactions are often dubious related-party

transactions and attract much attention from regulators and investors. Finally, we use the presence

of small profits indicator (SP), which equals to one if ROA is between 0% and 1%. This is because

34 Following Huang et al. (2015), our modified audit opinion includes three types of audit report modifications: (1) unqualified with an explanatory note (n=236), (2) qualified opinion (n=98), and (3) disclaimer of opinion (n=20). None of the companies in our sample received an adverse opinion. For detailed discussions and further examples of modified audit opinions in China, see DeFond et al. (2000) and Chen et al. (2010). We also estimate an ordered logit model for MAO following Chang et al. (2019) and find that our results remain the same.

27

Chinese companies have strong incentives to break even for regulatory reasons (Francis and Wang

2008; Francis and Yu 2009; Gul et al. 2013; Jorgensen et al. 2014).35 In estimating our difference-

in-differences model (Eq. 1), we use the same control variables as the ones in Panel A, except that

we include lag values of MAO and RESTATE for the audit opinion and restatement analyses. The

results presented in Panel B of Table 6 show that the localization rule has no impact on audit

quality, which is consistent with our main results in Panel A.

<Insert Table 6 here>

6 Additional analyses and robustness tests

6.1 New First and Second Signers

The Chinese regulators imposed the localization rule with the intention of promoting local

partners, rather than dismissing incumbent foreign partners (i.e., “addition rather than subtraction”).

To further investigate if our findings in the client-level analyses are attributable to new signing

partners, we restrict our sample to all new signing partners who appear on the audit reports for the

first time in any given year and replicate the analyses in Table 4 and Table 6, respectively. Our

reduced sample is composed of 979 audit reports (i.e., client-year observations) signed by new

first signers and 2,808 audit reports (i.e., client-year observations) signed by new second signers

over the full sample period. Table 7 Panel A shows that both the Big 4 and the other top-10 audit

firms experience a more rapid increase in the number of audit reports signed by new second signers

than those signed by new first signers, consistent with the fact that initial partner promotions

35 Chinese companies must be profitable for three consecutive years to qualify for issue of a seasoned equity offering. Further, a company that reports losses for two consecutive years will be subject to special treatment, for example, a daily stock price change limit of 5%, and will risk being delisted from the stock exchange if it cannot generate profit in the third year. Previous paper such as Chen et al., 2016 and Zeng et al., 2020 used SP as proxies for audit quality. We also construct an alternative proxy for SP by using a threshold based on ROE rather than ROA. Our results remain the same.

28

largely start with appearing as the second signer on audit reports.36 The t-test analyses reported in

column (7) demonstrates that that there is no significant difference in human capital of the new

first signing partners between Big 4 and other top-10 audit firms over the pre- and post-rule periods.

Interestingly, with respect to the new second signing partners, we find that Big 4 are less likely to

hire partners returning from overseas universities but prefer partners with a master’s degree and

rich local working experience. Panel B shows that results of multivariate analyses of human capital.

Supporting our main analyses in Table 4, we find that the new signing partners, in particular the

new second signers in the post-rule period are less likely to be graduated from overseas universities,

but more likely to hold higher educational degree, majoring in accounting, and obtain more

professional qualifications other than the CPA license. In Panel C of Table 7, we do not find

significant impacts of the localization rule on audit quality of all new signing partners. Taken

together, the results show that the Big 4 comply with the localization rule using the “addition rather

than subtraction” strategy proposed by MoF, they promote new signers with significantly different

characteristics in the post-rule period without compromising their audit quality.

<Insert Table 7 here>

6.2 Excluding New Audit Engagements in the Post Period

The findings documented in our client-level analyses might be attributable to the emergence

of new listed audit clients in the post-localization rule period, which might exhibit different

characteristics from those audited in the pre-localization rule period. To alleviate this concern, we

exclude all audit engagements (i.e., client-year observations) whose first presence in the database

was in 2012 or any year thereafter, and replicate analyses of human capital and audit quality. The

36 For the Big 4 group, the number of audit reports signed by new second signers more than doubled in the post-rule period, while the number of audit reports signed by new first signers remain stable. For the other Top 10 group, the number of audit reports signed by new second signers tripled in the post-rule period, while the number of reports signed by the first signers increased by 65 percent.

29

results presented in Table 8 Panel A and Panel B are consistent with those we documented in Table

4 and Table 6, respectively. Therefore, we conclude that the localization rule’s impact on Big 4’s

human capital is not limited to new audit engagements, and changes in the characteristics of

signing partners for existing clients do not impair audit quality.

<Insert Table 8 here>

6.3 Test of Parallel Trends Assumption

We employ a D-i-D research design to capture the impact of the localization rule on Big 4,

compared to other top-10 between the pre- and post-localization periods. Thus, it is important to

test the validity of the parallel trend assumption underlying the D-i-D estimation. To do that, we

replace POST by year indicator variables that track the impact of the localization rule before and

after it becomes effective, using 2012 as the benchmark year (year t=0). We first add seven

indicator variables, Year-7, Year-6, Year-5, Year-4, Year-3, Year-2, Year-1, for the pre-period and

six indicator variables, Year+6, Year+5, Year+4, Year+3, Year+2, Year+1, for the post-period.

We then interact these variables with Big4 and plot the time trend of the coefficient on these

interactions in untabulated analyses, generating evidence consistent with the main results on

partnership structure, human capital, and audit quality.

6.4 Placebo Test with Pseudo Adoption Year

In the main analyses, we observe significant influence of the localization rule on partnership

structure and human capital. However, our analyses might simply reflect the time trend of changes

in Big 4 and other top-10 and has nothing to do with the adoption of the localization rule. To

mitigate this concern, we conduct pseudo analyses (falsification tests) by restricting the sample

period from 2005 and 2011 and assigning year 2008 as a pseudo localization rule adoption year.

We define POST as one if audit reports are issued for fiscal year between 2008 and 2011, and zero

30

otherwise. The untabulated results show that we do not find consistent evidence as our main results

when the policy was not in place. We take these findings as further evidence that the localization

rule is a driving factor causing declines in partnership structure and changes in human capital

between Big 4 and other top-10 firms.

7 Conclusion

We examine a unique regulation that accelerated the localization of the Big 4 operations in

China, requiring these firms to move from approximately 60% of partners with foreign licenses in

2011 to less than 20% of partners with foreign licenses by the end of 2017 (i.e., have at least 80

percent locally licensed audit partners). This shift required a reorganization of the firms’ human

capital, which could have direct and indirect consequences for their partnerships’ structure and

audit quality.

First, we examine changes in the structure of the Big 4 partnerships and their human capital,

and next we examine whether the restructuring of Big 4 partnerships indirectly affected audit

quality. We use a sample including fourteen years around the implementation of the regulation and

rely on a difference-in-differences research design, comparing several outcomes between Big 4

and other top-10 local audit firms.

We demonstrate that the Big 4 met the requirements by promoting local talent, increasing the

number of incoming partners occupying junior roles, and diluting each partner’s share of the total

firm’s clients. However, we do not find evidence that the regulation had an incremental effect on

audit quality. Our findings suggest that the regulation achieved its intended objectives, primarily

developing local human capital, without impairing audit quality.

31

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35

Figure 1 Partnership structure. Panel A: Big 4 vs. Other top-10 average number of engagements and signers over year

Panel B: Big 4 vs. Other top-10 average ratio of engagement to signer over year

36

Table 1 Sample selection

Company-

years Client company-year observations from CSMAR and CNRDS during the years 2005-2018 15,543 Less: company-year observations without information on signers' individual characteristics (1,573) Less: company-year observations in financial industry (504) Less: company-year observations without sufficient data to compute discretionary accruals, abnormal working capital and control variables (2,247) Final sample for analyses 11,219

37

Table 2 Univariate difference-in-differences analyses

Mean Diff. in Means

(1) (2) (3) (4) (5) (6) (7)

Big4 Pre (n=291)

Big4 Post (n=788)

Other top-10 Pre

(n=1665)

Other top-10 Post

(n=8475) Column

(2) -(1) Mean Diff.

Column (4) -(3)

Mean Diff.

Column (5) -(6)

Diff-in-diff.

Panel A: Partnership structure S1PortSize 0.140 0.059 0.063 0.016 -0.081*** -0.046*** -0.035*** S2PortSize 0.096 0.044 0.037 0.009 -0.053*** -0.028*** -0.025*** AvgPortSize 0.119 0.052 0.050 0.013 -0.067*** -0.038*** -0.030***

Panel B: Audit firm human resources Variables specific to first signer: S1Male 0.656 0.673 0.718 0.763 0.017 0.046*** -0.029 S1OverseasUniv 0.141 0.066 0.008 0.009 -0.075*** 0.000 -0.075*** S1ChinaTopUniv 0.622 0.701 0.486 0.466 0.079** -0.020 0.098*** S1Master 0.168 0.150 0.262 0.206 -0.018 -0.056*** 0.037 S1ACCmajor 0.543 0.513 0.683 0.604 -0.030 -0.079*** 0.048 S1localCPAexp 9.591 14.603 11.938 15.531 5.012*** 3.594*** 1.418*** S1num_otherQ 0.107 0.084 0.145 0.166 -0.023 0.021* -0.044

Variables specific to second signer: S2Male 0.522 0.454 0.688 0.631 -0.068** -0.057*** -0.011 S2OverseasUniv 0.052 0.018 0.001 0.003 -0.034** 0.002** -0.036*** S2ChinaTopUniv 0.653 0.511 0.356 0.255 -0.142*** -0.102*** -0.040 S2Master 0.141 0.268 0.160 0.122 0.127*** -0.038*** 0.165*** S2ACCmajor 0.529 0.418 0.620 0.440 -0.111*** -0.180*** 0.068* S2localCPAexp 6.385 7.520 7.529 8.459 1.135*** 0.930*** 0.206

38

S2num_otherQ 0.041 0.032 0.140 0.060 -0.009 -0.080*** 0.070*** Panel C: Audit quality Abs_DA 0.067 0.046 0.082 0.060 -0.021*** -0.022*** 0.001 DA 0.002 -0.004 0.005 0.002 -0.006 -0.003 -0.003 |POS_DA|# 0.075 0.045 0.089 0.062 -0.030*** -0.027*** -0.003 |NEG_DA|† 0.060 0.046 0.076 0.058 -0.014*** -0.018*** 0.004 AWCA 0.000 -0.007 0.005 -0.003 -0.007 -0.008*** 0.001

Panel D: Client characteristics SIZE 23.532 24.104 21.827 22.151 0.572*** 0.324*** 0.248*** LIQ 1.250 1.510 1.868 2.417 0.260*** 0.549*** -0.289* LEV 0.531 0.530 0.498 0.428 -0.001 -0.070*** 0.068*** CFO 0.074 0.066 0.044 0.043 -0.008 -0.002 -0.006 REC 0.075 0.088 0.097 0.123 0.013** 0.026*** -0.014* INV 0.151 0.143 0.183 0.147 -0.008 -0.036*** 0.028*** ROA 0.053 0.045 0.039 0.036 -0.008** -0.002 -0.006 LOSS 0.058 0.055 0.094 0.100 -0.003 0.007 -0.011 GROWTH 0.269 0.149 0.233 0.182 -0.120*** -0.051*** -0.069** MTB 3.633 3.605 4.280 4.042 -0.028 -0.238*** 0.210 SOE 0.787 0.654 0.612 0.351 -0.133*** -0.261*** 0.127*** INSTOWN 9.519 7.233 7.271 6.237 -2.286*** -1.034*** -1.251** FIRMAGE 2.208 2.447 2.151 2.127 0.239*** -0.024 0.264***

This table shows the univariate difference-in-difference analyses for variables used in multivariate analyses. All variables are defined in Appendix A. All continuous variables are winsorized at the top and bottom one percent. *, **, and *** indicate two-tailed statistical significance at the ten, five, and one percent levels, respectively. # the sample size for |POS_DA| is 5,564 because it is restricted to observations with positive abnormal accruals. † the sample size for |NEG_DA| is 5,655 because it is restricted to observations with negative abnormal accruals.

39

Table 3 The localization rule and audit firm partnership structure

Dependent Variables (1) (2) (3) Variables S1PortSize S2PortSize AvgPortSize BIG4 0.066*** 0.052*** 0.060*** (12.06) (13.85) (14.31) POST -0.130*** -0.069*** -0.101*** (-22.26) (-12.93) (-20.85) BIG4*POST -0.022*** -0.017*** -0.020*** (-3.91) (-4.47) (-4.69) SIZE -0.000 0.000 0.000 (-0.36) (0.61) (0.04) LIQ -0.000 0.000 0.000 (-0.08) (0.22) (0.02) LEV 0.001 -0.000 0.001 (0.44) (-0.22) (0.25) CFO 0.001 -0.001 -0.001 (0.14) (-0.41) (-0.17) REC 0.004 0.002 0.003 (0.89) (0.63) (0.81) INV 0.004 0.006** 0.005** (1.36) (2.46) (2.10) ROA -0.001 -0.001 -0.001 (-0.11) (-0.29) (-0.09) LOSS 0.002 0.002* 0.001 (1.40) (1.89) (1.56) GROWTH -0.001 0.000 -0.000 (-1.59) (0.39) (-1.08) MTB -0.000 0.000 0.000 (-0.29) (0.85) (0.18) SOE -0.000 0.001 0.000 (-0.12) (1.25) (0.51) INSTOWN -0.000 -0.000 -0.000 (-0.58) (-0.12) (-0.46) FIRMAGE 0.000 -0.000 -0.000 (0.45) (-0.83) (-0.12) S1Male 0.004*** 0.001** 0.003*** (5.18) (2.36) (4.56) S1OverseasUniv 0.002 0.001 0.001 (0.57) (0.28) (0.46) S1ChinaTopUniv 0.001 0.000 0.001 (1.37) (1.18) (1.43) S1Master 0.003*** 0.002*** 0.003*** (3.47) (3.32) (3.83) S1ACCmajor 0.002** -0.000 0.001

40

(2.29) (-0.45) (1.32) S1localCPAexp -0.000 -0.000*** -0.000*** (-1.42) (-8.06) (-4.51) S1num_otherQ 0.002*** 0.002*** 0.002*** (2.98) (4.04) (3.79) S2Male 0.001 0.001* 0.001 (0.80) (1.89) (1.40) S2OverseasUniv -0.012*** -0.007* -0.010*** (-2.60) (-1.81) (-2.84) S2ChinaTopUniv -0.001 0.000 -0.000 (-1.07) (0.32) (-0.55) S2Master 0.003** 0.003*** 0.003*** (2.57) (4.22) (3.56) S2ACCmajor 0.001 -0.000 0.000 (0.89) (-0.76) (0.24) S2localCPAexp -0.000*** 0.000*** 0.000** (-2.69) (8.32) (2.19) S2num_otherQ 0.000 0.001 0.001 (0.11) (1.17) (0.67) Constant 0.141*** 0.073*** 0.108*** (13.02) (9.06) (12.85) Industry FE Yes Yes Yes Year FE Yes Yes Yes Observations 11,219 11,219 11,219 Adjusted R2 0.57 0.59 0.63

This table shows the impact of the localization rule on audit firm partnership structure. All variables are defined in Appendix A. All continuous variables are winsorized at the top and bottom one percent. *, **, and *** indicate two-tailed statistical significance at the ten, five, and one percent levels, respectively. Robust t-statistics in parentheses. Standard errors are clustered by each unique client company in the sample.

41

Table 4 The localization rule and human capital (i.e., characteristics of signers)

Panel A: The localization rule and the human capital variables of the first signer of audit reports Dependent Variables (1) (2) (3) (4) (5) (6) (7) Variables S1Male S1OverseasUniv S1ChinaTopUniv S1Master S1ACCmajor S1localCPAexp S1num_otherQ BIG4 -0.235 3.548*** 0.733*** -1.286*** -0.570** -1.756*** -0.006 (-1.12) (7.42) (3.57) (-3.61) (-2.50) (-5.13) (-0.13) POST 0.221 -0.569 -0.712*** -1.177*** -1.690*** 7.050*** -0.009 (0.98) (-0.62) (-3.29) (-4.52) (-7.68) (23.08) (-0.27) BIG4*POST -0.145 -0.902* 0.393* 0.535* 0.277 0.523 -0.053 (-0.68) (-1.94) (1.85) (1.68) (1.18) (1.47) (-1.16) SIZE -0.059 0.094 -0.045 0.019 0.021 0.027 -0.013 (-1.37) (0.74) (-1.16) (0.37) (0.49) (0.37) (-1.56) LIQ -0.001 -0.029 0.033** 0.081*** -0.011 -0.044 0.002 (-0.07) (-0.43) (2.04) (4.07) (-0.61) (-1.19) (0.64) LEV 0.249 -1.167 0.279 1.292*** 0.083 -0.348 0.032 (0.79) (-1.00) (0.96) (3.65) (0.28) (-0.62) (0.51) CFO 0.440 -1.170 -0.370 0.406 0.407 -0.258 -0.073 (1.00) (-0.54) (-0.93) (0.84) (0.97) (-0.33) (-0.89) REC 0.029 -2.515 0.299 0.605 -0.349 -0.803 -0.151* (0.07) (-1.36) (0.84) (1.28) (-0.94) (-1.17) (-1.88) INV 0.447 2.341** -0.053 -0.263 -0.233 -0.741 -0.010 (1.44) (2.23) (-0.18) (-0.67) (-0.70) (-1.27) (-0.17) ROA -0.582 -2.383 0.375 1.099 2.253*** 1.821 -0.097 (-0.74) (-0.71) (0.54) (1.24) (3.09) (1.32) (-0.61) LOSS 0.029 -1.003* -0.086 0.014 0.250** -0.177 -0.005 (0.24) (-1.78) (-0.86) (0.11) (2.33) (-0.90) (-0.22) GROWTH 0.073 0.031 -0.023 -0.073 -0.095* -0.188* 0.024** (1.34) (0.12) (-0.49) (-1.17) (-1.96) (-1.94) (2.12) MTB 0.007 0.021 0.013 -0.036** -0.006 0.019 -0.004 (0.45) (0.41) (0.96) (-2.00) (-0.39) (0.78) (-1.46) SOE 0.062 -0.297 0.075 0.177 -0.127 0.138 0.026

42

(0.65) (-0.80) (0.86) (1.63) (-1.37) (0.89) (1.26) INSTOWN 0.011** -0.006 0.007* 0.002 -0.005 -0.010 0.001 (2.33) (-0.32) (1.66) (0.41) (-1.03) (-1.32) (1.44) FIRMAGE -0.025 0.290 0.055 -0.070 0.049 0.146 -0.011 (-0.43) (1.11) (1.06) (-1.01) (0.90) (1.44) (-0.96) S1Male 0.172 -0.146* -0.055 -0.299*** 0.205 0.037** (0.57) (-1.86) (-0.56) (-3.66) (1.42) (2.35) S1OverseasUniv 0.254 2.466*** -0.847*** -0.061 0.201*** (0.83) (8.02) (-2.64) (-0.10) (2.78) S1ChinaTopUniv -0.137* 0.158* -0.232*** 1.121*** 0.056*** (-1.73) (1.81) (-3.13) (9.24) (3.74) S1Master -0.062 2.417*** 0.030 -0.771*** 0.849*** 0.101*** (-0.63) (8.83) (0.36) (-9.10) (5.53) (4.83) S1ACCmajor -0.300*** -0.898** -0.188** -0.767*** 1.571*** 0.026* (-3.66) (-2.45) (-2.57) (-9.10) (12.09) (1.79) S1localCPAexp 0.013 -0.042 0.069*** 0.055*** 0.094*** 0.020*** (1.47) (-1.17) (9.22) (5.17) (12.17) (13.35) S1num_otherQ 0.222** 0.576** 0.260*** 0.453*** 0.149* 1.772*** (2.23) (2.32) (3.10) (5.23) (1.79) (16.33) Constant 1.644* -6.326** -0.344 -2.390** 0.541 8.115*** 0.130 (1.71) (-2.31) (-0.40) (-2.14) (0.57) (5.42) (0.73) Industry FE Yes Yes Yes Yes Yes Yes Yes Year FE Yes Yes Yes Yes Yes Yes Yes Observations 11,219 11,219 11,219 11,219 11,219 11,219 11,219 Pseudo/ Adjusted R2

0.02 0.30 0.04 0.07 0.06 0.25 0.07

43

Panel B: The localization rule and the human capital variables of the second signer of audit reports Dependent Variables (1) (2) (3) (4) (5) (6) (7) Variables S2Male S2OverseasUniv S2ChinaTopUniv S2Master S2ACCmajor S2localCPAexp S2num_otherQ BIG4 -0.634*** 5.035*** 1.353*** -0.729** -0.590*** -1.267*** -0.101*** (-3.25) (4.04) (6.27) (-2.54) (-2.88) (-3.54) (-3.50) POST -0.555** -0.481 -1.055*** -0.535* -1.526*** 3.247*** -0.206*** (-2.49) (-0.30) (-4.45) (-1.65) (-6.78) (8.46) (-3.76) BIG4*POST 0.002 -3.313** -0.120 1.538*** 0.530** -0.442 0.087*** (0.01) (-2.35) (-0.53) (5.13) (2.29) (-1.12) (2.99) SIZE 0.023 -0.434 0.080* 0.059 0.002 -0.004 -0.005 (0.61) (-1.38) (1.86) (1.11) (0.06) (-0.06) (-0.92) LIQ 0.028* -0.224 -0.016 0.001 0.003 0.013 0.002 (1.66) (-1.08) (-0.76) (0.04) (0.18) (0.34) (0.89) LEV 0.494* 1.595 -0.296 -0.160 0.165 -0.565 0.049 (1.76) (0.83) (-0.93) (-0.39) (0.58) (-0.88) (1.18) CFO -0.305 0.582 0.156 -0.783 0.058 -1.014 0.005 (-0.77) (0.31) (0.34) (-1.36) (0.15) (-1.14) (0.08) REC 0.247 -4.497* 0.722* 0.024 0.047 -2.069*** -0.039 (0.72) (-1.80) (1.89) (0.05) (0.13) (-2.65) (-0.81) INV -0.294 2.954** 0.150 -0.461 0.128 -0.909 -0.101** (-0.96) (1.96) (0.43) (-1.00) (0.41) (-1.44) (-2.44) ROA -0.082 2.830 -0.506 -1.039 -0.267 -2.621* 0.021 (-0.12) (0.69) (-0.65) (-0.97) (-0.38) (-1.78) (0.22) LOSS -0.037 0.773 0.044 -0.283* -0.086 -0.187 0.017 (-0.36) (1.00) (0.37) (-1.87) (-0.85) (-0.85) (1.21) GROWTH -0.049 0.036 0.051 -0.049 -0.055 0.007 0.004 (-1.00) (0.09) (0.92) (-0.72) (-1.10) (0.07) (0.52) MTB 0.006 -0.163 0.010 -0.007 0.003 0.023 -0.002 (0.48) (-1.15) (0.72) (-0.38) (0.20) (0.80) (-0.74) SOE -0.118 0.018 0.117 0.190 -0.013 0.261 0.008 (-1.38) (0.03) (1.22) (1.55) (-0.15) (1.36) (0.58) INSTOWN -0.001 -0.022 -0.009** 0.019*** 0.006 -0.006 0.001*

44

(-0.15) (-0.75) (-2.06) (3.38) (1.57) (-0.65) (1.80) FIRMAGE 0.023 -0.685** 0.036 -0.172** 0.048 0.231** -0.001 (0.45) (-2.26) (0.57) (-2.38) (0.94) (1.98) (-0.11) S2Male -0.949* -0.142* -0.170* -0.149** 0.307** 0.003 (-1.78) (-1.83) (-1.82) (-2.23) (2.20) (0.31) S2OverseasUniv -0.713 3.026*** 1.150** 3.057*** -0.065** (-1.47) (5.68) (2.21) (2.76) (-2.24) S2ChinaTopUniv -0.158** 0.212* 0.475*** 2.437*** 0.018 (-2.03) (1.95) (6.03) (15.11) (1.38) S2Master -0.170* 3.197*** 0.164 -0.911*** 0.638*** 0.007 (-1.82) (6.59) (1.53) (-9.37) (3.31) (0.50) S2ACCmajor -0.154** 0.898* 0.483*** -0.901*** 1.552*** 0.002 (-2.30) (1.67) (6.24) (-9.41) (10.56) (0.18) S2localCPAexp 0.015** 0.113*** 0.113*** 0.029*** 0.073*** 0.013*** (2.20) (2.72) (14.68) (3.21) (10.14) (11.42) S2num_otherQ 0.036 -1.158 0.150 0.085 0.021 3.412*** (0.31) (-1.43) (1.16) (0.60) (0.16) (15.06) Constant 0.422 4.690 -3.672*** -3.023*** 0.562 3.475** 0.231** (0.51) (0.77) (-3.96) (-2.58) (0.68) (2.09) (2.04) Industry FE Yes Yes Yes Yes Yes Yes Yes Year FE Yes Yes Yes Yes Yes Yes Yes Observations 11,219 11,219 11,219 11,219 11,219 11,219 11,219 Pseudo/ Adjusted R2

0.02 0.37 0.11 0.06 0.08 0.16 0.08

This table shows the impact of the localization rule on human capital. Panel A reports the results for the first signer of audit reports and Panel B reports the result for the second signer of audit reports. All variables are defined in Appendix A. All continuous variables are winsorized at the top and bottom one percent. *, **, and *** indicate two-tailed statistical significance at the ten, five, and one percent levels, respectively. Robust t-statistics (z-statistics) in parentheses. Standard errors are clustered by each unique client company in the sample.

45

Table 5 Big 4 client assignment by year

Panel A. Client assignment between new vs. old S1

Year

New Clients Existing Clients

Annual total clients

new S1

% audited by new

S1

old S1

% audited

by old S1

Total new

clients

new S1

% audited by new

S1

old S1

% audited

by old S1

Total existing clients

2005 0 0% 0 0% 0 0 0% 31 100% 31 31 2006 0 0% 0 0% 0 0 0% 31 100% 31 31 2007 0 0% 0 0% 0 0 0% 40 100% 40 40 2008 0 0% 0 0% 0 0 0% 46 100% 46 46 2009 0 0% 0 0% 0 0 0% 24 100% 24 24 2010 0 0% 0 0% 0 0 0% 53 100% 53 53 2011 0 0% 0 0% 0 0 0% 66 100% 66 66 2012 0 0% 0 0% 0 3 4% 69 96% 72 72 2013 1 25% 3 75% 4 12 14% 72 86% 84 88 2014 2 50% 2 50% 4 12 14% 75 86% 87 91 2015 2 29% 5 71% 7 17 17% 86 83% 103 110 2016 1 9% 10 91% 11 28 24% 90 76% 118 129 2017 7 30% 16 70% 23 32 27% 85 73% 117 140 2018 14 42% 19 58% 33 32 26% 93 74% 125 158

Total/Avg. 27 33% 55 67% 82 136 14% 861 86% 997 1,079 Old S1 refers to first signers who signed at least one audit reports before 2012, new S1 refers to first signers who started to sign audit reports in or after 2012. Existing clients refer to client companies whose first audit report was signed before 2012, and new clients refer to client companies whose first audit report was signed in or after 2012.

46

Panel B. Client assignment between new vs. old S2

Year

New Clients Existing Clients

Annual total clients

new S2

% audited by new

S2

old S2

% audited by old

S2

Total new

clients

new S2

% audited by new

S2

old S2

% audited by old

S2

Total existing clients

2005 0 0% 0 0% 0 0 0% 31 100% 31 31 2006 0 0% 0 0% 0 0 0% 31 100% 31 31 2007 0 0% 0 0% 0 0 0% 40 100% 40 40 2008 0 0% 0 0% 0 0 0% 46 100% 46 46 2009 0 0% 0 0% 0 0 0% 24 100% 24 24 2010 0 0% 0 0% 0 0 0% 53 100% 53 53 2011 0 0% 0 0% 0 0 0% 66 100% 66 66 2012 0 0% 0 0% 0 27 38% 45 63% 72 72 2013 3 75% 1 25% 4 45 54% 39 46% 84 88 2014 4 100% 0 0% 4 61 70% 26 30% 87 91 2015 6 86% 1 14% 7 85 83% 18 17% 103 110 2016 10 91% 1 9% 11 104 88% 14 12% 118 129 2017 21 91% 2 9% 23 95 81% 22 19% 117 140 2018 31 94% 2 6% 33 113 90% 12 10% 125 158

Total/Avg. 75 91% 7 9% 82 530 53% 467 47% 997 1,079 Old S2 refers to second signers who signed at least one audit reports before 2012, new S2 refers to second signers who started to sign audit reports in or after 2012. Existing clients refer to client companies whose first audit report was signed before 2012, and new clients refer to client companies whose first audit report was signed in or after 2012.

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Table 6 The localization rule and audit quality

Panel A: The localization rule and main audit quality measures Dependent Variables (1) (2) (3) (4) (5) Variables Abs_DA DA |POS_DA| |NEG_DA| AWCA BIG4 -0.001 -0.003 -0.002 0.004 -0.003 (-0.28) (-0.54) (-0.32) (0.82) (-0.49) POST 0.014*** -0.021*** -0.009* 0.031*** -0.028*** (2.79) (-5.51) (-1.79) (6.52) (-2.96) BIG4*POST -0.004 -0.006 -0.008 -0.003 -0.002 (-0.94) (-1.09) (-1.13) (-0.57) (-0.41) SIZE -0.004*** 0.007*** 0.004*** -0.006*** 0.006*** (-4.01) (7.72) (3.69) (-6.53) (4.85) LIQ -0.000 -0.003*** -0.001*** 0.001*** 0.004*** (-0.02) (-7.38) (-2.62) (4.07) (5.55) LEV 0.013* 0.016** 0.024*** -0.007 -0.016 (1.69) (2.52) (2.93) (-1.01) (-1.53) CFO -0.056*** -1.062*** -0.818*** 0.724*** -0.596*** (-2.88) (-83.65) (-29.65) (34.47) (-25.47) REC 0.006 -0.056*** -0.046*** 0.035*** 0.006 (0.65) (-7.74) (-5.19) (4.54) (0.57) INV 0.009 -0.038*** -0.055*** 0.017** -0.010 (0.93) (-4.84) (-4.69) (2.20) (-1.00) ROA -0.048* 0.966*** 0.762*** -0.741*** 0.490*** (-1.69) (52.58) (22.39) (-29.33) (14.45) LOSS 0.026*** 0.008*** 0.017*** -0.002 0.009* (8.77) (3.30) (3.99) (-0.68) (1.95) GROWTH 0.032*** -0.008*** 0.025*** 0.037*** -0.073*** (11.77) (-2.73) (6.97) (11.63) (-12.78) MTB 0.001*** -0.001*** -0.000 0.001*** -0.001** (3.37) (-3.41) (-0.06) (3.29) (-2.37) SOE -0.006*** 0.001 -0.003 -0.004** 0.002

48

(-3.45) (0.64) (-1.20) (-2.21) (1.06) INSTOWN -0.000 -0.000 -0.000*** 0.000 0.000*** (-0.11) (-1.50) (-3.14) (1.53) (3.49) FIRMAGE 0.003*** 0.002** 0.004*** -0.001 0.006*** (2.69) (2.13) (2.67) (-0.60) (3.28) S1Male 0.000 0.000 -0.001 0.000 0.003 (0.14) (0.01) (-0.70) (0.30) (1.25) S1OverseasUniv -0.001 -0.001 0.002 0.001 0.007 (-0.29) (-0.24) (0.22) (0.42) (1.14) S1ChinaTopUniv -0.001 -0.001 -0.001 -0.000 -0.005** (-0.99) (-0.77) (-0.81) (-0.20) (-2.49) S1Master -0.001 -0.003** -0.003* 0.003* -0.001 (-0.41) (-2.00) (-1.67) (1.74) (-0.25) S1ACCmajor -0.000 -0.000 -0.001 0.001 -0.001 (-0.06) (-0.22) (-0.50) (0.54) (-0.52) S1localCPAexp -0.000 0.000** -0.000 -0.000** 0.000 (-1.44) (2.11) (-0.05) (-2.29) (0.38) S1num_otherQ 0.002 0.002 0.004** -0.000 -0.000 (0.95) (1.30) (2.05) (-0.35) (-0.06) S2Male 0.003** -0.001 0.001 0.003*** -0.001 (2.18) (-0.53) (0.66) (2.65) (-0.76) S2OverseasUniv 0.004 0.003 0.011 0.007 0.025** (0.46) (0.20) (0.74) (0.85) (2.15) S2ChinaTopUniv 0.003** -0.002 0.003 0.004*** 0.001 (2.04) (-1.38) (1.36) (2.74) (0.58) S2Master 0.001 0.001 -0.001 0.001 -0.006** (0.48) (0.34) (-0.43) (0.47) (-2.28) S2ACCmajor 0.000 0.001 0.001 -0.001 0.001 (0.07) (0.80) (0.88) (-0.55) (0.34) S2localCPAexp 0.000 -0.000 -0.000 0.000* -0.000 (0.43) (-1.33) (-0.64) (1.76) (-0.07) S2num_otherQ -0.002 0.003 0.001 -0.003 0.002 (-0.85) (1.11) (0.35) (-1.34) (0.64)

49

Constant 0.113*** -0.092*** -0.033 0.105*** -0.109*** (5.84) (-4.77) (-1.28) (5.00) (-4.09) Industry FE Yes Yes Yes Yes Yes Year FE Yes Yes Yes Yes Yes Observations 11,219 11,219 5,564 5,655 11,219 Adjusted R2 0.17 0.60 0.46 0.51 0.19

This panel shows the impact of the localization rule on our main proxies of audit quality: discretionary accruals, abnormal working capital accruals. All variables are defined in Appendix A. All continuous variables are winsorized at the top and bottom one percent. *, **, and *** indicate two-tailed statistical significance at the ten, five, and one percent levels, respectively. Robust t-statistics in parentheses. Standard errors are clustered by each unique client company in the sample.

50

Panel B: The localization rule and alternative audit quality measures Dependent Variables (1) (2) (3) (4) Variables MAO RESTATE BL SP BIG4 -0.096 -0.843* 0.004 -0.239 (-0.18) (-1.82) (1.05) (-0.82) POST -1.109** -3.900*** 0.003 -0.463 (-2.12) (-5.61) (1.17) (-1.40) BIG4*POST 0.301 0.523 -0.001 -0.064 (0.51) (0.94) (-0.28) (-0.20) Client variables Included Included Included Included Two signer variables

Included Included Included Included

Industry FE Yes Yes Yes Yes Year FE Yes Yes Yes Yes Observations 11,219 11,219 11,219 11,219 Pseudo/ Adjusted R2

0.44 0.22 0.18 0.10

This panel shows the impact of the localization rule on alternative measures of audit quality. All variables are defined in Appendix A. All continuous variables are winsorized at the top and bottom one percent. Both client and audit partner control variables are included in the regressions but not tabulated for brevity. *, **, and *** indicate two-tailed statistical significance at the ten, five, and one percent levels, respectively. Robust t-statistics (z-statistics) in parentheses. Standard errors are clustered by each unique client company in the sample.

51

Table 7 New signer subsamples

Panel A Univariate difference-in-differences analyses

Mean Diff. in Means (1) (2) (3) (4) (5) (6) (7)

Big4 Pre Big4 Post Other top-10 Pre

Other top-10 Post

Column (2) -(1)

Mean Diff.

Column (4) -(3)

Mean Diff.

Column (5)-(6) Diff-in-

diff. New first signer subsample S1Male 0.667 0.683 0.769 0.756 0.016 -0.013 0.030 S1OverseasUniv 0.077 0.024 0.024 0.011 -0.053 -0.013 -0.040 S1ChinaTopUniv 0.615 0.561 0.488 0.462 -0.054 -0.026 -0.028 S1Master 0.026 0.146 0.266 0.241 0.121* -0.026 0.146 S1ACCmajor 0.462 0.439 0.565 0.545 -0.023 -0.020 -0.003 S1localCPAexp 8.667 11.341 10.701 13.524 2.675** 2.823 -0.148 S1num_otherQ 0.026 0.122 0.204 0.246 0.096 0.042 0.054 Observations 39 41 338 561 80 899 979

New second signer subsample S2Male 0.500 0.438 0.675 0.586 -0.062 -0.089*** 0.027 S2OverseasUniv 0.036 0.012 0.000 0.008 -0.025 0.008*** -0.032*** S2ChinaTopUniv 0.564 0.438 0.327 0.212 -0.126** -0.115*** -0.011 S2Master 0.155 0.271 0.157 0.123 0.117*** -0.034** 0.150*** S2ACCmajor 0.473 0.372 0.564 0.371 -0.101* -0.193*** 0.092 S2localCPAexp 4.964 5.333 6.318 5.693 0.370 -0.625*** 0.995* S2num_otherQ 0.045 0.016 0.101 0.036 -0.030 -0.065*** 0.035 Observations 110 258 594 1,846 368 2,440 2,808

52

Panel B The localization rule and human capital (i.e., characteristics of signers) New first signer subsample Dependent Variables (1) (2) (3) (4) (5) (6) (7) Variables S1Male S1OverseasUniv S1ChinaTopUniv S1Master S1ACCmajor S1localCPAexp S1num_otherQ BIG4 -0.845* 4.811*** 1.490*** -3.573*** -1.226*** -0.663 -0.072 (-1.95) (3.47) (3.52) (-2.81) (-3.21) (-0.93) (-1.28) POST 0.083 -2.788 -0.645 -1.294 -1.382** 7.029*** -0.141 (0.12) (-1.01) (-0.95) (-1.61) (-2.06) (5.67) (-1.59) BIG4*POST 0.365 0.105 -0.477 3.098** 0.509 -1.771* 0.025 (0.71) (0.03) (-0.87) (2.27) (0.98) (-1.67) (0.29) Client variables Included Included Included Included Included Included Included First signer variables

Included Included Included Included Included Included Included

Industry FE Yes Yes Yes Yes Yes Yes Yes Year FE Yes Yes Yes Yes Yes Yes Yes Observations 979 979 979 979 979 979 979 Pseudo/ Adjusted R2

0.07 0.49 0.10 0.13 0.07 0.23 0.15

New second signer subsample Dependent Variables (1) (2) (3) (4) (5) (6) (7) Variables S2Male S2OverseasUniv S2ChinaTopUniv S2Master S2ACCmajor S2localCPAexp S2num_otherQ BIG4 -0.739*** 21.879*** 1.049*** -0.726** -0.588** -1.117*** -0.016 (-2.93) (8.74) (4.02) (-2.06) (-2.40) (-2.62) (-0.59) POST -1.286*** 16.261*** -1.630*** -0.578 -1.101*** 0.683 -0.054 (-2.84) (8.77) (-3.67) (-0.93) (-2.70) (0.96) (-1.18) BIG4* POST 0.244 -20.928*** 0.185 1.586*** 0.623** 0.510 0.011 (0.87) (-7.32) (0.63) (4.13) (2.15) (1.09) (0.40)

53

Client variables Included Included Included Included Included Included Included Second signer variables

Included Included Included Included Included Included Included

Industry FE Yes Yes Yes Yes Yes Yes Yes Year FE Yes Yes Yes Yes Yes Yes Yes Observations 2,808 2,808 2,808 2,808 2,808 2,808 2,808 Pseudo/ Adjusted R2

0.03 0.56 0.14 0.09 0.10 0.18 0.09

54

Panel C – The localization rule and audit quality

New first signer subsample Dependent Variables (1) (2) (3) (4) (5) Variables Abs_DA DA |POS_DA| |NEG_DA| AWCA BIG4 -0.000 0.007 -0.015 0.039** 0.021 (-0.01) (0.56) (-1.39) (2.07) (0.95) POST -0.008 -0.011 -0.015 0.018 -0.018 (-0.54) (-0.90) (-1.08) (1.03) (-0.48) BIG4*POST -0.010 -0.018 0.023 -0.043** -0.028 (-0.74) (-1.18) (1.61) (-2.06) (-1.04) Client variables

Included Included Included Included Included

Two signer variables

Included Included Included Included Included

Industry FE Yes Yes Yes Yes Yes Year FE Yes Yes Yes Yes Yes Observations 979 979 506 473 979 Adjusted R2 0.23 0.51 0.45 0.45 0.18

New second subsample Dependent Variables (1) (2) (3) (4) (5) Variables Abs_DA DA |POS_DA| |NEG_DA| AWCA BIG4 -0.002 -0.007 -0.012 0.010 -0.014 (-0.23) (-0.84) (-1.25) (1.31) (-1.26) POST -0.006 -0.030*** -0.022** 0.026** -0.047** (-0.68) (-3.79) (-2.44) (2.54) (-2.20) BIG4*POST -0.001 -0.002 0.009 -0.004 0.009 (-0.10) (-0.29) (0.88) (-0.54) (0.78) Client variables

Included Included Included Included Included

Two signer variables

Included Included Included Included Included

Industry FE Yes Yes Yes Yes Yes Year FE Yes Yes Yes Yes Yes Observations 2,808 2,808 1,367 1,441 2,808 Adjusted R2 0.18 0.58 0.45 0.49 0.20

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Table 8 Excluding new audit engagements (with first appearance in or after 2012)

Panel A – The localization rule and human capital (i.e., characteristics of signers) Specific to first signer characteristics Dependent Variables (1) (2) (3) (4) (5) (6) (7) Variables S1Male S1OverseasUniv S1ChinaTopUniv S1Master S1ACCmajor S1localCPAexp S1num_otherQ BIG4 -0.143 3.170*** 0.690*** -1.156*** -0.590** -1.484*** 0.001 (-0.62) (7.04) (3.23) (-3.29) (-2.43) (-4.12) (0.02) POST 0.332 -2.081** -0.977*** -1.435*** -2.294*** 7.334*** 0.016 (1.14) (-2.21) (-3.58) (-4.28) (-8.28) (17.80) (0.38) BIG4*POST -0.280 -0.203 0.416* 0.512* 0.312 0.241 -0.024 (-1.17) (-0.45) (1.88) (1.72) (1.25) (0.61) (-0.48) Client variables

Included Included Included Included Included Included Included

First signer variables

Included Included Included Included Included Included Included

Industry FE Yes Yes Yes Yes Yes Yes Yes Year FE Yes Yes Yes Yes Yes Yes Yes Observations 5,803 5,803 5,803 5,803 5,803 5,803 5,803 Pseudo/ Adjusted R2

0.03 0.34 0.04 0.07 0.08 0.30 0.04

Specific to second signer characteristics Dependent Variables (1) (2) (3) (4) (5) (6) (7) Variables S2Male S2OverseasUniv S2ChinaTopUniv S2Master S2ACCmajor S2localCPAexp S2num_otherQ BIG4 -0.613*** 6.082*** 1.211*** -0.714** -0.575*** -1.149*** -0.093*** (-3.08) (3.31) (5.55) (-2.50) (-2.73) (-3.01) (-3.09) POST -0.561** -2.921 -1.394*** -0.612 -1.596*** 3.350*** -0.227*** (-2.15) (-0.98) (-5.01) (-1.53) (-6.01) (6.93) (-3.77) BIG4* POST -0.021 -3.201* 0.178 1.607*** 0.531** -0.671 0.095*** (-0.10) (-1.92) (0.75) (5.14) (2.12) (-1.51) (3.10)

56

Client variables

Included Included Included Included Included Included Included

Second signer variables

Included Included Included Included Included Included Included

Industry FE Yes Yes Yes Yes Yes Yes Yes Year FE Yes Yes Yes Yes Yes Yes Yes Observations 5,803 5,803 5,803 5,803 5,803 5,803 5,803 Pseudo/ Adjusted R2

0.03 0.51 0.10 0.07 0.07 0.15 0.08

57

Panel B – The localization rule and audit quality

Main audit quality measures Dependent Variables (1) (2) (3) (4) (5) Variables Abs_DA DA |POS_DA| |NEG_DA| AWCA BIG4 -0.003 -0.007 -0.006 0.005 0.005 (-0.70) (-1.15) (-0.75) (0.97) (0.75) POST 0.007 -0.021*** -0.012* 0.021*** -0.013 (1.06) (-3.88) (-1.68) (3.49) (-1.24) BIG4*POST -0.003 -0.003 -0.005 -0.003 -0.001 (-0.69) (-0.57) (-0.68) (-0.69) (-0.18) Client variables

Included Included Included Included Included

Two signer variables

Included Included Included Included Included

Industry FE Yes Yes Yes Yes Yes Year FE Yes Yes Yes Yes Yes Observations 5,803 5,803 2,835 2,968 5,803 Adjusted R2 0.20 0.54 0.43 0.48 0.23

Alternative audit quality measures Dependent Variables (1) (2) (3) (4) Variables MAO RESTATE BL SP BIG4 0.031 -1.042** 0.003 -0.051 (0.05) (-2.21) (0.95) (-0.17) POST -1.268* -2.589*** -0.002 -0.432 (-1.79) (-4.09) (-0.66) (-1.06) BIG4*POST 0.839 1.081* 0.000 -0.208 (1.26) (1.87) (0.04) (-0.64) MAOt-1 3.598*** (11.50) RESTATEt-1 2.604*** (11.32) Client variables Included Included Included Included Two signer variables

Included Included Included Included

Industry FE Yes Yes Yes Yes Year FE Yes Yes Yes Yes Observations 5,803 5,803 5,803 5,803 Pseudo/ Adjusted R2

0.51 0.24 0.21 0.10

58

Appendix A. Variable definition

Variable Definition BIG4 Indicator variable equals one if the engagement is signed off by one of

Big 4 audit firms, and zero otherwise. POST Indicator variable equals one if the audit report is issued for fiscal year

2012 or any year thereafter, and zero otherwise. Audit quality variables: Abs_DA Absolute value of discretionary accruals (DA), measured as the residual

from the modified Jones model, adjusted by controlling for operating performance in last period (ROAt-1) (Kothari et al., 2005).

DA Signed value of discretionary accruals (DA), measured as the residual from the modified Jones model, adjusted by controlling for operating performance in last period (ROAt-1) (Kothari et al., 2005).

|POS_DA| Absolute value of positive DA. |NEG_DA| Absolute value of negative DA. AWCA Abnormal working capital accruals, scaled by average total assets in year

t and year t-1. MAO Indicator variable equals one if the client company receives a modified

audit opinion (including unqualified with explanatory language, qualified, disclaimer, and adverse), and zero otherwise.

RESTATE Indicator variable equals one if the financial report of a client-year observation is subsequently restated, and zero otherwise.

BL Sum of investment net income, profits from other operations, and non-operating net income, scaled by total assets by the end of year t-1.

SP Indicator variable equals one if the client company reports ROA between

0 and 0.01 for small positive profits, and zero otherwise. Partner information variables: S1PortSize Number of engagements the first signer takes in year t, scaled by the total

number of engagements signed by the same audit firm in the same year. S2PortSize Number of engagements the second signer takes in year t, scaled by the

total number of engagements signed by the same audit firm in the same year.

AvgPortSize Average values of S1PortSize and S2PortSize. Male Indicator variable equals one if the signer is male, and zero otherwise. OverseasUniv Indicator variable equals one if the signer was graduated from overseas

university, and zero otherwise. ChinaTopUniv Indicator variable equals one if the signer was graduated from prominent

Chinese university (985 or 211 universities), and zero otherwise. Master Indicator variable equals one if the signers' highest degree level is higher

than or equal to Masters, and zero otherwise. ACCmajor Indicator variable equals one if the signer was majored in accounting,

and zero otherwise. LocalCPAexp Number of years that an individual signer has been signing audit reports. Num_otherQ Number of other professional qualifications a given signer has.

59

Client characteristics variables: SIZE Natural logarithm of total assets. LIQ Current assets/current liabilities. LEV Total liabilities/total assets. REC Receivables/total assets. INV Inventory/total assets. ROA Net profit/total assets. LOSS Loss indicator, equals one if net profit is less than zero, and zero

otherwise. SOE Indicator variable equals one if a client is ultimately controlled by the

government, and zero otherwise. GROWTH (SALESt – SALESt-1) / SALESt-1, where SALES is the client’s sales

revenue. CFO Cash flows from operations/total assets. MTB Market value/book equity. INSTOWN Percentage of shares held by institutional investors. FIRMAGE The log value of the number of years the client company is listed on a

stock exchange. RESTATEt-1 Indicator variable equals one if the financial report of a client in year t-

1 is subsequently restated, and zero otherwise. MAOt-1 Indicator variable equals one if the client company receives a modified

audit opinion in year t-1, and zero otherwise.

60

Appendix B. The localization rule compliance by the end of 2018 and foreign partners

B1. The localization rule compliance (distribution of CICPA vs. non-CICPA licensed partners) by the end of 2018

Total CICPA Non-

CICPA Percentage of

CICPA KPMG 135 113 22 85.99% EY 163 132 31 80.98% PWC 174 129 45 74.13% DTT 150 120 30 80.00% # of Big-4 partners by the end of 2018 622 494 128 79.42%

# of other top-10 partners by the end of 2018 1,081 1,076 5 99.54% Total # of top-10 partners by the end of 2018 1,703 1,570 133 92.19%

61

B2. Examples of foreign partners background page on Chinese Big 4’s official websites

Examples of foreign partner appointed before the localization rule

62

An example of local partner appointed before the localization rule:

An example of local partner appointed after the localization rule: