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Migration, Ability and Audit Quality:
Evidence from Audit Partners in China*
Wen He
UQ Business School
University of Queensland
Chao Kevin Li
School of Accounting
UNSW Business School
UNSW Sydney
Yi Si†
School of Management
Xiamen University
March 2019
* We thank Pietro A. Bianchi, Kath Herbohn, Shushu Jiang, Jeong-bon Kim, Tina Gao, Liao Lin, Xinming Liu,
Jamie Tong, Chongwu Xia, Yang Xu, Xiao’ou Yu, Feida Zhang, Zhen Zheng, and seminar participants at 2019
AAA IAS Mid-year Miami Conference, Deakin University, Guangdong University of Finance and Economics,
Southwest University of Finance and Economics, University of Queensland, Xi’an Jiaotong University and
Xiamen University for helpful comments. Yi Si appreciates the financial support from the National Natural
Science Foundation of China (Grant Number: 71672141, 71602160, 71802171) and Chinese Universities
Scientific Fund (Grant Number: 20720191027). † Corresponding author.
Migration, Ability and Audit Quality:
Evidence from Audit Partners in China
Abstract
Research in labor economics proposes that migrants tend to be a group of self-selected individuals and
people with higher human capital migrate to more economic developed areas where wages are higher.
Based on this insight, we argue that university graduates who migrate to a more economically developed
city have higher human capital, relative to their peers staying in the city of the university where they
studied. When these upward migrating graduates become auditors, they provide higher quality audits.
Using data from China and multiple measures of audit quality, we find evidence supporting the
argument.
JEL Classifications: G41
Keywords: Audit quality; human capital; auditing; audit partner; China
1
1. Introduction
The importance of human capital for the auditing industry has been well recognized by audit
firms, the regulators and academics. For example, PWC (2018, p8) states “Our reputation depends on
our people”. The Public Company Accounting Oversight Board (PCAOB, 2013, p4) highlights that “the
audit inputs include six elements, each related to competent and talented people, who are essential for
audit quality”.1 A few studies have documented that audit quality is related to audit partners’ experience,
education background and cognitive ability (e.g., Chin and Chi 2009; Gul et al. 2013; He et al. 2018;
Kallunki et al. 2018; Knechel et al. 2015), audit personnel salaries (Hoopes et al. 2018; Knechel et al.
2013), and the aggregate supply of human capital in the local city where audit offices are located (Beck
et al. 2018). In this study, we extend this literature by examining a novel aspect of audit partners’ human
capital reflected in their decisions to migrate to another city to start their professional career after
obtaining academic degrees.
Our study is motivated by an insight from research on migrants in labor economics that migrants
are a group of self-selected individuals with differential human capital. In economics literature, human
capital is broadly defined to conceptualize individuals’ knowledge, education, skills, ability, training
and experience (Becker 1962). Chiswick (1999, p181) points out that “One of the standard propositions
in the migration literature is that migrants tend to be favourably ‘self-selected’ for labor-market success.
That is, economic migrants are described as tending, on average, to be more able, ambitious, aggressive,
entrepreneurial, or otherwise more favourably selected than similar individuals who choose to remain
in their place of origin.” The idea of migrants self-selecting to move to places with higher salaries and
1 The six elements are hiring and utilization, team-work, professional experience, training, review, and workloads.
Audit inputs are one of the three dimensions of audit quality in PCAOB’s Audit Quality Framework, the other
two dimensions are audit processes and results (PCAOB, 2013).
2
more job opportunities2 has been well studied in the literature (e.g., Roy 1951; Borjas 1987, 1991, 1999;
Greenwood 1985, 2005). For example, Chiswick (1999) uses an analytical model to show that,
assuming wages increase with ability and a constant cost of migration, both high ability and low ability
individuals choose to move to places where their ability can earn them higher wages. Empirical
evidence shows that immigrants from developing countries (e.g., Mexico) to developed countries (e.g.
U.S.A.) are more educated than non-immigrants and earn more than they could in their home country
(Chiquiar and Hanson 2005). Internal migrants have a similar pattern. While migrants have, on average,
a growth in wages (Böheim and Taylor 2007), Kopi and Clark (2015) use data from Sweden to show
that most of the wage growth for internal migrants is captured by those who are higher educated and
moving to the largest metropolitan regions. The evidence suggests that individuals’ human capital
including education and ability are important determinants of migration decisions and individuals with
higher human capital are more likely to move to more economically developed cities and countries.
Our setting is China where there are substantial variations in wages and economic development
across regions, and the average wage is much higher in more economically developed regions,
particularly for people working in the finance industry. Data from the National Bureau of Statistics of
China show that in 2016 the average annual salary in the finance industry is 239,085 yuan in Beijing
(the capital and one of the most economically developed cities) versus 60,252 yuan in Gansu (one of
the least economically developed provinces).3 Higher wages also suggest more competition: there are
over 500,000 workers in the finance industry in Beijing versus 76,000 in Gansu. The large wage
2 There are also other motives for migration, such as lifestyle, family reasons and religious beliefs (Greenwood
2005).
3 For comparison, the average wage for all industries in Beijing is 119,928 yuan, about twice the average wage
in Gansu (57,575 yuan) in 2016.
3
differences and strong competition suggest that high ability individuals in the finance industry in Gansu
are likely to move to Beijing where their ability is paid more. Furthermore, more economically
developed cities receive more investment, have better infrastructure, have better human capital, and
have more favourable policies for domestic and foreign business investors (Xing and Zhang 2017). In
more economically developed regions, promotions and career advances are more likely to be based on
merit and abilities rather than family or kin relationship (Allen et al. 2005; Lu and Hu 2014; Zai et al.
2014), which is particularly attractive to people with a higher ability who intend to move to a new city
where they have few social connections. Therefore, we argue that accounting graduates who move to
more economically developed cities to start their career are likely to have higher ability than those stay
in the same city where they studied at university. Following Chiswick (1999), we define ability broadly
including innate abilities such as ambition, entrepreneurship, and learning and adaptive ability. Our
main prediction is that when these upward migrating graduates become a senior auditor or an audit
partner in charge of audit engagements, their better ability helps them deliver audits of higher quality.
There are also cases where graduates move to cities that are less economically developed than their
university cities.4 We conjecture that these downward migrating graduates have a lower ability and
deliver lower quality audits when they become auditors.
Using education and employment background data for 2,917 engagement auditors who can sign
clients’ financial statements, we first validate the argument that upward migrating graduates have a
higher ability. Assuming that individuals with a higher ability are likely to progress faster in their career
paths, we predict that upward migrating graduates are more likely to have a larger portfolio of clients,
4 In our sample of graduates who become an engagement auditor in later years, 34.9% of graduates move to a
more economically developed city, while 11.3% of graduates move to a less economically developed city.
4
become an engagement auditor in a shorter time after obtaining their CPA certificate, and become an
audit partner at the end of our sample period (2016).5 The empirical results support all these predictions.
There is also some evidence that downward migrating graduates have a smaller portfolio of clients
within their audit firms. Overall the results support the assumption that upward migrating graduates
have a higher ability.
We then examine whether upward and downward migrating graduates are differentially
associated with two measures of audit quality, including clients’ performance-adjusted discretional
accruals and the likelihood of earnings restatement. The results show that clients with upward
(downward) migrating graduates have less (more) discretionary accruals and are less (more) likely to
restate their earnings. The results are obtained after we control for a number of client characteristics,
audit firm characteristics, university fixed effects, and year and industry fixed effects. We conduct a
range of robustness tests and obtain consistent results from tests using audit firm fixed effects to further
control for the effect of audit firms and using client location (province) fixed effects to control for
institutional factors in provinces where clients are located. We also use client fixed effects to examine
the effect of time-series variation in auditors’ ability (measured by upward and downward migration)
on audit quality of a given client. The results are consistently robust, suggesting that it is likely that
auditor ability has a distinct effect on audit quality.
We proceed to examine a few moderating factors that can affect the association between
graduate migration and audit quality. First, we consider gender and argue that, given the social prejudice
against women, it is more difficult for women to migrate upward. Therefore, female graduates who do
5 In China, both audit partners and senior audit managers can lead an engagement and sign clients’ annual reports
(Lennox et al. 2014; Lennox and Wu 2018). While audit partners have some ownership of the audit firm, audit
managers do not, so an audit partner is one level higher than an audit manager in the hierarchy in audit firms.
5
migrate upward and successfully become signing auditors are more likely to have better ability. Second,
we consider university reputation and argue that it is also more difficult for graduates from less
prestigious university to migrate upward because their universities do not provide a strong accreditation
of their superior ability. Consequently, upward migration provides a stronger signal of superior ability
of graduates from non-prestigious universities. In contrast, downward migrating graduates from
prestigious universities are likely to have a lower ability since they could not find a satisfactory job in
their university cities even with the help of the high reputation of their universities. Consistent with
these arguments, we find that upward migrating female graduates are associated with higher audit
quality. Upward migrating graduates from non-prestigious universities are related to higher audit
quality, while downward migrating graduates from prestigious universities are related to lower audit
quality. Third, we examine clients’ characteristics and expect that the impact of auditors’ ability on
audit quality is stronger when audit engagements are complex and challenging. Consistent with this
expectation, we find that the association between graduate migration and audit quality is stronger for
clients that have more business segments or have more industry specific noise in earnings as measured
by Francis and Gunn (2017).
Finally, we provide evidence that clients and investors seem to understand the differential
ability of migrating graduates. Downward migrating graduates receive lower audit fees for their
engagement, relative to their peers. Investors’ response to earnings surprises is stronger for clients with
an upward migrating graduate as the engagement auditor but weaker for clients hiring a downward
migrating graduate.
Our study contributes to the literature in two ways. First, we add to the growing literature
examining auditors’ human capital and audit quality. Francis (2011, p134) posits that “audits are of
6
higher quality when undertaken by competent people”. Although a number of experimental studies have
demonstrated that auditors’ cognitive ability and problem-solving ability affects auditors’ performance
in audit tasks (e.g., Libby and Tan 1994), archival evidence is limited, with one exception being a recent
study by Kallunki et al. (2018) who find a positive association between audit partners’ IQ scores and
measures of audit quality in Sweden. While Kallunki et al. (2018) focus on cognitive ability captured
by IQ scores, our study shows that the general ability of auditors has an impact on the quality of audits.
Furthermore, our study adds to the growing literature on the effect of individual auditors’ characteristics
on audit quality, as surveyed by Lennox and Wu (2018). These characteristics include audit partners’
expertise (Chin and Chi 2009; Knechel et al. 2015), early experience of economic recessions (He et al.
2018), gender (Ittonen et al. 2013; Cameran et al. 2016), and criminal records (Amir et al. 2014). These
studies enrich the early evidence based on the significant partner fixed effects in multivariate
regressions (e.g., Gul et al. 2013), and answer the call for more understanding of auditors who conduct
audits (Francis 2011; DeFond and Zhang 2014).
Second, our study also adds to a growing number of studies linking labor economics to
accounting. While it is well recognized that migrants have different ability and skills, the literature on
migrants mainly focuses on the determinants and consequences of migrants or immigrants in general
(see Grenwood 1985 and Borjas 1999 for a review of migration literature). Recent cross-disciplinary
studies examine the impact of immigrants on domestic audit and accounting job markets. Aobdia et al.
(2016b) find widespread employment of foreign-born graduates in Big N audit firms in the U.S. and
the immigrants complement the jobs of native graduates. Aobdia and Srivastava (2018) find that
employment of immigrants in the audit industry does not depress the wages of native auditors in the
U.S. We take a distinct approach by applying the insight that migrants are a group of self-selected
7
individuals to the setting of auditors. We validate the assumption that migration provides an observable
signal of auditor quality, and we show that upward (downward) migrating graduates provide higher
(lower) quality audits when the graduates become audit managers or partners.
The remainder of the paper proceeds as follows. Section 2 reviews the related studies and
develops the predictions. Section 3 describes the research design, the data and sample. Section 4 reports
the empirical results and Section 5 concludes.
2. Related Studies and Main Prediction
2.1 Auditor ability and audit quality
Auditing is a complex process and it is essential for auditors to make judgments and decisions
at all stages of the audit engagement (Hogarth 1991). Individual auditors’ traits, including their ability
and personality, knowledge and incentives can impact their judgments and decisions (Nelson and Tan
2005; Nelson 2009). Prior studies provide some evidence from experiments that individual auditors’
ability affects auditors’ performance. For example, Bonner and Lewis (1990) argue that auditors’
expertise is determined by their knowledge and ability. Their experimental results show that knowledge
and innate ability explain auditors’ performance in audit tasks better than experience. Libby and Tan
(1994) find that auditors’ problem-solving ability, assessed using GRE questions, affects auditors’
performance in audit tasks as well. Tan and Libby (1997) show that staff and senior auditors with higher
cognitive ability receive superior performance evaluations, particularly for their work on complex tasks.
McKnight and Wright (2011) also find that high performing auditors have better technical knowledge
and ability than low performing auditors.
8
Kallunki et al. (2018) use IQ score to measure auditors’ cognitive ability. Using archival data
from Sweden, they find a positive association between audit partners’ IQ scores and the accuracy of
going-concern opinion. High IQ audit partners are also associated with less income-increasing accruals,
but with higher audit fee premium. Their evidence suggests that auditors with high cognitive ability
deliver high quality audit services.
The effect of individual auditors’ characteristics on audit outcomes could be constrained by the
quality control mechanism in audit firms. Audit firms, particularly large ones, have systematic control
mechanisms in place to reduce individual auditors’ idiosyncratic effects and maintain consistency in
audit quality. However, the studies that find individual auditors’ characteristics impact audit quality
usually control for audit firm fixed effects (e.g., Gul et al. 2013; Kallunki et al. 2018), suggesting that
the control mechanisms in audit firms do not eliminate the idiosyncrasies of auditors. Furthermore, the
evidence shows that clients pay a higher fee for high quality audit partners (Kallunki et al. 2018) and
industry specialist partners (Zerni 2012; Goodwin and Wu 2014; Aobdia et al. 2016a), and investors
react more strongly to earnings surprises of clients of high quality audit partners. The evidence suggests
that both clients and investors can differentiate the quality of individual audit partners within an audit
firm.
In the setting of China, a few recent studies have documented that clients’ audit quality is
systematically related to characteristics of engagement auditors who sign clients’ annual reports. Gul et
al. (2013) find partner fixed effects have incremental explanatory power for variations in measures of
audit quality after controlling for audit firm fixed effects. Auditor characteristics such as education
background, Big N experience and political affiliation partially explain the effect of individual auditors.
Lennox et al. (2014) find that rotation of engagement partners within the same audit firm results in more
9
audit adjustments. Chen et al. (2016) find that clients can successfully engage in partner-level opinion
shopping. Li et al. (2017) show that auditors who had audit failures also deliver lower quality audits on
their other audit engagements, while audit quality of other partners in the same office or audit firms
does not seem to be negatively affected by the audit failure. Guan et al. (2016) and He et al. (2017) find
that audit partners’ social connections with client executives or audit committee members impair audit
quality. Aobdia et al. (2015) find that the differences in audit quality associated with partner fixed
effects are related to the earnings response coefficients of client firms, suggesting investors can identify
and value the quality of audit partners.
2.2 Migration and ability
Migrants are not a random group of people. Instead, as recognized in labor economics research,
individuals self-select to become migrants after calculating the costs and benefits of migration (see
Greenwood 1985 and Borjas 1999 for a review of literature on migration). The key benefit is the higher
wage or more job opportunities in destinations, while the costs include direct costs of relocation,
temporary loss of earnings and uncertainty. Chiswick (1999), among many others, posits that migrants,
on average, are more able, ambitious, aggressive and entrepreneurial than those choosing to stay in the
original place. Chiswick (1999) broadly defines ability as multi-faceted attributes including ambition,
entrepreneurial skills, aggressiveness and tenacity. If earnings increase with ability and there are out-
of-pocket migration costs, his model predicts that high ability individuals are more likely to migrate.
Assuming a constant cost of migration for high and low ability individuals, he shows that migration
incentives are determined by the ratio of wages in the destination relative to the origin. If this ratio is
higher for high-ability than for low-ability individuals, then those with high ability have a greater
incentive to migrate. However, if the ratio is greater for low-ability individuals, they would have a
10
greater propensity to migrate. The model in Chiswick (1999) thus predicts that individuals migrate to
places where their ability gets paid more.6
Empirical evidence that migrants on average earn more than they could in their original places
supports this argument (Chiquiar and Hanson 2005; Böheim and Taylor 2007). Furthermore, Kopi and
Clark (2015) find that most of the wage growth is captured by highly educated migrants and by those
moving to the largest metropolitan cities, suggesting that high skills earn higher returns in the largest
cities.
In the past decades, restrictions on migration within China have been greatly eased, resulting
in the population of internal migrants growing from 21.35 million in 1990 to 253 million in 2015 (Zhao
et al. 2018). Most migrants move from rural to urban areas, and internal migrants tend to be male,
younger and better educated (Zhao 2005). A key reason for rural-to-urban migration is the higher wages
in cities, and migrants prefer larger metropolitan cities (Xing and Zhang 2017). There are significant
differences in economic development and average wages across provinces and cities. According to data
released by the National Bureau of Statistics of China, the average wage in Beijing is about twice the
average wage in Gansu. The wage difference in the finance industry is even larger, with the average
wage in Beijing four times higher than the average in Gansu. As a result, there has been a ‘brain drain’
6 Borjas (1987) examines the effect of wage differentials on immigration incentives. He shows that if wages in
immigrants’ home country are sufficiently positively correlated with wages in the U.S. but distributed more
unequally in the home country, low income people are more likely to immigrate to the U.S. because the more
even wage distribution in the U.S. implies that low income workers get “insured” against negative labor market
outcomes while high income workers get “taxed”. Since most third-world countries have more uneven income
distribution than the U.S., Borjas’ (1987) model predicts that the U.S. will attract low income immigrants from
third-world countries. However, Chiquiar and Hanson (2005) show that Mexico emigrants to the U.S. have higher
education and better skills than non-emigrants and earn more in the U.S. than they could in Mexico. The evidence
does not support the prediction of Borjas (1987).
11
in the less economically developed western provinces because talent moves to eastern provinces where
wages are higher.
In our study, we focus on accounting graduates who complete their undergraduate degree and
decide where to start their professional career. Chinese universities are all located in cities, particularly
in large cities. Almost all the most prestigious Chinese universities are located in the capital city of a
province, which are usually the cultural and economic centers in the region.7 Entry to universities is
almost exclusively based on the students’ score in the nation-wide university admission tests,8 so
students in the same university study the same major usually have comparable academic performance
in the tests. Upon graduation, graduates are likely to stay in the same city to get a job, given the cost of
migration such as travel cost for job interviews, relocation cost, and cost of learning about new cities.
In our sample, about 55% of graduates chose to stay. Those who move to a different city consider the
benefits and costs of migrating relative to staying. Apart from family or religious reasons, we argue that
accounting graduates with higher ability are more likely to migrate to a city that is more economically
developed than their university city for two reasons. First, high ability graduates are likely to find a
good job (e.g., an audit assistant) in a city, but the wage for the job is much higher in the more
economically developed city so higher wages will motivate high ability graduates to migrate to more
economically developed cities (Chiswick 1999). While the higher wage is also attractive to low ability
graduates, they will find it more difficult to find a good job in more economically developed cities
7 Mainland China has 22 provinces, 4 municipalities, and 5 autonomous areas, all of which are at the same level
of administrative power in China’s government hierarchy. For brevity, we refer to them all as provinces.
8 Some universities give a very small number of places to students with special talent.
12
because of intensified competition in the job market there.9 Second, in more economically developed
areas, business practice is more market oriented and based on contracts, and promotions and career
advancements are based more on ability and merit rather than kin and political relations. This is
particularly attractive to high ability graduates who have fewer social connections in new cities.10
On the other hand, it is possible that low ability graduates find it is better for them to migrate
to a less economically developed city. Although they receive a lower wage, they could have a
competitive advantage in the job market in less developed cities. In other words, low ability graduates
could have difficulty in finding a job in their university city but become competitive in a less
economically developed city.
Therefore, we argue the direction of migration could reveal the innate ability of graduates, with
upward migrating graduates (those moving to a more economically developed city) having higher
ability and downward migrating graduates having lower ability. Following Chiswick (1999), we define
ability broadly including traits such as ambition, entrepreneurship and aggressiveness. These personal
traits are important for graduates’ success in their career, in addition to technical knowledge and
cognitive ability that have been studied in prior studies (e.g., Bonner and Lewis 1990; Kallunki et al.
2018). For example, ambition would motivate graduates to work harder and excel in job performance.
Entrepreneurship can help auditors expand their knowledge base by self-studying and acquire more
9 This assumes that employers can differentiate high and low ability graduates, which we believe is a reasonable
assumption given the rigorous recruitment practices in accounting and the auditing industry in economically
developed regions.
10 Some of graduates were born and brought up in areas or cities different from their university city, so they have
already migrated once before graduation. Like migration after graduation, students are more likely to migrate to
a more economically developed city for university education. It is unclear whether these migrating students are
more or less likely to migrate once again to start their career, relative to students who attend high schools and
universities in the same city. Due to data limitation, we cannot identify these migrating students and test if they
have different migration decisions after universities.
13
clients by exercising initiative. The downside of this broad definition, however, is that we could not
identify which specific types of ability underlie the auditors’ judgment and contribute to their success.
2.3 Main prediction
To the extent that upward (downward) migrating graduates have higher (lower) ability, we
expect that when they become senior auditors or audit partners leading audit engagements, the
differences in ability will be reflected in the quality of their audits. Based on prior findings that high
ability auditors deliver high quality audits (Libby and Tan 1994; Kallunki et al. 2018), we have the
following hypothesis stated in alternative form:
Hypothesis: Upward migrating graduates will produce higher quality audits while downward
migrating graduates will deliver lower quality audits, relative to non-migrating
graduates.
3. Research Design, Data and Sample
3.1 Research design
Our hypothesis is based on the premise that graduates’ migration status provides a signal of
their innate ability. We construct two indicator variables to capture graduates’ migration directions: UP
for upward migration and DOWN for downward migration. In particular, UP (DOWN) equals 1 when a
graduate who later becomes an engagement auditor starts their career in a more (less) economically
developed province relative to the province where the graduate’s university is located. To capture the
economic development of provinces and cities, we use an index developed by Fan and Wang (2003) ,
Fan, Wang and Zhu (2011, 2016) who measure the degree of marketization of a province on five aspects,
including the importance of local government in the economy, the importance of non-state-owned
14
businesses, the degree to which prices are determined by the market, the development of financial and
labor markets, and the development of market intermediaries (lawyers and accountants) and legal
environment. Fan and Wang (2003), Fan, Wang and Zhu (2011, 2016) give a score to each aspect and
use the weighted average of the scores to construct the index each year. The index has been normalized
to be in the range of 0 to 10, with a higher value indicating a higher level of economic development.
The index provides a relatively comprehensive measure of economic development and has been widely
used in prior studies (e.g., Wang, Wong and Xia 2008; Guan et al. 2016). We use the updated index for
the years between 1997 to 2014. 11 We also use some more traditional measures of economic
development in a province, including GDP per capita, GDP per capita in the finance industry, and the
number of listed firms. The results are largely consistent with or stronger than those reported in the
tables.
We start with an examination of the association between graduates’ migration directions (both
upward and downward migration) and some measures of auditors’ career outcomes, to provide evidence
that migration is related to auditors’ ability. Since it is difficult to directly observe and measure auditors’
ability in archival data, we examine their career outcomes based on the assumption that high ability
auditors are likely to have a fast-track in their professional career. Given the competitive nature of the
professional labor market, we think this is a reasonable assumption and expect high ability graduates
are more likely to audit important clients, become an audit partner, and become a signing auditor in
shorter time, relative to their peers who graduate from the same university in the same year. Accordingly,
we construct three measures of auditors’ career outcomes, including CPASIGN, AFPORTRANK and
11 Following Guan et al. (2016), we use the index value of 1997 for years before 1997 and the value of 2014 for
years after 2014, since the index and marketization process tend to be stable in adjacent years.
15
PARTNER. CPASIGN is the number of years between the year when an auditor obtained the CPA
qualification12 and the first year when they signed an annual report. A smaller CPASIGN indicates that
an auditor takes less time to be qualified as a signing auditor. AFPORTRANK is the quintile ranking of
an audit firm's clients according to total assets of each client, with the ranking conducted within the
audit firm in the year when an auditor started signing clients' annual reports. A larger AFPORTRANK
(from 4 to 0) indicates that an auditor has audited a larger client in the audit firm. PARTNER equals 1
if a signing auditor is an audit partner in 2016 (the last year in our sample period), and 0 otherwise.
Using data on individual signing auditors, we empirically estimate the following regression
models to investigate the career outcomes of migrating graduates:
CAREER = α0 +α1UP + α2DOWN + Controls + ε (1)
where CAREER stands for each of the three measures of graduates’ career outcomes. We use a negative
binomial model to estimate Equation 1 when CPASIGN is the dependent variable and expect α1 to be
negative and α2 to be positive based on the assumption that higher ability graduates can become signing
auditors in less time. We use ordered probit regressions when AFPORTRANK is the dependent variable
and use probit regressions when PARTNER is the dependent variable. In regressions with
AFPORTRANK and PARTNER as dependent variables, we expect α1 to be positive and α2 to be negative
assuming that higher ability graduates are more likely to join a larger audit firm and become an audit
partner.
12 In early periods when professional work experience was not compulsory for CPA qualifications, many auditors
obtained their CPA qualifications during their undergraduate years.
16
We include in Equation 1 a set of control variables for individual auditors’ characteristics that
have been examined in prior studies (e.g., Gul et al. 2013). These control variables include (1) MALE,
an indicator variable taking a value of 1 when an auditor is male, and 0 otherwise; (2) DEGREE, an
indicator variable equal to 3, 2, 1 or 0 for doctoral, postgraduate, undergraduate or other qualifications,
respectively; (3) MAJOR, an indicator variable equal to 1 for accounting and finance related majors (e.g.
accounting, finance, financial management and auditing), and 0 otherwise; (4) UNIVERSITY, an
indicator variable equal to 1 for prestigious universities in China, and 0 otherwise;13 (5) QUALIEXAM,
an indicator variable equal to 1 if an auditor obtained their CPA licence via qualification exams, and 0
otherwise; and (6) CPAAGE, the age when an auditor obtained their CPA licence. We include CPA year
fixed effects in regressions when using CPASIGN as the dependent variable, to control for the
differences in the years when graduates obtained their CPA qualifications. We add year fixed effects
with AFPORTRANK as dependent variables to control for the time series differences in the labor market
for auditors. We control for graduation year fixed effects when using PARTNER as the dependent
variables to control for differences associated with different cohorts of graduates.
To test our hypothesis that upward (downward) migrating graduates deliver higher quality
audits, we use both accrual and non-accrual-based measures. With respect to the accrual measure, we
follow Kothari et al. (2005) to estimate the performance-adjusted abnormal accruals for industry-years,
widely used in audit quality research (Gul et al. 2013; Aobdia et al. 2015; Kallunki et al. 2018): 14
13 More specifically, we define whether a university is prestigious based on whether it is listed as one of the
985/211 project universities. The 985/211 project universities have been well recognized in China as the top
universities and receive significantly more funding from the government.
14 We follow 2012 industry classifications by the CSRC to define an industry. The manufacturing industry has
been further classified into sub-industries given the dominance of manufacturing firms in China’s stock market.
17
TACCj,t/TAj,t-1 = α0 + α1(1/TAj,t-1) + α2(ΔREVj,t/TAj,t-1)+α3(PPEj,t/TAj,t-1)+ α4(IBt/ TAj,t-1) + εj,t (2)
where TACC is the total accruals, calculated as operating income minus operating cash flow as in Guan
et al. (2016). TA is the total assets. ΔREV is the change in revenues. PPE is property, plant and
equipment. IB is the operating income reported in income statements. Following Kothari et al (2005),
we augment the model with an intercept. We use signed residuals, termed as KLW, to capture audit
quality, because income-increasing earnings management presents high litigation risk for auditors (Lys
and Watts 1994) and auditors work harder to detect such misstatements (Barron et al. 2001).
For the non-accrual measure, we examine the presence of accounting restatements, which
signals audit failure. We define RESTATE as an indicator variable equal to 1 if a firm has to restate its
accounting numbers for year t, and 0 otherwise.
To examine the association between graduates’ migration status and audit quality, we estimate
the following cross-sectional regressions using annual observations from clients:
AQ = β0 + β1 UP + β2 DOWN + Controls + ε (3)
where AQ refers to KLW and RESTATE, and UP and DOWN are indicator variables for upward and
downward migrating graduates, respectively. Our hypothesis predicts a negative β1 and a positive β2 if
higher ability auditors deliver higher quality audits.
We also control for a range of client characteristics in Equation 3, when the dependent variable
is KLW, including: (1) OCF, operating cash flows deflated by average total assets; (2) LOSS, an
indicator variable taking a value of 1 for negative net profit, and 0 otherwise; (3) LEV, the ratio of total
liabilities divided by total assets; (4) TOBINQ, the sum of book value of total liability and market value
of total equity, divided by book value of total assets; (5) SIZEMV, the natural logarithm of fiscal year-
18
end market value of shareholder equity; 6) AGE, the number of years of a client since its listing year;
(7) SOE, an indicator variable equal to 1 if the ultimate controller of a firm is the government, and 0
otherwise; (8) BSHARE, an indicator variable equal to 1 if a client has B shares issued,15 and 0 otherwise;
(9) HSHARE, an indicator variable equal to 1 if a client has been listed in Hong Kong, and 0 otherwise;
(10) BIG4, an indicator variable equal to 1 if a client is audited by an international Big 4 audit firm, and
0 otherwise; (11) AFRANK, the annual percentile rankings of audit firm size based on the aggregate
clients’ total assets; and (12) FIRSTYEAR, an indicator variable equal to 1 if it is the first year for the
signing auditors to audit the client, and 0 otherwise.
When the dependent variable is the RESTATE, following He and Liu (2010), we select the
following control variables: (1) TOP1, the percentage of shares owned by the largest shareholder; (2)
INDEP, the percentage of board members who are independent directors; (3) SIZETAST, the natural
logarithm of total assets; (4) GROWTH, the year-to-year sales growth rate; and (5) ROA, net income
divided by average total assets. We also control for LEV and SOE, which are defined as previously.
In addition, industry and year fixed effects are included in the models to remove the effects of
the industry-wide time-invariant characteristics and of the year level constant omitted variables. We
add university fixed effects into the models to ensure that the effects of migration status are incremental
to those reflected in university reputation. We estimate Equation 3 using OLS regressions and adjust
standard errors for clustering effect at the firm level.
3.2 Data and sample
15 Some Chinese listed firms have multiple classes of shares. A shares are usually available to domestic investors
in mainland China, and B shares are available to foreign investors. Both A and B shares are traded in two stock
exchanges in mainland China. H shares are listed in Hong Kong Stock Exchange by Chinese firms, most of which
also have A shares.
19
We obtain stock return and financial report data from the CSMAR database. We manually
collect the identities of audit firms and signing auditors’ names from clients’ annual reports,
supplemented by online auditor databases maintained by the China Securities Regulatory Commission
(CSRC) and the Chinese Institute of Certified Public Accountants (CICPA). We compiled a web
crawler to collect individual auditors’ information, including gender, affiliations, birth date, position
within the audit firm, education background, and CPA ID from the CICPA official website.
Our sample period spans from 2008 to 2016 for our hypothesis examination. Our sample starts
from fiscal year 2008 because the new sets of Chinese Accounting Standards (CAS) and Auditing
Standards took effect on January 1, 2007 and accounting numbers are more consistent since then. We
implement the following procedures to construct our sample to examine how migration influences the
performance-adjusted accruals: (1) we remove firms with missing identities of audit firms or signing
auditors (388 observations); (2) we delete firms listed in stock exchanges for the first year (1,411
observations); (3) we remove 5,032 observations for which we cannot find the auditor’s graduating
universities or workplaces or for which we cannot calculate discretionary accruals or control variables.
After these procedures, there are 14,016 firm-year observations in the sample to estimate Equation 3
with KLW as the dependent variable. In this sample, 3,520 individual auditors are assigned to conduct
their audit work. To examine graduates’ career outcomes, we further require individual auditors to have
non-missing data on their personal characteristics. This leaves us with 2,917 individual auditors to
estimate Equation 1.
To examine the association between the migration status and the presence of accounting
restatements, we construct a matching sample following prior studies (Stanley and DeZoot 2007;
Kinney et al. 2004; He and Liu 2010). The matching protocol proceeds as follows: 1) we pool all firms
20
which never experienced accounting restatements throughout our sample period as the control sample;
2) For each restatement firm, we select one firm from the control sample. Both firms must belong to
the same industry. Specifically, for non-manufacturing firms, we categorize them into varying
industries according to the unique one letter assigned by the CSRC. For manufacturing firms, their
industry is identified by the unique one letter plus the first digit. Furthermore, the control firm must be
the one with the closest size to the restatement firm just before the restatement period; 3) if a firm
experiences multiple accounting restatements during the sample period, we match the same control firm.
The above matching protocol results in 3,010 firm-year observations to estimate Equation 3 with
RESTATE as the dependent variable.
Table 1 tabulates the sample distribution and information on economic development in each
province. The top five provinces ranked by the average market index, including Zhejiang, Guangdong,
Shanghai, Jiangsu and Beijing, are all located in coastal areas that have witnessed rapid economic
growth since the economic reform in the late 1970s.16 In contrast, the least economically developed
provinces, including Ningxia, Gansu, Xingjiang, Qinghai and Tibet, are all located in the west,
suggesting the imbalance in economic development across China. Provinces with a higher market index
also have higher GDP per capita in the finance industry, a higher salary for workers in the finance
industry, 17 and a larger number of listed firms. This suggests that more economically developed
provinces can provide higher wages and more job opportunities for high ability graduates. Table 1 also
reports the number of prestigious universities in each province from which auditors obtained their
degrees. While provinces with a higher market index tend to have more prestigious universities, some
16 A map of China can be found here: https://www.travelchinaguide.com/map/china_map.htm.
17 The salary in Tibet is particularly high because workers receive special subsidies due the tough natural
conditions here.
21
less economically developed provinces also have many prestigious universities (e.g., Shaanxi and
Heilongjiang). Thus, graduates from prestigious universities in less economically developed provinces
may have an incentive to migrate to a more economically developed province.
[Insert Table 1 about here]
4. Empirical Results
4.1 Descriptive statistics
Table 2 presents the summary statistics for all the variables used in Equations 1 and 3. Panel A
focuses on signing auditors’ characteristics. Mean CPASIGN is 8.7, indicating that, on average, it takes
8.7 years for a graduate to become a signing auditor after obtaining their CPA qualification. The mean
AFPORTRANK is 1.826 and median is 2, suggesting that signing auditors’ portfolios are of the average
size within their audit firms, given that AFPORTRANK ranges from 0 to 4. Both CPASIGN and
AFPORTRANK have considerable variations across auditors. About 12.3% of signing auditors are
partners by the end of 2016. About 34.9% (11.3%) of university graduates migrate to more (less)
developed provinces, compared with the provinces of their graduating universities. This suggests that
almost half of graduates migrate across provinces after obtaining their degrees while the rest tend to
stay in the same province to start their professional careers. Of signing auditors, 61% are male, 69.5%
graduated with an accounting/finance related major and 39.2% obtained a degree from a prestigious
university. The average age when signing auditors acquired their CPA licence is 27 years. These
statistics are comparable to those reported in Gul et al. (2013) and He et al. (2018).
Panel B reports the descriptive statistics for client-level variables. KLW have a mean close to 0
as they are residuals from regressions, but have substantial variations evidenced by standard deviations
and their ranges. The ratios of upward and downward migrants are only slightly different from that in
22
Panel A Table 2 because we drop auditors without personal information in Panel A. Of firm-year
observations, 14.2% report accounting losses and 42.4% are ultimately controlled by the government.
Mean BIG4 is 5.5%, implying that the international Big 4 only have a small market share in China in
terms of the number of clients. In about 60% of client-year observations, it is the first year in which an
auditor signed the client’s annual reports. This is likely due to mandatory rotation of auditors in China
after every five years of service. The distribution of the variables is comparable to that reported in prior
audit studies using data from China (Gul et al. 2013; Guan et al. 2016; Si et al. 2017).
Panel C presents the summary statistics for all variables for the matching sample. Mean
RESTATE is 0.5, reflecting our one to one matching procedures. Mean UP and DOWN are slightly
different from the values reported in Panel A and B, also resulting from the matching procedures. TOP1
has a mean of 0.317, indicating that the ownership concentration in the Chinese market is very pervasive.
Mean LEV and SOE are very similar to the that of the full sample.
[Insert Table 2 about here]
Table 3 tabulates the Pearson and Spearman correlation coefficients for the variables used to
estimate Equations 1 and 3. Pearson’s correlation coefficients are shown in the lower triangle while
Spearman’s rank correlations appear above the diagonal. The bold font indicates instances where the
correlation coefficients are significant at the 5% level (two-sided). Panel A shows that there is a
significantly negative correlation (-0.051) between CPASIGN and UP, implying that it takes less time
for upward migrating graduates to be qualified to sign clients’ annual reports. The correlation between
AFPORTRANK and UP is significantly positive (0.053), implying that upward migrating graduates have
a larger portfolio of clients within their audit firms. The correlation between PARTNER and UP is
23
significantly positive (0.066), indicating that upward migrating graduates are more likely to be partners
in 2016. The above three correlations are consistent with the conjecture that upward migrating auditors
have a higher ability and a fast track in their careers. The correlation between CPASIGN and DOWN
and the correlation between AFPORTRANK and DOWN have the predicted sign but are statistically
insignificant at the 5% level.
Panel B Table 3 documents a negative correlation (-0.027) between KLW and UP, implying
that upward migrating auditors are associated with lower level of discretionary accruals. The correlation
between KLW and DOWN is 0.030 which indicates that downward migrating auditors are associated
with a higher level of discretionary accruals. Panel C Table 3 shows a negative (positive) correlation
between RESTATE and UP (DOWN), implying that upward (downward) migration auditors are less
(more) likely to be associated with the presence of accounting restatements. As a result, the pairwise
correlations in these tables provide some preliminary evidence supporting our hypothesis that upward
migrating auditors have a higher ability and deliver higher quality audits.
[Insert Table 3 about here]
4.2 Migration and auditors’ ability
Table 4 presents the results from regressions estimating Equation 1. Columns (1) and (2)
examine the time lag between an auditor obtaining the CPA qualification and becoming a signing
auditor. In Column (1), UP has a negative coefficient (coefficient = -0.053, z-statistics = -2.724),
suggesting that upward migrating auditors become a signing auditor in a shorter time. In Column (2),
DOWN has a negative coefficient, consistent with downward migrating auditors taking more time to
become a signing auditor, but the coefficient is not statistically significant. In Column (3), both UP and
DOWN enter the model to compare migration auditors with peers who stay in their graduating places
24
(staying auditors). The coefficient on UP is still significant, albeit there is no significant loading on
DOWN. Columns (4) and (5) show that AFPORTRANK is positively related to UP and negatively
related to DOWN, implying that upward (downward) migrating auditors have a larger (smaller) portfolio
of clients. In column (6), benchmarked against staying auditors, there is still a significant coefficient on
UP. In Columns (7), (8) and (9) that examine the likelihood of signing auditors being audit partners,
UP has a positive and statistically significant coefficient (coefficient = 0.229, z-statistics = 3.192) and
DOWN has a negative coefficient, indicating that upward (downward) migrating auditors are more (less)
likely to be partners. Again, when including the two migration variables simultaneously in the model,
we only find a significant loading on UP. Regarding the control variables, we find auditors’ gender,
academic degrees, undergraduate major, graduating universities and age are all related to their career
outcomes in a way consistent with expectations. Overall, the evidence in Table 4 supports the argument
that upward migrating auditors are more able than peer auditors and thus have a fast track in their
professional careers. 18
[Insert Table 4 about here]
4.3 Migration and audit quality
Table 5 reports the results from multivariate regressions estimating Equation 3. Panel A
presents the results using KLW as dependent variables, while Panel B reports results from regressions
using RESTATE as dependent variables. In Column (1) Panel A, the coefficient on UP is -0.005 with a
t-statistic of -2.861, suggesting that upward migrating auditors are associated with lower level
18 One potential concern here is that some provinces have only small differences in the value of marketization
indices, such as Zhejiang (8.91) and Guangdong (8.64). To address this concern, we sort the provinces into five
quintiles based on the marketization index. Then we define UP (DOWN) to be 1 only if a graduate student moves
across quintiles. We re-estimate the models in Table 4 using newly defined UP and DOWN and obtain very similar
results.
25
discretionary accruals, compared with other auditors. In Column (2), there is a positive coefficient on
DOWN (coefficient = 0.008, t-statistic = 4.277), indicating that downward migrating auditors are
associated with higher discretionary accruals. In Column (3), we include both UP and DOWN
simultaneously in the regression to benchmark auditors experiencing migration against staying auditors.
The coefficient on UP remains negative and statistically significant, while DOWN continues to have a
positive and statistically significant coefficient. The results show that upward (downward) migrating
auditors are associated with low (high) discretionary accruals. 19 The results for the control variables are
consistent with those in prior research. We find discretionary accruals are lower for firms with higher
operating cash flows, an accounting loss, a higher leverage ratio, an older age, cross-listed shares in
Hong Kong and an international Big 4 auditor, consistent with findings in Ke et al. (2015) and Guan et
al. (2016). Large firms seem to have more accruals, as reported in Chen et al. (2016).
Panel B tabulates the results of estimating Equation 3 when RESTATE is the dependent variable.
The negative coefficient on UP (-0.106 with a t-statistic of -3.824) in column (1) illustrates that
compared with all other firms, firms audited by upward migration auditors are less likely to experience
accounting restatements subsequently. The negative coefficient in column (2) documents downward
migration auditors are associated with higher propensity of accounting restatements. When both
migration variables are included in column (3), the inferences remain unchanged. Therefore, the results
of estimating Equation 3 using the RESTATE corroborate the findings based on discretionary accruals.20
19 In a robustness test, we separately examine positive versus negative abnormal accruals. The untabulated results
show that UP (DOWN) is negatively (positively) related to income increasing discretionary accruals, but both UP
and DOWN are not related to income decreasing discretionary accruals. The results suggest the auditors’ ability
plays a more important role in detecting income increasing misstatements that present high audit risk.
20 In a robustness test, we re-estimate the models in Table 5 using the quintile-based definition of UP and DOWN
as described in Footnote 18, and we obtain very similar results.
26
Overall the results in Table 5 support our hypothesis that upward migrating auditors deliver higher
quality audits while downward migrating auditors provide lower quality audits.
[Insert Table 5 about here]
4.4 The effects of unobservable variables
One concern with the above findings is the correlated-omitted variables that could drive both
clients’ audit quality and migration decisions of graduates. To address this concern, we conduct a range
of tests by adding various fixed effects in Equation 3. Table 6 reports the results from the regressions
with additional fixed effects. First, it is possible that migrating graduates may concentrate in certain
type of audit firms and our results may be driven by differences between audit firms. To ensure that our
results are driven by differences between individual signing auditors rather than the differences between
audit firms, we add audit firm fixed effects to the regressions and report the results in Panel A.
Second, because there is mutual selection between auditors and clients, there is a concern that
clients’ characteristics, rather than auditor characteristics, drive the results. To address this concern, we
have included a number of observable client characteristics in the regressions. We further include client
fixed effects in regressions to control for unobservable client characteristics. One advantage of adding
client fixed effects is it allows us to examine time-series variations in audit quality associated with
auditors with different migration status. The evidence from client fixed effects thus provides stronger
evidence on the effect of changes in auditors’ migration status on clients’ audit quality, holding the
clients constant, which allows us to interpret the evidence as causal (Lennox and Wu 2018). We report
the results from client fixed effects in Panel B.
27
Third, the local economic and legal environment around clients’ headquarters could shape
clients’ audit choice and audit quality. This concern is particularly relevant since we use economic
development index for each province to classify auditors’ migration status. To address this concern, we
add client location (province) fixed effects and report the results in Panel C.
The results reported in Panels A to C in Table 6 consistently show that UP is negatively related
to the magnitude of discretionary accruals/the likelihood of restatements, while DOWN is positively
related to discretionary accruals/the likelihood of restatements, after controlling for various fixed effects.
The results suggest that our main results are unlikely to be explained by differences between audit firms,
clients or clients’ locations.
[Insert Table 6 about here]
4.5 Effect of auditor and client characteristics
In this subsection, we examine whether the effect of migrating graduates on audit quality varies
with auditor and client characteristics. We begin with auditor characteristics, including their gender and
graduating universities’ reputation. The rationale is that gender and university reputation may be related
to the cost of migration, given some general perceptions. For example, women can be perceived as less
able than men and there is some prejudice against women, so when female graduates do migrate to a
more economically developed province and become a signing auditor, they must overcome the
prejudice and have a higher ability. In contrast, when male graduates migrate downwards, it could be
that their ability is not competitive in the local job market and they thus have to move to a less
economically developed province to get a job. So upward migrating women and downward migrating
men are likely to have ability substantially different from their peers. Similarly, universities have
28
different reputations and graduates from prestigious universities are generally perceived to be of higher
quality,21 so graduates from a non-prestigious university would have a higher migration cost as their
universities are not well recognized in other provinces. When graduates from non-prestigious
universities do migrate to a more economically developed province and become signing auditors, they
are expected to have a much higher ability than their peers from the same university. On the other hand,
graduates from prestigious universities should find it relatively easy to get a job in their university cities.
If they migrate downwards to a less economically developed province, it is likely to indicate that their
ability could be insufficient to get a good job in their original province. To sum up, we expect that
graduates’ migration status presents a stronger signal of their ability for upward migrating female
graduates and for graduates from non-prestigious universities.
To test this expectation, we partition the sample into two subsamples based on (1) engagement
auditors’ gender and (2) whether signing auditors’ university is prestigious. We then separately estimate
Equation 3 for each subsample and report the results in Table 7. Panel A presents the regression results
for subsamples partitioned by auditors’ gender. Consistent with our expectation, we find UP has
negative and statistically significant coefficients only for female auditors, suggesting upward migrating
female graduates are more able to improve audit quality. In contrast, DOWN has larger positive and
statistically significant coefficient for male auditors, suggesting downward migrating male graduates
are more related to income increasing accruals. Panel B reports the results from subsamples partitioned
based on university reputation. UP are negatively related to discretionary accruals/the presence of
restatements only for graduates from non-prestigious universities, consistent with our prediction that
21 It has been observed that in China some employers state in their job advertisement that they only consider
applications from graduates from prestigious universities.
29
upward migrating graduates from non-prestigious universities have a much higher ability. DOWN has
a larger positive and statistically significant coefficients in Columns (1) where observations are from
auditors graduating from prestigious universities, consistent with downward migrating graduates from
prestigious universities having a lower ability. Column (3) and (4) do not yield strong evidence towards
our expectation in terms of downward migration auditors.
[Insert Table 7 about here]
We then proceed to investigate the effect of client characteristics with the assumption that high
ability auditors are more likely do a better job in complex and challenging audit engagements. This
assumption is based on evidence in Libby and Tan (1994) who show that auditors with a high ability
perform better in complex audit tasks, but not in easy tasks, compared with auditors with a low ability.
Therefore, we predict that the association between UP (DOWN) and audit quality is stronger in more
complex and challenging audit engagements. We capture the complexity of audit engagements in two
ways. First, we measure client complexity using the number of business segments reported by a client,
assuming more business segments make auditing more difficult (Fung et al. 2012). Second, we follow
Francis and Gunn (2017) to construct a measure of industry-specific earnings noise based on earnings
persistence. Specifically, industries in which firms have less persistent earnings have more noise in
earnings that makes auditing more challenging.
Each year, we divide our sample into “High” and “Low” groups according to (1) the median
number of segments; and (2) the median measure of earnings noise. We then estimate Equation 3
separately for each group and report the results in Table 8. Panel A shows that UP and DOWN have
statistically significant coefficients only for the group with above-median number of segments. Panel
30
B shows that the coefficients of UP and DOWN are statistically significant and larger in the group with
high earnings noise. The results are consistent with our expectation that differential ability of auditors
is more likely to have an effect on audit quality when audit engagements are complex.
[Insert Table 8 about here]
4.6 Alternative measures of audit quality
In our main results, we use performance matched discretional accruals (KLW) as a measure of
audit quality, with KLW estimated from a commonly used accrual model. Chen, Hribar and Melessa
(2018) show that using the residuals from first stage regressions, such as the accruals models, as
dependent variables in second stage regressions is likely to cause bias in the estimated coefficients.
They suggest researchers change the two-stage regression approach into a single step model by
including regressors from the first stage regression in the second stage model. Following their
suggestion, we modify Equation 3 and use the total accruals as the dependent variable and add the
regressors in Equation 2 to Equation 3. We re-estimate the model and report the results in Panel A of
Table 9. The results show that the coefficients on UP remain negative and statistically significant while
the coefficients of DOWN remain negative and statistically significant.
To further establish the robustness of our results, we repeat our main analyses using alternative
measures of audit quality, including the presence of small profit and the issuance of modified audit
opinion. We use OLS model to estimate the following regressions to examine whether auditors’
migration status is related to clients’ probability of announcing small profits or having qualified audit
opinion:
SP/MAO = λ0+ λ1UP+ λ2DOWN + controls + ε (4)
31
where SP, an indicator variable equal to 1 if clients’ ROA falls in the range of [0, 0.01], and 0 otherwise.
The second one is MAO, an indicator variable which takes the value of 1 if the audit opinion is adverse,
disclaimed qualified opinion, or unqualified opinion with explanatory notes, and 0 if the audit opinion
is a clean one.
When SP is the dependent variable, we follow Gul et al. (2013) to select a set of control
variables including OCF, LEV, TOBINQ, SIZEMV, AGE, SOE, BSHARE, HSHARE, BIG4, AFRANK
and FIRSTYEAR, all of which are defined as previously. When MAO is the dependent variable, the
control variables include: (1) CR, current assets divided by current liabilities at the end of year; (2) AR,
accounts receivable divided by total assets; (3) INV, inventory divided by total assets; 3) RPT, total
related party transactions divided by total assets; and (4) RET, market adjusted stock returns
compounded over the financial year. We also include OCF, LOSS, LEV, TOBINQ, SIZEMV, AGE, SOE,
BSHARE, HSHARE, BIG4, AFRANK and FIRSTYEAR as control variables. After removing
observations with missing information to calculate control variables and those with missing auditors’
information, we arrive at samples consisting of 14,077 and 13,552 observations to estimate Equation 4
with the dependent variable being SP and MAO, respectively.
Panel B of Table 9 reports the results of estimating Equation 4 using SP and MAO as the
dependent variables. Columns (1) to (3) show a positive loading on DOWN (statistically significant at
the level of 1%), implying that downward migrating auditors are more likely to report small profits
compared with other auditors. Although none of the coefficients of UP are statistically significant, the
signs are consistent with our predictions. Columns (4) to (6) depict similar findings when we use MAO
as the dependent variables. DOWN is negatively associated with MAO, suggesting that downward
migrating graduates are less likely to issue modified audit opinion. Therefore, results from alternative
32
proxies for audit quality are largely consistent with the main results using accrual-based measures of
audit quality.
4.7 Is there an audit fee premium or discount for migrating auditors?
In this subsection, we examine whether auditors’ migration status is related to audit fees for
two reasons. The first reason is that some prior studies use audit fees as a proxy for audit quality based
on the argument that higher audit fees indicate more auditor efforts and thus higher audit quality (e.g.,
Kallunki et al. 2018). The second reason is that, if clients can differentiate and value auditors’ ability,
we would expect higher ability auditors to receive higher audit fees, so the evidence from audit fees can
provide corroborating evidence for our hypothesis. We use the following model to investigate the
association between audit fees and auditors’ migration status:
FEE = λ0+ λ1UP + λ2DOWN + controls + ε (5)
where FEE is the natural logarithm of annual audit fees. Control variables include CR, AR, INV, RPT,
ROE, OCF, LOSS, LEV, TOBINQ, SIZEMV, AGE, SOE, BSHARE, HSHARE, BIG4, AFRANK and
FIRSTYEAR. We also control for audit opinion in the previous year (LAGMAO, an indicator variable
equal to 1 if the client received a modified audit opinion in the prior year, and 0 otherwise) and audited
interim reports (INTERIM, an indicator variable which takes a value of 1 if a client has its interim reports
audited, and 0 otherwise). The sample for estimating Equation (5) has 13,080 firm-year observations
from 2008 to 2016.
Panel C of Table 9 presents the results of regressions estimating Equation 5. The coefficient on
UP is 0.040 with a t-statistic of 2.929 in column (1), showing that upward migration auditors charge
higher audit fees. There is a negative loading on DOWN (coefficient = -0.065, t-statistic = -3.610) in
33
column (2), indicating that downward migrating auditors receive lower audit fees after controlling for
various characteristics of clients. Column (3) where we add both migration variables into the audit fee
model, the loadings on migration variables remain significant. The evidence from audit fees provides
some support to the argument that high (low) ability auditors receive higher (lower) audit fees.
[Insert Table 9 about here]
4.8 Do investors recognize the value of auditors’ migration status?
Finally, we investigate whether investors can differentiate audit quality provided by migrating
auditors, as Aobdia et al. (2015) find that investors react more strongly to earnings surprises of clients
whose audit partners have a higher quality. Following Baber et al. (2014) and Guan et al. (2016), we
estimate the following regression model:
CAR = δ0 + δ1UE + δ2UP + δ3UE* UP + δ4DOWN + δ5UE* DOWN + δ6 LOSS + δ7UE*LOSS
+δ8MAGUE + δ9UE*MAGUE + δ10BETA + δ1UE*BETA + δ12 LEVERC + δ13UE* LEVERC+
δ14 BM2 + δ15UE*BM2 + δ16 SIZEMV2 + δ17UE* SIZEMV2+ δ18BIG4 + δ19UE*BIG4 +
INDUSTRY/YEAR/UNIVERSITY DUMMIES + ε (6)
where CAR is the cumulative market-adjusted stock returns in the three-day [-1, 1] window around the
annual earnings announcements. UE is the unexpected earnings, measured as earnings in the fourth
quarter of year t minus earnings in the fourth quarter in year t-1, scaled by the market value of equity at
the end of two trading days before earnings announcements. The variables of interest are the interaction
term between UE and UP and the interaction term between UE and DOWN, which captures whether
the earnings response coefficients vary with upward and downward migrating auditors.
34
We also include a number of control variables for client characteristics and their interaction
terms with UE. MAGUE is the absolute value of UE, measuring the magnitude of earnings surprises.
BETA is the coefficient of market returns from the market model estimated using daily stock returns
and the value weighted market returns over the 120-trading day window ending two days before annual
earnings announcements. LEVERC is the total liabilities deflated by total assets at the end of third fiscal
quarter. BM2 is the book value of equity at the end of the third fiscal quarter divided by the market
value of equity two days before annual earnings announcements; SIZEMV2 is the natural logarithm of
the market value of equity two days before annual earnings announcements. We adjust standard errors
for clustering effect at the level of earnings announcement dates, as the announcements tend to
concentrate on a particular period of year. The sample to estimate Equation 6 has 14,171 client-year
observations over the period from 2008 to 2016.
Table 10 reports the results. The coefficient of UE×UP in Column (1) is positive and significant
at the 5% level, suggesting that the earnings response coefficient is larger for earnings audited by
upward migrating auditors. The coefficient of UE×DOWN in Column (2) is negative and significant at
the 5% level, indicating that the earnings response coefficient is lower for earnings audited by
downward migrating auditors. In Column (3) when we include both UE×UP and UE×DOWN in the
regression, the coefficients of UE×UP and UE×DOWN remain statistically significant. These results
suggest that investors appear to be able to differentiate and value the audit quality provided by migrating
auditors.
[Insert Table 10 about here]
5. Conclusion
35
Motivated by an insight in labor economics research that migrants are a group of self-selected
individuals, we argue that university graduates who migrate to a more economically developed province
have higher ability. When these graduates become engagement auditors, higher ability individuals
deliver higher quality audits. Using data from China, we document evidence consistent with the
argument. Specifically, we find that upward migrating graduates have a fast track in their auditing
careers, suggesting that they have higher ability. We further show that upward migrating auditors
provide higher quality audits. The results are robust to a range of robustness tests. Finally, we find
evidence suggesting that clients and investors are able to differentiate the auditors’ ability reflected in
their migration decisions.
Our study contributes to the growing literature on audit partners by providing novel archival
evidence that auditors’ ability affects audit quality. It also links auditing research with labor economics
research. There are some limitations of our study. First, while we provide evidence that upward
migrating graduates have a fast track in their career, we are unable to specify which ability helps them
achieve their career success. At the same time, it could be that auditors’ career success requires multiple
abilities including cognitive ability, problem solving ability and entrepreneurship, and the migration
decision reflects the combined ability of the individual. Second, our results are based on Chinese data,
and it is unclear if data from other markets can provide similar results. We thus caution readers when
generalizing the conclusions of the study.
36
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41
Appendix: Variable Definitions
Variables Definitions
Panel A: Dependent Variables (Listed in Order of Appearance)
CPASIGN Number of years between the CPA certificate year and first signing year.
AFPORTRANK Quintile ranking (ranging from 0 to 4) of an auditor’s portfolio size (based
on aggregate clients’ total assets) within the audit firms in the auditor’s first
signing year. PARTNER Indicator variable equal to 1 if the auditor is an audit partner, and zero
otherwise. KLW Discretionary accruals calculated using the model suggested by Kothari,
Leone and Wasley (2005). RESTATE Indicator variable equal to 1 for clients with financial restatement, and 0
otherwise. TACC Total accruals, difference between the operating income and operating cash
flow, deflated by the beginning total assets
SP Indicator for small profits, equal to 1 if ROA is between 0 and 0.01, and
zero otherwise. ROA is calculated as net income divided by average total
assets at the beginning and end of the year.
MAO Indicator variable equal to 1 for modified audit opinion (adverse, disclaimed
qualified opinions and unqualified opinions with explanatory notes), and
zero otherwise. FEE Natural logarithm of annual audit fees.
CAR Cumulative market-adjusted stock returns from trading day -1 to +1, where
day 0 is the annual earnings announcement day. Panel B: Test Variables (Listed in Order of Appearance)
DOWN Indicator variable equal to 1 when engagement partner starts a career in a
less developed province (measured in Market Index) than the university
location, and zero otherwise.
UP Indicator variable equal to 1 when engagement partner starts a career in a
more developed province (measured in Market Index) than the university
location, and zero otherwise.
Panel C: Control Variables (Listed in Order of Appearance)
MALE Indicator variable equal to 1 for male audit partner, and zero otherwise.
DEGREE Indicator variable for education degree, equal to 3 for PhD degree, 2 for
master degree, 1 for bachelor degree and zero otherwise.
MAJOR Indicator variable equal to 1 for accounting related major in the university
(such as accounting, auditing, financial management, and finance, and zero
otherwise. UNIVERSITY Indicator variable equal to 1 for prestigious universities in 985-211 project,
and zero otherwise.
QUALIEXAM Indicator variable equal to 1 for auditors who obtain their CPA license
through exams, and zero otherwise.
CPAAGE Age of audit partner when he or she gets the CPA licence.
OCF Operating cash flows by average of beginning and ending total assets.
LOSS Indicator variable equal to 1 for negative net income, and zero otherwise.
LEV Financial leverage, calculated as total liabilities divided by total assets at the
end of year.
TOBINQ Tobin’s Q, calculated as sum of book value of total assets and market value
of total equity, divided by book value of total assets.
SIZEMV Natural logarithm of year-end market value of shareholder equity.
AGE Number of years an audit client has been listed.
42
SOE Indicator variable equal to 1 for audit clients who are ultimately controlled
by the government, zero otherwise.
BSHARE Indicator variable equal to 1 for audit clients who have issued B-shares, and
zero otherwise.
HSHARE Indicator variable equal to 1 for audit clients who have issued H-shares, and
zero otherwise.
BIG4 Indicator variable equal to 1 for Big 4 audit firms, and zero otherwise.
AFRANK Annual percentile rankings of audit firm size, measured as natural logarithm
of total audited assets of listed audit clients.
FIRSTYEAR Indicator variable equal to 1 for audit clients where any of the signing
auditors is in his or her first year of tenure, zero otherwise.
CR Current ratio, calculated as current assets divided by total liabilities at the
end of year.
AR Accounts receivable intensity, calculated as accounts receivable divided by
total assets at the end of year.
INV Inventory intensity, calculated as inventory divided by total assets at the end
of year.
RPT Total related party transactions divided by total assets at the end of year.
ROE Core operating net income divided by average of beginning and ending
equity.
RET Market adjusted stock return during the year.
SIZETAST Natural logarithm of year-end total assets.
LAGMAO Indicator variable equal to 1 for clients with modified audit opinion
(adverse, disclaimed qualified opinions and unqualified opinions with
explanatory notes) in the previous year, and zero otherwise. INTERIM Indicator variable equal to 1 for clients whose interim (semi-annual) reports
are audited, and zero otherwise.
TOP1 The percentage of shares owned by the largest shareholder.
INDEP Board independence, measured as number of independent directors divided
by total number of board members.
GROWTH Annual growth rate of sales.
ROA Net income scaled by average assets.
UE Unexpected earnings, measured as earnings in Q4 of year t less earnings in
Q4 in year t-1, scaled by the market value of equity at the end of day -2,
where day 0 is the annual earnings announcement day.
MAGUE The absolute value of UE.
BETA BETA is estimated by the market model fitting on daily returns for 120
trading days before the [-1, +1] window, where day 0 is the annual earnings
announcement day. LEVERC Financial leverage, measured as the total liabilities to total assets ratio at the
end of third fiscal quarter.
BM2 Book-to-market value of equity at the end of day -2, where day 0 is the
annual earnings announcement day.
SIZEMV2 Natural logarithm of the market value at the end of day -2, where day 0 is
the annual earnings announcement day.
43
Table 1 Sample Distribution
This table reports the sample distribution by province and some descriptive statistics for each province.
Province Marketization
Index
GDP per
capita in
finance
industry
Average
annual
salary in
finance
industry
2006
Average
annual
salary in
finance
industry 2016
Number of
prestigious
universities
Number
of listed
firms
Number
of firm-
years
Zhejiang 8.91 1,211.47 53,667 130,813 1 427 1,274
Guangdong 8.64 1,561.23 53,079 135,412 4 597 1,936
Shanghai 8.59 1,285.64 66,016 226,500 9 294 1,125
Jiangsu 8.46 1,383.66 36,760 122,648 11 398 1,327
Beijing 7.58 1,257.28 88,408 239,085 26 316 1,140
Fujian 7.58 460.28 37,393 108,377 2 135 553
Tianjin 7.52 391.46 49,810 117,489 4 50 247
Shandong 7.05 922.55 28,207 93,405 3 197 855
Liaoning 6.61 429.14 28,549 80,323 4 84 420
Chongqing 6.46 326.62 33,511 126,739 2 49 226
Anhui 6.1 273.91 22,642 76,724 3 104 453
Sichuan 5.95 524.13 28,282 87,119 5 125 480
Henan 5.94 446.48 24,238 91,212 1 80 325
Hubei 5.86 387.49 22,161 93,701 7 102 436
Hebei 5.81 445.21 22,699 75,708 1 60 285
Hunan 5.68 287.1 22,846 97,704 3 109 391
Jiangxi 5.63 191.35 22,921 83,974 1 40 233
Hainan 5.43 51.36 33,667 102,747 1 32 139
Jilin 5.4 142.14 21,709 81,958 3 46 250
Guangxi 5.37 228.68 28,118 89,936 1 37 154
Heilongjiang 4.94 194.99 24,362 64,737 4 42 211
Inner Mongolia 4.74 201.37 23,521 78,570 1 27 149
Shanxi 4.72 285.95 22,080 75,683 1 38 240
Yunnan 4.62 260.63 27,792 121,529 1 35 177
Shaanxi 4.49 240.72 24,411 82,626 7 50 242
Guizhou 4.12 152.21 28,612 132,964 1 29 119
Ningxia 4.11 69.32 33,365 83,872 1 13 89
Gansu 3.85 94.57 19,343 60,252 1 34 160
Xinjiang 3.62 164 26,146 92,422 2 55 237
Qinghai 2.8 42.1 25,721 88,957 1 13 72
Tibet 1.04 14.79 56,768 184,146 1 17 71
44
Table 2 Descriptive Statistics
Table 2 presents the descriptive statistics of dependent and independent variables used in this study. Variable
definitions are given in the Appendix. To mitigate the concern of outliers, all the continuous variables are
winsorized at the top (bottom) 1% level.
Panel A: Audit Partner Migration and Ability (N=2,917)
Variables/Stats Mean Std Min P25 Median P75 Max
CPASIGN 8.700 4.331 0.000 6.000 8.000 12.000 20.000
AFPORTRANK 1.826 1.416 0.000 1.000 2.000 3.000 4.000
PARTNER 0.123 0.329 0.000 0.000 0.000 0.000 1.000
UP 0.349 0.477 0.000 0.000 0.000 1.000 1.000
DOWN 0.113 0.316 0.000 0.000 0.000 0.000 1.000
MALE 0.610 0.488 0.000 0.000 1.000 1.000 1.000
DEGREE 0.833 0.641 0.000 0.000 1.000 1.000 3.000
MAJOR 0.695 0.461 0.000 0.000 1.000 1.000 1.000
UNIVERSITY 0.392 0.488 0.000 0.000 0.000 1.000 1.000
QUALIEXAM 0.952 0.215 0.000 1.000 1.000 1.000 1.000
CPAAGE 27.151 5.009 18.000 24.000 26.000 30.000 38.000
Panel B: Audit Partner Ability and Audit Quality (N=14,016)
Variables/Stats Mean Std Min P25 Median P75 Max
KLW -0.001 0.084 -0.267 -0.043 0.000 0.042 0.254
MJONES 0.004 0.110 -0.366 -0.048 0.002 0.053 0.389
UP 0.343 0.475 0.000 0.000 0.000 1.000 1.000
DOWN 0.112 0.315 0.000 0.000 0.000 0.000 1.000
OCF 0.041 0.094 -0.292 -0.005 0.038 0.092 0.330
LOSS 0.142 0.349 0.000 0.000 0.000 0.000 1.000
LEV 0.415 0.235 0.007 0.229 0.404 0.584 1.173
TOBINQ 3.095 2.603 0.931 1.563 2.273 3.618 17.569
SIZEMV 22.389 1.002 20.386 21.669 22.307 22.976 25.368
AGE 9.914 6.072 1.000 4.000 10.000 15.000 25.000
SOE 0.424 0.494 0.000 0.000 0.000 1.000 1.000
BSHARE 0.035 0.185 0.000 0.000 0.000 0.000 1.000
HSHARE 0.033 0.179 0.000 0.000 0.000 0.000 1.000
BIG4 0.055 0.228 0.000 0.000 0.000 0.000 1.000
AFRANK 0.686 0.219 0.073 0.561 0.774 0.854 0.976
FIRSTYEAR 0.604 0.489 0.000 0.000 1.000 1.000 1.000
45
Panel C: Audit Partner Ability and Financial Restatement (N=3,010)
Variables/Stats Mean Std Min P25 Median P75 Max
RESTATE 0.500 0.500 0.000 0.000 0.500 1.000 1.000
UP 0.314 0.464 0.000 0.000 0.000 1.000 1.000
DOWN 0.137 0.344 0.000 0.000 0.000 0.000 1.000
TOP1 0.317 0.136 0.084 0.210 0.295 0.407 0.703
INDEP 0.371 0.053 0.300 0.333 0.333 0.400 0.571
LEV 0.411 0.244 0.007 0.212 0.393 0.580 1.173
SIZETAST 21.590 1.167 18.744 20.855 21.484 22.242 25.518
GROWTH -0.013 0.338 -0.643 -0.209 0.016 0.209 0.513
ROA 0.027 0.072 -0.313 0.005 0.025 0.057 0.239
SOE 0.387 0.487 0.000 0.000 0.000 1.000 1.000
46
Table 3 Correlation Matrix
This panel presents the Pearson and Spearman correlation matrix of audit partner migration and ability (Panel A), correlation matrix of audit partner migration status and audit
quality (Panel B) and correlation matrix between audit partner migration status and the presence of accounting restatements (Panel C). Pearson’s correlation coefficients are
shown in the lower triangle, including the diagonal, while Spearman’s rank correlations appear above the diagonal. The bold font indicates instances where the correlation
coefficients are significant at the 5% level or greater (two-sided). All the variable definitions are given in the Appendix.
Panel A: Audit Partner Migration and Ability
Variables 1 2 3 4 5 6 7 8 9 10 11
CPASIGN 1.000 -0.047 -0.083 -0.052 0.012 -0.075 -0.042 -0.055 0.023 -0.184 -0.161
AFPORTRANK -0.047 1.000 0.067 0.053 -0.030 0.040 -0.011 0.044 -0.002 0.057 -0.122
PARTNER -0.092 0.066 1.000 0.066 0.010 0.018 0.036 -0.025 0.042 -0.126 -0.053
UP -0.051 0.053 0.066 1.000 -0.261 0.051 -0.007 -0.024 -0.068 0.014 0.021
DOWN 0.015 -0.030 0.010 -0.261 1.000 0.025 0.064 -0.051 0.011 -0.011 0.068
MALE -0.077 0.041 0.018 0.051 0.025 1.000 -0.054 -0.021 -0.045 0.003 0.054
DEGREE -0.039 -0.012 0.034 -0.003 0.052 -0.049 1.000 -0.064 0.308 -0.021 -0.224
MAJOR -0.046 0.046 -0.025 -0.024 -0.051 -0.021 -0.062 1.000 -0.042 0.024 -0.116
UNIVERSITY 0.017 -0.004 0.042 -0.068 0.011 -0.045 0.297 -0.042 1.000 0.021 -0.103
QUALIEXAM -0.205 0.055 -0.126 0.014 -0.011 0.003 -0.024 0.024 0.021 1.000 -0.089
CPAAGE -0.153 -0.014 -0.050 0.002 0.023 0.009 -0.026 0.011 0.001 -0.021 1.000
47
Table 3 Correlation Matrix (Cont.)
Panel B: Audit Partner migration status and Audit Quality
Variables 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
KLW 1.000 -0.034 0.038 -0.675 -0.006 0.025 -0.022 -0.022 -0.022 -0.026 -0.013 -0.007 -0.041 -0.031 0.005
UP -0.027 1.000 -0.256 0.011 -0.002 0.020 0.007 0.009 -0.011 -0.016 0.023 -0.030 -0.047 0.034 0.027
DOWN 0.030 -0.256 1.000 -0.026 0.017 0.034 -0.016 -0.007 0.009 0.030 -0.011 0.009 -0.010 -0.057 -0.002
OCF -0.693 0.010 -0.015 1.000 -0.165 -0.074 0.046 0.101 -0.068 0.039 -0.013 0.033 0.059 0.037 0.005
LOSS -0.013 -0.002 0.017 -0.144 1.000 0.140 0.056 -0.131 0.161 0.006 0.030 -0.010 -0.038 -0.037 -0.008
LEV 0.027 0.017 0.033 -0.092 0.168 1.000 -0.387 -0.049 0.263 0.223 0.044 0.078 0.073 -0.004 -0.008
TOBINQ -0.021 0.009 -0.001 0.016 0.116 -0.166 1.000 0.161 -0.100 -0.270 0.003 -0.135 -0.160 -0.076 -0.006
SIZEMV -0.032 0.004 -0.006 0.109 -0.134 -0.057 0.107 1.000 0.131 0.135 0.057 0.237 0.270 0.201 -0.029
AGE -0.028 -0.009 0.010 -0.056 0.159 0.251 -0.002 0.120 1.000 0.344 0.216 0.013 0.041 -0.017 -0.049
SOE -0.029 -0.016 0.030 0.044 0.006 0.209 -0.191 0.159 0.341 1.000 0.085 0.144 0.146 0.095 -0.006
BSHARE -0.009 0.023 -0.011 -0.011 0.030 0.049 0.041 0.049 0.225 0.085 1.000 -0.027 0.163 0.072 0.006
HSHARE -0.006 -0.030 0.009 0.033 -0.010 0.072 -0.084 0.324 0.014 0.144 -0.027 1.000 0.472 0.217 -0.016
BIG4 -0.034 -0.047 -0.010 0.058 -0.038 0.065 -0.108 0.340 0.042 0.146 0.163 0.472 1.000 0.395 -0.007
AFRANK -0.025 0.041 -0.061 0.038 -0.037 -0.016 -0.041 0.194 -0.036 0.073 0.052 0.169 0.285 1.000 -0.006
FIRSTYEAR 0.006 0.027 -0.002 0.003 -0.008 -0.008 -0.006 -0.028 -0.047 -0.006 0.006 -0.016 -0.007 0.004 1.000
48
Panel C: Audit Partner migration status and Accounting Restatements
1 2 3 4 5 6 7 8 9 10
RESTATE 1.000 -0.044 0.049 -0.044 0.021 0.070 0.003 -0.011 -0.093 0.011
ENHIGH -0.044 1.000 -
0.268
-0.027 0.025 0.003 -0.006 -0.017 -0.007 -0.059
ENLOW 0.049 -0.268 1.000 0.023 0.011 0.036 0.022 -0.003 -0.029 0.027
TOP1 -0.036 -0.024 0.033 1.000 0.003 0.055 0.221 -0.006 0.101 0.279
INDEP 0.031 0.019 0.008 0.005 1.000 -0.042 -0.033 0.002 -0.005 -0.063
LEV 0.073 0.003 0.040 0.043 -0.047 1.000 0.255 0.032 -0.307 0.227
SIZETAST 0.002 -0.007 0.014 0.243 -0.011 0.198 1.000 0.043 0.044 0.288
GROWTH 0.022 -0.013 0.017 0.014 0.021 0.010 0.032 1.000 0.234 -0.025
ROA -0.074 -0.002 -
0.031
0.087 -0.008 -0.354 0.117 0.052 1.000 -0.067
SOE 0.011 -0.059 0.027 0.287 -0.067 0.218 0.299 0.015 -0.051 1.000
49
Table 4 Audit Partner Migration and Ability
This table presents results of audit partner migration and ability. Columns (1) and (2) report results of negative binomial regression based on Equation 1 with CPASIGN as the
dependent variable. Columns (3) and (4) report results of ordered probit regression based on Equation (1) with AFPORTRANK as the dependent variable. Columns (5) and (6)
report results of ordered probit regression based on Equation (1) with PARTNER as the dependent variable. In column (3), (6) and (9) we include both UP and DOWN in the model.
CPA licence year (CPA YEAR FE) and first signing year (SIGN YEAR FE) fixed effects are included in Column (1,2) and Column (3,4), respectively. Variable definitions are given
in the Appendix. ∗∗∗, ∗∗, and ∗ indicate two-tailed statistical significance at the 1%, 5%, and 10% levels, respectively.
Variables CPASIGN CPASIGN CPASIGN AFPORTRANK AFPORTRANK AFPORTRANK PARTNER PARTNER PARTNER
(1) (2) (3) (4) (5) (6) (7) (8) (9)
UP -0.053*** -0.048** 0.090** 0.077** 0.229*** 0.210***
(-2.724) (-2.378) (2.154) (1.988) (3.192) (2.800)
DOWN 0.014 0.012 -0.106* -0.075 -0.099 -0.096
(1.181) (0.902) (-1.692) (-1.163) (-1.284) (-1.084)
MALE -0.080*** -0.082*** -0.072*** 0.068* 0.074* 0.070* 0.044 0.036 0.050
(-4.223) (-4.373) (-4.646) (1.667) (1.814) (1.713) (0.659) (0.538) (0.740)
DEGREE -0.045*** -0.047*** -0.034*** 0.008 0.013 0.011 0.193*** 0.187*** 0.200***
(-2.945) (-3.080) (-2.688) (0.261) (0.390) (0.328) (3.593) (3.490) (3.707)
MAJOR -0.046** -0.043** -0.028* 0.123*** 0.118*** 0.120*** 0.081 0.086 0.075
(-2.276) (-2.155) (-1.720) (2.860) (2.733) (2.795) (1.131) (1.206) (1.040)
UNIVERSITY -0.037* -0.040** -0.031** 0.031 0.024 0.030 0.175** 0.186*** 0.172**
(-1.848) (-2.041) (-2.016) (0.731) (0.567) (0.698) (2.504) (2.673) (2.459)
QUALIEXAM -0.408*** -0.409*** -0.162*** 0.288*** 0.288*** 0.287*** -0.417*** -0.426*** -0.418***
(-10.232) (-10.240) (-4.730) (3.053) (3.058) (3.047) (-3.040) (-3.119) (-3.051)
CPAAGE -0.000*** -0.000*** -0.001*** -0.000 -0.000 -0.000 -0.000** -0.000** -0.000**
(-6.442) (-6.468) (-6.623) (-0.822) (-0.784) (-0.792) (-2.063) (-2.056) (-2.006)
Constant 3.319*** 3.307*** 3.313*** 4.880 4.663 4.912
(10.006) (9.951) (9.970) (0.059) (0.056) (0.059)
CPA YEAR FE YES YES YES NO NO NO NO NO NO
SIGN YEAR FE NO NO NO YES YES YES NO NO NO
50
GRADUATE YEAR FE NO NO NO NO NO NO YES YES YES
Pseudo R2 0.087 0.087 0.087 0.041 0.040 0.041 0.136 0.131 0.137
Observations 2,917 2,917 2,917 2,917 2,917 2,917 2,905 2,905 2,905
51
Table 5 Baseline results: Migration Status and Audit Quality
This table presents OLS regressions on audit partner ability and audit quality based on Equation 3. The dependent
variables of Panel A and Panel B are KLW and RESTATE, respectively. Industry, year and university fixed effects
are included. Industries are classified based on CSRC industry classifications with a two-digit code for the
manufacturing sector and a one-digit code for other sectors. Variable definitions are given in the Appendix.
Standard errors are clustered by audit client level. ∗∗∗, ∗∗, and ∗ indicate two-tailed statistical significance at the
1%, 5%, and 10% levels, respectively.
Panel A: Migration status and performance-adjusted abnormal accruals
Variables KLW KLW KLW
(1) (2) (3)
UP -0.005*** -0.003** (-3.125) (-2.194)
DOWN 0.008*** 0.007***
(4.277) (3.622)
OCF -0.675*** -0.675*** -0.675***
(-79.848) (-80.215) (-79.848)
LOSS -0.024*** -0.025*** -0.024***
(-12.357) (-12.400) (-12.357)
LEV -0.006* -0.007** -0.006*
(-1.938) (-2.051) (-1.938)
TOBINQ 0.000 0.000 0.000
(1.264) (1.299) (1.264)
SIZEMV 0.005*** 0.005*** 0.005***
(5.572) (5.450) (5.572)
AGE -0.001*** -0.001*** -0.001***
(-4.624) (-4.585) (-4.624)
SOE -0.001 -0.001 -0.001
(-1.009) (-0.993) (-1.009)
BSHARE -0.001 -0.001 -0.001
(-0.145) (-0.210) (-0.145)
HSHARE -0.001 -0.001 -0.001
(-0.194) (-0.168) (-0.194)
BIG4 -0.008** -0.007* -0.008**
(-2.084) (-1.915) (-2.084)
AFRANK -0.002 -0.002 -0.002
(-0.773) (-0.755) (-0.773)
FIRSTYEAR 0.001 0.001 0.001
(0.825) (0.777) (0.825)
Constant -0.045** -0.045** -0.046**
(-2.502) (-2.527) (-2.546)
YEAR FE YES YES YES
INDUSTRY FE YES YES YES
UNIVESITY FE YES YES YES
Adj R2 0.535 0.536 0.536
Observations 14,016 14,016 14,016
52
Panel B: Migration status and Accounting Restatements
Variables RESTATE RESTATE RESTATE
(1) (2) (3)
UP -0.106*** -0.087***
(-3.824) (-3.023)
DOWN 0.109*** 0.076**
(3.320) (2.261)
TOP1 -0.127 -0.127 -0.128
(-1.422) (-1.428) (-1.440)
INDEP -0.411** -0.397** -0.404**
(-2.065) (-1.998) (-2.034)
LEV 0.112** 0.109** 0.111**
(2.124) (2.060) (2.100)
SIZETAST -0.001 -0.002 -0.002
(-0.095) (-0.204) (-0.145)
GROWTH 0.005 0.005 0.005
(1.001) (0.969) (0.975)
ROA -0.279* -0.278* -0.271*
(-1.830) (-1.820) (-1.768)
SOE -0.005 0.000 -0.004
(-0.196) (0.011) (-0.155)
YEAR FE YES YES YES
INDUSTRY FE YES YES YES
UNIVERSITY FE YES YES YES
Adj R2 0.028 0.026 0.030
Observations 2,988 2,988 2,988
53
Table 6 Robustness Tests
This table presents OLS regressions on audit partner migration status and audit quality, measured by both accrual
and non-accrual proxies. Industry, year and university fixed effects are included except for Panel C (in Panel C,
industry fixed effects are omitted). For brevity, control variables are included in regressions but omitted from the
table. Industries are classified based on CSRC industry classifications with a two-digit code for the manufacturing
sector and a one-digit code for other sectors. Variable definitions are given in the Appendix. Standard errors are
clustered by audit client level. ∗∗∗, ∗∗, and ∗ indicate two-tailed statistical significance at the 1%, 5%, and 10%
levels, respectively.
Panel A: Audit Firm Fixed Effects
Variables KLW KLW KLW RESTATE RESTATE RESTATE
(1) (2) (3) (4) (5) (6)
UP -0.005*** -0.003** -0.111*** -0.091*** (-3.080) (-2.241) (-3.875) (-3.037)
DOWN 0.008*** 0.007*** 0.114*** 0.082**
(4.130) (3.512) (3.478) (2.388)
Controls Included Included Included Included Included Included
YEAR FE YES YES YES YES YES YES
INDUSTRY FE YES YES YES YES YES YES
UNIVERSITY FE YES YES YES YES YES YES
AUDIT FIRM FE YES YES YES YES YES YES
Adj R2 0.525 0.526 0.526 0.038 0.036 0.040
Observations 13,951 13,951 13,951 2,984 2,984 2,984
Panel B: Client Fixed Effects
Variables KLW KLW KLW RESTATE RESTATE RESTATE
(1) (2) (3) (4) (5) (6)
UP -0.004** -0.003* -0.102* -0.079 (-2.111) (-1.808) (-1.836) (-1.406)
DOWN 0.006*** 0.005** 0.103** 0.069**
(2.679) (2.204) (1.994) (2.053)
Controls Included Included Included Included Included Included
YEAR FE YES YES YES YES YES YES
UNIVERSITY FE YES YES YES YES YES YES
AUDIT CLIENT FE YES YES YES YES YES YES
Adj R2 0.552 0.552 0.552 0.222 0.221 0.222
Observations 13,682 13,682 13,682 2,684 2,684 2,684
Panel C: Audit Client Location Fixed Effects
Variables KLW KLW KLW RESTATE RESTATE RESTATE
(1) (2) (3) (4) (5) (6)
UP -0.003** -0.002** -0.092*** -0.078*** (-2.031) (-1.917) (-3.256) (-2.607)
DOWN 0.008*** 0.008*** 0.085*** 0.056**
(4.314) (4.044) (2.613) (2.143)
Controls Included Included Included Included Included Included
YEAR FE YES YES YES YES YES YES
INDUSTRY FE YES YES YES YES YES YES
UNIVERSITY FE YES YES YES YES YES YES
AUDIT CLIENT LOCATION
FE
YES YES YES YES YES YES
54
Adj R2 0.525 0.526 0.526 0.041 0.039 0.042
Observations 13,951 13,951 13,951 2,988 2,988 2,988
55
Table 7 The Effect of Audit Partner Characteristics
This table presents OLS regressions of subsample analyses on audit partner migration status and audit quality
based on Equation 3. Panel A is divided into two subsamples according to audit partner’s gender. Panel B is
divided into two subsamples according to university ranks. Industry, year and university fixed effects are included.
For brevity, control variables are included in regressions but omitted from the table. Industries are classified based
on CSRC industry classifications with a two-digit code for the manufacturing sector and a one-digit code for other
sectors. Variable definitions are given in the Appendix. Standard errors are clustered by audit client level. ∗∗∗, ∗∗,
and ∗ indicate two-tailed statistical significance at the 1%, 5%, and 10% levels, respectively.
Panel A: Gender
Variables KLW KLW RESTATE RESTATE
Female Male Female Male
(1) (2) (3) (4)
UP -0.006*** -0.003 -0.094** -0.072 (-2.998) (-1.259) (-2.522) (-1.317)
DOWN 0.004** 0.007** 0.060* 0.064**
(2.097) (2.074) (1.940) (2.169)
Controls YES YES YES YES
YEAR FE YES YES YES YES
INDUSTRY FE YES YES YES YES
UNIVERSITY FE YES YES YES YES
Adj R2 0.499 0.544 0.053 0.050
Observations 5,154 8,862 1,021 1,810
Panel B: University reputation
Variables KLW KLW RESTATE RESTATE
Prestigious Others Prestigious Others
(1) (2) (3) (4)
UP -0.001 -0.003** -0.022 -0.120*** (-0.319) (-2.279) (-0.473) (-3.101)
DOWN 0.007** 0.004** 0.057 0.075
(2.290) (1.968) (1.106) (1.564)
Controls YES YES YES YES
YEAR FE YES YES YES YES
INDUSTRY FE YES YES YES YES
UNIVERSITY FE YES YES YES YES
Adj R2 0.508 0.538 0.029 0.034
Observations 5,288 8,728 1,076 1,780
56
Table 8 The Effect of Client Characteristics
This table presents OLS regressions of subsample analyses on audit partner migration status and audit quality
based on Equation (4). Panel A is divided into High and Low groups according to year-industry median number
of business segments. Panel B is divided into High and Low groups according to median measure of industry-
specific earnings noise measure as in Francis and Gunn (2017). For brevity, control variables are included in
regressions but omitted from the table. Industries are classified based on CSRC industry classifications with a
two-digit code for the manufacturing sector and a one-digit code for other sectors. Variable definitions are given
in the Appendix. Standard errors are clustered by audit client level. ∗∗∗, ∗∗, and ∗ indicate two-tailed statistical
significance at the 1%, 5%, and 10% levels, respectively.
Panel A: Number of business segments
Variables KLW KLW RESTATE RESTATE
High Low High Low
(1) (2) (3) (4)
UP -0.003** -0.002 -0.137*** -0.022 (-2.045) (-1.181) (-3.497) (-0.470)
DOWN 0.008*** 0.003 0.101* 0.048
(2.747) (1.277) (1.882) (1.036)
Controls YES YES YES YES
YEAR FE YES YES YES YES
INDUSTRY FE YES YES YES YES
UNIVERSITY FE YES YES YES YES
Adj R2 0.549 0.510 0.069 0.066
Observations 6,433 7,415 1,251 1,503
Panel B: Earnings Noise
Variables KLW KLW RESTATE RESTATE
High Low High Low
(1) (2) (3) (4)
UP -0.004** -0.001 -0.076* -0.032*
(-2.021) (-0.943) (-1.855) (-1.957)
DOWN 0.005** 0.003* 0.067** 0.073
(2.031) (1.960) (2.095) (1.392)
Controls YES YES YES YES
YEAR FE YES YES YES YES
INDUSTRY FE YES YES YES YES
UNIVERSITY FE YES YES YES YES
Adj R2 0.538 0.518 0.035 0.031
Observations 7,606 6,410 1,540 1,254
57
Table 9 Evidence from Other Audit Quality Measures
This table presents OLS regressions on audit partner migration status and other alternative audit quality measures.
In Panel A, we use total accruals (TACC) as the dependent variable in Equation 3 and re-estimate the accrual
results of Panel A Table 5, following Chen et al. (2018). Panel B and C present results of small profit, modified
audit opinion and audit fee, respectively. Industry, year and university fixed effects are included. Industries are
classified based on CSRC industry classifications with a two-digit code for the manufacturing sector and a one-
digit code for other sectors. Variable definitions are given in the Appendix. Standard errors are clustered by audit
client level. ∗∗∗, ∗∗, and ∗ indicate two-tailed statistical significance at the 1%, 5%, and 10% levels, respectively.
Panel A: Using total accruals as dependent variables and adding discretionary accrual model regressions, as
suggested by Chen, Hribar and Melessa (2018, JAR)
Variables TACC TACC TACC
(1) (2) (3)
UP -0.004** -0.002** (-2.279) (-2.035)
DOWN 0.009*** 0.008***
(3.839) (3.381)
DA Model Regressors Included Included Included
Controls Included Included Included
YEAR FE YES YES YES
INDUSTRY FE YES YES YES
UNIVERSITY FE YES YES YES
Adj R2 0.671 0.671 0.671
Observations 13,950 13,950 13,950
58
Panel B: Using likelihood of small profit and modified audit opinion to measure audit quality
Variables SP SP SP MAO MAO MAO
(1) (2) (3) (4) (5) (6)
UP -0.017** -0.009 0.009 0.006 (-2.030) (-1.143) (1.504) (0.982)
DOWN 0.041*** 0.038*** -0.017*** -0.015**
(3.247) (2.971) (-2.725) (-2.256)
CR 0.002*** 0.002*** 0.002***
(3.778) (3.799) (3.798)
AR -0.085** -0.085** -0.086**
(-2.496) (-2.496) (-2.517)
INV -0.106*** -0.105*** -0.105***
(-4.797) (-4.762) (-4.764)
RPT 0.006 0.006 0.006
(1.310) (1.343) (1.317)
ROE -0.157*** -0.156*** -0.157***
(-5.112) (-5.097) (-5.109)
OCF -0.126*** -0.127*** -0.126*** -0.048** -0.047** -0.047**
(-4.485) (-4.519) (-4.468) (-2.031) (-1.992) (-2.033)
LOSS 0.053*** 0.053*** 0.053***
(5.611) (5.625) (5.625)
LEV 0.126*** 0.124*** 0.124*** 0.250*** 0.250*** 0.250***
(7.550) (7.437) (7.455) (11.656) (11.687) (11.673)
RET -0.027*** -0.027*** -0.027***
(-5.182) (-5.165) (-5.169)
TOBINQ -0.010*** -0.010*** -0.010*** 0.019*** 0.019*** 0.019***
(-8.693) (-8.740) (-8.743) (9.347) (9.349) (9.354)
SIZEMV -0.019*** -0.019*** -0.019*** -0.024*** -0.024*** -0.024***
(-4.809) (-4.853) (-4.835) (-7.227) (-7.202) (-7.215)
AGE 0.004*** 0.004*** 0.004*** 0.001* 0.001* 0.001*
(6.232) (6.293) (6.247) (1.692) (1.656) (1.679)
SOE -0.012 -0.011 -0.011 -0.025*** -0.025*** -0.025***
(-1.405) (-1.333) (-1.335) (-3.817) (-3.833) (-3.792)
BSHARE -0.005 -0.005 -0.005 0.017 0.018 0.017
(-0.237) (-0.250) (-0.245) (0.881) (0.906) (0.879)
HSHARE -0.019 -0.019 -0.019 0.022 0.021 0.022
(-0.977) (-0.964) (-0.967) (1.146) (1.143) (1.154)
BIG4 0.001 0.002 0.002 0.023* 0.021* 0.021*
(0.063) (0.118) (0.131) (1.860) (1.769) (1.779)
AFRANK -0.018 -0.017 -0.016 -0.016 -0.016 -0.017
(-1.063) (-1.006) (-0.949) (-1.166) (-1.167) (-1.217)
FIRSTYEAR 0.003 0.003 0.003 -0.000 -0.000 -0.000
(0.486) (0.457) (0.473) (-0.053) (-0.035) (-0.053)
YEAR FE YES YES YES YES YES YES
INDUSTRY FE YES YES YES YES YES YES
UNIVERSITY FE YES YES YES YES YES YES
Adj R2 0.084 0.085 0.085 0.208 0.208 0.208
Observations 14,012 14,012 14,012 13,490 13,490 13,490
59
Panel C: Audit fees
Variables FEE FEE FEE
(1) (2) (3)
UP 0.040*** 0.030** (2.929) (2.137)
DOWN -0.065*** -0.054***
(-3.610) (-2.978)
CR -0.004*** -0.004*** -0.004***
(-3.309) (-3.286) (-3.283)
AR 0.054 0.053 0.057
(0.717) (0.703) (0.749)
INV -0.538*** -0.535*** -0.536***
(-11.266) (-11.210) (-11.221)
RPT 0.055*** 0.056*** 0.055***
(6.649) (6.755) (6.691)
ROE -0.255*** -0.254*** -0.255***
(-7.409) (-7.406) (-7.427)
OCF -0.245*** -0.240*** -0.245***
(-5.081) (-4.999) (-5.083)
LEV 0.129*** 0.126*** 0.126***
(3.969) (3.885) (3.894)
TOBINQ 0.032*** 0.033*** 0.033***
(13.334) (13.412) (13.413)
SIZETAST 0.394*** 0.395*** 0.394***
(46.527) (46.609) (46.609)
AGE 0.011*** 0.011*** 0.011***
(8.098) (8.056) (8.097)
SOE -0.067*** -0.067*** -0.066***
(-3.916) (-3.937) (-3.878)
BSHARE 0.112*** 0.114*** 0.112***
(2.834) (2.890) (2.825)
HSHARE 0.773*** 0.772*** 0.774***
(14.054) (14.047) (14.087)
BIG4 0.471*** 0.466*** 0.467***
(9.794) (9.689) (9.706)
AFRANK 0.213*** 0.213*** 0.210***
(7.211) (7.203) (7.110)
FIRSTYEAR -0.021*** -0.021*** -0.021***
(-3.379) (-3.310) (-3.354)
LAGMAO 0.098*** 0.097*** 0.097***
(3.704) (3.691) (3.697)
INTERIM -0.087*** -0.085*** -0.086***
(-2.877) (-2.826) (-2.854)
YEAR FE YES YES YES
INDUSTRY FE YES YES YES
UNIVERSITY FE YES YES YES
Adj R2 0.707 0.707 0.707
Observations 13,016 13,016 13,016
60
Table 10 Evidence from Market Reactions to Earnings Announcements
The table reports the results from OLS regressions examining market reactions. The dependent variable is CAR,
cumulative market-adjusted stock returns from trading day -1 to +1, where day 0 is the earnings announcement
day. Definitions of other variables are presented in the Appendix. All the continuous control variables are
standardized (to have zero mean and unit standard deviation) to facilitate the interpretation of the coefficients.
∗∗∗, ∗∗, and ∗ indicate two-tailed statistical significance at the 1%, 5%, and 10% levels, respectively.
Variables CAR CAR CAR
(1) (2) (3)
UE 0.146*** 0.137*** 0.146*** (4.230) (4.380) (4.168)
UP 0.004 0.003
(1.591) (1.108)
UE×UP 0.028** 0.026**
(2.188) (2.097)
DOWN -0.002 -0.002
(-1.121) (-1.198)
UE×DOWN -0.019** -0.017*
(-2.052) (-1.907)
LOSS -0.003 -0.003 -0.003
(-1.017) (-1.018) (-1.017)
UE×LOSS -0.105** -0.105** -0.104**
(-2.372) (-2.416) (-2.401)
MAGUE 0.000 0.000 0.000
(0.261) (0.216) (0.266)
UE×MAGUE -0.021*** -0.021*** -0.021***
(-2.709) (-2.694) (-2.712)
BETA -0.001 -0.001 -0.001
(-1.099) (-1.094) (-1.098)
UE×BETA 0.022 0.023 0.022
(1.271) (1.296) (1.270)
LEVERC 0.001 0.001 0.001
(0.755) (0.736) (0.746)
UE×LEVERC 0.022 0.022 0.022
(1.417) (1.421) (1.417)
BM2 0.000 0.000 0.000
(0.173) (0.188) (0.173)
UE×BM2 0.002 0.003 0.002
(0.269) (0.274) (0.267)
SIZEMV2 -0.004*** -0.004*** -0.004***
(-5.218) (-5.204) (-5.220)
UE×SIZEMV2 0.014 0.014 0.014
(0.901) (0.878) (0.898)
BIG4 0.008*** 0.008*** 0.008***
(3.090) (3.133) (3.110)
UE×BIG4 0.008 0.012 0.007
(0.156) (0.263) (0.151)
CONSTANT -0.008* -0.008 -0.008*
(-1.696) (-1.615) (-1.666)
YEAR FE YES YES YES
61
INDUSTRY FE YES YES YES
UNIVERSITY FE YES YES YES
Adj R2 0.020 0.020 0.020
Observations 14,109 14,109 14,109
